Indexing is a powerful tool that significantly improves search performance in large databases. By creating indexes, you can quickly locate data without having to search every row in a table. Let’s explore how indexing works, with a simple example, and understand when to use it, when to avoid it, and best practices. First and foremost,…
In the world of databases, two primary types dominate: relational and non-relational. Understanding their differences is crucial for choosing the right database for your needs. Let’s explore these differences with simple examples and understand when to use each type. First and foremost, relational databases store data in structured tables with rows and columns. Each table…
In relational databases, ACID properties ensure reliable transactions. ACID stands for Atomicity, Consistency, Isolation, and Durability. Each property plays a crucial role in maintaining data integrity and reliability. Let’s delve into each property with simple examples and understand when to use them. First and foremost, Atomicity ensures that a transaction is all or nothing. Either…
Denormalization is an essential database optimization technique that modifies the schema to enhance read performance. Essentially, it involves combining tables or introducing redundancy. Therefore, let’s dive into why and when to use denormalization, accompanied by a simple example for clarity. First and foremost, denormalization aims to speed up read-heavy operations. For instance, consider a retail…