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AarushiShah
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databricks-lakebase-autoscale

Manage Lakebase Autoscaling PostgreSQL projects, branches, computes, and reverse ETL on Databricks.

Updated Mar 24, 2026
Search Tags:#postgresql#database branching#autoscaling#databricks#lakebase#scale-to-zero#reverse etl
One-click install
npx skills add https://github.com/AarushiShah/coding-agents-databricks-apps --skill databricks-lakebase-autoscale-aarushishah
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-lakebase-autoscale
Source: https://github.com/AarushiShah/coding-agents-databricks-apps/tree/main/.claude/skills/databricks-lakebase-autoscale
Command: npx skills add https://github.com/AarushiShah/coding-agents-databricks-apps --skill databricks-lakebase-autoscale-aarushishah

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires databricks-sdk, psycopg, sqlalchemy, and includes references (resource) components.

What problem does it solve? Setting up and operating a managed PostgreSQL database on Databricks involves unfamiliar concepts like projects, branches, autoscaling computes, OAuth token rotation, and scale-to-zero behavior. This Skill provides the patterns, SDK calls, CLI commands, and MCP tools needed to provision and operate Lakebase Autoscaling correctly without trial and error. ## Core Features & Use Cases - Project and Branch Management: Create projects, Git-like database branches with TTL expiration, branch protection, and point-in-time restore using the Databricks SDK w.postgres module or CLI. - Compute Autoscaling and Scale-to-Zero: Configure compute endpoints from 0.5 to 112 CU with autoscaling ranges and automatic suspension for cost savings. - Connection Patterns: Production-ready connection pooling with automatic OAuth token refresh, plus workarounds for macOS DNS resolution and scale-to-zero wake-up. - Reverse ETL: Sync Delta tables from Unity Catalog into PostgreSQL via synced tables with Snapshot, Triggered, or Continuous modes. - Use Case: Build a web application backend by creating a Lakebase project, branching a development environment from production, connecting via SQLAlchemy with token refresh, and syncing a product catalog from Delta Lake. ## Quick Start Ask the agent to create a Lakebase Autoscaling project named my-app with Postgres 17 and show how to connect to it from a Python notebook.

Frequently Asked Questions about databricks-lakebase-autoscale

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a Lakebase Autoscaling project on Databricks?▼

Use the Databricks SDK w.postgres.create_project() method with a ProjectSpec containing a display name and Postgres version 16 or 17. It is a long-running operation, so call .wait() on the returned operation. A production branch, default compute, and databricks_postgres database are created automatically.

How do I connect to Lakebase Autoscaling from Python?▼

Generate an OAuth token with w.postgres.generate_database_credential(), get the host from the endpoint status, and connect with psycopg using sslmode=require. Tokens expire after one hour, so production applications must implement a token refresh loop.

What is the difference between Lakebase Autoscaling and Lakebase Provisioned?▼

Autoscaling uses the w.postgres SDK module with projects as top-level resources, 0.5-112 CU sizing at 2 GB per CU, branching, and scale-to-zero. Provisioned uses w.database with fixed CU_1 to CU_8 instances at 16 GB per CU and no branching support.

Why does my Lakebase connection fail after idle periods?▼

Scale-to-zero suspends the compute after the inactivity timeout, and reactivation takes a few hundred milliseconds on the next connection. Implement connection retry logic, and note that session state like temporary tables is lost after reactivation.

How do I sync Delta tables to PostgreSQL with reverse ETL?▼

Create a synced table with w.database.create_synced_database_table() specifying the source Delta table, primary key columns, and a scheduling policy of Snapshot, Triggered, or Continuous. Triggered and Continuous modes require Change Data Feed enabled on the source table.

What are the limitations of Lakebase Autoscaling?▼

It does not support readable secondaries for high availability, Feature Store integration, Postgres-to-Delta sync, or direct migration from Lakebase Provisioned. Autoscaling ranges cannot exceed an 8 CU spread, and branches are limited to 500 per project with 8 TB logical size per branch.

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