Book Image

SQL Server 2017 Developer???s Guide

Book Image

SQL Server 2017 Developer???s Guide

Overview of this book

Microsoft SQL Server 2017 is a milestone in Microsoft's data platform timeline, as it brings in the power of R and Python for machine learning and containerization-based deployment on Windows and Linux. This book prepares you for advanced topics by starting with a quick introduction to SQL Server 2017's new features. Then, it introduces you to enhancements in the Transact-SQL language and new database engine capabilities before switching to a different technology: JSON support. You will take a look at the security enhancements and temporal tables. Furthermore, the book focuses on implementing advanced topics, including Query Store, columnstore indexes, and In-Memory OLTP. Toward the end of the book, you'll be introduced to R and how to use the R language with Transact-SQL for data exploration and analysis. You'll also learn to integrate Python code into SQL Server and graph database implementations as well as the deployment options on Linux and SQL Server in containers for development and testing. By the end of this book, you will be armed to design efficient, high-performance database applications without any hassle.
Table of Contents (25 chapters)
Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Preface
Free Chapter
1
Introduction to SQL Server 2017
Index

Chapter 13. Supporting R in SQL Server

SQL Server R Services in version 2017, because of added Python support renamed to Machine Learning Services, combines the power and flexibility of the open source R language with enterprise-level tools for data storage and management, workflow development, reporting,  and visualization. This chapter introduces R Machine Learning Services globally and the R language. R is developing quite fast, so it is worth mentioning that the R version used in this book is 3.2.2 (2015-08-14).

In the first section, you will learn about the free version of the R language and engine. You will also become familiar with the basic concepts of programming in R.

When developing an advanced analytical solution, you spend the vast majority of time with data. Typically, data is not in a shape useful for statistical and other algorithms. Data preparation is not really glamorous but is an essential part of analytical projects. You will learn how to create a new, or use an existing...