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Ingest data from Workday

Beta

This feature is in Beta. To use it, a workspace admin must turn on Lakeflow Connect for Workday Activity Logging from the Previews page. See Manage Databricks previews.

Create a managed Workday Activity Logging ingestion pipeline in Lakeflow Connect to ingest user activity log events into Databricks.

Requirements​

  • To create an ingestion pipeline, first meet the following requirements:

    • Your workspace must be enabled for Unity Catalog.

    • Serverless compute must be enabled for your workspace. See Serverless compute requirements.

    • To create a new connection, you must have CREATE CONNECTION privileges on the metastore. See Manage privileges in Unity Catalog.

      If the connector supports UI-based pipeline authoring, an admin can create the connection and the pipeline at the same time by completing the steps on this page. However, if the users who create pipelines use API-based pipeline authoring or are non-admin users, an admin must first create the connection in Catalog Explorer. See Connect to managed ingestion sources.

    • To use an existing connection, you must have USE CONNECTION privileges or ALL PRIVILEGES on the connection object.

    • You must have USE CATALOG privileges on the target catalog.

    • You must have USE SCHEMA and CREATE TABLE privileges on an existing schema or CREATE SCHEMA privileges on the target catalog.

  • To ingest from Workday, first configure authentication from Databricks and create a connection. See Configure authentication to Workday and Create a Workday Activity Logging connection.

Create an ingestion pipeline​

For the supported source table, see Supported source tables.

Use Declarative Automation Bundles to manage Workday Activity Logging pipelines as code. Bundles can contain YAML definitions of jobs and tasks, are managed using the Databricks CLI, and can be shared and run in different target workspaces (such as development, staging, and production). For more information, see What are Declarative Automation Bundles?.

  1. Create a bundle using the Databricks CLI:

    Bash
    databricks bundle init

  2. Add two new resource files to the bundle:

    • A pipeline definition file (for example, resources/workday_activity_logging_pipeline.yml). See pipeline.ingestion_definition and Examples.
    • A job definition file that controls the frequency of data ingestion (for example, resources/workday_activity_logging_job.yml).

  3. Deploy the pipeline using the Databricks CLI:

    Bash
    databricks bundle deploy

Examples​

The Workday Activity Logging connector makes available 1 source table in the default source schema. Ingest the table or the entire schema.

Ingest a specific table​

Use this option to customize destination naming for the table.

The following pipeline definition file ingests the Workday Activity Logging table:

YAML
resources:
  pipelines:
    workday_activity_logging_pipeline:
      name: workday_activity_logging_pipeline
      catalog: '<destination-catalog>'
      target: '<destination-schema>'
      channel: PREVIEW
      ingestion_definition:
        connection_name: '<workday-activity-logging-connection>'
        objects:
          - table:
              source_schema: 'default'
              source_table: 'activity_logging'
              destination_catalog: '<destination-catalog>'
              destination_schema: '<destination-schema>'
              destination_table: '<destination-table>'

Ingest the source schema​

Use this option to ingest the default source schema with one declaration. The schema currently contains only the activity_logging table.

The following pipeline definition file ingests the default source schema into a destination schema:

YAML
resources:
  pipelines:
    workday_activity_logging_pipeline:
      name: workday_activity_logging_pipeline
      catalog: '<destination-catalog>'
      target: '<destination-schema>'
      channel: PREVIEW
      ingestion_definition:
        connection_name: '<workday-activity-logging-connection>'
        objects:
          - schema:
              source_schema: 'default'
              destination_catalog: '<destination-catalog>'
              destination_schema: '<destination-schema>'

Declarative Automation Bundles job definition file​

The following is an example job definition file for use with Declarative Automation Bundles. The job runs daily.

YAML
resources:
  jobs:
    workday_activity_logging_job:
      name: workday_activity_logging_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: workday_activity_logging_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.workday_activity_logging_pipeline.id}

Common patterns​

For advanced pipeline configurations, see Common patterns for managed ingestion pipelines.

Next steps​

Start, schedule, and set alerts on your pipeline. See Common pipeline maintenance tasks.

Additional resources​

On this page