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Ddia data modeling

Skill ebarti/skills/ddia-data-modeling

Data modeling, storage engines, and encoding choices distilled from "Designing Data-Intensive Applications" (Kleppmann, 2nd ed) chapters 3-5. Covers relational, document, graph, and event-sourced models; LSM, B-tree, in-memory, and columnar storage; specialized indexes; encoding formats; and modes of dataflow. Use this skill when: - Choosing a database (relational/document/graph) - Picking a storage engine (LSM/B-tree/columnar) - Designing an analytics warehouse - Selecting a wire format (JSON/Avro/Protobuf) - Deciding REST vs RPC vs messaging - Modeling event-sourced systems - Building search/vector/geo indexesFrom its SKILL.md

Install
npx -y skills add ebarti/skills --skill ddia-data-modeling

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SKILL.md

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DDIA Data Modeling

A reference skill for designing data models, picking storage engines, and choosing encoding/dataflow strategies. Knowledge is split across 8 categories with progressive disclosure: load only the references relevant to the decision in front of you.

Quick Start

  1. Open guidelines.md — find your task, symptom, or topic.
  2. Load only the listed reference files (typically 1-3).
  3. Apply the rules and patterns; consult examples.md files for concrete code/schema.
  4. For multi-step decisions, follow the relevant workflow under workflows/.

Contents

References

CategoryFilesPurpose
relational-document-modelsknowledge, rules, examplesRelational vs document trade-offs, joins, schema-on-write vs schema-on-read
graph-modelsknowledge, rules, examplesProperty graphs, triple stores, Cypher/SPARQL/Datalog, traversal patterns
event-sourcing-cqrsknowledge, rules, examplesAppend-only event logs, CQRS read views, DataFrames for analytics
oltp-storageknowledge, rules, examplesLSM-trees, B-trees, in-memory engines, write/read trade-offs
olap-storageknowledge, rules, examplesColumnar layout, vectorized execution, separation of storage and compute
specialized-indexesknowledge, rules, examplesMultidimensional, geo (R-tree), full-text, vector indexes
encoding-formatsknowledge, rules, examplesJSON, Protobuf, Avro, Thrift; schema evolution and compatibility
dataflow-modesknowledge, rules, examplesDatabases, REST/RPC services, messaging, durable execution

Workflows

  • workflows/choosing-database-type.md — relational vs document vs graph vs columnar
  • workflows/choosing-storage-engine.md — LSM vs B-tree vs in-memory vs columnar
  • workflows/choosing-encoding-format.md — JSON vs Avro vs Protobuf vs Thrift
  • workflows/designing-event-sourced-system.md — event log + CQRS read views

Workflows

TaskWorkflow
Choose between relational/document/graph/columnar databaseworkflows/choosing-database-type.md
Choose storage engine (LSM/B-tree/in-memory/columnar)workflows/choosing-storage-engine.md
Pick wire encoding format & schema evolution strategyworkflows/choosing-encoding-format.md
Design an event-sourced system with CQRSworkflows/designing-event-sourced-system.md

Guidelines

See guidelines.md for:

  • Task-based file selection (database choice, storage choice, analytics, encoding, services)
  • Symptom/question lookup (joins, connected data, audit, write throughput, etc.)
  • Topic-by-topic file index (8 categories, 24 files)
  • Decision trees for the most common selection paths

What ships with it: 29 files

178.6 KB alongside SKILL.md

references/

workflows/

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.