>> fly from San Francisco. It's the Cube covering Google Cloud. Next nineteen, right Tio by Google Cloud and its ecosystem partners. >> Welcome back to Google Cloud next twenty nineteen Everybody, you're watching The Cube. The leader in live tech coverage. My name is Dave Volonte, and I'm here with my co host Stew Minutemen. John Ferrier is also here. Three days of wall to wall coverage of Google's Big Cloud Show customer event this day to a Shook Ramu is here is the vice president of Cloud and Customer Active Fio Boston based Great to see you again. Thanks for coming on to be here. So big show Active fio Category creator. Yeah, right. Yeah, drying it out. Battling in a very competitive space. Absolutely. Doing very well. Give us the update on what's going on with your company. So first >> to follow your super excited to be here Google next, right with one of the strategic partners for Google been working well in all departments. He had a great announcement. Today we announced active field goal for Global Bazaar SAS offering on it's dedicated to the Google platform. We want tohave the activity of experience be that much more better and easier for people running data sets anywhere, particularly in Google. So and Google has been one of our premier partners over the last, I would say three years or so we've gone from strength to strength, so very happy to be here and super excited to be launching this offering. You >> guys started active, Theo. It was clear you saw market beyond just back up beyond just insurance. You started to develop you populist copy data management. That term, everybody uses that today you sort of focused on other areas Dev offs, analytics and things of that nature. How is that gone? How is it resonated with customers? Where you getting the most traction today? >> So great question. I mean, it's gone really well, right? We've kind of been the leader, like you said, setting up the category and basically changing the way that it has looked at and being managed right data now, as a commodity is no longer a commodity. But it's an asset and we're kind of enabling companies to leverage that as it in many different ways on a cloud is here. Everybody wants to go to the cloud. Every customer we talked to every prospect we touch. Want to leverage Cloud And Google is coming in with a lot of strength, a lot of capabilities. So what we're building in terms of data transformation the data aware application of where technologies we have is a resonating very well. The devil of space we talked about, you know, is is the tip of the spear. For us, accounts are over seventy percent of our business, you know, And the last I checked, over sixty to seventy percent of our customers are leveraging cloud in some form. I'd be for Del Ops, cloud bursting D r and all of those categories and, you know, having a very strong enterprise. DNA makes his deal with scale very easily take complex applications and make it look simple. And that's been our strength for the past nine years. So we continue to in a way that strengthen work with Google to make the platform even more stronger. >> When, when I think back of those early days you said enterprise architect her it was like, Okay, let me understand that architecture, the building blocks, you know, the software i p that you have, but it's been quite a different discussion I've been having with your your team the last couple of years. Because, as you say, cloud is front and center and not surprising. To hear the devil is a big piece of help. Help us update kind of that journey. And, you know, a full SAS offering today. How you got from kind of the origin to the company, too, You know, a sass offering. Sure, >> right. I mean, we always knew we had a phenomenal product, right? And a phenomenal customers. We have a number of fourteen thousand two thousand customers with us. And you know what we realized is the adoption off. You know, to understand how cloud works and understand how customers can easily manage to cloud, the experience becomes much more important on. So the SAS offering is more about how do you experience the same great active Your technology with the push button is of use. So we enable the implementation installation ingestion of data in a minute. So by the time you're done with the whole process, you're already starting to love respect If your technology in the closet, your choice. An active field goal for Google. Particularly targets ASAP. Hana Sequel and other complex workload. So these workloads are traditionally been in a very infrastructure heavy, very people heavy in terms of managing. And what we've done is to radically transform how you manage those worthless. A lot of organizations and the conversations I've had over the last twenty four hours has been Hana this and Hannah that How do I make on a simple I've heard active you is the way to go for managing a safety. Hannah, how do you guys tackle it? And this is very interesting conversations with a lot of thought leaders who help us not only build a better product at all, it'll be improve the experience that they take it from there. So that's how I I would see the transformation for the company. >> Why? Why is active field make Hana simple? What is it specifically about? You guys >> don't differentiate. You think the great question. So Hana in general has been a very complicated, hard to install, hard to hard to hard to manage application. So what active you brings in is native application technology, right? So we don't go after infrastructure. We don't go after just storage. But we look at the application of the hole. So when you talk application down, we learn the application. We figure out how it works, how it works best, and how does the best way to capture it and present data back, which is what it's all about. And when you start from there, it's a hard problem to tackle, so it takes a little bit of time for us to tackle that problem. But when the solution comes out, it works one way across all platforms. So we've had customers moving data from on crime to the cloud, and they don't see a difference. They used to go left. Now they go right. But as part of the application to thin works, it works the same way a developer, using Hannah is using Hannah the same way yesterday that he was today. Because even though the databases moved from on creme of the club, so that transformation requires the level of abstraction and understanding the application that we have automated and building your engine >> okay, The hard question for data protection data managed folks today is how are you attacking SAS? Most companies that we asked that question, too, is that his roadmap roadmap Maybe that case for you too. But what is your strategy with regard to sass? Because something triggered me when you talked about the application yet and I know Ash knows background systems view application view has always been his expertise, your company's expertise. How eyes that opportunity for you guys. Is it one that you're actually actively pursuing? If so explain. If not, why not? Is it on the road map? >> So it's certainly an opportunity of pursuing and, you know, working with a number of sass vendors to figure out again a sense of, you know, where is the critical data mass? SAS is a number of components toe and essence off. Any particular application is you know, where is the workload? What is the state machine and how do you manage it? That's the key element. And once you tackle that, the fast application is like any other applications. So we have, you know, people working with us to build custom connectors for, like, office three, sixty five and other other elements of sass products. So as time of walls, you'LL see us, we'LL start working. We'Ll have announcements for the Cloud sequel and other Google platform of the service offerings. Amazon Rd s Those offerings are coming, and we will be basically building the platform. And once the platform comes just like active you has done, we will tackle the SAS applications. One >> of the first technical challenge. It's Roma business challenges. >> It's a business challenge. And you know, for us we have to focus on where the customers want to go, where the enterprise customers wanna go. And Stass at this point is, I would say, emerging to be a place where Enterprise wants to adopt it out of scale that they want adopted. So we're certainly focusing on that. >> And I think there's a perception to stew that, well, the SAS vendor there in the cloud, they got my data protected so good. >> Yeah, well, we know that's not the case that they need to worry about that. >> And I said, I said protected and that's not fair to you guys because >> I was a little, >> much wider scale. >> So But, you know, we were talking about ASAP, and we've watched some of these, you know, big tough application, and they're moving to the clouds. There's a lot of choices out there. You've announcement specifically about Google. What can you tell us about why customers are choosing Google? And if you have any stories about joint Google customers that you have love, >> I would say, Let's start off. You know, I would thank Google because it's one of the key partners for us. You've done over many, many million dollars last year, and we want to double the number of this year right on. It's been all the way from companies that have fifteen to twenty PM's two companies that have twenty thousand, so it spans the gamut. You know, from an infrastructure perspective, Google is the best of the brief. Nobody knows infrastructure computer memory better than Google. Nobody knows networking better than Google. Nobody knows security better than moving. So these are the choices. Why Enterprises? Now we're saying OK, Google is a choice. And as I see on the field flow today, last year was, I have a project. Maybe gold this year is how do I do ABC with gold So the conversations have shifted off. Should I do Google? Worse is how do I do ABC with Google and then you marry active use technology, which is infrastructure agnostic we don't care their application runs. And with that mantra you marry that Google infrastructure. It creates a very powerful combination for enterprises to adopt. >> So just as the follow ups that when we talk to customers here, multi cloud is the reality. So how does that play into your story? And where do you see that fit? >> We were always built multi cloud. So right from day one active use platform architecture Everything has been infrastructure diagnostic. So when you build something for Veum, where or Amazon it works as is in group. And with the latest capabilities on Claude Mobility that be announced a few months ago, you Khun move data seamlessly between different cloud platforms. In fact, I've just chosen in active field Iran be its de facto data protection platforms on all my old life. So you could hear. I know activity also being supporter Nolly Cloud s so that we'll be the only floor platform that is the golden standard to protect complex works lords like a safety nets. >> You mentioned you have a team in in Hyderabad. What? What are they working on? Is it sort of part of the broader development team? Your cloud Focus, Google Focus. What's >> the team in Hyderabad is very much integrated to our engineering team out of Boston. So, you know, they're basically equivalent. We all work together collaboratively. The talent in Hyderabad is now building a lot off our cloud technologies. And the spell is the emerging Technologies s. So we've been able to staff up a very strong team instead of very strong partner. Seems to kind of help us argument what we have here. So leave. Leaders here are basically leveraging. The resource is in Hyderabad kind of accelerate the development because, like, you know, there's never started to work. >> Okay, so you're following the sun and that and that and that the talent pool in that part of India has really exploded. You've seen that big companies hold all the club providers All the all the new ride share companies for their war for talent. Isn't there exactly good? So talk road map a little bit. What could we expect going forward, You know, show us a little leg, if you would. >> So you can see a lot more announcements around activity ago for Google will be enhancing the experience around, you know, adapting and ingesting ASAP and sequel, etcetera. You'LL be looking at a lot of our SAS integration offerings that are coming out. You talk about obviously sixty five Cloud Sequel Amazon RD s Things like that. We'LL have a migration sweet to talk about. How do you How do you ingest and manage communities? Containers? Because that's becoming a commonplace today, Right? How do you How do you tackle complex container in nine minutes? Micro Services. That's a maybe a focus for us and continue to, you know, build and integrate further into the application ecosystem. Because these applications not getting simpler ASAP is continuing to build more complex applications. How do you tackle that? The words road map and keep up with it. That's going to be what we going to be focusing on. >> So active Diogo. We talked about that a little bit. That's announcement here. That's that's your hard news. Yes, it's went to chipping, and once it available >> to go, it's a sass offering, so there's nothing to ship you know so well. Actual SAS pricing model. It's an actual SAS pricing model, fast offering one click purchase. Was it busy installed? So yes, >> Stewie's laughing because so many sass is, aren't a cloud pricing >> three years but only grow up? Can still nod. >> It's not an entity for reporting. It's not an entity that just gives you a bunch of glamour screens. It is actually taking your Hannah workloads and giving it to you for data protection, backup, disaster recovery. So it is. It is true active feel, the time test addictive you and a price product now being off for this test. So >> and how are you going to market with that product? >> So we have a number of vendors, this fellow's Kugel partners here. I get work with them to tow and to kind of generate the man and awareness. So this has been in works for over six months now, So it's not something that came out of the blue, and we've been working with Google in formulating the roadmap. For us, it is >> the active ecosystem looking like these days. How is that evolving? >> It's it's it's It's, um I would say, you know, the customers are the front and center of our ecosystem. We've always built a company with customers first mentality, and they drive a lot of our innovation because They give us a lot of requirements. They reach us in different angle. So they've helped us push the cloud road map. They've helped us push to the point where they want faster adoption. Is that adoption? And that's kind of where we're going, how the ecosystem is now still around enterprises. But the enterprise is tryingto innovate themselves because now data is that will be available. Eso abject with large financial institutions. GDP are so these are all the requirements and they're throwing at us. Okay, you can manage data. How do you air gap it? How do you work with object storage? How do you work with different kinds of technologies? They wanna work with us. And, you know, we've always stepped up to the plate saying, Sure, if it's a new piece of technology that we feel is viable and has the road map will jump at it and solve the problem with you. And that's always been the way of you the partner and growing the company >> you mentioned Air Gap. Some we haven't talked about this week is ransom. Where we talk about most most conferences. It's it's one of those unpleasant things that's a tailwind for companies like >> bank. Right. And we have an offering on ransomware rights. If you look at cyber resiliency, we're the only product in town Where and if you're hit by Ransomware, you can instantly the cover and say, Oh, my ransom or hit me on the seventeenth January, anything after that is gone. But at least I can get to seventy the January and sought my business up. Otherwise, everything else every other product out that this will take weeks or months to figure it out. So, you know, that's another type of a solution that came up. Not there, not there. Not happy about handsome. Where? But that does happen. So we have a solution for the problem. >> Thanks so much for coming in the cubes. Have you >> happy to be here? >> So we'LL see you back in Boston. All right, All right. Thanks. Thanks for watching everybody, This is the cube Will be here tomorrow Day three Student A mandate Volante and John Furrier Google Next Cloud Big Cloud Show We'LL See you tomorrow. Thanks for watching

(upbeat music) >> Hey, welcome back everybody. Jeff Frick here with the CUBE's 2018, a new year. I think this is actually my first interview of the year. I'm pretty excited. I have a CUBE conversation here in the Palo Alto studios to talk about a pretty interesting topic. It's been growing over time but it's getting more and more sophisticated and a much bigger reach. And that's dynamic pricing. It's not just stick the sticker on the item like it used to be back in the day. And that's the price and it's much more complicated. Much more sophisticated. And we're excited to have Alex Shartsis. He's the CEO of Perfect Price. Alex, good to see ya. >> Thanks for having me. >> So, dynamic pricing, right. We've saw it. I guess probably the airlines are maybe the first ones to do it. Or you know, Priceline.com was kind of the first one to talk about. You know, hotels have rooms they can't get rid of. But it's moved a lot further down the path than that. I mean now the Giants I think have have flex pricing. Whether it's the Dodgers on a Friday night or it's Toronto on a Tuesday. >> Yeah, I think it's kind of just a really interesting subject, cause everybody's experienced it, right? I mean, you may not know you've experienced it. But everybody. Whether you've taken an Uber, taken a flight, stayed in a hotel. Even at this point going to an A's game or a Giants game. You've been dynamically priced. And I think what people don't realize is a lot of times they benefit from it. You're able to get that flight for a little bit less. You're able to get the Uber for a little bit less, especially than a taxi. And yeah sometimes there's surge pricing. There's last minute fares. There's things that are more expensive but it's something that every consumer has dealt with. And I think a lot of us think about pricing from a consumer standpoint cause we're all consumers. But from a business standpoint there's nothing more impactful than dynamic pricing. >> Yea, and pricing in and of itself is such a complicated issue. You go through some of the stuff on your website. You know are you coming at it from a cost point of view? Is it a cost plus kind of a model? Or is it a value model? So there's a lot of factors, right? There is no kind of perfect price. You don't want a price at the top of the market. You know, then you're giving up some volume. So what are some of the factors when you talk to people as the pricing evolution is happening from kind of what they used to do to what they're trying to do now with dynamic pricing and how you can help them? >> Yeah, so I think if you think about sort of pricing evolving from. Cost plus was kind of the beginning. Like I bought the potato from the farmer for five bucks a pound. And I'm going to sell it for 10 bucks a pound. That covers my cost of shipping it. Having a stall at the Bazaar, whatever. I think, you know today, a lot of companies still do that. Which still shocks me. But there's you know, there became this sort of in the middle of the last century. Which is kind of weird to say. Value based pricing became a thing. So it wasn't that I would sell them for 10 bucks a pound cause it was just double what I paid for them. It's people are willing to pay 10 bucks a pound and then if I try and sell them for 12 nobody buys them. Or a lot fewer people buy them and if I sell them for seven I run out. And I could have made a lot more money. So what value based pricing was is really like what is my customer willing to pay? And the Bazaar was a great place. You have a conversation. You know, Alex, how much do you really need this potato? How much do you really want this thing? Oh, you're like wearing a nice suit. I think I'm going to charge you more for this. And that obviously went away when the department store was invented. And people would walk around and see a tag on the item. And so what we do and I think what our customers are really benefiting from is this notion of really accurately figuring out what that. Not only the value the customer's getting but also factoring in all the other business related costs and fixed costs and things like that. That should or should not be part of that equation. So that the company can sometimes sell maybe at a loss on that one unit. But you know, in the case of a travel business like an airline or hotel. Loss is a very subjective thing. And you're able to make money by lowering the price for a certain segment. Or for a certain time or for a certain origin, destination. Whatever that combination is. And increase your overall profitability by doing so. Plus bring in some customers that wouldn't have been able to buy from you before. >> So, that's an interesting point of view right. Cause always what are you optimizing for? Are you optimizing for the single transaction? Or are you optimizing for the bucket of transactions? And then that can get you to very different places. So as you seen it kind of evolve what are some of the key factors that tell one of your customers you've got a great opportunity to increase profitability. Increase revenue, increase client satisfaction. Again, what are you measuring? What are you optimizing for by incorporating a dynamic pricing and how did it get started? >> Right, those are great questions. So we went into this thinking there are a lot of businesses that are stuck in cost plus pricing. And they would benefit the most from dynamic pricing. Or from using AI to price things because they're doing such a bad job of it today. And it turns out they liked doing a bad job of it for whatever reason. And we have now been successful at convincing them that maybe there's a better way to do it. But the companies that already have a lot of people and a lot invested in pricing in some fashion. Some companies call it revenue management. Those companies are the ones that really benefit and the reason is they've already seen an impact. So one of the key things for us as you. One of the first questions we ask people is why are we talking about pricing? Did you do something? Did something change in your business? Did you notice it had an impact? And everyone of our customers has been able to say yes to that. Somebody made a mistake and they changed the price and they saw a huge swing in their business. And they realize maybe we should think about it this time. >> It's usually some kind of mistake that undercuts. >> Not usually but more than once it has happened. And sometimes it's like we should do software here or not. And not let people fat finger things in. But for the more sophisticated companies. They've already seen. Some of the companies we've worked with have had pricing teams since the 70's. And so they are constantly improving and they see using AI to do dynamic pricing is the next evolution. And they don't want to get left behind. They know know it's a core of their business. And just as Enterprise Software is moving to the Cloud. Machine learning people are starting to use or have been using the graphics core for a while. You can't ignore that trend if it's a core to their business. >> So that's interesting so and we didn't really kind of talk about the impact of AI. And just really AI. Or intelligence to do a better job of optimization because as you said if you've already invested in pricing it's a complicated thing. There's so many factors and another thing about. Kind of Amazon and the Amazon pricing strategy. Or the vendors within Amazon even. And then how do you factor in convenience? How do you factor in prime? I mean there's these other things that have absolutely nothing to do with the physical price that can enable you. You know as you said, get more revenue. Get more profitability in these factors. So now we have AI. We have these crazy big machines. We have Cloud computing and big data. Huge disrupter to this marketplace and then really new opportunities to bring a lot more power to bare I would imagine. >> So I think Amazon is a great example. Cause people have really experienced dynamic pricing with Amazon. Just cause you put something in your cart the next day it changes by five cents. And Amazon's January pricing is really interesting because Bezos is being very vocal about being consumer centric. And so they're looking at what the market is doing and what things are priced elsewhere. And they're always trying to be competitive and give you value because they recognize. You said earlier. What are you trying to optimize for? Is it revenue, is is profit? There are other things you can optimize for that actually improve both of those numbers. Like how frequently you come back to that as a customer. Do I go to Amazon or do I look at Target or Walmart first. That is a huge impact in Amazon's profitability. And you may do that because of price that one time or over your experience with Amazon as a retailer. So I think what's interesting about AI is that it enables us to go. Just like the ad industry did. Went from having a lot of humans. Trying to solve a problem that really wasn't solvable by humans. So taking a lot of shortcuts. Doing what they could. It actually solves a problem. So if you think about the ad industry. If you're spending 10 million dollars on ads which I'm sure some of your listeners would be. And you're running a campaign. You probably have an agency. They probably have 10 people managing your campaign. They're looking at the 30 or 40 creatives. They have a 1,000 publishers it's running on. But pretty soon the numbers get big. I'm not going to do it right now on camera. But you multiply it out. You're talking about billions. >> And they're all multi varied right. So there's all the different. >> Right, well is the purple creative doing well on the female focus websites for 20 to 30. But not for 40 to 50 and at some point you can't keep track of all the permutation. And one of the weird twists I learned working in that industry is that. When you get down to people who actually click and convert. That's a very small number. So you might have millions or tens of millions of impressions. But you might only have a thousand or two thousand customers that ended up out of it. So you're trying to back out. Okay, that was a customer. Where did they start? And that becomes a very, very thin line to draw. And 10 years ago that was all people. You know, you had your agency. You had literally thousands of people that we traffic those campaigns. And today 78% of those ads are served by AI. Those decisions aren't made by humans anymore. And I think if you think about dynamic pricing for businesses that are very large and have really complex businesses. Like rental car companies, hotels, airlines. Transportation trucking where you're dealing with thousands of different factors. Why would you trust that to people if you don't have to? >> Yeah, as long as you have the data right. And the sophistication gets pretty interesting. You guys have a better appeal to people that already understand the value of dynamic pricing. Which you're really offering them is a new way to do it. An AI based way to do it. A Cloud based way to do it. >> The one place where we found a lot of interest that haven't had sophisticated solutions in the past. The companies that don't have a lot of direct competition. Cause a lot of, at least in travel, a huge part of the revenue manager function is what are the Jones' doing? Right, find the Hilton. What's the Marriot around the corner selling their rooms for? And for better or for worse I think there's a place for it. But it don't think it's quite the same place it's just easy for a human to go to your boss and say well boss. The Marriot around the corner is at 250 a night so we're at 260 cause I think our rooms are nicer. And yet in your data is actually the optimal price. If you look at your data. You can actually get to that price. Maybe you set some rules or you put some limits on the AI. So if the Marriot is at 300 you're not at a 1,000. Maybe you should be, right. You should maybe think about that a little bit if that's what the AI is thinking. But if you don't have that crutch. If you don't have a direct competitor around the corner from you. Then it becomes really hard. And that's why Uber started doing this in the first place. Because they knew taxi pricing was wrong. But to Travis and Ryan and the people who started Uber. The key part of it. The value proposition was always being able to get a car. And so the only way you could do that is basically by pressing people out of the market when you don't have enough cars. And then that one person who really needs to go to the hospital. Or is in DC and needs to go to a New Year's party. Whatever it is. They can pay the $200 to get to that thing they really need to cause there still is a car as opposed to not having a car. >> So you bring up a whole other kind of layer of complexity and that's the third party provider. And it just fascinates me that everyday it seems like there's a new Trivago or Kayak. Or God knows how many other kind of secondary marketplaces there are. So how does that factor in when you not only are worrying about your own pricing? Vis-a-vis your competition around the street or kind of your classic set of competitors. But now you've got this whole other layer of distribution that's kind of outside of your direct control with a whole different type of a pricing structure I would imagine. In terms of supporting. Are you seeing that expand to other places or is travel such a unique thing because of the perishability of the assets? >> So I think it will expand to other places. We think transportation in general, also trucking. I mean everything that has these sort of high operating leverage models. Where you have a lot of vehicles or distribution centers or things. The more accurately you fit your pricing to your demand the more money you'll make. The better run your business will be. The more time you save. It has a lot of implications. One of the things that's really interesting about the different channels is traditionally they have played a roll. You know you think about Nordstrom Rack or TJ Max or Priceline. Hotwire, right. You as the Hilton don't want to ruin your brand by renting your rooms for 50 bucks a night even though you know they're going to be empty. So you give them to Hotwire or you give them to Priceline. That always going to play a roll. A lot of these other places are drawing from the same inventory. So it's just yet another front door for you as a hotel or airline or a rental car company to get business from. What's interesting is because of software. Because of legal agreements and also because of software. There isn't a lot of variation in price. Even though every travel site says cheapest prices or best price guaranteed or whatever. They're all getting their pricing data from the same place. It is the same price. And so it's sort of. Unless it is run in inventory. Unless it is Hotwire where it's opaque. Where you don't know what you're getting. If you're getting a room at a Hilton. You could pretty positive that where ever you book that room Hilton's going to be the same price. >> So it's just pure marketing when they're trying to compete. Because ultimately the system kicks out what that third party available price is or is that even dynamically? >> Well, if you think about. I worked in the travel industry for a while so I don't want to share things that I shouldn't share but if you just think about. If you were the company that powered all these different sites. And had your own big consumer facing website. Would you be okay if Hilton rented its rooms for 50 or a 100 bucks less on its website? Then it lets you rent them for it. >> Probably not. >> Alex: Probably not. (laughing) >> So before we run out of time. So what are the key kind of attributes to the business that really lend itself to having an opportunity to increase profitability and revenue with dynamic pricing? >> So the biggest one is that you've seen. You've had some experience. It could be how ever trivial. And you've seen an impact. Pricing did impact your business. The second one is having a significant number of things that you sell. So if your ring and you sell doorbells and you have one product. Dynamically pricing the product is going to cause a lot more problems than it solves. But if you're a rental car company with thousands of cars. An hotel company with thousands of rooms. Anything where's there's either a lot of variation over a small number of products or a large number of products with a lot of variation. And finally to us it seems like there's this. That you're already a data focused company. Other people have written about this but you know that there's value in their data. You haven't figured out how to get it out of there yet. Or maybe you're doing some things with it. But you are committed to running your business more efficiently. I guess the marketers would call it a psycho graphic profile but that kind of attitude. You know not being content with. Hey, we've done this for four years this way and its worked great. But really wanted to leverage your data and knowing that there is enough data there. Those are the three things that really give us. >> And we don't really worry about price protection I guess. Nobody goes back once they buy their item their like. This is what I wanted. This is perfect. So and I just wondered too. What industries are people not thinking about maybe that you're starting to see get more involved in dynamic pricing. I mean obviously we know travel and those types. You've mentioned cars a number of times. Talked about kind of some of the crazy stuff that goes on Amazon. But is there other kind of ones that people might never think about? >> I mean I think the two big ones are the transportation trucking industry. There a ton of permutation there and they kind of got left out and went web 1.0. And so I think there's a lot to be done there. The other one is event ticketing. You mentioned the A's and the Giants but they're kind of the exceptions. I think there's a lot of ink that's been spilled over price gouging and scalpers and things like that. And I think that if that is you take a hard look at pricing their products more effectively. Everybody would be better off. Consumers and the promoters and the venues themselves. >> Yes, in the Boss' Letter he likes to talk a lot about the concert industry. Alright well Alex Shartsis. CEO of Perfect Price. Thanks for taking a few minutes our of your day and sharing the story. >> Thank you. >> Alrighty, he's Alex. I'm Jeff you're watching the CUBE from our Palo Alto studios. Happy New Year everybody. See you next time. (upbeat music) Welcome back everybody Jeff Frick here with the CUBE. It's 2018, a new year. I think this is actually my first interview of the year. I'm pretty excited to have a CUBE conversation here in the Palo Alto studios to talk about a pretty interesting topic. It's been growing over time but it's getting more and more Sophisticated in a much bigger region. That's dynamic pricing. It's not just stick the sticker on the item like it used to be back in the day. And that's the price and it's much more complicated. Much more sophisticated and we're excited to have Alex Shartsis. He is the CEO of Perfect Price. Alex, good to see you. >> Thanks for having me. So dynamic pricing, right. We've saw it I guess probably the airlines maybe the first ones to do it. Or Priceline.com was kind of the first one to talk about. Hotels have rooms they can't get rid of. But it's moved a lot further down the path in that. I mean now even the Giants I think have flex pricing whether its the Dodgers on a Friday night. Or it's Toronto on a Tuesday. >> Yeah, I think it's king of a really interesting subject cause everybody has experienced it, right. I mean you may not know you've experienced it but everybody whether you've taken an Uber or taken a flight. Stayed in a hotel. Even at this point gone to an A's game or Giant's game. You've been dynamically priced. And what I think that people don't realize is a lot of times they benefit from it. They're able to get that flight for a little bit less. You're able to get the Uber for a little bit less especially than a taxi. And yeah, sometimes there's surge pricing.

>> [Announcer] Live from Boston, Massachusetts, this is theCUBE, covering Spark Summit East 2017, brought to you by Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. >> We're back, welcome to Boston, everybody, this is a special presentation that George Gilbert and I are going to provide to you now. SiliconANGLE Media is the umbrella brand of our company, and we've got three sub-brands. One of them is Wikibon, it's the research organization that Gorge works in, and then of course, we have theCUBE and then SiliconANGLE, which is the tech publication, and then we extensively, as you may know, use CrowdChat and other social data, but we want to drill down now on the Wikibon, Wikibon research side of things. Wikibon was the first research company ever to do a big data forecast. Many, many years ago, our friend Jeff Kelly produced that for several years, we opensourced it, and it really, I think helped the industry a lot, sort of framing the big data opportunity, and then George last year did the first Spark forecast, really Spark adoption, so what we want to do now is talk about some of the trends in the marketplace, this is going to be done in two parts, today's part one, and we're really going to talk about the overall market trends and the market conditions, and then we're going to go to part two tomorrow, where you're going to release some of the numbers, right? And we'll share some of the numbers today. So, we're going to start on the first slide here, we're going to share with you some slides. The Wikibon forecast review, and George is going to, I'm going to ask you to talk about where we are at with big data apps, everybody's saying it's peaked, big data's now going mainstream, where are we at with big data apps? >> [George] Okay, so, I want to quote, just to provide context, the former CTO on VMware, Steve Herrod. He said, "In the end, it wasn't big data, "it was big analytics." And what's interesting is that when we start thinking about it, there have been three classes of, there have been traditionally two classes of workloads, one batch, and in the context of analytics, that means running reports in the background, doing offline business intelligence, but then there was also the interactive-type work. What's emerging is something that's continuously happening, and it doesn't mean that all apps are going to be always on, it just means that there are, all apps will have a batch component, an interactive component, like with the user, and then a streaming, or continuous component. >> [Dave] So it's a new type of workload. >> Yes. >> Okay. Anything else you want to point out here? >> Yeah, what's worth mentioning, this is, it's not like it's going to burst fully-formed out of the clouds, and become sort of a new standard, there's two things that has to happen, the technology has to mature, so right now you have some pretty tough trade-offs between integration, which provides simplicity, and choice and optimization, which gives you fragmentation, and then skillset, and both of those need to develop. >> [Dave] Alright, we're going to talk about both of those a little bit later in this segment. Let's go to the next slide, which really talks to some of the high-level forecast that we released last year, so these are last year's numbers, correct? >> Yes, yes. >> [Dave] Okay, so, what's changed? You've got the ogive curve, which is sort of the streaming penetration, Spark/streaming, that's what, was last year, this is now reflective of continuous, you'll be updating that, how is this changing, what do you want us to know here? >> [George] Okay, so the key takeaways here are, first, we took three application patterns, the first being the data lake, which is sort of the original canonical repository of all your data. That never goes away, but on top of it, you layer what we were calling last year systems of engagement, which is where you've got the interactive machine learning component helping to anticipate and influence a user's decision, and then on top of that, which was the aqua color, was the self-tuning systems, which is probably more IIoT stuff, where you've got a whole ecosystem of devices and intelligence in the cloud and at the edge, and you don't necessarily need a human in the loop. But, these now, when you look at them, you can break them down as having three types of workloads, the batch, the interactive, and the continuous. >> Okay, and that is sort of a new workload here, and this is a real big theme of your research now is, we all remember, no, we don't all remember, I remember punch cards, that's the ultimate batch, and then of course, the terminals were interactive, and you think of that as closer to real time, but now, this notion of continuous, if you go to the next slide, Patrick, we can take a look at how workloads are changing, so George, take us through that dynamic. >> [George] Okay so, to understand where we're going, sometimes it helps to look at where we've come from, and the traditional workloads, if we talk about applications, they were divided into, now, we talked about sort of batch versus interactive, but now, they were also divided into online transaction processing, operational application, systems of record, and then there was the analytic side, which was reporting on it, but this was sort of backward-looking reporting, and we begin to see some convergence between the two with web and mobile apps, where a user was interacting both with the analytics that informed an interaction that they might have. That's looking backwards, and we're going to take a quick look at some of the new technologies that augmented those older application patterns. Then we're going to go look at the emergent workloads and what they look like. >> Okay so, let's have a quick conversation about this before we go on to the next segment. Hadoop obviously was batch. It really was a way, as we've talked about today and many other dates in theCUBE, a way to reduce the expense of doing data warehousing and business intelligence, I remember we were interviewing Jeff Hammerbacher, and he said, "When I was at Facebook, "my mission was to break the dependency "and the container, the storage container." So he really wanted to, needed to reduce costs, he saw that infrastructure needed to change, so if you look at the next slide, which is really sort of talking to Hadoop doing batch in traditional BI, take us through that, and then we'll sort of evolve to the future. >> Okay, so this is an example of traditional workloads, batch business intelligence, because Hadoop has not really gotten to the maturity point of view where you can really do interactive business intelligence. It's going to take a little more work. But here, you've basically put in a repository more data than you could possibly ever fit in a data warehouse, and the key is, this environment was very fragmented, there were many different engines involved, and so there was a high developer complexity, and a high operational complexity, and we're getting to the point where we can do somewhat better on the integration, and we're getting to the point where we might be able to do interactive business intelligence and start doing a little bit of advanced analytics like machine learning. >> Okay. Let's talk a little bit about why we're here, we're here 'cause it's Spark Summit, Spark was designed to simplify big data, simplify a lot of the complexity in Hadoop, so on the next slide, you've got this red line of Spark, so what is Spark's role, what does that red line represent? >> Okay, so the key takeaway from this slide is, couple things. One, it's interesting, but when you listen to Matei Zaharia, who is the creator of Spark, he said, "I built this to be a better MapReduce than MapReduce," which was the old crufty heart of Hadoop. And of course, they've stretched it far beyond their original intentions, but it's not the panacea yet, and if you put it in the context of a data lake, it can help you with what a data engineer does with exploring and munging the data, and what a data scientist might do in terms of processing the data and getting it ready for more advanced analytics, but it doesn't give you an end-to-end solution, not even within the data lake. The point of explaining this is important, because we want to explain how, even in the newer workloads, Spark isn't yet mature to handle the end-to-end integration, and by making that point, we'll show where it needs still more work, and where you have to substitute other products. >> Okay, so let's have a quick discussion about those workloads. Workloads really kind of drive everything, a lot of decisions for organizations, where to put things, and how to protect data, where the value is, so in this next slide you've got, you're juxtaposing traditional workloads with emerging workloads, so let's talk about these new continuous apps. >> Okay, so, this tees it up well, 'cause we focused on the traditional workloads. The emerging ones are where data is always coming in. You could take a big flow of data and sort of end it and bucket it, and turn it into a batch process, but now that we have the capability to keep processing it, and you want answers from it very near real time, you don't want to stop it from flowing, so the first one that took off like this was collecting telemetry about the operation and performance of your apps and your infrastructure, and Splunk sort of conquered that workload first. And then the second one, the one that everyone's talking about now is sort of Internet of Things, but more accurately, the Industrial Internet of Things, and that stream of data is, again, something you'll want to analyze and act on with as little delay as possible. The third one is interesting, asynchronous microservices. This is difficult, because this doesn't necessarily require a lot of new technology, so much as a new skillset for developers, and that's going to mean it takes off fairly slowly. Maybe new developers coming out of school will adopt it whole cloth, but this is where you don't rely on a big central database, this is where you break things into little pieces, and each piece manages itself. >> So you say the components of these arrows that you're showing in just explore processor, these are all sort of discrete elements of the data flow that you have to then integrate as a customer? >> [George] Yes, frankly, these are all steps that could be an end-to-end integrative process, but it's not yet mature enough really to do it end-to-end. For example, we don't even have a data store that can go all the way from ingest to serve, and by ingest, I mean taking the millions, potentially millions or more, events per second coming in from your Internet of Things devices, the explorer would be in that same data store, letting you visualize what's there, and process doing the analysis, and serving then is, from that same data store, letting your industrial devices, or your business intelligence workloads get real-time updates. For this to work as one whole, we need a data store, for example, that can go from end-to-end, in addition to the compute and analytic capabilities that go end-to-end. The point of this is, for continuous workloads, we do want to get to this integrated point somehow, sometime, but we're not there yet. >> Okay, let's go deeper, and take a look at the next slide, you've got this data feedback loop, and you've got this prediction on top of this, what does all that mean, let's double-click on that. >> Okay, so now we're unpacking the slide we just looked at, in that we're unpacking it into two different elements, one is what you're doing when you're running the system, and the next one will be what you're doing when you're designing it. And so for this one, what you're doing when you're running the system, I've grayed out the where's the data coming from and where's it going to, just to focus on how we're operating on the data, and again, to repeat the green part, which is storage, we don't have an end-to-end integrated store that could cost-effectively, scalably handle this whole chain of steps, but what we do have is that in the runtime, you're going to ingest the data, you're going to process it and make it ready for prediction, then there's a step that's called devops for data science, we know devops for developers, but devops for data science, as we're going to see, actually unpacks a whole 'nother level of complexity, but this devops for data science, this is where you get the prediction, of, okay, so, if this turbine is vibrating and has a heat spike, it means shut it down because something's going to fail. That's the prediction component, and the serve part then takes that prediction, and makes sure that that device gets it fast. >> So you're putting that capability in the hands of the data science component so they can effect that outcome virtually instantaneously? >> Yes, but in this case, the data scientist will have done that at design time. We're still at run time, so this is, once the data scientist has built that model, here, it's the engineer who's keeping it running. >> Yeah, but it's designed into the process, that's the devops analogy. Okay great, well let's go to that sort of next piece, which is design, so how does this all affect design, what are the implications there? >> So now, before we had ingest process, then prediction with devops for data science, and then serving, now when you're at design time, you ingest the data, and there's a whole unpacking of steps, which requires a handful, or two fistfuls of tools right now to make operate. This is to acquire the data, explore it, prepare it, model it, assess it, distribute it, all those things are today handled by a collection of tools that you have to stitch together, and then you have process at which could be typically done in Spark, where you do the analysis, and then serving it, Spark isn't ready to serve, that's typically a high-speed database, one that either has tons of data for history, or gets very, very fast updates, like a Redis that's almost like a cache. So the point of this is, we can't yet take Spark as gospel from end to end. >> Okay so, there's a lot of complexity here. >> [George] Right, that's the trade-off. >> So let's take a look at the next slide, which talks to where that complexity comes from, let's look at it first from the developer side, and then we'll look at the admin, so, so on the next slide, we're looking at the complexity from the dev perspective, explain the axes here. >> Okay, okay. So, there's two axes. If you look at the x-axis at the bottom, there's ingest, explore, process, serve. Those were the steps at a high level that we said a developer has to master, and it's going to be in separate products, because we don't have the maturity today. Then on the y-axis, we have some, but not all, this is not an exhaustive list of all the different things a developer has to deal with, with each product, so the complexity is multiplying all the steps on the y-axis, data model, addressing, programming model, persistence, all the stuff's on the y-axis, by all the products he needs on the x-axis, it's a mess, which is why it's very, very hard to build these types of systems today. >> Well, and why everybody's pushing on this whole unified integration, that was a major thing that we heard throughout the day today. What about from the admin's side, let's take a look at the next slide, which is our last slide, in terms of the operational complexity, take us through that. >> [George] Okay, so, the admin is when the system's running, and reading out the complexity, or inferring the complexity, follows the same process. On the y-axis, there's a separate set of tasks. These are admin-related. Governance, scheduling and orchestration, a high availability, all the different types of security, resource isolation, each of these is done differently for each product, and the products are on the x-axis, ingest, explore, process, serve, so that when you multiply those out, and again, this isn't exhaustive, you get, again, essentially a mess of complexity. >> Okay, so we got the message, if you're a practitioner of these so-called big data technologies, you're going to be dealing with more complexity, despite the industry's pace of trying to address that, but you're seeing new projects pop up, but nonetheless, it feels like the complexity curve is growing faster than customer's ability to absorb that complexity. Okay, well, is there hope? >> Yes. But here's where we've had this conundrum. The Apache opensource community has been the most amazing source of innovation I think we've ever seen in the industry, but the problem is, going back to the amazing book, The Cathedral and the Bazaar, about opensource innovation versus top-down, the cathedral has this central architecture that makes everything fit together harmoniously, and beautifully, with simplicity. But the bazaar is so much faster, 'cause it's sort of this free market of innovation. The Apache ecosystem is the bazaar, and the burden is on the developer and the administrator to make it work together, and it was most appropriate for the big internet companies that had the skills to do that. Now, the companies that are distributing these Apache opensource components are doing a Herculean job of putting them together, but they weren't designed to fit together. On the other hand, you've got the cloud service providers, who are building, to some extent, services that have standard APIs that might've been supported by some of the Apache products, but they have proprietary implementations, so you have lock-in, but they have more of the cathedral-type architecture that-- >> And they're delivering 'em their services, even though actually, many of those data services are discrete APIs, as you point out, are proprietary. Okay, so, very useful, George, thank you, if you have questions on this presentation, you can hit Wikibon.com and fire off a question to us, we'll make sure it gets to George and gets answered. This is part one, part two tomorrow is we're going to dig into some of the numbers, right? So if you care about where the trends are, what the numbers look like, what the market size looks like, we'll be sharing that with you tomorrow, all this stuff, of course, will be available on-demand, we'll be doing CrowdChats on this, George, excellent job, thank you very much for taking us through this. Thanks for watching today, it is a wrap of day one, Spark Summit East, we'll be back live tomorrow from Boston, this is theCUBE, so check out siliconangle.com for a review of all the action today, all the news, check out Wikibon.com for all the research, siliconangle.tv is where we house all these videos, check that out, we start again tomorrow at 11 o'clock east coast time, right after the keynotes, this is theCUBE, we're at Spark Summit, #SparkSummit, we're out, see you tomorrow. (electronic music jingle)