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Ingest data from Meta Ads

Beta

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

Learn how to create a managed ingestion pipeline to ingest data from Meta Ads into Databricks. For a list of supported objects, see Supported objects.

Requirements​

  • 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. 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 Meta Ads, you must complete the steps in Set up Meta Ads as a data source.

Create an ingestion pipeline​

This tab describes how to deploy an ingestion pipeline using Declarative Automation Bundles. 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 new bundle using the Databricks CLI:

    Bash
    databricks bundle init

  2. Add two new resource files to the bundle:

  3. Deploy the pipeline using the Databricks CLI:

    Bash
    databricks bundle deploy

ad_insights configuration​

When you ingest from ad_insights, you must configure the following settings in metamarketing_parameters:

Value

Description

level

Granularity level for insights: account, campaign, adset, or ad. Default is ad.

start_date

The start date for the insights data in YYYY-MM-DD format.

breakdowns

Optional. List of breakdown dimensions (for example, ["age", "gender", "country"]).

action_breakdowns

Optional. List of action breakdown dimensions (for example, ["action_type", "action_destination"]).

Examples​

Use these examples to configure your pipeline.

Ingest all current and future tables from an account​

The following is an example pipeline definition file:

YAML
resources:
  pipelines:
    pipeline_meta_ads:
      name: <pipeline-name>
      catalog: <destination-catalog>
      target: <destination-schema>
      channel: PREVIEW
      ingestion_definition:
        connection_name: <connection-name>
        objects:
          - schema:
              source_schema: <meta-ads-account-id>
              destination_catalog: <destination-catalog>
              destination_schema: <destination-schema>
              table_configuration:
                scd_type: SCD_TYPE_1

Select specific tables from an account to ingest​

The following is an example pipeline definition file:

YAML
resources:
  pipelines:
    pipeline_meta_ads:
      name: <pipeline-name>
      catalog: <destination-catalog>
      target: <destination-schema>
      channel: PREVIEW
      ingestion_definition:
        connection_name: <connection-name>
        objects:
          - table:
              source_schema: <meta-ads-account-id>
              source_table: campaigns
              destination_catalog: <destination-catalog>
              destination_schema: <destination-schema>
              table_configuration:
                scd_type: SCD_TYPE_1
          - table:
              source_schema: <meta-ads-account-id>
              source_table: ads
              destination_catalog: <destination-catalog>
              destination_schema: <destination-schema>
              table_configuration:
                scd_type: SCD_TYPE_1

Ingest ad_insights with metamarketing_parameters​

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

YAML
resources:
  pipelines:
    pipeline_meta_ads:
      name: <pipeline-name>
      catalog: <destination-catalog>
      target: <destination-schema>
      channel: PREVIEW
      ingestion_definition:
        connection_name: <connection-name>
        objects:
          - table:
              source_schema: <meta-ads-account-id>
              source_table: ad_insights
              destination_catalog: <destination-catalog>
              destination_schema: <destination-schema>
              table_configuration:
                scd_type: SCD_TYPE_1
                metamarketing_parameters:
                  level: ad
                  start_date: '2024-01-01'
                  breakdowns:
                    - age
                    - gender
                  action_breakdowns:
                    - action_type

Declarative Automation Bundles job definition file​

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

YAML
resources:
  jobs:
    meta_ads_dab_job:
      name: meta_ads_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: <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​