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

Data warehouse architects are facing the need to integrate many types of data. Cloud data integration can be a real challenge for on-premises data warehouses for the following reasons:

  • The data sources are obviously not stored on-premises and the data stores differ a lot from what ETL tools such as SSIS are usually made for. As we saw earlier, the out-of-the-box SSIS toolbox has sources, destinations, and transformation tools that deal with on-premises data only.
  • The data transformation toolset is quite different to the cloud one. In the cloud, we don't necessarily use SSIS to transform data. There are specific data transformation languages such as Hive and Pig that are used by the cloud developers. The reason for this is that the volume of data may be huge and these languages are running on clusters. as opposed to SSIS, which is running on a single machine...