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<title>DataOps Archives - Datagaps | Gen AI-Powered Automated Cloud Data Testing</title>
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<title>How Datagaps Enables End-to-End Data Observability on Fabric</title>
<link>https://www.datagaps.com/blog/how-datagaps-enables-end-to-end-data-observability-on-fabric/</link>
<dc:creator><![CDATA[Raj Mohan Achanta]]></dc:creator>
<pubDate>Fri, 23 Oct 2026 09:14:02 +0000</pubDate>
<category><![CDATA[Data Observability]]></category>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[Anomaly Detection]]></category>
<category><![CDATA[data observability]]></category>
<category><![CDATA[Microsoft Fabric]]></category>
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<description><![CDATA[<p>Monitor Microsoft Fabric data health end to end with Datagaps DataOps Suite, using AI-powered rules, validation, profiling, and anomaly detection.</p>
<p>The post <a href="https://www.datagaps.com/blog/how-datagaps-enables-end-to-end-data-observability-on-fabric/">How Datagaps Enables End-to-End Data Observability on Fabric</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<p><span class="TextRun SCXW176038018 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW176038018 BCX8">Data observability is the continuous monitoring of data health across the full lifecycle, from ingestion to reporting. The </span><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener"><span class="NormalTextRun SpellingErrorV2Themed SCXW176038018 BCX8">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW176038018 BCX8">DataOps</span></a></span><span class="NormalTextRun SCXW176038018 BCX8"><a href="_wp_link_placeholder"> Suite</a> delivers it on Microsoft Fabric with AI-generated rules, data quality scoring, ETL and BI validation, </span><span class="NormalTextRun SCXW176038018 BCX8">profiling</span><span class="NormalTextRun SCXW176038018 BCX8"> and ML-based anomaly detection across </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW176038018 BCX8">Bronze, Silver and Gold</span><span class="NormalTextRun SCXW176038018 BCX8"> layers.</span></span><span class="EOP Selected SCXW176038018 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><span data-contrast="auto">A green pipeline and a green dashboard can still hide wrong data, such as overnight duplicate records.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Data quality checks data at rest; data observability tracks data in motion across the full lifecycle.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">The DataOps Suite maps onto Fabric’s medallion architecture across Bronze, Silver and Gold layers on OneLake.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Generated rules remain the same more than 80% of the time, so they can be exported and reused.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">AI spans column descriptions, rule generation, PII classification, natural-language SQL and anomaly detection.</span><span data-ccp-props="{}"> </span></li></ul> </div>
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/request-a-demo/" target="_blank" rel="noopener"><span class="NormalTextRun SCXW117171178 BCX8">See how the </span><span class="NormalTextRun SpellingErrorV2Themed SCXW117171178 BCX8">DataOps</span><span class="NormalTextRun SCXW117171178 BCX8"> Suite monitors your Fabric pipelines end to end →</span></a></span></p> </div>
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<p><span class="TextRun SCXW54637806 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW54637806 BCX8"><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">End-to-end data observability</a></span> on Microsoft Fabric means </span><span class="NormalTextRun SCXW54637806 BCX8">monitoring</span><span class="NormalTextRun SCXW54637806 BCX8"> data health continuously as it moves from ingestion through transformation, </span><span class="NormalTextRun SCXW54637806 BCX8">storage</span><span class="NormalTextRun SCXW54637806 BCX8"> and reporting — not just running data quality checks on tables. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW54637806 BCX8">Datagaps</span><span class="NormalTextRun SCXW54637806 BCX8"> delivers this with the </span><span class="NormalTextRun SpellingErrorV2Themed SCXW54637806 BCX8">DataOps</span><span class="NormalTextRun SCXW54637806 BCX8"> Suite, which </span><span class="NormalTextRun SCXW54637806 BCX8">validates</span><span class="NormalTextRun SCXW54637806 BCX8"> and monitors data at multiple levels and integrates with existing architectures.</span></span><span class="EOP Selected SCXW54637806 BCX8" data-ccp-props="{}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why do green pipelines still produce untrustworthy data?</h2> </div>
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<p><span class="TextRun SCXW110844424 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW110844424 BCX8">Imagine waking up in the morning and seeing revenue on your dashboard jump by 40%. Everyone celebrates until someone realises that a pipeline duplicated records overnight. The pipeline was green. The dashboard was green. Everything looked fine, except the data itself was wrong.</span></span><span class="EOP Selected SCXW110844424 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><span class="NormalTextRun SCXW105398632 BCX8">Over the last five years, the importance of data has grown exponentially. Organisations have started using platforms like Microsoft Fabric and Databricks to build solutions, but the trust factor </span><span class="NormalTextRun SCXW105398632 BCX8">remains</span><span class="NormalTextRun SCXW105398632 BCX8"> missing. The reason is straightforward: <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/database-testing/" target="_blank" rel="noopener">data pipelines cannot be fully trusted until they are fully tested.</a></span> Modern solutions require not just AI development, but also end-to-end AI testing.</span></p> </div>
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<p><span class="TextRun SCXW56266028 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW56266028 BCX8">Beyond trust, data observability also addresses accountability. Without clear visibility into where data went wrong and why, issues become difficult to </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW56266028 BCX8">trace</span><span class="NormalTextRun SCXW56266028 BCX8"> and responsibility becomes hard to assign across engineering, analytics, and business teams.</span></span><span class="EOP Selected SCXW56266028 BCX8" data-ccp-props="{}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">What is data observability? </h2> </div>
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<p><span class="NormalTextRun SCXW111125693 BCX8">Data observability is the monitoring of the entire data lifecycle, from ingestion to reporting. It is a concept that is </span><span class="NormalTextRun SCXW111125693 BCX8">frequently</span><span class="NormalTextRun SCXW111125693 BCX8"> misunderstood, and </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW111125693 BCX8">its</span><span class="NormalTextRun SCXW111125693 BCX8"> meaning often varies depending on who is working with the data and how they interpret it. To understand it properly, it helps to look at it through the lens of </span><span class="NormalTextRun SCXW111125693 BCX8">different roles</span><span class="NormalTextRun SCXW111125693 BCX8">.</span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">What does data observability mean for a data engineer?</h3> </div>
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<p><span class="NormalTextRun SCXW189491273 BCX8">As a data engineer, the primary concern</span><span class="NormalTextRun SCXW189491273 BCX8"> is separating good data from bad data. If a data quality check fails, you want to know exactly where the issue lies, whether it is in the source, a transformation, or a downstream table. That visibility is what observability means at this level.</span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">What does data observability mean for an analytics manager? </h3> </div>
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<p><span class="TextRun SCXW142045581 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW142045581 BCX8">At a management level,</span><span class="NormalTextRun SCXW142045581 BCX8"> observability goes further. It is not just about finding data quality issues; it is about linking failures to data flow lineage to understand root causes, monitoring data quality health over time, prioritising fixes that require more attention, and having a measurable view of the entire pipeline to report to stakeholders.</span></span><span class="EOP Selected SCXW142045581 BCX8" data-ccp-props="{}"> </span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">What does data observability mean for a data scientist? </h3> </div>
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<p><span class="NormalTextRun SCXW210518157 BCX8">For a data scientist, observability means performing more aggressive testing of the data before it reaches models or dashboards. This includes anomaly detection using ML techniques, such as </span><span class="NormalTextRun SCXW210518157 BCX8">identifying</span><span class="NormalTextRun SCXW210518157 BCX8"> an unusual shift in data volume or a sudden increase in values within a column beyond its expected range. These signals serve as early warnings, giving the team the opportunity to investigate and resolve issues before potentially corrupted or drifted data is fed into production models.</span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">How is data observability different from data quality? </h2> </div>
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<p><span class="TextRun SCXW59353193 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW59353193 BCX8"><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-quality-monitoring-tools/" target="_blank" rel="noopener">Data quality</a></span> focuses on whether data meets predefined standards at a point in time, while data observability tracks data health continuously as it moves through systems, pipelines, and reports. The two terms are often used interchangeably, but they address different concerns. The comparison below clarifies the distinction:</span></span></p> </div>
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<th style="text-align:left;">Dimension</th>
<th style="text-align:left;">Data Observability</th>
<th style="text-align:left;">Data Quality</th>
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<td style="text-align:left;">Scope</td>
<td style="text-align:left;">Data in motion; full lifecycle from ingestion to reporting</td>
<td style="text-align:left;">Data at rest; focuses on tables and datasets</td>
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<td style="text-align:left;">Detects known and unknown issues proactively</td>
<td style="text-align:left;">Checks known issues; mostly reactive</td>
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<td style="text-align:left;">Coverage</td>
<td style="text-align:left;">Drift, anomalies, lineage, and ML-based monitoring</td>
<td style="text-align:left;">Nulls, duplicates, and static rule validation</td>
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<td style="text-align:left;">End-to-end pipeline health and traceability</td>
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<p><span class="TextRun SCXW175478236 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW175478236 BCX8">Data quality focuses on whether data meets predefined standards at a point in time. Data observability takes a broader view, tracking data health continuously as it moves through systems, pipelines, and reports.</span></span><span class="EOP Selected SCXW175478236 BCX8" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":1,"335551620":1,"335559685":0,"335559737":0,"335559738":0,"335559739":0,"335559740":259}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">How does Datagaps enable end-to-end data observability on Microsoft Fabric? </h2> </div>
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<p><span class="TextRun SCXW51743337 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SpellingErrorV2Themed SCXW51743337 BCX8">Datagaps</span><span class="NormalTextRun SCXW51743337 BCX8"> offers the </span><span class="NormalTextRun SpellingErrorV2Themed SCXW51743337 BCX8">DataOps</span><span class="NormalTextRun SCXW51743337 BCX8"> Suite to address </span><span class="NormalTextRun SCXW51743337 BCX8">the trust problem described above</span><span class="NormalTextRun SCXW51743337 BCX8">. It is an end-to-end testing and monitoring platform that integrates with existing architectures and performs validation and monitoring at multiple levels. T</span><span class="NormalTextRun SpellingErrorV2Themed SCXW51743337 BCX8">he comp</span><span class="NormalTextRun SCXW51743337 BCX8">onents of the </span><span class="NormalTextRun SpellingErrorV2Themed SCXW51743337 BCX8">DataOps</span><span class="NormalTextRun SCXW51743337 BCX8"> Suite work <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/analytics-ops/" target="_blank" rel="noopener">across the full analytics pipeline</a></span>, regardless of the platform.</span></span><span class="EOP Selected SCXW51743337 BCX8" data-ccp-props="{}"> </span></p> </div>
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<img fetchpriority="high" decoding="async" width="2025" height="776" src="https://www.datagaps.com/wp-content/uploads/Cloud-Analytics-Pipeline-Infographic.png" class="attachment-full size-full wp-image-63780" alt="DataOps Suite architecture overview showing components across Landing Zone, Curated Zone, Warehouse, and BI layers" srcset="https://www.datagaps.com/wp-content/uploads/Cloud-Analytics-Pipeline-Infographic.png 2025w, https://www.datagaps.com/wp-content/uploads/Cloud-Analytics-Pipeline-Infographic-300x115.png 300w, https://www.datagaps.com/wp-content/uploads/Cloud-Analytics-Pipeline-Infographic-1024x392.png 1024w, https://www.datagaps.com/wp-content/uploads/Cloud-Analytics-Pipeline-Infographic-768x294.png 768w, https://www.datagaps.com/wp-content/uploads/Cloud-Analytics-Pipeline-Infographic-1536x589.png 1536w" sizes="(max-width: 2025px) 100vw, 2025px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite architecture overview showing components across Landing Zone, Curated Zone, Warehouse, and BI layers</figcaption>
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<p><span class="TextRun SCXW36041825 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW36041825 BCX8">For Microsoft Fabric environments specifically, the integration maps natively onto the <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/azure-synapse-testing/" target="_blank" rel="noopener">medallion architecture</a></span>, with </span><span class="NormalTextRun SpellingErrorV2Themed SCXW36041825 BCX8">Datagaps</span><span class="NormalTextRun SCXW36041825 BCX8"> components </span><span class="NormalTextRun SCXW36041825 BCX8">operating</span><span class="NormalTextRun SCXW36041825 BCX8"> across </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW36041825 BCX8">Bronze, Silver, and Gold</span><span class="NormalTextRun SCXW36041825 BCX8"> layers on </span><span class="NormalTextRun SpellingErrorV2Themed SCXW36041825 BCX8">OneLake</span><span class="NormalTextRun SCXW36041825 BCX8">.</span></span><span class="EOP Selected SCXW36041825 BCX8" data-ccp-props="{}"> </span></p> </div>
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<img decoding="async" width="1044" height="439" src="https://www.datagaps.com/wp-content/uploads/Microsoft-Fabric-integration-with-datagaps.2.png" class="attachment-full size-full wp-image-63781" alt="Microsoft Fabric integration with Datagaps showing Data Quality Monitor, ETL Validation, and BI Validation mapped across Bronze, Silver, and Gold layers on OneLake" srcset="https://www.datagaps.com/wp-content/uploads/Microsoft-Fabric-integration-with-datagaps.2.png 1044w, https://www.datagaps.com/wp-content/uploads/Microsoft-Fabric-integration-with-datagaps.2-300x126.png 300w, https://www.datagaps.com/wp-content/uploads/Microsoft-Fabric-integration-with-datagaps.2-1024x431.png 1024w, https://www.datagaps.com/wp-content/uploads/Microsoft-Fabric-integration-with-datagaps.2-768x323.png 768w" sizes="(max-width: 1044px) 100vw, 1044px" /> <figcaption class="widget-image-caption wp-caption-text">Microsoft Fabric integration with Datagaps showing Data Quality Monitor, ETL Validation, and BI Validation mapped across Bronze, Silver, and Gold layers on OneLake</figcaption>
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How do AI-generated column descriptions set up the data model? </span>
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The process begins as soon as you connect your data source and create a data model. The first task is generating descriptions for your columns using AI. This produces consistent metadata that the platform uses to understand your dataset and generate better rules and queries wherever required. </p>
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<img decoding="async" width="1120" height="527" src="https://www.datagaps.com/wp-content/uploads/Datagaps-column-description.3.png" class="attachment-full size-full wp-image-63785" alt="Datagaps column description generation using AI on DimCustomer table with Generate Descriptions button highlighted]" srcset="https://www.datagaps.com/wp-content/uploads/Datagaps-column-description.3.png 1120w, https://www.datagaps.com/wp-content/uploads/Datagaps-column-description.3-300x141.png 300w, https://www.datagaps.com/wp-content/uploads/Datagaps-column-description.3-1024x482.png 1024w, https://www.datagaps.com/wp-content/uploads/Datagaps-column-description.3-768x361.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> <figcaption class="widget-image-caption wp-caption-text">Datagaps column description generation using AI on DimCustomer table with Generate Descriptions button highlighted</figcaption>
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<p><span class="TextRun SCXW212699605 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW212699605 BCX8">Once descriptions are generated and sample data has been reviewed, the next step is building data quality processes, starting with defining data quality rules.</span></span><span class="EOP Selected SCXW212699605 BCX8" data-ccp-props="{}"> </span></p> </div>
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How does Datagaps generate data quality rules automatically? </span>
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Datagaps generates <a href="https://www.datagaps.com/data-quality-monitoring-tools/" target="_blank" rel="noopener noreferrer" style="color: #1967d2;text-decoration: underline;font-weight: 400">data quality rules</a> automatically for all selected tables, without requiring manual checks to be written. The AI uses sample data and metadata to produce rules covering dimensions such as: </p>
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<ul><li><span data-contrast="auto">Validity: ensuring values conform to expected formats and constraints</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Uniqueness: detecting duplicate records or primary key violations</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Completeness: identifying null or missing values</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Consistency: validating conformity of related fields</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Timeliness: checking that data arrives within expected time windows</span></li></ul> </div>
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<img loading="lazy" decoding="async" width="1100" height="500" src="https://www.datagaps.com/wp-content/uploads/DimCustomer-mail-address.4.webp" class="attachment-full size-full wp-image-63795" alt="Auto-generated data quality rules for DimCustomer table including email validity, birth date validity, duplicate check, and SQL expression rule" srcset="https://www.datagaps.com/wp-content/uploads/DimCustomer-mail-address.4.webp 1100w, https://www.datagaps.com/wp-content/uploads/DimCustomer-mail-address.4-300x136.webp 300w, https://www.datagaps.com/wp-content/uploads/DimCustomer-mail-address.4-1024x465.webp 1024w, https://www.datagaps.com/wp-content/uploads/DimCustomer-mail-address.4-768x349.webp 768w" sizes="(max-width: 1100px) 100vw, 1100px" /> <figcaption class="widget-image-caption wp-caption-text">Auto-generated data quality rules for DimCustomer table including email validity, birth date validity, duplicate check, and SQL expression rule</figcaption>
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<p><span class="TextRun SCXW209800348 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW209800348 BCX8">A practical advantage of this approach is reusability. Once rules are generated for a given set of tables, more than 80% of the time those rules </span><span class="NormalTextRun SCXW209800348 BCX8">remain</span><span class="NormalTextRun SCXW209800348 BCX8"> the same across runs. They can be exported, imported, and customised rather than regenerated each time, which also reduces AI processing costs.</span></span><span class="EOP Selected SCXW209800348 BCX8" data-ccp-props="{}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="595" src="https://www.datagaps.com/wp-content/uploads/Rules-list-with-Export-option.5.png" class="attachment-full size-full wp-image-63801" alt="Rules list with Export option" srcset="https://www.datagaps.com/wp-content/uploads/Rules-list-with-Export-option.5.png 1120w, https://www.datagaps.com/wp-content/uploads/Rules-list-with-Export-option.5-300x159.png 300w, https://www.datagaps.com/wp-content/uploads/Rules-list-with-Export-option.5-1024x544.png 1024w, https://www.datagaps.com/wp-content/uploads/Rules-list-with-Export-option.5-768x408.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> <figcaption class="widget-image-caption wp-caption-text">Rules list with Export option highlighted, showing rule reusability across environments</figcaption>
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How does Datagaps detect and classify PII automatically? </span>
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For datasets containing personally identifiable information, Datagaps uses machine learning to automatically detect and classify PII columns across all tables. Rather than manually tagging sensitive fields, the platform classifies each column into a known entity type such as email address, phone number, or national identifier. These columns can then be <a href="https://www.datagaps.com/test-data-manager/" target="_blank" rel="noopener noreferrer" style="color: #1967d2;text-decoration: underline;font-weight: 400">masked</a> or flagged for <a href="https://www.datagaps.com/compliance-solutions/" target="_blank" rel="noopener noreferrer" style="color: #1967d2;text-decoration: underline;font-weight: 400">governance purposes</a> without any additional code. </p>
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<span >
How is the data quality score calculated? </span>
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Every rule defined in a table contributes to an individual data quality score at the column level. These roll up into an aggregated score visible in the data quality dashboard. This gives teams a standardised, quantifiable view of data health that can be <a href="https://www.datagaps.com/ebook/data-quality-maturity-assessment-guide/" target="_blank" rel="noopener noreferrer" style="color: #1967d2;text-decoration: underline;font-weight: 400">tracked over time</a> and broken down by dimension. </p>
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<div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="1 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/SQL-Expression.6.png" alt="SQL Expression" /><figcaption class="elementor-image-carousel-caption">SQL Expression</figcaption></figure></div><div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="2 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/Dashboard.7.png" alt="Dashboard" /><figcaption class="elementor-image-carousel-caption">Dashboard</figcaption></figure></div> </div>
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<p><span class="TextRun SCXW102776981 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW102776981 BCX8">All records that fail defined quality checks are surfaced in the Data Quality Issues section.</span></span><span class="EOP Selected SCXW102776981 BCX8" data-ccp-props="{}"> </span></p> </div>
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How are data quality issues managed and remediated? </span>
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From the Data Quality Issues interface, teams can choose to save, quarantine, or delete bad records. Notifications, stakeholder alerts, and ticket creation can all be automated from within the platform, ensuring full accountability throughout the remediation process.
For example, if a check is added to ensure that first and last names in a table contain no special characters or numbers, the platform will show exactly where that check failed, how many bad records were found, and the sample rows that triggered the failure. The team can then decide whether to retain, isolate, or remove those records before proceeding. </p>
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<img loading="lazy" decoding="async" width="1107" height="857" src="https://www.datagaps.com/wp-content/uploads/Data-Quality-issues.8.png" class="attachment-full size-full wp-image-63829" alt="Data Quality issues in DQM" srcset="https://www.datagaps.com/wp-content/uploads/Data-Quality-issues.8.png 1107w, https://www.datagaps.com/wp-content/uploads/Data-Quality-issues.8-300x232.png 300w, https://www.datagaps.com/wp-content/uploads/Data-Quality-issues.8-1024x793.png 1024w, https://www.datagaps.com/wp-content/uploads/Data-Quality-issues.8-768x595.png 768w" sizes="(max-width: 1107px) 100vw, 1107px" /> </div>
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How do dataflows validate ETL transformations and compare data? </span>
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A key part of end-to-end observability is <a href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener noreferrer" style="color: #1967d2;text-decoration: underline;font-weight: 400">verifying that transformations produce the expected output</a>. The best way to find mismatches is by creating separate dataflows for testing. A dataflow connects a raw source, applies a transformation, and then <a href="https://www.datagaps.com/data-reconciliation/" target="_blank" rel="noopener noreferrer" style="color: #1967d2;text-decoration: underline;font-weight: 400">runs a comparison</a> to surface any rows where the output does not match expectations. </p>
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<img loading="lazy" decoding="async" width="1120" height="558" src="https://www.datagaps.com/wp-content/uploads/Dataflow-diagram.9.png" class="attachment-full size-full wp-image-63840" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Dataflow-diagram.9.png 1120w, https://www.datagaps.com/wp-content/uploads/Dataflow-diagram.9-300x149.png 300w, https://www.datagaps.com/wp-content/uploads/Dataflow-diagram.9-1024x510.png 1024w, https://www.datagaps.com/wp-content/uploads/Dataflow-diagram.9-768x383.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<p><span class="NormalTextRun SCXW42687982 BCX8">The result view shows exactly which rows were mismatched, the original value versus the changed value per column, and a column-wise breakdown of discrepancies. This level of clarity is essential before moving any data solution to </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW42687982 BCX8">production, and</span><span class="NormalTextRun SCXW42687982 BCX8"> is particularly valuable during <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-migration-testing-automation/" target="_blank" rel="noopener">data migration scenarios</a></span> where column mappings and type conversions need to be </span><span class="NormalTextRun SCXW42687982 BCX8">validated</span><span class="NormalTextRun SCXW42687982 BCX8">.</span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="544" src="https://www.datagaps.com/wp-content/uploads/Dataflow-mapping-result.10.png" class="attachment-full size-full wp-image-63842" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Dataflow-mapping-result.10.png 1120w, https://www.datagaps.com/wp-content/uploads/Dataflow-mapping-result.10-300x146.png 300w, https://www.datagaps.com/wp-content/uploads/Dataflow-mapping-result.10-1024x497.png 1024w, https://www.datagaps.com/wp-content/uploads/Dataflow-mapping-result.10-768x373.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<p><span class="NormalTextRun SCXW8542091 BCX8">Within Dataflows, an AI Assistant is also available to help write correct and complex SQL queries using simple natural language prompts. To give the AI </span><span class="NormalTextRun SCXW8542091 BCX8">accurate</span><span class="NormalTextRun SCXW8542091 BCX8"> context, a RAG model of the data schema can be configured, allowing the assistant to generate queries that are grounded in the actual metadata of the dataset.</span></p> </div>
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How are BI reports validated against source data? </span>
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Observability does not end at the database layer. Reports also need to be <a href="https://www.datagaps.com/bi-testing-tools/" target="_blank" rel="noopener noreferrer" style="color: #1967d2;text-decoration: underline;font-weight: 400">validated against the original dataset</a>. As an analytics manager, checking data quality scores alone is insufficient; the figures shown in a report must also match what is in the source tables. Dataflows support this by allowing a comparison between a BI tool dataset and the underlying source.
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For example, if <a href="https://www.datagaps.com/automate-tableau-testing/" target="_blank" rel="noopener noreferrer" style="color: #1967d2;text-decoration: underline;font-weight: 400">a report on Tableau</a> is aggregating yearly income by number of children, the same aggregation can be written against the source table and compared directly. The tool picks up the dataset from the dashboard automatically and validates whether the numbers match. Any discrepancy in values is surfaced as a mismatched row, including type mismatches such as a column appearing as a decimal in the source but as a string in the report. </p>
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How do data profiling and anomaly detection work? </span>
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A minimal yet important capability that also falls under data observability is <a href="https://www.datagaps.com/blog/data-profiling-in-etl-types-and-best-practices/" target="_blank" rel="noopener noreferrer" style="color: #1967d2;text-decoration: underline;font-weight: 400">profiling your data</a> and identifying patterns. For datasets that change every day, profiling provides a report on what has changed, what the distribution looks like, and where anomalies may have appeared.
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Beyond standard aggregates such as minimum value, maximum value, null count, and mean, the platform supports machine learning-based analysis to detect skewness and hidden patterns in datasets. This does not require any custom code; selecting the desired profiling options is sufficient and the platform generates the report automatically. </p>
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<img loading="lazy" decoding="async" width="1120" height="521" src="https://www.datagaps.com/wp-content/uploads/Data-profiling-configuration.16.png" class="attachment-full size-full wp-image-63865" alt="Data profiling configuration" srcset="https://www.datagaps.com/wp-content/uploads/Data-profiling-configuration.16.png 1120w, https://www.datagaps.com/wp-content/uploads/Data-profiling-configuration.16-300x140.png 300w, https://www.datagaps.com/wp-content/uploads/Data-profiling-configuration.16-1024x476.png 1024w, https://www.datagaps.com/wp-content/uploads/Data-profiling-configuration.16-768x357.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> <figcaption class="widget-image-caption wp-caption-text">Data profiling configuration screen showing aggregate options, frequency analysis, pattern analysis selection, and anomaly detection settings with standard deviation method</figcaption>
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<p><span class="TextRun SCXW37014659 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW37014659 BCX8">The output is a detailed profile showing observed patterns per column, frequency of values, and trend comparisons against </span><span class="NormalTextRun SCXW37014659 BCX8">previous</span><span class="NormalTextRun SCXW37014659 BCX8"> runs, giving teams a continuous view of how their data is behaving over time.</span></span><span class="EOP Selected SCXW37014659 BCX8" data-ccp-props="{}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="640" src="https://www.datagaps.com/wp-content/uploads/Data-profile-result.17.png" class="attachment-full size-full wp-image-63875" alt="Data profile result" srcset="https://www.datagaps.com/wp-content/uploads/Data-profile-result.17.png 1120w, https://www.datagaps.com/wp-content/uploads/Data-profile-result.17-300x171.png 300w, https://www.datagaps.com/wp-content/uploads/Data-profile-result.17-1024x585.png 1024w, https://www.datagaps.com/wp-content/uploads/Data-profile-result.17-768x439.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> <figcaption class="widget-image-caption wp-caption-text">Data profile result showing Customer_Key column with row count, null percentage, distinct count, frequency analysis, and pattern analysis breakdown</figcaption>
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<h2 class="elementor-heading-title elementor-size-default">What role does AI play across the Datagaps observability framework?</h2> </div>
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<p><span data-contrast="auto">In the DataOps Suite, AI is embedded across the entire observability workflow rather than sitting in one feature. AI used for development in Microsoft Fabric is now matched by AI for testing as well. The AI capabilities span the following areas:</span><span data-ccp-props="{}"> </span></p><ul><li><span data-contrast="auto">AI-generated column descriptions to establish consistent and machine-readable metadata</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">AI-powered rule generation based on sample data and metadata context</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">ML-based entity classification for automated PII detection across all tables</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">An AI Assistant within Dataflows for generating SQL queries using natural language prompts, grounded in a RAG model of the data schema</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">ML-based anomaly detection for identifying distribution shifts and statistical irregularities without requiring custom code</span><span data-ccp-props="{}"> </span></li><li style="list-style-type: none;"> </li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Conclusion </h2> </div>
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<p><span data-contrast="auto">The best thing about having a data observability setup in place is that you can compare what goes into the system against what is being reflected on the dashboard or in your tables. You can introduce tests at every stage to validate what is happening and whether it is correct.</span><span data-ccp-props="{}"> </span></p><p><span data-contrast="auto">As organisations scale their data operations on Microsoft Fabric, data quality checks alone are not sufficient. Observability needs continuous visibility across the full data lifecycle, from ingestion through transformation, storage, and reporting. Datagaps and its DataOps Suite provide the tooling to achieve this, with AI accelerating every stage of the process and integrating natively with existing architectures.</span></p> </div>
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<p><span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/request-a-demo/" target="_blank" rel="noopener"><span style="text-decoration: underline;"><span class="NormalTextRun SCXW211184476 BCX8">See how the </span><span class="NormalTextRun SpellingErrorV2Themed SCXW211184476 BCX8">DataOps</span><span class="NormalTextRun SCXW211184476 BCX8"> Suite validates Fabric data from Bronze to BI report →</span></span></a></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Frequently Asked Questions </h2> </div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="1" aria-controls="elementor-tab-content-1531"><h3 class="eael-accordion-tab-title">What is the difference between data observability and data quality? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1531" class="eael-accordion-content clearfix" data-tab="1" aria-labelledby="faq-1"><p><span class="TextRun SCXW133730243 BCX8"><span class="NormalTextRun SCXW133730243 BCX8">Data quality checks whether data at rest meets predefined standards, using static rules for issues like nulls and duplicates. Data observability covers data in motion across the full lifecycle, proactively detecting known and unknown issues through drift, anomaly, </span><span class="NormalTextRun SCXW133730243 BCX8">lineage</span><span class="NormalTextRun SCXW133730243 BCX8"> and ML-based monitoring with automated alerting. Data quality checks are one part of observability.</span></span><span class="EOP Selected SCXW133730243 BCX8"> </span></p></div>
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<div id="faq-2" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-1532"><h3 class="eael-accordion-tab-title">How does Datagaps support data observability on Microsoft Fabric? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1532" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-2"><p><span class="TextRun SCXW81688219 BCX8"><span class="NormalTextRun SCXW81688219 BCX8">The </span><span class="NormalTextRun SpellingErrorV2Themed SCXW81688219 BCX8">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW81688219 BCX8">DataOps</span><span class="NormalTextRun SCXW81688219 BCX8"> Suite integrates with existing architectures and maps natively onto Fabric’s medallion architecture, with Data Quality Monitor, ETL Validation and BI Validation </span><span class="NormalTextRun SCXW81688219 BCX8">operating</span><span class="NormalTextRun SCXW81688219 BCX8"> across </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW81688219 BCX8">Bronze, Silver and Gold</span><span class="NormalTextRun SCXW81688219 BCX8"> layers on </span><span class="NormalTextRun SpellingErrorV2Themed SCXW81688219 BCX8">OneLake</span><span class="NormalTextRun SCXW81688219 BCX8">. It covers AI-generated rules, DQ scoring, issue management, dataflow-based comparisons, BI report </span><span class="NormalTextRun SCXW81688219 BCX8">validation</span><span class="NormalTextRun SCXW81688219 BCX8"> and profiling.</span></span><span class="EOP Selected SCXW81688219 BCX8"> </span></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-1533"><h3 class="eael-accordion-tab-title">Can AI-generated data quality rules be reused across runs?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1533" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-3"><p><span class="TextRun SCXW160290695 BCX8"><span class="NormalTextRun SCXW160290695 BCX8">Yes. Once rules are generated for a given set of tables, more than 80% of the time those rules </span><span class="NormalTextRun SCXW160290695 BCX8">remain</span><span class="NormalTextRun SCXW160290695 BCX8"> the same across runs. They can be exported, </span><span class="NormalTextRun SCXW160290695 BCX8">imported</span><span class="NormalTextRun SCXW160290695 BCX8"> and customised rather than regenerated each time, which also reduces AI processing costs. Rules cover validity, uniqueness, completeness, </span><span class="NormalTextRun SCXW160290695 BCX8">consistency</span><span class="NormalTextRun SCXW160290695 BCX8"> and timeliness.</span></span><span class="EOP Selected SCXW160290695 BCX8"> </span></p></div>
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<div id="faq-4" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-1534"><h3 class="eael-accordion-tab-title">What happens to bad records that fail data quality checks in Datagaps?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1534" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-4"><p><span class="NormalTextRun SCXW194459487 BCX8">Failed records are surfaced in the Data Quality Issues section, where teams can save, quarantine or </span><span class="NormalTextRun SCXW194459487 BCX8">delete</span><span class="NormalTextRun SCXW194459487 BCX8"> them. The platform shows where a check failed, how many bad records were found and sample rows. Notifications, stakeholder </span><span class="NormalTextRun SCXW194459487 BCX8">alerts</span><span class="NormalTextRun SCXW194459487 BCX8"> and ticket creation can be automated, ensuring accountability throughout remediation.</span></p></div>
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<div id="faq-5" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="5" aria-controls="elementor-tab-content-1535"><h3 class="eael-accordion-tab-title">How do you validate that a BI report matches its source data?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1535" class="eael-accordion-content clearfix" data-tab="5" aria-labelledby="faq-5"><p><span class="TextRun SCXW156902476 BCX8"><span class="NormalTextRun SpellingErrorV2Themed SCXW156902476 BCX8">Datagaps</span><span class="NormalTextRun SCXW156902476 BCX8"> dataflows compare a BI tool dataset with the underlying source. For a Tableau report aggregating yearly income by number of children, the same aggregation is written against the source table and compared directly. Discrepancies appear as mismatched rows, including type mismatches such as a decimal in the source shown as a string in the report.</span></span><span class="EOP Selected SCXW156902476 BCX8"> </span></p></div>
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<a href="https://www.linkedin.com/in/raj-mohan-achanta-41454b1a2/" >
RajMohan Achanta </a>
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Associate Product Manager, Datagaps </p>
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Avinash Keshri </a>
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Head, Product Marketing </p>
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</div>
</div>
</div>
</div>
</div>
<p>The post <a href="https://www.datagaps.com/blog/how-datagaps-enables-end-to-end-data-observability-on-fabric/">How Datagaps Enables End-to-End Data Observability on Fabric</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 to Generate Synthetic Test Data for Microsoft Fabric with TDM</title>
<link>https://www.datagaps.com/blog/how-to-generate-synthetic-test-data-for-microsoft-fabric-with-tdm/</link>
<dc:creator><![CDATA[Raj Mohan Achanta]]></dc:creator>
<pubDate>Thu, 22 Oct 2026 12:27:27 +0000</pubDate>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[Datagaps TDM]]></category>
<category><![CDATA[Microsoft Fabric]]></category>
<category><![CDATA[PII Masking]]></category>
<category><![CDATA[Synthetic Data]]></category>
<category><![CDATA[Test Data Management]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=63404</guid>
<description><![CDATA[<p>Generate synthetic test data for Microsoft Fabric with Datagaps TDM. Use AI to create masked, realistic data for secure, scalable testing.</p>
<p>The post <a href="https://www.datagaps.com/blog/how-to-generate-synthetic-test-data-for-microsoft-fabric-with-tdm/">How to Generate Synthetic Test Data for Microsoft Fabric with TDM</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><span class="TextRun SCXW99550397 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SpellingErrorV2Themed SCXW99550397 BCX8">Datagaps</span><span class="NormalTextRun SCXW99550397 BCX8"><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> (TDM) is an AI-powered synthetic data tool that learns patterns from sample data to generate privacy-safe test data for Microsoft Fabric </span><span class="NormalTextRun SpellingErrorV2Themed SCXW99550397 BCX8">lakehouses</span><span class="NormalTextRun SCXW99550397 BCX8"> and warehouses, masking PII automatically and reducing work that could take weeks to under an hour.</span></span><span class="EOP Selected SCXW99550397 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><span data-contrast="auto">Manually copying or creating test data breaks down at 30 to 40 or more tables per testing cycle.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">TDM has two components: Data Models configure generation, and Datasets generate and load the data.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">AI auto-detects entities, treats classified columns as PII or confidential, and masks them during generation.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Sample data is a prerequisite; by default TDM takes 5,000 rows from the connected source.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Generating 100 or 1,000 rows completes in about five seconds, with multiple tables processed in parallel.</span></li></ul> </div>
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/test-data-manager-trial-request/" target="_blank" rel="noopener"><span class="NormalTextRun SCXW77785008 BCX8">See how </span><span class="NormalTextRun SpellingErrorV2Themed SCXW77785008 BCX8">Datagaps</span><span class="NormalTextRun SCXW77785008 BCX8"> TDM generates masked test data for your Fabric tables →</span></a></span></p> </div>
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<p><span class="TextRun SCXW95773873 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW95773873 BCX8">To generate synthetic test data for Microsoft Fabric, configure a </span></span><span class="TextRun SCXW95773873 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW95773873 BCX8">Data Model</span></span><span class="TextRun SCXW95773873 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW95773873 BCX8"> in </span><span class="NormalTextRun SpellingErrorV2Themed SCXW95773873 BCX8">Datagaps</span><span class="NormalTextRun SCXW95773873 BCX8"> Test Data Manager (TDM) — connect your </span><span class="NormalTextRun SpellingErrorV2Themed SCXW95773873 BCX8">lakehouse</span><span class="NormalTextRun SCXW95773873 BCX8"> or warehouse, let AI detect and mask entities, load sample data and confirm target data types — then use a </span></span><span class="TextRun SCXW95773873 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW95773873 BCX8">Dataset</span></span><span class="TextRun SCXW95773873 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW95773873 BCX8"> to generate the volume you need and load it into tables or files.</span></span><span class="EOP Selected SCXW95773873 BCX8" data-ccp-props="{}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why is test data for Microsoft Fabric pipelines hard to manage? </h2> </div>
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<p><span class="TextRun SCXW193107267 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW193107267 BCX8">In <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/microsoft-fabric-testing-automation/" target="_blank" rel="noopener">Microsoft Fabric</a></span>, pipelines and notebooks are only as good as the data used to test them. In a typical enterprise setting, developers work with minimal or dummy data, and QA teams manually copy production data or create new entries for staging. In large setups with 30 to 40 or more tables per testing cycle, this becomes unsustainable fast, especially when PII masking and relational integrity are also in scope.</span></span><span class="EOP SCXW193107267 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><span class="TextRun SCXW45042379 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SpellingErrorV2Themed SCXW45042379 BCX8">Datagaps</span><span class="NormalTextRun SCXW45042379 BCX8"> Test Data Manager (TDM)</span></span><span class="TextRun SCXW45042379 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW45042379 BCX8"> addresses this directly. Its AI capabilities generate synthetic data that covers all production-level nuances without manual effort, reducing what could take weeks to under an hour.</span></span><span class="EOP Selected SCXW45042379 BCX8" data-ccp-props="{}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">How does Datagaps TDM generate synthetic data for Microsoft Fabric? </h2> </div>
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<p><span class="TextRun SCXW68732214 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SpellingErrorV2Themed SCXW68732214 BCX8">Datagaps</span><span class="NormalTextRun SCXW68732214 BCX8"> TDM has two core components: </span></span><strong><span class="TextRun SCXW68732214 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW68732214 BCX8">Data Models</span></span></strong><span class="TextRun SCXW68732214 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW68732214 BCX8"> and </span></span><strong><span class="TextRun SCXW68732214 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW68732214 BCX8">Datasets</span></span><span class="TextRun SCXW68732214 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW68732214 BCX8">.</span></span></strong></p> </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/test-data-manager/" target="_blank" rel="noopener"><strong><span class="TextRun SCXW78120396 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW78120396 BCX8">Data Models</span></span></strong></a></span><span class="TextRun SCXW78120396 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW78120396 BCX8"> are where you configure the data you want to generate for each table. This includes masking data, fetching sample data for generation, defining the target data type, and all other configuration requirements.</span></span></p> </div>
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<p><strong><span class="TextRun SCXW117414207 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW117414207 BCX8">Datasets</span></span></strong><span class="TextRun SCXW117414207 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW117414207 BCX8"> use the configured data model and generate data using AI/ML capabilities. You define the volume of data to be generated and then choose to either save it to an external location or load it directly into tables for testing.</span></span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="677" src="https://www.datagaps.com/wp-content/uploads/image-1-1.png" class="attachment-full size-full wp-image-63571" alt="Data Models & Datasets of DQ in Datagaps" srcset="https://www.datagaps.com/wp-content/uploads/image-1-1.png 1120w, https://www.datagaps.com/wp-content/uploads/image-1-1-300x181.png 300w, https://www.datagaps.com/wp-content/uploads/image-1-1-1024x619.png 1024w, https://www.datagaps.com/wp-content/uploads/image-1-1-768x464.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> <figcaption class="widget-image-caption wp-caption-text">Test Data Manager — Data Models and Datasets</figcaption>
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<h2 class="elementor-heading-title elementor-size-default">How do you configure a TDM data model for Microsoft Fabric tables?</h2> </div>
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<h3 class="elementor-heading-title elementor-size-default">Connect the Fabric lakehouse or warehouse </h3> </div>
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<p><span class="TextRun SCXW19038420 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW19038420 BCX8">For demonstration purposes, two schemas were connected into the data model, </span><span class="NormalTextRun SCXW19038420 BCX8">containing</span><span class="NormalTextRun SCXW19038420 BCX8"> 5 to 10 dimension and fact tables, each with </span><span class="NormalTextRun SCXW19038420 BCX8">approximately 20</span><span class="NormalTextRun SCXW19038420 BCX8"> to 30 columns. When you connect your Fabric data source, you connect your specific </span><span class="NormalTextRun SpellingErrorV2Themed SCXW19038420 BCX8">lakehouse</span><span class="NormalTextRun SCXW19038420 BCX8"> or warehouse. The moment a data source is connected, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW19038420 BCX8">Datagaps</span><span class="NormalTextRun SCXW19038420 BCX8"> fetches relevant metadata and automatically reflects it in the interface.</span></span><span class="EOP Selected SCXW19038420 BCX8" data-ccp-props="{}"> </span></p> </div>
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How are entities classified and PII masked? </span>
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There is an additional column called Entity, which is used to define the type of a column and whether it needs to be masked. When any column is classified as one of the defined entities, Datagaps treats that data as PII or confidential and masks it during generation. Datagaps already supports GDPR, CCPA, and HIPAA-aligned workflows, which means data security concerns are addressed at the platform level. </p>
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<img loading="lazy" decoding="async" width="1120" height="539" src="https://www.datagaps.com/wp-content/uploads/image-2-1-1.png" class="attachment-full size-full wp-image-63576" alt="Data Models of TDM" srcset="https://www.datagaps.com/wp-content/uploads/image-2-1-1.png 1120w, https://www.datagaps.com/wp-content/uploads/image-2-1-1-300x144.png 300w, https://www.datagaps.com/wp-content/uploads/image-2-1-1-1024x493.png 1024w, https://www.datagaps.com/wp-content/uploads/image-2-1-1-768x370.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<p><span class="TextRun SCXW151286073 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW151286073 BCX8">Entity detection can be done manually, or </span><span class="NormalTextRun SpellingErrorV2Themed SCXW151286073 BCX8">Datagaps</span><span class="NormalTextRun SCXW151286073 BCX8"> can handle it automatically using AI. There is a built-in </span><span class="NormalTextRun SCXW151286073 BCX8">option</span><span class="NormalTextRun SCXW151286073 BCX8"> to auto-detect entities for your tables.</span></span><span class="EOP Selected SCXW151286073 BCX8" data-ccp-props="{}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="529" src="https://www.datagaps.com/wp-content/uploads/image-3-1.png" class="attachment-full size-full wp-image-63587" alt="Auto-detect entities" srcset="https://www.datagaps.com/wp-content/uploads/image-3-1.png 1120w, https://www.datagaps.com/wp-content/uploads/image-3-1-300x142.png 300w, https://www.datagaps.com/wp-content/uploads/image-3-1-1024x484.png 1024w, https://www.datagaps.com/wp-content/uploads/image-3-1-768x363.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<p><span class="NormalTextRun SCXW16242282 BCX8">When auto-detect entities </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW16242282 BCX8">is</span><span class="NormalTextRun SCXW16242282 BCX8"> used, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW16242282 BCX8">Datagaps</span><span class="NormalTextRun SCXW16242282 BCX8"> applies its internal AI/ML models to classify each column into one of its predefined entity categories. Columns </span><span class="NormalTextRun SCXW16242282 BCX8">containing</span><span class="NormalTextRun SCXW16242282 BCX8"> names, payment details, addresses, date-time values, and similar data are auto-classified without requiring manual intervention. This is a particularly powerful feature when connecting tables for the first time and configuring them at scale. The platform can generate millions of records across formats with built-in masking.</span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="516" src="https://www.datagaps.com/wp-content/uploads/image-4-1.png" class="attachment-full size-full wp-image-63591" alt="" srcset="https://www.datagaps.com/wp-content/uploads/image-4-1.png 1120w, https://www.datagaps.com/wp-content/uploads/image-4-1-300x138.png 300w, https://www.datagaps.com/wp-content/uploads/image-4-1-1024x472.png 1024w, https://www.datagaps.com/wp-content/uploads/image-4-1-768x354.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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Why is sample data required? </span>
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The only prerequisite is that some sample data must be loaded before using this feature. To auto-detect entities and generate synthetic data, existing data is required so the system can identify patterns in those values. You can filter that sample however you choose, whether using a specific set of values or a random selection. This sample enables the AI to understand the patterns needed for accurate synthetic data generation. </p>
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<img loading="lazy" decoding="async" width="1120" height="537" src="https://www.datagaps.com/wp-content/uploads/image-5-1.png" class="attachment-full size-full wp-image-63599" alt="synthetic data generation" srcset="https://www.datagaps.com/wp-content/uploads/image-5-1.png 1120w, https://www.datagaps.com/wp-content/uploads/image-5-1-300x144.png 300w, https://www.datagaps.com/wp-content/uploads/image-5-1-1024x491.png 1024w, https://www.datagaps.com/wp-content/uploads/image-5-1-768x368.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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How do you preserve referential integrity between tables? </span>
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For projects with relationships between tables, these need to be accounted for carefully. A foreign key, for example, must be mapped correctly and must be available in its source table. Otherwise, the generated data will lack referential integrity. Datagaps allows you to define these relationships and map columns to their parent column in another table. </p>
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<img loading="lazy" decoding="async" width="1120" height="482" src="https://www.datagaps.com/wp-content/uploads/image-6.png" class="attachment-full size-full wp-image-63600" alt="Relation - child, parent column" srcset="https://www.datagaps.com/wp-content/uploads/image-6.png 1120w, https://www.datagaps.com/wp-content/uploads/image-6-300x129.png 300w, https://www.datagaps.com/wp-content/uploads/image-6-1024x441.png 1024w, https://www.datagaps.com/wp-content/uploads/image-6-768x331.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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How much sample data does TDM use? </span>
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<p class="elementor-icon-box-description">
By default, Datagaps takes 5,000 rows of sample data from the connected source. This can be increased or decreased after initial testing. It also allows you to add a WHERE clause to filter specific data for sample generation, which is useful when testing for a particular region or segment. </p>
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<img loading="lazy" decoding="async" width="1120" height="617" src="https://www.datagaps.com/wp-content/uploads/image-7.png" class="attachment-full size-full wp-image-63605" alt="" srcset="https://www.datagaps.com/wp-content/uploads/image-7.png 1120w, https://www.datagaps.com/wp-content/uploads/image-7-300x165.png 300w, https://www.datagaps.com/wp-content/uploads/image-7-1024x564.png 1024w, https://www.datagaps.com/wp-content/uploads/image-7-768x423.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<p><span class="TextRun SCXW128503419 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW128503419 BCX8">When you generate your sample data, you will notice that certain columns are masked. These are the same columns for which an entity was defined in the configuration step.</span></span><span class="EOP Selected SCXW128503419 BCX8" data-ccp-props="{}"> </span></p> </div>
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<div class="elementor-image-carousel-wrapper swiper" role="region" aria-roledescription="carousel" aria-label="Image Carousel" dir="ltr">
<div class="elementor-image-carousel swiper-wrapper" aria-live="off">
<div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="1 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/image-8-1-1024x585.png" alt="Sample data" /></figure></div><div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="2 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/image-9-1-1024x595.png" alt="Synthetic data" /></figure></div> </div>
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<i aria-hidden="true" class="eicon-chevron-left"></i> </div>
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<i aria-hidden="true" class="eicon-chevron-right"></i> </div>
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How are target data types defined? </span>
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<p class="elementor-icon-box-description">
Once that configuration is complete, the next step is defining the quality of your synthetic data. This is where you define the target data types for generation. For entity columns, there are predefined data types that Datagaps refers to during generation. This too does not need to be done manually. The auto-recommend feature assigns entities and correct target data types for each table. All that is required is a review to confirm accuracy. </p>
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<div class="elementor-image-carousel-wrapper swiper" role="region" aria-roledescription="carousel" aria-label="Image Carousel" dir="ltr">
<div class="elementor-image-carousel swiper-wrapper" aria-live="off">
<div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="1 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/image-10-1024x474.png" alt="Data Type in Synthetic data" /></figure></div><div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="2 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/image-11-1024x571.png" alt="Generator data in synthetic data" /></figure></div> </div>
<div class="elementor-swiper-button elementor-swiper-button-prev" role="button" tabindex="0">
<i aria-hidden="true" class="eicon-chevron-left"></i> </div>
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<i aria-hidden="true" class="eicon-chevron-right"></i> </div>
<div class="swiper-pagination"></div>
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What is Fake Data in Datagaps TDM? </span>
</h3>
<p class="elementor-icon-box-description">
The real capability of synthetic data generation becomes apparent at this step. For columns containing names, payment details, or other entity types, the system automatically classifies the target type and sets the data up as realistic fake data. This is what Datagaps refers to as Fake Data. </p>
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<img loading="lazy" decoding="async" width="444" height="574" src="https://www.datagaps.com/wp-content/uploads/image-13.png" class="attachment-large size-large wp-image-63624" alt="Fake data consists of AI or machine-generated values" srcset="https://www.datagaps.com/wp-content/uploads/image-13.png 444w, https://www.datagaps.com/wp-content/uploads/image-13-232x300.png 232w" sizes="(max-width: 444px) 100vw, 444px" /> </div>
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<p><span class="NormalTextRun SCXW39084132 BCX8">Fake data consists of AI or machine-generated values defined across multiple categories. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW39084132 BCX8">Datagaps</span><span class="NormalTextRun SCXW39084132 BCX8"> maintains a predefined list of categories aligned with real-world data, and it classifies columns into one of those categories before generation. While the AI handles classification accurately in most cases, it is strongly recommended to review the target type of each column at this step. Verify that a column is not misclassified, for example, as an address when it </span><span class="NormalTextRun AdvancedProofingIssueV2Themed SCXW39084132 BCX8">actually contains</span><span class="NormalTextRun SCXW39084132 BCX8"> a name.</span></p> </div>
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<img loading="lazy" decoding="async" width="462" height="748" src="https://www.datagaps.com/wp-content/uploads/image-12.png" class="attachment-medium_large size-medium_large wp-image-63620" alt="Fake Data in TDM" srcset="https://www.datagaps.com/wp-content/uploads/image-12.png 462w, https://www.datagaps.com/wp-content/uploads/image-12-185x300.png 185w" sizes="(max-width: 462px) 100vw, 462px" /> </div>
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<p><span class="TextRun SCXW199547311 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW199547311 BCX8">Almost any category of data you can think of is predefined within TDM, including blood group, card details, age, email, contact information, and more. All of them can be mapped correctly to your tables without manual classification, and the AI generates these values as part of the synthetic data generation process.</span></span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="826" src="https://www.datagaps.com/wp-content/uploads/image-14.png" class="attachment-full size-full wp-image-63626" alt="Fake Data providers" srcset="https://www.datagaps.com/wp-content/uploads/image-14.png 1120w, https://www.datagaps.com/wp-content/uploads/image-14-300x221.png 300w, https://www.datagaps.com/wp-content/uploads/image-14-1024x755.png 1024w, https://www.datagaps.com/wp-content/uploads/image-14-768x566.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<span >
Can you apply constraints, gender and locale options? </span>
</h3>
<p class="elementor-icon-box-description">
Yes. Constraints can also be applied during generation. For datetime columns, for example, you can define a specific range to ensure the generated values fall within a desired window. </p>
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<img loading="lazy" decoding="async" width="439" height="707" src="https://www.datagaps.com/wp-content/uploads/image-15.png" class="attachment-large size-large wp-image-63633" alt="Fake data valid till date" srcset="https://www.datagaps.com/wp-content/uploads/image-15.png 439w, https://www.datagaps.com/wp-content/uploads/image-15-186x300.png 186w" sizes="(max-width: 439px) 100vw, 439px" /> </div>
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<p><span class="TextRun SCXW232524628 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW232524628 BCX8">If you want generated names to reflect a particular gender, that can be configured as well. And if you need the synthetic data to reflect details from a specific locale, that option is available too.</span></span><span class="EOP Selected SCXW232524628 BCX8" data-ccp-props="{}"> </span></p> </div>
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<div class="elementor-image-carousel-wrapper swiper" role="region" aria-roledescription="carousel" aria-label="Image Carousel" dir="ltr">
<div class="elementor-image-carousel swiper-wrapper" aria-live="off">
<div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="1 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/image16-1024x413.png" alt="preview of synthetic data in TDM" /></figure></div><div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="2 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/image-17-1.png" alt="Locale Type" /></figure></div> </div>
<div class="elementor-swiper-button elementor-swiper-button-prev" role="button" tabindex="0">
<i aria-hidden="true" class="eicon-chevron-left"></i> </div>
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<i aria-hidden="true" class="eicon-chevron-right"></i> </div>
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<span >
Data model configuration at a glance </span>
</h3>
<p class="elementor-icon-box-description">
All of the above configuration is done within the Data Model component of TDM. This can be done across all tables in a single pass before moving to the generation phase. <br><br>1. Go to the data model <br>
2. Use AI to auto-detect entities and verify them <br>
3. Load sample data <br>
4. Let AI define target types for synthetic data and verify again <br>
5. Make adjustments as required for enterprise needs </p>
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<span >
How do you generate and load synthetic data with TDM datasets? </span>
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<p class="elementor-icon-box-description">
Once configuration is complete, the process moves to the Dataset component of TDM, where you choose the output destination, select tables, generate the data and load it into the target. </p>
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<span >
Choose where the generated data is saved </span>
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<p class="elementor-icon-box-description">
Here, you define where you want to save the generated data and how. This includes choosing whether to save the synthetic data to CSV files, load it directly into tables in an existing database, into a specific schema, and so on. </p>
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<img loading="lazy" decoding="async" width="1120" height="514" src="https://www.datagaps.com/wp-content/uploads/image-18.png" class="attachment-full size-full wp-image-63644" alt="Properties of test data" srcset="https://www.datagaps.com/wp-content/uploads/image-18.png 1120w, https://www.datagaps.com/wp-content/uploads/image-18-300x138.png 300w, https://www.datagaps.com/wp-content/uploads/image-18-1024x470.png 1024w, https://www.datagaps.com/wp-content/uploads/image-18-768x352.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<span >
Select tables and run generation </span>
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<p class="elementor-icon-box-description">
Next, you select the tables for which you want to generate data, with the option to do this for all tables or only selected ones. </p>
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<img loading="lazy" decoding="async" width="1120" height="320" src="https://www.datagaps.com/wp-content/uploads/image-19.png" class="attachment-full size-full wp-image-63646" alt="Select tables to generate data" srcset="https://www.datagaps.com/wp-content/uploads/image-19.png 1120w, https://www.datagaps.com/wp-content/uploads/image-19-300x86.png 300w, https://www.datagaps.com/wp-content/uploads/image-19-1024x293.png 1024w, https://www.datagaps.com/wp-content/uploads/image-19-768x219.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<p><span class="NormalTextRun SCXW29224212 BCX8">Data generation typically takes a few seconds to a few minutes, depending on the volume configured. For 100 or 1,000 rows, generation completes in approximately 5 seconds. When generating across multiple tables, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW29224212 BCX8">Datagaps</span><span class="NormalTextRun SCXW29224212 BCX8"> processes them in parallel within the same </span><span class="NormalTextRun SCXW29224212 BCX8">time frame</span><span class="NormalTextRun SCXW29224212 BCX8">.</span></p> </div>
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<div class="elementor-image-carousel swiper-wrapper" aria-live="off">
<div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="1 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/image-20.png" alt="Generation result showing Order, Transaction, and Customer tables being processed simultaneously" /></figure></div><div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="2 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/image-21.png" alt="Generation result showing Order, Transaction, and Customer tables being processed simultaneously" /></figure></div> </div>
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<span >
Load data into the target table or file </span>
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<p class="elementor-icon-box-description">
Once data is generated, it can be loaded into the target table or file. If a table is selected as the target, the schemas must be clearly defined to avoid conflicts during insertion. </p>
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<img loading="lazy" decoding="async" width="1120" height="269" src="https://www.datagaps.com/wp-content/uploads/image-22.png" class="attachment-full size-full wp-image-63652" alt="Load data into the target table" srcset="https://www.datagaps.com/wp-content/uploads/image-22.png 1120w, https://www.datagaps.com/wp-content/uploads/image-22-300x72.png 300w, https://www.datagaps.com/wp-content/uploads/image-22-1024x246.png 1024w, https://www.datagaps.com/wp-content/uploads/image-22-768x184.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<span >
What happens if sample data is missing? </span>
</h3>
<p class="elementor-icon-box-description">
If sample data was not loaded for some tables during configuration, the tool will fail during generation and prompt you to go back and add sample data. This is by design. Sample data is what enables the system to identify patterns within your data so the output remains relevant to your use case.<br><br>When all items are configured correctly, data generation will succeed consistently. </p>
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Choose save behavior and confirm the load </span>
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When loading into the target table, you can choose the save behavior: append, drop, or truncate. The appropriate choice depends on how you intend to proceed with testing once the data is available. </p>
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<img loading="lazy" decoding="async" width="445" height="304" src="https://www.datagaps.com/wp-content/uploads/Append-the-result.png" class="attachment-large size-large wp-image-63662" alt="Append the result of generated data" srcset="https://www.datagaps.com/wp-content/uploads/Append-the-result.png 445w, https://www.datagaps.com/wp-content/uploads/Append-the-result-300x205.png 300w" sizes="(max-width: 445px) 100vw, 445px" /> </div>
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<ul><li><span class="TextRun SCXW170806998 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW170806998 BCX8">If the load is successful, a confirmation is shown. If not, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW170806998 BCX8">Datagaps</span><span class="NormalTextRun SCXW170806998 BCX8"> provides logs so you can trace and resolve the exact issue. Loading of final synthetic data is straightforward whether you are saving to a file such as CSV or directly to a table. For table targets, </span><span class="NormalTextRun SCXW170806998 BCX8">validate</span><span class="NormalTextRun SCXW170806998 BCX8"> the schema and the process is complete.</span></span><span class="EOP Selected SCXW170806998 BCX8" data-ccp-props="{}"> </span></li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Conclusion </h2> </div>
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<p><span data-contrast="auto">That is the complete process for generating synthetic data at scale for enterprise environments, regardless of the data source, whether Microsoft Fabric or others. Datagaps supports each step of the testing process and provides all the tools required for testing any part of an analytical workflow.</span><span data-ccp-props="{}"> </span></p><p><span data-contrast="auto">The key differentiator is AI integration at every step. The same work done manually could take weeks to replicate data correctly, and even then, it would be difficult to introduce the same level of diversity, especially for edge cases and unexpected patterns.</span><span data-ccp-props="{}"> </span></p><p><span data-contrast="auto">With AI-powered generation, teams can produce large volumes of synthetic data, introduce varied patterns and data rules, and run comprehensive testing of pipelines before anything reaches production.</span></p> </div>
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<p><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/test-data-manager-trial-request/" target="_blank" rel="noopener"><span class="EOP Selected SCXW265958618 BCX8" data-ccp-props="{}"><span class="TextRun SCXW10738096 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW10738096 BCX8">See how TDM generates masked, relationally consistent test data for Fabric →</span></span> </span></a></span></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Frequently Asked Questions </h2> </div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="1" aria-controls="elementor-tab-content-1531"><h3 class="eael-accordion-tab-title">How does Datagaps TDM generate synthetic data for Microsoft Fabric? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1531" class="eael-accordion-content clearfix" data-tab="1" aria-labelledby="faq-1"><p><span class="TextRun SCXW3880589 BCX8"><span class="NormalTextRun SCXW3880589 BCX8">TDM uses two components. In the Data Model you connect a Fabric </span><span class="NormalTextRun SpellingErrorV2Themed SCXW3880589 BCX8">lakehouse</span><span class="NormalTextRun SCXW3880589 BCX8"> or warehouse, classify entities, load sample </span><span class="NormalTextRun SCXW3880589 BCX8">data</span><span class="NormalTextRun SCXW3880589 BCX8"> and set target data types. In the Dataset you choose the volume, select </span><span class="NormalTextRun SCXW3880589 BCX8">tables</span><span class="NormalTextRun SCXW3880589 BCX8"> and save the output to files such as CSV or load it directly into tables. AI/ML capabilities generate the data from patterns in the sample.</span></span><span class="EOP Selected SCXW3880589 BCX8"> </span></p></div>
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<div id="faq-2" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-1532"><h3 class="eael-accordion-tab-title">Does Datagaps TDM mask PII in synthetic test data?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1532" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-2"><p><span class="NormalTextRun SCXW208588374 BCX8">Yes. When a column is classified as one of TDM’s defined entities, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW208588374 BCX8">Datagaps</span><span class="NormalTextRun SCXW208588374 BCX8"> treats it as PII or confidential and masks it during generation. Entities can be set manually or auto-detected with AI, which classifies names, payment details, addresses and date-time values. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW208588374 BCX8">Datagaps</span><span class="NormalTextRun SCXW208588374 BCX8"> supports GDPR, CCPA and HIPAA-aligned workflows.</span></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-1533"><h3 class="eael-accordion-tab-title">Why does TDM need sample data before generating synthetic data?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1533" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-3"><p><span class="NormalTextRun SCXW221225326 BCX8">Sample data lets the AI </span><span class="NormalTextRun SCXW221225326 BCX8">identify</span><span class="NormalTextRun SCXW221225326 BCX8"> patterns in existing values so generated output stays relevant to your use case. By </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW221225326 BCX8">default</span><span class="NormalTextRun SCXW221225326 BCX8"> TDM takes 5,000 rows from the connected source, which you can increase, </span><span class="NormalTextRun SCXW221225326 BCX8">decrease</span><span class="NormalTextRun SCXW221225326 BCX8"> or filter with a WHERE clause. If sample data is missing for a table, generation fails by design and prompts you to add it.</span></p></div>
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<div id="faq-4" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-1534"><h3 class="eael-accordion-tab-title">How does TDM keep referential integrity across related Fabric tables? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1534" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-4"><p><span class="TextRun SCXW18591511 BCX8"><span class="NormalTextRun SCXW18591511 BCX8">TDM lets you define relationships between tables and map columns to their parent column in another table. A foreign key, for example, must be mapped correctly and must be available in its source table; </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW18591511 BCX8">otherwise</span><span class="NormalTextRun SCXW18591511 BCX8"> the generated data will lack referential integrity. Defining these mappings during data model configuration keeps related tables consistent.</span></span><span class="EOP Selected SCXW18591511 BCX8"> </span></p></div>
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<div id="faq-5" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="5" aria-controls="elementor-tab-content-1535"><h3 class="eael-accordion-tab-title">How fast is synthetic data generation with Datagaps TDM? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1535" class="eael-accordion-content clearfix" data-tab="5" aria-labelledby="faq-5"><p><span class="TextRun SCXW45868271 BCX8"><span class="NormalTextRun SCXW45868271 BCX8">Generation typically takes a few seconds to a few minutes, depending on the configured volume. For 100 or 1,000 rows it completes in approximately five seconds, and multiple tables are processed in parallel within the same </span><span class="NormalTextRun SCXW45868271 BCX8">time frame</span><span class="NormalTextRun SCXW45868271 BCX8">. The full workflow reduces what could take weeks of manual effort to under an hour.</span></span><span class="EOP Selected SCXW45868271 BCX8"> </span></p></div>
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Avinash Keshri </a>
</h5>
<p class="elementor-icon-box-description">
Head, Product Marketing </p>
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<p>Head of Product Marketing at Datagaps and IIM Bangalore alumnus. 13+ years of experience in commercializing AI and data platforms across global markets.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/how-to-generate-synthetic-test-data-for-microsoft-fabric-with-tdm/">How to Generate Synthetic Test Data for Microsoft Fabric with TDM</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>How to Generate Data Quality Rules for Microsoft Fabric with AI</title>
<link>https://www.datagaps.com/blog/how-to-generate-data-quality-rules-for-microsoft-fabric-with-ai/</link>
<dc:creator><![CDATA[Raj Mohan Achanta]]></dc:creator>
<pubDate>Wed, 21 Oct 2026 09:09:26 +0000</pubDate>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[AI Rule Generation]]></category>
<category><![CDATA[dataops]]></category>
<category><![CDATA[Microsoft Fabric]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=63199</guid>
<description><![CDATA[<p>Generate data quality rules for Microsoft Fabric using AI. Learn how AI simplifies rule creation, improves data accuracy, and strengthens data governance.</p>
<p>The post <a href="https://www.datagaps.com/blog/how-to-generate-data-quality-rules-for-microsoft-fabric-with-ai/">How to Generate Data Quality Rules for Microsoft Fabric with AI</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><span class="TextRun SCXW26841463 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW26841463 BCX8">AI-assisted data quality rule generation in </span><span class="NormalTextRun SpellingErrorV2Themed SCXW26841463 BCX8">Datagaps</span><span class="NormalTextRun SCXW26841463 BCX8"> is a prompt-driven workflow that creates validation rules, including rule names, descriptions, quality dimensions and SQL logic, for selected Microsoft Fabric tables and columns, then executes them to produce column-level DQ scores, pass/fail results and sample bad records.</span></span><span class="EOP Selected SCXW26841463 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><span data-contrast="auto">Successful Fabric pipelines do not guarantee output trustworthy enough for reporting, operations and AI use cases.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Defining checks manually across hundreds of tables and thousands of columns is hard to standardize and sustain.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Datagaps generates rules from selected columns plus a generic prompt and a business-specific domain prompt.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Each AI-generated rule includes SQL logic and dimension mapping and should be reviewed before execution.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Results show column-wise DQ scores, rule pass/fail outcomes, sample bad records and an overall run summary.</span><span data-ccp-props="{}"> </span></li></ul> </div>
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/data-ops-suite-trial-request/" target="_blank" rel="noopener"><span class="TextRun SCXW176770195 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW176770195 BCX8">See how </span><span class="NormalTextRun SpellingErrorV2Themed SCXW176770195 BCX8">Datagaps</span><span class="NormalTextRun SCXW176770195 BCX8"> generates data quality rules for your Fabric tables →</span></span></a></span></p> </div>
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<p><span class="TextRun SCXW195147135 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW195147135 BCX8">To generate <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-quality-monitoring-tools/" target="_blank" rel="noopener">data quality rules</a></span> for Microsoft Fabric at scale, connect Fabric as a data source in </span><span class="NormalTextRun SpellingErrorV2Themed SCXW195147135 BCX8">Datagaps</span><span class="NormalTextRun SCXW195147135 BCX8">, create a data model over the tables you want to validate, and use the Data Rules Wizard’s AI-assisted mode to generate rules from your selected columns and prompts. You then review the rules, run </span><span class="NormalTextRun SCXW195147135 BCX8">validations</span><span class="NormalTextRun SCXW195147135 BCX8"> and use the DQ score and bad-record samples to prioritize remediation.</span></span><span class="EOP Selected SCXW195147135 BCX8" data-ccp-props="{}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why does data quality matter once Microsoft Fabric pipelines are running? </h2> </div>
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<p><span class="TextRun SCXW184782545 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW184782545 BCX8">As organizations adopt <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/microsoft-fabric-testing-automation/" target="_blank" rel="noopener">Microsoft Fabric</a></span> to modernize analytics and accelerate AI-driven use cases, the focus quickly shifts from pipeline execution to outcome reliability. Pipelines may run successfully, but the real question is whether the output is trustworthy enough for reporting, operations, and AI experiences.</span></span><span class="EOP Selected SCXW184782545 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><span class="TextRun SCXW102124142 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW102124142 BCX8">This becomes even more important when Fabric data powers automated dashboards and natural language use cases such as text to SQL. In these scenarios, quality issues can silently propagate and impact decisions at scale.</span></span><span class="EOP Selected SCXW102124142 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><span class="NormalTextRun SpellingErrorV2Themed SCXW249505822 BCX8">Datagaps</span><span class="NormalTextRun SCXW249505822 BCX8"> helps address this by providing a structured workflow for data quality and validation, including AI-assisted rule generation to accelerate coverage and improve consistency.</span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why is data quality rule generation difficult in Fabric environments? </h2> </div>
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<p><span class="TextRun SCXW35418886 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW35418886 BCX8">Rule generation is difficult because enterprise data estates often include hundreds of tables and thousands of columns, each with business meaning and validation expectations. Defining and </span><span class="NormalTextRun SCXW35418886 BCX8">maintaining</span><span class="NormalTextRun SCXW35418886 BCX8"> tests manually across that footprint is time-consuming, difficult to standardize, and hard to sustain as schemas evolve.</span></span><span class="EOP Selected SCXW35418886 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><span class="TextRun SCXW225488128 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW225488128 BCX8">Many projects follow layered architectures across environments like DEV, STG, and PROD. Data changes at each layer due to ingestion and transformations. To </span><span class="NormalTextRun SCXW225488128 BCX8">maintain</span><span class="NormalTextRun SCXW225488128 BCX8"> trust, teams typically need validation at table level and column level, and sometimes across layers.</span></span><span class="EOP Selected SCXW225488128 BCX8" data-ccp-props="{}"> </span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">A typical gold layer might include dozens of tables with dozens of columns per table. Common checks include:</h3> </div>
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<ul><li><span data-contrast="auto">Numeric constraints such as revenue greater than zero</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Format checks such as valid email patterns</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Null checks for required attributes</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Uniqueness checks to prevent duplicate keys</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Cross-table consistency checks to ensure reconciliation across datasets</span><span data-ccp-props="{}"> </span></li></ul><p><span data-contrast="auto">Manually implementing these checks at scale can become difficult to manage, especially when new columns appear and business logic changes.</span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">How do you generate and validate data quality rules for Microsoft Fabric in Datagaps? </h2> </div>
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<p><span class="TextRun SCXW262476204 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW262476204 BCX8">The end-to-end workflow has ten steps: connect Fabric, open the <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-quality-monitoring-tools/" target="_blank" rel="noopener">Data Quality section</a></span>, build a data model, baseline the DQ score, create rules with the Data Rules Wizard, generate them with AI, scope them with prompts, review them, run validations, and interpret the results.</span></span><span class="EOP Selected SCXW262476204 BCX8" data-ccp-props="{}"> </span></p> </div>
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<span >
Step 1: Connect Microsoft Fabric as a data source </span>
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Start by adding Microsoft Fabric as a data source in Datagaps using the available connectors and the integration flow. </p>
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<img loading="lazy" decoding="async" width="1790" height="800" src="https://www.datagaps.com/wp-content/uploads/Modern-Data-Sources-Connector-Dashboard-1.png" class="attachment-full size-full wp-image-63428" alt="Modern Data Sources Connector Dashboard" srcset="https://www.datagaps.com/wp-content/uploads/Modern-Data-Sources-Connector-Dashboard-1.png 1790w, https://www.datagaps.com/wp-content/uploads/Modern-Data-Sources-Connector-Dashboard-1-300x134.png 300w, https://www.datagaps.com/wp-content/uploads/Modern-Data-Sources-Connector-Dashboard-1-1024x458.png 1024w, https://www.datagaps.com/wp-content/uploads/Modern-Data-Sources-Connector-Dashboard-1-768x343.png 768w, https://www.datagaps.com/wp-content/uploads/Modern-Data-Sources-Connector-Dashboard-1-1536x686.png 1536w" sizes="(max-width: 1790px) 100vw, 1790px" /> <figcaption class="widget-image-caption wp-caption-text">Connect Microsoft Fabric as a data source using available connectors</figcaption>
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<span >
Step 2: Move to the Data Quality section to access DQ capabilities </span>
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After the Fabric integration is set up, move to the Data Quality section in Datagaps. This area provides multiple data quality related capabilities, such as: </p>
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<ul><li><span data-contrast="auto">Viewing and generating a </span><b><span data-contrast="auto">Data Quality Score</span></b><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Exploring </span><b><span data-contrast="auto">Data Lineage</span></b><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Reviewing data quality issues and related artifacts</span><span data-ccp-props="{}"> </span></li></ul> </div>
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<p><span class="TextRun SCXW57799901 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW57799901 BCX8">This step is important because it is where you begin the quality workflow before building rules.</span></span><span class="EOP Selected SCXW57799901 BCX8" data-ccp-props="{}"> </span></p> </div>
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<img decoding="async" src="https://www.datagaps.com/wp-content/uploads/elementor/thumbs/Data-quality-sections-for-DQ-Score-rv7seeo4ugzmvk7w51gibr8mz21lhwwv158py0myb4.png" title="Data quality sections for DQ Score" alt="Data quality sections for DQ Score" loading="lazy" /> <figcaption class="widget-image-caption wp-caption-text">The Data Quality section provides access to DQ Score, Data Lineage, and other quality workflows.</figcaption>
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<span >
Step 3: Create a data model and select tables </span>
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Create a data model as the base layer on which rules are generated and executed. Select the Fabric tables you want to validate. This can scale from a single table to hundreds depending on your scope. </p>
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<img decoding="async" src="https://www.datagaps.com/wp-content/uploads/elementor/thumbs/Data-Model-to-organise-tables-for-rules-s3-rv7sherkoezci9sq3a35t77keivaiy04av1sxn8dlw.png" title="Data Model to organise tables for rules" alt="Data Model to organise tables for rules" loading="lazy" /> <figcaption class="widget-image-caption wp-caption-text">Create a data model to organize tables for rule creation and scoring.</figcaption>
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Step 4: Generate and review baseline DQ score and trends </span>
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Once the data model is set up, generate the DQ score to establish a baseline. Review trends and drilldowns to understand quality distribution across tables and dimensions. </p>
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<img decoding="async" src="https://www.datagaps.com/wp-content/uploads/elementor/thumbs/Generate-and-review-DQ-Score-rv7sohhu4ipck1239adv4ptwnwuxz579xfknes0tsc.png" title="Generate and review DQ Score" alt="Generate and review DQ Score" loading="lazy" /> <figcaption class="widget-image-caption wp-caption-text"></figcaption>
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<span >
Step 5: Create rules using the Data Rules Wizard </span>
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Datagaps provides a Data Rules Wizard that supports both manual rule definition and AI-assisted rule generation. </p>
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<span >
Step 6: Use AI-assisted rule generation to accelerate rule creation </span>
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<p class="elementor-icon-box-description">
Switch the rule type to the AI-assisted mode and define execution criteria like success conditions and severity. This enables Datagaps to generate rule suggestions based on your selected columns and the prompt you provide. </p>
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Step 7: Select tables and columns, then provide prompts </span>
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Select the table and columns to include in rule generation. Use two prompts to improve output quality: <br><br>
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Step 8: Review AI-generated rules before execution </span>
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AI-generated rules typically include rule names, descriptions, mapped quality dimensions, and SQL logic that identifies bad records according to the chosen success criteria. Review these rules before running them to ensure they align with internal standards and domain expectations. </p>
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Step 9: Configure and run validations </span>
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Before execution, configure how the run should behave, including where to store bad records, parallel execution settings, and sampling limits for captured bad data. </p>
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Step 10: Interpret results and prioritize remediation </span>
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After execution, results can be reviewed across multiple levels, including: </p>
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<ul><li><span data-contrast="auto">Column-wise DQ score based on executed rules</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Rule-level pass or fail outcomes</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Sample bad records for investigation</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Summary view across rules and columns</span><span data-ccp-props="{}"> </span></li></ul> </div>
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<p><span class="TextRun SCXW185869050 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW185869050 BCX8"><strong>Work through the results top-down:</strong> start with the high-level scoring view, then review one passing rule and one failing rule, and close with the overall summary.</span></span><span class="EOP Selected SCXW185869050 BCX8" data-ccp-props="{}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">How can teams operationalize AI-generated data quality rules in production? </h2> </div>
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<p><span data-contrast="auto">To operationalize this at enterprise scale, standardize how rules are generated, reviewed and gated:</span><span data-ccp-props="{}"> </span></p><ul><li><span data-contrast="auto">Standardize prompts for repeatable rule generation across datasets</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Maintain domain prompts in a shared repository per business domain</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Add a review workflow for AI-generated rules, especially for cross-table logic</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Track DQ score trends and define thresholds for release gates</span><span data-ccp-props="{}"> </span><span data-contrast="auto">Integrate validation runs into pipeline schedules for continuous monitoring</span><span data-ccp-props="{}"> </span></li><li><span class="TextRun SCXW108358148 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW108358148 BCX8">Integrate validation runs into pipeline schedules for continuous monitoring</span></span><span class="EOP Selected SCXW108358148 BCX8" data-ccp-props="{}"> </span></li><li style="list-style-type: none;"> </li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Conclusion </h2> </div>
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<p><span class="TextRun SCXW181035878 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW181035878 BCX8">Microsoft Fabric enables fast analytics and AI experiences, but reliable outcomes depend on trustworthy data. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW181035878 BCX8">Datagaps</span><span class="NormalTextRun SCXW181035878 BCX8"> supports Fabric data quality workflows by helping teams generate rules faster, execute validations consistently, and interpret results with actionable detail. This reduces risk, improves reliability, and helps scale data quality practices across teams and environments.</span></span><span class="EOP Selected SCXW181035878 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/request-a-demo/" target="_blank" rel="noopener"><span class="TextRun SCXW265958618 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW265958618 BCX8">See how </span><span class="NormalTextRun SpellingErrorV2Themed SCXW265958618 BCX8">Datagaps</span><span class="NormalTextRun SCXW265958618 BCX8"> AI rule generation scores your Fabric tables → </span></span><span class="EOP Selected SCXW265958618 BCX8" data-ccp-props="{}"> </span></a></span></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Frequently Asked Questions </h2> </div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="1" aria-controls="elementor-tab-content-1531"><h3 class="eael-accordion-tab-title">How does Datagaps generate data quality rules for Microsoft Fabric tables? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1531" class="eael-accordion-content clearfix" data-tab="1" aria-labelledby="faq-1"><p><span class="TextRun SCXW197349388 BCX8"><span class="NormalTextRun SpellingErrorV2Themed SCXW197349388 BCX8">Datagaps</span><span class="NormalTextRun SCXW197349388 BCX8"> generates rules through the AI-assisted mode of its Data Rules Wizard. You select the Fabric table and columns, define success criteria and severity, and provide a generic prompt plus a domain prompt. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW197349388 BCX8">Datagaps</span><span class="NormalTextRun SCXW197349388 BCX8"> then returns rule suggestions with rule names, descriptions, mapped quality </span><span class="NormalTextRun SCXW197349388 BCX8">dimensions</span><span class="NormalTextRun SCXW197349388 BCX8"> and SQL logic that </span><span class="NormalTextRun SCXW197349388 BCX8">identifies</span><span class="NormalTextRun SCXW197349388 BCX8"> bad records.</span></span><span class="EOP Selected SCXW197349388 BCX8"> </span></p></div>
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<div id="faq-2" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-1532"><h3 class="eael-accordion-tab-title">What data quality checks are typical for a Microsoft Fabric gold layer?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1532" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-2"><p><span class="TextRun SCXW99668558 BCX8"><span class="NormalTextRun SCXW99668558 BCX8">Common checks include numeric constraints such as revenue greater than zero, format checks such as valid email patterns, null checks for required attributes, uniqueness checks to prevent duplicate keys, and cross-table consistency checks that reconcile datasets. A gold layer often holds dozens of tables with dozens of columns each, which is why manual implementation becomes hard to manage.</span></span></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-1533"><h3 class="eael-accordion-tab-title">Should AI-generated data quality rules be reviewed before they run?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1533" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-3"><p><span class="TextRun SCXW47403614 BCX8"><span class="NormalTextRun SCXW47403614 BCX8">Yes. AI-generated rules should be reviewed before execution to confirm they align with internal standards and domain expectations. Review covers the rule name, description, mapped </span><span class="NormalTextRun SCXW47403614 BCX8">dimension</span><span class="NormalTextRun SCXW47403614 BCX8"> and SQL logic. For production adoption, add a formal review workflow, especially for cross-table logic, and keep domain prompts in a shared repository per business domain.</span></span><span class="EOP Selected SCXW47403614 BCX8"> </span></p></div>
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<div id="faq-4" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-1534"><h3 class="eael-accordion-tab-title">What can you configure before running data quality validations on Fabric data? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1534" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-4"><p><span class="TextRun SCXW15312659 BCX8"><span class="NormalTextRun SCXW15312659 BCX8">Before execution, you configure how the run behaves: where bad records should be stored, parallel execution settings, and sampling limits for the bad data that is captured. These settings are applied to the rules managed centrally within the data model, so validations run consistently each time they are triggered.</span></span></p></div>
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<div id="faq-5" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="5" aria-controls="elementor-tab-content-1535"><h3 class="eael-accordion-tab-title">How do you interpret Datagaps data quality results for Microsoft Fabric? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1535" class="eael-accordion-content clearfix" data-tab="5" aria-labelledby="faq-5"><p><span class="NormalTextRun SCXW220250160 BCX8">Results are reviewed at several levels: a column-wise DQ score based on executed rules, rule-level pass or fail outcomes, sample bad records for investigation, and a summary across rules, </span><span class="NormalTextRun SCXW220250160 BCX8">columns</span><span class="NormalTextRun SCXW220250160 BCX8"> and dimensions. Start with the high-level scoring view, then examine a passing and a failing rule, and finish with the overall run summary.</span></p></div>
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<a href="https://www.linkedin.com/in/raj-mohan-achanta-41454b1a2/" >
RajMohan Achanta </a>
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<p class="elementor-icon-box-description">
Associate Product Manager, Datagaps </p>
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<p>Associate Product Manager at Datagaps. Shapes the product experience across ETL Validator, BI Validator, and Data Quality Monitor.</p> </div>
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<a href="https://www.linkedin.com/in/avinashkeshri-iimb/" >
Avinash Keshri </a>
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<p class="elementor-icon-box-description">
Head, Product Marketing </p>
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</div>
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<div class="elementor-element elementor-element-1e2303c elementor-widget elementor-widget-text-editor" data-id="1e2303c" data-element_type="widget" data-e-type="widget" data-widget_type="text-editor.default">
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<p>Head of Product Marketing at Datagaps and IIM Bangalore alumnus. 13+ years of experience in commercializing AI and data platforms across global markets.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/how-to-generate-data-quality-rules-for-microsoft-fabric-with-ai/">How to Generate Data Quality Rules for Microsoft Fabric with AI</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>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>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=7381</guid>
<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>
]]></description>
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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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<table style="width: 100%; border-collapse: collapse; margin: 20px 0; font-size: 16px;">
<thead>
<tr style="background: #d6e3f5;">
<th style="padding: 12px; border: 1px solid #ccc;">Component</th>
<th style="padding: 12px; border: 1px solid #ccc;">Why It Matters</th>
</tr>
</thead>
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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>
</tr>
<tr style="background: #f8f9fa;">
<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>
</tr>
<tr>
<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>
</tr>
<tr style="background: #f8f9fa;">
<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>
</tr>
<tr>
<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>
</tr>
<tr style="background: #f8f9fa;">
<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>
</tr>
</tbody>
</table> </div>
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<h2 class="elementor-heading-title elementor-size-default">Extensibility</h2> </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;"><span style="color: #1967d2; text-decoration: underline;"><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></span>This <span style="color: #000000;">eliminates</span> the need for complex workarounds that you have to shoehorn into other solutions.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Advanced API Components</h2> </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 APIs that we needed to invoke as part of ETL testing 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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<h2 class="elementor-heading-title elementor-size-default">AI Based Observability</h2> </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 Data Observability 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 Data Quality Monitor for this.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Ability To Handle Large Volumes in the Billions</h2> </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. Learn more about automating your Big Data.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Integration with your DevOps Platform</h2> </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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<h2 class="elementor-heading-title elementor-size-default">Integration with an RPA Platform </h2> </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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<h2 class="elementor-heading-title elementor-size-default">Conclusion</h2> </div>
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<p>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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<!-- FAQs: Data Testing Limits, AI Observability, and Extensibility -->
<section class="faq-section" aria-labelledby="faq-heading">
<h2 id="faq-heading">Frequently Asked Questions: Data Testing Limits, AI Observability, and Extensibility</h2>
<div class="faq-list">
<details>
<summary>1) Why isn’t standard data testing enough for most organizations?</summary>
<p>
Standard testing (data access, quality rules, comparisons) typically only covers about 75% of real-world
scenarios — edge cases like complex APIs, massive data volumes, and unknown anomalies require additional
capabilities.
</p>
</details>
<details>
<summary>2) What’s the difference between Data Quality rules and AI-based Observability?</summary>
<p>
Data Quality rules catch known, predefined issues you can anticipate and codify. AI-based Observability
uses machine learning to detect unexpected anomalies and patterns you wouldn’t think to write a rule for —
the two work best together.
</p>
</details>
<details>
<summary>3) How does extensibility help with data testing?</summary>
<p>
Extensibility (like Python-based plugins) lets teams build custom logic for unique data issues on the fly,
instead of being limited to a tool’s out-of-the-box features or waiting on vendor updates.
</p>
</details>
<details>
<summary>4) Why is DevOps and RPA integration important for data testing?</summary>
<p>
Integrating testing into DevOps pipelines and RPA workflows allows validation to run automatically as part
of continuous delivery, rather than as a separate manual step — critical for enterprise-scale,
high-velocity environments.
</p>
</details>
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<p>The post <a href="https://www.datagaps.com/blog/6-critical-components-of-data-testing/">6 Critical Components of Data Testing</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<title>Data Drift Using DataOps Data Profiling</title>
<link>https://www.datagaps.com/blog/data-drift-using-dataops-data-profiling/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Sat, 06 Jun 2026 16:34:00 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Dataflow]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[ETL Testing]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=7349</guid>
<description><![CDATA[<p>What is Data Drift? Within the data space, the only constant thing is “change”. The drift in data here refers to a multitude of changes in the input data primarily in terms of frequency, aggregates, and heterogeneity. These are not regarded as errors as these types of shifts and changes</p>
<p>The post <a href="https://www.datagaps.com/blog/data-drift-using-dataops-data-profiling/">Data Drift Using DataOps Data Profiling</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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<p>Data drift refers to changes in input data over time — in frequency, aggregates, or heterogeneity — that reflect real-world shifts rather than errors. This post explains drift types (Sudden, Gradual, Incremental, Reoccurring) and detection methods using DataOps Suite’s Profiling Nodes, covering statistical shifts (mean, min-max, deviation, skewness, kurtosis), key/GUID pattern changes, and domain shifts from new values. It also distinguishes data drift from model drift, showing how early detection prevents downstream quality and model performance issues.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li>Data drift has four cadence types — Sudden, Gradual, Incremental, and Reoccurring Drift, each describing how data distribution changes relate to time and metric aggregates.</li><li>Profiling Nodes detect drift through statistical baselines — tracking mean, min-max values, standard deviation, skewness, and kurtosis helps identify when a dataset’s distribution has shifted from expected norms.</li><li>Key/GUID pattern changes signal structural drift — unexpected changes in primary key formats (e.g., a 5-digit number becoming alphanumeric) can cause duplication and incorrect aggregations if undetected.</li><li>Data drift and model drift are distinct but related — data drift reflects changes in input data itself, while model drift refers to degradation in model performance; fixing data drift alone doesn’t resolve model drift, which needs separate detection techniques.</li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">What Is Data Drift?</h2> </div>
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<p>Data drift is a change in the statistical properties of input data over time — in frequency, aggregates, or heterogeneity — that causes a dataset to diverge from the benchmark a pipeline, analysis, or ML model was originally built on. Within the data space, the only constant is change, and data drift isn’t inherently an error: these shifts are factual and representative of how real-world data evolves.</p><p>Model Drift comes as the other side of the coin to <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-monitor-datasets?tabs=python" target="_blank" rel="noopener">data drift</a></span> that is closely related to how the statistical nature and the probabilities as well as the intended logic translation have been altered. While model drift is closely associated with AI-ML models, data drift affects every pipeline that has been made using past production data.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">A quick way to comprehend data drift is via a couple of real-world examples
</h3> </div>
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<p>An ML model that predicts house prices based on a myriad of property aspects such as the number of rooms, area, location, floor, and such which was originally trained in 2019 will not work correctly in 2020 due to the variety of changes in the aforementioned aspects. Certain areas went up in demand as did a certain number of bedrooms and such. If the model is not re-trained or corrected with updated bias, the predicted prices cannot be used.<br />Assume a statistical regression-based model predicts if a customer might default on a loan. The bank’s majority of clients at this point were new families. A few months after the model has been running, the marketing department unveils a new type of campaign targeted toward young students. While the campaign is successful the model is no longer accurate as there are new types of distributions among the various inputs the model is fed. Therefore, the prediction of defaulters itself is incorrect.<br />A reporting system that showcases the mean forecasts across multiple regions suddenly has a higher mean temperature than expected. Under the hood, a few areas had updated their sensors to one of a different brand that resulted in the dimensions being recorded in Fahrenheit as opposed to Celsius on which the system was based.</p><p>A couple of distinctions in the various types of data drifts are the cadence of the drift and the type of the drift. The use cases showed a focus on the type of drift. The cadence of drift segregates drifts into 4 types. These are Sudden Drift, Gradual Drift, Incremental Drift, and Reoccurring Drift. These are usually defined against data distribution and time, but the concept translates with specific aggregates of the metrics themselves.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="435" src="https://www.datagaps.com/wp-content/uploads/Different-Classifications-of-Drift-1024x696.webp" class="attachment-large size-large wp-image-5586" alt="Different-Classifications-of-Drift" srcset="https://www.datagaps.com/wp-content/uploads/Different-Classifications-of-Drift-1024x696.webp 1024w, https://www.datagaps.com/wp-content/uploads/Different-Classifications-of-Drift-300x204.webp 300w, https://www.datagaps.com/wp-content/uploads/Different-Classifications-of-Drift-768x522.webp 768w, https://www.datagaps.com/wp-content/uploads/Different-Classifications-of-Drift-1536x1044.webp 1536w, https://www.datagaps.com/wp-content/uploads/Different-Classifications-of-Drift.webp 1600w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 1. Different Classifications of Drift</p> </div>
</div>
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<img loading="lazy" decoding="async" width="640" height="324" src="https://www.datagaps.com/wp-content/uploads/Sudden-Drift-1024x518.webp" class="attachment-large size-large wp-image-5590" alt="Sudden-Drift" srcset="https://www.datagaps.com/wp-content/uploads/Sudden-Drift-1024x518.webp 1024w, https://www.datagaps.com/wp-content/uploads/Sudden-Drift-300x152.webp 300w, https://www.datagaps.com/wp-content/uploads/Sudden-Drift-768x388.webp 768w, https://www.datagaps.com/wp-content/uploads/Sudden-Drift.webp 1300w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>Figure 2. The above graph showcases a “Sudden” Drift in Yearly Income where the overall values of the metric have increased sharply</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Profiling as Drift Detection in Data Drift</h2> </div>
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<p>Data Profiling is an integral part of <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-observability-tool/" target="_blank" rel="noopener">data observability</a></span> within the DataOps Suite, helping users create profiles that hold every aspect of information that can be derived from a dataset — aggregates such as mean, deviations, min-max, nulls, and more, along with frequency and pattern analysis.</p><p>A dataset can be directly pulled into a profiling node. The <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/dataflow/" target="_blank" rel="noopener">DataOps Suite</a></span> Profile node provides a variety of aggregation and pattern analysis options.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="522" src="https://www.datagaps.com/wp-content/uploads/DataOps-Profile-Node.webp" class="attachment-large size-large wp-image-5596" alt="DataOps-Profile-Node" srcset="https://www.datagaps.com/wp-content/uploads/DataOps-Profile-Node.webp 956w, https://www.datagaps.com/wp-content/uploads/DataOps-Profile-Node-300x244.webp 300w, https://www.datagaps.com/wp-content/uploads/DataOps-Profile-Node-768x626.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 3. DataOps Profile Node</p><p>Each of the aggregations works to create a profile of the dataset, maintaining an average value, upper and lower bounds, deviations, patterns, null counts, and such. This help creates a baseline of the expectations in the datasets and something for the users to use for comparisons. Let’s have a closer look at a few real-life examples.</p> </div>
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<th style="padding: 12px; border: 1px solid #ccc;">Type of Drift</th>
<th style="padding: 12px; border: 1px solid #ccc;">What Changes</th>
<th style="padding: 12px; border: 1px solid #ccc;">Example</th>
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<td style="padding: 12px; border: 1px solid #ccc;">Covariate Drift / Metric Stats</td>
<td style="padding: 12px; border: 1px solid #ccc;">Statistical aggregates like mean, min-max, standard deviation, skewness, and kurtosis</td>
<td style="padding: 12px; border: 1px solid #ccc;">Yearly income values shift upward with less variance across customers</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Change in Keys / GUID</td>
<td style="padding: 12px; border: 1px solid #ccc;">The pattern or format of primary keys used in relational datasets</td>
<td style="padding: 12px; border: 1px solid #ccc;">A 5-digit numeric Customer Key suddenly becomes alphanumeric</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Domain Shift / New Values</td>
<td style="padding: 12px; border: 1px solid #ccc;">The set of valid values (domain) for a column, such as new categories being added</td>
<td style="padding: 12px; border: 1px solid #ccc;">New geography IDs appear, changing distinct counts and distributions</td>
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<h3 class="elementor-heading-title elementor-size-default">Data Drift & Variety of Drift Detection – Covariate Drift or Drift in Metrics Stats</h3> </div>
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<p>Every numerical metric holds certain statistical aggregates that can help keep the baseline of the dataset. The most basic ones of these are average, min-max values, and standard deviation. Skewness and Kurtosis also help keep the distribution in check.</p><p>A change in mean implies that in general the average value of the metrics has been altered. In the example below, the yearly income has overall increased.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="331" src="https://www.datagaps.com/wp-content/uploads/Mean.webp" class="attachment-large size-large wp-image-5600" alt="Mean" srcset="https://www.datagaps.com/wp-content/uploads/Mean.webp 877w, https://www.datagaps.com/wp-content/uploads/Mean-300x155.webp 300w, https://www.datagaps.com/wp-content/uploads/Mean-768x398.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 4. Mean</p><p>While Min-Max values show the upper and lower hard bounds of the metrics, the variability, and the weights away from the mean are showcased by the deviation. In the example we see that while the min and max values of the yearly income have shifted up with the mean, there is less variance in this metric as well, implying that there is less variance in the customers.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="245" src="https://www.datagaps.com/wp-content/uploads/Minimum-Value.webp" class="attachment-large size-large wp-image-5604" alt="Minimum-Value" srcset="https://www.datagaps.com/wp-content/uploads/Minimum-Value.webp 876w, https://www.datagaps.com/wp-content/uploads/Minimum-Value-300x115.webp 300w, https://www.datagaps.com/wp-content/uploads/Minimum-Value-768x295.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<div class="et_pb_module et_pb_text et_pb_text_10 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><p style="text-align: center;">Figure 5. Minimum Value</p></div></div> </div>
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<img loading="lazy" decoding="async" width="640" height="174" src="https://www.datagaps.com/wp-content/uploads/Maximum-Value.webp" class="attachment-large size-large wp-image-5608" alt="Maximum-Value" srcset="https://www.datagaps.com/wp-content/uploads/Maximum-Value.webp 884w, https://www.datagaps.com/wp-content/uploads/Maximum-Value-300x81.webp 300w, https://www.datagaps.com/wp-content/uploads/Maximum-Value-768x209.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 6. Maximum Value</p> </div>
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<img loading="lazy" decoding="async" width="640" height="276" src="https://www.datagaps.com/wp-content/uploads/Standard-Deviation.webp" class="attachment-large size-large wp-image-5941" alt="Standard-Deviation" srcset="https://www.datagaps.com/wp-content/uploads/Standard-Deviation.webp 883w, https://www.datagaps.com/wp-content/uploads/Standard-Deviation-300x129.webp 300w, https://www.datagaps.com/wp-content/uploads/Standard-Deviation-768x331.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 7. Standard Deviation [The decrease showcases that most of the values in the past 2 runs are much closer to the mean]</p> </div>
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<p><span style="text-align: justify; background-color: #ffffff;"><strong>Skewness</strong> identifies how skewed a dataset is, as in how many values lie evenly away from the mean in both directions while Kurtosis identifies the degree of curve of the distribution of a dataset. Any changes in these datasets represent changes in the distribution and therefore critically affect any statistical tests like the p-test or t-test. In our example, these do not alter as much, however, in more sensitive models such as an AI / ML model, these tiny changes would affect the results more drastically.</span></p> </div>
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<img loading="lazy" decoding="async" width="640" height="333" src="https://www.datagaps.com/wp-content/uploads/Skeness.webp" class="attachment-large size-large wp-image-5966" alt="Skeness" srcset="https://www.datagaps.com/wp-content/uploads/Skeness.webp 885w, https://www.datagaps.com/wp-content/uploads/Skeness-300x156.webp 300w, https://www.datagaps.com/wp-content/uploads/Skeness-768x399.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 8. Skewness</p> </div>
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<img loading="lazy" decoding="async" width="640" height="327" src="https://www.datagaps.com/wp-content/uploads/Kurtosis.webp" class="attachment-large size-large wp-image-5969" alt="Kurtosis" srcset="https://www.datagaps.com/wp-content/uploads/Kurtosis.webp 870w, https://www.datagaps.com/wp-content/uploads/Kurtosis-300x153.webp 300w, https://www.datagaps.com/wp-content/uploads/Kurtosis-768x393.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 9. Kurtosis</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Change in Keys / GUID</h5> </div>
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<p>A GUID or a primary key is on the most important columns in relational datasets. In terms of delta datasets, they are critical in ensuring duplicity doesn’t enter the system. Any changes in these patterns will result in incorrect aggregations and reports, especially when checked against pre-change datasets.</p> </div>
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<img loading="lazy" decoding="async" width="304" height="177" src="https://www.datagaps.com/wp-content/uploads/Before.webp" class="attachment-large size-large wp-image-5971" alt="Before" srcset="https://www.datagaps.com/wp-content/uploads/Before.webp 304w, https://www.datagaps.com/wp-content/uploads/Before-300x175.webp 300w" sizes="(max-width: 304px) 100vw, 304px" /> </div>
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<p style="text-align: center;">Before</p> </div>
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<p style="text-align: center;">After</p> </div>
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<p>In the example above, we see that pattern of the Customer Key was a 5-digit number which was suddenly updated to an alphanumeric key.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Domain Shift or Addition of New Values</h4> </div>
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<p>As per the example in the introduction of this blog post if a new campaign type or a new geography id is added to a system the corresponding joins have to be checked. Additionally, if geography id is one of the group columns for any aggregations the aggregates in question are affected as well. While addition that is not in the expected domain is ruled out as a bad record, segregation in teams can result in new validated domain LOVs that the analysis team might not be aware of.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="229" src="https://www.datagaps.com/wp-content/uploads/Distinct-Count.webp" class="attachment-large size-large wp-image-5979" alt="Distinct-Count" srcset="https://www.datagaps.com/wp-content/uploads/Distinct-Count.webp 888w, https://www.datagaps.com/wp-content/uploads/Distinct-Count-300x107.webp 300w, https://www.datagaps.com/wp-content/uploads/Distinct-Count-768x275.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p style="text-align: center;">Figure 10. Distinct Count</p> </div>
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<p>In the example, we see the addition of a few geography ids causing the number of distinct values to vary as well as changes in the distribution of the customers in various geographies.</p> </div>
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<p style="text-align: center;">Before</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Model Drift and Final Thoughts</h5> </div>
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<p>Model Drift is the other side of the coin that is affected mainly due to data drift. It refers to degradation in model performance due to changes in data and outdatedness of the model parameters. In a machine learning system only fixing the data drift will not be sufficient and separate techniques will have to be used to detect model drift against production data and model.</p><p>Data Drift affects not just ML models but any system that works with functions, aggregates, and systems where statistical tests are being performed. Gradual changes over time creep up in the datasets, resulting in lower <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-quality-testing/" target="_blank" rel="noopener">data quality</a></span> and model quality.</p><p>Detecting data drift is often de-prioritized, but it doesn’t have to be complex — it can be easily deployed, documented, and monitored using the DataOps Profiling Nodes covered above. This ensures that any type of drift, whether in metrics, domains, patterns, or keys, is identified early, before it causes severe dips in model or pipeline quality.</p> </div>
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<p>Data drift isn’t a system malfunction — it’s simply the natural byproduct of a world that keeps changing, whether that’s shifting customer demographics, a new marketing campaign, or something as mundane as a sensor being swapped out for a different brand. Left untracked, these gradual shifts in frequency, distribution, and structure quietly erode the accuracy of dashboards, reports, and ML models until the gap between reality and the system’s assumptions becomes too large to ignore. The real risk isn’t drift itself — it’s drift that goes undetected, since diagnosing what changed becomes exponentially harder the longer it’s allowed to accumulate. By building drift detection into everyday data profiling — tracking statistical baselines like mean, deviation, skewness, and key patterns — teams can catch these shifts early, well before they cascade into degraded model performance or unreliable business insights, and DataOps Suite’s Profiling Nodes make this a continuous, low-effort practice rather than a reactive scramble.</p> </div>
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<h3 id="faq-heading">FAQs: Data Drift Detection</h3>
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<details>
<summary>1) What is data drift?</summary>
<p>
Data drift refers to changes in the characteristics of input data over time, such
as shifts in value distributions, frequencies, or aggregated metrics. While these
changes often reflect real-world trends rather than data errors, they can affect
downstream analytics, reporting, and machine learning performance if left
unmonitored.
</p>
</details>
<details>
<summary>2) What are the different types of data drift?</summary>
<p>
Data drift is commonly categorized into four types: <strong>Sudden Drift</strong>,
where changes occur abruptly; <strong>Gradual Drift</strong>, where values shift
slowly over time; <strong>Incremental Drift</strong>, involving small continuous
changes that accumulate; and <strong>Recurring Drift</strong>, where predictable
patterns reappear, such as seasonal fluctuations.
</p>
</details>
<details>
<summary>3) How does DataOps Suite detect data drift?</summary>
<p>
DataOps Suite uses Profiling Nodes to establish statistical baselines for datasets,
including metrics such as mean, minimum and maximum values, standard deviation,
skewness, and kurtosis. It detects drift by comparing new data against these
historical profiles and highlighting significant deviations.
</p>
</details>
<details>
<summary>4) What’s the difference between data drift and model drift?</summary>
<p>
Data drift refers to changes in the input data distribution, whereas model drift
occurs when a machine learning model’s predictive accuracy declines over time.
Although data drift can contribute to model drift, each requires its own monitoring
and validation strategy.
</p>
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<p>The post <a href="https://www.datagaps.com/blog/data-drift-using-dataops-data-profiling/">Data Drift Using DataOps Data Profiling</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<title>Why FinOps is Essential for Fintech Companies</title>
<link>https://www.datagaps.com/blog/why-finops-is-important-for-fintech-companies/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Wed, 20 May 2026 13:32:00 +0000</pubDate>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[Analyitcs FinOps]]></category>
<category><![CDATA[Analytics Ops]]></category>
<category><![CDATA[FinOps]]></category>
<category><![CDATA[FinOps Best Practices]]></category>
<category><![CDATA[FinOps Cloud]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=33674</guid>
<description><![CDATA[<p>FinOps (financial operations) helps fintech companies manage cloud spending through better collaboration between finance, technology, and business teams. This post covers FinOps’ core benefits — cost optimization, financial accountability, real-time financial management, and enhanced cross-team collaboration — and how it bridges traditionally siloed financial and technical practices. It also explains how Datagaps’ DataOps Suite supports […]</p>
<p>The post <a href="https://www.datagaps.com/blog/why-finops-is-important-for-fintech-companies/">Why FinOps is Essential for Fintech Companies</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>FinOps (financial operations) helps fintech companies manage cloud spending through better collaboration between finance, technology, and business teams. This post covers FinOps’ core benefits — cost optimization, financial accountability, real-time financial management, and enhanced cross-team collaboration — and how it bridges traditionally siloed financial and technical practices. It also explains how Datagaps’ DataOps Suite supports FinOps by automating data reconciliation, validation, and testing, strengthening data governance and ensuring reliable financial data for decision-making.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li>FinOps bridges finance and technology — it replaces siloed traditional financial management with a collaborative approach involving finance, operations, and engineering teams working toward shared cost and performance goals.</li><li>Four core benefits define its value for fintech — cost optimization, financial accountability, real-time financial management, and enhanced cross-team collaboration all support better decision-making in a fast-moving industry.</li><li>DataOps Suite automates FinOps-related workflows — by automating data reconciliation, validation, and testing, it reduces manual effort and helps ensure financial data used in FinOps decisions is accurate and reliable.</li><li>Data governance is a key enabler — Datagaps strengthens FinOps practices by providing oversight of data quality and integrity, which is essential for maintaining financial accountability and compliance.</li></ul> </div>
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<p><span class="TextRun SCXW103207625 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW103207625 BCX0">Fintech comp</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nies must m</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">n</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">ge costs, </span><span class="NormalTextRun SCXW103207625 BCX0">optimize</span><span class="NormalTextRun SCXW103207625 BCX0"> resources, </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nd </span><span class="NormalTextRun SCXW103207625 BCX0">m</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">int</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">in</span><span class="NormalTextRun SCXW103207625 BCX0"> fin</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nci</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">l </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">ccount</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">bility while delivering innov</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">tive services. This is where FinOps, or financial operations</span><span class="NormalTextRun SCXW103207625 BCX0">, comes into pl</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">y. <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.finops.org/introduction/what-is-finops/" target="_blank" rel="noopener">FinOps is </a></span></span><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.finops.org/introduction/what-is-finops/"><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0"> fr</span><span class="NormalTextRun SCXW103207625 BCX0">a</span></a></span><span class="NormalTextRun SCXW103207625 BCX0"><a href="https://www.finops.org/introduction/what-is-finops/"><span style="text-decoration: underline; color: #1967d2;">mework</span></a> th</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">t combines </span><span class="NormalTextRun SCXW103207625 BCX0">fin</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nci</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">l m</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">n</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">gement</span><span class="NormalTextRun SCXW103207625 BCX0">, oper</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">tion</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">l pr</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">ctices, </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nd technology to help org</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">niz</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">tions m</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">n</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">ge their </span><span class="NormalTextRun SCXW103207625 BCX0">clo</span><span class="NormalTextRun SCXW103207625 BCX0">u</span><span class="NormalTextRun SCXW103207625 BCX0">d</span><span class="NormalTextRun SCXW103207625 BCX0"> spending efficiently </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nd effectively. It brings together fin</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nce, technology, </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nd business te</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">ms to </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">chieve fin</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nci</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">l </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">ccount</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">bility </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nd m</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">ximize the v</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">lue of every doll</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">r spent on </span><span class="NormalTextRun SCXW103207625 BCX0">clo</span><span class="NormalTextRun SCXW103207625 BCX0">u</span><span class="NormalTextRun SCXW103207625 BCX0">d</span><span class="NormalTextRun SCXW103207625 BCX0"> services. </span></span><span class="EOP SCXW103207625 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why FinOps is Important in Fintech Companies</h2> </div>
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Aligning Finance with Technology </span>
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<a href="https://www.datagaps.com/dataops-suite/" style="color:#1967d2"><u>FinOps</u></a> is a transformative approach that bridges the gap between financial operations and technological advancements. FinOps ensures that financial practices keep pace with rapid technological changes in the fintech sector, where agility and innovation are paramount. It enables organizations to optimize cloud spending, allocating resources efficiently without compromising innovation. </p>
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The Shift from Traditional Financial Management </span>
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Unlike traditional financial management, which often operates in silos, FinOps promotes a collaborative culture. It brings together finance, operations, and engineering teams to work towards common goals, such as cost optimization, performance improvement, and business value creation. This collaboration is essential in fintech, where financial efficiency and technological excellence go hand in hand. </p>
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<h2 class="elementor-heading-title elementor-size-default">Benefits of FinOps: Why It's Vital for Fintech Companies</h2> </div>
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<img loading="lazy" decoding="async" width="1200" height="628" src="https://www.datagaps.com/wp-content/uploads/Benefits-of-FinOps.jpg" class="attachment-full size-full wp-image-33680" alt="benefits of finops in cloud" srcset="https://www.datagaps.com/wp-content/uploads/Benefits-of-FinOps.jpg 1200w, https://www.datagaps.com/wp-content/uploads/Benefits-of-FinOps-300x157.jpg 300w, https://www.datagaps.com/wp-content/uploads/Benefits-of-FinOps-1024x536.jpg 1024w, https://www.datagaps.com/wp-content/uploads/Benefits-of-FinOps-768x402.jpg 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> </div>
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<th style="padding: 12px; border: 1px solid #ccc;">What It Delivers</th>
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<td style="padding: 12px; border: 1px solid #ccc;">Cost Optimization</td>
<td style="padding: 12px; border: 1px solid #ccc;">Real-time visibility into cloud spending to identify cost reductions without hurting performance</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Financial Accountability</td>
<td style="padding: 12px; border: 1px solid #ccc;">Every department owns its cloud spending, fostering data-driven resource decisions</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Real-Time Financial Management</td>
<td style="padding: 12px; border: 1px solid #ccc;">Continuous monitoring of financial performance for fast, informed decisions</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Enhanced Collaboration</td>
<td style="padding: 12px; border: 1px solid #ccc;">Breaks down silos between finance, operations, and engineering teams</td>
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1. Cost Optimization </span>
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This priority is echoed industry-wide. According to the <a href="https://data.finops.org/2025-report/" target="_blank">FinOps Foundation's 2025 State of FinOps Report</a> — surveying organizations responsible for more than $69 billion in cloud spend — workload optimization and waste reduction remains the clear top priority for FinOps practitioners, with roughly half ranking it as their primary focus. </p>
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2. Financial Accountability </span>
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FinOps ensures that every department within a fintech organization is accountable for its cloud spending. This accountability fosters a culture of financial responsibility, where teams are motivated to optimize their resource usage and make data-driven decisions that align with the company's financial goals. </p>
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3. Real-Time Financial Management </span>
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FinOps enables real-time financial management, allowing fintech companies to monitor their financial performance continuously. This real-time insight is essential for making informed decisions quickly, which is critical in an industry that thrives on agility and responsiveness. </p>
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4. Enhanced Collaboration </span>
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FinOps fosters collaboration between finance, operations, and engineering teams, breaking down silos and ensuring that everyone is working towards the same financial goals. This collaboration leads to better decision-making and a more unified approach to financial management. </p>
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<h2 class="elementor-heading-title elementor-size-default">How does Datagaps DataOps Suite Empower FinOps for Fintech Companies?</h2> </div>
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The Role of DataOps in FinOps </span>
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<a href="https://www.datagaps.com/dataops-suite/" style="color:#1967d2"><u>Datagaps' DataOps Suite</u></a> is a powerful tool that enhances FinOps practices within fintech companies. DataOps Suite allows fintech companies to gain deeper insights into their cloud spending by automating data workflows and ensuring data accuracy. It streamlines data validation and testing, ensuring that financial data is accurate and reliable for real-time decision-making. </p>
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Automation and Efficiency </span>
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The DataOps Suite integrates seamlessly with FinOps strategies, automating repetitive tasks such as data reconciliation, validation, and testing. This automation reduces the manual effort required for financial management, freeing up resources to focus on strategic initiatives. With Datagaps, fintech companies can ensure that their FinOps processes are efficient, accurate, and aligned with their financial goals. </p>
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Enhanced Data Governance </span>
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Incorporating DataOps Suite into FinOps practices also strengthens data governance. By providing comprehensive oversight of data quality and integrity, Datagaps ensures that all financial data used in FinOps is trustworthy. This enhanced governance is crucial for maintaining financial accountability and compliance within fintech companies. </p>
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<h3 class="elementor-heading-title elementor-size-default">Analytics FinOps for Financial Efficiency and Success</h3> </div>
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<p><span class="TextRun SCXW120797502 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW120797502 BCX0"><a href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener"><span style="text-decoration: underline; color: #1967d2;">Analytics FinOps</span></a> offers fintech comp</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nies </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0"> powerful fr</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">mework for m</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">n</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ging their fin</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nces in </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0"> world where fin</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nci</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">l efficiency is critic</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">l to success. By </span><span class="NormalTextRun SCXW120797502 BCX0">optimizing</span><span class="NormalTextRun SCXW120797502 BCX0"> costs, ensuring fin</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nci</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">l </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ccount</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">bility, </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nd en</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">bling re</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">l-time </span><span class="NormalTextRun SCXW120797502 BCX0">fin</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nci</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">l m</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">n</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">gement</span><span class="NormalTextRun SCXW120797502 BCX0">, <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.finops.org/introduction/what-is-finops/" target="_blank" rel="noopener">FinOps</a></span> helps fintech comp</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nies st</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">y competitive </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nd </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">chieve their business go</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ls. Embr</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">cing Analytics FinOps is not just </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0"> str</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">tegic choice; </span><span class="NormalTextRun SCXW120797502 BCX0">It’s</span><span class="NormalTextRun SCXW120797502 BCX0"> necess</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ry for </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ny fintech comp</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ny th</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">t w</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nts to thrive in tod</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">y’s f</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">st-p</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ced fin</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nci</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">l l</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ndsc</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">pe. </span></span><span class="EOP SCXW120797502 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Conclusion</h2> </div>
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<p>For fintech companies operating in a sector where speed, innovation, and cost discipline all have to coexist, FinOps isn’t just a cloud cost-cutting exercise — it’s the framework that finally gets finance, engineering, and operations teams working from the same playbook instead of siloed goals. The real payoff comes from combining that collaborative culture with financial data teams can actually trust: real-time visibility into spending only matters if the numbers behind it are accurate and reconciled. That’s where a platform like Datagaps’ DataOps Suite fits in, automating the data validation and governance work that keeps FinOps decisions grounded in reliable data rather than guesswork. As fintech companies scale their cloud footprint, pairing FinOps’ financial discipline with strong data quality practices isn’t optional — it’s what separates sustainable growth from costly blind spots.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Ready to transform your financial operations?</h2> </div>
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<p><span class="TextRun SCXW188263925 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW188263925 BCX0">Explore how </span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">D</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">a</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">t</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">a</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">g</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">a</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">ps</span><span class="NormalTextRun SCXW188263925 BCX0">‘ </span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">D</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">a</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">t</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">a</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">Ops</span><span class="NormalTextRun SCXW188263925 BCX0"> Suite c</span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0">n help you implement FinOps pr</span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0">ctices </span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0">nd </span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0">chieve fin</span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0">nci</span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0">l excellence. Schedule </span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0"> demo tod</span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0">y!</span></span></p> </div>
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<h3 id="faq-heading">FAQs: FinOps and DataOps Suite</h3>
<section class="faq-section" aria-labelledby="faq-heading">
<div class="faq-list">
<details>
<summary>1) What is FinOps and why does it matter for fintech companies?</summary>
<p>
FinOps (Financial Operations) is a collaborative framework that brings together
finance, engineering, and business teams to manage cloud spending effectively.
It helps fintech organizations optimize costs, improve financial accountability,
and make faster, data-driven decisions while scaling cloud operations.
</p>
</details>
<details>
<summary>2) What are the core benefits of adopting FinOps?</summary>
<p>
FinOps enables organizations to optimize cloud costs, improve financial
accountability, gain real-time visibility into spending, and strengthen
collaboration between finance, operations, and engineering teams, replacing
traditional siloed approaches to cloud cost management.
</p>
</details>
<details>
<summary>3) How does DataOps Suite support FinOps practices?</summary>
<p>
DataOps Suite automates data reconciliation, validation, and testing to help
ensure the financial data used for FinOps reporting and decision-making is
accurate, complete, and reliable while reducing manual effort.
</p>
</details>
<details>
<summary>4) Why is data governance important for FinOps success?</summary>
<p>
Effective data governance ensures financial information remains accurate,
consistent, and trustworthy. By maintaining high-quality data, organizations
can make better FinOps decisions, improve accountability, and support
regulatory compliance.
</p>
</details>
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<p>The post <a href="https://www.datagaps.com/blog/why-finops-is-important-for-fintech-companies/">Why FinOps is Essential for Fintech Companies</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>DataOps Suite Accelerates CI/CD for Data Pipelines Through Testing Automation </title>
<link>https://www.datagaps.com/blog/dataops-suite-accelerates-ci-cd-for-data-pipelines-through-testing-automation/</link>
<comments>https://www.datagaps.com/blog/dataops-suite-accelerates-ci-cd-for-data-pipelines-through-testing-automation/#respond</comments>
<dc:creator><![CDATA[avinash keshri]]></dc:creator>
<pubDate>Tue, 19 May 2026 14:24:00 +0000</pubDate>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[Dataflow]]></category>
<category><![CDATA[DataOps]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=30291</guid>
<description><![CDATA[<p>DataOps Suite can help with your existing CI/CD pipelines and Accelerate CI/CD for Data Pipelines with Testing Automation</p>
<p>The post <a href="https://www.datagaps.com/blog/dataops-suite-accelerates-ci-cd-for-data-pipelines-through-testing-automation/">DataOps Suite Accelerates CI/CD for Data Pipelines Through Testing Automation </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="30291" class="elementor elementor-30291" data-elementor-post-type="post">
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<p>Drawing from a Datagaps webinar, this post explains how DataOps Suite accelerates CI/CD for data pipelines through four capabilities: dual pipeline orchestration across data and code workflows, advanced data observability, deployment automation, and testing automation. It frames catching data bugs early the same way early bug detection works in software development — reducing pressure on data engineers and preventing downstream issues. A case study cites a tech company that achieved a 40% decrease in deployment cycles and a 30% improvement in data and code quality.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>DataOps Suite covers four core CI/CD capabilities</strong> — dual pipeline orchestration, advanced data observability, deployment automation, and testing automation, together streamlining both data and code pipelines.</li><li><strong>Catching data bugs early mirrors software development best practices</strong> — early detection reduces pressure on data engineers, prevents downstream issues, and enables faster resolution.</li><li><strong>Automated testing improves collaboration and reduces MTTR</strong> — running tests continually gives prompt feedback, helps development/testing/operations teams work together more effectively, and shortens mean time to recovery from failures.</li><li><strong>Real-world impact is measurable</strong> — one technology company using DataOps Suite for CI/CD saw a 40% decrease in deployment cycles and a 30% improvement in data and code quality.</li></ul> </div>
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<p>CI/CD for data pipelines means applying continuous integration and continuous deployment principles — automated testing and validation on every change — to data and code, not just application code. The <span style="color: #1967d2;"><a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/">DataOps Suite</a></span> brings this to data pipelines, enhancing cloud development’s speed, quality, and reliability.</p> </div>
</div>
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<h2 class="elementor-heading-title elementor-size-default">What is DataOps?</h2> </div>
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<p><span class="NormalTextRun SCXW148730300 BCX0">As <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.gartner.com/en/information-technology/glossary/dataops" target="_blank" rel="noopener">Gartner</a></span> defines it, “</span><span class="NormalTextRun SpellingErrorV2Themed SCXW148730300 BCX0">DataOps</span><span class="NormalTextRun SCXW148730300 BCX0"> is a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW148730300 BCX0">DataOps</span><span class="NormalTextRun SCXW148730300 BCX0"> aims to deliver value faster by creating predictable delivery and change management of data, data models, and related artifacts.”</span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Advantages of Automated Testing in CI/CD Pipelines</h2> </div>
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<thead>
<tr style="background: #d6e3f5;">
<th style="padding: 12px; border: 1px solid #ccc;">Advantage</th>
<th style="padding: 12px; border: 1px solid #ccc;">What It Delivers</th>
</tr>
</thead>
<tbody>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Prompt Feedback</td>
<td style="padding: 12px; border: 1px solid #ccc;">Provides continuous, fast testing that identifies defects and delivers rapid feedback.</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Improved Collaboration</td>
<td style="padding: 12px; border: 1px solid #ccc;">Enables development, testing, and operations teams to work more efficiently with fewer errors and miscommunications.</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Rapid Deployment</td>
<td style="padding: 12px; border: 1px solid #ccc;">Accelerates build, test, and deployment cycles for applications, data pipelines, and AI models.</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Improved MTTR</td>
<td style="padding: 12px; border: 1px solid #ccc;"><span class="TextRun SCXW148730300 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW148730300 BCX0"><a href="https://en.wikipedia.org/wiki/CI/CD" target="_blank" rel="noopener"><span style="text-decoration: underline; color: #1967d2;">CI/CD</span></a> can support reducing the average time it takes to recover from a probable failure, measured by the MTTR. </span></span><span class="LineBreakBlob BlobObject DragDrop SCXW148730300 BCX0"><span class="SCXW148730300 BCX0"> </span></span></td>
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<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Transparency</td>
<td style="padding: 12px; border: 1px solid #ccc;"><span class="TextRun SCXW148730300 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW148730300 BCX0">Automated <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> and <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/compliance-solutions/" target="_blank" rel="noopener">compliance</a></span> checks can offer complete transparency by allowing authorized personnel to access updated compliance data immediately. </span></span></td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Reduced Manual Effort</td>
<td style="padding: 12px; border: 1px solid #ccc;">Automates repetitive testing tasks, allowing teams to focus on more complex manual validation.</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Boost Data Accuracy</td>
<td style="padding: 12px; border: 1px solid #ccc;">Improves data quality through more precise tests and broader validation coverage.</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Product Consistency</td>
<td style="padding: 12px; border: 1px solid #ccc;">Generates and compares large volumes of test results to ensure consistent application behavior.</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Faster Delivery of High-Quality Software</td>
<td style="padding: 12px; border: 1px solid #ccc;">Uses automated GUI testing to detect and resolve issues earlier, enabling faster software releases.</td>
</tr>
</tbody>
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<h2 class="elementor-heading-title elementor-size-default">Integration and Impact </h2> </div>
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<p><span class="TextRun SCXW148730300 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW148730300 BCX0">The </span><span class="NormalTextRun SpellingErrorV2Themed SCXW148730300 BCX0">DataOps</span><span class="NormalTextRun SCXW148730300 BCX0"> Suite provides easy-to-use connectivity for data sources and coding environments, thorough testing and validation by development and operational standards, and smooth transitions and upgrades seamlessly integrating into existing CI/CD frameworks. </span></span><span class="LineBreakBlob BlobObject DragDrop SCXW148730300 BCX0"><br class="SCXW148730300 BCX0" /></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Success Story </h2> </div>
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<p>Customers using DataOps Suite to reconcile data and code pipelines have reported 50% reductions in deployment cycle time and measurable improvements in data and code quality.</p> </div>
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<p>Conclusion</p> </div>
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<p><span class="TextRun SCXW191101251 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW191101251 BCX0"><span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener"><span style="text-decoration: underline;">DataOps Suite</span></a></span> can help with CI/CD to easily streamline the overall cloud development process and </span><span class="NormalTextRun SCXW191101251 BCX0">maintain</span><span class="NormalTextRun SCXW191101251 BCX0"> high data quality and reliability. </span><span class="NormalTextRun SCXW191101251 BCX0">DataGaps</span><span class="NormalTextRun SCXW191101251 BCX0"> recently went live on </span><span class="NormalTextRun SCXW191101251 BCX0">a webinar</span><span class="NormalTextRun SCXW191101251 BCX0"> sharing “Accelerate CI/CD for Data Pipelines with Testing Automation” in which we shared how our Industry Agnostic </span><span class="NormalTextRun SpellingErrorV2Themed SCXW191101251 BCX0">D</span><span class="NormalTextRun SpellingErrorV2Themed SCXW191101251 BCX0">ataOps</span> <span class="NormalTextRun SCXW191101251 BCX0">Suite aids in accelerating data pipelines within CI/CD. They automate repetitive tasks, bring teams together, and give you a clear view of what is happening. This makes your data pipelines run smoother and faster and, most importantly, delivers high-quality data you can trust for your applications and reports. </span></span><span class="LineBreakBlob BlobObject DragDrop SCXW191101251 BCX0"><span class="SCXW191101251 BCX0"> </span><br class="SCXW191101251 BCX0" /></span><span class="LineBreakBlob BlobObject DragDrop SCXW191101251 BCX0"><span class="SCXW191101251 BCX0"> </span><br class="SCXW191101251 BCX0" /></span><span class="TextRun SCXW191101251 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW191101251 BCX0">In CI/CD data pipelines, catching bugs early mimics the benefits of early bug detection in software development. It alleviates pressure on data engineers, fosters higher code quality, prevents downstream issues, and allows for quicker resolution, </span><span class="NormalTextRun SCXW191101251 BCX0">u</span><span class="NormalTextRun SCXW191101251 BCX0">ltimately building</span> <span class="NormalTextRun SCXW191101251 BCX0">a solid foundation for reliable data flow.</span></span><span class="EOP SCXW191101251 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335557856":16777215,"335559738":0,"335559739":240,"335559740":279}"> </span></p> </div>
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<p style="text-align: left;"><span class="TextRun SCXW184731117 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW184731117 BCX0">Hidden Defects in your Data Pipeline?</span></span></p> </div>
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<p><span class="TextRun SCXW184731117 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW184731117 BCX0">Looking for Reduced Downstream Impacts?</span></span></p> </div>
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<summary>1) How does DataOps Suite support CI/CD for data pipelines?</summary>
<p>
DataOps Suite provides dual pipeline orchestration for data and code workflows, advanced data
observability, deployment automation built on CI/CD principles, and testing automation that validates
data integrity and code functionality throughout the pipeline.
</p>
</details>
<details>
<summary>2) Why does early bug detection matter in data pipeline CI/CD?</summary>
<p>
Catching data issues early mirrors the benefits of early bug detection in software development. It
reduces pressure on data engineers, improves code quality, prevents downstream issues, and speeds up
issue resolution.
</p>
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<summary>3) What business impact can automated CI/CD testing deliver for data pipelines?</summary>
<p>
In one case cited, a technology company using DataOps Suite for CI/CD achieved a 40% decrease in
deployment cycles and a 30% improvement in overall data and code quality.
</p>
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<summary>4) What benefits does test automation bring to CI/CD data pipelines?</summary>
<p>
Automated testing provides prompt feedback, improves collaboration across development, testing, and
operations teams, reduces mean time to recovery (MTTR), cuts manual effort, boosts data accuracy, and
speeds delivery of high-quality outputs.
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<p>Head of Product Marketing at Datagaps and IIM Bangalore alumnus. 13+ years of experience in commercializing AI and data platforms across global markets.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/dataops-suite-accelerates-ci-cd-for-data-pipelines-through-testing-automation/">DataOps Suite Accelerates CI/CD for Data Pipelines Through Testing Automation </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>Leveraging DataOps Suite to comply with BCBS, SOC2 and other regulations</title>
<link>https://www.datagaps.com/blog/leveraging-dataops-suite-to-comply-with-bcbs-soc2-and-other-regulations/</link>
<dc:creator><![CDATA[Adithya Buddhavarapu]]></dc:creator>
<pubDate>Mon, 20 Apr 2026 11:59:00 +0000</pubDate>
<category><![CDATA[DataOps]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=7382</guid>
<description><![CDATA[<p>Many industries are regulated and have to adhere to a set of guidelines and industry standards. While we are a software company, we have been learning more about these standards and how our software is helping the financial industry adhere to the regulations.</p>
<p>The post <a href="https://www.datagaps.com/blog/leveraging-dataops-suite-to-comply-with-bcbs-soc2-and-other-regulations/">Leveraging DataOps Suite to comply with BCBS, SOC2 and other regulations</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 SOC 2’s Processing Integrity criteria and BCBS 239’s risk data aggregation principles apply to financial data systems, using General Ledger (GL) file validation as a concrete example. It highlights common compliance challenges — data scattered across systems, manual testing, and reliance on Excel-based checks — that make achieving trustworthy data difficult. The post sets up (without fully detailing) how DataOps Suite can help banks automate testing and reduce manual intervention to support these regulatory standards.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li>SOC 2’s Processing Integrity has 6 criteria — covering error prevention/correction, accurate system inputs, timely data processing, proper data storage, complete system outputs, and authorized data modifications.</li><li>BCBS 239 emerged from the 2008 financial crisis — it established 14 principles across governance, risk data aggregation, risk reporting, and supervisory review to fix inadequate bank data management practices exposed during the crisis.</li><li>General Ledger (GL) integrity is a compliance risk point — since GL data feeds Enterprise Reporting and multiple downstream systems, errors introduced during scheduler/ETL updates can cascade into inaccurate financial reporting and regulatory fines.</li><li>Current compliance testing relies heavily on manual work — banks commonly use Excel-based “Checks” worksheets and manual eyeballing to validate risk metrics, since data is distributed across systems with no unified automation in place.</li></ul> </div>
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<p>Many industries are regulated and have to adhere to a set of guidelines and industry standards. While we are a software company, we have been learning more about these standards and how our software is helping the financial industry adhere to the regulations.</p> </div>
</div>
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<p>Two of the standards we have been hearing about in the recent past are mentioned below:</p><ul><li><strong>The Systems and Organizational Controls (SOC) 2</strong></li><li><strong>Basel Committee on Banking Supervision’s standard number 239</strong></li></ul> </div>
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<p><span style="text-decoration: underline;"><strong>The Systems and Organizational Controls (SOC) 2,</strong></span></p><p>A comprehensive reporting framework was set forth by the American Institute of Chartered Public Accountants (ACPA). As part of this framework, organizations should engage independent auditors to execute a series of assessments to test the controls related to Trust Services Criteria (TSC) such as Security, Availability, <strong>Processing Integrity</strong>, Confidentiality, or Privacy.</p><p>From our perspective at Datagaps, achieving Processing Integrity is where we can add value and thus explaining our understanding below.</p><p><strong>The Processing Integrity component has 6 criteria.</strong></p><ul><li>Procedures exist to prevent, or detect and correct, processing errors to meet the entity’s processing integrity commitments and system requirements.</li></ul> </div>
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<img loading="lazy" decoding="async" width="640" height="640" src="https://www.datagaps.com/wp-content/uploads/datagaps_data_integrity-1024x1024.webp" class="attachment-large size-large wp-image-6255" alt="datagaps_data_integrity" srcset="https://www.datagaps.com/wp-content/uploads/datagaps_data_integrity-1024x1024.webp 1024w, https://www.datagaps.com/wp-content/uploads/datagaps_data_integrity-300x300.webp 300w, https://www.datagaps.com/wp-content/uploads/datagaps_data_integrity-150x150.webp 150w, https://www.datagaps.com/wp-content/uploads/datagaps_data_integrity-768x768.webp 768w, https://www.datagaps.com/wp-content/uploads/datagaps_data_integrity.webp 1200w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<ul><li>System inputs are measured and recorded completely, accurately, and time to meet the entity’s processing integrity commitments and system requirements.</li><li>Data is processed completely, accurately, and timely as authorized to meet the entity’s processing integrity commitments and system requirements.</li><li>Data is stored and maintained completely, accurately, and in a timely manner for its specified life span to meet the entity’s processing integrity commitments and system requirements.</li><li>System output is complete, accurate, and distributed to meet the entity’s processing integrity commitments and system requirements.</li><li>Modification of data, other than routine transaction processing, is authorized and processed to meet the entity’s processing integrity commitments and system requirements.</li></ul> </div>
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<p><strong>After the Global Financial Crisis in 2007-2008</strong>, one of the key lessons learned was that the Data Management frameworks and IT architectures in place at many banks at that time were inadequate to support and report risks associated with global banking. In lieu of that, the <strong>Basel Committee on Banking Supervision’s standard number 239</strong> was issued as principles for effective risk data aggregation and Risk Reporting.</p> </div>
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<p>These are <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.bis.org/publ/bcbs239.pdf" target="_blank" rel="noopener">14 principles</a></span></span> that were laid out to strengthen banks’ risk data aggregation capabilities and internal risk reporting practices and they fall under the following categories.</p> </div>
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<ul><li><strong>Overarching Governance and infrastructure</strong> – Principle 1 & 2</li><li><strong>Risk Data Aggregation Capabilities – </strong>Principles 3 through 6</li></ul> </div>
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<p>In this blog, we will focus on few aspects of Principles 3 and 7 since they are the areas which are relevant from our perspective at Datagaps. If you want a complete description of the principles, please refer to this <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.bis.org/publ/bcbs239.pdf" target="_blank" rel="noopener">link</a></span></span>.</p> </div>
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<div class="elementor-tab-title elementor-tab-mobile-title" aria-selected="true" data-tab="1" role="tab" tabindex="0" aria-controls="elementor-tab-content-1421" aria-expanded="false">Principle 3</div>
<div id="elementor-tab-content-1421" class="elementor-tab-content elementor-clearfix" data-tab="1" role="tabpanel" aria-labelledby="elementor-tab-title-1421" tabindex="0" hidden="false"><p><strong>Accuracy and Integrity</strong> A bank should be able to generate accurate and reliable risk data to meet normal and stress/crisis reporting accuracy requirements. Data should be aggregated on a largely automated basis so as to minimize the probability of errors.</p><ul><li>Where a bank relies on manual processes and desktop applications (eg<br />spreadsheets, databases) and has specific risk units that use these applications for software development, it should have effective mitigants in place (eg end-user computing policies and procedures) and other effective controls that are consistently applied across the bank’s processes.</li><li>Risk data should be reconciled with the bank’s sources, including accounting data<br />where appropriate, to ensure that the risk data is accurate</li><li>There should be an appropriate balance between automated and manual systems. Where professional judgments are required, human intervention may be appropriate. A higher degree of automation is desirable to reduce the risk of errors.</li></ul></div>
<div class="elementor-tab-title elementor-tab-mobile-title" aria-selected="false" data-tab="2" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1422" aria-expanded="false">Principle 7:</div>
<div id="elementor-tab-content-1422" class="elementor-tab-content elementor-clearfix" data-tab="2" role="tabpanel" aria-labelledby="elementor-tab-title-1422" tabindex="0" hidden="hidden"><p><strong>Accuracy</strong>: Risk management reports should accurately and precisely convey<br />aggregated risk data and reflect risk in an exact manner. Reports should be reconciled<br />and validated.</p><ol><li>Risk management reports should be accurate and precise to ensure a bank’s board and senior management can rely with confidence on the aggregated information to make critical decisions about risk.</li><li>To ensure the accuracy of the reports, a bank should maintain, at a minimum, the following</li></ol><ul><li>Defined requirements and processes to reconcile reports to risk data;</li><li>Automated and manual edit and reasonableness checks, including an inventory of the validation rules that are applied to quantitative information. The inventory should include explanations of the conventions used to describe any mathematical or logical relationships that should be verified through these validations or checks;</li><li>Integrated procedures for identifying, reporting, and explaining data errors or weaknesses in data integrity via exceptions reports</li></ul></div>
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<ul><li><strong>Risk Reporting Practices</strong> – Principles 7 through 11</li><li><strong>Supervisory Review tools and Cooperation – </strong>Principles 12 through 14</li></ul> </div>
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<p>As we are collaborating more with the financial industry, we are learning that while the guidelines are fairly clear, the actual implementation is complex and there are no clear tools and implementation strategies to help banks achieve these principles. In this blog, we want to delve a bit deeper with a concrete example of how our software can help achieve the Process Integrity required for the audits and enable trust in enterprise data and systems.</p> </div>
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<p>Consider General Ledger (GL), one of the most important elements in the Accounting world. Data is entered manually and automatically into the GL and oftentimes data is extracted from the GL into Enterprise Reporting systems or other downstream systems because the GL is a treasure trove of valuable information from Accounts Receivable, Accounts Payable, Purchasing, Payroll, and the sub-ledgers within the enterprise as shown in the image.</p> </div>
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<p>If anything goes wrong with the GL, it can have huge implications within the enterprise because if GL is not trustworthy, then, the reporting will not be trustworthy as well and wrong numbers might get accidentally reported to Wall Street because which organizations have to pay huge fines to governing entities.</p> </div>
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<p>Now, the question is how and where can something go wrong with the process integrity when it comes to General Ledger? To answer this question, let’s take a look at a potential file output from the GL system in Oracle. As mentioned in the <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://docs.oracle.com/cd/E69185_01/cwdirect/pdf/180/cwdirect_user_reference/SO02_09.htm#Ravr95793" target="_blank" rel="noopener">Oracle Documentation</a></span></span>, a GL file can be imported or extracted in multiple formats through automated scheduler jobs such as AutoSys and one such format (IXGLDN) has the below columns.</p> </div>
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<table style="width: 100%; border-collapse: collapse; margin: 20px 0; font-size: 16px;">
<thead>
<tr style="background: #d6e3f5;">
<th style="padding: 12px; border: 1px solid #ccc;">Field Name</th>
<th style="padding: 12px; border: 1px solid #ccc;">Attributes</th>
<th style="padding: 12px; border: 1px solid #ccc;">Field Description/Contents</th>
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</thead>
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<td style="padding: 12px; border: 1px solid #ccc;">Run group</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 12 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">Composed of CW + the date the file was populated, in CCYYMMDD format. For example, the run group for a record created on February 19, 2003 is CW20030219.</td>
</tr>
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<td style="padding: 12px; border: 1px solid #ccc;">Sequence number</td>
<td style="padding: 12px; border: 1px solid #ccc;">Numeric, 6 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">A unique sequence number assigned by the system to each record each time you generate the download file.</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Company</td>
<td style="padding: 12px; border: 1px solid #ccc;">Numeric, 4 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">The first 4 positions of the Cross reference number field in Working with Chart of Accounts (WCA). If the first 4 positions include a non-numeric character, such as a hyphen (-) the system skips this character and takes the first 4 numeric values. For example, if the Cross reference number is 100-10-12345, the Company will be 1000. If the Cross reference number field does not contain numeric characters, the Company field will not be populated consistently.</td>
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<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Company old</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 31 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">The first 11 positions of the Cross reference number field in Work with Chart of Accounts (fast path = WCA).</td>
</tr>
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<td style="padding: 12px; border: 1px solid #ccc;">Account number old</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 31 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">Positions 12-19 of the Cross reference number field in Work with Chart of Accounts (fast path = WCA).</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Source code</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 1 position</td>
<td style="padding: 12px; border: 1px solid #ccc;">The Journal source field from the General Ledger Interface file, indicating the type of transaction that generated the posting. See Accounts Payable Chapter 41: Displaying the General Ledger Interface (DGLI) for a list of journal source codes.</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Transaction date</td>
<td style="padding: 12px; border: 1px solid #ccc;">Numeric, 8 positions (CCYYMMDD format)</td>
<td style="padding: 12px; border: 1px solid #ccc;">The date this record was downloaded to the General Ledger Download file. For example, the transaction date for a record created on February 19, 1999 is 19990219.</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Reference</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 10 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">Composed of CW + the date the download file was created. For example, the Reference for a record created on February 19, 1999 is CW19990219.</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Description</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 30 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">Same as the Reference field.</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Currency code</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 5 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">Blank</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Units amount</td>
<td style="padding: 12px; border: 1px solid #ccc;">Numeric, 15 positions with a 2-place decimal</td>
<td style="padding: 12px; border: 1px solid #ccc;">0.00</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Units amount sign</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 1 position</td>
<td style="padding: 12px; border: 1px solid #ccc;">Blank</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Transaction amount</td>
<td style="padding: 12px; border: 1px solid #ccc;">Numeric, 15 positions with a 2-place decimal</td>
<td style="padding: 12px; border: 1px solid #ccc;">The Transaction amount field from the General Ledger Interface file.</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Transaction amount sign</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 1 position</td>
<td style="padding: 12px; border: 1px solid #ccc;">+ if the transaction amount is positive. – if the transaction amount is negative.</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Base amount</td>
<td style="padding: 12px; border: 1px solid #ccc;">Numeric, 15 positions with a 2-place decimal</td>
<td style="padding: 12px; border: 1px solid #ccc;">The transaction amount field from the General Ledger Interface file.</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Base amount sign</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 1 position</td>
<td style="padding: 12px; border: 1px solid #ccc;">+ if the transaction amount is positive. – if the transaction amount is negative.</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Base rate</td>
<td style="padding: 12px; border: 1px solid #ccc;">Numeric, 12 positions with a 6-place decimal</td>
<td style="padding: 12px; border: 1px solid #ccc;">.000000</td>
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<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">System</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 2 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">CW</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Program code</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 5 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">Blank</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Auto rev</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 1 position</td>
<td style="padding: 12px; border: 1px solid #ccc;">N</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Posting date</td>
<td style="padding: 12px; border: 1px solid #ccc;">Numeric, 8 positions (CCYYMMDD format)</td>
<td style="padding: 12px; border: 1px solid #ccc;">The date of the transaction from the General Ledger Interface file, converted into 8-position format. For example, the posting date for a record from February 19, 1999 is 19990219.</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Activity</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 15 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">Blank</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Account category</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 5 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">Blank</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Document number</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 15 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">CW + the date the download file was created + the sequence number for this record. For example, the document number for the first record created on February 19, 1999 is CW19990219000001.</td>
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<td style="padding: 12px; border: 1px solid #ccc;">To base amount</td>
<td style="padding: 12px; border: 1px solid #ccc;">Numeric, 15 positions with a 2-place decimal</td>
<td style="padding: 12px; border: 1px solid #ccc;">1.00</td>
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<td style="padding: 12px; border: 1px solid #ccc;">To base amount Sign</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 1 position</td>
<td style="padding: 12px; border: 1px solid #ccc;">+</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Effect date</td>
<td style="padding: 12px; border: 1px solid #ccc;">Numeric, 8 positions (CCYYMMDD format)</td>
<td style="padding: 12px; border: 1px solid #ccc;">0</td>
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<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Journal book number</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 12 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">Blank</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">MX value 1</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 20 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">Blank</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">MX value 2</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 20 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">Blank</td>
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<td style="padding: 12px; border: 1px solid #ccc;">MX value 3</td>
<td style="padding: 12px; border: 1px solid #ccc;">Alphanumeric, 20 positions</td>
<td style="padding: 12px; border: 1px solid #ccc;">Blank</td>
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<p>Whenever the <strong>version of the software related to scheduling or ETL or GL is updated</strong>, there is a possibility for the files to not be in sync with the expected formats and thus result in wrong data sets. If testing teams are able to achieve the desired quality, there are various challenges such as </p> </div>
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<ul><li>While the principles recommend banks have one source of data, it is impossible in large banks because the data is distributed across multiple systems.</li><li>Manual intervention related to all the files involved in testing is unavoidable with the current set of tools and frameworks.</li><li>Lack of end-to-end automation that instills trust in Quality Assurance and Reporting. </li><li>Lost productivity in setting up test files and data.</li></ul> </div>
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<p>Relying on Excel and manual eye-balling to check values and validate rules. As an example, consider the Excel file shared by the BIS as a reference to help banks adhere to the principles. It has multiple worksheets and one specifically called “Checks” that supervisors and other governance body members can use to validate the risk-related metrics. All this has to be done manually.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="281" src="https://www.datagaps.com/wp-content/uploads/BIS_Excel-1024x450.webp" class="attachment-large size-large wp-image-6358" alt="BIS_Excel" srcset="https://www.datagaps.com/wp-content/uploads/BIS_Excel-1024x450.webp 1024w, https://www.datagaps.com/wp-content/uploads/BIS_Excel-300x132.webp 300w, https://www.datagaps.com/wp-content/uploads/BIS_Excel-768x338.webp 768w, https://www.datagaps.com/wp-content/uploads/BIS_Excel.webp 1456w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>In our future blogs, we will explain how DataOps Suite can help banks achieve the following benefits and adhere to the principles laid out by organizations such as AICPA, BIS, etc. </p> </div>
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<ul><li>Elimination of Manual Intervention</li><li>End to end automation of test scripts</li><li>Extremely high performance to test billions of records in little time.</li><li>Integrated scheduling</li></ul> </div>
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<p><strong>References:</strong></p><p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" title="Referral Link 1" href="https://linfordco.com/blog/processing-integrity/#:~:text=Processing%20Integrity%20is%20one%20of%20the%20five%20trust,transactions%20or%20completing%20processing%20on%20behalf%20of%20clients.">Referral Link 1</a> </span></p><p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" title="Referral Link 2" href="https://docs.oracle.com/cd/E69185_01/cwdirect/pdf/180/cwdirect_user_reference/SO02_09.htm#Ravr95793">Referral Link 2</a></span></p><p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" title="Referral Link 3" href="https://linfordco.com/blog/processing-integrity/#:~:text=Processing%20Integrity%20is%20one%20of%20the%20five%20trust,transactions%20or%20completing%20processing%20on%20behalf%20of%20clients.">Referral Link 3</a></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Conclusion</h2> </div>
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<p>Frameworks like SOC 2’s Processing Integrity criteria and BCBS 239’s risk data aggregation principles exist because the 2007–2008 financial crisis made painfully clear what happens when banks can’t trust their own data — inaccurate risk reporting isn’t just a compliance footnote, it’s a systemic risk. Yet as this General Ledger example shows, achieving that trust in practice is far harder than the principles suggest: a single scheduling or ETL version update can silently break file formats, and most banks are still relying on distributed systems, manual file checks, and error-prone spreadsheets like the BIS reference workbook to catch these issues. Closing that gap isn’t just about better intentions — it requires automation that can validate data completeness, accuracy, and format consistency at the scale banks actually operate at, without depending on manual eyeballing of every file. That’s the direction we’ll dig into further in upcoming posts, as we explore how DataOps Suite helps eliminate this manual burden and bring real end-to-end automation to processing integrity and risk reporting compliance.</p> </div>
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<h3 id="faq-heading">FAQs: SOC 2 Processing Integrity and BCBS 239 Compliance</h3>
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<summary>1) What is SOC 2 Processing Integrity, and what does it cover?</summary>
<p>
SOC 2 Processing Integrity is one of the five Trust Services Criteria. It focuses
on ensuring that systems process data accurately, completely, and in a timely
manner by addressing error prevention and correction, accurate inputs, complete
processing, secure data storage, reliable outputs, and authorized data
modifications.
</p>
</details>
<details>
<summary>2) What is BCBS 239 and why was it introduced?</summary>
<p>
BCBS 239 is a regulatory framework introduced following the 2008 financial crisis
to strengthen how banks aggregate and report risk data. It establishes 14
principles covering governance, risk data aggregation, risk reporting, and
supervisory review to improve enterprise risk management.
</p>
</details>
<details>
<summary>3) Why is General Ledger (GL) data validation important for compliance?</summary>
<p>
General Ledger (GL) data serves as the foundation for enterprise financial
reporting and downstream regulatory systems. Errors introduced during ETL
processes or scheduled jobs can propagate across reports, increasing operational
risk and the likelihood of regulatory non-compliance.
</p>
</details>
<details>
<summary>4) How do banks typically validate compliance-related data today?</summary>
<p>
Many financial institutions still depend on manual validation methods, including
Excel-based checklists and spreadsheet reviews, to verify risk metrics and
financial data. Because information is often spread across multiple systems, these
manual processes can be time-consuming and difficult to scale.
</p>
</details>
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<p>The post <a href="https://www.datagaps.com/blog/leveraging-dataops-suite-to-comply-with-bcbs-soc2-and-other-regulations/">Leveraging DataOps Suite to comply with BCBS, SOC2 and other regulations</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></content:encoded>
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<item>
<title>Beyond Green Pipelines: Why DataOps and Data Observability Are Converging and Why Datagaps Bridges Both</title>
<link>https://www.datagaps.com/blog/dataops-data-observability-trusted-data-pipelines/</link>
<comments>https://www.datagaps.com/blog/dataops-data-observability-trusted-data-pipelines/#respond</comments>
<dc:creator><![CDATA[Anand Rao]]></dc:creator>
<pubDate>Fri, 13 Mar 2026 12:39:52 +0000</pubDate>
<category><![CDATA[Data Observability]]></category>
<category><![CDATA[DataOps]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=45615</guid>
<description><![CDATA[<p>If you’ve ever celebrated a successful pipeline run – only to discover the business dashboard was showing complete nonsense – you’ve already learned the uncomfortable truth: job status is not data trust. Modern data environments are sprawling across warehouses, lakehouses, streaming pipelines, APIs, and BI-layers – and now AI pipelines that amplify the blast radius […]</p>
<p>The post <a href="https://www.datagaps.com/blog/dataops-data-observability-trusted-data-pipelines/">Beyond Green Pipelines: Why DataOps and Data Observability Are Converging and Why Datagaps Bridges Both</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="45615" class="elementor elementor-45615" data-elementor-post-type="post">
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<p>If you’ve ever celebrated a successful pipeline run – only to discover the business dashboard was showing complete nonsense – you’ve already learned the uncomfortable truth: job status is not data trust.</p><p>Modern data environments are sprawling across warehouses, lakehouses, streaming pipelines, APIs, and BI-layers – and now AI pipelines that amplify the blast radius of bad data. In that world, monitoring jobs is table stakes. What teams need is operationalized trust: repeatable, testable, observable data delivery that holds up from ingestion all the way to business consumption.</p> </div>
</div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>Job status isn’t data trust</strong> — a pipeline can run successfully while the downstream dashboard shows completely wrong numbers, and AI pipelines now amplify the blast radius of that gap.</li><li><strong>DataOps and observability are converging into one operating model</strong> — DataOps handles orchestration, deployment, and CI/CD discipline, while observability provides continuous visibility across data content, pipeline flow, infrastructure, usage, and cost.</li><li><strong>Neither discipline works well alone</strong> — reliable DataOps needs the visibility observability provides, and observability is only actionable when DataOps frameworks exist to remediate what it finds.</li><li><strong>Datagaps bridges both through four capabilities</strong> — validating data in place (source-to-target, transformation, distribution drift), turning detection into evidence-backed root-cause analysis, extending validation into BI dashboards, and using AI-assisted rule creation to scale coverage without scaling headcount.</li></ul> </div>
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<p>Book a Datagaps walkthrough to see end-to-end validation – pipeline and BI – on real scenarios.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why DataOps and Data Observability Are Converging?</h2> </div>
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We’re seeing the same issue everywhere: data teams can’t deliver trusted results on time when people and tools are stuck in separate silos. That’s why DataOps and data observability are starting to blend into one operating model. </div>
</div>
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<h3 class="elementor-heading-title elementor-size-default">The distinction between these two is significant: </h3> </div>
</div>
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<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 32 32"><g id="Group_20826" data-name="Group 20826" transform="translate(-4197 14921)"><g id="Group_601" data-name="Group 601" transform="translate(4197 -14921)"><circle id="Ellipse_30" data-name="Ellipse 30" cx="16" cy="16" r="16" fill="#1eb473"></circle><path id="Path_426" data-name="Path 426" d="M4732.163-15573.172l4.563,4.191,8.547-9.346" transform="translate(-4722.81 15589.505)" fill="none" stroke="#fff" stroke-linecap="round" stroke-linejoin="round" stroke-width="3"></path></g></g></svg> </span>
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<div class="elementor-icon-box-content">
<h4 class="elementor-icon-box-title">
<span >
DataOps </span>
</h4>
<p class="elementor-icon-box-description">
DataOps addresses the execution and management of data workflows - orchestrating dependencies, automating deployments, and enabling CI/CD discipline. </p>
</div>
</div>
</div>
</div>
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<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 32 32"><g id="Group_20826" data-name="Group 20826" transform="translate(-4197 14921)"><g id="Group_601" data-name="Group 601" transform="translate(4197 -14921)"><circle id="Ellipse_30" data-name="Ellipse 30" cx="16" cy="16" r="16" fill="#1eb473"></circle><path id="Path_426" data-name="Path 426" d="M4732.163-15573.172l4.563,4.191,8.547-9.346" transform="translate(-4722.81 15589.505)" fill="none" stroke="#fff" stroke-linecap="round" stroke-linejoin="round" stroke-width="3"></path></g></g></svg> </span>
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<div class="elementor-icon-box-content">
<h4 class="elementor-icon-box-title">
<span >
Data observability </span>
</h4>
<p class="elementor-icon-box-description">
Data observability is about continuous visibility into data health and context across pipelines and environments. A simple way to think about it is watching five areas: the data itself, how it moves through pipelines, the compute/infrastructure it runs on, how people use it, and how costs get allocated. </p>
</div>
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<p>In practice, these disciplines are becoming inseparable. You cannot have reliable DataOps without the visibility provided by observability, and observability is only actionable if you have the DataOps frameworks to remediate issues.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">See How Datagaps Unifies DataOps and Data Observability</h2> </div>
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<p>AI based proactive detection of anomalies, drift and inconsistencies.</p> </div>
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<a class="premium-button premium-button-none premium-btn-md premium-button-none" href="https://www.datagaps.com/data-observability-tool/">
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<span >
Learn More </span>
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<h2 class="elementor-heading-title elementor-size-default">How to Build Operationalized Data Trust?</h2> </div>
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<p>Datagaps is named as a Representative Vendor in two Gartner® reports – <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://www.gartner.com/document-reader/document/7099730" target="_blank" rel="noopener"><span>Market Guide for DataOps Tools</span></a></span> and <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.gartner.com/document-reader/document/7490153" target="_blank" rel="noopener">Market Guide for Data Observability Tools</a></span></span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">We built Datagaps to make data trust measurable across the full delivery chain by bridging DataOps execution and observability outcomes:</h3> </div>
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Validate data where it lives: </span>
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<p class="elementor-icon-box-description">
Datagaps verifies data in place at key stages - <a href="https://www.datagaps.com/data-reconciliation/" target="_blank" style="color:#1967d2;text-decoration: underline">source-to-target reconciliation</a>, transformation validation, completeness checks, uniqueness checks, distribution drift detection, and regression testing. </p>
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Turn detection into action with evidence: </span>
</h4>
<p class="elementor-icon-box-description">
To support DataOps excellence, Datagaps provides run histories and evidence-backed outputs that let teams pinpoint exactly what failed and when - moving beyond simple alerts to actionable root-cause analysis. </p>
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<span >
Extend trust into BI dashboards: </span>
</h4>
<p class="elementor-icon-box-description">
Datagaps also validates <a href="https://www.datagaps.com/automate-power-bi-testing/" target="_blank" style="color:#1967d2;text-decoration: underline">dashboards and reports</a> for regressions and filter inconsistencies, so the last mile - what business users see - is tested just like the upstream pipeline. </p>
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Scale coverage with AI-assisted rule creation: </span>
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<p class="elementor-icon-box-description">
Datagaps uses profiling and anomaly detection to suggest and generate validation rules, helping teams expand test coverage without expanding headcount at the same rate. </p>
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<h2 class="elementor-heading-title elementor-size-default">Ready to Make Data Trust Measurable? </h2> </div>
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<p>Your data strategy shouldn’t be about stitching together another standalone monitoring tool. It should be an integrated part of how you run DataOps to ensure continuous data health. If your operations still rely on manual validation, sampling, or last-minute heroics to prove trust – it’s time to re-evaluate.</p> </div>
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<blockquote class="custom-blockquote">
Read the
<a href="https://www.gartner.com/document-reader/document/7099730" target="_blank" rel="noopener">
Market Guide for DataOps Tools
</a>
and
<a href="https://www.gartner.com/document-reader/document/7490153" target="_blank" rel="noopener">
Market Guide for Data Observability Tools
</a>
on Gartner and learn more.
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<a href="https://www.datagaps.com/request-a-demo/" target="_blank">Request a pilot plan</a> and we’ll help you identify the highest-impact gap to prove ROI fast.
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<p>Conclusion</p> </div>
</div>
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<p class="PDq2pG_selectionAnchorContainer" data-start="1072" data-end="1495">Modern data teams can no longer rely on pipeline success alone to measure reliability. A pipeline that finishes successfully can still deliver incomplete, inaccurate, or outdated data to business users and AI applications. That is why DataOps and Data Observability are no longer separate disciplines—they work together to ensure data is not only delivered efficiently but also remains trustworthy throughout its lifecycle.</p><p data-start="1500" data-end="1890">By combining automated validation, continuous monitoring, root-cause analysis, and AI-assisted rule generation, organizations can move from reactive issue resolution to proactive data reliability. Datagaps brings these capabilities together in a unified platform, enabling enterprises to build trusted data pipelines that support confident business decisions, analytics, and AI initiatives.</p> </div>
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<p><em>Source: Gartner Report, Market Guide for DataOps Tools, By Michael Simone, Sharat Menon, etc., October 2025.</em></p><p><em>Gartner Report, Market Guide for Data Observability Tools, By Melody Chien and Michael Simone, February 2026.</em></p> </div>
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<p><em>Gartner is a trademark of Gartner, Inc. and/or its affiliates.</em></p> </div>
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<p><em>Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.</em></p> </div>
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<h2 id="faq-heading">Frequently Asked Questions: DataOps and Data Observability Convergence</h2>
<div class="faq-list">
<details>
<summary>1) Why isn’t a successful pipeline run enough to trust the data?</summary>
<p>
Job status only confirms the pipeline executed — it doesn’t catch downstream issues like schema drift or
transformation errors that leave dashboards showing inaccurate numbers.
</p>
</details>
<details>
<summary>2) What’s the difference between DataOps and data observability?</summary>
<p>
DataOps focuses on orchestrating and automating data workflows, while data observability provides
continuous visibility into data health across content, pipelines, infrastructure, usage, and cost.
</p>
</details>
<details>
<summary>3) Why are DataOps and data observability converging?</summary>
<p>
Reliable DataOps requires the visibility observability provides, and observability is only actionable
when paired with DataOps frameworks that can remediate the issues it detects.
</p>
</details>
<details>
<summary>4) How does Datagaps combine DataOps and observability in one platform?</summary>
<p>
It validates data at key pipeline stages, provides evidence-backed run histories for root-cause analysis,
extends validation into BI dashboards, and uses AI to suggest new validation rules as coverage needs
grow.
</p>
</details>
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Anand Rao Vala </a>
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<p>VP of Marketing at Datagaps. Go-to-market leader for enterprise data and analytics, with prior roles at Qlik, Informatica, IBM, and Hitachi Vantara.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/dataops-data-observability-trusted-data-pipelines/">Beyond Green Pipelines: Why DataOps and Data Observability Are Converging and Why Datagaps Bridges Both</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>Stop Trusting Green Pipelines: Gartner’s Data Observability Wake-Up Call and How Datagaps Helps You Act</title>
<link>https://www.datagaps.com/blog/data-observability-tools-gartner-guide/</link>
<comments>https://www.datagaps.com/blog/data-observability-tools-gartner-guide/#respond</comments>
<dc:creator><![CDATA[Anand Rao]]></dc:creator>
<pubDate>Wed, 04 Mar 2026 05:50:31 +0000</pubDate>
<category><![CDATA[Data Observability]]></category>
<category><![CDATA[DataOps]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=44793</guid>
<description><![CDATA[<p>A pipeline can “succeed” while a business dashboard quietly drifts into nonsense — job status isn’t data trust. Drawing on Gartner’s 2026 Market Guide for Data Observability Tools, this post argues observability means five things: continuously watching data workflows, detecting issues early, alerting the right people, enabling fast troubleshooting, and supporting operations with context. It […]</p>
<p>The post <a href="https://www.datagaps.com/blog/data-observability-tools-gartner-guide/">Stop Trusting Green Pipelines: Gartner’s Data Observability Wake-Up Call and How Datagaps Helps You Act</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="44793" class="elementor elementor-44793" data-elementor-post-type="post">
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<p>A pipeline can “succeed” while a business dashboard quietly drifts into nonsense — job status isn’t data trust. Drawing on Gartner’s 2026 Market Guide for Data Observability Tools, this post argues observability means five things: continuously watching data workflows, detecting issues early, alerting the right people, enabling fast troubleshooting, and supporting operations with context. It frames reliability across five lenses (data content, flow, infrastructure, usage, cost) and outlines a five-step pilot blueprint for adopting observability without ripping out existing monitoring.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>Data failures are often silent, unlike traditional job failures</strong> — a schema changes, a join breaks, or a distribution shifts without triggering any alert, so event-based monitoring alone misses them.</li><li><strong>Observability spans five lenses, not just pipeline status</strong> — data content, data flow/pipeline, infrastructure/compute, user usage, and financial allocation together define true data health.</li><li><strong>Two forces are shaping the buying market</strong> — AI augmentation (dynamic thresholds, faster root-cause analysis, automated remediation) and unified platforms that reduce tool sprawl across monitoring, governance, and security.</li><li><strong>A high-value pilot follows five concrete steps</strong> — pick one high-impact data product, define trust signals, implement validation from ingestion through BI consumption, operationalize outcomes with ownership and alerts, and measure business results like fewer incidents.</li></ul> </div>
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<p>If you’ve ever had a pipeline “succeed” while the business dashboard quietly drifted into nonsense, you already know the uncomfortable truth: <span style="color: #000000;"><strong>job status isn’t data trust.</strong></span></p> </div>
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<p>Modern data stacks are bigger, faster, and more distributed than ever – cloud warehouses, streaming ingestion, ELT frameworks, data products, and now AI systems that amplify the impact of bad data. In this reality, we believe the old approach (reactive monitoring + a handful of checks + lots of tribal knowledge) can’t keep up.</p> </div>
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<p>If you want a solid overview of the data observability category and what buyers look for, <a href="https://www.gartner.com/document-reader/document/7490153" target="_blank">the best Gartner® report - Market Guide for Data Observability Tools</a> is a useful place to start.</p>
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<h2 class="elementor-heading-title elementor-size-default">Our perspective: observability is “data health”, not just monitoring </h2> </div>
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<p>Traditional monitoring tends to be event-based: a job fails, a system goes down, an alert fires. The challenge is that data failures are often <em>silent</em> – a schema changes, a join breaks, a distribution shifts, a transformation logic regresses, or a dashboard filter starts behaving differently.</p> </div>
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<p>We think <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-observability-tool/" target="_blank" rel="noopener">data observability</a></span></span> should do five jobs well: continuously watch data workflows, detect issues early, alert the right people, help teams troubleshoot quickly, and support day-to-day operations with context (lineage, collaboration, incident workflows, and cost visibility). Our takeaway is simple: if data drives decisions, then data reliability has to be engineered like uptime.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">The five lenses you need to see reliability end-to-end</h2> </div>
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<p>We like to think of observability as five lenses that work together:</p> </div>
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<p><strong><span style="color: #000000;">1. Data content</span> </strong>– Is the data accurate, complete, consistent, and within expected bounds?<br /><strong><span style="color: #000000;">2. Data flow & pipeline</span> </strong>– Is data moving correctly through ingestion, transformation, orchestration, and delivery?<br /><strong><span style="color: #000000;">3. Infrastructure & compute</span> </strong>– Are resources sufficient, stable, and performant?<br /><strong><span style="color: #000000;">4. User usage & utilization</span></strong> – Who is using data, how, and what changed?<br /><strong><span style="color: #000000;">5. Financial allocation</span> </strong>– What is this pipeline/data product costing, and who owns that spend?</p> </div>
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<p>This is the shift teams are making: from “Is the job green?” to “Is the data healthy, used, and worth what it costs?”</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">We see two big directions shaping buying decisions: </h2> </div>
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AI augmentation </span>
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Expect tools to become better at dynamic thresholds, anomaly prediction, faster root-cause analysis, and even automated remediation actions. In plain terms: fewer false alarms, earlier detection, and less time spent guessing. </p>
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Unified platforms </span>
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Organizations are increasingly looking for consolidated experiences that reduce tool sprawl. Instead of stitching together monitoring, governance, and security across multiple products, the market is moving toward more unified “single pane” operations. </p>
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<p>For buyers, this means your “<strong>observability strategy</strong>” shouldn’t be another standalone tool – it should be part of how you run DataOps.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">How Datagaps aligns with Gartner’s observability roadmap</h3> </div>
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<p><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-observability-tool/" target="_blank" rel="noopener">Datagaps</a></span></span> is built for the outcomes Gartner emphasizes – trusted data across the lifecycle, operationalized with repeatability and evidence.</p> </div>
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<th style="padding: 12px; border: 1px solid #ccc;">Lens</th>
<th style="padding: 12px; border: 1px solid #ccc;">Key Question</th>
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</thead>
<tbody>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Data Content</td>
<td style="padding: 12px; border: 1px solid #ccc;">Is the data accurate, complete, consistent, and within expected quality thresholds?</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Data Flow & Pipeline</td>
<td style="padding: 12px; border: 1px solid #ccc;">Is data moving correctly through ingestion, transformation, orchestration, and delivery processes?</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Infrastructure & Compute</td>
<td style="padding: 12px; border: 1px solid #ccc;">Are compute resources sufficient, reliable, stable, and performing as expected?</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">User Usage & Utilization</td>
<td style="padding: 12px; border: 1px solid #ccc;">Who is using the data, how is it being used, and what usage patterns have changed?</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Financial Allocation</td>
<td style="padding: 12px; border: 1px solid #ccc;">What are the costs associated with this data pipeline or product, and who is accountable for that spend?</td>
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</tbody>
</table> </div>
</div>
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<h2 class="elementor-heading-title elementor-size-default">How to use the report: A simple pilot blueprint that works</h2> </div>
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<p>Our advice is practical: don’t rip and replace. Start where today’s monitoring fails, then pilot observability where the business impact is real. A high-value pilot looks like this:</p> </div>
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<ol><li>Pick one <b>high-impact data product</b> (revenue dashboard, <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/compliance-solutions/" target="_blank" rel="noopener">regulatory pipeline</a></span>, AI feature dataset).</li><li>Define <b>trust signals</b> (freshness, <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">reconciliation</a></span>, drift thresholds, KPI integrity).</li><li>Implement <b>validations</b> across ingestion → transformations → consumption (including BI).</li><li>Operationalize <b>outcomes</b> (ownership, alerts, run history, incident workflow).</li><li>Measure <b>business results</b>: fewer incidents, faster root cause analysis, and fewer post-release surprises.</li></ol> </div>
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<p>That’s exactly the kind of real-world adoption path Datagaps is designed to support.</p> </div>
</div>
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<h3 class="elementor-heading-title elementor-size-default">Ready to make data trust measurable?</h3> </div>
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<p><span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.gartner.com/document-reader/document/7490153"><span style="text-decoration: underline;">Download the Gartner Market Guide</span></a></span> and learn more.</p> </div>
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<p>Book a Datagaps walkthrough to see end-to-end validation (pipeline + dashboard) on real scenarios.</p> </div>
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<a href="https://www.datagaps.com/request-a-demo/" target="_blank">Request a pilot plan</a> and we’ll help you identify the highest-impact gap to prove ROI fast.
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<p>If <strong>“trusted data”</strong> is a priority, now’s the time to move from monitoring jobs to managing <strong><span style="color: #000000;">data health.</span></strong></p> </div>
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<p>Conclusion</p> </div>
</div>
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<p>The core shift Gartner is pointing to isn’t a new tool category — it’s a new question. “Is the job green?” only confirms a process ran; it says nothing about whether the data itself is accurate, complete, or trustworthy by the time it reaches a dashboard. Observability across all five lenses — content, flow, infrastructure, usage, and cost — is what closes that gap, and doing it well increasingly means fewer disconnected tools, not more. Teams don’t need to rip out existing monitoring to get there; a single high-impact pilot, with clear trust signals and measurable outcomes, is often enough to prove the value before scaling further.</p> </div>
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<p><em>Source: Gartner Report, Market Guide for Data Observability Tools, By Melody Chien and Michael Simone, February 2026.</em></p> </div>
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<p>Gartner is a trademark of Gartner, Inc. and/or its affiliates.</p> </div>
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<details>
<summary>1) Why isn’t a “successful” pipeline run enough to trust the data?</summary>
<p>
Pipeline success only confirms the job ran — it doesn’t catch silent failures like schema changes,
broken joins, distribution shifts, or transformation logic regressions that leave the report technically
complete but factually wrong.
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<summary>2) What are the five lenses of data observability?</summary>
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<summary>4) How should a team start piloting data observability?</summary>
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Start with one high-impact data product, define trust signals like freshness and reconciliation
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Anand Rao Vala </a>
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VP Marketing, Datagaps </p>
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<p>VP of Marketing at Datagaps. Go-to-market leader for enterprise data and analytics, with prior roles at Qlik, Informatica, IBM, and Hitachi Vantara.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/data-observability-tools-gartner-guide/">Stop Trusting Green Pipelines: Gartner’s Data Observability Wake-Up Call and How Datagaps Helps You Act</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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