Load Shopify data to Databricks

Shopify Admin REST API provides authenticated access to store administration resources such as products, orders, customers, inventory, and shop configuration. Everything needed to build a working Shopify → Databricks 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 Databricks 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These values come from the Shopify API reference — the authoritative source if anything here looks out of date.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?

Send the app's Shopify Admin API access token in the X-Shopify-Access-Token request header on every REST Admin API request. Shopify's examples use a token placeholder such as {access_token} or {password}; the token is obtained through the app's applicable OAuth or Shopify-admin authentication flow. Content-Type: application/json is shown for JSON requests.

1. Get your credentials

For a new integration, create an app in Shopify’s Dev Dashboard: 1) Sign in to the Shopify Dev Dashboard and create/select your app. 2) Configure the app’s Admin API access scopes, requesting only the minimum scopes required. 3) Create and release an app version with those scopes. 4) Install the app on the target store and complete the authorization flow. For an app acting on stores in your own Shopify organization, open the app in the Dev Dashboard, copy its Client ID and Client secret, then exchange them programmatically at Shopify’s token endpoint using the client-credentials grant; cache and refresh the resulting access token (tokens expire after 24 hours). For a traditional public/custom app authorization, complete OAuth and use the returned Admin API access token. For a legacy custom app created directly in Shopify admin, go to Shopify admin > Settings > Apps and sales channels > Develop apps, create/configure the app, select Admin API scopes, install it, and reveal/copy its Admin API access token; Shopify’s current documentation says admin-created custom apps are authenticated in Shopify admin and that reinstalling is required to generate a new token. Send the token on every REST request as X-Shopify-Access-Token. Use the store’s myshopify domain in https://{shop}.myshopify.com/admin/api/{api-version}/... . Note that Shopify classifies REST Admin API as legacy as of October 1, 2024 and recommends GraphQL Admin API for new integrations.

2. Add them to .dlt/secrets.toml

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[sources.shopify_source]
api_key = "your_shopify_admin_api_access_token"
shop = "your-store-subdomain.myshopify.com"
api_version = "2026-10"

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 Databricks?

These are the Shopify endpoints dlt can load into Databricks:

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How do I load only new Shopify records?

Shopify exposes updated_at on products.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": "products", "endpoint": {
    "path": "products.json",
    "data_selector": "products",
    "incremental": {"cursor_path": "updated_at", "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 products and orders from the Shopify API into Databricks:

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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": "products", "endpoint": {"path": "products.json", "data_selector": "products"}},
            {"name": "orders", "endpoint": {"path": "orders.json", "data_selector": "orders"}}
        ],
    }
    yield from rest_api_resources(config)


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


if __name__ == "__main__":
    load_shopify_to_databricks()

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 Databricks?

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 Databricks 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