Advanced|17 hours|40 lessons

Distributed Systems Design

The hard core of distributed systems. Core concepts, storage, tradeoffs, the distributed systems fundamentals (partitions, clocks, consensus, CRDTs), and distributed transactions. Built for senior engineers who need to genuinely understand how distributed systems work, not just name the patterns. Goes deeper than typical system design interview prep.

Text-based, no videos

7 modules, 40 lessons

Lifetime access

What you'll learn

Reason precisely about scalability, availability, reliability, and latency

Explain CAP and PACELC accurately, and place a system on the consistency spectrum

Choose between block, file, and object storage, and between replication and erasure coding

Handle network partitions, split brain, failure detection, and partial failure

Order events without trusted clocks using Lamport timestamps and vector clocks

Explain Paxos and Raft, leader election, distributed locks, gossip, and CRDTs

Design multi-service consistency with sagas, the outbox pattern, and 2PC trade-offs

Curriculum

7 modules · 40 lessons

Core Concepts

The vocabulary of distributed systems: scalability, availability, reliability, failure domains, performance, hashing, and the consistency landscape.

8 lessons

Scalability25 minFREE Availability25 minFREE Reliability25 minFREE Single Point of Failure (SPOF)25 minFREE Latency vs Throughput vs Bandwidth25 minFREE Consistent Hashing30 minFREE CAP Theorem30 minFREE Consistency Models30 minFREE

Storage Systems

Where data actually lives: block, file, and object storage, distributed file systems, and how large systems buy durability cheaply.

4 lessons

Block vs File vs Object Storage25 min Object Storage25 min Distributed File Systems30 min Erasure Coding25 min

Tradeoffs

The recurring either/or decisions in distributed design: scaling direction, concurrency model, data flow direction, state, transport, and consistency.

6 lessons

Vertical vs Horizontal Scaling20 min Concurrency vs Parallelism20 min Push vs Pull Architecture25 min Stateful vs Stateless Architecture25 min Long Polling vs WebSockets25 min Strong vs Eventual Consistency25 min

Distributed Systems Fundamentals

What changes the moment you cross the network boundary: partial failure, partitions, split brain, failure detection, and the failure-handling toolkit.

5 lessons

Challenges of Distribution25 min Network Partitions25 min Split Brain Problem25 min Heartbeats25 min Handling Failures in Distributed Systems30 min

Time and Ordering

There is no single "now" in a distributed system. Clock drift, logical time, Lamport timestamps, and vector clocks.

4 lessons

Clock Synchronization Problem25 min Logical Clocks20 min Lamport Timestamps25 min Vector Clocks30 min

Coordination and Consensus

Getting unreliable nodes to agree: Paxos, Raft, leader election, distributed locks, gossip, CRDTs, and operational transformation.

8 lessons

Consensus Algorithms25 min Paxos Algorithm35 min Raft Algorithm35 min Leader Election25 min Distributed Locks30 min Gossip Protocol25 min CRDTs30 min Operational Transformation30 min

Distributed Transactions

Atomicity across service boundaries: why it is hard, why 2PC blocks, why 3PC did not save us, and what modern systems do instead.

5 lessons

The Problem with Distributed Transactions25 min Two-Phase Commit (2PC)30 min Three-Phase Commit (3PC)25 min SAGA Pattern30 min Outbox Pattern25 min

About the Author

Sharon Sahadevan

AI Infrastructure Engineer

Building production GPU clusters on Kubernetes. H100s, large-scale model serving, and end-to-end ML infrastructure across Azure and AWS.

10+ years designing cloud-native platforms with deep expertise in Kubernetes orchestration, GitOps (Argo CD), Terraform, and MLOps pipelines for LLM deployment.

Author of KubeNatives, a weekly newsletter read by 3,000+ DevOps and ML engineers for production insights on K8s internals, GPU scheduling, and model-serving patterns.

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