Book Image

SQL Server 2016 Developer's Guide

By : Miloš Radivojević, Dejan Sarka, William Durkin
Book Image

SQL Server 2016 Developer's Guide

By: Miloš Radivojević, Dejan Sarka, William Durkin

Overview of this book

Microsoft SQL Server 2016 is considered the biggest leap in the data platform history of the Microsoft, in the ongoing era of Big Data and data science. This book introduces you to the new features of SQL Server 2016 that will open a completely new set of possibilities for you as a developer. It prepares you for the more advanced topics by starting with a quick introduction to SQL Server 2016's new features and a recapitulation of the possibilities you may have already explored with previous versions of SQL Server. The next part introduces you to small delights in the Transact-SQL language and then switches to a completely new technology inside SQL Server - JSON support. We also take a look at the Stretch database, security enhancements, and temporal tables. The last chapters concentrate on implementing advanced topics, including Query Store, column store indexes, and In-Memory OLTP. You will finally be introduced to R and learn how to use the R language with Transact-SQL for data exploration and analysis. By the end of this book, you will have the required information to design efficient, high-performance database applications without any hassle.
Table of Contents (21 chapters)
SQL Server 2016 Developer's Guide
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
12
In-Memory OLTP Improvements in SQL Server 2016

Introducing R


R is the most widely used language for statistics, data mining, and machine learning. Besides the language, R is also the environment and the engine that executes the R code. You need to learn how to develop R programs, just as you need to learn any other programming language you intend to use.

Before going deeper into the R language, let's explain what the terms statistics, data mining, and machine learning mean. Statistics is the study and analysis of data collections, and interpretation and presentation of the results of the analysis. Typically, you don't have all population data, or census data, collected. You have to use samples—often survey samples. Data mining is again a set of powerful analysis techniques used on your data in order to discover patterns and rules that might improve your business. Machine learning is programming to use data to solve a given problem automatically. You can immediately see that all three definitions overlap. There is not a big distinction...