This post helps you with loading your data from AfterShip to PostgreSQL. If you are looking to get analytics-ready data without the manual hassle, you can integrate Aftership to PostgreSQL with RudderStack, so you can focus on what matters, getting value out of your shipping data.
Access your data on Aftership
The first step in loading your Aftership data to any kind of data warehouse solution is to access your data and start extracting it.
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 to Aftership using a private API key that is linked to your account.
- Pagination. API endpoints that return a collection of items are always paginated.
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 from some of the biggest e-commerce companies like Wish and Etsy. Among the features the Aftership offers, the following are included:
- Customer engagement with branded tracking pages: Customers are directed to the company’s website for tracking in order to further engage them after-sales.
- 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 PostgreSQL
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 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.
Also, you have to choose the right data types. Again, depending on the system that you will send the data to 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.
Each table is a collection of columns with a predefined data type as an integer or VARCHAR. PostgreSQL, like any other SQL database, supports a wide range of different data types.
A typical strategy for loading data from a source like Aftership to Postgres database is to create a schema to map each API endpoint to a table. Each key inside the Aftership API endpoint response should be mapped to a column of that table, and you should ensure the right conversion to a Postgres compatible data type.
Load from Aftership to PostgreSQL
For example, if an endpoint from Aftership returns a value as String, you should convert it into a VARCHAR with a predefined max size or TEXT data type. Tables can then be created on your database using the CREATE SQL statement.
Once you have defined your schema and you have created your tables with the proper data types, you can start loading data into your database.
The preferred way of adding larger datasets into a PostgreSQL database is by using the COPY command. COPY is copying data from a file on a file system that is accessible by the Postgres instance, in this way much larger datasets can be inserted into the database in less time. COPY requires physical access to a file system in order to load any data.
Nowadays, with cloud-based, fully managed databases, getting direct access to a file system is not always possible. If this is the case and you cannot use a COPY statement, then another option is to use PREPARE together with INSERT, to end up with optimized and more performant INSERT queries.
Updating your Aftership data on PostgreSQL
As you will be generating more data on Aftership, you will need to update your older data on PostgreSQL. This includes new records together with updates to older records that, for any reason, have been updated on Aftership.
You will need to periodically check Aftership 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 PostgreSQL table is achieved by creating UPDATE statements.
Another issue that you need to take care of is the identification and removal of any duplicate records on your database. Either because Aftership 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 the data that is inserted in your database is a big and difficult issue, and PostgreSQL features like TRANSACTIONS can help tremendously, although they do not solve the problem in the general case.
The best way to load data from Aftership to PostgreSQL
So far, we just scraped the surface of what you can do with PostgreSQL and how you can load data into it. Things can get even more complicated if you want to integrate data coming from different sources.
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