Database

A database is an organized collection of data that can be stored and accessed electronically. They are designed to store large amounts of information by structuring data that optimized information retrieval and modification. The structuring can include using tables, columns, rows, objects and indexes and other mechanisms that facilitate effective data management.

The structure of a database can be file-based, relational, record-based, document-based, and key-value based among other structures. The relational databases with tables, columns and rows and the use of SQL - Structured Query Language is the most common database used for transactional use today. MongoDB is an example of NoSQL Database.

Database Types

graph TD
    DB[Database Systems] --> Rel[Relational]
    DB --> NoSQL[NoSQL]
    DB --> Special[Specialized]
    Rel --> PostgreSQL["[[PostgreSQL]]"]
    Rel --> MySQL["[[MySQL]]"]
    Rel --> SQLite["[[SQLite]]"]
    Rel --> Oracle[Oracle]
    NoSQL --> Doc["Document ([[MongoDB]])"]
    NoSQL --> KV["Key-Value ([[Redis]])"]
    NoSQL --> WC["Wide-Column ([[Cassandra]])"]
    NoSQL --> Graph["Graph ([[Neo4j]])"]
    Special --> TS["Time-Series ([[InfluxDB]])"]
    Special --> Search["Search (Elasticsearch)"]
    Special --> InMem["In-Memory (Memcached)"]

See NoSQL Databases for when to choose non-relational and History of Relational Databases for how we got here.

Structured vs Unstructured Data

Aspect	Structured	Unstructured
Format	Tables, rows, columns	Text, images, video, audio, documents
Schema	Predefined, rigid	No predefined schema
Querying	SQL	Requires specialized tools (NLP, ML)
Storage	Relational databases (PostgreSQL, MySQL, Oracle)	NoSQL Databases (MongoDB, Cassandra), data lakes
Examples	Customer records, financial transactions, inventory	Emails, social media posts, sensor data, videos

Semi-structured data (JSON, XML) falls between the two — it has some organizational properties (tags, keys) but no rigid tabular schema. Document databases like MongoDB are designed for this.

Core Concepts

• Managed by a Database Management Systems (DBMS)
• Structured according to a three-level architecture (internal, conceptual, external)
• Designed using ERDs and Normalization
• Queried with SQL using joins, subqueries, and Window Functions
• Protected by ACID properties for transactional integrity
• Scaled via replication, partitioning, and sharding