Ticketmaster Discovery Feed Python API Docs | dltHub

Ticketmaster Discovery Feed is a feed-based API that provides downloadable, per-country event data (JSON/CSV) sourced from Ticketmaster and related platforms. The REST API base URL is https://app.ticketmaster.com/discovery-feed/v2/ and All requests require an API key passed as the apikey query 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 pip install "dlt[workspace]" and start loading Ticketmaster Discovery Feed data in under 10 minutes.

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What data can I load from Ticketmaster Discovery Feed?

Here are some of the endpoints you can load from Ticketmaster Discovery Feed:

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How do I authenticate with the Ticketmaster Discovery Feed API?

The API uses an API key passed as the apikey query parameter on every request (HTTPS required).

1. Get your credentials

1. Register for a Ticketmaster developer account: https://developer-acct.ticketmaster.com/user/register 2) Request access to the Discovery Feed (if required) via devportalinquiry@ticketmaster.com or the developer portal; an API key will be issued. 3) Copy the provided API key and add it as the apikey query parameter in requests.

2. Add them to .dlt/secrets.toml

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[sources.ticketmaster_discovery_feed_source]
api_key = "your_api_key_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.

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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 Ticketmaster Discovery Feed 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 ticketmaster_discovery_feed_pipeline.py

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

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Pipeline ticketmaster_discovery_feed_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ticketmaster_discovery_feed_data
The duckdb destination used duckdb:/ticketmaster_discovery_feed.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 ticketmaster_discovery_feed_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 venues from the Ticketmaster Discovery Feed 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 ticketmaster_discovery_feed_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.ticketmaster.com/discovery-feed/v2/",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "events", "endpoint": {"path": "discovery/v2/events.json", "data_selector": "_embedded.events"}},
            {"name": "venues", "endpoint": {"path": "discovery/v2/venues.json", "data_selector": "_embedded.venues"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM ticketmaster_discovery_feed_data.events LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ticketmaster_discovery_feed_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 Ticketmaster Discovery Feed 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 omit or send an invalid apikey the API responds with 401 and a JSON fault payload: {"fault":{"faultstring":"Invalid ApiKey","detail":{"errorcode":"oauth.v2.InvalidApiKey"}}}. Verify the apikey query parameter is present and correct.

Rate limits & quota exceeded

Default quota: 5000 calls/day and rate limit ~5 requests/second. Relevant headers are Rate-Limit, Rate-Limit-Available, Rate-Limit-Over, Rate-Limit-Reset. When quota is exceeded you receive HTTP 429 and a fault JSON: {"fault":{"faultstring":"Rate limit quota violation. Quota limit exceeded. Identifier : {apikey}" ,"detail":{"errorcode":"policies.ratelimit.QuotaViolation"}}}.

Pagination / deep paging

Discovery API uses page/size parameters; response includes page metadata (page.size, page.totalElements, page.totalPages, page.number) and next/self links in _links. Deep paging beyond item index ~1000 is not supported (size * page < 1000 limit).

Common API errors

• 401 Unauthorized: invalid/missing apikey (see Authentication failures)
• 429 Too Many Requests: quota exceeded (see Rate limits)
• 400 Bad Request: invalid query parameters
• 404 Not Found: invalid resource id

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