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

Using JSON Data types in PostgreSQL

While arrays can be useful for storing a list of values in a single field, sometimes your data structures can be complex. You might want to store multiple values of different types in a single field, and you might want data to be keyed with labels rather than stored sequentially. These are common issues with log-level data, as well as alternative data. For example, a healthcare patient database may contain a field called prescription, which contains all the prescriptions of a patient. Some patients may not have any prescriptions, thus this field may be empty. Other patients may have multiple prescriptions, and each patient's prescription may be different from the others. One patient may have a hypertension drug of 10mg per day. Another may have an insomnia drug of two pills per night. Yet another patient may have both. It is very hard to store these in a predefined format, so they are usually stored as key-value pairs using the JSON format.