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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

Database Scanning Methods

You have learned that all database operations are carried out by database management systems (DBMSs) such as PostgreSQL. Typically, the DBMS will run these operations in a server's memory, which stores the data to be processed. The problem with this approach is that memory storage is not large enough for modern databases, which are frequently in a scale of gigabytes, if not terabytes. Data in the majority of modern databases is saved on hard disks and uploaded into memory when it is used in a database operation. Yet again, a DBMS can only upload a small part of the database into memory. Whenever it figures that it needs a certain dataset, it must go to the hard disk to retrieve the unit of storage (which is called a hard disk block) that has the required data in it. The process that the PostgreSQL server uses to search through a database is known as scanning.

SQL-compliant databases, such as PostgreSQL, provide several different methods for scanning...

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