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

Transforming Data

Each dataset is unique along with each of the business use cases for the datasets. That means the processing and transforming of datasets are unique in their own way. However, there are some processing logics that you will frequently run into in the real world. You will learn some of these in the sections in this section.


When looking through a dataset, you may be interested in determining the unique values in a column or group of columns. This is the primary use case of the DISTINCT keyword.

For example, if you wanted to know all the unique model years in the products table, you could use the following query:

FROM products

This should give the following result:

Figure 3.24: Distinct model years

You can also use it with multiple columns to get all the distinct column combinations present. For example, to find all distinct years and what product types...