This page provides you with instructions on how to extract data from Mailjet and load it into Redshift. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
Pulling Data Out of Mailjet
You can collect Mailjet data using Webhooks. Once you’ve set up HTTP endpoints, Mailjet will begin sending data via the POST request method. Data will be enclosed in the body of the request in JSON format.
Preparing Mailjet Data for Redshift
With the JSON in hand, you now need to map all those data fields into a schema that can be inserted into your Redshift database. This means that, for each value in the response, you need to identify a predefined data type (i.e. INTEGER, DATETIME, etc.) and build a table that can receive them.
The Mailjet documentation can give you a good sense of what fields will be provided by each endpoint, along with their corresponding data types. Once you have identified all of the columns you will want to insert, use the CREATE TABLE statement in Redshift to define a table that can receive all of this data.
Inserting Mailjet Data into Redshift
It may seem like the easiest way to add your data is to build tried-and-true INSERT statements that add data to your Redshift table row-by-row. If you have any experience with SQL this will be your gut reaction but it isn’t very efficient.
Redshift actually offers some good documentation for how to best bulk insert data into new tables. The COPY command is particularly useful for this task, as it allows you to insert multiple rows without needing to build individual INSERT statements for each row.
If you cannot use COPY, it might help to use PREPARE to create a prepared INSERT statement, and then use EXECUTE as many times as required. This avoids some of the overhead of repeatedly parsing and planning INSERT.
Keeping Data Up-To-Date
So what’s next? You’ve built a script that collects data from Mailjet and moves it into Redshift. What happens when Mailjet sends a data type that your script doesn’t recognize? It’s also important to consider the situation where an entry in Redshift needs to be updated to a new value. Once you’ve build in that functionality, you can set your script up as a cron job or continuous loop to keep pulling new data as it appears.
Other Data Warehouse Options
Redshift is totally awesome, but sometimes you need to start smaller or optimize for different things. In this case, many people choose to get started with Postgres, which is an open source RDBMS that uses nearly identical SQL syntax to Redshift. If you’re interested in seeing the relevant steps for loading this data into Postgres, check out Mailjet to Postgres
Easier and Faster Alternatives
If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.
Thankfully, products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Mailjet data via the webhook API, structuring it in a way that is optimized for analysis, and inserting that data into your Redshift data warehouse.