دوره جامع Apache Flink

آپاچی فلینک (Apache Flink) یک فریمورک قدرتمند و موتور پردازش توزیع‌شده است که برای محاسبات حالت‌مند (stateful computations) بر روی جریان‌های داده نامحدود و محدود طراحی شده است. این فریمورک به طور گسترده برای پردازش داده‌های بلادرنگ، تجزیه و تحلیل جریان و پردازش دسته‌ای استفاده می‌شود و به کسب‌وکارها این امکان را می‌دهد که داده‌های با سرعت و حجم بالا را با تأخیر کم مدیریت کنند. یادگیری آپاچی فلینک برای کسانی که به دنبال شغل در صنعت کلان‌داده هستند بسیار مهم است، زیرا این یک انتخاب محبوب برای تجزیه و تحلیل بلادرنگ، تشخیص تخلف، سیستم‌های مبتنی بر رویداد و سایر برنامه‌های حساس به زمان در صنایعی مانند امور مالی، تجارت الکترونیک و اینترنت اشیا است.
یک مدرس خصوصی می‌تواند با ارائه یک برنامه درسی ساختاریافته، مثال‌های عملی و پروژه‌های عملی که سناریوهای دنیای واقعی را شبیه‌سازی می‌کنند، یادگیری شما را تسریع کند و به شما کمک کند تا به سرعت تخصص لازم برای برجسته شدن در نقش‌هایی مانند توسعه‌دهنده کلان‌داده یا مهندس داده را بدست آورید.

Chapter 1: Introduction to Distributed Processing and Big Data

Lesson 1: What Is Big Data?
Lesson 2: Distributed Processing Fundamentals
Lesson 3: Overview of Big Data Frameworks
Lesson 4: Batch vs. Stream Processing Paradigms
Lesson 5: Positioning Apache Flink in the Big Data Ecosystem

Chapter 2: Introduction to Apache Flink

Lesson 1: What Is Apache Flink?
Lesson 2: History and Evolution of Apache Flink
Lesson 3: Key Features and Innovations in Flink
Lesson 4: Comparing Apache Flink with Other Big Data Tools
Lesson 5: Real-World Applications and Use Cases

Chapter 3: Setting Up the Apache Flink Environment

Lesson 1: System Requirements and Prerequisites
Lesson 2: Installing Apache Flink Locally
Lesson 3: Configuring a Flink Cluster
Lesson 4: IDE Integration for Flink Development
Lesson 5: Deploying Flink on Cloud Platforms
Lesson 6: Flink Configuration Files and Environment Settings

Chapter 4: Apache Flink Architecture and Core Concepts

Lesson 1: Flink’s Distributed Architecture
Lesson 2: Runtime Execution Model and Parallelism
Lesson 3: State Management in Flink
Lesson 4: Time Semantics in Flink
Lesson 5: Data Abstractions: DataStream and DataSet APIs
Lesson 6: Windowing Mechanisms

Chapter 5: Apache Flink Commands and CLI Tools

Lesson 1: Overview of the Flink Command-Line Interface
Lesson 2: Running and Managing Jobs
Lesson 3: Job Submission and Deployment Options
Lesson 4: Monitoring and Debugging Through CLI
Lesson 5: Using Flink’s REST API for Management
Lesson 6: Troubleshooting Common Command Issues

Chapter 6: Developing Stream Processing Applications

Lesson 1: Introduction to the Flink DataStream API
Lesson 2: Building a Simple Streaming Application
Lesson 3: Advanced Stream Processing Concepts
Lesson 4: Implementing Window Operations
Lesson 5: Integrating External Data Sources and Sinks
Lesson 6: Custom Operators and Functions

Chapter 7: Developing Batch Processing Applications

Lesson 1: Introduction to the Flink DataSet API
Lesson 2: Building Batch Processing Jobs
Lesson 3: Optimization Techniques for Batch Jobs
Lesson 4: Integrating with External Storage Systems
Lesson 5: Transitioning Between Batch and Streaming
Lesson 6: Command-Line Execution and IDE Debugging for Batch Jobs

Chapter 8: Advanced Stream Processing Features

Lesson 1: Deep Dive into Stateful Stream Processing
Lesson 2: Checkpointing and Savepoints
Lesson 3: Exactly-Once Processing Semantics
Lesson 4: Advanced Windowing and Triggering
Lesson 5: Custom State Backends and Memory Management
Lesson 6: Handling High-Volume and High-Velocity Data Streams

Chapter 9: Flink Connectors and Integration with External Systems

Lesson 1: Overview of Flink Connectors
Lesson 2: Configuring Source and Sink Connectors
Lesson 3: Real-Time Data Ingestion and Egress
Lesson 4: Integrating Flink with NoSQL Databases
Lesson 5: File System and Cloud Storage Connectors
Lesson 6: Case Studies: End-to-End Connector Implementations

Chapter 10: Flink SQL and Table API

Lesson 1: Introduction to Flink SQL and Its Capabilities
Lesson 2: Querying Streaming Data Using SQL
Lesson 3: Table API Essentials
Lesson 4: Advanced SQL Operations in Flink
Lesson 5: Integrating Flink SQL with External Systems
Lesson 6: Real-World Examples of Flink SQL Applications

Chapter 11: Apache Flink in Machine Learning and Graph Processing

Lesson 1: Introduction to FlinkML
Lesson 2: Building ML Pipelines in Flink
Lesson 3: Introduction to Flink Gelly for Graph Processing
Lesson 4: Integrating External ML Frameworks
Lesson 5: Performance Considerations for ML and Graph Workloads

Chapter 12: Performance Tuning and Optimization

Lesson 1: Monitoring Flink Performance
Lesson 2: Tuning for Low Latency and High Throughput
Lesson 3: Memory Management and Garbage Collection
Lesson 4: Profiling and Debugging Performance Issues
Lesson 5: Optimization Case Studies and Real-World Tips

Chapter 13: Security, Monitoring, and Operational Best Practices

Lesson 1: Securing Apache Flink Deployments
Lesson 2: Configuring SSL/TLS and Secure Communication
Lesson 3: Cluster Monitoring and Logging
Lesson 4: Operational Best Practices and Health Checks
Lesson 5: Auditing, Alerting, and Incident Response Strategies

Chapter 14: Deploying Apache Flink on Cloud and Containerized Platforms

Lesson 1: Cloud Deployment Strategies
Lesson 2: Containerization with Docker and Kubernetes
Lesson 3: Managing Resources and Auto-Scaling in the Cloud
Lesson 4: High Availability and Disaster Recovery
Lesson 5: Real-World Cloud Deployment Case Studies

Chapter 15: New Features and Updates in Recent Apache Flink Releases

Lesson 1: Overview of the Latest Flink Releases
Lesson 2: Deep Dive: Improvements in State Management and Fault Tolerance
Lesson 3: Enhanced Connectors and Integrations
Lesson 4: Performance Enhancements and API Refinements
Lesson 5: Migration and Compatibility Considerations
Lesson 6: Future Roadmap and Emerging Trends in Stream Processing

Chapter 16: Troubleshooting, Debugging, and Best Practices

Lesson 1: Common Errors and How to Resolve Them
Lesson 2: Debugging Techniques for Streaming and Batch Jobs
Lesson 3: Best Practices for Job Reliability and Maintainability
Lesson 4: Handling Version Incompatibilities and Dependencies
Lesson 5: Community Resources and Support Channels

Chapter 17: Real-World Applications and Industry Use Cases

Lesson 1: Apache Flink in Financial Services and Real-Time Analytics
Lesson 2: Telecommunications and IoT Applications
Lesson 3: E-Commerce, Retail, and User Behavior Analysis
Lesson 4: Case Studies of End-to-End Flink Implementations
Lesson 5: Integrating Apache Flink into Modern Data Architectures

مدت دوره: 50 + 10 ساعت

تمامی کدهای Flink این دوره و همچنین فایل pdf کامل تدریس دوره در اختیار دانشجویانی که در این دوره ثبت نام نمایند، قرار خواهد گرفت. در پایان دوره، یک پروژه عملی به مدت حدود 10 ساعت با همکاری مدرس و دانشجو انجام خواهد شد، که آمادگی کامل برای ورود به بازار کار را ایجاد نماید.
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