Ingest data from Meta Ads

Important

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

Learn how to create a managed ingestion pipeline to ingest data from Meta Ads into Azure 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

Declarative Automation Bundles

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?.

Create a bundle using the Databricks CLI:
```
databricks bundle init
```
Add two new resource files to the bundle:
- A pipeline definition file (for example, resources/meta_ads_pipeline.yml). See pipeline.ingestion_definition and Examples.
- A job definition file that controls the frequency of data ingestion (for example, resources/meta_ads_job.yml). See Declarative Automation Bundles job definition file.
Deploy the pipeline using the Databricks CLI:
```
databricks bundle deploy
```

Databricks notebook

Import the following notebook into your Azure Databricks workspace:

Get notebook
Leave cell one as-is.
Modify cell three with your pipeline configuration details. See pipeline.ingestion_definition and Examples.
Click Run all.

`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

Declarative Automation Bundles

The following is an example pipeline definition file:

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

The following is an example pipeline specification:

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

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

Select specific tables from an account to ingest

Declarative Automation Bundles

The following is an example pipeline definition file:

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

The following is an example pipeline specification:

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

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

Ingest `ad_insights` with `meta_ads_options`

Declarative Automation Bundles

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

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

Databricks notebook

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

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

Declarative Automation Bundles job definition file

Declarative Automation Bundles

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.

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

Feedback

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Last updated on 2026-05-04

Ingest data from Meta Ads

Requirements

Create an ingestion pipeline

Declarative Automation Bundles

Databricks notebook

ad_insights configuration

Examples

Ingest all current and future tables from an account

Declarative Automation Bundles

Databricks notebook

Select specific tables from an account to ingest

Declarative Automation Bundles

Databricks notebook

Ingest ad_insights with meta_ads_options

Declarative Automation Bundles

Databricks notebook

Declarative Automation Bundles job definition file

Declarative Automation Bundles

Common patterns

Next steps

Additional resources

Feedback

Additional resources

`ad_insights` configuration

Ingest `ad_insights` with `meta_ads_options`