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


In this chapter, you covered special data types, including date and time, geospatial, complex data structures, and text data types. For date and time data types, you explored how to manipulate time series data, extract components, and represent the information in practical ways that would allow you to build analysis. For geospatial data types, you learned how to convert latitude and longitude into POINT data types that allow you to calculate distances between locations.

For complex data types, you explored several powerful data types: arrays, JSON, and JSONB. For these data types, you learned how to create these values, as well as how to write complex queries to navigate their structure.

Finally, you learned that text data can be useful in analytics—first in running an analysis on keywords, and also in the context of text search, which can be a valuable analytical tool.

As your datasets grow larger and larger, these complex analyses become slower and slower...