This is the code repository for SQL Server 2017 Developer’s Guide, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
Microsoft SQL Server 2017 is the next big step in the data platform history of Microsoft as it brings in the power of R and Python for machine learning and containerization-based deployment on Windows and Linux. Compared to its predecessor, SQL Server 2017 has evolved into Machine Learning with R services for statistical analysis and Python packages for analytical processing. This book prepares you for more advanced topics by starting with a quick introduction to SQL Server 2017’s new features and a recapitulation of the possibilities you may have already explored with previous versions of SQL Server. The next part introduces you to enhancements in the Transact-SQL language and new database engine capabilities and then switches to a completely new technology inside SQL Server: JSON support. We also take a look at the Stretch database, security enhancements, and temporal tables.
Furthermore, the book focuses on implementing advanced topics, including Query Store, columnstore indexes, and In-Memory OLTP. Towards 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 in SQL Server and graph database implementations along with deployment options on Linux and SQL Server in containers for development and testing.
By the end of this book, you will have the required information to design efficient, high-performance database applications without any hassle.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
USE WideWorldImportersDW;
SELECT *
FROM Dimension.Customer;
- In order to run all of the demo code in this book, you will need SQL Server 2017 Developer or Enterprise Edition. In addition, you will extensively use SQL Server Management Studio.
- You will also need the RStudio IDE and/or SQL Server Data Tools with R Tools for Visual Studio plug-in.
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