Datafold
Datafold helps data teams prevent data quality issues through automated testing and data diffing, enabling continuous integration for data pipelines and ensuring trust in data assets before they impact business decisions.
Last updated May 11, 2026
- Industry
- Data Infrastructure/DataOps (Not AdTech)
- Business Model
- SaaS
- Target Market
- Mid-Market, Enterprise
- Employee Count
- 11-50
- Funding
- $21M
- API Available
- Yes
Market Position
Emerging player in data quality and data reliability platform space
Overview
Datafold is a data reliability platform, not an AdTech company. It operates in the data engineering and DataOps space, providing tools for data teams to ensure data quality and reliability across their data pipelines. The company focuses on preventing data quality issues through automated testing, data diffing, and observability capabilities. Datafold serves data engineers, analytics engineers, and data teams who need to maintain trust in their data infrastructure and prevent errors from propagating through data transformations and pipelines. Datafold's platform integrates with modern data stacks including data warehouses like Snowflake, BigQuery, Redshift, and Databricks, as well as transformation tools like dbt. The company has positioned itself as a critical tool for organizations practicing continuous integration and deployment (CI/CD) for data, enabling teams to catch data quality issues before they reach production. While Datafold may be used by companies in the AdTech industry to ensure their data quality, it is not itself an AdTech platform and does not provide advertising technology solutions.
Products & Features
Data Diff
Automated data diffing tool that compares datasets across environments to identify discrepancies and changes
CI/CD for dbt
Continuous integration and deployment capabilities specifically designed for dbt projects
Data Observability
Monitoring and alerting for data quality issues across data pipelines
Column-level Lineage
Tracks data lineage at the column level to understand data dependencies and impact
Automated Testing
Generates and runs automated tests for data transformations and pipelines
Key Features
Automated data diffing across environmentsCI/CD integration for data pipelinesColumn-level lineage trackingData quality monitoring and alertingIntegration with dbt and modern data stack toolsPull request integration for data changesAnomaly detectionImpact analysis for data changes
Use Cases
Preventing data quality issues in productionTesting data transformations before deploymentValidating data migrationsMonitoring data pipeline healthEnsuring accuracy of analytics and reportingDetecting data anomalies and regressionsUnderstanding impact of schema changes
Customer Segments
Data engineering teamsAnalytics engineering teamsData platform teamsCompanies using dbtOrganizations with complex data pipelines
Connections