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Run npx skillmds@latest add ebarti/ddia-architecture in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Ddia Architecture free to use?

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Who published Ddia Architecture?

ebarti (@ebarti) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ddia Architecture by ebarti — Agent Skill

Name: Ddia Architecture
Author: ebarti

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 fundamentals

ebarti f818ef9 26 files · 164.4 KB Updated Sep 17, 2026

File contents

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.md first — 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.

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

---name: ddia-architecturedescription: 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 fundamentals---# DDIA ArchitectureFoundational 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 Start1. 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| 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` |## GuidelinesSee `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

Frequently asked questions