Apache Kafka for Developers Training Course

Introduction

• Fundamentals of Apache Kafka
• Concepts of event streaming and real-time processing
• Key features and capabilities of Apache Kafka
• Practical applications and advantages in modern software development

Architectural Foundations and Core Concepts

• Exploring Kafka architecture: brokers, topics, partitions, and replicas
• Roles of producers and consumers in message publishing and consumption
• The function of ZooKeeper within the Kafka ecosystem

Managing Topics and Partitions

• Creating and configuring topics
• Implications of partitioning strategies
• Replication mechanisms and fault tolerance

Building Producers

• Implementing Kafka producers across various programming languages
• Serialization techniques and message handling
• Producer configuration strategies and best practices

Developing Consumers

• Constructing Kafka consumers in multiple programming languages
• Managing consumer groups and offset management
• Message processing strategies and delivery guarantee models

System Integration via Kafka Connect

• Understanding Kafka Connect and its connector framework
• Configuring Kafka Connect for seamless data integration
• Connecting Kafka to diverse external systems

Stream Processing with Kafka Streams & ksqlDB

• Overview of Kafka Streams and stream processing features
• Building stream processing applications with Kafka Streams
• Utilizing ksqlDB for real-time analytics and processing

Cloud Integration with Confluent Cloud

• Features and benefits of Confluent Cloud
• Configuring Kafka client applications for Confluent Cloud connection

Security Considerations

• Introduction to Kafka security protocols
• Implementing SSL/TLS for secure communication
• Authentication and authorization mechanisms

Monitoring and Operational Management

• Utilizing metrics and tools for Kafka monitoring
• Managing cluster resources and health
• Performance tuning and resolving common operational issues

Best Practices and Industry Applications

• Design principles for robust Kafka-based applications
• Case studies demonstrating Kafka implementations across industries
• Strategies for scalable and fault-tolerant architecture

Wrap-up and Future Directions