did, uh, my undergraduate at BYU. Most people at the University of Utah would not like to hear that, but at the university down south and I was looking for opportunities to get to know more people and look outside of that bubble. What? BYU. And so I chose to do my graduate at the University of Utah. And, uh, that's where I came into contact it, and it was the same degree that I did in my undergrad. Uh, it was I did birth information systems at BYU and the University of Utah. And, uh, and part of that was because I enjoyed that discipline and what I had learned that BYU and I want to further that, Uh, that's where I met the likes of Dr Borough, who's probably not there as well as Rohit Agarwal. Uh, and, uh, I continued to enjoy my studies at the University of Utah. Uh, that's where I learned some PHP from road ahead, as well as about emerging technologies and that Emerging Technologies course we had, I believe a Manning Ted Dunning who came from map are or map our sense from a produce, uh, that that lecture in and of itself intrigued me, and I wanted to learn more about Hadoop. I looked into possible places where I could learn more about that. Between my undergraduate in my graduate, I had done on internship at A T and T in Dallas, and, uh, A T and T probably has more data than 99% of most companies out there. And so I started to ask A T and T and some of the people that I knew it a t and t about Hadoop and big Data and they said, Yes, we're looking to get into that right now. And so it was kind of perfect timing. I returned after my internship for a time there and went straight into their big data program where we did our things to dupe. So for five years at A T and T, we were upgrading Hadoop clusters. We were working with job AP eyes to get applications to work correctly. We were doing all sorts of craziness. I was working crazy hours. Ah, and after working five years in that atmosphere of crazy hours, I wanted to slow down a bit. And so then I looked for opportunities outside of a T and T, And that's how I ended up at State Farm. And today I've been at State Farm for about burning on five years now, uh, also So it's been quite a journey, and I've loved every minute of it.

like I mentioned I came to state Farm was to slow down a bit. So 18 t I was working, you know, 70 hour weeks, we were getting cars, and at times of the night, we were just trying to make application drum successfully. And I was part of the operations team that at A T and T actually and tryingto make the big data program successful. We're also trying to get up and running on. So there was a lot of work that went behind that. And after five years, I was somewhat burnout. So I was looking like I said, for a little bit slower pace. State Farm actually has a 38 hour work week. And, uh, once I heard that, that was kind of my selling point. Eso, even though I still am on car occasionally and have to work the occasional weekend. By and large, I'm working fewer than 40 hours a week now. And so, um, like I said, we do with the occasional call in the night when a server service goes down. But, um, generally, uh, it's a lot slower here at State Farm, and that's why I enjoy it. Uh, yet state. From today, I do a lot of Hadoop admin, but at the same time, I'm working on a lot of applications Are so so working with a P eyes Kafka ap eyes H base ap eyes. Uh, so and most of that is Java programming. Uh, however, we do do a lot of pie phone, and then a lot of the automation that goes behind the admin Burke is bash Oh, are either shot scripting.

Yes. So that's that's a good leading eso. Ah, a lot of others are the tools that I said wins adroll that state farm. We also did it 18 90. So it's address programming, a lot of pair programming. That's kind of the methodology behind it. However, the languages, I'd say the biggest language used a state farmers python Ah, the biggest language of support that we used it. A t and t. Um, a lot of the data scientists there used our and so we were forced to support. Are we used to tour at 18 t carve our cloud? Actually, that, um, is similar to our studio today. Except think, eight years ago, when our studio wasn't in the cloud, that was our cloud. Uh, so, um, a lot of tools that you would think that data scientists used today we support those Ah, narrative is that State Farm we're looking at migrating to, uh, the merger between Cloudera and Horton works. That product is called CDP or clutter Cloudera data platform. And so we're going to see a lot of new tools as part of that. My grocery, um, most notably Tez, Apache, Phoenix, that sits on top of the each base. Uh, I think stay farm today has one of the largest H each base instances in the world. And, uh, one of our instances H base instances, is about 10 petabytes and eso. We work religiously day in and day out to keep that instance up and running. Think are of the policy documents and pictures and you name it. That deal with State Farm's core business of the insurance are in that H base instance, and so H base is probably the name tool that I support and use day in and day out.