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


Columnar storage brings a completely new set of possibilities in SQL Server. You can get lightning performance of analytical queries right from your data warehouse, without a special analytical database management system. This chapter started by describing features that support analytical queries in SQL Server other that columnar storage. You can use row or page data compression levels, bitmap filtered hash joins, filtered indexes, indexed views, window analytical and aggregate functions, table partitioning, and more. However, columnar storage adds an additional level of compression and performance boost. You learned about the algorithms behind the fantastic compression with columnar storage. This chapter also includes a lot of code, showing you how to create and use the nonclustered and the clustered columnstore indexes, including updating the data, creating constraints, and adding additional B-tree nonclustered indexes.

In the next two chapters, you are going to learn about a completely...