Load Flight Duration API data to DuckDB

The SITA Flight Duration API provides statistics on flight times between two airports, including average, minimum, and maximum durations. Everything needed to build a working Flight Duration 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 Flight Duration API 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 Flight Duration 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 Flight Duration 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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Flight Duration API API at a glance

These values come from the Flight Duration API API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Flight Duration API API?

Authentication requires obtaining an OAuth access token via the OAuth2 Client Credential flow using an API key and consumer secret. The resulting access token is supplied as a Bearer token in the Authorization header for all API requests.

1. Get your credentials

1. Navigate to the developer portal (e.g., https://www.developer.aero/) and create an account. 2. Register your interest or application to obtain an API key (client_id) and consumer secret (client_secret). 3. To authenticate, use the OAuth2 Client Credential flow: base64-encode your 'client_id:client_secret' pair and send it in the Authorization header to the token endpoint (e.g., https://sitaopen.api.aero/duration/oauth/token) to receive an OAuth access token. Use this token as a Bearer token in subsequent API requests.

2. Add them to .dlt/secrets.toml

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[sources.flight_duration_api_source]
api_key = "your_client_id_here"
client_secret = "your_consumer_secret_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 Flight Duration API data can I load into DuckDB?

These are the Flight Duration API endpoints dlt can load into DuckDB:

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How do I load only new Flight Duration API records?

The Flight Duration API 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": "flight_duration", "endpoint": {
    "path": "v2/{originAirport}/{destinationAirport}",
    # 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 Flight Duration API pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v2/{originAirport}/{destinationAirport} and /v2/{originAirport}/{destinationAirport}?split=airline from the Flight Duration API API into DuckDB:

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

@dlt.source
def flight_duration_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://sitaopen.api.aero/duration",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "flight_duration", "endpoint": {"path": "v2/{originAirport}/{destinationAirport}"}},
            {"name": "flight_duration_split_by_airline", "endpoint": {"path": "v2/{originAirport}/{destinationAirport}?split=airline"}}
        ],
    }
    yield from rest_api_resources(config)


def load_flight_duration_api_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="flight_duration_api_pipeline",
        destination="duckdb",
        dataset_name="flight_duration_api_data",
    )
    load_info = pipeline.run(flight_duration_api_source())
    print(load_info)


if __name__ == "__main__":
    load_flight_duration_api_to_duckdb()

Run it with python flight_duration_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 Flight Duration 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("flight_duration_api_pipeline").dataset()
df = data.flight_duration.df()
print(df.head())

SQL:

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SELECT * FROM flight_duration_api_data.flight_duration LIMIT 10;

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

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How do I deploy the Flight Duration 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 Flight Duration 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 Flight Duration 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