Название: SQL Server Analytical Toolkit: Using Windowing, Analytical, Ranking, and Aggregate Functions for Data and Statistical Analysis
Автор: Angelo Bobak
Издательство: Apress
Год: 2023
Страниц: 1069
Язык: английский
Формат: pdf
Размер: 84.1 MB

Learn window function foundational concepts through a cookbook-style approach, beginning with an introduction to the OVER() clause, its various configurations in terms of how partitions and window frames are created, and how data is sorted in the partition so that the window function can operate on the partition data sets. You will build a toolkit based not only on the window functions but also on the performance tuning tools, use of Microsoft Excel to graph results, and future tools you can learn such as PowerBI, SSIS, and SSAS to enhance your data architecture skills.

This book goes beyond just showing how each function works. It presents four unique use-case scenarios (sales, financial, engineering, and inventory control) related to statistical analysis, data analysis, and BI. Each section is covered in three chapters, one chapter for each of the window aggregate, ranking, and analytical function categories.

This is a book on applying Microsoft SQL Server aggregate, analytical, and ranking functions across various industries for the purpose of statistical, reporting, analytical, and historical performance analysis using a series of built-in SQL Server functions affectionately known as the window functions!

No, not window functions like the ones used in the C# or other Microsoft Windows application programming. They are called window functions because they implement windows into the data set generated by a query. These windows allow you to control where the functions are applied in the data by creating partitions in the query data set. “What’s a partition?” you might ask. This is a key concept you need to understand to get the most out of this book. Suppose you have a data set that has six rows for product category A and six rows for product category B. Each row has a column that stores sales values that you wish to analyze. The data set can be divided into two sections, one for each product category. These are the partitions that the window functions use. You can analyze each partition by applying the window functions (more on this in Chapter 1).

Each chapter includes several TSQL code examples and is re-enforced with graphic output plus Microsoft Excel graphs created from the query output. SQL Server estimated query plans are generated and described so you can see how SQL Server processes the query. These together with IO, TIME, and PROFILE statistics are used to performance tune the query. You will know how to use indexes and when not to use indexes.

You will learn how to use techniques such as creating report tables, memory enhanced tables, and creating clustered indexes to enhance performance. And you will wrap up your learning with suggested steps related to business intelligence and its relevance to other Microsoft Tools such as Power BI and Analysis Services.

All code examples, including code to create and load each of the databases, are available online.

What You Will Learn:
Use SQL Server window functions in the context of statistical and data analysis
Re-purpose code so it can be modified for your unique applications
Study use-case scenarios that span four critical industries
Get started with statistical data analysis and data mining using TSQL queries to dive deep into data
Study discussions on statistics, how to use SSMS, SSAS, performance tuning, and TSQL queries using the OVER() clause.
Follow prescriptive guidance on good coding standards to improve code legibility

Who This Book Is For:
Intermediate to advanced SQL Server developers and data architects. Technical and savvy business analysts who need to apply sophisticated data analysis for their business users and clients will also benefit. This book offers critical tools and analysis techniques they can apply to their daily job in the disciplines of data mining, data engineering, and business intelligence.