Ataccama launches data quality app on Snowflake to improve data validation

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Ataccama has launched a new data quality app designed to work seamlessly within Snowflake, allowing joint customers to validate data quality directly through the Snowflake Native App Framework. The app, available on Snowflake Marketplace, provides a set of pre-defined rules that users can apply effortlessly within their existing workflows, ensuring that data meets high-quality standards before it’s used in business processes.

Jay Limburn, Chief Product Officer at Ataccama, highlighted the app’s potential to enhance decision-making across industries. “The real value of data lies in being able to trust it and apply it confidently to business use cases,” he said. Limburn explained that maintaining high data quality within complex enterprise environments empowers data engineers and scientists to deliver accurate, reliable data to business teams, which in turn supports better decision-making for areas like marketing, risk management, and product development.

The collaboration between Ataccama and Snowflake is part of a broader effort to improve data quality and enable organisations to extract more value from their data. The Snowflake AI Data Cloud is central to this initiative, offering a platform where data experts can efficiently manage and validate data quality, enabling seamless integration of Ataccama’s tools within Snowflake. This, according to Tarik Dwiek, Snowflake’s Head of Technology Alliances, provides businesses with a foundation of trusted data to fuel insights and innovation. “Data quality is key to maximising the value of the Snowflake AI Data Cloud, enabling businesses to trust their data for better insights and innovation,” Dwiek said.

Built with Snowflake’s Native App Framework, Ataccama’s Data Quality app allows users to validate data quality directly as they write SQL code within Snowflake. By offering these capabilities through a few simple clicks on Snowflake Marketplace, customers can integrate the app in minutes, streamlining the data quality process without disrupting existing workflows.

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