Databricks has unified analytical and transactional processing in its Lakehouse, and added large-scale real-time query handling and agentic marketing features.
These announcements came at its an Francisco Data + AI Summit with more than 30,000 in-person attendees.
It’s combining OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) features for digging into data in its data store with the LTAP (Lake Transactional/Analytical Processing) architecture. 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.
Various attempts have been made to unify OLAP and OTLP with a single engine. Databricks says LTAP unifies data at the storage layer. 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. As LTAP is built on open standards, it works with any application that uses Postgres and any reader that understands open table formats like Iceberg and Delta.
Databricks Co-founder and CEO Ali Ghodsi stated: “For decades, complicated data infrastructure was a tax that teams were forced to pay. 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.”
We’re told The first step toward LTAP was Lakebase, which brought Postgres-native transactions to object storage, the same layer powering the Lakehouse. By separating compute from storage, Lakebase transforms the economics of running thousands of applications and agents at once. Launched just last year, Lakebase already serves thousands of customers, including Block, Ensemble, Superhuman, and Zillow, and handles 12 million database launches per day.
Lakebase and the Lakehouse shared a storage layer, but each maintained its own copy of data in its own format. LTAP ends that separate copying, and stores data directly in Unity Catalog, using the same open formats as the Lakehouse.
Databricks says LTAP provides unified governance with one source of truth. Everything is governed through Unity Catalog with a single identity, permissions, and audit model, so every engine reads the same copy and agents share a single governed surface to act on.
There are no performance trade-offs. Transactional workloads run in standard Postgres with full ACID semantics. Analytical workloads run across the full Lakehouse at any scale and concurrency. Each scales independently, and because there's no data movement between systems, operational and analytical results are always in sync.
No ETL (Extract, Transform and Load) pipelines are used at all. There are three more Lakebase additions;
Cross-cloud, cross-region disaster recovery,
Git-style branching and snapshots enable safe experimentation against production data,
Autonomous database operations let AI agents monitor health, detect slowdowns, propose indexes, and assist with recovery.
Lakehouse//RT
This is a real-time Lakehouse powered by a new compute engine, called Reyden, that delivers millisecond query latency at tens of thousands of concurrent users and agents, directly on governed Delta Lake and Apache Iceberg tables. A Lakehouse//RT query runs natively within Unity Catalog's governance framework with no separate permissions layer, no proprietary formats, and no sync/CDC pipelines, eliminating the cost and complexity of maintaining a separate real-time serving layer alongside the lakehouse.
Ghodsi said: “Over the past decade, we've unified the major workloads of the modern data stack on a single open foundation: data engineering and data science with Spark, and data warehousing with Photon and the Lakehouse. Lakehouse//RT completes the engine spectrum, providing the millisecond speed layer that people want and agents require. Just as we proved that the best data warehouse is a lakehouse, now, the best real-time analytics engine is the lakehouse, too.”
Customers have seen response times as low as 10ms on smaller datasets and sub-100ms performance on larger ones. Databricks says that, on standard analytical benchmarks, Lakehouse//RT delivers sub-100 millisecond latency at 12,000 queries per second, and customers have seen up to 16x better performance than their existing specialized real-time serving stacks.
Read more about Lakehouse//RT in a blog. Databrick’s Customer Lake
CustomerLake is an agentic Customer Data Platform (CDP), built natively on Databricks’ Lakehouse, that unifies customer data, AI models, agents, identity resolution, audience building, and activation to provide agentic marketing.
In the marketing and advertizing world a CDP, as defined by the Customer Data Platform Institute (CDPI), is “packaged software that creates a persistent, unified customer database that is accessible to other systems.” It combines data from separate sources: websites, apps, CRMs, POS systems, and so forth, to create a single and comprehensive profile for each customer.
Databricks says Customer Lake replaces one-off marketing campaigns with “infinity campaigns,” continuous agentic loops that react to customer context in real time, enabling enterprises to deliver 1:1 personalized experiences a billion times a day. It uses a workforce of agents that continuously analyze behavior, decide, and act,
It has campaign and profile agents, an open partner ecosystem, and native integrations and reverse ETL to ingest, unify, and activate customer data across the marketing and advertising technology stack.
This is Databrick’s second entry into specific enterprise SW markets, closely following on from its Lakewatch security lakehouse. CustomerLake pricing will be designed around a specialized, value-aligned consumption model rather than a traditional software license.
Learn more about CustomerLake from a blog. Availability
Lakehouse//RT is available in Beta. LTAP is coming soon as a part of Lakebase. CustomerLake is now available in Private Preview, with current customers including HP, Circle K, AB InBev, and Getnet by Santander.
Bootnote
Vector database supplier Zilliz uses the Lakebase term in a diffrent way with its Milvus Vector Lakebase.