Introduction to Database Systems

What is a database? A database is an organized collection of structured data stored electronically and accessed through a database management system (DBMS). Databases provide a way to store, retrieve, modify, and delete data efficiently while maintaining data integrity, security, and consistency.

The evolution of data storage systems: The journey from manual filing systems to hierarchical databases in the 1960s, through network databases, the revolutionary relational databases of the 1970s, object-oriented databases of the 1980s-90s, and finally to modern NoSQL and NewSQL systems illustrates how data storage has continually adapted to meet increasing data volume, variety, and velocity requirements.

Database Examples in Daily Life: Databases power everything from social media platforms and online shopping experiences to banking systems, healthcare records, and mobile applications. Every time someone checks their email, uses a streaming service, or makes an online purchase, they're interacting with sophisticated database systems.

Data Models Made Simple

Data Models: What They Represent and Why They Matter Data models provide an abstract framework for organizing and representing data. They serve as a blueprint for database design, helping developers translate real-world entities and relationships into structured data that computers can store and process efficiently.

Comparing Database Models—Relational vs. Nonrelational:

• Relational databases: Organize data into tables with rows and columns, emphasizing relationships between data entities through keys. They excel in structured data scenarios requiring ACID compliance and complex querying.
• Nonrelational databases: Offer flexible schema designs and various data models (document, key-value, wide-column, graph) optimized for specific use cases. They prioritize scalability, performance, and handling unstructured data.

Database Types and Characteristics: Major database types include relational (MySQL, PostgreSQL, Oracle), document stores (MongoDB), key-value stores (Redis), wide-column stores (Cassandra), graph databases (Neo4j), and time-series databases—each with distinct strengths for specific data management challenges.

Query Languages – Communicating with Your Database

What are query languages? Query languages are specialized programming languages designed to retrieve and manipulate data stored in databases. They provide a standardized way to communicate with database systems, hiding the complexity of underlying storage structures.

A beginner-friendly introduction to SQL: SQL (Structured Query Language) is the standard language for relational database management, consisting of:

• Data Definition Language (DDL) for creating and modifying database structures
• Data Manipulation Language (DML) for inserting, updating, and retrieving data
• Data Control Language (DCL) for managing access permissions

Examples of simple queries: Fundamental SQL operations include SELECT statements with WHERE clauses, JOIN operations to combine data from multiple tables, aggregations with GROUP BY, and sorting with ORDER BY.

Your First SQL Query: Basic SQL syntax and structure helps build a foundation for writing queries that retrieve and manipulate data stored in relational database systems.

Fundamentals of Database Design

The Database Design Process: A systematic approach to database design includes:

1. Requirements gathering and analysis
2. Conceptual design (entity-relationship modeling)
3. Logical design (translating conceptual models to database schemas)
4. Physical design (optimizing for performance)
5. Implementation and maintenance

Modeling Real-World Data: Normalization techniques help eliminate redundancy and dependency issues. Identifying entities, attributes, and relationships creates schemas that balance performance requirements with data integrity needs.

Diving into Nonrelational Data Storage (NoSQL)

Introduction to Nonrelational Databases: NoSQL systems differ from traditional relational databases in terms of data models, consistency guarantees, and scaling approaches.

When and Why to Use NoSQL: Scenarios where NoSQL databases excel include:

• Handling massive data volumes with horizontal scalability
• Managing semi-structured or unstructured data
• Supporting high-throughput operations with low latency
• Enabling flexible schema evolution as application requirements change
• Providing specialized data models for specific use cases (graphs, time series, etc.)

Case Studies: Examples of NoSQL in Modern Applications: Various NoSQL solutions solve specific business challenges, from MongoDB powering content management systems to graph databases supporting recommendation engines.