Distributed Systems
Course Index
28 Lessons · 3 Sections
Master distributed systems — the theory and practice behind every large-scale system at Google, Amazon & Meta. Covers CAP theorem, consensus algorithms, consistent hashing, distributed transactions, Kafka & observability. Includes 5 production builds: distributed cache, message queue, consensus implementation, rate limiter & distributed lock.
28Lessons
3Sections
4Projects
FreeAccess
Section 1Distributed Systems TheoryLessons 1–10
Lesson 1
What are Distributed Systems?
Lesson 2
Fallacies of Distributed Computing
Lesson 3
CAP Theorem
Lesson 4
PACELC Theorem
Lesson 5
Consistency Models: Strong, Eventual & Causal
Lesson 6
Distributed Consensus: Raft & Paxos
Lesson 7
Leader Election
Lesson 8
Distributed Clocks: Lamport & Vector Clocks
Lesson 9
Replication Strategies
Lesson 10
Partitioning & Sharding
Section 2Patterns, Infrastructure & CloudLessons 11–22
Lesson 11
Distributed Transactions: 2PC & Sagas
Lesson 12
Distributed Caching: Redis & Memcached
Lesson 13
Service Discovery in Distributed Systems
Lesson 14
Load Balancing Strategies
Lesson 15
Circuit Breaker & Bulkhead Patterns
Lesson 16
Distributed Tracing: OpenTelemetry & Jaeger
Lesson 17
Message Passing vs Shared Memory
Lesson 18
Distributed File Systems: HDFS & GFS
Lesson 19
Conflict-Free Replicated Data Types (CRDTs)
Lesson 20
Byzantine Fault Tolerance
Lesson 21
Distributed Systems in Cloud: AWS, GCP & Azure
Lesson 22
AI & Distributed Systems 2026
Section 3Anti-Patterns, Interview Prep & ProjectsLessons 23–28