30-Day Series - Learn Kafka

30-Day Plan to learn Kafka

I drafted the following plan and will add content to each topic in this page as I progress.

Week 1: Kafka Basics

• Day 1-3: Introduction to Kafka. What is Kafka and why is it used? Kafka’s architecture and core concepts: Producers, Consumers, Brokers, Topics, Partitions. Setting up a Kafka cluster on your local machine.
• Day 4-5: Deep Dive into Kafka Producers and Consumers. Producing messages to Kafka. Consuming messages from Kafka. Committing offsets and understanding delivery semantics.
• Day 6-7: Working with Kafka Streams. Introduction to Kafka Streams. Creating a simple stream processing application.

  Week 2: Kafka Internals and Administration

• Day 8-10: Kafka Storage and Log Compaction. Understanding the Kafka storage format. Log retention and compaction. How Kafka ensures durability.
• Day 11-12: Kafka Cluster Setup and Scaling. Setting up a multi-broker Kafka cluster. Adding and removing brokers. Replication and fault tolerance.
• Day 13-14: Monitoring and Administration. Important Kafka metrics to monitor. Using Kafka command-line tools. Configuring alerts and understanding common issues.

Week 3: Advanced Kafka Topics

• Day 15-17: Introduction to Kafka Connect. Building source and sink connectors. Managing connectors.
• Day 18-20: Kafka Streams Deep Dive. Windowed operations. Stateful stream processing. Interactive Queries.
• Day 21-22: Security in Kafka. Configuring SSL for Kafka. Setting up authentication and authorization using ACLs.
• Day 23-24: Kafka Performance Tuning. Optimizing producer and consumer configurations. Tuning broker configurations. Handling large data loads.

Week 4: Real-World Applications and Best Practices

• Day 25-26: Kafka Patterns and Anti-Patterns. Common use cases for Kafka. When and how to use Kafka correctly. Mistakes to avoid.
• Day 27-28: Integrating Kafka with Other Systems. Kafka with databases (CDC). Kafka with big data systems like Hadoop and Spark.
• Day 29: Case Study Analysis: Study a real-world application of Kafka in industries like retail, finance, or social media to understand its implementation.
• Day 30: Final Project: Design and implement a small end-to-end system utilizing Kafka. This could be a data pipeline, a streaming analytics platform, or any other use case that interests you.

Recommendations

1. Hands-On Practice: Spend at least 50% of your time practicing hands-on. You can use platforms like Confluent Cloud or set up your environment using Docker.
2. Documentation: The official Kafka documentation is a gold mine. Keep it handy.
3. Community and Forums: Join Kafka forums and communities. They can be invaluable for troubleshooting and understanding real-world challenges.
4. Supplementary Resources: There are many good books, online courses, and articles on Kafka. Consider resources from Confluent, Jay Kreps (Kafka’s co-creator), and other industry leaders.

By the end of this 30-day plan, you should have a comprehensive understanding of Kafka, its ecosystem, and how it fits into the modern data landscape.