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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

Understanding data

As already mentioned, understanding data is interleaved with data preparation. In order to know what to do, which variables need recoding, which variables have missing values, and how to combine variables into a new one, you need to deeply understand the data you are dealing with. You can get this understanding with a simple overview of the data, which might be a method good enough for small datasets, or a method for checking just a small subset of a large dataset.

You can get more information about the distribution of variables by showing the distributions graphically. Basic statistical methods are also useful for data overview. Finally, sometimes these basic statistical results and graphs are already exactly what you need for a report.

R is an extremely powerful language and environment for both visualizations and statistics. You will learn how to:

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