Hopefully, when you are done running through this you will have something like this. All jobs that have been running longer than x minutes over a 24 hour period and what jobs they overlap, which is not trivial if you are looking at management studio output. You can also use the facet_wrap that you were introduced to in the last blog to look at one job over weeks and months.
R Script for this is here on github, grab it and walk along with the blog.
Now that you have a connection from R to SQL, WOO HOO, what the heck do you do with it? Well for starters all of the reports that you wish Microsoft would write and ship with SSMS, now is your chance to do it yourself.
I will give you a few scripts every now and then just to get you started, I don’t have a production environment and I don’t have access to one so when I offer t-sql and R it will be from whatever data I can generate for a rudimentary test. If you have more data over a longer period of time, I may be interested in looking at it just to test out a bit. I am not going to write a system for you, but I can get you started. And i make no promises that when you run my code it wont blow chunks, my life time running joke is that i would never run my code in production, so i would certainly advise you not to either. Just consider everything i do introductory demos.
So here you are, you know SQL or you at least do something with it everyday and are wondering what all the hoopla is about R and data science. Lets break down the first barrier, R and data science actually have little to do with each other, R is a language, data science is an abstract field of work, sort of like saying I am a computer scientist, that will narrow it down but not by much. What is your industry, what languages do you use, what is your education, hacker, bachelors, masters, phd…? You can be a Data Scientist and never use R.
But we are going to use R, today, right now, get ready.