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DataFreemiumOpen Source
SODA
Data quality monitoring and observability for modern data stacks
NOASSERTION
ABOUT
Data quality issues — missing values, schema changes, referential integrity violations, and distribution shifts — silently propagate from source systems into analytics and ML pipelines, producing unreliable results that erode trust. Soda allows data teams to define data quality checks as code, run them on schedules or in CI/CD pipelines, and get alerts when data falls outside expected parameters. It integrates with dbt, Airflow, and modern data stacks for automated data observability.
INSTALL
pip install soda-coreINTEGRATION GUIDE
1. Define freshness, completeness, and accuracy checks on source tables and SQL views
2. Run data quality checks in your CI/CD pipeline to prevent bad data from reaching production
3. Monitor for schema drift and anomalous row counts in production data warehouses
4. Integrate with dbt to automatically test every model after materialization
5. Set up Slack alerts for data quality failures detected during nightly Soda scans
TAGS
pythondata-qualitydata-observabilitydata-testingdata-monitoringdata-contractsdbt