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

Nonclustered columnstore indexes


After a theoretical introduction, it is time to start using columnar storage. You will start by learning how to create and use NCCI. You already know from the previous section that an NCCI can be filtered. Now you will learn how to create, use, and ignore an 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 ETL process for scheduled loading of the data warehouse. This means that the data you analyze is somehow stalled. Frequently, the data is loaded overnight and is thus one day old when you analyze it. For many analytical purposes...