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Datafold
Data Analytics
Datafold is a data quality and testing company providing tools that help data engineering teams detect data discrepancies and validate changes to data pipelines before they cause downstream problems.
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Datafold was founded in 2020 by Gleb Mezhanskiy, drawing on his prior experience leading data engineering teams at companies including Lyft, where he directly experienced how difficult it was to confidently make changes to complex data pipelines and data transformation code without inadvertently introducing subtle data quality issues or discrepancies that might not be discovered until well after the change was deployed, sometimes causing significant downstream problems for business analytics, reporting, or other systems that depend on that data being accurate. Datafold's core product provides automated data diffing and testing capabilities that let data engineering teams compare data before and after a proposed change to a data pipeline or transformation, automatically identifying any discrepancies in the resulting data that the change might introduce, functioning conceptually similarly to how software engineers use automated testing to verify that code changes don't break existing functionality before those changes are deployed to production, a practice that had been comparatively underdeveloped in the data engineering discipline relative to more mature software engineering testing practices. The company's technology has become particularly relevant alongside the broader growth of the "modern data stack," including tools like dbt (data build tool) for data transformation, as data engineering teams have needed increasingly rigorous testing and validation practices to manage growing numbers of complex, interdependent data pipelines feeding into critical business analytics, machine learning models, and increasingly generative AI applications that depend on high-quality underlying data. Datafold has built integrations with popular data warehouse and data transformation tools to fit into data teams' existing workflows, positioning its data quality and testing capabilities as complementary to rather than a replacement for tools like dbt. The company has raised venture funding from investors including Amplify Partners and Insight Partners, competing in a growing data observability and quality category that includes companies such as Monte Carlo and Bigeye.
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