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SQL Server 2017 Developer???s Guide

SQL Server 2017 Developer???s Guide

3.6 (5)
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SQL Server 2017 Developer???s Guide

SQL Server 2017 Developer???s Guide

3.6 (5)

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 (19 chapters)
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1
Introduction to SQL Server 2017

Exploring dynamic data masking


With the new SQL Server 2016 and Dynamic Data Masking (DDM), you have an additional tool that helps you limit the exposure of sensitive data by masking it to non-privileged users. The masking is done on the SQL Server side, and thus you don't need to implement any changes to applications so they can start using it. DDM is available in the Standard, Enterprise, and Developer Editions; you can read the official documentation about it at:

https://docs.microsoft.com/en-us/sql/relational-databases/security/dynamic-data-masking.

This section introduces DDM, including:

  • Defining masked columns
  • DDM limitations

Defining masked columns

You define DDM at the column level. You can obfuscate values from a column in a table by using four different masking functions:

  • The default function implements full masking. The mask depends on the data type of the column. A string is masked by changing each character of a string to X. Numeric values are masked to zero. Date and time data type...

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