Looker Python API Docs | dltHub

Looker is a data exploration and analytics platform that provides a JSON‑oriented REST API. The REST API base URL is https://<instance_name>.cloud.looker.com:443 and All requests require an OAuth 2.0 Bearer access token in the Authorization header..

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 pip install "dlt[workspace]" and start loading Looker data in under 10 minutes.

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What data can I load from Looker?

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

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How do I authenticate with the Looker API?

Obtain an access token by calling the login endpoint with your client ID and client secret, then include the token in the Authorization: Bearer <token> header on every request.

1. Get your credentials

1. Sign in to Looker as an admin.
2. Navigate to Admin → Users → API3 Keys (or Admin → API depending on version).
3. Click Create New API3 Key to generate a client ID and client secret.
4. Store the client ID and client secret securely.
5. Use them to call the login endpoint (POST /login) to receive a short‑lived OAuth 2.0 access token.

2. Add them to .dlt/secrets.toml

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[sources.looker_data_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
access_token = "your_access_token"

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.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt 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:

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dlt ai toolkit rest-api-pipeline install

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 Looker 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:

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python looker_data_pipeline.py

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

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

Inspect your pipeline and data:

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dlt pipeline looker_data_pipeline show

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

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Python pipeline example

This example loads users and looks from the Looker API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def looker_data_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<instance_name>.cloud.looker.com:443",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users"}},
            {"name": "looks", "endpoint": {"path": "looks"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="looker_data_pipeline",
        destination="duckdb",
        dataset_name="looker_data_data",
    )
    load_info = pipeline.run(looker_data_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.

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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):

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import dlt

data = dlt.pipeline("looker_data_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM looker_data_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("looker_data_pipeline").dataset()
data.users.df().head()

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

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What destinations can I load Looker 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.

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Troubleshooting

Authentication errors

• 401 Authorization Required – Returned when the Bearer token is missing, invalid, or expired. Obtain a fresh token via the login endpoint.

Rate limiting

• The API may respond with 429 Too Many Requests if limits are exceeded. Implement exponential back‑off and respect the Retry-After header.

Pagination

• List endpoints return a maximum of 100 records per page. Use the page and per_page query parameters to navigate through results.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

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

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

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• 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