SQL databases follow a structured schema, use tables (rows & columns), and guarantee strong data consistency. They follow the ACID properties, making them ideal when data integrity and relationships are important.

They are used in banking systems, ERPs, eCommerce, transactions, and any system where correctness is critical.

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Why SQL Databases?

• Fixed schema and defined data types
• Strong data consistency and integrity
• Support for transactions using ACID
• Joins and relationships between tables
• Powerful querying using SQL
• Well-suited for transactional systems (OLTP)

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Popular SQL Databases

PostgreSQL

• Open-source, highly reliable, ACID-compliant
• Supports advanced data types (JSON, GIS, arrays)
• Known for performance & strong consistency 👉 Best for enterprise apps, analytics, and complex data models.

MySQL

• Widely used open-source database
• Fast reads, easy to set up, community support
• Common in web applications 👉 Best for websites, CMS systems, SaaS apps.

Oracle Database

• Enterprise-grade commercial database
• Powerful for large financial & mission-critical systems
• Supports advanced transaction management 👉 Best for banking, telecom, ERPs, enterprise-level applications.

SQL Server (Microsoft)

• Strong integration with Windows & Azure
• Good for BI/reporting and enterprise apps 👉 Best when using Microsoft ecosystem or Windows stack.

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ACID Properties (Core of SQL)

• Atomicity – All operations in a transaction succeed or none do
• Consistency – Data always remains valid and rules are enforced
• Isolation – Concurrent transactions do not affect each other
• Durability – Once committed, data is never lost

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Transactions

Used when multiple operations must succeed together.

Example:

BEGIN;
UPDATE accounts SET balance = balance - 100 WHERE id = 1;
UPDATE accounts SET balance = balance + 100 WHERE id = 2;
COMMIT;

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When to Use SQL Databases?

Scenario	SQL Recommended?
Financial transactions	✔ Yes
Strong data consistency required	✔ Yes
Fixed schema & validations	✔ Yes
High scalability & unstructured data	❌ No → Use NoSQL
Analytics on huge data	⚠ Depends (OLAP DB or Data Warehouse)

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Strengths of SQL Databases

• Strong consistency (ACID)
• Structured & validated data
• Easy to query & analyze
• Mature ecosystem & tooling
• Data integrity via constraints

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Limitations of SQL Databases

• Schema changes are difficult
• Not ideal for huge scale-out systems
• Horizontal scaling is harder than NoSQL
• Performance can suffer with millions of writes per second