Description

Introduction

Apache Kafka is a distributed streaming platform used to build real-time data pipelines and streaming applications. As organizations increasingly rely on real-time data processing, mastering Kafka’s advanced capabilities for stream processing and fault-tolerant systems becomes essential. This course is designed for professionals who are already familiar with the basics of Kafka and want to enhance their skills in building fault-tolerant, scalable, and highly available stream processing systems.

Prerequisites

• Basic understanding of Apache Kafka and its core components
• Familiarity with concepts such as producers, consumers, topics, partitions, and brokers
• Experience in programming (Java, Scala, or Python)
• Understanding of basic distributed systems concepts
• Familiarity with stream processing concepts

Table of Contents

1. Introduction to Advanced Kafka Concepts
   1.1 Overview of Kafka and Its Ecosystem
   1.2 Kafka Cluster Architecture and Operations
   1.3 Understanding Kafka’s Fault Tolerance Mechanism
   1.4 Kafka Broker Internals: Partitions, Replication, and Log Segments
   1.5 Kafka’s Role in Event-Driven Architectures
2. Kafka Stream Processing
   2.1 Introduction to Kafka Streams API
   2.2 Stream Processing Concepts: Stream, Table, and Joins
   2.3 Kafka Streams State Stores: RocksDB Integration
   2.4 Kafka Streams Processing Topology: Building, Configuring, and Managing
   2.5 Advanced Stream Transformations: Map, Filter, Aggregate, and Windowing
   2.6 Handling Late Data and Stream Reprocessing
3. Fault Tolerance and High Availability in Kafka
   3.1 Kafka Replication: Understanding and Configuring Replication Factors
   3.2 Broker and Partition Failover Mechanisms
   3.3 Handling Node Failures and Partition Rebalancing
   3.4 Configuring Kafka for High Availability (HA)
   3.5 Managing Kafka Consumer Group Offsets and Fault Tolerance
   3.6 Zookeeper and Kafka: Managing Coordination for Fault Tolerance
4. Kafka Streams Performance Optimization
   4.1 Performance Considerations for Kafka Streams Applications
   4.2 Configuring and Tuning Kafka Streams for Scalability and Efficiency
   4.3 Minimizing Latency in Stream Processing
   4.4 Partitioning Strategies for Load Balancing
   4.5 Monitoring Kafka Streams with JMX and Metrics
5. Advanced Kafka Consumer and Producer Concepts
   5.1 Fine-Tuning Kafka Producers for Efficient Data Publishing
   5.2 Consumer Group Strategies for Parallel Processing
   5.3 Managing Backpressure and Message Delivery Guarantees
   5.4 Exactly Once Semantics (EOS): Configuring Producers and Consumers
   5.5 Advanced Kafka Consumer Group Rebalancing
   5.6 Optimizing Kafka Consumers for Low Latency
6. Stream Processing with Kafka Connect
   6.1 Introduction to Kafka Connect Framework
   6.2 Building and Managing Kafka Connectors for Data Integration
   6.3 Configuring Source and Sink Connectors
   6.4 Kafka Connect Fault Tolerance and Scaling
   6.5 Monitoring and Managing Kafka Connect Workers
   6.6 Use Cases: Database Sync, File Ingestion, and More
7. Kafka Security for Stream Processing
   7.1 Overview of Kafka Security Features
   7.2 Configuring SSL/TLS Encryption for Kafka Producers and Consumers
   7.3 Implementing Authentication with SASL/Kerberos
   7.4 Access Control Lists (ACLs) for Kafka Topics and Consumers
   7.5 Kafka Security Best Practices for Protecting Data Streams
8. Integrating Kafka with Big Data and Cloud Ecosystems
   8.1 Kafka and Apache Flink for Real-Time Stream Processing
   8.2 Kafka Integration with Apache Spark for Advanced Analytics
   8.3 Streaming Data into Data Lakes and Warehouses (HDFS, S3, and BigQuery)
   8.4 Kafka and Kubernetes: Scaling Stream Processing with Containers
   8.5 Using Kafka in Cloud Environments (AWS, Azure, GCP)
9. Advanced Kafka Monitoring and Troubleshooting
   9.1 Key Metrics for Monitoring Kafka Clusters and Stream Applications
   9.2 Setting Up Prometheus and Grafana for Kafka Metrics
   9.3 Debugging Kafka Consumers and Producers(Ref: AWS Security Specialty: In-Depth Cloud Security and Risk Management)
   9.4 Troubleshooting Kafka Streams Applications
   9.5 Logging and Analyzing Kafka Cluster Health with ELK Stack
10. Case Studies and Real-World Kafka Applications
    10.1 Real-Time Data Processing in Financial Systems
    10.2 Stream Processing for E-commerce and Online Transactions
    10.3 Log Aggregation and Event Sourcing in Distributed Systems
    10.4 IoT Data Processing at Scale with Kafka
    10.5 Building Fault-Tolerant Data Pipelines with Kafka
11. Capstone Project: Building a Fault-Tolerant Stream Processing System
    11.1 Defining the Problem and System Requirements
    11.2 Designing the Kafka Cluster for High Availability
    11.3 Developing Kafka Streams Applications with Advanced Features
    11.4 Implementing Fault-Tolerance and Data Integrity Strategies
    11.5 Monitoring and Testing the Stream Processing System
12. Conclusion and Next Steps
    12.1 Key Takeaways and Best Practices for Advanced Kafka Usage
    12.2 Preparing for Kafka Certifications and Advanced Roles
    12.3 Advanced Kafka Resources: Books, Documentation, and Tools
    12.4 Expanding Your Kafka Skills: Integrating with New Technologies
    12.5 Continuing the Journey with Stream Processing and Real-Time Data

Conclusion

This course empowers you with advanced knowledge and hands-on skills to harness the full power of Apache Kafka in stream processing and fault-tolerant systems. You’ll learn how to build scalable, resilient, and low-latency data pipelines while mastering Kafka’s fault-tolerant capabilities. Whether you’re developing real-time applications, integrating Kafka with other big data tools, or ensuring the reliability of stream processing, this course will provide you with the expertise needed to optimize Kafka in complex, production environments.

Reference