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Designing Scalable Reliable Systems
Created by Anuj Jadhav
Build a rock-solid understanding of how modern applications scale from one server to worldwide traffic. You’ll move from core definitions through architecture blocks, scaling patterns, data partitioning, caching, consistency, and monitoring, finishing with trade-off frameworks used by expert engineers.
Requirements
- Basic programming knowledge
- Familiarity with client-server concept
- Comfort with reading simple system diagrams
What you'll learn
- Accurately define and measure scalability, reliability, and availability
- Identify and diagram key components of a scalable web architecture
- Select appropriate scaling, partitioning, and caching strategies for given loads
- Evaluate monitoring data and cost constraints to inform design choices
- Apply consistency models and CAP theorem to justify real-world trade-offs
Learning path
9 modules • Each builds on the previous one
1
System Design Basics Overview
Introduce what system design means, why it matters, and common goals like scalability, reliability, and maintainability.
1 video5 min
2
Scalability Definition and Metrics
Clarify what scalability is, common metrics (throughput, latency), and why it differs from performance.
1 video5 min
3
Reliability and Availability Fundamentals
Explain reliability, availability, and how they relate but differ from scalability, using real-world analogies.
1 video6 min
4
Architecture Building Block Components
Cover key components—clients, load balancers, web servers, application servers, databases, caches—and their roles.
1 video5 min
5
Vertical and Horizontal Scaling Strategies
Introduce vertical vs horizontal scaling, autoscaling, and typical load-balancer patterns.
2 videos11 min
6
Data Partitioning and Replication Techniques
Explain sharding, consistent hashing, master-slave and multi-leader replication, and how these affect performance and reliability.
2 videos11 min
7
Caching Fundamentals and Patterns
Cover caching purposes, cache eviction policies, CDN use, and write-through vs write-back patterns.
2 videos13 min
8
Monitoring, Observability, and Cost Choices
Discuss metrics, logging, alerting, and balancing performance improvements against infrastructure cost.
2 videos9 min
9
Consistency Models and CAP Trade-offs
Introduce strong vs eventual consistency, the CAP theorem, and practical decision-making in distributed systems.
2 videos20 min
Start Learning
Begin your learning journey
Modules9
Duration80 min
Science-backed learning
In-video quizzes and scaffolded content to maximize retention.
Key concepts
System Design Purpose And ScopeScalability Definition And MetricsReliability And Availability TargetsCore Web Architecture ComponentsVertical Versus Horizontal ScalingLoad Balancing MechanicsDatabase Sharding And ReplicationCaching Layers And Eviction PoliciesMonitoring And Observability PracticesCost-Performance-Availability Trade-OffsConsistency Models In Distributed DataCAP Theorem Decision Framework