Centra Python API Docs | dltHub

Centra is a headless e‑commerce platform and API suite for managing products, orders, customers, stock and related commerce data. The REST API base URL is https://{your-centra-store-domain} and All requests require a plugin API key for legacy REST endpoints or a Bearer token for the Integration (GraphQL) 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 Centra data in under 10 minutes.

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What data can I load from Centra?

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

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How do I authenticate with the Centra API?

Centra uses an API‑plugin key for legacy REST endpoints (sent via request headers) and a Bearer access token for the Integration (GraphQL) API (Authorization: Bearer ).

1. Get your credentials

1. Log in to your Centra admin (store backend).
2. Navigate to Store settings → Plugins → Add API plugin (Order API / Shop API / Integration API as needed).
3. Create a new API plugin and enable the required endpoints/scopes (products, orders, customers, etc.).
4. After creation, copy the provided Base URL and API Key (or Integration API access token) shown in the plugin details.
5. Store the API key in secrets.toml as api_key = "..." and configure the Base URL in your connector.

2. Add them to .dlt/secrets.toml

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[sources.centra_source]
api_key = "your_centra_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 Centra 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 centra_pipeline.py

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

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Pipeline centra_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset centra_data
The duckdb destination used duckdb:/centra.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 centra_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 Centra 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 centra_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your-centra-store-domain}",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "products", "endpoint": {"path": "products", "data_selector": "products"}},
            {"name": "orders", "endpoint": {"path": "orders", "data_selector": "orders"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM centra_data.products LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("centra_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 Centra 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 requests return 401/403, verify you used the plugin API key for REST endpoints or a Bearer token for Integration API in the Authorization header. Ensure the API plugin in Centra Admin has the required scope/endpoints enabled.

Rate limits and throttling

Centra documentation notes standard API behavior; if you encounter 429 responses, implement exponential backoff and respect plugin limits. Use pagination (limit/offset or page) to reduce load.

Pagination quirks

Most legacy REST GET endpoints support limit and offset (or page) and return a next/"next" or page metadata — use limit and offset to iterate. Some endpoints return a top‑level status and a named array (e.g. {"status":"ok","products":[...]}).

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