Has anyone prototyped Databricks Lakebase or deployed it in a production environment?

[DPatil]

Has anyone prototyped Databricks Lakebase or deployed it in a production environment? I'd be interested in hearing about your experience, including any lessons learned, performance observations, or challenges you encountered.

I’ve spent quite a bit of time reviewing this OLTP database and was able to go through several of its capabilities. I think it’s going to become very popular and much needed in the future. 

Please your experience? I'm putting together architecture diagram and what I have explored later.

[balajij8]

Lakebase is solving a key architectural requirement by finally allowing transactional workloads inside the Data Intelligence platform alongside the analytical data in Lakehouse.

Performance & Core

• Speed & Concurrency - Because it operates as a Postgres engine under the hood, it handles high-concurrency row-level reads/writes effortlessly. You can use it to power real-time AI agents (acting as the agent's memory/state store) and the latency was consistently good.

• Branching - The copy-on-write branching feature is good for CI/CD & also for compliance. You can use it for secure Time Travel and compliance trials. You can spin up isolated staging environments from production data in seconds without duplicating massive storage costs.

Architecture & Integration

• Zero-Code APIs - If you are building frontend applications (like integrating with Databricks Apps), Lakebase’s Data API allows for incredibly fast, zero-code REST integrations. You can use it for pushing real-time patient vitals directly into the operational layer.

• Governance - Wrapping Unity Catalog around both analytical lakehouse and the Lakebase OLTP layer is a key area for consideration. Choose controls based on need either at Unity Catalog level or directly at the Lakebase level (Postgres role controls)

• OLTP Boundary - You can keep heavy BI and aggregations on Databricks SQL Warehouses, and strictly use Lakebase for fast, operational application serving.

Key Consideration

• Scale-to-Zero - Lakebase's serverless scaling is great for lowering costs in dev environments. However, if you allow a production instance to scale entirely to zero, the first application request that triggers a wake-up will experience a cold-start latency spike. Make sure you configure your minimum compute to keep production warm.

• Connection Pooling - Opening and closing direct connections for every single transaction will exhaust your limits and kill performance. Ensure the application architecture leverages connection pooling when interacting with Lakebase at a high volume.

You can refer the Lakebase architectures given below for your prototypes.

Security, Governance & Compliance

• Fortifying Enterprise Healthcare Databricks Lakebase with the Security Triad

• Databricks Lake base Time Travel - Secure Healthcare Compliance Trial via Lake base Branching

Integration & Bidirectional Data Sync

• Zero Code REST Integration for Modern HealthCare Vitals via Databricks Lakebase Data API

• Databricks Lake base - Enterprise Healthcare Data Intelligence via Bidirectional Data Sync

AI

• HealthCare Prior Authorizations with Databricks Lakebase Vector Search

• Databricks Lakebase - Healthcare Patient Risk Scoring using Feature Stores powered by Lake base

• Databricks Lake base - Modern Enterprise Healthcare Agents with Lake base Memory

Applications

• Databricks Lake base - Modern Medical Inventory Management with Databricks Apps & Lake base

• Modern HealthCare Capacity Planning with Databricks Lake base & AI BI