Microsoft SQL Server is developing faster than ever before in its almost 30-year history. The latest versions, SQL Server 2016 and 2017, bring with them many important new features. Some of these new features just extend or improve features that were introduced in the previous versions of SQL Server, and some of them open a completely new set of possibilities for a database developer.
This book prepares its readers for more advanced topics by starting with a quick introduction to SQL Server 2016 and 2017's new features and a recapitulation of the possibilities database developers already had in previous versions of SQL Server. It then goes on to, the new tools are introduced. The next part introduces small delights in the Transact-SQL language. The book then switches to a completely new technology inside SQL Server—JSON support. This is where the basic chapters end and the more complex chapters begin. Stretch Database, security enhancements, and temporal tables are medium-level topics. The latter chapters of the book cover advanced topics, including Query Store, columnstore indexes, and In-Memory OLTP. The next two chapters introduce R and R support in SQL Server, and show how to use the R language for data exploration and analysis beyond what a developer can achieve with Transact-SQL. Python language support is then introduced. The next chapter deals with new possibilities for using data structures called graphs in SQL Server 2017. The final chapter introduces SQL Server on Linux and in containers.
By reading this book, you will explore all of the new features added to SQL Server 2016 and 2017. You will become capable of identifying opportunities for using the In-Memory OLTP technology. You will also learn how to use columnstore indexes to get significant storage and performance improvements for analytical applications. You will also be able to extend database design using temporal tables. You will learn how to exchange JSON data between applications and SQL Server in a more efficient way. For very large tables with some historical data, you will be able to migrate the historical data transparently and securely to Microsoft Azure by using Stretch Database. You will tighten security using the new security features to encrypt data or to get more granular control over access to rows in a table. You will be able to tune workload performance more efficiently than ever with Query Store, and use SQL Server on Linux platforms and in containers. Finally, you will discover the potential of R and Python integration with SQL Server.
