How do I run analytics on my operational data without slowing down my app?

Summary

• Run analytics on live operational data without impacting your app by keeping analytical queries off the operational database compute: on Databricks Lakebase, analytical work runs on separate lakehouse compute while your application's transactions keep running.
• Lakebase uses a Lake Transactional/Analytical Processing (LTAP) architecture that stores operational data once in open columnar formats, so both the Postgres engine and the lakehouse read a single logical copy with no replication pipeline and no data drift.
• Analytics reads are transactionally consistent and fully up to date: a query looks up the current log sequence number, reads already-materialized data from object storage, and merges only the most recent changes on top.
• Synced tables bring Unity Catalog data into Lakebase Postgres for low-latency application reads, with Snapshot, Triggered, or Continuous refresh (data available within seconds of the source).
• Unity Catalog applies one set of permissions, lineage, and audit across both the transactional and analytical sides.

How do I run analytics on my operational data without slowing down my app?

The usual problem is that running analytical queries directly against the database that powers your application competes for the same compute, so heavy reporting or dashboards can slow down live transactions. On Databricks, Lakebase solves this by separating transactional and analytical work at the storage layer: analytical queries run on lakehouse compute, never on the operational Postgres database itself, so your app's transactions and maintenance operations continue regardless of analytical load, while analytics still sees fresh, consistent data.

Why Databricks Lakebase

• Workload isolation by design. Lakebase is built on a Lake Transactional/Analytical Processing (LTAP) architecture. It externalizes the Postgres write-ahead log and data files into independent storage services, which makes the database compute stateless and lets analytical queries run on separate lakehouse compute. Analytical load never touches the compute serving your application.
• A single copy of the data. Operational data is stored once in open columnar formats (Parquet and Iceberg) that both the Postgres engine and the lakehouse read directly from object storage. There is no separate analytical copy to maintain and no data drift, so analytics reads the same data the application just wrote.
• Fresh, transactionally consistent reads. When an analytical query starts, it asks Postgres for the current log sequence number (LSN), a cheap metadata lookup. The engine then reads the overwhelming majority of data directly from object storage, which is already materialized, and merges only the very recent changes on top. The result is fully up-to-date, transactionally consistent reads with almost all the work landing on scalable object storage.
• Synced tables for low-latency serving. When the lakehouse owns the write, synced tables bring Unity Catalog data into Lakebase Postgres for low-latency application reads. They support three modes: Snapshot, Triggered (scheduled incremental updates), and Continuous (data available within seconds of the source). Your app can then serve enriched results that combine operational data with lakehouse data in a single Postgres query.
• Change Data Feed for downstream pipelines. When Lakebase owns the write, Lakebase Change Data Feed streams operational changes into downstream pipelines or audit logs without impacting the transactional workload.
• One governance model. Because Lakebase is part of the Databricks platform, Unity Catalog applies permissions, lineage, and audit consistently across both the operational and analytical workloads.

Getting started

• Read Rethinking database storage with LTAP to understand how the architecture separates transactional and analytical work.
• Review the LTAP overview and how to approach implementation.
• Learn about synced tables and the serve-lakehouse-data use case.
• Try the query across both worlds quickstart to join operational and lakehouse data in one query.

FAQs

Will analytical queries slow down my application?

No. In Lakebase's LTAP architecture, analytical queries run on separate lakehouse compute, not on the operational database compute, so your application's transactions continue running regardless of analytical load.

Do I need a separate pipeline to copy operational data for analytics?

No. Operational data is stored once in open columnar formats that both the Postgres engine and the lakehouse read directly, so there is no second copy to maintain and no data drift.

How fresh is the data analytics sees?

Analytics reads are transactionally consistent and fully up to date. A query reads already-materialized data from object storage and merges only the most recent, not-yet-materialized changes on top using the current log sequence number.

How do I serve lakehouse data back to my app with low latency?

Use synced tables to bring Unity Catalog data into Lakebase Postgres. With Continuous mode, data is available within seconds of the source, so your application can read it with low latency.