Tinybird Python API Docs | dltHub

Tinybird is a real-time data platform for building APIs from SQL queries over streaming and batch data sources. The REST API base URL is https://api.tinybird.co and all requests require a token provided via Authorization header or URL parameter.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv add "dlt[hub]" and start loading Tinybird data in under 10 minutes.

---

What data can I load from Tinybird?

Here are some of the endpoints you can load from Tinybird:

---

How do I authenticate with the Tinybird API?

All requests must be authenticated using a Tinybird Token or JWT, provided either as a Bearer token in the 'Authorization' header ('Authorization: Bearer ') or as a 'token' URL parameter.

1. Get your credentials

To obtain your API credentials (Static Token), follow these steps in the Tinybird dashboard: 1. Log in to your Tinybird account. 2. Navigate to the "Tokens" section (usually located in the sidebar). 3. Click the "Create Token" button. 4. Select the necessary scopes for your use case (e.g., DATASOURCES

, PIPES, ADMIN). 5. Copy the generated token value immediately, as it may not be displayed again. Store this value securely.

2. Add them to .dlt/secrets.toml

Copy
[sources.tinybird_source]
token = "p.your_generated_token_value_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

Copy
uv run dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

Copy
uv run dlthub ai toolkit install rest-api-pipeline

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Tinybird API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

Copy
uv run python tinybird_pipeline.py

If everything is configured correctly, you'll see output like this:

Copy
Pipeline tinybird_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset tinybird_data
The duckdb destination used duckdb:/tinybird.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

---

Python pipeline example

This example loads /v0/pipes and /v0/sql from the Tinybird API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def tinybird_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.tinybird.co",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "data_sources", "endpoint": {"path": "v0/datasources", "data_selector": "datasources"}},
            {"name": "pipes", "endpoint": {"path": "v0/pipes", "data_selector": "pipes"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tinybird_pipeline",
        destination="duckdb",
        dataset_name="tinybird_data",
    )
    load_info = pipeline.run(tinybird_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

---

How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

Copy
import dlt

data = dlt.pipeline("tinybird_pipeline").dataset()
sessions_df = data.pipes.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM tinybird_data.pipes LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("tinybird_pipeline").dataset()
data.pipes.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Tinybird data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

Copy
uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack