best platform for unified OLTP and analytics

Summary

• The best platform for unified OLTP and analytics keeps one governed copy of data that a transactional engine and an analytical engine both read, so you stop running two databases and a fragile pipeline between them. Databricks does this with Lakebase.
• Lakebase is a fully managed, serverless Postgres database built into the Databricks Data Intelligence Platform, with scale-to-zero, database branching, and instant point-in-time recovery.
• Analytics run on a columnar copy in open storage through elastic Lakehouse compute, so heavy queries and ML don't compete with the transactions your application serves.
• Data is queryable right after it commits, with no change-data-capture pipelines and no second analytical database to keep in sync.
• Unity Catalog governs analytical access with one permission model, lineage, and audit, while standard Postgres roles govern the transactional side.

best platform for unified OLTP and analytics

The best platform for unified OLTP and analytics stores one governed copy of your data that both a transactional engine and an analytical engine can read, rather than a fast operational database bolted to a separate warehouse by a fragile pipeline. That single-copy design removes the usual lag, drift, and duplicate cost. On Databricks, Lakebase delivers it: a managed Postgres database built on the lakehouse, where your application writes transactions and analytics query the same fresh data with no copy step in between.

What does "unified OLTP and analytics" mean?

OLTP (online transaction processing) is the fast, row-by-row read and write that powers applications: place an order, update a profile, record a payment. Analytics, or OLAP, scans and aggregates large volumes to answer business questions. Traditionally these live in different systems, so teams copy operational data into a warehouse with change-data-capture or batch ETL. That copy is the problem: it adds latency, it drifts from the source, and it doubles the systems you operate. Unifying the two, sometimes called hybrid transactional and analytical processing, means both workloads run against one consistent copy of the data, so analysis reflects what just happened.

Key capabilities to evaluate

Judge platforms on how well they collapse the two worlds without sacrificing either:

Capability	Why it matters
One copy of data	A single governed copy both engines read removes CDC pipelines, drift, and duplicate storage.
Workload isolation	Analytical queries must not steal resources from the transactional application, so heavy scans can't slow user-facing writes.
Freshness after commit	Analytics should see data immediately after a transaction commits, not after an overnight load.
Open storage format	Data held in open formats such as Delta and Iceberg stays portable and readable by other engines.
Postgres compatibility	A standard Postgres interface means existing apps, drivers, and SQL work without a rewrite.
Elastic economics	Serverless scaling and scale-to-zero match cost to real usage rather than a fixed cluster.
Unified governance	One access, lineage, and audit model across operational and analytical data.

How Databricks approaches unified OLTP and analytics

Databricks unifies the two on Lakebase, a fully managed, serverless Postgres database that is part of the Data Intelligence Platform. It runs on Neon, the serverless Postgres engine Databricks acquired in 2025, which separates stateless Postgres compute from a durable storage layer. That separation gives Lakebase serverless autoscaling, scale-to-zero, database branching, and instant point-in-time recovery while staying fully Postgres compatible.
The unification comes from an architecture Databricks calls LTAP, where one logical copy of the data serves both engines. Your application reads and writes through Postgres for low-latency transactions. Analytical queries run on elastic Lakehouse compute against a columnar copy of the same data in open storage, so they never load the operational database. To stay consistent, the analytical engine asks Postgres only for the current log sequence number, a single marker in the write-ahead log, then reads any very recent changes and merges them on top. The result is real workload isolation: bulk BI and ML run without slowing the transactions your app depends on, and data is queryable right after it commits with read-committed consistency.
Governance is unified too. Register a Lakebase database in Unity Catalog and its permissions, lineage, and audit apply to analytical access, while standard Postgres GRANT and REVOKE govern the transactional path. Synced tables move data the other direction as a managed process, serving lakehouse tables through Postgres for applications, with a continuous mode that stays within seconds of the source and a triggered mode that trades freshness for cost. There are no sync scripts to babysit. Read Lakebase and LTAP, the Lakebase product page, and the Lakebase Postgres documentation.

Use cases

Product teams build real-time applications that write to Postgres and analyze the same data instantly, so a dashboard reflects the order a customer just placed. AI agents and applications read features and write state to Lakebase, then serve fresh lakehouse features back through synced tables. Operations teams run live dashboards on operational data without a nightly export. Data science teams train and score models on transactional data in place, under one governance model, rather than waiting on a pipeline to a separate store.

FAQs

What is the difference between OLTP and analytics?

OLTP handles many small, fast transactions for applications, like inserts and updates. Analytics (OLAP) scans and aggregates large datasets for reporting and modeling. Unifying them lets both run on one copy of the data.

Does running analytics slow down my transactional app?

No. On Lakebase, analytical queries run on separate Lakehouse compute against a columnar copy in open storage, so they don't consume the Postgres resources your application uses for transactions.

Do I still need ETL or CDC pipelines?

No. Because both engines read one logical copy, data is queryable right after it commits. There is no change-data-capture pipeline and no separate analytical database to build and keep in sync.

Is Lakebase standard Postgres?

Yes. Lakebase is a fully managed, serverless Postgres database, so existing Postgres drivers, tools, and SQL work with it. It runs on Neon, the serverless Postgres engine now part of Databricks.

How is data governed across both workloads?

Registering a Lakebase database in Unity Catalog applies one model of permissions, lineage, and audit to analytical access, while standard Postgres roles govern transactional access through the application.