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System Design Deep Dive: Real-World Distributed Systems
Ready to become a System Design pro? Unlock the world’s largest distributed systems, including file systems, data processing systems, and databases from hyperscalers like Google, Meta, and Amazon.
4.5
158 Lessons
20h
Updated this week
Join 3 million developers at
Join 3 million developers at
LEARNING OBJECTIVES
- Analyze design trade-offs in real-world distributed systems using case studies from leading companies
- Evaluate the architecture and performance of distributed file systems like Google File System, Colossus, and Tectonic
- Design scalable and fault-tolerant distributed databases by understanding principles from Bigtable, Megastore, and Spanner
- Implement concurrency management techniques using Two-Phase Locking and distributed coordination services like Chubby and ZooKeeper
- Apply consensus algorithms such as Paxos and Raft to ensure data consistency and fault tolerance in distributed systems
KEY OUTCOMES
Ace System Design Interviews
Demonstrate your ability to analyze and design scalable distributed systems, impressing interviewers with real-world examples and trade-off reasoning.
Architect Robust Distributed Systems
Design and evaluate distributed file systems and databases that meet high availability and performance standards in production environments.
Implement Concurrency Control Mechanisms
Effectively manage concurrent access and ensure data integrity in distributed applications using proven techniques and tools.
Apply Consensus Protocols in Real-World Scenarios
Utilize consensus algorithms to maintain data consistency and fault tolerance in distributed systems, ensuring reliable operations across nodes.
Learning Roadmap
3.
Google File System (GFS)
Google File System (GFS)
11 Lessons
11 Lessons
Examine the Google File System's design for scalability, performance, and fault tolerance in distributed environments.
4.
Google Colossus File System
Google Colossus File System
3 Lessons
3 Lessons
Break down the steps to Colossus's scalability, low-latency, and enhanced control in data management.
5.
Facebook's Tectonic File System
Facebook's Tectonic File System
8 Lessons
8 Lessons
Consolidate storage resources and ensure scalability, efficiency, and performance isolation in advanced systems.
7.
Google Bigtable
Google Bigtable
7 Lessons
7 Lessons
Piece together the parts of Google's Bigtable, focusing on its data model, architecture, and design refinements.
8.
Google Megastore
Google Megastore
6 Lessons
6 Lessons
Learn how to use Megastore for scalable, reliable, and ACID-compliant cloud storage.
9.
Google Spanner
Google Spanner
9 Lessons
9 Lessons
Look at Google Spanner’s unique combination of SQL consistency, NoSQL scalability, and global performance.
11.
Many-core Key-value Store
Many-core Key-value Store
5 Lessons
5 Lessons
Break down the steps to design an efficient, power-saving many-core key-value store system
12.
Scaling Memcache
Scaling Memcache
7 Lessons
7 Lessons
Solve problems in scaling Memcache, managing data replication, performance optimization, and consistency.
13.
SILT
SILT
12 Lessons
12 Lessons
Investigate optimizing key-value stores for efficient resource use, low latency, and scalability.
14.
Amazon DynamoDB
Amazon DynamoDB
8 Lessons
8 Lessons
Build on DynamoDB's scalability, flexible schemas, and robust high availability mechanisms.
16.
Two-phase Locking (2PL)
Two-phase Locking (2PL)
3 Lessons
3 Lessons
Unpack the core of Two-Phase Locking (2PL), ensuring serializability while addressing deadlocks and performance issues.
17.
Google Chubby Locking Service
Google Chubby Locking Service
8 Lessons
8 Lessons
Examine Chubby's innovative, robust design for distributed systems, enhancing fault tolerance, replication, and client coordination.
18.
ZooKeeper
ZooKeeper
5 Lessons
5 Lessons
Break down complex ideas of ZooKeeper's architecture, primitives, and performance in distributed systems.
20.
MapReduce
MapReduce
8 Lessons
8 Lessons
See how it works to streamline parallel data processing, fault tolerance, and scalability using MapReduce.
21.
Spark
Spark
10 Lessons
10 Lessons
Master the steps to leverage Spark's in-memory computation for scalable, low-latency data processing.
22.
Kafka
Kafka
8 Lessons
8 Lessons
Learn how to use Kafka for efficient, scalable, and reliable real-time data streaming.
24.
Understanding Consensus: Two Generals, FLP, & Byzantine Generals
Understanding Consensus: Two Generals, FLP, & Byzantine Generals
4 Lessons
4 Lessons
Examine consensus challenges in distributed systems, including the Two Generals, FLP, and Byzantine Generals problems.
25.
Two-phase Commit
Two-phase Commit
4 Lessons
4 Lessons
Grasp the fundamentals of the Two-Phase Commit protocol for ensuring distributed transaction consistency.
26.
State Machine Replication
State Machine Replication
10 Lessons
10 Lessons
Take a closer look at replicating state machines for fault tolerance, coordinated requests, and fault-tolerant outputs.
27.
Paxos
Paxos
6 Lessons
6 Lessons
See how Paxos maintains consensus in distributed systems, addressing safety, liveness, and fault tolerance.
28.
Raft
Raft
8 Lessons
8 Lessons
Piece together the parts of the Raft algorithm, ensuring consistent, reliable distributed systems.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Complete more lessons to unlock your certificate
Developed by MAANG Engineers
ABOUT THIS COURSE
Modern software systems are expected to operate at a massive scale while meeting strict reliability and latency requirements. Whether it’s a feed refresh, a payment request, or a real-time analytics query, users expect systems to respond instantly and consistently. That expectation has raised the bar for engineers today, understanding that System Design isn’t optional. It’s a core skill for building and evaluating production-grade systems.
I built this course from my experience working on large-scale distributed systems at Microsoft (Azure) and Meta (Scuba), and from interviewing hundreds of candidates across both companies. The pattern I kept seeing was this: candidates understood individual components, but struggled to combine them into a coherent system. They knew what a cache or load balancer was, but not when or why to use it. This course is designed to bridge that gap.
We start with the foundational building blocks of System Design, including databases, caching layers, load balancing, and messaging systems, and focus on how they interact under real-world constraints. From there, we analyze systems built by companies like Google, Facebook, and Amazon, breaking them down to understand the trade-offs behind each design decision. The goal is not just to learn concepts, but to develop the ability to reason through them in practice. This approach has helped a large number of engineers build stronger intuition for System Design and perform better in interviews.
If you want to understand how real systems are designed and be able to design them yourself, this course gives you a clear, practical path forward.
ABOUT THE AUTHOR
Fahim ul Haq
Software Engineer, Distributed Storage at Meta and Microsoft, Educative (Co-founder & CEO)
Trusted by 3 million developers working at companies
D
Desh S
Huawei Technologies
S
Sumit S
Learner
J
Jayanth H
Learner
D
Desh S
Huawei Technologies
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