The Developer's Guide to Data Modelling with Document Databases
summary
A three‑phase method: gather real queries, sizes and peak traffic; map entities and relationships and decide link vs embed per access patterns; then apply targeted patterns only when needed. Example uses MongoDB for a casino analytics app that records card‑insert writes, play‑times, jackpot growth and top slots; embedding avoids joins for frequent reads, the attribute pattern collapses many release fields into a small indexed array to cut index count, and the document versioning pattern preserves history but doubles writes. Watch for one-to-zillions cases where linking is mandatory.
AI-written summary · about
chapters (6)
- 0:00Start
- 1:53The Developer's Guide to Data Modeling with Document Databases
- 3:01data model for document databases
- 4:33Building a Data Model in Three Phases
- 16:37A data structuring model that solves common document data model use cases
- 42:43Simplicity Performance
related talks
26mNoSQL Data Modeling for Front-end Developers
46mFrom Tables to Documents -- Changing Your Database Mindset
45mNoSQL Data Modeling for Front-end Developers
55mData Modeling in a Non-Relational World
30mHelp! I Need To UnSQLize My Application
01h 00mThe Modern Database Debate: Documents, Tables, or Documents in Tables?
36mMongoDB Schema Design - best practices
41mFrom Tables to Documents - Changing Your Database Mindset