Extract your data from Shopify

Shopify exposes its complete platform to developers through its API. It is used by thousands of developers to create applications that are then sold through the Shopify marketplace.

As a Web API following the RESTful architecture principles, it can be accessed through HTTP. As a RESTful API, interacting with it can be achieved by using tools like CURL or Postman or by using HTTP clients for your favorite language or framework. A few suggestions:

• Apache HttpClient for Java
• Spray-client for Scala
• Hyper for Rust
• Ruby rest-client
• Python http-client

Shopify also offers a number of SDKs that are officially supported and maintained by them and that can be used to access their platform for different uses. For example, by using the iOS and Android Buy SDK it is possible to add Shopify checkout capabilities to your mobile application. Other SDKs that are offered are:

• Embedded App SDK
• Javascript Buy SDK
• Shopify POS App SDK
• Sales Channel SDK

Shopify API Authentication

There are two different types of applications in Shopify when it comes to authentication, private and public apps.

Private apps will only function on individual stores, so you don’t need to authenticate them through Oauth. You can get Private app credentials through your Partner dashboard or by logging into any Shopify admin.

You can also use this API key to manipulate your store using the API console, without the need for building a fully functional app.

The apps you create in your Partners dashboard function as public apps which can be made available for download in Shopify’s App Store if you meet the criteria.

Shopify Rate Limiting

The API call limit operates using a “leaky bucket” algorithm as a controller. This allows for infrequent bursts of calls and allows your app to continue to make an unlimited amount of calls over time. The bucket size is 40 calls (which cannot be exceeded at any given time), with a “leak rate” of 2 calls per second that continually empties the bucket. If your app averages 2 calls per second, it will never trip a 429 error (“bucket overflow”).

Endpoints and Available Resources

Shopify exposes 35+ endpoints covering all the possible touchpoints of e-commerce. Some of the most important resources that can be accessed through these endpoints are the following:

• Abandoned checkouts: used to return abandoned checkouts. A checkout is considered abandoned when a customer has entered their billing & shipping info but has yet to complete the purchase.
• Article: Operations concerning articles in your Blog.
• Asset: files that make up the theme of a shop.
• Blog: Shopify, in addition to the e-commerce features also offers an environment where the merchant can create a Blog for her shop.
• Collect: An object that connects a product to a custom collection.
• Customer: A customer resource instance represents a customer account with the shop.
• Event: Events are generated by specific Shopify resources when specific things happen, such as the creation of an article.
• Order: An order is a customer’s completed request to purchase one or more products from a shop.
• Product: A product is an individual item for sale in a Shopify shop.
• Transaction: Transactions are created for every order that results in an exchange of money.

For a complete list of endpoints, you can see here.

It is clear that with such a rich platform and API the data that can be pulled out of Shopify are both valuable and come in large quantities.

So, let’s assume that we want to pull all events out of Shopify to use them for further analysis. To do so we need to make a request to the Event endpoint like this.

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GET /admin/events.json?filter=Product,Order

This request will retrieve all the events that are related to Products and Orders for our shop. Inside the response, there will be an array of objects with each one representing one Order or Product.

Events are generated for the following resources:

• Articles
• Blogs
• Custom Collections
• Comments
• Orders
• Pages
• Products
• Smart Collections

By using the “limit” and “page” parameters it is possible to page your results in the case that you need to pull out a large number of events. Additionally, there are a number of ways that you can filter the results for example based on a date, so in a continuous data extraction process, it is possible to pull only new data and avoid duplicates. This can be achieved by using the “

created_at_min

created_at_max

After you have successfully pulled data from the Shopify API you are ready to extract and prepare them for Amazon Redshift. Of course, the above process is only for one of the available resources, if you would like to have a complete view of all the available data then you will have to create a much complex ETL process including the majority of the 35+ resources that Shopify has.

Transform and prepare your Shopify data

After you have accessed the data on Shopify, you will have to transform it based on two main factors,

1. The limitations of the database that the data will be loaded onto
2. The type of analysis that you plan to perform

Each system has specific limitations on the data types and data structures that it supports. If for example, you want to push data into Google BigQuery, then you can send nested data like JSON directly. But when you are dealing with tabular data stores, like Microsoft SQL Server, this is not an option. Instead, you will have to flatten out your data before loading it into the database.

Transform and prepare your Shopify data for Amazon Redshift

Amazon Redshift is built around industry-standard SQL with added functionality to manage very large data sets and high-performance analysis. So, in order to load the data into it, you will have to follow its data model which is a typical relational database model. The data you extract from a data source should be mapped into tables and columns. Where you can consider the table as a map to the resource you want to store and columns the attributes of that resource.

Also, each attribute should adhere to the data types that are supported by Redshift.

As data is probably coming in a representation like JSON that supports a much smaller range of data types you have to be really careful about what data you feed into Redshift and make sure that you have mapped your types into one of the data types supported by Redshift.

Designing a Schema to use in Redshift and mapping the data from your data source to it is a process that you should take seriously as it can both affect the performance of your cluster and the questions that you can answer. It’s always a good idea to have in your mind the best practices that Amazon has published regarding the design of a Redshift database. When you have concluded on the design of your database you need to load your data on one of the data sources that are supported as input by Redshift, these are the following:

1. Amazon S3
2. Amazon DynamoDB
3. Amazon Kinesis Firehose

Load your Shopify data into Amazon Redshift

To upload data to Amazon S3 you will have to use the AWS API, as we see again APIs play an important role in both the extraction but also the loading of data into our data warehouse. The first task that you have to perform is to create a bucket, you do that by executing an HTTP PUT on the Amazon AWS API endpoints for S3.

You can do this by using a tool like CURL or Postman. Or use the libraries provided by Amazon for your favorite language. You can find more information by reading the API reference for the Bucket operations on Amazon AWS documentation.

After you have created your bucket you can start sending your data to Amazon S3, using again the same API but by using the endpoints for Object operations. As in the Bucket case you can either access the HTTP endpoints directly or use the library of your preference.

DynamoDB imports data again from S3, it adds another step between S3 and Amazon Redshift so if you don’t need it for other reasons you can avoid it.

Amazon Kinesis Firehose is the latest addition as a way to insert data into Redshift and offers a real-time streaming approach to data importing. The necessary steps for adding data to Redshift through Kinesis Firehose are the following:

1. create a delivery stream
2. add data to the stream

whenever you add new data to the stream, Kinesis takes care of adding these data to S3 or Redshift, again going through S3, in this case, is redundant if your goal is to move your data to Redshift. The execution of the previous two steps can be performed either through REST or through your favorite library just as in the previous two cases. The difference here is that for pushing data into the stream you’ll be using a Kinesis Agent.

Amazon Redshift supports two methods for loading data into it. The first one is by invoking an INSERT command. You can connect to your Amazon Redshift instance with your client, using either a JDBC or ODBC connection and then you perform an INSERT command.

JAVASCRIPT
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insert into category_stage values
(12, 'Concerts', 'Comedy', 'All stand-up comedy performances');

The way you invoke the INSERT command is the same as you would do with any other SQL database, for more information you can check the INSERT examples page on the Amazon Redshift documentation.

Redshift is not designed for INSERT like operations, on the contrary, the most efficient way of loading data into it is by doing bulk uploads using a COPY command. You can perform a COPY command for data that lives as flat files on S3 or from an Amazon DynamoDB table. When you perform COPY commands, Redshift is able to read multiple files simultaneously and it automatically distributes the workload to the cluster nodes and performs the load in parallel. As a command COPY is quite flexible and allows for many different ways of using it, depending on your use case. Performing a COPY on Amazon S3 is as simple as the following command:

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copy listing
from 's3://mybucket/data/listing/'
credentials 'aws_access_key_id=;aws_secret_access_key=';

For more examples on how to invoke a COPY command, you can check the COPY examples page on Amazon Redshift documentation. As in the INSERT case, the way to perform the COPY command is by connecting to your Amazon Redshift instance using a JDBC or ODBC connection and then invoke the commands you want using the SQL Reference from Amazon Redshift documentation.

The best way to load data from Shopify to Amazon Redshift and possible alternatives

So far we just scraped the surface of what can be done with Amazon Redshift and how to load data into it. The way to proceed relies heavily on the data you want to load, from which service they are coming from, and the requirements of your use case. Things can get even more complicated if you want to integrate data coming from different sources.

A possible alternative, instead of writing, hosting, and maintaining a flexible data infrastructure, is to use an ETL as a service product like RudderStack that can handle this kind of problem automatically for you.

RudderStack integrates with multiple sources or services like databases, CRM, email campaigns, analytics, and more. Quickly and safely move all your data from Shopify into your data warehouse and start generating insights from your data.