This page provides you with instructions on how to extract data from Mailjet and load it into Delta Lake. (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 Delta Lake?
Delta Lake is an open source storage layer that sits on top of existing data lake file storage, such AWS S3, Azure Data Lake Storage, or HDFS. It uses versioned Apache Parquet files to store data, and a transaction log to keep track of commits, to provide capabilities like ACID transactions, data versioning, and audit history.
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 Delta Lake on Databricks
To create a Delta table, you can use existing Apache Spark SQL code and change the format from
delta. Once you have a Delta table, you can write data into it using Apache Spark's Structured Streaming API. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table. Databricks provides quickstart documentation that explains the whole process.
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
Delta Lake on Databricks 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, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3. 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 Panoply, and To S3.
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 Delta Lake 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 Delta Lake data warehouse.