A Definitive Guide to NoSQL Databases
The evolution of data has changed every aspect of modern society. Today, massive amounts of data are collected from various digital sources, including IoT devices, smart devices, applications, and websites. Data has become such an important part of society that it is taught at all levels of education.
For example, The Computer Science Principles course for CHS students includes a whole section on data and information. In order to store these vast volumes of data, data management systems have developed to allow developers to effectively store, organize, and use the data. Today, the majority of the data collected, approximately 80%, is unstructured data, such as text documents, images, video and audio files, social media posts, and data from artificial intelligence applications. The need to store this new type of data, which is constantly evolving, has led to more developers and companies using NoSQL databases.
While not as widely used as SQL databases, the NoSQL database market is growing and is projected to be worth $86.3 billion by 2032, growing at a CAGR of 28.1% from 2023 to 2032. In this article, we will provide a definitive guide to NoSQL databases and explain why they have become so popular.
NoSQL vs. SQL
When people talk of a NoSQL database, they are referring to any non-relational database. The name can stand for “non-SQL” or “not only SQL.” SQL (Structured Query Language) is a domain-specific language used to organize data and is used in a relational database management system. This type of database stores data in tables and columns, just like a spreadsheet. These databases are used to store structured data, such as personal details, financial information, and quantitative data. A NoSQL database can store unstructured data because it has a more flexible schema where each set of documents/row-column/key-value pairs can contain different data types.
A guide to NoSQL databases on MongoDB outlines how this makes NoSQL databases more suitable for real-time processing, big data analytics, and distributed environments. On the other hand, MongoDB notes how SQL databases are more suitable for read-heavy and transaction workloads. A key advantage of NoSQL databases over SQL databases is that they scale horizontally compared to vertically, which means that they can expand without a drop in performance by adding more nodes rather than having to add more physical power, such as added CPUs. This allows NoSQL databases to scale in line with the data needs of the developer or company, and with the amount of data increasing every year, it is becoming an essential function for developers.
Four Types of NoSQL Databases
There are four common types of NoSQL databases: document databases, key-value databases, wide-column stores, and graph databases. Each database stores data differently, allowing developers to fit the data model to the data.
Document Databases store data in the form of schema-less JSON structures, or “documents.” A NoSQL feature by Info World notes that “the data could be anything from integers to strings to free-form text.” A document database offers a more flexible data model, which makes it much more suited for semi-structured and typically unstructured data sets.
Key-value databases are simpler, as each item contains just keys and values. The data is fetched by a unique key or several unique keys and used to retrieve the related value. The values can be simple data types like strings and numbers or more complex objects.
Wide Column Stores are similar to SQL databases in that they store data in tables, rows, and dynamic columns. However, compared to SQL databases, wide-column stores are flexible, and different rows can have different sets of columns. Wide Column Stores also employ column compression techniques to reduce the storage space and enhance performance.
Graph databases store data in the form of nodes and edges. As per Info World, the “data is represented as a network or graph of entities and their relationships, where each node in the graph is a free-form chunk of data.”. These databases work well for highly connected data, where the relationships or patterns may not be very obvious initially.
Use Cases For NoSQL Database
NoSQL databases are now used in most industries because of their flexibility and ability to store different data types. Procedia Computer Science’s paper details how the financial industry is one domain where dynamic and large amounts of data are produced daily, which is why NoSQL databases are an ideal choice for data management. The databases are used to market data, customer information, and real-time analytics. NoSQL databases are popular with e-commerce platforms because they store information on consumers and their favored products to provide data for personalized recommendations.
The health industry also frequently uses NoSQL databases for patient data management to store patient records, medications, and lab results. Wearable devices store patient data on NoSQL databases because they can handle unstructured data.
NoSQL databases are quickly becoming a vital part of the data ecosystem due to their versatility. As the amount of data increases, so will the demand for these databases. We have plenty of information in our Featured section for articles on data, technology, and future innovations.
