Apache Kafka Certification Training Course Syllabus

Overview: This comprehensive Apache Kafka Certification Training Course is designed for data engineers and SREs seeking hands-on expertise in building, securing, and operating real-time streaming platforms. The course spans eight modules, each requiring approximately 6–8 hours of engagement, totaling 50–60 hours of learning. Participants will progress from Kafka fundamentals to advanced operations, including stream processing, integration, security, and performance tuning. The program culminates in a capstone project that integrates all components into a production-style event-driven architecture. With lifetime access and practical labs, learners gain real-world experience in deploying scalable, fault-tolerant Kafka pipelines.

Module 1: Introduction to Kafka & Cluster Setup

Estimated time: 7 hours

• Event streaming vs. messaging systems
• Kafka ecosystem components and use cases
• Zookeeper vs. KRaft consensus protocols
• Hands-on: Install Kafka and configure a multi-broker cluster
• Create and manage topics in a local and distributed environment

Module 2: Developing Producers & Consumers

Estimated time: 7 hours

• Core Producer and Consumer APIs
• Serializers and deserializers (SerDe)
• Consumer groups and offset management
• Hands-on: Build a Java producer to publish events
• Develop a Python consumer to process streaming data

Module 3: Kafka Streams Fundamentals

Estimated time: 7 hours

• Stream processing topologies and DSL basics
• Understanding KStream vs. KTable
• Stateless and stateful operations
• Hands-on: Implement stream transformation and windowed aggregations

Module 4: ksqlDB & Interactive SQL on Streams

Estimated time: 7 hours

• ksqlDB architecture and deployment modes
• Defining streams and tables from topics
• Pull queries vs. push queries
• Hands-on: Create persistent queries to filter, enrich, and join streaming data

Module 5: Kafka Connect & Connector Management

Estimated time: 7 hours

• Source vs. sink connectors
• Single Message Transforms (SMTs)
• Offset and configuration management
• Hands-on: Configure JDBC source and Elasticsearch sink connectors
• Apply transformations for data enrichment

Module 6: Security, Monitoring & Operations

Estimated time: 7 hours

• SSL/TLS encryption for data in transit
• SASL authentication and ACLs for authorization
• JMX metrics and integration with Prometheus
• Hands-on: Secure client connections and set up Grafana dashboard

Module 7: Performance Tuning & Cluster Scaling

Estimated time: 7 hours

• Partitioning strategies and replication factors
• Throughput tuning and latency optimization
• Broker configuration best practices
• Hands-on: Benchmark producer/consumer performance
• Optimize cluster settings under load

Module 8: Capstone Project

Estimated time: 8 hours

• Design an end-to-end real-time analytics pipeline
• Incorporate fault tolerance and disaster recovery mechanisms
• Integrate Kafka producers, consumers, Streams, Connect, and ksqlDB

Prerequisites

• Familiarity with distributed systems concepts
• Basic knowledge of Java or Python
• Understanding of command-line and Linux environment

What You'll Be Able to Do After

• Build and manage scalable Kafka clusters using KRaft or Zookeeper
• Develop robust producers and consumers in Java and Python
• Implement real-time stream processing using Kafka Streams and ksqlDB
• Integrate Kafka with external systems via Kafka Connect
• Secure, monitor, and tune Kafka clusters for production workloads