Complete Course of Apache Kafka
Apache Kafka is an open-source, distributed event-streaming platform designed for high-throughput, fault-tolerant, and real-time data processing.
It enables businesses to build scalable pipelines for collecting, storing, and analyzing continuous streams of data from various sources.
Kafka is widely used for real-time analytics, log aggregation, event sourcing, and integrating microservices in industries like e-commerce, finance, and IoT.
Learning Apache Kafka is crucial for big data professionals, as it is a key technology for managing and processing large-scale data flows in modern data-driven organizations.
Chapter 1: Introduction to Big Data, Distributed Systems, and Messaging Frameworks
Lesson 1: Understanding Big Data: Concepts, Characteristics, and Challenges
Lesson 2: Fundamentals of Distributed Systems and Data Processing
Lesson 3: Messaging Paradigms and the Role of Event Streaming
Lesson 4: Overview of Big Data Tools and Frameworks (e.g., Hadoop, Spark, Flink)
Lesson 5: Comparing Apache Kafka with Traditional Big Data and Messaging Tools
Chapter 2: Introduction to Apache Kafka
Lesson 1: What is Apache Kafka? Origins, Evolution, and Core Use Cases
Lesson 2: The Role of Kafka in Modern Big Data Architectures
Lesson 3: Key Features and Advantages of Kafka
Lesson 4: Overview of the Kafka Ecosystem and Related Technologies
Chapter 3: Apache Kafka Architecture and Core Concepts
Lesson 1: Distributed Architecture: Brokers, Clusters, and ZooKeeper (or KRaft mode)
Lesson 2: Data Organization: Topics, Partitions, and Replication
Lesson 3: Producer, Consumer, and Consumer Groups Explained
Lesson 4: Understanding Offsets, Commit Strategies, and Log Management
Lesson 5: Messaging Semantics: At-Least-Once, At-Most-Once, and Exactly-Once Delivery
Chapter 4: Setting Up Apache Kafka
Lesson 1: System Requirements and Prerequisites for Kafka Deployment
Lesson 2: Installation Methods: Local Setup, Cluster Installation, Docker Containers, and Kubernetes
Lesson 3: Configuring Kafka: Editing server.properties and Related Files
Lesson 4: IDE Integration and Development Environment Setup (using IntelliJ/Eclipse with Maven/Gradle)
Lesson 5: Hands-On Lab: Installing and Running a Single-Node Kafka Cluster
Chapter 5: Apache Kafka Command Line Tools and Administration
Lesson 1: Overview of Kafka’s CLI Tools
Lesson 2: Managing Topics with kafka-topics.sh (create, list, describe, delete)
Lesson 3: Producing Messages Using kafka-console-producer.sh
Lesson 4: Consuming Messages Using kafka-console-consumer.sh
Lesson 5: Administering Consumer Groups via kafka-consumer-groups.sh
Lesson 6: Modifying Broker and Topic Configurations with kafka-configs.sh
Lesson 7: Monitoring and Logging via Command Line Tools
Chapter 6: Developing with Kafka: Producers and Consumers
Lesson 1: Introduction to the Kafka Producer API: Setup, Configuration, and Code Examples
Lesson 2: Deep Dive into the Kafka Consumer API: Polling, Deserialization, and Processing
Lesson 3: Advanced Producer Configurations: Acknowledgments, Retries, Batching, and Compression
Lesson 4: Consumer Group Coordination, Offset Management (auto vs. manual), and Rebalancing
Lesson 5: Error Handling, Idempotence, and Recovery Strategies in Producer/Consumer Applications
Chapter 7: Stream Processing with Kafka Streams
Lesson 1: Introduction to Kafka Streams: Concepts and Use Cases
Lesson 2: Building Stream Processing Applications with the Kafka Streams DSL
Lesson 3: Advanced Topics: Stateful vs. Stateless Operations, Windowing, and Aggregations
Lesson 4: Exploring the Processor API for Custom Stream Processing
Lesson 5: Hands-On Lab: Creating a Real-Time Analytics Application Using Kafka Streams
Chapter 8: Data Integration with Kafka Connect
Lesson 1: Overview of Kafka Connect Framework and Its Role in Data Integration
Lesson 2: Configuring and Deploying Source and Sink Connectors
Lesson 3: Developing Custom Connectors for Specialized Data Sources
Lesson 4: Operating Kafka Connect in Distributed vs. Standalone Modes
Lesson 5: Hands-On Lab: Integrating Kafka with Databases, File Systems, and Other Systems
Chapter 9: Kafka Security and Authentication
Lesson 1: Security Overview: Threats and Requirements for a Kafka Cluster
Lesson 2: Configuring SSL/TLS Encryption for Secure Data Transmission
Lesson 3: Setting Up SASL for Authentication (PLAIN, SCRAM, GSSAPI)
Lesson 4: Implementing Access Control Lists (ACLs) and Role-Based Access
Lesson 5: Best Practices for Securing Kafka Clusters in Production Environments
Chapter 10: Performance Tuning and Optimization
Lesson 1: Tuning Broker Configurations and Hardware Considerations
Lesson 2: Producer and Consumer Tuning for Throughput and Latency Optimization
Lesson 3: Load Balancing, Partitioning Strategies, and Resource Allocation
Lesson 4: Benchmarking Kafka Performance and Using Profiling Tools
Lesson 5: Troubleshooting and Resolving Common Performance Issues
Chapter 11: Kafka Fault Tolerance and High Availability
Lesson 1: Understanding Kafka’s Replication Mechanism and Data Durability
Lesson 2: Leader-Follower Dynamics and In-Sync Replicas (ISR)
Lesson 3: Configuring Failover and Recovery Strategies in a Kafka Cluster
Lesson 4: Multi-Datacenter Deployments and Disaster Recovery Planning
Lesson 5: Hands-On Lab: Simulating Broker Failures and Recovery Procedures
Chapter 12: Monitoring, Management, and Administration
Lesson 1: Key Metrics and Logs: What to Monitor in a Kafka Cluster
Lesson 2: Tools and Dashboards: Prometheus, Grafana, Confluent Control Center, and Kafka Manager
Lesson 3: Setting Up Alerts and Automated Health Checks
Lesson 4: Maintenance Tasks: Log Retention, Cleanup Policies, and Cluster Upgrades
Lesson 5: Best Practices for Daily Kafka Administration and Troubleshooting
Chapter 13: Kafka in Cloud and Containerized Environments
Lesson 1: Deploying Kafka on Major Cloud Platforms (AWS, Azure, GCP)
Lesson 2: Containerizing Kafka with Docker: Best Practices and Examples
Lesson 3: Orchestrating Kafka Clusters Using Kubernetes and Helm Charts
Lesson 4: Overview of Managed Kafka Services (Confluent Cloud, Amazon MSK, Azure Event Hubs)
Lesson 5: Optimizing Cloud-Based Kafka Deployments for Scalability and Reliability
Chapter 14: Advanced Kafka Concepts
Lesson 1: Exactly-Once Semantics: Theory and Implementation
Lesson 2: Kafka Transactions: Achieving Atomicity in Message Processing
Lesson 3: Log Compaction: Use Cases and Configuration
Lesson 4: In-Depth Look at Kafka’s Internal Mechanics: Storage, Caching, and Compression
Lesson 5: Fine-Tuning Advanced Configuration Parameters for Specialized Workloads
Chapter 15: Integrating Kafka with Other Big Data Tools
Lesson 1: Real-Time Data Processing: Kafka with Apache Spark and Flink
Lesson 2: Combining Kafka with Hadoop Ecosystem Components and NoSQL Databases
Lesson 3: Leveraging Kafka for Real-Time Search with Elasticsearch
Lesson 4: Integrating Kafka with Machine Learning Pipelines
Lesson 5: Building End-to-End Data Pipelines and Microservices Architectures
Chapter 16: New Features and Innovations in Recent Kafka Releases
Lesson 1: Overview of the Latest Apache Kafka Releases (e.g., Kafka 3.x)
Lesson 2: Enhancements in Performance, Scalability, and Security
Lesson 3: New and Improved CLI Tools, APIs, and Connector Capabilities
Lesson 4: Innovations in Kafka Streams and KSQL/ksqlDB
Lesson 5: Community Contributions and the Future Roadmap for Kafka
Chapter 17: Best Practices, Case Studies, and Real-World Applications
Lesson 1: Best Practices for Designing and Operating Kafka-Based Systems
Lesson 2: Case Studies: Kafka in E-Commerce, Finance, IoT, and More
Lesson 3: Lessons Learned from Large-Scale Kafka Deployments
Lesson 4: Troubleshooting Complex Kafka Scenarios in Production
Lesson 5: Future Trends in Event Streaming and the Evolving Big Data Landscape
Chapter 18: Hands-On Projects and Course Wrap-Up
Lesson 1: End-to-End Project: Designing a Real-Time Streaming Data Pipeline with Kafka
Lesson 2: Lab Project: Building a Microservices Architecture Leveraging Kafka
Lesson 3: Capstone Project: Integrating Kafka with Multiple Data Sources and Sinks
Lesson 4: Course Recap: Key Concepts, Best Practices, and Takeaways
Lesson 5: Next Steps: Advanced Resources, Community Engagement, and Further Learning
