Full-Stack Software EngineerIntermediate8 modules~4.0 hours hands-on
High-Availability & Distributed System Design
Scale applications to handle millions of concurrent users — master caching, message queues, database sharding, and the CAP theorem trade-offs that define modern system architecture.
// What you'll be able to do- Vertical, horizontal, and load balancing
- The fundamental trade-off in distributed systems
- Replicas, connection pooling, and sharding
- Redis, cache patterns, and invalidation strategies
8 modules, built to run
Every module ships runnable code. Expand any one to see what's inside.
00IntroductionWhat problem this solves and how the course works30 min
Objective — What problem this solves and how the course works
Start moduleFree preview
01Scaling StrategiesVertical, horizontal, and load balancing30 min
Objective — Vertical, horizontal, and load balancing
02CAP Theorem & ConsistencyThe fundamental trade-off in distributed systems30 min
Objective — The fundamental trade-off in distributed systems
03Database ScalingReplicas, connection pooling, and sharding30 min
Objective — Replicas, connection pooling, and sharding
04Caching ArchitectureRedis, cache patterns, and invalidation strategies30 min
Objective — Redis, cache patterns, and invalidation strategies
05Message Queues & Async DesignRabbitMQ, event-driven architecture, and decoupling services30 min
Objective — RabbitMQ, event-driven architecture, and decoupling services
06Distributed ResilienceCircuit breakers, idempotency, and observability30 min
Objective — Circuit breakers, idempotency, and observability
07Pattern SynthesisChoosing the right strategy for any system design problem30 min
Objective — Choosing the right strategy for any system design problem
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