Modern Database Management E-Book

Chand Kumawat

• Herausgeber: via tolino media
• Kategorie: Fachliteratur
• Sprache: Englisch

Veröffentlichungsjahr: 2023

Beschreibung

Modern Database Management is a comprehensive guide that provides an in-depth understanding of database management concepts and techniques. This book covers a wide range of topics, from the basics of database design to advanced topics such as data warehousing, data mining, and machine learning with databases. In this book, you will learn how to design, implement, and manage databases that meet the needs of your organization. You will also gain a deep understanding of the various database management systems, their advantages, and limitations.

Leseprobe

Inhaltsverzeichnis

Introduction to modern database management

Types of Databases and Their Applications

Query Languages and SQL

Database Design Principles

Data Modeling Techniques

ER Diagrams and Entity Relationships

Relational Database Management Systems

Database Security and Authorization

Database Backup and Recovery

Database Performance Tuning and Optimization

Indexing and Query Optimization

Transactions and concurrency control

Cloud databases and database as a service

Big data and NoSQL databases

Data warehousing and business intelligence

OLAP and Data Mining

Data analytics and machine learning with databases

Database administration and monitoring

Database Auditing and Compliance

Database architecture and scalability

Database deployment and migration

Data integration and ETL processes

Data governance and data quality management

Data privacy and ethical considerations

Future Trends in Database Management

Best practices and tips for effective database management

Impressum

Modern Database Management

Chand Kumawat

© 2023 Chand Kumawat. All rights reserved.

Imprint: Independently Published.

Email: [email protected]

Introduction to modern database management

Modern database management is the process of storing, organizing, managing, and retrieving data in a structured and efficient manner. It is a critical aspect of any business or organization that deals with large volumes of data. A database is an organized collection of data that can be accessed, managed, and updated easily. Database management systems (DBMS) are software tools that facilitate the creation, maintenance, and use of databases.

The evolution of database management systems has been quite significant over the past few decades. The early DBMS were simple file systems that stored data in flat files, making it difficult to manage and access. However, the advent of the relational model in the 1970s revolutionized the field of database management. This model introduced the concept of tables, which could be related to each other through common fields, making it easier to organize and retrieve data.

Modern DBMS use advanced technologies to manage data efficiently, such as indexing, data compression, and encryption. Cloud-based databases have also gained popularity in recent years, allowing organizations to store and manage their data on remote servers accessible from anywhere.

This chapter provides an overview of modern database management, including the types of databases, the relational model, and the tools and techniques used to design, develop, and maintain databases.

Types of Databases

There are several types of databases, each with its unique features and applications. Some of the most common types of databases include:

Relational databases: These databases are based on the relational model and store data in tables that are related to each other through common fields. The tables can be manipulated using SQL, which is a powerful query language.

Object-oriented databases: These databases store data as objects rather than tables, making them suitable for applications that require complex data structures.

NoSQL databases: These databases do not follow the relational model and are designed to handle large volumes of unstructured data, such as social media feeds or sensor data.

Hierarchical databases: These databases organize data in a tree-like structure, where each node has only one parent but can have multiple children.

Network databases: These databases are similar to hierarchical databases but allow each node to have multiple parents.

Cloud databases: These are databases hosted on remote servers accessible over the internet, offering a scalable and cost-effective solution for storing and managing data.

The Relational Model and Data Normalization

The relational model, proposed by E.F. Codd in the 1970s, is based on the concept of tables that are related to each other through common fields. Each table represents a specific entity, and the rows in the table represent instances of that entity. The columns in the table represent attributes of the entity.

Data normalization is the process of organizing data in a database to minimize redundancy and improve data integrity. Normalization is achieved by breaking down large tables into smaller, related tables that are free from redundant data.

There are several levels of normalization, known as normal forms. The most commonly used normal forms are:

First normal form (1NF): This requires that each column in a table contains atomic values, i.e., values that cannot be broken down further.

Second normal form (2NF): This requires that each non-key column in a table is fully dependent on the primary key.

Third normal form (3NF): This requires that each non-key column in a table is not dependent on other non-key columns.

Query Languages and SQL

Structured Query Language (SQL) is a powerful query language used to manage relational databases. SQL allows users to create, modify, and manipulate databases and tables, as well as perform complex queries to retrieve data.

Types of Databases and Their Applications

Databases are an essential part of any organization that deals with a significant amount of data. Databases can be classified into various types based on their functionality, data models, and data storage mechanisms. In this chapter, we will discuss the various types of databases and their applications.

Relational Databases

Relational databases are the most commonly used type of database. They are based on the relational data model, which uses tables to store data. Tables are made up of rows and columns, with each row representing a record, and each column representing an attribute.

Relational databases are widely used in many applications, such as finance, healthcare, education, and e-commerce. They are ideal for storing structured data that can be easily queried, analyzed, and updated. Relational databases are also used in customer relationship management systems, human resource management systems, and inventory management systems.

Object-Oriented Databases

Object-oriented databases are designed to store complex data types, such as images, videos, and audio files. They use the object-oriented data model, which represents data as objects. Objects contain data and the methods that operate on that data.

Object-oriented databases are suitable for applications that require the management of complex data structures, such as computer-aided design (CAD) systems, multimedia applications, and scientific simulations.

NoSQL Databases

NoSQL (Not Only SQL) databases are designed to handle large volumes of unstructured data, such as social media feeds, sensor data, and log files. Unlike relational databases, NoSQL databases do not rely on a fixed schema. Instead, they use flexible data models, such as key-value, document, or graph models.

NoSQL databases are used in many applications, such as content management systems, e-commerce platforms, and real-time analytics systems. They are also used in big data applications, such as Hadoop and Apache Spark.

Graph Databases

Graph databases use graph data models to store data. Graphs consist of nodes and edges, where nodes represent entities, and edges represent relationships between entities. Graph databases are ideal for applications that involve complex relationships between data, such as social networks, recommendation systems, and fraud detection systems.

Time-Series Databases

Time-series databases are designed to store and manage time-stamped data. They are widely used in IoT (Internet of Things) applications, where data is generated at high velocity and needs to be analyzed in real-time. Time-series databases are optimized for querying and analyzing data based on time intervals.

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