Book Image

SQL Server 2017 Machine Learning Services with R

By : Julie Koesmarno, Tomaž Kaštrun
Book Image

SQL Server 2017 Machine Learning Services with R

By: Julie Koesmarno, Tomaž Kaštrun

Overview of this book

R Services was one of the most anticipated features in SQL Server 2016, improved significantly and rebranded as SQL Server 2017 Machine Learning Services. Prior to SQL Server 2016, many developers and data scientists were already using R to connect to SQL Server in siloed environments that left a lot to be desired, in order to do additional data analysis, superseding SSAS Data Mining or additional CLR programming functions. With R integrated within SQL Server 2017, these developers and data scientists can now benefit from its integrated, effective, efficient, and more streamlined analytics environment. This book gives you foundational knowledge and insights to help you understand SQL Server 2017 Machine Learning Services with R. First and foremost, the book provides practical examples on how to implement, use, and understand SQL Server and R integration in corporate environments, and also provides explanations and underlying motivations. It covers installing Machine Learning Services;maintaining, deploying, and managing code;and monitoring your services. Delving more deeply into predictive modeling and the RevoScaleR package, this book also provides insights into operationalizing code and exploring and visualizing data. To complete the journey, this book covers the new features in SQL Server 2017 and how they are compatible with R, amplifying their combined power.
Table of Contents (12 chapters)

Summary

Using SQL Server R for any kind of DBA task, as we have seen here, it is not always hardcore statistics or predictive analytics; we might also be some simple statistical understanding underlying the connection and relationships between the attribute's queries, gathered statistics, and indexes. Prognosing and predicting, for example, information from execution plans in order to prepare a better understanding of the query of cover missing index, is a crucial point. Parameter sniffing or a cardinality estimator would also be a great task to tackle along the usual statistics.

But we have seen that predicting events that are usually only monitored can be a huge advantage for a DBA and a very welcome feature for core systems.

With R integration into SQL Server, such daily, weekly, or monthly tasks can be automated to different, before not uses yet, extent. And as such,...