Shopify to Grafana

This page provides you with instructions on how to extract data from Shopify and analyze it in Grafana. (If the mechanics of extracting data from Shopify seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Shopify?

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.

What is Grafana?

Grafana is an open source platform for time series analytics. It can run on-premises on all major operating systems or be hosted by Grafana Labs via GrafanaCloud. Grafana allows users to create, explore, and share dashboards to query, visualize, and alert on data.

Getting data out of Shopify

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.

Sample Shopify data

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"
    }
  ]
}

Loading data into Grafana

Analyzing data in Grafana requires putting it into a format that Grafana can read. Grafana natively supports nine data sources, and offers plugins that provide access to more than 50 more. Generally, it's a good idea to move all your data into a data warehouse for analysis. MySQL, Microsoft SQL Server, and PostgreSQL are among the supported data sources, and because Amazon Redshift is built on PostgreSQL and Panoply is built on Redshift, those popular data warehouses are also supported. However, Snowflake and Google BigQuery are not currently supported.

Analyzing data in Grafana

Grafana provides a getting started guide that walks new users through the process of creating panels and dashboards. Panel data is powered by queries you build in Grafana's Query Editor. You can create graphs with as many metrics and series as you want. You can use variable strings within panel configuration to create template dashboards. Time ranges generally apply to an entire dashboard, but you can override them for individual panels.

Keeping Shopify data up to date

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.

From Shopify to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Shopify data in Grafana is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Shopify to Redshift, Shopify to BigQuery, Shopify to Azure SQL Data Warehouse, Shopify to PostgreSQL, Shopify to Panoply, and Shopify to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data from Shopify to Grafana automatically. With just a few clicks, Stitch starts extracting your Shopify data via the API, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Grafana.