Data is growing exponentially. It is no secret, but it is hard to imagine exactly how fast and how large a dataset can grow. However, no dataset, big or small, is useful when it is unprocessed. So a variety of database management systems store this data and SQL helps organize it for processing. We clean up the information, sort it, analyze it, remove the unwanted bits, and use the conclusions to the best of our ability. Managing all these tasks would be difficult without SQL.

SQL is a programming language for databases. Sometimes SQL is used interchangeably with relational database management systems (RDBMS) like: mySQL, Oracle, SQLite, and SQL Server. However the RDBMS is like a library and SQL is the language the librarian speaks. Some examples of what SQL can do are: create tables for visualization, request a piece of information, insert or modify records, and set security measures.
Compared to other programming languages, SQL is straight-forward to learn. For many developers, SQL is a fundamental language to know, but it is not the main focus. Many applications are built on top of a database, so it is important to learn SQL for when it interacts with a development programming language. Imagine a video sharing app, like YouTube. It will store information by creator, category, trends, playlists, and more. Then it also stores information on user accounts, likes, views, and more. New content is being uploaded every second. On top of that, there is developer code so someone could search for and view videos, interact with videos, and there is code in place to find videos you may like based on your previous views. Imagining the amount of data for YouTube is unfathomable, and that is only one website!

SQL does not always come paired with another programming language. In many businesses, SQL is used for data analysis or database management. In retail businesses, there is data on inventory, shipping and billing information, employee personal information, and business operation data. Another example is medicine. For a single patient, there could be multiple medications, strengths, quantities, doctors who prescribed them, and multiple insurance information. The point being we gather more and more data by the day, and SQL is just the start of processing it.