Load UK Carbon Intensity data to DuckDB
Build a UK Carbon Intensity to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the UK Carbon Intensity API base URL, auth, endpoints, and incremental loading.
The Carbon Intensity API provides real-time, forecast, and historical carbon intensity data for the electricity system in Great Britain. Everything needed to build a working UK Carbon Intensity → 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.
Build your UK Carbon Intensity to DuckDB pipeline
Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.
PromptRunuvx dlthub-init@latestto build a pipeline from UK Carbon Intensity 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 UK Carbon Intensity 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.
UK Carbon Intensity API at a glance
| Base URL | https://api.carbonintensity.org.uk |
| Example endpoint | GET intensity |
| Authentication | publicly accessible, no authentication required |
| Pagination | Not paginated |
| API reference | https://carbon-intensity.github.io/api-definitions/index.html |
These values come from the UK Carbon Intensity API reference — the authoritative source if anything here looks out of date.
How do I authenticate with the UK Carbon Intensity API?
The API is publicly accessible and does not require any authentication or headers.
No credentials required. The UK Carbon Intensity API is public, so there is nothing to obtain and nothing to add to .dlt/secrets.toml — the pipeline above runs as written.
What UK Carbon Intensity data can I load into DuckDB?
These are the UK Carbon Intensity endpoints dlt can load into DuckDB:
| Resource | Endpoint | Method | Data selector | Description |
|---|---|---|---|---|
| intensity | intensity | GET | data | Get current carbon intensity |
| intensity_date | intensity/date | GET | data | Get carbon intensity for today |
| intensity_by_date | intensity/date/{date} | GET | data | Get carbon intensity for specific date |
| intensity_factors | intensity/factors | GET | data | Get carbon intensity factors for fuel types |
| generation | generation | GET | data | Get current generation mix |
How do I load only new UK Carbon Intensity records?
The UK Carbon Intensity 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.
{"name": "intensity", "endpoint": { "path": "intensity", # 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.
What does the generated UK Carbon Intensity pipeline look like?
A standard dlt REST API pipeline — the same code you would write by hand, loading /intensity and /regional from the UK Carbon Intensity API into DuckDB:
import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def uk_carbon_intensity_source(): config: RESTAPIConfig = { "client": { "base_url": "https://api.carbonintensity.org.uk", }, "resources": [ {"name": "intensity", "endpoint": {"path": "intensity"}}, {"name": "generation", "endpoint": {"path": "generation"}} ], } yield from rest_api_resources(config) def load_uk_carbon_intensity_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="uk_carbon_intensity_pipeline", destination="duckdb", dataset_name="uk_carbon_intensity_data", ) load_info = pipeline.run(uk_carbon_intensity_source()) print(load_info) if __name__ == "__main__": load_uk_carbon_intensity_to_duckdb()
Run it with python uk_carbon_intensity_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.
How do I query UK Carbon Intensity 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):
import dlt data = dlt.pipeline("uk_carbon_intensity_pipeline").dataset() df = data.intensity.df() print(df.head())
SQL:
SELECT * FROM uk_carbon_intensity_data.intensity LIMIT 10;
See querying your data with dataset and exploring it in marimo notebooks.
How do I deploy the UK Carbon Intensity 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 UK Carbon Intensity loads into governed, documented models.
- Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.
What other destinations can I load UK Carbon Intensity data to?
dlt loads into any of these — only the destination argument changes:
| Destination | Example value |
|---|---|
| PostgreSQL | "postgres" |
| BigQuery | "bigquery" |
| Snowflake | "snowflake" |
| Redshift | "redshift" |
| Databricks | "databricks" |
| Filesystem (S3, GCS, Azure) | "filesystem" |
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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