Introduction

Data has fundamentally transformed the 21st century. Thanks to easy access to computers, companies, and organizations have been able to change the way they work with larger and more complex datasets. Using data, insights that would have been virtually impossible to derive 50 years ago can now be found with just a few lines of computer code. Two of the most important tools in this revolution are the relational database and its primary language, Structured Query Language (SQL).

While we could, in theory, analyze all data by hand, computers are far better at the task and are certainly the preferred tool for storing, organizing, and processing data. Among the most critical of these data tools are the relational database and language used to access it, SQL. These two technologies have been cornerstones of data processing and continue to be the backbone of most companies that deal with substantial amounts of data.

Companies use SQL as the primary method for storing much of their data. Furthermore, companies now take much of this data and put it into specialized databases called data warehouses and data lakes so that they can perform advanced analytics on their data. Virtually all of these data warehouses and data lakes are accessed using SQL. We'll be looking at working with SQL using analytics platforms such as data warehouses.

We assume that every person following this chapter has had some basic exposure to SQL. However, for those users who have very limited exposure to SQL, or have just not used it for some time, this chapter will provide a basic refresher of what relational databases and SQL are, along with a basic review of SQL operations and syntax. We will also go over practice exercises to help reinforce these concepts.

Let's first understand data and its types in the next section.