Apache Kafka Mastery: Complete Course from Fundamentals to Production
Master Distributed Systems, Streaming Architecture & Microservices Patterns
Learn Apache Kafka from the ground up with our comprehensive course covering distributed systems, event-driven architecture, real-time processing, and production deployment. Master the technologies used by companies processing billions of events daily including Netflix, Uber, and LinkedIn.
What Makes This Course Different
Production-Focused
Learn from real-world scenarios and war stories from companies processing billions of events daily.
Hands-On Labs
Build actual systems, not just toy examples. Each lesson includes practical exercises.
Advanced Patterns
Master Event Sourcing, CQRS, Saga patterns, and Change Data Capture.
Troubleshooting
Debug common production issues with confidence using proven techniques.
Modern Stack
Learn Kubernetes, KRaft, Schema Registry, and cloud-native patterns.
Real Monitoring
Build complete monitoring dashboards with Prometheus and Grafana.
Course Curriculum
This course is structured in 4 progressive modules, each building on the previous one:
Module 1: Foundation
Core Concepts & Architecture (Lessons 1-3)
Lesson 1: Kafka Fundamentals and Architecture
- • Understanding Kafka as a distributed commit log (not a queue)
- • Topics, partitions, and offsets explained
- • Broker architecture and cluster coordination
- • ZooKeeper vs KRaft mode
- • Replication protocol and ISR (In-Sync Replicas)
- • When to use Kafka vs other messaging systems
Hands-on Lab: Set up a 3-broker Kafka cluster
Lesson 2: Producer Mastery and Message Delivery
- • Producer internals: batching, compression, and partitioning
- • Partition key design strategies
- • Delivery semantics: at-most-once, at-least-once, exactly-once
- • Idempotent producers and transactions
- • Performance tuning: batch.size, linger.ms, compression types
- • Handling backpressure and rate limiting
Hands-on Lab: Build a high-throughput producer
Lesson 3: Consumer Groups and Concurrency Models
- • Consumer groups and partition assignment
- • Scaling consumers: vertical vs horizontal
- • Partition planning formula and concurrency limits
- • Offset management strategies (auto vs manual commit)
- • Static membership for stable deployments
- • Multi-threaded and multi-process consumption patterns
Hands-on Lab: Create multiple consumer groups on same topic
Module 2: Performance
Scaling & Optimization (Lessons 4-6)
Lesson 4: Rebalancing Deep Dive and Optimization
- • Understanding the rebalancing lifecycle
- • Cooperative-Sticky Assignor vs Eager rebalancing
- • Heartbeat and session management
- • Tuning: session.timeout.ms, max.poll.interval.ms
- • Preventing rebalance storms in Kubernetes
- • Static group membership for containerized apps
Hands-on Lab: Monitor rebalancing in real-time
Lesson 5: Lag Management and Performance Monitoring
- • Understanding consumer lag metrics
- • Lag diagnosis framework (7 common patterns)
- • Monitoring with Burrow, Prometheus, and Grafana
- • End-to-end latency measurement
- • JMX metrics: broker, producer, consumer
- • Setting up alerts for production systems
Hands-on Lab: Build complete monitoring dashboard
Lesson 6: Storage, Retention, and Log Management
- • How Kafka stores data: segments, indexes, and page cache
- • Log retention strategies (time vs size-based)
- • Log compaction for stateful data
- • Disk I/O optimization and RAID configuration
- • Partition count planning and limits
- • OS-level tuning for Kafka workloads
Hands-on Lab: Configure log compaction for CDC use case
Module 3: Real-time Processing
Streaming & Data Governance (Lessons 7-8)
Lesson 7: Kafka Streams and Real-Time Processing
- • Kafka Streams API fundamentals
- • KStream vs KTable vs GlobalKTable
- • Windowing operations (tumbling, hopping, session)
- • Stateful processing with RocksDB
- • Stream-stream and stream-table joins
- • Exactly-once processing in Streams
Hands-on Lab: Build real-time aggregation pipeline
Lesson 8: Schema Registry and Data Governance
- • Schema Registry architecture and integration
- • Avro, Protobuf, and JSON Schema comparison
- • Schema evolution strategies (backward, forward, full compatibility)
- • Managing schema versions in production
- • Best practices for schema design
- • Integration with Kafka Connect
Hands-on Lab: Set up Schema Registry
Module 4: Production
Security & Operations (Lessons 9-10)
Lesson 9: Security, Authentication, and Authorization
- • SASL mechanisms (PLAIN, SCRAM, GSSAPI, OAUTHBEARER)
- • SSL/TLS encryption setup
- • ACL (Access Control Lists) configuration
- • Quotas for resource management
- • Encryption at rest strategies
- • Security audit logging
Hands-on Lab: Configure SASL/SCRAM authentication
Lesson 10: Production Operations and Advanced Patterns
- • Kafka Connect and ecosystem integration
- • Multi-datacenter replication with MirrorMaker 2
- • Running Kafka on Kubernetes (Strimzi operator)
- • Advanced patterns: Event Sourcing, CQRS, Saga, CDC
- • Troubleshooting production issues (7 war stories)
- • Disaster recovery and capacity planning
- • Performance tuning checklist
Hands-on Lab: Deploy Kafka with Strimzi on Kubernetes
Course Format
Video Lectures
30-45 minutes each
Written Docs
Comprehensive guides
Hands-on Labs
Practical exercises
Quiz
10 questions per lesson
Prerequisites
- • Basic understanding of distributed systems
- • Familiarity with command line
- • Programming experience (Python or Java preferred)
- • Understanding of networking concepts
What You'll Build
By the end of this course, you'll have built:
- • High-throughput event streaming pipeline
- • Real-time analytics system with Kafka Streams
- • Multi-consumer architecture with proper lag monitoring
- • Production-ready Kafka cluster on Kubernetes
- • Change Data Capture pipeline with Debezium
- • Complete monitoring and alerting system
Frequently Asked Questions
What is Apache Kafka and why should I learn it?
Apache Kafka is a distributed event streaming platform used by thousands of companies for real-time data pipelines, streaming analytics, and data integration. Learning Kafka is essential for modern software engineers working with microservices, real-time systems, and big data architectures.
Is this course suitable for beginners?
This course is designed for intermediate to advanced developers. While we cover fundamentals, having basic knowledge of distributed systems, command line tools, and programming (Python or Java) will help you get the most out of the course. If you're completely new to Kafka, we recommend starting with our introductory articles first.
What's the difference between Kafka and RabbitMQ?
Kafka is a distributed event streaming platform designed for high-throughput, fault-tolerant data pipelines, while RabbitMQ is a traditional message broker. Kafka excels at real-time data streaming, log aggregation, and event sourcing, while RabbitMQ is better for complex routing and request-reply patterns. This course covers when to use Kafka vs other messaging systems.
What will I build during this course?
You'll build a complete event streaming pipeline, real-time analytics system, multi-consumer architecture with monitoring, production-ready Kafka cluster on Kubernetes, and Change Data Capture pipeline with Debezium.
How long does it take to complete?
The course is designed to take 20-25 hours to complete, including hands-on labs and practical exercises. You can learn at your own pace and revisit lessons as needed.
What technologies are covered?
We cover Apache Kafka, Kafka Streams, Schema Registry, Kafka Connect, Kubernetes, Prometheus, Grafana, Docker, and various programming languages including Python and Java.
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