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

Introduction

Since its inception, SSIS was meant to execute on a single machine running Windows. The service by itself could not scale on multiple machines. Although it would have been possible to call package execution with custom orchestration mechanism, it didn't have anything built in. You needed to manually develop an orchestration service and that was tedious to do and maintain. See this article for a custom scale-out pattern with SSIS: https://msdn.microsoft.com/en-us/dn887191.aspx.

What lots of developers wanted was a way to use SSIS a bit like the way Hadoop works: call a package execution from a master server and scale it on multiple workers (servers). The SSIS team is delivering a similar functionality in 2017, enabling us to enhance scalability and performance in our package executions.

As mentioned before, the scale out functionality is like Hadoop. The difference...