Shopify is an ecommerce platform for online and retail point-of-sale systems. It lets businesses set up and manage online stores, accept credit card payments, and track and respond to orders.

Power BI is Microsoft’s business intelligence offering. It's a powerful platform that includes capabilities for data modeling, visualization, dashboarding, and collaboration. Many enterprises that use Microsoft's other products can get easy access to Power BI and choose it for its convenience, security, and power.

With high-value use cases across analysts, IT, business users, and developers, Power BI offers a comprehensive set of functionality that has consistently landed Microsoft in Gartner's "Leaders" quadrant for Business Intelligence.

The first step to getting Shopify data into your data warehouse is pulling that data off of Shopify's servers using either the Shopify REST API or webhooks. We'll focus on the API here because it allows you to retrieve all of your historical data rather than just new real-time data.

Shopify's API offers numerous endpoints that can provide information on transactions, customers, refunds, and more. Using methods outlined in the API documentation, you can retrieve the data you need. For example, to get a list of all transactions for a given ID, you could call GET /admin/orders/#[id]/transactions.json.

The Shopify API returns JSON-formatted data. Here's an example of the kind of response you might see when querying the transactions endpoint.

{
  "transactions": [
    {
      "id": 179259969,
      "order_id": 450789469,
      "kind": "refund",
      "gateway": "bogus",
      "message": null,
      "created_at": "2017-08-05T12:59:12-04:00",
      "test": false,
      "authorization": "authorization-key",
      "status": "success",
      "amount": "209.00",
      "currency": "USD",
      "location_id": null,
      "user_id": null,
      "parent_id": null,
      "device_id": null,
      "receipt": {},
      "error_code": null,
      "source_name": "web"
    },
    {
      "id": 389404469,
      "order_id": 450789469,
      "kind": "authorization",
      "gateway": "bogus",
      "message": null,
      "created_at": "2017-08-01T11:57:11-04:00",
      "test": false,
      "authorization": "authorization-key",
      "status": "success",
      "amount": "409.94",
      "currency": "USD",
      "location_id": null,
      "user_id": null,
      "parent_id": null,
      "device_id": null,
      "receipt": {
        "testcase": true,
        "authorization": "123456"
      },
      "error_code": null,
      "source_name": "web",
      "payment_details": {
        "credit_card_bin": null,
        "avs_result_code": null,
        "cvv_result_code": null,
        "credit_card_number": "•••• •••• •••• 4242",
        "credit_card_company": "Visa"
      }
    },
    {
      "id": 801038806,
      "order_id": 450789469,
      "kind": "capture",
      "gateway": "bogus",
      "message": null,
      "created_at": "2017-08-05T10:22:51-04:00",
      "test": false,
      "authorization": "authorization-key",
      "status": "success",
      "amount": "250.94",
      "currency": "USD",
      "location_id": null,
      "user_id": null,
      "parent_id": null,
      "device_id": null,
      "receipt": {},
      "error_code": null,
      "source_name": "web"
    }
  ]
}

You can analyze any data in Power BI, as long as that data exists in a data warehouse that's connected to your Power BI account. The most common data warehouses include Amazon Redshift, Google BigQuery, and Snowflake. Microsoft also has its own data warehousing platform called Azure SQL Data Warehouse.

Connecting these data warehouses to Power BI is relatively simple. The Get Data menu in the Power BI interface allows you to import data from a number of sources, including static files and data warehouses. You'll find each of the warehouses mentioned above among the options in the Database list. The Power BI documentation provides more details on each.

In Power BI, each table in the data warehouse you connect is known as a dataset, and the analyses conducted on these datasets are known as reports. To create a report, use Power BI’s report editor, a visual interface for building and editing reports.

The report editor guides you through several selections in the course of building a report: the visualization type, fields being used in the report, filters being applied, any formatting you wish to apply, and additional analytics you may wish to layer onto your report, such as trendlines or averages. You can explore all of the features related to analyzing and tracking data in the Power BI documentation.

Once you've created a report, Power BI lets you share it with report "consumers" in your organization.

So, now what? You've built a script that pulls data from Shopify and loads it into your data warehouse, but what happens tomorrow when you have new transactions?

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Shopify's API results include fields like created_at that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.