Load Shopify data to Snowflake

Shopify REST API is an interface for developers to programmatically read and write store data such as products, orders, and customers. Everything needed to build a working Shopify → Snowflake 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 Shopify to Snowflake pipeline

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

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 Shopify 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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Shopify API at a glance

These values come from the Shopify API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Shopify API?

Requests to the Shopify Admin REST API must include an 'X-Shopify-Access-Token' header containing the access token granted during the OAuth flow. Do not use 'Authorization: Bearer' as it is not supported for this API.

1. Get your credentials

1. Log in to the Shopify Developers Dev Dashboard (https://dev.shopify.com/dashboard/). 2. Navigate to the Apps section and select your specific app. 3. Open the Settings tab. 4. Locate the Credentials section to view or copy your Client ID and Client Secret. Note that these are used to programmatically request short-lived access tokens via the client credentials grant, as non-expiring tokens are no longer provided in the UI.

2. Add them to .dlt/secrets.toml

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[sources.shopify_source]
shop_url = "your-store-name"
access_token = "your_access_token_here"
# If using client credentials (for automated token exchange):
# SHOPIFY_CLIENT_ID = "your-client-id"
# SHOPIFY_CLIENT_SECRET = "your-client-secret"

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 Shopify data can I load into Snowflake?

These are the Shopify endpoints dlt can load into Snowflake:

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How do I load only new Shopify records?

Shopify exposes page_info on orders.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": "orders", "endpoint": {
    "path": "orders.json",
    "data_selector": "orders",
    "incremental": {"cursor_path": "page_info", "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 Shopify pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading admin/api/{api_version}/products.json and admin/api/{api_version}/orders.json from the Shopify API into Snowflake:

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

@dlt.source
def shopify_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{shop}.myshopify.com/admin/api/{api_version}",
            "auth": {"type": "api_key", "api_key": access_token, "name": "X-Shopify-Access-Token", "location": "header"},
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "orders.json", "data_selector": "orders"}},
            {"name": "products", "endpoint": {"path": "products.json", "data_selector": "products"}}
        ],
    }
    yield from rest_api_resources(config)


def load_shopify_to_snowflake() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="shopify_pipeline",
        destination="snowflake",
        dataset_name="shopify_data",
    )
    load_info = pipeline.run(shopify_source())
    print(load_info)


if __name__ == "__main__":
    load_shopify_to_snowflake()

Run it with uv run python shopify_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 Shopify data in Snowflake?

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("shopify_pipeline").dataset()
df = data.products.df()
print(df.head())

SQL:

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SELECT * FROM shopify_data.products LIMIT 10;

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

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How do I deploy the Shopify to Snowflake 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 Shopify 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 Shopify 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