Messaging & Event Streaming

Design and operate event-driven systems, message brokers, and stream processing at scale.

75 minutes

8Detailed Sections

Senior Level

Event-driven architectures decouple producers from consumers, enable asynchronous processing, and make systems more resilient to load spikes.

They are commonly used for audit trails, user activity streams, metrics pipelines, and cross-service notifications. Key components include producers, brokers, topics/queues, and consumers.

The trade-offs include operational complexity, eventual consistency, and the need for careful schema evolution.

This section frames common use-cases (analytics pipelines, notifications, materialized views) and the design decisions that follow.

Key Takeaways

Core Benefit: Decouples services and enables asynchronous processing

Core Benefit: Improves system resilience by handling load spikes

Real-world Uses: Analytics pipelines, notifications, audit trails, materialized views

Requirements: Schema versioning, versioning tooling, operational expertise

Trade-off: Gains decoupling at cost of eventual consistency complexity

Visual Diagram

Producer -> Broker (topic/partition) -> Consumer Groups

All Tutorials Practice Questions

Messaging & Event Streaming

Table of Contents

Overview: Why Event-Driven Architectures

Key Takeaways

Visual Diagram

Kafka vs RabbitMQ: When to choose which

Event Sourcing & CQRS Patterns

Delivery Semantics: At-least-once, At-most-once, Exactly-once

Ordering, Partitioning & Consumer Groups

Stream Processing: Stateless vs Stateful, Tools and Patterns

Operational Concerns, Pitfalls and Real-World Examples

Case Studies: Messaging & Streaming at Scale