دوره جامع Apache Kafka

آپاچی کافکا (Apache Kafka) یک پلتفرم متن‌باز و توزیع‌شده‌ی پخش رویداد (event-streaming) است که برای پردازش داده‌های با توان عملیاتی بالا، تحمل‌پذیری خطا و بلادرنگ طراحی شده است. این پلتفرم به کسب‌وکارها این امکان را می‌دهد تا خطوط لوله‌ی مقیاس‌پذیر برای جمع‌آوری، ذخیره‌سازی و تجزیه و تحلیل جریان‌های مداوم داده‌ها از منابع مختلف ایجاد کنند. کافکا به طور گسترده برای تجزیه و تحلیل بلادرنگ، تجمیع لاگ‌ها، منبع‌یابی رویداد و ادغام میکروسرویس‌ها در صنایعی مانند تجارت الکترونیک، امور مالی و اینترنت اشیا استفاده می‌شود. یادگیری آپاچی کافکا برای متخصصان کلان‌داده بسیار مهم است، زیرا یک فناوری کلیدی برای مدیریت و پردازش جریان‌های داده در مقیاس بزرگ در سازمان‌های مدرن داده‌محور است.
یک مدرس خصوصی می‌تواند با ارائه درس‌های ساختاریافته، پروژه‌های عملی و بینش‌های عملی در مورد معماری، تولیدکنندگان، مصرف‌کنندگان، موضوعات و ادغام آن با اکوسیستم‌های کلان‌داده‌ی کافکا، این فرآیند یادگیری را تسریع کند و تضمین کند که شما برای مدیریت چالش‌های دنیای واقعی در نقش‌هایی مانند مهندس کلان‌داده یا متخصص پخش داده، مجهز هستید.

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

دوره جامع Apache Kafka

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