Apache Flink – Real-Time Stream Processing and Data Pipelines

Description:

This hands-on training introduces participants to Apache Flink, a powerful open-source stream processing framework for real-time data processing and analytics. The course starts with the fundamentals of Flink’s architecture, event-time processing, and DataStream API, then moves into advanced concepts such as fault tolerance, state management, performance tuning, and integration with external systems such as Kafka, Elasticsearch, and Hadoop.

Through practical labs and real-world use cases, participants will gain experience building Flink applications capable of handling large-scale data streams with low latency and high throughput. This training is ideal for data engineers, backend developers, and real-time analytics professionals looking to develop resilient and scalable stream processing solutions.

Duration: 2 Days

Course Code: BDT 510

Learning Objectives:

By the end of this course, participants will be able to:

1. Understand Apache Flink’s architecture and its core components.
2. Build and deploy real-time streaming applications using the DataStream API.
3. Manage state, event time, and watermarking effectively in stream applications.
4. Configure checkpoints, recovery, and fault-tolerant streaming pipelines.
5. Integrate Flink with external systems like Kafka, HDFS, and Elasticsearch.
6. Use Flink SQL and Table API for declarative stream processing.

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