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
9. Using SQL to Uncover the Truth: A Case Study

9. Using SQL to Uncover the Truth: A Case Study 

Activity 9.01: Quantifying the Sales Drop

Solution:

Perform the following steps to complete this activity:

  1. Load the sqlda database with psql.
  2. Using the OVER and ORDER BY statements, compute the daily cumulative sum of sales. This provides you with a discrete count of sales over a period of time on a daily basis. Insert the results into a new table called bat_sales_growth:
    SELECT 
      *, 
      sum(count) OVER (ORDER BY sales_date) 
    INTO 
      bat_sales_growth 
    FROM 
      bat_sales_daily;
  3. Compute a seven-day lag of the sum column, and then insert all the columns of bat_sales_daily and the new lag column into a new table, bat_sales_daily_delay. This lag column indicates the sales amount a week prior to the given record, allowing you to compare sales with the previous week:
    SELECT 
      *, 
      lag(sum, 7) OVER (ORDER BY sales_date) 
    INTO 
      bat_sales_daily_delay...