Kafka & Event-Driven Architecture with AI
Description
This hands-on course develops practical skills in building event-driven systems using Kafka while incorporating AI-assisted development workflows. Participants practice designing reliable event flows and learn how AI can support faster configuration, code scaffolding, and architectural refinement—without replacing sound engineering judgment. The outcome is a clearer understanding of asynchronous system design and increased confidence in implementing resilient event-driven solutions.
Indicative Duration: 16 training hours
*Duration is adjusted based on the final scope and the target audience.
Scope
| 1. Event-Driven Architecture Foundations | • Introduction to event-driven architecture & Kafka • AI Practice: AI explains Kafka concepts: topics, partitions, producers/consumers, brokers |
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2. Kafka Messaging Fundamentals
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• Kafka producer & consumer fundamentals • AI Practice: AI scaffolds producer/consumer code examples |
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| • Kafka messaging patterns (pub/sub, point-to-point) • AI Practice: AI generates diagrams for event flows |
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| 3. Topic Design & Scaling | • Kafka topic design & partitioning • AI Practice: AI suggests partition strategies, consumer group setups |
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| 4. Reliability & Configuration | • Kafka configuration & reliability (acks, retries, DLQs) • AI Practice: AI generates configuration examples and code snippets |
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| 5. Application Integration | • Integration of Kafka in Java/Spring applications • AI Practice: AI scaffolds service + Kafka integration code |
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| 6. Observability & Error Handling | • Monitoring • Logging • Error handling • AI Practice: AI proposes monitoring metrics, retry strategies, logging templates |
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| 7. Capstone Project | • Event-driven mini-system • AI Practice: AI scaffolds end-to-end Kafka event flow, diagrams, and code |
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Learning Objectives
Upon completion of the course participants will be able to:
- Explain core event-driven architecture principles and Kafka fundamentals
- Design event flows with appropriate topic structures and partition strategies
- Implement producers and consumers within Java/Spring applications
- Configure reliability mechanisms and error-handling strategies in messaging systems
- Use AI tools to scaffold, review, and refine event-driven implementations responsibly
Target Audience
- Roles: Backend Developers, Software Engineers, Software Architects
- Seniority: Junior to Mid-Level Professionals or Senior Professionals exploring AI-assisted backend workflows
Prerequisite Knowledge
- Basic Java programming knowledge
- Familiarity with backend application development
- Introductory understanding of REST-based systems
Delivery Method
Sessions can be delivered via the following formats:
- Live Online – Interactive virtual sessions via video conferencing
- On-Site – At your organization’s premises
- In-Person – At Code.Hub’s training center
- Hybrid – A combination of online and in-person sessions
