Types of databases

Relational Databases (SQL Databases)

Definition: Data is stored in structured tables with predefined schemas.

Use Case

Best for structured data and complex queries.

Examples

• PostgreSQL – Open-source, powerful, supports JSON.
• MySQL – Fast, widely used for web applications.
• Microsoft SQL Server – Enterprise-level, good for Windows environments.
• Oracle Database – Large-scale enterprise applications.
• SQLite – Lightweight, file-based, used in mobile apps.

Pros: ACID compliance, strong query capabilities with SQL. Cons: Less flexible for unstructured data, can be slow for massive horizontal scaling.

NoSQL Databases (Non-Relational Databases)

Definition: No fixed schema, optimized for flexibility, scalability, and unstructured data.

Use Case

Ideal for big data, real-time analytics, and distributed systems.

Types of NoSQL Databases

Document Databases

Store data as JSON or BSON documents.

Examples

• MongoDB – Most popular, schema-flexible.
• CouchDB – Replication-friendly JSON storage.
• Firebase Firestore – Google’s cloud NoSQL.

Pros: Great for dynamic schemas (e.g., user profiles, catalogs).
Cons: Not as strong for complex queries as SQL.

Key-Value Stores

Simple key-value pairs (like a dictionary).

Examples

• Redis – In-memory, ultra-fast.
• DynamoDB – AWS-managed, highly scalable.
• Riak – Distributed, fault-tolerant.

Pros: Blazing fast for caching and real-time apps.
Cons: Not great for relationships between data.

Column-Family Databases

Store data in columns instead of rows for better read/write performance.

Examples

• Apache Cassandra – Distributed, fault-tolerant.
• HBase – Built on Hadoop for big data.

Pros: Great for high-scale analytics (Big Data, IoT, logs).
Cons: Not ideal for small apps with frequent updates.

Graph Databases

Designed for highly connected data.

Examples

• Neo4j – Most popular, used for social networks.
• ArangoDB – Multi-model (graph + document + key-value).
• Amazon Neptune – Cloud-based graph DB.

Pros: Great for relationships (social networks, fraud detection).
Cons: More niche use case compared to SQL/NoSQL.

Time-Series Databases

Optimized for time-stamped data (IoT, financial markets, logs).

Examples

• InfluxDB – Fast for real-time analytics.
• TimescaleDB – SQL-based time-series database.
• Prometheus – Monitoring and alerting system.

Pros: Best for metrics, monitoring, IoT data.
Cons: Not ideal for general-purpose applications.

NewSQL Databases

Hybrid between SQL & NoSQL (scalable SQL databases).

Examples

• Google Spanner – Distributed SQL with strong consistency.
• CockroachDB – Horizontally scalable SQL.
• TiDB – Open-source MySQL-compatible NewSQL.

Pros: SQL benefits but designed for scalability.
Cons: Still emerging and not as widely adopted.

Object-Oriented Databases

Stores data as objects instead of tables (used in OOP).

Examples

• db4o
• ObjectDB

Pros: Good for applications using object-oriented languages (Java, C#).
Cons: Less common, niche use case.

Multi-Model Databases

Can handle multiple database types (SQL, NoSQL, Graph).

Examples

• ArangoDB – Supports document, key-value, graph.
• OrientDB – Combines document and graph databases.

Pros: Flexibility to use different models in one system.
Cons: More complex to manage.

Which One Should You Choose?

Use Case	Best Database Type	Examples
General web apps	Relational (SQL)	PostgreSQL, MySQL
Flexible data, JSON storage	Document (NoSQL)	MongoDB, Firestore
Fast caching, real-time data	Key-Value	Redis, DynamoDB
Big Data, analytics	Column-Family	Cassandra, HBase
Relationships (social networks, fraud detection)	Graph	Neo4j, ArangoDB
Time-stamped data (IoT, logs)	Time-Series	InfluxDB, TimescaleDB
Cloud-native, distributed SQL	NewSQL	Google Spanner, CockroachDB