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

Summary


For SQL Server developers, this must have been quite an exhausting chapter. Of course, the whole chapter is not about the T-SQL language; it's about the R language, and about statistics and advanced analytics. Of course, developers can also profit from the capabilities that the new language has to offer. You learned how to measure associations between discrete, continuous, and combinations of discrete and continuous variables. You learned about directed and undirected data mining and machine learning methods. Finally, you saw how to produce quite advanced graphs in R.

Please be aware that if you want to become a real data scientist, you need to learn more about statistics, data mining and machine learning algorithms, and practice programming in R. Data science is a long learning process, just like programming and development. Therefore, when you start using R, you should have your code double-checked by a senior data scientist for all the tricks and tips that I haven't covered in...