From monolith to Lakebase to LTAP: rethinking the database from storage up
Summary
Lakebase makes Postgres compute stateless by externalizing the log and data files into independent cloud services, unlocking unlimited storage, elastic compute, durable writes, and instant branching. LTAP further stores operational data once in open columnar formats that both Postgres and Lakehouse engines read, enabling analytics on fresh data without CDC pipelines or a second copy.
Summary generated by brickster.ai. For the full article, follow the source link above.
More from Databricks Blog
A practical guide to cost optimization with Lakebase Postgres
Lakebase Postgres delivers low, predictable costs when you sync only the working subset of Lakehouse data, match sync modes to actual freshness needs, and right-size compute so hot data fits in cache. Combining these optimization practices with serverless scale-to-zero, tuned PITR, and shared storage ensures strong performance and availability without paying for unused resources.
How to scale agentic applications without creating AI sprawl
Scaling autonomous agentic applications without creating AI sprawl requires shared infrastructure for context, tooling, governance, and observability instead of rebuilding capabilities for every agent. Establishing centralized choice, governed enterprise context, and rigorous control ensures teams can adopt new models while enforcing scoped permissions, consistent policies, and operational tracing.
Your data, your storage, your rules: a 2026 guide to storing Unity Catalog managed tables
You can redirect where Unity Catalog managed tables land by using SET MANAGED LOCATION and moving existing data through external table conversion. Defining managed storage paths at the metastore, catalog, or schema level keeps you in control of your cloud storage to support compliance and accurate cost attribution.
How Databricks rolls out frontier models to 12,000 employees on Day 1
Databricks delivers Day 1 access to frontier AI models for all 12,000 employees to ensure cutting-edge capabilities are immediately available internally. See how the company prioritizes and operationalizes large-scale model rollouts across its entire workforce.
How Databricks rolls out frontier models to 12,000 employees on Day 1
Databricks makes workforce access to frontier AI capabilities a top priority by rolling out newly released models to 12,000 employees on Day 1. This organization-wide rollout demonstrates how the platform delivers immediate, day-one access to cutting-edge AI capabilities at scale.
Lakebase Search: State-of-the-art full text and vector search for Postgres
Lakebase Postgres now includes a built-in search engine, generally available on AWS and Azure, that enables native semantic, keyword, and hybrid search directly alongside operational data without separate ETL pipelines. Featuring a serverless architecture that scales to zero and decouples storage from compute, it delivers twice the throughput at a quarter of the cost of cloud Postgres with pgvector on 100-million-vector benchmarks.
