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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

5. Window Functions for Data Analysis

Activity 5.01: Analyzing Sales Using Window Frames and Window Functions

Solution:

The solution to this activity is as follows:

  1. Open pgAdmin, connect to the sqlda database, and open SQL query editor.
  2. Calculate the total sales amount by day for all the days in the year 2021 (that is, before the date January 1, 2022).
  3. The query for this step will be:
    SELECT 
      sales_transaction_date::date, 
      SUM(sales_amount) sales_amount
    FROM 
      sales
    WHERE
      sales_transaction_date::date BETWEEN '20210101' AND '20211231'
    GROUP BY
      sales_transaction_date::date;

The result is:

Figure 5.18: Daily Sales of 2021

  1. Calculate the rolling 30-day average for the daily number of sales deals. The query for this step will be:

Activity5.01.sql

1 WITH 
2  daily_sales as (
3    SELECT 
4      sales_transaction_date::date, 
5      SUM(sales_amount) sales_amount
6    FROM 
7      sales
8...
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