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 or data migration automation, Datafold ensures data quality at every stage of the data pipeline.

Key features

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

Use cases

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.

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