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we're recording on Halloween, and I guess we'll put it in the not-so-secret back end

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so that people can listen on Halloween. Now, that is, last I checked, October 31st.

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Now, here's a question I have for you all. You can choose to answer it however you want.

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What Halloween costumes are going to be in your domiciles today? And as an example,

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I don't think my wife and I are going to have a costume. We haven't really discussed that.

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Our son is in first grade, so he's had to have two costumes, so we're costumed out.

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But my daughter is going to be, if you're familiar with the Wild Kratts,

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she's going to be a Viva in an octopus creature power suit, and that's exciting.

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And then my son's school costume is Jacques Cousteau, which is fun.

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And then his regular costume is he's going to be a scorpion.

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So how about over in your neck of the woods, Richard?

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What's going on?

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Yeah, my daughter's about to turn three, so getting her to wear any costume has still been

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a struggle. So we've got both Wonder Woman and a princess, and she's been wearing them around

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the house. I think we're in good shape. The downside is it's going to be like 45 degrees

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tonight, so both seem like four choices.

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Yeah, you know, I think maybe at the three-year-old level, a good costume is clothes.

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Right, yeah, a shirt right now is a great outfit for her.

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And then my eight-year-old son's going to go as the Grim Reaper, which will also go well

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because it's supposed to rain tonight, and nothing looks scarier than holding an umbrella.

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And then this is an opportune time. Let's bring in our guest here. Why don't you introduce

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yourself before you answer the question?

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Sure. So, hey, everyone. This is Saurabh Gupta. I'm an advisory platform architect at Pivotal,

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working out of the New York City office.

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And I just help customers' current end perspective with all things Pivotal, especially on the

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technical side.

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So when your prospects and customers call you up and say, hey, I don't have a Halloween costume

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yet, what are you advising them on?

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So we start off by doing some pairing on it, I suppose. That's always a good way to break

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problems down at Pivotal. But, you know, there's only one type of costume I personally know,

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and that's nerd. But that's a power suit these days. So you have to kind of...

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I was going to say, is this like 80s nerd with a pocket protector and taped up glasses? Or like,

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how has it evolved over the past, what would that be, 20 years or so?

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Oh, yeah. It's like a hipster nerd now, right? Like, these are all, you know, shorter, those

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ankle-high pants with the funny socks, you know, artism shoes.

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Yeah, yeah.

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It can get expensive to dress like that these days.

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You know, it's funny, the hipster nerd, as it were, I think the only thing it shares

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with the 80s nerd is the high pants and socks. I think both of them had that.

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But they were white socks in the 80s. They were absolutely monochromatic, bleached white

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socks in the 80s. Now it's like these absolutely rainbow colors.

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And these socks, no doubt, are delivered on a subscription basis weekly from some sort of

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service with a funny name. And they probably have silver in them.

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That's right. Socks as a service.

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Well, I think it'll be an exciting Halloween here. My kids woke up just bouncing off the

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walls, as it were. And so I was thinking, like, I know what we should do this evening.

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Load them up with candy. That'll go great.

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How much of that candy do you end up eating?

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You know, I actually, I don't want to be self-aggrandizing, but I actually don't like

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candy that much. So I don't actually eat it. But I'll probably, I'll tell you what, if there's

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one of those, those little butterfingers in there, you know, have you seen the man, I,

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for someone who doesn't really like candy, I'm a fool for butterfingers. I love those. I feel

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like I can feel them like drilling into my teeth as I eat them. And it's good stuff. Also,

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for some reason, some, you know, I book all of my own travel. So whoever, whatever stupid person

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books my travel booked me on an early morning flight tomorrow. And I don't know how that's

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related, but, but we'll see what happens. Be very excited.

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Yeah. You should get up early with that.

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Yes. But I'm going to be narrowly missing by a day. I'm sure they're doing some hilarious,

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spooky, like safety talks on flights today. And it's just, just such a shame. I'm going to miss

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all of that. I don't know. Things, things have gotten pretty sedate lately. Maybe, maybe there'll

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be a Samsung galaxy seven demonstration. Exactly. That's the scariest part of the plane demo right

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now. Yeah. If you dress up as one, will you be allowed on the flight though?

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That's right. You can come melt our chocolate for the free hot chocolate we're giving people in

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first class. Right. Making s'mores. Well, speaking of industry news, before, before we get to the

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second half of our show, I wanted to bring our guests on to talk a little bit about, you know,

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stuff we do, we pivotal do in the industrial world and the internet of things and, and stuff like that.

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But we've got a little bit of news items to go over at first. I think, I think there's a, as always,

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just about every time we talk, there's some interesting partnership acquisition thing going

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on and a few cloud related things. So why don't you kick it off for us, Richard? What's, what's,

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what's catching your eye over there? Well, there's earning season. So we've seen all the

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hand-wringing and celebratory information about, you know, Apple did fine. They think they had

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earnings drop in general, which was unusual, but still printing money. Microsoft's doing well.

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Google's doing well. Amazon, again, had a good quarter. I think it was less than expected. So

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they saw a bit of a stock drop, but it's also seems to be executives shifting between jobs season. So

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Adrian Cockroft joining AWS, him, Caroline McCrory joining Microsoft. Just it's that season. I think

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people get through some bonuses and they get through the summer and they rotate between positions. So

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always interesting to see that moving and shaking. And then my alma mater CenturyLink acquired level

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three communications this morning for lots of billions. So always a end of year sales apparently

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going on in the, in the enterprise world where companies are buying each other just before Black

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Friday. That I'm going to try to use that as a anti McKinsey title sometime on a slide, just in

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quotes for lots of billions. That's, that's a, at some point it gets to be silly money. So it's not

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even worth talking to numbers. So, so since it's your alma mater and I always feel a little awkward

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talking about acquisitions at places I used to work, so I don't, don't go all awkward or whatever,

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but, but like, what is, uh, what's up with CenturyLink and level three? Like I don't,

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I know CenturyLink, but I mean, how would you characterize both of their businesses? Like what,

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what do they do?

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I mean, I think this point it's, it's network, network, network. I mean, level, level three is,

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you know, has a lot of fiber. I think I saw, you know, CenturyLink's adding a couple hundred

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thousand miles of fiber now. And so, you know, you get a lot more connectivity to office buildings,

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to residential as, as companies like CenturyLink are trying to compete more with the Comcasts and

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Verizon's of the world for, for end residential users. But I think this is, you know, a move back

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towards pure telecommunications for both of these companies. It's a, a giant company now that may

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be able to take on some of the traditional telcos, but I think this represents a bigger shift back to

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what they were new and were comfortable with versus some of the more recent flirtations with more

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cloudy stuff.

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Hmm. The pipes, the pipes. Meanwhile, AT&T wants to buy a Time Warner going in the opposite direction,

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I guess.

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Well, and Google stopped doing their Google fiber stuff.

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Oh yeah. I saw that.

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Fiber is expensive. And so these things are tough and it's a capital,

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intensive business. There's only a few things that seem to be really hard to enter nowadays.

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And it's either making planes or laying fiber. Other than that, everybody can be disrupted.

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And, and of course, this is going to skate on the edge of my knowledge of the world,

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but you guys, don't you guys have a CenturyLink stadium up there, like up and up in your head?

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Oh yeah. My, I'm sitting in the office, uh, right across the street from CenturyLink field,

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which is home of the Seattle Seahawks who will finally be Superbowl winners again this season.

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Now I'm hoping that it has more than three levels and in the future, whatever level three is,

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has some sort of special denotation on it. Just something, you know, for the employees to

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appreciate.

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You would hope so. You would hope so.

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So also, uh, last week, you know, I didn't actually follow up on this news, but there's,

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there's new MacBooks out, right? And didn't they put like a touch bar on it? Was,

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I saw a bunch of jokes about them removing the escape key. Did that actually happen? Or,

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or I think so. I, it's just funny because I mean, Microsoft had an event that day before.

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And if you, again, for following my Twitter feed or other people, it was the Microsoft event was,

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wow, this is really some neat stuff. Maybe it's not all practical. Am I going to spend,

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you know, three or six grand on a giant monitor that turns into a screen that I can write on,

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whatever. But it's a very creative play for surface studio versus the Mac event was,

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it seemed like pretty widely panned as, gosh, we waited three or four years for,

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you know, a touch bar and some more memory. Like what else was going to change here? Not a touch

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screen, not these other things that people might look for from a MacBook pro. So, Hey, good news.

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Things, things did evolve, but they're killing more of the ports on the side and, you know,

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adding a touch bar, but not a touch screen. So like, okay, not, not bad, but you wonder if

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Microsoft's at innovating them at this point.

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Good old surface. Remember the surface table? That was, that was ahead of its time.

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I like that.

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Yeah. That was something you see in those old arcades or the old pizza parlors where you get

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to play across from each other. Yeah.

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Yeah. You know, it did have a little miniature rant. Now I got one of those, uh, iPhone seven pluses,

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which is a great phone, uh, as my, you know, it's, it's good stuff, but it is the, uh, the prediction

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about the, uh, the lack of a headphone jack is a little tedious. There have been many times

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over the past since I've had it, uh, so to speak, where I, I, I think, you know, I was traveling last

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week and I think it was like a day trip. And I think three times I sat by a plug and I was thinking

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like, cause I want to charge this. And I was like, Oh, right. I'm actually listening to something

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cause I had it wired in. So it is like, uh, I would categorize that under not cool, right? Like

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it's, it's bearable and sure I should use Bluetooth, but like Bluetooth, like a disaster, like it's

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just really annoying. Uh, so it is, uh, you know, that's, that's an annoyance. Like whenever a ports

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are removed, uh, it's, it's kind of not good, but.

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And I understand that to try to move to progress, but when you're yanking out, you know, HDMI ports,

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like that's how I plug in my monitor at home to my Mac book. So what am I going to do? Buy

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another dongle? Like the dongle market is going to explode, which is maybe the title of this,

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this podcast now, but you know, it's just, do I want this many appendages to my computer or should

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my computer just do the things I want it to do? Yeah. Start investing in big dongle. That's big

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dongle is going to take over the industry. You watch. Yeah. Or at the very least have a dongle

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Halloween costume. Oh, see now that's why you're an advising platform person. It's, it's power

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without accountability. My friend, it's a great place to be. Yeah. Dongle costumes. That would

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be hilarious. Well, I, uh, so, so what is the surface studio like for, uh, for like, uh, graphics

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pros or like what's, what's the, uh, is it similar? What's the, the iPad pro? Is it a similar

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idea that this is like a work item that you would use? That's what it looks like. I mean,

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it's again, it's a big, almost all in one computer that can also turn into a writable screen and this

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and that. So Microsoft going hard against the creative audience, which is, you know, micro,

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uh, Apple's home turf and arguably doing it better right now, just given their focus. And if you look

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at, you know, Apple's earnings, they don't make much money at this point on the computer market.

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They make a heck of a lot more on music and software and things like that. Right. And iPhones.

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So I guess you can forgive them for not paying as much attention to it, but you just wonder if

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they'll, they'll actually start to lose some of the vibe of being the best platform for creative

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people. Yeah. Yeah. Yeah. We'll see. Yeah. I noticed that final cut pro is now only $300

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in the app store, which is, uh, back when I did video editing, it used to be like, I don't know,

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$2,500 or something. It's crazy. Uh, well, I had a couple of, uh, little items that aren't

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really like newsworthy, but I wanted to point them out. Cause I think they're, uh, they're

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highly relevant to things we talk about and things we talk about. So the first one is a

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bit of a, uh, would you call it a jeer? I was reading this piece about, uh, our friends

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at IBM and how like Watson is great for them and happening. And there's this little mention

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that IDC had predicted that, uh, by 2020, uh, the, uh, let's, I can't do the math, but

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the, the, uh, the AR market was going to be something like $45 billion or $46

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billion, which on the surface of it, like, you know, you know, if long time listeners

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know, I'm always like, what's up with AI? Like, could you just help me schedule meetings

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across organizations? That would, that would be good to have some artificial intelligence

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in that. But what's sort of astounding about this, and maybe I have it completely wrong

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or I've done my math incorrectly, but it's, this is 2016 last I checked. So 2020 is less

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than three years away. And then if you read this article, it's basically like currently the, uh,

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the AR market is like seven or $8 billion. So somehow over three years, there's going to be

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almost $40 billion worth like generated on AI stuff. So, so listen, there's going to be some

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gigantic steps that obviously we haven't, uh, taken a, taken a gander on that are going to be

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happening. So we should just get ready. It's going to be awesome.

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There you go. No, those are, those are big numbers. I mean, you do see more software providers,

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obviously baking AI in, but it's amazing to see it getting that big.

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I mean, that amount of progress over three years would be, would be fantastic.

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Right. But that's a, that's a big job.

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And then, and then there's another, another, uh, more nerdy, uh, sort of mixed thing. I,

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I think this is an older study if I remember from 2014 and it had been updated recently,

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but I, I came across this, uh, academic study. I forget who it's from, but academics. And they were

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studying the effect of Airbnb on hotel pricing in Austin and Dallas over the course of, I forget

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how many years. And, you know, the, the, the headline grabbing top of the thing was that basically

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to nuance it a little bit, basically if you were in the lower end hotel market, what we used to call

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motels as, as I recall, but the non-business traveler, uh, luxury market, you can expect your

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prices to drop like eight, eight to 10% because Airbnb is in your market for whatever science

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reason. Like I love reading academic papers because like most of them are methodological

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stuff with funny Greek letters and I can pretend like I'm smart, but anyways, like it's a, so there's

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established that, but I think, I think what's really interesting for people who listen to this

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is, um, uh, there's a, there's an explanation at some point in the paper going over the,

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let's call it elasticity and lack of elasticity and in, uh, the inventory of hotels that beds,

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I think they say. And basically it says in a traditional hotel business, it takes about

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four years from idea to opening the hotel doors to build it out. So if you want to increase your

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capacity, uh, it's a long time versus what's interesting about Airbnb in this discussion is

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that they can basically do it, uh, instantaneously. People who are renting out their houses or rooms

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can either list them or delist them like on their whim. Now, of course they can't build a house

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on, on their whim, but the idea is that you've got this infinite supply of beds that you could

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be in. And because you've got this, all the software and the system and the business model

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enabled by all that software between Airbnb, you like get around this constraint, this four

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year constraint that existed in the hotel market. And it's just, it's an interesting way of thinking

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about a lot of the, uh, kind of small batch thinking and even kind of cloud dynamics that

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we're always saying businesses need to, uh, get onto that, uh, is explained pretty well.

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And, you know, eight to 10% ain't, ain't, uh, ain't nothing to shake your head at when

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it comes to reducing your top line revenue. So it, uh, it does point towards to wrap up

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my rambling here. Like in my mind, I filed as way is one way to look at, uh, all this software

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defined business cloud native, or let's finally do software and in a good way stuff is what are

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constraints that your current business model has that just seem impossible to break? Like

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it takes us four years to build a new hotel. So we had to plan four years and you should

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go through those constraints and ask like, is there a way that we could use software to

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change our thinking and enable a new process that would remove that constraint from us?

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Which is, you know, it's a good way to, uh, set the course of your strategy.

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Right. No, I like that. That's a, that's a great example. It's a more out of the box and

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just always looking software, software tech industry, but that's a, that's a cool example.

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Yeah. So there you go. So we'll put links to those in, uh, in the show notes there. And

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so now, so now let me reintroduce my guest. Hello again. And, uh, or our guest, I should say. So I,

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I wanted to have you on because, uh, over the past couple of weeks. So, so someone had the bright

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idea. Hey, let's go get Coté to talk about something he doesn't know that much about, uh,

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which is always challenging. So, uh, you, you were like, you were like my secret professor on all of

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this and, and you helped like, uh, train me up on, on what, what, uh, some interesting things that we

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do in the industrial space and internet of things and so forth and so on. But as we'll get into,

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like, I think for me, the thing that was most interesting is that, uh, you had come up with

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a, the way that I describe it as a way of applying small batch thinking to IOT cases, which is to say,

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what is like a process by which we can introduce a feedback loop, uh, into industrial stuff that we

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actually do something with it. Right. And, and, you know, as the jokes always go, whenever you hear

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about internet of things, it's always sort of like, uh, I think Harrell from GE puts it this way.

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It's, he's always like, ah, there's, you know, blinking toothbrushes and thermostats, you know,

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like there's all this consumer world stuff, but, uh, also to crib another phrase, uh, phrase from him,

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right. There's also like the big dirty noise machines, noisy machines. And that's where, um,

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you know, a lot of what pivotal does operates in that area and where there's a lot of interesting,

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almost kind of hidden stuff going on that it's all, it's hidden by the fact of it being,

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I guess, boring to most people, right? Like, like managing turbines and agriculture and all this

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stuff. They have really interesting effects, but the, uh, what's happening there is, is not so

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exciting as USB toothbrushes, I guess, but yeah. And it's all these names, you know, with, with fancy,

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fancy names with vowels missing and, you know, fancy new top level domain names as, as endings like

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.io in many cases, right. That, that are trying to be IOT companies, but the surprise to most developers

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even is, is that there's really a lot of truth in the GE ad where they're saying we're an industrial

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company that is also a software company because industrial companies have in, you know, even in

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the analog world really done this for a long time. Um, when you look at how the electrical grid works and

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some of these, how these major systems work, how, how power plants work, there's a lot of, um, custom

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stuff that had to be done to, to continuously monitor things at very, very large scale. And, you know, I always get a chuckle

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out of folks doing cute things by combining different Arduinos together. Oh, I did an IOT project.

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Yeah. It's a connected thing project, which is not an IOT project. And, you know, that was, that was

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something that you and I, we were chatting about last week as well.

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Yeah. And, and, and so, so keep, uh, I was about to lead the witness as a word to get you to say all

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that, but, but you led yourself, uh, the horse and the water and the whatnot, but, uh, lay, lay some

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more of that groundwork. Like what, what are some examples of, of let's call it industrial IOT or

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whatever? Like what are, what are like the machines that were previously like very analog driven, but now

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they either you can rapidly get data off of them or, and, or they have computers embedded on them

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or they're like hooked up to the network as it were. Like what are, what are some interesting, um,

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examples of that?

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Right. So, uh, so let's actually kind of take one step back and, and go over a lot of this, uh,

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glossary in a way, because there are many different ways in which people interpret IOT. So when you go to,

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you know, the tech crunch website, or you go to some of these tech blogs, you have, oh,

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here's an IOT company that is, you know, creating these cute little things to teach children about

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coding and all that. Like, you know, not to, not to poopoo any of the work that those companies are

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doing. Some of it is very, very fantastic work and very essential. The, the fundamental difference that,

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you know, or even when you take in hue lights or some of these, you know,

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a wifi enabled dimmers and stuff, what you're really replacing is a wire with a wireless protocol.

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Basically that's what you're doing. Oh, instead of there being a light switch that I flick on and

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off with human intervention, I can push a button on my smartphone, which will essentially convey a

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message to something which will then electronically cause that switch to flip, which will turn my light

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on or off or, you know, increase my, or decrease the intensity. That is just a connected device.

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It is not necessarily IOT because what you need in IOT is a decision-making loop where there is a cause

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and effect. And if you look at how all of industrial machinery operates, it is designed to be in a feedback

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loop. There's the homeostasis that is continuously monitored where, and, and you have these analog feedback

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systems that are essentially sampling the system continuously. It's, you know, the, the frequency,

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the sample frequency is, you know, either hundreds or thousands of times per second, because when you

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have this massive machine that's burning fuel and it's spinning and it starts to wobble, you have to

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really very carefully figure out whether there's an issue here or not. And very, very quickly.

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So for there to be, you know, you know, when you have, when you have an electronic doorbell where you

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press a button on your smartphone and a message goes up and it, you know, bounces off a data center

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that's 2000 miles away and comes back through your wifi router. And so you're essentially making a 5,000

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mile round trip to cross three feet. That's not IOT. Like that, that would actually be an anti-pattern.

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Yeah. It's, it's, it's like the plastic bag of cyberspace where you have to travel all that way

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and be, yeah, you know, it's, and, and, and this, this, this will kind of reveal, uh, my complete,

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uh, lack of industrial, uh, hardware knowledge being a pure sort of like software person. But

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it's interesting in studying a lot of IOT cases, it seems like one of the Uber things is you, you

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alluded to alluded to it is like wobbles bad. We've got to detect wobble. Like whether you're doing

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drilling or turbines, but I guess a lot of most many things spin very rapidly. And there's, there's

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almost like if there were, if there becomes a, uh, uh, design patterns of IOT, I'm sure one of them

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will be like wobble detection. And, uh, yeah. Yeah. I mean, any, yeah, if it wobbles, it, it's not

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going to last long, no matter, no matter what it is. Um, even, even, even if it's not spinning,

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you look at a human, a wobbling human. Yeah. You got to have it looked at. So,

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so basically in, in all of these, and, and a lot of these machines have been connected online for a

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long time, you know, they were physical cables before the internet. So what the internet does

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is break up a signal into packets, but, and then figures out a way to route them. But if you don't

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have that system and, you know, this is, this is Edison's era, this is, you know, the 1940s,

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you're building these massive machinery plants and, and, and, you know, refining stuff and building

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stuff and turning, uh, rubber, coal and steel into, into cars, really, that's what's happening in

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Detroit. Uh, how are you monitoring this stuff? So, so there, there is always this loop where we will

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extract data. The data is going to be some kind of a, some kind of an electrical pattern, which we will

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then put through some semi electronic device. Um, you know, whether those are resistors or capacitors

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or some other sort of like simpler, non microchip style things, and then create some indication of

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how we control the input into that and, and how those two things start to get more and more separated.

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So then you, you have a control panel, which is again, starts to get more and more removed so that

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you can bring in more scale. So you have one central sort of mission control style system, and then you

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have plants in different places. And then the distance between those plants starts to increase. And

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with increasing automation, increasing sort of computational sophistication, you can get into

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situations where in Canada, you have completely almost autonomous oil, uh, refineries running in

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the tar sands. And you have, you know, the, the engineers that are monitoring it sitting comfortably

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in Alberta in a, in a heated building and going home to their families at five o'clock, uh, that sort of

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thing. So, so it enables that and, and companies have been doing this for a long time. And that's really

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a surprise to a lot of folks who are trying to get into IoT is that there's so much that's already been

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going on for so many years, um, that these use case specific things, um, you know, the, the,

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what we deem the classic quote unquote dinosaur industry really has a leg up just in terms of

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size and scale to tackle some of these problems. Can I ask you a question about that? So,

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I mean, are, are the industrial companies are using this, are they retrofitting to collect some of those,

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that information or how, how are systems, you know, modern applications or rather systems

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collecting some of that data coming off of these things? Are they modern pieces of hardware or are

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they, how does that work? Yes, that's, uh, that's, that's actually coming from the OEMs. So, so that's

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a whole, like, there's, there's this, there's a whole industry that does nothing but these process

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control loops. And, and Pivotal works very closely with some of these major industrial manufacturers that do

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one of a bunch of things, either they make the machines themselves or they make these process control

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systems. I see. And, you know, there, there's an increasing sort of digitization of this, but

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fundamentally there's, there's, um, there's sort of three, three steps to this. You have the thing that is

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producing this output. You have something that's taking that output in. You have some, some other

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mechanism where a decision is being made about what to do with that sort of received output. And then

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something that is transmitting an instruction to change state, which could even be an instruction to

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maintain state, but some, some instruction to, to the machine to, to maintain, manage, monitor, whatever

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with their state. Now, these are, these are classic industrial control systems and they've been

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analog for a while and they're getting more and more digital as we go. The question is,

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is correctness and security. And, you know, where, where do you want to put in, um, you know, the

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monitoring and where do you want to do the data capture to record it somewhere? And how do you

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actually then store that analog signal for later use? So those are the, like the answer is yes and no,

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because it's very, very, very use case specific. If you go into highly regulated environments,

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those things don't change very well or very often because you need to put in a lot of certification

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before any change can occur in more newer fields as, you know, as more, um, uh, you know, if,

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if it's a newer technology, then it will be built digital from, from the get go, but then it'll have

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to be sort of as that. So, you know, I mean, the, the regulatory burden here is significant.

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So, you know, to, to sort of, uh, uh, teeter totter between the abstract and this was abstract

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and the specific, um, uh, what, you know, I, I was kind of going over that Airbnb study earlier,

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or I don't know if kind of, I was shallowly going over it. And, and one of the things I liked

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about it is it sort of like exposed an interesting constraint that, that you can, uh, exploit or limit

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yourself by. And in, in so much that many, many industrial things have been connected and, and at

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least had some like, you know, uh, controls in them, like, what are, what are some in, in more of

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an IOT era? Like, what are some of the constraints that get removed, uh, that allow you to do something

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new and interesting? So if, if you're, if your tar sand buddies have been up there, like remotely

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drilling forever, like what's different about stuff now that it's something to be all agog about?

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Right. Yeah. That's, that's actually a great question. So, so let's actually make it, make it

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more, um, uh, more relatable. So case in point is you have these, uh, utility meters or, uh, you know,

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that, that, that folks come around and, you know, read from the outside, the, the utility workers come

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and read, read their meters or, you know, do some work with that. Um, and then you say, oh,

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let's make them connected so that we don't have to send people out to each house to read it. We'll

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just keep getting their readings, um, ourselves, uh, from central office. So there's, they put,

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they either put a cellular chip in it or they wire that to your telephone line or something,

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um, whereby the meter reading can be, uh, sort of, uh, extracted from a central mission controlled

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style, uh, room or facility. But what you've done really is just done a status reporting.

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So where this starts to get interesting and, and these sorts of things have existed for a long time.

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Another case in point is your, your electronic, uh, key cards when you go into a hotel. Well,

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that is that IOT. Well, it's an electronic doorknob, but it's not really IOT. Um, and, and there are

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many other situations where you have these connected devices, but they're not necessarily IOT. The,

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the, the significant thing about IOT is that you're able to capture the, the time series data from that

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device and are able to analyze that and use that analytic and machine learning input to inform how

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you control that device, whether that control isn't, uh, affected through a human means or to an

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automated means. But this control loop extending that stretching out this control loop by saying,

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we are also going to inject decision database decision-making into how this thing is controlled,

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how its behavior is controlled. That's what is so exciting, which absolutely exist before.

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And, and, and, and correct me where I'm wrong or fill in things I'm missing, but, but the,

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let's say over the last five years, the, the, the major, the technological advances that make

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this possible is probably, and, and cheaper is spread across all of these. It always has to be cheaper,

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but, and maybe that's the only thing is, is you basically, you have cheaper networking,

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whether it's cell networking or whatever, or so it's, it's always possible to like get a dedicated

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line to your convenience store as they do. And you got all those satellite dishes up there,

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but now it's cheaper for everything to, to be online. And then also, uh, I guess just raw compute

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and storage is cheap enough that you could actually do something with that, that you could store it

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and do something with those devices. And then in addition to that, because compute is cheaper and I

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guess storage as well, you can actually do, uh, more analytics than, and you do them faster than

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you could previously. And it seems like, at least when I look at it, the, the upshot of like,

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we've always had connected doorknobs, so to speak to whatever beyond connected doorknobs is,

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is that you, whoever you is a controller, I keep thinking of Homer Simpson sitting in the atomic

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control unit thing there, like at the control panels. But so when Homer's sitting there,

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he can actually very rapidly, uh, do some sort of decision. Right. Whereas I think, I think the loop,

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the speed at which you could do analysis before was both expensive and a lot slower, uh, than it used

375
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to be previously. Right. And it's not just that, that Homer Simpson can do a decision, but he can actually

376
00:32:23,460 --> 00:32:31,140
get three options that there's a 90% probability that this is a meltdown or there is a, you know, 95%

377
00:32:31,140 --> 00:32:36,100
possibility that there's a pump error here, or the, these are your sort of choices because we can infer

378
00:32:36,820 --> 00:32:41,860
from this data that we're getting, here's how it matches with previous patterns. Right. That's interesting.

379
00:32:42,660 --> 00:32:49,620
Right. So, so connecting your electrical meter to, to the utility is, is like low jack. It's like,

380
00:32:49,620 --> 00:32:54,100
where's my meter? What is it's reading? Where's my car? What's this odometer? Right. Very simple.

381
00:32:54,100 --> 00:32:59,940
Okay. Doable, nice, has value, but it's not really interesting. The fun stuff happens when your

382
00:32:59,940 --> 00:33:06,020
utility says, here are what all the meters are doing. So how the house next door is not using as much as

383
00:33:06,020 --> 00:33:12,180
you are. So how do we now create a demand response system whereby not everybody turns their washers on

384
00:33:12,180 --> 00:33:16,340
or dryers on at the same time? So we don't have to fire up a new turbine, which means now they're

385
00:33:16,340 --> 00:33:21,060
sending data back to the meter. Right. Not only is it, is the data being sent back to the meter,

386
00:33:21,060 --> 00:33:25,300
that meter now can communicate with the washing machine and say, don't turn your cell phone right

387
00:33:25,300 --> 00:33:29,860
now. Wait till the load next door is done or wait till we give you the green light because,

388
00:33:29,860 --> 00:33:33,620
hey, as an incentive, we'll actually make it cheaper for you to do that. Right. That loop,

389
00:33:33,620 --> 00:33:40,500
that closed decision loop that says we've analyzed terabytes, petabytes of data. We know when things

390
00:33:40,500 --> 00:33:45,620
are happening. We know what these patterns correlate to. There is an insight here that is actionable,

391
00:33:45,620 --> 00:33:54,340
that has business value. That's IoT. So when I see connect your Arduino sensor and have it send a tweet,

392
00:33:55,460 --> 00:33:58,100
very cute, very nice, but sorry, that is not IoT.

393
00:33:58,100 --> 00:34:06,820
Yeah. I'll put a link to it in the notes. I didn't appreciate this talk until recently,

394
00:34:06,820 --> 00:34:15,780
but way back at the 2015 CF summit, Harrell from GE gave this great talk about how they're using cloud

395
00:34:15,780 --> 00:34:23,220
foundry and various GE things. And the case he has of wind turbines has all sorts of good examples in it.

396
00:34:23,220 --> 00:34:29,060
But I mean, basically they, you know, industrialize up their wind turbine farms and there's all sorts of

397
00:34:29,060 --> 00:34:35,140
interesting, uh, things. And, and once again, wobble comes into play. Uh, but, but one of the more

398
00:34:35,140 --> 00:34:40,100
interesting things is, is that it used to be, they would have to schedule, uh, maintenance. They would

399
00:34:40,100 --> 00:34:44,100
have scheduled maintenance. Like, I don't know what the period was every two weeks or something.

400
00:34:44,100 --> 00:34:49,380
They would just send people out to check all the turbines just because they didn't know if something

401
00:34:49,380 --> 00:34:54,580
needed to be serviced. So they would have to check it. But now, uh, you know, I like this example

402
00:34:54,580 --> 00:35:00,500
because it bleeds into the, uh, the meatware space, so to speak now, because they have like extremely rich

403
00:35:00,500 --> 00:35:05,940
data about wobble and other things, they can just send out, uh, basically maintenance people on demand,

404
00:35:05,940 --> 00:35:10,900
which has like a tremendous cost saving to it. You're not just like wasting rolling trucks out and

405
00:35:10,900 --> 00:35:15,380
everything when everything's, uh, correct. So, you know, I, I thought that was a good example of like,

406
00:35:15,860 --> 00:35:20,500
uh, a pretty simple, straightforward thing, but maybe not obvious that you can't do that unless you

407
00:35:20,500 --> 00:35:24,500
have this, uh, a bunch of connected things sending it to you. And then it plays right into the loop you

408
00:35:24,500 --> 00:35:29,460
have of like, now you can make a decision, which is to send out or not send out a truck to check on

409
00:35:29,460 --> 00:35:34,900
something. Right. And, and that's why it's important to have lots and lots of these, which is where scale

410
00:35:34,900 --> 00:35:40,580
becomes important. And scale is enabled, like you said, just a few minutes ago with, with lowered costs.

411
00:35:40,580 --> 00:35:47,620
So the moment you have a commoditized sort of, uh, substrate of, of very cheap storage network and

412
00:35:47,620 --> 00:35:53,140
compute, that's when you can say, okay, now this becomes interesting. And, you know, like, like it,

413
00:35:53,140 --> 00:35:59,140
it sort of makes me think of, you know, what if, what if you had infinite energy, what would you do with

414
00:35:59,140 --> 00:36:02,820
that? Well, that's when you, things get interesting because you can have things like the transport and

415
00:36:02,820 --> 00:36:08,020
replicators you see in Star Trek. But if you don't have that, then you can't do any of those fun things.

416
00:36:08,660 --> 00:36:13,860
And that's the same thing here, unless you have this elastic on demand, infinite or near infinite

417
00:36:14,420 --> 00:36:20,980
way to, to manage this ingest, this, this amount of data, this amount of repeated analytics from

418
00:36:20,980 --> 00:36:25,460
multiple devices. So you can create a statistical models because, you know, you can't ever make a

419
00:36:25,460 --> 00:36:30,420
model from one data point. Um, because if, if you could, then you would have one person in the country

420
00:36:30,420 --> 00:36:34,100
vote and the election would be over. Cause you could essentially use that as a proxy for many,

421
00:36:34,100 --> 00:36:39,140
many, many other things. Thankfully it's not that. So you really do need a large statistical

422
00:36:39,140 --> 00:36:46,500
sample before you can derive anything meaningful. But then with that large data comes the, the issue

423
00:36:46,500 --> 00:36:53,060
of, of handling it at scale. And, and so, so along those lines, like what, uh, like, like,

424
00:36:53,860 --> 00:36:58,420
you know, so we work, we work to this other slide you have with the loop that's in my head.

425
00:36:58,420 --> 00:37:02,340
And there, there's, there's a division of like, I think you have different words for it, but like

426
00:37:02,340 --> 00:37:07,460
the hot data that you have and the cold data and, and then like, uh, some analytics and then finally

427
00:37:07,460 --> 00:37:12,820
building applications over it. But, but what are like the patterns of the types of, I don't know

428
00:37:12,820 --> 00:37:16,500
what to call them technologies. You have it at least at all the points on this loop.

429
00:37:17,700 --> 00:37:20,900
Sure. So actually let me, let me, you know, for the benefit of the listeners,

430
00:37:21,460 --> 00:37:27,620
let me kind of verbally sketch one of these out and try to imagine that you have a device sitting

431
00:37:27,620 --> 00:37:32,900
out somewhere and it's, it's producing this, this data, it's producing something, it's producing a

432
00:37:32,900 --> 00:37:39,380
signal and you're somehow capturing the signal. So that's the ingest. Now that signal is ingested

433
00:37:39,380 --> 00:37:44,740
and it has to be passed on to something that is reacting to that signal. It could be, um,

434
00:37:44,740 --> 00:37:50,180
a pre-baked algorithm. It could be a pre-baked sort of, uh, cutoff could be fused. It could be

435
00:37:50,180 --> 00:37:53,060
something very simple or something very complicated. It doesn't matter, but something has to take the

436
00:37:53,060 --> 00:38:00,020
signal and react to it. And that reaction then has to go back out to the, you know, the thing,

437
00:38:00,020 --> 00:38:03,860
whether it's the same thing that produces signal or something else that can impact that or,

438
00:38:03,860 --> 00:38:07,140
you know, something entirely different doesn't matter. But fundamentally there's

439
00:38:07,140 --> 00:38:12,100
the data production, data ingest, data reaction, and then data outbound, like the impact of it.

440
00:38:12,820 --> 00:38:17,460
And that's sort of your, and this loop can be very close to where the data has been produced,

441
00:38:17,460 --> 00:38:23,060
or it can be very remote, but it's, it's supposed to be very fast. And that's sort of your basic

442
00:38:23,060 --> 00:38:30,020
process control. Now, one thing you can do is once you've ingested your data, you can also put this in,

443
00:38:30,020 --> 00:38:34,980
in what's known in fancy terminology as a device shadow, where you're saying,

444
00:38:34,980 --> 00:38:41,860
how do I accurately represent the current state of my system? And, and, and what that's, what that's

445
00:38:41,860 --> 00:38:47,940
doing is saying here's an object model or something that's in memory. So it can be updated very, very

446
00:38:47,940 --> 00:38:52,900
fast. It's not a high latency operation. And then it can be queried very, very fast. And additionally,

447
00:38:52,900 --> 00:38:56,340
then you can do some fun things with it, like a real-time analytics. So as I'm,

448
00:38:57,060 --> 00:39:03,540
as I'm putting data into it, I'm conducting a real-time test on that data sample to see if there's any kind of

449
00:39:03,540 --> 00:39:08,260
event I should generate or some kind of trigger I should, I should initiate based on the input that

450
00:39:08,260 --> 00:39:12,740
I just got. But you can theoretically update this thousands of times a second.

451
00:39:14,020 --> 00:39:20,180
And then you're passing this on. So you've done your, you've done your sort of hot data slash device

452
00:39:20,180 --> 00:39:25,060
shadow. You've done some real-time analytics and you're then you then have to surface that. So

453
00:39:25,060 --> 00:39:30,580
someone has to be able to see it on demand. And that's kind of your dashboard. So this is sort of

454
00:39:30,580 --> 00:39:35,540
the, the near-term loop. So you have the, the real-time, you have the near real-time, and then you have

455
00:39:35,540 --> 00:39:42,500
the, the last layer of it, which is the archival. So as you're, as you're sending data to, to the hot

456
00:39:42,500 --> 00:39:46,660
data, to the device shadow system, you're essentially splitting the pipe and sending it to your archival

457
00:39:46,660 --> 00:39:51,940
store. That's your time series database. So that's not just the here and now. It is also the past,

458
00:39:51,940 --> 00:39:58,420
you know, three, six, eight, you know, 12 months of data relating to your entire system. So this is

459
00:39:58,420 --> 00:40:02,340
your big, massive, structured, semi-structured, unstructured data store.

460
00:40:02,340 --> 00:40:04,260
Is that the historian sort of thing?

461
00:40:04,260 --> 00:40:12,260
Yeah, the data story. Yeah. And you can then use, you know, ever more sophisticated or even

462
00:40:12,260 --> 00:40:18,820
time-consuming technologies to scan this data every X amount of time, whether every 10 minutes,

463
00:40:18,820 --> 00:40:23,940
15 minutes, whatever. And this is where highly parallel data analytics technologies become

464
00:40:23,940 --> 00:40:28,580
relevant. And again, these are the challenges because traditional data systems, traditional

465
00:40:29,220 --> 00:40:33,620
RDBMS systems, et cetera, they're not up to this task. You need fundamentally new types of systems

466
00:40:33,620 --> 00:40:37,540
that are built to be distributed, parallel, concurrent, resilient from the ground up.

467
00:40:38,980 --> 00:40:44,260
And you run those machine analytics, your big machine learning, your big analytics on that data. And then

468
00:40:44,260 --> 00:40:49,540
you can essentially do a couple of very interesting things. You can obviously surface those insights,

469
00:40:49,540 --> 00:40:56,500
again, so your KPI dashboards can take those batch analytics insights. But you can also update any

470
00:40:56,500 --> 00:41:04,420
real-time analytics that are occurring. You can actually say, you know what, we've noticed that there's this X amount of drift.

471
00:41:04,660 --> 00:41:11,620
So you should really rebase your wobble detector by this little bit amount because we've detected that everything is kind of

472
00:41:11,620 --> 00:41:17,620
wearing down a little bit. So the tolerances are changing. So why don't you update yourself with this new data?

473
00:41:17,620 --> 00:41:23,780
And none of this involves any downtime. It's all continuous. It's all automated. But what you're

474
00:41:23,780 --> 00:41:30,340
now doing is saying we can take insights from batch analytics, from big machine learning, and inject them

475
00:41:30,340 --> 00:41:36,980
into a real-time analytics pipeline essentially instantaneously. That's a game changer. And then all

476
00:41:36,980 --> 00:41:42,740
of this gets surfaced. And then you can also, depending on the use case and the security requirements,

477
00:41:42,740 --> 00:41:47,860
you can actually integrate your outbound based on the insights and the triggers that just got created

478
00:41:47,860 --> 00:41:52,900
based on these machine learning models. So you can say anytime, you know, the machine learning says,

479
00:41:52,900 --> 00:41:58,820
yeah, this is okay, or we approved this model, why don't you just apply it and send these state change

480
00:41:58,820 --> 00:42:05,780
messages out to the edge or the device? And that kind of completes your whole automated decision-making loop.

481
00:42:06,740 --> 00:42:14,340
So basically, you can imagine it either as tiers or layers or concentric circles, whichever mental

482
00:42:14,340 --> 00:42:19,460
model makes the most sense. But there's, you know, starting from the edge or the device, there's a

483
00:42:19,460 --> 00:42:24,580
real-time, near real-time, and time series loops. And each of them has a different value and a different

484
00:42:25,220 --> 00:42:32,340
play. But from an IoT standpoint, what you have to do is choose which ones are the most relevant use

485
00:42:32,340 --> 00:42:40,020
cases for you. And then how you sort of solve for those, and then how you complete that decision-making

486
00:42:40,020 --> 00:42:45,780
loop and where that occurs. And then you come into the more pragmatic considerations of what's going

487
00:42:45,780 --> 00:42:49,700
to be my data fabric? What's going to be my ingest rate? How big should I make it? What's it going to

488
00:42:49,700 --> 00:42:55,700
cost? Because even if, you know, even very, very small amounts multiplied by infinity become very,

489
00:42:55,700 --> 00:42:58,980
very large amounts. It's like Arthur Dent leaving the light on in his

490
00:42:59,780 --> 00:43:05,460
bathroom in Hitchhiker's Guide to the Galaxy, right? Like, you know, it's pennies, but over the

491
00:43:05,460 --> 00:43:11,540
age of the universe, it's a big amount. So costs become very significant. And there are some numbers

492
00:43:11,540 --> 00:43:17,700
that I ran where even if you have, you know, very small amount of devices out there, a few million,

493
00:43:17,700 --> 00:43:23,300
and they're producing data every X minutes, the amount of data you start to get is significant

494
00:43:23,300 --> 00:43:30,020
enough for it to be a Google-scale problem for your systems. So, you know, that's sort of the high

495
00:43:30,020 --> 00:43:35,060
level of how to think about IoT, how pivotal things about IoT. And these are non-trivial problems.

496
00:43:36,180 --> 00:43:42,820
And, you know, we solve them with customers. And customers are still our customers, so we must be

497
00:43:42,820 --> 00:43:43,540
doing something right there.

498
00:43:43,540 --> 00:43:47,620
The ultimate feedback loop, remaining customer.

499
00:43:48,420 --> 00:43:48,660
Yes.

500
00:43:49,220 --> 00:43:52,900
I mean, you talked about some of the tech. So yeah, I mean, tell me more a little bit about how some of

501
00:43:52,900 --> 00:43:57,220
the pivotal tech fits into something like that. I mean, you've touched on a bit of it, but, you know,

502
00:43:57,220 --> 00:44:02,020
when it comes to that whole loop of taking in data and making decisions and pushing things back out to

503
00:44:02,020 --> 00:44:05,060
the edge and all those components, where do you see that intersection?

504
00:44:05,060 --> 00:44:16,580
Sure. So let's start at the ingest. And you need something that's very lightweight, that's very

505
00:44:16,580 --> 00:44:22,500
resilient, that's built for scale. And, you know, one thing that Pivotal has in its technology

506
00:44:22,500 --> 00:44:27,780
toolkit is RabbitMQ. And RabbitMQ is a very, very capable system. There are almost an infinite

507
00:44:27,780 --> 00:44:34,420
number of permutations you can have it to and tune it to for it to meet the use case that you want it

508
00:44:34,420 --> 00:44:44,100
to. It's very flexible. It's the system that runs WhatsApp, for instance. And I mean, I don't know if

509
00:44:44,100 --> 00:44:47,700
it's the Pivotal version or it's the open source version, but there's some version of RabbitMQ that's

510
00:44:47,700 --> 00:44:54,500
running underneath WhatsApp. But it's billions of messages and very high reliability. And I recently

511
00:44:54,500 --> 00:45:03,780
had a customer ask me, can you give us some numbers, some performance metrics for RabbitMQ? And the only answer to

512
00:45:03,780 --> 00:45:09,380
that is, what do you need? Because you start with what you need, and then you can build these things

513
00:45:09,380 --> 00:45:16,100
out to be the right scale to handle that traffic. But from a technology standpoint, it's a really good

514
00:45:16,100 --> 00:45:23,220
technology. So that's the ingest part. From a hot data part, we have, you know, the scale out in-memory

515
00:45:23,220 --> 00:45:30,500
data grids. One example is Gemfire, that is a Pivotal product. And again, Gemfire is this amazing product.

516
00:45:30,500 --> 00:45:37,060
It's battle tested. It's run in some very, very mission critical environments. And it has this

517
00:45:37,060 --> 00:45:42,420
aspect that you can actually add more nodes and rebalance your clusters. And, you know, it actually

518
00:45:42,420 --> 00:45:49,220
gives you scale out memory. And it also has the advantage that you can run process analytics on the

519
00:45:49,220 --> 00:45:55,940
inbound. As data is coming in, you can actually do an eval on it before you stick it into an in-memory

520
00:45:55,940 --> 00:46:02,260
table. So again, very mature technology, very high speed, very well understood and known and deployed,

521
00:46:02,260 --> 00:46:08,020
and operating very successfully in different places. Then you have sort of the archival data store,

522
00:46:09,380 --> 00:46:14,260
where you could use any number of technologies, you could use any kind of Hadoop distributions, etc.

523
00:46:15,540 --> 00:46:21,060
And if you're using one of the open data platform compatible Hadoop distributions, you can set Hawk on

524
00:46:21,060 --> 00:46:26,820
top of that. Or you could use something like Greenplum database, where you get SQL like access to your

525
00:46:26,820 --> 00:46:32,580
entire data store, but you are able to query it in parallel. So you're essentially moving little

526
00:46:32,580 --> 00:46:39,620
bits of query code and not petabytes of data to run your queries, which then makes batch analytics,

527
00:46:39,620 --> 00:46:45,860
large-scale machine learning very, very feasible. And then, of course, there are things like Madlib

528
00:46:45,860 --> 00:46:50,820
that our data scientists have produced. So these are open source machine learning libraries. And then

529
00:46:50,820 --> 00:46:56,260
there's Cloud Foundry, where all this insight becomes actionable as software. Fundamentally,

530
00:46:56,260 --> 00:47:01,060
you have to take this stuff and do something with it. You can make a PowerPoint or a presentation out of

531
00:47:01,060 --> 00:47:08,020
it and give it to a meeting. And then that insight just goes away, because it's okay, that was interesting.

532
00:47:08,020 --> 00:47:15,860
Now what do we do about it? And the actionability comes from software. So software lives on Cloud Foundry.

533
00:47:15,860 --> 00:47:23,220
It is essentially the application dial tone, where even parts of the ingest can live on it. You can have

534
00:47:23,220 --> 00:47:28,260
different types of stream processing. But the fact that it transforms your IaaS into this elastically

535
00:47:29,060 --> 00:47:34,500
scalable application fabric with very little work on your part. That's pretty incredible.

536
00:47:35,540 --> 00:47:40,420
And then there are other sorts of applications you can write that are actually doing the outbound,

537
00:47:40,420 --> 00:47:45,060
or you can integrate with existing systems, etc. But really, you have to carve out the pieces that

538
00:47:45,060 --> 00:47:51,940
work best for the use case that you have at hand and select from a fairly good, rich, diverse,

539
00:47:51,940 --> 00:47:57,700
and mature toolkit to build it out and get speed to market. Because if you start to develop this

540
00:47:57,700 --> 00:48:02,660
from first principles from the ground up, you wouldn't go in and build your own Hadoop. You

541
00:48:02,660 --> 00:48:08,980
wouldn't go in and build your own networking IP stack. These are completely not value added things.

542
00:48:09,540 --> 00:48:13,940
But what you want to do is build those models, those algorithms where you actually have a demand

543
00:48:13,940 --> 00:48:19,220
response utility business running, where you can charge customers, where it's accruing to your bottom

544
00:48:19,220 --> 00:48:23,700
line. So this stuff helps you get started very, very quickly.

545
00:48:23,700 --> 00:48:30,100
Awesome. How do people learn more about this? Do you find that people are publishing some reference

546
00:48:30,100 --> 00:48:34,340
architectures and seeing how you piece this sort of thing together? Are people learning collaboratively,

547
00:48:34,340 --> 00:48:39,540
or is it just kind of organic as these systems start to collect the data? You start to emit the data.

548
00:48:39,540 --> 00:48:44,100
All right, now I want to be smart with the data. I mean, how do people take that next step? Even if they

549
00:48:44,100 --> 00:48:49,620
don't have these industrial cases, I'm assuming many of these things aren't just connected doorbells. You might

550
00:48:49,620 --> 00:48:55,220
actually have insurance and other reasons you're getting car data or what have you. So how do people

551
00:48:55,220 --> 00:48:59,860
get past the hype, in your opinion, and get into actually doing something that's meaningful and

552
00:48:59,860 --> 00:49:00,980
helping them be more efficient?

553
00:49:00,980 --> 00:49:06,980
I think the most important thing is to start with a use case. Because what happens is,

554
00:49:08,100 --> 00:49:16,340
the business says, we want an IoT platform. And tech here is, oh, they want an IoT platform. And tech is

555
00:49:16,340 --> 00:49:21,380
great at building things, like in-house corporate tech. And they start to build out, quote unquote,

556
00:49:21,380 --> 00:49:27,380
an IoT platform. But that means so many things to so many different people. So actually going back

557
00:49:27,380 --> 00:49:31,540
to the business and saying, hey, what do you mean when you say this? What do you really want to do with

558
00:49:31,540 --> 00:49:35,940
this data? How often will this data be collected? What kind of insights are you looking for?

559
00:49:37,140 --> 00:49:43,860
Is device state really relevant to you? Is the feedback loop, is the React going to go back to the

560
00:49:43,860 --> 00:49:49,220
edge? Or is it something that you want to consume internally? What's a tie into revenue for this?

561
00:49:49,220 --> 00:49:55,060
What kind of scale are you looking for? Those things start to become significant. So the first

562
00:49:55,060 --> 00:50:00,580
step is to say, yes, we need to handle data from things at a very, very rapid rate. But then the next

563
00:50:00,580 --> 00:50:08,900
most important question is, what do you plan to do with it? And that's where many companies

564
00:50:08,900 --> 00:50:14,020
currently have a disconnect, because they're not really clear about that. So to have a clear line

565
00:50:14,020 --> 00:50:18,980
from what you want to do with it, then it can get distilled into, here's how you do it. And let's get

566
00:50:18,980 --> 00:50:25,380
this going. Because once that clarity exists, these things can get implemented very, very fast.

567
00:50:27,380 --> 00:50:31,540
Yeah. Well, I think, I think, yeah, that's fantastic. So that, that was what I was looking for. And I

568
00:50:31,540 --> 00:50:35,780
think, I think to the, uh, to, to the point of clarity, right, that's what I liked so much about

569
00:50:35,780 --> 00:50:41,540
your initial model was it was, it was very simple. And then it creates a, uh, like, oh, this is what I'm

570
00:50:41,540 --> 00:50:47,540
doing. So sort of realization, like why I would want to go about doing that. So, so thanks for being on

571
00:50:47,540 --> 00:50:52,740
to go over that. I think it was helpful. And I'll, um, I'll, uh, in the show notes and I'll probably

572
00:50:52,740 --> 00:50:58,100
make it the album art for this episode, if I can, uh, wangle that out, but, uh, I'll, I'll put the,

573
00:50:58,100 --> 00:51:03,620
those, those slides I was talking about that have the, uh, the loop in it. So it, but, but before I

574
00:51:03,620 --> 00:51:07,940
start to wrap up, is there any, where should people to go to, uh, to find you, uh, well, not to find

575
00:51:07,940 --> 00:51:13,540
you in the real world, but online to, you know, engage with your brand as it were, talk with you more,

576
00:51:13,540 --> 00:51:18,580
whatever you, you got a Twitter account or just like LinkedIn where you set up. Uh, yeah,

577
00:51:18,580 --> 00:51:27,220
you can find me at, on Twitter. It's Saurabh Gupta SG, all one word. Um, or, you know, just,

578
00:51:27,220 --> 00:51:35,020
I'm in the New York office. So just, uh, you know, send in, send in, uh, uh, a request. I suppose

579
00:51:35,020 --> 00:51:43,020
my email is as Gupta at Pivotal.io. So just, just write in, um, and, uh, any, any other ways,

580
00:51:43,080 --> 00:51:46,100
like any time you contact Pivotal, if you just say, Hey, I want to talk to this guy,

581
00:51:46,540 --> 00:51:47,820
I'm sure they'll figure out a way to get it to me.

582
00:51:47,820 --> 00:51:53,260
That's right. Go, go through our concierge program. Yeah. You know, I, I have actually

583
00:51:53,260 --> 00:51:58,380
been at the doorstep of the New York Pivotal office, but never inside yet. So, so one day

584
00:51:58,380 --> 00:52:03,380
I'll go inside. I'm sure it's great. Yeah. And get me some, uh, get me some Sriracha cashews.

585
00:52:03,700 --> 00:52:09,240
I gotta, gotta check that out. Well, before we wrap up, I just wanted to mention, uh, that we, uh,

586
00:52:09,240 --> 00:52:15,040
we at Pivotal, we're, we're, we're doing our, our cloud native roadshows again, uh, throughout the

587
00:52:15,040 --> 00:52:19,960
rest of the year, if I remember. And they're, they're in Dallas, Cincinnati, St. Louis, Hartford,

588
00:52:20,140 --> 00:52:26,240
Denver, New York, and Los Angeles. And, uh, they're all in November. So, uh, my colleagues

589
00:52:26,240 --> 00:52:30,900
are going to be getting lots of miles to end out their year traveling around. But the, these

590
00:52:30,900 --> 00:52:34,640
are, these are basically, I wouldn't call them full day. I think they wrap up around like

591
00:52:34,640 --> 00:52:39,740
two or 3 PM, but you get, you get a breakfast and a lunch and you get to see people like, uh, Casey

592
00:52:39,740 --> 00:52:45,120
West and Kenny and other people. Uh, I think, I think Fred shows up there to talk about data and

593
00:52:45,120 --> 00:52:50,780
they just go over, uh, in general, like what Pivotal is up to and some, some fun zany things. So

594
00:52:50,780 --> 00:52:54,980
I'll put a link to it in the show notes. Or if you just search for Pivotal cloud native roadshow,

595
00:52:55,100 --> 00:52:59,020
you can no doubt find that, but, uh, you should come check those out if you're, you're in one of

596
00:52:59,020 --> 00:53:03,720
those cities. And also if you're very timely and listening to this, I'm going to be up in Kansas

597
00:53:03,720 --> 00:53:09,020
city, uh, on the first and second, which is tomorrow and the day after tomorrow, uh, at a

598
00:53:09,020 --> 00:53:14,940
miniature roadshow and also to DevOps meetup. And then if you're, uh, in Omaha, uh, enjoying a steak

599
00:53:14,940 --> 00:53:19,720
or talking with your buddy, Warren Buffett on November 16th, I'll be up there doing the, uh, the

600
00:53:19,720 --> 00:53:25,180
same thing as well. So there's some exciting, uh, speaking of engaging with brands, you can come

601
00:53:25,180 --> 00:53:31,500
engage with us all throughout, uh, the North American area. So with that, uh, thanks for

602
00:53:31,500 --> 00:53:35,780
listening as always, if you've enjoyed this episode, or you just want to do something nice,

603
00:53:35,780 --> 00:53:40,760
regardless of your sentiment about this episode, you go to the iTunes store and leave us a review

604
00:53:40,760 --> 00:53:45,560
or a rating. And the best thing is always to just, uh, recommend this podcast to other people to

605
00:53:45,560 --> 00:53:51,040
subscribe to and listen, or just tell us that you like it and then we'll be happy. And the rest of

606
00:53:51,040 --> 00:53:56,300
our weeks will be, uh, will be great. And with that, we'll see everyone next time. Bye-bye.

607
00:54:00,160 --> 00:54:06,540
Well, we're, uh, clearing out our throats, apparently. Let me get a sip of coffee here.

608
00:54:07,640 --> 00:54:08,360
Good call.

609
00:54:08,480 --> 00:54:13,740
Because when you want to clear your throat, there's nothing like a pipe and hot cup of black coffee

610
00:54:13,740 --> 00:54:15,200
to really clear it out, right?

611
00:54:15,780 --> 00:54:17,780
Yeah, well, I'll just swallow some cinnamon right now, then.

612
00:54:18,500 --> 00:54:19,300
Second choice.

613
00:54:20,140 --> 00:54:23,980
Cinnamon. Did I ever tell you the story that I remember reading maybe in Herodotus,

614
00:54:24,120 --> 00:54:31,080
Herodotus, where, uh, the people who controlled the cinnamon trade claimed, uh, one of the reasons

615
00:54:31,080 --> 00:54:36,900
it was so expensive was because the cinnamon only grew way up on a mountain guarded by like harpies

616
00:54:36,900 --> 00:54:41,300
or dragons or something. And so it was very dangerous. So they would have to take rocks and

617
00:54:41,300 --> 00:54:46,160
throw it up at the cinnamon trees. And so the cinnamon trees would break and fall down the side of the

618
00:54:46,160 --> 00:54:51,640
cliff. So, you know, ergo, uh, you know, Henry the first had to pay a lot for cinnamon or something.

619
00:54:52,080 --> 00:54:54,860
I don't know. That, that seems like a lie by made up by big cinnamon.