STiX Cloud Ticketing Python API Docs | dltHub

STiX Cloud is a ticketing and event booking platform exposing a REST API for event discovery, seat selection, cart/checkout, and transaction management. The REST API base URL is https://api.stixcloudtest.com/api/v0 and OAuth 2.0 client_credentials; requests use a Bearer access token.

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 STiX Cloud Ticketing data in under 10 minutes.

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What data can I load from STiX Cloud Ticketing?

Here are some of the endpoints you can load from STiX Cloud Ticketing:

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How do I authenticate with the STiX Cloud Ticketing API?

Obtain an access token via POST to the auth endpoint using client_id and client_secret (grant_type=client_credentials). Include Authorization: Bearer <access_token> and Content-Type: application/json on API requests. The tenant code must be supplied in API paths.

1. Get your credentials

1. Contact STiX Cloud (or your SISTIC account manager) to provision a tenant and OAuth client. 2. Note the tenant code, client_id and client_secret provided. 3. Request an access token: POST https://auth.stixcloud.com/auth/v0/{tenant}/oauth/token?grant_type=client_credentials using HTTP Basic with client_id:client_secret or equivalent. 4. Use returned access_token in Authorization: Bearer for subsequent API calls.

2. Add them to .dlt/secrets.toml

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[sources.stix_cloud_ticketing_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
tenant = "your_tenant_code"

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 STiX Cloud Ticketing 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 stix_cloud_ticketing_pipeline.py

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

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Pipeline stix_cloud_ticketing_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset stix_cloud_ticketing_data
The duckdb destination used duckdb:/stix_cloud_ticketing.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 stix_cloud_ticketing_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 events and patron_transactions from the STiX Cloud Ticketing 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 stix_cloud_ticketing_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.stixcloudtest.com/api/v0",
            "auth": {
                "type": "bearer",
                "access_token": client_secret,
            },
        },
        "resources": [
            {"name": "events", "endpoint": {"path": "{tenant}/icc/{icc}/products", "data_selector": "content"}},
            {"name": "patron_transactions", "endpoint": {"path": "{tenant}/patrons/account/transactions", "data_selector": "content"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="stix_cloud_ticketing_pipeline",
        destination="duckdb",
        dataset_name="stix_cloud_ticketing_data",
    )
    load_info = pipeline.run(stix_cloud_ticketing_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("stix_cloud_ticketing_pipeline").dataset()
sessions_df = data.events.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM stix_cloud_ticketing_data.events LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("stix_cloud_ticketing_pipeline").dataset()
data.events.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 STiX Cloud Ticketing 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 the token is missing, invalid or expired the API returns 401 with body like: { "error": "invalid_token", "error_description": "Access token expired: ..." }. Re-request token via client_credentials flow. Ensure tenant path variable is correct.

Pagination and data selector notes

Many list endpoints return an envelope with keys "content" (list of records) and "links" (pagination). Use the "content" key to extract records. Some endpoints return a top-level JSON array instead of an envelope (e.g., seat section availability) — inspect endpoint sample responses.

Common API error format

Errors use a standard JSON shape with fields such as httpStatus, errorCode, statusMessage, exceptionName, errorTime and url. Example 400: { "httpStatus": "400", "errorCode": "error.invalid.request.parameter", "statusMessage": "Invalid request parameter." }

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