Ticket Tailor Python API Docs | dltHub

Ticket Tailor is an online event ticketing platform that provides a REST API to manage events, orders, tickets and related resources. The REST API base URL is https://api.tickettailor.com and All requests require an API key via HTTP Basic 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 Ticket Tailor data in under 10 minutes.

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What data can I load from Ticket Tailor?

Here are some of the endpoints you can load from Ticket Tailor:

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How do I authenticate with the Ticket Tailor API?

Provide the API key as the HTTP Basic auth username; the password can be left empty. Include the header Accept: application/json.

1. Get your credentials

1. Sign in to your Ticket Tailor account and open the Box Office you want to access.
2. Go to Box Office Settings > API (or Manage Billing > API) in the app.
3. Click Generate a New Key, give the key a name and create it.
4. Copy the generated API key (starts like sk_...) and store it securely — this is the value used as the Basic auth username.
5. Use the key as the username in Basic auth (password left empty) or set Authorization: Basic Base64Encode(api_key).

2. Add them to .dlt/secrets.toml

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[sources.ticket_tailor_source]
api_key = "sk_XXXXXXXXXXXX"

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 Ticket Tailor 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 ticket_tailor_pipeline.py

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

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Pipeline ticket_tailor_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ticket_tailor_data
The duckdb destination used duckdb:/ticket_tailor.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 ticket_tailor_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 orders from the Ticket Tailor 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 ticket_tailor_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.tickettailor.com",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "events", "endpoint": {"path": "v1/events", "data_selector": "data"}},
            {"name": "orders", "endpoint": {"path": "v1/orders", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM ticket_tailor_data.events LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ticket_tailor_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 Ticket Tailor 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 Unauthorized, verify you supplied the API key as HTTP Basic auth username (password empty) or Authorization: Basic <Base64(api_key)> and include Accept: application/json. The API key is box‑office scoped; ensure the key corresponds to the box office you are querying.

Rate limits

Ticket Tailor enforces rate limits (default: 5000 requests per 30 minutes). Check X-Rate-Limit-Limit, X-Rate-Limit-Remaining and X-Rate-Limit-Reset headers, and respect Retry-After when present.

Pagination and list semantics

List endpoints use cursor‑based pagination via starting_after and ending_before with a limit (max 100). Responses contain a data array of records.

Common API errors

Errors return conventional HTTP codes and a JSON body with status, error_code and message. Common statuses: 400, 401, 403, 404, 422, 429, 5XX.

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