According to the latest data published in early 2019, more than 4.3 billion people worldwide are connected to the Internet, representing an increase of nearly 10% compared to the previous year.
Furthermore, there are more than 5 billion unique mobile users (about 67% of the world's population), and more than 3.4 billion people regularly use social media.
In this context, the amount of information generated continues to increase each year, reaching 33 zettabytes (33 billion terabytes) in 2018. However, this figure pales in comparison to the more than 2,000 zettabytes that will be generated in 2035, according to the study. Statista's Digital Economy Compass.
Against this backdrop, Big Data has emerged as a solution capable of managing this vast volume of data, analyzing it, and extracting insights that are of enormous value to businesses.
One of the most important pillars of Big Data is databases, which come in various types, notably relational and non-relational databases. In this article, we will focus on the former—which are currently the most widely used—and examine whether this type of database possesses the necessary characteristics for use in Big Data.
What are relational databases?
A relational database is one in which data are related to one another through tables and links that serve as bridges between the different tables, thereby allowing the information to be broken down.
These types of databases are based on a standard language known as SQL (Structured Query Language). Their main advantage lies in their ability to be linked together without the need to duplicate large amounts of information.
Relational Databases and Big Data: Are They Compatible?
As we mentioned earlier, in relational databases, data is stored with a defined relationship within a structure that is typically based on tables containing rows and columns.
The main challenges that relational databases face with Big Data are as follows:
- They are not flexible and are therefore not designed to accommodate potential changes.
- They have trouble handling heterogeneous data.
- Their design is not optimized for operational and analytical tasks, making them inefficient.
- They are not equipped to handle the development of modern applications because these applications use object-oriented programming languages.
However, the challenges faced by SQL databases (or relational databases) do not mean they will disappear; non-relational databases are better suited to the Big Data environment, which means they will be used more and more.
In addition, thanks to advances in the use of non-relational databases, it will be possible to combine them with relational databases for certain applications.
What is certain is that the coming years will be marked by the continuous generation of data, its management, and subsequent analysis.
