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.

In row-oriented storage, how is data physically arranged on disk?

All columns of a single row are stored contiguously, Row-oriented storage keeps all columns of each row together on disk. When you read row 1, you get [id, name, email, age] as a contiguous block of bytes.

Why does SELECT AVG(age) FROM users perform poorly on a row-oriented database with millions of rows?

The database must read entire rows (name, email, etc.) just to extract the age column, Because all columns are stored together, the database reads every byte of every row, names, emails, all of it, just to pull out the age field. Most of the I/O is wasted on data the query does not need.

Which workload type is row-oriented storage best suited for?

OLTP, reading and writing individual rows in transactions, Row storage is built for OLTP workloads: insert a user, update an order, fetch a profile. These are row-at-a-time operations where reading all columns of specific rows is the dominant pattern.

Why do most companies use BOTH a row store and a column store?

Row stores handle transactions well but analytical queries poorly, so analytics go to a column store, Row stores are fast at single-row reads and writes (OLTP). Column stores are fast at scanning specific columns across millions of rows (OLAP). Running analytics on a row store wastes I/O. Running transactions on a column store wastes time on scattered writes. The OLTP/OLAP split lets each engine do what it does best.

Row-Oriented Storage

Name: System Design Masterclass
Availability: InStock

in Database Types Storage

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