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)

Boosting analytics with SQL Server R integration

Data science is in the forefront of the SQL Server and R integration. Every task performed by DBA, sysadmin, the analyst, wrangler, or any other role that is working with SQL server can have these tasks supported with any kind of statistics, data correlation, data analysis, or data prediction. R integration should not be restricted only to the fields of data science. Instead, it should be explored and used in all tasks. DBA can gain from R integration by using switching from monitoring tasks to understanding and predicting what might or will happen next. Likewise, this idea can be applied to sysadmin, data wranglers, and so on. R integration also brings different roles of people closer to understand statistics, metrics, measures, and learn how to improve them by using statistical analysis and predictions.

Besides bringing siloed individual teamwork into more coherent and cohesive teams, R integration also brings less data movement, because different users can now—with the help of R code—execute, drill down, and feel the data, instead of waiting to have data first prepared, exported, and imported again. With smoother workflows comes faster time to deployment, whether it is a simple report, a predictive model, or analysis. This allows the boundaries of data ownership to shift into insights ownership, which is a positive aspect of faster reactions to business needs.

In the past year, we have also seen much more interest in data science in Microsoft stack. With R integration, Azure Machine Learning, and Power BI, all users who want to learn new skills and virtues have great starting points from the available products.