Databricks has shared a deeper look at how Lakebase rethinks database architecture by separating Postgres compute from storage, and how that design leads to LTAP, a model where transactions and analytics can run on the same underlying data without traditional ETL pipelines or separate copies.
Whatโs new
- Why traditional databases hit limits: Databricks argues that many database pain points trace back to a monolithic design where the write-ahead log and data files live on one machine, making durability, scaling, replicas, and workload isolation harder than they need to be.
- Lakebase separates compute from storage: In Lakebase, Postgres compute becomes stateless while storage is externalized, with data living in low-cost cloud object storage and compute scaling independently on top.
- That architecture unlocks practical benefits: Databricks highlights elastic serverless compute, durable storage, instant branching and cloning, and a more flexible operating model for transactional workloads.
- Lakebase also improves the path to LTAP: Databricks says LTAP unifies transactional and analytical processing at the storage layer, so operational and analytical workloads can work from a single governed copy of data in the lake instead of relying on ETL, replicas, or hidden sync pipelines.
- Storage-layer unification is the key idea: Rather than forcing one engine to do everything, Databricks keeps Postgres for transactions and lakehouse engines for analytics, while making the data underneath shared, current, and governed through Unity Catalog.
Databricks also points to performance work already happening in Lakebase itself. In one architecture update, the company said Lakebase can deliver up to 5x faster Postgres writes by pushing certain recovery-related work into its distributed storage layer instead of leaving it on the compute node.
๐ Read the full post here
