Load Amadeus Airport Nearest Relevant data to DuckDB

The Amadeus Airport Nearest Relevant API provides a list of relevant airports near a specified latitude and longitude coordinate pair. Everything needed to build a working Amadeus Airport Nearest Relevant → 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 Amadeus Airport Nearest Relevant 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 Amadeus Airport Nearest Relevant 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 Amadeus Airport Nearest Relevant 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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Amadeus Airport Nearest Relevant API at a glance

These values come from the Amadeus Airport Nearest Relevant API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Amadeus Airport Nearest Relevant API?

Amadeus APIs require a Bearer token obtained by sending a POST request with client_id and client_secret to the /v1/security/oauth2/token endpoint. The token must be included in the Authorization header as 'Bearer {access_token}'.

1. Get your credentials

The Amadeus for Developers self-service portal was decommissioned on July 17th, 2026. Developers must now transition to the Amadeus Enterprise API Portal. To obtain credentials for Enterprise APIs: 1. Navigate to the Amadeus for Developers website. 2. Request access to Enterprise APIs by registering through the portal. 3. Work with an Amadeus travel consultant who will guide you through the selection and setup process for enterprise-scale API access. Authentication for these APIs typically utilizes the OAuth 2.0 Client Credentials flow, requiring you to exchange a client_id and client_secret for a short-lived Bearer access token via the /v1/security/oauth2/token endpoint.

2. Add them to .dlt/secrets.toml

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[sources.amadeus_airport_nearest_relevant_source]
client_id, client_secret = "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 Amadeus Airport Nearest Relevant data can I load into DuckDB?

These are the Amadeus Airport Nearest Relevant endpoints dlt can load into DuckDB:

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How do I load only new Amadeus Airport Nearest Relevant records?

The Amadeus Airport Nearest Relevant 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": "airport_nearest_relevant", "endpoint": {
    "path": "v1/reference-data/locations/airports",
    # 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 Amadeus Airport Nearest Relevant pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v1/security/oauth2/token and /v1/reference-data/locations/airports from the Amadeus Airport Nearest Relevant API into DuckDB:

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

@dlt.source
def amadeus_airport_nearest_relevant_source(client_id_client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://test.api.amadeus.com (test) or https://api.amadeus.com (production)",
            "auth": {"type": "bearer", "token": client_id_client_secret},
        },
        "resources": [
            {"name": "airport_nearest_relevant", "endpoint": {"path": "v1/reference-data/locations/airports", "data_selector": "data"}},
            {"name": "airport_city_search", "endpoint": {"path": "v1/reference-data/locations", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_amadeus_airport_nearest_relevant_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="amadeus_airport_nearest_relevant_pipeline",
        destination="duckdb",
        dataset_name="amadeus_airport_nearest_relevant_data",
    )
    load_info = pipeline.run(amadeus_airport_nearest_relevant_source())
    print(load_info)


if __name__ == "__main__":
    load_amadeus_airport_nearest_relevant_to_duckdb()

Run it with python amadeus_airport_nearest_relevant_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 Amadeus Airport Nearest Relevant 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("amadeus_airport_nearest_relevant_pipeline").dataset()
df = data.airport_nearest_relevant.df()
print(df.head())

SQL:

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SELECT * FROM amadeus_airport_nearest_relevant_data.airport_nearest_relevant LIMIT 10;

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

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How do I deploy the Amadeus Airport Nearest Relevant 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 Amadeus Airport Nearest Relevant 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 Amadeus Airport Nearest Relevant 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