DataChat Achieves Google Cloud Ready – BigQuery Designation

DataChat, the no-code, conversational analytics platform that delivers speed to insights and a current Google Cloud partner, has successfully earned the Google Cloud Ready – BigQuery designation, giving current and future customers confidence that DataChat products can work with Google Cloud’s BigQuery, a unified, AI-ready data analytics platform. Specifically, DataChat connects directly to a customer’s BigQuery database, allowing the customer to analyze massive datasets and get quick answers with no SQL required.

“Every move we make as a company, from product enhancements to partnerships, is focused on one thing – providing businesses with a fast, easy, no-code way to turn their data into insights,” said Viken Eldemir, CEO, DataChat. “By working closely with Google Cloud, we are giving enterprises the ability to instantly ask multidimensional questions of the data contained in their BigQuery database, refine queries on the fly and uncover meaningful insights.”

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Google Cloud Ready – BigQuery is a partner integration validation program that intends to increase the customer confidence in partner integrations into BigQuery. As part of this initiative, Google Cloud engineering teams validate partner integrations into BigQuery by running a series of data integration tests and comparing results against benchmarks as well as working closely with partners to fill any gaps and refine documentation.

In earning this designation, DataChat has proven its data analytics product has met a core set of functional and interoperability requirements when integrating with the BigQuery platform, paving the way for DataChat to collaborate closely with the engineering and BigQuery teams at Google Cloud to develop road maps.

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Benefits of DataChat + BigQuery include the ability to:

• Process millions of rows of data in seconds with built-in analytics tools
• Leverage Google Cloud’s BigQuery ML and DataChat’s ML engine for forecasting, clustering, and more—without writing a single line of code
• Select, customize, and train BigQuery ML models with automated workflows and built-in visualizations
• Create reusable, verifiable workflows for ongoing and future BigQuery analysis