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Why Datafold?

Datafold automates the most error-prone and time-consuming aspects of the data engineering workflow by preventing and detecting data quality issues. In addition to standard observability features like monitoring, profiling, and lineage, we integrate deeply into the development cycle with automated CI/CD testing. This enables data teams to prevent bad code deployments and detect issues upstream of the data warehouse.

Whether it’s for CI/CD testing, data migrations, or ongoing data replication, Datafold helps ensure data quality at every stage of the data pipeline.

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Key features

Data quality is a complex and multifaceted problem. Datafold’s unified platform helps embed proactive data quality testing in your workflows:

Data Diffs

Use value-level data diffs to isolate and identify changes in your data. Catch unintended modifications before they disrupt production or downstream data usage.

Data Monitors

Create monitors for data diffs, data quality metrics, SQL metrics, SQL rules, and schema changes to send alerts when inconsistencies are detected.

Data Explorer & Column-Level Lineage

Learn how your data assets move and change across systems with column-level lineage, metadata, and profiles, to track the impacts of changes made upstream.

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Use cases

CI/CD Data Testing

Catch data quality issues early with automated testing during development and deployment.

Accelerated Data Migrations

Speed up migrations with our full-cycle migration automation solution for data teams.

Data Monitoring & Observability

Shift monitoring upstream to proactively prevent disruptions and ensure data quality.

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Getting started

There are a few ways to get started with your first data diff:

1

Create a data diff

Once you’ve integrated a data connection and code repository, you can run a new in-database or cross-database data diff or explore your data lineage.

2

Create automated monitors

Create monitors to send alerts when data diffs fall outside predefined ranges.

3

Set up CI/CD testing

Get started with deployment testing through our universal (No-Code, API) or dbt integrations.

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Learn more

Curious to learn more about why and how data quality matters? We wrote a whole guide (with illustrations of medieval castles, moats, and knights) called the Data Quality Guide which covers:

  • A practical roadmap towards creating a robust data quality system
  • Data quality metrics to keep, and metrics to ignore
  • Nurturing a strong data quality culture within and beyond data teams
What's a Data Diff?