What Is This Course About
Every large-scale system you use daily -- Google Search, Amazon's shopping cart, Netflix streaming, WhatsApp messaging -- is a distributed system. Behind the scenes, thousands of machines coordinate to give you a seamless experience. But how?
The honest answer: most engineers never get to see the full picture. You work on a service, maybe two. You know your corner of the system. Then you walk into a system design interview and get asked to design something at the scale of Kafka or DynamoDB, and you realize there's a gap between writing code and architecting systems.
This course exists to close that gap.
The approach: learn from the masters
Instead of teaching distributed systems theory in isolation, we take a different approach: study the actual systems that run the internet. Amazon's Dynamo, Google's BigTable, Apache Kafka -- these aren't textbook exercises. They're battle-tested architectures that serve billions of requests daily. Each one made deliberate trade-offs, and understanding why they made those trade-offs is worth more than memorizing definitions.
Here's the key insight: once you deeply understand how one well-designed distributed system works, every other system starts to feel familiar. That's because they all draw from the same pool of fundamental patterns -- consistent hashing, quorum-based replication, write-ahead logs, gossip protocols. The systems change, but the patterns repeat.
What to expect
The course is structured in two parts that reinforce each other:
Part 1: System design case studies
We dissect the architecture of seven influential distributed systems, chosen because they represent fundamentally different design philosophies:
| System | Category | Key Trade-off |
|---|---|---|
| Dynamo | Key-value store | Sacrifices consistency for availability |
| Cassandra | Wide-column NoSQL | Tunable consistency with decentralized design |
| BigTable | Wide-column NoSQL | Strong consistency with centralized coordination |
| Kafka | Messaging & streaming | Durability and throughput over low latency |
| GFS | Distributed file system | Optimized for large sequential reads/writes |
| HDFS | Distributed file system | Open-source GFS with different trade-offs |
| Chubby | Coordination service | Strong consistency for small metadata |
For each system, we cover: the problem it was built to solve, the design decisions and their trade-offs, how reads and writes actually work under the hood, and what happens when things go wrong.
Part 2: System design patterns
After studying these systems, you'll notice recurring solutions to recurring problems. We extract these into 20 reusable patterns -- the building blocks that appear across every distributed system:
| Pattern | The Problem It Solves |
|---|---|
| Consistent Hashing | How to distribute data across nodes without reshuffling everything when nodes join or leave |
| Quorum | How to ensure data consistency without waiting for every single node |
| Write-ahead Log | How to survive crashes without losing data |
| Vector Clocks | How to track causality when multiple nodes write concurrently |
| Gossip Protocol | How to spread information without a central coordinator |
| Bloom Filters | How to quickly check if data exists without reading from disk |
| ...and 14 more |
These aren't abstract concepts -- each pattern is grounded in the real systems from Part 1. When you read about Dynamo using vector clocks, you'll link back to the pattern. When you see Kafka's write-ahead log, you'll recognize the same technique GFS uses.
How to use this course
- For interview prep: Read the case studies for breadth, then the patterns for the toolkit you'll apply in any design question.
- For deep understanding: Follow the cross-references between systems and patterns -- the connections are where the real learning happens.
- As a reference: Each summary page and pattern page is designed to be useful on its own when you need a quick refresher.
The systems in this course were chosen not just because they're famous, but because they collectively cover the most important distributed systems challenges: partitioning, replication, consistency, fault tolerance, and coordination. Master these, and you'll have the vocabulary and intuition to reason about any distributed system you encounter.