Datafold
Data testing platform with column-level lineage and automated diffs for dbt - prevents data quality regressions before they reach production dashboards.
Datafold is a data reliability platform that helps analytics and data engineering teams test, compare, and validate data changes before they reach production dashboards and business reports. Its core feature is column-level data diffing, which automatically compares datasets row by row and column by column to surface unexpected changes introduced by SQL or dbt model modifications. Founded in 2020 by Gleb Mezhanskiy and Boris Trofimov, Datafold raised a $20M Series A in 2021 backed by Altimeter Capital. The platform integrates directly into CI/CD pipelines for dbt projects, showing data impact analysis alongside code reviews in GitHub and GitLab pull requests so data engineers can catch regressions before merging. Datafold also ships an open-source `data-diff` command-line tool, making column-level diffing accessible for teams that want the core capability without the full cloud platform.
Key Features
- Column-level data diffing - compares datasets row by row and column by column to identify exactly what changed and by how much
- dbt CI integration - runs automated data diffs on every pull request and posts results as GitHub/GitLab PR comments
- Column-level lineage - visualizes how data flows through dbt models so teams understand the downstream impact of any change
- Cross-database diff - compares tables across different database systems including Snowflake, BigQuery, Redshift, and Postgres
- Anomaly detection - flags statistical outliers and unexpected value distributions that signal data pipeline failures
- Open-source data-diff CLI - free command-line tool for running column-level diffs without the cloud platform
Use Cases
- Analytics engineers validating dbt model refactors by confirming that row counts, null rates, and value distributions are unchanged
- Data engineering teams integrating automated data quality gates into CI/CD pipelines to block broken models from reaching production
- Data platform teams debugging cross-system data discrepancies when migrating from one warehouse to another
- BI developers investigating why a dashboard metric changed after a recent SQL model update
Pros
- Column-level data diffs in CI/CD is a genuinely unique capability that catches data regressions invisible to standard code review
- Open-source data-diff CLI gives smaller teams access to the core diffing capability for free without the cloud platform
- Integrates natively into the dbt workflow where analytics engineers already spend their time - no new tools to context-switch into
Cons
- Primarily focused on dbt-based workflows - teams using custom SQL pipelines or other orchestrators get limited out-of-box integration
- Cloud platform pricing is enterprise-tier, making it a significant budget line for early-stage data teams
- Column-level lineage visualization can become cluttered and hard to navigate in large dbt projects with hundreds of models
Datafold Alternatives
Explore similar tools and alternatives
Looking for alternatives to Datafold? Here are some similar tools you might like:
Metabase
Open-source business intelligence platform with AI that lets anyone ask questions about data in plain English - trusted by 50,000+ companies globally.
Hex
AI analytics platform combining collaborative SQL/Python notebooks, data apps, and natural-language queries for data teams; $19.8M ARR.
Deepnote
Collaborative AI-native data notebook for data scientists and analysts with real-time multiplayer editing and AI code assistance.
Ready to try Datafold?
Visit the official website to explore all features and get started with Datafold today.
Reviews
0 reviews for Datafold
Based on 0 reviews
Share your experience
Log in to write a review for Datafold
Julius AI
AI data analyst that reads your spreadsheets, databases, and files - answering questions, building charts, and running analyses in plain English.
Hex
AI analytics platform combining collaborative SQL/Python notebooks, data apps, and natural-language queries for data teams; $19.8M ARR.
Weights & Biases
ML experiment tracking, model monitoring, and dataset versioning platform - used by OpenAI, Toyota, and 1,000+ organizations to ship better models faster.
Have an AI Tool?
List your AI tool for free, or go featured for top placement in your category - and reach thousands of potential users.
Submit Your Tool