Load Facebook-ads-cm data to DuckDB

The Facebook Marketing API is a collection of Graph API endpoints used to manage and analyze ad campaigns across Meta technologies. Everything needed to build a working Facebook-ads-cm → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Facebook-ads-cm to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Facebook-ads-cm to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Facebook-ads-cm API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Facebook-ads-cm API at a glance

These values come from the Facebook-ads-cm API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Facebook-ads-cm API?

Every Marketing API call requires a valid Graph API access token, which can be passed either as an 'access_token' query parameter or as a Bearer token in the 'Authorization' header.

1. Get your credentials

1. Create a Meta Developer account at developers.facebook.com and register a new Meta App in the App Dashboard.\n2. In your App Dashboard, navigate to the App Review section to request necessary permissions (e.g., ads_read, ads_management) required for your integration.\n3. Navigate to the Graph API Explorer tool.\n4. Select your app in the 'Meta App' field.\n5. In the 'User or Page' field, select 'User Token'.\n6. Select the required permissions (ads_read, ads_management) and click 'Generate Access Token'.\n7. To generate a long-lived token (recommended for production pipelines), paste this token into the Access Token Debugger and click 'Extend Access Token' at the bottom of the page.\n8. Ensure you store your App ID, App Secret, and the long-lived Access Token securely. For server-to-server applications, you may alternatively use the System User token flow described in official documentation.

2. Add them to .dlt/secrets.toml

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[sources.facebook_ads_cm_source]
access_token = "REPLACE_ME"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Facebook-ads-cm data can I load into DuckDB?

These are the Facebook-ads-cm endpoints dlt can load into DuckDB:

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How do I load only new Facebook-ads-cm records?

Facebook-ads-cm exposes after on act_{ad_account_id}/campaigns, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "campaigns", "endpoint": {
    "path": "act_{ad_account_id}/campaigns",
    "data_selector": "data",
    "incremental": {"cursor_path": "after", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Facebook-ads-cm pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading act_{AD_ACCOUNT_ID}/campaigns and act_{AD_ACCOUNT_ID}/insights from the Facebook-ads-cm API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def facebook_ads_cm_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://graph.facebook.com/v25.0",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "campaigns", "endpoint": {"path": "act_{ad_account_id}/campaigns", "data_selector": "data"}},
            {"name": "ads", "endpoint": {"path": "act_{ad_account_id}/ads", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_facebook_ads_cm_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="facebook_ads_cm_pipeline",
        destination="duckdb",
        dataset_name="facebook_ads_cm_data",
    )
    load_info = pipeline.run(facebook_ads_cm_source())
    print(load_info)


if __name__ == "__main__":
    load_facebook_ads_cm_to_duckdb()

Run it with python facebook_ads_cm_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Facebook-ads-cm data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("facebook_ads_cm_pipeline").dataset()
df = data.campaigns.df()
print(df.head())

SQL:

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SELECT * FROM facebook_ads_cm_data.campaigns LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Facebook-ads-cm to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Facebook-ads-cm loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Facebook-ads-cm data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack