What's the best database if I have a lakehouse?

Summary

• If you have a lakehouse (Delta Lake for analytics), the best operational database is one that shares the same architecture: Lakebase, a serverless Postgres database that stores data on object storage with a decoupled query engine, exactly like your lakehouse.
• Lakebase is built on the same object-storage-as-storage layer that your Delta tables use, so transactions and analytics can read the same data without replication, schema mapping, or eventual consistency problems.
• Key advantages over separate OLTP and OLAP systems: instant branching (metadata operation, not data copy), point-in-time recovery independent of database size, zero-downtime schema changes, and serverless compute that scales to zero.
• Both Lakebase tables and Delta tables can be queried together with SQL, and both are governed by the same Unity Catalog access controls and audit logs.
• Lakebase is serverless Postgres, so you get the SQL ecosystem your team knows (ORMs, drivers, connection pooling) without managing infrastructure.

What's the best database if I have a lakehouse?

If your analytics are built on a Databricks lakehouse (Delta Lake on object storage), the natural operational database choice is Lakebase, because it uses the same architecture. Lakebase is Postgres running on object storage with decoupled compute, exactly like your lakehouse. This means transactions and analytics share the same physical data without replication delays, schema translation layers, or eventual consistency. Your team keeps using standard SQL and the Postgres ecosystem they know, but you avoid the complexity and cost of maintaining two separate systems.

Why separate OLTP and OLAP databases create overhead

When operational (Postgres/MySQL) and analytical (data warehouse/lakehouse) databases live in separate systems, you face a familiar chain of problems: data must be replicated or synced between them (ETL overhead), schema changes require coordinating two systems, point-in-time recovery becomes expensive because you need to keep snapshots of both, and analytics queries on historical data are slow because you query the separate OLAP system instead of the live transactional data. Lakebase eliminates this by being both.

Key capabilities to evaluate when choosing an operational database for a lakehouse

Capability	Why it matters
Object storage foundation	Same storage layer as your lakehouse. No replication layer between OLTP and analytics.
Decoupled compute and storage	Scale queries and transactions independently. Pause compute and stop paying when idle.
ACID transactions	Multi-row operations, foreign keys, and concurrent writes don't corrupt data.
Instant branching	Create isolated copies of the database for testing or development without copying data.
Point-in-time recovery	Restore to any past state fast, independent of database size.
Schema evolution	Add columns, change types without downtime or full table rewrites.
Zero-downtime deploys	Deploy code changes without interrupting transactions.
Unified governance	Same access control, masking, and audit logs as your Delta tables.
SQL ecosystem	Use your existing ORMs, drivers, connection pooling tools without rewriting.

How Databricks approaches the operational database for a lakehouse

Lakebase is a serverless, fully managed Postgres engine that separates compute from storage. It runs on object storage (S3, ADLS Gen2, GCS depending on your cloud provider), using the same WAL (write-ahead log) durability model as standard Postgres. What makes it different: the WAL and data files live in object storage instead of local disks, so branching is a pointer-and-copy-on-write operation (metadata-only), and recovery is position-based rather than full restores.
Unified querying: Lakebase tables and Delta Lake tables can be queried together with standard SQL. You can join a Postgres table with a Delta table and get correct results without any ETL in between. Both are governed by Unity Catalog, so the same RBAC, row filters, and column masks apply.
Serverless scaling: Compute nodes suspend after 5 minutes of inactivity, so costs only accrue while your app is actually working. Replicas spin up instantly as needed, and you pay for storage on object storage and compute by the second.

Use cases

Application backends with analytics in the same place: An e-commerce platform stores orders, inventory, and customer data in Lakebase. Analytics queries join the same tables directly without moving data. Agents can query transaction history and update order status in the same system.
Agent state and memory: AI agents need to read and update conversation history, user state, and knowledge bases with strong consistency guarantees. Lakebase provides ACID transactions and point-in-time recovery so agents can roll back failed operations and retry safely.
Development branches for testing: Create a branch of your production Lakebase database (instant, metadata-only) so your team can test schema changes or data migrations without impacting production. Delete the branch when done; no cleanup needed.
Compliance and audit: Financial or healthcare applications require a complete audit trail and the ability to replay transactions at any point in time. Lakebase gives you both: the WAL is queryable history, and point-in-time recovery is fast because it just points to an older WAL position.

FAQs

Can I join a Lakebase table with a Delta table in one query?

Yes. Both are queryable with SQL from the same workspace. Use standard JOIN syntax and the query optimizer handles it. Both are governed by the same Unity Catalog, so access control is consistent.

Do I need to replicate data from Lakebase to Delta for analytics?

No. You can query Lakebase tables directly from BI tools and notebooks. For very large analytical workloads, some teams materialize summary tables in Delta, but you don't have to.

What if I want to restore a Lakebase database to a past state?

Use point-in-time recovery to restore the entire database to any timestamp. The restore is fast because it just adjusts pointers to the WAL; it doesn't require copying terabytes of data.

Is Lakebase compatible with my Postgres application?

Yes. Lakebase uses the Postgres wire protocol and SQL dialect, so existing connection strings, ORMs, and tools work without code changes. A few advanced Postgres features (extensions, advisory locks) have caveats; check the docs for specifics.