Data Engineering System Design

Sizing, throughput, tradeoffs and communicating an architecture under interview pressure. This path sequences the category's 15 resources by roadmap stage and composite score — work top-down inside each step.

System Design

1. Hold the system design mental model — data systems as tradeoffs 34.15 — Hold the system design mental model — data systems as tradeoffs
2. Run the back-of-envelope drill — numbers before architecture 34.15 — Run the back-of-envelope drill — numbers before architecture
3. Choose storage engines on tradeoffs — reads, writes, and consistency 34.15 — Choose storage engines on tradeoffs — reads, writes, and consistency
4. Define reliability in numbers — SLIs, SLOs, and error budgets 34.15 — Define reliability in numbers — SLIs, SLOs, and error budgets
5. Design resilient pipelines — retry, circuit breaker, bulkhead 34.15 — Design resilient pipelines — retry, circuit breaker, bulkhead
6. Read reference architectures like an engineer — AWS as precedent 34.15 — Read reference architectures like an engineer — AWS as precedent

9 more on the category page.

• Frame any data system as a tradeoff among reliability, scalability, and maintainability
• Name the six mechanisms DDIA teaches and map each one to a system you operate
• Argue a design decision in tradeoff language instead of tool-brand language
• Estimate QPS, storage per day, and retention footprint from a workload description
• Apply peak-versus-average factors instead of quoting averages alone
• Ground an estimate in a real deployed reference architecture
• Argue a storage choice from replication, partitioning, and consistency needs
• Explain what each consistency semantic means for a merge-based pipeline

Verification on these records: 15 community. Nothing here is independently audited — the trust scan and your own review are the controls.

Phase 4 of the roadmap. Stage context: System Design · side-by-side comparison · all learning topics