Load Data DC data to DuckDB

Data DC is the District of Columbia's open data portal providing access to government datasets and GIS services via REST APIs. Everything needed to build a working Data DC → 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 Data DC 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 Data DC 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 Data DC 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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Data DC API at a glance

These values come from the Data DC API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Data DC API?

Authentication is required for protected ArcGIS REST services and can be provided either as an 'apikey' query parameter or via an Authorization header, depending on the specific service requirements. Public datasets on the Open Data portal are generally accessible without credentials.

1. Get your credentials

To obtain an API key for the Data Commons REST API, navigate to the official API key management portal at https://apikeys.datacommons.org. Sign in or create an account if prompted, then request a key for the relevant hostnames (such as api.datacommons.org). You can manage and enable access for the specific APIs you intend to use through this dashboard.

2. Add them to .dlt/secrets.toml

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[sources.data_dc_source]
api_key = "your_api_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 Data DC data can I load into DuckDB?

These are the Data DC endpoints dlt can load into DuckDB:

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How do I load only new Data DC records?

Data DC exposes resultOffset on maps2.dcgis.dc.gov/dcgis/rest/services/{service}/FeatureServer/0/query, 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": "arcgis_feature_query", "endpoint": {
    "path": "maps2.dcgis.dc.gov/dcgis/rest/services/{service}/FeatureServer/0/query",
    "data_selector": "features",
    "incremental": {"cursor_path": "resultOffset", "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 Data DC pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading node and resolve from the Data DC API into DuckDB:

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

@dlt.source
def data_dc_source(apikey=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://opendata.dc.gov",
            "auth": {"type": "api_key", "api_key": apikey, "name": "X-API-Key", "location": "header"},
        },
        "resources": [
            {"name": "arcgis_feature_query", "endpoint": {"path": "maps2.dcgis.dc.gov/dcgis/rest/services/{service}/FeatureServer/0/query", "data_selector": "features"}},
            {"name": "datasets", "endpoint": {"path": "datasets", "data_selector": "datasetRecords"}}
        ],
    }
    yield from rest_api_resources(config)


def load_data_dc_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="data_dc_pipeline",
        destination="duckdb",
        dataset_name="data_dc_data",
    )
    load_info = pipeline.run(data_dc_source())
    print(load_info)


if __name__ == "__main__":
    load_data_dc_to_duckdb()

Run it with python data_dc_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 Data DC 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("data_dc_pipeline").dataset()
df = data.arcgis_feature_query.df()
print(df.head())

SQL:

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SELECT * FROM data_dc_data.arcgis_feature_query LIMIT 10;

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

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How do I deploy the Data DC 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 Data DC 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 Data DC 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