Is this a video course?

No. This is an interactive, slide-based learning platform. Each lesson has rich text, animated diagrams, live code editors, and quizzes. You learn by reading, interacting, and doing, not by watching videos passively.

How long do I have access?

Forever. Both pricing tiers are one-time payments with lifetime access. This includes all current 766 lessons and any future content we add.

What level of experience do I need?

None. We start from absolute basics like 'What is latency?' and build up to distributed consensus protocols. The Foundation level assumes zero prior knowledge of system design.

How much does the system design course cost?

7.99 US dollars for lifetime access globally, or 499 Indian rupees for lifetime access in India. One-time payment, no subscription, no hidden fees. 11 lessons are free with no signup required.

What technologies are covered?

Everything from DNS and load balancers to Kubernetes, Kafka, distributed databases, consensus protocols, stream processing, security architecture, and observability. We cover principles and real-world implementations used at Netflix, Google, Amazon, Uber, Stripe, and more.

Is this useful for system design interview preparation?

Yes. The lessons are structured around the exact topics asked in system design interviews at FAANG and top-tier companies. Interactive diagrams help you practice whiteboard-style explanations. Covers everything from URL shortener design to distributed payment systems.

How is this different from ByteByteGo or Educative?

766 interactive lessons (4x more than most competitors), 16 different diagram types that build step by step, real production examples from Netflix, Google, Amazon, Uber, and Stripe, and lifetime access for a one-time payment of $7.99 instead of annual subscriptions costing 100 to 200 dollars per year.

Why does a columnar storage engine read 10x fewer bytes than a row store for a typical analytics query?

Analytics queries typically select a few columns out of many, and columnar layout stores each column contiguously so the engine skips unneeded columns entirely, A typical dashboard query selects 3 columns out of 40. A columnar engine reads only those 3 columns. A row store reads all 40 columns for every row because they are interleaved on disk. The MIT C-Store paper demonstrated 10x to 100x speedups from this single design choice.

In a star schema, why should the fact table contain only measurements and foreign keys?

Keeping the fact table narrow reduces storage cost per row and speeds up every scan, since attributes like product name belong in small dimension tables, A narrow fact table (8 foreign keys plus a few measures at roughly 80 bytes per row) scans much faster than a wide one. Dimension tables are small enough to broadcast as in-memory hash joins, keeping the query fast even at trillion-row scale.

What is the main risk of an OLAP cube with too many dimensions?

Every additional dimension multiplies the cube's row count, potentially making it as large as the raw fact table and defeating the purpose, A cube across 4 dimensions might be 4 million rows. Adding 2 more dimensions can explode it to 400 million rows, at which point querying the cube is nearly as expensive as querying the raw facts. Pick 4 to 6 dimensions and freeze the list.

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Name: System Design Masterclass
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This lesson is part of the full System Design Masterclass. Unlock it along with 765 other lessons for a one-time payment.

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Foundation lessons free, try before you buy

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“The interactive diagrams alone are worth way more than the price.”

Anish K., Senior Engineer at a Bengaluru startup

All 766 interactive lessons

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