Apache Kafka Complete

Seminarziel

• Understand the core concepts and architecture of Apache Kafka.
• Set up and configure Kafka clusters for different deployment scenarios.
• Produce and consume data from Kafka topics using Kafka producers and consumers.
• Integrate Kafka with Spring Boot to build scalable and resilient applications.
• Explore advanced Kafka concepts such as performance tuning, partition management, and monitoring.
• Implement security measures to protect Kafka clusters.
• Learn real-world use cases and best practices for designing Kafka-based architectures.
• Manage Kafka operations, troubleshoot common issues, and ensure high availability.
• Gain hands-on experience through practical exercises and examples.

Inhalt

• Introduction to Apache Kafka
  • Overview of Apache Kafka and its role in modern data streaming architectures.
  • Understanding Kafka's core concepts: topics, partitions, producers, consumers, and brokers.
  • Architecture of Kafka: brokers, ZooKeeper, and the role of each component.
  • Kafka deployment options: standalone mode, distributed mode, and high availability.
• Setting up Kafka Cluster
  • Installing Kafka: step-by-step guide for setting up a Kafka cluster on local machines or cloud platforms.
  • Configuring Kafka brokers and ZooKeeper ensemble.
  • Testing Kafka cluster connectivity and ensuring a healthy cluster.
  • Hands-on exercises: Setting up a basic Kafka cluster and performing cluster management tasks.
• Working with Kafka Topics
  • Creating Kafka topics: configuring topic properties and replication factor.
  • Producing data to Kafka topics: using Kafka producers and understanding message keys and values.
  • Consuming data from Kafka topics: implementing Kafka consumers and consuming messages in various modes (e.g., from the beginning, from a specific offset).
  • Hands-on exercises: Creating topics, producing and consuming data, and exploring different consumer configurations.
• Integration of Spring Boot with Kafka
  • Introduction to Spring for Apache Kafka: a Spring-based integration for building Kafka-based applications.
  • Configuring Spring Boot for Kafka integration: dependencies, configuration properties, and bean definitions.
  • Implementing Kafka producers and consumers using Spring Boot.
  • Hands-on exercises: Building a simple Spring Boot application that integrates with Kafka for producing and consuming messages.
• Advanced Kafka Concepts
  • Understanding Kafka message serialization and deserialization: Avro, JSON, and custom formats.
  • Configuring Kafka for optimal performance and scalability: tuning broker settings, producer and consumer configurations.
  • Managing Kafka partitions: understanding partitioning strategies and rebalancing.
  • Monitoring Kafka cluster and performance metrics: using tools like Kafka Manager, Prometheus, and Grafana.
  • Hands-on exercises: Performance tuning, managing partitions, and monitoring Kafka cluster.
• Kafka Security
  • Introduction to Kafka security: securing Kafka clusters with authentication and authorization.
  • Configuring SSL encryption for Kafka communication.
  • Implementing access control lists (ACLs) to restrict Kafka operations.
  • Securing Kafka ZooKeeper ensemble.
  • Hands-on exercises: Configuring SSL for Kafka, enabling authentication and authorization, and managing ACLs.
• Real-world Kafka Use Cases
  • Use cases for Kafka in real-world applications: event streaming, log aggregation, microservices, and more.
  • Best practices for designing Kafka-based architectures.
  • Kafka ecosystem overview: integrating Kafka with other tools like Apache Spark, Apache Flink, and Elasticsearch.
  • Hands-on exercises: Building a sample real-world Kafka application using Spring Boot and the concepts covered throughout the training.
• Kafka Operations and Troubleshooting
  • Kafka cluster management: scaling Kafka, adding and removing brokers, and handling failures.
  • Troubleshooting common issues in Kafka: monitoring, debugging, and diagnosing problems.
  • Kafka data backup and disaster recovery strategies.
  • Hands-on exercises: Performing Kafka cluster operations, troubleshooting common issues, and practicing data recovery.