Welcome
Datafold is the unified platform proactive data quality that combines automated data testing, data reconciliation, and observability to help data teams prevent data quality issues and accelerate their development velocity.
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:
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.
Datafold Migration Agent
Discover how DMA provides full-cycle migration automation with SQL code translation and cross-database validation.
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.
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.
Getting started
There are a few ways to get started with your first data diff:
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.
Create automated monitors
Create monitors to send alerts when data diffs fall outside predefined ranges.
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
Welcome
Datafold is the unified platform proactive data quality that combines automated data testing, data reconciliation, and observability to help data teams prevent data quality issues and accelerate their development velocity.
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:
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.
Datafold Migration Agent
Discover how DMA provides full-cycle migration automation with SQL code translation and cross-database validation.
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.
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.
Getting started
There are a few ways to get started with your first data diff:
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.
Create automated monitors
Create monitors to send alerts when data diffs fall outside predefined ranges.
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