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Datafold

Data CI: diff datasets row-by-row in pull requests and catch regressions before they reach production.

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About

Datafold brings CI practices to data engineering: its core data-diff engine compares datasets at row and column level — down to individual value changes — and surfaces the impact of SQL or dbt model changes directly in pull requests, so reviewers see data impact, not just code diffs. Column-level lineage derived from SQL static analysis traces how fields flow through transformations, and scheduled cross-database diffs validate migrations and replications. It's SOC 2 Type II, HIPAA, and GDPR compliant with multi-tenant SaaS or single-tenant VPC deployment, and integrates with dbt, Snowflake, BigQuery, Airflow, and GitHub. Caveats from user reviews: pricing isn't public (quote-based, and reviewers describe it as reasonable but enterprise-tier), setup can challenge non-technical users, and reporting features are considered weak. A strong fit for dbt-heavy teams that want pre-production regression testing.

What it can do

  • Row- and column-level data diffing; data diffs in CI pull requests; column-level lineage from SQL analysis; cross-database diffing for migrations; scheduled checks and alerting; REST API; SOC 2/HIPAA/GDPR compliance.

Setup

Connect your warehouse and dbt project, install the GitHub integration, and Datafold starts commenting data diffs on pull requests automatically.

Prerequisites

A SQL data warehouse; dbt project or SQL pipelines; GitHub or GitLab for CI integration.

Use cases

Reviewing data impact of dbt model changes in pull requestsValidating large warehouse migrations with cross-database diffsMonitoring production tables with scheduled discrepancy checks

Works with

dbtGitHubSnowflakeBigQuerySlack

Trust score — 7/10

/10 trustVerified

Scored 7/10 (verified tier, metrics-v1). Evidence: Data-diff in CI, column-level lineage from SQL analysis, cross-database diffing corroborated by official docs and user reviews.; SOC 2/HIPAA/GDPR compliance and SaaS/VPC deployment from official docs.; Pricing not public (quote-based); reviewer sentiment on value labeled as reviewer-reported.; Limitations disclosed: weak reporting, setup friction for non-technical users..

Sources

Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.