Relational vs. Non-Relational Database

In today’s digital landscape, data is the crux for organisations across industries. In a second, zillions of pieces of data are generated globally from websites and applications. Each bit of data empowers organisations to make well-informed decisions and gain a competitive edge. Businesses can uncover valuable insights and create tailored experiences when data is utilised effectively.

To handle this large amount of data and derive insights, it is essential that businesses understand which database is suitable for them. Not only this, choosing the right database helps them be robust, agile, and scalable with ever-changing business needs.

Each database has its own strengths and weaknesses, but before we delve further into the types of databases, let us understand the simple term database.

What is a database?

A database is a structured way of storing data in a table in rows and columns that allows easy retrieving, manipulating, querying, updating, and deleting data.

Each row and column represents individual records and their attributes. There are two main types of databases: relational and non-relational databases.

In this article, we will explore the difference between relational and non-relational databases, examine their usage, and explore the various types within each category. Gaining a comprehensive understanding of these distinct types can empower businesses and organisations to make well-informed decisions when selecting the optimal database solution for their specific requirements.

What is a relational database?

The term relational database is based on the relational model developed by Edgar F. Codd in the 1970s. Relational database is a type of database management system which stores data in a structured manner in tables organised into rows and columns. The creation of tables within a relational database involves the use of predefined schemas that define the data types and constraints for every column. Each individual table represents an entity or a relationship between entities.

For example, consider a database of a hospital with tables for doctors, patients, and illness. Each column of the table has attributes or properties of the entities, such as the doctor’s name, patient’s name, or illness.

Features of relational databases

Relational databases provide a reliable means of storing and retrieving data consistently while effectively managing complex data relationships.
They enable the establishment of seamless relationships between tables through primary and foreign keys. A primary key serves as a unique identifier for each row in a table, while a foreign key establishes a link between two tables.
Structured Query Language (SQL) used to query relational databases empowers users to effortlessly construct queries for data retrieval, analysis, data manipulation, performing joins, and extracting specific information from multiple tables.
Relational databases offer numerous advantages, such as maintaining data integrity, providing flexibility and scalability, and enforcing data integrity constraints.
Moreover, they possess the capability to handle substantial data volumes and can be expanded to accommodate the growing demands of data storage.

Common Examples of Relational Databases

Some of the commonly used relational databases are:

Microsoft SQL Server

Developed by the Microsoft Corporation, it is one of the most widely used relational database management systems. With its extensive set of features, it offers scalable, robust, and efficient data storage, retrieval, and manipulation of structured data. Microsoft SQL Server has a simple yet intuitive user interface that makes the job of a database administrator and developer easy. Its overall features make it popular among enterprise-level applications.

MySQL

MySQL is an open-source RDBMS with a large community continuously contributing to its enhancement. The highly scalable platform makes it suitable for both small-scale and large web applications. It provides a reliable and scalable platform for storing and retrieving structured data and performing various operations, such as querying, updating, and managing databases. It is robust and supports multiple programming languages.

Snowflake

Snowflake is a cloud-based relational database management system, enabling it to be faster and more efficient in data loading and querying. It makes it ideal for making data-driven decisions and performing real-time analytics. Its separate storage and computing architecture allows scalable and elastic data processing, which helps in large-scale data warehousing and analytics. Like other RDBMS, it also supports SQL, which makes it easy for developers to handle.

PostgreSQL

Like MySQL, PostgreSQL is also an open-source relational database management system with a dedicated community of developers to help in continuous development. Being ACID-compliant makes it a robust platform with high data integrity and performance that supports concurrency. Enables data manipulation operations and writing complex queries with support for various data types, indexing options, and advanced features like triggers and stored procedures.

What is a non-relational database?

Non-relational databases, also known as NoSQL databases, differ from relational databases by employing a “not only SQL” approach. They are designed to handle unstructured or semi-structured data and store it in a non-tabular format. These databases offer excellent scalability and can efficiently manage large volumes of data.

Due to their flexible structure, non-relational databases can accommodate various data types such as documents, graphs, key-value pairs, and columns. They are particularly suitable for applications that generate a substantial amount of real-time data with evolving requirements.

Many real-time applications, such as IoT and sensor data, inventory and catalogue management, fraud detection, and identity authentication, generate lots of unstructured data and use non-relational databases to store and manage it.

Features of non-relational databases

Unlike relational databases, non-relational databases have flexible and dynamic schemas that allow tables or columns to evolve as and when required.
Non-relational databases are horizontally scalable that allows addition of new servers or nodes to the database to match with changing data volume.
They are high performance databases with faster response times and large-scale data processing capabilities. Its data storage and indexing capabilities makes it unique and fast.
Designed with built-in fault tolerance and high availability which means even if one node fails, the system would be still operational.
As mentioned earlier, non-relational databases can handle multiple data types.

Common Examples of Non-Relational Databases

MongoDB

MongoDB is one of the most popular non-relational databases. It is document-oriented and stores data in JSON-like documents. Dynamic and schema-less data modelling helps it handle semi-structured and unstructured data. Efficient indexing, sharding capabilities, and horizontal scalability make it a high-performance database suitable for web applications and real-time analytics that require high availability and scalability.

IBM Cloudant

IBM Cloudant is a database as a service (DBaaS) NoSQL database managed by IBM. It supports a flexible data model, stores and retrieves data in JSON format, and is designed for handling large-scale and high-velocity data. Cloudant’s fault-tolerant storage solution and automatic scaling and load balancing properties make it a high-availability data solution. It is based on Apache CouchDB and has a distributed architecture with built-in indexing and querying capabilities. It is best suited for geographically distributed data.

Amazon DynamoDB

DynamoDB is a NoSQL database managed by AWS. It is super fast, requires low latency, has high scalability, and can handle up to millions of requests per second. With its unique property of storing data in key-value pairs, it can support a wide range of data models. It is fault-tolerant, has on-demand capacity provisioning, and has built-in security to store and protect business data.

Apache Cassandra

Apache Cassandra is a NoSQL database with multiple commodity servers that allow it to handle large amounts of data. Cassandra is a highly scalable, available, distributed, and fault tolerant database, making it suitable for data-critical applications. It can seamlessly handle increasing data volumes and have high write throughput because of its linear scalability and wide-column store data model.

Comparing & Contrasting Relational & Non-Relational databases

	Agile	DevOps
Focus	Iterative and incremental software development	Entire software delivery lifecycle including development, operations, and continuous delivery
Values	Collaboration, customer satisfaction, continuous improvement	Collaboration, customer satisfaction, continuous improvement
Teams	Cross-functional teams collaborate closely	Cross-functional teams collaborate closely
Delivery	Frequent delivery of working software in small increments	Continuous integration, delivery, and deployment of software
Automation	Automation of testing and other development processes	Automation of the entire software delivery pipeline
Improvement	Continuous improvement and adaptation	Continuous learning and feedback loops for process enhancement
Customer	Frequent customer collaboration and feedback	Customer-centric focus on delivering value and meeting needs
Alignment	Bridging the gap between development and operations	Bridging the gap between development and operations

What is SQL and NoSQL?

SQL and NoSQL are both types of query languages used to interact with databases.

SQL or Structured Query Language is used to interact with databases where data is stored in a table in rows and columns with predefined relationships between them. NoSQL or Not Only SQL is used to interact with non-relational databases and generally do not use SQL.

SQL is used to handle structured and complex data with well defined relationships. On the other hand, NoSQL is used to handle large volumes of unstructured or semi-structured data with no predefined data structure.

SQL is used to handle data of mid-range to enterprise level applications while NoSQL is used to handle data of big data applications, real-time analytics, IoT, etc.

Final thoughts

In conclusion, it is not about choosing the superior database or which database outperforms the other in certain scenarios. It is all about choosing the right tool for your business needs, type of data to be handled, scalability, performance, development requirements, etc. Once you are clear with that, it will become much easier for you to select the right database for your organisation.

If you are still confused about which direction to take, hire a database developer or a consultant from our vetted pool of resources, and ensure that your data management processes are efficient, scalable, and aligned with your organization’s growth objectives.

Abhishek Ghosh

Experienced B2B Content Marketer, Writer, Editor, and Strategist. He is from an engineering background who now loves to play around with words. He is an SEO and a social media enthusiast.