Relational Databases Vs Non-Relational Databases | Overview.

An Overview of  Relational Databases Vs  Non-Relational Databases.

Focus:

• Tailored for SRE, DevOps, Cloud, and DevSecOps Engineers

Breakdown:

• Intro,
• Relational Databases (RDBMS),
• Examples of Relational Databases (RDBMS),
• Non-Relational Databases (NoSQL),
• Examples of Non-Relational Databases (NoSQL),
• Key Differences,
• Comparison table,
• Common Use Cases.
• Insights.

Intro:

• Relational databases store data in structured, predefined tables and use SQL (language) for querying.
•  while non-relational databases (NoSQL) store varied data types with flexible schemas for scalability and handling large amounts of unstructured data.
• The main difference between relational databases and non-relational databases Primarily lies in their structure, data storage, and use cases.

Relational Databases (RDBMS)

• Structure: Uses structured tables with rows and columns, following a strict schema.
• Schema: Predefined schema that enforces data consistency.
• Data Storage: Uses relations (tables) to store data and maintains relationships using primary keys and foreign keys.
• Query Language: Uses SQL (Structured Query Language) for querying and managing data.
• ACID Compliance: Ensures Atomicity, Consistency, Isolation, and Durability for transactions, making them reliable.
• Scalability: Typically vertically scalable (adding more power to a single server).
• Best Use Cases:
• Banking and financial systems
• Enterprise applications (ERP, CRM)
• E-commerce transactions

Examples of Relational Databases (RDBMS) 

•  MySQL, PostgreSQL, Microsoft SQL Server, Oracle Database

Non-Relational Databases (NoSQL)

• Structure: Stores data in various formats such as key-value pairs, documents, column-families, or graphs.
• Schema: Flexible or schema-less, allowing for dynamic data storage.
• Data Storage: Does not use fixed table structures; instead, it organizes data in formats optimized for specific use cases.
• Query Language: Uses varied query languages (e.g., JSON-based queries for document stores).
• Eventual Consistency: Prioritizes speed and scalability over strict ACID compliance.
• Scalability: Typically horizontally scalable (distributes data across multiple servers).
• Best Use Cases:
• Big data and real-time applications
• Content management and recommendation engines
• Internet of Things (IoT)
• Social media platforms.

Examples of Non-Relational Databases (NoSQL)

•  MongoDB (Document-based), Cassandra (Column-based), Redis (Key-value store), Neo4j (Graph-based)

Key Differences

• Query Language: 
• Relational databases typically use Structured Query Language (SQL) for defining and manipulating data. 
• Non-relational databases use different query methods that are specific to their data model (e.g., document-specific query APIs).
• Consistency:
•  Relational databases enforce strong data integrity and consistency through features like primary and foreign keys and ACID properties, which ensure data is always correct. 
• Non-relational databases may prioritize speed and availability over immediate consistency (often using eventual consistency).
• Scalability:
•  Relational databases typically scale vertically (upgrading a single server with more power), which has limits. 
• Non-relational databases are designed to scale horizontally (distributing data across multiple servers or clusters), making them ideal for handling massive data volumes and high traffic.

Common Use Cases

Relational Databases	Non-Relational Databases
* Systems requiring complex transactions (e.g., e-commerce platforms, financial systems).	* Applications with rapidly changing data requirements (e.g., web and mobile applications).
* Applications where data integrity and consistency are paramount (e.g., medical records).	* Big data analytics and content management systems (e.g., social media feeds).
* Applications with well-defined, stable data relationships.	* Real-time applications needing high speed and scalability (e.g., online gaming, IoT data).

NB:

• Many modern applications use a combination of both types of databases to leverage their respective strengths.

Comparison table

Feature	Relational Databases (RDBMS)	Non-Relational Databases (NoSQL)
Structure	Tables (rows & columns)	Key-value, Document, Column, Graph
Schema	Predefined, strict	Dynamic, flexible
Query Language	SQL	Varies (JSON, Key-Value, etc.)
Scalability	Vertical (scale-up)	Horizontal (scale-out)
ACID Compliance	Strong	Weaker (often BASE: Basically Available, Soft state, Eventual consistency)
Best for	Structured data, Transactions	Big data, Real-time applications

twtech-insights

Examples of Relational Databases (RDBMS) and Non-Relational Databases (NoSQL):

Relational Databases (RDBMS) Examples

1. MySQL – Popular open-source relational database for web applications.
2. PostgreSQL – Advanced RDBMS with strong support for complex queries and ACID compliance.
3. Microsoft SQL Server – Enterprise-grade RDBMS by Microsoft, commonly used in corporate environments.
4. Oracle Database – High-performance database used in large-scale applications.
5. IBM Db2 – Enterprise database with AI-driven optimizations.

Non-Relational Databases (NoSQL) Examples

1. MongoDB (Document Store) – Stores JSON-like documents, used in flexible and scalable applications.
2. Cassandra (Column Store) – Distributed NoSQL database optimized for high availability and scalability.
3. Redis (Key-Value Store) – In-memory data store used for caching and real-time applications.
4. Neo4j (Graph Database) – Optimized for handling relationships in social networks, fraud detection, etc.
5. DynamoDB (AWS Managed NoSQL) – Fully managed NoSQL database service for high-performance applications.