Create a Meta Ads ingestion pipeline

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.

  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 the pipeline

Databricks Asset Bundles

This tab describes how to deploy an ingestion pipeline using Databricks Asset 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 Databricks Asset Bundles?.

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/meta_ads_pipeline.yml). See Values to modify and Examples.
   • A workflow file that controls the frequency of data ingestion (resources/meta_ads_job.yml). See Databricks Asset Bundles workflow file.

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 tab

2. Leave cell one as-is.

3. Modify cell two or three with your pipeline configuration details. See Values to modify and Examples.

4. Click Run all.

Values to modify

Value	Description
name	A unique name for the pipeline.
connection_name	The name of the Unity Catalog connection that stores authentication details for Meta Ads.
source_schema	Your Meta Ads account ID.
source_table	The name of the object you want to ingest (for example, ads, campaigns, or ad_insights).
destination_catalog	The name of the catalog you want to write to in Databricks.
destination_schema	The name of the schema you want to write to in Databricks.
destination_table	Optional. A unique name for the table you want to write to in Databricks. If you don't provide this, the connector automatically uses the source table name.
scd_type	The SCD method to use: SCD_TYPE_1 or SCD_TYPE_2. The default is SCD type 1. For more information, see Enable history tracking (SCD type 2).

For ad_insights only:

When you ingest from ad_insights, you must configure the following additional 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

Ingest all current and future tables from an account

Databricks Asset Bundles

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:
          - schema:
              source_schema: <meta-ads-account-id>
              destination_catalog: <destination-catalog>
              destination_schema: <destination-schema>
              table_configuration:
                scd_type: SCD_TYPE_1

Databricks notebook

Python

pipeline_spec = {
  "name": "<pipeline-name>",
  "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"
          }
        }
      }
    ]
  }
}

json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Select specific tables from an account to ingest

Databricks Asset Bundles

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: 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

Databricks notebook

Python

pipeline_spec = {
  "name": "<pipeline-name>",
  "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"
          }
        }
      }
    ]
  }
}

json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Ingest ad_insights with metamarketing_parameters

Databricks Asset Bundles

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

Databricks notebook

Python

pipeline_spec = {
  "name": "<pipeline-name>",
  "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"]
            }
          }
        }
      }
    ]
  }
}

json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Databricks Asset Bundles workflow 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.

Additional resources

• Meta Ads ingestion connector limitations
• Troubleshoot Meta Ads ingestion
• Meta Ads ingestion connector reference
• Meta Ads ingestion connector FAQs