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SQL Server 2017 Developer???s Guide

SQL Server 2017 Developer???s Guide

3.6 (5)
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SQL Server 2017 Developer???s Guide

SQL Server 2017 Developer???s Guide

3.6 (5)

Overview of this book

Microsoft SQL Server 2017 is a milestone in Microsoft's data platform timeline, as it brings in the power of R and Python for machine learning and containerization-based deployment on Windows and Linux. This book prepares you for advanced topics by starting with a quick introduction to SQL Server 2017's new features. Then, it introduces you to enhancements in the Transact-SQL language and new database engine capabilities before switching to a different technology: JSON support. You will take a look at the security enhancements and temporal tables. Furthermore, the book focuses on implementing advanced topics, including Query Store, columnstore indexes, and In-Memory OLTP. Toward the end of the book, you'll be introduced to R and how to use the R language with Transact-SQL for data exploration and analysis. You'll also learn to integrate Python code into SQL Server and graph database implementations as well as the deployment options on Linux and SQL Server in containers for development and testing. By the end of this book, you will be armed to design efficient, high-performance database applications without any hassle.
Table of Contents (19 chapters)
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1
Introduction to SQL Server 2017

Summary

In this chapter, we got an overview of how SQL Server can be hosted in a container environment (natively on Windows), allowing us to easily deploy new instances of SQL Server with effectively a single line of code. This lightweight virtualization technology fits well into a development scenario and encourages us to experiment with new versions of SQL Server without the traditional overhead of enterprise IT workflows for deploying new servers.

We also saw how simple it is to install SQL Server on Linux, which is, in fact, even simpler than installing SQL Server on Windows. A single repository command can download the desired release of SQL Server and deploy it into a Linux installation. Depending on the internet connection, we can potentially spin up an instance of SQL Server in minutes rather than multiples of that time when working with Windows (including server restarts...

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