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Making Data Simple: Cloud computing, part 2

Making Data Simple: Cloud computing, part 2

Overview

Five years ago, people were talking about it. Today, it’s completely mainstream. Learn how to use cloud to enhance your business in part two of our podcast with IBM Fellow Sam Lightstone. Sam discusses how data is moved, stored, and automated in the cloud. Also, learn the steps to invention and how to get your products noticed.

Show notes

00.30 Connect with Al Martin on Twitter and LinkedIn.

01.00 Connect with Sam Lightstone on TwitterLinkedIn, and his website.

04.45 Learn more about IBM's Hybrid Cloud Platform.

05.35 Learn more about autonomous systems (AS).

09.50 Learn more about IBM's QueryPlex.

10.30 Learn more about IBM Fellows.

12.00 Learn more about The Pomodoro Technique.

19.35 Find the MIT Technology Review.

20.20 Find Making it Big in Software: Get the Job.Work the Org. Become Great by Sam Lightstone.

20.25 Connect with Steve Wozniak on Twitter and LinkedIn.

20.45 Learn more about IBMs Db2 Database here.

21.45 Learn more about James Gosling here.

21.50 Connect with Bjarne Striustrup on Twitter,  LinkedIn, and his personal website.

21.50 Connect with Robert Elliot Kahn on Twitter and learn more about him here.

22.15 Learn more about Ray Tomlinson here.

Hungry for more? Check out our previous podcast episodes of Making Data Simple:

Transcript

Al Martin:             Hey, folks, welcome to Making Data Simple this Al Martin speaking, this week we have a continuation from the previous week so, enjoy. 

                              So, here’s  a question. I visit a lot of clients, like you do, and honestly, many clients are struggling in terms of how to get started. In fact, one of the go-to, you know, strategy sessions that I often take with me is the maturity curve, you know, starting everything, talking about client servers, data science, machine learning, you know, and everything in between. 

                              But since many of these clients went digital, they’ve been on premises, right? So, I think their tendency is to be reluctant, to be cautious toward the cloud. Some of them are — like you said, some of them are going all in, but some of them are just kind of playing with like new use cases and what not, but with breaches, with the security of their beloved data, and I get it right, if data is the new loyal, you’re going to give it away, if somebody takes it, or you know, somehow you open it up and it’s not safe. 

                              So, I guess, back to the simple question again, how does a company get started with cloud? What do you see as the first step to making that leap? 

Sam Lightstone:    Yes, I think the first step really is to start small. Pick something just, you know, put your foot into it, so to speak, but dip your foot in the pool. Pick some application, and maybe something that’s got a relatively small amount of data, and try it out, you know, and have that experience of optimization on tap, resources on tap. Someone else is worrying about how to make it run smoothly, excellently, continuously available, distributed. 

                              Let that be someone else’s problem. And, you know, the great thing about the cloud is you’re always getting the latest. You’re always getting the latest software, you’re always getting the latest optimizations, you’re always getting the latest hardware. Someone else takes care of that for you. 

                              So start with something small, and I  — and you know, many cloud providers  — cloud services offer free versions, or sometimes what we call freemium versions where they’re free up until a certain amount of usage, so you can really start playing with this stuff literally for free.

                               And I think that, you know, what I hear from clients is that once they start using cloud services, especially the fully managed services where we take care of all the configuration tuning for you, they get hooked. They’re like wow this is great, I can up and running so fast, I want more of that. Just start small. 

                              Start small, start with something that’s close to free and try it out, and, you know, sort of find some examples and opportunities where you just got a few gigabytes of data. And with usually with a few gigabytes of data you can start using cloud services for free. 

Al Martin:             Awesome. So, back to IBM and our point of view in hybrid data management, and the key word, I guess if any of them, is hybrid, and you mentioned this earlier, and we really do that through, what I would call, a common analytic engine across multiple form factors. You know, from our appliance, to our — or to using, you know, you can use your own hardware with the software behind the firewall, or you can take that and take our database to the cloud.

                              That could be managed, that could be hosted, it could be in memory, it could be transactional, it could be analytics, I mean, that is the simplicity that we’re driving across the business. And I’ll be the first to say, as you know, as an executive I don’t often care how the sausage is made, I just want it to be made great with quality and I want it to work. 

                              So my question to you is, and I think, you know, some people may hear that the pitch that I give, and it almost sounds like magic, it’s not. I see demos, and people are taking me through this on a regular basis. You talked a little bit about this earlier. Can you talk a little bit more about some of that magic out of that hood that actually makes that possible? 

Sam Lightstone:    Yes, okay, interesting question. I know we have a long history in this topic of how do we make this all so manageable and so simple? You know, I talk to a cloud as, you know, we take care of it for you, but how do we do that? How do we take care of that for you? 

                              So, let me start by maybe just elaborating, or painting a picture of the problem. Imagine that you’re a company that provides cloud services like IBM or any of our competitors. You know, we don’t have one database that we’re managing, or a hundred, or a thousand, and you know, we’re talking a scale of data and a scale of database instances that is measured  — I probably can’t give exact numbers, it’s all, you know, you’ll probably all get fired or something if I talk exact numbers. But imagine you have hundreds of thousands… 

Al Martin:             Then let’s not talk exact numbers. 

Sam Lightstone:    Right. So, but you know, anyway I’ll just make up a number, let’s say millions. Let’s say you have millions of databases, so you know, there’s no human being that’s going round — going around and tune all that for you.

                              So, most companies, most cloud companies, automate some aspects of this, and they leave the rest for the end user, those who are using the cloud, to keep tuning the system beyond that. 

                              But that wasn’t an approach that we wanted to take at IBM, and so we believe very strongly in the notion of fully self-managing, self-maintaining, self-tuning systems, what some people call autonomous data systems. And whether you call it autonomous, or autonomic, or self-managing, or machine learning, these are all variations on a theme. 

                              And we invested a tremendous amount of research and development in this, to make these systems hum. I’ll share with you some examples under the hood, of how we made this simple and how it works. So, for example, you talk to the how we wanted to play our systems in the cloud on — and many different shapes and sizes in the cloud, and then on premises on any hardware that a customer provides us. 

                              So, how do you create technology that’s really easy to use, when it’s landing on so many different foreign factors? So, the first step in that, was when we deployed our technology we then viewed with the ability to sniff the hardware that it’s landing on. And the first thing that happens when this technology is deployed, it sniffs the hardware and says oh how much — how many CPUs have I got here? How much memory have I got here? And automatically configures itself to the size of the target servers that it’s landing on. Like, bam, within two seconds it’s configured to work with management, and memory configuration, and process model, and parallelism, and many of the query compiler decision points. 

                              Bam, it’s two seconds right at the point of deployment. And that happens to automatic detect the hardware. Then we automate many of the — of the run-time attributes, the maintenance attributes, automatic back-ups, automatic statistics collection, automatic reclaim and storage for — storage blocks that comes free in the system, and on and on.

                              We adaptively self-tune the memory distribution that responds directly to the workload that you’re running. Your personal workload, not some mathematical average of the last 10,000 customers we’ve seen, but actual specific run-time adaptation to whatever you are running at this moment, and we adapt memory resources accordingly.

                              And to make it all stable — and it’s one thing to have all this automation, but it’s got to be stable, and so we’ve imbued the technology with really world-class control theory systems that monitor the stability of the system, the pressures on the system, and help adapt how quickly we are responding, how aggressively we are responding, so that the system stays stable at all times. 

                              I think we’re actually the only — the only company in the world, that I know have — that I know of that has imbued control theoretical models directly into their data management systems. 

                              So, all these things together give us this load and go simplicity, but also runtime adaptation that gives us, we believe, this self-managing, autonomous, data platform that we’re very, very proud of, and I’d like to believe it’s the best in the world. Although, you know, okay I guess I’m a little biased, but. But yes, you know, we don’t talk about it enough, it is a huge amount of technology that we are very proud of. 

Al Martin:             Fantastic. I think if we’ve done anything today, we’ve reiterated that you know your stuff. I’m glad – I’m absolutely glad you’re on our team, and not somebody else’s. So look, I think we nailed the cloud piece, anything you think we left unsaid?

Sam Lightstone:    Well, I think maybe just again, something to reflect on, you know, Cloud’s been with us for a long time. Cloud isn’t new. What’s interesting to me is, you know, and I spoke at conferences a couple of years ago, and I asked people how many of you are, you know, have a corporate directive to move to cloud, or move some out – some of your applications to the cloud. 

                              And a couple of years ago, you know, you’d ask that question and you’d get like a quarter of the people in the room stick up their hands. Now days you ask that question, just two years later, and it’s pretty much every one in the room puts up their hand. 

                              So, we’ve gone from something that is, you know, that was important for the enterprise, and for the Fortune 500, to something that is now ubiquitous and everybody’s on this train. So, it is a fascinating inflection point in time, and we’ll see where it all goes. 

Al Martin:             I agree. I think the plummeting costs of compute storage is changing the game, you know, so now we’re out performing more (unintelligible) at this point in time. But the one thing I do think, and I was listening to — back to the (Andreessen Horowitz) video blog when I looked at it, he said the best way to predict the future is to subtract something important today, and pull it to something else. 

                              Pretty simple but, in his mind, that’s where it comes back to the Edge. So it’ll be interesting to see where we go in the future, I know you’re all over that just as well in terms of Query Plex, and I have high expectations and (terms of where that lies). 

Sam Lightstone:    Yes, no pressure. 

Al Martin:             Yes. You’ll deliver, I know it. Hey, before we go, I would like to — I always do what I call a lightning round. So it gets a little bit personal, just so folks get to know Mr. Sam Lightstone. You up for it? 

Sam Lightstone:    Sure, let’s do it. 

Al Martin:             All right, so just a few things. First of all, there is this characterization of fellow, this stature of fellow, how does one actually get to become an IBM fellow? 

Sam Lightstone:    Oh, wow. 

Al Martin:             That will probably take all day long, right? 

Sam Lightstone:    Okay, I’ll try to be quick. First of all, I don’t think anyone really knows. There are about — so IBM’s a company of, I don’t know, 400,000 some people and about 200,000 of those are technical people. And fellow is the, you know, it’s the highest honor that you can get for those of us who are in the technical path. There are about a hundred, just under 100 active IBM fellows. I think there’s been about 200 in the history of the company. It’s really an honor, it’s a great honor, but it is mostly an honor more than a position. And the way you get there is by having had pretty dramatic impact on the industry, and on the IBM technology story over a sustained period of time. 

                              There’s no formula to it, unfortunately. I wish I could — I wish I could just tell people follow these five steps, but I think all the fellows I know got there in different ways. Some of them by founding, you know, great billion-dollar businesses, others by having a collection of industry transforming patents. So everybody’s got there in a different way. 

Al Martin:             So you must be unique, that one way or another, as you just said. The question I would have is, just something simple that the audience can take from them, in terms of if you could help me here, one thing that you do, one practice that you perform regularly that makes you great at what you do.

                              It could be something tactical, it doesn’t matter. I like to take something away, like a good idea. Like, for me, I often use a Pomodoro Technique. I don’t know if you know about it, it’s like look I’m like, you know, if there’s a squirrel, I’m like a dog. I’m like squirrel, and I’m off doing something else. I cannot keep focused. 

                              So I use this Pomodoro Technique where I, you know, I literally will put a timer on 30 minutes and I’ll focus on whatever I’m going to do, so then I can break after that, then get on the next one and I hit the timer again. It really works for me because I’m always go, go, go, high energy. What do you do that’s unique that keeps you on focus, and help you achieve your goals? 

Sam Lightstone:    Interesting. Okay, so I would first of all start by saying let me talk about what I don’t do. I don’t walk into a room trying to be the smartest person in the room. In fact, I’d much prefer that I’m not the smartest person in the room, because then I know I’ve got a good team. 

                              And, you know, the goal of trying to be the smartest person in the room is sort of an egotistical and pointless goal. So I see my role as something a little bit different, which is not to be the smartest person, but try to think about the things that everybody, for whatever reason, is not currently thinking about. So now let me answer your question a little bit more directly. 

                              I have this feeling that most of us operate, you know, our mental attitude, our mental focus, operates in two spaces pretty easily. There’s one space which is very, very focused. You come into work, you got tasks to do, you’ve got business problems, you’ve got operational needs, you’ve got personnel issues, you’ve got customer requirements, and those things, you know, they’re very cold, hard math, you know, they’ve got to be dealt with. 

                              And most of us are reasonably proficient at dealing with practical tasks. That’s one mode of mental operation. So, we’re all good at that, and then there’s another domain which I’ll call the domain of mental fantasy, where you just dream. And you can dream big, and, you know, what should I do today? 

                              Oh, I’ll make a Star Trek transporter, you know? I will — I will — wouldn’t it be nice if we had cars that could fly to the moon on two cents of gas? So, those things are easy to dream on, but they’re not very practical. In fact, they’re entirely useless because you can’t build them, and nobody knows how to build them.

                              So they’re fantasy, but they’re useless fantasy. Most great ideas that really help advance society come from this mental space somewhere in-between, the domain I’ll call tactical fantasy. When you can get your mind dreaming, but not so far out that it’s useless, though it’s not dealing with cold hard math, and it’s not dealing with pure fantasy.

                              It’s this small sliver of space in between, and that’s where great ideas come from. And most of us have a hard time getting our minds into that zone. I try, I would say every week, to do things that help me get my mind in that zone. I’ll find I’m often in that space, for example, when I’m exercising, or I’m riding a bike, or going for a jog. 

                              Somehow certain kinds of activities, they’re not totally in fantasy land, they’re not totally cold hard math, they’re somewhere in between, and that’s where my best ideas come from. Probably everyone has a different way to get their mind into that mental state, but doing that a few hours a week can really help you get on your game, and come up with the great ideas. 

Al Martin:             So now I envision you like in a laboratory, haven’t showered in three weeks, dreaming, smoking a pipe, with like hockey in the – just in the distance in the background, just going like in a constant recursive… 

Sam Lightstone:    Yes, (go east, go east). 

Al Martin:             Am I close? 

Sam Lightstone:    Yes, you’re pretty good. 

Al Martin:             Am I close? 

Sam Lightstone:    You’re pretty close. 

Al Martin:             All right, good, good. Hey, there’s two things that you didn’t talk about there, and I’ve got to hit these before we go, is two things that I’ve heard you say. Number one is creating a repeatable pattern of invention, that’s number one.                             

                              And number two, your view on the value of hobbies.

Sam Lightstone:    Oh, okay, wow. Yes, repeatable process for inventions, so again, I think it’s really important to have this mental space where you can come up with ideas that are practical, and a little bit dreamy. But that’s not enough, you’ve got to find time to work on it, and probably the thing I have found most useful in my career is not only to have the idea, but to find a way to capture the imagination of the people who have the money. 

                              The people who are going to make staffing decisions, you’ve got to capture – you have to captivate their interest. You’ve got to get them excited a little bit, and that’s a bit of an art form. So one of the things I would recommend to aspiring inventors and dreamers is actually spend a bit of time, and listen to some great pitches. 

                              Listen to some people who are great at public speaking. How do they get the audience excited and, you know, tap into that style because getting people excited about your idea is necessary if you’re going to get your idea off the ground. Second thing I would say is, especially nowadays, the number one thing you can do to get people excited is have a working prototype, show them something running.

                              And that might mean you’ve got to work weekends and nights, but a few weeks or months of doing that can make the difference of getting people excited, getting your idea off the ground. So, those are some things that can help with that repeatable process. 

Al Martin:             I’ve got to say — I’ve got to say this makes a lot of sense now. Now I know why you’re poking every day. I don’t think — he practices what he preaches, folks, I’ll tell you that right now. He pokes me every day, and by the way, he does bring demos that are pretty freaking cool, I have to say. Anyway, keep going with the value of hobbies. 

Sam Lightstone:    So a few words about hobbies, you know, my Dad always used to tell me it was good to have hobbies — good to have hobbies, and I — when I young I thought he meant it’s good to have hobbies because it will enrich your life and, oh you’ll have something to do other than work, work, work.                                   

                              But as I’ve gotten older, I’ve come to believe that hobbies has — of course have that value that enriches your life, and of course, hobbies can be fun whether it’s photography, music, sports, whatever it is you’re interested in. But there’s another element to it all, which is that hobbies actually take your mind to a different place — a place where you can dream a little bit, where you can relax a little bit. 

                              I find that in a lot of the hobbies I do, help me get into that mental space of practical imagination. And one of those things that I started doing that I come – sort of come back to recently. When I was a teenager I was a competitive fencer. I was training four or five days a week, I was super into it, and then as I got into minor league (twines) I just stopped.

                              And about a year ago I picked it up again. Now you might think well, why — what value is there in my professional life of running around, jumping around stabbing people with a sword? But I can somehow… 

Al Martin:             Stop, stop, stop. 

Sam Lightstone:    After a hard day at the office, man, that... but seriously, having these hobbies, really, it’s a great way to unwind, and it’s a great way to get your mind into the space where you can think about problems in a new light. 

Al Martin:             Awesome, I like it. So a couple more, I like books, and I always ask this question because I read a ton and I always put them down and I try to get them on my list and prioritize. 

                              Any books that you would absolutely recommend, and if you have a favorite book it doesn’t have to be — it could be fiction, it could be, you know, leadership oriented, I don’t care, just one or two books that you recommend. 

Sam Lightstone:    I would say the thing people to read, it’s not actually a book, it’s a journal. I love reading the MIT Technology Review. It’s a publication that’s associated with MIT, and it talks about the latest and greatest technology trends. 

                              Comes out I think every two months and I have a subscription to that, so they actually deliver a published, you know, paper version of this journal to my house every two months. I love it, it’s written just at the right level. I don’t have to be an expert in any of the domains to understand what they’re talking about. 

                              So, you know, unlike a scientific journal where you can’t really parish what they’re talking about, unless you’re, you know, a graduate student in that domain. So, I recommend MIT Technology Review, it’s excellent, it keeps me abreast of what’s going on blockchain, and AI, and data systems machine learning. An excellent journal.

Al Martin:             Hey, you wrote a book, right? You wrote Making it Big in Software, several years ago. Can you talk about how you did that? I know you even had a discussion with the great Steve Wozniak, if I understand right. 

Sam Lightstone:    Yes, that’s right. Wow, I didn’t know you knew about that. 

Al Martin:             I know everything, man. 

Sam Lightstone:    Yes, I wrote this book. So let me tell you about why I wrote the book. I wrote the book when I became a manager at IBM. This is going back some time, as a technology manager and, you know, the core colonel of our DB2 System. 

                              And I went on a training course — IBM sent me on a training course, and had to be a manager, so I knew the IBM approach to managing people and projects, and I knew the technology. But there was this big gap, and what I didn’t know, was I didn’t know how to advise my staff on how to get on with their careers as a software engineer. 

                              So, that actually become painfully obvious to me, so I started researching what advice should I give to my team on how do — how do you do a software sizing estimate, and schedule estimates, how do you get ahead in your career in a professionally appropriate way? 

                              How do you get things done in a software organization? And I began to collect pros of wisdom on this, and after a while I compiled those in a book, which became Making it Big in Software, and I used that as an opportunity to interview a number of famous people. 

                              So, you mentioned Steve Wozniak, who was the co-founder of Apple; some other folks I had the opportunity to meet with, James Gosling, who was the inventor of Java; Bjarne Stroustrup, who invented C++; Robert Kahn, co-inventor of the Internet; and on and on. 

                              There is one guy, though, I’ll mention who worked for me — one guy who stood out for me, and you’ve probably never heard of him. I would be surprised if most people have heard of him. If I were to ask you who invented the Apple computer, you all know that, and if I ask you who founded Microsoft, you all know that. 

                              So if I were to ask you, who invented e-mail? Anybody know? No one’s heard of this guy. Nobody — his name is Ray Tomlinson, he just passed away recently actually, sadly. Ray Tomlinson invented e-mail in — at the end of 1971. He’s the guy who put that @ sign in there. Nobody’s ever heard of him. 

Al Martin:             Very good. 

Sam Lightstone:    And when I interviewed this guy, he was still sitting at the same desk, doing the same old job, not famous, no extra money. The guy changed the world more than anyone. And I spoke to him, and he was happy as a pig in mud. I’ve never met somebody who was so delighted with his life and what he did, and what he accomplished. 

                              He was just happy. He was just happy. And I think if there’s one piece of advice that came out from that interview, and probably the majority to the interviews I did with these pretty famous and impressive people, is that you’ve got to be happy to be successful, and not successful to be happy. 

                              You’ve got to love what you do, and when you love what you do you’ll have more energy for it, you’ll wake up earlier, you’ll bounce out of bed, you’ll work longer and harder, you’ll be more passionate and success will follow from that, and… 

Al Martin:             Wow, that’s a good way to wrap this up. I have to say, you know, I travel a ton and — by the way, I knew Ray — or I knew of Ray, but I couldn’t give you that name just nowhere. But, you know, I travel a ton and, you know, I see celebrities all the time. All the time and, you know, I’m one of these guys that just, you know, ignores them and acts like they’re not even there. 

                              But I ran into this Steve Wozniak, and he was just sitting relatively close to me in the airport, and I just couldn’t control myself. You know, it’s like this guy changed the world, and he’s sitting right over there. And nobody noticed, you know. I’m looking around, saying does anybody see that Steve Wozniak is just sitting right across from me. 

                              So finally I got up, I went over there, and he was the nicest, genuine guy that I’ve ever met. I mean, he actually seemed like he was nervous taking pictures of me. I must’ve took three or four because I kept looking around and I didn’t – that didn’t work out let’s take another one, but I’ll take another one. 

                              And he was good the whole freaking time. And meanwhile, everybody around is looking around going, who the hell is he taking a picture of? Which was fine with me, I mean, I was very happy. I just couldn’t believe that somebody had changed the world right there, and nobody even, you know… 

Sam Lightstone:    Yes, and a very — a humble soul, a humble soul for sure. A lot of these people are, you know, they’re not — we have the image sometimes that these people are rock stars, and mega famous, and winning big corporations but actually, at the end of the day, they’re people. They’re people like you and me, and they’ve got families and kids, and they’re getting on with their life. 

Al Martin:             He was fabulous, I have to say. Hey, how can listeners connect with you? You’ve got a Twitter handle that you favor, or anything? 

Sam Lightstone:    Yes, they can find me on LinkedIn — Sam Lightstone on LinkedIn, and they can find me on Twitter as well, Sam Lightstone. 

Al Martin:             Thanks for listening to Making Data Simple. If you’re interested in show notes, check out IBMBigDataHub.com