These docs cover the Datafold application only. They do not cover Datafold’s AI agent platform, which our team uses to deliver migrations and other data engineering projects. The platform is changing quickly, so we share its capabilities directly rather than in public docs. To learn what it can do for your project, book a demo.
How does DMA work?
Datafold translates your SQL codebase and validates every translated asset against the source with value-level data diffs. Datafold engineers own delivery end to end. Datafold provides a comprehensive report at the end of the migration. This report includes links to data diffs validating parity and highlighting any discrepancies at the dataset, column, and row levels between the source and target databases.Why migrate with DMA?
Unlike traditional deterministic transpilers, DMA offers several distinct benefits:- Full parity between source and target: DMA ensures not just code that compiles, but code that delivers the same results in your new database, complete with explicit validation.
- Flexible dialect handling: DMA can adapt to any arbitrary input/output dialect without requiring a full grammar definition, which is especially valuable for legacy systems.
- Modernizing code structure: DMA can convert complex stored procedures into clean, modern formats such as dbt projects, following best practices.
Getting started with DMA
Want to learn more?Book a demo to connect with the Datafold team.
- Connect your source and target data sources to Datafold.
- Provide Datafold access to your codebase, typically by installing the Datafold GitHub/GitLab/ADO app or via system catalog access for stored procedures.
