Load SDMX data to DuckDB

SDMX REST API is a standard for offering programmatic access to statistical data and metadata over HTTP. Everything needed to build a working SDMX → 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 SDMX 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 SDMX 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 SDMX 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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SDMX API at a glance

These values come from the SDMX API documentation. Check them against the vendor's current reference before relying on them in production.

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How do I authenticate with the SDMX API?

The SDMX REST API specification does not define a standard authentication mechanism for general data retrieval, but specific implementations (like Fusion Registry or IMF) may require HTTP Basic Authentication or a Bearer token in the 'Authorization' header depending on the resource and provider.

1. Get your credentials

The SDMX REST API specification does not mandate a universal API key system, as it is an open standard implemented by various statistical organizations (e.g., Eurostat, IMF, OECD). For public data retrieval, authentication is typically not required. If you are interacting with a private or secure registry (such as a Fusion Metadata Registry) that requires elevated privileges to upload or modify data, you must obtain a username and password directly from the administrator of that specific registry instance. These services typically use HTTP Basic Authentication.

2. Add them to .dlt/secrets.toml

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[sources.sdmx_source]
sdmx_username = "your_username_here"
sdmx_password = "your_password_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 SDMX data can I load into DuckDB?

These are the SDMX endpoints dlt can load into DuckDB:

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How do I load only new SDMX records?

SDMX exposes offset on data/{context}/{agencyID}/{resourceID}/{version}/{key}, 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": "data", "endpoint": {
    "path": "data/{context}/{agencyID}/{resourceID}/{version}/{key}",
    "incremental": {"cursor_path": "offset", "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 SDMX pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading data and dataflow (or structure) from the SDMX API into DuckDB:

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

@dlt.source
def sdmx_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL is an implementation-specific entry point defined by each provider, typically following the pattern '{protocol}://{ws-entry-point}/'",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "data", "endpoint": {"path": "data/{context}/{agencyID}/{resourceID}/{version}/{key}"}},
            {"name": "structure_codelist", "endpoint": {"path": "structure/codelist/{agencyID}/{resourceID}/{version}"}}
        ],
    }
    yield from rest_api_resources(config)


def load_sdmx_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sdmx_pipeline",
        destination="duckdb",
        dataset_name="sdmx_data",
    )
    load_info = pipeline.run(sdmx_source())
    print(load_info)


if __name__ == "__main__":
    load_sdmx_to_duckdb()

Run it with python sdmx_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 SDMX 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("sdmx_pipeline").dataset()
df = data.data.df()
print(df.head())

SQL:

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SELECT * FROM sdmx_data.data LIMIT 10;

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

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How do I deploy the SDMX 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 SDMX 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 SDMX 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