ETL Validator

ETL Validator is an AI-powered automated data validation platform built by Datagaps for testing and validating extract-transform-load (ETL) and ELT pipelines.

Reviewed by 7wData

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Publisher review

ETL Validator is an AI-powered automated data validation platform built by Datagaps for testing and validating extract-transform-load (ETL) and ELT pipelines. The platform uses low-code visual test builders and AI-driven automation to reduce manual testing effort while ensuring data accuracy, completeness, and integrity as data moves between source and target systems. It supports over 200 data connectors including Snowflake, Databricks, BigQuery, and AWS S3, with Apache Spark-powered parallel processing for validating billions of records.

The tool includes self-healing validations that adapt to schema changes, auto-generates test cases from SQL prompts, and integrates with CI/CD pipelines and test management tools like Azure DevOps and TestRail. Deployable on-premise, cloud, or hybrid environments, ETL Validator targets data engineering teams and QA organizations managing pipelines across legacy and modern platforms who need reliable validation without extensive programming knowledge. As of 2026, Datagaps is the only vendor listed in Gartner's DataOps Tools & Data Observability market guides, positioning the tool in the mainstream commercial segment.

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How it works

  1. Low-code visual test builder

    Drag-and-drop interface for creating test cases without manual query writing, enabling non-technical users to build complex validations.

  2. AI-powered test generation

    Automatically generates test cases from ETL mapping documents, data profiles, and natural language prompts to reduce manual test authoring.

  3. 200+ data connectors

    Supports Snowflake, Databricks, BigQuery, AWS S3, ADLS, GCS, Oracle, SQL Server, Postgres, and other databases and data lakes for source and target validation.

  4. Parallel and incremental execution

    Apache Spark-powered parallel processing for validating billions of records; incremental testing for delta changes and targeted record verification.

  5. Self-healing validations

    Automatically adapts test cases to schema changes, reducing maintenance burden when data structures evolve.

  6. Flat file and automated discovery

    File watcher capability detects and validates new files automatically; supports structured and unstructured file formats.

  7. CI/CD and pipeline integration

    Scheduling, automated triggering, and integration with test management tools (ADO, TestRail) and notification systems for DevOps workflows.

Strengths and trade-offs

Strengths

  • No-code/low-code interface lowers barrier to entry compared to query-based competitors; supports junior resources without SQL expertise.
  • AI-driven test generation and self-healing validations reduce ongoing maintenance and manual effort, addressing a real pain point in data validation.
  • Broad connector support (200+) and cloud-native architecture (on-prem, cloud, hybrid) fit modern data stacks; Spark parallelization scales to large pipelines.

Trade-offs

  • Pricing is custom and not published; requires sales contact, making budget planning difficult for smaller teams or proof-of-concept work.
  • Commercial tool with no open-source option; competitors like Great Expectations and Talend Open Studio offer free alternatives, though with steeper learning curves.
  • Initial setup and configuration can be time-consuming despite the low-code interface; post-launch maintenance is lighter but first deployment requires investment.

Pricing context

ETL Validator uses a custom, quote-based pricing model tailored to team size and data volume. No public pricing tiers are available. The platform offers a 14-day free trial in a sandbox environment.

Datagaps' broader DataOps Suite (which includes ETL Validator, BI Validator, Data Quality Monitor, and Test Data Manager) follows the same custom-quote approach. Enterprise deployments and volume discounts are negotiated directly with sales.

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Sources

Reporting on this tool draws on these publicly available sources.

  1. www.datagaps.com — ETL Validator features, AI capabilities, connectors, deployment options, and product overview.
  2. www.datagaps.com — Datagaps company background, product portfolio, and recognition in Gartner's DataOps market guides.
  3. www.crunchbase.com — Datagaps company founding year (2010) and headquarters location (Herndon, Virginia).
  4. www.softwaretestinghelp.com — Comparative analysis of ETL testing tools, including strengths/weaknesses of Datagaps ETL Validator, QuerySurge, Informatica, Talend, and iCEDQ.
  5. www.saasworthy.com — Datagaps ETL Validator pricing model (custom/quote-based) and free trial details.
  6. www.rudderstack.com — Overview of modern data warehouse and ETL testing tool landscape in 2026.