In our retail analytics project (CPG domain), Lakebase transformed how we handled operational data

[KVNARK]

We had ADF pipelines extracting POS data to Snowflake, but needed real-time operational tracking—job statuses, data quality alerts, user audit logs. Traditional RDS/SQL databases created ETL sync nightmares between ops and analytics layers.

Lakebase solution:
Migrated all those tables to Lakebase Provisioned (serverless Postgres).

Key wins:
Zero-copy sync: Lakebase tables auto-materialize as Delta Live Tables in lakehouse—no more dual maintenance
Unity Catalog governance: Single access control for ops + analytics teams
Scale-to-zero: It costs us only during pipeline runs (vs. always-on VMs). It has resulted in reducing the cost to the customer.

[rdokala]

This is great! I was wondering if there is a specific rationale behind Lakebase Provisioned instance as opposed to Autoscaling instance. More insights would be helpful.

[balajij8]

Lake base Autoscaling instances are the way forward as they are cost efficient with predictable costs, optimized for performance with simplified operations. Autoscaling automatically adjusts resources based on workload demands. Scale to Zero is an excellent feature reducing idle compute spend. Granular Cost efficient scaling is possible in Autoscaling mode with native support for Branching, Cloning & Point Restores. Provisioned is considered legacy while Autoscaling is the new standard.

There is a mandatory shift to Autoscaling part of the Lakebase evolution. More details here