Ingest data from Box

Important

This feature is in Beta. To use it, a workspace admin must turn on Lakeflow Connect for Box Audit Logs from the Previews page. See Manage Azure Databricks previews.

Create a managed Box Audit Logs ingestion pipeline in Lakeflow Connect to ingest enterprise audit events into Azure 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 Box, first configure authentication from Azure Databricks and create a connection. See Configure authentication to Box and Create a Box Audit Logs connection.

Create an ingestion pipeline

For the list of supported source tables, see Supported source tables.

Databricks UI

  1. In the sidebar of the Azure Databricks workspace, click Data Ingestion.
  2. On the Add data page, under Databricks connectors, click Box Audit Logs.
  3. On the Connection page of the ingestion wizard, select the connection that stores your Box credentials. If you have the CREATE CONNECTION privilege on the metastore, click Plus icon. Create connection to create a connection with the credentials from Configure authentication to Box.
  4. Click Next.
  5. On the Ingestion setup page, enter a name for the pipeline.
  6. Select a catalog and a schema to write pipeline event logs to. If you have USE CATALOG and CREATE SCHEMA privileges on the catalog, click Plus icon. Create schema in the drop-down menu to create a schema.
  7. Click Create pipeline and continue.
  8. On the Source page, select the tables to ingest.
  9. Click Save and continue.
  10. On the Destination page, select a catalog and a schema to load data into. If you have USE CATALOG and CREATE SCHEMA privileges on the catalog, click Plus icon. Create schema in the drop-down menu to create a schema.
  11. Click Save and continue.
  12. (Optional) On the Schedules and notifications page, click Plus icon. Create schedule. Set the frequency to refresh the destination tables.
  13. (Optional) Click Plus icon. Add notification to set email notifications for pipeline operation success or failure.
  14. Click Save and run pipeline.

Declarative Automation Bundles

Use Declarative Automation Bundles to manage Box Audit Logs 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). See What are Declarative Automation Bundles?.

  1. Create a bundle using the Databricks CLI:

    databricks bundle init

  2. Add two new resource files to the bundle:

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

  3. Deploy the pipeline using the Databricks CLI:

    databricks bundle deploy

Databricks notebook

  1. Import the following notebook into your Azure Databricks workspace:

    Get notebook

  2. Leave cells one and two as they are. Do not modify.

  3. Modify cell three with your pipeline configuration details. See pipeline.ingestion_definition and Examples.

  4. Optionally configure advanced pipeline settings. See Common patterns for managed ingestion pipelines.

  5. Click Run all.

Examples

The Box Audit Logs connector provides one source table in the default source schema. Ingest the table or the entire schema.

Ingest the event_logs table

Use this option to customize destination naming for the table.

Declarative Automation Bundles

The following pipeline definition file ingests the Box Audit Logs table:

resources:
  pipelines:
    box_audit_logs_pipeline:
      name: box_audit_logs_pipeline
      catalog: 'main'
      target: 'box_audit_logs_data'
      ingestion_definition:
        connection_name: box_audit_logs_connection
        objects:
          - table:
              source_schema: 'default'
              source_table: 'event_logs'
              destination_catalog: 'main'
              destination_schema: 'box_audit_logs_data'
              destination_table: 'event_logs'

Databricks notebook

The following pipeline specification ingests the Box Audit Logs table:

pipeline_name = "box_audit_logs_pipeline"
connection_name = "<box-audit-logs-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "event_logs",
          "destination_catalog": "main",
          "destination_schema": "box_audit_logs_data",
          "destination_table": "event_logs"
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Ingest the entire schema

Use this option to ingest all Box Audit Logs source tables into a single destination schema with one declaration.

Declarative Automation Bundles

The following pipeline definition file ingests all supported Box Audit Logs tables into a destination schema:

resources:
  pipelines:
    box_audit_logs_pipeline:
      name: box_audit_logs_pipeline
      catalog: 'main'
      target: 'box_audit_logs_data'
      ingestion_definition:
        connection_name: box_audit_logs_connection
        objects:
          - schema:
              source_schema: 'default'
              destination_catalog: 'main'
              destination_schema: 'box_audit_logs_data'

Databricks notebook

The following pipeline specification ingests all supported Box Audit Logs tables into a destination schema:

pipeline_name = "box_audit_logs_pipeline"
connection_name = "<box-audit-logs-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "schema": {
          "source_schema": "default",
          "destination_catalog": "main",
          "destination_schema": "box_audit_logs_data"
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Declarative Automation Bundles job definition file

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

Declarative Automation Bundles

resources:
  jobs:
    box_audit_logs_job:
      name: box_audit_logs_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: box_audit_logs_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.box_audit_logs_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

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