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Ingest data from GitLab

Beta

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

This page shows how to create a managed GitLab ingestion pipeline using Lakeflow Connect.

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 GitLab, first configure authentication from Databricks and create a connection. See Configure authentication to GitLab and Create a GitLab connection.

Connector options​

Set pipeline-scoped options in source_configurations. See Examples for usage.

Option

Scope

Required

Applies to

Description

start_datetime

Pipeline

No

All incremental tables

The earliest record timestamp to ingest on the first sync, in yyyy-MM-dd'T'HH:mm:ss.SSS'Z' format. For the default, see Connector options.

Option

Scope

Required

Applies to

Description

start_datetime

Pipeline

No

All incremental tables

The earliest record timestamp to ingest on the first sync, in yyyy-MM-dd'T'HH:mm:ss.SSS'Z' format. For the default, see Connector options.

Create an ingestion pipeline​

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

  1. In the sidebar of the Databricks workspace, click Data Ingestion.
  2. On the Add data page, under Databricks connectors, click GitLab.
  3. On the Connection page of the ingestion wizard, select the connection that stores your GitLab 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 GitLab.
  4. Click Next.
  5. On the Ingestion setup page, enter a name for the pipeline.
  6. Select a catalog and a schema to write data 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, then click Save and run pipeline.

Examples​

The GitLab connector makes 28 source tables available in the default source schema. The following examples ingest specific tables.

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 GitLab tables:

YAML
resources:
  pipelines:
    gitlab_pipeline:
      name: gitlab_pipeline
      catalog: 'main'
      target: 'gitlab_data'
      ingestion_definition:
        connection_name: gitlab_connection
        source_configurations:
          - api_source_connector_config:
              configs:
                start_datetime: '<start-datetime>'
        objects:
          - table:
              source_schema: 'default'
              source_table: 'projects'
              destination_catalog: 'main'
              destination_schema: 'gitlab_data'
              destination_table: 'projects'
          - table:
              source_schema: 'default'
              source_table: 'issues'
              destination_catalog: 'main'
              destination_schema: 'gitlab_data'
              destination_table: 'issues'
          - table:
              source_schema: 'default'
              source_table: 'merge_requests'
              destination_catalog: 'main'
              destination_schema: 'gitlab_data'
              destination_table: 'merge_requests'
          - table:
              source_schema: 'default'
              source_table: 'commits'
              destination_catalog: 'main'
              destination_schema: 'gitlab_data'
              destination_table: 'commits'

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:
    gitlab_job:
      name: gitlab_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: gitlab_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.gitlab_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