Bigcommerce Python API Docs | dltHub

BigCommerce is a cloud eCommerce platform that exposes REST and GraphQL APIs to manage store data (products, orders, customers, catalogs, stores, channels, webhooks, themes, etc.). The REST API base URL is https://api.bigcommerce.com/stores/{store_hash}/v3 and All Management API requests require store-level API credentials (Bearer-style API token) provided via request headers..

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 Bigcommerce data in under 10 minutes.

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What data can I load from Bigcommerce?

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

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How do I authenticate with the Bigcommerce API?

Generate a Store API Account in the BigCommerce control panel to obtain API credentials; include the API token in request headers (X-Auth-Token) and use the API Path / store_hash from the account when building the base URL. Requests must include appropriate OAuth scopes chosen when the API account was created.

1. Get your credentials

1. Log into your store Control Panel as store owner or a user with high-risk permissions.
2. Go to Settings → Store API Accounts (or Account-level API accounts for account tokens).
3. Click '+ Create API Account'.
4. Choose Token type (V2/V3 API token), name the account, select required OAuth scopes, and Save.
5. Immediately copy or download the displayed credentials (.txt) — this contains the Access Token (API token) and the API Path (store_hash).
6. Use the API Path to form the base URL and the Access Token in headers for requests.

2. Add them to .dlt/secrets.toml

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[sources.bigcommerce_source]
access_token = "your_store_api_token_here"
store_hash = "your_store_hash_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 Bigcommerce 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 bigcommerce_pipeline.py

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

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Pipeline bigcommerce_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bigcommerce_data
The duckdb destination used duckdb:/bigcommerce.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 bigcommerce_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 products and orders from the Bigcommerce 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 bigcommerce_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.bigcommerce.com/stores/{store_hash}/v3",
            "auth": {
                "type": "api_key",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "products", "endpoint": {"path": "catalog/products", "data_selector": "data"}},
            {"name": "orders", "endpoint": {"path": "orders", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bigcommerce_pipeline",
        destination="duckdb",
        dataset_name="bigcommerce_data",
    )
    load_info = pipeline.run(bigcommerce_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("bigcommerce_pipeline").dataset()
sessions_df = data.products.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bigcommerce_data.products LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bigcommerce_pipeline").dataset()
data.products.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 Bigcommerce 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 get 401 Unauthorized or 403 Forbidden: confirm you used the Access Token from the API Account popup (store-level token), include it in the X-Auth-Token header, and that the token has the required OAuth scopes. Remember the credentials popup is shown only once when the API account is created.

Rate limiting and throttling

BigCommerce enforces rate limits on Management API calls; clients should inspect headers and implement retry/backoff on 429 responses. Use pagination to reduce payload sizes.

Pagination quirks

Most v3 endpoints return paginated responses in the structure { "data": [...], "meta": { "pagination": { ... } } }. Use meta.pagination.total, limit, and page to page through results.

Common API errors and responses

400 Bad Request — malformed request; 401 Unauthorized — invalid/missing token; 403 Forbidden — insufficient scopes/permissions; 404 Not Found — resource missing or wrong store_hash; 429 Too Many Requests — rate limit exceeded; 500/502/503 — server errors (retry with backoff).

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