1 00:00:00,000 --> 00:00:05,220 Hey there. This is another interview from Mark in our SpringOne Platform speaker series. 2 00:00:05,620 --> 00:00:09,920 It's a pretty good one. I hope you enjoy it. Why don't you check out the conference that this is 3 00:00:09,920 --> 00:00:15,780 pulled from, SpringOne Platform. It's over at springoneplatform.io. It's October 7th and 10th 4 00:00:15,780 --> 00:00:20,940 in my hometown of Austin, Texas. And if you want to get $200 off registration when you go to 5 00:00:20,940 --> 00:00:30,220 springoneplatform.io and register, use the code S1P200 underscore M Heckler. That's S1P200 6 00:00:30,220 --> 00:00:38,540 underscore M H E C K L E R. You can also check the show notes at pivotal.io slash podcast to get that 7 00:00:38,540 --> 00:00:44,840 spelled out to you. So with that, enjoy the episode. Hi, this is Mark Heckler and welcome back to this 8 00:00:44,840 --> 00:00:51,500 episode of our Women of SpringOne Platform series. And today we're joined by Zoe Vance. Zoe, if you 9 00:00:51,500 --> 00:00:55,840 would, please tell our audience a bit about you. Sure. Thanks, Mark. I'm Zoe. I'm the product lead 10 00:00:55,840 --> 00:01:01,940 for messaging at Pivotal. So thinking about enabling developers at our customers to use and access the 11 00:01:01,940 --> 00:01:07,340 best tools they need across streaming event driven microservices and message oriented middleware. And 12 00:01:07,340 --> 00:01:14,080 how can we help those folks be productive and have a seamless experience? Outstanding. That's a hot 13 00:01:14,080 --> 00:01:19,520 topic nowadays. So that's really a good thing. Let's take things back just a little bit. How did you get 14 00:01:19,520 --> 00:01:26,360 into tech to begin with? It was a bit of a winding road. I've definitely been a bit all over the place. 15 00:01:26,580 --> 00:01:32,740 I graduated with a degree in mechanical engineering, but wanted to understand a little bit more 16 00:01:32,740 --> 00:01:38,180 of different industries and different types of businesses out there. So went to management consulting 17 00:01:38,180 --> 00:01:44,520 and worked at McKinsey for two years. And from there really wanted more operational experience. 18 00:01:45,120 --> 00:01:51,640 So I saw I joined a early stage startup as their first employee. And the premise of that startup was 19 00:01:51,640 --> 00:01:58,240 to build tools for developers to structure information on the web. And that was my kind of how I got into 20 00:01:58,240 --> 00:02:04,880 product at that company. And, you know, seeing developer focused tools and having developers as customers 21 00:02:04,880 --> 00:02:10,960 made me want to understand more of the technical side there. So I actually then went to get my master's in 22 00:02:10,960 --> 00:02:17,320 computer science, after which I joined Pivotal, which let me merge some of the technical background 23 00:02:17,320 --> 00:02:23,340 and the product piece. Oh, very cool. That actually is a really good, diverse background to bring 24 00:02:23,340 --> 00:02:29,280 to bear on a lot of the problems that we face on a daily basis. If I could ask, what do you love most 25 00:02:29,280 --> 00:02:34,520 about what you're doing or your current position, or maybe how you got here from your point of origin? 26 00:02:35,660 --> 00:02:41,580 I think I really enjoy, I really love the function of my current job in the day to day. I think what's 27 00:02:41,580 --> 00:02:47,740 interesting about tech is it's a really complex and rapidly changing environment, which leads to a lot 28 00:02:47,740 --> 00:02:54,220 of, you know, a lot of need for creative problem solving. And things are constantly shifting. So you 29 00:02:54,220 --> 00:02:58,940 always need to be looking ahead a little bit. And I like thinking about strategically, what does that mean 30 00:02:58,940 --> 00:03:03,040 for us? And what does that mean for our customers? And how can we help be a part of their journey there? 31 00:03:03,820 --> 00:03:09,220 Yeah, it's funny, you mentioned the constantly shifting, because sometimes it seems like our 32 00:03:09,220 --> 00:03:15,080 industry changes so much, you can either love it because of how dynamic it is, or it can be terribly 33 00:03:15,080 --> 00:03:20,780 frustrating because of the same reason. How do you filter out all of the various different inputs, 34 00:03:20,900 --> 00:03:26,720 all of the different things that could possibly be coming down the road at you, at us, in terms of what 35 00:03:26,720 --> 00:03:30,940 we need to know, what we need to be able to position for our customers? 36 00:03:32,020 --> 00:03:37,860 I think I try to pay attention to all the signals to just get a sense of making sure I'm talking to a 37 00:03:37,860 --> 00:03:44,040 diverse range of people, because it's really easy to get into your own echo chamber about a particular 38 00:03:44,040 --> 00:03:49,260 opinion on a certain technology, or just the market opinion on a certain technology without doing your 39 00:03:49,260 --> 00:03:57,160 own due diligence. So I'd say it's a lot of talking to different people and having a strong opinion. 40 00:03:58,340 --> 00:04:03,880 I often feel like I'll pose the opposing opinion just to make sure I understand what is the substance 41 00:04:03,880 --> 00:04:07,660 behind the argument, because a lot of times people will say things and not necessarily carry through 42 00:04:07,660 --> 00:04:12,020 the implications or the rationale for that. So it's really trying to dig in and make sure I understand the 43 00:04:12,020 --> 00:04:14,140 drivers behind the trends we're seeing. 44 00:04:14,820 --> 00:04:20,260 I'm looking back at your sessions from previous years, and it's obvious where your strengths and 45 00:04:20,260 --> 00:04:24,100 your interests lie, I guess, from looking at that, or it seems obvious to me, because last year, 46 00:04:24,200 --> 00:04:29,880 you presented about data-driven decision making. Sounds familiar, right? As well as decoding 47 00:04:29,880 --> 00:04:35,520 distributed systems. And I think both of those tie in really nicely with what you've been saying, 48 00:04:35,620 --> 00:04:42,500 which is having to make decisions. Filter may not be the right word, but how to accommodate, how to 49 00:04:42,500 --> 00:04:47,640 absorb all of these various different inputs and decide or determine what are the most critical 50 00:04:47,640 --> 00:04:53,700 things to act on or to incorporate. So with that in mind, the data-driven decision making, decoding 51 00:04:53,700 --> 00:04:59,120 distributed systems, which again, both really tie in really nicely, has your thinking on either of 52 00:04:59,120 --> 00:05:01,880 those, both of those changed over the last year? Has it evolved? 53 00:05:02,880 --> 00:05:09,760 I think especially with data-driven decision making, the talk was initially about adapting for 54 00:05:09,760 --> 00:05:14,680 environments where you might not have perfect data and how to leverage data to get to better, 55 00:05:14,800 --> 00:05:21,400 even if you can't get to perfect. I think as I have more and more experience in these complex 56 00:05:21,400 --> 00:05:28,480 landscapes where it's not entirely clear what right is, I'm more and more of the mind that, you know, 57 00:05:28,720 --> 00:05:32,760 do what works. So I definitely believe still in all the best practices that we talked about, 58 00:05:32,760 --> 00:05:37,640 with data-driven decision making. But there are also things that you just can't quantify and that 59 00:05:37,640 --> 00:05:42,860 sometimes you do have to really merge those best practices with, you know, common sense and what's 60 00:05:42,860 --> 00:05:47,800 going on. And, you know, if you want to be keeping up with what's going on in our ever-changing 61 00:05:47,800 --> 00:05:57,780 industry. So, so true. You know, and that practicality that you're bringing out, the idea that you have to 62 00:05:57,780 --> 00:06:02,660 kind of sift through the things that are shiny. And as you mentioned, our industry does tend to focus a 63 00:06:02,660 --> 00:06:07,780 lot on that. And I think part of that is because perhaps, trying to be an optimist here, but part of 64 00:06:07,780 --> 00:06:13,320 that is perhaps that we do wrestle with problems that we always seem to solve imperfectly, right? I mean, 65 00:06:13,320 --> 00:06:17,780 we're kind of trying to improve every time and we're almost there, but we never feel like it's the ideal 66 00:06:17,780 --> 00:06:23,560 fit. Does that make sense? Yeah, that makes sense. I think there are perennial problems that we all 67 00:06:23,560 --> 00:06:30,080 have that we keep hoping will go away. But there are many things, there are many problems in that are 68 00:06:30,080 --> 00:06:36,540 just hard, that are hard to solve that continue to plague us even as we move from new tech to new tech. 69 00:06:37,300 --> 00:06:42,660 Yeah. And this year, I'm so excited about this because this kind of ties in, in my mind, really 70 00:06:42,660 --> 00:06:47,220 nicely with what you're going to be speaking about this year, which are, you have a couple of talks, 71 00:06:47,280 --> 00:06:52,720 actually. One is titled RabbitMQ and Kafka. These are both near and dear to my heart because I think 72 00:06:52,720 --> 00:06:58,080 that streaming is very important. I talk a lot about Spring Cloud Stream with our customers and with the 73 00:06:58,080 --> 00:07:03,420 community as well. And RabbitMQ and Kafka both are kind of best of breed solutions in that space, 74 00:07:03,660 --> 00:07:09,240 as well as you've got a talk on building reliable services on Kubernetes. And Kubernetes seems to be 75 00:07:09,240 --> 00:07:15,300 at least mildly popular in the industry, right? Yes, that would seem to be the trend. 76 00:07:15,760 --> 00:07:22,400 Right. So could you tell us a bit about both of those talks? And again, I'm particularly keyed up 77 00:07:22,400 --> 00:07:26,500 about the one, but obviously both of them are absolutely critical at this point in our industry 78 00:07:26,500 --> 00:07:32,680 and the technology stack in general. Sure. So maybe I'm taking the lazy way out. I'm kind of doing 79 00:07:32,680 --> 00:07:40,000 talks on the FAQ that I get day in and day out. The RabbitMQ and Kafka talk is definitely the question 80 00:07:40,000 --> 00:07:45,640 that I talk about the most with customers when they're making choices about the tools that meet 81 00:07:45,640 --> 00:07:52,240 the requirements for their application workloads. And in this talk, it's quite a nuanced conversation, 82 00:07:52,240 --> 00:07:59,100 but there are clear areas where each tool is a really good fit. And so I'm excited to step through 83 00:07:59,100 --> 00:08:04,240 the architectures that they both have and talk about some of the pros and cons of each for different 84 00:08:04,240 --> 00:08:10,380 use cases because they are both really strong tools. And the building reliable services on Kubernetes 85 00:08:10,380 --> 00:08:16,560 comes from my team's experience building Rabbit for Kubernetes. And what we've learned there is we 86 00:08:16,560 --> 00:08:22,020 start thinking about the criteria that enterprises really need, especially as we try to navigate, 87 00:08:22,220 --> 00:08:26,260 you know, this nascent space in terms of, you know, delivering data services on Kubernetes. 88 00:08:27,320 --> 00:08:33,480 That makes a ton of sense. And I love the, it's not the lazy way out at all. I find it's so frustrating 89 00:08:33,480 --> 00:08:39,720 at times that sometimes people want to answer the questions that no one is asking. And when the community 90 00:08:39,720 --> 00:08:44,720 keeps asking the same questions over and over, it means it's absolutely critical that it gets an answer 91 00:08:44,720 --> 00:08:49,840 because for whatever reason, it's not being answered. So I love that you're covering the ground that is the most 92 00:08:49,840 --> 00:08:54,960 essential to cover, which is the stuff that everyone really wants and needs to know the most. So I 93 00:08:54,960 --> 00:09:01,960 personally find that exciting. So, well, I mean, we're talking a lot about SpringOne Platform because 94 00:09:01,960 --> 00:09:07,460 SpringOne Platform is coming up soon. So if I could ask, what is your favorite thing about SpringOne 95 00:09:07,460 --> 00:09:08,080 Platform, Zoe? 96 00:09:09,360 --> 00:09:14,480 I would say this is, I think this is my third SpringOne Platform. I 97 00:09:14,480 --> 00:09:20,480 have done it a little differently each year. I think I really enjoy, if you look through the agenda, there's 98 00:09:20,480 --> 00:09:25,920 always a series of interesting talks that are on topics that I haven't spent a lot of time thinking about. So it's a 99 00:09:25,920 --> 00:09:30,700 great opportunity to learn. I went to some really amazing talks last year from the Air Force. 100 00:09:31,660 --> 00:09:37,900 And the, I mean, I think the favorite of anyone's conference is just seeing, getting to meet people in person and 101 00:09:37,900 --> 00:09:42,740 talk more casually about the challenges and things we're all working on and seeing in the industry. 102 00:09:44,020 --> 00:09:48,900 Now, what would you say to someone who's just considering coming to SpringOne Platform? I know this is kind of 103 00:09:48,900 --> 00:09:52,780 late in the game. I know that a lot of people booked as soon as the site went up for the 104 00:09:52,780 --> 00:09:58,100 first time. But if you were talking to someone who was just now getting around to looking at the 105 00:09:58,100 --> 00:10:02,900 tickets, who are just now getting around to making their reservations, what would you say to someone 106 00:10:02,900 --> 00:10:06,120 who's giving it some thought, who's planning to come, maybe considering coming? 107 00:10:06,980 --> 00:10:12,500 I would say that SpringOne Platform is a really nice opportunity to lift your head out a little bit 108 00:10:12,500 --> 00:10:17,520 of whatever, you know, tunnel you're in, whatever you're doing day in, day out, and see what else is 109 00:10:17,520 --> 00:10:21,720 going on and what are the other things that people are talking about. It's a really great opportunity to 110 00:10:21,720 --> 00:10:26,960 learn. And I think the things that you don't know, you don't know can often be the most productive 111 00:10:26,960 --> 00:10:33,140 thing to have exposure to. So I definitely encourage people to take the opportunity to learn about new 112 00:10:33,140 --> 00:10:39,220 spaces and new things and new trends. Well, that's great. But you omitted one of the absolutely 113 00:10:39,220 --> 00:10:45,220 essential reasons to come to SpringOne Platform this year, and that's to meet Zoe Vance. So if you 114 00:10:45,220 --> 00:10:50,940 haven't already gotten your ticket, now is the time to go get that ticket to go sign up, because Zoe's 115 00:10:50,940 --> 00:10:54,800 going to be talking about RabbitMQ and Kafka, about building reliable services on Kubernetes. 116 00:10:55,320 --> 00:11:01,340 And this is your chance to come meet Zoe in person. Zoe, in between then and now, how would 117 00:11:01,340 --> 00:11:05,960 people get in touch with you? How would they keep up with what you're doing? And maybe arrange a time 118 00:11:05,960 --> 00:11:07,900 where they could just stop by and shake your hand and say hi. 119 00:11:09,060 --> 00:11:13,620 Yes, I would love to hear from anyone who's listening and meet up at SpringOne. You can reach 120 00:11:13,620 --> 00:11:20,420 me at zvance, Z-V-A-N-C-E, at pivotal.io. And I will see you in Austin. 121 00:11:21,140 --> 00:11:25,760 Sounds great. Looking forward to it. Hope to see you all there. And Zoe, thank you so much for your 122 00:11:25,760 --> 00:11:28,060 time today. And we'll let you get back to your work. 123 00:11:28,700 --> 00:11:29,360 Thank you, Mark. 124 00:11:30,480 --> 00:11:35,660 Well, I hope you enjoyed that interview. I sure did. Hey, and as a bonus for listening this far, 125 00:11:35,660 --> 00:11:41,060 if you want to register for SpringOne Platform, the wonderful conference we have coming up October 126 00:11:41,060 --> 00:11:46,360 7th to 10th in my hometown, Austin, Texas, when you go to springoneplatform.io to register, 127 00:11:46,500 --> 00:11:56,600 you can use the code S1P200 underscore M Heckler. That's M-H-E-C-K-L-E-R. That's Mark's last name. 128 00:11:56,760 --> 00:12:02,360 You can find the discount code if you go to pivotal.io slash podcast as well. And that'll 129 00:12:02,360 --> 00:12:09,160 get you $200 off the registration fee, price, winning admission, however you want to think 130 00:12:09,160 --> 00:12:13,640 about it. Well, we've got several of these coming out. So if you enjoyed this one, you 131 00:12:13,640 --> 00:12:18,300 should check out the other ones. And otherwise, tune into the regular Pivotal Conversations each 132 00:12:18,300 --> 00:12:20,700 week or so. And we'll see you next time. Bye-bye.