Ingest data from Vimeo

Beta

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

Create a managed Vimeo ingestion pipeline in Lakeflow Connect using the Databricks UI, Declarative Automation Bundles, or a Databricks notebook. The pipeline ingests the teams, team_members, team_roles, and audit_logs tables into Unity Catalog.

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

Connector options

Set the pipeline-scoped start_datetime option in source_configurations to choose where the first audit_logs sync starts. For the option's details, see Connector options. For usage, see Examples.

Create an ingestion pipeline

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

Databricks UI

1. In the sidebar of the Databricks workspace, click Data Ingestion.
2. On the Add data page, under Databricks connectors, click Vimeo.
3. On the Connection page of the ingestion wizard, select the connection that stores your Vimeo credentials. If you have the CREATE CONNECTION privilege on the metastore, click Create connection to create a connection with the credentials from Configure authentication to Vimeo.
4. Click Next.
5. On the Ingestion setup page, enter a name for the pipeline.
6. Select a catalog and a schema to write event logs to. If you have USE CATALOG and CREATE SCHEMA privileges on the catalog, click 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 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 Create schedule. Set the frequency to refresh the destination tables.
13. (Optional) Click Add notification to set email notifications for pipeline operation success or failure, then click Save and run pipeline.

Declarative Automation Bundles

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

3. Deploy the pipeline using the Databricks CLI:

   Bash

   databricks bundle deploy

Databricks notebook

1. Import the following notebook into your Databricks workspace:

   Open notebook in new tabOpen in Databricks

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 Vimeo connector makes available 4 source tables in the default source schema. 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.

Declarative Automation Bundles

The following pipeline definition file ingests individual Vimeo tables:

YAML

resources:
  pipelines:
    vimeo_pipeline:
      name: vimeo_pipeline
      catalog: 'main'
      target: 'vimeo_data'
      ingestion_definition:
        connection_name: vimeo_connection
        source_configurations:
          - api_source_connector_config:
              configs:
                start_datetime: '<start-date>'
        objects:
          - table:
              source_schema: 'default'
              source_table: 'teams'
              destination_catalog: 'main'
              destination_schema: 'vimeo_data'
              destination_table: 'teams'
          - table:
              source_schema: 'default'
              source_table: 'team_members'
              destination_catalog: 'main'
              destination_schema: 'vimeo_data'
              destination_table: 'team_members'
          - table:
              source_schema: 'default'
              source_table: 'team_roles'
              destination_catalog: 'main'
              destination_schema: 'vimeo_data'
              destination_table: 'team_roles'
          - table:
              source_schema: 'default'
              source_table: 'audit_logs'
              destination_catalog: 'main'
              destination_schema: 'vimeo_data'
              destination_table: 'audit_logs'

Databricks notebook

The following pipeline specification ingests individual Vimeo tables:

Python

pipeline_name = "vimeo_pipeline"
connection_name = "<vimeo-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "source_configurations": [
      {
        "api_source_connector_config": {
          "configs": {
            "start_datetime": "<start-date>"
          }
        }
      }
    ],
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "teams",
          "destination_catalog": "main",
          "destination_schema": "vimeo_data",
          "destination_table": "teams"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "team_members",
          "destination_catalog": "main",
          "destination_schema": "vimeo_data",
          "destination_table": "team_members"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "team_roles",
          "destination_catalog": "main",
          "destination_schema": "vimeo_data",
          "destination_table": "team_roles"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "audit_logs",
          "destination_catalog": "main",
          "destination_schema": "vimeo_data",
          "destination_table": "audit_logs"
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Ingest the entire schema

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

Declarative Automation Bundles

The following pipeline definition file ingests all supported Vimeo tables into a destination schema:

YAML

resources:
  pipelines:
    vimeo_pipeline:
      name: vimeo_pipeline
      catalog: 'main'
      target: 'vimeo_data'
      ingestion_definition:
        connection_name: vimeo_connection
        source_configurations:
          - api_source_connector_config:
              configs:
                start_datetime: '<start-date>'
        objects:
          - schema:
              source_schema: 'default'
              destination_catalog: 'main'
              destination_schema: 'vimeo_data'

Databricks notebook

The following pipeline specification ingests all supported Vimeo tables into a destination schema:

Python

pipeline_name = "vimeo_pipeline"
connection_name = "<vimeo-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "source_configurations": [
      {
        "api_source_connector_config": {
          "configs": {
            "start_datetime": "<start-date>"
          }
        }
      }
    ],
    "objects": [
      {
        "schema": {
          "source_schema": "default",
          "destination_catalog": "main",
          "destination_schema": "vimeo_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

YAML

resources:
  jobs:
    vimeo_job:
      name: vimeo_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: vimeo_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.vimeo_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

• Vimeo connector FAQ
• Vimeo connector limitations
• Vimeo connector reference
• Troubleshoot the Vimeo connector