Complete Course of Apache Flink

Apache Flink is a powerful framework and distributed processing engine designed for stateful computations over unbounded and bounded data streams. It is widely used for real-time data processing, stream analytics, and batch processing, enabling businesses to handle high-velocity and high-volume data with low latency. Learning Apache Flink is crucial for those seeking careers in the big data industry, as it is a popular choice for real-time analytics, fraud detection, event-driven systems, and other time-sensitive applications in industries like finance, e-commerce, and IoT.
A tutor can accelerate your learning by providing a structured curriculum, practical examples, and hands-on projects that simulate real-world scenarios, helping you quickly develop the expertise needed to stand out in roles like Big Data Developer or Data Engineer.

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

The online class is held via Skype (or Zoom or Microsoft Teams) and the cost per hour of tutoring is only $15. At the end of this long course, you will master all the required basic and advanced concepts of Apache Flink and we will develop a real world project together for about 10 hours, that fully prepares you to find a job as a professional Database Administrator or entry-level Big Data Engineer.
To book this class, message or call my telegram or WhatsApp:
+98 (912) 490-8372 or +98 (935) 490-8372
You can also send email to me:
abolfazl.mohammadijoo@gmail.com

GET IN TOUCH

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+98 9354908372
info@mohammadijoo.com

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