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

Nonclustered columnstore indexes


After all of the theoretical introduction, it is time to start using the columnar storage. You will start by learning how to create and use nonclustered columnstore indexes (NCCI). You already know from the previous section that a NCCI can be filtered. Now you will learn how to create, use, and ignore a NCCI. In addition, you will measure the compression rate of the columnar storage.

Because of the different burdens on SQL Server when a transactional application uses it compared to analytical applications usage, traditionally, companies split these applications and created data warehouses. Analytical queries are diverted to the data warehouse database. This means that you have a copy of data in your data warehouse, of course with a different schema. You also need to implement the Extract Transform Load (ETL) process for scheduled loading of the data warehouse. This means that the data you analyze is somehow stall. Frequently, the data is loaded overnight and...