Apache Flink Stream Processing Essentials

Learn the fundamentals of real-time stream processing using Apache Flink, from architecture to hands-on data pipeline development.

Course Objectives

• Understand the architecture and runtime model of Apache Flink.
• Build and deploy real-time data processing applications.
• Work with event time, windows, watermarks, and stateful stream operations.
• Integrate Flink with sources like Kafka and sinks like PostgreSQL or Elasticsearch.

Course Overview

This course offers a practical, hands-on introduction to Apache Flink, the leading open-source stream processing engine for distributed, stateful computations over unbounded and bounded data streams. Participants will develop streaming jobs, apply transformations, configure fault tolerance, and monitor performance using Flink’s APIs and deployment tools. Ideal for data engineers, developers, and analytics professionals seeking to unlock the power of real-time processing.

Sample Module: Flink Architecture and Setup

This module introduces Apache Flink’s architecture, including its job manager, task managers, and distributed runtime. Learners will also get hands-on with local and Docker-based Flink environments.

Lesson: Setting Up a Local Flink Cluster

In this lesson, participants will install and launch a Flink cluster on their local machine, explore the Flink dashboard, and run their first example job. Emphasis is placed on understanding deployment modes and resource management.