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Brief
Datafold

Datafold

Data Infrastructuredatafold.com

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 Jun 23, 2026

Founded
2020
HQ
San Francisco, California, United States
Connections
5

At a glance

Employees
11-50
Funding
$21M
5integrations

About

Emerging player in data quality and data reliability platform space

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.

Business model

SaaS

Target market

Mid-Market, Enterprise

What they offer

  • 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

Tech & specs

Technology stack

PythonSQLCloud data warehousesdbtGit

Security & compliance

SOC 2 Type IIGDPR

Deployment

Cloud

API

Yes

Corporate history
  1. 2020 · Founded
Connection details
See integrations with Datafold (5)

Explore further

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