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.
Dauer/zeitlicher Ablauf:
2 Tage
Zielgruppe:
Software Developers: Developers who want to enhance their skills in building real-time data streaming applications and event-driven architectures using Apache Kafka and Spring Boot.
System Architects: System architects who seek a comprehensive understanding of Apache Kafka and its integration with Spring Boot to design scalable and resilient systems.
Data Engineers: Professionals responsible for data pipelines, data integration, and real-time data processing who can benefit from learning how to effectively utilize Apache Kafka for efficient data streaming.
Technical Leads: Technical leads overseeing the design and development of applications that require data streaming and event-driven processing.
DevOps Engineers: DevOps professionals involved in the deployment, management, and monitoring of Kafka clusters and ensuring high availability and performance.
Technology Managers: Managers who want to gain an understanding of the capabilities and potential of Apache Kafka and Spring Boot to drive technical decision-making and oversee projects utilizing these technologies.
IT Professionals and Enthusiasts: Individuals with an interest in data streaming, event-driven architectures, and modern data processing technologies, regardless of their specific role.
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