# ddia-systems

Guides database choice, replication setup and consistency trade-offs when queries slow down at scale or replicas drift apart.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 5400 installs · ★ 2508
- Author: wondelai
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/wondelai/skills/tree/HEAD/ddia-systems/SKILL.md
- Updated: 2026-09-10
- HTML: https://affpapa.org/en/nevibecode/skills/ddia-systems

## What it does

The skill lays out seven domains for reasoning about data-intensive systems: data models and query languages, storage engines, replication, partitioning, transactions, derived data and fault handling. Each comes with context-pattern-example tables. User profiles with nested data point to a document model, social graph traversal to a graph model, a financial ledger with joins to a relational one. Deeper material on data models and storage engines sits in `references/`.

There is a scoring pass too. Seven diagnostic rows, roughly 1.4 points each when a trade-off is deliberate and documented. Unknown default isolation level or untested failover keeps you at 5-6. The skill names the failed rows and what to fix for 10/10.

It triggers on phrasing like SQL or NoSQL, replication lag, hot partitions, eventual consistency. General system design lives in the separate `system-design` skill, resilience in `release-it`; this one stays on the data layer.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add wondelai/skills --skill ddia-systems -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/ddia-systems/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/ddia-systems/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add wondelai/skills --skill ddia-systems -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/ddia-systems/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add wondelai/skills --skill ddia-systems -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/ddia-systems/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install wondelai/skills ddia-systems
```

2. **Manually: put the files in place**
- File: `.github/skills/ddia-systems/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add wondelai/skills --skill ddia-systems -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/wondelai/skills --path ddia-systems
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/ddia-systems/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add wondelai/skills --skill ddia-systems -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/wondelai/skills/tree/HEAD/ddia-systems/SKILL.md

## When to use it
- pick between relational, document and graph models for a given access pattern
- explain why replicas serve stale reads after a write
- spread hot keys across partitions before they take down a cluster
- find out which transaction isolation level is actually in effect
- design a batch or stream processing pipeline

## Example prompts
- Help me choose a database: user profiles with nested addresses and settings, read-heavy, few writes
- Replicas lag and users don't see their own writes — what should I configure?
- Score our architecture with ddia-systems and tell me which diagnostic rows fail

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 8042a540aae6ceb7.

BEGIN THIRD-PARTY FILE 8042a540aae6ceb7
```text
---
name: ddia-systems
description: 'Design data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "which database should I use", "SQL or NoSQL", "replication lag", "partitioning strategy", "consistency vs availability", "stream processing", "ACID transactions", "eventual consistency", "my queries are slow at scale", or "data is inconsistent across replicas". Also trigger when choosing a datastore, designing data pipelines, or debugging distributed-system consistency issues. Covers data models, batch/stream processing, and distributed consensus. For system design, see system-design. For resilience, see release-it.'
license: MIT
metadata:
  author: wondelai
  version: "1.4.0"
---

# Designing Data-Intensive Applications Framework

A principled approach to building reliable, scalable, and maintainable data systems. Apply these principles when choosing databases, designing schemas, architecting distributed systems, or reasoning about consistency and fault tolerance.

## Core Principle

**Data outlives code.** Applications are rewritten and frameworks come and go, but data persists for decades -- prioritize the long-term correctness, durability, and evolvability of the data layer. Most applications are data-intensive, not compute-intensive: the hard problems are data volume, complexity, and rate of change, and explicit consistency/availability/latency trade-offs separate robust systems from fragile ones.

## Scoring

**Goal: 10/10.** Score a data architecture by the seven Quick Diagnostic rows below: award ~1.4 points per row answered "yes" with evidence (deliberate, documented trade-off), 0 where the answer is "no" or unknown.

- **9-10:** every domain choice -- data model, storage engine, replication, partitioning, isolation, derived-data, fault handling -- is deliberate, documented, and matched to actual read/write/consistency requirements; failover tested.
- **5-6:** core choices made but two or three diagnostic rows fail -- e.g. default isolation level unknown, hot-key risk unhandled, or failover untested.
- **<=3:** choices driven by familiarity, not requirements; ignored failure modes (replication lag, write skew, hot partitions) and accidental complexity dominate.

Report the current score, which diagnostic rows failed, and the improvements needed to reach 10/10.

## The DDIA Framework

Seven domains for reasoning about data-intensive systems:

### 1. Data Models and Query Languages

**Core concept:** The data model shapes how you think about the problem. Relational, document, and graph models each impose different constraints and enable different query patterns.
```
END THIRD-PARTY FILE 8042a540aae6ceb7

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