Datafold
Data diffing and regression testing for data teams
Datafold is profiled here as a Data Quality tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Datafold is a data reliability platform, founded in 2020 by Gleb Mezhanskiy, that catches data quality issues before they ship by comparing datasets across environments. Its data diff compares a development branch against production row by row, so an engineer sees exactly how a code change alters the data a pipeline produces. Datafold wires this into continuous integration for dbt projects, and column-level lineage traces how a change ripples through downstream tables, which gives data teams a way to test changes like software.
Key Capabilities:
Data diff that compares datasets across environments value by value
Regression testing in CI that flags data changes on each pull request
Column-level lineage tracing impact through downstream tables
Native integration with dbt development workflows
Cross-database diffing for validating migrations and replication
Monitoring that detects anomalies in production tables
Alternative tools
- Anomalo
Automated data quality monitoring with machine learning
- Elementary
dbt-native data observability and anomaly detection
- Soda
Data quality testing defined in a readable check language
- GX (Great Expectations)
Declarative data quality testing for pipelines