⚡ Free course

Streaming & Kafka

Reason about events, ordering and lag the way a platform team does.

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What you will learn

1. CDC with Debezium: reading the database log instead of the database
   Polling a production database every five minutes is how analytics teams get their access revoked. CDC reads the log the database already writes — and gets every change, in order, including the deletes.
2. Kafka hands-on: your first producer and consumer
   Twenty minutes from nothing to watching your own events flow. Concepts stick once you have broken something yourself.
3. Events and the Log — the Kafka mental model
   Kafka is not a queue. It is an append-only log that many readers can read at their own speed.
4. Choosing a Partition Key
   The partition key decides two things at once: what stays ordered, and what gets overloaded.
5. Consumer groups, rebalancing and offsets
   Three mechanisms decide whether your consumer loses data, duplicates it, or quietly stops: where the offset is committed, when the group rebalances, and how long a slow poll is tolerated.
6. Producer guarantees: acks, retries, idempotent producers (coming soon)
7. Schema registry: Avro, Protobuf, compatibility (coming soon)
8. Spark Structured Streaming vs Flink (coming soon)
9. Windowing and joins on streams (coming soon)
10. Kafka Connect and CDC with Debezium (coming soon)

How the course works

• Each chapter is a short book: the basics, the explanation in several ways, the words to know, and a two-minute summary.
• Then you test yourself — quiz, puzzles — and recall it on two different days, so it stays.
• Blinks (one idea per screen), games and a daily plan keep you coming back. It is free.