The Data Metric Function Integration will enable joint customers to automate data quality checks and implement data contracts using Foundational’s native git integration and code analysis technology.
Foundational, a data management solution that proactively prevents issues in data platforms, announced at Snowflake’s annual user conference, Snowflake Data Cloud Summit 2024, the launch of its Data Metric Function (DMF) integration. This new integration on the Snowflake AI Data Cloud will enable organizations to use Foundational’s code quality and validation product to automate data contract implementation, using Data Metric Functions, Snowflake’s latest feature release for improving data quality.
“At the core of Foundational’s mission is making code development for data easier, scalable, and a lot more trustworthy,” said Barak Fargoun, Foundational’s CTO and co-founder. “Contracts and testing are two key elements in software engineering that are still fundamentally hard in data. This new functionality expands our existing capabilities to enforce additional aspects of data quality in a very streamlined way for our customers.”
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By leveraging the Snowflake AI Data Cloud, Foundational is joining Snowflake in mobilizing the world’s data to help organizations to enforce data quality efficiently with cost, consistency, and performance. Foundational’s unique capabilities for inspecting code before it’s deployed, and preventing data incidents, will be now joined by the new functionality to further increase organizational trust in data, and accelerate the development and adoption of data products in the enterprise.
“Foundational’s commitment to helping Snowflake mobilize the world’s data can be seen through the launch of its Data Metric Functions Integration,” said Tarik Dwiek, Head of Technology Alliances at Snowflake. “This new integration will provide joint customers with the top tier governance and quality they’ve come to expect from the AI Data Cloud.”
Foundational’s Data Metric Functions Integration will enable joint customers to streamline data contract implementation and efficiently tackle common data quality challenges, such as upstream schema changes, which often lead to incidents. The new integration allows for better coverage against more types of data issues, which can be caught and prevented before any code is deployed, leading to much greater efficiency and minimal business impact.
Source: Businesswire
