This page provides you with instructions on how to extract data from Mailjet and load it into Panoply. (If this manual process sounds onerous, 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 Panoply?
Panoply is an end-to-end data platform that can spin up an Amazon Redshift instance in just a few clicks. It uses machine learning and natural language processing (NLP) to learn, model, and automate standard data management activities performed by data scientists, data engineers, and analysts. It can import data with no schema, no modeling, and no configuration. With Panoply, you can use your favorite analysis, SQL, and visualization tools just as you would if you were creating a Redshift data warehouse on your own.
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 Panoply
When you've identified all the columns you want to insert, use the Reshift CREATE TABLE statement to make a table in your data warehouse to receive the data.
Now you can replicate your data. It may seem as if the easiest way to do that (especially if there isn't much of it) is to build INSERT statements and add data to your table row by row. If you have any experience with SQL, this probably will be your first inclination. But beware! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you should instead load the data into Amazon S3 and then use the Redshift COPY command to import it into Redshift.
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.
Other data warehouse options
Panoply is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Azure SQL Data Warehouse, To S3, and To Delta Lake.
Easier and faster alternatives
If all this sounds a bit overwhelming, don’t be alarmed. 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 move data from Mailjet to Panoply automatically. 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 your Panoply data warehouse.