By Rudderstack Team

How to load data from Aftership to Redshift

This post helps you with loading your data from Aftership to Redshift. If you are looking to get analytics-ready data without the manual hassle you can integrate Aftership to Redshift with RudderStack, so you can focus on what matters, getting value out of your business.

Access your data on Aftership

The first step in loading the Aftership data to any kind of data warehouse solution is to access them and start extracting them.

Using the REST API that Aftership offers you can programmatically interact with your account in order to gain access to your order tracking data. By doing so you can:

  • Get the list of all supported couriers.
  • Retrieve tracking results
  • Get tracking information of the last checkpoint of a tracking
  • Gain access to contacts (SMS or email) to be notified when the status of tracking has changed.

You can also retrieve some basic aggregated metrics for any user-defined time period such as the average score of all your surveys or of a specific trend or client.

In addition to the above, the things that you have to keep in mind when dealing with the Aftership API, are:

  • Rate limits. In order to guarantee a high quality of service to all users of the API, Aftership applies certain rate limits. Currently, users are limited to 600 requests per minute per account.
  • Authentication. You can authenticate Aftership to use a private API key that is linked to your account.
  • Pagination. API endpoints that return a collection of items are always paginated.

About Aftership

Aftership is a package tracking platform for online retailers and e-commerce businesses, supporting. It was first introduced in 2011 and since then it has been widely adopted by some of the biggest e-commerce companies like Wish and Etsy. Among the features the Aftership offers, the following are included:

  1. Customer engagement with branded tracking pages: Customers are directed to the company’s website for tracking in order to further engage them after sales.
  2. Proactive delivery updates: Customers remain informed regarding the latest status of their orders via push notifications, email, or SMS.

Aftership is also one of the top apps and extensions at various shopping cart solutions like Shopify, Bigcommerce, eBay, and Magento, with millions of active shipments each month.

Transform and prepare your Aftership data for Amazon Redshift

After you have accessed your data on Aftership, you will have to transform it based on two main factors,

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

Each system has specific limitations on the data types and structures that it supports. If for example, you want to push them into Google BigQuery, then you can send nested data like JSON directly.

Also, you have to choose the right data types. Again, depending on the system that you will send the data and the data types that the API exposes to you, you will have to make the right choices. These choices are important because they can limit the expressivity of your queries and limit your analysts on what they can do directly out of the database.

Also, you have to consider that the reports you’ll get from Aftership are like CSV files in terms of their structure and you need to somehow identify what and how to map to a table into your database.

Transform and prepare your Aftership data for Amazon Redshift

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 your Aftership data into it, you will have to follow its model which is a typical relational database model. The information you extract from your 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 datatypes that are supported by Redshift.

Designing a Schema for 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 sources that are supported as input by Redshift, these are the following:

Load your data from Aftership to Amazon Redshift

To upload your Aftership data to Amazon S3 you will have to use the AWS REST API, as we see again APIs play an important role in both the extraction but also the loading of data into our 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 REST 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 data to Amazon S3, using again the same AWS REST 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.

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

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.

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