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.

Preface

About the Authors

About the Chapters

Setting up Your Environment

Loading the Sample Datasets – Windows

Loading the Sample Datasets – Linux

Loading the Sample Datasets – macOS

Running SQL files

Accessing the Code Files

1. Understanding and Describing Data

1. Understanding and Describing Data

Data Analytics and Statistics

Types of Statistics

Working with Missing Data

Statistical Significance Testing

SQL and Analytics

Free Chapter

2. The Basics of SQL for Analytics

2. The Basics of SQL for Analytics

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

3. SQL for Data Preparation

3. SQL for Data Preparation

Assembling Data

Transforming Data

4. Aggregate Functions for Data Analysis

4. Aggregate Functions for Data Analysis

Aggregate Functions

Aggregate Functions with the GROUP BY Clause

Aggregate Functions with the HAVING Clause

Using Aggregates to Clean Data and Examine Data Quality

5. Window Functions for Data Analysis

5. Window Functions for Data Analysis

Window Functions

Statistics with Window Functions

6. Importing and Exporting Data

6. Importing and Exporting Data

The COPY Command

Using Python with your Database

Going Passwordless

7. Analytics Using Complex Data Types

7. Analytics Using Complex Data Types

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

8. Performant SQL

8. Performant SQL

The Importance of Highly Efficient SQL

Database Scanning Methods

Killing Queries

Functions and Triggers

9. Using SQL to Uncover the Truth: A Case Study

9. Using SQL to Uncover the Truth: A Case Study

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

Customer Reviews

5 star

0

4 star

0

3 star

0

2 star

0

1 star

0

9. Using SQL to Uncover the Truth: A Case Study

Activity 9.01: Quantifying the Sales Drop

Solution:

Perform the following steps to complete this activity:

Load the sqlda database with psql.
Using the OVER and ORDER BY statements, compute the daily cumulative sum of sales. This provides you with a discrete count of sales over a period of time on a daily basis. Insert the results into a new table called bat_sales_growth:
```
SELECT 
  *, 
  sum(count) OVER (ORDER BY sales_date) 
INTO 
  bat_sales_growth 
FROM 
  bat_sales_daily;
```
Compute a seven-day lag of the sum column, and then insert all the columns of bat_sales_daily and the new lag column into a new table, bat_sales_daily_delay. This lag column indicates the sales amount a week prior to the given record, allowing you to compare sales with the previous week:
```
SELECT 
  *, 
  lag(sum, 7) OVER (ORDER BY sales_date) 
INTO 
  bat_sales_daily_delay...
```