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SQL for Data Analytics

You're reading from   SQL for Data Analytics Harness the power of SQL to extract insights from data

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801812870
Length 540 pages
Edition 3rd Edition
Languages
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Authors (4):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Matt Goldwasser Matt Goldwasser
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Matt Goldwasser
Jun Shan Jun Shan
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Jun Shan
Upom Malik Upom Malik
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Upom Malik
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Toc

Table of Contents (11) Chapters Close

Preface 1. Understanding and Describing Data 2. The Basics of SQL for Analytics FREE CHAPTER 3. SQL for Data Preparation 4. Aggregate Functions for Data Analysis 5. Window Functions for Data Analysis 6. Importing and Exporting Data 7. Analytics Using Complex Data Types 8. Performant SQL 9. Using SQL to Uncover the Truth: A Case Study Appendix

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...
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