A database is an organized collection of inter-related data, generally stored and accessed electronically from a computer system.
Thank you for reading this post, don't forget to subscribe!- They are structured to facilitate the storage, retrieval, modification, and deletion of data in conjunction with various data-processing operations.
- A database can hold various forms of data, including text, numbers, images, and more, and is designed to manage large amounts of information efficiently.
- It serves as the backbone for many modern applications, such as websites, business systems, and research tools.
Examples:
- Relational databases like MySQL, PostgreSQL, and Oracle.
- NoSQL databases like MongoDB, Cassandra, and Redis.
Database Design:
Database design is the process of creating a structured and efficient schema for storing and managing data within a system.
- A well-designed database ensures data consistency, integrity, scalability, and optimal performance.
The database design process is divided into several key stages:
1.) Requirement Analysis:
Requirement analysis is the initial phase where the data needs of the system are gathered and understood.
- This step involves discussions with stakeholders, including business users, developers, and database administrators, to identify what data needs to be stored, processed, and retrieved.
- Key aspects considered include data volume, security requirements, access control, and expected queries.
- A requirement specification document is created to serve as a reference for the next stages of design.
2.) Conceptual Design:
Conceptual design involves creating a high-level data model that represents the entities, attributes, and relationships in the system.
- The Entity-Relationship (E-R) diagram is a widely used tool in this phase.
- Entities represent real-world objects or concepts (e.g., Customer, Order, Product).
- Attributes are properties of entities (e.g., Customer has Customer_ID, Name, Email).
- Relationships define associations between entities (e.g., Customer places Orders).
- Cardinality constraints (e.g., one-to-many, many-to-many) are specified to define the nature of relationships.
- This phase ensures a clear understanding of the database structure before implementation.
3.) Logical Design:
Logical design involves converting the conceptual data model into a structured relational schema using tables, attributes, and keys while applying normalization rules.
- The E-R model is transformed into a relational schema consisting of tables with primary keys, foreign keys, and attributes.
- Normalization is applied to eliminate data redundancy and improve data integrity by organizing tables into forms such as:
- First Normal Form (1NF): Eliminates duplicate columns and ensures atomic values.
- Second Normal Form (2NF): Ensures all non-key attributes depend on the whole primary key.
- Third Normal Form (3NF): Eliminates transitive dependencies.
- Logical design does not consider physical storage but focuses on structuring data logically for efficient management.
4.) Physical Design:
Physical design involves implementing the logical schema in a specific database management system (DBMS) while considering performance, storage, and indexing.
- This step involves defining the tables, data types, constraints (primary key, foreign key, unique, check, etc.), and indexes to optimize query performance.
- Indexes are created to speed up searches and retrieval operations.
- Partitioning and clustering strategies may be applied for large datasets to enhance efficiency.
- Storage considerations, such as disk space, caching mechanisms, and backup strategies, are addressed.
- Security measures like user access controls, encryption, and auditing are implemented.