This chapter will cover the basics of how to install SQL Server 2016 to properly go through the examples in the rest of this book. The version of SQL Server used in the following chapters is the Developer edition of SQL Server 2016. It's available for free as long as you subscribe to Visual Studio Dev Essentials.
Mastering SQL Server 2017
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Mastering SQL Server 2017
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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
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Mastering SQL Server 2017
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Table of Contents (20 chapters)
Title Page
Copyright
About Packt
Preface
SQL Server Tools
JSON Support in SQL Server
Stretch Database
Temporal Tables
Columnstore Indexes
SSIS Setup
What Is New in SSIS 2016
Key Components of a Modern ETL Solution
Dealing with Data Quality
Unleash the Power of SSIS Script Task and Component
On-Premises and Azure Big Data Integration
Extending SSIS Custom Tasks and Transformations
Scale Out with SSIS 2017
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