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.

A window fires at 10:01 and then a record with event_time 10:00:42 arrives 30 seconds later with allowedLateness set to 2 minutes. What happens?

The window state is still alive, the record folds in, and a new pane is emitted (with a retraction if accumulatingAndRetracting is set), Allowed lateness keeps window state alive past the watermark close. A record arriving inside the grace period folds back into the pane. In accumulatingAndRetracting mode, Flink emits a retraction of the previous result followed by the new pane, so downstream sinks that support upserts converge to the correct answer.

Why does a retraction based pipeline pair naturally with a Kafka compacted topic?

Compaction keeps only the latest value per key, so repeated writes for the same window simply converge to the corrected value with bounded storage, A retraction pipeline writes the same key multiple times as new panes fire. Without compaction, the topic grows forever. With compaction (cleanup.policy=compact) the broker garbage collects older values in the background and downstream consumers see only the latest correct result per key.

When should you drop late records silently instead of routing them to a side output?

Only when the business truly does not care about the data and you have explicitly reviewed and documented that decision, Silent drops are almost always a bug waiting to happen. Wire a side output even if you throw it into a dead letter topic, so auditors can answer what happened to late data. Explicit drops are fine for low value streams like coarse dashboards, but the decision must be reviewed and documented, not left as a default.

Late Data Handling

Name: System Design Masterclass
Availability: InStock

in Stream Batch Processing

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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