The importance of storing, organizing, and using data has increased every year. We are living in a data era where being competitive and agile means being able to manage data from a wide range of sources. As a result, data flexibility has become a major concern for companies to keep up with massive volumes of new data, especially since studies have found that 90% of data is only two years old and only around 5% of businesses feel they have data management under control.
In order to manage this data, companies are investing in data management systems that can cater to their data storage needs. The two main types of databases that companies use are relational databases (RDBMS) and NoSQL databases, which have key differences that make them suitable for different purposes. In this article, we will examine the key differences.
Schema and Data Model
The biggest difference between RDBMS and NoSQL databases is their schemas. A RDBMS requires predefined rigid schemas to store structured data, while NoSQL databases use flexible, evolving schemas to store unstructured and semi-structured data. Structured data is information that is organized in a consistent, predefined format and is usually made up of alphanumeric characters. Examples include financial transactions, inventory records, or customer lists, which is why the data model for RDBMS is tabular. Unstructured data doesn’t have a predefined data model and can update and change rapidly, while structured data have a much lower rate of change. Examples of unstructured data include pictures, audio files, videos, and social media posts. NoSQLs are much more flexible as different data models can be chosen to suit the data.
Fault Tolerance
A database’s fault tolerance is its ability to keep operating and functioning as it should despite failures or faults in one or more of its components. MongoDB’s NoSQL database guide explains how there is a key difference between the fault tolerance of the two databases. A NoSQL database has built-in fault tolerance and high availability due to data replication, while RDBMS uses replication, backup, and recovery mechanisms. However, for RDBMS, additional measures like disaster recovery mechanisms may need to be implemented during application development.
Scalability
Both databases scale in a different way. RDBMS are usually scaled vertically, where additional resources (e.g., CPUs, storage, or memory) are added to a single server to allow the database to grow. NoSQL databases scale horizontally, and this is achievable because the databases are often configured in a distributed system, where many independent nodes are linked via a network and work together. To scale horizontally involves increasing the capacity of a distributed system by adding more nodes. In doing so, more nodes can support the system’s workload. This means there is no limit to how large a NoSQL database can grow, which is why they are increasingly used to manage massive datasets required to train and support AI applications.
Query Language
RDBMS uses structured query language (SQL) for its database creation and operation. SQL defines the structure and organization of the stored data as well as the relationships among data items and is versatile enough to be used for both front-end and back-end applications. NoSQL databases use a selection of query languages to retrieve and manage data, depending on the data model and management system. For example, MongoDB has its own MQL query language, while Neo4J uses Cypher.
Use Cases
Each database is suited to different use cases, with RDBMS used to store data that must meet certain governmental or industry standards. They are also used to store data that results from an interaction between two or more parties, such as retailer point-of-sale (PoS) databases, healthcare prescription databases, and financial recordkeeping databases. NoSQL databases are used to store unstructured data that results from an interaction between two or more parties. Examples include AI applications, media streaming services, and social network analysis. As we move into a fintech ecosystem, NoSQL databases’ compatibility with AI will also be increasingly used to train robo-advisors and AI-powered tools that can offer tailored financial advice based on individual data. With 80% of data being unstructured, NoSQL databases could soon overtake RDBMS in the near future.
Both relational and NoSQL databases play a key role in the data-driven society that we live in. As the volume of data and the demand to effectively store it increases, both types of databases will continue to be vital systems in the future.
