The ability to store and organize data has become one of the most pressing issues of the twenty-first century.
The arrival of smart technology, big data, and artificial intelligence (AI) has dramatically increased the amount and types of data that can be harvested and used. This has led to an increasing demand for databases that can store data differently than relational databases, which store and organize data in tables, rows, and columns. The NoSQL database is fast becoming favored by developers due to its ability to store and support unstructured data, especially from the increasing consumption of multimedia, with the NoSQL database market projected to be worth $37.61 billion in 2028 from $11.6 billion in 2024. This article will explore what a NoSQL database is and why developers are increasingly using them.
What is a NoSQL Database?
A NoSQL database stands for “not only SQL database,” and they store a much wider range of data more naturally and flexibly than traditional SQL databases. This guide to NoSQL databases on MongoDB outlines how the database was developed in the late 2000s due to the dramatically decreasing cost of data storage, which, in turn, quickly resulted in the increasing amount of data that applications needed to store and query. The evolution and implementation of cloud computing, smart technology, big data, and, more recently, AI and machine learning (ML) mean that developers must adapt rapidly to changing requirements, iterate quickly, and constantly change their software stack. The NoSQL database offers this flexibility and scalability through the different types of databases.
Four Types of NoSQL Databases
There are four types of NoSQL databases: key-value databases, wide-column stores, document databases, and graph databases.
Key-value databases are where each item stored contains keys and values, and each key is unique and associated with a single value. Using a telephone directory as an example, the key would be the name, and the value would be the phone number. The advantage of a key-value database for developers is that it uses less memory to store data because they don’t necessitate placeholders for optional values.
Wide-column databases are similar to traditional SQL databases in that they store data in tables, rows, and dynamic columns. Unlike the rigid traditional database, however, wide-column databases are much more flexible. Different rows can have different sets of columns, and the columns can be compressed to reduce storage space and enhance performance. This makes them ideal for storing and organizing vast quantities of data across multiple servers or database nodes.
Document databases store data in documents, each containing pairs of fields and values. The values are usually strings, booleans, numbers, or arrays, but they can be a wide range of modern formats, including unstructured data, making the document database very flexible. Many developers use document databases because they store data in documents similar to JSON (JavaScript Object Notation) objects, which makes it easier for those familiar with coding.
Graph databases store data in the form of nodes and edges. The nodes usually store data about people, places, and things, and the edges store information about the relationships between the nodes. For example, if data is stored on social media posts, the node stores information on the content of the post, while the edge stores data on who likes the post or who follows it. This allows graph databases to identify connections between data and seamlessly analyze large datasets.
Why Developers Are Favoring NoSQL Databases
NoSQL databases have several characteristics that make them increasingly popular with developers across different industries. A Medium article on NoSQL databases lists them as non-relational, flexible schema, horizontally scalable, eventually consistent, and NoSQL query languages.
Non-Relational: As explained above, data can be stored in different types of databases. A key advantage for developers is that many NoSQL databases are multi-model databases that support more than one type of NoSQL data model. This means that developers can easily switch between different data models based on their application requirements.
Flexible Schema: Technology is constantly evolving, especially through AI and ML. We have covered how there has been an increasing integration of Generative AI in Manufacturing and Logistics, which is enhancing operations, streamlining processes, and boosting efficiency. The flexible schema of NoSQL databases allows developers to keep up with the evolving technology and store the new types of data generated by modern innovations.
Horizontally Scalable: As the amount of data increases, so does the demand to expand the datasets without any issue. NoSQL databases store data horizontally by adding more servers or nodes to the system. This increases the system’s capacity by allowing nodes to distribute the load, making NoSQL databases ideal for developers who need to scale their systems rapidly.
Eventually Consistent: This prioritizes availability over strong consistency, which means that while there may be temporary inconsistencies, all the data will eventually converge to the same state. This makes the data much more available, as even if the data is not exactly up-to-date across distributed systems, the databases are still operational.
NoSQL Query Languages: NoSQL databases can use both proprietary APIs and non-SQL syntax like UnQL or CQL. This makes them applicable to developers across a wide range of applications.
NoSQL databases are fast becoming the future of data management as technology evolves and developers look for new ways to store and organize the increasing amount of data generated. As this article shows, the flexible nature of NoSQL databases will allow developers to keep up with modern innovations.