System Design for Builders
Most system design content is written for FAANG interviews, not for people shipping products. This course is different. Every concept is grounded in decisions you actually face: should I add a cache here? When do I need a queue? How do I structure this so it does not fall apart at 10,000 users? You will learn to reason about trade-offs, read load, and design systems that grow with your product without over-engineering them.
What you'll learn
Course outline
Free — no account needed
Full course — $89 one-time
Caching Strategies
Cut database load by 80% — cache-aside, write-through, TTL, and cache invalidation patterns
Queues and Async Processing
Move slow work off the request path — analytics, webhooks, AI inference, and background jobs
Data Consistency and the CAP Theorem
ACID vs BASE, eventual consistency, distributed transactions, and when Postgres is the right answer
API Design and Versioning
REST, RPC, and GraphQL — when to use each and how to version without breaking clients
Rate Limiting and Abuse Prevention
Token bucket, sliding window, Redis-based limits, DDoS patterns, and API key management
Load Balancing and Horizontal Scaling
Scale your app horizontally — stateless servers, sticky sessions, and auto-scaling patterns
CDN and Edge Architecture
Static vs dynamic caching, Cloudflare Workers, cache-control headers, and when to use edge functions
Observability and Reliability
Know when something breaks before your users do — logging, metrics, alerting, and SLOs
Incident Response and Postmortems
On-call fundamentals, runbooks, the 5-step incident process, SLAs, and blameless postmortems
System Design Patterns in Practice
Complete architecture review — every pattern applied, the next three bottlenecks, and what to build next
Get the full course
12 lessons — from the mental model through CAP theorem, rate limiting, CDN architecture, incident response, and a complete URL shortener architecture at 100M req/day.
Written by the RadarTrek editorial team · Reviewed June 2026
About this course
System design is the skill of architecting software systems that are reliable, scalable, and maintainable — making the right tradeoffs between competing concerns and knowing which problems are important to solve now versus later. Learning system design as a builder means understanding databases, caching, queues, CDNs, load balancing, and the distributed system principles that determine how a system behaves under load. This system design tutorial focuses on the real architectural decisions that developers face when scaling web applications beyond the single-server stage.
System design skills are essential for senior engineers, technical co-founders, and anyone building products they plan to scale. After this course you will be able to design a high-level architecture for common web application patterns, explain tradeoffs between different approaches, identify the bottlenecks in an existing system, and make the incremental architectural decisions that allow a product to scale from hundreds to millions of users.
Frequently asked questions
Do I need to know system design to build a startup product?
At early stage, premature optimisation is the primary risk — building for scale before you have users wastes time on problems you do not have. But knowing system design concepts prevents catastrophic mistakes: choosing a schema that cannot be extended, skipping caching for a query that runs a million times a day, or building on infrastructure that cannot scale past a certain point without a complete rewrite. The goal is informed pragmatism, not over-engineering.
What is the difference between horizontal and vertical scaling?
Vertical scaling means making a single server more powerful — adding CPU, RAM, or disk. Horizontal scaling means adding more servers and distributing load across them. Vertical scaling is simpler but has a ceiling — there is a limit to how powerful one machine can be. Horizontal scaling can theoretically scale without limit but requires stateless application design, load balancing, and distributed data management.
What is caching and when should I use it?
Caching stores the result of an expensive operation (a database query, an external API call, a computed value) so that future requests can use the stored result without repeating the computation. Use caching when: a computation is expensive and its result changes infrequently, the same computation is requested often, and the data being cached can tolerate being slightly stale. Common caching layers: in-memory cache (Redis), CDN caching for static assets, and database query caching.
What is a message queue and when do I need one?
A message queue decouples the producer of work from the consumer. Instead of a web server directly processing a task (sending an email, generating a report, processing an image), it enqueues a message describing the work. A worker process dequeues and processes messages asynchronously. Use queues when: a task takes too long to complete in a web request, you need to retry failed tasks, or you need to buffer work during traffic spikes.
What is the CAP theorem?
The CAP theorem states that a distributed system can only guarantee two of three properties: Consistency (all nodes see the same data simultaneously), Availability (every request receives a response), and Partition tolerance (the system continues operating despite network failures). In practice, network partitions happen, so the real choice is between consistency and availability. Most web applications choose eventual consistency with high availability.