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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 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 meta_ads_options in the connector_options for the table or schema:

Value

Description

level

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

start_date

Optional. The start date for the insights data in YYYY-MM-DD format. Must be within the last 36 months. If unset, the connector defaults to 36 months before the current date.

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"]).

action_attribution_windows

Optional. List of attribution windows used to report action statistics (for example, ["7d_click", "1d_view"]). If unset, the connector uses your Meta Ads account's default attribution settings. See Attribution windows for supported values.

action_report_time

Optional. Determines the timestamp used to report action statistics: impression, conversion, mixed, and lifetime. For example, with impression, the connector reports a conversion on the date of the impression that drove it. With conversion, it reports a conversion on the date it occurred.

time_increment

Optional. Aggregation period for the returned statistics: all_days, monthly, or an integer number of days (from 1 to 90) as a string (for example, "1" for daily buckets, "7" for weekly). If unset, the Insights API default of all_days is used.

custom_insights_lookback_window

Optional. Number of days to reingest on each subsequent sync to capture late-arriving conversions. If unset, the connector reingests the last 7 days. When set explicitly and action_attribution_windows isn't set, this value is also used to derive the attribution window sent to Meta as <N>d_click,1d_view.

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 meta_ads_options​

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
              connector_options:
                meta_ads_options:
                  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 job definition file to use with Declarative Automation Bundles. The job runs every day, exactly one day from the last run.

YAML
resources:
  jobs:
    meta_ads_dab_job:
      name: meta_ads_dab_job

      trigger:
        periodic:
          interval: 1
          unit: DAYS

      email_notifications:
        on_failure:
          - <email-address>

      tasks:
        - task_key: refresh_pipeline
          pipeline_task:
            pipeline_id: ${resources.pipelines.pipeline_meta_ads.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​

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