RunSQL

Grai

Grai is an open-source data lineage and catalog tool designed to help data teams understand and test how data flows across their entire stack. It automatically builds column-level lineage from databases, warehouses, BI tools, and transformation layers, then integrates this metadata into CI/CD workflows to validate changes before they reach production. ## Key Features - **Pre-built Connectors** — Automatically synchronizes lineage from popular data sources like Snowflake, BigQuery, dbt, Fivetran, and Metabase. - **Centralized Data Tests** — Write validation tests that run whenever upstream data sources change, ensuring data quality. - **CI/CD Integration** — Runs data validation tasks as part of GitHub Actions to test changes everywhere data is used. - **Self-Hosted Deployment** — Fully open-source and deployable on your own infrastructure with Docker, Kubernetes, or Helm. - **Column-Level Lineage** — Provides detailed visibility into how data columns relate across systems, not just table-level dependencies. ## Philosophy Grai believes data teams should have full control over their metadata and be able to test data changes with the same rigor as code changes, preventing broken pipelines and dashboards before they impact production.

DQOps

Data Quality and Observability platform for the whole data lifecycle, from profiling new data sources to full automation with Data Observability. Configure data quality checks from the UI or in YAML files, let DQOps run the data quality checks daily to detect data quality issues.

GreatExpectation

Open Source data validation framework to manage data quality. Users can define and document “expectations” rules about how data should look and behave

DataKitchen

Open Source Data Observability for end-to-end Data Journey Observability, data profiling, anomaly detection, and auto-created data quality validation tests