California-based data engineering company Prophecy has announced the release of a new version of its core platform, Prophecy 3.0. This new version comes with low-code SQL capabilities,that could give business data users a great visual drag-and-drop canvas to build data pipelines natively on cloud data platforms. In the past, this sort of data piping requires complex SQL codes, creating delays and accuracy issues on downstream analytics and machine learning efforts.
Business teams with limited coding skills can now prepare their data for analytics quickly and simply with low-code SQL, enabling them to deliver analytics faster and adapting to changing business needs. The Prophecy 3.0’s platform technology goes beyond low-code Spark for data engineers and Reverse, allowing users to open existing dbt Core projects in Prophecy and edit the SQL code as visual pipelines, with the changes saved back as SQL.
With this new development, Prophecy has opened up new possibilities like consistently applying data quality checks irrespective of the language, and enabling a self-service framework to create data products. As indicated by Kevin Petrie, VP of research at Eckerson Group, the addition of low-code SQL to Prophecy’s platform increases its addressable market.
To hear more about the advantages and insights of Prophecy 3.0, Dreamforce Summit has invited executives in San Francisco on July 11 – 12, to share information about how AI investments can be integrated and optimized for success.
Prophecy, founded in 2017, is an automated analytics engineering platform that works with customers to improve the data infrastructure, and get their teams up to speed with the latest machine learning projects. In building their product, the company focused on enabling teams to move away from manual coding and into an automated pipeline; creating production-grade pipelines for complete data foundations for data engineers and data scientists. Their platform provides enterprises with a low-code/no-code data engineering solution that bridges the gap between data engineering and business users.
