Shopify Python API Docs | dltHub

Shopify Admin REST API is Shopify’s versioned administrative REST API for managing store data (products, orders, customers, collections, etc.). The REST API base URL is https://{store_name}.myshopify.com/admin/api/{api_version} and All requests require an access token supplied in an HTTP header..

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 Shopify data in under 10 minutes.

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What data can I load from Shopify?

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

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How do I authenticate with the Shopify API?

Apps (public/custom) obtain OAuth access tokens or admin app access tokens; include the token in the X-Shopify-Access-Token header on every request. Example cURL header: -H 'X-Shopify-Access-Token: {access_token}'.

1. Get your credentials

1. Create an app in Shopify Partner Dashboard (public app) or a custom app in the store admin (custom app).
2. For public apps, perform the OAuth installation flow to obtain an access token with requested scopes.
3. For custom/admin apps, create the app in the store admin and copy the “access token” (admin API access token).
4. Use that token as X-Shopify-Access-Token on requests.

2. Add them to .dlt/secrets.toml

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[sources.shopify_store_source]
shop = "your-store.myshopify.com"
access_token = "your_access_token_here"
api_version = "2026-01"

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 Shopify 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 shopify_store_pipeline.py

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

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Pipeline shopify_store_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset shopify_store_data
The duckdb destination used duckdb:/shopify_store.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 shopify_store_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 Shopify 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 shopify_store_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{store_name}.myshopify.com/admin/api/{api_version}",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "products", "endpoint": {"path": "admin/api/{api_version}/products.json", "data_selector": "products"}},
            {"name": "orders", "endpoint": {"path": "admin/api/{api_version}/orders.json", "data_selector": "orders"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM shopify_store_data.products LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("shopify_store_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 Shopify 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 (401/403)

If X-Shopify-Access-Token is missing/invalid you’ll get 401 Unauthorized (body: {"errors": "[API] Invalid API key or access token (unrecognized login or wrong password)"}). Ensure you are using a valid OAuth/admin access token and requested scopes.

Rate limiting (429)

REST Admin API limit: 40 requests per app per store per minute (replenishes 2/sec). Responses include X-Shopify-Shop-Api-Call-Limit header and 429 responses include Retry-After header. On 429 back off and retry after the indicated seconds.

Pagination quirks

REST uses cursor‑based pagination. Use the Link headers or page_info query param to fetch subsequent pages (client libraries expose next_page_info/prev_page_info). Avoid using since_id for cursor‑style endpoints; follow docs for cursor‑based pagination.

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