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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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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

Window Functions

Continuing with the discussion on Window Functions, you want to find the earliest customers for ZoomZoom. In a more technical term, this means you want to rank every customer according to the date they became a customer, with the earliest customer being ranked 1, the second-earliest customer being ranked 2, and so on. You can get all the customers using the following query:

SELECT 
  customer_id, first_name, last_name, date_added
FROM 
  customers
ORDER BY 
  date_added;

The result is:

Figure 5.1: Customers ordered by date_added

You can order the customers from the earliest to the most recent, copy the output to an Excel spreadsheet, and assign a row number to each row so that you have the rank for each customer. But this is not automatic and is prone to errors. SQL provides several ways using which you can achieve it. Later in this chapter, you will learn how to assign numbers to ordered records by using...

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