How can I add analytics to my Postgres app without standing up a second system?

Summary

• Lakebase runs OLTP and OLAP workloads together on the same operational data with built-in workload isolation.
• Analytical queries execute on Lakehouse compute, leaving your app's Postgres database unaffected by heavy BI or ML workloads.
• Real-time analytics queries see live data instantly, with no pipelines, ETL tools, or data copies required.
• Query across operational and analytical data in a single SQL statement using synced tables and Lakehouse//RT compute.
• All data governance flows through Unity Catalog, providing unified security policies and lineage across both systems.

How can I add analytics to my Postgres app without standing up a second system?

Lakebase solves this by running your operational database and analytics layer on the same data. Instead of building brittle ETL pipelines or maintaining separate systems, analytical queries execute directly on live Postgres data, isolated from your app's transactions through dedicated compute. This architecture eliminates the traditional tax of syncing data across systems while keeping your app's performance unaffected.

What is workload isolation?

Workload isolation means that analytical queries and your app's operational transactions never compete for resources. In Lakebase, OLTP (operational) transactions run on your Postgres compute, while OLAP (analytical) queries run on separate Lakehouse compute. This design ensures that a 1000-row full-table scan or machine learning job doesn't slow down your app's user-facing queries.

Key capabilities to evaluate

Capability	Why it matters
Real-time analytics on live data	Analytics queries see data immediately after commit, eliminating data freshness delays.
Workload isolation	Analytical workloads run on separate compute, so they don't starve operational transactions.
Unified data access	Query across operational and analytical data in one SQL statement without API calls or sync logic.
Built-in governance	All data, whether OLTP or analytical, is under Unity Catalog control with consistent security policies.
No ETL required	Data is immediately queryable after being written to your app's database. No CDC, pipelines, or maintenance overhead.

How Databricks approaches this

Lakebase implements a Lakehouse-Transactional Analytical Processing (LTAP) architecture. Your Postgres database runs as the operational layer, storing OLTP data in Delta tables within object storage. When your app writes or modifies data, Lakebase immediately makes it queryable from Lakehouse compute without a separate sync step.
You can then use SQL queries or Databricks apps to query this live data directly. For example, join your app's customer records with a Lakehouse-computed user segments table to enrich your operational data:

SELECT 
  app.id, 
  app.name, 
  app.value, 
  segments.segment, 
  segments.engagement_score 
FROM your_app_table app 
JOIN "default".user_segments_computed segments ON app.id = segments.user_id;

This cross-layer query uses Lakehouse//RT compute, leaving your app's Postgres database untouched. You can also connect Databricks apps to Lakebase to build custom analytics interfaces, dashboards, or user-facing reports.
All data access is governed by Unity Catalog. The same access policies and audit logs apply to queries from your app and from Lakehouse compute. This means you don't need separate governance for your OLTP and OLAP layers.

Use cases

Real-time operational dashboards: Build live dashboards that reflect your app's data without slowing transactions. Display customer activity, inventory levels, or system health metrics updated after every write.
AI and ML feature engineering: Train models on live transactional data without building pipelines. Your data science team queries the same table your app writes to, guaranteed to be fresh.
Compliance and audit reporting: Generate audit reports and compliance snapshots directly from your live operational data under Unity Catalog governance, with full lineage tracking.
Customer-facing analytics: Add analytics pages to your app by connecting Databricks apps to Lakebase. Show customers their own operational metrics in real time.

FAQs

Will analytical queries slow down my app?

No. Analytical queries run on Lakehouse compute, not on your Postgres database. Your app's transactions are unaffected.

Do I need to build ETL pipelines to sync data?

No. Data is queryable immediately after being written to Lakebase. No CDC tools, pipelines, or maintenance overhead required.

Can I query across my app's data and my Lakehouse data in one query?

Yes. You can join Lakebase tables with Lakehouse tables in a single SQL statement using synced tables and Lakehouse//RT compute.

How do I add analytics to an existing Postgres app?

Provision a Lakebase instance and point your app's connection string to it. You can also add Lakebase as a resource in a Databricks app to build analytics interfaces.

Is all my data governed consistently?

Yes. Unity Catalog manages access control and audit logs for all data, whether OLTP or analytical.