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<title>Snowflake Archives - Datagaps | Gen AI-Powered Automated Cloud Data Testing</title>
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<title>Snowflake Archives - Datagaps | Gen AI-Powered Automated Cloud Data Testing</title>
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<title>Top 3 ETL Testing Tools: How to Choose the Best Tool</title>
<link>https://www.datagaps.com/blog/top-3-etl-testing-tools/</link>
<dc:creator><![CDATA[Raj Mohan Achanta]]></dc:creator>
<pubDate>Thu, 30 Apr 2026 19:05:05 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Databricks]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[ETL Testing]]></category>
<category><![CDATA[Snowflake]]></category>
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<description><![CDATA[<p>ETL Testing refers to the testing, validation, and analysis of the Extraction, Transformation, and Loading Processes that are part of ETL and ELT Pipelines. As ETL testing refers to “Data-in-Motion” Testing, the unit test architecture and principles slightly differ from “Data-at-Rest” Testing (Warehouse/DB Validation).</p>
<p>The post <a href="https://www.datagaps.com/blog/top-3-etl-testing-tools/">Top 3 ETL Testing Tools: How to Choose the Best Tool</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<h2 class="elementor-heading-title elementor-size-default">What are ETL Testing Tools?</h2> </div>
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<p><span style="text-decoration: underline;"><span style="color: #0000ff; text-decoration: underline;"><a style="color: #0000ff; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener"><span style="color: #1967d2; text-decoration: underline;">ETL testing tools</span></a></span></span> are purpose-built platforms that validate data as it moves through extract, transform, and load pipelines. As data pipelines become more complex, organizations rely on ETL testing tools to verify transformations, detect data issues, and maintain trust in analytics.</p><p>While many teams explore general ETL tools, it is important to distinguish between ETL tools used for data movement and ETL testing tools used for validation and quality assurance.</p><p>Looking for a structured starting point? Check out our <span style="text-decoration: underline;"><span style="color: #1967d2;"><a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/blog/how-to-validate-etl-testing-checklist/" target="_blank" rel="noopener">ETL Testing Checklist</a></span></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">When are ETL Testing Tools Used?</h2> </div>
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<p>ETL testing tools are primarily used across two major categories of projects where data accuracy is critical:</p> </div>
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<span >
1. Data Migration Projects </span>
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These involve moving data across systems while ensuring consistency and completeness. Common scenarios include: </p>
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<ul><li>Application migrations</li><li>Cloud migrations such as moving to <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/snowflake-testing-automation/" target="_blank" rel="noopener">Snowflake</a></span></span> or <span style="text-decoration: underline;"><span style="color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/databricks-testing-automation/" target="_blank" rel="noopener">Databricks</a></span></span></li><li>Data warehouse migrations such as Teradata to Redshift or Teradata to Databricks</li></ul> </div>
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<p>In these cases, ETL testing tools and data testing tools are essential for validating large-scale data movement and ensuring no data loss or transformation errors.</p><p>Need help with data migration? Explore our <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-migration-testing-automation/" target="_blank" rel="noopener">Data Migration Solution page</a>.</span></span></p> </div>
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<span >
2. Data Pipeline Testing </span>
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These focus on ongoing validation of data pipelines in production environments. Key use cases include: </p>
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<ul><li>Verifying data transformations across pipelines</li><li>Ensuring consistency between source and target systems</li><li>Detecting data quality issues early</li><li>Supporting continuous validation as pipelines scale Here, ETL automation testing tools help teams scale validation, reduce manual effort, and maintain data quality across evolving pipelines.<p>Read more on <span style="text-decoration: underline; color: #1967d2;"><a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/etl-testing/" target="_blank" rel="noopener">ETL Testing</a></span> for data pipeline environments.</p></li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Evaluation Criteria: How We Selected and Assessed ETL Testing Tools?</h2> </div>
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<p class="font-claude-response-body">Modern ETL testing tools are expected to deliver multi-source validation, transformation testing, automation, AI-assisted test creation, and scalability across large data environments. These capabilities formed the basis of our evaluation.</p> </div>
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<p class="font-claude-response-body">Several tools come up frequently in this space. iceDQ, Tosca DI, and Informatica DVO were considered but excluded for specific reasons:</p> </div>
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<p><strong>iceDQ:</strong> The on-premise version of iceDQ lacks several core ETL testing capabilities that enterprise teams typically require. The SaaS version is more feature-complete but not suited for teams that need on-premise deployment.</p><p><strong>Informatica DVO:</strong> Informatica DVO is not a standalone ETL testing tool. It runs only within the Informatica platform, making it irrelevant for teams outside that ecosystem.</p><p><strong>Tosca DI:</strong> While Tosca is a popular choice for application and UI testing, Tosca DI is found to be limited in scope for ETL testing and end-to-end pipeline validation, making it a less suitable option for teams with comprehensive data pipeline testing requirements.</p> </div>
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<p>ETL testing tools broadly fall into three categories: purpose-built ETL testing platforms, open-source tools, and developer-first testing frameworks. This comparison selects one representative from each category to highlight how different approaches address the same validation challenges. In this comparison, Datagaps ETL Validator represents the purpose-built category, Great Expectations the open-source data quality framework approach, and dbt Tests the developer-first framework.</p> </div>
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<p class="font-claude-response-body">Evaluation was based on nine criteria that reflect real production requirements: core ETL testing capabilities, automation and CI/CD integration, usability and test authoring, data quality and observability, data contracts and governance, testing scope and coverage, enterprise readiness, scalability and performance, and pricing and accessibility.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Top 3 ETL Testing Tools: Detailed Comparison</h2> </div>
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<p>Below is a detailed comparison of three widely considered options: <span style="text-decoration: underline;"><span style="color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">Datagaps ETL Validator</a></span></span>, Great Expectations, and dbt tests.</p> </div>
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<div class="etl-legend__title">Legend</div>
<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--star">★</span>
<span>Unique / standout feature</span>
</div>
<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--check">✔</span>
<span>Strong / full support</span>
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<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--half">◐</span>
<span>Partial / limited support</span>
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<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--cross">✘</span>
<span>Not supported / not available</span>
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</div>
<p class="etl-scroll-hint">← Scroll to see full table →</p>
<div class="etl-table-wrapper">
<table class="etl-table">
<colgroup>
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</colgroup>
<thead>
<tr>
<th>Feature / Capability</th>
<th class="tool-col"><span class="etl-head-nowrap">Datagaps<br/>ETL Validator</span></th>
<th class="tool-col"><span class="etl-head-nowrap">Great Expectations</span></th>
<th class="tool-col"><span class="etl-head-nowrap">dbt Tests</span></th>
<th>Verdict</th>
</tr>
</thead>
<tbody>
<tr class="etl-cat-row"><td colspan="5">1. Core ETL Testing</td></tr>
<tr class="etl-data-row">
<td>ETL Test Authoring & Execution</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator is purpose-built for end-to-end ETL test authoring and execution. Great Expectations and dbt Tests define data quality checks but are not designed for full ETL test execution.</td>
</tr>
<tr class="etl-data-row">
<td>ELT / In-Database Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-check">✔</span></td>
<td>ETL Validator and dbt Tests push validation to the warehouse natively. ETL Validator leads on orchestration across multiple platforms. Great Expectations requires custom configuration.</td>
</tr>
<tr class="etl-data-row">
<td>Flat File / CSV Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator handles flat file and CSV validation natively. Great Expectations supports file-based validation with setup. dbt Tests are database-only.</td>
</tr>
<tr class="etl-data-row">
<td>Multiple Source / Target Support</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator supports multiple heterogeneous sources and targets in a single test run. Great Expectations supports multiple backends but requires per-datasource configuration. dbt Tests operate within a single warehouse.</td>
</tr>
<tr class="etl-data-row">
<td>Transformation Validation</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-check">✔</span></td>
<td>ETL Validator adds GenAI-assisted rule authoring across any ecosystem. dbt Tests are strong for validating dbt model outputs. Great Expectations validates expectations on data but is not transformation-aware.</td>
</tr>
<tr class="etl-data-row">
<td>Source-to-Target Reconciliation</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator uniquely supports Data Profile reconciliation across source and target. Great Expectations and dbt have no cross-system reconciliation capability.</td>
</tr>
<tr class="etl-data-row">
<td>Source-to-Report Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator validates the full chain from raw source through to the BI report layer. Neither Great Expectations nor dbt Tests reach the reporting layer.</td>
</tr>
<tr class="etl-data-row">
<td>Non-dbt Pipeline Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and Great Expectations can test pipelines outside dbt. dbt Tests are locked to dbt models.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">2. Automation & CI/CD</td></tr>
<tr class="etl-data-row">
<td>Automated Regression Testing</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator adds GenAI-assisted test maintenance. Great Expectations supports checkpoint-based runs but lacks structured regression management. dbt Tests re-run on every invocation but have no dedicated regression management.</td>
</tr>
<tr class="etl-data-row">
<td>CI/CD Pipeline Integration</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-star">★</span></td>
<td>dbt Tests have first-class CI/CD integration. ETL Validator and Great Expectations both support CI/CD with broad pipeline trigger options.</td>
</tr>
<tr class="etl-data-row">
<td>Scheduled / Triggered Test Runs</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator supports native scheduling and REST API triggers. Great Expectations and dbt Tests rely on external orchestrators such as Airflow or Prefect.</td>
</tr>
<tr class="etl-data-row">
<td>Test Case Reusability</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td>All three support reusable test definitions. ETL Validator and Great Expectations offer reusable templates via their platforms.</td>
</tr>
<tr class="etl-data-row">
<td>Test Maintenance Overhead</td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">Medium-High</span></td>
<td><span class="sym-text">Medium-High</span></td>
<td>ETL Validator's GenAI-assisted maintenance significantly reduces upkeep. Great Expectations and dbt Tests require engineers to update definitions manually for every schema or pipeline change.</td>
</tr>
<tr class="etl-data-row">
<td>Cross-Pipeline Orchestration</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator orchestrates tests across multiple pipelines in a single run. Great Expectations is partial. dbt Tests are scoped to the dbt DAG.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">3. Usability & Test Authoring</td></tr>
<tr class="etl-data-row">
<td>No-Code / Visual Test Builder</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator is the only tool with a drag-and-drop no-code interface for ETL testing. Great Expectations and dbt Tests require coding.</td>
</tr>
<tr class="etl-data-row">
<td>Ease of Setup</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator deploys in days. Great Expectations requires configuration of datasources and expectation suites. dbt Tests require an existing dbt project.</td>
</tr>
<tr class="etl-data-row">
<td>Business User Accessibility</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator is designed for QA analysts and business users without coding skills. Great Expectations and dbt Tests both require Python or SQL proficiency.</td>
</tr>
<tr class="etl-data-row">
<td>GenAI / AI-Assisted Test Creation</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator generates tests automatically from ETL mapping documents using agentic AI, cutting initial test creation time by over 60%. Neither Great Expectations nor dbt Tests offer this.</td>
</tr>
<tr class="etl-data-row">
<td>Test Documentation & Visibility</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator provides customisable stakeholder dashboards. Great Expectations generates Data Docs but they are technical in nature. dbt generates docs automatically but visibility for non-engineers is limited.</td>
</tr>
<tr class="etl-data-row">
<td>Learning Curve</td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">High</span></td>
<td><span class="sym-text">High</span></td>
<td>ETL Validator is the fastest to productive use for any team profile. Great Expectations and dbt Tests require mastery of Python or the full dbt framework.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">4. Data Quality & Observability</td></tr>
<tr class="etl-data-row">
<td>Data Quality Monitoring</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator provides continuous DQ monitoring with scoring and alerting. Great Expectations supports expectation-based monitoring. dbt Tests run at job execution time only.</td>
</tr>
<tr class="etl-data-row">
<td>Anomaly Detection</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator automatically detects data anomalies across pipelines using AI. Neither Great Expectations nor dbt Tests offer automated anomaly detection.</td>
</tr>
<tr class="etl-data-row">
<td>Data Profiling</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator provides rich data profiling alongside test execution. Great Expectations offers profiling through its Profiler API. dbt Tests require separate tools.</td>
</tr>
<tr class="etl-data-row">
<td>Data Lineage</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-star">★</span></td>
<td>dbt auto-generates column-level lineage across the entire DAG. ETL Validator provides pipeline-level lineage tied to DQ scoring. Great Expectations has no lineage support.</td>
</tr>
<tr class="etl-data-row">
<td>DQ Scoring & Health Dashboards</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator uniquely provides quantified DQ scores and health dashboards across pipelines. Neither Great Expectations nor dbt offer this natively.</td>
</tr>
<tr class="etl-data-row">
<td>Alerting & Notifications</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator supports native alerting on test failures. Great Expectations and dbt alerting depend on the orchestration layer.</td>
</tr>
<tr class="etl-data-row">
<td>BI Regression Testing</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator's visual BI report regression testing across Power BI, Tableau, QuickSight, and Oracle Analytics has no equivalent in Great Expectations or dbt.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">5. Data Contracts & Governance</td></tr>
<tr class="etl-data-row">
<td>Data Contracts</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator supports formal data contracts across pipeline boundaries. Great Expectations supports expectation-as-contract patterns. dbt has partial support via dbt contracts (1.5+).</td>
</tr>
<tr class="etl-data-row">
<td>Schema Validation & Drift Detection</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td>All three detect schema drift. ETL Validator and dbt Tests are more automated. Great Expectations requires expectation suite updates.</td>
</tr>
<tr class="etl-data-row">
<td>Data Observability Integration</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator provides built-in observability across the full pipeline. dbt and Great Expectations integrate with third-party observability tools.</td>
</tr>
<tr class="etl-data-row">
<td>Audit Trails & Compliance Reporting</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator provides compliance-grade audit trails out of the box. Great Expectations generates run history logs but requires additional tooling for audit reports. dbt requires significant custom engineering.</td>
</tr>
<tr class="etl-data-row">
<td>Role-Based Access Control</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator supports enterprise RBAC natively. Great Expectations has no built-in RBAC. dbt Cloud offers team-level permissions.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">6. Testing Scope & Coverage</td></tr>
<tr class="etl-data-row">
<td>Mixed-Source Pipelines (DB + Files + APIs)</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator's Apache Spark engine supports the largest number of heterogeneous sources. Great Expectations supports multiple backends. dbt is warehouse-only.</td>
</tr>
<tr class="etl-data-row">
<td>Legacy System Testing (SSIS, Informatica, ODI)</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator tests pipelines built in any ETL tool including legacy platforms. Great Expectations requires custom datasource connectors. dbt Tests are not suitable for non-dbt pipelines.</td>
</tr>
<tr class="etl-data-row">
<td>Streaming / Real-Time Data Validation</td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and Great Expectations have partial streaming support. dbt is mainly a batch transformation tool.</td>
</tr>
<tr class="etl-data-row">
<td>Extensibility</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator provides custom plugins using Python. Great Expectations is highly extensible via its custom expectation framework. dbt has a fixed set of capabilities.</td>
</tr>
<tr class="etl-data-row">
<td>Test Data Generation</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator uniquely generates synthetic test data for automating pipeline testing. Neither Great Expectations nor dbt Tests offer this.</td>
</tr>
<tr class="etl-data-row">
<td>End-to-End Pipeline Coverage</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator covers ingestion, transformation, loading, and BI reporting. Great Expectations and dbt Tests cover only portions of the pipeline.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">7. Enterprise Readiness</td></tr>
<tr class="etl-data-row">
<td>Enterprise Support & SLAs</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator offers dedicated commercial support with SLAs. Great Expectations has commercial support via GX Cloud. dbt Core is open-source with community support only.</td>
</tr>
<tr class="etl-data-row">
<td>On-Premise Deployment</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and Great Expectations support on-premise deployment. dbt Cloud is SaaS-based.</td>
</tr>
<tr class="etl-data-row">
<td>Multi-Project / Multi-Team Support</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator supports multiple projects in a single deployment with container isolation. Great Expectations and dbt support multi-team setups with configuration.</td>
</tr>
<tr class="etl-data-row">
<td>Custom Dashboards for Stakeholders</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator provides fully customisable stakeholder-facing dashboards. Great Expectations generates Data Docs but they are developer-facing. dbt has no stakeholder dashboard capability.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">8. Scalability & Performance</td></tr>
<tr class="etl-data-row">
<td>Handling Large Data Volumes</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-check">✔</span></td>
<td>ETL Validator's Spark-based execution engine is built for billions of records. Great Expectations performance is dependent on the compute backend.</td>
</tr>
<tr class="etl-data-row">
<td>Auto-Scaling</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator has native on-demand auto-scaling. dbt and Great Expectations rely on underlying infrastructure.</td>
</tr>
<tr class="etl-data-row">
<td>Parallel Test Execution</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator's Spark engine enables high-parallelism across hundreds of tests simultaneously. Great Expectations and dbt test parallelism are infrastructure-dependent.</td>
</tr>
<tr class="etl-data-row">
<td>Cloud-Native Deployment</td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td><span class="sym-check">✔</span></td>
<td>All three are cloud-native. ETL Validator supports AKS, EKS, GKE, and Databricks. Great Expectations supports GX Cloud. dbt Cloud is fully managed.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">9. Pricing & Accessibility</td></tr>
<tr class="etl-data-row">
<td>Licensing Model</td>
<td><span class="sym-text">Commercial</span></td>
<td><span class="sym-text">Open-Source / GX Cloud</span></td>
<td><span class="sym-text">Open-Source / dbt Cloud</span></td>
<td>Great Expectations Core is open-source; GX Cloud adds a managed tier. dbt Core is free; dbt Cloud is commercial. The true cost of both includes significant engineering time to build and maintain.</td>
</tr>
<tr class="etl-data-row">
<td>Relative Cost</td>
<td><span class="sym-text">Best value</span></td>
<td><span class="sym-text">Free + engineering cost</span></td>
<td><span class="sym-text">Free + engineering cost</span></td>
<td>Both Great Expectations and dbt Tests appear free but carry hidden engineering costs. ETL Validator delivers the broadest feature coverage across total cost of ownership.</td>
</tr>
<tr class="etl-data-row">
<td>ETL Vendor Lock-in Risk</td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">Medium</span></td>
<td>dbt Tests are tightly coupled to the dbt ecosystem. ETL Validator and Great Expectations carry low lock-in risk.</td>
</tr>
<tr class="etl-data-row">
<td>Ideal Team Profile</td>
<td><span class="sym-text">Data Engineering & QA teams of all sizes</span></td>
<td><span class="sym-text">Python-proficient data engineers</span></td>
<td><span class="sym-text">dbt-native analytics engineers</span></td>
<td>Great Expectations and dbt Tests require engineering depth. ETL Validator serves QA, engineering, and business users of all profiles.</td>
</tr>
</tbody>
</table>
</div>
</div> </div>
</div>
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<h3 class="elementor-heading-title elementor-size-default">Which ETL Testing Tool Should You Choose?</h3> </div>
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<p class="font-claude-response-body">Choosing the right <span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener"><span style="text-decoration: underline;">ETL testing tool</span></a></span> depends on how comprehensive your testing needs are across data pipelines. While multiple tools offer specific capabilities, they differ significantly in scope, flexibility, and coverage.</p> </div>
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Datagaps ETL Validator provides a more complete approach by supporting end-to-end ETL testing across heterogeneous data sources, including databases, files, APIs and BI layers. It also offers automation, AI-driven test generation, and scalability required for modern data environments. </p>
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Great Expectations is a powerful open-source framework for defining and validating data quality expectations. It works well for Python-proficient data engineering teams who need flexible, code-driven validation. However, it requires significant setup and engineering effort, has no no-code interface, and does not support end-to-end ETL testing or BI layer validation out of the box. </p>
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dbt Tests are limited to rule-based data checks within a single data warehouse. They are not built for complete ETL testing and do not address pipeline validation across systems. </p>
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<h3 class="elementor-heading-title elementor-size-default">Why Datagaps ETL Validator Is the Right ETL Testing Tool</h3> </div>
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<p><span style="font-weight: 600;">For teams that need comprehensive coverage across the full pipeline, <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; font-weight: 600;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">Datagaps ETL Validator </a></span>is the clear choice. Three reasons stand out:</span></p><ul><li><strong>End-to-end pipeline coverage</strong>: Where Great Expectations covers data quality checks at specific points and dbt Tests stay within the warehouse, ETL Validator goes further: across heterogeneous sources, through transformations, and all the way to the BI reporting layer. No stitching of multiple tools required.</li><li><strong>Scalability built in:</strong> ETL Validator is built on a Spark-based engine, purpose-designed to handle enterprise data volumes without compromising on performance. Great Expectations performance is dependent on the underlying compute backend, and dbt Tests do not scale independently of the warehouse.</li><li><strong>Accessible to the whole team:</strong> ETL Validator is the only tool in this comparison with a no-code interface, making it usable by QA analysts and business users alongside data engineers. Great Expectations and dbt Tests both require Python or SQL proficiency, limiting who can build and maintain tests.</li></ul> </div>
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<p>Datagaps is recognized as a data pipelines test automation specialist in Gartner’s Market Guide for DataOps Tools. If reliable, end-to-end data validation matters to your team, <span style="text-decoration: underline;"><span style="color: #1967d2;"><strong><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">Datagaps ETL Validator</a></strong></span></span> is the tool built for that job.</p> </div>
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<p><span style="text-decoration: underline;">Disclaimer</span>: The above-mentioned list is purely an outcome of the conversations and feedback received from various industry users in the ETL/Data Warehouse testing space. Any concerns or views can be shared at <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="mailto:contact@datagaps.com">contact@datagaps.com</a></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Watch ETL Validator in Action with Demo</h2> </div>
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Check out how ETL Validator simplifies ETL Testing, data validation through automation across pipelines from this playlist </div>
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<span >
Demo Playlist </span>
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<p>Start your 14-day free trial in our sandbox. Explore and optimize your ETL processes. Start your trial today!</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Get Started with ETL Validator – An ETL & Data Testing tool</h2> </div>
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Request a Demo </span>
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<p>The post <a href="https://www.datagaps.com/blog/top-3-etl-testing-tools/">Top 3 ETL Testing Tools: How to Choose the Best Tool</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<title>ETL Testing In Snowflake Using DataOps Suite</title>
<link>https://www.datagaps.com/blog/etl-testing-in-snowflake-using-dataops-suite/</link>
<comments>https://www.datagaps.com/blog/etl-testing-in-snowflake-using-dataops-suite/#respond</comments>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Sat, 14 Feb 2026 13:18:00 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[ETL Testing]]></category>
<category><![CDATA[Snowflake]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=11775</guid>
<description><![CDATA[<p>ETL stands for Extract, Transform, and Load. It is the process by which data is extracted from one or more sources, transformed into compatible formats, and then loaded into a target Database or Data Warehouse.</p>
<p>The post <a href="https://www.datagaps.com/blog/etl-testing-in-snowflake-using-dataops-suite/">ETL Testing In Snowflake Using DataOps Suite</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<p>ETL testing in <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/snowflake-testing-automation/" target="_blank" rel="noopener">Snowflake</a></span> involves three stages — <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">extraction, transformation, and loading</a></span> — each of which needs validation to confirm data quality throughout the pipeline. This post walks through a practical example: extracting customer data directly from Snowflake, transforming it per business requirements, loading it to a target using a DB Sink component, and finally validating the generated reports using <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/bi-testing-tools/" target="_blank" rel="noopener">BI Validator</a></span>. It’s a straightforward look at how DataOps Suite handles each ETL stage for Snowflake specifically.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>ETL testing in Snowflake follows the standard three-stage process</strong> — extraction, transformation, and loading — each needing its own validation checkpoint.</li><li><strong>Data extraction can pull from the same or different source locations</strong> — the example in this post extracts customer data directly from a Snowflake table.</li><li><strong>The DB Sink component handles data loading</strong> — moving transformed data to its target file location as the final ETL step.</li><li><strong>BI Validator closes the loop on report accuracy</strong> — once ETL processing completes, generated reports are checked and validated to confirm they reflect the transformed data correctly.</li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Introduction and Overview of ETL Testing Snowflake</h2> </div>
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.ibm.com/in-en/topics/etl" target="_blank" rel="noopener">ETL</a></span> stands for Extract, Transform, and Load. It is the process by which data is extracted from one or more sources, transformed into compatible formats, and then loaded into a target Database or Data Warehouse. The sources may include Flat Files, Third-Party Applications, Databases, etc.<span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"> </span></span><a href="https://www.datagaps.com/data-testing-concepts/etl-testing/" target="_blank" rel="noopener"><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;">ETL testing</span></span></a> is necessary to ensure that data moving from external sources to the data warehouse is accurate at each point between the source and destination.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">Purpose of ETL</h3> </div>
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<p>ETL allows businesses to consolidate data from multiple databases and other sources into a single repository with the data that has been modified and used during the analysis of data. This unified data repository allows for simplified access to analysis and additional processing of the data. There are many advantages of using ETL tools for the migration of data. It reduces delivery time, reduces unnecessary expenses, makes the process easy to use, and also will be simple for data migrations. Data Integration, Data Warehousing, and Data Migration are the three common uses of ETL.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">ETL Testing Process in Snowflake</h3> </div>
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<p>The data will be migrated from one data warehouse to another cloud-based <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://aws.amazon.com/data-warehouse/" target="_blank" rel="noopener">data warehouse</a></span> using various steps present in ETL Testing. The multiple steps involved in this process are the extraction of data, the transformation of the data, and finally the loading of data to the different data sources. This process is essential for proper testing such the quality of data can be checked efficiently. The DataOps Suite tool can be used efficiently for ETL Testing. <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/request-demo/" target="_blank" rel="noopener">Request Demo</a></span></p> </div>
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<h5><strong>The various steps involved in ETL Testing are as follows:</strong></h5> </div>
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<h3 class="elementor-heading-title elementor-size-default">Step 1: Extraction Of Data</h3> </div>
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<p>Data Extraction is the first step that will be performed in the <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/etl-testing/" target="_blank" rel="noopener">ETL Testing</a></span>. In this procedure, the data will usually be extracted from the same data source, or it can be extracted from different source locations also. Here, for example, the data is extracted from the same source i.e. Snowflake, and Customer data is extracted. After extracting the data from the source location, then further the data can be transformed according to the client’s requirements.</p> </div>
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<img fetchpriority="high" decoding="async" width="1320" height="697" src="https://www.datagaps.com/wp-content/uploads/Data-Extraction-From-Customers-Table.png" class="attachment-full size-full wp-image-11776" alt="Data-Extraction-From-Customers-Table" srcset="https://www.datagaps.com/wp-content/uploads/Data-Extraction-From-Customers-Table.png 1320w, https://www.datagaps.com/wp-content/uploads/Data-Extraction-From-Customers-Table-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Data-Extraction-From-Customers-Table-1024x541.png 1024w, https://www.datagaps.com/wp-content/uploads/Data-Extraction-From-Customers-Table-768x406.png 768w" sizes="(max-width: 1320px) 100vw, 1320px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Data Extraction From Customers Table</figcaption>
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<h3 class="elementor-heading-title elementor-size-default">Step 2: Transformation Of Data</h3> </div>
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<p>After the data is extracted from the same or different data source to the same or the other source, a few changes or transformations in the customers’ data are done. Generally, data transformations include changes in data types or other changes according to the client’s requirements.</p><p>The below screenshot depicts the Customer data that is being transformed.</p> </div>
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<img decoding="async" width="1321" height="695" src="https://www.datagaps.com/wp-content/uploads/Data-Transformation-Using-SQL-Component.png" class="attachment-full size-full wp-image-11777" alt="Data-Transformation-Using-SQL-Component" srcset="https://www.datagaps.com/wp-content/uploads/Data-Transformation-Using-SQL-Component.png 1321w, https://www.datagaps.com/wp-content/uploads/Data-Transformation-Using-SQL-Component-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Data-Transformation-Using-SQL-Component-1024x539.png 1024w, https://www.datagaps.com/wp-content/uploads/Data-Transformation-Using-SQL-Component-768x404.png 768w" sizes="(max-width: 1321px) 100vw, 1321px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Data Transformation Using SQL Component</figcaption>
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<p>Once the data is transformed, <strong><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-reconciliation/" target="_blank" rel="noopener">data comparison</a></span> </strong>can be performed to view the changes after transformation.</p> </div>
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<img decoding="async" width="1320" height="700" src="https://www.datagaps.com/wp-content/uploads/Comparison-Of-Data-Using-Data-Compare-Component.png" class="attachment-full size-full wp-image-11778" alt="Comparison-Of-Data-Using-Data-Compare-Component" srcset="https://www.datagaps.com/wp-content/uploads/Comparison-Of-Data-Using-Data-Compare-Component.png 1320w, https://www.datagaps.com/wp-content/uploads/Comparison-Of-Data-Using-Data-Compare-Component-300x159.png 300w, https://www.datagaps.com/wp-content/uploads/Comparison-Of-Data-Using-Data-Compare-Component-1024x543.png 1024w, https://www.datagaps.com/wp-content/uploads/Comparison-Of-Data-Using-Data-Compare-Component-768x407.png 768w" sizes="(max-width: 1320px) 100vw, 1320px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Comparison Of Data Using Data Compare Component</figcaption>
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<p><span class="fontSizeMediumPlus">Further, the quality of data can be checked by using the <strong>Data Rules Component. </strong></span><span class="fontSizeMediumPlus"><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-quality-testing/" target="_blank" rel="noopener">Data quality checks</a></span> are done to find out the issues in the quality of data. The <strong>Data</strong><strong> Profile Component</strong> can also be used to find out the data quality issues.</span></p> </div>
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<p>In the below screenshot, the quality of data is checked by verifying the email address as well as the name string check by using different data rules in the data rules component.</p> </div>
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<img loading="lazy" decoding="async" width="1323" height="696" src="https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component.png" class="attachment-full size-full wp-image-11779" alt="Data-Quality-Check-Using-Data-Rules-Component" srcset="https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component.png 1323w, https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component-1024x539.png 1024w, https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component-768x404.png 768w" sizes="(max-width: 1323px) 100vw, 1323px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Data Quality Check Using Data Rules Component</figcaption>
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<p>Data profiling is also done to check the quality of data.</p> </div>
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<img loading="lazy" decoding="async" width="1320" height="695" src="https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component.png" class="attachment-full size-full wp-image-11780" alt="Profiling-Data-Using-Data-Profile-Component" srcset="https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component.png 1320w, https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component-1024x539.png 1024w, https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component-768x404.png 768w" sizes="(max-width: 1320px) 100vw, 1320px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Profiling Data Using Data Profile Component</figcaption>
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<h3 class="elementor-heading-title elementor-size-default">Step 3: Loading Of The Data</h3> </div>
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<p>Once the transformation of data is performed, further the data will be loaded from one source to a particular file location. Here the data is loaded by using the <strong>DB Sink component.</strong> This is the general testing process followed in the DataOps Suite tool. <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/request-demo/" target="_blank" rel="noopener">Request Demo</a></span></p> </div>
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<p>The below screenshot depicts the data loaded to the desired data source after the data transformations are done.</p> </div>
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<img loading="lazy" decoding="async" width="1320" height="695" src="https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component.png" class="attachment-full size-full wp-image-11781" alt="Data-Loading-Using-DB-Sink-Component" srcset="https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component.png 1320w, https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component-1024x539.png 1024w, https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component-768x404.png 768w" sizes="(max-width: 1320px) 100vw, 1320px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Data Loading Using DB Sink Component</figcaption>
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<thead>
<tr style="background: #d6e3f5;">
<th style="padding: 12px; border: 1px solid #ccc;">Stage</th>
<th style="padding: 12px; border: 1px solid #ccc;">Component(s) Used</th>
<th style="padding: 12px; border: 1px solid #ccc;">What Gets Validated</th>
</tr>
</thead>
<tbody>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">1. Extraction</td>
<td style="padding: 12px; border: 1px solid #ccc;">Source Connection to Snowflake</td>
<td style="padding: 12px; border: 1px solid #ccc;">Verifies that customer data is extracted correctly from the source table.</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">2. Transformation</td>
<td style="padding: 12px; border: 1px solid #ccc;">SQL Component, Data Compare Component, Data Rules Component, Data Profile Component</td>
<td style="padding: 12px; border: 1px solid #ccc;">Validates data type transformations, compares transformed data, enforces data quality rules (such as email and name format checks), and performs data profiling.</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">3. Loading</td>
<td style="padding: 12px; border: 1px solid #ccc;">DB Sink Component</td>
<td style="padding: 12px; border: 1px solid #ccc;">Confirms that transformed data is loaded accurately into the target destination.</td>
</tr>
</tbody>
</table> </div>
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<p>Once the ETL Testing process is completed, the reports generated need to be checked and evaluated as there will be some differences. In our DataOps Suite tool, BI Validator can be used to check and evaluate the reports.</p><p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/data-testing-concepts/data-warehouse-testing-checklist/" target="_blank" rel="noopener">Read: Data Warehouse Testing Checklist</a></span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">Conclusion</h3> </div>
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/etl-testing/" target="_blank" rel="noopener">ETL testing</a></span> is an important process when data is transferred from one or multiple databases to another database, especially when a huge amount of data is used. It makes sure that the data loaded in the destination source is accurate enough. The step-by-step procedure of ETL Testing can be checked by using different components in our DataOps Suite tool. By using ETL Testing, the performance can be increased. Once the entire ETL Testing is completed in Snowflake, then finally the reports will be generated. The reports generated will be checked and validated finally by using the <a href="https://www.datagaps.com/bi-validator/">BI Validator</a>.</p> </div>
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<summary>1) What are the main stages of ETL testing in Snowflake?</summary>
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ETL testing in Snowflake follows extraction, transformation, and loading — extracting data (often from
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<p>Digital Marketing Manager at Datagaps. Drives data-driven growth through content, performance campaigns, and marketing technology.</p> </div>
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S P S Murthy Akella </a>
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Director, Technology Strategy, Datagaps </p>
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<p>Director of Technology Strategy at Datagaps. Business solutions architect and Certified Scrum Master in data engineering, responsible AI, and ML across BFSI, telecom, aviation, and energy.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/etl-testing-in-snowflake-using-dataops-suite/">ETL Testing In Snowflake Using DataOps Suite</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<title>Datagaps Data Validation and Migration To Snowflake</title>
<link>https://www.datagaps.com/blog/data-validation-in-snowflake-data-migration/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Mon, 19 Jan 2026 12:31:00 +0000</pubDate>
<category><![CDATA[Snowflake]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=7286</guid>
<description><![CDATA[<p>This article describes ways in which we can test the data movement between Snowflake and on-premises data and between instances of Snowflake itself. We will also provide some benchmarks for doing comparison testing for large volumes of data moved into Snowflake.</p>
<p>The post <a href="https://www.datagaps.com/blog/data-validation-in-snowflake-data-migration/">Datagaps Data Validation and Migration To Snowflake</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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<p>This blog explains how Datagaps validates data movement between on-premises databases and Snowflake, and between Snowflake instances, using DataFlow. A real client case study covers migrating 400 SQL Server tables (500M+ rows each) to <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/cloud-data-test-automation/snowflake/" target="_blank" rel="noopener">Snowflake</a></span>, surfacing issues like numeric precision differences, null inconsistencies, and truncation. It also covers transitioning from ETL to ELT, BI Validator-driven regression testing, and AWS EMR cluster auto-scaling to over 30 nodes—achieving roughly a 50% reduction in test cycle time.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li>Bulk migration testing catches issues at scale — using the Data Migration Wizard, a client generated comparison tests for 400 SQL Server tables (500M+ rows each) in just a few hours, surfacing numeric precision, null value, and truncation issues.</li><li>DataFlow supports the ETL-to-ELT transition — as the client shifted their pipeline architecture toward Snowflake, DataFlow validated accuracy across iterations until source and target data were fully in sync.</li><li>BI Validator extends testing to the reporting layer — clients use it for regression, performance, and stress testing to compare BI dashboards and reports between the old warehouse and new Snowflake implementation.</li><li>AWS EMR auto-scaling improves testing efficiency — DataOps Suite’s cluster integration scaled EMR nodes up to 30+ during comparisons of 500M-record tables, then scaled down after, cutting testing costs while achieving an estimated 50% reduction in test cycle time.</li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Data validation in snowflake</h2> </div>
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<p>Validating data during a migration to Snowflake means confirming that records, data types, and precision all carry over correctly from the source system — and this article discusses why and how to do that, diving into the challenges of bulk <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/cloud-data-test-automation/snowflake/" target="_blank" rel="noopener">Data Migration to Snowflake.</a></span></span></p> </div>
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<section class="elementor-section elementor-inner-section elementor-element elementor-element-b696c59 elementor-section-content-top bw-ac elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="b696c59" data-element_type="section" data-e-type="section">
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<h5 class="elementor-heading-title elementor-size-default">Why and How?</h5> </div>
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People are rapidly adopting cloud architectures for Data Warehouses and Machine Learning projects due to the economies of scale in the cloud. One obstacle in achieving rapid success is the data and data types inconsistencies between on-premise structures and the modern data stacks in the cloud. </div>
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When you migrate vast amounts of data to the cloud, the opportunity to introduce mistakes is multiplied due to these reasons and others. The earlier you catch the issues, the less costly it is to resolve the discrepancies. </div>
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<p><strong>This is where </strong><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/" target="_blank" rel="noopener">Datagaps</a></span><strong> come in to play.</strong></p> </div>
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<p><a href="https://www.datagaps.com/wp-content/uploads/2022/01/database-and-Snowflake.svg"><br /><img loading="lazy" decoding="async" class="attachment-thumbnail size-thumbnail wp-image-6747" src="https://www.datagaps.com/wp-content/uploads/database-and-Snowflake.svg" alt="database-and-Snowflake" width="129" height="150" /> </a><br />We provide the ability to run <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-reconciliation/" target="_blank" rel="noopener">data reconciliation</a></span> on millions & even billions of records between source and target structures of different types, such as your on-premises database and Snowflake.</p> </div>
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This article describes ways in which we can test the data movement between Snowflake and on-premises data and between instances of Snowflake itself. We will also provide some benchmarks for doing comparison testing for large volumes of data moved into Snowflake. Another benefit of the Datagaps approach is, as additional data is moved into the Snowflake structure from other sources, Datagaps provides the ability to monitor the quality of your data structures by continually providing an up to date scoring so that you can determine when data is becoming corrupted. </div>
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<h2 class="elementor-heading-title elementor-size-default">Benefits of using Datagaps to test data movement into Snowflake</h2> </div>
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<p>We recently sat down with one of our clients that uses our DataFlow product for testing the data migration from on-Prem SQL Server to Snowflake in the cloud running in <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://aws.amazon.com/" target="_blank" rel="noopener">AWS.</a></span></p> </div>
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<img loading="lazy" decoding="async" width="464" height="53" src="https://www.datagaps.com/wp-content/uploads/Datagaps-Snowflake_1.svg" class="attachment-full size-full wp-image-6812" alt="Datagaps-Snowflake" /> </div>
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<h5 class="elementor-heading-title elementor-size-default">Implementation</h5> </div>
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<p>They started the initial migration by performing bulk loads from 400 SQL Server tables to Snowflake with minimal transformations. This was stage 0, where they could perform source and target data comparison for over 500 million rows of data per table. Making use of the Data Migration wizard, the client was able to generate comparison tests for 400 tables in just a few hours. Even though there were few changes at this stage, they still encountered errors that were surfaced by our<br />DataFlow product.</p> </div>
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<strong>Examples:</strong> Issues include numeric precision differences, null value inconsistencies, truncation issues and character interpolations. </div>
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<p>Next, they began to perform incremental new data migrations where they continued to find similar issues that had to be corrected. As this continued, they wanted to transition from this incremental new data migration from SQL Server to loading the new data directly into Snowflake to reap the benefits stated earlier. To accomplish this, their initial ETL processes needed to be migrated to an ELT process aimed at Snowflake.</p> </div>
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<p>DataFlow was used once again to check the accuracy between the two systems once the new processes were in place. The validations exposed issues in the new <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/etl-testing/" target="_blank" rel="noopener">ELT process</a></span> through several iterations until the transformation were in sync. After a short period of testing, they could cut over to the new system and deprecate the SQL server environment. Now DataFlow continued to validate the incremental data as it was moved into Snowflake, finding issues earlier in the cycle where they are less costly to fix in time and lost credibility.</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">How it makes a difference?</h5> </div>
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Many of our clients take this one step further by testing their BI tools against the old warehouse and the new Snowflake implementation. They do regression, performance, and stress testing using our BI Validator tool to compare the old with the new. They can find differences in the look and feel in the output generation of the reports and dashboards. Differences are exposed between the report queries when compared to a database query. Often this is the last task necessary to validate the migration process. </div>
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Making use of the inbuilt cluster integration with AWS EMR in the DataOps suite, the client was able to automatically scale the EMR cluster on demand to over 30 nodes depending on the data volumes being compared and scale down once the testing has been completed. This capability helped reduce the cost of the testing while still achieving high performance when comparing tables of size <strong>500 Million Records</strong> </div>
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<h6 class="elementor-heading-title elementor-size-default">Reduction in Testing <br> Time by</h6> </div>
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<h6 class="elementor-heading-title elementor-size-default">Improved Testing ROI by</h6> </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;">Facts</th>
<th style="padding: 12px; border: 1px solid #ccc;">Detail</th>
</tr>
</thead>
<tbody>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Migration path</td>
<td style="padding: 12px; border: 1px solid #ccc;">On-premise SQL Server → Snowflake (AWS)</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Initial bulk load scope</td>
<td style="padding: 12px; border: 1px solid #ccc;">400 SQL Server tables, over 500 million rows per table compared</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Test case generation time</td>
<td style="padding: 12px; border: 1px solid #ccc;">Comparison tests for 400 tables generated in a few hours via the Data Migration wizard</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Compute scaling</td>
<td style="padding: 12px; border: 1px solid #ccc;">AWS EMR cluster auto-scaled up to 30 nodes based on data volume, then scaled back down</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Manual vs. automated testing coverage</td>
<td style="padding: 12px; border: 1px solid #ccc;">From manual testing of 10,000 sample records to complete testing of 500 million</td>
</tr>
</tbody>
</table> </div>
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<h3 class="elementor-heading-title elementor-size-default">Amount of data tested increased from manual testing of <strong>10,000 sample records</strong> to complete testing of <strong>500 M</strong>.</h3> </div>
</div>
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</section>
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<img loading="lazy" decoding="async" width="595" height="96" src="https://www.datagaps.com/wp-content/uploads/Datagaps-Snowflake_Banner.svg" class="attachment-full size-full wp-image-6856" alt="Datagaps-Snowflake_Banner" /> </div>
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<h5 class="elementor-heading-title elementor-size-default">Conclusion</h5> </div>
</div>
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<p>In conclusion, the goals of the migration project of agility, cost savings and performance improvements were achieved.</p><p>They also realized these benefits months earlier as a result of the improvement in the migration process due to the impact of the DataFlow products contribution in an estimated 50% test cycle reduction.</p><p>One of our clients reports comparing a file against a <strong>Snowflake</strong> instance with</p> </div>
</div>
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<span class="elementor-counter-number" data-duration="2000" data-to-value="23" data-from-value="0" data-delimiter=",">0</span>
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<h3 id="faq-heading">FAQs: Snowflake Data Migration Validation</h3>
<section class="faq-section" aria-labelledby="faq-heading">
<div class="faq-list">
<details>
<summary>1) What kind of data validation is needed when migrating to Snowflake?</summary>
<p>
Snowflake migrations require validating data transferred from on-premises
databases as well as comparisons between Snowflake environments. Validation helps
identify issues such as numeric precision differences, null value mismatches, data
truncation, and other inconsistencies introduced during migration.
</p>
</details>
<details>
<summary>2) How does the Data Migration Wizard help with large-scale migrations?</summary>
<p>
The Data Migration Wizard automatically generates comparison tests for large
numbers of tables, significantly reducing manual effort. For example, it has been
used to validate hundreds of SQL Server tables containing hundreds of millions of
rows, enabling organizations to quickly identify migration discrepancies at scale.
</p>
</details>
<details>
<summary>3) How does BI Validator support Snowflake migrations beyond the database layer?</summary>
<p>
BI Validator extends migration validation to reports and dashboards by performing
automated regression testing, performance testing, and stress testing. This helps
ensure reporting outputs remain accurate and consistent after migrating from a
legacy data warehouse to Snowflake.
</p>
</details>
<details>
<summary>4) How does AWS EMR cluster integration improve testing performance during migration?</summary>
<p>
DataOps Suite can automatically scale AWS EMR clusters to support resource-intensive
comparisons of very large datasets and then scale them down when processing is
complete. This approach improves performance, optimizes infrastructure costs, and
helps reduce overall migration testing time.
</p>
</details>
</div>
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<p>Director of Technology Strategy at Datagaps. Business solutions architect and Certified Scrum Master in data engineering, responsible AI, and ML across BFSI, telecom, aviation, and energy.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/data-validation-in-snowflake-data-migration/">Datagaps Data Validation and Migration To Snowflake</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></content:encoded>
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<title>Data Validation for Snowflake</title>
<link>https://www.datagaps.com/blog/snowflake-data-migration-testing/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Fri, 18 Feb 2022 14:28:42 +0000</pubDate>
<category><![CDATA[Snowflake]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=7365</guid>
<description><![CDATA[<p>Migration from a legacy data warehouse such as Netezza to a cloud-based Snowflake data warehouse requires multiple steps. Data Validation is the key to the success of data migration projects. Datagaps Data Flow can be used to validate data in each step of the data migration as</p>
<p>The post <a href="https://www.datagaps.com/blog/snowflake-data-migration-testing/">Data Validation for Snowflake</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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<img loading="lazy" decoding="async" width="640" height="254" src="https://www.datagaps.com/wp-content/uploads/snowflake-data-migration-1-1024x407.webp" class="attachment-large size-large wp-image-6390" alt="snowflake-data-migration-1" srcset="https://www.datagaps.com/wp-content/uploads/snowflake-data-migration-1-1024x407.webp 1024w, https://www.datagaps.com/wp-content/uploads/snowflake-data-migration-1-300x119.webp 300w, https://www.datagaps.com/wp-content/uploads/snowflake-data-migration-1-768x305.webp 768w, https://www.datagaps.com/wp-content/uploads/snowflake-data-migration-1-1536x611.webp 1536w, https://www.datagaps.com/wp-content/uploads/snowflake-data-migration-1.webp 1798w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>Migrating from a legacy data warehouse to Snowflake requires validation at each step. This guide outlines a four-step process using Datagaps DataFlow: comparing extracted data (row counts, encoding, completeness) between the legacy warehouse and AWS S3, validating transformations using Data Profile and Data Rules components, verifying data loaded into Snowflake against staging files, and end-to-end validation. It also covers using BI Validator to test report accuracy, layout, performance, and security after switching reports to Snowflake.</p> </div>
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<p><strong>Key Takeaways</strong></p><p> </p><ul><li>Extraction validation confirms lossless data movement — comparing row counts, data encoding, completeness, and values between the legacy warehouse and AWS S3 landing zone ensures nothing was lost during extraction.</li><li>Transformation step requires data quality checks before load — Data Profile and Data Rules components help identify and curate data quality issues before syncing data to the staging zone, ahead of loading into Snowflake.</li><li>DataFlow supports single-test, end-to-end validation — a single DataFlow can validate data between the legacy warehouse and S3, as well as between the legacy warehouse and Snowflake, in one unified test.</li><li>Reports need separate validation after migration — BI Validator checks report data accuracy, layout, performance, security, and stress-tests reports under concurrent user loads to confirm they function correctly once switched to Snowflake.</li></ul> </div>
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<h5 class="elementor-heading-title elementor-size-default">Migrating to Snowflake Cloud Data Warehouse</h5> </div>
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<p>Migration from a legacy data warehouse such as Netezza to a cloud-based <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.snowflake.com/en/fundamentals/data-warehouse/" target="_blank" rel="noopener">Snowflake data warehouse</a></span> requires multiple steps. Data Validation is the key to the success of data migration projects. Datagaps DataFlow can be used to validate data in each step of the data migration as well as the end-to-end data validation scenarios. If you are looking for Snowflake testing tools – <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/etl-testing-tools/etl-validator-download/" target="_blank" rel="noopener">Try Dataflow free for 14 days</a></span> </p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Step 1: Extract data from the Legacy data warehouse</h5> </div>
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<p>Data is typically extracted into CSV or Parquet format and moved to a landing zone in AWS S3. Depending on the data volumes, AWS offers multiple options for moving the files to S3. Once the data has been moved to AWS S3, data validations need to be performed to ensure that all the data was properly extracted and migrated to AWS S3. Since there are not many transformations in this step, these tests are typically one-to-one comparisons of the data in the tables in the legacy data warehouse and the files in the AWS S3 landing zone.</p> </div>
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<p>– Compare table to file row counts<br />– Compare data encoding<br />– Compare data completeness<br />– Compare data values</p> </div>
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<p>A sample test case diagram is shown to the right. JDBC Component can be used to read data from the legacy data warehouse. File Component can be used to read data from AWS S3. Finally, the Data Compare component can be used to compare the two datasets. Sample output for a data comparison component is shown below.</p> </div>
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<img loading="lazy" decoding="async" width="584" height="219" src="https://www.datagaps.com/wp-content/uploads/oracle-to-S3.webp" class="attachment-large size-large wp-image-6400" alt="oracle-to-S3" srcset="https://www.datagaps.com/wp-content/uploads/oracle-to-S3.webp 584w, https://www.datagaps.com/wp-content/uploads/oracle-to-S3-300x113.webp 300w" sizes="(max-width: 584px) 100vw, 584px" /> </div>
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<p>Data comparison test case</p> </div>
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<img loading="lazy" decoding="async" width="640" height="327" src="https://www.datagaps.com/wp-content/uploads/data-compare.webp" class="attachment-large size-large wp-image-6402" alt="data-compare" srcset="https://www.datagaps.com/wp-content/uploads/data-compare.webp 1000w, https://www.datagaps.com/wp-content/uploads/data-compare-300x153.webp 300w, https://www.datagaps.com/wp-content/uploads/data-compare-768x392.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>The output of data comparison</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Step 2: Transform data</h5> </div>
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<p>Transformations such as data type conversions can be performed in this step. Data curation can be also done to improve the <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/data-quality/" target="_blank" rel="noopener">Data Quality</a></span> before the data is loaded into Snowflake. Before curating the data, it is important to profile the data and run data quality tests to identify data quality issues with the data. DataFlow can be used to perform these tasks.</p> </div>
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<p>– Compare data between landing zone and staging (curated) zone in S3<br />– Use Data Profile and Data Rules components to identify data quality issues<br />– Curate data and sync to the staging zone</p> </div>
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<img loading="lazy" decoding="async" width="640" height="398" src="https://www.datagaps.com/wp-content/uploads/data-profile.webp" class="attachment-large size-large wp-image-6412" alt="data-profile" srcset="https://www.datagaps.com/wp-content/uploads/data-profile.webp 961w, https://www.datagaps.com/wp-content/uploads/data-profile-300x186.webp 300w, https://www.datagaps.com/wp-content/uploads/data-profile-768x477.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<img loading="lazy" decoding="async" width="640" height="284" src="https://www.datagaps.com/wp-content/uploads/data-rules-1024x455.webp" class="attachment-large size-large wp-image-6413" alt="data-rules" srcset="https://www.datagaps.com/wp-content/uploads/data-rules-1024x455.webp 1024w, https://www.datagaps.com/wp-content/uploads/data-rules-300x133.webp 300w, https://www.datagaps.com/wp-content/uploads/data-rules-768x342.webp 768w, https://www.datagaps.com/wp-content/uploads/data-rules.webp 1493w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>Data Rules component</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Step 3: Copy data to Snowflake</h5> </div>
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<p>Assuming that the Snowflake tables have been created, the last step is to copy the data to the snowflake. Use the VALIDATE function to validate the data files and identify any errors. DataFlow can be used to compare the data between the Staging Zone (S3) files and Snowflake after the load.</p> </div>
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<p>– Compare table to file row counts<br />– Compare data encoding<br />– Compare data completeness<br />– Compare data values<br />– End-to-end data validation (Legacy data warehouse to Snowflake)</p> </div>
</div>
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<p>DataFlow can be used to perform end-to-end <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/blog/how-to-perform-continuous-data-validation/" target="_blank" rel="noopener">Data Validation</a></span> in a single test as shown to the right. A single DataFlow can be used to compare data between legacy data warehouse and S3 as well as legacy data warehouse and Snowflake.</p> </div>
</div>
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<img loading="lazy" decoding="async" width="633" height="338" src="https://www.datagaps.com/wp-content/uploads/End-to-end-data-validation.webp" class="attachment-large size-large wp-image-6423" alt="End-to-end-data-validation" srcset="https://www.datagaps.com/wp-content/uploads/End-to-end-data-validation.webp 633w, https://www.datagaps.com/wp-content/uploads/End-to-end-data-validation-300x160.webp 300w" sizes="(max-width: 633px) 100vw, 633px" /> </div>
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<p>End-to-end test case</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Step 4: Modify reports to Use Snowflake</h5> </div>
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<div class="elementor-element elementor-element-b02f822 elementor-widget elementor-widget-text-editor" data-id="b02f822" data-element_type="widget" data-e-type="widget" data-widget_type="text-editor.default">
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<p>While snowflake provides JDBC/ODBC drivers and supports most of the commonly used SQL functions, there are going to be some differences between the way reports are developed and executed in the legacy <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/services/data-warehouse-services/" target="_blank" rel="noopener">Data Warehouse</a></span> and Snowflake. Once these changes are made, thorough testing needs to be performed between the reports using the legacy data warehouse and the equivalent reports using Snowflake.</p> </div>
</div>
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<p>– Compare report data<br />– Compare report layout<br />– Compare report performance<br />– Stress test reports in the new environments by simulating concurrent user loads<br />– Compare security</p> </div>
</div>
<div class="elementor-element elementor-element-0e76ec0 elementor-widget elementor-widget-text-editor" data-id="0e76ec0" data-element_type="widget" data-e-type="widget" data-widget_type="text-editor.default">
<div class="elementor-widget-container">
<p>Datagaps BI Validator is a no-code <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/bi-testing-tools/bi-validator/">BI Testing Tool</a></span> that can help automate all these tests for the supported BI tools.</p> </div>
</div>
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<p>Try BI Validator free for 14 days for your Snowflake BI testing needs – <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/bi-testing-tools/bi-validator-download/" target="_blank" rel="noopener">Download Now</a></span></p> </div>
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<h3 id="faq-heading">FAQs: Snowflake Data Migration Validation</h3>
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<details>
<summary>1) What are the main steps involved in validating a Snowflake data migration?</summary>
<p>
Snowflake migration validation typically includes four stages: validating data
extraction from the legacy data warehouse to AWS S3, verifying data
transformations and quality in the staging area, comparing staged data with data
loaded into Snowflake, and validating BI reports after they are connected to the
new Snowflake environment.
</p>
</details>
<details>
<summary>2) How is data extraction validated when migrating to Snowflake?</summary>
<p>
Extraction validation compares row counts, data completeness, encoding, and
actual data values between the legacy source system and the files written to AWS
S3. This ensures data is transferred accurately before any transformation or
loading takes place.
</p>
</details>
<details>
<summary>3) Why is data profiling important before loading data into Snowflake?</summary>
<p>
Data profiling identifies quality issues such as missing values, invalid formats,
and inconsistencies before data is loaded into Snowflake. Cleaning and validating
data in the staging area helps ensure only accurate, high-quality data reaches the
target warehouse.
</p>
</details>
<details>
<summary>4) What does report validation involve after migrating to Snowflake?</summary>
<p>
Post-migration report validation verifies that dashboards and reports produce the
same results as before migration. It includes checking data accuracy, visual
consistency, report performance, stress testing under concurrent usage, and
confirming that security and user access permissions remain correctly configured.
</p>
</details>
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Digital Marketing Manager, Datagaps </p>
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<p>Digital Marketing Manager at Datagaps. Drives data-driven growth through content, performance campaigns, and marketing technology.</p> </div>
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Subrahmanya Narayana Chirravuri </a>
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<p>Senior Director of Technology at Datagaps. Leads engineering for the ETL, BI, and data-quality validation platforms.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/snowflake-data-migration-testing/">Data Validation for Snowflake</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<title>Datagaps Attains Snowflake Select Technology Partner Status</title>
<link>https://www.datagaps.com/blog/datagaps-attains-snowflake-select-technology-partner-status/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Tue, 08 Feb 2022 19:52:46 +0000</pubDate>
<category><![CDATA[BI Testing]]></category>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Dataflow]]></category>
<category><![CDATA[ETL Testing]]></category>
<category><![CDATA[Snowflake]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=3955</guid>
<description><![CDATA[<p>Datagaps Inc. announces a new partnership with Snowflake, the Data Cloud company. Datagaps attains Snowflake Select Technology Partner in the space of Data and Business Intelligence Validation to help organizations validate their migration to Snowflake’s Data Cloud.</p>
<p>The post <a href="https://www.datagaps.com/blog/datagaps-attains-snowflake-select-technology-partner-status/">Datagaps Attains Snowflake Select Technology Partner Status</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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<p>Datagaps achieved Snowflake Select Technology Partner status in the Data and Business Intelligence Validation space, helping organizations validate migrations to Snowflake’s Data Cloud. Datagaps’ ETL Validator confirms ETL processes run correctly against new Snowflake data structures, while the Data Migration Wizard validates that hundreds of legacy tables migrated accurately. BI Validator supports the transition further with regression testing, performance testing, stress testing, and old-vs-new GUI comparisons to ensure BI applications work correctly after migration.</p> </div>
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<p><strong>Key Takeaways</strong></p><p> </p><ul><li>Datagaps is now a Snowflake Select Technology Partner, recognized specifically for Data and Business Intelligence Validation capabilities supporting Snowflake migrations.</li><li>ETL Validator confirms post-migration pipeline accuracy — verifying that ETL processes run correctly once data structures are pointed to the new Snowflake environment.</li><li>The Data Migration Wizard validates large-scale table migrations, offering a quick way to confirm hundreds of legacy tables moved to the cloud correctly.</li><li>BI Validator supports the BI-layer transition through regression testing, performance testing, stress testing, and old-vs-new GUI comparisons as reports get redirected to Snowflake.</li></ul> </div>
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<p><a title="Datagaps" href="https://www.datagaps.com/" target="_blank" rel="noopener"><span style="text-decoration: underline; color: #1967d2;">Datagaps Inc</span>.</a> announces a new partnership with <a href="https://www.snowflake.com/en/"><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;">Snowflake</span></span></a>, the Data Cloud company. Datagaps attains Snowflake Select Technology Partner in the space of Data and Business Intelligence Validation to help organizations validate their migration to Snowflake’s Data Cloud.</p> </div>
</div>
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<p>Snowflake delivers the Data Cloud, where thousands of organizations have seamless access to explore, share, and unlock the true value of their data.</p> </div>
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<p>Datagaps as a Select Technology Partner helps organizations validate their legacy migration to Snowflake’s platform. Datagaps ETL Validation helps ensure that the ETL processes are running correctly when the new data structures are pointed to. The Data Migration Wizard provides a quick method for validating that hundreds of legacy tables moved to the cloud correctly. Finally, as the BI applications need to be redirected to the cloud, BI Validator includes regression testing, performance testing, stress testing, and old vs new GUI comparisons to test the BI migration.</p> </div>
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<p class="et_pb_module_header">Naren <br />Yalamanchilli</p> </div>
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<p><em><i>Snowflake’s Platform</i><i> presents key advantages over traditional on-premises data solutions. However, migrating to Snowflake can requires careful planning and execution. Datagaps helps simplify this process by automating the data migration testing and analytics regression testing. Using our validation, customers have successfully validated billions of records and thousands of reports. We are looking forward to helping more Snowflake customers with this partnership</i></em></p> </div>
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<p>Moving data freely from one solution to another, and then to all your business users for truly democratized analytics, is not only vital to your organization but also one of your biggest challenges. Snowflake’s platform natively connects to dozens of other solutions, turning data into an asset you can easily derive valuable insights from without the cost, risk, and headache of trying to connect disparate, legacy systems.</p> </div>
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<p>With data today being the core of every business, it is essential that enterprises work with the right technology experts to get their outputs and analytics right. Datagaps data validation solutions, be it ETL Validator, BI Validator, or the DataOps Suite- (A complete suite for data validation and data quality with test data manager), have been very effective in achieving set data needs with all our customers.</p> </div>
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<p>We have seen a significant increase in inquiries regarding our ability to help validate the data migration process to Snowflake. People have turned to us to help validate the data migration, new vs. old ETL/ELT process data validation and on the backend, validate the data analytics old vs. new now pointing to Snowflake. We are excited about the opportunity to work with Snowflake’s clients and are thrilled to have achieved Select Partner Status with Snowflake.</p> </div>
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<p class="et_pb_module_header"><strong>David Small- VP Sales and Marketing, Datagaps Inc.</strong></p> </div>
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<h2 id="faq-heading">FAQs: Datagaps and Snowflake Migrations</h2>
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<summary>1) What is Datagaps’ Snowflake Select Technology Partner status?</summary>
<p>
Datagaps is recognized as a Snowflake Select Technology Partner for its expertise
in Data and Business Intelligence Validation, helping organizations validate data
quality and reporting accuracy during migrations to the Snowflake Data Cloud.
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<summary>2) How does ETL Validator help with Snowflake migrations?</summary>
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ETL Validator verifies that ETL processes continue to execute correctly after data
structures and pipelines are migrated to Snowflake, identifying data transformation,
mapping, and processing issues introduced during the migration.
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<summary>3) What does the Data Migration Wizard do?</summary>
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The Data Migration Wizard validates that legacy tables have been accurately migrated
to Snowflake by comparing source and target datasets, enabling organizations to
confirm data integrity across large-scale migrations.
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<summary>4) How does BI Validator support Snowflake migrations at the reporting layer?</summary>
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BI Validator performs automated regression testing, performance testing, stress
testing, and side-by-side visual comparisons to verify that BI reports and dashboards
continue to function correctly after being connected to Snowflake.
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<p>The post <a href="https://www.datagaps.com/blog/datagaps-attains-snowflake-select-technology-partner-status/">Datagaps Attains Snowflake Select Technology Partner Status</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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