Dashboard developer workflows

AI/BI dashboards support programmatic and DevOps-oriented workflows for managing dashboards at scale. You can manage dashboards as code using Declarative Automation Bundles and REST APIs, transfer dashboards across workspaces using import and export, and apply source control using Databricks Git folders.

Capability	Description
Programmatic management	Manage dashboards as code using Declarative Automation Bundles or Terraform. Automate creation, updates, and sharing using REST APIs. Schedule routine dashboard refreshes with Lakeflow Jobs.
Import and export	Export dashboards as portable .lvdash.json files and import them into other workspaces. Edit serialized dashboard files directly to make bulk updates without using the UI.
Source control	Version-control dashboard files using Databricks Git folders. Implement CI/CD workflows to develop dashboards in branches and deploy them across environments.

Manage dashboards with Declarative Automation Bundles

To learn how to manage an AI/BI dashboard using Declarative Automation Bundles, see dashboard. For an example bundle that defines a dashboard, see the bundle-examples GitHub repository.

Databricks also offers a Terraform provider. See the Databricks Terraform documentation.

Manage dashboards with REST APIs

See Use Databricks APIs to manage dashboards for tutorials that demonstrate how to use Databricks REST APIs to manage dashboards. The included tutorials explain how to convert legacy dashboards into Lakeview dashboards, and how to create, manage, and share them.

Schedule updates using Lakeflow Jobs

You can configure a task to routinely update an existing published dashboard. To learn more about orchestrating workflows with Lakeflow Jobs, see Lakeflow Jobs. To learn how to configure a dashboard task, see Dashboard task for jobs.

note

Schedule and subscriber lists that you create using the dashboard UI or API are distinct from scheduling and automation associated with a job. See Automating jobs with schedules and triggers.