Elastic Path Python API Docs | dltHub

Elastic Path is a headless, composable eCommerce platform providing REST APIs for commerce resources. The REST API base URL is https://useast.api.elasticpath.com and All requests require a Bearer token for authentication..

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 Elastic Path data in under 10 minutes.

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What data can I load from Elastic Path?

Here are some of the endpoints you can load from Elastic Path:

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How do I authenticate with the Elastic Path API?

Requests must include an Authorization header with a Bearer access token. Tokens are obtained via the OAuth2 token endpoint using your client_id and client_secret.

1. Get your credentials

1. In Commerce Manager go to System > Application Keys. 2) Create or view an Application Key (client_id / client_secret) for your project/region. 3) Exchange the keys at the region OAuth endpoint to obtain an access token (POST https://.api.elasticpath.com/oauth/access_token with client_id and grant_type). 4) Use the returned access_token in Authorization: Bearer <access_token> for API requests.

2. Add them to .dlt/secrets.toml

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[sources.elastic_path_source]
access_token = "your_access_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 Elastic Path 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 elastic_path_pipeline.py

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

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Pipeline elastic_path_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset elastic_path_data
The duckdb destination used duckdb:/elastic_path.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 elastic_path_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 carts from the Elastic Path 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 elastic_path_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://useast.api.elasticpath.com",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "products", "endpoint": {"path": "v2/products", "data_selector": "data"}},
            {"name": "carts", "endpoint": {"path": "v2/carts", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM elastic_path_data.products LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("elastic_path_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 Elastic Path 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 receive 401 Unauthorized, verify Authorization header is present: Authorization: Bearer <access_token>. Tokens expire (expires_in) and must be re‑issued; ensure you requested a token from the region‑specific /oauth/access_token endpoint using your client credentials.

Token expiration and grant types

Elastic Path supports multiple grant types (implicit for client‑side/read‑only and client_credentials for server‑side). An expired or wrong grant type will lead to failed requests; refresh or re‑request tokens as appropriate.

Pagination and response format

The API follows JSON:API conventions; collection responses place records under the "data" key and support pagination parameters. Use the API's page query parameters to iterate pages (page[size], page[number] or the documented pagination scheme for your service region).

Rate limits and errors

Handle 429 Too Many Requests by backing off and retrying after the window. Other common errors include 400 for invalid requests and 403 for insufficient scope/permissions—check your application key scopes in Commerce Manager.

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