Databricks Lakebase

Lakebase Postgres Autoscaling

FIRST: Use the parent databricks-core skill for CLI basics, authentication, and profile selection.

Lakebase is Databricks' serverless Postgres-compatible database, available on both AWS and Azure (GA). It provides fully managed OLTP storage with autoscaling, branching, and scale-to-zero.

Provisioned Lakebase no longer exists — never create it. The old Provisioned tier (static, non-scalable Postgres; databricks database CLI / w.database SDK) is being migrated to Autoscaling over summer 2026, with no customer action required. Lakebase today is Autoscaling-only (databricks postgres / w.postgres). Do not attempt to create Provisioned instances; if a user asks for one, tell them Provisioned instances don't exist anymore and point them to Autoscaling. Treat the legacy database resource key in app configs as retired — migrate to the postgres resource key. For the legacy DAB synced_database_tables resource, use the databricks postgres create-synced-table CLI flow instead (see the Common Issues row and references/synced-tables.md).

For context when reading older configs or docs, here is how the retired Provisioned tier maps to Autoscaling:

Compliance: Supports HIPAA, C5, TISAX, or None.

Capabilities

• Project lifecycle -- create, update, delete Lakebase Postgres Autoscaling projects
• Branching -- copy-on-write branches with TTL, point-in-time recovery, and reset
• Compute scaling -- autoscale 0.5--32 CU, fixed 36--112 CU, scale-to-zero
• High availability -- 1 primary + 1--3 secondaries, automatic failover
• PostgreSQL connectivity -- OAuth token refresh, connection pooling, SSL
• Data API -- PostgREST-compatible HTTP CRUD (Autoscaling only)
• Lakebase synced tables -- sync Unity Catalog Delta tables into Postgres (previously known as Reverse ETL)
• Databricks App integration -- scaffold apps with Lakebase feature, deploy-first workflow
• Cloud support -- AWS and Azure (GA)

Reference docs:

• computes-and-scaling.md — Sizing, endpoint management, scale-to-zero, HA
• connectivity.md — Connection patterns, token refresh, Data API
• synced-tables.md — Lakebase synced tables, data type mapping, capacity planning
• lakehouse-sync.md — CDC from Lakebase Postgres to Unity Catalog Delta tables (programmable via databricks postgres *-cdf-config / w.postgres.*_cdf_config, Beta)
• pgvector.md — Vector similarity search with pgvector extension
• off-platform.md — Off-platform Lakebase (NOT Databricks Apps): external Node.js apps connecting via @databricks/lakebase, env management, token refresh, Drizzle ORM

Resource Hierarchy

Project (top-level container)
  └── Branch (isolated database environment, copy-on-write)
        ├── Endpoint (read-write or read-only)
        ├── Database (standard Postgres DB)
        └── Role (Postgres role)

• Project: Top-level container. Creating one auto-provisions a production branch and a primary read-write endpoint.
• Branch: Isolated database environment sharing storage with parent (copy-on-write). States: READY, ARCHIVED.
• Endpoint (called Compute in UI): Compute resource powering a branch. Types: ENDPOINT_TYPE_READ_WRITE, ENDPOINT_TYPE_READ_ONLY.
• Database: Standard Postgres database within a branch. Default: databricks_postgres.
• Role: Postgres role within a branch.

Resource Name Formats

All IDs: 1-63 characters, start with lowercase letter, lowercase letters/numbers/hyphens only (RFC 1123).

CLI Discovery -- ALWAYS Do This First

Note: "Lakebase" is the product name; the CLI command group is postgres. All commands use databricks postgres ....

Do NOT guess command syntax. Discover available commands dynamically:

databricks postgres -h                    # List all subcommands
databricks postgres <subcommand> -h       # Flags, args, JSON fields

Create a Project

First decide: reuse or create. When building or attaching to an app, ask the user whether to reuse an existing project/branch/database — list them with databricks postgres list-projects (then list-branches / list-databases), let the user pick, and confirm which schema the app will own — or create a new project. Only skip listing and create directly when the user explicitly asked for a brand-new project.

databricks postgres create-project <PROJECT_ID> \
  --json '{"spec": {"display_name": "<DISPLAY_NAME>"}}' \
  --profile <PROFILE>

Auto-creates: production branch + primary read-write endpoint (1 CU min/max, scale-to-zero). Long-running operation; CLI waits by default. Use --no-wait to return immediately.

After creation, verify:

databricks postgres list-branches projects/<PROJECT_ID> --profile <PROFILE>
databricks postgres list-endpoints projects/<PROJECT_ID>/branches/<BRANCH_ID> --profile <PROFILE>
databricks postgres list-databases projects/<PROJECT_ID>/branches/<BRANCH_ID> --profile <PROFILE>

Extract connection values from JSON output:

Updating a Project

databricks postgres update-project projects/<PROJECT_ID> spec.display_name \
  --json '{"spec": {"display_name": "My Updated Application"}}' \
  --profile <PROFILE>

Deleting a Project

WARNING: Permanent -- deletes all branches, computes, databases, roles, and data. Do not delete without explicit user permission.

databricks postgres delete-project projects/<PROJECT_ID> --profile <PROFILE>

Autoscaling

Endpoints use compute units (CU) (~2 GB RAM per CU). Range: 0.5--32 CU (dynamic), 36--112 CU (fixed). Scale-to-zero enabled by default (5 min timeout).

See computes-and-scaling.md for sizing tables, endpoint CRUD, and configuration details.

Branches

Branches are copy-on-write snapshots. Use for testing schema migrations, trying queries, or previewing data changes without affecting production.

databricks postgres create-branch projects/<PROJECT_ID> <BRANCH_ID> \
  --json '{"spec": {"source_branch": "projects/<PROJECT_ID>/branches/<SOURCE>", "no_expiry": true}}' \
  --profile <PROFILE>

Branches require an expiration policy: "no_expiry": true for permanent, or "ttl": "<seconds>s" (max 30 days).

Limits: 10 unarchived branches per project. 8 TB logical data per branch. 1,000 projects per workspace.