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)

Creating predictions with R - disk usage

Predictions involve spotting any unplanned and unwanted activities or unusual system behavior, especially when compared it to the baseline. In this manner, raising a red flag would result in fewer false positive states.

In addition, we always come across disk-size problems. Based on this problem, we will demo database growth, store the data, and then run predictions against the collected data to be able at the end to predict when a DBA can expect disk space problems.

To illustrate this scenario, I will create a small database of 8 MB and no possibility of growth. I will create two tables. One will serve as a baseline, DataPack_Info_SMALL, and the other will serve as a so-called everyday log, where everything will be stored for unexpected cases or undesired behavior. This will persist in the DataPack_Info_LARGE table.

First, create a database...