Databricks, the data and AI company and pioneer of the data lakehouse paradigm, unveiled the evolution of the Databricks Lakehouse Platform to a sold-out crowd at the annual Data + AI Summit in San Francisco. New capabilities revealed include best-in-class data warehousing performance and functionality, expanded data governance, new data sharing innovations to include an analytics marketplace and data clean rooms for secure data collaboration, automatic cost optimization for ETL operations, and machine learning (ML) lifecycle improvements.
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“Our customers want to be able to do business intelligence, AI, and machine learning on one platform, where their data already resides. This requires best-in-class data warehousing capabilities that can run directly on their data lake. Benchmarking ourselves against the highest standards, we have proven time and again that the Databricks Lakehouse Platform gives data teams the best of both worlds on a simple, open, and multi-cloud platform,” said Ali Ghodsi, Co-founder and CEO of Databricks. “Today’s announcements are a significant step forward in advancing our Lakehouse vision, as we are making it faster and easier than ever to maximize the value of data, both within and across companies.”
The Best Data Warehouse is the Lakehouse
Organizations like Amgen, AT&T, Northwestern Mutual and Walgreens, are making the move to the lakehouse because of its ability to deliver analytics on both structured and unstructured data. Today, Databricks unveiled new data warehousing capabilities in its platform to further enhance analytics workloads:
- Databricks SQL Serverless, available in preview on AWS, provides instant, secure, and fully managed elastic compute for improved performance at a lower cost.
- Photon, the record-setting query engine for lakehouse systems, will be generally available on Databricks Workspaces in the coming weeks, further expanding Photon’s reach across the platform. In the two years since Photon was announced, it has processed exabytes of data, run billions of queries, delivered benchmark-setting price/performance at up to 12x better than traditional cloud data warehouses.
- Open source connectors for Go, Node.js, and Python now make it even simpler to access the lakehouse from operational applications.
- Databricks SQL CLI now enables developers and analysts to run queries directly from their local computers.
- Databricks SQL now provides query federation, offering the ability to query remote data sources including PostgreSQL, MySQL, AWS Redshift, and others without the need to first extract and load the data from the source systems.