Book Image

Mastering SQL Server 2017

By : Miloš Radivojević, Dejan Sarka, William Durkin, Christian Cote, Matija Lah
Book Image

Mastering SQL Server 2017

By: Miloš Radivojević, Dejan Sarka, William Durkin, Christian Cote, Matija Lah

Overview of this book

Microsoft SQL Server 2017 uses the power of R and Python for machine learning and containerization-based deployment on Windows and Linux. By learning how to use the features of SQL Server 2017 effectively, you can build scalable apps and easily perform data integration and transformation. You’ll start by brushing up on the features of SQL Server 2017. This Learning Path will then demonstrate how you can use Query Store, columnstore indexes, and In-Memory OLTP in your apps. You'll also learn to integrate Python code in SQL Server and graph database implementations for development and testing. Next, you'll get up to speed with designing and building SQL Server Integration Services (SSIS) data warehouse packages using SQL server data tools. Toward the concluding chapters, you’ll discover how to develop SSIS packages designed to maintain a data warehouse using the data flow and other control flow tasks. By the end of this Learning Path, you'll be equipped with the skills you need to design efficient, high-performance database applications with confidence. This Learning Path includes content from the following Packt books: SQL Server 2017 Developer's Guide by Miloš Radivojevi?, Dejan Sarka, et. al SQL Server 2017 Integration Services Cookbook by Christian Cote, Dejan Sarka, et. al
Table of Contents (20 chapters)
Title Page
Free Chapter
1
Introduction to SQL Server 2017

What is missing in SQL Server 2017?

SQL Server 2016 is the first SQL Server version that has some built-in support for temporal data. However, even in SQL Server version 2017, the support is still quite basic. SQL Server 2016 and 2017 support system-versioned tables only. You have seen at the beginning of this chapter that application versioned tables, and of course bitemporal tables, add much more complexity to temporal problems. Unfortunately, in order to deal with application validity times, you need to develop your own solution, including your own implementation of all constraints, on which you need to enforce data integrity. In addition, you need to deal with the optimization of temporal queries by yourself as well.

In SQL Server 2017, you can define the retention period for historical rows. Therefore, you do not have to do the history data cleanup by yourself, like you...