SQL Server Analytical Toolkit: Using Windowing, Analytical, Ranking, and Aggregate Functions for Data and Statistical Analysis

Learn foundational concepts through a cookbook-style approach, beginning with an introduction to the SQL Server functions used for data mining, data analysis, and business intelligence. Each function is described in detail, accompanied by simple coding examples designed to familiarize you with the application of the functions. All code used in the technical chapters is available as a download so you can practice what you have learned.

This book goes beyond just showing how each function works by presenting four unique use-case scenarios (sales, financial, engineering, and inventory control) related to statistical analysis, data analysis, and BI. Each section is covered in two chapters, one for introducing the data model and the second for applying the various data processing recipes and functions discussed. You will wrap up your learning with next 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 database toolkits to learn the application of each function, are available online.

• Use SQL Server functions in the context of statistical and data analysis
• Re-purpose code templates that can be modified for your unique applications
• Study use-case scenarios that span four critical industries
• Try tutorials on statistical and analytical formulas that can be applied to data analysis with SQL Server
• Get started with statistical data analysis and data mining using TSQL
• 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.