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

Columnstore Indexes

Analytical queries that scan huge amounts of data are always problematic in relational databases. Nonclustered balanced tree indexes are efficient for transactional query seeks; however, they rarely help with analytical queries. A great idea occurred nearly 30 years ago: why do we need to store data physically in the same way we work with it logically, row by row? Why don't we store it column by column and transform columns back into rows when we interact with the data? Microsoft played with this idea for a long time and finally implemented it in SQL Server.

Columnar storage was first added to SQL Server in the 2012 version. It included nonclustered columnstore indexes (NCCI) only. Clustered columnstore indexes (CCIs) were added in the 2014 version. In this chapter, readers can revise columnar storage and then explore huge improvements for...