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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

Going Passwordless

In addition to everything mentioned so far, it is also a good idea to set up a .pgpass file. A .pgpass file specifies the parameters that you use to connect to your database, including your password. All of the programmatic methods of accessing the database discussed in this chapter (using either psql or Python) will allow you to skip the password parameter if your .pgpass file contains the password for the matching hostname, database, and username. This not only saves you time but also increases the security of your database because you can freely share your code without having to worry about passwords embedded in the code.

On Unix-based systems and macOS, you can create the .pgpass file in your home directory. On Windows, you can create the file in %APPDATA%\postgresql\pgpass.conf. %APPDATA% is a Windows system value that points to the current application data folder. You can get the actual value of it by opening Windows Explorer, typing the exact word %APPDATA...

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