Databricks declares the end of pipelines with a unified platform for operational and analytical data
Databricks Inc. is using its Data + AI Summit today in San Francisco to unveil a new data architecture designed to eliminate one of enterprise computing’s oldest bottlenecks: the separation between transactional databases and analytical systems.
The company is also introducing a real-time analytics engine that it says removes the need for separate serving infrastructure while delivering millisecond response times.
The new architecture, called Lake Transactional/Analytical Processing, unifies operational and analytical workloads on a single copy of data stored in a data lake. Databricks said the approach enables applications, analytics systems and artificial intelligence agents to access the same data without the change data capture pipelines, extract/transform/load processes and replicated databases that have traditionally connected operational and analytical environments.
The company said conventional architectures are ill-suited to the emerging world of AI agents, which continuously read, analyze and act upon data in near-real time.
