6 Different Types of Database Management Systems

Let’s for a minute imagine that you’re a librarian in charge of managing a vast library of books. You have to keep track of each book’s location, author, title, and many other details. Now imagine that you have millions of books to manage! It would be a nightmare to keep track of everything without a proper system in place.

This is where the role of the database management system (DBMS) becomes important. A DBMS is a software program that helps manage vast amounts of data by allowing users to store, access, and manipulate information efficiently. Database management systems come in various types, and in this article, we’ll be exploring some of the most common ones.

Relational Database Management Systems (RDBMS)

The backbone of most modern relational data storage systems is the Relational Database Management System (RDBMS). This type of DBMS is incredibly versatile and uses tables with columns and rows to organize data. 

One of the biggest advantages of the RDBMS is their ability to use Structured Query Language (SQL), which allows users to access data from multiple tables at once with ease. The RDBMS is widely used in many different applications, such as CRM, HRMS, and CMS systems. 

The RDBMS uses a unique system to store data in a tabular format where each table has a primary key that identifies it and a relationship with other tables. This allows for seamless data extraction across tables, such as in a school where information about students and their grades can be stored across two interrelated tables.

MySQL is a great example of a relational database management system (RDBMS). Developed by Oracle, this open-source software uses SQL to efficiently manage and organize data. Enterprises globally use MySQL thanks to its reliability, speed, and user-friendliness. 

As a result, it’s a popular choice for a range of applications, such as e-commerce websites, CRMs, and digital financial systems. CMS platforms like WordPress and Joomla also rely on MySQL on their server-side.

Object-Oriented Database Management Systems (OODBMS)

Object-oriented programming has changed the way we approach data, and with it, Object-oriented Database Management Systems (OODBMS) have emerged. The OODBMS stores data as objects, with fields and behaviors acting on those fields. 

An OODBMS is similar to real-world objects and can store data and methods to manipulate that data. For instance, an online bookstore can represent a book as an object with attributes such as title, author, and publication date. 

Similarly, a customer can be an object with attributes like name, address, and email. With an OODBMS, retrieving information about books and customers is quick and easy, making it a powerful tool for modern data management.

One example of an OODBMS is db4o, which is an open-source object database for Java and .NET environments. It allows developers to store and retrieve objects directly, making it easier to work with object-oriented programming languages.

The OODBMS is particularly useful in applications that require complex data structures, such as computer-aided design (CAD) and multimedia systems. Scientific and engineering applications with large amounts of data that needs manipulation often rely on a type or other of an OODBMS.

Hierarchical Database Management Systems

The Hierarchical Database Management Systems (HDBMS) type organizes data in a tree-like structure, where each record has a parent record, making such perfect for data that follows a natural hierarchy, like organizational charts or file systems. 

The HDBMS may not be as flexible as a RDBMS, but they’re much faster and more efficient at retrieving data. In contrast to a RDBMS, which use tables to store data, an HDBMS utilize a parent-child relationship that enables the easy retrieval of information about a specific product and its components. 

For instance, in a manufacturing company, products can be parent records, while their components are child records. This hierarchy makes it easy to access information about a particular product and all its components. With the fast and efficient retrieval of data, the HDBMS is an excellent choice for hierarchical data storage.

IMS (Information Management System), a hierarchical DBMS developed by IBM, is popular in large organizations and government agencies. These entities require data to be organized in a strict hierarchy, which IMS provides.

Network Database Management Systems

Network Database Management Systems (NDBMS) are similar to the HDBMS but with a more flexible structure. Instead of a strict hierarchy, the NDBMS uses a network model to represent data relationships.

The NDBMS allows for complex relationships between data records. Unlike an HDBMS, an NDBMS permits each record to have multiple parents, making them more flexible and useful in a variety of applications. 

The NDBMS also finds wide applications in manufacturing, finance, government bureaus, and scientific applications where data relationships are crucial for analysis. With an NDBMS, you can efficiently and effectively store, manage, and retrieve data with complex relationships, providing you with the insights you need to make informed decisions.

CA Technologies (formerly Computer Associates) developed IDMS (Integrated Database Management System), a network DBMS commonly used in large organizations that need to manage complex relationships between data entities.

Object-Relational Database Management Systems (ORDBMS)

Object-Relational Database Management Systems (ORDBMS) combine the best of both the RDBMS and the OODBMS. The ORDBMS bridges the gap between the structured data of the RDBMS and the complex data modeling capabilities of the OODBMS. They allow users to store and manipulate data as objects while still using SQL to access the data.

In an ORDBMS, data is stored in tables, similar to an RDBMS. However, an ORDBMS allows users to define new data types and methods, just like an OODBMS. This means that an ORDBMS can handle more complex data structures, such as images, audio, and video files, as well as more complex relationships between data.

One of the primary benefits of using an ORDBMS is that it allows for better data modeling. An ORDBMS can store complex data types, such as arrays, structures, and objects, as well as perform complex operations on those data types, such as inheritance, polymorphism, and encapsulation. This makes it easier to model complex business processes and relationships, resulting in more accurate and efficient data management.

Another advantage of an ORDBMS is that they allow for better integration with object-oriented languages, such as Java or Python. This also allows developers to work with a single language throughout the entire development process, improving efficiency and reducing errors.

One of the most popular ORDBMS is Oracle Database. Oracle Database offers a wide range of features, including support for complex data types, object-oriented programming, and data modeling. Oracle Database also offers scalability and high availability, making it suitable for large-scale enterprise applications.

PostgreSQL is another popular ORDBMS that combines the features of a traditional RDBMS with those of an OODBMS. It allows developers to store complex data types, such as arrays and user-defined types, while also supporting advanced SQL operations.

NoSQL Database Management Systems

When most people think of databases, they likely think of traditional, structured databases that use SQL (Structured Query Language) to store and manage data. However, with the rise of big data, cloud computing, and real-time data processing, a new type of database management system has emerged – NoSQL (Not only SQL) databases.

NoSQL databases handle large volumes of unstructured or semi-structured data, such as social media data, sensor data, or log files, and offer high scalability and performance. Unlike traditional SQL databases that use a fixed schema to structure data, NoSQL databases use a flexible, dynamic schema that allows for easy addition or modification of data fields.

There are several types of NoSQL databases, including:

Document-Based Databases

These databases store data in the form of documents, usually in JSON or XML format. Each document is self-contained and can have a different structure from other documents in the same database. Document-based/document-oriented databases are highly scalable and performant, making them well-suited for real-time data processing.

Developers can store and retrieve data as JSON documents with MongoDB and Firebase, a couple of popular document-based NoSQL databases. Several large organizations, such as eBay, use MongoDB for its search engine and personalizing recommendations.

Firebase, on the other hand, is popular with mobile and web application development. It’s used by a wide range of companies, from startups to large e-commerce organizations like Alibaba.

Column-Family Databases

A Column-Family Database Management System (CFDBMS) is a type of NoSQL DBMS that stores data in columns rather than rows. In a CFDBMS, data is organized in column families, which are groups of columns that are frequently accessed together. This allows for efficient querying and retrieval of specific columns.

For example, imagine you’re running an e-commerce platform, and you want to store information about products, including their name, price, and availability. Using a CFDBMS, you can group these columns into a column family, allowing you to retrieve information about a specific product quickly.

Organizations often use column-family databases for data warehousing and analytics due to their high efficiency in handling large datasets. Apache Cassandra is a widely used column-family NoSQL database. Developers often use it for large-scale, distributed applications, such as social media platforms and financial systems, which need high availability and scalability.

Graph Databases

A Graph Database Management System (GDBMS) stores data in a graph-like structure, where nodes represent entities, and edges represent relationships between those entities. In a GDBMS, data is stored based on its relationships, allowing for efficient querying of complex, highly interconnected data.

For instance, imagine you’re running a healthcare platform, and you want to store information about patients, doctors, and medical conditions. Using a GDBMS, you can represent patients, doctors, and medical conditions as nodes and their relationships as edges. This allows for easy retrieval of information about patients, their doctors, and their medical conditions.

Neo4j is a popular graph database that allows developers to store and query data as nodes and relationships. Social networks, recommendation engines, and fraud detection systems often rely on it.

Some Pros of NoSQL Databases

NoSQL databases offer several advantages over traditional SQL databases. 

1. They are highly scalable, allowing them to handle large volumes of data and high traffic loads. 
2. They are also highly flexible, allowing for easy addition or modification of data fields. 
3. NoSQL databases also offer high availability and real-time access to data, making them ideal for applications that need real-time data processing.

Cons of NoSQL Databases

NoSQL databases are not without their drawbacks.

1. They lack the mature tooling and ecosystem of traditional SQL databases, making it harder to find skilled developers and third-party tools.
2. NoSQL databases may not provide the same level of consistency and durability as traditional SQL databases, making them less suitable for certain applications.

Summing Up

Database Management Systems are essential tools for managing large amounts of data efficiently. As we’ve seen there are various types of database management systems and each has its unique features and functionalities.

Choosing the right type of DBMS depends on the specific needs of your business or organization. A RDBMS is ideal for managing structured data, while NoSQL database systems are highly suitable for handling unstructured data. Some NoSQL database systems like Graph Databases are ideal for querying complex relationships such as in social media while an OODBMS is great for complex data modeling.