Kafka Streams Fundamentals

The "Kafka Streams Fundamentals" course is designed to equip participants with the skills to process and analyze streaming data effectively. This course provides an in-depth exploration of the Kafka Streams library, focusing on its powerful capabilities for processing real-time data at scale. Through a mix of theoretical lessons and hands-on projects, participants will gain the expertise required to build, deploy, and optimize Kafka Streams applications.

What You’ll Learn:

• Understand the architecture and core concepts of Kafka Streams.
• Develop streaming applications using the Kafka Streams Java API.
• Process and analyze streaming data in real time.
• Integrate Kafka Streams with external systems such as databases and cloud services.
• Debug, monitor, and optimize Kafka Streams applications.
This course is suitable for:
• Developers and Data Engineers aiming to master stream processing.
• Solution Architects looking to integrate streaming solutions into enterprise architectures.
• Technical professionals transitioning into real-time data processing roles.

Course Program

1. Introduction to Stream Processing and Kafka Streams (1 hour - Theory)

• Overview of stream processing concepts
• Role of Kafka Streams in the Apache Kafka ecosystem
• Core architecture of Kafka Streams
• Introduction to Apache Kafka: topics, producers, consumers, and brokers
• Differences between batch and stream processing
2. Fundamentals of Kafka Streams API (3 hours - 2 hours theory, 1 hour practice)
• Managing topics: reading and writing data
• Basic operations: filtering, mapping, and aggregation
• Managing stream state: state stores
• Practical Exercise:
• Setup and run a basic Kafka Streams application
• Implement basic operations using the Kafka Streams Java API
3. Advanced Event Processing (2 hours - 1 hour theory, 1 hour practice)
• Windowed operations
• Joins and aggregations
• Real-time data transformations
• Practical Exercise:
• Create an application implementing windowed operations
• Perform joins between multiple data streams
4. Integration and Deployment of Kafka Streams Applications (3 hours - 2 hours theory, 1 hour practice)
• Integrating with external systems (e.g., databases, cloud storage)
• Monitoring and debugging Kafka Streams applications
• Deployment practices for containerized environments

Practical Exercise:
• Deploy a Kafka Streams application using Docker
• Set up monitoring with Kafka-compatible tools

By the end of the course, participants will:

• Have a solid understanding of stream processing and Kafka Streams.
• Be able to build, debug, and deploy Kafka Streams applications.
• Confidently manage and analyze real-time data streams.

• By the end of the course, the students should be comfortable applying what they learned (building their own datasets and applying ML algorithms on them) and extending their knowledge in this domain confidently.

Course Program

1. Introduction to Stream Processing and Kafka Streams (1 hour - Theory)

• Overview of stream processing concepts
• Role of Kafka Streams in the Apache Kafka ecosystem
• Core architecture of Kafka Streams
• Introduction to Apache Kafka: topics, producers, consumers, and brokers
• Differences between batch and stream processing
• Managing topics: reading and writing data
• Basic operations: filtering, mapping, and aggregation
• Managing stream state: state stores

2. Fundamentals of Kafka Streams API (3 hours - 2 hours theory, 1 hour practice)

Practical Exercise:

• Setup and run a basic Kafka Streams application
• Implement basic operations using the Kafka Streams Java API
• Windowed operations
• Joins and aggregations
• Real-time data transformations

3. Advanced Event Processing (2 hours - 1 hour theory, 1 hour practice)

Practical Exercise:

• Create an application implementing windowed operations
• Perform joins between multiple data streams
• Integrating with external systems (e.g., databases, cloud storage)
• Monitoring and debugging Kafka Streams applications
• Deployment practices for containerized environments

4. Integration and Deployment of Kafka Streams Applications (3 hours - 2 hours theory, 1 hour practice)

Practical Exercise:

• Deploy a Kafka Streams application using Docker

Set up monitoring with Kafka-compatible tools