This course is ideal for:

1. Data Engineers and Software Developers
2. Backend Engineers working with event-driven systems
3. Big Data professionals focused on real-time analytics
4. Engineers using Kafka, Spark Streaming, or Flink-like systems

1. Basic knowledge of Java or Scala
2. Familiarity with stream processing concepts is helpful
3. Understanding of distributed systems is a plus

Course Outline:

Module 1: Introduction to Apache Flink

• Overview of Stream Processing vs Batch Processing
• Key Features and Architecture of Apache Flink
• Use Cases and Real-world Applications

Module 2: Flink Architecture and Core Concepts

• Streams, Transformations, and Execution Model
• DataStream API vs DataSet API
• Understanding Flink’s Execution Pipeline
• Hands-on: Create and run a simple Flink application

Module 3: Setting Up the Flink Environment

• Installing Flink and Cluster Configuration
• Using the Flink Web UI
• Deploying Jobs on Local and Cluster Setup
• Hands-on: Deploy and monitor a sample Flink job

Module 4: DataStream API Basics

• Creating DataStreams
• Transformations: map, filter, flatMap, keyBy, reduce
• Time Semantics: Event Time vs Processing Time
• Windowing Basics: Tumbling, Sliding, Session Windows
• Hands-on: Use transformations and windows in a streaming job

Module 5: State Management in Flink

• Introduction to State: Keyed and Operator State
• Configuring State Backends
• Best Practices for Stateful Applications
• Hands-on: Using managed state in custom transformations

Module 6: Event Time and Watermarking

• Understanding Watermarks
• Dealing with Late Events and Allowed Lateness
• Event Time vs Ingestion Time
• Hands-on: Implement event time processing with watermarks

Module 7: Fault Tolerance and Checkpointing

• Flink’s Fault Tolerance Guarantees
• Configuring Checkpoints and Savepoints
• Application State Management and Recovery
• Hands-on: Enable checkpointing and simulate failure recovery

Module 8: Advanced DataStream Operations

• Using ProcessFunction and Timers
• Integrating Flink with Kafka, Cassandra, and Elasticsearch
• Hands-on: Ingest data from Kafka and write to Elasticsearch

Module 9: Performance Tuning and Debugging

• Job Parallelism and Task Slots
• Monitoring Jobs with Metrics and Logs
• Debugging Failures and Bottlenecks
• Hands-on: Optimize job resource usage and performance

Module 10: Flink SQL and Table API

• Introduction to Flink SQL and Declarative APIs
• Table API vs DataStream API
• Writing and Executing Streaming SQL Queries
• Hands-on: Create streaming queries with Flink SQL

Module 11: Integration with Big Data Ecosystem

• Reading from and Writing to HDFS, S3, and Cloud Storage
• End-to-End Use Case: Stream to Storage Pipeline
• Hands-on: Integrate Flink with Hadoop/HDFS

Training Material Provided:

• Course slides and reference guides