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

7. Analytics Using Complex Data Types


This chapter covers how to make the most of your data by analyzing complex and alternative data types. While data is typically thought of as numbers, in the real world, it frequently exists in other formats: text, dates and times, and latitude and longitude. In addition to these specialty data types, other data types provide the context regarding sequential or non-predeterministic attributes. The goal of this chapter is to show how you can use SQL and analytics techniques to produce insights from these other data types.

By the end of this chapter, you will be able to perform descriptive analytics on time series data using datetime. You will use geospatial data to identify relationships, then extract insights from complex data types (that is, arrays, JSON, and JSONB) and perform text analytics.