Bazaarvoice Python API Docs | dltHub

Bazaarvoice is a platform that provides APIs for managing and retrieving consumer‑generated content such as reviews and questions. The REST API base URL is https://api.bazaarvoice.com and Requests require either a Passkey query parameter (Conversations API) or an OAuth2 Bearer token (Response API)..

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 Bazaarvoice data in under 10 minutes.

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What data can I load from Bazaarvoice?

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

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How do I authenticate with the Bazaarvoice API?

Conversations API calls include a Passkey query parameter; Response API calls must include an Authorization: Bearer header.

1. Get your credentials

1. Log in to your Bazaarvoice account.
2. Navigate to the Developer or API Keys section.
3. Create a new API key (Passkey) for the Conversations API and copy the generated value.
4. For the Response API, register an application in the OAuth2 client area and note the client ID and secret.
5. Use the client credentials to obtain an access token via the token endpoint.
6. Store the Passkey and access token securely for use in API calls.

2. Add them to .dlt/secrets.toml

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[sources.bazaarvoice_source]
api_key = "your_api_key_here"
# For OAuth2 protected endpoints, optionally:
access_token = "your_oauth2_token_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 Bazaarvoice 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 bazaarvoice_pipeline.py

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

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Pipeline bazaarvoice_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bazaarvoice_data
The duckdb destination used duckdb:/bazaarvoice.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 bazaarvoice_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 reviews and questions from the Bazaarvoice 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 bazaarvoice_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.bazaarvoice.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "reviews", "endpoint": {"path": "reviews.json", "data_selector": "Results"}},
            {"name": "questions", "endpoint": {"path": "questions.json", "data_selector": "Results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bazaarvoice_pipeline",
        destination="duckdb",
        dataset_name="bazaarvoice_data",
    )
    load_info = pipeline.run(bazaarvoice_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("bazaarvoice_pipeline").dataset()
sessions_df = data.reviews.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bazaarvoice_data.reviews LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bazaarvoice_pipeline").dataset()
data.reviews.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 Bazaarvoice 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

If the Passkey query parameter is missing or invalid, the API returns a 401 Unauthorized response. For the Response API, omitting the Bearer token or using an expired token also results in 401.

Rate limiting

Bazaarvoice may enforce request limits per minute. Exceeding this limit returns a 429 Too Many Requests response; clients should implement exponential back‑off.

Pagination quirks

Responses include Offset, Limit, and TotalResults. Use the Offset parameter to request subsequent pages. If Offset + Limit exceeds TotalResults, the API returns an empty Results array.

Common data issues

When a required parameter such as ProductId is omitted, the API returns a 400 Bad Request with an error message in the HasErrors field.

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