Three Data Models | Databases

Overview

The course covers three data models. The relational model has been the industry mainstay for over 48 years; the other two are representatives of the NoSQL movement.

Model	Structure	Query language	Optimised for
Relational	Tables	SQL	High throughput of concurrent updates
Document-oriented	Nested documents (JSON)	Python API, path queries	Read-heavy workloads, semi-structured data
Graph-oriented	Nodes and edges	Cypher	Path-oriented queries, network traversal

All three primarily hold discrete data. The Lent term MLRD course deals with soft/continuous decision making.

Relational model

Data is stored in 2-D tables:

Each row is a record (tuple).
Each column is a field (attribute).
The schema indicates field names, data formats, and which column(s) comprise the key.
Row and column ordering is unimportant.
SQL is the main query language.

Example table:

Name	CRSID	College	Year
Alice	abc12	King’s	2
Bob	def34	Trinity	1
Charlie	ghi56	St John’s	3

The primary key (underlined in schema diagrams) is CRSID.

The relational model has been the industry mainstay since the 1970s due to its strong theoretical foundation (relational algebra, normalisation) and robust ACID transaction support.

Document-oriented model

Also called aggregate-oriented database. Instead of splitting data across normalised tables, related data is stored together as a single document.

Optimised for read-oriented databases with few updates.
Uses semi-structured data (typically JSON).
No rigid schema: different documents can have different fields.
Queries are expressed in the host programming language or via path expressions.

Example document (JSON):

{
  "name": "Alice",
  "crsid": "abc12",
  "college": "King's",
  "papers": [
    {"code": "CS1", "title": "Foundations of CS"},
    {"code": "CS2", "title": "Software Engineering"}
  ]
}

Graph-oriented model

Data is represented as nodes (entities) and edges (relationships). Both nodes and edges can carry properties.

Query languages tend to have path-oriented capabilities.
Extensive support for standard graph techniques: shortest path, breadth-first search, PageRank.
Well-suited to highly connected data (social networks, recommendation engines, transport networks).

Example (Cypher):

CREATE (alice:Student {name: 'Alice', crsid: 'abc12'})
CREATE (bob:Student {name: 'Bob', crsid: 'def34'})
CREATE (alice)-[:FRIEND_OF]->(bob)

Three database systems used in the course

System	Type	Query language	Notes
SQLite	Relational	SQL	Open-source, embedded, single-file database
TinyDB	Document-oriented	Python API	Open-source, in-process, no server required
Neo4j	Graph-oriented	Cypher	Java-based, client-server architecture

Examination

For examination purposes, students must learn everything in the slide deck and know all glossary terms. A relational database consists of 2-D tables; the schema indicates field names, data formats, and which column(s) comprise the key (conventionally underlined). Row and column ordering is unimportant.

Summary

The three data models are relational (tables + SQL), document-oriented (JSON + path queries), and graph-oriented (nodes/edges + Cypher).
The relational model dominates industry; the NoSQL models address specific use cases (semi-structured data, highly connected data).
The course uses SQLite, TinyDB, and Neo4j as concrete exemplars.