Load Apache Airflow data to DuckDB

Apache Airflow provides a public REST API for interacting with DAGs, tasks, and other system resources. Everything needed to build a working Apache Airflow → 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 Apache Airflow 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 Apache Airflow 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 Apache Airflow 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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Apache Airflow API at a glance

These values come from the Apache Airflow API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Apache Airflow API?

Authentication is performed via JSON Web Tokens (JWT) obtained by POSTing credentials to the /auth/token endpoint. Requests must include an Authorization header with the value 'Bearer '.

1. Get your credentials

To obtain credentials for the Apache Airflow REST API, you must authenticate using your existing Airflow user credentials (username and password) to generate a JSON Web Token (JWT). Send a POST request to the /auth/token endpoint of your Airflow instance. The request body should be a JSON object containing username and password. The response will contain an access_token field, which is your JWT. This token is used in the Authorization header of subsequent API requests in the format: Authorization: Bearer . Note that the specific auth manager configured in your environment (e.g., FAB, LDAP, or custom) governs the exact behavior of the /auth/token endpoint.

2. Add them to .dlt/secrets.toml

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[sources.apache_airflow_source]
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 Apache Airflow data can I load into DuckDB?

These are the Apache Airflow endpoints dlt can load into DuckDB:

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How do I load only new Apache Airflow records?

Apache Airflow exposes cursor on api/v2/assets, 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": "assets", "endpoint": {
    "path": "api/v2/assets",
    "data_selector": "assets",
    "incremental": {"cursor_path": "cursor", "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 Apache Airflow pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /auth/token and /api/v2/dags from the Apache Airflow API into DuckDB:

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

@dlt.source
def apache_airflow_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "{AIRFLOW_BASE_URL}/api/v2",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "assets", "endpoint": {"path": "api/v2/assets", "data_selector": "assets"}},
            {"name": "dag_runs", "endpoint": {"path": "api/v2/dags/{dag_id}/dagRuns", "data_selector": "dag_runs"}}
        ],
    }
    yield from rest_api_resources(config)


def load_apache_airflow_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="apache_airflow_pipeline",
        destination="duckdb",
        dataset_name="apache_airflow_data",
    )
    load_info = pipeline.run(apache_airflow_source())
    print(load_info)


if __name__ == "__main__":
    load_apache_airflow_to_duckdb()

Run it with python apache_airflow_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 Apache Airflow 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("apache_airflow_pipeline").dataset()
df = data.assets.df()
print(df.head())

SQL:

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SELECT * FROM apache_airflow_data.assets LIMIT 10;

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

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How do I deploy the Apache Airflow 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 Apache Airflow 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 Apache Airflow 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