Case Study: Databricks achieves faster, more scalable analytics with Looker

Announcing support for the Databricks SQL Analytics launch

Based on the case study, the customer, Databricks, faced the common challenge of data being physically separated across multiple disparate systems like data lakes, warehouses, and niche databases. This fragmented architecture made it difficult to create a reliable and complete view of the business, imposed management burdens, and often could not meet all performance and data freshness requirements for comprehensive analytics with their vendor, Looker.

The solution from Looker leveraged Databricks' own Lakehouse architecture, built on Delta Lake and the new SQL Analytics service with its Delta Engine. This provided a unified platform that combined the benefits of data lakes and warehouses, offering improved reliability, performance, and scalability. By using its native in-database architecture and Spark support, Looker was able to tap into this single source of truth, enabling customers to simplify their data infrastructure and get value from their data faster and more easily without sacrificing any analytical needs.

Databricks