Apache Kafka for Programmers Training Course

Introduction

• An overview of Apache Kafka
• Fundamentals of event streaming and real-time data processing
• Key features and capabilities of Apache Kafka
• Practical applications and benefits of Kafka in modern development

Kafka Architecture and Core Concepts

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

Managing Kafka Topics and Partitions

• Processes for topic creation and configuration
• The significance of partitions and their operational implications
• Mechanisms for replication and fault tolerance in Kafka

Implementing Kafka Producers

• Creating producers using various programming languages
• Techniques for message serialization
• Optimising producer configurations and adhering to best practices

Developing Kafka Consumers

• Building consumers across different programming languages
• Setting up consumer groups and managing offsets
• Strategies for efficient message processing and ensuring delivery guarantees

Kafka Integration with External Systems via Kafka Connect

• An introduction to Kafka Connect and its available connectors
• Configuring and deploying Kafka Connect for seamless data integration
• Connecting Kafka with diverse external systems

Building Streaming Applications with Kafka Streams and ksqlDB

• An overview of Kafka Streams and its stream processing capabilities
• Developing applications for stream processing using Kafka Streams
• Leveraging ksqlDB for real-time data processing and analytics

Connecting Kafka Clients to Confluent Cloud

• An overview of Confluent Cloud and its key features
• Steps to configure and connect Kafka client applications to Confluent Cloud

Securing Kafka

• An introduction to Kafka's security features
• Implementing SSL/TLS for secure communication channels
• Configuring authentication and authorization mechanisms

Monitoring and Operational Management

• Utilising metrics and tools to monitor Kafka performance
• Managing cluster resources and operations
• Troubleshooting common issues and optimising performance

Best Practices and Real-World Applications

• Best practices for designing robust Kafka-based applications
• Case studies demonstrating Kafka implementations across various industries
• Strategies for building scalable and fault-tolerant Kafka architectures

Conclusion and Future Directions