---
title: Glossary For Database
date: 2024-02-27
tags: [DataBase, Glossary]
excerpt: "A database glossary covering structured, unstructured, and semi-structured data, plus relational and NoSQL data models."
updated: 2026-07-08 08:16:04
lang: en
i18n:
  cn: /DB-Glossary
  translation: 2
---

## Structured/Unstructured Data

According to whether data has a predefined data model, we can divide it into three categories.
### 1.1 Structured Data

**Definition**:
Structured data is highly organized and neatly formatted data that follows a strict predefined schema. It is the cornerstone of traditional relational databases (RDBMS).

*   **Core features**:
    *   **Two-dimensional table structure**: data can fit perfectly into rows and columns.
    *   **Strong types**: each field has a clear data type, such as Integer, Varchar, or Date.
    *   **Easy to compute**: computers can retrieve, aggregate, and analyze it very efficiently through SQL.
*   **Typical sources**:
    *   Core enterprise systems: ERP, CRM, financial systems.
    *   Transaction records: bank transfers, e-commerce orders.
*   **Technical extension**:
    > In big data, structured data usually uses a Schema-on-Write[^1] strategy. The structure must be defined before data is written. This guarantees data consistency, but sacrifices write flexibility.

### 1.2 Unstructured Data

**Definition**:
Unstructured data has no predefined model or organization and is usually treated as binary objects (BLOBs). This kind of data accounts for **80% - 90%** of global data volume. It is both the main challenge and a major source of value in big data analysis.

*   **Common forms**:
    *   **Multimedia**: images, audio, video.
    *   **Documents**: PDF, Word, design drawings.
    *   **Machine data**: although some logs have a format, they are often treated as unstructured streams before cleaning.
*   **Processing difficulty**:
    It cannot be queried directly with SQL. Usually, **OCR** (optical character recognition), **NLP** (natural language processing), or **CV** (computer vision) is needed to extract features and convert them into structured labels for storage.

### 1.3 Semi-Structured Data

**Definition**:
Semi-structured data sits between the two. It has some self-describing structure, but does not conform to the strict table form of relational databases. It organizes data through tags or keys.

*   **Core technical carriers**:
    *   **XML** (Extensible Markup Language): an early Internet data exchange standard.
    *   **JSON** (JavaScript Object Notation): the mainstream format for modern Web APIs and NoSQL databases such as MongoDB.
    *   **HTML**: webpage structural markup.
*   **Example**:
    An HTML document itself is an unstructured text stream, but through tags such as `{html} <h1>` and `{html} <div>`, machines can identify headings and paragraphs, giving it semantic structure.


## Data Models

Relational data models and non-relational data models.

### Relational Data Model

## 2. Relational Data Model (RDBMS): Classic Order

Relational Database Management Systems are based on mathematical set theory and relational algebra. Since the 1970s, they have been the standard for enterprise applications.

### 2.1 Core Concepts

In an RDBMS, data is organized into **Tables**.

| Term        | Alias                         | Description                                           |
| :---------- | :---------------------------- | :---------------------------------------------------- |
| **Table**   | Relation                      | A two-dimensional grid made of rows and columns, corresponding to an entity set, such as a user table. |
| **Record**  | Row, Tuple                    | Represents a concrete entity instance, such as the complete information for user Alice. |
| **Field**   | Column, Attribute             | Represents one kind of feature in the data, such as the age of all users. |

**Data example: User Table**

| User ID (PK) | Username | Email         | Status   |
| :----------- | :------- | :------------ | :------- |
| 1001         | Alice    | alice@tech.io | Active   |
| 1002         | Bob      | bob@data.net  | Inactive |

## Non-Relational Data Model (NoSQL)

With the explosion of Web 2.0, traditional RDBMS encountered bottlenecks in high concurrency, massive storage, and scalability. **NoSQL (Not Only SQL)** emerged in response.
### 3.1 Four Main Types

NoSQL is not a specific technology, but a technology family. It mainly includes the following four categories:

1.  **Key-Value Stores**
    *   **Principle**: similar to Map/Hash in programming languages, using a key to quickly read and write a value.
    *   **Examples**: Redis, Memcached.
    *   **Scenarios**: caching, session management, real-time leaderboards.

2.  **Document Stores**
    *   **Principle**: store documents in JSON/BSON format with flexible schema (schema-free).
    *   **Examples**: MongoDB, Couchbase.
    *   **Scenarios**: content management systems (CMS), e-commerce product details with highly variable attributes, fast-iterating businesses.

3.  **Column-family Stores**
    *   **Principle**: store data by columns, good at writes and large-scale aggregation queries.
    *   **Examples**: Apache Cassandra, HBase.
    *   **Scenarios**: IoT time-series data, massive logs, user behavior traces.

4.  **Graph Databases**
    *   **Principle**: store nodes and edges, focusing on complex relationship networks.
    *   **Examples**: Neo4j, JanusGraph.
    *   **Scenarios**: social network recommendations, anti-fraud risk-control relationship networks, knowledge graphs.

[^1]: [What is Schema-on-write? Examples in Analytics | PlainSignal](https://plainsignal.com/glossary/schema-on-write)