Load Ifttt data to DuckDB

IFTTT Connect API allows developers to connect their services and products to the IFTTT ecosystem for managing connections and triggering events. Everything needed to build a working Ifttt → 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 Ifttt 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 Ifttt 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 Ifttt 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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Ifttt API at a glance

These values come from the Ifttt API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Ifttt API?

The API supports two main authentication methods: Service-Key (passed in the 'IFTTT-Service-Key' header) for backend-to-backend communication, and Bearer token authentication (passed in the 'Authorization' header) for user-authenticated requests.

1. Get your credentials

To obtain your credentials for the IFTTT Webhooks service, first ensure the Webhooks service is connected to your IFTTT account. Navigate to the Webhooks service page on the IFTTT website and click the 'Connect' button if it is not yet active. Once connected, scroll to the 'About Webhooks' section at the bottom of the page and click the 'Documentation' link. Your unique Webhooks key will be displayed prominently at the top of the resulting documentation page. If you need to refresh your credentials, you can navigate to the Webhooks settings page (typically https://ifttt.com/maker_webhooks/settings) and click 'Regenerate key'. Note that if you are developing a professional IFTTT service (as opposed to using personal Webhooks), you obtain your 'Service Key' from the 'Details' tab within the IFTTT Platform dashboard.

2. Add them to .dlt/secrets.toml

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[sources.ifttt_source]
ifttt_webhooks_key = "your_key_here"

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 Ifttt data can I load into DuckDB?

These are the Ifttt endpoints dlt can load into DuckDB:

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How do I load only new Ifttt records?

The Ifttt API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "service_status", "endpoint": {
    "path": "ifttt/v1/status",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 Ifttt pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading GET /v2/me and POST /v2/connections/{connection_id}/actions/{action_slug}/run (for Connect API) or standard Webhooks trigger URLs like https://maker.ifttt.com/trigger/{event}/with/key/{key}. from the Ifttt API into DuckDB:

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

@dlt.source
def ifttt_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://connect.ifttt.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "service_status", "endpoint": {"path": "ifttt/v1/status"}},
            {"name": "user_info", "endpoint": {"path": "ifttt/v1/user/info"}}
        ],
    }
    yield from rest_api_resources(config)


def load_ifttt_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ifttt_pipeline",
        destination="duckdb",
        dataset_name="ifttt_data",
    )
    load_info = pipeline.run(ifttt_source())
    print(load_info)


if __name__ == "__main__":
    load_ifttt_to_duckdb()

Run it with python ifttt_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 Ifttt 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("ifttt_pipeline").dataset()
df = data.user_info.df()
print(df.head())

SQL:

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SELECT * FROM ifttt_data.user_info LIMIT 10;

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

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How do I deploy the Ifttt 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 Ifttt 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 Ifttt 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