Day 41 of 45 · Pro lesson

DE System Design at Scale

The curriculum for this day stays visible. The lesson, code, and workspace unlock with Pro.

Teaches: 500M events/day data platform blueprint, Throughput, storage & compute capacity sizing, Lambda vs Kappa vs Delta lakehouse architectures, Decoupling ingestion, processing, and analytical serving, Multi-tenant isolation & noisy neighbor prevention, Scaling from 500M to 5B events/day: 10x bottlenecks

Build: System Design interactive sizing calculator: daily event volume → throughput/sec → storage decay → worker cluster sizing → cost estimation model.

•Complete 15 drills on throughput calculations, storage IOPS sizing, network egress estimation, and multi-region failover.
•Run 6 Python modules simulating peak traffic spikes, partition scaling, and storage cost projections.
•Analyze 5 production design trade-offs: Kafka vs SQS, Parquet vs Delta, Redshift vs Snowflake, and Batch vs Streaming.
•Defend the complete 500M events/day system design interview defense and pass the blank-page architecture challenge.

In system design interviews, always start with requirements, SLA numbers, and back-of-the-envelope calculations before drawing single boxes or choosing technologies.

This section walks through the idea with a short example, then the trade-offs you should mention in an interview.

In practice you start from the raw rows, apply the transform step by step, and check the shape of the result before you move on.

A common mistake is to jump straight to the final query without naming the grain or the join keys that keep the result correct.

Once the core path works, you harden it for nulls, duplicates, and late data so the pipeline stays reliable under load.

The Pro write-up covers the full explanation, worked examples, and the code you can run in the studio.

# Locked example
result = transform(frame)
print(result.head())

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