Understanding Databases

Introduction to Data and Database

• Data is vital in the digital world, impacting everything from governments to companies.
• Success of a company relies on effective data utilization.
• Definition of Data: Any information or fact can be considered data (e.g., name, age, address, bank balance).
• Data can take various forms: images, videos, files, plain text.
• Examples of data in a school setting: details of teachers, students, subjects.

What is a Database?

• A Database is an electronically stored container for data.
• Purpose: To easily access, modify, protect, and analyze stored data.
• Examples of applications using databases: Google, Instagram, WhatsApp, Facebook.
• Example Project: College timesheet web application.
  • Needs a database to store and retrieve data.
  • Database software is installed on a computer, connecting to the application.

Database vs. DBMS

• Database: just a data storage container.
• DBMS (Database Management System): software to manage databases.
  • Allows users to store, modify, retrieve, and protect data.
  • Examples of DBMS: MySQL, PostgreSQL, MongoDB, Neo4j, Cassandra.

Evolution of Databases

• 1960s: First type of database - flat file databases (simple files like CSV).
• Progressed to hierarchical and network databases (parent-child relationships).
• Replaced by Relational Databases due to ability to store complex relationships.

Types of Databases Today

• Relational Databases: 74% of databases used today.
• Non-Relational Databases: Gaining popularity due to increased data usage, especially from social media.
• Companies often use a combination of both.
• Examples: Oracle (relational), MongoDB (non-relational).

Relational Database

• Data stored in tables that are related to one another.
  • Tables consist of columns and rows.
  • Columns have names and data types (rules for data).
  • Rows represent records.
• Example: Office database contains employee, manager, and department information.
• Foreign Key: used to establish relationships between different tables.
• Data retrieval through SQL (Structured Query Language).
• Commonly used by financial institutions (banks, insurance).
• Examples: Oracle, MySQL, Microsoft SQL Server, PostgreSQL.

Non-Relational Database

• Categories include:
  • Key-Value Store: Simplest non-relational database, associates keys with data blobs (e.g., images, JSON).
    • Examples: Redis, Memcache.
  • Document Database: Stores data in structured documents (JSON, XML) with unique keys.
    • Examples: MongoDB, CouchDB.
  • Graph Database: Uses nodes, edges, and properties to represent data relationships.
    • Useful for identifying patterns (e.g., fraud detection).
    • Example: Neo4j.
  • Wide-Column Database: Stores data in rows and columns but uses column families without traditional tables.
    • Example: Cassandra, Edge Space.