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SQL Server 2017 Developer???s Guide

You're reading from   SQL Server 2017 Developer???s Guide A professional guide to designing and developing enterprise database applications

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788476195
Length 816 pages
Edition 1st Edition
Languages
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Authors (3):
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Dejan Sarka Dejan Sarka
Author Profile Icon Dejan Sarka
Dejan Sarka
Miloš Radivojević Miloš Radivojević
Author Profile Icon Miloš Radivojević
Miloš Radivojević
William Durkin William Durkin
Author Profile Icon William Durkin
William Durkin
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Table of Contents (19) Chapters Close

Preface 1. Introduction to SQL Server 2017 FREE CHAPTER 2. Review of SQL Server Features for Developers 3. SQL Server Tools 4. Transact-SQL and Database Engine Enhancements 5. JSON Support in SQL Server 6. Stretch Database 7. Temporal Tables 8. Tightening Security 9. Query Store 10. Columnstore Indexes 11. Introducing SQL Server In-Memory OLTP 12. In-Memory OLTP Improvements in SQL Server 2017 13. Supporting R in SQL Server 14. Data Exploration and Predictive Modeling with R 15. Introducing Python 16. Graph Database 17. Containers and SQL on Linux 18. Other Books You May Enjoy

SQL Server R Machine Learning Services


In SQL Server suite, SQL Server Analysis Services (SSAS) supports data mining from version 2000. SSAS includes some of the most popular algorithms with very explanatory visualizations. SSAS data mining is very simple to use. However, the number of algorithms is limited, and the whole statistical analysis is missing in the SQL Server suite. By introducing R in SQL Server, Microsoft made a quantum leap forward in statistics, data mining, and machine learning.

Of course, the R language and engine have their own issues. For example, installing packages directly from code might not be in accordance with the security policies of an enterprise. In addition, most calculations are not scalable. Scalability might not be an issue for statistical and data mining analyses, because you typically work with samples. However, machine learning algorithms can consume huge amounts of data.

With SQL Server 2016 and 2017, you get a highly scalable R engine. Not every function...

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