Data Fundamentals and Databases
📌 Data is defined as any information or fact, including names, addresses, images, or files, essential for the functioning of systems from local companies to governments.
💾 A database acts as an electronic container for storing data, enabling easy access, modification, protection, and analysis.
💻 Applications like Google, Instagram, and WhatsApp rely on databases to store and retrieve user information, whether on personal computers or large servers.

DBMS vs. Database
⚙️ A database is merely the container for data, while DBMS (Database Management System) is the software used to manage and interact with that data (e.g., storing, modifying, or protecting it).
🛠️ Users interact with the DBMS using specific languages (like SQL) to execute commands on the database.
📈 Popular examples of DBMS software include MySQL, PostgreSQL, MongoDB, and Neo4j.

Database Evolution and Types
💾 The evolution started in the 1960s with file databases, followed by hierarchical and network databases, which struggled with complex relationships.
📊 Currently, relational databases (RDBMS) constitute over 74% of current usage, while non-relational (NoSQL) databases have grown popular due to massive data growth from social media.
🔗 Major companies often use a combination of both relational and non-relational databases to handle vast amounts of user data.

Relational Databases (RDBMS)
🗂️ Data is stored in collections of related tables, composed of columns (defined by name and data type) and rows (records).
🔗 Relationships between tables are established using keys, such as the foreign key constraint, allowing data retrieval across multiple, interconnected tables.
💻 SQL (Structured Query Language) is the standard language used with RDBMS like Oracle, MySQL, and Microsoft SQL Server to query, store, and modify data.

Non-Relational Databases (NoSQL)
🧩 NoSQL encompasses several categories designed for specific data management needs, including Key-Value, Document, Graph, and Wide Column stores.
🔑 Key-Value Stores (e.g., Redis) are the simplest, storing data as a blob associated with a unique key, useful for configuration data.
📝 Document Databases (e.g., MongoDB) store data in structured documents (like JSON), where each document can have a unique internal structure.
🔗 Graph Databases (e.g., Neo4j) prioritize relationships, storing data as nodes connected by edges, ideal for pattern searching like fraud detection.

Key Points & Insights
➡️ Understand the distinction: the database stores data, and the DBMS manages it.
➡️ Relational Databases rely on structured tables and SQL for complex, interconnected data common in financial institutions.
➡️ For unstructured or relationship-heavy data (like social graphs), consider Non-Relational Databases such as Document or Graph stores.
➡️ Oracle is noted as the most widely used relational database, while MongoDB leads among non-relational options.

📸 Video summarized with SummaryTube.com on Dec 12, 2025, 16:10 UTC