Skip to main content
Last updated on

Ingest data from Slack Access and Integration Logs

Beta

This feature is in Beta. Workspace admins can control access to this feature from the Previews page. See Manage Databricks previews.

This page shows how to create a managed Slack Access and Integration Logs ingestion pipeline using Lakeflow Connect.

Requirements​

  • To create an ingestion pipeline, you must 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.

    • If you plan 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.

    • If you plan 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 Slack, you must first configure authentication from Databricks and create a connection. See Configure authentication to Slack and Create a Slack Access and Integration Logs connection.

Create an ingestion pipeline​

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

Use Declarative Automation Bundles to manage Slack Access and Integration 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). 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/slack_access_integration_logs_pipeline.yml). See pipeline.ingestion_definition and Examples.
    • A job definition file that controls the frequency of data ingestion (for example, resources/slack_access_integration_logs_job.yml).

  3. Deploy the pipeline using the Databricks CLI:

    Bash
    databricks bundle deploy

Examples​

The Slack Access and Integration Logs connector exposes two source tables (access_logs and integration_logs) under the default source schema. You can ingest individual tables or the entire schema.

Ingest specific tables​

Use this option to ingest a specific subset of tables, or to customize destination naming per table.

The following pipeline definition file ingests individual Slack Access and Integration Logs tables:

YAML
resources:
  pipelines:
    slack_access_integration_logs_pipeline:
      name: slack_access_integration_logs_pipeline
      catalog: 'main'
      target: 'slack_access_integration_logs_data'
      ingestion_definition:
        connection_name: slack_access_integration_logs_connection
        objects:
          - table:
              source_schema: 'default'
              source_table: 'access_logs'
              destination_catalog: 'main'
              destination_schema: 'slack_access_integration_logs_data'
              destination_table: 'access_logs'
          - table:
              source_schema: 'default'
              source_table: 'integration_logs'
              destination_catalog: 'main'
              destination_schema: 'slack_access_integration_logs_data'
              destination_table: 'integration_logs'

Ingest the entire schema​

Use this option to ingest all Slack Access and Integration Logs source tables into a single destination schema with one declaration.

The following pipeline definition file ingests all supported Slack Access and Integration Logs tables into a destination schema:

YAML
resources:
  pipelines:
    slack_access_integration_logs_pipeline:
      name: slack_access_integration_logs_pipeline
      catalog: 'main'
      target: 'slack_access_integration_logs_data'
      ingestion_definition:
        connection_name: slack_access_integration_logs_connection
        objects:
          - schema:
              source_schema: 'default'
              destination_catalog: 'main'
              destination_schema: 'slack_access_integration_logs_data'

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:
    slack_access_integration_logs_job:
      name: slack_access_integration_logs_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: slack_access_integration_logs_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.slack_access_integration_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​

On this page