Databricks Introduces Lakebase, a PostgreSQL Database for AI Workloads

"Because every query competes for the same fixed CPU and memory resources, a single query can affect all live operations. These constraints slow teams down and make it risky to work against live data. As applications become more automated and systems act on data in real time, this kind of shared, fragile infrastructure becomes an even bigger liability (...) To remove this architectural bottleneck, we created the lakebase category, a new architecture for operational databases that separates compute from storage."

"Lakebase is designed to integrate with the Databricks platform, providing a hybrid solution that combines both transactional and analytical capabilities. According to Databricks, the goal of the new serverless service is to simplify real-time apps and AI workloads by integrating database, analytics, and governance on a single platform. Lakebase provides instant data branching, point-in-time recovery, and unified access controls, designed to speed up development, improve reliability, and keep operational and analytical data in sync."

Lakebase delivers a serverless PostgreSQL-based OLTP database that scales compute and storage independently and integrates transactional and analytical functionality on the Databricks platform. The service offers instant data branching, point-in-time recovery, unified access controls, automatic scaling, and managed Postgres hosting tied to Databricks services. Lakebase uses lightweight, ephemeral compute on top of durable data lake storage to prevent query contention and improve reliability. The architecture aims to simplify real-time applications and AI workloads, speed development, keep operational and analytical data in sync, and reduce risk when working with live data.

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