Apache Kafka for Programmers Training Course

Introduction

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

Kafka Architecture and Core Principles

• Architectural components: brokers, topics, partitions, and replicas
• The role of producers and consumers in message publishing and consumption
• The function of ZooKeeper within the Kafka ecosystem

Managing Topics and Partitions

• Processes for creating and configuring topics
• The significance of partitions and their impact on performance
• Ensuring replication and fault tolerance in Kafka

Working with Kafka Producers

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

Working with Kafka Consumers

• Development of consumers in multiple programming environments
• Management of consumer groups and offsets
• Approaches to message processing and ensuring delivery guarantees

External Integration via Kafka Connect

• Understanding Kafka Connect and its connector ecosystem
• Configuration and application of Kafka Connect for data integration
• Methods for connecting Kafka with third-party systems

Streaming Application Development with Kafka Streams & ksqlDB

• Overview of Kafka Streams and its processing capabilities
• Building stream processing applications with Kafka Streams
• Leveraging ksqlDB for real-time analytics and data processing

Connecting to Confluent Cloud

• Introduction to Confluent Cloud features
• Steps to configure and link Kafka client applications with Confluent Cloud

Kafka Security Protocols

• Essentials of Kafka security features
• Setting up SSL/TLS for encrypted communication
• Implementing authentication and authorization in Kafka

Monitoring and Operational Management

• Tracking performance via Kafka metrics and monitoring tools
• Administration of Kafka clusters and resource allocation
• Diagnosing common issues and optimizing performance

Best Practices and Industry Applications

• Strategic guidelines for designing Kafka-based solutions
• Case studies illustrating Kafka implementations across different sectors
• Building scalable and fault-tolerant Kafka architectures

Conclusion and Future Directions