How nOps rebuilt their cloud optimization platform on Databricks Lakebase, and why other ISVs should too

Summary

nOps migrated its production cloud optimization platform to Databricks Lakebase, a fully managed PostgreSQL database integrated with the Databricks Lakehouse. Its previous setup separated customer-facing operational data from analytics, requiring scheduled jobs, cron-based change-detection logic, and synchronization work that introduced lag and operational overhead, especially as workloads expanded to GCP and Azure. In the new architecture, Lakebase is the central Postgres database and single source of truth for the front end and AI infrastructure, while the Lakehouse continuously consumes its data for analytics and metric computation. Databricks Metric Views are automatically surfaced in the application, and data flows one way without direct write-back. The reported results are faster data pipelines, lower operational overhead, and a better customer experience, with auto-scaling, auto-stop, restore, OAuth, and compatibility features supporting adoption.

Takeaways

1. 1

   Lakebase replaced nOps’s scheduled synchronization and change-detection work with direct Lakehouse access to frequently changing customer data.

2. 2

   The architecture uses Lakebase for operational data and the Lakehouse for analytics, with one-way data flow and no direct analytical write-back.

3. 3

   nOps cites serverless auto-scaling, scale-to-zero, point-in-time restore, OAuth roles, and PostgreSQL compatibility as practical migration benefits.