This page provides you with instructions on how to extract data from Mailjet and analyze it in Power BI. (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 Power BI?
Power BI is Microsoft’s business intelligence offering. It's a powerful platform that includes capabilities for data modeling, visualization, dashboarding, and collaboration. Many enterprises that use Microsoft's other products can get easy access to Power BI and choose it for its convenience, security, and power.
With high-value use cases across analysts, IT, business users, and developers, Power BI offers a comprehensive set of functionality that has consistently landed Microsoft in Gartner's "Leaders" quadrant for Business Intelligence.
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 Power BI
You can analyze any data in Power BI, as long as that data exists in a data warehouse that's connected to your Power BI account. The most common data warehouses include Amazon Redshift, Google BigQuery, and Snowflake. Microsoft also has its own data warehousing platform called Azure SQL Data Warehouse.
Connecting these data warehouses to Power BI is relatively simple. The Get Data menu in the Power BI interface allows you to import data from a number of sources, including static files and data warehouses. You'll find each of the warehouses mentioned above among the options in the Database list. The Power BI documentation provides more details on each.
Analyzing data in Power BI
In Power BI, each table in the data warehouse you connect is known as a dataset, and the analyses conducted on these datasets are known as reports. To create a report, use Power BI’s report editor, a visual interface for building and editing reports.
The report editor guides you through several selections in the course of building a report: the visualization type, fields being used in the report, filters being applied, any formatting you wish to apply, and additional analytics you may wish to layer onto your report, such as trendlines or averages. You can explore all of the features related to analyzing and tracking data in the Power BI documentation.
Once you've created a report, Power BI lets you share it with report "consumers" in your organization.
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 Power BI 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 from Mailjet to Power BI automatically. With just a few clicks, Stitch starts extracting your Mailjet data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Power BI.