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)

Microsoft's commitment to the open source R language

With a growing popularity and community, R has become and continues to be a big player in the field of advanced analytics and data visualization. R and machine learning servers (or services) are not just buzzword that will be forgotten in the next cycle of SQL Server, but it is infiltrating more and more into different layers of open source and corporate software. In the past five years, many big analytical players have introduced R integration, interpreters, and wrappers for the R language, because of the language's practicality, usability, and inter-disciplinarily and open source orientation. As Microsoft's making a bold and strategic move toward being open source friendly, the use cases for integrating R in SQL Server are growing, making this move even more natural and at the right point in time. This move had been very well appreciated in the SQL community and the business as well.

In comparison to other big analytical tools, Microsoft took integration very seriously. It addressed many of the issues and limitations of the language itself, and created complete integration of R with the SQL Server in order to give the best user experience. Many competitors (such as SAS, IBM, SAP, and Oracle) have done similar integration, but failed to take into account many aspects that contribute to a holistic user experience. Microsoft has announced that joining the R consortium will give them the ability to help the development of the R language and to support future development. In addition, Microsoft has created its own package repository called MRAN (from CRAN, where M stands for Microsoft) and is giving support and SLA agreement for R as well, even though the language and engine is based on Open R (a free, open-sourced version). All these steps tell us how dedicated Microsoft is in bringing an open source, statistical and programming language into the SQL Server environment.

We can only expect more R integration into other services. For example, Power BI supports native R visuals (https://powerbi.microsoft.com/en-us/blog/r-powered-custom-visuals) since October 2016, and R language since December 2015. Therefore, I am a strong believer that R will soon be part of the whole SQL Server ecosystem such as SSAS, SSIS, and SSRS natively as well. With Azure Analysis Services, R is again one step closer to analysis services.