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.
Table of Contents (11 chapters)
9
9. Using SQL to Uncover the Truth: A Case Study

Window Frame

As mentioned in the earlier sections discussing the basics of window functions, by default, a window is set for each value group to encompass all the rows from the first to the current row in the partition, as shown in Figure 5.6. However, this is the default and can be adjusted using the window frame clause. A window function query using the window frame clause would look as follows:

SELECT 
  {columns},
  {window_func} OVER (
    PARTITION BY {partition_key} 
    ORDER BY {order_key} 
    {rangeorrows} BETWEEN {frame_start} AND {frame_end}
  )
FROM 
  {table1};

Here, {columns} are the columns to retrieve from tables for the query, {window_func} is the window function you want to use, {partition_key} is the column or columns you want to partition on, {order_key} is the column or columns you want to order by, {rangeorrows} is either the RANGE keyword or the ROWS keyword...