kafka-connect-twitter VS debezium

I'm reading O'Reilly's "Mastering Kafka Streams and ksqlDB" to start learning Kafka, it was suggested for me on an ad by Confluent. Unsurprisingly it uses Confluent's software throughout the book. One of the first projects is a simple app that does sentiment analysis on tweets. The book uses kafka-console-producer and a sample .json file for the tweets, but for my app I wanted to read actual tweets. To do that I've been reading about Kafka Connect and looking at this repository, but I'm having a hard time understating how to best deploy this for my local setup. So far I've been using docker-compose.yml files provided by the book, which in turn uses Confluent's docker images for kafka, zookeeper, etc. As for this Twitter Connect repository, it seems the recommended way of setting it up is to use Confluent's platform and its CLI tool to automagically install it, which is fine, but I wanted to learn how things work under the hood (to some extend) and if possible not rely so heavily upon Confluent's software. Is it a good idea to just stick with Confluent and the book, or should I be reading a different material for a first Kafka project and working with a different kind of setup? Perhaps I'm getting ahead of myself trying to use Kafka Connect at this point?

They manage data in the application layer and your original data stays where it is. This way data consistency is no longer an issue as it was with streaming databases. You can use Change Data Capture (CDC) services like Debezium by directly connecting to your primary database, doing computational work, and saving the result back or sending real-time data to output streams.

Both of these articles mention a key player, Debezium. In fact, Debezium has had a place in the modern infrastructure. Let's use a diagram to understand why.

This might be overkill, but you could use an extra tool like https://debezium.io to capture logs about all creates, updates, and deletes in your table

If you're considering a relational database for an event-driven architecture, check out Debezium. It lets you stream changes to relational databases, and subscribe to change events.

Debezium

https://debezium.io/ comes to mind as an open source product, but there are a gazillion of these tools out there.

Contrary to what it sounds, it’s quite straightforward. The database changes are captured via Debezium and published as events on Apache Kafka. ClickHouse consumes those changes in partial order by Kafka Engine. Real-time and eventually consistent.