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#### Overview of this book

Understanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don't know how to use it to gain the most effective business insights from data, this book is for you. SQL for Data Analytics helps you build the skills to move beyond basic SQL and instead learn to spot patterns and explain the logic hidden in data. You'll discover how to explore and understand data by identifying trends and unlocking deeper insights. You'll also gain experience working with different types of data in SQL, including time-series, geospatial, and text data. Finally, you'll learn how to increase your productivity with the help of profiling and automation. By the end of this book, you'll be able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of an analytics professional. Please note: if you are having difficulty loading the sample datasets, there are new instructions uploaded to the GitHub repository. The link to the GitHub repository can be found in the book's preface.
Preface
Free Chapter
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

# 9. Using SQL to Uncover the Truth – a Case Study

## Activity 18: Quantifying the Sales Drop

Solution

1. Load the `sqlda` database:
`\$ psql sqlda`
2. Compute the daily cumulative sum of sales using the `OVER` and `ORDER BY` statements. Insert the results into a new table called `bat_sales_growth`:
`sqlda=# SELECT *, sum(count) OVER (ORDER BY sales_transaction_date) INTO bat_sales_growth FROM bat_sales_daily;`

The following table shows the daily cumulative sum of sales:

##### Figure 9.48: Daily sales count
3. Compute a 7-day `lag` function of the `sum` column and 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 what the sales were like 1 week before the given record:
`sqlda=# SELECT *, lag(sum, 7) OVER (ORDER BY sales_transaction_date) INTO bat_sales_daily_delay FROM bat_sales_growth;`
4. Inspect the first 15 rows of `bat_sales_growth`:
`sqlda=# SELECT * FROM bat_sales_daily_delay LIMIT 15;`

The following is the output of...