What Are the Different Types of Databases?

Last updated on July 24th, 2024 at 11:59 am

What Are the Different Types of Databases?

Understanding data and its storage is integral to anyone looking to kick start their career in data science. Today, even non-technical roles demand an understanding of data handling and management.

Data is any information that is stored in a system. Now if you are working with a small dataset, you can handle it manually. However, owing to the rapid digitisation of businesses, the influx of data has grown manifold. This brings in the need for different types of databases.

However, if you are someone who nurtures an interest in all things data, it is never too late. Consider enrolling in a data science course today to give your resume the edge it deserves!

What are Databases

Owing to all the hubbub about data management today, you must have come across the word “database”. A database is a structured collection of data that is stored on a system of your choice.

Typically, you would think of a database as a computer system, but your phone is also a database and so is your smartwatch. Any platform or device that stores information for you is a database. Broadly classifying, there are two different types of databases– internal and external.

Internal databases are the ones that companies themselves own such as data lakes and warehouses. External databases, on the other hand, are owned by third-party organisations and can be accessed on the Internet such as cloud-based storage solutions.

What is a Database Management System (DBMS)?

Now that you know what are databases, the next step after you have stored your data is to manipulate it, right? As in, you might want to perform some function on a selected section of the data, or even delete a part of it. This is where a different type of DBMS comes in handy.

A DBMS is a software system that is used to manage a database. It allows the user to store, retrieve or manipulate the data to make it more consumable and accessible. DBMS serves as an interface between the database and the user, without hampering data integrity.

Here, there is a provision for a database administrator (DBA) as well. The DBA is responsible for maintaining databases and ensuring their integrity, security, performance and availability.

Database Components

Different components work together to facilitate the creation, management and utilisation of different types of databases. Each component plays a crucial role in the overall functionality and performance of a database system.

• Hardware
  The hardware aspect of a database is undoubtedly one of the integral components ensuring the smooth functioning of the system. It includes devices such as servers, networking equipment and storage devices that work together to optimise database scalability and accessibility.
• Software
  Now that we have the hardware in place, we need to integrate database software into the system. Database software includes the database management system (DBMS), which is used to manipulate and interact with databases. DBMS provides functionalities such as data storage, retrieval, manipulation, deletion and security. To make it simpler, think of a DBMS as a librarian and the different types of databases as their library.
• Data Procedures
  Data procedures refer to the methods and protocols used to manage and manipulate data within a database. Data procedures ensure that data remains accurate, consistent and consumable to authorised users. This includes defining data structures, implementing data integrity rules and establishing backup and recovery procedures.
• Database Access Language
  Database access languages interact with databases and perform operations such as querying, updating and deleting data. SQL (Structured Query Language) is the most widely used database access language, allowing users to write queries to retrieve and manipulate data.

About Primary Database Types

Depending on the user’s requirements there are different types of databases. They are classified based on the storage pattern, technique and accessibility. Broadly classifying the different types of databases, we have:

Centralised database

This is one of the different types of databases where all data is stored in a centrally monitored unit. The data can be accessed from any device after a user passes verification measures.

In this arrangement vendor intervention is significantly less as the datasets are all centrally stored. Hence, it is cost-effective and provides for better data consistency. However, centrally storing the data does have some demerits to it.

Owing to the large size of the repository, the buffer time is high and retrieval takes time. On top of that, the database may be relatively inexpensive to install but requires extensive maintenance.

Distributed database

Think of the distributed database as the polar opposite mechanism to the centralised database system. Distributed databases divide and compartmentalise datasets into different systems. This is done so that users can access only relevant information from their respective databases.

Two different types of databases constitute distributed databases– homogenous and heterogenous. The difference is simple. Homogeneous databases use the same devices and operating systems across units. For heterogeneous databases, the operating systems, application processes and hardware devices are all different.

Distributed database systems facilitate better scalability and growth across the organisation. It is also beneficial in a server-error scenario. As in, one anomaly does not disrupt the entire database and hinder smooth functioning. However, they are costlier to build and maintain due to the compartmentalised infrastructure mechanism.

NoSQL database

Structured query language (SQL) servers store extensive amounts of structured data. Now, NoSQL is the exact opposite of that. These databases store a wide variety and range of datasets. Also known as non-SQL database, this system stores data outside traditional storage norms of a tabular format as in relational databases.

This data storage system is also cost effective when compared to the traditional RDBMS. Maintaining a NoSQL database does not require powerful and extensive infrastructure. This allows companies to allocate resources effectively and provides for high scalability.

NoSQL database can be further divided into the following types:–

• Key value storage
  This type of NoSQL database stores every data input with a key value or identification mark. It is the most commonly used method.
• Graph database
  When working with an exponentially growing amount of data, graph databases come in handy. Social media networks use graph databases to store all the information they handle in graph formats.
• Document oriented database
  Suppose you want to store your data in documents that are grouped as per the application code. This is where document oriented databases should be your pick. They group the data in JSON-like documents, in open interchange format that are both human and machine readable.
• Wide-column store or column store database
  It is similar to storing data in relational databases. Data is stored in columns instead of rows to make it more consumable.

Cloud database

Imagine an organisation that has a huge volume of data to be managed. So, it might not want to get into the hassle of owning and maintaining an extensive database system. This is where cloud databases come in. These are data storage solutions that run in public or hybrid cloud environments.

Other than being cost-effective, cloud databases are also relatively more accessible than traditional databases. Owing to the huge influx of disparate data the need to scale and compartmentalise databases is essential to growing businesses.

Furthermore, cloud databases are customisable to the user’s needs. They are equipped with robust security measures and in-built data recovery mechanisms in addition to their high accessibility.

Relational database

You must have heard about storing data in rows and columns. Well, that is the principle of relational databases. Invented in 1970 by E.F. Codd, a relational database stores the data in tuples and attributes. It uses structured query language (SQL) to create a unique identification for each data that is entered into the system.

The relational database has four components which are popularly known as the ACID properties. They are:

• Atomicity
  Like atoms are the smallest independent units of matter, operation is the smallest unit of a relational database. This means that an operation or transaction has to be completed in its entirety. In case that is not possible, the operation is aborted. This is in accordance with the “all or nothing” policy of an SQL server.
• Consistency
  This property of a relational database ensures that the value of a data remains consistent before and after a function is performed on it. If the data is manipulated during an operation, the database should reflect the changes correctly.
• Isolation
  This property ensures that simultaneous operations do not affect each other. Operations must not be visible to one another until they are completed and the changes are reflected in the database. This ensures that multiple users can access the same database independently without interfering with one another.
• Durability
  Once an operation is completed, the changes it brings about are stored permanently in a nonvolatile memory. This means that even if a system failure occurs, the memory of the transaction will remain intact. The system will not revert to its previous state and the effect of the operation updates the database.

Object-oriented database

Also known as OODB, an object-oriented database functions on the basic definition of object-oriented programming (OOP). It stores data in the form of objects and classes. The object-oriented data model has three major components– object structure, object classes and object identity.

Object-oriented databases are used when you’re working with weaving complex data structures and techniques. It integrates all the core principles of OOPS such as polymorphism, inheritance and encapsulation. This significantly reduces the amount of translation required and thus makes retrieval efficient.

However, OODB structures are costly to implement, maintain and integrate in the system. On the other hand, OODB eliminates the concept of mapping, directly giving developers access to the database objects. In addition to supporting challenging data types, it also provides for efficient scalability of the database.

Hierarchical database

The hierarchical database structure is based on the parent-child model. Developed in the 1950s by IBM, it operates in a tree-like structure. This model views data as a collection of segments that form hierarchical relationships.

Segments can be connected in a chain-like structure through logical, directional associations. In this model, segments pointed to by logical associations are called child segments, while the segment pointing to them is the parent segment.

Hierarchical models are widely used due to their semantic and physical alignment with real-world biological, political and social structures. Also, they are suitable as physical models because of the hierarchical organisation of disk storage systems, like tracks and cylinders.

Network database

Although based on the principle of traditional hierarchical databases, network databases allow records with multiple parents. Although easy to design and maintain, network databases do not embody the tree structure.

Instead, they follow a graphical pattern defined by a schema containing the intra-data node relationships. Although network databases are preferred over hierarchical databases for more data independence, they still lack a flexible structure.

While creating the database there is a lot of flexibility, however, once populated, the structure is very rigid.

Conclusion

Different types of databases allow for greater accessibility of options for users with varied requirements. Depending on the resources at hand, users can opt for the database that best suits their needs.

However, maintaining these databases needs efficient experts who supervise the entire operation. If you are someone who is fascinated by data science this is your chance. The global demand for data scientists is projected to increase by 200% by 2026. So why wait? Head over to Imarticus’ Postgraduate Program In Data Science And Analytics and learn as you earn hands-on experience.

FAQ's