💡Note: this guide was written as of Metabase v0.46. If you find anything no longer working please let us know in the comments or at hello@grai.io Connecting the data used by data consumers in the business or analytics end of an organization is often one of the

Extracting data lineage from a data warehouse like BigQuery can be complicated because unlike traditional relational databases we rarely have foreign keys or database constraints to fall back upon. Complicating the picture even further, common data workflows like ELT (Extract, Load, Transform) incentivize users to duplicate data making it difficult

Grai is a robust data lineage solution designed to aid in managing your data stack. This blog post guides you through configuring Grai for a data stack using DBT, Snowflake, Fivetran, and Postgres, and demonstrates how to validate data changes in your version control system to prevent outages rather than

As data engineers, we all know that production database changes can be a bit of a headache. Whether it's a schema change, a column type change, or a column name change, it can feel like we're always playing catch-up with the rest of the organization. But

Quick update, Grai was admitted to the S22 batch of Y-Combinator. We are so excited to be part of this amazing community and can't wait to share more soon.