Book Image

SQL Server 2017 Developer???s Guide

Book Image

SQL Server 2017 Developer???s Guide

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 (25 chapters)
Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Preface
Free Chapter
1
Introduction to SQL Server 2017
Index

Introduction to graph databases


As mentioned in this chapter's introduction, relational databases do not provide a good enough answer for the actual software development and data challenges. In today's world, you need to be fast; faster than the others. This means that data is created, updated, and changed faster than ever. Relational databases are inert and are not designed to handle fast changes and meet new business requirements as well as agile development. Handling changes in relational databases is too expensive and too slow. Achieving scalability and elasticity is also a huge challenge for relational databases. Nowadays, changes occur frequently, and data modeling is a huge challenge: you only partly know the necessary requirements and you have less time for development and deployment than ever. Furthermore, relational databases are not suitable for processing highly related and nested data or hierarchical application objects.

To handle tremendous volumes of data as well as a variety...