What is DBMS?

What is a Database?

A Database is an organized collection of data that can be easily accessed, managed, and updated. It is like a digital locker where your data is stored in a structured way.

What is a Database Management System (DBMS)?

A DBMS is a software system that interacts with users, applications, and the database itself to capture and analyze data.

It provides the tools to:

• Define (create tables, schemas, etc.)
• Manipulate (insert, update, delete)
• Retrieve (querying using SQL)
• Control access (authorization, backup)

Why do we need a DBMS?

Types of DBMS

1. Hierarchical DBMS
2. Network DBMS
3. Relational DBMS (RDBMS)
4. Object-Oriented DBMS
5. NoSQL DBMS (Non-relational)

We moved from one to another because of increasing data complexity, need for flexibility, and better performance.

Hierarchical DBMS

• Data is stored in tree-like structure.
• Each record has only one parent and can have multiple children.
• Think like a file system (C:\Folder\Subfolder\Files)

Example:

• An employee record belongs to one department, and each department has multiple employees.

Pros:

• Easy to navigate with parent-child links.
• Fast access if hierarchy is known.

Cons:

• Very rigid.
• Difficult to reorganize structure.
• Doesn’t support complex relationships (e.g. many-to-many).

Example DBMS: IBM’s IMS

Network DBMS

• Extension of hierarchical model.
• Records can have multiple parents and multiple children.
• Uses pointers (like graphs) to define relationships.

Example:

• A student can enroll in multiple courses, and each course can have many students.

Pros:

• More flexible than hierarchical.
• Handles complex relationships.

Cons:

• Still needs predefined structure.
• Data access via pointers is complex to manage.

Example DBMS: Integrated Data Store (IDS)

Relational DBMS (RDBMS)

• Introduced by E.F. Codd in 1970 (revolutionary idea!)
• Stores data in tables (relations).
• Tables have rows (records) and columns (fields).
• Uses SQL (Structured Query Language) for operations.

Example:

• A Users table with columns: ID, Name, Email
• A Orders table with columns: OrderID, UserID, Amount
• We can join tables using keys (UserID)

Pros:

• Simple and powerful querying with SQL.
• Data independence and integrity.
• Widely adopted and supported.

Cons:

• Not ideal for very complex or hierarchical data.
• Slower for huge scale data (billions of rows).
• Relational constraints add overhead.

Example DBMS: MySQL, PostgreSQL, Oracle, MS SQL Server

Object-Oriented DBMS (OODBMS)

• Stores data as objects, like in Object-Oriented Programming.
• Each object contains both data and methods.
• Useful when your app is written in OOP languages like Java, Python, C++

Example:

• An object Car with attributes (color, model) and methods (start(), stop())

Pros:

• Great for complex data like CAD, multimedia, engineering.
• Matches programming model.

Cons:

• Not easy for traditional data analytics.
• Slower adoption.

Example DBMS: db4o, ObjectDB

NoSQL DBMS (Non-relational)

• Designed for Big Data, real-time web apps, and scalability.
• Doesn’t require fixed schema.
• Handles unstructured, semi-structured data (JSON, XML, etc.)
• 4 Types:
  • Key-Value Store (Redis)
  • Document Store (MongoDB)
  • Column Store (Cassandra)
  • Graph DB (Neo4j)

Example:

• MongoDB stores: { “_id”: 1, “name”: “Alice”, “orders”: [ {“item”: “Book”, “price”: 250}, {“item”: “Pen”, “price”: 20} ] }

Pros:

• Highly scalable and fast.
• Flexible data formats.
• Great for distributed systems and cloud-based apps.

Cons:

• Less standardization.
• ACID compliance not guaranteed (eventual consistency instead).
• Querying is not as powerful as SQL for relational data.

Why the Transition?

Real-World Use Cases

• RDBMS: Banking systems, ERP software, online shopping (Amazon orders)
• NoSQL: Social media apps (Instagram, Facebook), IoT devices, recommendation engines
• OODBMS: CAD/CAM tools, scientific applications

Summary Table: