Yelp Python API Docs | dltHub

Yelp is a platform that provides business information, reviews, and local data via its Places API. The REST API base URL is https://api.yelp.com/v3 and All requests require a Bearer API key..

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 Yelp data in under 10 minutes.

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What data can I load from Yelp?

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

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How do I authenticate with the Yelp API?

Authentication is performed by sending an HTTP header Authorization: Bearer <API_KEY> with each request.

1. Get your credentials

1. Visit https://www.yelp.com/developers and sign in with your Yelp account.
2. Click “Create App”.
3. Fill in the required app name and description fields.
4. After the app is created, the dashboard will display an “API Key”.
5. Copy this API key; it will be used as the Bearer token in the Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.yelp_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 Yelp 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 yelp_pipeline.py

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

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Pipeline yelp_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset yelp_data
The duckdb destination used duckdb:/yelp.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 yelp_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 businesses_search and business_reviews from the Yelp 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 yelp_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.yelp.com/v3",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "businesses_search", "endpoint": {"path": "businesses/search", "data_selector": "businesses"}},
            {"name": "business_reviews", "endpoint": {"path": "businesses/{id}/reviews", "data_selector": "reviews"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="yelp_pipeline",
        destination="duckdb",
        dataset_name="yelp_data",
    )
    load_info = pipeline.run(yelp_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("yelp_pipeline").dataset()
sessions_df = data.businesses_search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM yelp_data.businesses_search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("yelp_pipeline").dataset()
data.businesses_search.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 Yelp 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 Unauthorized – The API key is missing, malformed, or invalid. Verify that the Authorization: Bearer <API_KEY> header is correctly set.
• 403 Forbidden – The key does not have permission for the requested resource.

Rate Limiting

• 429 Too Many Requests – You have exceeded the per‑minute request quota. Respect the Retry-After header and implement exponential back‑off.

Pagination Quirks

• The limit parameter defaults to 20 and caps at 50.
• The offset parameter can be set from 0 up to 1000; requesting beyond this range returns an empty list.
• Responses include a total field indicating the total number of matching records.
• Use limit and offset together to paginate through results safely.

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