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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

Aggregate Functions with the HAVING Clause

You learned about the WHERE clause in this chapter when you worked on SELECT statements, which select only certain rows meeting the condition from the original table for later queries. You also learned how to use aggregate functions with the WHERE clause in the previous section. Bear in mind that the WHERE clause will always be applied to the original dataset. This behavior is defined by the SQL SELECT statement syntax, regardless of whether there is a GROUP BY clause or not. Meanwhile, GROUP BY is a two-step process. In the first step, SQL selects rows from the original table or table set to form aggregate groups. In the second step, SQL calculates the aggregate function results. When you apply a WHERE clause, its conditions are applied to the original table or table set, which means it will always be applied in the first step. Sometimes, you are only interested in certain rows in the aggregate function result with certain characteristics...

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