Disclaimer: I’m one of the Technical Reviewers for this book.

With the rising demand for cloud computing and wide adaptation of cloud-based analytics platforms such as Data Lake Analytics and the likes, the chasm between the traditional Data Professionals and their need for analytics literacy is becoming wider and wider. There seems to be an insurmountable gap between the analytics discipline and the in-house SQL Server database platform pros who are looking to transition to Analytics and Data Science. If you’re a SQL Server pro and want to go after a data science career path, you have to “go out” of the SQL Server path and learn, for example, R or Python.

SQL Server 2016 has changed the data science landscape for the SQL Server platform. SQL Server 2016 R Services provided the data pro a direct path to Analytics and Data Science. It facilitated an easier way to and eliminated the barrier to entry for Data Science. The next version of SQL Server pushed the envelope further. SQL Server 2017 has basically become a database with built-in Artificial Intelligence with its In-database Machine Learning feature. Aside from R, you have another option to use Python. There is much, much more about SQL Server 2017 to be excited about!

If you are looking for a reading material for Machine Learning With R, there is this book published by Packt titled SQL Server 2017 Machine Learning with R: Data Exploration, Modeling and Advanced Analytics (Amazon) written by Tomaž Kaštrun (T | B) and Julie Koesmarno (T | B); and reviewed by Dave Wentzel (B) and yours truly. This book provides essential knowledge to get started with Machine Learning. “It gives you the foundational knowledge and insights to help you understand SQL Server 2017 Machine Learning with R,” the back cover says.

One of the strong features of R is its Visualization. Chapter 4 shows you around Data Exploration and Data Munging. You’ll learn about data frames and dplyr, how to use R with TSQL, integrating R in SSRS and Power BI.

Some statistics is covered in Chapter 5 as well as dataset subsetting and merging. Also covered in the chapter are statistical tests and sampling. Predictive Modeling is covered in Chapter 6.

In Chapter 7, Operationalizing R Code, you can see how R and TSQL work together to train and save a model, then operationalize the model using real-time scoring and native scoring. So, TSQL still plays a big part in Machine Learning; and, you’ll see that all throughout the book.

Chapter 8 covers information about deploying, managing, and monitoring database solutions containing R codes. If you are a SQL Server DBA, the whole Chapter 9, Machine Learning Services with R for DBAs, is for you. It talks about how you can use R to do DBA tasks such as prediction of disk usage. This is an example that you can build upon for more complicated DBA metrics or monitoring tasks. Chapter 10 deals with the extended built-in capabilities of JSON. It also discusses Columnstore and in-memory OLTP.

The book is an easy-to-follow step-by-step guide that will bring you up to speed with Machine Learning with R in SQL Server 2017.

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