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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

Data Structures: JSON and Arrays

Many versions of modern SQL also support data structures, such as JavaScript Object Notation (JSON) and arrays. Arrays are simply lists of data usually written as members enclosed in square brackets. For example, ['cat', 'dog', 'horse'] is an array. A JSON object is a series of key-value pairs that are separated by commas and enclosed in curly braces. For example, {'name': 'Bob', 'age': 27, 'city': 'New York'} is a valid JSON object. These data structures show up constantly in technology applications, and being able to use them in a database makes it easier to perform many kinds of analysis work.

You will explore data structures in more detail in Chapter 7, Analytics Using Complex Data Types. Before that, you will learn about some basic operations in an RDBMS using SQL.

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SQL for Data Analytics - Third Edition
Published in: Aug 2022
Publisher: Packt
ISBN-13: 9781801812870
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