During its Data + AI Summit, Databricks introduced LTAP, an innovative data processing system architecture that aims to unify OLAP and OLTP operations by consolidating all copies of the data into a single instance of the data lakehouse. Eliminating the challenges presented by CDC pipelines, ETL process, and multiple replicas of databases, LTAP solves an enterprise problem that has remained unsolved since decades ago – isolating analytical and transactional workloads from each other. With the help of open-source PostgreSQL, Iceberg, and Delta Lake technologies, LTAP combines all the benefits of OLAP and OLTP operations, and ensures independent scalability with strict isolation, which makes data accessible for downstream applications.
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In addition to LTAP, Databricks has introduced enterprise capabilities to its Postgres-based Lakebase solution, which is responsible for 12 million database launches per day, including cross-cloud disaster recovery, automated optimization of the health status, and git branching. Pointing to the massive proliferation of autonomous AI agents driving this real-time infrastructure shift. “For decades, complicated data infrastructure was a tax that teams were forced to pay,” said Ali Ghodsi, Co-founder and CEO of Databricks. “Then agents arrived. In a matter of months, organizations effectively doubled their workforce, just not with humans. Agents write code, make calls, and run loops at a pace human teams never could. The infrastructure that powered the last era of computing is now the bottleneck that no one can afford. LTAP removes it.”
