Well, here we go, its finally time to take this horse out of the barn! I’m Shep, some of you know me some of you do not, you can check my linked in profile to learn more about me, I’m just a bloke. I have been screwing around with SQL Server since 1994 SQL 4.21a, I am pretty sure we had the OS2 version laying around somewhere but we all ignored the purple ALR server and the dozen or so disks needed to install everything, until one night I got bored and decided to turn it on. Jump a head a dozen or so years and I am working for Microsoft five years as a SQL PFE, one year as frontline Windows Engineer (lapse in judgement) and eventually the SQL Server Customer Advisory Team (SQLCAT) in Redmond running the Lab. “PC (server) Recycle it” became my mantra, I took great joy in PC recycling an HP SuperDome, I know you’re clutching your pearls at the thought, especially considering the server cost $700,000 – $1,400,000. But, damn thing wouldn’t boot anymore what did you expect. For what its worth, HP brought it back, and the lab got another one.
While running the lab we had fewer customers than I would have liked to have seen since the entire world turned upside down and suddenly cloud computing was all the rage. SQL CAT became low priority, and everything cloud became everything. Most lab managers run for 1-2 years and move on to something else, many lab managers take the job for the sole purpose of getting on the CAT team, certainly a noble endeavor. I made it pretty clear up front that I never wanted to be on CAT, I just wanted to run the lab, so at the end of my two years I was at a precipice, leave CAT, leave Microsoft, do something else?
Like many SQL Server experts, once you do it long enough you are only really good at being a SQL Server expert, you know data, you know many data domains, you can think pretty fast on your feet, data and structures are living logic problems. Those all certainly seem like noble skills, so what’s next?
About ten years ago every C* suite executive started demanding big data after getting off an airplane having read CIO magazine, or some airline rag that regurgitates smart looking articles. The problem was they didn’t know what big data was, quite frankly neither did we. What happened that caused it was we stopped deleting data, compression became main stream in databases, devices started creating data, people essentially became devices to be tracked, the web began to dominate everything, so your mouse became a device to be tracked, where was it, and what was it doing, your phone, your path through a mall was now data on how to sell more stuff, your car. It is a toxic waste dump of unlimited data, all of it available to be mined and sadly be hacked.
Well, Data Scientists and statisticians to the rescue, or so the line goes. Statistics and data science is claimed to be the sexist job, I really don’t know what that means, how does one define a sexy job?
At the end of my CAT Lab tenure, R was in beta with SQL Server shipping in the CTPs, Azure ML (Machine Learning) was new and shiny and it certainly seemed like a good thing to invest in, so I put together a training plan for me and ended up working with the MS data scientists working on customer problems. The customer engagements were very much like a CAT engagement, phone call to triage, verify they had the data needed, make sure they have an actual question to be answered from the data, then a few of us would fly to the customer site and do a 5 day hackathon to attempt to give them a machine learning solution in Azure ML. Sometimes we could, sometimes we could not, but this gave me the opportunity to get my feet wet in the data science space and work with the Microsoft Data Science PhDs, which led me to quit Microsoft to focus full time on filling in the academic gaps needed to be good in that role.
But, why…? What I learned after working with customer SQL experts, their statisticians, their data scientists, and our data scientist is that 60%-80% of the time consuming portion of the job can be done by a SQL gal or guy. No Shit! The problem I learned was that first off they are speaking very different languages, one is coming from academia and the other quite frankly is computer science, or hard knock learning much like me. Second, on average 80% of the job of the data scientist is data wrangling and feature engineering, and unless they have been doing for a very long time and learned, they are trying to do all of it on their laptop in R or Python using a sample, something that SQL Server and most relational database engines are exceptionally good at and if well written, very fast at. Most data scientist would love help in this area, but there is still the failure of communication, they are speaking different languages. SQL folks have the innate ability to break walls down between groups (if they want to), there typically is no better data or domain expert than the SQL folks, so they would seem to be the perfect fit.
So the next question is how does one get there? Unfortunately its not just a 40 hour immersion class, but depending on deep and how far you want to go, that can be the beginning!
This blog and eventually the training the public talks I am developing will whittle away at the boundaries. Today the path looks intimidating and since data science is the sexy new job, there are a lot of companies telling you that in 12 weeks you can be a data scientist, though if you dig deeper you will find many of their students have graduated with math upper level math degrees. Microsoft is claiming tools like Azure ML will commoditize data science and bring it to the masses, but if you call and ask for statistical model help you will likely end up on the phone with a PhD, so there is a tiny discrepancy in what they are selling and what they are doing, but, magic black box machine learning is industry wide. I do believe ten years from now that machine learning will be built into everything and the knowledge required to take advantage of it will be widespread.
I will be sharing my academic experiences, as much knowledge as I can articulate, where I think academia is blowing it, where I think we are blowing it. What classes are worthwhile, lots and lots of samples in R and SQL along the way.