1 00:00:00,720 --> 00:00:04,160 Well, here we are at Cloud Camp Austin, one. 2 00:00:04,420 --> 00:00:05,200 I almost said two. 3 00:00:05,340 --> 00:00:06,280 I don't know why it would be two. 4 00:00:06,580 --> 00:00:06,800 Right. 5 00:00:06,980 --> 00:00:10,520 And we've got another edition of Profiles in Courage. 6 00:00:11,060 --> 00:00:13,860 I'm one of your hosts, Michael Coté, as always. 7 00:00:13,940 --> 00:00:15,020 And we've got another guest with us. 8 00:00:15,060 --> 00:00:16,300 Why don't you introduce yourself real quick? 9 00:00:16,320 --> 00:00:16,600 Are you on camera? 10 00:00:17,040 --> 00:00:17,580 No, I'm not. 11 00:00:17,680 --> 00:00:18,140 It's just you. 12 00:00:18,140 --> 00:00:19,780 How do people know that that's not me? 13 00:00:19,960 --> 00:00:21,220 Well, they just recognize my voice. 14 00:00:21,220 --> 00:00:22,440 My name is actually Michael. 15 00:00:22,800 --> 00:00:23,300 That's right. 16 00:00:23,560 --> 00:00:24,780 But everyone calls me Coté. 17 00:00:25,360 --> 00:00:25,960 That's true. 18 00:00:26,540 --> 00:00:29,820 If people start calling me Coté, then I think we have something to worry about. 19 00:00:29,820 --> 00:00:30,280 That's true. 20 00:00:30,940 --> 00:00:32,040 My name is Michael Wild. 21 00:00:32,460 --> 00:00:37,040 And, you know, people from Red Monk frequent listeners may know you from the IT Management 22 00:00:37,040 --> 00:00:39,400 Podcast episode you were on, as I recall. 23 00:00:39,560 --> 00:00:45,080 I was on one episode, which apparently was the highest listened to and the highest highly 24 00:00:45,080 --> 00:00:46,260 subscribed episode. 25 00:00:47,260 --> 00:00:51,000 And I constantly get, you know, I'm signing autographs now. 26 00:00:51,240 --> 00:00:51,520 That's right. 27 00:00:51,700 --> 00:00:53,480 It's become a launch pad. 28 00:00:53,640 --> 00:00:56,300 Well, I'm sorry we've created that problem opportunity for you. 29 00:00:56,580 --> 00:00:57,620 Because nothing is worse. 30 00:00:57,700 --> 00:01:00,200 You want to go to the grocery store like you need a red bell pepper. 31 00:01:00,700 --> 00:01:04,240 And the next thing you know, three hours later, you know, you're often to call in the bounty 32 00:01:04,240 --> 00:01:07,320 guards and the big, the bounty, you know, the bounty, not bounty guards. 33 00:01:07,780 --> 00:01:08,280 What do they call them? 34 00:01:08,320 --> 00:01:08,920 Bodyguards. 35 00:01:09,420 --> 00:01:10,740 In the big SUVs. 36 00:01:10,900 --> 00:01:11,340 That's right. 37 00:01:11,380 --> 00:01:11,980 The Mounties. 38 00:01:12,240 --> 00:01:12,840 The Mounties. 39 00:01:13,120 --> 00:01:17,040 So, so, so, so why don't you tell us, here we are at Cloud Camp Austin and you've got 40 00:01:17,040 --> 00:01:21,040 a shirt that's basically like advertising something as a silver lining to the cloud. 41 00:01:21,360 --> 00:01:24,600 So how does, what does, what does a Splunk guy care about the cloud? 42 00:01:25,400 --> 00:01:26,620 That's a great question. 43 00:01:26,820 --> 00:01:33,880 Um, I work for a company called Splunk and we make a cool software engine, um, that is best 44 00:01:33,880 --> 00:01:36,240 described as a search engine for IT data. 45 00:01:36,880 --> 00:01:41,280 And, you know, my shirt says, Cloud Computing meets Silver Lining. 46 00:01:41,880 --> 00:01:48,540 Um, earlier we were in the UnConference session and we were, the session we were talking about 47 00:01:48,540 --> 00:01:52,140 is public versus private clouds and Amazon. 48 00:01:52,720 --> 00:02:00,120 I made a statement that, unfortunately, with folks like Amazon, GoGrid, Maso, we all have 49 00:02:00,120 --> 00:02:06,040 the same problem, you're still buying IT infrastructure and you still need IT people and you still need 50 00:02:06,040 --> 00:02:07,760 to manage all that stuff. 51 00:02:08,680 --> 00:02:14,160 And so Splunk is a really easy application that just indexes, allows you to search and 52 00:02:14,160 --> 00:02:18,920 report on any of the data that's happening up in the cloud, like all your transactional 53 00:02:18,920 --> 00:02:24,400 logs, web logs, a lot of the stuff that's just generated from the apps that you have running. 54 00:02:24,680 --> 00:02:29,400 So, you know, we, we generally are seen as someone who gives you a great deal of visibility 55 00:02:29,400 --> 00:02:33,600 in your infrastructure and hopefully puts a silver lining on cloud computing. 56 00:02:34,040 --> 00:02:38,300 And, and, uh, now, now you've actually been in the IT management business for quite some 57 00:02:38,300 --> 00:02:38,660 time. 58 00:02:39,100 --> 00:02:40,080 Hence the podcast. 59 00:02:41,220 --> 00:02:42,220 I can't, you know. 60 00:02:42,480 --> 00:02:45,620 And, and you, uh, you used to work at Tivoli, if I remember correctly, right? 61 00:02:45,620 --> 00:02:49,400 I used to work at Tivoli, um, after it was acquired by IBM. 62 00:02:49,700 --> 00:02:51,820 So I'm not an OT guy. 63 00:02:52,520 --> 00:02:57,100 And, and so, so looking at like being part of the, you know, Splunk is sort of what I would 64 00:02:57,100 --> 00:03:02,020 categorize as sort of one of the new, the, the wave of sort of new ways of doing IT management 65 00:03:02,020 --> 00:03:03,220 along with lots of other folks. 66 00:03:03,220 --> 00:03:03,440 Right. 67 00:03:03,660 --> 00:03:08,380 And like how, when you look back on the, whether it was OT or not, on, on sort of like the Tivoli 68 00:03:08,380 --> 00:03:09,300 way of doing things. 69 00:03:09,300 --> 00:03:09,300 Right. 70 00:03:09,300 --> 00:03:13,260 So the old, the old traditional way and sort of the newer way, like what do you see the differences 71 00:03:13,260 --> 00:03:19,260 are between these two different approaches or, or groups of products, if you will, to give 72 00:03:19,260 --> 00:03:21,840 you a broad question or is there no difference? 73 00:03:23,240 --> 00:03:32,500 Well, um, that's a good question because when I was at Tivoli, um, which hopefully the explanation 74 00:03:32,500 --> 00:03:40,660 of on Tivoli is pretty easy, but, um, we, the company started with its products on standardizing 75 00:03:40,660 --> 00:03:44,720 and wrapping software around the process of managing IT. 76 00:03:44,720 --> 00:03:50,160 And we used to say, you don't want to buy point tools because they're outside of your 77 00:03:50,160 --> 00:03:55,000 own process and they're really, um, completely separate. 78 00:03:55,140 --> 00:03:56,420 They have their own interfaces, their own training. 79 00:03:56,420 --> 00:03:57,680 You wanted to buy a whole suite of tools. 80 00:03:57,700 --> 00:04:00,520 Yeah, it's like, there's no reason why you need to buy QuickBooks. 81 00:04:00,640 --> 00:04:01,820 You could just buy SAP. 82 00:04:02,320 --> 00:04:02,940 Right, right, right. 83 00:04:03,120 --> 00:04:04,960 So we had to switch batteries, but you were going on. 84 00:04:05,200 --> 00:04:07,680 So Tivoli, it was like encouraging buying a whole suite of tools. 85 00:04:07,680 --> 00:04:14,760 Well, buying a whole suite, um, you know, like buying SAP versus buying QuickBooks and a number 86 00:04:14,760 --> 00:04:25,420 of other products and, um, um, the, the products are designed in a, with a general theme of how 87 00:04:25,420 --> 00:04:26,880 management should be done. 88 00:04:26,880 --> 00:04:33,580 And then you have the whole other side of the equation, which is, you know, guys in the 89 00:04:33,580 --> 00:04:38,380 trenches need the tools that they can use to get work done. 90 00:04:38,380 --> 00:04:46,040 Um, Tivoli and CA and the big five or six or two or three, now, whatever it is, great. 91 00:04:46,660 --> 00:04:50,360 Um, when you have a multi-billion dollar IT business, you want to manage the whole process. 92 00:04:51,260 --> 00:05:00,120 But, so Splunk comes out of, um, out of that era of, well, you know, Tivoli and IBM, they've 93 00:05:00,120 --> 00:05:05,420 covered a lot of things, but, you know, I'm in a diamond mine and I need a special tool 94 00:05:05,420 --> 00:05:10,700 to do that and that's sort of where the Splunk or Spelunking idea comes with, you know, getting 95 00:05:10,700 --> 00:05:11,820 me inside of a data center. 96 00:05:11,820 --> 00:05:17,540 So we built this extremely powerful tool that is used by a lot of the technical folks in 97 00:05:17,540 --> 00:05:17,800 IT. 98 00:05:17,960 --> 00:05:20,540 I mean, we've built our business on the backs of the geeks in IT. 99 00:05:21,260 --> 00:05:25,520 We're actually moving up the stack now to gain, to allow, you know, levels of management 100 00:05:25,520 --> 00:05:27,360 to gain visibility on what the heck's going on. 101 00:05:28,220 --> 00:05:37,000 Um, I, most of the time those worlds, um, work really well together because if there's 102 00:05:37,000 --> 00:05:42,400 a product like Splunk, none of the big five have anything even close to what Splunk does. 103 00:05:42,760 --> 00:05:44,500 They don't deal with all the unstructured data. 104 00:05:44,620 --> 00:05:46,620 They're awesome at monitoring and all this other great stuff. 105 00:05:47,140 --> 00:05:51,020 So when a customer asks me, hey, how does Splunk work with Tivoli? 106 00:05:51,120 --> 00:05:52,400 How does Splunk work with Nagios? 107 00:05:52,440 --> 00:05:53,320 I get that all the time. 108 00:05:54,320 --> 00:05:59,040 Um, you know, it's pretty much a human process that's plugged together where the IT management 109 00:05:59,040 --> 00:06:02,400 system will say, hey, something is wrong over here. 110 00:06:03,260 --> 00:06:06,680 A task would be doled out to a human to then go look over here. 111 00:06:06,780 --> 00:06:09,220 Splunk makes it really easy to go look over here fast. 112 00:06:09,220 --> 00:06:09,420 Right. 113 00:06:09,540 --> 00:06:13,840 So there's sort of a, nowadays there's a proliferation of sort of best of breed or niche tools. 114 00:06:14,020 --> 00:06:14,180 Sure. 115 00:06:14,520 --> 00:06:17,620 Instead of the complete solution that you might have expected in the past. 116 00:06:17,760 --> 00:06:19,820 We used to battle against that best of breed. 117 00:06:20,160 --> 00:06:20,720 Right, right, right. 118 00:06:20,720 --> 00:06:25,300 You need the, you know, the framework and, you know. 119 00:06:25,900 --> 00:06:28,640 But now both seem to be able to coexist quite pretty well. 120 00:06:28,920 --> 00:06:32,600 You know, it's a happy world and it's probably because of the cloud. 121 00:06:33,600 --> 00:06:35,280 So I have one last question for you. 122 00:06:35,280 --> 00:06:41,920 Now, here in Texas, a couple of years ago, we had this local distillery, like fifth generation, 123 00:06:42,040 --> 00:06:43,000 that makes Tito's vodka. 124 00:06:43,520 --> 00:06:47,140 And I've noticed in the liquor store, there's now a Dripping Springs vodka. 125 00:06:47,620 --> 00:06:51,800 And since you live in Dripping Springs, as I recall, have you come across this vodka? 126 00:06:51,900 --> 00:06:52,760 Do you know anything about it? 127 00:06:52,840 --> 00:06:54,880 Your memory is amazing. 128 00:06:55,280 --> 00:06:55,600 Yes. 129 00:06:56,540 --> 00:06:58,280 I live in Dripping Springs, Texas. 130 00:06:59,920 --> 00:07:04,080 And the Dripping Springs vodka is apparently supposed to be quite good. 131 00:07:05,400 --> 00:07:12,240 However, there was like a huge explosion and fire at the place where Dripping Springs vodka is made. 132 00:07:12,900 --> 00:07:15,140 It was on, I read about this on DS. 133 00:07:15,220 --> 00:07:16,560 So we should be buying this up. 134 00:07:16,620 --> 00:07:17,600 It may not come anymore. 135 00:07:18,240 --> 00:07:19,580 I'm going to go buy some tonight. 136 00:07:19,980 --> 00:07:23,640 I think we're going out after this. 137 00:07:23,720 --> 00:07:27,540 Maybe we'll have to stop by Specs on the way to Little Wondros. 138 00:07:27,620 --> 00:07:28,240 There you go. 139 00:07:28,420 --> 00:07:32,520 Well, I think we've gotten some actionable advice here at the end. 140 00:07:33,000 --> 00:07:33,380 That's good. 141 00:07:33,540 --> 00:07:35,800 And so thanks for being on this Profiles in Courage. 142 00:07:37,120 --> 00:07:41,280 Profiles in Courage seems like it needs some sort of da-da-da-da-da-da-da theme. 143 00:07:41,700 --> 00:07:42,760 Well, maybe we'll put that in there. 144 00:07:42,760 --> 00:07:44,080 You know, you need something like that.