Shopify Raw Data Guide

This guide shows you how to use the raw data coming from this specific data source APIs, so we can support you achieve your reporting needs. In this guide, some recommendations are provided as well as examples of use cases for the most commonly used fields. If you’d like to dig deeper into the raw data from each API, we recommend you to refer to our integrations documentation which provides links to each API documentation. For more guidance on how to build your reports and BI metrics, please inquire us about the Beginners Journey, Data Modelling Guide, Marketing Attribution and/or BI Metrics Guide.

Note: There may be several ways to achieve the use cases that has been indicated below, but these columns are the ones commonly used for most of the use cases we've encountered.


Most Used Tables

Table Name



The orders table presents data on an order level. Here, each row is an order, with a unique order id column. You can accomplish most of your BI use cases using the data from Orders table.


The Customer table stores information about a shop's customers, such as their contact details, their order history, and so on. Note: customers who haven't placed any order are also stored in this table. Shopify starts recording them as soon as they start the checkout process with entering their address details


The Product table lets you update and create products in a merchant's store. You can use product variants with the Product to create or update different versions of the same product. You can also add or update product images. This can be used to get information such as variant id for inventory level analysis.


An inventory level represents the available quantity of an inventory item at a specific location. Each inventory level belongs to one inventory item and has one location. For every location where an inventory item is available, there's an inventory level that represents the inventory item's quantity at that location


Most Used Fields

Column Name


Use Cases

Table Name


The auto generated date and time (ISO 8601 format) when the order was created in Shopify. The value for this property cannot be changed.

This is your order date column. You can use this value to analyze how much orders or revenue was generated at a specific date, week, month, year and so on. The order date also plays a crucial role for more advanced analysis such as calculating the first order of your customers, recency, frequency, etc.



Information about the customer. The order might not have a customer and apps should not depend on the existence of a customer object. This value might be null if the order was created through Shopify Point of Sale.

Using the JSON EXTRACTION node you can get the Customer ID, email, creation date, etc. The Customer ID can be used for segmenting customers, calculating new customers, and so on.



A list of discounts applied to the order. Information about the discounts is nested in a JSON object

Using the JSON EXTRACTION node you can get the discount code, discount amount, and type of discount.



A list of line item objects, each containing information about an item in the order. This information is nested in a JSON object.

Using the JSON EXTRACTION node line item id, item price, product id, item quantity, sku, variant id, and so on. This can be used for calculating cost of items sold, map to inventory level, etc.



The order 's position in the shop's count of orders starting at 1001.

This field can be used for counting the amount of orders in a certain time frame, or even finding the first order of a specific customer.



A list of refunds applied to the order nested in a JSON object.

This field can be used to calculate the number of refunded orders, amounts and the date of the refund.



The price of the order in the shop currency after discounts but before shipping, duties, taxes, and tips.

For different types of revenue calculations.



The total discounts applied to the price of the order in the shop currency.

For being included in different types of revenue calculations.



The sum of all line item prices, discounts, shipping, taxes, and tips in the shop currency.

For being included in different types of revenue calculations.


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