Jen Stirrup has started a new series on getting started with R. First, installing R:
First up, what do you need to know about SQL Server installation with R? The installation sequence is well documented here. However, if you want to make sure that the R piece is installed, then you will need to make sure that you do one thing: tick the Advanced Analytics Extension box.
Her next post covers language basics in contrast to SQL Server:
There are similarities and differences between SQL and R, which might be confusing. However, I think it can be illuminating to understand these similarities and differences since it tells you something about each language. I got this idea from one of the attendees at PASS Summit 2015 and my kudos and thanks go to her. I’m sorry I didn’t get her name, but if you see this you will know who you are, so please feel free to leave a comment so that I can give you a proper shout out.
I’m looking forward to the rest of this series.
Our basic architecture was:
- Multiple VLANs containing SQL Servers to be monitored
- VLAN containing the monitoring server
Probably not the best for what we were wanting to do, but you work with what you’re given. I installed SQL Monitor, fired it up, and nothing worked.
After much trial and error, and a lot of network monitoring by a very enthusiastic young infrastructure guy, here are the inbound rules that we needed to put in place on each SQL Server VLAN to get this working
Note that this is Azure IaaS, not Azure SQL Database.
Buck Woody’s back to blogging, and his focus is data science. Over the past month, he’s looked at R and Python.
In future notebook entries we’ll explore working with R, but for now, we need to install it. That really isn’t that difficult, but it does bring up something we need to deal with first. While the R environment is truly amazing, it has some limitations. It’s most glaring issue is that the data you want to work with is loaded into memory as a frame, which of course limits the amount of data you can process for a given task. It’s also not terribly suited for parallelism – many things are handled as in-line tasks. And if you use a package in your script, you have to ensure others load that script, and at the right version.
Enter Revolution Analytics – a company that changed R to include more features and capabilities to correct these issues, along with a few others. They have a great name in the industry, bright people, and great products – so Microsoft bought them. That means the “RRE” engine they created is going to start popping up in all sorts of places, like SQL Server 2016, Azure Machine Learning, and many others. But the “stand-alone” RRE products are still available, and at the current version. So that’s what we’ll install.
Python has some distinct differences that make it attractive for working in data analytics. It scales well, is fairly easy to learn and use, has an extensible framework, has support for almost every platform around, and you can use it to write extensive programs that work with almost any other system and platform.
R and Python are the two biggest languages in this slice of the field, and you’ll gain a lot from learning at least one of these languages.