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The Last Mile of Data Intelligence - Databricks Lakebase

balajij8
New Contributor

We spent the last decade building a wall in the middle of the data stack. Data Lakehouse on one side - massive, powerful & analytical. The Operational Database on the other - fast, transactional, and vital for apps. The separation created a “last mile” problem that created technical challenges as both are optimized for different things.

We burnt weeks building brittle pipelines and RETL workflows to push insights - like customer risk scores back into a transactional system where an app can use them. Lakebase brings the transactional layer inside the platform. Its a ACID compliant PostgreSQL engine. By putting an operational SQL engine right next to your Lakehouse, Lakebase tears down the wall between analytics and operations.

Eliminating the Sync “Friction”
The biggest friction point has always been latency. In the past, if you wanted to serve predictions to a user-facing app, you were stuck writing and maintaining writing custom code. With Lakebase Native Data Sync, you can configure Delta tables in the Lakehouse to push data automatically to the operational database without additional plumbing.

Powering Intelligent Applications
We can finally stop obsessing over the mechanics of data movement and enable timely experiences as the friction between the lake and the app is now removed by Lakebase.

The “Last Mile” isn’t a distance anymore

1 REPLY 1

Louis_Frolio
Databricks Employee
Databricks Employee

Great framing, @balajij8 . The “last mile” analogy really lands — especially for anyone who’s spent weeks maintaining brittle reverse ETL pipelines just to get insights back into an app where they can actually be used.

Bringing an ACID-compliant Postgres engine directly alongside the Lakehouse fundamentally changes the conversation. Native Data Sync removing latency and plumbing friction is the quiet superpower here — it lets teams focus on building intelligent experiences instead of data gymnastics.

This feels like a very pragmatic step toward truly operationalizing analytics. Nicely articulated.

Cheers, Lou.