Data analytics can be enhanced by the power of relational databases. Relational databases are a mature and ubiquitous technology used for storing and querying structured data. Relational databases store data in the form of relations, also known as tables, which allow an excellent combination of performance, efficiency, and ease of use.
SQL is the language used to access relational databases. SQL supports many different data types, including numeric data, text data, and even data structures.
SQL can be used to perform all the tasks in the lifecycle of Create, Read, Update, and Delete (CRUD). SQL can be used to create and drop tables, as well as insert, delete, and update data elements. When querying data, SQL allows a user to pick which fields to pull, as well as how to filter the data. This data can also be ordered, and SQL allows as much or as little data as you need to be pulled.
Having reviewed the basics of data analytics and SQL, you will move on to the next chapter's discussion of how SQL can be used to perform the first step in data analytics: cleaning and transformation of data.