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Course guide

Distributed Systems Engineering: A Practical Walkthrough

A production-engineering walkthrough of distributed systems: CAP, consensus, replication, scalability, observability, and Zero Trust. Free 12-module course.

Distributed systems engineering is the discipline of building software that survives partial failure, scales horizontally, and stays observable across many machines. This is the field most modern infrastructure runs on - Kubernetes, Kafka, Cassandra, Spanner, every cloud platform.

The free Distributed Systems Engineering course walks you through 12 modules covering everything from CAP and latency to consensus, observability, Zero Trust, Kubernetes-native architecture, and real failure scenarios.

Learning path

Continue from concept to implementation

These course modules place this topic in context, connect it to adjacent decisions, and provide the practical next step.

Module 1

Foundations of Distributed Systems

What a distributed system actually is, why we build them, and the trade-offs that define every design decision after this point.

3 hours | 5 learning objectives

Module 2

Networking & Distributed Communication

How services actually talk: TCP, HTTP/2, gRPC, service discovery, load balancing, retries, and the timeout discipline that keeps systems from melting.

4 hours | 5 learning objectives

Module 3

Event-Driven & Asynchronous Systems

How Kafka, RabbitMQ, NATS, and pub/sub patterns let services decouple in time and scale - and the failure modes that come with them.

4 hours | 5 learning objectives

Practical outcomes

What the recommended module teaches

  • Define a distributed system from a production-engineering perspective
  • Understand why distributed systems replace monoliths and what it costs you
  • Internalise CAP and PACELC as decision frameworks, not academic theorems
  • Reason about latency, availability, fault tolerance, and consistency as a coupled system
  • Build the mental model that every later module depends on