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DP-750 · 4.3.6 · Deploy and maintain data pipelines and workloads

Deploy with REST APIs

Deploy a bundle by using REST APIs

Automate Databricks resource deployment through authenticated REST calls that create or update the jobs, pipelines, and objects represented by project configuration.

Put API deployment in the automation flow

Microsoft Learn Declarative Automation Bundles architecture showing local development, version control, CI/CD, and development, staging, and production workspaces.

REST API deployment is usually one automation mechanism inside a broader CI/CD path. This architecture shows that path; the diagrams below focus on request sequencing, authentication, and deployment state.

Understand the lower-level path

  1. 1Automation identityAuthenticate with OAuth
  2. 2API endpointAddress the workspace or account resource
  3. 3Desired stateDecide create or update
  4. 4REST requestSend resource configuration
  5. 5VerifyRead back resulting state
REST deployment

Direct REST deployment gives lower-level lifecycle control than a bundle deployment.

Bundles versus direct REST

  • Bundle

    Desired state
    Declarative project
    Create/update
    Managed by bundle tooling
    Best fit
    Repeatable project deployment

  • Direct REST

    Desired state
    Your automation logic
    Create/update
    You decide
    Best fit
    Custom lifecycle control
Deployment abstraction

Direct API automation must converge resources on the intended state instead of blindly creating duplicates.

Use the correct scope

Workspace resources such as jobs use workspace APIs. Account-level objects use account APIs. Authenticate unattended automation with a dedicated service principal.

Key points

  • REST endpoint: Use the correct workspace or account API
  • OAuth: Prefer service-principal authentication for unattended deployment
  • Resource mapping: Bundle resource fields mirror corresponding API objects
  • Idempotence: Use create or update logic that converges on desired state

Exam trap

REST automation operates on Databricks resources directly; it must reproduce the desired-state and update logic that the CLI normally manages.

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