Looking for the best data quality tools for GitHub Workflows? This list covers 3 tools that natively integrate with GitHub Workflows — from data testing and data observability to shift-left data quality and unified platforms.
Each tool below links directly to its GitHub Workflows integration documentation so you can evaluate support.
By Ari Bajo - Data Engineer turned Writer.
See the full data quality tools list
All 36 data quality, data testing, and data observability tools.
AI-native data quality platform with data contracts written in YAML, UI, or AI, anomaly detection, and AI agents that explain and fix data issues.
Best for data teams that want engineers and business users to agree on data contracts in one place and resolve bad data together.
Open-source data contracts engine (Soda 4) to write data quality checks and contracts in YAML and run them in pipelines and CI/CD.
Best for data engineers who prefer YAML over Python for checks and want them running free inside pipelines and CI/CD.
Data product marketplace (formerly Data Mesh Manager) where every data product has an owner and data contracts based on the Open Data Contract Standard, from the team behind the open-source Data Contract CLI.
Best for organizations adopting data mesh that want a data product marketplace with ODCS data contracts, policy checks, and test results published from the Data Contract CLI.
AI data review agent for dbt pull requests, with open-source data diff, schema diff, downstream impact analysis, and column-level lineage.
Best for dbt teams that want reviewers to see the data impact of code changes before approving a pull request.
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By Ari Bajo - Data Engineer turned Writer.