Mastering Database: A Comprehensive Guide for Beginners

Since 1960, databases have come a long way. Recently, due to the advent of the internet and also the need for extensive storage and speed, cloud databases and self-driving databases have begun to break new ground. With the popularity of data-driven decision-making, more tech graduates have been learning data science to enter the job market.

While R and Python are popular for machine learning and analysis, Database management and SQL are often overlooked. Typically, data is stored in databases and needs business intelligence tools or SQL to access. This guide gives you an elaborate picture to begin your database journey. It contains different types of databases along with their differences. Let’s explore!

Defining Database

A database is referred to as an organized or systematic collection of related information and data that is stored in a manner that can be easily managed, accessed, updated, deleted, and retrieved as needed. Databases are designed to store, organize, handle, and manage large volumes of data and information effectively and efficiently in a structured manner.

Simply put, it is a collection of data organized in a particular way that makes it easy to sort, search, and analyze. It is very much like a digital filing cabinet where information is stored and accessed by various systems, applications, and users.

There are different types of databases, such as object-oriented, NoSQL, and relational, each with unique applications and characteristics. The main objective of any database is to provide a secure and centralized location for managing and storing data, ensuring data accuracy and consistency, and making it accessible to authorized applications and users.

Different Types of Database Systems

The classification of databases into various types is done on the basis of the underlying data model used in every case. Below we have listed different types of databases.

• Relational Databases

A relational database is quite a common type of database that stores data in tables that are related to one another through keys. In a relational database, each table has a unique primary key to link it to the other tables. They used SQL (StructuredQuerry Language) to manage and query data. Some common examples are PostgreSQL, MySQL, Microsoft SQL Server, and Oracle).

• NoSQL Databases

NoSQL databases are used for semi-structured and unstructured data. They do not use rows, columns, and tables like relational databases. Rather they store data in flexible formats like graph-based, document-based, or key-value pairs. NoSQL is commonly used in real-time and big data applications. Some of its popular examples are Couchbase, Cassandra, and MongoDB.

• Object-oriented Databases

Object-oriented databases are used to store data in objects similar to those used in object-oriented programming languages like C# and Java. They allow for complex data relationships and offer a more natural way to store data for object-oriented applications. Commonly they are used in artificial intelligence, web development, and computer-aided design. Some of its common examples are db4o and ObjectDB.

• Hierarchical Databases

Hierarchical Databases organize data in a tree-like structure, with every record with one parent and several child records. They are appropriate for storing data having a predictable and fixed structure. In the past, these were popular, but they have been replaced largely by other databases. IMS (Information Management Systems) is a popular example of this type.

• Network Databases

Similar to Hierarchical databases, Network databases allow more complex relationships between the records. Every record in a network database can have numerous parent and child records. They are appropriate for storing data with a complex structure that cannot easily be represented in a hierarchical database. Today, they are not widely used. Some of its examples include CA-IDMS and IDS (Integrated Data Stores).

Understanding RDBMS

Relational Database Management System (RDBMS) is a type of database management system based on the relational model. Here, data is organized into tables, and relationships between tables allow for easy manipulation and retrieval of the information. The popular RDBMSs include SQLite, SQL Server, PostgreSQL, Oracle, and MySQL.

• SQLite: SQLite is a lightweight, small RDBMS embedded into the application. It is famous for its ease of use, reliability, and high performance. SQLite is compatible with various operating systems, including macOS, Linux, and Windows.
• SQL Server: SQL Server is one commercial RDBMS maintained and developed by Microsoft. It is famous for its security, scalability, and high performance. It is well-suited only to the Windows operating system.
• PostgreSQL: PostgreSQL is an open-source RDBMS famous for its advanced features like full-text search, concurrency control, and complex data types. Widely it is used in scientific applications, business intelligence, and data warehousing.
• Oracle: Oracle is a commercial RDBMS used widely in enterprise environments. It is famous for its security, scalability, and high performance. It is compatible with various applications, including Solaris, Linux, and Windows.
• MySQL: MySQL is an open-source RDBMS used widely for web-based applications. It is famous for its ease of use, reliability, and high performance. This is compatible with various operating systems, including macOS, Linux, and Windows.

Database Design

Database design is a vital step in creating an efficient and functional database system. It includes creating a structure to organize the data and enable efficient manipulation, retrieval, and storage.

Key components of Database design

• Designing a Database: It involves identifying the data that has to be stored and then organizing it into tables that are related to one another. The tables must be designed to ensure data consistency and minimize redundancy.
• ERD (Entity-relationship diagrams): It is a virtual representation of its structure that shows the tables, their attributes, and the relationships that are stored in every table. ERDs are important as they offer a concise and clear view of the structure of the database.
• Normalization: This is a process of organizing data in a database to ensure data consistency and minimize redundancy. It involves breaking down large tables into more manageable, smaller ones related to one another. It ensures that every table includes only the data that is relevant to it and helps to eliminate data redundancy. Normalization is a vital aspect of design as it helps to improve its performance, ensure data consistency, and minimize data redundancy.

There are various levels of normalization, with one level building upon the previous level. The common levels of normalization involved are:

1. 1NF (Fist Normal Form)
2. 2NF (Second Normal Form)
3. 3NF (Third Normal Form)
4. BCNF (Boyce-Codd Normal Form)

SQL (Structured Query Language)

SQL is used to manipulate and manage databases. Whether you are a beginner or a seasoned developer, understanding the basics of SQL is vital for the ones working with data.

Different types of SQL

SQL commands are grouped into four key categories that include:

• DDL (Data Definition Language): DDL commands are used to modify and create the structure of a database, such as creating tables, deleting tables, and altering table structures. Some of its examples include CREATE, DROP, and ALTER.
• DML (Data Manipulation Language):DML commands manipulate the data within a database. The commands include INSERT, SELECT, DELETE, and UPDATE.
• DCL (Data Control Language): DCL commands are used for managing access, such as revoking and granting permissions. The examples include REVOKE and GRANT.
• DQL (Data Query Language): DQL commands are primarily used to query the data. Commands most used include SELECT (used in retrieving the data from a table).

SQL VsNoSQL

The key difference between SQL and NoSQL databases lies in how they store and retrieve data. SQL databases use rows and tables to store data, whereas NoSQL databases use key-value pairs, collections, and databases. For structured data, SQL databases are better suited, while for unstructured data, NoSQL databases are better suited.

Another difference between the both lies in the way they handle scalability. These databases are vertically scalable, so SQL databases can handle more load by adding more resources to the same server. NoSQL databases can handle the additional load by adding more servers, and these are horizontally scalable.

Wrapping Up

Hope this guide was helpful in providing you with a comprehensive overview of different types of databases and their differences. Designing a database is a vital step in creating an efficient and functional database system. By understanding the various types and their unique features, you can choose the database perfect for your specific use and design one that can meet your data management needs. Praxis is a comprehensive learning and career platform offering database courses to help students gain practical experiences and develop critical skills. Our programs empower students to create a professional portfolio and help them stand out in today’s competitive job market. Reach us today and take the first step towards a fulfilling career in databases with Praxis.

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