Skip to main content
Last updated on

Create a Zendesk Support ingestion pipeline

Beta

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

This page describes how to create a Zendesk Support ingestion pipeline using Databricks Lakeflow Connect.

Prerequisites​

To create an ingestion pipeline, you must 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.

    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 Zendesk Support, you must complete the steps in Configure Zendesk Support for OAuth.

Create the ingestion pipeline​

  1. Create a new bundle using the Databricks CLI:

    Bash
    databricks bundle init

  2. Add two new resource files to the bundle:

    • A pipeline definition file (resources/zendesk_pipeline.yml).
    • A workflow file that controls the frequency of data ingestion (resources/zendesk_job.yml).

    The following is an example resources/zendesk_pipeline.yml file:

    YAML
    variables:
      destination_catalog:
        default: main
      destination_schema:
        default: ingest_destination_schema

    # The main pipeline for zendesk_dab
    resources:
      pipelines:
        pipeline_zendesk:
          name: zendesk_pipeline
          catalog: ${var.destination_catalog}
          target: ${var.destination_schema}
          ingestion_definition:
            connection_name: zendesk_connection
            objects:
              - table:
                  source_schema: <source-schame-name>
                  source_table: <source-table-name>
                  destination_catalog: ${var.destination_catalog}
                  destination_schema: ${var.destination_schema}

    The following is an example resources/zendesk_job.yml file:

    YAML
    resources:
      jobs:
        zendesk_dab_job:
          name: zendesk_dab_job

          trigger:
            # Run this job every day, exactly one day from the last run
            # See https://docs.databricks.com/api/workspace/jobs/create#trigger
            periodic:
              interval: 1
              unit: DAYS

          email_notifications:
            on_failure:
              - <email-address>

          tasks:
            - task_key: refresh_pipeline
              pipeline_task:
                pipeline_id: ${resources.pipelines.pipeline_zendesk.id}

  3. Deploy the pipeline using the Databricks CLI:

    Bash
    databricks bundle deploy

Common patterns​

Optionally configure advanced options, like history tracking (SCD type 2). See Common patterns for managed ingestion pipelines.

Additional resources​