Apache Flink Training is a hands-on program designed to equip learners with advanced skills in real-time stream processing and batch data processing using Apache Flink. This training focuses on Flink architecture, event-driven processing, stateful stream processing, windowing concepts, and building scalable data pipelines. Participants will gain practical experience in processing high-volume, low-latency data streams used in modern big data analytics, IoT systems, fraud detection, and real-time monitoring applications.

Participants will gain strong expertise in real-time stream processing using Apache Flink, enabling them to build scalable data pipelines, process event-driven data, and implement stateful streaming applications.

• Data Engineers and Big Data Developers
• Software Engineers working on real-time systems
• Backend and Distributed Systems Developers
• Cloud and DevOps Engineers
• Professionals working in analytics and streaming platforms

• Basic understanding of programming concepts (Java or Python preferred)
• Familiarity with distributed systems and data processing fundamentals
• Basic knowledge of streaming or big data concepts is helpful
• Understanding of databases and data pipelines is beneficial

• Apache Flink architecture and runtime understanding
• Stream processing and batch processing concepts
• Stateful stream processing implementation
• Event time, processing time, and watermarks handling
• Windowing and aggregation techniques
• Fault tolerance and checkpointing mechanisms
• Building real-time data streaming applications

Upon completion of this training, participants will be able to design and implement real-time stream processing applications using Apache Flink. They will be capable of building scalable, fault-tolerant data pipelines for processing high-velocity data in real-time business and analytical environments.

Introduction to Apache Flink and Streaming Concepts
• Overview of Flink architecture and ecosystem
• Data stream vs batch processing
• Job managers, task managers, and execution model
• Setting up Flink environment

Core Stream Processing in Flink
• DataStream API fundamentals
• Transformations and operations
• Event time vs processing time
• Introduction to windowing concepts
Advanced Stream Processing Techniques
• Stateful stream processing concepts
• Watermarks and late event handling
• Fault tolerance and checkpointing
• Parallelism and performance optimization

Real-Time Data Pipeline Development
• Building end-to-end streaming applications
• Integration with Kafka and external systems
• Monitoring and debugging Flink jobs
• Real-world use cases and architecture design

• Flink architecture and execution model
• Stream vs batch processing
• DataStream API and transformations
• Windowing and event time processing
• Stateful processing and checkpointing
• Real-time pipeline development and integration

• Hands-on Flink job development exercises
• Practical stream processing assignments
• Mini project on real-time data pipeline creation
• Trainer-led evaluation of Flink applications and outputs

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in Apache Flink Training, validating their expertise in real-time stream processing, Flink architecture, stateful streaming, and distributed data pipeline development.