As part of your agenda, read these articles, as many as you can. You might even decide to follow one through to the end. But that's up to you.
Kafka is one of the shiniest tech out there. Someplaces know it and use it, others are not sure what it is, but the key thing when discussing it with folks who don't know what it is always point out: it sits at the very core of LinkedIn's business. They wrote the first one, and ALL data at LinkedIn goes through ONE kafka system. Billions of messages (transactions) a day.
Understanding its applicability, architecture, and use will differentiate you from many Data people who are not strong in it.
Scan over all the articles quickly (2-3 min per article) first. Jot down notes on what seems to be in each article. Then, looking over your notes on eacha rticle, read in the order that seems most natural to you. By repetition, reading the same terms used by different authors, helps you to catalog in your own mind your understanding of how the network of concepts comes together to form your own expertise.
- Great intro video
- Intro to Kafka
- Kafka vs. RabbitMQ
- Kafka & RabbitMQ "part deux"
- Wow, long
- Building Data Pipelines With Kafka
- Kafka Gotchas
- Kafdrop (get it) & Kafka
- AWS, Kafka, etc, overview
- Kafka for Beginners
- All? you need to know about Kafka
- Messaging: Kafka Amazes
Scala. So scala is a language that in some ways is a cross between Java and Python. (Well, no, but for my purposes here, it's a stretch but not a wide one). Some of these articles use scala as the language to talk to Kafka with. Like scala for spark, it's just different, but not that hard to look at the code and get a feel for what it is doing. HOWEVER, when you get to the article about the 6-minutes, well, that article is thick. Scala has things in it that your knowledge of Python simply does not prepare you for. So treat scala like I do, with gentle movements, slowly, carefully, and don't drop it.