Scalability fundamentals, database selection, caching strategies, load balancers, message queues, and end-to-end system design exercises (URL shortener, Twitter, ride-sharing). The course that works for both senior engineering interviews and real architectural decisions.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on system design you can find — even without producing a single minute of custom video.
This course is built by engineers who ship system design systems in production. It reflects how these tools actually behave at scale.
Every day includes working code examples you can copy, run, and modify right now. Understanding comes through doing.
Instead of re-explaining existing documentation, this course links to the definitive open-source implementations and the best reference material on system design available.
Each day is designed for about an hour of focused reading plus hands-on work. Do the whole course over a week of lunch breaks. No live classes, no quizzes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
Vertical vs horizontal scaling, stateless services, the CAP theorem and what it actually means in practice, consistency models (strong, eventual, causal), and the mental framework for reasoning about distributed system trade-offs.
When to choose PostgreSQL, MongoDB, Cassandra, or Redis — not generic rules but specific scenarios where each wins. Sharding strategies, replication topology, and the database anti-patterns that kill scaling.
Cache-aside vs write-through vs write-behind, Redis vs Memcached, CDN edge caching, cache invalidation strategies, the thundering herd problem, and consistent hashing for distributed caches.
Kafka vs RabbitMQ vs SQS, producer/consumer patterns, exactly-once vs at-least-once delivery, fan-out patterns, backpressure, and the event-driven architecture patterns that decouple services at scale.
Three end-to-end system design walkthroughs that cover every component from days 1-4. URL shortener (read-heavy, global distribution), Twitter feed (fan-out on write vs read), ride-sharing (geospatial, real-time matching).
Instead of shooting our own videos, we link to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
Complete system design interview preparation — the mental framework, component vocabulary, and the approach to designing any system under pressure.
Kafka vs RabbitMQ vs SQS — when each makes sense, producer/consumer patterns, and event-driven architecture at scale.
The CAP theorem in plain language — what consistency, availability, and partition tolerance mean in practice for real distributed systems.
Cache-aside, write-through, CDN configuration, and the cache invalidation patterns that work in distributed systems.
End-to-end Twitter system design walkthroughs covering timeline generation, fan-out on write vs read, and the infrastructure decisions at scale.
The best way to deepen understanding is to read the canonical open-source implementations. Clone them, trace the code, understand how the concepts in this course get applied in production.
The most comprehensive system design reference on GitHub — 200k+ stars. Every component and pattern in this course is covered with diagrams and examples.
Apache Kafka source. Reading the producer and consumer group implementations shows how message queues achieve high throughput and consumer group coordination.
Curated collection of scalability war stories, system design case studies, and infrastructure engineering articles from companies operating at scale.
Reference implementations of consistent hashing with virtual nodes — the algorithm behind distributed cache and database sharding.
System design questions are the filter for senior and staff engineering roles. This course covers the components, trade-offs, and design patterns examiners expect you to know.
The patterns in this course aren't just for interviews — they're the actual decisions production teams make. This course gives you the vocabulary and trade-off framework for real architectural discussions.
AI inference systems, vector databases, and embedding pipelines have the same distributed systems challenges as web applications. This course covers the infrastructure patterns that apply to both.
The 2-day in-person Precision AI Academy bootcamp covers system design and AI architecture in depth — hands-on, with practitioners who build AI systems for a living. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
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