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.

Panoply is an end-to-end data platform that can spin up an Amazon Redshift instance in just a few clicks. It uses machine learning and natural language processing (NLP) to learn, model, and automate standard data management activities performed by data scientists, data engineers, and analysts. It can import data with no schema, no modeling, and no configuration. With Panoply, you can use your favorite analysis, SQL, and visualization tools just as you would if you were creating a Redshift data warehouse on your own.

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"
    }
  ]
}

Once you've identified the columns you want to insert, you can use the Redshift CREATE TABLE statement to set up a table to receive all of the data.

To populate that table, you might be tempted to use INSERT statements to add data to your Redshift table row by row. Don't do that; Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, a better approach is to load the data into Amazon S3 and use the COPY command to migrate it into Redshift.

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.

Panoply is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Azure SQL Data Warehouse, To S3, and To Delta Lake.