Cinchy Python API Docs | dltHub

Cinchy is an enterprise data platform that provides a REST API for executing CQL queries, accessing saved queries, health checks, jobs, secrets manager and other platform functions. The REST API base URL is https://cinchy.net and Supports Basic authentication, Bearer token (OAuth2) and Personal Access Token authentication..

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 Cinchy data in under 10 minutes.

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What data can I load from Cinchy?

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

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How do I authenticate with the Cinchy API?

APIs accept Basic authentication for BasicAuthAPI routes, and Bearer tokens (OAuth2 access tokens or PATs) via the Authorization header: "Authorization: Bearer ".

1. Get your credentials

1. In Cinchy, register a client in the Integrated Clients table (set client_id, grant type, permitted scopes).
2. Note the generated client_id and client_secret (GUID).
3. Request a token with a POST to https://{Cinchy SSO URL}/identity/connect/token, supplying client_id, client_secret, grant_type=password, username, password, and scope.
4. Alternatively, create a Personal Access Token from the user Settings page and use it as a Bearer token.

2. Add them to .dlt/secrets.toml

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[sources.cinchy_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
username = "your_username"
password = "your_password"

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 Cinchy 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 cinchy_pipeline.py

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

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Pipeline cinchy_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cinchy_data
The duckdb destination used duckdb:/cinchy.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 cinchy_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 execute_cql and MyDomain/MyQuery from the Cinchy 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 cinchy_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://cinchy.net",
            "auth": {
                "type": "bearer",
                "access_token": client_secret,
            },
        },
        "resources": [
            {"name": "execute_cql", "endpoint": {"path": "API/ExecuteCQL"}},
            {"name": "saved_query", "endpoint": {"path": "API/v[version]/{domain}/{saved_query}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cinchy_pipeline",
        destination="duckdb",
        dataset_name="cinchy_data",
    )
    load_info = pipeline.run(cinchy_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("cinchy_pipeline").dataset()
sessions_df = data.execute_cql.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cinchy_data.execute_cql LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cinchy_pipeline").dataset()
data.execute_cql.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 Cinchy 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 failures

If you receive 401/400 when requesting tokens, verify client_id/client_secret, grant_type and that the client has appropriate scopes. The token endpoint returns { "error": "invalid_grant", "error_description": "Invalid username or password" } for bad credentials.

Using tokens

Include the access token or PAT in every request header: Authorization: Bearer <token>. Tokens expire (e.g. expires_in: 3600); refresh or re‑request when expired.

Basic auth vs BasicAuthAPI

Basic auth endpoints require using the BasicAuthAPI path: https://<Cinchy Web URL>/BasicAuthAPI/MyDomain/MyQuery and a Basic Authorization header (base64 username:password).

Pagination and compressed JSON

ExecuteCQL supports StartRow and RowCount for pagination. The CompressJSON parameter defaults to true (schema returned separately). Set CompressJSON=false to receive expanded JSON.

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