databases_history_key_characteristics_of_databases_types_of_databases_Presentation (1)

1.

A Journey Through Databases
Introduction: Databases are fundamental to how we store and retrieve information today. From simple
spreadsheets to complex cloud-based systems, they underpin countless applications and services. This
presentation explores the evolution of databases, their core characteristics, and the various types
available, providing a comprehensive overview of this essential technology.

2.

Early
Management
Systems
• HierarchicalDatabase
Databases (1960s): Modeled
like a tree structure, with parent-child
relationships.
Limited flexibility for complex relationships. Popularized by IBM's Information Management System
(IMS). Used in early airline reservation systems.
Network Databases (1970s): Allowed more complex relationships than hierarchical models.
Implemented using the CODASYL standard. Challenging to manage and update. Provided more
flexibility for representing real-world relationships.
Relational Databases (1970s): Introduced the concept of tables with rows and columns. Data
organized based on relationships between tables. Foundation for SQL and modern database
systems. Offered a more structured and intuitive approach to data management.

3.

4.

The
Relational
Revolution
• Structured
Query Language (SQL): Model
Standardized language
for interacting with relational databases.
Used for defining, querying, and manipulating data. Powerful and widely adopted. Became the
industry standard for relational database management.
Normalization: Process of organizing data to reduce redundancy and improve data integrity.
Involves breaking down large tables into smaller, related tables. Ensures efficient storage and
retrieval. Crucial for maintaining data consistency.
ACID Properties: Guarantees reliable transaction processing (Atomicity, Consistency, Isolation,
Durability). Essential for data integrity in financial and other critical systems. Ensures data accuracy
and reliability.

5.

6.

Key Characteristics of Databases
Data Integrity: Ensuring data accuracy and consistency. Enforced through constraints and validation
rules. Critical for reliable information retrieval.
Data Redundancy Reduction: Minimizing duplicate data. Improves storage efficiency and reduces
update anomalies. Simplifies data maintenance.
Data Sharing and Concurrency: Enabling multiple users to access and modify data concurrently.
Managed through locking mechanisms. Facilitates collaboration.
Data Security: Protecting sensitive information from unauthorized access. Achieved through access
controls and encryption. Crucial for data privacy.

7.

NoSQL Databases
Document Databases: Store data in flexible, self-describing documents (e.g., JSON). Schema-less
design. Suitable for unstructured or semi-structured data. Popular for content management and
web applications.
Key-Value Stores: Simple data model based on key-value pairs. Highly scalable and performant.
Ideal for caching and session management. Used extensively in web applications.
Graph Databases: Represent data as nodes and relationships. Excellent for modeling complex
interconnected data. Used in social networks and recommendation engines. Powerful for analyzing
relationships.

8.

NewSQL Databases
Combines scalability of NoSQL with ACID guarantees of relational databases. Addresses the
limitations of traditional relational databases in distributed environments. Provides high availability
and performance.
Suitable for high-volume transactional workloads. Offers the benefits of both relational and NoSQL
approaches. Emerging as a powerful solution for modern applications.
Examples include Google Spanner and CockroachDB. Designed to handle massive datasets and
high transaction rates. Provides the robustness of ACID properties with horizontal scalability.

9.

Conclusion: Databases have evolved significantly over time, adapting to the changing needs of
businesses and applications. Understanding the different types of databases and their key
characteristics is crucial for making informed decisions about data management strategies. From
traditional relational systems to modern NoSQL and NewSQL databases, the landscape continues to
evolve, offering powerful tools for storing and retrieving information in the digital age.