Load Meta WhatsApp Cloud API data to DuckDB

The WhatsApp Cloud API enables businesses to programmatically send and receive messages on the WhatsApp Business Platform. Everything needed to build a working Meta WhatsApp Cloud API → 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 Meta WhatsApp Cloud API to DuckDB pipeline

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

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Run uvx dlthub-init@latest to build a pipeline from Meta WhatsApp Cloud API 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 Meta WhatsApp Cloud API 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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Meta WhatsApp Cloud API API at a glance

These values come from the Meta WhatsApp Cloud API API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Meta WhatsApp Cloud API API?

All requests require an Authorization header with the value 'Bearer <ACCESS_TOKEN>', along with a 'Content-Type: application/json' header.

1. Get your credentials

To obtain a permanent access token for the WhatsApp Cloud API: 1. Navigate to the Meta Business Settings (business.facebook.com/settings). 2. Select your business account. 3. Under the Users section in the left sidebar, click System Users. 4. Click Add to create a new system user (assign an Admin role). 5. With the system user selected, click Assign Assets, select your app, and toggle Manage app under Full control. Also, select your WhatsApp account and toggle Manage WhatsApp Business accounts under Full control. 6. Click Generate token. 7. Select your app and ensure the following permissions are included: business_management, whatsapp_business_messaging, and whatsapp_business_management. 8. Copy the token immediately and store it securely.

2. Add them to .dlt/secrets.toml

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[sources.meta_whatsapp_cloud_api_source]
access_token = "EAAG..."

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 Meta WhatsApp Cloud API data can I load into DuckDB?

These are the Meta WhatsApp Cloud API endpoints dlt can load into DuckDB:

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How do I load only new Meta WhatsApp Cloud API records?

Meta WhatsApp Cloud API exposes after on {waba-id}/phone_numbers, 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": "phone_numbers", "endpoint": {
    "path": "{waba-id}/phone_numbers",
    "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 Meta WhatsApp Cloud API pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /{version}/{phone-number-id}/messages and /{whatsapp-business-account-id}/message_templates from the Meta WhatsApp Cloud API API into DuckDB:

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

@dlt.source
def meta_whatsapp_cloud_api_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://graph.facebook.com",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "phone_numbers", "endpoint": {"path": "{waba-id}/phone_numbers", "data_selector": "data"}},
            {"name": "message_templates", "endpoint": {"path": "{waba-id}/message_templates", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_meta_whatsapp_cloud_api_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="meta_whatsapp_cloud_api_pipeline",
        destination="duckdb",
        dataset_name="meta_whatsapp_cloud_api_data",
    )
    load_info = pipeline.run(meta_whatsapp_cloud_api_source())
    print(load_info)


if __name__ == "__main__":
    load_meta_whatsapp_cloud_api_to_duckdb()

Run it with python meta_whatsapp_cloud_api_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 Meta WhatsApp Cloud API 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("meta_whatsapp_cloud_api_pipeline").dataset()
df = data.phone_numbers.df()
print(df.head())

SQL:

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SELECT * FROM meta_whatsapp_cloud_api_data.phone_numbers LIMIT 10;

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

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How do I deploy the Meta WhatsApp Cloud API 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 Meta WhatsApp Cloud API 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 Meta WhatsApp Cloud API 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