How to load data from Shopify to Snowflake

Extract your data from Shopify

Shopify exposes its complete platform to developers through their 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 Ieak 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.
• ApplicationCharge: Request to charge a shop a one-time fee by issuing this call.
• 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 offer an environment where the merchant can create a Blog for her shop.
• CarrierService: A Carrier Service (also known as a Carrier Calculated Service or Shipping Service) provides real-time shipping rates to Shopify.
• 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, any 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.

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

This request will get us back to all the events that are related to Products and Orders for our shop. The response will be in JSON and will look like this:

JAVASCRIPT
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HTTP/1.1 200 OK
{
 "events": [
   {
     "id": 677313116,
     "subject_id": 921728736,
     "created_at": "2008-01-10T08:00:00-05:00",
     "subject_type": "Product",
     "verb": "create",
     "arguments": [
       "IPod Touch 8GB"
     ],
     "body": null,
     "message": "created a new product: <a href=\"\/admin\/products\/921728736\">IPod Touch 8GB<\/a>.",
     "author": "Shopify",
     "description": "created a new product: IPod Touch 8GB.",
     "path": "\/admin\/products\/921728736"
   },
   {
     "id": 365755215,
     "subject_id": 632910392,
     "created_at": "2008-01-10T07:00:00-05:00",
     "subject_type": "Product",
     "verb": "create",
     "arguments": [
       "IPod Nano - 8GB"
     ],
     "body": null,
     "message": "created a new product: <a href=\"\/admin\/products\/632910392\">IPod Nano - 8GB<\/a>.",
     "author": "Shopify",
     "description": "created a new product: IPod Nano - 8GB.",
     "path": "\/admin\/products\/632910392"
   }
 ]
}

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 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 and created_at_max parameters in your GET request.

After you have successfully pulled your data from the Shopify API, you are ready to extract and prepare them for Snowflake. 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.

Shopify Data Preparation for Snowflake

The first step, before you start ingesting your data into a Snowflake data warehouse instance, is to have a well-defined schema of your data.

Data in Snowflake is organized around tables with a well-defined set of columns, with each one having a specific data type.

Snowflake supports a rich set of data types. It is worth mentioning that a number of semi-structured data types is also supported. With Snowflake, it is possible to load data directly in JSON, Avro, ORC, Parquet, or XML format. Hierarchical data is treated as a first-class citizen, similar to what Google BigQuery offers.

There are also one notable common data type that is not supported by Snowflake. LOB or large object data type is not supported. Instead you should use a BINARY or VARCHAR type instead. But these types are not that useful for data warehouse use cases.

A typical strategy for loading data from Shopify to Snowflake is to create a schema where you will map each API endpoint to a table.

Each key inside the Shopify API endpoint response should be mapped to a column of that table, and you should ensure the right conversion to a Snowflake data type.

Of course, you will need to ensure that as data types from the Shopify API might change, you will adapt your database tables accordingly, there’s no such thing as automatic data type casting.

After you have a complete and well-defined data model or schema for Snowflake, you can move forward and start loading your data into the database.

Load data from Shopify to Snowflake

Usually, data is loaded into Snowflake in a bulk way, using the COPY INTO command. Files containing data, usually in JSON format, are stored in a local file system or Amazon S3 buckets. Then a COPY INTO command is invoked on the Snowflake instance and data is copied into a data warehouse.

The files can be pushed into Snowflake using the PUT command into a staging environment before the COPY command is invoked.

Another alternative is to upload data directly into a service like Amazon S3, from where Snowflake can access any data directly.

Finally, Snowflake offers a web interface as a data loading wizard where someone can visually setup and copy data into a data warehouse. Just keep in mind that the functionality of this wizard is limited compared to the rest of the methods.

Snowflake, in contrast to other technologies like Redshift, does not require a data schema to be packed together with the data that will be copied. Instead, the schema is part of the query that will copy every data into the data warehouse. This simplifies data loading process and offers more flexibility on data type management.

Updating your Shopify data on Snowflake

As you will be generating more data on Shopify, you will need to update your older data on Snowflake. This includes new records together with updates to older records that for any reason have been updated on Shopify.

You will need to periodically check Shopify for new data and repeat the process that has been described previously, while updating your currently available data if needed. Updating an already existing row on a Snowflake table is achieved by creating UPDATE statements.

Snowflake has a great tutorial on the different ways of handling updates, especially using primary keys.

Another issue that you need to take care of is the identification and removal of any duplicate records on your database. Either because Shopify does not have a mechanism to identify new and updated records or because of errors on your data pipelines, duplicate records might be introduced to your database.

In general, ensuring the quality of data that is inserted in your database is a big and difficult issue.

The best way to load data from Shopify to Snowflake

So far, we just scraped the surface of what you can do with Snowflake and how to load data into it. Things can get even more complicated if you want to integrate data coming from different sources.

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