Kafka

Target Audience: Developers and professionals who will be implementing producers and consumers in a Kafka topology. Prior experience with Java is required.

Course Objectives:

• Gain a thorough understanding of Kafka, its architecture, and key concepts.

• Explore how Kafka integrates with the Hadoop ecosystem and enterprise environments.

• Master the development of producers and consumers within a Kafka topology.

• Discover strategies for scaling Kafka to handle increasing data volumes.

Course Length: 2 days

Module 1: Introduction to Kafka and Fundamentals

• What is Kafka? - Demystifying Kafka's role as a distributed streaming platform.

• The Need for Kafka: - Exploring the limitations of traditional messaging systems and how Kafka addresses them.

• Kafka Use Cases: - Identifying real-world applications of Kafka across various industries (e.g., log aggregation, stream processing, microservices communication).

• Core Concepts: - Deep dive into key Kafka terminology like topics, partitions, producers, consumers, and brokers.

• Kafka Architecture: - Understanding the components of a Kafka cluster (brokers, ZooKeeper) and their interaction.

• Setting Up a Kafka Cluster: - Hands-on lab for installing and configuring a Kafka cluster locally (single node or distributed).

Module 2: Building, Administering, and Scaling with Kafka

• Kafka vs. Other Messaging Systems: - Comparative Analysis - Understanding Kafka's unique features and advantages compared to other messaging solutions (e.g., RabbitMQ, JMS).

• Building with Kafka:

  • Kafka Components: - Detailed exploration of Kafka producers, consumers, topics, partitions, and brokers.

  • Kafka Producer and Consumer APIs: - Hands-on labs for creating producers and consumers using the Java API. Learn to send and receive messages to/from topics.

• Data Durability and Reliability:

  • Replication Strategies: - Understanding how replication ensures data availability and fault tolerance (leader election).

  • Configuring Replication: - Learning to set up and manage replication for data redundancy.

• Kafka Administration and Monitoring:

  • Logging with Kafka: - Exploring Kafka's log management capabilities (retention, compaction) to optimize storage usage.

  • Administration and Monitoring: - Understanding key administration tasks (e.g., user management, topic creation) and monitoring tools (e.g., Kafka Manager, JMX) for maintaining a healthy Kafka cluster.

• Scaling and Performance Optimization:

  • Hardware and Runtime Configuration: - Learning best practices for configuring Kafka for optimal performance based on hardware capabilities.

  • Performance Tuning: - Strategies for tuning Kafka to meet specific data processing needs (e.g., adjusting batch sizes, buffer settings).

Optional Modules (Can be covered in separate sessions if time permits):

• Module 3: Integrating Kafka with Other Systems

  • Storm and Spark: - Exploring how Kafka integrates with these big data processing frameworks for real-time stream processing.

  • Kafka Connect: - Understanding Kafka Connect for data integration with various sources (databases) and sinks (data warehouses).

  • Kafka Streams: - Introduction to Kafka Streams for building real-time stream processing applications (microservices communication).

• Module 4: Kafka Security

  • Authentication and Authorization: - Implementing user authentication and access control (ACLs) for secure Kafka access.