About the Course

What we study in the course

System Design is about designing systems that remain fast, reliable, and manageable as load grows. That is why the course does not start with specific technologies, but with a way of thinking: define the requirements, estimate the scale, split the system into components, and only then choose technical solutions.

To make complex topics easier to understand and remember, the material uses diagrams, small examples, and step-by-step explanations of how an architecture changes when new requirements and constraints appear.

To make sure the theory does not remain just a collection of terms, the course uses different exercise formats:

• Multiple-choice tests;
• Matching components with their responsibilities;
• Putting design steps in the correct order;
• Finding an incorrect decision or a bottleneck;
• Entering a precise answer to an architecture question;
• Working with configuration and code fragments;
• Practical tasks where you choose and justify a solution.

Different formats force you not only to recognize the correct answer, but also to apply architectural ideas in new situations — exactly what matters in System Design.

Course roadmap

The topics are arranged so that you gradually move from basic principles to designing distributed and high-load systems:

• Foundation. Requirements, capacity estimation, scaling, load balancing, network communication, and basic architectural decisions.
• Data and performance. Storage systems, indexes, replication, partitioning, caching, and ways to reduce latency as load grows.
• Distributed systems. Message queues, asynchronous communication, consistency, fault tolerance, and designing systems without a single point of failure.
• Architectural approaches. Decomposition, event-driven systems, and other techniques that help manage the complexity of large applications.

As you progress, the questions will become less clear-cut. That is normal: real-world design almost always means choosing between several viable options and accounting for their cost, complexity, and limitations.

How we explain architecture

We look at every decision through cause and effect: first the problem, then the possible options, and finally the trade-offs of the chosen approach.

Client
  ↓
Load Balancer
  ↓
Application Servers
  ↓
Cache → Database

A diagram like this proves nothing by itself. What matters is understanding why the load balancer appeared, what data can be cached, what happens if one server fails, and where the next bottleneck will appear.