GoKawiilDatabricks today launched LTAP (Lake Transactional/Analytical Processing), a new data processing architecture that unifies OLAP and OLTP on a single copy of data in the lake, eliminating ETL, replicas, and pipelines by design.
Lakebase, the foundation of the LTAP architecture, now serves thousands of customers and handles 12 million database launches per day across the platform.
Databricks is the world's first LTAP platform. It combines Lakebase (serverless Postgres on open object storage) with the Lakehouse under a single governance model, source of truth, and storage layer for all operational, analytical, and streaming data.
DATA + AI SUMMIT – June 16, 2026 – Databricks, the Data and AI company, today introduced Lake Transactional/Analytical Processing (LTAP), a new data processing architecture that unifies transactions, analytics, streaming, and operational data on a single copy of storage in the lake. With LTAP, enterprises have a single governed foundation to read, reason, and act on, without pipelines, replicas, or the ETL overhead that has defined data infrastructure for decades. Powered by major advances in Lakebase, LTAP provides a new data foundation for the AI application era.
The New Data Foundation for the Agentic Era
For four decades, transactional and analytical workloads have lived in separate systems: operational databases served applications, analytical systems answered questions. Bridging them meant building CDC pipelines that are brittle and prone to breaking under pressure. That was already a bad tradeoff when humans wrote software at human speed. Today, AI helps developers write ~50x more applications than ever before, many of which are powered by agents that need to read, reason, and act on data in near real time. The old architecture wasn't built for this.
The data industry has tried to solve the problem of disparate systems before. HTAP promised to unify transactional and analytical data in a single engine, but collapsed workload isolation in the process, compromised performance for both, and left organizations with a massive, expensive proprietary footprint. Zero ETL took a different approach, hiding the CDC pipeline rather than eliminating it. The underlying architectural problem remained.
LTAP takes a fundamentally different approach: rather than forcing both workloads into one engine or concealing the pipeline, it unifies data at the storage layer. All operational data is immediately queryable and available in the lake for analytics, with no pipelines. Transactional and analytical workloads scale independently with full performance and strict isolation. And because LTAP is built on open standards, it works with any application that speaks Postgres and any reader that understands open table formats like Iceberg and Delta.
“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.”
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