This page provides you with instructions on how to extract data from Mailjet and analyze it in Superset. (If the mechanics of extracting data from Mailjet seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is Mailjet?
Mailjet is an email automation platform used to set up marketing campaigns and send transactional emails. It boasts an easy-to-use interface and a scalable pricing structure. Mailjet stores data on bounce rate, click stats, and opening information: data that's useful when it comes time to quantify the effectiveness of your email strategy.
What is Superset?
Apache Superset is a cloud-native data exploration and visualization platform that businesses can use to create business intelligence reports and dashboards. It includes a state-of-the-art SQL IDE, and it's open source software, free of cost. The platform was originally developed at Airbnb and donated to the Apache Software Foundation.
Getting data out of Mailjet
Mailjet exposes data through webhooks, which you can use to push data to a defined HTTP endpoint as events happen. It's up to you to parse the objects you catch via your webhooks and decide how to load them into your data warehouse.
Loading data into Superset
You must replicate data from your SaaS applications to a data warehouse before you can report on it using Superset. Superset can connect to almost 30 databases and data warehouses. Once you choose a data source you want to connect to, you must specify a host name and port, database name, and username and password to get access to the data. You then specify the database schema or tables you want to work with.
Keeping Mailjet data up to date
Once you've set up the webhooks you want and have begun collecting data, you can relax – as long as everything continues to work correctly. You’ll have to keep an eye out for any changes to Mailjet's webhooks implementation.
From Mailjet to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Mailjet data in Superset is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Mailjet to Redshift, Mailjet to BigQuery, Mailjet to Azure SQL Data Warehouse, Mailjet to PostgreSQL, Mailjet to Panoply, and Mailjet to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Mailjet with Superset. With just a few clicks, Stitch starts extracting your Mailjet data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Superset.