Load Bazaarvoice data to DuckDB

Bazaarvoice offers a suite of APIs for managing consumer-generated content, client responses, and sentiment insights. Everything needed to build a working Bazaarvoice → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Bazaarvoice to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Bazaarvoice to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Bazaarvoice API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Bazaarvoice API at a glance

These values come from the Bazaarvoice API documentation. Check them against the vendor's current reference before relying on them in production.

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How do I authenticate with the Bazaarvoice API?

Bazaarvoice APIs primarily use either an API passkey (provided as a query parameter or header) or OAuth2 (2-legged or 3-legged) for authentication. When using OAuth2, client credentials (ID and secret) are used to retrieve an access token.

1. Get your credentials

To obtain Bazaarvoice API credentials: 1. Sign in to the Bazaarvoice Portal. 2. Select the required client instance from the Instance Selector in the top-right corner. 3. Navigate to Settings, then under the Developer section, select API. 4. Click Request API key. 5. Choose the desired API type (e.g., Conversations API, Response API) and click Next. 6. Complete the request form. 7. Wait for your company's Technical Administrator to approve the request, after which you will receive notification via email. Once approved, the keys will appear in the API management dashboard.

2. Add them to .dlt/secrets.toml

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[sources.bazaarvoice_source]
passkey = "REPLACE_ME"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Bazaarvoice data can I load into DuckDB?

These are the Bazaarvoice endpoints dlt can load into DuckDB:

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How do I load only new Bazaarvoice records?

Bazaarvoice exposes Offset on data/reviews.json, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "reviews", "endpoint": {
    "path": "data/reviews.json",
    "data_selector": "Results",
    "incremental": {"cursor_path": "Offset", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Bazaarvoice pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading reviews.json and products.json from the Bazaarvoice API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def bazaarvoice_source(passkey=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.bazaarvoice.com",
            "auth": {"type": "api_key", "api_key": passkey, "name": "access_token"},
        },
        "resources": [
            {"name": "reviews", "endpoint": {"path": "data/reviews.json", "data_selector": "Results"}},
            {"name": "questions", "endpoint": {"path": "data/questions.json", "data_selector": "Results"}}
        ],
    }
    yield from rest_api_resources(config)


def load_bazaarvoice_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bazaarvoice_pipeline",
        destination="duckdb",
        dataset_name="bazaarvoice_data",
    )
    load_info = pipeline.run(bazaarvoice_source())
    print(load_info)


if __name__ == "__main__":
    load_bazaarvoice_to_duckdb()

Run it with python bazaarvoice_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Bazaarvoice data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("bazaarvoice_pipeline").dataset()
df = data.reviews.df()
print(df.head())

SQL:

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SELECT * FROM bazaarvoice_data.reviews LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Bazaarvoice to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Bazaarvoice loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Bazaarvoice data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack