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Ddia architecture

Skill ebarti/skills/ddia-architecture

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). Use this skill when: - Choosing between OLTP (operational) and analytical (OLAP) storage - Deciding cloud vs self-hosted / managed service vs build-your-own - Evaluating distributed (microservices, multi-node) vs single-node architectures - Defining performance SLOs and reasoning about response-time percentiles - Designing for reliability, defining a fault model, and choosing fault tolerance strategies - Planning capacity / scalability and choosing scale-up vs scale-out - Architecting for long-term maintainability (operability, simplicity, evolvability) - Reviewing an architecture proposal against DDIA fundamentalsFrom its SKILL.md

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npx -y skills add ebarti/skills --skill ddia-architecture

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SKILL.md

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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

  1. Read guidelines.md first — it routes you from your task or symptom to the right files.
  2. Load only the files relevant to your task (each category has knowledge.md, rules.md, examples.md).
  3. Apply the concepts to the architecture decision at hand.

Contents

References

CategoryFilesPurpose
references/operational-vs-analytical/knowledge, rules, examplesOLTP vs OLAP; warehouses, lakes, lakehouses; system of record vs derived data
references/cloud-vs-self-hosted/knowledge, rules, examplesBuild vs buy; IaaS/PaaS/SaaS; cloud-native vs on-prem trade-offs
references/distributed-systems-intro/knowledge, rules, examplesWhen to distribute; partial failure; microservices vs monolith vs serverless
references/performance/knowledge, rules, examplesResponse time vs throughput; percentiles; tail latency; SLOs/SLAs
references/reliability/knowledge, rules, examplesFault vs failure; fault tolerance; hardware/software/human faults
references/scalability/knowledge, rules, examplesLoad parameters; scale-up vs scale-out; shared-nothing
references/maintainability/knowledge, rules, examplesOperability, simplicity, evolvability; abstractions; legacy systems

Workflows

TaskWorkflow
Decide cloud vs self-hosted deploymentworkflows/choosing-cloud-vs-self-hosted.md
Define performance SLOs (latency, throughput, error budget)workflows/defining-performance-slos.md
Assess reliability requirements & identify SPOFsworkflows/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

What ships with it: 25 files

161.0 KB alongside SKILL.md

references/

workflows/

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