Mailjet to Metabase

This page provides you with instructions on how to extract data from Mailjet and analyze it in Metabase. (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 Metabase?

Metabase provides a visual query builder that lets users generate simple charts and dashboards, and supports SQL for gathering data for more complex business intelligence visualizations. It runs as a JAR file, and its developers make it available in a Docker container and on Heroku and AWS. Metabase is free of cost and open source, licensed under the AGPL.

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 Metabase

Metabase works with data in databases; you can't use it as a front end for a SaaS application without replicating the data to a data warehouse first. Out of the box Metabase supports 15 database sources, and you can download 10 additional third-party database drivers, or write your own. Once you specify the source, you must specify a host name and port, database name, and username and password to get access to the data.

Using data in Metabase

Metabase supports three kinds of queries: simple, custom, and SQL. Users create simple queries entirely through a visual drag-and-drop interface. Custom queries use a notebook-style editor that lets users select, filter, summarize, and otherwise customize the presentation of the data. The SQL editor lets users type or paste in SQL queries.

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 Metabase 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 Synapse Analytics, 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 Metabase. 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 Metabase.