Book Image

SQL for Data Analytics - Third Edition

By : Jun Shan, Matt Goldwasser, Upom Malik, Benjamin Johnston
Book Image

SQL for Data Analytics - Third Edition

By: Jun Shan, Matt Goldwasser, Upom Malik, Benjamin Johnston

Overview of this book

Every day, businesses operate around the clock, and a huge amount of data is generated at a rapid pace. This book helps you analyze this data and identify key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level. SQL for Data Analytics, Third Edition is a great way to get started with data analysis, showing how to effectively sort and process information from raw data, even without any prior experience. You will begin by learning how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you will learn how to write SQL queries to aggregate, calculate, and combine SQL data from sources outside of your current dataset. You will also discover how to work with advanced data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you will be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation. By the end of this book, you will be proficient in the efficient application of SQL techniques in everyday business scenarios and looking at data with the critical eye of analytics professional.

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Table of Contents (11 chapters)
Preface
About the Book
About the Authors
Audience
About the Chapters
Conventions
Setting up Your Environment
Installing Git
Loading the Sample Datasets – Windows
Loading the Sample Datasets – Linux
Loading the Sample Datasets – macOS
Running SQL files
Accessing the Code Files
1
1. Understanding and Describing Data
1. Understanding and Describing Data
Introduction
Data Analytics and Statistics
Types of Statistics
Working with Missing Data
Statistical Significance Testing
SQL and Analytics
Summary
Free Chapter
2
2. The Basics of SQL for Analytics
2. The Basics of SQL for Analytics
Introduction
The World of Data
Relational Databases and SQL
PostgreSQL Relational Database Management System (RDBMS)
Creating Tables
Basic Data Types of SQL
Data Structures: JSON and Arrays
Column Constraints
Updating Tables
SQL and Analytics
Summary
3
3. SQL for Data Preparation
3. SQL for Data Preparation
Introduction
Assembling Data
Cleaning Data
Transforming Data
Summary
4
4. Aggregate Functions for Data Analysis
4. Aggregate Functions for Data Analysis
Introduction
Aggregate Functions
Aggregate Functions with the GROUP BY Clause
Aggregate Functions with the HAVING Clause
Using Aggregates to Clean Data and Examine Data Quality
Summary
5
5. Window Functions for Data Analysis
5. Window Functions for Data Analysis
Introduction
Window Functions
Statistics with Window Functions
Window Frame
Summary
6
6. Importing and Exporting Data
6. Importing and Exporting Data
Introduction
The COPY Command
Using Python with your Database
Going Passwordless
Summary
7
7. Analytics Using Complex Data Types
7. Analytics Using Complex Data Types
Introduction
Date and Time Data types for Analysis
Performing Geospatial Analysis in PostgreSQL
Using Array Data types in PostgreSQL
Using JSON Data types in PostgreSQL
Text Analytics Using PostgreSQL
Summary
8
8. Performant SQL
8. Performant SQL
Introduction
The Importance of Highly Efficient SQL
Database Scanning Methods
Killing Queries
Functions and Triggers
Summary
9
9. Using SQL to Uncover the Truth: A Case Study
9. Using SQL to Uncover the Truth: A Case Study
Introduction
Case Study
Summary
Appendix
1. Understanding and Describing Data
2. The Basics of SQL for Analytics
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

The Importance of Highly Efficient SQL

To understand why performance is so important, consider the following scenarios.

You are performing post hoc analysis (that is, analysis after the fact or event). You have completed a study and collected a large dataset of individual observations of various factors or features. One such example is described within your ZoomZoom database, which analyzes the sales data for each customer.

With the data collection process, you want to analyze the data for patterns and insights as specified by your problem statement. If your dataset is sufficiently large, you could quickly encounter issues if you do not optimize the queries first; the most common issue would simply be the time taken to execute the queries. While this does not sound like a significant issue, unnecessarily long processing times can cause the following problems:

  • Reduction in the depth of the completed analysis: As each query takes a long time, the practicalities of project...
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