I ran across a blog post from the very talented Joe Sack recenty, who I hope makes a few more minutes for me the next time we're in the same town together ;).
The post is a look at some of the customers that are using R Services in SQL Server 2016. As you might expect, there are highlights from customers that show dramatic improvement in performance. Going from 200 valuations/hour to 12,000/minute and taking a process from 2 days to 1 hour. I'm sure not all operations improved that much, but I bet most had some improvement. R Services is a big improvement in the way data is analyzed with the R language.
What I really like, however, is that the piece includes some of the gotchas customers experienced, with links on how one might go about fixing the issues. There are also hints on visualizations and performance tuning options. I like looks at technology that include some details on those items that work well or don't work well. This is a good overview that I hope gets more customers interested in using R services.
But, I hope that we see deeper pieces that can give technical guidance on specific scenarios. Which models scale well and which don't? Which options in some analysis improved (or hurt) performance? There are plenty of items which are best answered with specific examples, rather than general advice. With all the customers and data Microsoft gathers, especially for companies that might use R Services in azure, I'd expect that we could get some useful, detailed examples on how this (and other) technologies actually work in the real world. I'd like to see this guidance, not just with R Services, but with other technologies as well. Certainly providing some sample data sets and code that performs well really (or tuning options), is what most of us want to see.
Of course, I'd like to see more of these stories and details at SQLServerCentral as well, so if you are solving problems and want to publish something, drop us a note. We'd love articles from the real world, whether on SQL Server 2016, or any prior version. The more reference problems and solutions we have, the more people learn to code better.