Book Image

SQL for Data Analytics - Third Edition

By : Jun Shan, Matt Goldwasser, Upom Malik, Benjamin Johnston
Book Image

SQL for Data Analytics - Third Edition

By: Jun Shan, Matt Goldwasser, Upom Malik, Benjamin Johnston

Overview of this book

Every day, businesses operate around the clock, and a huge amount of data is generated at a rapid pace. This book helps you analyze this data and identify key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level. SQL for Data Analytics, Third Edition is a great way to get started with data analysis, showing how to effectively sort and process information from raw data, even without any prior experience. You will begin by learning how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you will learn how to write SQL queries to aggregate, calculate, and combine SQL data from sources outside of your current dataset. You will also discover how to work with advanced data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you will be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation. By the end of this book, you will be proficient in the efficient application of SQL techniques in everyday business scenarios and looking at data with the critical eye of analytics professional.
Table of Contents (11 chapters)
9. Using SQL to Uncover the Truth: A Case Study


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.