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<title>DevOps Archives - Datagaps | Gen AI-Powered Automated Cloud Data Testing</title>
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<title>DevOps 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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<span style="color: #ffffff;">AI Based Observabilitys</span> </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>
]]></content:encoded>
</item>
<item>
<title>How Datagaps DataOps Suite Aligns with DORA Metrics to Maximize ROI</title>
<link>https://www.datagaps.com/blog/how-datagaps-dataops-suite-aligns-with-dora-metrics-to-maximize-roi/</link>
<comments>https://www.datagaps.com/blog/how-datagaps-dataops-suite-aligns-with-dora-metrics-to-maximize-roi/#respond</comments>
<dc:creator><![CDATA[Anand Rao]]></dc:creator>
<pubDate>Fri, 23 May 2025 13:29:01 +0000</pubDate>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[DevOps]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=38299</guid>
<description><![CDATA[<p>In the era of data-driven decisions, enterprise success hinges not just on innovation, but on the speed, stability, and quality of data delivery. The DORA metrics – Deployment Frequency, Lead Time for Changes, Mean Time to Restore (MTTR), and Change Failure Rate (CFR) – have emerged as critical indicators of DevOps and DataOps performance. These […]</p>
<p>The post <a href="https://www.datagaps.com/blog/how-datagaps-dataops-suite-aligns-with-dora-metrics-to-maximize-roi/">How Datagaps DataOps Suite Aligns with DORA Metrics to Maximize ROI</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="38299" class="elementor elementor-38299" data-elementor-post-type="post">
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<p>In the era of data-driven decisions, enterprise success hinges not just on innovation, but on the <em>speed</em>, <em>stability</em>, and <em>quality</em> of data delivery. The <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://dora.dev/" target="_blank" rel="noopener"><span>DORA metrics</span></a></span> – Deployment Frequency, Lead Time for Changes, Mean Time to Restore (MTTR), and Change Failure Rate (CFR) – have emerged as critical indicators of DevOps and DataOps performance. These metrics help organizations assess how effectively they build, test, deploy, and recover their software and data systems.</p> </div>
</div>
<div class="elementor-element elementor-element-6f327c6 elementor-widget elementor-widget-text-editor" data-id="6f327c6" data-element_type="widget" data-e-type="widget" data-widget_type="text-editor.default">
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<p>The <span style="text-decoration: underline; color: #1967d2;"><a style="color: rgb(25, 103, 210); text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener">Datagaps DataOps Suite</a></span> stands out as a powerful enabler of DORA excellence by integrating <strong>automation, observability, AI/ML-driven insights</strong>, and <strong>low-code test pipelines</strong> across the entire data lifecycle. But beyond technical efficiency, it also delivers <strong>hard business outcomes</strong>, including faster time-to-market, increased trust in analytics, and measurable ROI from client feedback.</p> </div>
</div>
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<h2 class="elementor-heading-title elementor-size-default">Let’s explore how Datagaps directly maps to the four DORA metrics and amplifies both operational performance and financial impact.</h2> </div>
</div>
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<img loading="lazy" decoding="async" width="900" height="628" src="https://www.datagaps.com/wp-content/uploads/how-Datagaps-directly-maps-to-the-four-DORA-metrics.jpg" class="attachment-full size-full wp-image-38322" alt="DataOps Suite DORA Metrics" srcset="https://www.datagaps.com/wp-content/uploads/how-Datagaps-directly-maps-to-the-four-DORA-metrics.jpg 900w, https://www.datagaps.com/wp-content/uploads/how-Datagaps-directly-maps-to-the-four-DORA-metrics-300x209.jpg 300w, https://www.datagaps.com/wp-content/uploads/how-Datagaps-directly-maps-to-the-four-DORA-metrics-768x536.jpg 768w" sizes="(max-width: 900px) 100vw, 900px" /> </div>
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<h3 class="elementor-heading-title elementor-size-default">1. Deployment Frequency: Accelerating BI and Analytics Delivery </h3> </div>
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<span class="elementor-icon-list-text"><span style="color: #1D1D33"><b>DORA Goal:</b></span> Increase how often new releases are deployed into production.</span>
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<span class="elementor-icon-list-text"><span style="color: #1D1D33"><b>How Datagaps Helps:</b></span></span>
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<ul style="list-style-type: none; padding-left: 0; margin: 0; font-family: 'inter', sans-serif; font-size: 18px; color: #17253d;"><li style="display: flex; align-items: flex-start; gap: 12px; margin-bottom: 15px;"><span style="font-size: 24px; color: #19935e; flex-shrink: 0;">●</span><div><span style="text-decoration: underline;"><a style="color: #0000ff; text-decoration: underline;" href="https://www.datagaps.com/bi-validator/" target="_blank" rel="noopener"><strong><span style="color: #1967d2; text-decoration: underline;">BI Validator:</span></strong></a></span><br />Enables automated regression, upgrade, and performance testing across BI platforms like Power BI, Tableau, and Oracle Analytics.</div></li><li style="display: flex; align-items: flex-start; gap: 12px; margin-bottom: 15px;"><span style="font-size: 24px; color: #19935e; flex-shrink: 0;">●</span><div><strong>CI/CD Integration:</strong><br />Datagaps integrates seamlessly with DevOps tools like Jenkins, GitHub Actions, and Azure Pipelines.</div></li><li style="display: flex; align-items: flex-start; gap: 12px; margin-bottom: 15px;"><span style="font-size: 24px; color: #19935e; flex-shrink: 0;">●</span><div><strong style="color: #000000;">Test Automation Coverage >80%:</strong><br />This high degree of test coverage allows teams to ship updates frequently without regression risks.</div></li><li style="display: flex; align-items: flex-start; gap: 12px; margin-bottom: 15px;"><span style="font-size: 24px; color: #19935e; flex-shrink: 0;">●</span><div><strong style="color: #000000;">Environment Reusability:</strong><br />Shared test cases across multiple environments enable quicker deployments across UAT, Dev, and Prod.</div></li></ul> </div>
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<span class="elementor-icon-list-text"><span style="color: #1D1D33"><b>Business Impact:</b></span></span>
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<div>Speeds up dashboard and data report updates, ensuring analytics teams keep pace with business needs.</div></li>
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<div>Cuts manual validation bottlenecks, accelerating the cadence of releases.</div></li>
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<div>Enables agile, continuous improvement cycles—a hallmark of high-performing DevOps teams.</div></li>
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<h3 class="elementor-heading-title elementor-size-default">2. Lead Time for Changes: Reducing Time to Market</h3> </div>
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<span class="elementor-icon-list-text"><span style="color: #1D1D33"><b>DORA Goal:</b></span> Minimize the time it takes from code committed to code running in production.</span>
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<div><strong>No-Code Test Pipelines:</strong> Rapid drag-and-drop test case creation minimizes the need for complex scripting.</div></li>
<li style="display: flex; align-items: flex-start; gap: 12px; margin-bottom: 15px;"><span style="font-size: 24px; color: #19935e; flex-shrink: 0;">●</span>
<div><strong><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/etl-validator/" target="_blank" rel="noopener">ETL Validator:</a></span></strong> Performs full-spectrum validations (schema, metadata, row-by-row comparisons) from ingestion to visualization.</div></li>
<li style="display: flex; align-items: flex-start; gap: 12px; margin-bottom: 15px;"><span style="font-size: 24px; color: #19935e; flex-shrink: 0;">●</span>
<div><strong style="color: #000000;">Automated Test Generation:</strong> Powered by Generative AI and ML, accelerates test development.</div></li>
<li style="display: flex; align-items: flex-start; gap: 12px; margin-bottom: 15px;"><span style="font-size: 24px; color: #19935e; flex-shrink: 0;">●</span>
<div><strong style="color: #000000;">TDM (<span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/test-data-manager/" target="_blank" rel="noopener">Test Data Manager</a></span>):</strong> Generates statistically similar test data while masking PII, ensuring that test cycles don’t wait on sensitive data approvals.</div></li>
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<span class="elementor-icon-list-text"><span style="color: #1D1D33"><b>Business Impact:</b></span></span>
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<div>Shrinks the cycle time from development to release, improving responsiveness to business requirements.</div></li>
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<div>Reduces development overhead and testing effort – freeing up dev bandwidth.</div></li>
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<div>Enables data products to reach stakeholders faster, improving decision agility.</div></li>
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<h3 class="elementor-heading-title elementor-size-default">3. Mean Time to Restore (MTTR): Restoring Services Fast</h3> </div>
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<span class="elementor-icon-list-text"><span style="color: #1D1D33"><b>DORA Goal:</b></span> Decrease the time required to recover from an incident or defect.</span>
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<span class="elementor-icon-list-text"><span style="color: #1D1D33"><b>How Datagaps Helps:</b></span></span>
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<div><strong style="color: #000000;">ML-Based Anomaly Detection:</strong> The <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-data-quality/" target="_blank" rel="noopener">Data Quality Monitor</a></span> continuously observes both pipeline-level and report-level data for drift, outliers, or missing values.</div></li>
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<div><strong style="color: #000000;">Historical Snapshot Comparisons:</strong> Teams can quickly pinpoint when and where issues were introduced by comparing historical baselines.</div></li>
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<div><strong style="color: #000000;">Auto-generated Rule Failures:</strong> These facilitate root-cause analysis with pinpointed insights into what failed and why.</div></li>
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<div><strong style="color: #000000;">Integrated Alerts & Dashboards:</strong> Real-time alerting ensures teams respond to failures as they happen, minimizing downtime.</div></li>
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<div>Faster recovery reduces lost revenue from broken dashboards or delayed reports.</div></li>
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<div>Maintains trust in business insights, critical for executive decisions.</div></li>
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<div>Strengthens SLA compliance by reducing incident resolution times.</div></li>
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<h3 class="elementor-heading-title elementor-size-default">4. Change Failure Rate (CFR): Delivering Stability with Every Update</h3> </div>
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<span class="elementor-icon-list-text"><span style="color: #1D1D33"><b>DORA Goal:</b></span> Lower the percentage of changes that result in failure in production.</span>
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<span class="elementor-icon-list-text"><span style="color: #1D1D33"><b>How Datagaps Helps:</b></span></span>
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<div><strong style="color: #000000;">End-to-End Regression Testing</strong> for data pipelines and BI dashboards catches issues before deployment.</div></li>
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<div><strong style="color: #000000;">Visual Comparison Engine</strong> in BI Validator detects even subtle UI/report inconsistencies.</div></li>
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<div><strong style="color: #000000;">Security and Stress Testing</strong> ensures dashboards are resilient under peak loads and correctly enforce role-based access.</div></li>
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<div><strong style="color: #000000;">Business Rule Validation:</strong> Logical assertions and KPI checks are applied automatically to confirm accuracy against defined metrics.</div></li>
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<div>Reduces bugs and inconsistencies in production reports.</div></li>
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<div>Improves confidence in analytics, increasing BI adoption across business teams.</div></li>
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<div>A client reported a reduction of five-person-months of testing to a few days, driving substantial cost savings and quality assurance.</div></li>
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<h2 class="elementor-heading-title elementor-size-default">ROI: Bridging DORA and Business Value</h2> </div>
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<p>Aligning DORA metrics to business ROI is where Datagaps shines. The platform does not stop at automation – it enables enterprises to <span style="color: #000000;"><strong>measure</strong>, <strong>track</strong></span>, and <span style="color: #000000;"><strong>monetize</strong></span> their improvements:</p> </div>
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<div><strong style="color: #000000;">2x ROI:</strong> through reduction in QA costs, manual testing time, and faster incident resolution.</div></li>
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<div><strong style="color: #000000;">Increased Productivity:</strong> Gartner projects a 10x productivity increase for DataOps teams using automated tools by 2026.</div></li>
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<div><strong style="color: #000000;">BI Confidence Growth:</strong> More frequent, reliable report releases drive higher user adoption and improved decision-making outcomes.</div></li>
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<div><strong style="color: #000000;">Operational Efficiency:</strong> From sandbox environments to CI/CD orchestration, Datagaps supports scalable, repeatable deployments – key to sustainable digital transformation.</div></li>
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<div style="overflow-x: auto;"><table style="width: 100%; border-collapse: collapse; font-family: 'inter', sans-serif; font-weight: 400; margin: 20px 0; font-size: 18px; min-width: 600px;"><thead><tr style="background-color: #d6e3f5; color: #19935e; font-weight: bold;"><th style="padding: 14px; border: 1px solid #ccc;"><span style="color: #17253d;"><strong>DORA Metric</strong></span></th><th style="padding: 14px; border: 1px solid #ccc;"><strong><span style="color: #17253d;">ROI Impact</span></strong></th><th style="padding: 14px; border: 1px solid #ccc;"><strong><span style="color: #17253d;">Datagaps Capabilities</span></strong></th></tr></thead><tbody><tr><td style="padding: 14px; border: 1px solid #ccc;">Deployment Frequency</td><td style="padding: 14px; border: 1px solid #ccc;">More frequent BI/report deployments</td><td style="padding: 14px; border: 1px solid #ccc;">CI/CD Integration, Automated Regression, Upgrade and Performance Testing</td></tr><tr style="background-color: #f8f9fa;"><td style="padding: 14px; border: 1px solid #ccc;">Lead Time for Changes</td><td style="padding: 14px; border: 1px solid #ccc;">Reduction in test creation & execution time</td><td style="padding: 14px; border: 1px solid #ccc;">Test Case Generation, API/CLI Automation</td></tr><tr><td style="padding: 14px; border: 1px solid #ccc;">Mean Time to Restore</td><td style="padding: 14px; border: 1px solid #ccc;">Reduced downtime from BI/report issues</td><td style="padding: 14px; border: 1px solid #ccc;">Anomaly Detection, Alerts, Historical Snapshots, Post-restore Validation</td></tr></tbody></table></div> </div>
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<h2 class="elementor-heading-title elementor-size-default">Turn DORA Metrics Into Measurable ROI</h2> </div>
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<p>Datagaps goes beyond supporting DORA – it puts them into action. With automation, AI-driven quality checks, and DevOps integration, Datagaps boosts agility and business impact.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Ready to accelerate deployments, reduce downtime, and improve data quality?</h2> </div>
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<p>Request a Demo and talk to a DataOps Expert today</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">FAQ's about DORA (DevOps Research and Assessment) </h3> </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-5461"><h3 class="eael-accordion-tab-title">What are the four DORA metrics?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-5461" class="eael-accordion-content clearfix" data-tab="1" aria-labelledby="faq-1"><p>The DORA metrics are Deployment Frequency, Lead Time for Changes, Mean Time to Restore (MTTR), and Change Failure Rate (CFR).</p></div>
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<div id="faq-3" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-5462"><h3 class="eael-accordion-tab-title">In what ways does the DataOps Suite reduce Lead Time for Changes?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-5462" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-3"><p>No-code test pipelines, automated test generation, and efficient test data management all contribute to faster development-to-release cycles.</p></div>
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<div id="faq-3" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="3" aria-controls="elementor-tab-content-5463"><h3 class="eael-accordion-tab-title">How does the DataOps Suite support continuous improvement cycles?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-5463" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-3"><p>By enabling agile, automated testing and deployment, the suite allows teams to iterate quickly and improve continuously the key principles measured by DORA metrics.</p></div>
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<div id="faq-3" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-5464"><h3 class="eael-accordion-tab-title">Why are DORA metrics important for data and analytics teams?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-5464" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-3"><p>They provide a standardized way to assess and improve the speed, stability, and quality of software and data system delivery.</p></div>
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<div id="faq-3" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="5" aria-controls="elementor-tab-content-5465"><h3 class="eael-accordion-tab-title">What features of the DataOps Suite help lower Change Failure Rate (CFR)?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-5465" class="eael-accordion-content clearfix" data-tab="5" aria-labelledby="faq-3"><p>End-to-end regression testing, visual comparison engines, and automated business rule validation catch issues before deployment, improving release stability.</p></div>
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<p>The post <a href="https://www.datagaps.com/blog/how-datagaps-dataops-suite-aligns-with-dora-metrics-to-maximize-roi/">How Datagaps DataOps Suite Aligns with DORA Metrics to Maximize ROI</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>DataOps Suite Feature Updates – Version 2023.4.0.0</title>
<link>https://www.datagaps.com/blog/dataops-suite-feature-updates-version-2023-4-0-0/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Sat, 06 Jan 2024 11:32:19 +0000</pubDate>
<category><![CDATA[BI Testing]]></category>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[DevOps]]></category>
<category><![CDATA[ETL Testing]]></category>
<category><![CDATA[Tableau Testing]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=25392</guid>
<description><![CDATA[<p>Datagaps Inc. announced its latest product feature updates for the DataOps Suite Version 2023.4.0.0 – The theme of this update is Integration and Easy to apply Analysis. If you are interested in getting a demo of DataOps Suite, you can fill your details here.</p>
<p>The post <a href="https://www.datagaps.com/blog/dataops-suite-feature-updates-version-2023-4-0-0/">DataOps Suite Feature Updates – Version 2023.4.0.0</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>DataOps Suite 4.0.0 centers on GIT Integration, letting teams manage ETL/ELT code, BI reports, and test cases in a single Git repository across providers like Azure Repos and Bitbucket for version control, rollback, and CI/CD-enabled collaboration. The release also expands BI testing with Tableau Upgrade, Power BI Regression, and Power BI Upgrade comparisons, introduces Fixed and Delta deviation techniques for anomaly detection, and adds Microsoft Teams integration for real-time notifications and status updates across the data team.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>GIT Integration enables CI/CD for DataOps testing</strong> — teams can now store and version dataflows, ETL/ELT code, BI reports, and test cases in a single Git repository (Azure Repos, Bitbucket), with rollback and multi-user collaboration built in.</li><li><strong>BI regression and upgrade testing expands beyond Tableau</strong> — the suite now supports Tableau Upgrade, Power BI Regression, and Power BI Upgrade, comparing baseline vs. live dashboards for both text changes and pixel-level appearance shifts.</li><li><strong>Two new anomaly detection techniques improve data quality monitoring</strong> — Fixed deviation flags data points that cross a set threshold, while Delta deviation flags data points that shift too much from the prior value.</li><li><strong>Microsoft Teams integration strengthens collaboration</strong> — real-time notifications and status updates now surface directly in Teams, keeping the data team informed without switching tools.</li></ul> </div>
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<p>If you are interested in getting a <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">demo</a></span> of DataOps Suite, <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">you can fill your details here.</a></span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">DataOps Suite Feature Updates – Version 2023.4.0.0</h3> </div>
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<h2 class="elementor-heading-title elementor-size-default">1. Enable CI/CD for your DataOps testing with GIT integration</h2> </div>
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<img loading="lazy" decoding="async" width="980" height="549" src="https://www.datagaps.com/wp-content/uploads/DataOps-Suite-with-GIT-providers-Azure-DevOps-and-Bitbucket.webp" class="attachment-full size-full wp-image-25396" alt="" srcset="https://www.datagaps.com/wp-content/uploads/DataOps-Suite-with-GIT-providers-Azure-DevOps-and-Bitbucket.webp 980w, https://www.datagaps.com/wp-content/uploads/DataOps-Suite-with-GIT-providers-Azure-DevOps-and-Bitbucket-300x168.webp 300w, https://www.datagaps.com/wp-content/uploads/DataOps-Suite-with-GIT-providers-Azure-DevOps-and-Bitbucket-768x430.webp 768w" sizes="(max-width: 980px) 100vw, 980px" /> </div>
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<center>DataOps Suite with GIT providers Azure DevOps and Bitbucket</center> </div>
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<p>DataOps Suite supports various GIT providers like <strong>Azure Repos</strong> and <strong>Bitbucket</strong> to store, organize, manage, and track the dataflows of different versions, making it easier to roll back to previous versions if necessary. They provide a centralized repository to manage the dataflows in one place and make it easy for multiple team members to collaborate on maintaining the dataflows.</p><p><span class="fontSizeLarge">GIT Integration empowers the Data team to manage their ETL/ELT code, BI reports/workbooks and test cases in a single GIT repository.</span></p><h5><a href="https://help.datagaps.com/articles/#!dataops-suite/git-integration" target="_blank" rel="noopener"><span style="text-decoration: underline;">Learn More – GIT Integration</span></a></h5> </div>
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<h2 class="elementor-heading-title elementor-size-default">2. Improved Testing Capabilities for Tableau & Power BI Reports</h2> </div>
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DataOps Suite 4.0.0 elevates BI Analytics with enhanced features, catering to both Tableau and Power BI users.
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<img loading="lazy" decoding="async" width="980" height="549" src="https://www.datagaps.com/wp-content/uploads/Improved-features-of-BI-Analytics.webp" class="attachment-full size-full wp-image-25400" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Improved-features-of-BI-Analytics.webp 980w, https://www.datagaps.com/wp-content/uploads/Improved-features-of-BI-Analytics-300x168.webp 300w, https://www.datagaps.com/wp-content/uploads/Improved-features-of-BI-Analytics-768x430.webp 768w" sizes="(max-width: 980px) 100vw, 980px" /> </div>
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<center>Improved features of BI Analytics</center>
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<div class="et_pb_module et_pb_text et_pb_text_9 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><p>In addition to Tableau Regression, DataOps Suite now supports <strong>Tableau Upgrade</strong>, <strong>Power BI Regression</strong>, and <strong>Power BI upgrade</strong>.</p><p><a href="https://help.datagaps.com/articles/#!dataops-suite/tableau-upgrade" target="_blank" rel="noopener"><span style="text-decoration: underline;">Table</span><u>au Upgrade</u></a> <span class="fontSizeLarge">compares the views and worksheets in the newer version with the same views and the worksheets in the older version in the same or different Tableau environments to ensure that no data or formatting has been lost.</span></p><p><span class="fontSizeLarge"><a href="https://help.datagaps.com/articles/#!dataops-suite/power-bi-regression" target="_blank" rel="noopener"><span style="text-decoration: underline;">Power BI Regression</span></a> performs a </span>regression testing of the Power BI dashboards by comparing the baseline version of the dashboard (PDF) with the live version. This comparison can be done for ‘text’ which highlights the changes in text even if the text is shifted by a little and ‘appearance’ which performs the pixel-to-pixel comparison and highlights text, chart, etc.,</p><p><a href="https://help.datagaps.com/articles/#!dataops-suite/power-bi-regression" target="_blank" rel="noopener"><span style="text-decoration: underline;">Power BI Upgrade</span></a>c<span class="fontSizeLarge">ompares the PDF report in the newer version with the same PDF report in the older version in the same or different Power BI environments to ensure that no data or formatting has been lost.</span></p><p><span class="fontSizeLarge">By automating the regression and migration testing, these components can save organizations time and effort, and help them to ensure that their dashboards and reports are trustworthy and working as expected.</span></p></div></div><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"><h5><a href="https://help.datagaps.com/articles/#!dataops-suite/bi-analytics" target="_blank" rel="noopener"><u>Learn More</u></a></h5></div></div> </div>
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<h2 class="elementor-heading-title elementor-size-default">3. “Data Observability” Enhancements</h2> </div>
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<p>DataOps Suite introduces two innovative detection techniques—Fixed and Delta deviation—for identifying data anomalies.</p> </div>
</div>
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<img loading="lazy" decoding="async" width="980" height="549" src="https://www.datagaps.com/wp-content/uploads/Data-Observability-enhancements.webp" class="attachment-full size-full wp-image-25404" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Data-Observability-enhancements.webp 980w, https://www.datagaps.com/wp-content/uploads/Data-Observability-enhancements-300x168.webp 300w, https://www.datagaps.com/wp-content/uploads/Data-Observability-enhancements-768x430.webp 768w" sizes="(max-width: 980px) 100vw, 980px" /> </div>
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<center>Data Anomaly techniques</center>
</div>
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<div class="et_pb_module et_pb_text et_pb_text_14 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><p>DataOps Suite has introduced two new detection techniques, <strong>Fixed and Delta deviation,</strong> for reporting the data anomalies.</p><p><strong>Fixed deviation</strong> is a simple technique where we can set a fixed threshold, and if any data point deviates from the fixed threshold, it is flagged as an anomaly.</p><p><strong>Delta deviation</strong> is a sophisticated technique <span class="fontSizeLarge">that compares each data point to the previous one. If the change in the value of the data point is more than a certain amount, then it is flagged as an anomaly.</span></p><p><span class="fontSizeLarge">Both fixed and delta deviation are valuable tools that can be used to improve the <strong>quality</strong>, <strong>accuracy</strong>, and<strong> efficiency</strong> of data ingestion projects.</span></p></div></div><div class="et_pb_module et_pb_text et_pb_text_15 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><h5><a href="https://help.datagaps.com/articles/#!dataops-suite/data-observability" target="_blank" rel="noopener"><u>Learn More</u></a></h5></div></div> </div>
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<h2 class="elementor-heading-title elementor-size-default">4. Collaboration with Microsoft Teams
</h2> </div>
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DataOps Suite strengthens collaboration by seamlessly integrating with Microsoft Teams, providing real-time notifications and status updates. </div>
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<center>Collaboration with Microsoft Teams</center>
</div>
</div>
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<div class="et_pb_module et_pb_text et_pb_text_14 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><div class="et_pb_module et_pb_text et_pb_text_19 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><p>DataOps Suite collaborates with <a href="https://www.microsoft.com/en-us/microsoft-teams/collaboration" target="_blank" rel="noopener"><u>Microsoft Teams</u></a> by sending the status of Pipeline and <span class="fontSizeLarge">Data Quality rules of the Data Model execution </span>to the channels through “Webhook URL<strong>”</strong>.</p><p><span class="fontSizeLarge">By integrating DataOps Suite with Microsoft Teams, team members can receive notifications about pipeline or data quality rule execution status, errors, and other important notification events directly in Microsoft Teams </span>based on the configured Teams template<span class="fontSizeLarge">. This can help to improve communication and collaboration between team members, and ensure that everyone is on the same page.</span></p></div></div><div class="et_pb_module et_pb_text et_pb_text_20 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><h5><a href="https://help.datagaps.com/articles/#!dataops-suite/collaboration-tools" target="_blank" rel="noopener"><u>Learn More</u></a></h5></div></div></div></div> </div>
</div>
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<div class="et_pb_module et_pb_text et_pb_text_14 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><div class="et_pb_module et_pb_text et_pb_text_19 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><div class="et_pb_module et_pb_text et_pb_text_21 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner">For more information on other enhancements of DataOps Suite version 4.0.0, read our <a href="https://help.datagaps.com/articles/#!dataops-suite/dataops-suite-releases/a/h2_203948859" target="_blank" rel="noopener"><u>Release Notes.</u></a></div></div><div class="et_pb_module et_pb_text et_pb_text_22 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><a href="https://help.datagaps.com/articles/#!dataops-suite/dataops-suite-releases" target="_blank" rel="noopener"><u>Full Release Notes of DataOps Suite – Learn More</u></a></div></div></div></div></div></div> </div>
</div>
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<h3 class="elementor-heading-title elementor-size-default">DataOps Suite – Free Trial</h3> </div>
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The Datagaps’ DataOps Suite now comes with new components that add extensibility and connectivity with other applications as well as a focus on ease of creating tests by automatically creating SQL Queries and identifying anomalies based on data profile. </div>
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<h4 class="elementor-heading-title elementor-size-default">Try DataOps Suite Free for 14 days…</h4> </div>
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<summary>1) What is GIT Integration in DataOps Suite 4.0.0?</summary>
<p>
GIT Integration lets teams store, organize, and version-control their ETL/ELT code, BI reports and
workbooks, and test cases in a single Git repository, using providers like Azure Repos and Bitbucket,
with easy rollback to previous versions.
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<summary>2) What new BI testing capabilities did version 4.0.0 add?</summary>
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Yes, it integrates with Microsoft Teams to deliver real-time notifications and status updates, keeping
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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/dataops-suite-feature-updates-version-2023-4-0-0/">DataOps Suite Feature Updates – Version 2023.4.0.0</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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