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system-design
12 registry resources · 10 tutorials, 2 references · roadmap: System Design
What this topic covers
DE//AGENT currently lists 12 resources under system-design: 10 tutorials, 2 references. Everything below is derived from the live registry — counts, capabilities, verification state and security posture come from the curated records, never from marketing copy.
What you can learn here
Documented learning objectives across these resources:
- Frame any data system as a tradeoff among reliability, scalability, and maintainability — via Hold the system design mental model — data systems as tradeoffs
- Name the six mechanisms DDIA teaches and map each one to a system you operate — via Hold the system design mental model — data systems as tradeoffs
- Argue a design decision in tradeoff language instead of tool-brand language — via Hold the system design mental model — data systems as tradeoffs
- Estimate QPS, storage per day, and retention footprint from a workload description — via Run the back-of-envelope drill — numbers before architecture
- Apply peak-versus-average factors instead of quoting averages alone — via Run the back-of-envelope drill — numbers before architecture
- Ground an estimate in a real deployed reference architecture — via Run the back-of-envelope drill — numbers before architecture
- Argue a storage choice from replication, partitioning, and consistency needs — via Choose storage engines on tradeoffs — reads, writes, and consistency
- Explain what each consistency semantic means for a merge-based pipeline — via Choose storage engines on tradeoffs — reads, writes, and consistency
22 more objectives live on the individual resource pages.
Where this fits in the learning roadmap
These resources map to stage: System Design (12). Follow the four-phase roadmap to place this topic inside a progression instead of learning it in isolation.
Compare the registry's options
Every entry, its shape and its trust state — sorted by the registry's composite score. This table is the honest version of a marketing comparison: no sponsored placements, no unverifiable claims.
How scores are computed: source quality score.
Where to start
Highest composite score first:
- Hold the system design mental model — data systems as tradeoffs score 34.15 — beginner
- Run the back-of-envelope drill — numbers before architecture score 34.15 — beginner
- Choose storage engines on tradeoffs — reads, writes, and consistency score 34.15 — intermediate
Scores rank provenance, completeness and trust — read how verification works before adopting anything.
Good fits
Start here if you're new: Hold the system design mental model — data systems as tradeoffs, Run the back-of-envelope drill — numbers before architecture.
Reach for these with production experience: Capstone checklist — a design is done when these all hold, Interview walkthrough — design a data platform end-to-end, Production gotchas — premature scaling and the missing capacity plan.
Production use cases
- Hold the system design mental model — data systems as tradeoffs — Before any diagram, data systems are tradeoffs, not products. This lesson fixes the vocabulary — reliability, scalability, maintainability, and the six canonical mechanisms behind … details
- Run the back-of-envelope drill — numbers before architecture — Estimation is the warm-up act of every design conversation: QPS, storage per day, retention, peak factor, all written down before a single box is drawn. This lesson drills the powe… details
- Choose storage engines on tradeoffs — reads, writes, and consistency — Storage choice is where a design's promises become real: the engine you pick decides what replication buys, what partitioning costs, and which consistency semantics your merges get… details
- Define reliability in numbers — SLIs, SLOs, and error budgets — A design without reliability numbers is a wish. This lesson borrows the SRE toolkit — SLIs, SLOs, error budgets, incident management, postmortems, and capacity planning — and point… details
Further study
Adjacent topics: tutorial (10 shared), architecture (2 shared), reliability (2 shared), fundamentals (2 shared), scalability (2 shared).
Study path: the roadmap places this topic in a progression · the scoring model explains the numbers · the trust rule catalog shows every security check these entries passed through.
Full resource list
| Resource | Type | Score |
|---|---|---|
| Hold the system design mental model — data systems as tradeoffs | Tutorial | 34.15 |
| Run the back-of-envelope drill — numbers before architecture | Tutorial | 34.15 |
| Choose storage engines on tradeoffs — reads, writes, and consistency | Tutorial | 34.15 |
| Define reliability in numbers — SLIs, SLOs, and error budgets | Tutorial | 34.15 |
| Design resilient pipelines — retry, circuit breaker, bulkhead | Tutorial | 34.15 |
| Read reference architectures like an engineer — AWS as precedent | Tutorial | 34.15 |
| Speak the scaling vocabulary — load balancing, caching, sharding | Tutorial | 34.15 |
| Capstone checklist — a design is done when these all hold | Tutorial | 34.15 |
| Interview walkthrough — design a data platform end-to-end | Tutorial | 34.15 |
| Production gotchas — premature scaling and the missing capacity plan | Tutorial | 34.15 |
| The System Design Primer | Reference | 27.7 |
| Designing Data-Intensive Applications (O'Reilly) | Reference | 26.7 |