Shopify to BigQuery: 2 Easy Methods

on Tutorial, Data Warehouse, ETL, Hevo • February 25th, 2020 • Write for Hevo

You have your complete E-Commerce store set up on Shopify. You Collect data on the orders placed, Carts abandoned, Products viewed, and so on. You now want to move all of this data on Shopify to a robust Data Warehouse such as Google BigQuery so that you can combine this information with data from many other sources and gain deep insights. Well, you have landed on the right blog.

This blog will discuss 2 step-by-step methods for moving data from Shopify to BigQuery for analytics. First, it will provide a brief introduction to Shopify and BigQuery. Afterwards, the blog will explain the 2 methods of setting up your Shopify BigQuery integration. Read along to find out which method is the best for you!

Table of Contents


  • A Shopify account.
  • A BigQuery account.
  • Working knowledge of RESTful APIs.
  • A clear idea of what data you need to transfer.

What is Shopify

Shopify Logo
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With the advent of the internet and cloud, Shopify has emerged as one of the easiest ways for individuals and businesses to set up an online eCommerce store. Shopify liberates the user of all the technical complexities and development effort making it super simple to set up a business online. Shopify has a range of products that handle everything from landing page set up to, payments to reporting. 

Although Shopify gives out a range of analytics reports, it is not always easy to unify Shopify data with the other data that a business may have. Eg: Advertising data from Facebook Ads or Google Ads might have to be combined with Shopify orders to understand the true ROI from these campaigns. That is where the need to move data from Shopify to BigQuery arises. 

To learn more about Shopify, visit here.

What is Google BigQuery

BigQuery Logo
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Google BigQuery, a Cloud-based Data Warehouse solution offered by Google allows users to build SQL-like queries to answer deep analytical questions. BigQuery speed of execution combined with its pay for what you use payment model makes it a lucrative option for modern businesses that want to run analytics on large data sets. 

Businesses often tend to load data from different data sources – Shopify for eCommerce, Facebook/Google Ads for Advertising, Intercom for Support, and more to BigQuery and in turn answer deeper analytical questions by querying this data. 

To learn more about Google BigQuery visit here.

Data Prerequisites required before exporting from Shopify to Google BigQuery

Before you load data into BigQuery, make sure it’s in a format it can comprehend. For example, if the API you’re using returns XML, you’ll need to convert it to a serialization BigQuery can understand first. There are now two data formats supported:

  • CSV, as well as
  • JSON

You must also ensure that the data types you are utilizing are those that BigQuery supports, which include the following:


Methods to Set Up Shopify to BigQuery Integration

Method 1: Using a Custom Code to Set Up Shopify to BigQuery Integration

You would need to invest in some engineering resources that can understand Shopify APIs and build code to extract data from here. Once the infrastructure is ready, you would need to test, deploy and validate the consistency of data. Given this is a manual process, the time to insights would be high.

Method 2: Using Hevo Data to Set Up Shopify to BigQuery Integration

Hevo Data provides a hassle-free solution and helps you directly transfer data from Shopify to BigQuery and numerous other Databases/Data Warehouses or destinations of your choice instantly without having to write any code. Hevo comes with a graphical interface that allows you to configure your Shopify as the source and load data in real-time. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. It helps transfer data from Shopify to a destination of your choice for free.

Get Started with Hevo for Free

Hevo’s pre-built with 100+ data sources (including 30+ free data sources) will take full charge of the data transfer process, allowing you to focus on key business activities.

Get started with Hevo today! Sign up for the 14-day free trial!

Methods to Set Up Shopify to BigQuery Integration

The following 2 methods can be used to transfer data from Shopify to Bigquery :

Method 1: Using a Custom Code to Set Up Shopify to BigQuery Integration

This approach requires you to custom code the ETL process manually by using various API calls. The whole process of transferring data from Shopify to BigQuery can be completed using the following 3 steps:

Step 1: Pulling Data from Shopify using API Calls

Shopify exposes a wide set of RESTful APIs to its customers to extract data on orders, cart updates, inventory status, customers, and more. You can explore the complete set of APIs here.

Some things to note when using Shopify APIs: 

  1. Since Shopify allows you to extract a wide variety of data sets, you would need to first list down the data points you would need and learn about the APIs that will help you to extract this data
  2. To make a Shopify API call, you would need to provide API Authentication. This can be done by generating a username and password using the Shopify admin account.

Once you have these pre-requisites addressed, initiating an API call to extract product data from Shopify would look like this: 

curl --user user:password GET /admin/api/2019-10/products.json?limit=100

Shopify imposes limits on the amount of data that it sends out in a single API call. The default pagination limit is 50 if the limit parameter is not given. For the API call made above 250-page results will be shared. 

If you need to extract more data, then you would need to store the ID value of the last product that the API returned and then use this id to get the next data set. 

This is done as below.

curl --user user:password GET /admin/api/2019-10/products.json?limit=100&since_id=632910392 -o products.json

Where since_id is the last product id that was received on the previous API call. 

Once you have successfully executed this API call, you will now receive a response in JSON format. This is a nested JSON that contains all the information related to the products such as title, description, images, etc., and more importantly, the variants sub JSON which provides all the variant-specific information like inventory_quantity,  barcode, product price, etc.

Step 2: Modifying the Extracted Data

Now that you have extracted the data from Shopify, you will next need to load it to BigQuery. 

You can load data into BigQuery directly using API call or can upload the CSV / JSON file generated in the previous step using BigQuery UI.

Before you move data to BigQuery, make sure that the data types returned by the API response are supported by BigQuery. You can read more about BigQuery data types and conversions here.

Step 3: Loading Data into BigQuery

The 3rd and final step involves loading the modified data into Bigquery. This can be done as follows:

  1. Upload the prepared data to Google Cloud Storage (using Gsutil or the Web Console)
  2. Create a BigQuery table to store this data
  3. Load to BigQuery from your GCS bucket using BigQuery’s command-line tool (bq) or any cloud SDK. Read more about bq commands here.

To learn more about loading data into BigQuery, read through this guide.

Limitations of the above approach:

  1. Effort Intensive: Using code to move data from Shopify to BigQuery using custom code requires you to learn and bring together many different technologies. Given the learning curve involved, your data projects’ timelines can be affected.
  2. Not Real-time: The process mentioned above does not help you bring data in real-time. You would have to configure a cron job and write extra code to bring data in real-time.
  3. Data Consistency: Since you are extracting business-critical data out of Shopify, you would need to set up a separate process to validate the data received in BigQuery. Any inconsistency would negatively impact the decisions you may take as a business.
  4. Cannot Transform Data: Many times, you would encounter use cases where you need to convert currencies or standardize time zones to perform efficient analytics. The above approach does not cover that.
  5. Maintainance Overhead: If Shopify makes any changes on the API or say Google Bigquery is down, any break in your script will result in irretrievable data loss. Hence, you would need to constantly monitor and maintain the data

Method 2: Using Hevo Data to Set Up Shopify to BigQuery Integration

Hevo Data Logo
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Hevo Data, a No-code Data Pipeline, helps you directly transfer data from Shopify and 100+ other data sources to Data Warehouses such as BigQuery, Redshift, Databases, BI tools, or a destination of your choice in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. It helps transfer data from Shopify to a destination of your choice for free.

Sign up here for a 14-Day Free Trial!

Hevo Data takes care of all your Data Preprocessing needs and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination.  Hevo can set up your Shopify BigQuery integration process in the following 2 steps:

  • Configure Shopify as Data Source: In this step, you need to connect your Shopify account with Hevo which will then set up a data pipeline for your data transfer process. Moreover, you can use the automapping feature to automate the schema management process as shown in the below image.
Data Mapping in Hevo
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To learn more about connecting your Shopify account to Hevo, visit here.

  • Configuring BigQuery as Destination: You need to connect your BigQuery account to Hevo as the destination for your data transfer process. This is shown in the below image.
Configuing Bigquery as Destination in Hevo
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To learn more about connecting your BigQuery account to Hevo, visit here.

Hevo takes care of automatically mapping all your data to relevant tables in BigQuery, giving you access to Shopify data in real-time. Sign up for a risk-free 14-day free trial with Hevo and experience a hassle-free data migration from Shopify to BigQuery.

Additionally, Hevo helps you clean, transform and enrich data both before and after moving the data warehouse, ensuring that you have analysis-ready data in your warehouse at any point.

The Advantages of Using Hevo

Hevo automates your Shopify to BigQuery process and also provides the following benefits :

  • Simplicity: Hevo is super intuitive, super simple to use. With Hevo, you can start loading data from Shopify to BigQuery in just a few clicks.
  • Real-time Data: Hevo’s real-time streaming architecture ensures that you move data from Shopify to BigQuery instantly, without any delay. This allows you to gain meaningful insights in real-time.
  • Reliable Data Load: Hevo’s fault-tolerant architecture ensures that your data is loaded consistently and reliably without any loss of data.
  • Scalability: Hevo is built to handle data of any scale. Additionally, Hevo can bring data from a multitude of data sources. Both these aspects ensure that Hevo can help you scale your data infrastructure as your data needs grow.


The blog provided a brief introduction to Shopify and BigQuery. It also explained 2 step by step methods by which you can connect Shopify to BigQuery and transfer your data easily. The first method involves manually building custom code using RESTful APIs. This method has certain limitations which were discussed in the previous section.

Visit our Website to Explore Hevo

The challenges of the first method can be avoided by implementing Hevo Data, which can automate the data transfer process using its No-code Data Pipeline. Hevo provides a point and clicks interface that will allow you to move data without writing any code. Since Hevo is fully managed, the setup time is minimal and you will have your Shopify data in BigQuery in minutes. It helps transfer data from Shopify to a destination of your choice for free.

Sign Up for a 14-day free trial to experience a hassle-free data load from Shopify to Google BigQuery.

Share your experience of this blog in the comments section!

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