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<title>Snowflake Archives - Datagaps | Gen AI-Powered Automated Cloud Data Testing</title>
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<title>Snowflake Archives - Datagaps | Gen AI-Powered Automated Cloud Data Testing</title>
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<title>ETL Testing In Snowflake Using DataOps Suite</title>
<link>https://www.datagaps.com/blog/etl-testing-in-snowflake-using-dataops-suite/</link>
<comments>https://www.datagaps.com/blog/etl-testing-in-snowflake-using-dataops-suite/#respond</comments>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Sat, 14 Feb 2026 13:18:00 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[ETL Testing]]></category>
<category><![CDATA[Snowflake]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=11775</guid>
<description><![CDATA[<p>ETL stands for Extract, Transform, and Load. It is the process by which data is extracted from one or more sources, transformed into compatible formats, and then loaded into a target Database or Data Warehouse.</p>
<p>The post <a href="https://www.datagaps.com/blog/etl-testing-in-snowflake-using-dataops-suite/">ETL Testing In Snowflake Using DataOps Suite</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>ETL testing in <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/snowflake-testing-automation/" target="_blank" rel="noopener">Snowflake</a></span> involves three stages — <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">extraction, transformation, and loading</a></span> — each of which needs validation to confirm data quality throughout the pipeline. This post walks through a practical example: extracting customer data directly from Snowflake, transforming it per business requirements, loading it to a target using a DB Sink component, and finally validating the generated reports using <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/bi-testing-tools/" target="_blank" rel="noopener">BI Validator</a></span>. It’s a straightforward look at how DataOps Suite handles each ETL stage for Snowflake specifically.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>ETL testing in Snowflake follows the standard three-stage process</strong> — extraction, transformation, and loading — each needing its own validation checkpoint.</li><li><strong>Data extraction can pull from the same or different source locations</strong> — the example in this post extracts customer data directly from a Snowflake table.</li><li><strong>The DB Sink component handles data loading</strong> — moving transformed data to its target file location as the final ETL step.</li><li><strong>BI Validator closes the loop on report accuracy</strong> — once ETL processing completes, generated reports are checked and validated to confirm they reflect the transformed data correctly.</li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Introduction and Overview of ETL Testing Snowflake</h2> </div>
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.ibm.com/in-en/topics/etl" target="_blank" rel="noopener">ETL</a></span> stands for Extract, Transform, and Load. It is the process by which data is extracted from one or more sources, transformed into compatible formats, and then loaded into a target Database or Data Warehouse. The sources may include Flat Files, Third-Party Applications, Databases, etc.<span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"> </span></span><a href="https://www.datagaps.com/data-testing-concepts/etl-testing/" target="_blank" rel="noopener"><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;">ETL testing</span></span></a> is necessary to ensure that data moving from external sources to the data warehouse is accurate at each point between the source and destination.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">Purpose of ETL</h3> </div>
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<p>ETL allows businesses to consolidate data from multiple databases and other sources into a single repository with the data that has been modified and used during the analysis of data. This unified data repository allows for simplified access to analysis and additional processing of the data. There are many advantages of using ETL tools for the migration of data. It reduces delivery time, reduces unnecessary expenses, makes the process easy to use, and also will be simple for data migrations. Data Integration, Data Warehousing, and Data Migration are the three common uses of ETL.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">ETL Testing Process in Snowflake</h3> </div>
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<p>The data will be migrated from one data warehouse to another cloud-based <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://aws.amazon.com/data-warehouse/" target="_blank" rel="noopener">data warehouse</a></span> using various steps present in ETL Testing. The multiple steps involved in this process are the extraction of data, the transformation of the data, and finally the loading of data to the different data sources. This process is essential for proper testing such the quality of data can be checked efficiently. The DataOps Suite tool can be used efficiently for ETL Testing. <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/request-demo/" target="_blank" rel="noopener">Request Demo</a></span></p> </div>
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<h5><strong>The various steps involved in ETL Testing are as follows:</strong></h5> </div>
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<h3 class="elementor-heading-title elementor-size-default">Step 1: Extraction Of Data</h3> </div>
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<p>Data Extraction is the first step that will be performed in the <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">ETL Testing</a></span>. In this procedure, the data will usually be extracted from the same data source, or it can be extracted from different source locations also. Here, for example, the data is extracted from the same source i.e. Snowflake, and Customer data is extracted. After extracting the data from the source location, then further the data can be transformed according to the client’s requirements.</p> </div>
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<img fetchpriority="high" decoding="async" width="1424" height="752" src="https://www.datagaps.com/wp-content/uploads/Gemini_Generated_Image_op6oc5op6oc5op6o.png" class="attachment-full size-full wp-image-57924" alt="Datagaps Data Quality Check" srcset="https://www.datagaps.com/wp-content/uploads/Gemini_Generated_Image_op6oc5op6oc5op6o.png 1424w, https://www.datagaps.com/wp-content/uploads/Gemini_Generated_Image_op6oc5op6oc5op6o-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Gemini_Generated_Image_op6oc5op6oc5op6o-1024x541.png 1024w, https://www.datagaps.com/wp-content/uploads/Gemini_Generated_Image_op6oc5op6oc5op6o-768x406.png 768w" sizes="(max-width: 1424px) 100vw, 1424px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Data Extraction From Customers Table</figcaption>
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<h3 class="elementor-heading-title elementor-size-default">Step 2: Transformation Of Data</h3> </div>
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<p>After the data is extracted from the same or different data source to the same or the other source, a few changes or transformations in the customers’ data are done. Generally, data transformations include changes in data types or other changes according to the client’s requirements.</p><p>The below screenshot depicts the Customer data that is being transformed.</p> </div>
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<img decoding="async" width="1424" height="749" src="https://www.datagaps.com/wp-content/uploads/Gemini_Generated_Image_ayanckayanckayan.png" class="attachment-full size-full wp-image-58019" alt="Datagaps Data Quality Check" srcset="https://www.datagaps.com/wp-content/uploads/Gemini_Generated_Image_ayanckayanckayan.png 1424w, https://www.datagaps.com/wp-content/uploads/Gemini_Generated_Image_ayanckayanckayan-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Gemini_Generated_Image_ayanckayanckayan-1024x539.png 1024w, https://www.datagaps.com/wp-content/uploads/Gemini_Generated_Image_ayanckayanckayan-768x404.png 768w" sizes="(max-width: 1424px) 100vw, 1424px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Data Transformation Using SQL Component</figcaption>
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<p>Once the data is transformed, <strong><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-reconciliation/" target="_blank" rel="noopener">data comparison</a></span> </strong>can be performed to view the changes after transformation.</p> </div>
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<p><span class="fontSizeMediumPlus">Further, the quality of data can be checked by using the <strong>Data Rules Component. </strong></span><span class="fontSizeMediumPlus"><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 checks</a></span> are done to find out the issues in the quality of data. The <strong>Data</strong><strong> Profile Component</strong> can also be used to find out the data quality issues.</span></p> </div>
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<p>In the below screenshot, the quality of data is checked by verifying the email address as well as the name string check by using different data rules in the data rules component.</p> </div>
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<img loading="lazy" decoding="async" width="1323" height="696" src="https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component.png" class="attachment-full size-full wp-image-11779" alt="Data-Quality-Check-Using-Data-Rules-Component" srcset="https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component.png 1323w, https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component-1024x539.png 1024w, https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component-768x404.png 768w" sizes="(max-width: 1323px) 100vw, 1323px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Data Quality Check Using Data Rules Component</figcaption>
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<p>Data profiling is also done to check the quality of data.</p> </div>
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<img loading="lazy" decoding="async" width="1320" height="695" src="https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component.png" class="attachment-full size-full wp-image-11780" alt="Profiling-Data-Using-Data-Profile-Component" srcset="https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component.png 1320w, https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component-1024x539.png 1024w, https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component-768x404.png 768w" sizes="(max-width: 1320px) 100vw, 1320px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Profiling Data Using Data Profile Component</figcaption>
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<h3 class="elementor-heading-title elementor-size-default">Step 3: Loading Of The Data</h3> </div>
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<p>Once the transformation of data is performed, further the data will be loaded from one source to a particular file location. Here the data is loaded by using the <strong>DB Sink component.</strong> This is the general testing process followed in the DataOps Suite tool. <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/request-demo/" target="_blank" rel="noopener">Request Demo</a></span></p> </div>
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<p>The below screenshot depicts the data loaded to the desired data source after the data transformations are done.</p> </div>
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<img loading="lazy" decoding="async" width="1320" height="695" src="https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component.png" class="attachment-full size-full wp-image-11781" alt="Data-Loading-Using-DB-Sink-Component" srcset="https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component.png 1320w, https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component-1024x539.png 1024w, https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component-768x404.png 768w" sizes="(max-width: 1320px) 100vw, 1320px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Data Loading Using DB Sink Component</figcaption>
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<th style="padding: 12px; border: 1px solid #ccc;">Stage</th>
<th style="padding: 12px; border: 1px solid #ccc;">Component(s) Used</th>
<th style="padding: 12px; border: 1px solid #ccc;">What Gets Validated</th>
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<td style="padding: 12px; border: 1px solid #ccc;">1. Extraction</td>
<td style="padding: 12px; border: 1px solid #ccc;">Source Connection to Snowflake</td>
<td style="padding: 12px; border: 1px solid #ccc;">Verifies that customer data is extracted correctly from the source table.</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">2. Transformation</td>
<td style="padding: 12px; border: 1px solid #ccc;">SQL Component, Data Compare Component, Data Rules Component, Data Profile Component</td>
<td style="padding: 12px; border: 1px solid #ccc;">Validates data type transformations, compares transformed data, enforces data quality rules (such as email and name format checks), and performs data profiling.</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">3. Loading</td>
<td style="padding: 12px; border: 1px solid #ccc;">DB Sink Component</td>
<td style="padding: 12px; border: 1px solid #ccc;">Confirms that transformed data is loaded accurately into the target destination.</td>
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</tbody>
</table> </div>
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<p>Once the ETL Testing process is completed, the reports generated need to be checked and evaluated as there will be some differences. In our DataOps Suite tool, BI Validator can be used to check and evaluate the reports.</p><p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/data-testing-concepts/data-warehouse-testing-checklist/" target="_blank" rel="noopener">Read: Data Warehouse Testing Checklist</a></span></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/etl-testing/" target="_blank" rel="noopener">ETL testing</a></span> matters most when large volumes of data move between databases, since even small errors can compound across millions of records. DataOps Suite makes this process transparent, using dedicated components to verify data at every stage — extraction, transformation, and loading — rather than only checking the final output. Once loading is complete, <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/bi-testing-tools/" target="_blank" rel="noopener">BI Validator</a></span> closes the loop by validating that generated reports accurately reflect the transformed data. Together, this step-by-step validation is what keeps performance gains from Snowflake migrations from coming at the cost of data accuracy.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/etl-testing-in-snowflake-using-dataops-suite/">ETL Testing In Snowflake Using DataOps Suite</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>Datagaps Data Validation and Migration To Snowflake</title>
<link>https://www.datagaps.com/blog/data-validation-in-snowflake-data-migration/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Mon, 19 Jan 2026 12:31:00 +0000</pubDate>
<category><![CDATA[Snowflake]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=7286</guid>
<description><![CDATA[<p>This article describes ways in which we can test the data movement between Snowflake and on-premises data and between instances of Snowflake itself. We will also provide some benchmarks for doing comparison testing for large volumes of data moved into Snowflake.</p>
<p>The post <a href="https://www.datagaps.com/blog/data-validation-in-snowflake-data-migration/">Datagaps Data Validation and Migration To Snowflake</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 blog explains how Datagaps validates data movement between on-premises databases and Snowflake, and between Snowflake instances, using DataFlow. A real client case study covers migrating 400 SQL Server tables (500M+ rows each) to <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/cloud-data-test-automation/snowflake/" target="_blank" rel="noopener">Snowflake</a></span>, surfacing issues like numeric precision differences, null inconsistencies, and truncation. It also covers transitioning from ETL to ELT, BI Validator-driven regression testing, and AWS EMR cluster auto-scaling to over 30 nodes—achieving roughly a 50% reduction in test cycle time.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li>Bulk migration testing catches issues at scale — using the Data Migration Wizard, a client generated comparison tests for 400 SQL Server tables (500M+ rows each) in just a few hours, surfacing numeric precision, null value, and truncation issues.</li><li>DataFlow supports the ETL-to-ELT transition — as the client shifted their pipeline architecture toward Snowflake, DataFlow validated accuracy across iterations until source and target data were fully in sync.</li><li>BI Validator extends testing to the reporting layer — clients use it for regression, performance, and stress testing to compare BI dashboards and reports between the old warehouse and new Snowflake implementation.</li><li>AWS EMR auto-scaling improves testing efficiency — DataOps Suite’s cluster integration scaled EMR nodes up to 30+ during comparisons of 500M-record tables, then scaled down after, cutting testing costs while achieving an estimated 50% reduction in test cycle time.</li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Data validation in snowflake</h2> </div>
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<p>Validating data during a migration to Snowflake means confirming that records, data types, and precision all carry over correctly from the source system — and this article discusses why and how to do that, diving into the challenges of bulk <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/cloud-data-test-automation/snowflake/" target="_blank" rel="noopener">Data Migration to Snowflake.</a></span></span></p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Why and How?</h5> </div>
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People are rapidly adopting cloud architectures for Data Warehouses and Machine Learning projects due to the economies of scale in the cloud. One obstacle in achieving rapid success is the data and data types inconsistencies between on-premise structures and the modern data stacks in the cloud. </div>
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When you migrate vast amounts of data to the cloud, the opportunity to introduce mistakes is multiplied due to these reasons and others. The earlier you catch the issues, the less costly it is to resolve the discrepancies. </div>
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<p><strong>This is where </strong><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/" target="_blank" rel="noopener">Datagaps</a></span><strong> come in to play.</strong></p> </div>
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<p>We provide the ability to run <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-reconciliation/" target="_blank" rel="noopener">data reconciliation</a></span>on millions & even billions of records between source and target structures of different types, such as your on-premises database and Snowflake.</p> </div>
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This article describes ways in which we can test the data movement between Snowflake and on-premises data and between instances of Snowflake itself. We will also provide some benchmarks for doing comparison testing for large volumes of data moved into Snowflake. Another benefit of the Datagaps approach is, as additional data is moved into the Snowflake structure from other sources, Datagaps provides the ability to monitor the quality of your data structures by continually providing an up to date scoring so that you can determine when data is becoming corrupted. </div>
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<h2 class="elementor-heading-title elementor-size-default">Benefits of using Datagaps to test data movement into Snowflake</h2> </div>
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<p>We recently sat down with one of our clients that uses our DataFlow product for testing the data migration from on-Prem SQL Server to Snowflake in the cloud running in <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://aws.amazon.com/" target="_blank" rel="noopener">AWS.</a></span></p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Implementation</h5> </div>
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<p>They started the initial migration by performing bulk loads from 400 SQL Server tables to Snowflake with minimal transformations. This was stage 0, where they could perform source and target data comparison for over 500 million rows of data per table. Making use of the Data Migration wizard, the client was able to generate comparison tests for 400 tables in just a few hours. Even though there were few changes at this stage, they still encountered errors that were surfaced by our<br />DataFlow product.</p> </div>
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<p><strong>Examples:</strong> Issues include numeric precision differences, null value inconsistencies, truncation issues and character interpolations.</p> </div>
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<p>Next, they began to perform incremental new data migrations where they continued to find similar issues that had to be corrected. As this continued, they wanted to transition from this incremental new data migration from SQL Server to loading the new data directly into Snowflake to reap the benefits stated earlier. To accomplish this, their initial ETL processes needed to be migrated to an ELT process aimed at Snowflake.</p> </div>
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<p>DataFlow was used once again to check the accuracy between the two systems once the new processes were in place. The validations exposed issues in the new <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">ELT process</a></span> through several iterations until the transformation were in sync. After a short period of testing, they could cut over to the new system and deprecate the SQL server environment. Now DataFlow continued to validate the incremental data as it was moved into Snowflake, finding issues earlier in the cycle where they are less costly to fix in time and lost credibility.</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">How it makes a difference?</h5> </div>
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Many of our clients take this one step further by testing their BI tools against the old warehouse and the new Snowflake implementation. They do regression, performance, and stress testing using our BI Validator tool to compare the old with the new. They can find differences in the look and feel in the output generation of the reports and dashboards. Differences are exposed between the report queries when compared to a database query. Often this is the last task necessary to validate the migration process. </div>
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Making use of the inbuilt cluster integration with AWS EMR in the DataOps suite, the client was able to automatically scale the EMR cluster on demand to over 30 nodes depending on the data volumes being compared and scale down once the testing has been completed. This capability helped reduce the cost of the testing while still achieving high performance when comparing tables of size <strong>500 Million Records</strong> </div>
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<h6 class="elementor-heading-title elementor-size-default">Reduction in Testing <br> Time by</h6> </div>
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<h3 class="elementor-heading-title elementor-size-default">Amount of data tested increased from manual testing of <strong>10,000 sample records</strong> to complete testing of <strong>500 M</strong>.</h3> </div>
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<td style="padding: 12px; border: 1px solid #ccc;">Migration path</td>
<td style="padding: 12px; border: 1px solid #ccc;">On-premise SQL Server → Snowflake (AWS)</td>
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<td style="padding: 12px; border: 1px solid #ccc;">400 SQL Server tables, over 500 million rows per table compared</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Test case generation time</td>
<td style="padding: 12px; border: 1px solid #ccc;">Comparison tests for 400 tables generated in a few hours via the Data Migration wizard</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Compute scaling</td>
<td style="padding: 12px; border: 1px solid #ccc;">AWS EMR cluster auto-scaled up to 30 nodes based on data volume, then scaled back down</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Manual vs. automated testing coverage</td>
<td style="padding: 12px; border: 1px solid #ccc;">From manual testing of 10,000 sample records to complete testing of 500 million</td>
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<p>In conclusion, the goals of the migration project of agility, cost savings and performance improvements were achieved.</p><p>They also realized these benefits months earlier as a result of the improvement in the migration process due to the impact of the DataFlow products contribution in an estimated 50% test cycle reduction.</p><p>One of our clients reports comparing a file against a <strong>Snowflake</strong> instance with</p> </div>
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<h3 id="faq-heading">FAQs: Snowflake Data Migration Validation</h3>
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<summary>1) What kind of data validation is needed when migrating to Snowflake?</summary>
<p>
Snowflake migrations require validating data transferred from on-premises
databases as well as comparisons between Snowflake environments. Validation helps
identify issues such as numeric precision differences, null value mismatches, data
truncation, and other inconsistencies introduced during migration.
</p>
</details>
<details>
<summary>2) How does the Data Migration Wizard help with large-scale migrations?</summary>
<p>
The Data Migration Wizard automatically generates comparison tests for large
numbers of tables, significantly reducing manual effort. For example, it has been
used to validate hundreds of SQL Server tables containing hundreds of millions of
rows, enabling organizations to quickly identify migration discrepancies at scale.
</p>
</details>
<details>
<summary>3) How does BI Validator support Snowflake migrations beyond the database layer?</summary>
<p>
BI Validator extends migration validation to reports and dashboards by performing
automated regression testing, performance testing, and stress testing. This helps
ensure reporting outputs remain accurate and consistent after migrating from a
legacy data warehouse to Snowflake.
</p>
</details>
<details>
<summary>4) How does AWS EMR cluster integration improve testing performance during migration?</summary>
<p>
DataOps Suite can automatically scale AWS EMR clusters to support resource-intensive
comparisons of very large datasets and then scale them down when processing is
complete. This approach improves performance, optimizes infrastructure costs, and
helps reduce overall migration testing time.
</p>
</details>
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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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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/data-validation-in-snowflake-data-migration/">Datagaps Data Validation and Migration To Snowflake</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>Data Validation for Snowflake</title>
<link>https://www.datagaps.com/blog/snowflake-data-migration-testing/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Tue, 18 Feb 2025 14:28:00 +0000</pubDate>
<category><![CDATA[Snowflake]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=7365</guid>
<description><![CDATA[<p>Migration from a legacy data warehouse such as Netezza to a cloud-based Snowflake data warehouse requires multiple steps. Data Validation is the key to the success of data migration projects. Datagaps Data Flow can be used to validate data in each step of the data migration as</p>
<p>The post <a href="https://www.datagaps.com/blog/snowflake-data-migration-testing/">Data Validation for Snowflake</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>Migrating from a legacy data warehouse to Snowflake requires validation at each step. This guide outlines a four-step process using Datagaps DataFlow: comparing extracted data (row counts, encoding, completeness) between the legacy warehouse and AWS S3, validating transformations using Data Profile and Data Rules components, verifying data loaded into Snowflake against staging files, and end-to-end validation. It also covers using BI Validator to test report accuracy, layout, performance, and security after switching reports to Snowflake.</p> </div>
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<p><strong>Key Takeaways</strong></p><p> </p><ul><li>Extraction validation confirms lossless data movement — comparing row counts, data encoding, completeness, and values between the legacy warehouse and AWS S3 landing zone ensures nothing was lost during extraction.</li><li>Transformation step requires data quality checks before load — Data Profile and Data Rules components help identify and curate data quality issues before syncing data to the staging zone, ahead of loading into Snowflake.</li><li>DataFlow supports single-test, end-to-end validation — a single DataFlow can validate data between the legacy warehouse and S3, as well as between the legacy warehouse and Snowflake, in one unified test.</li><li>Reports need separate validation after migration — BI Validator checks report data accuracy, layout, performance, security, and stress-tests reports under concurrent user loads to confirm they function correctly once switched to Snowflake.</li></ul> </div>
</div>
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<h5 class="elementor-heading-title elementor-size-default">Migrating to Snowflake Cloud Data Warehouse</h5> </div>
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<p>Snowflake data migration testing is the process of validating data at each stage of a move from a legacy data warehouse — such as Netezza — to a cloud-based <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.snowflake.com/en/fundamentals/data-warehouse/" target="_blank" rel="noopener">Snowflake data warehouse:</a></span> extraction, transformation, load, and the reports built on top of it. Datagaps DataFlow can be used to validate data at each of these steps, as well as run end-to-end validation across the entire migration. If you are looking for Snowflake testing tools – <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/" target="_blank" rel="noopener">Try Dataflow free for 14 days</a></span> </p> </div>
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<img loading="lazy" decoding="async" width="640" height="254" src="https://www.datagaps.com/wp-content/uploads/snowflake-data-migration-1-1024x407.webp" class="attachment-large size-large wp-image-6390" alt="snowflake-data-migration-1" srcset="https://www.datagaps.com/wp-content/uploads/snowflake-data-migration-1-1024x407.webp 1024w, https://www.datagaps.com/wp-content/uploads/snowflake-data-migration-1-300x119.webp 300w, https://www.datagaps.com/wp-content/uploads/snowflake-data-migration-1-768x305.webp 768w, https://www.datagaps.com/wp-content/uploads/snowflake-data-migration-1-1536x611.webp 1536w, https://www.datagaps.com/wp-content/uploads/snowflake-data-migration-1.webp 1798w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<th style="padding: 12px; border: 1px solid #ccc;">Step</th>
<th style="padding: 12px; border: 1px solid #ccc;">What Gets Validated</th>
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<td style="padding: 12px; border: 1px solid #ccc;">1. Extract from legacy warehouse</td>
<td style="padding: 12px; border: 1px solid #ccc;">Row counts, data encoding, completeness, and values between the legacy warehouse and the AWS S3 landing zone</td>
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<td style="padding: 12px; border: 1px solid #ccc;">2. Transform data</td>
<td style="padding: 12px; border: 1px solid #ccc;">Data quality issues via profiling and data rules; comparison between landing and staging (curated) zones</td>
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<tr>
<td style="padding: 12px; border: 1px solid #ccc;">3. Copy data to Snowflake</td>
<td style="padding: 12px; border: 1px solid #ccc;">Row counts, encoding, completeness, and values between S3 staging files and Snowflake; end-to-end legacy-to-Snowflake validation</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">4. Modify reports to use Snowflake</td>
<td style="padding: 12px; border: 1px solid #ccc;">Report data, layout, performance, security, and behavior under concurrent user load, compared against the legacy reports</td>
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<p>Data is typically extracted into CSV or Parquet format and moved to a landing zone in AWS S3. Depending on the data volumes, AWS offers multiple options for moving the files to S3. Once the data has been moved to AWS S3, data validations need to be performed to ensure that all the data was properly extracted and migrated to AWS S3. Since there are not many transformations in this step, these tests are typically one-to-one comparisons of the data in the tables in the legacy data warehouse and the files in the AWS S3 landing zone.</p> </div>
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<p>– Compare table to file row counts<br />– Compare data encoding<br />– Compare data completeness<br />– Compare data values</p> </div>
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A sample test case diagram is shown to the right. JDBC Component can be used to read data from the legacy data warehouse. File Component can be used to read data from AWS S3. Finally, the Data Compare component can be used to run <a href="https://www.datagaps.com/data-reconciliation/" target="_blank" style="color:#1967d2; text-decoration: underline;">data reconciliation</a> between the two datasets. Sample output for a data comparison component is shown below. </div>
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<img loading="lazy" decoding="async" width="584" height="219" src="https://www.datagaps.com/wp-content/uploads/oracle-to-S3.webp" class="attachment-large size-large wp-image-6400" alt="oracle-to-S3" srcset="https://www.datagaps.com/wp-content/uploads/oracle-to-S3.webp 584w, https://www.datagaps.com/wp-content/uploads/oracle-to-S3-300x113.webp 300w" sizes="(max-width: 584px) 100vw, 584px" /> </div>
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<p>Data comparison test case</p> </div>
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<img loading="lazy" decoding="async" width="640" height="327" src="https://www.datagaps.com/wp-content/uploads/data-compare-1024x523.png" class="attachment-large size-large wp-image-57204" alt="data-compare" srcset="https://www.datagaps.com/wp-content/uploads/data-compare-1024x523.png 1024w, https://www.datagaps.com/wp-content/uploads/data-compare-300x153.png 300w, https://www.datagaps.com/wp-content/uploads/data-compare-768x393.png 768w, https://www.datagaps.com/wp-content/uploads/data-compare.png 1440w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>The output of data comparison</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Step 2: Transform data</h5> </div>
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<p>Transformations such as data type conversions can be performed in this step. Data curation can be also done to improve the <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</a></span> before the data is loaded into Snowflake. Before curating the data, it is important to profile the data and run data quality tests to identify data quality issues with the data. DataFlow can be used to perform these tasks.</p> </div>
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<p>– Compare data between landing zone and staging (curated) zone in S3<br />– Use Data Profile and Data Rules components to identify data quality issues<br />– Curate data and sync to the staging zone</p> </div>
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<img loading="lazy" decoding="async" width="640" height="398" src="https://www.datagaps.com/wp-content/uploads/data-profile.webp" class="attachment-large size-large wp-image-6412" alt="data-profile" srcset="https://www.datagaps.com/wp-content/uploads/data-profile.webp 961w, https://www.datagaps.com/wp-content/uploads/data-profile-300x186.webp 300w, https://www.datagaps.com/wp-content/uploads/data-profile-768x477.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<img loading="lazy" decoding="async" width="640" height="284" src="https://www.datagaps.com/wp-content/uploads/data-rules-1024x455.png" class="attachment-large size-large wp-image-57206" alt="data-rules" srcset="https://www.datagaps.com/wp-content/uploads/data-rules-1024x455.png 1024w, https://www.datagaps.com/wp-content/uploads/data-rules-300x133.png 300w, https://www.datagaps.com/wp-content/uploads/data-rules-768x341.png 768w, https://www.datagaps.com/wp-content/uploads/data-rules-1536x683.png 1536w, https://www.datagaps.com/wp-content/uploads/data-rules.png 1548w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>Data Rules component</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Step 3: Copy data to Snowflake</h5> </div>
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Assuming that the <a href="https://www.datagaps.com/snowflake-testing-automation/" target="_blank" style="color:#1967d2; text-decoration: underline;">Snowflake</a> tables have been created, the last step is to copy the data to Snowflake. Use the VALIDATE function to validate the data files and identify any errors. DataFlow can be used to compare the data between the Staging Zone (S3) files and Snowflake after the load. </div>
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<p>– Compare table to file row counts<br />– Compare data encoding<br />– Compare data completeness<br />– Compare data values<br />– End-to-end data validation (Legacy data warehouse to Snowflake)</p> </div>
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<p>DataFlow can be used to perform end-to-end <span style="text-decoration: underline; color: #1967d2;"><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</a></span> in a single test as shown to the right. A single DataFlow can be used to compare data between legacy data warehouse and S3 as well as legacy data warehouse and Snowflake.</p> </div>
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<img loading="lazy" decoding="async" width="633" height="338" src="https://www.datagaps.com/wp-content/uploads/End-to-end-data-validation.webp" class="attachment-large size-large wp-image-6423" alt="End-to-end-data-validation" srcset="https://www.datagaps.com/wp-content/uploads/End-to-end-data-validation.webp 633w, https://www.datagaps.com/wp-content/uploads/End-to-end-data-validation-300x160.webp 300w" sizes="(max-width: 633px) 100vw, 633px" /> </div>
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<p>End-to-end test case</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Step 4: Modify reports to Use Snowflake</h5> </div>
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<p>While snowflake provides JDBC/ODBC drivers and supports most of the commonly used SQL functions, there are going to be some differences between the way reports are developed and executed in the legacy <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/services/data-warehouse-services/" target="_blank" rel="noopener">Data Warehouse</a></span> and Snowflake. Once these changes are made, thorough testing needs to be performed between the reports using the legacy data warehouse and the equivalent reports using Snowflake.</p> </div>
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<p>– Compare report data<br />– Compare report layout<br />– Compare report performance<br />– Stress test reports in the new environments by simulating concurrent user loads<br />– Compare security</p> </div>
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<p>Datagaps BI Validator is a no-code <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/bi-testing-tools/bi-validator/">BI Testing Tool</a></span> that can help automate all these tests for the supported BI tools.</p> </div>
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<p>Try BI Validator free for 14 days for your Snowflake BI testing needs – <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/bi-testing-tools/bi-validator-download/" target="_blank" rel="noopener">Download Now</a></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Conclusion</h2> </div>
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<p>Migrating to Snowflake is never just a lift-and-shift — it’s a multi-stage process where errors introduced at extraction or transformation can silently propagate all the way through to the reports business users rely on every day. By validating data at each stage — comparing row counts and values as data moves from the legacy warehouse to S3, profiling and curating data during transformation, and reconciling loaded data against Snowflake — teams can catch discrepancies before they compound. And because reports rarely translate one-to-one across platforms, testing report accuracy, layout, performance, and security after the switch is just as critical as validating the underlying data. With tools like Datagaps DataFlow and BI Validator automating these checks at every step, organizations can move to Snowflake with confidence that their data — and the reports built on it — remain accurate and trustworthy from day one.</p> </div>
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<h3 id="faq-heading">FAQs: Snowflake Data Migration Validation</h3>
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<div class="faq-list">
<details>
<summary>1) What are the main steps involved in validating a Snowflake data migration?</summary>
<p>
Snowflake migration validation typically includes four stages: validating data
extraction from the legacy data warehouse to AWS S3, verifying data
transformations and quality in the staging area, comparing staged data with data
loaded into Snowflake, and validating BI reports after they are connected to the
new Snowflake environment.
</p>
</details>
<details>
<summary>2) How is data extraction validated when migrating to Snowflake?</summary>
<p>
Extraction validation compares row counts, data completeness, encoding, and
actual data values between the legacy source system and the files written to AWS
S3. This ensures data is transferred accurately before any transformation or
loading takes place.
</p>
</details>
<details>
<summary>3) Why is data profiling important before loading data into Snowflake?</summary>
<p>
Data profiling identifies quality issues such as missing values, invalid formats,
and inconsistencies before data is loaded into Snowflake. Cleaning and validating
data in the staging area helps ensure only accurate, high-quality data reaches the
target warehouse.
</p>
</details>
<details>
<summary>4) What does report validation involve after migrating to Snowflake?</summary>
<p>
Post-migration report validation verifies that dashboards and reports produce the
same results as before migration. It includes checking data accuracy, visual
consistency, report performance, stress testing under concurrent usage, and
confirming that security and user access permissions remain correctly configured.
</p>
</details>
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<p>The post <a href="https://www.datagaps.com/blog/snowflake-data-migration-testing/">Data Validation for Snowflake</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>Datagaps Attains Snowflake Select Technology Partner Status</title>
<link>https://www.datagaps.com/blog/datagaps-attains-snowflake-select-technology-partner-status/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Tue, 08 Feb 2022 19:52:46 +0000</pubDate>
<category><![CDATA[BI Testing]]></category>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Dataflow]]></category>
<category><![CDATA[ETL Testing]]></category>
<category><![CDATA[Snowflake]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=3955</guid>
<description><![CDATA[<p>Datagaps Inc. announces a new partnership with Snowflake, the Data Cloud company. Datagaps attains Snowflake Select Technology Partner in the space of Data and Business Intelligence Validation to help organizations validate their migration to Snowflake’s Data Cloud.</p>
<p>The post <a href="https://www.datagaps.com/blog/datagaps-attains-snowflake-select-technology-partner-status/">Datagaps Attains Snowflake Select Technology Partner Status</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>Datagaps achieved Snowflake Select Technology Partner status in the Data and Business Intelligence Validation space, helping organizations validate migrations to Snowflake’s Data Cloud. Datagaps’ ETL Validator confirms ETL processes run correctly against new Snowflake data structures, while the Data Migration Wizard validates that hundreds of legacy tables migrated accurately. BI Validator supports the transition further with regression testing, performance testing, stress testing, and old-vs-new GUI comparisons to ensure BI applications work correctly after migration.</p> </div>
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<p><strong>Key Takeaways</strong></p><p> </p><ul><li>Datagaps is now a Snowflake Select Technology Partner, recognized specifically for Data and Business Intelligence Validation capabilities supporting Snowflake migrations.</li><li>ETL Validator confirms post-migration pipeline accuracy — verifying that ETL processes run correctly once data structures are pointed to the new Snowflake environment.</li><li>The Data Migration Wizard validates large-scale table migrations, offering a quick way to confirm hundreds of legacy tables moved to the cloud correctly.</li><li>BI Validator supports the BI-layer transition through regression testing, performance testing, stress testing, and old-vs-new GUI comparisons as reports get redirected to Snowflake.</li></ul> </div>
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<p><a title="Datagaps" href="https://www.datagaps.com/" target="_blank" rel="noopener"><span style="text-decoration: underline; color: #1967d2;">Datagaps Inc</span>.</a> announces a new partnership with <a href="https://www.snowflake.com/en/"><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;">Snowflake</span></span></a>, the Data Cloud company. Datagaps attains Snowflake Select Technology Partner in the space of Data and Business Intelligence Validation to help organizations validate their migration to Snowflake’s Data Cloud.</p> </div>
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<p>Snowflake delivers the Data Cloud, where thousands of organizations have seamless access to explore, share, and unlock the true value of their data.</p> </div>
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<p>Datagaps as a Select Technology Partner helps organizations validate their legacy migration to Snowflake’s platform. Datagaps ETL Validation helps ensure that the ETL processes are running correctly when the new data structures are pointed to. The Data Migration Wizard provides a quick method for validating that hundreds of legacy tables moved to the cloud correctly. Finally, as the BI applications need to be redirected to the cloud, BI Validator includes regression testing, performance testing, stress testing, and old vs new GUI comparisons to test the BI migration.</p> </div>
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<p><em><i>Snowflake’s Platform</i><i> presents key advantages over traditional on-premises data solutions. However, migrating to Snowflake can requires careful planning and execution. Datagaps helps simplify this process by automating the data migration testing and analytics regression testing. Using our validation, customers have successfully validated billions of records and thousands of reports. We are looking forward to helping more Snowflake customers with this partnership</i></em></p> </div>
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<p>Moving data freely from one solution to another, and then to all your business users for truly democratized analytics, is not only vital to your organization but also one of your biggest challenges. Snowflake’s platform natively connects to dozens of other solutions, turning data into an asset you can easily derive valuable insights from without the cost, risk, and headache of trying to connect disparate, legacy systems.</p> </div>
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<p>With data today being the core of every business, it is essential that enterprises work with the right technology experts to get their outputs and analytics right. Datagaps data validation solutions, be it ETL Validator, BI Validator, or the DataOps Suite- (A complete suite for data validation and data quality with test data manager), have been very effective in achieving set data needs with all our customers.</p> </div>
</div>
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<h2 class="elementor-heading-title elementor-size-default">Conclusion</h2> </div>
</div>
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<p>Achieving Select Technology Partner status with Snowflake reflects the growing need organizations have for trustworthy validation as they move critical workloads to the Data Cloud. With ETL Validator, the Data Migration Wizard, and BI Validator working together to verify everything from table-level migration accuracy to post-migration report performance and visual consistency, Datagaps gives Snowflake customers a proven way to confirm their migration is complete, correct, and production-ready — not just that the data moved, but that it moved right. As more enterprises look to modernize on Snowflake’s platform, this partnership positions Datagaps to support that shift with validation built for scale.</p> </div>
</div>
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<div class="elementor-icon-wrapper">
<div class="elementor-icon">
<i aria-hidden="true" class="icon icon-double-quote"></i> </div>
</div>
</div>
</div>
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<p>We have seen a significant increase in inquiries regarding our ability to help validate the data migration process to Snowflake. People have turned to us to help validate the data migration, new vs. old ETL/ELT process data validation and on the backend, validate the data analytics old vs. new now pointing to Snowflake. We are excited about the opportunity to work with Snowflake’s clients and are thrilled to have achieved Select Partner Status with Snowflake.</p> </div>
</div>
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<p class="et_pb_module_header"><strong>David Small- VP Sales and Marketing, Datagaps Inc.</strong></p> </div>
</div>
</div>
</div>
</div>
</section>
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<h2 id="faq-heading">FAQs: Datagaps and Snowflake Migrations</h2>
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<details>
<summary>1) What is Datagaps’ Snowflake Select Technology Partner status?</summary>
<p>
Datagaps is recognized as a Snowflake Select Technology Partner for its expertise
in Data and Business Intelligence Validation, helping organizations validate data
quality and reporting accuracy during migrations to the Snowflake Data Cloud.
</p>
</details>
<details>
<summary>2) How does ETL Validator help with Snowflake migrations?</summary>
<p>
ETL Validator verifies that ETL processes continue to execute correctly after data
structures and pipelines are migrated to Snowflake, identifying data transformation,
mapping, and processing issues introduced during the migration.
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<details>
<summary>3) What does the Data Migration Wizard do?</summary>
<p>
The Data Migration Wizard validates that legacy tables have been accurately migrated
to Snowflake by comparing source and target datasets, enabling organizations to
confirm data integrity across large-scale migrations.
</p>
</details>
<details>
<summary>4) How does BI Validator support Snowflake migrations at the reporting layer?</summary>
<p>
BI Validator performs automated regression testing, performance testing, stress
testing, and side-by-side visual comparisons to verify that BI reports and dashboards
continue to function correctly after being connected to Snowflake.
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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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Founder & CEO, Datagaps </p>
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<p>Founder and CEO of Datagaps. Sets the vision for automated ETL, BI, and data-quality validation, built on deep enterprise data-architecture expertise.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/datagaps-attains-snowflake-select-technology-partner-status/">Datagaps Attains Snowflake Select Technology Partner Status</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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