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<title>Cloud Data Migration Archives - Datagaps | Gen AI-Powered Automated Cloud Data Testing</title>
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<title>Cloud Data Migration Archives - Datagaps | Gen AI-Powered Automated Cloud Data Testing</title>
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<title>6 Critical Components of Data Testing</title>
<link>https://www.datagaps.com/blog/6-critical-components-of-data-testing/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Sun, 05 Jul 2026 12:41:00 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Dataflow]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[DevOps]]></category>
<category><![CDATA[ETL Testing]]></category>
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<description><![CDATA[<p>Data is a precious asset that has to be validated at various stages of use. One stage is at the point of ingestion, and another as it moves through your enterprise and lands in your data warehouse or data lake. Finally, when it is consumed in your data analytics platform.</p>
<p>The post <a href="https://www.datagaps.com/blog/6-critical-components-of-data-testing/">6 Critical Components of Data Testing</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://en.wikipedia.org/wiki/Database_testing" target="_blank" rel="noopener">Data testing</a></span> is often assumed to be a solved problem, but standard capabilities — data access, quality rules, and comparison methods — only cover about 75% of what enterprises actually encounter in production. The remaining gap shows up in complex APIs, billion-row datasets, and anomalies no one thought to write a rule for. This blog breaks down the 6 critical components — extensibility, advanced API handling, AI-based observability, large volume handling, DevOps integration, and RPA integration — that close that gap and make data testing enterprise-ready.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>Extensibility matters</strong> — Python-based plugins let teams solve unexpected data issues without workarounds.</li><li><strong>APIs are essential</strong> — Complex sources (e.g., hierarchical JSON via multiple APIs) need advanced API handling.</li><li><strong>AI + rules beat rules alone</strong> — Combining Data Quality rules with AI-driven Observability catches both known and unknown issues.</li><li><strong>Scalability and integration close the gaps</strong> — Handling billion-row volumes (DB engine or Spark) plus tight DevOps/RPA integration rounds out enterprise-grade data testing.</li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Importance of Data and Data Testing</h2> </div>
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<p>Data is a precious asset that has to be validated at various stages of use. One stage is at the point of ingestion, and another as it moves through your enterprise and lands in your data warehouse or data lake. Finally, when it is consumed in your data analytics platform. This is from the point of view of analyzing data.</p><p>What about all of the production data that you have in the enterprise?</p><p>How is that going to be monitored?</p><p>So, table stakes for <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://en.wikipedia.org/wiki/Database_testing" target="_blank" rel="noopener">data testing</a></span> start with access to all the data in your environment, whether in your analytics platform or stored within your production applications. Along with the data access, data quality rules have to be available, as well as a method of comparing data sources of like or mixed data structures and varying volumes, often in the billions.</p> </div>
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<ul><li>With these core capabilities, you can develop good testing workflows that take care of <strong>75%</strong> of your testing needs.</li><li>But what about the other <strong>25%</strong>?</li><li>What if your data is in complex hierarchical JSON structures?</li><li>What if the data testing needs are not anticipated and solved?</li></ul><p>The last 25% brings about the 6 critical components where you can solve those unexpected needs.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Here are the 6 critical components</h2> </div>
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<td style="padding: 12px; border: 1px solid #ccc;">Extensibility</td>
<td style="padding: 12px; border: 1px solid #ccc;">Lets teams resolve unanticipated data problems via Python or other extensible methods, without complex workarounds</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Advanced API Components</td>
<td style="padding: 12px; border: 1px solid #ccc;">Handles data access via APIs, including complex hierarchical JSON structures requiring multiple calls</td>
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<td style="padding: 12px; border: 1px solid #ccc;">AI-Based Observability</td>
<td style="padding: 12px; border: 1px solid #ccc;">Combines Data Quality rules with Data Observability to catch both known and previously unanticipated data issues</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Ability to Handle Large Volumes</td>
<td style="padding: 12px; border: 1px solid #ccc;">Scales from database-engine comparisons (up to 40 million rows) to Apache Spark in-memory comparisons for higher volumes</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Integration with DevOps Platforms</td>
<td style="padding: 12px; border: 1px solid #ccc;">Keeps DataOps and DevOps process execution and management consistent</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Integration with RPA Platforms</td>
<td style="padding: 12px; border: 1px solid #ccc;">Extends testing to business users and scenarios that mimic human interaction, beyond what Python, Scala, or SQL alone can cover</td>
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<p><span style="color: #ffffff;">Extensibility</span></p> </div>
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<p>In data testing, there are often times when you need to be able to extend your solution to other areas that weren’t anticipated. A unique data problem is encountered that is outside the norm and could not be thought of beforehand. For example, If your solution is extensible through Python or some other method, the issue can be resolved quickly. <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.youtube.com/watch?v=hZnVo7nZGpg" target="_blank" rel="noopener">With Datagaps, we provide a Plugin component that can be selected from a library of components that is extensible by using Python.</a></span> This eliminates the need for complex workarounds that you have to shoehorn into other solutions.</p> </div>
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<p><span style="color: #ffffff;">Advanced API Components</span></p> </div>
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<p>In today’s world, data comes to us in a variety of ways. Often as simple as CSV files, feeds from production applications or data that is FTP’d to a location. Quite often, there are requirements to use an Advanced API to get access to the data. In one recent example our client had 8 API’s that we needed to invoke In one recent example our client had 8 APIs that we needed to invoke as part of <a href="https://www.datagaps.com/data-testing-concepts/etl-testing/" target="_blank" rel="noopener">ETL testing</a> to gain access to their Hierarchical JSON data. We needed to create multiple files from each of the APIs, which meant that we needed advanced capabilities.</p> </div>
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<p>Writing Data Quality rules is effective in most situations, but often it may not be needed if your solution can learn from the data being ingested. A combination of Data Quality rules and <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-observability-tool/" target="_blank" rel="noopener">Data Observability</a></span> is the best approach. Data Quality rules can surface likely data issues efficiently while Data Observability will find outliers that haven’t been anticipated before. You may try Datagaps <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/data-quality/" target="_blank" rel="noopener">Data Quality Monitor</a></span> for this.</p> </div>
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<p><span style="color: #ffffff;">Ability To Handle Large Volumes in the Billions</span></p> </div>
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<p>As data volumes continue to grow, at some point your normal processing requirements will outgrow your data testing capabilities. Recommended approach: start with a database engine for comparisons up to 40 million rows, since it’s easier to set up and less costly; once volumes exceed that, switch to Apache Spark-based in-memory comparisons for higher-volume workloads.This method takes advantage of native cloud capabilities such as clusters and auto scaling. So if you volumes are low currently the DB Engine will take care of the volumes but as your data scales you have an option to swap out the DB Engine for the Apache spark implementation that can meet your current of future needs. <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/blog/how-do-you-automate-big-data-testing-everything-to-know/" target="_blank" rel="noopener">Learn more about automating your Big Data.</a></span></p> </div>
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<p><span style="color: #ffffff;">Integration with your DevOps Platform</span></p> </div>
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<p>Your DevOps organization has spent an enormous amount of time and cost to implement a DevOps platform. As you introduce your DataOps platform it is important to be able to integrate with the DevOps platform such as x,y,z. This ensures consistency between how your DevOps ad DataOps process execution and management.</p> </div>
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<p><span style="color: #ffffff;">Integration with an RPA Platform</span></p> </div>
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<p>Python, Scala and SQL use cases can be extended to handle a limitless number of variations in your data test plans. However, these languages, while easy to use for developers aren’t meant for the business user. Additionally, they aren’t designed to mimic human behavior. There is a Billion dollar industry that caters to Robotic Process Automation. In other words, RPA mimics the human interaction</p> </div>
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<p>In conclusion, data testing needs have risen in importance as organizations monetize the use of the data or make critical decisions based on the data flowing through their enterprise. Volumes are increasing, sources take on different access methods, and often, data needs to be accessed through alternative means via API or other methods. Your processing needs have certainly grown substantially in the past few years. Methods of testing are changing rapidly. That is why we believe extensibility is so important. As all of these dynamics impact your business and future needs, a platform like <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener">DataOps Suite</a></span></span> that will scale and extend capabilities will be critical for current and future needs.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/6-critical-components-of-data-testing/">6 Critical Components of Data Testing</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<item>
<title>Data Drift Using DataOps Data Profiling</title>
<link>https://www.datagaps.com/blog/data-drift-using-dataops-data-profiling/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Sat, 06 Jun 2026 16:34:00 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Dataflow]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[ETL Testing]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=7349</guid>
<description><![CDATA[<p>What is Data Drift? Within the data space, the only constant thing is “change”. The drift in data here refers to a multitude of changes in the input data primarily in terms of frequency, aggregates, and heterogeneity. These are not regarded as errors as these types of shifts and changes</p>
<p>The post <a href="https://www.datagaps.com/blog/data-drift-using-dataops-data-profiling/">Data Drift Using DataOps Data Profiling</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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<p>Data drift refers to changes in input data over time — in frequency, aggregates, or heterogeneity — that reflect real-world shifts rather than errors. This post explains drift types (Sudden, Gradual, Incremental, Reoccurring) and detection methods using DataOps Suite’s Profiling Nodes, covering statistical shifts (mean, min-max, deviation, skewness, kurtosis), key/GUID pattern changes, and domain shifts from new values. It also distinguishes data drift from model drift, showing how early detection prevents downstream quality and model performance issues.</p> </div>
</div>
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<p><strong>Key Takeaways</strong></p><ul><li>Data drift has four cadence types — Sudden, Gradual, Incremental, and Reoccurring Drift, each describing how data distribution changes relate to time and metric aggregates.</li><li>Profiling Nodes detect drift through statistical baselines — tracking mean, min-max values, standard deviation, skewness, and kurtosis helps identify when a dataset’s distribution has shifted from expected norms.</li><li>Key/GUID pattern changes signal structural drift — unexpected changes in primary key formats (e.g., a 5-digit number becoming alphanumeric) can cause duplication and incorrect aggregations if undetected.</li><li>Data drift and model drift are distinct but related — data drift reflects changes in input data itself, while model drift refers to degradation in model performance; fixing data drift alone doesn’t resolve model drift, which needs separate detection techniques.</li></ul> </div>
</div>
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<h2 class="elementor-heading-title elementor-size-default">What Is Data Drift?</h2> </div>
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<p>Data drift is a change in the statistical properties of input data over time — in frequency, aggregates, or heterogeneity — that causes a dataset to diverge from the benchmark a pipeline, analysis, or ML model was originally built on. Within the data space, the only constant is change, and data drift isn’t inherently an error: these shifts are factual and representative of how real-world data evolves.</p><p>Model Drift comes as the other side of the coin to <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-monitor-datasets?tabs=python" target="_blank" rel="noopener">data drift</a></span> that is closely related to how the statistical nature and the probabilities as well as the intended logic translation have been altered. While model drift is closely associated with AI-ML models, data drift affects every pipeline that has been made using past production data.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">A quick way to comprehend data drift is via a couple of real-world examples
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<p>An ML model that predicts house prices based on a myriad of property aspects such as the number of rooms, area, location, floor, and such which was originally trained in 2019 will not work correctly in 2020 due to the variety of changes in the aforementioned aspects. Certain areas went up in demand as did a certain number of bedrooms and such. If the model is not re-trained or corrected with updated bias, the predicted prices cannot be used.<br />Assume a statistical regression-based model predicts if a customer might default on a loan. The bank’s majority of clients at this point were new families. A few months after the model has been running, the marketing department unveils a new type of campaign targeted toward young students. While the campaign is successful the model is no longer accurate as there are new types of distributions among the various inputs the model is fed. Therefore, the prediction of defaulters itself is incorrect.<br />A reporting system that showcases the mean forecasts across multiple regions suddenly has a higher mean temperature than expected. Under the hood, a few areas had updated their sensors to one of a different brand that resulted in the dimensions being recorded in Fahrenheit as opposed to Celsius on which the system was based.</p><p>A couple of distinctions in the various types of data drifts are the cadence of the drift and the type of the drift. The use cases showed a focus on the type of drift. The cadence of drift segregates drifts into 4 types. These are Sudden Drift, Gradual Drift, Incremental Drift, and Reoccurring Drift. These are usually defined against data distribution and time, but the concept translates with specific aggregates of the metrics themselves.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="435" src="https://www.datagaps.com/wp-content/uploads/Different-Classifications-of-Drift-1024x696.webp" class="attachment-large size-large wp-image-5586" alt="Different-Classifications-of-Drift" srcset="https://www.datagaps.com/wp-content/uploads/Different-Classifications-of-Drift-1024x696.webp 1024w, https://www.datagaps.com/wp-content/uploads/Different-Classifications-of-Drift-300x204.webp 300w, https://www.datagaps.com/wp-content/uploads/Different-Classifications-of-Drift-768x522.webp 768w, https://www.datagaps.com/wp-content/uploads/Different-Classifications-of-Drift-1536x1044.webp 1536w, https://www.datagaps.com/wp-content/uploads/Different-Classifications-of-Drift.webp 1600w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 1. Different Classifications of Drift</p> </div>
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<img loading="lazy" decoding="async" width="640" height="324" src="https://www.datagaps.com/wp-content/uploads/Sudden-Drift-1024x518.webp" class="attachment-large size-large wp-image-5590" alt="Sudden-Drift" srcset="https://www.datagaps.com/wp-content/uploads/Sudden-Drift-1024x518.webp 1024w, https://www.datagaps.com/wp-content/uploads/Sudden-Drift-300x152.webp 300w, https://www.datagaps.com/wp-content/uploads/Sudden-Drift-768x388.webp 768w, https://www.datagaps.com/wp-content/uploads/Sudden-Drift.webp 1300w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>Figure 2. The above graph showcases a “Sudden” Drift in Yearly Income where the overall values of the metric have increased sharply</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Profiling as Drift Detection in Data Drift</h2> </div>
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<p>Data Profiling is an integral part of <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-observability-tool/" target="_blank" rel="noopener">data observability</a></span> within the DataOps Suite, helping users create profiles that hold every aspect of information that can be derived from a dataset — aggregates such as mean, deviations, min-max, nulls, and more, along with frequency and pattern analysis.</p><p>A dataset can be directly pulled into a profiling node. The <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/dataflow/" target="_blank" rel="noopener">DataOps Suite</a></span> Profile node provides a variety of aggregation and pattern analysis options.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="522" src="https://www.datagaps.com/wp-content/uploads/DataOps-Profile-Node.webp" class="attachment-large size-large wp-image-5596" alt="DataOps-Profile-Node" srcset="https://www.datagaps.com/wp-content/uploads/DataOps-Profile-Node.webp 956w, https://www.datagaps.com/wp-content/uploads/DataOps-Profile-Node-300x244.webp 300w, https://www.datagaps.com/wp-content/uploads/DataOps-Profile-Node-768x626.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 3. DataOps Profile Node</p><p>Each of the aggregations works to create a profile of the dataset, maintaining an average value, upper and lower bounds, deviations, patterns, null counts, and such. This help creates a baseline of the expectations in the datasets and something for the users to use for comparisons. Let’s have a closer look at a few real-life examples.</p> </div>
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<th style="padding: 12px; border: 1px solid #ccc;">Type of Drift</th>
<th style="padding: 12px; border: 1px solid #ccc;">What Changes</th>
<th style="padding: 12px; border: 1px solid #ccc;">Example</th>
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<td style="padding: 12px; border: 1px solid #ccc;">Covariate Drift / Metric Stats</td>
<td style="padding: 12px; border: 1px solid #ccc;">Statistical aggregates like mean, min-max, standard deviation, skewness, and kurtosis</td>
<td style="padding: 12px; border: 1px solid #ccc;">Yearly income values shift upward with less variance across customers</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Change in Keys / GUID</td>
<td style="padding: 12px; border: 1px solid #ccc;">The pattern or format of primary keys used in relational datasets</td>
<td style="padding: 12px; border: 1px solid #ccc;">A 5-digit numeric Customer Key suddenly becomes alphanumeric</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Domain Shift / New Values</td>
<td style="padding: 12px; border: 1px solid #ccc;">The set of valid values (domain) for a column, such as new categories being added</td>
<td style="padding: 12px; border: 1px solid #ccc;">New geography IDs appear, changing distinct counts and distributions</td>
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<h3 class="elementor-heading-title elementor-size-default">Data Drift & Variety of Drift Detection – Covariate Drift or Drift in Metrics Stats</h3> </div>
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<p>Every numerical metric holds certain statistical aggregates that can help keep the baseline of the dataset. The most basic ones of these are average, min-max values, and standard deviation. Skewness and Kurtosis also help keep the distribution in check.</p><p>A change in mean implies that in general the average value of the metrics has been altered. In the example below, the yearly income has overall increased.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="331" src="https://www.datagaps.com/wp-content/uploads/Mean.webp" class="attachment-large size-large wp-image-5600" alt="Mean" srcset="https://www.datagaps.com/wp-content/uploads/Mean.webp 877w, https://www.datagaps.com/wp-content/uploads/Mean-300x155.webp 300w, https://www.datagaps.com/wp-content/uploads/Mean-768x398.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 4. Mean</p><p>While Min-Max values show the upper and lower hard bounds of the metrics, the variability, and the weights away from the mean are showcased by the deviation. In the example we see that while the min and max values of the yearly income have shifted up with the mean, there is less variance in this metric as well, implying that there is less variance in the customers.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="245" src="https://www.datagaps.com/wp-content/uploads/Minimum-Value.webp" class="attachment-large size-large wp-image-5604" alt="Minimum-Value" srcset="https://www.datagaps.com/wp-content/uploads/Minimum-Value.webp 876w, https://www.datagaps.com/wp-content/uploads/Minimum-Value-300x115.webp 300w, https://www.datagaps.com/wp-content/uploads/Minimum-Value-768x295.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<div class="et_pb_module et_pb_text et_pb_text_10 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><p style="text-align: center;">Figure 5. Minimum Value</p></div></div> </div>
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<img loading="lazy" decoding="async" width="640" height="174" src="https://www.datagaps.com/wp-content/uploads/Maximum-Value.webp" class="attachment-large size-large wp-image-5608" alt="Maximum-Value" srcset="https://www.datagaps.com/wp-content/uploads/Maximum-Value.webp 884w, https://www.datagaps.com/wp-content/uploads/Maximum-Value-300x81.webp 300w, https://www.datagaps.com/wp-content/uploads/Maximum-Value-768x209.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 6. Maximum Value</p> </div>
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<img loading="lazy" decoding="async" width="640" height="276" src="https://www.datagaps.com/wp-content/uploads/Standard-Deviation.webp" class="attachment-large size-large wp-image-5941" alt="Standard-Deviation" srcset="https://www.datagaps.com/wp-content/uploads/Standard-Deviation.webp 883w, https://www.datagaps.com/wp-content/uploads/Standard-Deviation-300x129.webp 300w, https://www.datagaps.com/wp-content/uploads/Standard-Deviation-768x331.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 7. Standard Deviation [The decrease showcases that most of the values in the past 2 runs are much closer to the mean]</p> </div>
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<p><span style="text-align: justify; background-color: #ffffff;">Skewness identifies how skewed a dataset is, as in how many values lie evenly away from the mean in both directions while Kurtosis identifies the degree of curve of the distribution of a dataset. Any changes in these datasets represent changes in the distribution and therefore critically affect any statistical tests like the p-test or t-test. In our example, these do not alter as much, however, in more sensitive models such as an AI / ML model, these tiny changes would affect the results more drastically.</span></p> </div>
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<img loading="lazy" decoding="async" width="640" height="333" src="https://www.datagaps.com/wp-content/uploads/Skeness.webp" class="attachment-large size-large wp-image-5966" alt="Skeness" srcset="https://www.datagaps.com/wp-content/uploads/Skeness.webp 885w, https://www.datagaps.com/wp-content/uploads/Skeness-300x156.webp 300w, https://www.datagaps.com/wp-content/uploads/Skeness-768x399.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 8. Skewness</p> </div>
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<img loading="lazy" decoding="async" width="640" height="327" src="https://www.datagaps.com/wp-content/uploads/Kurtosis.webp" class="attachment-large size-large wp-image-5969" alt="Kurtosis" srcset="https://www.datagaps.com/wp-content/uploads/Kurtosis.webp 870w, https://www.datagaps.com/wp-content/uploads/Kurtosis-300x153.webp 300w, https://www.datagaps.com/wp-content/uploads/Kurtosis-768x393.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 9. Kurtosis</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Change in Keys / GUID</h5> </div>
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<p>A GUID or a primary key is on the most important columns in relational datasets. In terms of delta datasets, they are critical in ensuring duplicity doesn’t enter the system. Any changes in these patterns will result in incorrect aggregations and reports, especially when checked against pre-change datasets.</p> </div>
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<p style="text-align: center;">Before</p> </div>
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<img loading="lazy" decoding="async" width="314" height="171" src="https://www.datagaps.com/wp-content/uploads/After.webp" class="attachment-large size-large wp-image-5972" alt="After" srcset="https://www.datagaps.com/wp-content/uploads/After.webp 314w, https://www.datagaps.com/wp-content/uploads/After-300x163.webp 300w" sizes="(max-width: 314px) 100vw, 314px" /> </div>
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<p style="text-align: center;">After</p> </div>
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<p>In the example above, we see that pattern of the Customer Key was a 5-digit number which was suddenly updated to an alphanumeric key.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Domain Shift or Addition of New Values</h4> </div>
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<p>As per the example in the introduction of this blog post if a new campaign type or a new geography id is added to a system the corresponding joins have to be checked. Additionally, if geography id is one of the group columns for any aggregations the aggregates in question are affected as well. While addition that is not in the expected domain is ruled out as a bad record, segregation in teams can result in new validated domain LOVs that the analysis team might not be aware of.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="229" src="https://www.datagaps.com/wp-content/uploads/Distinct-Count.webp" class="attachment-large size-large wp-image-5979" alt="Distinct-Count" srcset="https://www.datagaps.com/wp-content/uploads/Distinct-Count.webp 888w, https://www.datagaps.com/wp-content/uploads/Distinct-Count-300x107.webp 300w, https://www.datagaps.com/wp-content/uploads/Distinct-Count-768x275.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 10. Distinct Count</p> </div>
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<p>In the example, we see the addition of a few geography ids causing the number of distinct values to vary as well as changes in the distribution of the customers in various geographies.</p> </div>
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<p>Model Drift is the other side of the coin that is affected mainly due to data drift. It refers to degradation in model performance due to changes in data and outdatedness of the model parameters. In a machine learning system only fixing the data drift will not be sufficient and separate techniques will have to be used to detect model drift against production data and model.</p><p>Data Drift affects not just ML models but any system that works with functions, aggregates, and systems where statistical tests are being performed. Gradual changes over time creep up in the datasets, resulting in lower <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-quality-testing/" target="_blank" rel="noopener">data quality</a></span> and model quality.</p><p>Detecting data drift is often de-prioritized, but it doesn’t have to be complex — it can be easily deployed, documented, and monitored using the DataOps Profiling Nodes covered above. This ensures that any type of drift, whether in metrics, domains, patterns, or keys, is identified early, before it causes severe dips in model or pipeline quality.</p> </div>
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<details>
<summary>1) What is data drift?</summary>
<p>
Data drift refers to changes in the characteristics of input data over time, such
as shifts in value distributions, frequencies, or aggregated metrics. While these
changes often reflect real-world trends rather than data errors, they can affect
downstream analytics, reporting, and machine learning performance if left
unmonitored.
</p>
</details>
<details>
<summary>2) What are the different types of data drift?</summary>
<p>
Data drift is commonly categorized into four types: <strong>Sudden Drift</strong>,
where changes occur abruptly; <strong>Gradual Drift</strong>, where values shift
slowly over time; <strong>Incremental Drift</strong>, involving small continuous
changes that accumulate; and <strong>Recurring Drift</strong>, where predictable
patterns reappear, such as seasonal fluctuations.
</p>
</details>
<details>
<summary>3) How does DataOps Suite detect data drift?</summary>
<p>
DataOps Suite uses Profiling Nodes to establish statistical baselines for datasets,
including metrics such as mean, minimum and maximum values, standard deviation,
skewness, and kurtosis. It detects drift by comparing new data against these
historical profiles and highlighting significant deviations.
</p>
</details>
<details>
<summary>4) What’s the difference between data drift and model drift?</summary>
<p>
Data drift refers to changes in the input data distribution, whereas model drift
occurs when a machine learning model’s predictive accuracy declines over time.
Although data drift can contribute to model drift, each requires its own monitoring
and validation strategy.
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<p>The post <a href="https://www.datagaps.com/blog/data-drift-using-dataops-data-profiling/">Data Drift Using DataOps Data Profiling</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></content:encoded>
</item>
<item>
<title>ERP Implementations Still Fail at Alarming Rates – Here’s Why Testing Automation With Robust Data Validation Is the Fix</title>
<link>https://www.datagaps.com/blog/erp-implementation-failures-testing-automation-data-validation/</link>
<comments>https://www.datagaps.com/blog/erp-implementation-failures-testing-automation-data-validation/#respond</comments>
<dc:creator><![CDATA[Adithya Buddhavarapu]]></dc:creator>
<pubDate>Thu, 04 Jun 2026 14:57:39 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[ETL Testing]]></category>
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<description><![CDATA[<p>Citing recent ERP Implementation Failure Statistics research, this post explains why most ERP implementations still fail to meet their objectives, even with strong budgets and vendor support. It breaks down the most common root causes—weak change management, poor data migration, and inexperienced implementation teams—and argues that testing automation systematically addresses most of them. The post […]</p>
<p>The post <a href="https://www.datagaps.com/blog/erp-implementation-failures-testing-automation-data-validation/">ERP Implementations Still Fail at Alarming Rates – Here’s Why Testing Automation With Robust Data Validation Is the Fix</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<p>Citing recent ERP Implementation Failure Statistics research, this post explains why most ERP implementations still fail to meet their objectives, even with strong budgets and vendor support. It breaks down the most common root causes—weak change management, poor data migration, and inexperienced implementation teams—and argues that testing automation systematically addresses most of them. The post highlights how manufacturing complexity escalates migration risk, and recommends treating testing as a continuous, first-class workstream rather than a final-phase checkbox.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>ERP failure rates remain persistently high</strong> — the majority of implementations still fail to meet their stated objectives, a pattern that has held steady across the industry for years despite strong budgets and executive backing.</li><li><strong>Poor data migration is a leading, preventable failure cause</strong> — alongside weak change management and inexperienced implementation teams, it accounts for the bulk of ERP project failures, and is precisely the kind of issue automated validation is built to catch.</li><li><strong>Manufacturing complexity directly escalates migration risk</strong> — simpler models like Make-to-Stock carry lower risk, while highly configurable models like Engineer-to-Order introduce far more custom logic and testing surface area.</li><li><strong>Automation pays for itself well beyond its upfront cost</strong> — a modest investment in test automation can prevent the much larger cost overruns typical of poorly tested ERP migrations, making it a fiduciary decision as much as a technical one.</li></ul> </div>
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<p>Modern ERP transformations require a dual focus on testing automation and datavalidation to ensure quality, accuracy, and long-term system reliability.</p> </div>
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<img loading="lazy" decoding="async" width="1200" height="534" src="https://www.datagaps.com/wp-content/uploads/Validation-vs-Migration-Effort-Analytical-View-1.jpg" class="attachment-full size-full wp-image-52007" alt="Validation vs Migration Effort Analytical View" srcset="https://www.datagaps.com/wp-content/uploads/Validation-vs-Migration-Effort-Analytical-View-1.jpg 1200w, https://www.datagaps.com/wp-content/uploads/Validation-vs-Migration-Effort-Analytical-View-1-300x134.jpg 300w, https://www.datagaps.com/wp-content/uploads/Validation-vs-Migration-Effort-Analytical-View-1-1024x456.jpg 1024w, https://www.datagaps.com/wp-content/uploads/Validation-vs-Migration-Effort-Analytical-View-1-768x342.jpg 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> </div>
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<p>S/4HANA success is driven by a strong foundation built on both testing automation and data validation, ensuring processes run correctly and data drives the right decisions.</p> </div>
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I recently came across Godlan’s <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://godlan.com/erp-implementation-failure-statistics/" target="_blank" rel="noopener">2025 ERP Implementation Failure Statistics research</a></span></span>, and the numbers stopped me cold. Not because they were surprising — anyone who’s lived through a botched ERP rollout knows the pain — but because the industry keeps repeating the same mistakes, year after year, at an industrial scale. </div>
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<p>Let me walk you through what the data says, why it matters for anyone planning an SAP S/4HANA migration, and what I believe is the single most impactful lever to bend these failure curves: testing automation.</p> </div>
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<img loading="lazy" decoding="async" width="1200" height="534" src="https://www.datagaps.com/wp-content/uploads/SAP-Landscape-for-Data-Migration-ECC-to-S-4HANA.jpg" class="attachment-full size-full wp-image-52008" alt="SAP Landscape for Data Migration ECC to S/4HANA" srcset="https://www.datagaps.com/wp-content/uploads/SAP-Landscape-for-Data-Migration-ECC-to-S-4HANA.jpg 1200w, https://www.datagaps.com/wp-content/uploads/SAP-Landscape-for-Data-Migration-ECC-to-S-4HANA-300x134.jpg 300w, https://www.datagaps.com/wp-content/uploads/SAP-Landscape-for-Data-Migration-ECC-to-S-4HANA-1024x456.jpg 1024w, https://www.datagaps.com/wp-content/uploads/SAP-Landscape-for-Data-Migration-ECC-to-S-4HANA-768x342.jpg 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> </div>
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<h2 class="elementor-heading-title elementor-size-default">The Numbers Are Brutal</h2> </div>
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Godlan’s research, drawing on Panorama Consulting Group’s 2025 ERP Report and
Gartner analysis, paints a stark picture: </div>
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<h3 class="elementor-heading-title elementor-size-default">Industry-wide ERP implementation failure rates:</h3> </div>
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<p>• <strong>68%</strong> of ERP implementations fail to meet their objectives — and that’s theaverage <br />• <strong>73%</strong> failure rate for discrete manufacturing specifically <br />• <strong>189%</strong> average budget overrun across all industries <br />• <strong>215%</strong> budget overrun in discrete manufacturing <br />•<strong> 25–30%</strong> timeline extensions beyond original plans <br />• Only<strong> 27–32%</strong> of projects actually achieve their stated objectives</p> </div>
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That last number deserves a pause. Fewer than one in three ERP projects delivers what
was promised. And Gartner’s forward-looking analysis projects that 70% of ERP
implementations over the next three years will fail to meet objectives. </div>
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<p>These aren’t fringe projects failing. These are major enterprise investments often tensof millions of dollars that go sideways despite massive budgets, executive sponsorship, and vendor involvement.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">The Root Causes Are Predictable (and Preventable)</h2> </div>
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Godlan’s analysis of over 2,400 ERP implementations identified consistent failure patterns. The top root causes and their frequency: </div>
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<img loading="lazy" decoding="async" width="1200" height="534" src="https://www.datagaps.com/wp-content/uploads/SAP-Data-Migration-Stages-with-Pre-Post-Validation.jpg" class="attachment-full size-full wp-image-52009" alt="SAP-Data-Migration Stages with Pre & Post Validation" srcset="https://www.datagaps.com/wp-content/uploads/SAP-Data-Migration-Stages-with-Pre-Post-Validation.jpg 1200w, https://www.datagaps.com/wp-content/uploads/SAP-Data-Migration-Stages-with-Pre-Post-Validation-300x134.jpg 300w, https://www.datagaps.com/wp-content/uploads/SAP-Data-Migration-Stages-with-Pre-Post-Validation-1024x456.jpg 1024w, https://www.datagaps.com/wp-content/uploads/SAP-Data-Migration-Stages-with-Pre-Post-Validation-768x342.jpg 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> </div>
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<p>• <strong>Inadequate change management</strong> — 42% of failures <br />• <strong>Poor data migration</strong> — 38% <br />• <strong>Inexperienced implementation teams</strong> — 35% <br />• <strong>Lack of executive sponsorship</strong> — 31% <br />• <strong>Insufficient end-user training</strong> — 29% <br />•<strong> Scope creep</strong> — 26% <br />• <strong>Over-customization</strong> — 23% <br />• <strong>Vendor selection errors</strong> — 19%</p> </div>
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<p>The top three causes alone – change management, data migration, and team inexperience — account for over 75% of failures. And here’s what struck me: every single one of these failure modes is amplified by inadequate testing, and most of them are detectable through proper test automation before they become production crises.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">Think about it:</h3> </div>
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Poor data migration (38% of failures) is precisely the problem that automated data validation catches. When you’re moving hundreds of thousands of material master records, customer masters, vendor records, and BOMs from ECC to S/4HANA, manual spot-checking misses the long tail of data corruption, truncation, and transformation errors. Automated comparison scripts that verify source-to-target integrity field by field, table by table, catch what human eyes cannot. The Complexity Escalation Is Real </div>
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<p>One of the most useful frameworks in Godlan’s research is the business model risk analysis. Implementation risk doesn’t stay flat — it escalates dramatically based on operational complexity:</p> </div>
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<p>• <strong>Make-to-Stock</strong> — Medium risk (65/100) <br />• <strong>Make-to-Order</strong> — High risk (78/100) <br />• <strong>Configure-to-Order</strong> — Very High risk (85/100) <br />•<strong> Engineer-to-Order</strong> — Critical risk (92/100)</p> </div>
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<p>This matters enormously for SAP S/4HANA migrations. The more complex your manufacturing model, the more business logic is encoded in custom code, BOM structures, routing configurations, and pricing rules and the more surface area there is for migration defects.</p> </div>
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Manual testing simply cannot cover this surface area. A configure-to-order
manufacturer might have thousands of configuration variants, each producing different
BOMs and routing sequences. Testing even 5% of those combinations manually would
take months. Automated parameterized tests can cover them in hours. </div>
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<h2 class="elementor-heading-title elementor-size-default">Testing Automation as the Common Denominator </h2> </div>
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<p>Testing automation has emerged as the common denominator across successful ERP implementations especially in complex S/4HANA transformations where speed, scale, and accuracy are critical. In modern implementations, it is most effective when consistently used along with data validation as a standard practice, not an option</p> </div>
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Here’s my thesis: testing automation doesn’t just address one root cause of ERP failure — it systematically mitigates the majority of them. </div>
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<p><strong>Accelerates project timelines</strong>, enabling rapid testing cycles alongside continuous data validation during iterative migrations</p><p><strong>Enables early detection of both system defects and data inconsistencies</strong>, preventing issues from reaching production</p><p><strong>Change management failures?</strong> Automated test suites demonstrate to end users and stakeholders that the new system works. They build confidence through evidence, not promises.</p><p><strong>Data migration failures?</strong> Automated source-to-target validation catches discrepancies at scale before go-live, not after. </p><p><strong>Inexperienced teams?</strong> A well-designed test automation framework provides guardrails. it encodes the business process knowledge that experienced consultants carry in their heads, making it available to the entire project team.<br /><br /><strong>Scope creep?</strong> Automated regression testing gives project leaders the confidence to say “the current scope works” and the data to evaluate whether proposed additions are worth the risk.<br /><strong><br />Over-customization?</strong> Automated tests that validate standard vs. custom behavior help teams identify where customization adds value vs. where it introduces risk. <br /><br />The organizations that beat the 68–73% failure rate aren’t doing anything exotic. They’re investing in structured, automated quality assurance from day one of the project not bolting it on at the end when everything is already on fire.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">The Cost of Inaction vs. the Cost of Automation</h2> </div>
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<p>Let’s put the Godlan numbers in financial context. If the average ERP implementation runs 189–215% over budget, and a mid-market SAP S/4HANA migration typically budgets $5–15 million, the overrun exposure is $9.5–32 million.</p> </div>
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<p>Meanwhile, a well-structured test automation initiative including tool licensing, framework development, and test creation typically runs 5–10% of total project budget and delivers ROI within 4–7 months.</p><p>The Forrester Total Economic Impact study on Tricentis SAP QA solutions documented 403% ROI over three years.</p> </div>
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<p>The asymmetry is stark: spend 5–10% upfront on automation to avoid 100–115% in cost overruns. That’s not a technology decision. That’s a fiduciary one.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">What Should You Do About It?</h2> </div>
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<p>If you’re planning, mid-flight, or recovering from an SAP S/4HANA migration, here’s what the data suggests:</p> </div>
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1. Treat testing as a first-class workstream, not a phase. </span>
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Testing should start in discovery and run continuously through hypercare. The organizations that succeed embed quality engineering from day one. </p>
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2. Automate data migration validation early. </span>
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Don't wait until your third mock
migration to discover that 20% of your material masters are corrupted. Build
automated comparison scripts after your first test load. </p>
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3. Invest in end-to-end process automation, not just unit tests. </span>
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The defects that kill ERP go-lives aren't syntax errors — they're cross-module process failures. Order-to-cash, procure-to-pay, plan-to-produce: these need automated end-to
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4. Build the regression suite as a permanent asset. </span>
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S/4HANA updates come faster than ECC. The regression suite you build during migration becomes your insurance policy for every future release. </p>
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5. Choose implementation partners with testing DNA. </span>
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The Godlan research is clear: inexperienced teams are a top-three failure driver. Your implementation partner should have a proven test automation methodology, not a slide deck about one. </p>
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<h2 class="elementor-heading-title elementor-size-default">Final Thought</h2> </div>
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<p>The ERP implementation failure statistics haven’t improved meaningfully in a decade. The industry keeps building billion-dollar systems and testing them with spreadsheets and hope. The organizations that break the pattern are the ones that treat quality as <br />infrastructure – automated, repeatable, and non-negotiable.</p> </div>
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Testing automation with data validation is not optionalit is critical in S/4HANA because: </div>
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<p>• <strong>Systems are real-time and highly integrated,</strong> requiring both automated testing and validated data to ensure accuracy across processes</p><p>• <strong>Errors directly affect business operations,</strong> making it essential to validate both system behavior and the data driving it<br /><br />• <strong>Fixing issues later is costly,</strong> especially when both defects and data inconsistencies are embedded in production<br /><br />• <strong>Clean, validated data combined with automated testing</strong> ensures a successful and stable transformation</p> </div>
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<p>Testing automation with data validation creates a controlled and reliable environment where both system functionality and data accuracy are continuously verified across every stage of the S/4HANA migration. </p> </div>
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<p>“In S/4HANA, testing automation with data validation is not just a technical requirement – it is a business-critical discipline that directly determines the success or failure of the entire implementation”.</p><p>The data is clear. The question is whether you’ll act on it.</p> </div>
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<p><em>Statistics referenced from Godlan’s <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://godlan.com/erp-implementation-failure-statistics/" target="_blank" rel="noopener">2025 ERP Implementation Failure Statistics research</a></span></span>: citing Panorama Consulting Group’s 2025 ERP Report and Gartner analysis.</em></p> </div>
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<p>Also read : <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/blog/sap-material-master-migration-testing-automation-s4hana" target="_blank" rel="noopener">Sap Material Master Migration Testing Automation S4Hana</a></span> </p> </div>
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<h2 class="elementor-heading-title elementor-size-default">FAQ's</h2> </div>
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<div class="eael-adv-accordion" id="eael-adv-accordion-613a8e8" data-scroll-on-click="no" data-scroll-speed="300" data-accordion-id="613a8e8" data-accordion-type="accordion" data-toogle-speed="300">
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="1" aria-controls="elementor-tab-content-1011"><h3 class="eael-accordion-tab-title">Why do last-minute data issues arise in UAT?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1011" class="eael-accordion-content clearfix" data-tab="1" aria-labelledby="faq-1"><p>Because business users identify real-world mismatches not caught in earlier testing.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-1012"><h3 class="eael-accordion-tab-title">Why is incomplete business validation a major mistake in UAT? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1012" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-1"><p>It allows technically correct but business-incorrect data to move into production.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="3" aria-controls="elementor-tab-content-1013"><h3 class="eael-accordion-tab-title">Why do critical failures occur post go-live despite successful migrations? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1013" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-1"><p>Because real transactional loads expose hidden master data inconsistencies.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-1014"><h3 class="eael-accordion-tab-title">Why is dependency on “technical success” instead of “data accuracy” a mistake?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1014" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-1"><p>Data may load successfully but still fail during actual business execution.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="5" aria-controls="elementor-tab-content-1015"><h3 class="eael-accordion-tab-title">Why is lack of data consistency across landscapes a common issue? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1015" class="eael-accordion-content clearfix" data-tab="5" aria-labelledby="faq-1"><p>Because changes made in one system (DEV) are not synchronized properly across QA and PRD.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="6" aria-controls="elementor-tab-content-1016"><h3 class="eael-accordion-tab-title">Why do data inconsistencies originate in the DEV landscape?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1016" class="eael-accordion-content clearfix" data-tab="6" aria-labelledby="faq-1"><p>Because incomplete validation rules in DEV allow incorrect configurations to pass into higher environments.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="7" aria-controls="elementor-tab-content-1017"><h3 class="eael-accordion-tab-title">Why do migration issues often go unnoticed in QA?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1017" class="eael-accordion-content clearfix" data-tab="7" aria-labelledby="faq-1"><p>Because test data is limited and does not fully simulate real production scenarios.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="8" aria-controls="elementor-tab-content-1018"><h3 class="eael-accordion-tab-title">Why is pre-migration validation considered a critical success factor?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1018" class="eael-accordion-content clearfix" data-tab="8" aria-labelledby="faq-1"><p>Incorrect data migration leads to faulty transactions, reporting issues, and business disruptions.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="9" aria-controls="elementor-tab-content-1019"><h3 class="eael-accordion-tab-title">Why is missing reconciliation between legacy and target systems a mistake?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1019" class="eael-accordion-content clearfix" data-tab="9" aria-labelledby="faq-1"><p>It leads to mismatched stock, valuation, and reporting after migration.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="10" aria-controls="elementor-tab-content-10110"><h3 class="eael-accordion-tab-title">Why is repeated data cleansing ignored across cycles?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-10110" class="eael-accordion-content clearfix" data-tab="10" aria-labelledby="faq-1"><p>Because teams assume initial fixes are sufficient, allowing recurring errors to persist.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="11" aria-controls="elementor-tab-content-10111"><h3 class="eael-accordion-tab-title">Why is absence of automated validation checks a major gap?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-10111" class="eael-accordion-content clearfix" data-tab="11" aria-labelledby="faq-1"><p>Manual validations miss large-scale inconsistencies in complex datasets.</p></div>
</div></div> </div>
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Adithya Buddhavarapu </a>
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Advisor & Co-Founder, Datagaps </p>
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<p>Cofounder and Advisor at Datagaps. Deep expertise in enterprise data platforms, BI, and analytics architecture.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/erp-implementation-failures-testing-automation-data-validation/">ERP Implementations Still Fail at Alarming Rates – Here’s Why Testing Automation With Robust Data Validation Is the Fix</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<title>Testing Automation of Material Master in SAP During Migration to S/4HANA</title>
<link>https://www.datagaps.com/blog/sap-material-master-migration-testing-automation-s4hana/</link>
<comments>https://www.datagaps.com/blog/sap-material-master-migration-testing-automation-s4hana/#respond</comments>
<dc:creator><![CDATA[Adithya Buddhavarapu]]></dc:creator>
<pubDate>Thu, 04 Jun 2026 14:54:51 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[ETL Testing]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=49939</guid>
<description><![CDATA[<p>As 59% of companies now run S/4HANA (up from 2024), Material Master migration remains a high-risk blind spot—touching procurement, inventory, sales, and finance. This post explains why manual testing can’t scale across hundreds of thousands of records, how S/4HANA’s shift to real-time MATDOC-based stock calculation changes testing requirements, and outlines four test types: data migration […]</p>
<p>The post <a href="https://www.datagaps.com/blog/sap-material-master-migration-testing-automation-s4hana/">Testing Automation of Material Master in SAP During Migration to S/4HANA</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
<content:encoded><![CDATA[ <div data-elementor-type="wp-post" data-elementor-id="49939" class="elementor elementor-49939" data-elementor-post-type="post">
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<p>As 59% of companies now run S/4HANA (up from 2024), Material Master migration remains a high-risk blind spot—touching procurement, inventory, sales, and finance. This post explains why manual testing can’t scale across hundreds of thousands of records, how S/4HANA’s shift to real-time MATDOC-based stock calculation changes testing requirements, and outlines four test types: data migration validation, functional regression, custom code validation, and performance testing. It closes with a five-phase automation framework spanning pre-migration through post-go-live hypercare.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>Material Master is a high-risk, low-visibility migration point</strong> — a single inconsistency in MARA or MARC tables can cascade across procurement, inventory, sales, and finance on day one of go-live.</li><li><strong>S/4HANA fundamentally changes the underlying data model</strong> — stock values are now calculated in real time via MATDOC and CDS views rather than stored statically in MARD/MARC, changing how custom code and reports must be tested.</li><li><strong>Four test types are required for full coverage</strong> — data migration validation, functional regression testing (P2P, O2C, Plan-to-Produce), custom code validation, and performance testing under realistic transaction loads.</li><li><strong>A five-phase framework structures the automation effort</strong> — pre-migration baselining, mock migration cycles, dress rehearsal/mock cutover, go-live validation, and hypercare regression over the following 2-4 weeks.</li></ul> </div>
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<p>The clock is ticking. SAP’s 2027 mainstream maintenance deadline for ECC is driving a massive wave of S/4HANA migrations, with 59% of companies now fully or partially live on S/4HANA as of late 2025 — up 13 points from 2024. Yet one of the most underestimated risks in every migration sits quietly in the background: the Material Master.</p> </div>
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<p>Material Master isn’t glamorous. It doesn’t get keynote stage time. But it touches everything — procurement, inventory, sales, production planning, quality management, finance. A single data inconsistency in your MARA or MARC tables can cascade through your entire supply chain on day one of go-live. And when you’re migrating hundreds of thousands (or millions) of material records from ECC to S/4HANA, manual testing simply doesn’t scale.</p> </div>
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<img loading="lazy" decoding="async" width="1200" height="534" src="https://www.datagaps.com/wp-content/uploads/Material-Master-Integration-Issues-Across-SAP-Modules.jpg" class="attachment-full size-full wp-image-52026" alt="Material Master Integration Issues Across SAP Modules" srcset="https://www.datagaps.com/wp-content/uploads/Material-Master-Integration-Issues-Across-SAP-Modules.jpg 1200w, https://www.datagaps.com/wp-content/uploads/Material-Master-Integration-Issues-Across-SAP-Modules-300x134.jpg 300w, https://www.datagaps.com/wp-content/uploads/Material-Master-Integration-Issues-Across-SAP-Modules-1024x456.jpg 1024w, https://www.datagaps.com/wp-content/uploads/Material-Master-Integration-Issues-Across-SAP-Modules-768x342.jpg 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> </div>
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<p>This blog lays out why Material Master testing automation is non-negotiable during S/4HANA migration, what changes in the data model demand it, and how to approach it practically.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why Material Master Is the Migration Minefield</h2> </div>
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<img loading="lazy" decoding="async" width="1200" height="572" src="https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Key-Focus-AreasSimple.jpg" class="attachment-full size-full wp-image-52027" alt="Material Master Data Migration Key Focus Areas(Simple)" srcset="https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Key-Focus-AreasSimple.jpg 1200w, https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Key-Focus-AreasSimple-300x143.jpg 300w, https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Key-Focus-AreasSimple-1024x488.jpg 1024w, https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Key-Focus-AreasSimple-768x366.jpg 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> </div>
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<p>Material Master is often called a “<strong>migration minefield</strong>” because it is one of the <strong>most complex, interdependent, and business-critical data objects in SAP</strong>. Even small inconsistencies can cascade into major operational issues across procurement, production, sales, and finance.</p> </div>
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<p><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.sap.com/" target="_blank" rel="noopener">In SAP</a></span></span>, a material master is not a single entity but a collection of multiple views including Basic Data, Sales, Purchasing, MRP, Plant Data, Storage Location, Accounting, Costing, and Quality Management. Each view aligns with specific organizational levels and is supported by different underlying tables, creating a highly distributed data structure. This multi-dimensional complexity makes material master data one of the most sensitive and error-prone areas during migration.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">During an S/4HANA migration, several things change simultaneously: </h3> </div>
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<p>The data model <strong>has fundamentally shifted in S/4HANA.</strong> While the core Material Master tables (MARA, MARC, MARD, MBEW) still exist, <strong>they are no longer always the primary source of truth for transactional data.</strong> Inventory quantities in tables like <strong>MARD are now derived rather than persistently stored for reporting purposes</strong> when a material document is posted.</p> </div>
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<p>Instead, stock values are <strong>calculated in real time using the MATDOC table and accessed via CDS views.</strong> The old aggregate and index tables <strong>have been removed as part of the S/4HANA data simplification initiative </strong>and replaced by CDS view proxies.</p> </div>
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<p>This means any custom code or reports that read stock fields from MARD or MARC<strong> may now retrieve data through compatibility views or CDS layers rather than direct physical storage,</strong> and <strong>the performance behavior, data accuracy, and read patterns have fundamentally changed.</strong></p> </div>
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The Business Partner migration complicates vendor relationships. </span>
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In ECC, vendor masters lived separately. In S/4HANA, they're merged into the Business Partner framework. Material Master records with vendor-specific info (source lists, purchasing info records, quota arrangements) need their vendor references reconciled against the new BP structure. This is a cross-domain dependency that's easy to miss in isolated Material Master testing. </p>
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Custom fields and Z-tables are everywhere. </span>
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Most ECC systems are heavily customized. Custom fields appended to MARA, MARC, or MBEW need to be carried forward through the S/4HANA Migration Cockpit (LTMC/LTMOM) using BAPI extension structures like BAPI_TE_E1MARA and BAPI_TE_E1MARC. If the field selection group assignments (T-code OMSR) aren't configured correctly, data simply won't make it to the target database. This is the kind of silent failure that only shows up if you're testing at scale. </p>
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Data quality issues that were tolerable in ECC become blockers in S/4HANA. </span>
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Duplicate materials, incomplete mandatory fields, mismatched units of measure, inconsistent material type assignments — all of these can cause the SUM/DMO conversion process to fail or produce corrupt records. One global food manufacturer found a 20% duplication rate in their Material Master during pre-migration audit. </p>
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<img loading="lazy" decoding="async" width="1200" height="534" src="https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Top-5-Real-Time-Challenges.jpg" class="attachment-full size-full wp-image-52028" alt="Material Master Data Migration Top 5 Real Time Challenges" srcset="https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Top-5-Real-Time-Challenges.jpg 1200w, https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Top-5-Real-Time-Challenges-300x134.jpg 300w, https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Top-5-Real-Time-Challenges-1024x456.jpg 1024w, https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Top-5-Real-Time-Challenges-768x342.jpg 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> </div>
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<h2 class="elementor-heading-title elementor-size-default">What Material Master Testing Actually Looks Like </h2> </div>
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<p>Testing Material Master during an S/4HANA migration isn’t a single activity. It spans multiple test types, each of which benefits enormously from automation:</p> </div>
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1. Data Migration Validation </span>
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This is the most obvious layer: verifying that every material record migrated correctly from ECC to S/4HANA. </p>
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<strong>For automated testing, this means</strong> </div>
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<p>• Record count reconciliation across source (ECC) and target (S/4HANA) for every Material Master table — MARA, MARC, MARD, MBEW, MAKT, MVKE, and custom extensions.</p><p>• Field-by-field comparison for a statistically significant sample (or ideally all records), checking that values in every view transferred accurately.</p><p>• Checksum validation helps detect subtle data issues such as truncated descriptions, character encoding problems in the 40-character MAKTX field, and unit of measure mismatches.<br /><br />• Cross-referencing material-to-vendor relationships against the migrated Business Partner records.<br /><br />• <strong>Material type and valuation class validation</strong>, ensuring correct account determination and financial postings in S/4HANA.<br /><br />• Validation of custom (Z) fields through BAPI extension structures, confirming that enhancements in MARA/MARC are correctly populated in the target system.<br /><br />• Integration validation with dependent objects, such as pricing conditions, BOMs, and purchasing info records, to ensure materials function correctly in end-to end processes.<br /><br />• Data completeness checks, ensuring mandatory fields required in S/4HANA (e.g., Business Partner linkage, valuation data) are not missing.</p> </div>
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Automating this with tools like Tricentis Tosca, SAP CBTA, or even purpose-built SQL/ABAP comparison scripts can reduce what would be weeks of manual spot checking into hours of comprehensive, repeatable validation. </div>
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2. Functional Regression Testing </span>
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Once the data lands in S/4HANA, does it actually work? Can you create a purchase order for a migrated material? Does MRP run correctly against the migrated plant data? Does the material show up in Fiori apps the way users expect? </div>
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<p>Functional regression for Material Master means automating end-to-end business process scenarios that exercise the migrated data:</p> </div>
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<p>•<strong> Procure-to-Pay (P2P): </strong>Create a purchase requisition → convert to PO → goods receipt → invoice verification, all using migrated materials</p><p><strong>• Order-to-Cash (O2C):</strong> Create a sales order → delivery → billing using migrated materials with sales org data</p><p><strong>• Plan-to-Produce:</strong> Run MRP for migrated materials, verify planned orders, confirm production orders</p><p><strong>• Inventory Management:</strong> Post goods movements (MIGO) for migrated materials, verify stock levels in the new MATDOC-based data model.</p><p><strong>• Account determination validation,</strong> confirming that goods movements and invoices post correctly to the right GL accounts based on valuation class and material type.</p><p><strong>• Cross-module integration validation,</strong> ensuring material data works consistently across MM, SD, PP, and FI without breaks in data flow.</p><p>• <strong>Fiori app validation and user behavior checks,</strong> confirming that migrated materials appear correctly in apps like Manage Product Master Data, Stock Overview, and Create Purchase Order</p><p><strong>• Warehouse and storage integration validation,</strong> ensuring materials function properly with WM/EWM processes, including bin determination and stock placement</p><p><strong>• Tax and compliance validation,</strong> confirming that materials trigger correct tax codes and localization logic across regions</p><p><strong>• Batch management and serial number validation,</strong> ensuring batch-controlled or serialized materials behave correctly in procurement, production, and delivery processes</p><p><strong>• Availability check (ATP) validation,</strong> verifying that stock availability and confirmation logic work correctly with migrated inventory data</p> </div>
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<p>These scenarios should be scripted and parameterized so they can run against hundreds of representative materials, not just the three or four that someone happened to pick for manual testing.</p> </div>
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3. Custom Code Validation </span>
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S/4HANA's Simplification List identifies thousands of changes that affect custom ABAP code. For Material Master specifically, any custom code that directly reads from deprecated tables, uses obsolete function modules, or references fields that have been removed or repurposed needs to be identified and tested. </p>
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Automated custom code scanning (using SAP’s Custom Code Migration app or the ATC checks in Eclipse/ADT) should be followed by automated functional tests of every Z program, Z-report, and user exit that touches Material Master data. The goal is to catch the programs that pass the static code check but still produce wrong results because of the changed data model semantics. </div>
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4. Performance Testing </span>
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This is the layer most teams skip — and pay for dearly after go-live. The shift from statically maintained stock fields to dynamically calculated CDS views means that transactions and reports reading MARD or MARC stock data will behave differently under load. A report that ran in 3 seconds in ECC against pre-aggregated stock tables might take 30 seconds in S/4HANA if the MATDOC table has millions of entries and the CDS view stack isn't optimized. </p>
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<p>Automated performance testing should simulate realistic transaction volumes for key Material Master operations: mass material creation (MM01/API), MRP runs across plant level data, stock overview queries (MMBE), and batch material document postings. Identify the performance cliffs before your users find them.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Building the Automation Framework</h2> </div>
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<p>Here’s a practical approach to structuring Material Master test automation for an S/4HANA migration:</p> </div>
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<strong>Phase 1: Pre-Migration (ECC Side)</strong> Extract baseline data from ECC Material Master
tables. Build automated comparison datasets. Identify the full inventory of custom
fields, custom code, and cross-module dependencies. This is your “source of truth”
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<p><strong>Phase 2: Mock Migration Cycles</strong> Run the migration (via Migration Cockpit or SUM/DMO) in a sandbox environment. Execute the full automated test suite – <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener">data validation</a></span></span>, functional regression, custom code validation. Log every discrepancy. Fix, re-migrate, re-test. This cycle typically runs 3–5 times before the data and configuration are clean enough for dress rehearsal.</p> </div>
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<p><strong>Phase 3: Dress Rehearsal / Mock Cutover</strong> Full-scale migration in a production-mirror environment. Complete automated test suite plus performance testing under simulated production load. This is where you validate not just data correctness but also cutover timing and rollback procedures.</p> </div>
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<p><strong>Phase 4: Go-Live Validation</strong> Smoke test suite runs immediately post-cutover. Automated checks confirm record counts, critical material availability, and key transaction execution. Any failures trigger the rollback decision.</p> </div>
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<div class="elementor-widget-container">
<p><strong>Phase 5: Hypercare Regression</strong> Continuous automated regression during the first 2–4 weeks post-go-live, catching issues that emerge as users interact with migrated data in real business scenarios. SAP delivers S/4HANA updates at a faster cadence than ECC, so the regression suite you build here becomes a permanent asset.</p> </div>
</div>
</div>
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<h2 class="elementor-heading-title elementor-size-default">Tool Landscape</h2> </div>
</div>
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<p>For <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-migration-testing-automation/" target="_blank" rel="noopener">data migration validation</a></span> specifically, purpose-built SQL comparison scripts (running against both ECC and S/4HANA databases) or tools like Precisely’s Automate Evolve can validate millions of records with checksum and business-rule logic that goes beyond simple row counting.</p> </div>
</div>
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<h2 class="elementor-heading-title elementor-size-default">The Cost of Not Automating</h2> </div>
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<p>The math is straightforward. A typical mid-size manufacturer has 200,000+ material records across dozens of plants. Each record has 15–20 views. Manual testing of even 1% of records across all views would take months. And a single missed defect – a wrong unit of measure in a purchasing view, a missing MRP profile at one plant – can halt production lines or create procurement chaos on day one.</p> </div>
</div>
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<p>The 2026 ASUG/Precisely survey found that 49% of organizations cite business process change as their top migration barrier, and data quality emerged as a critical but often overlooked challenge. Automation doesn’t just accelerate testing – it’s the only way to achieve the coverage required to de-risk a Material Master migration at enterprise scale.</p> </div>
</div>
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<p>Also read : <span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/blog/erp-implementation-failures-testing-automation-data-validation" target="_blank" rel="noopener"><span style="text-decoration: underline;">Erp Implementation Failures Testing Automation Data Validation</span></a></span></p> </div>
</div>
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<h2 class="elementor-heading-title elementor-size-default">FAQ's</h2> </div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="1" aria-controls="elementor-tab-content-1241"><h3 class="eael-accordion-tab-title">Why is Material Master validation required before data migration?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1241" class="eael-accordion-content clearfix" data-tab="1" aria-labelledby="faq-1"><p>Validation is required to ensure that only accurate, complete, and consistent data is migrated into the target system. Poor-quality material data leads to downstream failures in procurement, planning, sales, and finance processes.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-1242"><h3 class="eael-accordion-tab-title">Why is cross-module validation (MM, SD, FI) required before migration? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1242" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-1"><p>Validation is required because material data impacts multiple modules. Even if data appears correct in MM, inconsistencies with SD or FI can result in end-to-end process failures</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="3" aria-controls="elementor-tab-content-1243"><h3 class="eael-accordion-tab-title">Why did MRP fail to generate purchase requisitions for materials? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1243" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-1"><p>Because procurement type (MARC-BESKZ) was incorrectly assigned in material master, leading to wrong planning behaviour.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-1244"><h3 class="eael-accordion-tab-title">Why is data consistency validation across tables required? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1244" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-1"><p>Validation is required to maintain referential integrity. Inconsistent data relationships can lead to system errors, incorrect reporting, and transaction failures.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="5" aria-controls="elementor-tab-content-1245"><h3 class="eael-accordion-tab-title">Why is validation of valuation class and account assignment consistency required during material migration? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1245" class="eael-accordion-content clearfix" data-tab="5" aria-labelledby="faq-1"><p>A batch of raw materials was migrated with an incorrect valuation class (mapped to finished goods accounts). As a result, inventory postings flowed into the wrong GL accounts, causing incorrect cost reporting and audit discrepancies. The issue went unnoticed until month-end financial closing, requiring extensive corrections.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="6" aria-controls="elementor-tab-content-1246"><h3 class="eael-accordion-tab-title">Why is validation of storage location stock data required before migration?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1246" class="eael-accordion-content clearfix" data-tab="6" aria-labelledby="faq-1"><p>During migration, storage location stock totals were not reconciled with plant-level stock. After go-live, inventory reports showed mismatches, and FI reported stock valuation differences. This resulted in manual adjustments and audit concerns, delaying financial closing</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="7" aria-controls="elementor-tab-content-1247"><h3 class="eael-accordion-tab-title">Why is validation of automatic account determination (OBYC) required before material master migration?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1247" class="eael-accordion-content clearfix" data-tab="7" aria-labelledby="faq-1"><p>During migration, valuation classes were loaded without validating OBYC configuration. After go-live, goods receipts failed with “Account determination error”, blocking procurement operations. In some cases, postings hit incorrect GL accounts, leading to financial misstatements and manual reclassification efforts.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="8" aria-controls="elementor-tab-content-1248"><h3 class="eael-accordion-tab-title">Why did subcontracting fail after material master migration?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1248" class="eael-accordion-content clearfix" data-tab="8" aria-labelledby="faq-1"><p>Because Special Procurement Keys (MARC-SOBSL) were incorrectly migrated, causing MRP to ignore subcontracting requirements.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="9" aria-controls="elementor-tab-content-1249"><h3 class="eael-accordion-tab-title">Why was batch traceability lost after material master migration?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1249" class="eael-accordion-content clearfix" data-tab="9" aria-labelledby="faq-1"><p>Because batch management indicator (MARC-XCHPF) was not properly maintained, breaking material tracking.</p></div>
</div></div> </div>
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<p>Cofounder and Advisor at Datagaps. Deep expertise in enterprise data platforms, BI, and analytics architecture.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/sap-material-master-migration-testing-automation-s4hana/">Testing Automation of Material Master in SAP During Migration to S/4HANA</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<item>
<title>Top 3 ETL Testing Tools: How to Choose the Best Tool</title>
<link>https://www.datagaps.com/blog/top-3-etl-testing-tools/</link>
<dc:creator><![CDATA[Raj Mohan Achanta]]></dc:creator>
<pubDate>Thu, 30 Apr 2026 19:05:05 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Databricks]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[ETL Testing]]></category>
<category><![CDATA[Snowflake]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=7034</guid>
<description><![CDATA[<p>ETL Testing refers to the testing, validation, and analysis of the Extraction, Transformation, and Loading Processes that are part of ETL and ELT Pipelines. As ETL testing refers to “Data-in-Motion” Testing, the unit test architecture and principles slightly differ from “Data-at-Rest” Testing (Warehouse/DB Validation).</p>
<p>The post <a href="https://www.datagaps.com/blog/top-3-etl-testing-tools/">Top 3 ETL Testing Tools: How to Choose the Best Tool</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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<h2 class="elementor-heading-title elementor-size-default">What are ETL Testing Tools?</h2> </div>
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<p><span style="text-decoration: underline;"><span style="color: #0000ff; text-decoration: underline;"><a style="color: #0000ff; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener"><span style="color: #1967d2; text-decoration: underline;">ETL testing tools</span></a></span></span> are purpose-built platforms that validate data as it moves through extract, transform, and load pipelines. As data pipelines become more complex, organizations rely on ETL testing tools to verify transformations, detect data issues, and maintain trust in analytics.</p><p>While many teams explore general ETL tools, it is important to distinguish between ETL tools used for data movement and ETL testing tools used for validation and quality assurance.</p><p>Looking for a structured starting point? Check out our <span style="text-decoration: underline;"><span style="color: #1967d2;"><a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/blog/how-to-validate-etl-testing-checklist/" target="_blank" rel="noopener">ETL Testing Checklist</a></span></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">When are ETL Testing Tools Used?</h2> </div>
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<p>ETL testing tools are primarily used across two major categories of projects where data accuracy is critical:</p> </div>
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1. Data Migration Projects </span>
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These involve moving data across systems while ensuring consistency and completeness. Common scenarios include: </p>
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<ul><li>Application migrations</li><li>Cloud migrations such as moving to <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/snowflake-testing-automation/" target="_blank" rel="noopener">Snowflake</a></span></span> or <span style="text-decoration: underline;"><span style="color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/databricks-testing-automation/" target="_blank" rel="noopener">Databricks</a></span></span></li><li>Data warehouse migrations such as Teradata to Redshift or Teradata to Databricks</li></ul> </div>
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<p>In these cases, ETL testing tools and data testing tools are essential for validating large-scale data movement and ensuring no data loss or transformation errors.</p><p>Need help with data migration? Explore our <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-migration-testing-automation/" target="_blank" rel="noopener">Data Migration Solution page</a>.</span></span></p> </div>
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<span >
2. Data Pipeline Testing </span>
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These focus on ongoing validation of data pipelines in production environments. Key use cases include: </p>
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<ul><li>Verifying data transformations across pipelines</li><li>Ensuring consistency between source and target systems</li><li>Detecting data quality issues early</li><li>Supporting continuous validation as pipelines scale Here, ETL automation testing tools help teams scale validation, reduce manual effort, and maintain data quality across evolving pipelines.<p>Read more on <span style="text-decoration: underline; color: #1967d2;"><a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/etl-testing/" target="_blank" rel="noopener">ETL Testing</a></span> for data pipeline environments.</p></li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Evaluation Criteria: How We Selected and Assessed ETL Testing Tools?</h2> </div>
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<p class="font-claude-response-body">Modern ETL testing tools are expected to deliver multi-source validation, transformation testing, automation, AI-assisted test creation, and scalability across large data environments. These capabilities formed the basis of our evaluation.</p> </div>
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<p class="font-claude-response-body">Several tools come up frequently in this space. iceDQ, Tosca DI, and Informatica DVO were considered but excluded for specific reasons:</p> </div>
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<p><strong>iceDQ:</strong> The on-premise version of iceDQ lacks several core ETL testing capabilities that enterprise teams typically require. The SaaS version is more feature-complete but not suited for teams that need on-premise deployment.</p><p><strong>Informatica DVO:</strong> Informatica DVO is not a standalone ETL testing tool. It runs only within the Informatica platform, making it irrelevant for teams outside that ecosystem.</p><p><strong>Tosca DI:</strong> While Tosca is a popular choice for application and UI testing, Tosca DI is found to be limited in scope for ETL testing and end-to-end pipeline validation, making it a less suitable option for teams with comprehensive data pipeline testing requirements.</p> </div>
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<p>ETL testing tools broadly fall into three categories: purpose-built ETL testing platforms, open-source tools, and developer-first testing frameworks. This comparison selects one representative from each category to highlight how different approaches address the same validation challenges. In this comparison, Datagaps ETL Validator represents the purpose-built category, Great Expectations the open-source data quality framework approach, and dbt Tests the developer-first framework.</p> </div>
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<p class="font-claude-response-body">Evaluation was based on nine criteria that reflect real production requirements: core ETL testing capabilities, automation and CI/CD integration, usability and test authoring, data quality and observability, data contracts and governance, testing scope and coverage, enterprise readiness, scalability and performance, and pricing and accessibility.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Top 3 ETL Testing Tools: Detailed Comparison</h2> </div>
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<p>Below is a detailed comparison of three widely considered options: <span style="text-decoration: underline;"><span style="color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">Datagaps ETL Validator</a></span></span>, Great Expectations, and dbt tests.</p> </div>
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<!-- ============================================================
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/* ===== Mobile ===== */
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</style>
<div class="etl-section">
<div class="etl-legend">
<div class="etl-legend__title">Legend</div>
<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--star">★</span>
<span>Unique / standout feature</span>
</div>
<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--check">✔</span>
<span>Strong / full support</span>
</div>
<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--half">◐</span>
<span>Partial / limited support</span>
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<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--cross">✘</span>
<span>Not supported / not available</span>
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<p class="etl-scroll-hint">← Scroll to see full table →</p>
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<table class="etl-table">
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<tr>
<th>Feature / Capability</th>
<th class="tool-col"><span class="etl-head-nowrap">Datagaps<br/>ETL Validator</span></th>
<th class="tool-col"><span class="etl-head-nowrap">Great Expectations</span></th>
<th class="tool-col"><span class="etl-head-nowrap">dbt Tests</span></th>
<th>Verdict</th>
</tr>
</thead>
<tbody>
<tr class="etl-cat-row"><td colspan="5">1. Core ETL Testing</td></tr>
<tr class="etl-data-row">
<td>ETL Test Authoring & Execution</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator is purpose-built for end-to-end ETL test authoring and execution. Great Expectations and dbt Tests define data quality checks but are not designed for full ETL test execution.</td>
</tr>
<tr class="etl-data-row">
<td>ELT / In-Database Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-check">✔</span></td>
<td>ETL Validator and dbt Tests push validation to the warehouse natively. ETL Validator leads on orchestration across multiple platforms. Great Expectations requires custom configuration.</td>
</tr>
<tr class="etl-data-row">
<td>Flat File / CSV Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator handles flat file and CSV validation natively. Great Expectations supports file-based validation with setup. dbt Tests are database-only.</td>
</tr>
<tr class="etl-data-row">
<td>Multiple Source / Target Support</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator supports multiple heterogeneous sources and targets in a single test run. Great Expectations supports multiple backends but requires per-datasource configuration. dbt Tests operate within a single warehouse.</td>
</tr>
<tr class="etl-data-row">
<td>Transformation Validation</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-check">✔</span></td>
<td>ETL Validator adds GenAI-assisted rule authoring across any ecosystem. dbt Tests are strong for validating dbt model outputs. Great Expectations validates expectations on data but is not transformation-aware.</td>
</tr>
<tr class="etl-data-row">
<td>Source-to-Target Reconciliation</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator uniquely supports Data Profile reconciliation across source and target. Great Expectations and dbt have no cross-system reconciliation capability.</td>
</tr>
<tr class="etl-data-row">
<td>Source-to-Report Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator validates the full chain from raw source through to the BI report layer. Neither Great Expectations nor dbt Tests reach the reporting layer.</td>
</tr>
<tr class="etl-data-row">
<td>Non-dbt Pipeline Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and Great Expectations can test pipelines outside dbt. dbt Tests are locked to dbt models.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">2. Automation & CI/CD</td></tr>
<tr class="etl-data-row">
<td>Automated Regression Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator adds GenAI-assisted test maintenance. Great Expectations supports checkpoint-based runs but lacks structured regression management. dbt Tests re-run on every invocation but have no dedicated regression management.</td>
</tr>
<tr class="etl-data-row">
<td>CI/CD Pipeline Integration</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-star">★</span></td>
<td>dbt Tests have first-class CI/CD integration. ETL Validator and Great Expectations both support CI/CD with broad pipeline trigger options.</td>
</tr>
<tr class="etl-data-row">
<td>Scheduled / Triggered Test Runs</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator supports native scheduling and REST API triggers. Great Expectations and dbt Tests rely on external orchestrators such as Airflow or Prefect.</td>
</tr>
<tr class="etl-data-row">
<td>Test Case Reusability</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td>All three support reusable test definitions. ETL Validator and Great Expectations offer reusable templates via their platforms.</td>
</tr>
<tr class="etl-data-row">
<td>Test Maintenance Overhead</td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">Medium-High</span></td>
<td><span class="sym-text">Medium-High</span></td>
<td>ETL Validator's GenAI-assisted maintenance significantly reduces upkeep. Great Expectations and dbt Tests require engineers to update definitions manually for every schema or pipeline change.</td>
</tr>
<tr class="etl-data-row">
<td>Cross-Pipeline Orchestration</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator orchestrates tests across multiple pipelines in a single run. Great Expectations is partial. dbt Tests are scoped to the dbt DAG.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">3. Usability & Test Authoring</td></tr>
<tr class="etl-data-row">
<td>No-Code / Visual Test Builder</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator is the only tool with a drag-and-drop no-code interface for ETL testing. Great Expectations and dbt Tests require coding.</td>
</tr>
<tr class="etl-data-row">
<td>Ease of Setup</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator deploys in days. Great Expectations requires configuration of datasources and expectation suites. dbt Tests require an existing dbt project.</td>
</tr>
<tr class="etl-data-row">
<td>Business User Accessibility</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator is designed for QA analysts and business users without coding skills. Great Expectations and dbt Tests both require Python or SQL proficiency.</td>
</tr>
<tr class="etl-data-row">
<td>GenAI / AI-Assisted Test Creation</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator generates tests automatically from ETL mapping documents using agentic AI, cutting initial test creation time by over 60%. Neither Great Expectations nor dbt Tests offer this.</td>
</tr>
<tr class="etl-data-row">
<td>Test Documentation & Visibility</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator provides customisable stakeholder dashboards. Great Expectations generates Data Docs but they are technical in nature. dbt generates docs automatically but visibility for non-engineers is limited.</td>
</tr>
<tr class="etl-data-row">
<td>Learning Curve</td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">High</span></td>
<td><span class="sym-text">High</span></td>
<td>ETL Validator is the fastest to productive use for any team profile. Great Expectations and dbt Tests require mastery of Python or the full dbt framework.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">4. Data Quality & Observability</td></tr>
<tr class="etl-data-row">
<td>Data Quality Monitoring</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator provides continuous DQ monitoring with scoring and alerting. Great Expectations supports expectation-based monitoring. dbt Tests run at job execution time only.</td>
</tr>
<tr class="etl-data-row">
<td>Anomaly Detection</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator automatically detects data anomalies across pipelines using AI. Neither Great Expectations nor dbt Tests offer automated anomaly detection.</td>
</tr>
<tr class="etl-data-row">
<td>Data Profiling</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator provides rich data profiling alongside test execution. Great Expectations offers profiling through its Profiler API. dbt Tests require separate tools.</td>
</tr>
<tr class="etl-data-row">
<td>Data Lineage</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-star">★</span></td>
<td>dbt auto-generates column-level lineage across the entire DAG. ETL Validator provides pipeline-level lineage tied to DQ scoring. Great Expectations has no lineage support.</td>
</tr>
<tr class="etl-data-row">
<td>DQ Scoring & Health Dashboards</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator uniquely provides quantified DQ scores and health dashboards across pipelines. Neither Great Expectations nor dbt offer this natively.</td>
</tr>
<tr class="etl-data-row">
<td>Alerting & Notifications</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator supports native alerting on test failures. Great Expectations and dbt alerting depend on the orchestration layer.</td>
</tr>
<tr class="etl-data-row">
<td>BI Regression Testing</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator's visual BI report regression testing across Power BI, Tableau, QuickSight, and Oracle Analytics has no equivalent in Great Expectations or dbt.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">5. Data Contracts & Governance</td></tr>
<tr class="etl-data-row">
<td>Data Contracts</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator supports formal data contracts across pipeline boundaries. Great Expectations supports expectation-as-contract patterns. dbt has partial support via dbt contracts (1.5+).</td>
</tr>
<tr class="etl-data-row">
<td>Schema Validation & Drift Detection</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td>All three detect schema drift. ETL Validator and dbt Tests are more automated. Great Expectations requires expectation suite updates.</td>
</tr>
<tr class="etl-data-row">
<td>Data Observability Integration</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator provides built-in observability across the full pipeline. dbt and Great Expectations integrate with third-party observability tools.</td>
</tr>
<tr class="etl-data-row">
<td>Audit Trails & Compliance Reporting</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator provides compliance-grade audit trails out of the box. Great Expectations generates run history logs but requires additional tooling for audit reports. dbt requires significant custom engineering.</td>
</tr>
<tr class="etl-data-row">
<td>Role-Based Access Control</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator supports enterprise RBAC natively. Great Expectations has no built-in RBAC. dbt Cloud offers team-level permissions.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">6. Testing Scope & Coverage</td></tr>
<tr class="etl-data-row">
<td>Mixed-Source Pipelines (DB + Files + APIs)</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator's Apache Spark engine supports the largest number of heterogeneous sources. Great Expectations supports multiple backends. dbt is warehouse-only.</td>
</tr>
<tr class="etl-data-row">
<td>Legacy System Testing (SSIS, Informatica, ODI)</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator tests pipelines built in any ETL tool including legacy platforms. Great Expectations requires custom datasource connectors. dbt Tests are not suitable for non-dbt pipelines.</td>
</tr>
<tr class="etl-data-row">
<td>Streaming / Real-Time Data Validation</td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and Great Expectations have partial streaming support. dbt is mainly a batch transformation tool.</td>
</tr>
<tr class="etl-data-row">
<td>Extensibility</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator provides custom plugins using Python. Great Expectations is highly extensible via its custom expectation framework. dbt has a fixed set of capabilities.</td>
</tr>
<tr class="etl-data-row">
<td>Test Data Generation</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator uniquely generates synthetic test data for automating pipeline testing. Neither Great Expectations nor dbt Tests offer this.</td>
</tr>
<tr class="etl-data-row">
<td>End-to-End Pipeline Coverage</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator covers ingestion, transformation, loading, and BI reporting. Great Expectations and dbt Tests cover only portions of the pipeline.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">7. Enterprise Readiness</td></tr>
<tr class="etl-data-row">
<td>Enterprise Support & SLAs</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator offers dedicated commercial support with SLAs. Great Expectations has commercial support via GX Cloud. dbt Core is open-source with community support only.</td>
</tr>
<tr class="etl-data-row">
<td>On-Premise Deployment</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and Great Expectations support on-premise deployment. dbt Cloud is SaaS-based.</td>
</tr>
<tr class="etl-data-row">
<td>Multi-Project / Multi-Team Support</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator supports multiple projects in a single deployment with container isolation. Great Expectations and dbt support multi-team setups with configuration.</td>
</tr>
<tr class="etl-data-row">
<td>Custom Dashboards for Stakeholders</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator provides fully customisable stakeholder-facing dashboards. Great Expectations generates Data Docs but they are developer-facing. dbt has no stakeholder dashboard capability.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">8. Scalability & Performance</td></tr>
<tr class="etl-data-row">
<td>Handling Large Data Volumes</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-check">✔</span></td>
<td>ETL Validator's Spark-based execution engine is built for billions of records. Great Expectations performance is dependent on the compute backend.</td>
</tr>
<tr class="etl-data-row">
<td>Auto-Scaling</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator has native on-demand auto-scaling. dbt and Great Expectations rely on underlying infrastructure.</td>
</tr>
<tr class="etl-data-row">
<td>Parallel Test Execution</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator's Spark engine enables high-parallelism across hundreds of tests simultaneously. Great Expectations and dbt test parallelism are infrastructure-dependent.</td>
</tr>
<tr class="etl-data-row">
<td>Cloud-Native Deployment</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td>All three are cloud-native. ETL Validator supports AKS, EKS, GKE, and Databricks. Great Expectations supports GX Cloud. dbt Cloud is fully managed.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">9. Pricing & Accessibility</td></tr>
<tr class="etl-data-row">
<td>Licensing Model</td>
<td><span class="sym-text">Commercial</span></td>
<td><span class="sym-text">Open-Source / GX Cloud</span></td>
<td><span class="sym-text">Open-Source / dbt Cloud</span></td>
<td>Great Expectations Core is open-source; GX Cloud adds a managed tier. dbt Core is free; dbt Cloud is commercial. The true cost of both includes significant engineering time to build and maintain.</td>
</tr>
<tr class="etl-data-row">
<td>Relative Cost</td>
<td><span class="sym-text">Best value</span></td>
<td><span class="sym-text">Free + engineering cost</span></td>
<td><span class="sym-text">Free + engineering cost</span></td>
<td>Both Great Expectations and dbt Tests appear free but carry hidden engineering costs. ETL Validator delivers the broadest feature coverage across total cost of ownership.</td>
</tr>
<tr class="etl-data-row">
<td>ETL Vendor Lock-in Risk</td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">Medium</span></td>
<td>dbt Tests are tightly coupled to the dbt ecosystem. ETL Validator and Great Expectations carry low lock-in risk.</td>
</tr>
<tr class="etl-data-row">
<td>Ideal Team Profile</td>
<td><span class="sym-text">Data Engineering & QA teams of all sizes</span></td>
<td><span class="sym-text">Python-proficient data engineers</span></td>
<td><span class="sym-text">dbt-native analytics engineers</span></td>
<td>Great Expectations and dbt Tests require engineering depth. ETL Validator serves QA, engineering, and business users of all profiles.</td>
</tr>
</tbody>
</table>
</div>
</div> </div>
</div>
</div>
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<h3 class="elementor-heading-title elementor-size-default">Which ETL Testing Tool Should You Choose?</h3> </div>
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<p class="font-claude-response-body">Choosing the right <span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener"><span style="text-decoration: underline;">ETL testing tool</span></a></span> depends on how comprehensive your testing needs are across data pipelines. While multiple tools offer specific capabilities, they differ significantly in scope, flexibility, and coverage.</p> </div>
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<h4 class="elementor-icon-box-title">
<span >
Datagaps ETL Validator </span>
</h4>
<p class="elementor-icon-box-description">
Datagaps ETL Validator provides a more complete approach by supporting end-to-end ETL testing across heterogeneous data sources, including databases, files, APIs and BI layers. It also offers automation, AI-driven test generation, and scalability required for modern data environments. </p>
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Great Expectations </span>
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<p class="elementor-icon-box-description">
Great Expectations is a powerful open-source framework for defining and validating data quality expectations. It works well for Python-proficient data engineering teams who need flexible, code-driven validation. However, it requires significant setup and engineering effort, has no no-code interface, and does not support end-to-end ETL testing or BI layer validation out of the box. </p>
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dbt Tests are limited to rule-based data checks within a single data warehouse. They are not built for complete ETL testing and do not address pipeline validation across systems. </p>
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<h3 class="elementor-heading-title elementor-size-default">Why Datagaps ETL Validator Is the Right ETL Testing Tool</h3> </div>
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<p><span style="font-weight: 600;">For teams that need comprehensive coverage across the full pipeline, <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; font-weight: 600;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">Datagaps ETL Validator </a></span>is the clear choice. Three reasons stand out:</span></p><ul><li><strong>End-to-end pipeline coverage</strong>: Where Great Expectations covers data quality checks at specific points and dbt Tests stay within the warehouse, ETL Validator goes further: across heterogeneous sources, through transformations, and all the way to the BI reporting layer. No stitching of multiple tools required.</li><li><strong>Scalability built in:</strong> ETL Validator is built on a Spark-based engine, purpose-designed to handle enterprise data volumes without compromising on performance. Great Expectations performance is dependent on the underlying compute backend, and dbt Tests do not scale independently of the warehouse.</li><li><strong>Accessible to the whole team:</strong> ETL Validator is the only tool in this comparison with a no-code interface, making it usable by QA analysts and business users alongside data engineers. Great Expectations and dbt Tests both require Python or SQL proficiency, limiting who can build and maintain tests.</li></ul> </div>
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<p>Datagaps is recognized as a data pipelines test automation specialist in Gartner’s Market Guide for DataOps Tools. If reliable, end-to-end data validation matters to your team, <span style="text-decoration: underline;"><span style="color: #1967d2;"><strong><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">Datagaps ETL Validator</a></strong></span></span> is the tool built for that job.</p> </div>
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<p><span style="text-decoration: underline;">Disclaimer</span>: The above-mentioned list is purely an outcome of the conversations and feedback received from various industry users in the ETL/Data Warehouse testing space. Any concerns or views can be shared at <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="mailto:contact@datagaps.com">contact@datagaps.com</a></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Watch ETL Validator in Action with Demo</h2> </div>
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Check out how ETL Validator simplifies ETL Testing, data validation through automation across pipelines from this playlist </div>
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<span >
Demo Playlist </span>
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<p>Start your 14-day free trial in our sandbox. Explore and optimize your ETL processes. Start your trial today!</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Get Started with ETL Validator – An ETL & Data Testing tool</h2> </div>
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Request a Demo </span>
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<p>The post <a href="https://www.datagaps.com/blog/top-3-etl-testing-tools/">Top 3 ETL Testing Tools: How to Choose the Best Tool</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<title>Validation of Salesforce Objects, Uploads and Updates</title>
<link>https://www.datagaps.com/blog/validation-of-salesforce-objects-uploads-and-updates/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Thu, 29 Jan 2026 16:34:00 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[Dataflow]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[ETL Testing]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=6967</guid>
<description><![CDATA[<p>Salesforce is a cloud-based CRM platform that helps businesses manage and analyze customer interactions and data throughout the customer lifecycle. It is used to store and organize information about customers, such as their contact details, communication history, and preferences.</p>
<p>The post <a href="https://www.datagaps.com/blog/validation-of-salesforce-objects-uploads-and-updates/">Validation of Salesforce Objects, Uploads and Updates</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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<p>This guide covers how to validate Salesforce objects, uploads, and updates using DataOps Suite. It explains how Metadata Validation keeps Dev and Production Salesforce schemas in sync, catching unintended changes to fields, page layouts, or workflow rules before deployment. It also covers common upload issues (formatting, integrity, permissions), and how the Data Compare Node determines which records need “upsert” (insert/update) versus “update” operations by identifying differences between Salesforce objects and source files.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li>Dev and Prod metadata often drift apart — testing-related changes to fields, page layouts, workflow rules, or custom objects in a sandbox environment can unintentionally differ from production if not validated before deployment.</li><li>Metadata Validation Node keeps schemas in sync — DataOps Suite can directly compare Dev and Prod Salesforce schemas to catch structural mismatches before they cause issues.</li><li>Data Compare Node validates uploads against source files — it pulls data directly from Salesforce objects and compares it to an on-premise file or dataset to catch formatting, integrity, and permission issues.</li><li>Choosing upsert vs. update requires precision — using upsert on duplicate external IDs updates existing records instead of inserting new ones, so <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/dataflow/" target="_blank" rel="noopener">DataOps Suite</a></span> uses Data Compare results to correctly split records into “new” (upsert) and “existing” (update) groups.</li></ul> </div>
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<h4 class="elementor-heading-title elementor-size-default">Understanding Validation of Salesforce Objects, Uploads,Updates and How Datagaps DataOps Can Help</h4> </div>
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<h5 class="elementor-heading-title elementor-size-default">Intro to Salesforce</h5> </div>
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<p><a href="https://www.salesforce.com/" target="_blank" rel="noopener"><u>Salesforce</u></a> is a cloud-based CRM platform businesses use to manage and analyze customer interactions and data throughout the customer lifecycle — storing contact details, communication history, and preferences to build a complete view of each customer. This helps businesses understand customer needs and personalize their interactions. Validating that this data is accurate and consistent, across sandboxes, uploads, and updates, is what this post covers.</p> </div>
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<p>This makes data quality inside Salesforce especially high-stakes. According to <a href="https://www.salesforce.com/in/hub/analytics/data-validation-practices/" target="_blank" rel="noopener">Salesforce’s own research</a>, a little more than one-fifth of sales reps’ time is spent researching bad data — time that validated objects, uploads, and updates are meant to give back.</p> </div>
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<p>In addition to its CRM capabilities, Salesforce also provides a range of tools and features for data management and integration, including data import and export, data modeling, and data governance. This makes it possible for businesses to manage and analyze their customer data in a centralized location and to integrate it with other systems and applications.</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Production vs Development – Metadata Validation</h5> </div>
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<div><p>In a Salesforce deployment, it is common for there to be differences in metadata between the development environment (also known as a sandbox or dev environment) and the production environment (also known as prod). This is because the development environment is often used for testing and experimentation, which can result in changes to the metadata that are not intended for the production environment.</p></div><p>Some examples of metadata changes that may occur in the development environment but not be intended for the production environment include:</p> </div>
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<p><strong>Modifying the structure of objects or fields:</strong> This could involve adding or deleting fields, or changing field data types. For example, a developer may be testing a new feature that requires adding a new field to the Account object to store additional data. If this field is not needed in the production environment, it would be important to remove it before deploying the changes to prod.</p> </div>
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<p><strong>Changing page layouts or field-level security settings:</strong> This could involve modifying the layout of a page to display new fields or rearranging existing ones, or changing the visibility of fields based on user roles or profiles. For example, a business user may be testing a new page layout for the Account object in the dev environment, but this layout may not be ready for production.</p> </div>
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<p><strong>Modifying workflow rules or approval processes:</strong> This could involve adding or modifying rules that trigger actions based on certain conditions, or changing the steps or participants in an approval process. For example, a developer may be testing a new workflow rule in the dev environment that sends an email notification when an Account is created, but this rule may not be ready for production.</p> </div>
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<p><strong>Adding or modifying custom objects or custom fields:</strong> This could involve creating new objects to store custom data or adding new fields to existing objects. For example, a business user may be testing a new custom object in the dev environment to track project tasks, but this object may not be ready for production.</p> </div>
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<p>These changes may occur in the development environment for a variety of reasons. For example, a developer may be testing a new feature or functionality and need to make changes to the metadata to support it. Or, a business user may be exploring different options for customizing the Salesforce instance and may make a series of changes as they iterate on their design.</p> </div>
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<p>Here, the Metadata Validation Node of the <a href="https://www.datagaps.com/dataops-suite/dataflow/" target="_blank" rel="noopener"><u> DataOps Suite</u>.</a> can be used to ensure the metadata of the Dev and Prod Objects are in sync</p> </div>
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<p>A Metadata Validation Node against Dev and Prod Salesforce Schemas</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Upload Validation</h5> </div>
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<p>Several distinct types of issues can arise while uploading data to Salesforce, and which ones you hit often depends on the type of data, its source, and the Salesforce object you’re uploading to. The four most common categories are:</p> </div>
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<div id="elementor-tab-title-1771" class="elementor-tab-title elementor-tab-desktop-title" aria-selected="true" data-tab="1" role="tab" tabindex="0" aria-controls="elementor-tab-content-1771" aria-expanded="false">Data Formatting</div>
<div id="elementor-tab-title-1772" class="elementor-tab-title elementor-tab-desktop-title" aria-selected="false" data-tab="2" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1772" aria-expanded="false">Data Integrity Issues</div>
<div id="elementor-tab-title-1773" class="elementor-tab-title elementor-tab-desktop-title" aria-selected="false" data-tab="3" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1773" aria-expanded="false">Object-Specific Issues</div>
<div id="elementor-tab-title-1774" class="elementor-tab-title elementor-tab-desktop-title" aria-selected="false" data-tab="4" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1774" aria-expanded="false">Permission Issues</div>
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<div class="elementor-tab-title elementor-tab-mobile-title" aria-selected="true" data-tab="1" role="tab" tabindex="0" aria-controls="elementor-tab-content-1771" aria-expanded="false">Data Formatting</div>
<div id="elementor-tab-content-1771" class="elementor-tab-content elementor-clearfix" data-tab="1" role="tabpanel" aria-labelledby="elementor-tab-title-1771" tabindex="0" hidden="false"><p>If the data you are uploading is not properly formatted, it may not be accepted by Salesforce. For example, if you are uploading a CSV file and the data in the file is not properly structured, Salesforce may not be able to parse the data correctly.</p></div>
<div class="elementor-tab-title elementor-tab-mobile-title" aria-selected="false" data-tab="2" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1772" aria-expanded="false">Data Integrity Issues</div>
<div id="elementor-tab-content-1772" class="elementor-tab-content elementor-clearfix" data-tab="2" role="tabpanel" aria-labelledby="elementor-tab-title-1772" tabindex="0" hidden="hidden"><p>If the data you are uploading contains errors or inconsistencies, it may cause issues with the integrity of your Salesforce data. For example, if you are uploading a list of leads and some of the leads are missing required fields, the upload may fail.</p></div>
<div class="elementor-tab-title elementor-tab-mobile-title" aria-selected="false" data-tab="3" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1773" aria-expanded="false">Object-Specific Issues</div>
<div id="elementor-tab-content-1773" class="elementor-tab-content elementor-clearfix" data-tab="3" role="tabpanel" aria-labelledby="elementor-tab-title-1773" tabindex="0" hidden="hidden"><p>Each Salesforce object has its own set of fields and requirements, and if the data you are uploading does not meet these requirements, the upload may fail. For example, if you are uploading data to the Account object and the data does not contain a value for the required “Name” field, the upload may fail.</p></div>
<div class="elementor-tab-title elementor-tab-mobile-title" aria-selected="false" data-tab="4" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1774" aria-expanded="false">Permission Issues</div>
<div id="elementor-tab-content-1774" class="elementor-tab-content elementor-clearfix" data-tab="4" role="tabpanel" aria-labelledby="elementor-tab-title-1774" tabindex="0" hidden="hidden"><p>If you do not have the correct permissions in Salesforce, you may not be able to upload data to certain objects or fields. For example, if you are trying to upload data to a custom object that you do not have permission to access, the upload may fail.</p></div>
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<p>The Data Compare Node can be used to pull data directly from the Salesforce Object and compare it against the file or dataset on premise as seen below.</p> </div>
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<p>A Basic Data Compare checking salesforce against a dataset</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Upsert vs Update</h5> </div>
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It is possible to encounter issues when uploading a large set of records that contain duplicates or need to be updated rather than inserted as new records. </div>
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If you are using the upsert function and the records you are uploading contain duplicates based on the external ID field, the upsert function will treat these as updates rather than inserts and will update the existing records with the new data. This can be problematic if you want to insert the records as new records rather than updating the existing ones. </div>
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<p>In this case, you may want to use the update function instead of the upsert function. The update function allows you to specify a query to select the records you want to modify, rather than relying on the external ID field to identify matching records. This can be useful if you want to update records based on criteria other than the external ID, or if you want to insert records as new records rather than updating existing ones.</p> </div>
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<p>There are several issues that can arise while uploading data to Salesforce, and the specific issues you may encounter can depend on the type of data you are uploading, the source of the data, and the type of Salesforce object you are uploading to. Here are a few examples of issues that can arise while uploading data to Salesforce:</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Upsert</h5> </div>
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<p>The “upsert” function is used to either update existing records or create new records in an object, depending on whether a matching record already exists. When using the upsert function, you specify a field in the object that will be used as the unique identifier, called the “external ID”. If a record with a matching external ID already exists, the upsert function will update that record with the new data. If no matching record is found, the upsert function will create a new record with the provided data.</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Update</h5> </div>
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<p>The “update” function is used to modify existing records in an object. When using the update function, you specify the records that you want to update using a query, and then specify the new field values that you want to set for those records. The update function will only modify records that already exist in the object, and will not create new records.</p> </div>
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<p>Both the upsert and update functions can be used to modify a single record or multiple records at once. They can be useful for updating or creating records in bulk, or for keeping data in Salesforce synchronized with data from other sources.</p> </div>
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<p>To solve the complex job of figuring out which records to use with Upsert and which to use Upload, the Data Compare Node comes in handy once again. Every node in the <span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/dataops-suite/dataflow/" target="_blank" rel="noopener"><span style="text-decoration: underline;">DataOps Suite</span></a></span> has its results, and comparisons saved as views that can be called upon internally. This allows for reference and loops of the system to ensure complex solutions can be easily defined and solved in the Suite.</p> </div>
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<p>In this case, after defining the data comparison between the Salesforce object and the CSV file, the node generates a set of related views, including records that exist only in the Salesforce object, records that exist only in the CSV file, records identified as different, and others. Our focus is on the dataset containing the differences and the dataset containing records that exist only in the CSV file. The dataset with records found only in the CSV file contains records that are not present in the Salesforce object and therefore requires the upsert function to create these new records in the Salesforce object.</p> </div>
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<p>The other dataset contains records that already exist in the Salesforce object but require updates to specific value sets. In this case, the update function is used along with prefixed Python code to identify the exact values that need to be updated.</p> </div>
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<p>The different views created by the Data Compare Node and the corresponding upsert and update nodes.</p> </div>
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<p>Note that distinguishing between the addition of a new external ID field and updating an existing record is a highly quintessential task especially when the objects are called by CRM and reporting tools themselves where the difference between a new record and an updated record is really critical.</p> </div>
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The below image shows all views created by the Data Compare Node and the corresponding the upsert and update nodes </div>
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<img loading="lazy" decoding="async" width="1600" height="900" src="https://www.datagaps.com/wp-content/uploads/All-views-created-by-the-Data-Compare-Node-and-the-correcponsing-the-upsert-and-update-nodes-01.webp" class="attachment-full size-full wp-image-5970" alt="All-views-created-by-the-Data-Compare-Node-and-the-correcponsing-the-upsert-and-update-nodes-01" srcset="https://www.datagaps.com/wp-content/uploads/All-views-created-by-the-Data-Compare-Node-and-the-correcponsing-the-upsert-and-update-nodes-01.webp 1600w, https://www.datagaps.com/wp-content/uploads/All-views-created-by-the-Data-Compare-Node-and-the-correcponsing-the-upsert-and-update-nodes-01-300x169.webp 300w, https://www.datagaps.com/wp-content/uploads/All-views-created-by-the-Data-Compare-Node-and-the-correcponsing-the-upsert-and-update-nodes-01-1024x576.webp 1024w, https://www.datagaps.com/wp-content/uploads/All-views-created-by-the-Data-Compare-Node-and-the-correcponsing-the-upsert-and-update-nodes-01-768x432.webp 768w, https://www.datagaps.com/wp-content/uploads/All-views-created-by-the-Data-Compare-Node-and-the-correcponsing-the-upsert-and-update-nodes-01-1536x864.webp 1536w" sizes="(max-width: 1600px) 100vw, 1600px" /> </div>
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<h5 class="elementor-heading-title elementor-size-default">Conclusion</h5> </div>
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<p>In conclusion, it is important to carefully validate Salesforce objects, uploads, and updates to ensure that your data is accurate and consistent. By following best practices and using the appropriate tools and techniques, such as the DataOps Suite, you can avoid common issues such as data formatting errors, data integrity problems, and object-specific issues. Whether you are working with Veeva objects, pre-sales CRM objects, or any other type of object in Salesforce, taking the time to validate your data will help you maintain the quality and reliability of your Salesforce data.</p> </div>
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<h3 id="faq-heading">FAQs: Salesforce Metadata Validation and Data Deployment</h3>
<section class="faq-section" aria-labelledby="faq-heading">
<div class="faq-list">
<details>
<summary>1) Why do Salesforce Dev and Production environments sometimes fall out of sync?</summary>
<p>
Differences can arise when changes made in a Salesforce sandbox environment—such
as updates to fields, page layouts, workflow rules, or custom objects—are not fully
validated before deployment. These inconsistencies can lead to unexpected behavior
in the Production environment.
</p>
</details>
<details>
<summary>2) How does Metadata Validation help with Salesforce deployments?</summary>
<p>
The Metadata Validation Node in DataOps Suite compares metadata between Salesforce
Development and Production environments to identify schema differences before
deployment, reducing the risk of failed releases and configuration issues.
</p>
</details>
<details>
<summary>3) What issues commonly occur during Salesforce data uploads?</summary>
<p>
Salesforce data uploads can fail because of formatting inconsistencies, data
integrity violations, validation rule failures, or insufficient user permissions.
Identifying these issues before loading data helps ensure successful updates.
</p>
</details>
<details>
<summary>4) How does DataOps Suite decide whether to upsert or update a Salesforce record?</summary>
<p>
Using the Data Compare Node, DataOps Suite compares Salesforce object data with a
source dataset to determine whether a record already exists. Records with matching
external IDs are updated, while new records are upserted, helping prevent duplicate
data and ensuring accurate synchronization.
</p>
</details>
</div>
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<p>The post <a href="https://www.datagaps.com/blog/validation-of-salesforce-objects-uploads-and-updates/">Validation of Salesforce Objects, Uploads and Updates</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<title>Validation Of Complex and Hierarchical JSON and XML files</title>
<link>https://www.datagaps.com/blog/validation-of-complex-and-hierarchical-json-and-xml-files/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Fri, 09 Jan 2026 11:16:00 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[ETL Testing]]></category>
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<description><![CDATA[<p>While most google searches for “JSON Validation” led to validation tools and articles that focus on structure, a severely under-looked topic is about validating the data within these complex files. The same applies to XML files.</p>
<p>The post <a href="https://www.datagaps.com/blog/validation-of-complex-and-hierarchical-json-and-xml-files/">Validation Of Complex and Hierarchical JSON and XML files</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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<p>Complex, hierarchical JSON and XML files are hard to validate because their nested arrays and key pairs don’t fit traditional relational datasets. DataOps Suite solves this with auto-parsing capabilities that flatten these files into related, key-linked views, enabling standard data rules, profiling, and validation to be applied just like normal tables. The Suite also offers a Query Builder for extracting and flattening nested data from JSON/XML pulled via APIs, simplifying access for both technical and non-technical users.</p> </div>
</div>
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<p><strong>Key Takeaways</strong></p><ul><li>Validating data within JSON/XML is harder than validating structure — most tools focus on structural validation, but checking the actual data values inside complex, nested files is often overlooked and difficult to solve.</li><li>Auto-parsing breaks complex files into relational views — DataOps Suite flattens nested JSON/XML sub-structures into separate, key-linked views, letting teams apply standard data rules, profiling, and validation without custom scripting.</li><li>Parsed views retain built-in relational connections — the generated tables come with connection keys and relationships already established, so any ETL pipeline, DB system, or query builder can work with them immediately.</li><li>Query Builder simplifies API-sourced JSON/XML data — it enables filtering, sorting, aggregation, and flattening of nested data pulled from APIs, making it accessible even to non-technical users without writing complex SQL.</li></ul> </div>
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<h4 class="elementor-heading-title elementor-size-default">Complex Structural Hierarchical JSON and XML files</h4> </div>
</div>
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<h4 class="elementor-heading-title elementor-size-default">Why are complex structural and deeply hierarchical JSON and XML files so difficult to validate?</h4> </div>
</div>
<div class="elementor-element elementor-element-6bd83dc elementor-widget elementor-widget-text-editor" data-id="6bd83dc" data-element_type="widget" data-e-type="widget" data-widget_type="text-editor.default">
<div class="elementor-widget-container">
<p>Validating hierarchical JSON and XML files means checking not just that the file’s structure is well-formed, but that the actual data values nested inside it — down to the deepest levels — are correct. Most guidance on “<a href="https://www.datagaps.com/blog/etl-validator-for-data-migration-testing/" target="_blank" rel="noopener"><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;">JSON Validation</span></span></a>” focuses only on structure, leaving data-level validation an under-addressed problem for both JSON and XML files. Created by and for applications to communicate a multitude of variables interlinked via non-linear many-to-many relationships, these files become increasingly complicated to untangle and validate properly as they grow.</p> </div>
</div>
<section class="elementor-section elementor-inner-section elementor-element elementor-element-2dd7d5e elementor-section-content-top bw-ac raw-json elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="2dd7d5e" data-element_type="section" data-e-type="section">
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<p>Fig. Raw JSON</p> </div>
</div>
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<img loading="lazy" decoding="async" width="300" height="281" src="https://www.datagaps.com/wp-content/uploads/Raw-JSON-1-300x281.webp" class="attachment-medium size-medium wp-image-5575" alt="Raw-JSON" srcset="https://www.datagaps.com/wp-content/uploads/Raw-JSON-1-300x281.webp 300w, https://www.datagaps.com/wp-content/uploads/Raw-JSON-1.webp 635w" sizes="(max-width: 300px) 100vw, 300px" /> </div>
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<p>A common use case seen apart from JSON/XML data validation only is the inclusion of these file types into more traditional datasets. A typical DB with a good number of related views and tables suddenly has to ingest a set of complex JSON files. While parsing and pulling individual data values out from the JSON itself is deemed highly situation specific and requires special functions, validating these data values is usually a nightmare for the developers.</p><p>As seen in image even loading a JSON or XML as a data frame into Spark results in a complex dataset that cannot be combined with traditional datasets.</p> </div>
</div>
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<img loading="lazy" decoding="async" width="640" height="107" src="https://www.datagaps.com/wp-content/uploads/Raw-Loaded-Tables-1-1024x171.webp" class="attachment-large size-large wp-image-5595" alt="Raw-Loaded-Tables-1" srcset="https://www.datagaps.com/wp-content/uploads/Raw-Loaded-Tables-1-1024x171.webp 1024w, https://www.datagaps.com/wp-content/uploads/Raw-Loaded-Tables-1-300x50.webp 300w, https://www.datagaps.com/wp-content/uploads/Raw-Loaded-Tables-1-768x128.webp 768w, https://www.datagaps.com/wp-content/uploads/Raw-Loaded-Tables-1.webp 1345w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>Fig. Raw Loaded Tables</p> </div>
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<p>As XMLs and JSONs get more complex, the amount of structured columns consisting of nested arrays, key pairs, and lists only increases. While this is a start, we cannot validate any of these values or use the specifics unless we use complex functions to extract individual values. Here we introduce the auto-parsing capabilities in <a href="https://www.datagaps.com/dataops-suite/dataflow/"><span style="text-decoration: underline; color: #1967d2;"> DataOps Suite</span>.</a></p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Auto-Parsing and Validation of JSON and XML</h4> </div>
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<p>Recommended approach: rather than writing custom parsing functions for each file structure, use the DataOps Suite’s built-in libraries to parse JSON and XML files with a single command. These commands flatten different types of JSONs and XMLs into a set of relational datasets. By converting the complex sub-structures of these files into separate views with related primary keys, <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/etl-testing/" target="_blank" rel="noopener">validation</a></span>, application of data rules, profiling, and analysis can all be applied to those views just like any other relational data.</p> </div>
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<p>In the example, the two complex sub-structures are broken into separate views with a key-relation table. As the key pairs for the various bases (names) remain consistent, the functions ensure that no excessive data is created. In the example, each type of donut has a sub-set of the pre-existing list of batters and in the tables created we see only the distinct values and a combination of ids to correctly tag the list of values.</p> </div>
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<p>The segregated views can either be analyzed and validated separately to check the complete list of values and key-pairs combinations or these views can be combined with filters to perform node-specific validation.</p> </div>
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<img loading="lazy" decoding="async" width="113" height="300" src="https://www.datagaps.com/wp-content/uploads/JSON-1-113x300.webp" class="attachment-medium size-medium wp-image-5619" alt="JSON" srcset="https://www.datagaps.com/wp-content/uploads/JSON-1-113x300.webp 113w, https://www.datagaps.com/wp-content/uploads/JSON-1.webp 297w" sizes="(max-width: 113px) 100vw, 113px" /> </div>
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<p>Breaking sub-structures into Separate Views</p> </div>
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<img loading="lazy" decoding="async" width="300" height="203" src="https://www.datagaps.com/wp-content/uploads/Breaking-sub-structures-into-Seperate-Views-1-300x203.webp" class="attachment-medium size-medium wp-image-5620" alt="Breaking-sub-structures-into-Seperate-Views" srcset="https://www.datagaps.com/wp-content/uploads/Breaking-sub-structures-into-Seperate-Views-1-300x203.webp 300w, https://www.datagaps.com/wp-content/uploads/Breaking-sub-structures-into-Seperate-Views-1-768x519.webp 768w, https://www.datagaps.com/wp-content/uploads/Breaking-sub-structures-into-Seperate-Views-1.webp 946w" sizes="(max-width: 300px) 100vw, 300px" /> </div>
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<p>One of the most important aspects that might be overlooked is the fact that the tables and sub-tables created by the function are part of the relational DB system. That is, these datasets have assigned connection keys and relations built the instant they are made. This makes it to that any<span style="text-decoration: underline; color: #1967d2;"> </span><a href="https://www.datagaps.com/etl-testing-tools/etl-validator/"><span style="text-decoration: underline; color: #1967d2;">Query Builder system</span></a>, ETL Pipeline, or DB system can instantly work with related and work with them easily without the user having to keep track of the complex relations or joining keys.</p> </div>
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<p>In the example, the user makes a view to taking into consideration only the combinations of “Batters” and “Toppings” of an “Old Fashioned” Donut. This is later run across a set of data rules which include a domain check on the toppings, a null check, a duplicity check, and a space character check.</p><p>Performing a similar set of tests with custom tools on specific nodes will require a framework to be developed and maintained and each new set of rules, analysis, and profiling would require a lot more setup time and maintenance resources. This also excludes the reporting system that would have to be created and connected to the customized validation system.</p> </div>
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<img loading="lazy" decoding="async" width="280" height="300" src="https://www.datagaps.com/wp-content/uploads/Donut-Rules-1-280x300.webp" class="attachment-medium size-medium wp-image-5621" alt="Donut-Rules" srcset="https://www.datagaps.com/wp-content/uploads/Donut-Rules-1-280x300.webp 280w, https://www.datagaps.com/wp-content/uploads/Donut-Rules-1.webp 331w" sizes="(max-width: 280px) 100vw, 280px" /> </div>
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<p>Fig. Donut Rules</p> </div>
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<div class="et_pb_column et_pb_column_3_5 et_pb_column_6 et_pb_css_mix_blend_mode_passthrough"><div class="et_pb_module et_pb_text et_pb_text_11 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/etl-testing-tools/etl-validator-download/">Try DataOps Suite – Free Trial</a></span></p></div></div></div><p>In the Suite, an XML file is imported into the flow, converted into relational views, then validated across a set of rules and profiling systems. XML files are notorious to use outside the intended applications and therefore there are fewer validation systems that work to validate the values. The flow showcases how a typical validation and analysis flow for an XML file will look in the Suite. </p> </div>
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<img loading="lazy" decoding="async" width="640" height="131" src="https://www.datagaps.com/wp-content/uploads/Overall-Flow-1-1024x210.webp" class="attachment-large size-large wp-image-5622" alt="Overall-Flow" srcset="https://www.datagaps.com/wp-content/uploads/Overall-Flow-1-1024x210.webp 1024w, https://www.datagaps.com/wp-content/uploads/Overall-Flow-1-300x62.webp 300w, https://www.datagaps.com/wp-content/uploads/Overall-Flow-1-768x158.webp 768w, https://www.datagaps.com/wp-content/uploads/Overall-Flow-1.webp 1027w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>Fig. Overall Flow</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">APIs with Query Builder</h4> </div>
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The most common place to find JSONs or XMLs in a relational DB or warehouses is via APIs which come up with specific use cases. And with complex JSONs or XMLs come complex queries. In these specific cases, there are a few reasons why using the query builder provided by Datagaps can be really beneficial.
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<th style="padding: 12px; border: 1px solid #ccc;">Query Builder Benefit</th>
<th style="padding: 12px; border: 1px solid #ccc;">Why It Matters for Hierarchical JSON/XML</th>
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<td style="padding: 12px; border: 1px solid #ccc;">User-friendly interface</td>
<td style="padding: 12px; border: 1px solid #ccc;">Non-technical users can access and manipulate data without writing complex SQL</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Data manipulation functions</td>
<td style="padding: 12px; border: 1px solid #ccc;">Filtering, sorting, and aggregation help explore large datasets and identify patterns</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Nested data extraction</td>
<td style="padding: 12px; border: 1px solid #ccc;">Flattens complex JSON/XML structures into a more manageable, workable format</td>
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First, query builders typically provide a user-friendly interface that allows users to easily access and manipulate data without having to write complex SQL code. This can be especially helpful for non-technical users who may not be familiar with SQL or may not have the technical expertise to write queries themselves. Second, query builders often support a wide range of <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-quality-testing/" target="_blank" rel="noopener">data quality</a></span> and manipulation functions, such as filtering, sorting, and aggregation, which can be used to transform and analyze data in various ways. This can be useful for exploring and understanding large datasets, and for identifying trends and patterns within the data. In addition, query builders can also be used to extract and flatten nested data from JSON or XML files. This can be useful for breaking down complex data structures and transforming the data into a more manageable format that is easier to work with.
Overall, using a query builder to work with data from JSON or XML files that have been pulled via an API can provide a more convenient and powerful way to access and manipulate data, and can help users to unlock the full potential of the data. </div>
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<img loading="lazy" decoding="async" width="640" height="348" src="https://www.datagaps.com/wp-content/uploads/Query-Builder-with-Relational-Datasets-in-the-JSON-1-1024x557.webp" class="attachment-large size-large wp-image-5623" alt="Query-Builder-with-Relational-Datasets-in-the-JSON" srcset="https://www.datagaps.com/wp-content/uploads/Query-Builder-with-Relational-Datasets-in-the-JSON-1-1024x557.webp 1024w, https://www.datagaps.com/wp-content/uploads/Query-Builder-with-Relational-Datasets-in-the-JSON-1-300x163.webp 300w, https://www.datagaps.com/wp-content/uploads/Query-Builder-with-Relational-Datasets-in-the-JSON-1-768x418.webp 768w, https://www.datagaps.com/wp-content/uploads/Query-Builder-with-Relational-Datasets-in-the-JSON-1.webp 1454w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>Fig. Query Builder with Relational Datasets in the JSON</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Conclusion</h5> </div>
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<p>JSONs and XMLs that are under constant structural or value-type change need a flexible validation solution that can adapt without a rebuild each time — whether the change is structural, a data type shift, or a new anomaly to catch. Since most databases work with default-style views, complex nested columns otherwise disrupt the flow of quality checks and pipelines.</p><p>The combination of parsing and extraction functions that create relational views, working in tandem with the data quality nodes in the Datagaps DataOps Suite, ensures that validating, analyzing, and detecting anomalies in these datasets stays just a few clicks away — even as the underlying XML and JSON files keep changing.</p><p>Overall, the DataGaps ETL Validator is a valuable and Top ETL Testing tool for organizations that need to ensure the quality and integrity of their data as it is transferred from one system to another. By providing fast, efficient, and accurate data validation, the Validator can help organizations avoid costly errors and improve the reliability and effectiveness of their ETL processes.</p> </div>
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<h2 id="faq-heading">FAQs: JSON/XML Data Validation in DataOps Suite</h2>
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<details>
<summary>1) Why is it difficult to validate data inside complex JSON or XML files?</summary>
<p>
Unlike relational data organized into rows and columns, JSON and XML files often
contain nested objects, arrays, and key-value pairs that do not map directly to
standard tables. This makes it more challenging to apply conventional data quality
rules, profiling, and comparison techniques.
</p>
</details>
<details>
<summary>2) How does DataOps Suite make JSON/XML files easier to validate?</summary>
<p>
DataOps Suite automatically parses hierarchical JSON and XML files into flattened,
relational views connected by keys. This enables users to apply standard validation
rules, profiling, and comparison logic just as they would for relational database
tables.
</p>
</details>
<details>
<summary>3) Do the auto-generated views maintain relationships between nested data?</summary>
<p>
Yes. The generated relational views include built-in connection keys that preserve
relationships between parent and nested records, allowing the data to be used
directly in ETL workflows, databases, and query tools without additional
configuration.
</p>
</details>
<details>
<summary>4) How does the Query Builder help with JSON/XML data pulled from APIs?</summary>
<p>
The Query Builder enables users to filter, sort, aggregate, and flatten nested
JSON or XML data returned from APIs. This simplifies working with complex API
responses and reduces the need for custom coding by both technical and business
users.
</p>
</details>
</div>
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<a href="https://www.linkedin.com/in/getmeatrajesh/" >
Rajesh Kumar A </a>
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Digital Marketing Manager, Datagaps </p>
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<p>Digital Marketing Manager at Datagaps. Drives data-driven growth through content, performance campaigns, and marketing technology.</p> </div>
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S P S Murthy Akella </a>
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Director, Technology Strategy, Datagaps </p>
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<p>Director of Technology Strategy at Datagaps. Business solutions architect and Certified Scrum Master in data engineering, responsible AI, and ML across BFSI, telecom, aviation, and energy.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/validation-of-complex-and-hierarchical-json-and-xml-files/">Validation Of Complex and Hierarchical JSON and XML files</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<title>Challenges In Data Migration To Cloud</title>
<link>https://www.datagaps.com/blog/challenges-in-data-migration-to-cloud/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Sat, 03 Jan 2026 13:14:00 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=7416</guid>
<description><![CDATA[<p>We’ve recently seen an uptick in data validation inquiries with Cloud Data Migration projects. Obviously, this is driven by the undeniable success and cost savings associated with moving to the Cloud. We are well past the Hype Cycle of the Trough of Disillusionment as described by Gartner.</p>
<p>The post <a href="https://www.datagaps.com/blog/challenges-in-data-migration-to-cloud/">Challenges In Data Migration To Cloud</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<p><span class="_animating_6ta1u_10" data-newtext-seq="0">Migrating to a </span><span class="_animating_6ta1u_10" data-newtext-seq="15">cloud data warehouse introduces three </span><span class="_animating_6ta1u_10" data-newtext-seq="53">critical validation challenges: </span><span class="_animating_6ta1u_10" data-newtext-seq="85">ensuring 100% data accuracy during bulk </span><span class="_animating_6ta1u_10" data-newtext-seq="125">migration, running old and new <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/etl-testing-tools/etl-validator/">ETL </a></span></span><span class="_animating_6ta1u_10" data-newtext-seq="160"><span style="text-decoration: underline; color: #1967d2;">processes</span> in parallel without data </span><span class="_animating_6ta1u_10" data-newtext-seq="195">loss, and confirming analytics/BI tools </span><span class="_animating_6ta1u_10" data-newtext-seq="235">function correctly post-migration. </span><span class="_animating_6ta1u_10" data-newtext-seq="270">Manual validation across truncations, </span><span class="_animating_6ta1u_10" data-newtext-seq="308">precision mismatches, and referential </span><span class="_animating_6ta1u_10" data-newtext-seq="346">integrity issues can derail timelines </span><span class="_animating_6ta1u_10" data-newtext-seq="384">and erode trust in the new environment. </span><span class="_animating_6ta1u_10" data-newtext-seq="424">Datagaps automates this entire </span><span class="_animating_6ta1u_10" data-newtext-seq="455">validation lifecycle—bulk migration </span><span class="_animating_6ta1u_10" data-newtext-seq="491">testing, incremental ETL testing, and </span><span class="_animating_6ta1u_10" data-newtext-seq="529">analytics regression testing—cutting </span><span class="_animating_6ta1u_10" data-newtext-seq="566">migration testing time and QA costs </span><span class="_animating_6ta1u_10" data-newtext-seq="602">while ensuring source-to-target data </span><span class="_animating_6ta1u_10" data-newtext-seq="639">integrity throughout the cloud </span><span class="_animating_6ta1u_10" data-newtext-seq="670">transition.</span></p> </div>
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<p><strong>Key Takeaways</strong></p><p> </p><ul><li><strong>Modern Data Warehouse migration demands 100% data validation</strong> — format incompatibilities between on-premise and cloud structures are a leading cause of mistrust in migrated data.</li><li><strong>Old and new ETL processes must run in parallel and be continuously tested</strong> — switching data stores (Snowflake, Databricks, Redshift) often forces a shift in ETL tooling or a move to ELT.</li><li><strong>Analytics cutover requires multi-layer testing</strong> — regression, metadata, dashboard-to-dashboard, and performance/stress testing all need to pass before cutting over to the new environment.</li><li><strong>Automated testing shrinks migration risk and cost</strong> — Datagaps’ test case builder reduces migration testing time, data quality testing time, and QA costs versus manual validation.</li></ul> </div>
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<h4 class="elementor-heading-title elementor-size-default">We’ve decided on a new Cloud-based Data Warehouse. Now what?</h4> </div>
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<p><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/blog/data-migration-testing/">Cloud data migration</a></span></span> is a project where an organization moves its data warehouse, ETL processes, and analytics tools from on-premise infrastructure to a cloud platform such as <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/snowflake-testing-automation/">Snowflake</a></span>, <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/databricks-testing-automation/">Databricks</a></span>, or Redshift — and validating that move correctly is the hard part. Datagaps has recently seen an uptick in data validation inquiries tied to Cloud Data Migration projects, driven by the undeniable success and cost savings organizations see from moving to the Cloud. Per Gartner’s Hype Cycle framework, we believe the market is well past the Trough of Disillusionment and well down the path of the Plateau of Productivity, though some argue the Trough was never fully experienced.</p> </div>
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<p>When you pass through the Slope of Enlightenment, the challenges are surfaced more clearly. As we are a data and <a href="https://www.datagaps.com/bi-testing-tools/bi-validator/">BI validation</a> company, our clients that are migrating to the cloud ask three things.</p> </div>
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<ul><li>How do I validate that the data is migrated correctly?</li><li>How will this affect my ETL processes?</li><li>How do I check that my analytics tools function correctly in the new environment?</li></ul> </div>
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<strong>Gartner Hype Cycle</strong> </div>
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<img loading="lazy" decoding="async" width="640" height="416" src="https://www.datagaps.com/wp-content/uploads/Cost-Of-Data-Migration-copy-03.svg" class="attachment-medium_large size-medium_large wp-image-6572" alt="Cost-Of-Data-Migration-copy-03" /> </div>
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<h5 class="elementor-heading-title elementor-size-default">Challenges in Migrating to the modern Data Stack</h5> </div>
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<p><strong>1. Implementing the Modern Data Warehouse</strong>. Moving to the Modern Data Warehouse typically takes the shape of fork lifting the current data in the on-premise Data Warehouse and migrating that data with minimal changes into the modern data stack. Often this movement of the data results in mistakes due to a variety of reasons, not the least of which is the data formats in the new warehouse aren’t completely compatible with the old. The new Data Warehouse and the old need to be validated by comparing 100% of the data elements in each. Not doing so can lead to mistrust and loss of credibility.</p> </div>
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<p><strong>2. Incremental ETL Testing.</strong> The <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/etl-testing-tools/etl-validator/" data-wplink-edit="true">ETL processes</a></span> that once feed the on-premise Data Warehouse incrementally now have to feed the cloud data warehouse generally in a different data store such as Snowflake, Databricks, or Redshift. The change in datastore typically drives a shift in the ETL tool and/or the incremental ETL processes. So now, there are two ETL processes. One is the old ETL process, and the other is what feeds the new <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/data-warehouse-testing/">Cloud Data Warehouse</a>.</span></span></p> </div>
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<p>Often, a change is made to swap out the <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/etl-testing-tools/etl-validator/">ETL tool</a></span> to take advantage of some of the differences in the new modern data stacks. Sometimes, this takes the shape of the new process as an ELT process to transform the data once it is loaded. Again, these results need to be validated together to determine if the new techniques replicate all of the data transformations required to support the data analytics to follow.</p> </div>
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<p><strong>3. Impact on Data Analytics</strong>. Finally, you need to point your analytics tools to the new Cloud platform. Often multiple data analytics platforms are involved as well. In some cases, companies take this opportunity to consolidate analytics platforms, and most don’t due to the enormous amount of additional development and testing required.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="418" src="https://www.datagaps.com/wp-content/uploads/Cost-Of-Data-Migration-copy-02-1.svg" class="attachment-medium_large size-medium_large wp-image-6576" alt="Cost-Of-Data-Migration-copy-02-1" /> </div>
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<p>In each of these steps, data must be extensively validated.</p> </div>
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<th style="padding: 12px; border: 1px solid #ccc;">Migration Challenge</th>
<th style="padding: 12px; border: 1px solid #ccc;">How Datagaps Addresses It</th>
</tr>
</thead>
<tbody>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Implementing the Modern Data Warehouse — moving 100% of data elements from on-premise to Cloud without introducing format mismatches</td>
<td style="padding: 12px; border: 1px solid #ccc;">Bulk Migration Testing: an automated data migration test case builder compares the new Cloud data structure against the older on-premise structure</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Incremental ETL Testing — running old and new ETL/ELT processes in parallel to confirm the new process replicates all required data transformations</td>
<td style="padding: 12px; border: 1px solid #ccc;">Incremental ETL Testing: old and new ETL processes are continuously tested in parallel, without extensive manual transformation work, to confirm they produce matching results</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Impact on Data Analytics — repointing analytics platforms to the new Cloud environment and validating they still function correctly</td>
<td style="padding: 12px; border: 1px solid #ccc;">Analytics Testing: regression testing, metadata testing, data validation between old and new systems, report-to-database query verification, GUI/dashboard pixel comparison, and performance and stress testing</td>
</tr>
</tbody>
</table> </div>
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<p><strong>1. Bulk Migration Testing.</strong> Bulk migration to the Cloud introduces truncation errors, precision mismatches, and broken referential integrity — fundamental <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-quality-monitoring-tools/">data quality</a></span> problems that creep into large-scale data moves. Datagaps’ <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-migration-testing-automation/">data migration test</a></span> case builder automatically creates source-to-target comparisons between the new Cloud data structure and the older on-premise structure. This approach shaves months off the data migration project timeline.</p> </div>
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<p><strong>2. Incremental ETL Testing</strong>. In conjunction with getting the data moved successfully, most organizations run a parallel ETL replacement project to prepare the <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/etl-testing-tools/etl-validator/" data-wplink-edit="true">ETL processes</a></span> to do the incremental loads in the new data warehouse. Whether the <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/etl-testing-tools/etl-validator/" target="_blank" rel="noopener" data-wplink-edit="true">ETL tool</a></span> is being replaced with newer technology or not, <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/blog/how-to-perform-continuous-data-validation/" target="_blank" rel="noopener">data validation testing</a></span></span> is still an essential requirement. The old and new ETL processes run in parallel for a period of time and need to be continuously tested to ensure the processes are working correctly. Much like Datagaps provides <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/blog/pitfalls-of-cloud-data-migration/" target="_blank" rel="noopener">data migration testing</a></span></span> for the Bulk migration test, we can produce these test cases in an automated fashion without the need to do extensive transformations, which save time and cost significantly.</p> </div>
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<p><strong>3. Analytics Testing</strong>. When the data analytics platforms are redirected to the new cloud implementation, several tests need to be performed in parallel.</p><ul><li>Regression testing between the new and the old platform.</li><li>Metadata testing to make sure the schemas haven’t been changed inadvertently and to validate the ones that need to be changed have been.</li><li>Data validation testing is needed between the old and the Cloud to ensure the data is correct in both instances.</li><li>A report to database query in both systems is needed to verify the expected results.</li><li>Finally, old pixel to new cloud app pixel and old dashboard data to new dashboard data comparisons at the GUI interface level should be performed to check the technologies are performing the same in each environment.</li><li>Performance and stress testing in the new environment.</li></ul> </div>
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<p>A cloud data migration is only as trustworthy as the validation behind it. Bulk migration testing confirms the new Cloud data structure matches the old on-premise structure record for record, incremental <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/etl-testing/">ETL testing</a></span> confirms the new pipeline reproduces every transformation the old one did, and analytics testing confirms dashboards and reports return the same results in the new environment. Skipping any one of these three checks is how migrations lose stakeholder trust even after the technical cutover succeeds.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">Benefits of Automating the Data Migration Testing</h3> </div>
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<p>Reduction in Migration Testing Time by</p> </div>
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<p>Overall Reduction of TCO by</p> </div>
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<p>Reduction in Data Quality Testing Time by</p> </div>
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<p>Reduction in QA Cost by</p> </div>
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<a href="https://www.linkedin.com/in/getmeatrajesh/" >
Rajesh Kumar A </a>
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<p class="elementor-icon-box-description">
Digital Marketing Manager, Datagaps </p>
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<p>Digital Marketing Manager at Datagaps. Drives data-driven growth through content, performance campaigns, and marketing technology.</p> </div>
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S P S Murthy Akella </a>
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<p class="elementor-icon-box-description">
Director, Technology Strategy, Datagaps </p>
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<p>Director of Technology Strategy at Datagaps. Business solutions architect and Certified Scrum Master in data engineering, responsible AI, and ML across BFSI, telecom, aviation, and energy.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/challenges-in-data-migration-to-cloud/">Challenges In Data Migration To Cloud</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></content:encoded>
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<item>
<title>Oracle to Azure Synapse Data Warehouse Migration Validation</title>
<link>https://www.datagaps.com/blog/oracle-to-azure-synapse-data-warehouse-migration-validation/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Fri, 28 Nov 2025 11:58:00 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[ETL Testing]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=7185</guid>
<description><![CDATA[<p>any organizations continue to use Oracle database for storing transactional and warehouse data in their on-premise data centers. However, with data growing exponentially, their data environments are struggling to handle the growing load which requires more processing power and</p>
<p>The post <a href="https://www.datagaps.com/blog/oracle-to-azure-synapse-data-warehouse-migration-validation/">Oracle to Azure Synapse Data Warehouse Migration Validation</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
<content:encoded><![CDATA[ <div data-elementor-type="wp-post" data-elementor-id="7185" class="elementor elementor-7185" data-elementor-post-type="post">
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<p>This guide explains how to validate Oracle-to-Azure Synapse data warehouse migrations, covering key motivations (reduced licensing costs, better performance, easier in-database analytics) and vulnerabilities like Oracle-specific features and data type mapping differences. It details validation for initial (full load) vs. incremental ETL migrations, including source, constraint, and null checks. A major focus is validating Slowly Changing Dimensions (SCD Types 1, 2, and 3), each requiring different checks like count, duplicate, transformation, and referential integrity validation.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li>Data type mapping differences drive migration risk — Oracle and Azure Synapse interpret SQL ANSI standards differently, especially for numeric and date types (e.g., Oracle’s DATE vs. Azure’s DATETIME truncates fractional seconds), requiring careful validation.</li><li>Initial vs. incremental loads need different validation approaches — initial (full) loads require source, constraint, and null checks, while incremental ETL runs need additional validation to confirm only new/modified records are inserted or updated correctly.</li><li>SCD Type validation requires distinct techniques — SCD Type 1 (overwrite) needs count/duplicate/transformation checks, SCD Type 2 (history-preserving) needs delta and referential integrity checks, and SCD Type 3 (limited history) needs checks across original and current value columns.</li><li>Post-migration validation follows a structured sequence — comparing source vs. target record counts, confirming transformations match business rules, then running data reconciliation, duplicate, NULL, and referential integrity checks ensures migration quality.</li></ul> </div>
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<p>Oracle to azure synapse migration: This article discusses how a migration from Oracle to Azure Synapse data warehouse can be validated from an End-to-End perspective.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why migrate from Oracle to Azure Synapse Data Warehouse?</h2> </div>
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<p>Any organizations continue to use Oracle database for storing transactional and warehouse data in their on-premise data centers. However, with data growing exponentially, their data environments are struggling to handle the growing load which requires more processing power and increased storage, adding to data costs. So, migrating Oracle data to the cloud is an extremely viable option for optimizing costs and performance, and migrating the Oracle database to Azure SQL Server (or Azure Synapse) is a popular alternative.</p> </div>
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<p><strong>Migration from Oracle to </strong><a href="https://azure.microsoft.com/en-in/products/synapse-analytics/">Azure Synapse </a><strong> DWH can be justified mainly by below:</strong></p> </div>
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<ul><li>Reduced Licensing Costs (especially post exponential upscaling, by virtualizing servers, database consolidation, and compression of data)</li><li>Superior Performance</li><li>Easier Implementation of In-Database Analytics</li></ul> </div>
</div>
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<p>If you looking to automate your <span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/data-testing-concepts/etl-testing/" target="_blank" rel="noopener"><u>ETL testing</u></a></span>, try Datagaps <span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/etl-testing-tools/etl-validator/" target="_blank" rel="noopener"><u> ETL Validator</u> </a></span> ETL testing tool.</p> </div>
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<p><span style="color: #1967d2;"><strong><a style="color: #1967d2;" href="https://www.datagaps.com/etl-testing-tools/etl-validator-download/" target="_blank" rel="noopener"><u>Request Free Trial</u></a></strong></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Primary Challenges in Migration and Points of Vulnerability</h2> </div>
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While Oracle and Azure Synapse both are RDMS with SQL as the main language, there are a few sets of differences that can cause issues during or post-migration. </div>
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<ul><li><b>Oracle Specific Features </b><ul><li>If any features such as Package Variables, Collections (Nested Tables, Objects VARRAYS), System Packages, Wrapped Source Code, Row-level Triggers, and User-defined Types are used, they might not be properly translated in the Azure Synapse.</li></ul></li><li><b>Data Type Differences </b><ul><li>As both the systems interpret SQL ANSI standards slightly differently, there are variations in the data type conversions, especially in terms of numeric and date data types & formats</li></ul></li><li><b>Migration Indexes</b></li><li><b>Performance Comparisons</b></li></ul> </div>
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<img loading="lazy" decoding="async" width="300" height="244" src="https://www.datagaps.com/wp-content/uploads/Challenge-300x244.webp" class="attachment-medium size-medium wp-image-6072" alt="Challenge" srcset="https://www.datagaps.com/wp-content/uploads/Challenge-300x244.webp 300w, https://www.datagaps.com/wp-content/uploads/Challenge-768x624.webp 768w, https://www.datagaps.com/wp-content/uploads/Challenge.webp 1016w" sizes="(max-width: 300px) 100vw, 300px" /> </div>
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<span class="elementor-button-text">Migration Expert</span>
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<a class="elementor-accordion-title" tabindex="0">Datatype Mapping</a>
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<div id="elementor-tab-content-1401" class="elementor-tab-content elementor-clearfix" data-tab="1" role="region" aria-labelledby="elementor-tab-title-1401"><table data-tablestyle="MsoNormalTable" data-tablelook="1184" aria-rowcount="25">
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<td data-celllook="4369"><span data-contrast="none">Azure Synapse </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="4369"><b><span data-contrast="none">Oracle</span></b><span data-contrast="none"> </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="4369"><b><span data-contrast="none">Comment</span></b><span data-contrast="none"> </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="2">
<td data-celllook="69905"><span data-contrast="none">BIGINT </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">NUMBER (19) </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none"> </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="3">
<td data-celllook="69905"><span data-contrast="none">BINARY </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">RAW </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="4">
<td data-celllook="69905"><span data-contrast="none">BIT </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">NUMBER (3) </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="5">
<td data-celllook="69905"><span data-contrast="none">CHAR </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">CHAR </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="6">
<td data-celllook="69905"><span data-contrast="none">DATETIME </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">DATE </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">Fractional parts of a second are truncated </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="7">
<td data-celllook="69905"><span data-contrast="none">DECIMAL </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">NUMBER (p[, s]) </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="8">
<td data-celllook="69905"><span data-contrast="none">FLOAT </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">FLOAT (49) </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="9">
<td data-celllook="69905"><span data-contrast="none">IMAGE </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">LONG RAW </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="10">
<td data-celllook="69905"><span data-contrast="none">INTEGER </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">NUMBER (10) </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">NUMBER range is -2,147,483,647 to 2,147,483,647 </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="11">
<td data-celllook="69905"><span data-contrast="none">MONEY </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">NUMBER (19,4) </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="12">
<td data-celllook="69905"><span data-contrast="none">NCHAR </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">NCHAR </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="13">
<td data-celllook="69905"><span data-contrast="none">NTEXT </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">LONG </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="14">
<td data-celllook="69905"><span data-contrast="none">NVARCHAR </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">NCHAR </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="15">
<td data-celllook="69905"><span data-contrast="none">NUMERIC </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">NUMBER(p[,s]) </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="16">
<td data-celllook="69905"><span data-contrast="none">REAL </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">FLOAT (23) </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="17">
<td data-celllook="69905"><span data-contrast="none">SMALL DATETIME </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">DATE </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">The value for seconds is returned as 0 </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="18">
<td data-celllook="69905"><span data-contrast="none">SMALL MONEY </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">NUMBER (10,4) </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="19">
<td data-celllook="69905"><span data-contrast="none">SMALLINT </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">NUMBER (5) </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">NUMBER range is -32,767 to 32,767 </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="20">
<td data-celllook="69905"><span data-contrast="none">TEXT </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">LONG </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="21">
<td data-celllook="69905"><span data-contrast="none">TIMESTAMP </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">RAW </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="22">
<td data-celllook="69905"><span data-contrast="none">TINYINT </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">NUMBER (3) </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="23">
<td data-celllook="69905"><span data-contrast="none">UNIQUEIDENTIFIER </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">CHAR (36) </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none"> </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="24">
<td data-celllook="69905"><span data-contrast="none">VARBINARY </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">RAW </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
<tr aria-rowindex="25">
<td data-celllook="69905"><span data-contrast="none">VARCHAR </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">VARCHAR2 </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
<td data-celllook="69905"><span data-contrast="none">– </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td>
</tr>
</tbody>
</table></div>
</div>
</div>
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<h3 class="elementor-heading-title elementor-size-default">Pitfalls and Validation Techniques</h3> </div>
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<p><strong>Pitfalls/Drawback – </strong> Since retaining the accuracy of data is the utmost priority in loading data from a source to a target system, data needs to be verified and consistent. Therefore, there could be the following downfall during the overall process of ETL which may cause businesses to lose in different aspects if it is not cared for, such as.</p> </div>
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<img loading="lazy" decoding="async" width="512" height="512" src="https://www.datagaps.com/wp-content/uploads/save-money.webp" class="attachment-large size-large wp-image-6065" alt="save-money" srcset="https://www.datagaps.com/wp-content/uploads/save-money.webp 512w, https://www.datagaps.com/wp-content/uploads/save-money-300x300.webp 300w, https://www.datagaps.com/wp-content/uploads/save-money-150x150.webp 150w" sizes="(max-width: 512px) 100vw, 512px" /> </div>
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<p>Cost Of Migration</p> </div>
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<img loading="lazy" decoding="async" width="150" height="150" src="https://www.datagaps.com/wp-content/uploads/time-left-150x150.webp" class="attachment-thumbnail size-thumbnail wp-image-6067" alt="time-left" srcset="https://www.datagaps.com/wp-content/uploads/time-left-150x150.webp 150w, https://www.datagaps.com/wp-content/uploads/time-left-300x300.webp 300w, https://www.datagaps.com/wp-content/uploads/time-left.webp 512w" sizes="(max-width: 150px) 100vw, 150px" /> </div>
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Time To Market </div>
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<img fetchpriority="high" decoding="async" width="512" height="512" src="https://www.datagaps.com/wp-content/uploads/data.webp" class="attachment-large size-large wp-image-5591" alt="data" srcset="https://www.datagaps.com/wp-content/uploads/data.webp 512w, https://www.datagaps.com/wp-content/uploads/data-300x300.webp 300w, https://www.datagaps.com/wp-content/uploads/data-150x150.webp 150w" sizes="(max-width: 512px) 100vw, 512px" /> </div>
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Poor Data Quality </div>
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</section>
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<p><b>Read:</b> <a href="https://www.datagaps.com/blog/pitfalls-of-cloud-data-migration/">Pitfalls of<b> Cloud Data Migration</b></a></p> </div>
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<strong>Validation Techniques – </strong> Migrating data is not simply a process of migrating the data from its source system to the destination system, but it is also a complex task of retaining the quality of the data which requires a dedicated validation team with quality assurance skills, trusted validation tools, and the expertise in this domain. </div>
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From the validation point of view, it means that the data must be validated thoroughly end-to-end along with migrating from the existing system to the new destination system successfully. Therefore, Validation of already migrated data could be achieved by following some standard steps. </div>
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<div id="elementor-tab-title-1981" class="elementor-tab-title elementor-tab-desktop-title" aria-selected="true" data-tab="1" role="tab" tabindex="0" aria-controls="elementor-tab-content-1981" aria-expanded="false">Step 1:</div>
<div id="elementor-tab-title-1982" class="elementor-tab-title elementor-tab-desktop-title" aria-selected="false" data-tab="2" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1982" aria-expanded="false">Step 2:</div>
<div id="elementor-tab-title-1983" class="elementor-tab-title elementor-tab-desktop-title" aria-selected="false" data-tab="3" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1983" aria-expanded="false">Step 3:</div>
<div id="elementor-tab-title-1984" class="elementor-tab-title elementor-tab-desktop-title" aria-selected="false" data-tab="4" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1984" aria-expanded="false">Step 4:</div>
<div id="elementor-tab-title-1985" class="elementor-tab-title elementor-tab-desktop-title" aria-selected="false" data-tab="5" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1985" aria-expanded="false">Step 5:</div>
<div id="elementor-tab-title-1986" class="elementor-tab-title elementor-tab-desktop-title" aria-selected="false" data-tab="6" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1986" aria-expanded="false">Step 6:</div>
</div>
<div class="elementor-tabs-content-wrapper" role="tablist" aria-orientation="vertical">
<div class="elementor-tab-title elementor-tab-mobile-title" aria-selected="true" data-tab="1" role="tab" tabindex="0" aria-controls="elementor-tab-content-1981" aria-expanded="false">Step 1:</div>
<div id="elementor-tab-content-1981" class="elementor-tab-content elementor-clearfix" data-tab="1" role="tabpanel" aria-labelledby="elementor-tab-title-1981" tabindex="0" hidden="false"><p><span data-contrast="none"><strong>Migration Validation Scope</strong></span></p><p>Having a clear understanding and accurate analysis of the scope of the migration test as to <strong> when</strong> and <strong>what </strong>needs to be tested.</p></div>
<div class="elementor-tab-title elementor-tab-mobile-title" aria-selected="false" data-tab="2" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1982" aria-expanded="false">Step 2:</div>
<div id="elementor-tab-content-1982" class="elementor-tab-content elementor-clearfix" data-tab="2" role="tabpanel" aria-labelledby="elementor-tab-title-1982" tabindex="0" hidden="hidden"><p><strong>Understanding the source system and metadata.<br /></strong></p><p>The Subset of data to be validated. During the validation, one must have a proper understanding of the volume of data that is moved from the source database to the destination database.</p><p>This will help to validate and analyze the performance issue.</p><ul><li>What type of data is it?</li><li>What format is the data in? Are there multiple formats that you need to plan for?</li><li>What attributes does this data have?</li></ul></div>
<div class="elementor-tab-title elementor-tab-mobile-title" aria-selected="false" data-tab="3" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1983" aria-expanded="false">Step 3:</div>
<div id="elementor-tab-content-1983" class="elementor-tab-content elementor-clearfix" data-tab="3" role="tabpanel" aria-labelledby="elementor-tab-title-1983" tabindex="0" hidden="hidden"><p data-contrast="none"><strong> Data Transformation check</strong></p><p>A Data Transformation check is a process used to validate if the data transformation is working in accordance with the mapping logic document and business rules to check if new tables or columns are created with the proper data types and lengths (as specified in the mapping document).</p></div>
<div class="elementor-tab-title elementor-tab-mobile-title" aria-selected="false" data-tab="4" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1984" aria-expanded="false">Step 4:</div>
<div id="elementor-tab-content-1984" class="elementor-tab-content elementor-clearfix" data-tab="4" role="tabpanel" aria-labelledby="elementor-tab-title-1984" tabindex="0" hidden="hidden"><p data-contrast="none"><strong>Data Integrity and Uniqueness check</strong></p><p><strong>A. Data Integrity check:</strong> In the data integrity validation, the key attributes are validated to check if the referential integrity between parent and child tables is mapped correctly and if any uncertain data is removed or loaded.</p><p><strong>B. Data Uniqueness check: </strong> A data Uniqueness check is nothing but a data duplication check, it is evaluated to verify the uniqueness of data of a table/entity as per the data model rule (as specified in the mapping document).</p><p><strong>Example:</strong> verify the customer table if it has authentic data and does not contain any duplicate records based on ‘Customer_Key’ as the key column.</p></div>
<div class="elementor-tab-title elementor-tab-mobile-title" aria-selected="false" data-tab="5" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1985" aria-expanded="false">Step 5:</div>
<div id="elementor-tab-content-1985" class="elementor-tab-content elementor-clearfix" data-tab="5" role="tabpanel" aria-labelledby="elementor-tab-title-1985" tabindex="0" hidden="hidden"><p data-contrast="none"><strong>Data Completeness check</strong></p><p>Traditionally, in the data warehouse, data completeness is evaluated through ETL testing that uses aggregate functions like (sum, max, min, count) to evaluate the average completeness of a column or record. Throughout the evaluation process, it also needs to be verified that the data in the target system is as per expectation after loading which can help in achieving data completeness.</p><ul><li><span data-contrast="none">Record count verification.</span></li><li><span data-contrast="none">Comparing source and target data sets.</span><span data-contrast="none"> </span></li><li><span data-contrast="none">Aggregated records validation.</span></li></ul></div>
<div class="elementor-tab-title elementor-tab-mobile-title" aria-selected="false" data-tab="6" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1986" aria-expanded="false">Step 6:</div>
<div id="elementor-tab-content-1986" class="elementor-tab-content elementor-clearfix" data-tab="6" role="tabpanel" aria-labelledby="elementor-tab-title-1986" tabindex="0" hidden="hidden"><p data-contrast="none"><strong>Data Quality Check</strong></p><p><span data-contrast="none">Data Quality check is critical, in order to make sure that the data is correctly loaded into the destination tables/fields, it must be ensured that the application correctly rejects, substitutes default values, corrects issues, ignores unwanted characters, and reports invalid records if any.</span></p></div>
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Thus, by following the above 6 steps, data migration validation could be achieved with 100% error-free data without having any impediments to the business in decision-making. </div>
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<h3 class="elementor-heading-title elementor-size-default">Validation of Initial Data Migration</h3> </div>
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<p>Initial <span style="color: #0000ff;"><span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/data-migration-testing/" target="_blank" rel="noopener"><span style="text-decoration: underline;">Data Migration Validation</span></a></span>,</span> first and foremost before evaluating and checking the initial data migration, it is important to understand the meaning of Initial data migration. Thus, it basically truncates and loads the entire data into the data warehouse whenever an ETL process/ETL job is said to be run hence this process is known as an initial load or full load. Here, in this process, historical data is not maintained, and only current data is maintained in the data warehouse. The old data is erased and loaded with new data on the next ETL job run. This type of load is performed in type1 SCD (Slowly Changing Dimension).</p> </div>
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<p>Therefore, to validate the initial migration, follow the <strong>Validation Techniques</strong> as mentioned above.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">Validation of ETL Changes</h3> </div>
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Since the data warehouse doesn’t contain any data initially, after full load/initial load when the ETL process happens for the second, third, or fourth time, data is loaded in an incremental fashion where changes are appended to the data warehouse to keep the historical/modified records. </div>
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The Insertion of a record happens only if a record doesn’t already exist in the data warehouse else the update is done only for modified records. </div>
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<strong>Validate data sources </strong> – Perform a data count check and verify that the table and column data type meet the specifications of the data model. Make sure check keys are in place and remove duplicate data. If not done correctly, the aggregate report could be inaccurate or misleading. </div>
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<strong>Validate constraints </strong> – This involves validating the constraints and making sure they are applied on the expected tables. This involves checking the misuse of integrity constraints like Foreign Key. </div>
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<strong>Validate nulls</strong> – This involves validating the full data set in the source and the target tables by using the minus query. We need to perform both source minus target and target minus source. If the minus query returns a value, then that should be considered as mismatched rows. </div>
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<h3 class="elementor-heading-title elementor-size-default">Validation approach for SCD types</h3> </div>
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<p><strong>What are SCDs – Slowly Changing Dimensions?</strong></p><ul><li>A Slowly Changing Dimension (SCD) is a dimension that stores and manages both current and historical data over time in a data warehouse.</li><li>This applies to cases where the attribute for a record varies over time.</li></ul> </div>
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There are in general ways to solve this type of problem, and they are categorized as follows. </div>
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<a class="elementor-accordion-title" tabindex="0">SCD Type 1:</a>
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<div id="elementor-tab-content-9451" class="elementor-tab-content elementor-clearfix" data-tab="1" role="region" aria-labelledby="elementor-tab-title-9451"><p>SCD 1, is used to keep the latest data, the old data is overwritten. In other words, no historical data is managed.</p><p><strong>Advantage –</strong> This is the easiest way to handle the Slowly Changing Dimension problem as there is no requirement to keep track of the old/historical records (data).</p><p><strong>Disadvantages –</strong> By applying this methodology, it is not possible to keep track back in the history of changes made periodically for a record, for example in the below example the company/organization will not be able to know if James was in New York earlier.</p><p><strong>Example:</strong></p><p><strong>Source Table: </strong>Customer</p><table data-tablestyle="MsoTableGrid" data-tablelook="1184" aria-rowcount="4"><tbody><tr aria-rowindex="1"><td data-celllook="4369"><b><span data-contrast="none">Customer_Key 🔑</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">Name</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">City</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="2"><td data-celllook="4369"><span data-contrast="none">10001</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">James</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">New York</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="3"><td data-celllook="4369"><span data-contrast="none">10002</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Luis</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Arizona</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="4"><td data-celllook="4369"><span data-contrast="none">10003</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Alexander</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Texas</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr></tbody></table><p><strong>After Migration: </strong></p><p>Let’s assume customer <strong> James </strong>has moved from<br /><strong>New York </strong>City to<br /><strong>California </strong>, in this case,<br /><strong>SCD Type 1 </strong>will overwrite the existing record with the newly updated record as shown in the below dimension table.</p><p><strong> Target Table: </strong>Customer_Dim</p><table data-tablestyle="MsoTableGrid" data-tablelook="1184" aria-rowcount="4"><tbody><tr aria-rowindex="1"><td data-celllook="4369"><b><span data-contrast="none">Customer_Key 🔑</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">Name</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">City</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="2"><td data-celllook="4369"><span data-contrast="none">10001</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">James</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span style="color: #ff0000;"><b>California</b></span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="3"><td data-celllook="4369"><span data-contrast="none">10002</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Luis</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Arizona</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="4"><td data-celllook="4369"><span data-contrast="none">10003</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Alexander</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Texas</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr></tbody></table><h3><span style="color: #0e1726;">Validation to be performed:</span></h3><p><strong>Count Check</strong> – Validate that the count of updated records in the target table matches with the updated count’s records in the source table./p></p><p><strong>Duplicate check</strong> – Validate that the uniqueness of key columns in the target(dimension) is maintained./p></p><p><strong>Transformation Check </strong> – Validate that the transformation logic applied to any individual columns is transformed correctly and the records are getting loaded as per the mapping document./p></p><p><strong>Referential Integrity Check</strong> – Validate that the parent table (Primary Key column) has the same number of counts as the child table (Foreign Key column)./p></p><h3><span style="color: #0e1726;">When to use SCD Type 1:</span></h3><p>A Slowly Changing Dimension Type 1 should be used only when it is not necessary for the data warehouse to keep track of historical changes</p></div>
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<a class="elementor-accordion-title" tabindex="0">SCD Type 2:</a>
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<div id="elementor-tab-content-9452" class="elementor-tab-content elementor-clearfix" data-tab="2" role="region" aria-labelledby="elementor-tab-title-9452"><p>SCD Type 2, is used to keep the history of old data by adding a new record in the target table if any record was updated in the source table. Therefore, both the original and the new record will be present, and the new record gets its own primary key.</p><p><strong>Advantage –</strong> SCD type 2 allows us to accurately keep all historical information.</p><p><strong>Disadvantages –</strong> This will cause the size of the table to grow fast. In cases where the number of rows for the table is very high to start with, storage and performance can become a concern.</p><p><strong>Example:</strong></p><p><strong>Source Table: </strong>Customer</p><table data-tablestyle="MsoTableGrid" data-tablelook="1184" aria-rowcount="4"><caption> </caption><tbody><tr aria-rowindex="1"><td data-celllook="4369"><b><span data-contrast="none">Customer_Key 🔑</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">Name</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">City</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="2"><td data-celllook="4369"><span data-contrast="none">10001</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">James</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">New York</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="3"><td data-celllook="4369"><span data-contrast="none">10002</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Luis</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Arizona</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="4"><td data-celllook="4369"><span data-contrast="none">10003</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Alexander</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Texas</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr></tbody></table><p><strong>After Migration: </strong></p><p>Let’s assume customer <strong> James </strong>has moved from <strong>New York </strong>City to<br /><strong>California </strong>and from California moved to Bloomberg in this case,<br /><strong>SCD Type 2 </strong>will add a new record with the existing record as shown in the below dimension table.</p><p><strong> Target Table: </strong>Customer_Dim</p><table data-tablestyle="MsoTableGrid" data-tablelook="1184" aria-rowcount="6"><tbody><tr aria-rowindex="1"><td style="width: 123.188px;" data-celllook="4369"><b><span data-contrast="none">Customer_Key 🔑</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td style="width: 75.9875px;" data-celllook="4369"><b><span data-contrast="none">Name</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td style="width: 95.1125px;" data-celllook="4369"><b><span data-contrast="none">City</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="2"><td style="width: 123.188px;" data-celllook="4369"><span data-contrast="none">10001</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td style="width: 75.9875px;" data-celllook="4369"><span data-contrast="none">James</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td style="width: 95.1125px;" data-celllook="4369"><b><span data-contrast="none">New York</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="3"><td style="width: 123.188px;" data-celllook="4369"><span data-contrast="none">10002</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td style="width: 75.9875px;" data-celllook="4369"><span data-contrast="none">Luis</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td style="width: 95.1125px;" data-celllook="4369"><span data-contrast="none">Arizona</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="4"><td style="width: 123.188px;" data-celllook="4369"><span data-contrast="none">10003</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td style="width: 75.9875px;" data-celllook="4369"><span data-contrast="none">Alexander</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td style="width: 95.1125px;" data-celllook="4369"><span data-contrast="none">Texas</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="5"><td style="width: 123.188px;" data-celllook="4369"><span data-contrast="none">10004</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td style="width: 75.9875px;" data-celllook="4369"><span data-contrast="none">James</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td style="width: 95.1125px;" data-celllook="4369"><span style="color: #ff0000;"><b>California</b></span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="6"><td style="width: 123.188px;" data-celllook="4369"><span data-contrast="none">10005</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td style="width: 75.9875px;" data-celllook="4369"><span data-contrast="none">James</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td style="width: 95.1125px;" data-celllook="4369"><span style="color: #ff0000;"><b>Bloomberg</b></span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr></tbody></table><h3><span style="color: #0e1726;">Validation to be done:</span></h3><p><strong>Delta Check</strong> – Verify whether the historical data is preserved, and whether new records are being created or not.</p><p><strong>Duplicate check</strong> – Verify the uniqueness of records (Minus Query).</p><p><strong>Referential Integrity Check</strong> – Validate that the parent table (Primary Key column) has the same number of counts as the child table (Foreign Key column)</p><h3><span style="color: #0e1726;">When to use SCD Type 2:</span></h3><p>SCD Type 2 should be used when it is necessary for the data warehouse to track the historical changes.</p></div>
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<a class="elementor-accordion-title" tabindex="0">SCD Type 3:</a>
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<div id="elementor-tab-content-9453" class="elementor-tab-content elementor-clearfix" data-tab="3" role="region" aria-labelledby="elementor-tab-title-9453"><p>In Type-3 Slowly Changing Dimension, a few technical columns such as <strong>Original_City, Current_City</strong>, and <strong>Effective_Date</strong> are added as new attributes to store the changed values and their cities.</p><p>Attribute <strong>Original_City</strong>, indicates the original city (before update/changed city) of the customer, while attribute <strong>Current_City</strong> indicates the present city (latest update/changed city) of the customer. There will also be a column that indicates when the current value becomes active.</p><p><strong>Advantage:</strong></p><ul><li>SCD Type 3, does not increase the size of the table since new information is updated in an individual attribute.</li><li>This allows us to keep some part of history.</li></ul><p><strong>Note:</strong> SCD Type 3 is minimally used in an organization.</p><p><strong>Disadvantages –</strong> Type 3 will not be able to keep all historical changes where an attribute is changed more than once. For example, if James later moves to Texas on December 31, 2010, the California information will get lost.</p><p><strong>Example:</strong></p><p><strong>Source Table: </strong>Customer</p><table data-tablestyle="MsoTableGrid" data-tablelook="1184" aria-rowcount="4"><caption> </caption><tbody><tr aria-rowindex="1"><td data-celllook="4369"><b><span data-contrast="none">Customer_Key 🔑</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">Name</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">City</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="2"><td data-celllook="4369"><span data-contrast="none">10001</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">James</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">New York</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="3"><td data-celllook="4369"><span data-contrast="none">10002</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Luis</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Arizona</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="4"><td data-celllook="4369"><span data-contrast="none">10003</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Alexander</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Texas</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr></tbody></table><p>If any updates or changes are made in the source table, then the SCD Type 3 table will capture the records as shown below.</p><p>Let’s assume, <strong>James</strong> moved from <strong>New York</strong> to <strong>California</strong> , the original information gets updated, and the records will get populated as the below table (assuming the effective date of change is January 01, 2010).</p><p><strong> Target Table:</strong> Customer_Dim</p><table data-tablestyle="MsoTableGrid" data-tablelook="1184" aria-rowcount="4"><tbody><tr aria-rowindex="1"><td data-celllook="4369"><b><span data-contrast="none">Customer_Key 🔑</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">Name</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">Original_City</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">Current_City</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">Effective_Date</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="2"><td data-celllook="4369"><span data-contrast="none">10001</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">James</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">New York</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">California</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><b><span data-contrast="none">01-01-2010</span></b><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="3"><td data-celllook="4369"><span data-contrast="none">10002</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Luis</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Arizona</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none"> </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none"> </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr><tr aria-rowindex="4"><td data-celllook="4369"><span data-contrast="none">10003</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Alexander</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none">Texas</span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none"> </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td><td data-celllook="4369"><span data-contrast="none"> </span><span data-ccp-props="{"201341983":0,"335559740":259}"> </span></td></tr></tbody></table><h3><span style="color: #0e1726;">When to use SCD Type 3:</span></h3><p>Type 3 Slowly Changing Dimension should only be used when it is necessary for the data warehouse to track historical changes, and when such changes will only occur a finite(limited) number of times.</p></div>
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<h3 class="elementor-heading-title elementor-size-default">Conclusion:</h3> </div>
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/data-warehouse-testing/" target="_blank" rel="noopener">Data warehouse migration</a></span> validation matters as much as the migration itself. During post-migration testing, first compare record counts between source and target, then confirm records were transformed per the business requirements in the mapping documents — a mismatch here can harm the business potentially. From there, data reconciliation, duplicate checks, NULL checks, and referential integrity checks round out the validation needed to ensure data quality.</p> </div>
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<p>Then, data reconciliation, Duplicate check, NULL check, and referential integrity check must be performed to ensure the quality of data.</p> </div>
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<p><strong>Also, read – </strong><span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/blog/oracle-to-azure-sql-server-migration-validation/" target="_blank" rel="noopener"><u>Oracle to Azure SQL Server Migration Validation</u></a></span></p> </div>
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<h3 id="faq-heading">FAQs: Oracle to Azure Synapse Migration Validation</h3>
<section class="faq-section" aria-labelledby="faq-heading">
<div class="faq-list">
<details>
<summary>1) Why do companies migrate from Oracle to Azure Synapse?</summary>
<p>
Organizations migrate from Oracle to Azure Synapse to reduce licensing costs,
improve performance for large-scale analytics, and take advantage of cloud-native
capabilities such as scalable storage and in-database analytics. Successful
migration requires careful validation to account for platform-specific differences.
</p>
</details>
<details>
<summary>2) What are the key data type differences between Oracle and Azure Synapse?</summary>
<p>
Oracle and Azure Synapse implement certain SQL data types differently,
particularly numeric and date/time fields. For example, Oracle’s <strong>DATE</strong>
data type stores both date and time, while Azure Synapse uses
<strong>DATETIME</strong>, which can handle values differently and lead to subtle
inconsistencies if data type mappings are not validated.
</p>
</details>
<details>
<summary>3) How does validation differ between initial and incremental data loads?</summary>
<p>
Initial (full) load validation focuses on record counts, source-to-target
comparisons, constraint validation, and null checks. Incremental ETL validation
additionally verifies that only new or modified records are inserted or updated,
ensuring existing data is not duplicated or overwritten incorrectly.
</p>
</details>
<details>
<summary>4) How are Slowly Changing Dimensions (SCDs) validated during migration?</summary>
<p>
Validation techniques vary by SCD type. Type 1 requires count, duplicate, and
transformation checks; Type 2 adds delta and referential integrity validation to
preserve history; and Type 3 validates both current and historical value columns
to ensure accurate tracking of changes.
</p>
</details>
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<p>The post <a href="https://www.datagaps.com/blog/oracle-to-azure-synapse-data-warehouse-migration-validation/">Oracle to Azure Synapse Data Warehouse Migration Validation</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></content:encoded>
</item>
<item>
<title>Top 3 ETL Testing Tools: How to Choose the Best Tool Clone</title>
<link>https://www.datagaps.com/blog/top-3-etl-testing-tools-clone/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Wed, 23 Apr 2025 08:41:00 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[ETL Testing]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=47619</guid>
<description><![CDATA[<p>ETL Testing refers to the testing, validation, and analysis of the Extraction, Transformation, and Loading Processes that are part of ETL and ELT Pipelines. As ETL testing refers to “Data-in-Motion” Testing, the unit test architecture and principles slightly differ from “Data-at-Rest” Testing (Warehouse/DB Validation).</p>
<p>The post <a href="https://www.datagaps.com/blog/top-3-etl-testing-tools-clone/">Top 3 ETL Testing Tools: How to Choose the Best Tool Clone</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
<content:encoded><![CDATA[ <div data-elementor-type="wp-post" data-elementor-id="47619" class="elementor elementor-47619" data-elementor-post-type="post">
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<h2 class="elementor-heading-title elementor-size-default">What are ETL Testing Tools?</h2> </div>
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<p><span style="text-decoration: underline;"><span style="color: #0000ff; text-decoration: underline;"><a style="color: #0000ff; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener"><span style="color: #1967d2; text-decoration: underline;">ETL testing tools</span></a></span></span> are purpose-built platforms that validate data as it moves through extract, transform, and load pipelines. As data pipelines become more complex, organizations rely on ETL testing tools to verify transformations, detect data issues, and maintain trust in analytics.</p><p>While many teams explore general ETL tools, it is important to distinguish between ETL tools used for data movement and ETL testing tools used for validation and quality assurance.</p><p>Looking for a structured starting point? Check out our <span style="text-decoration: underline;"><span style="color: #1967d2;"><a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/blog/how-to-validate-etl-testing-checklist/" target="_blank" rel="noopener">ETL Testing Checklist</a></span></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">When are ETL Testing Tools Used?</h2> </div>
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<p>ETL testing tools are primarily used across two major categories of projects where data accuracy is critical:</p> </div>
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<span >
1. Data Migration Projects </span>
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<p class="elementor-icon-box-description">
These involve moving data across systems while ensuring consistency and completeness. Common scenarios include: </p>
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<ul><li>Application migrations</li><li>Cloud migrations such as moving to <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/snowflake-testing-automation/" target="_blank" rel="noopener">Snowflake</a></span></span> or <span style="text-decoration: underline;"><span style="color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/databricks-testing-automation/" target="_blank" rel="noopener">Databricks</a></span></span></li><li>Data warehouse migrations such as Teradata to Redshift or Teradata to Databricks</li></ul> </div>
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<p>In these cases, ETL testing tools and data testing tools are essential for validating large-scale data movement and ensuring no data loss or transformation errors.</p><p>Need help with data migration? Explore our <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-migration-testing-automation/" target="_blank" rel="noopener">Data Migration Solution page</a>.</span></span></p> </div>
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<span >
2. Data Pipeline Testing </span>
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<p class="elementor-icon-box-description">
These focus on ongoing validation of data pipelines in production environments. Key use cases include: </p>
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<ul><li>Verifying data transformations across pipelines</li><li>Ensuring consistency between source and target systems</li><li>Detecting data quality issues early</li><li>Supporting continuous validation as pipelines scale Here, ETL automation testing tools help teams scale validation, reduce manual effort, and maintain data quality across evolving pipelines.<br /><br />Read more on <span style="text-decoration: underline; color: #1967d2;"><a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/etl-testing/" target="_blank" rel="noopener">ETL Testing</a></span> for data pipeline environments.</li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Evaluation Criteria: How We Selected and Assessed ETL Testing Tools?</h2> </div>
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<p class="font-claude-response-body">Modern ETL testing tools are expected to deliver multi-source validation, transformation testing, automation, AI-assisted test creation, and scalability across large data environments. These capabilities formed the basis of our evaluation.</p> </div>
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<p class="font-claude-response-body">Several tools come up frequently in this space. iceDQ, Tosca DI, and Informatica DVO were considered but excluded for specific reasons:</p> </div>
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<p><strong>iceDQ:</strong> The on-premise version of iceDQ lacks several core ETL testing capabilities that enterprise teams typically require. The SaaS version is more feature-complete but not suited for teams that need on-premise deployment.</p><p><strong>Informatica DVO:</strong> Informatica DVO is not a standalone ETL testing tool. It runs only within the Informatica platform, making it irrelevant for teams outside that ecosystem.</p><p><strong>Tosca DI:</strong> While Tosca is a popular choice for application and UI testing, Tosca DI is found to be limited in scope for ETL testing and end-to-end pipeline validation, making it a less suitable option for teams with comprehensive data pipeline testing requirements.</p> </div>
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<p class="font-claude-response-body">ETL testing tools broadly fall into three categories: purpose-built ETL testing platforms, query-based validation tools, and developer-first testing frameworks. This comparison selects one representative from each category to highlight how different approaches address the same validation challenges. In this comparison, Datagaps ETL Validator represents the purpose-built category, QuerySurge the query-based approach, and dbt Tests the developer-first framework.</p> </div>
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<p class="font-claude-response-body">Evaluation was based on nine criteria that reflect real production requirements: core ETL testing capabilities, automation and CI/CD integration, usability and test authoring, data quality and observability, data contracts and governance, testing scope and coverage, enterprise readiness, scalability and performance, and pricing and accessibility.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Top 3 ETL Testing Tools: Detailed Comparison</h2> </div>
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<p>Below is a detailed comparison of three widely considered options: <span style="text-decoration: underline;"><span style="color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">Datagaps ETL Validator</a></span></span>, QuerySurge, and dbt tests.</p> </div>
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<div class="etl-legend">
<div class="etl-legend__title">Legend</div>
<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--star">★</span>
<span>Unique / standout feature</span>
</div>
<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--check">✔</span>
<span>Strong / full support</span>
</div>
<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--half">◐</span>
<span>Partial / limited support</span>
</div>
<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--cross">✘</span>
<span>Not supported / not available</span>
</div>
</div>
<p class="etl-scroll-hint">← Scroll to see full table →</p>
<div class="etl-table-wrapper">
<table class="etl-table">
<colgroup>
<col>
<col>
<col>
<col>
<col>
</colgroup>
<thead>
<tr>
<th>Feature / Capability</th>
<th class="tool-col"><span class="etl-head-nowrap">Datagaps<br>ETL Validator</span></th>
<th class="tool-col"><span class="etl-head-nowrap">QuerySurge</span></th>
<th class="tool-col"><span class="etl-head-nowrap">dbt Tests</span></th>
<th>Verdict</th>
</tr>
</thead>
<tbody>
<tr class="etl-cat-row"><td colspan="5">1. Core ETL Testing</td></tr>
<tr class="etl-data-row">
<td>ETL Test Authoring & Execution</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and QuerySurge are purpose-built for end-to-end ETL test authoring and execution. dbt Tests define quality checks on dbt models only.</td>
</tr>
<tr class="etl-data-row">
<td>ELT / In-Database Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-check">✔</span></td>
<td>ETL Validator and dbt Tests push validation to the warehouse natively. ETL Validator leads on orchestration across multiple platforms. QuerySurge is partial.</td>
</tr>
<tr class="etl-data-row">
<td>Flat File / CSV Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and QuerySurge handle flat file and CSV validation natively. dbt Tests are database-only.</td>
</tr>
<tr class="etl-data-row">
<td>Multiple Source / Target Support</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator supports multiple heterogeneous sources and targets in a single test run. QuerySurge supports only a single source-target pair. dbt Tests operate within a single warehouse.</td>
</tr>
<tr class="etl-data-row">
<td>Transformation Validation</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td>ETL Validator adds GenAI-assisted rule authoring across any ecosystem. dbt Tests are strong for validating dbt model outputs. QuerySurge uses SQL-based validation.</td>
</tr>
<tr class="etl-data-row">
<td>Source-to-Target Reconciliation</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator uniquely supports Data Profile reconciliation. QuerySurge covers row counts and aggregations. dbt has no cross-system reconciliation.</td>
</tr>
<tr class="etl-data-row">
<td>Source-to-Report Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator validates the full chain from raw source through to the BI report layer. QuerySurge has limited support. dbt Tests do not reach the reporting layer.</td>
</tr>
<tr class="etl-data-row">
<td>Non-dbt Pipeline Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and QuerySurge test any pipeline regardless of transformation tool. dbt Tests are locked to dbt models.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">2. Automation & CI/CD</td></tr>
<tr class="etl-data-row">
<td>Automated Regression Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator adds GenAI-assisted test maintenance. QuerySurge offers structured ETL regression automation. dbt Tests re-run on every invocation but have no dedicated regression management.</td>
</tr>
<tr class="etl-data-row">
<td>CI/CD Pipeline Integration</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-star">★</span></td>
<td>dbt Tests have first-class CI/CD integration. ETL Validator and QuerySurge both support CI/CD with broad pipeline trigger options.</td>
</tr>
<tr class="etl-data-row">
<td>Scheduled / Triggered Test Runs</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and QuerySurge support native scheduling and REST API triggers. dbt Tests depend on dbt Cloud or an external orchestrator such as Airflow.</td>
</tr>
<tr class="etl-data-row">
<td>Test Case Reusability</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td>All three support reusable test definitions. ETL Validator and QuerySurge offer reusable templates via their UIs and test libraries.</td>
</tr>
<tr class="etl-data-row">
<td>Test Maintenance Overhead</td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">Medium</span></td>
<td><span class="sym-text">Medium-High</span></td>
<td>ETL Validator's GenAI-assisted maintenance significantly reduces upkeep as pipelines change. dbt Tests require engineers to update definitions manually for every model or schema change.</td>
</tr>
<tr class="etl-data-row">
<td>Cross-Pipeline Orchestration</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and QuerySurge orchestrate tests across multiple pipelines in a single run. dbt Tests are scoped to the dbt DAG.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">3. Usability & Test Authoring</td></tr>
<tr class="etl-data-row">
<td>No-Code / Visual Test Builder</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator is the only tool with a drag-and-drop no-code interface for ETL testing. QuerySurge is partial. dbt Tests are written entirely in YAML and SQL.</td>
</tr>
<tr class="etl-data-row">
<td>Ease of Setup</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and QuerySurge deploy in days. dbt Tests require an existing dbt project before writing a single test.</td>
</tr>
<tr class="etl-data-row">
<td>Business User Accessibility</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator is designed for QA analysts and business users without coding skills. QuerySurge requires SQL knowledge. dbt Tests require proficiency in dbt, YAML, SQL, and version control.</td>
</tr>
<tr class="etl-data-row">
<td>GenAI / AI-Assisted Test Creation</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator generates tests automatically from ETL mapping documents using agentic AI, cutting initial test creation time by over 60%. QuerySurge offers limited GenAI support.</td>
</tr>
<tr class="etl-data-row">
<td>Test Documentation & Visibility</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator provides customisable stakeholder dashboards. QuerySurge offers detailed reporting. dbt generates docs automatically but test visibility for non-engineers is limited.</td>
</tr>
<tr class="etl-data-row">
<td>Learning Curve</td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">Low-Medium</span></td>
<td><span class="sym-text">High</span></td>
<td>ETL Validator is the fastest to productive use for any team profile. dbt Tests require mastery of the full dbt framework.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">4. Data Quality & Observability</td></tr>
<tr class="etl-data-row">
<td>Data Quality Monitoring</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator provides continuous DQ monitoring with scoring and alerting. dbt Tests and QuerySurge run at job execution time only.</td>
</tr>
<tr class="etl-data-row">
<td>Anomaly Detection</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator automatically detects data anomalies across pipelines using AI. Neither QuerySurge nor dbt Tests offer automated anomaly detection.</td>
</tr>
<tr class="etl-data-row">
<td>Data Profiling</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator provides rich data profiling alongside test execution. QuerySurge offers basic profiling. dbt Tests require separate tools such as dbt-profiler or Elementary.</td>
</tr>
<tr class="etl-data-row">
<td>Data Lineage</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-star">★</span></td>
<td>dbt auto-generates column-level lineage across the entire DAG. ETL Validator provides pipeline-level lineage tied to DQ scoring. QuerySurge has no lineage support.</td>
</tr>
<tr class="etl-data-row">
<td>DQ Scoring & Health Dashboards</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator uniquely provides quantified DQ scores and health dashboards across pipelines. Neither QuerySurge nor dbt offer this natively.</td>
</tr>
<tr class="etl-data-row">
<td>Alerting & Notifications</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and QuerySurge support native alerting on test failures. dbt alerting depends on the orchestration layer.</td>
</tr>
<tr class="etl-data-row">
<td>BI Regression Testing</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator's visual BI report regression testing across Power BI, Tableau, QuickSight, and Oracle Analytics has no equivalent in QuerySurge or dbt.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">5. Data Contracts & Governance</td></tr>
<tr class="etl-data-row">
<td>Data Contracts</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator supports formal data contracts for validating data and schema obligations across pipeline boundaries. dbt has partial support via dbt contracts. QuerySurge has none.</td>
</tr>
<tr class="etl-data-row">
<td>Schema Validation & Drift Detection</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-check">✔</span></td>
<td>ETL Validator and dbt Tests both detect schema drift. QuerySurge offers partial schema validation.</td>
</tr>
<tr class="etl-data-row">
<td>Data Observability Integration</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator provides built-in observability across the full pipeline. dbt integrates with third-party tools. QuerySurge is less observability-focused.</td>
</tr>
<tr class="etl-data-row">
<td>Audit Trails & Compliance Reporting</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and QuerySurge provide compliance-grade audit trails out of the box. dbt requires significant custom engineering to produce audit-ready reports.</td>
</tr>
<tr class="etl-data-row">
<td>Role-Based Access Control</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and QuerySurge support enterprise RBAC natively. dbt Cloud offers team-level permissions; dbt Core has no access control layer.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">6. Testing Scope & Coverage</td></tr>
<tr class="etl-data-row">
<td>Mixed-Source Pipelines</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator's Apache Spark engine supports heterogeneous sources including databases, files, and APIs. dbt is warehouse-only.</td>
</tr>
<tr class="etl-data-row">
<td>Legacy System Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and QuerySurge test pipelines built in any ETL tool including legacy platforms. dbt Tests are not suitable for non-dbt pipelines.</td>
</tr>
<tr class="etl-data-row">
<td>Streaming / Real-Time Data Validation</td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and QuerySurge have partial streaming support. dbt is mainly a batch transformation tool.</td>
</tr>
<tr class="etl-data-row">
<td>Extensibility</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator provides the capability to add custom plugins using Python, making it highly extensible. QuerySurge and dbt have a fixed set of capabilities.</td>
</tr>
<tr class="etl-data-row">
<td>Test Data Generation</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator uniquely generates synthetic test data for automating pipeline testing, reducing reliance on production data copies.</td>
</tr>
<tr class="etl-data-row">
<td>End-to-End Pipeline Coverage</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator covers ingestion, transformation, loading, and BI reporting. dbt Tests cover only the transformation layer within dbt models.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">7. Enterprise Readiness</td></tr>
<tr class="etl-data-row">
<td>Enterprise Support & SLAs</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and QuerySurge offer dedicated commercial support with SLAs. dbt Core is open-source with community support only.</td>
</tr>
<tr class="etl-data-row">
<td>On-Premise Deployment</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and QuerySurge support on-premise deployment. dbt Cloud is SaaS-based.</td>
</tr>
<tr class="etl-data-row">
<td>Multi-Project / Multi-Team Support</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator supports multiple projects in a single deployment with container isolation. QuerySurge supports multi-team setups.</td>
</tr>
<tr class="etl-data-row">
<td>Custom Dashboards for Stakeholders</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator uniquely provides customisable stakeholder-facing dashboards for sharing test results and data quality scores.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">8. Scalability & Performance</td></tr>
<tr class="etl-data-row">
<td>Handling Large Data Volumes</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-check">✔</span></td>
<td>ETL Validator's Spark-based execution engine is built for billions of records. QuerySurge is comparatively limited for enterprise-scale data volumes.</td>
</tr>
<tr class="etl-data-row">
<td>Auto-Scaling</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator has native on-demand auto-scaling. dbt and QuerySurge rely on underlying infrastructure.</td>
</tr>
<tr class="etl-data-row">
<td>Parallel Test Execution</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator's Spark engine enables high-parallelism across hundreds of tests simultaneously. dbt test parallelism is warehouse-dependent.</td>
</tr>
<tr class="etl-data-row">
<td>Cloud-Native Deployment</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td>All three are cloud-native. ETL Validator supports AKS, EKS, GKE, and Databricks. dbt Cloud is fully managed.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">9. Pricing & Accessibility</td></tr>
<tr class="etl-data-row">
<td>Licensing Model</td>
<td><span class="sym-text">Commercial</span></td>
<td><span class="sym-text">Commercial</span></td>
<td><span class="sym-text">Open-Source / dbt Cloud</span></td>
<td>dbt Core is free and open-source; dbt Cloud adds a managed commercial tier. The true cost of dbt Tests includes engineering time to build, maintain, and extend.</td>
</tr>
<tr class="etl-data-row">
<td>Relative Cost</td>
<td><span class="sym-text">Best value</span></td>
<td><span class="sym-text">Mid-range</span></td>
<td><span class="sym-text">Free + engineering cost</span></td>
<td>dbt Tests appear free, but the hidden cost is engineering hours to configure and maintain them. ETL Validator delivers broad feature coverage across total cost of ownership.</td>
</tr>
<tr class="etl-data-row">
<td>ETL Vendor Lock-in Risk</td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">Medium</span></td>
<td>dbt Tests are tightly coupled to the dbt ecosystem. ETL Validator and QuerySurge carry low lock-in risk.</td>
</tr>
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<td>Ideal Team Profile</td>
<td><span class="sym-text">Data Engineering & QA teams of all sizes</span></td>
<td><span class="sym-text">QA Teams</span></td>
<td><span class="sym-text">dbt-native analytics engineers</span></td>
<td>dbt Tests only make sense for teams already running dbt. ETL Validator serves QA, engineering, and business users.</td>
</tr>
</tbody>
</table>
</div>
</div> </div>
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<h3 class="elementor-heading-title elementor-size-default">Which ETL Testing Tool Should You Choose?</h3> </div>
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<p class="font-claude-response-body">Choosing the right <span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener"><span style="text-decoration: underline;">ETL testing tool</span></a></span> depends on how comprehensive your testing needs are across data pipelines. While multiple tools offer specific capabilities, they differ significantly in scope, flexibility, and coverage.</p> </div>
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<h4 class="elementor-icon-box-title">
<span >
Datagaps ETL Validator </span>
</h4>
<p class="elementor-icon-box-description">
Datagaps ETL Validator provides a more complete approach by supporting end-to-end ETL testing across heterogeneous data sources, including databases, files, APIs and BI layers. It also offers automation, AI-driven test generation, and scalability required for modern data environments. </p>
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<h4 class="elementor-icon-box-title">
<span >
QuerySurge </span>
</h4>
<p class="elementor-icon-box-description">
QuerySurge is effective for SQL-based validation but is largely limited to query-pair comparisons and does not support broader multi-system or end-to-end pipeline testing scenarios. </p>
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<span >
dbt tests </span>
</h4>
<p class="elementor-icon-box-description">
dbt Tests are limited to rule-based data checks within a single data warehouse. They are not built for complete ETL testing and do not address pipeline validation across systems. </p>
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<h3 class="elementor-heading-title elementor-size-default">Our Recommendation for ETL Testing Tool</h3> </div>
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<p><span style="font-weight: 600;"><br />For teams that need comprehensive coverage across the full pipeline, <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; font-weight: 600;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">Datagaps ETL Validator </a></span>is the clear choice. </span>Where QuerySurge stops at query-pair validation and does not scale effectively for large data volumes, and dbt Tests stay within the warehouse running rule-based checks, Datagaps ETL Validator goes further: across sources, through transformations, and all the way to the BI reporting layer. Built on a Spark-based engine, Datagaps ETL Validator is designed to scale for enterprise data volumes without compromising on performance. It is purpose-built for ETL testing and Datagaps is recognized as a data pipelines test automation specialist in Gartner’s Market Guide for DataOps Tools. If reliable, end-to-end data validation matters to your team, Datagaps ETL Validator is the tool built for that job.</p> </div>
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<p>For teams looking beyond framework-specific validation toward complete pipeline testing and ETL automation, <span style="text-decoration: underline;"><span style="color: #1967d2;"><strong><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">Datagaps ETL Validator</a></strong></span></span> offers a more comprehensive approach.</p> </div>
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<p><span style="text-decoration: underline;">Disclaimer</span>: The above-mentioned list is purely an outcome of the conversations and feedback received from various industry users in the ETL/Data Warehouse testing space. Any concerns or views can be shared at <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="mailto:contact@datagaps.com">contact@datagaps.com</a></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Watch ETL Validator in Action with Demo</h2> </div>
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Check out how ETL Validator simplifies ETL Testing, data validation through automation across pipelines from this playlist </div>
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Demo Playlist </span>
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</a>
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Start your 14-day free trial in our sandbox. Explore and optimize your ETL processes. Start your trial today! </div>
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<h2 class="elementor-heading-title elementor-size-default">Get Started with ETL Validator – An ETL & Data Testing tool</h2> </div>
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<p>The post <a href="https://www.datagaps.com/blog/top-3-etl-testing-tools-clone/">Top 3 ETL Testing Tools: How to Choose the Best Tool Clone</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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