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.
What You Will Learn
Explore the new development features introduced in SQL Server 2017
Identify opportunities for In-Memory OLTP technology
Use columnstore indexes to get storage and performance improvements
Extend database design solutions using temporal tables
Exchange JSON data between applications and SQL Server
Migrate historical data to Microsoft Azure by using Stretch database
Use the new security features to encrypt or mask the data
Control the access to the data on the row levels
Simplify performance troubleshooting with Query Store
Discover the potential of R and Python integration
Model complex relationships with the graph databases in SQL Server 2017