ddia-architecture
Skill1 starsChanged 6 months agoMITView on GitHub
Foundational architectural concepts for data-intensive applications, distilled from "Designing Data-Intensive Applications" (Kleppmann, 2nd ed) chapters 1-2. Covers the operational vs analytical split, cloud vs self-hosted trade-offs, distributed systems trade-offs, and the core nonfunctional requirements (performance, reliability, scalability, maintainability).
In this repository21 other files it gives its agents.
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The file
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Distinctive · estimate
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1
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- Distinctive
- Very specific
- Very specific: line 23
Read guidelines.md first — it routes you from your task or symptom to the right files.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
DDIA Architecture
Foundational concepts every data-intensive system architect needs: how operational and analytical systems differ, when to use cloud vs self-hosted, when to distribute, and how to reason about performance, reliability, scalability, and maintainability. Built from chapters 1-2 of "Designing Data-Intensive Applications" (Kleppmann, 2nd ed).
Quick Start
- Read
guidelines.mdfirst — it routes you from your task or symptom to the right files. - Load only the files relevant to your task (each category has
knowledge.md,rules.md,examples.md). - Apply the concepts to the architecture decision at hand.
Contents
References
| Category | Files | Purpose |
references/operational-vs-analytical/ | knowledge, rules, examples | OLTP vs OLAP; warehouses, lakes, lakehouses; system of record vs derived data |
references/cloud-vs-self-hosted/ | knowledge, rules, examples | Build vs buy; IaaS/PaaS/SaaS; cloud-native vs on-prem trade-offs |
references/distributed-systems-intro/ | knowledge, rules, examples | When to distribute; partial failure; microservices vs monolith vs serverless |
references/performance/ | knowledge, rules, examples | Response time vs throughput; percentiles; tail latency; SLOs/SLAs |
references/reliability/ | knowledge, rules, examples | Fault vs failure; fault tolerance; hardware/software/human faults |
references/scalability/ | knowledge, rules, examples | Load parameters; scale-up vs scale-out; shared-nothing |
references/maintainability/ | knowledge, rules, examples | Operability, simplicity, evolvability; abstractions; legacy systems |
Workflows
| Task | Workflow |
| Decide cloud vs self-hosted deployment | workflows/choosing-cloud-vs-self-hosted.md |
| Define performance SLOs (latency, throughput, error budget) | workflows/defining-performance-slos.md |
| Assess reliability requirements & identify SPOFs | workflows/assessing-reliability-requirements.md |
Guidelines
See guidelines.md for:
- Task-based file selection (architecture decisions, capacity planning, operational design)
- Symptom/question lookup ("latency is unpredictable", "system breaks on deploy", etc.)
- Topic index with paths to every reference file
- Decision tree for common architecture questions
- Complete file index
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