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Data engineers and analytics teams use Grai to trace how changes in databases and data pipelines may affect downstream models and tools. It builds a view of data lineage across connected systems and keeps metadata in sync through integrations such as Snowflake, BigQuery, PostgreSQL, dbt, and Fivetran. ([github.com](https://github.com/grai-io/grai-core?utm_source=openai)) Teams can add Grai to GitHub Actions to run data validation tests on pull requests and see results before changes are merged. This can help identify issues when an upstream data source changes, including potential effects on warehouses or dbt projects. Grai is suited to teams that want to include data checks in their CI workflows and manage metadata across a connected data stack. ([github.com](https://github.com/grai-io/grai-actions))
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