Apache Kafka for Programmers Training Course

Introduction

• Overview of Apache Kafka
• Foundations of event streaming and real-time data processing
• Detailed look at Apache Kafka and its core features
• Practical applications and advantages of Kafka in modern development

Exploring Kafka Architecture and Core Concepts

• Kafka architecture: brokers, topics, partitions, and replicas
• Producers and consumers: mechanisms for publishing and consuming messages
• The role of ZooKeeper within the Kafka ecosystem

Managing Kafka Topics and Partitions

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

Implementing Kafka Producers

• Building producers across various programming languages
• Techniques for message serialization
• Optimal producer configurations and best practices

Developing Kafka Consumers

• Creating consumers in different programming languages
• Managing consumer groups and offset tracking
• Delivery guarantees and message processing strategies

Integrating External Systems via Kafka Connect

• Overview of Kafka Connect and its connector ecosystem
• Configuration and application of Kafka Connect for data integration
• Connecting Kafka to external data sources and sinks

Building Streaming Apps with Kafka Streams & ksqlDB

• Introduction to Kafka Streams and stream processing
• Developing processing applications with Kafka Streams
• Leveraging ksqlDB for real-time analytics and processing

Connecting to Confluent Cloud

• Features and overview of Confluent Cloud
• Configuring and linking Kafka client applications with Confluent Cloud

Kafka Security Fundamentals

• Overview of Kafka security capabilities
• Securing communication through SSL/TLS configuration
• Implementing authentication and authorization protocols

Monitoring and Operational Management

• Utilizing metrics and tools for Kafka monitoring
• Cluster and resource management strategies
• Debugging common issues and performance optimization

Best Practices and Industry Use Cases

• Design principles for robust Kafka-based applications
• Case studies of Kafka implementations across industries
• Architecting for scalability and fault tolerance

Conclusion and Future Directions