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LTAP Explained: How Databricks Unifies OLTP and OLAP

Databricks21 July 2026Watch on YouTube

Description

Databricks recently announced LTAP: Lake Transactional/Analytical Processing. LTAP does not try to force transactions and analytics into the same engine. Instead, it unifies them at the storage layer: → Lakebase and Postgres handle transactions → Lakehouse engines handle analytics → Both access one governed copy of data in open formats on object storage → Each compute layer scales independently → No CDC pipeline or second analytical copy to keep synchronized A Postgres-compatible engine remains specialized for low-latency transactional workloads, while Lakehouse engines remain specialized for analytics, ML and AI. Read the technical deep dive: https://www.databricks.com/blog/lakebase-ltap-rethinking-database-storage

What you'll learn

  • LTAP unifies transactional and analytical workloads at the storage layer instead of forcing them into the same engine.
  • Postgres handles transactional tasks while Lakehouse engines specialize in analytics, ML and AI.
  • Both systems access a single dataset in open formats, eliminating the need for CDC pipelines and keeping synchronized copies.
  • Each compute layer scales independently based on specific workload requirements.

Frequently asked questions

How does LTAP differ from traditional OLTP/OLAP architectures?
LTAP unifies OLTP and OLAP at the storage layer instead of combining them in a single engine. Postgres and Lakehouse engines work together through one governed dataset in open formats, without needing CDC pipelines or synchronizing multiple copies.
What are the core components of LTAP?
LTAP comprises Lakebase and Postgres for transactional processing, Lakehouse engines for analytical processing, and a single dataset in open formats on object storage used by both layers.
What are the benefits of independent scaling in LTAP?
Independent scaling allows each compute layer (transactional and analytical) to grow separately based on its own workload demands, resulting in more efficient resource usage and better performance.
Why is a CDC pipeline no longer needed in LTAP?
Because LTAP uses a single governed dataset in open formats that both transactional and analytical engines work on directly, eliminating the need for a Change Data Capture pipeline to synchronize data between systems.

Topics

database architectureoltp/olaplakehousedatabricks