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

Summary


For an SQL Server developer, this must have been quite an exhaustive chapter. Of course, the whole chapter is not about the T-SQL language, it is about the R language, and about statistics and advanced analytics. Of course, developers can also profit from the capabilities that the new language has to offer. You learned how to measure the associations between discrete, continuous, and the combination of discrete and continuous variables. You learned about directed and undirected data mining and machine learning methods. Finally, you saw how to produce quite advanced graphs in R.

Please be aware that if you want to become a real data scientist, you need to learn more about statistics, data mining and machine learning algorithms, and practice programming in R. Data science is a long learning process, just like programming and development. Therefore, when you start using R, you should have your code double-checked by a senior data scientist for all the tricks and tips that I haven't covered...