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What are the Types of Database Systems?

What are the Types of Database Systems?

Data is everywhere. You create and consume data daily, from social media posts to online transactions. But how do you store, manage, and retrieve data efficiently and effectively? You need database systems. These software applications help you create, manipulate, and query data in a structured and organized way. However, not all database systems are the same. There are different database systems, each with its features, benefits, and drawbacks. In this blog post, you’ll learn about the main kinds of databases and how to use these systems for various purposes!

What is a Database System?

You may have heard the term "database" before, but do you know what it means? A database is not just a fancy word for a spreadsheet or a file folder. A database is an organized collection of data or information that a computer can store, access, and manipulate. 

There are different types according to how you organize data. For example, in Navigational databases, the data is shown as a tree-like structure (hierarchical databases), or it can be more flexible and have multiple and complex relationships between them (network databases). There are also cloud databases where data is on a cloud rather than a local server, which could be great for enterprise databases where many people need access to the data. On the other hand, if a small group of people or just one person is going to manage the data, they can use a personal database.

Sounds simple enough? Wait, there's more! A database is not just a bunch of data. It also needs a way to manage that data. That's where the different types of database management systems come in.

A database system, also known as a Database Management System (DBMS), is software that handles the interaction between the user, the application, and the database. The DBMS allows you to create, update, delete, and query data in the database using a special language called SQL (Structured Query Language). The DBMS also handles the data's security, performance, backup, and recovery. The DBMS and the database together are called a database system.

How Do Database Systems Work?

You may be wondering how database systems work: how do DBMS store and retrieve data quickly and efficiently? How do they handle complex queries and operations on the data? Well, a DBMS has three main components: the internal level, the conceptual level, and the external level.

The internal level represents how data gets physically stored in the entire database. It deals with data internal structures, file formats, indexes, hashing, compression, encryption, and other techniques to optimize storage and access. The DBMS manages the inner level.

The database organizes and represents the data at the conceptual level. It defines the logical schema, which includes tables, columns, keys, constraints, relationships, and other elements that describe the data model. It provides a common view of the data for all users.

The external level represents how users and applications see and use the data. It defines multiple views or subsets of the data for specific purposes or contexts. It allows users to access only the necessary data without exposing unnecessary details.

How To Choose a Database System?

There are many factors to consider when choosing a database system for your product, like type, size, and complexity of your data, performance and scalability requirements, security and reliability aspects, development and maintenance costs, and compatibility and integration.

1. Data Model

The data model defines how the data is structured, organized, and manipulated in the database. Database systems support different data models, such as relational, hierarchical structures, networks, documents, key-value stores, graphs, etc. The data model affects how easy or difficult it is to store and query your data and how flexible or rigid your schema is. You should choose a data model that matches your data's natural shape and complexity and the query patterns you need to support.

2. Performance

Performance requirements depend on many factors, such as the hardware resources, the network latency, the data size and distribution, the indexing and caching strategies, the concurrency and locking mechanisms, etc. For example, if you need to manage and store data in daily operations, you should stick to operational databases, which are also an excellent option for business analytics. However, you should choose a database system that can meet your performance expectations and Service Level Agreements (SLAs) and handle peak loads and spikes in traffic.

3. Scalability

DBMS have different scalability models and architectures, such as master-slave, master-master, sharding, replication, etc. You should choose a database system that can scale with your business needs and budget constraints and provide high availability and fault tolerance.

4. Security

Security refers to how well the different categories of databases can protect your data from unauthorized access, modification, or deletion. Security involves many aspects, such as authentication, authorization, encryption, auditing, backup, recovery, etc. You should choose a database system that can meet your security standards and regulations and prevent common threats such as SQL injections and data breaches.

5- Cost

Cost includes many components: license fees, hardware expenses, Cloud Services fees, personnel salaries, training costs, support contracts, etc. You should choose a database system that fits your budget and provides a good Return On Investment (ROI).

What are the Different Databases Types?

1. Centralized Database Systems

A centralized database system is in which all the data is stored and managed in a single unit. This unit can be a computer, a server, or a mainframe. The unit that keeps the data is called the server, and the computers or devices that access the data are called the clients. The clients communicate with the server through a network, such as a Local Area Network (LAN) or a Wide Area Network (WAN). The server handles all client requests and performs all the operations on the data, such as creating, updating, deleting, and querying. The server also manages the data's security, performance, backup, and recovery.

Pros of Centralized Database Systems

Integrity: A centralized database system ensures that there is only one copy of each piece of data in the database. That reduces data redundancy and inconsistency and enhances data reliability.

Security: It can protect the data from unauthorized access, modification, or deletion by using authentication, encryption, and other mechanisms.

Cons of Centralized Database Systems

Accessibility: This system relies on network speed and availability to have real-time access to the data. Network failures or congestion cause delays or errors in data processing.

Availability: A centralized database system relies on the server's uptime and performance to provide access to the data. If there is a server failure or downtime, database administrators may be unable to access data.

2. Distributed Database Systems 

A distributed database system is a system in which the data is stored and managed on multiple units. These units can be computers, servers, or mainframes. The unit that keeps the data is called a node, and the network of nodes is called a cluster. The nodes communicate with each other through the network to exchange data and coordinate operations. Each node can run its Database Management System (DBMS), which provides the functionalities.

Pros of Distributed Database Systems 

Performance: A distributed database system can improve performance by spreading the workload among multiple nodes. That reduces the load on each node and increases the throughput and response time.

Availability: It can boost availability by replicating data across multiple nodes. That ensures that if one node fails or becomes inaccessible, another node can provide access to the same data. 

Cons of Distributed Database Systems

Cost: This system requires more hardware and software than other database systems. The cost of distributing the data is significantly higher.

Complexity: Distributed database systems are more complex and require more skills and resources for their design, implementation, maintenance, and troubleshooting. Additionally, challenges arise from network and node failures, data consistency, and distributed recovery.

3. Relational Database Systems

Relational systems are among the most popular ones to store and manage data in a series of tables. Tables consist of rows and column-oriented databases, where each row represents a record, and each column represents an attribute. Tables can link to each other using keys and unique identifiers that establish the relationships among data.

Pros of Relational Database Systems

Integrity: Relational database models ensure accurate and consistent data across all tables. They use constraints, such as primary keys, foreign keys, and check controls, to prevent data duplication, inconsistency, and errors.

Portability: They make transferring or migrating data from one location to another easy because it gets stored in a standardized format and a logical structure.

Cons of Relational Database Systems

Access: These depend on the network speed and availability for real-time data access. If there is a network failure or congestion, users may experience delays or errors in accessing or processing the data.

Diversity: The database systems may be unable to accommodate different types or formats of data that require extra storage or processing methods. It may need modifying or integrating multiple databases to support diverse data sources or applications.

4. Object-Oriented Database Systems

Object-oriented databases are a type of Database Management System that store and manipulate data as objects rather than tables or records. Objects are entities with data and behavior, and the user or the programming language can define them. They use the same model and concepts as object-oriented programming languages, such as classes, inheritance, polymorphism, encapsulation, and abstraction. Object-Oriented Database Management Systems can work for applications that deal with complex and diverse data types, such as CAD, GIS, or Artificial Intelligence.

Pros of Object-Oriented Database Systems

Modeling: Object-oriented database systems can model complex and diverse data types more naturally and easily than other types of systems. They can also support user-defined types and methods that extend the system's functionality.

Manipulation: These databases offer efficient and effective data manipulation, performing complex operations on objects without requiring multiple tables or records.

Cons of Object-Oriented Database Systems

Administration: They may have more difficulty or overhead than other systems in managing and maintaining the data. They may also lack standardization or support for common features or functions, such as views, triggers, stored procedures, etc.

Access: These systems may have lower performance or compatibility than others when accessing data through standard interfaces or tools. They may also require more network bandwidth or resources to transfer complex objects across nodes or sites.

NoSQL Database Systems

NoSQL or non-relational databases store data in various formats, such as documents, key-value pairs, graphs, or columns. Unlike Relational Database Management Systems (RDBMSs), which use tables and rows to organize data, these databases use different data models to represent and manipulate data. For example, document databases store data as JSON or XML documents, key-value databases store data as simple key-value pairs, graph databases store data as nodes and edges, and column databases store data as columns of values, which are perfect features for web-based applications.

Pros of NoSQL Database Systems

Performance: NoSQL database systems can provide faster and more consistent performance than other systems. They can use caching, indexing, or replication techniques to optimize data access and processing. 

Integration: They can integrate data more seamlessly with object-oriented programming languages than other systems. They can use the same model and concepts as the programming languages, reducing their impedance mismatch.

Cons of NoSQL Database Systems

Oversight: These may have more difficulty or overhead than other systems in managing and maintaining the data. They may also lack standardization or support for common features or functions, such as views, triggers, stored procedures, etc.

Admission: NoSQL database systems may have lower performance or compatibility than others when accessing data through standard interfaces or tools. They may also require more network bandwidth or resources to transfer complex objects across nodes or sites.


Database systems are Business Intelligence -based applications that can help you manage and manipulate data organizationally. When deciding on a database system, thee is no one-size-fits-all solution, so you should consider aspects like data model, performance, scalability, security, and cost. Depending on your needs, you may also need to use more than a single database type system for a wide range of purposes or even consider combining them in a hybrid or polyglot approach according to your requirements.