(upbeat music) >> Okay, welcome back everyone at Supercloud 2 event, live here in Palo Alto, theCUBE Studios live stage performance, virtually syndicating it all over the world. I'm John Furrier with Dave Vellante here as Cube alumni, and special influencer guest, Howie Xu, VP of Machine Learning and Zscaler, also part-time as a CUBE analyst 'cause he is that good. Comes on all the time. You're basically a CUBE analyst as well. Thanks for coming on. >> Thanks for inviting me. >> John: Technically, you're not really a CUBE analyst, but you're kind of like a CUBE analyst. >> Happy New Year to everyone. >> Dave: Great to see you. >> Great to see you, Dave and John. >> John: We've been talking about ChatGPT online. You wrote a great post about it being more like Amazon, not like Google. >> Howie: More than just Google Search. >> More than Google Search. Oh, it's going to compete with Google Search, which it kind of does a little bit, but more its infrastructure. So a clever point, good segue into this conversation, because this is kind of the beginning of these kinds of next gen things we're going to see. Things where it's like an obvious next gen, it's getting real. Kind of like seeing the browser for the first time, Mosaic browser. Whoa, this internet thing's real. I think this is that moment and Supercloud like enablement is coming. So this has been a big part of the Supercloud kind of theme. >> Yeah, you talk about Supercloud, you talk about, you know, AI, ChatGPT. I really think the ChatGPT is really another Netscape moment, the browser moment. Because if you think about internet technology, right? It was brewing for 20 years before early 90s. Not until you had a, you know, browser, people realize, "Wow, this is how wonderful this technology could do." Right? You know, all the wonderful things. Then you have Yahoo and Amazon. I think we have brewing, you know, the AI technology for, you know, quite some time. Even then, you know, neural networks, deep learning. But not until ChatGPT came along, people realize, "Wow, you know, the user interface, user experience could be that great," right? So I really think, you know, if you look at the last 30 years, there is a browser moment, there is iPhone moment. I think ChatGPT moment is as big as those. >> Dave: What do you see as the intersection of things like ChatGPT and the Supercloud? Of course, the media's going to focus, journalists are going to focus on all the negatives and the privacy. Okay. You know we're going to get by that, right? Always do. Where do you see the Supercloud and sort of the distributed data fitting in with ChatGPT? Does it use that as a data source? What's the link? >> Howie: I think there are number of use cases. One of the use cases, we talked about why we even have Supercloud because of the complexity, because of the, you know, heterogeneous nature of different clouds. In order for me as a developer, in order for me to create applications, I have so many things to worry about, right? It's a complexity. But with ChatGPT, with the AI, I don't have to worry about it, right? Those kind of details will be taken care of by, you know, the underlying layer. So we have been talking about on this show, you know, over the last, what, year or so about the Supercloud, hey, defining that, you know, API layer spanning across, you know, multiple clouds. I think that will be happening. However, for a lot of the things, that will be more hidden, right? A lot of that will be automated by the bots. You know, we were just talking about it right before the show. One of the profound statement I heard from Adrian Cockcroft about 10 years ago was, "Hey Howie, you know, at Netflix, right? You know, IT is just one API call away." That's a profound statement I heard about a decade ago. I think next decade, right? You know, the IT is just one English language away, right? So when it's one English language away, it's no longer as important, API this, API that. You still need API just like hardware, right? You still need all of those things. That's going to be more hidden. The high level thing will be more, you know, English language or the language, right? Any language for that matter. >> Dave: And so through language, you'll tap services that live across the Supercloud, is what you're saying? >> Howie: You just tell what you want, what you desire, right? You know, the bots will help you to figure out where the complexity is, right? You know, like you said, a lot of criticism about, "Hey, ChatGPT doesn't do this, doesn't do that." But if you think about how to break things down, right? For instance, right, you know, ChatGPT doesn't have Microsoft stock price today, obviously, right? However, you can ask ChatGPT to write a program for you, retrieve the Microsoft stock price, (laughs) and then just run it, right? >> Dave: Yeah. >> So the thing to think about- >> John: It's only going to get better. It's only going to get better. >> The thing people kind of unfairly criticize ChatGPT is it doesn't do this. But can you not break down humans' task into smaller things and get complex things to be done by the ChatGPT? I think we are there already, you know- >> John: That to me is the real game changer. That's the assembly of atomic elements at the top of the stack, whether the interface is voice or some programmatic gesture based thing, you know, wave your hand or- >> Howie: One of the analogy I used in my blog was, you know, each person, each professional now is a quarterback. And we suddenly have, you know, a lot more linebacks or you know, any backs to work for you, right? For free even, right? You know, and then that's sort of, you should think about it. You are the quarterback of your day-to-day job, right? Your job is not to do everything manually yourself. >> Dave: You call the play- >> Yes. >> Dave: And they execute. Do your job. >> Yes, exactly. >> Yeah, all the players are there. All the elves are in the North Pole making the toys, Dave, as we say. But this is the thing, I want to get your point. This change is going to require a new kind of infrastructure software relationship, a new kind of operating runtime, a new kind of assembler, a new kind of loader link things. This very operating systems kind of concepts. >> Data intensive, right? How to process the data, how to, you know, process so gigantic data in parallel, right? That's actually a tough job, right? So if you think about ChatGPT, why OpenAI is ahead of the game, right? You know, Google may not want to acknowledge it, right? It's not necessarily they do, you know, not have enough data scientist, but the software engineering pieces, you know, behind it, right? To train the model, to actually do all those things in parallel, to do all those things in a cost effective way. So I think, you know, a lot of those still- >> Let me ask you a question. Let me ask you a question because we've had this conversation privately, but I want to do it while we're on stage here. Where are all the alpha geeks and developers and creators and entrepreneurs going to gravitate to? You know, in every wave, you see it in crypto, all the alphas went into crypto. Now I think with ChatGPT, you're going to start to see, like, "Wow, it's that moment." A lot of people are going to, you know, scrum and do startups. CTOs will invent stuff. There's a lot of invention, a lot of computer science and customer requirements to figure out. That's new. Where are the alpha entrepreneurs going to go to? What do you think they're going to gravitate to? If you could point to the next layer to enable this super environment, super app environment, Supercloud. 'Cause there's a lot to do to enable what you just said. >> Howie: Right. You know, if you think about using internet as the analogy, right? You know, in the early 90s, internet came along, browser came along. You had two kind of companies, right? One is Amazon, the other one is walmart.com. And then there were company, like maybe GE or whatnot, right? Really didn't take advantage of internet that much. I think, you know, for entrepreneurs, suddenly created the Yahoo, Amazon of the ChatGPT native era. That's what we should be all excited about. But for most of the Fortune 500 companies, your job is to surviving sort of the big revolution. So you at least need to do your walmart.com sooner than later, right? (laughs) So not be like GE, right? You know, hand waving, hey, I do a lot of the internet, but you know, when you look back last 20, 30 years, what did they do much with leveraging the- >> So you think they're going to jump in, they're going to build service companies or SaaS tech companies or Supercloud companies? >> Howie: Okay, so there are two type of opportunities from that perspective. One is, you know, the OpenAI ish kind of the companies, I think the OpenAI, the game is still open, right? You know, it's really Close AI today. (laughs) >> John: There's room for competition, you mean? >> There's room for competition, right. You know, you can still spend you know, 50, $100 million to build something interesting. You know, there are company like Cohere and so on and so on. There are a bunch of companies, I think there is that. And then there are companies who's going to leverage those sort of the new AI primitives. I think, you know, we have been talking about AI forever, but finally, finally, it's no longer just good, but also super useful. I think, you know, the time is now. >> John: And if you have the cloud behind you, what do you make the Amazon do differently? 'Cause Amazon Web Services is only going to grow with this. It's not going to get smaller. There's more horsepower to handle, there's more needs. >> Howie: Well, Microsoft already showed what's the future, right? You know, you know, yes, there is a kind of the container, you know, the serverless that will continue to grow. But the future is really not about- >> John: Microsoft's shown the future? >> Well, showing that, you know, working with OpenAI, right? >> Oh okay. >> They already said that, you know, we are going to have ChatGPT service. >> $10 billion, I think they're putting it. >> $10 billion putting, and also open up the Open API services, right? You know, I actually made a prediction that Microsoft future hinges on OpenAI. I think, you know- >> John: They believe that $10 billion bet. >> Dave: Yeah. $10 billion bet. So I want to ask you a question. It's somewhat academic, but it's relevant. For a number of years, it looked like having first mover advantage wasn't an advantage. PCs, spreadsheets, the browser, right? Social media, Friendster, right? Mobile. Apple wasn't first to mobile. But that's somewhat changed. The cloud, AWS was first. You could debate whether or not, but AWS okay, they have first mover advantage. Crypto, Bitcoin, first mover advantage. Do you think OpenAI will have first mover advantage? >> It certainly has its advantage today. I think it's year two. I mean, I think the game is still out there, right? You know, we're still in the first inning, early inning of the game. So I don't think that the game is over for the rest of the players, whether the big players or the OpenAI kind of the, sort of competitors. So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest, to get, you know, another shot to the OpenAI sort of the level?" You know, I did a- (laughs) >> Line up. >> That's classic VC. "How much does it cost me to replicate?" >> I'm pretty sure he asked the question to a bunch of guys, right? >> Good luck with that. (laughs) >> So we kind of did some napkin- >> What'd you come up with? (laughs) >> $100 million is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So 100 million. >> John: Hundreds of millions. >> Yeah, yeah, yeah. 100 million order of magnitude is what I came up with. You know, we can get into details, you know, in other sort of the time, but- >> Dave: That's actually not that much if you think about it. >> Howie: Exactly. So when he heard me articulating why is that, you know, he's thinking, right? You know, he actually, you know, asked me, "Hey, you know, there's this company. Do you happen to know this company? Can I reach out?" You know, those things. So I truly believe it's not a billion or 10 billion issue, it's more like 100. >> John: And also, your other point about referencing the internet revolution as a good comparable. The other thing there is online user population was a big driver of the growth of that. So what's the equivalent here for online user population for AI? Is it more apps, more users? I mean, we're still early on, it's first inning. >> Yeah. We're kind of the, you know- >> What's the key metric for success of this sector? Do you have a read on that? >> I think the, you know, the number of users is a good metrics, but I think it's going to be a lot of people are going to use AI services without even knowing they're using it, right? You know, I think a lot of the applications are being already built on top of OpenAI, and then they are kind of, you know, help people to do marketing, legal documents, you know, so they're already inherently OpenAI kind of the users already. So I think yeah. >> Well, Howie, we've got to wrap, but I really appreciate you coming on. I want to give you a last minute to wrap up here. In your experience, and you've seen many waves of innovation. You've even had your hands in a lot of the big waves past three inflection points. And obviously, machine learning you're doing now, you're deep end. Why is this Supercloud movement, this wave of Supercloud and the discussion of this next inflection point, why is it so important? For the folks watching, why should they be paying attention to this particular moment in time? Could you share your super clip on Supercloud? >> Howie: Right. So this is simple from my point of view. So why do you even have cloud to begin with, right? IT is too complex, too complex to operate or too expensive. So there's a newer model. There is a better model, right? Let someone else operate it, there is elasticity out of it, right? That's great. Until you have multiple vendors, right? Many vendors even, you know, we're talking about kind of how to make multiple vendors look like the same, but frankly speaking, even one vendor has, you know, thousand services. Now it's kind of getting, what Kid was talking about what, cloud chaos, right? It's the evolution. You know, the history repeats itself, right? You know, you have, you know, next great things and then too many great things, and then people need to sort of abstract this out. So it's almost that you must do this. But I think how to abstract this out is something that at this time, AI is going to help a lot, right? You know, like I mentioned, right? A lot of the abstraction, you don't have to think about API anymore. I bet 10 years from now, you know, IT is one language away, not API away. So think about that world, right? So Supercloud in, in my opinion, sure, you kind of abstract things out. You have, you know, consistent layers. But who's going to do that? Is that like we all agreed upon the model, agreed upon those APIs? Not necessary. There are certain, you know, truth in that, but there are other truths, let bots take care of, right? Whether you know, I want some X happens, whether it's going to be done by Azure, by AWS, by GCP, bots will figure out at a given time with certain contacts with your security requirement, posture requirement. I'll think that out. >> John: That's awesome. And you know, Dave, you and I have been talking about this. We think scale is the new ratification. If you have first mover advantage, I'll see the benefit, but scale is a huge thing. OpenAI, AWS. >> Howie: Yeah. Every day, we are using OpenAI. Today, we are labeling data for them. So you know, that's a little bit of the- (laughs) >> John: Yeah. >> First mover advantage that other people don't have, right? So it's kind of scary. So I'm very sure that Google is a little bit- (laughs) >> When we do our super AI event, you're definitely going to be keynoting. (laughs) >> Howie: I think, you know, we're talking about Supercloud, you know, before long, we are going to talk about super intelligent cloud. (laughs) >> I'm super excited, Howie, about this. Thanks for coming on. Great to see you, Howie Xu. Always a great analyst for us contributing to the community. VP of Machine Learning and Zscaler, industry legend and friend of theCUBE. Thanks for coming on and sharing really, really great advice and insight into what this next wave means. This Supercloud is the next wave. "If you're not on it, you're driftwood," says Pat Gelsinger. So you're going to see a lot more discussion. We'll be back more here live in Palo Alto after this short break. >> Thank you. (upbeat music)

(upbeat electronic music) >> Okay, welcome back to Supercloud 2, live here in Palo Alto. I'm John Furrier, with Dave Vellante. Again, all day wall-to-wall coverage, just had a great interview with Walmart, we've got a Next interview coming up, you're going to hear from Bob Muglia and Tristan Handy, two experts, both experienced entrepreneurs, executives in technology. We're here to break down what just happened with Walmart, and what's coming up with George Gilbert, former colleague, Wikibon analyst, Gartner Analyst, and now independent investor and expert. George, great to see you, I know you're following this space. Like you read about it, remember the first days when Dataverse came out, we were talking about them coming out of Berkeley? >> Dave: Snowflake. >> John: Snowflake. >> Dave: Snowflake In the early days. >> We, collectively, have been chronicling the data movement since 2010, you were part of our team, now you've got your nose to the grindstone, you're seeing the next wave. What's this all about? Walmart building their own super cloud, we got Bob Muglia talking about how these next wave of apps are coming. What are the super apps? What's the super cloud to you? >> Well, this key's off Dave's really interesting questions to Walmart, which was like, how are they building their supercloud? 'Cause it makes a concrete example. But what was most interesting about his description of the Walmart WCMP, I forgot what it stood for. >> Dave: Walmart Cloud Native Platform. >> Walmart, okay. He was describing where the logic could run in these stateless containers, and maybe eventually serverless functions. But that's just it, and that's the paradigm of microservices, where the logic is in this stateless thing, where you can shoot it, or it fails, and you can spin up another one, and you've lost nothing. >> That was their triplet model. >> Yeah, in fact, and that was what they were trying to move to, where these things move fluidly between data centers. >> But there's a but, right? Which is they're all stateless apps in the cloud. >> George: Yeah. >> And all their stateful apps are on-prem and VMs. >> Or the stateful part of the apps are in VMs. >> Okay. >> And so if they really want to lift their super cloud layer off of this different provider's infrastructure, they're going to need a much more advanced software platform that manages data. And that goes to the -- >> Muglia and Handy, that you and I did, that's coming up next. So the big takeaway there, George, was, I'll set it up and you can chime in, a new breed of data apps is emerging, and this highly decentralized infrastructure. And Tristan Handy of DBT Labs has a sort of a solution to begin the journey today, Muglia is working on something that's way out there, describe what you learned from it. >> Okay. So to talk about what the new data apps are, and then the platform to run them, I go back to the using what will probably be seen as one of the first data app examples, was Uber, where you're describing entities in the real world, riders, drivers, routes, city, like a city plan, these are all defined by data. And the data is described in a structure called a knowledge graph, for lack of a, no one's come up with a better term. But that means the tough, the stuff that Jack built, which was all stateless and sits above cloud vendors' infrastructure, it needs an entirely different type of software that's much, much harder to build. And the way Bob described it is, you're going to need an entirely new data management infrastructure to handle this. But where, you know, we had this really colorful interview where it was like Rock 'Em Sock 'Em, but they weren't really that much in opposition to each other, because Tristan is going to define this layer, starting with like business intelligence metrics, where you're defining things like bookings, billings, and revenue, in business terms, not in SQL terms -- >> Well, business terms, if I can interrupt, he said the one thing we haven't figured out how to APIify is KPIs that sit inside of a data warehouse, and that's essentially what he's doing. >> George: That's what he's doing, yes. >> Right. And so then you can now expose those APIs, those KPIs, that sit inside of a data warehouse, or a data lake, a data store, whatever, through APIs. >> George: And the difference -- >> So what does that do for you? >> Okay, so all of a sudden, instead of working at technical data terms, where you're dealing with tables and columns and rows, you're dealing instead with business entities, using the Uber example of drivers, riders, routes, you know, ETA prices. But you can define, DBT will be able to define those progressively in richer terms, today they're just doing things like bookings, billings, and revenue. But Bob's point was, today, the data warehouse that actually runs that stuff, whereas DBT defines it, the data warehouse that runs it, you can't do it with relational technology >> Dave: Relational totality, cashing architecture. >> SQL, you can't -- >> SQL caching architectures in memory, you can't do it, you've got to rethink down to the way the data lake is laid out on the disk or cache. Which by the way, Thomas Hazel, who's speaking later, he's the chief scientist and founder at Chaos Search, he says, "I've actually done this," basically leave it in an S3 bucket, and I'm going to query it, you know, with no caching. >> All right, so what I hear you saying then, tell me if I got this right, there are some some things that are inadequate in today's world, that's not compatible with the Supercloud wave. >> Yeah. >> Specifically how you're using storage, and data, and stateful. >> Yes. >> And then the software that makes it run, is that what you're saying? >> George: Yeah. >> There's one other thing you mentioned to me, it's like, when you're using a CRM system, a human is inputting data. >> George: Nothing happens till the human does something. >> Right, nothing happens until that data entry occurs. What you're talking about is a world that self forms, polling data from the transaction system, or the ERP system, and then builds a plan without human intervention. >> Yeah. Something in the real world happens, where the user says, "I want a ride." And then the software goes out and says, "Okay, we got to match a driver to the rider, we got to calculate how long it takes to get there, how long to deliver 'em." That's not driven by a form, other than the first person hitting a button and saying, "I want a ride." All the other stuff happens autonomously, driven by data and analytics. >> But my question was different, Dave, so I want to get specific, because this is where the startups are going to come in, this is the disruption. Snowflake is a data warehouse that's in the cloud, they call it a data cloud, they refactored it, they did it differently, the success, we all know it looks like. These areas where it's inadequate for the future are areas that'll probably be either disrupted, or refactored. What is that? >> That's what Muglia's contention is, that the DBT can start adding that layer where you define these business entities, they're like mini digital twins, you can define them, but the data warehouse isn't strong enough to actually manage and run them. And Muglia is behind a company that is rethinking the database, really in a fundamental way that hasn't been done in 40 or 50 years. It's the first, in his contention, the first real rethink of database technology in a fundamental way since the rise of the relational database 50 years ago. >> And I think you admit it's a real Hail Mary, I mean it's quite a long shot right? >> George: Yes. >> Huge potential. >> But they're pretty far along. >> Well, we've been talking on theCUBE for 12 years, and what, 10 years going to AWS Reinvent, Dave, that no one database will rule the world, Amazon kind of showed that with them. What's different, is it databases are changing, or you can have multiple databases, or? >> It's a good question. And the reason we've had multiple different types of databases, each one specialized for a different type of workload, but actually what Muglia is behind is a new engine that would essentially, you'll never get rid of the data warehouse, or the equivalent engine in like a Databricks datalake house, but it's a new engine that manages the thing that describes all the data and holds it together, and that's the new application platform. >> George, we have one minute left, I want to get real quick thought, you're an investor, and we know your history, and the folks watching, George's got a deep pedigree in investment data, and we can testify against that. If you're going to invest in a company right now, if you're a customer, I got to make a bet, what does success look like for me, what do I want walking through my door, and what do I want to send out? What companies do I want to look at? What's the kind of of vendor do I want to evaluate? Which ones do I want to send home? >> Well, the first thing a customer really has to do when they're thinking about next gen applications, all the people have told you guys, "we got to get our data in order," getting that data in order means building an integrated view of all your data landscape, which is data coming out of all your applications. It starts with the data model, so, today, you basically extract data from all your operational systems, put it in this one giant, central place, like a warehouse or lake house, but eventually you want this, whether you call it a fabric or a mesh, it's all the data that describes how everything hangs together as in one big knowledge graph. There's different ways to implement that. And that's the most critical thing, 'cause that describes your Uber landscape, your Uber platform. >> That's going to power the digital transformation, which will power the business transformation, which powers the business model, which allows the builders to build -- >> Yes. >> Coders to code. That's Supercloud application. >> Yeah. >> George, great stuff. Next interview you're going to see right here is Bob Muglia and Tristan Handy, they're going to unpack this new wave. Great segment, really worth unpacking and reading between the lines with George, and Dave Vellante, and those two great guests. And then we'll come back here for the studio for more of the live coverage of Supercloud 2. Thanks for watching. (upbeat electronic music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> OpenAI The company, and ChatGPT have taken the world by storm. Microsoft reportedly is investing an additional 10 billion dollars into the company. But in our view, while the hype around ChatGPT is justified, we don't believe OpenAI will lock up the market with its first mover advantage. Rather, we believe that success in this market will be directly proportional to the quality and quantity of data that a technology company has at its disposal, and the compute power that it could deploy to run its system. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we unpack the excitement around ChatGPT, and debate the premise that the company's early entry into the space may not confer winner take all advantage to OpenAI. And to do so, we welcome CUBE collaborator, alum, Sarbjeet Johal, (chuckles) and John Furrier, co-host of the Cube. Great to see you Sarbjeet, John. Really appreciate you guys coming to the program. >> Great to be on. >> Okay, so what is ChatGPT? Well, actually we asked ChatGPT, what is ChatGPT? So here's what it said. ChatGPT is a state-of-the-art language model developed by OpenAI that can generate human-like text. It could be fine tuned for a variety of language tasks, such as conversation, summarization, and language translation. So I asked it, give it to me in 50 words or less. How did it do? Anything to add? >> Yeah, think it did good. It's large language model, like previous models, but it started applying the transformers sort of mechanism to focus on what prompt you have given it to itself. And then also the what answer it gave you in the first, sort of, one sentence or two sentences, and then introspect on itself, like what I have already said to you. And so just work on that. So it it's self sort of focus if you will. It does, the transformers help the large language models to do that. >> So to your point, it's a large language model, and GPT stands for generative pre-trained transformer. >> And if you put the definition back up there again, if you put it back up on the screen, let's see it back up. Okay, it actually missed the large, word large. So one of the problems with ChatGPT, it's not always accurate. It's actually a large language model, and it says state of the art language model. And if you look at Google, Google has dominated AI for many times and they're well known as being the best at this. And apparently Google has their own large language model, LLM, in play and have been holding it back to release because of backlash on the accuracy. Like just in that example you showed is a great point. They got almost right, but they missed the key word. >> You know what's funny about that John, is I had previously asked it in my prompt to give me it in less than a hundred words, and it was too long, I said I was too long for Breaking Analysis, and there it went into the fact that it's a large language model. So it largely, it gave me a really different answer the, for both times. So, but it's still pretty amazing for those of you who haven't played with it yet. And one of the best examples that I saw was Ben Charrington from This Week In ML AI podcast. And I stumbled on this thanks to Brian Gracely, who was listening to one of his Cloudcasts. Basically what Ben did is he took, he prompted ChatGPT to interview ChatGPT, and he simply gave the system the prompts, and then he ran the questions and answers into this avatar builder and sped it up 2X so it didn't sound like a machine. And voila, it was amazing. So John is ChatGPT going to take over as a cube host? >> Well, I was thinking, we get the questions in advance sometimes from PR people. We should actually just plug it in ChatGPT, add it to our notes, and saying, "Is this good enough for you? Let's ask the real question." So I think, you know, I think there's a lot of heavy lifting that gets done. I think the ChatGPT is a phenomenal revolution. I think it highlights the use case. Like that example we showed earlier. It gets most of it right. So it's directionally correct and it feels like it's an answer, but it's not a hundred percent accurate. And I think that's where people are seeing value in it. Writing marketing, copy, brainstorming, guest list, gift list for somebody. Write me some lyrics to a song. Give me a thesis about healthcare policy in the United States. It'll do a bang up job, and then you got to go in and you can massage it. So we're going to do three quarters of the work. That's why plagiarism and schools are kind of freaking out. And that's why Microsoft put 10 billion in, because why wouldn't this be a feature of Word, or the OS to help it do stuff on behalf of the user. So linguistically it's a beautiful thing. You can input a string and get a good answer. It's not a search result. >> And we're going to get your take on on Microsoft and, but it kind of levels the playing- but ChatGPT writes better than I do, Sarbjeet, and I know you have some good examples too. You mentioned the Reed Hastings example. >> Yeah, I was listening to Reed Hastings fireside chat with ChatGPT, and the answers were coming as sort of voice, in the voice format. And it was amazing what, he was having very sort of philosophy kind of talk with the ChatGPT, the longer sentences, like he was going on, like, just like we are talking, he was talking for like almost two minutes and then ChatGPT was answering. It was not one sentence question, and then a lot of answers from ChatGPT and yeah, you're right. I, this is our ability. I've been thinking deep about this since yesterday, we talked about, like, we want to do this segment. The data is fed into the data model. It can be the current data as well, but I think that, like, models like ChatGPT, other companies will have those too. They can, they're democratizing the intelligence, but they're not creating intelligence yet, definitely yet I can say that. They will give you all the finite answers. Like, okay, how do you do this for loop in Java, versus, you know, C sharp, and as a programmer you can do that, in, but they can't tell you that, how to write a new algorithm or write a new search algorithm for you. They cannot create a secretive code for you to- >> Not yet. >> Have competitive advantage. >> Not yet, not yet. >> but you- >> Can Google do that today? >> No one really can. The reasoning side of the data is, we talked about at our Supercloud event, with Zhamak Dehghani who's was CEO of, now of Nextdata. This next wave of data intelligence is going to come from entrepreneurs that are probably cross discipline, computer science and some other discipline. But they're going to be new things, for example, data, metadata, and data. It's hard to do reasoning like a human being, so that needs more data to train itself. So I think the first gen of this training module for the large language model they have is a corpus of text. Lot of that's why blog posts are, but the facts are wrong and sometimes out of context, because that contextual reasoning takes time, it takes intelligence. So machines need to become intelligent, and so therefore they need to be trained. So you're going to start to see, I think, a lot of acceleration on training the data sets. And again, it's only as good as the data you can get. And again, proprietary data sets will be a huge winner. Anyone who's got a large corpus of content, proprietary content like theCUBE or SiliconANGLE as a publisher will benefit from this. Large FinTech companies, anyone with large proprietary data will probably be a big winner on this generative AI wave, because it just, it will eat that up, and turn that back into something better. So I think there's going to be a lot of interesting things to look at here. And certainly productivity's going to be off the charts for vanilla and the internet is going to get swarmed with vanilla content. So if you're in the content business, and you're an original content producer of any kind, you're going to be not vanilla, so you're going to be better. So I think there's so much at play Dave (indistinct). >> I think the playing field has been risen, so we- >> Risen and leveled? >> Yeah, and leveled to certain extent. So it's now like that few people as consumers, as consumers of AI, we will have a advantage and others cannot have that advantage. So it will be democratized. That's, I'm sure about that. But if you take the example of calculator, when the calculator came in, and a lot of people are, "Oh, people can't do math anymore because calculator is there." right? So it's a similar sort of moment, just like a calculator for the next level. But, again- >> I see it more like open source, Sarbjeet, because like if you think about what ChatGPT's doing, you do a query and it comes from somewhere the value of a post from ChatGPT is just a reuse of AI. The original content accent will be come from a human. So if I lay out a paragraph from ChatGPT, did some heavy lifting on some facts, I check the facts, save me about maybe- >> Yeah, it's productive. >> An hour writing, and then I write a killer two, three sentences of, like, sharp original thinking or critical analysis. I then took that body of work, open source content, and then laid something on top of it. >> And Sarbjeet's example is a good one, because like if the calculator kids don't do math as well anymore, the slide rule, remember we had slide rules as kids, remember we first started using Waze, you know, we were this minority and you had an advantage over other drivers. Now Waze is like, you know, social traffic, you know, navigation, everybody had, you know- >> All the back roads are crowded. >> They're car crowded. (group laughs) Exactly. All right, let's, let's move on. What about this notion that futurist Ray Amara put forth and really Amara's Law that we're showing here, it's, the law is we, you know, "We tend to overestimate the effect of technology in the short run and underestimate it in the long run." Is that the case, do you think, with ChatGPT? What do you think Sarbjeet? >> I think that's true actually. There's a lot of, >> We don't debate this. >> There's a lot of awe, like when people see the results from ChatGPT, they say what, what the heck? Like, it can do this? But then if you use it more and more and more, and I ask the set of similar question, not the same question, and it gives you like same answer. It's like reading from the same bucket of text in, the interior read (indistinct) where the ChatGPT, you will see that in some couple of segments. It's very, it sounds so boring that the ChatGPT is coming out the same two sentences every time. So it is kind of good, but it's not as good as people think it is right now. But we will have, go through this, you know, hype sort of cycle and get realistic with it. And then in the long term, I think it's a great thing in the short term, it's not something which will (indistinct) >> What's your counter point? You're saying it's not. >> I, no I think the question was, it's hyped up in the short term and not it's underestimated long term. That's what I think what he said, quote. >> Yes, yeah. That's what he said. >> Okay, I think that's wrong with this, because this is a unique, ChatGPT is a unique kind of impact and it's very generational. People have been comparing it, I have been comparing to the internet, like the web, web browser Mosaic and Netscape, right, Navigator. I mean, I clearly still remember the days seeing Navigator for the first time, wow. And there weren't not many sites you could go to, everyone typed in, you know, cars.com, you know. >> That (indistinct) wasn't that overestimated, the overhyped at the beginning and underestimated. >> No, it was, it was underestimated long run, people thought. >> But that Amara's law. >> That's what is. >> No, they said overestimated? >> Overestimated near term underestimated- overhyped near term, underestimated long term. I got, right I mean? >> Well, I, yeah okay, so I would then agree, okay then- >> We were off the charts about the internet in the early days, and it actually exceeded our expectations. >> Well there were people who were, like, poo-pooing it early on. So when the browser came out, people were like, "Oh, the web's a toy for kids." I mean, in 1995 the web was a joke, right? So '96, you had online populations growing, so you had structural changes going on around the browser, internet population. And then that replaced other things, direct mail, other business activities that were once analog then went to the web, kind of read only as you, as we always talk about. So I think that's a moment where the hype long term, the smart money, and the smart industry experts all get the long term. And in this case, there's more poo-pooing in the short term. "Ah, it's not a big deal, it's just AI." I've heard many people poo-pooing ChatGPT, and a lot of smart people saying, "No this is next gen, this is different and it's only going to get better." So I think people are estimating a big long game on this one. >> So you're saying it's bifurcated. There's those who say- >> Yes. >> Okay, all right, let's get to the heart of the premise, and possibly the debate for today's episode. Will OpenAI's early entry into the market confer sustainable competitive advantage for the company. And if you look at the history of tech, the technology industry, it's kind of littered with first mover failures. Altair, IBM, Tandy, Commodore, they and Apple even, they were really early in the PC game. They took a backseat to Dell who came in the scene years later with a better business model. Netscape, you were just talking about, was all the rage in Silicon Valley, with the first browser, drove up all the housing prices out here. AltaVista was the first search engine to really, you know, index full text. >> Owned by Dell, I mean DEC. >> Owned by Digital. >> Yeah, Digital Equipment >> Compaq bought it. And of course as an aside, Digital, they wanted to showcase their hardware, right? Their super computer stuff. And then so Friendster and MySpace, they came before Facebook. The iPhone certainly wasn't the first mobile device. So lots of failed examples, but there are some recent successes like AWS and cloud. >> You could say smartphone. So I mean. >> Well I know, and you can, we can parse this so we'll debate it. Now Twitter, you could argue, had first mover advantage. You kind of gave me that one John. Bitcoin and crypto clearly had first mover advantage, and sustaining that. Guys, will OpenAI make it to the list on the right with ChatGPT, what do you think? >> I think categorically as a company, it probably won't, but as a category, I think what they're doing will, so OpenAI as a company, they get funding, there's power dynamics involved. Microsoft put a billion dollars in early on, then they just pony it up. Now they're reporting 10 billion more. So, like, if the browsers, Microsoft had competitive advantage over Netscape, and used monopoly power, and convicted by the Department of Justice for killing Netscape with their monopoly, Netscape should have had won that battle, but Microsoft killed it. In this case, Microsoft's not killing it, they're buying into it. So I think the embrace extend Microsoft power here makes OpenAI vulnerable for that one vendor solution. So the AI as a company might not make the list, but the category of what this is, large language model AI, is probably will be on the right hand side. >> Okay, we're going to come back to the government intervention and maybe do some comparisons, but what are your thoughts on this premise here? That, it will basically set- put forth the premise that it, that ChatGPT, its early entry into the market will not confer competitive advantage to >> For OpenAI. >> To Open- Yeah, do you agree with that? >> I agree with that actually. It, because Google has been at it, and they have been holding back, as John said because of the scrutiny from the Fed, right, so- >> And privacy too. >> And the privacy and the accuracy as well. But I think Sam Altman and the company on those guys, right? They have put this in a hasty way out there, you know, because it makes mistakes, and there are a lot of questions around the, sort of, where the content is coming from. You saw that as your example, it just stole the content, and without your permission, you know? >> Yeah. So as quick this aside- >> And it codes on people's behalf and the, those codes are wrong. So there's a lot of, sort of, false information it's putting out there. So it's a very vulnerable thing to do what Sam Altman- >> So even though it'll get better, others will compete. >> So look, just side note, a term which Reid Hoffman used a little bit. Like he said, it's experimental launch, like, you know, it's- >> It's pretty damn good. >> It is clever because according to Sam- >> It's more than clever. It's good. >> It's awesome, if you haven't used it. I mean you write- you read what it writes and you go, "This thing writes so well, it writes so much better than you." >> The human emotion drives that too. I think that's a big thing. But- >> I Want to add one more- >> Make your last point. >> Last one. Okay. So, but he's still holding back. He's conducting quite a few interviews. If you want to get the gist of it, there's an interview with StrictlyVC interview from yesterday with Sam Altman. Listen to that one it's an eye opening what they want- where they want to take it. But my last one I want to make it on this point is that Satya Nadella yesterday did an interview with Wall Street Journal. I think he was doing- >> You were not impressed. >> I was not impressed because he was pushing it too much. So Sam Altman's holding back so there's less backlash. >> Got 10 billion reasons to push. >> I think he's almost- >> Microsoft just laid off 10000 people. Hey ChatGPT, find me a job. You know like. (group laughs) >> He's overselling it to an extent that I think it will backfire on Microsoft. And he's over promising a lot of stuff right now, I think. I don't know why he's very jittery about all these things. And he did the same thing during Ignite as well. So he said, "Oh, this AI will write code for you and this and that." Like you called him out- >> The hyperbole- >> During your- >> from Satya Nadella, he's got a lot of hyperbole. (group talks over each other) >> All right, Let's, go ahead. >> Well, can I weigh in on the whole- >> Yeah, sure. >> Microsoft thing on whether OpenAI, here's the take on this. I think it's more like the browser moment to me, because I could relate to that experience with ChatG, personally, emotionally, when I saw that, and I remember vividly- >> You mean that aha moment (indistinct). >> Like this is obviously the future. Anything else in the old world is dead, website's going to be everywhere. It was just instant dot connection for me. And a lot of other smart people who saw this. Lot of people by the way, didn't see it. Someone said the web's a toy. At the company I was worked for at the time, Hewlett Packard, they like, they could have been in, they had invented HTML, and so like all this stuff was, like, they just passed, the web was just being passed over. But at that time, the browser got better, more websites came on board. So the structural advantage there was online web usage was growing, online user population. So that was growing exponentially with the rise of the Netscape browser. So OpenAI could stay on the right side of your list as durable, if they leverage the category that they're creating, can get the scale. And if they can get the scale, just like Twitter, that failed so many times that they still hung around. So it was a product that was always successful, right? So I mean, it should have- >> You're right, it was terrible, we kept coming back. >> The fail whale, but it still grew. So OpenAI has that moment. They could do it if Microsoft doesn't meddle too much with too much power as a vendor. They could be the Netscape Navigator, without the anti-competitive behavior of somebody else. So to me, they have the pole position. So they have an opportunity. So if not, if they don't execute, then there's opportunity. There's not a lot of barriers to entry, vis-a-vis say the CapEx of say a cloud company like AWS. You can't replicate that, Many have tried, but I think you can replicate OpenAI. >> And we're going to talk about that. Okay, so real quick, I want to bring in some ETR data. This isn't an ETR heavy segment, only because this so new, you know, they haven't coverage yet, but they do cover AI. So basically what we're seeing here is a slide on the vertical axis's net score, which is a measure of spending momentum, and in the horizontal axis's is presence in the dataset. Think of it as, like, market presence. And in the insert right there, you can see how the dots are plotted, the two columns. And so, but the key point here that we want to make, there's a bunch of companies on the left, is he like, you know, DataRobot and C3 AI and some others, but the big whales, Google, AWS, Microsoft, are really dominant in this market. So that's really the key takeaway that, can we- >> I notice IBM is way low. >> Yeah, IBM's low, and actually bring that back up and you, but then you see Oracle who actually is injecting. So I guess that's the other point is, you're not necessarily going to go buy AI, and you know, build your own AI, you're going to, it's going to be there and, it, Salesforce is going to embed it into its platform, the SaaS companies, and you're going to purchase AI. You're not necessarily going to build it. But some companies obviously are. >> I mean to quote IBM's general manager Rob Thomas, "You can't have AI with IA." information architecture and David Flynn- >> You can't Have AI without IA >> without, you can't have AI without IA. You can't have, if you have an Information Architecture, you then can power AI. Yesterday David Flynn, with Hammersmith, was on our Supercloud. He was pointing out that the relationship of storage, where you store things, also impacts the data and stressablity, and Zhamak from Nextdata, she was pointing out that same thing. So the data problem factors into all this too, Dave. >> So you got the big cloud and internet giants, they're all poised to go after this opportunity. Microsoft is investing up to 10 billion. Google's code red, which was, you know, the headline in the New York Times. Of course Apple is there and several alternatives in the market today. Guys like Chinchilla, Bloom, and there's a company Jasper and several others, and then Lena Khan looms large and the government's around the world, EU, US, China, all taking notice before the market really is coalesced around a single player. You know, John, you mentioned Netscape, they kind of really, the US government was way late to that game. It was kind of game over. And Netscape, I remember Barksdale was like, "Eh, we're going to be selling software in the enterprise anyway." and then, pshew, the company just dissipated. So, but it looks like the US government, especially with Lena Khan, they're changing the definition of antitrust and what the cause is to go after people, and they're really much more aggressive. It's only what, two years ago that (indistinct). >> Yeah, the problem I have with the federal oversight is this, they're always like late to the game, and they're slow to catch up. So in other words, they're working on stuff that should have been solved a year and a half, two years ago around some of the social networks hiding behind some of the rules around open web back in the days, and I think- >> But they're like 15 years late to that. >> Yeah, and now they got this new thing on top of it. So like, I just worry about them getting their fingers. >> But there's only two years, you know, OpenAI. >> No, but the thing (indistinct). >> No, they're still fighting other battles. But the problem with government is that they're going to label Big Tech as like a evil thing like Pharma, it's like smoke- >> You know Lena Khan wants to kill Big Tech, there's no question. >> So I think Big Tech is getting a very seriously bad rap. And I think anything that the government does that shades darkness on tech, is politically motivated in most cases. You can almost look at everything, and my 80 20 rule is in play here. 80% of the government activity around tech is bullshit, it's politically motivated, and the 20% is probably relevant, but off the mark and not organized. >> Well market forces have always been the determining factor of success. The governments, you know, have been pretty much failed. I mean you look at IBM's antitrust, that, what did that do? The market ultimately beat them. You look at Microsoft back in the day, right? Windows 95 was peaking, the government came in. But you know, like you said, they missed the web, right, and >> so they were hanging on- >> There's nobody in government >> to Windows. >> that actually knows- >> And so, you, I think you're right. It's market forces that are going to determine this. But Sarbjeet, what do you make of Microsoft's big bet here, you weren't impressed with with Nadella. How do you think, where are they going to apply it? Is this going to be a Hail Mary for Bing, or is it going to be applied elsewhere? What do you think. >> They are saying that they will, sort of, weave this into their products, office products, productivity and also to write code as well, developer productivity as well. That's a big play for them. But coming back to your antitrust sort of comments, right? I believe the, your comment was like, oh, fed was late 10 years or 15 years earlier, but now they're two years. But things are moving very fast now as compared to they used to move. >> So two years is like 10 Years. >> Yeah, two years is like 10 years. Just want to make that point. (Dave laughs) This thing is going like wildfire. Any new tech which comes in that I think they're going against distribution channels. Lina Khan has commented time and again that the marketplace model is that she wants to have some grip on. Cloud marketplaces are a kind of monopolistic kind of way. >> I don't, I don't see this, I don't see a Chat AI. >> You told me it's not Bing, you had an interesting comment. >> No, no. First of all, this is great from Microsoft. If you're Microsoft- >> Why? >> Because Microsoft doesn't have the AI chops that Google has, right? Google is got so much core competency on how they run their search, how they run their backends, their cloud, even though they don't get a lot of cloud market share in the enterprise, they got a kick ass cloud cause they needed one. >> Totally. >> They've invented SRE. I mean Google's development and engineering chops are off the scales, right? Amazon's got some good chops, but Google's got like 10 times more chops than AWS in my opinion. Cloud's a whole different story. Microsoft gets AI, they get a playbook, they get a product they can render into, the not only Bing, productivity software, helping people write papers, PowerPoint, also don't forget the cloud AI can super help. We had this conversation on our Supercloud event, where AI's going to do a lot of the heavy lifting around understanding observability and managing service meshes, to managing microservices, to turning on and off applications, and or maybe writing code in real time. So there's a plethora of use cases for Microsoft to deploy this. combined with their R and D budgets, they can then turbocharge more research, build on it. So I think this gives them a car in the game, Google may have pole position with AI, but this puts Microsoft right in the game, and they already have a lot of stuff going on. But this just, I mean everything gets lifted up. Security, cloud, productivity suite, everything. >> What's under the hood at Google, and why aren't they talking about it? I mean they got to be freaked out about this. No? Or do they have kind of a magic bullet? >> I think they have the, they have the chops definitely. Magic bullet, I don't know where they are, as compared to the ChatGPT 3 or 4 models. Like they, but if you look at the online sort of activity and the videos put out there from Google folks, Google technology folks, that's account you should look at if you are looking there, they have put all these distinctions what ChatGPT 3 has used, they have been talking about for a while as well. So it's not like it's a secret thing that you cannot replicate. As you said earlier, like in the beginning of this segment, that anybody who has more data and the capacity to process that data, which Google has both, I think they will win this. >> Obviously living in Palo Alto where the Google founders are, and Google's headquarters next town over we have- >> We're so close to them. We have inside information on some of the thinking and that hasn't been reported by any outlet yet. And that is, is that, from what I'm hearing from my sources, is Google has it, they don't want to release it for many reasons. One is it might screw up their search monopoly, one, two, they're worried about the accuracy, 'cause Google will get sued. 'Cause a lot of people are jamming on this ChatGPT as, "Oh it does everything for me." when it's clearly not a hundred percent accurate all the time. >> So Lina Kahn is looming, and so Google's like be careful. >> Yeah so Google's just like, this is the third, could be a third rail. >> But the first thing you said is a concern. >> Well no. >> The disruptive (indistinct) >> What they will do is do a Waymo kind of thing, where they spin out a separate company. >> They're doing that. >> The discussions happening, they're going to spin out the separate company and put it over there, and saying, "This is AI, got search over there, don't touch that search, 'cause that's where all the revenue is." (chuckles) >> So, okay, so that's how they deal with the Clay Christensen dilemma. What's the business model here? I mean it's not advertising, right? Is it to charge you for a query? What, how do you make money at this? >> It's a good question, I mean my thinking is, first of all, it's cool to type stuff in and see a paper get written, or write a blog post, or gimme a marketing slogan for this or that or write some code. I think the API side of the business will be critical. And I think Howie Xu, I know you're going to reference some of his comments yesterday on Supercloud, I think this brings a whole 'nother user interface into technology consumption. I think the business model, not yet clear, but it will probably be some sort of either API and developer environment or just a straight up free consumer product, with some sort of freemium backend thing for business. >> And he was saying too, it's natural language is the way in which you're going to interact with these systems. >> I think it's APIs, it's APIs, APIs, APIs, because these people who are cooking up these models, and it takes a lot of compute power to train these and to, for inference as well. Somebody did the analysis on the how many cents a Google search costs to Google, and how many cents the ChatGPT query costs. It's, you know, 100x or something on that. You can take a look at that. >> A 100x on which side? >> You're saying two orders of magnitude more expensive for ChatGPT >> Much more, yeah. >> Than for Google. >> It's very expensive. >> So Google's got the data, they got the infrastructure and they got, you're saying they got the cost (indistinct) >> No actually it's a simple query as well, but they are trying to put together the answers, and they're going through a lot more data versus index data already, you know. >> Let me clarify, you're saying that Google's version of ChatGPT is more efficient? >> No, I'm, I'm saying Google search results. >> Ah, search results. >> What are used to today, but cheaper. >> But that, does that, is that going to confer advantage to Google's large language (indistinct)? >> It will, because there were deep science (indistinct). >> Google, I don't think Google search is doing a large language model on their search, it's keyword search. You know, what's the weather in Santa Cruz? Or how, what's the weather going to be? Or you know, how do I find this? Now they have done a smart job of doing some things with those queries, auto complete, re direct navigation. But it's, it's not entity. It's not like, "Hey, what's Dave Vellante thinking this week in Breaking Analysis?" ChatGPT might get that, because it'll get your Breaking Analysis, it'll synthesize it. There'll be some, maybe some clips. It'll be like, you know, I mean. >> Well I got to tell you, I asked ChatGPT to, like, I said, I'm going to enter a transcript of a discussion I had with Nir Zuk, the CTO of Palo Alto Networks, And I want you to write a 750 word blog. I never input the transcript. It wrote a 750 word blog. It attributed quotes to him, and it just pulled a bunch of stuff that, and said, okay, here it is. It talked about Supercloud, it defined Supercloud. >> It's made, it makes you- >> Wow, But it was a big lie. It was fraudulent, but still, blew me away. >> Again, vanilla content and non accurate content. So we are going to see a surge of misinformation on steroids, but I call it the vanilla content. Wow, that's just so boring, (indistinct). >> There's so many dangers. >> Make your point, cause we got to, almost out of time. >> Okay, so the consumption, like how do you consume this thing. As humans, we are consuming it and we are, like, getting a nicely, like, surprisingly shocked, you know, wow, that's cool. It's going to increase productivity and all that stuff, right? And on the danger side as well, the bad actors can take hold of it and create fake content and we have the fake sort of intelligence, if you go out there. So that's one thing. The second thing is, we are as humans are consuming this as language. Like we read that, we listen to it, whatever format we consume that is, but the ultimate usage of that will be when the machines can take that output from likes of ChatGPT, and do actions based on that. The robots can work, the robot can paint your house, we were talking about, right? Right now we can't do that. >> Data apps. >> So the data has to be ingested by the machines. It has to be digestible by the machines. And the machines cannot digest unorganized data right now, we will get better on the ingestion side as well. So we are getting better. >> Data, reasoning, insights, and action. >> I like that mall, paint my house. >> So, okay- >> By the way, that means drones that'll come in. Spray painting your house. >> Hey, it wasn't too long ago that robots couldn't climb stairs, as I like to point out. Okay, and of course it's no surprise the venture capitalists are lining up to eat at the trough, as I'd like to say. Let's hear, you'd referenced this earlier, John, let's hear what AI expert Howie Xu said at the Supercloud event, about what it takes to clone ChatGPT. Please, play the clip. >> So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest to get a, you know, another shot to the openAI sort of the level." You know, I did a (indistinct) >> Line up. >> A hundred million dollar is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So a hundred- >> Guys a hundred million dollars, that's an astoundingly low figure. What do you make of it? >> I was in an interview with, I was interviewing, I think he said hundred million or so, but in the hundreds of millions, not a billion right? >> You were trying to get him up, you were like "Hundreds of millions." >> Well I think, I- >> He's like, eh, not 10, not a billion. >> Well first of all, Howie Xu's an expert machine learning. He's at Zscaler, he's a machine learning AI guy. But he comes from VMware, he's got his technology pedigrees really off the chart. Great friend of theCUBE and kind of like a CUBE analyst for us. And he's smart. He's right. I think the barriers to entry from a dollar standpoint are lower than say the CapEx required to compete with AWS. Clearly, the CapEx spending to build all the tech for the run a cloud. >> And you don't need a huge sales force. >> And in some case apps too, it's the same thing. But I think it's not that hard. >> But am I right about that? You don't need a huge sales force either. It's, what, you know >> If the product's good, it will sell, this is a new era. The better mouse trap will win. This is the new economics in software, right? So- >> Because you look at the amount of money Lacework, and Snyk, Snowflake, Databrooks. Look at the amount of money they've raised. I mean it's like a billion dollars before they get to IPO or more. 'Cause they need promotion, they need go to market. You don't need (indistinct) >> OpenAI's been working on this for multiple five years plus it's, hasn't, wasn't born yesterday. Took a lot of years to get going. And Sam is depositioning all the success, because he's trying to manage expectations, To your point Sarbjeet, earlier. It's like, yeah, he's trying to "Whoa, whoa, settle down everybody, (Dave laughs) it's not that great." because he doesn't want to fall into that, you know, hero and then get taken down, so. >> It may take a 100 million or 150 or 200 million to train the model. But to, for the inference to, yeah to for the inference machine, It will take a lot more, I believe. >> Give it, so imagine, >> Because- >> Go ahead, sorry. >> Go ahead. But because it consumes a lot more compute cycles and it's certain level of storage and everything, right, which they already have. So I think to compute is different. To frame the model is a different cost. But to run the business is different, because I think 100 million can go into just fighting the Fed. >> Well there's a flywheel too. >> Oh that's (indistinct) >> (indistinct) >> We are running the business, right? >> It's an interesting number, but it's also kind of, like, context to it. So here, a hundred million spend it, you get there, but you got to factor in the fact that the ways companies win these days is critical mass scale, hitting a flywheel. If they can keep that flywheel of the value that they got going on and get better, you can almost imagine a marketplace where, hey, we have proprietary data, we're SiliconANGLE in theCUBE. We have proprietary content, CUBE videos, transcripts. Well wouldn't it be great if someone in a marketplace could sell a module for us, right? We buy that, Amazon's thing and things like that. So if they can get a marketplace going where you can apply to data sets that may be proprietary, you can start to see this become bigger. And so I think the key barriers to entry is going to be success. I'll give you an example, Reddit. Reddit is successful and it's hard to copy, not because of the software. >> They built the moat. >> Because you can, buy Reddit open source software and try To compete. >> They built the moat with their community. >> Their community, their scale, their user expectation. Twitter, we referenced earlier, that thing should have gone under the first two years, but there was such a great emotional product. People would tolerate the fail whale. And then, you know, well that was a whole 'nother thing. >> Then a plane landed in (John laughs) the Hudson and it was over. >> I think verticals, a lot of verticals will build applications using these models like for lawyers, for doctors, for scientists, for content creators, for- >> So you'll have many hundreds of millions of dollars investments that are going to be seeping out. If, all right, we got to wrap, if you had to put odds on it that that OpenAI is going to be the leader, maybe not a winner take all leader, but like you look at like Amazon and cloud, they're not winner take all, these aren't necessarily winner take all markets. It's not necessarily a zero sum game, but let's call it winner take most. What odds would you give that open AI 10 years from now will be in that position. >> If I'm 0 to 10 kind of thing? >> Yeah, it's like horse race, 3 to 1, 2 to 1, even money, 10 to 1, 50 to 1. >> Maybe 2 to 1, >> 2 to 1, that's pretty low odds. That's basically saying they're the favorite, they're the front runner. Would you agree with that? >> I'd say 4 to 1. >> Yeah, I was going to say I'm like a 5 to 1, 7 to 1 type of person, 'cause I'm a skeptic with, you know, there's so much competition, but- >> I think they're definitely the leader. I mean you got to say, I mean. >> Oh there's no question. There's no question about it. >> The question is can they execute? >> They're not Friendster, is what you're saying. >> They're not Friendster and they're more like Twitter and Reddit where they have momentum. If they can execute on the product side, and if they don't stumble on that, they will continue to have the lead. >> If they say stay neutral, as Sam is, has been saying, that, hey, Microsoft is one of our partners, if you look at their company model, how they have structured the company, then they're going to pay back to the investors, like Microsoft is the biggest one, up to certain, like by certain number of years, they're going to pay back from all the money they make, and after that, they're going to give the money back to the public, to the, I don't know who they give it to, like non-profit or something. (indistinct) >> Okay, the odds are dropping. (group talks over each other) That's a good point though >> Actually they might have done that to fend off the criticism of this. But it's really interesting to see the model they have adopted. >> The wildcard in all this, My last word on this is that, if there's a developer shift in how developers and data can come together again, we have conferences around the future of data, Supercloud and meshs versus, you know, how the data world, coding with data, how that evolves will also dictate, 'cause a wild card could be a shift in the landscape around how developers are using either machine learning or AI like techniques to code into their apps, so. >> That's fantastic insight. I can't thank you enough for your time, on the heels of Supercloud 2, really appreciate it. All right, thanks to John and Sarbjeet for the outstanding conversation today. Special thanks to the Palo Alto studio team. My goodness, Anderson, this great backdrop. You guys got it all out here, I'm jealous. And Noah, really appreciate it, Chuck, Andrew Frick and Cameron, Andrew Frick switching, Cameron on the video lake, great job. And Alex Myerson, he's on production, manages the podcast for us, Ken Schiffman as well. Kristen Martin and Cheryl Knight help get the word out on social media and our newsletters. Rob Hof is our editor-in-chief over at SiliconANGLE, does some great editing, thanks to all. Remember, all these episodes are available as podcasts. All you got to do is search Breaking Analysis podcast, wherever you listen. Publish each week on wikibon.com and siliconangle.com. Want to get in touch, email me directly, david.vellante@siliconangle.com or DM me at dvellante, or comment on our LinkedIn post. And by all means, check out etr.ai. They got really great survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, We'll see you next time on Breaking Analysis. (electronic music)

>> Narrator: From theCUBE Studios in Palo Alto and Boston bringing you data-driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante >> Enterprise tech practitioners, like most of us they want to make their lives easier so they can focus on delivering more value to their businesses. And to do so, they want to tap best of breed services in the public cloud, but at the same time connect their on-prem intellectual property to emerging applications which drive top line revenue and bottom line profits. But creating a consistent experience across clouds and on-prem estates has been an elusive capability for most organizations, forcing trade-offs and injecting friction into the system. The need to create seamless experiences is clear and the technology industry is starting to respond with platforms, architectures, and visions of what we've called the Supercloud. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis we give you a preview of Supercloud 2, the second event of its kind that we've had on the topic. Yes, folks that's right Supercloud 2 is here. As of this recording, it's just about four days away 33 guests, 21 sessions, combining live discussions and fireside chats from theCUBE's Palo Alto Studio with prerecorded conversations on the future of cloud and data. You can register for free at supercloud.world. And we are super excited about the Supercloud 2 lineup of guests whereas Supercloud 22 in August, was all about refining the definition of Supercloud testing its technical feasibility and understanding various deployment models. Supercloud 2 features practitioners, technologists and analysts discussing what customers need with real-world examples of Supercloud and will expose thinking around a new breed of cross-cloud apps, data apps, if you will that change the way machines and humans interact with each other. Now the example we'd use if you think about applications today, say a CRM system, sales reps, what are they doing? They're entering data into opportunities they're choosing products they're importing contacts, et cetera. And sure the machine can then take all that data and spit out a forecast by rep, by region, by product, et cetera. But today's applications are largely about filling in forms and or codifying processes. In the future, the Supercloud community sees a new breed of applications emerging where data resides on different clouds, in different data storages, databases, Lakehouse, et cetera. And the machine uses AI to inspect the e-commerce system the inventory data, supply chain information and other systems, and puts together a plan without any human intervention whatsoever. Think about a system that orchestrates people, places and things like an Uber for business. So at Supercloud 2, you'll hear about this vision along with some of today's challenges facing practitioners. Zhamak Dehghani, the founder of Data Mesh is a headliner. Kit Colbert also is headlining. He laid out at the first Supercloud an initial architecture for what that's going to look like. That was last August. And he's going to present his most current thinking on the topic. Veronika Durgin of Sachs will be featured and talk about data sharing across clouds and you know what she needs in the future. One of the main highlights of Supercloud 2 is a dive into Walmart's Supercloud. Other featured practitioners include Western Union Ionis Pharmaceuticals, Warner Media. We've got deep, deep technology dives with folks like Bob Muglia, David Flynn Tristan Handy of DBT Labs, Nir Zuk, the founder of Palo Alto Networks focused on security. Thomas Hazel, who's going to talk about a new type of database for Supercloud. It's several analysts including Keith Townsend Maribel Lopez, George Gilbert, Sanjeev Mohan and so many more guests, we don't have time to list them all. They're all up on supercloud.world with a full agenda, so you can check that out. Now let's take a look at some of the things that we're exploring in more detail starting with the Walmart Cloud native platform, they call it WCNP. We definitely see this as a Supercloud and we dig into it with Jack Greenfield. He's the head of architecture at Walmart. Here's a quote from Jack. "WCNP is an implementation of Kubernetes for the Walmart ecosystem. We've taken Kubernetes off the shelf as open source." By the way, they do the same thing with OpenStack. "And we have integrated it with a number of foundational services that provide other aspects of our computational environment. Kubernetes off the shelf doesn't do everything." And so what Walmart chose to do, they took a do-it-yourself approach to build a Supercloud for a variety of reasons that Jack will explain, along with Walmart's so-called triplet architecture connecting on-prem, Azure and GCP. No surprise, there's no Amazon at Walmart for obvious reasons. And what they do is they create a common experience for devs across clouds. Jack is going to talk about how Walmart is evolving its Supercloud in the future. You don't want to miss that. Now, next, let's take a look at how Veronica Durgin of SAKS thinks about data sharing across clouds. Data sharing we think is a potential killer use case for Supercloud. In fact, let's hear it in Veronica's own words. Please play the clip. >> How do we talk to each other? And more importantly, how do we data share? You know, I work with data, you know this is what I do. So if you know I want to get data from a company that's using, say Google, how do we share it in a smooth way where it doesn't have to be this crazy I don't know, SFTP file moving? So that's where I think Supercloud comes to me in my mind, is like practical applications. How do we create that mesh, that network that we can easily share data with each other? >> Now data mesh is a possible architectural approach that will enable more facile data sharing and the monetization of data products. You'll hear Zhamak Dehghani live in studio talking about what standards are missing to make this vision a reality across the Supercloud. Now one of the other things that we're really excited about is digging deeper into the right approach for Supercloud adoption. And we're going to share a preview of a debate that's going on right now in the community. Bob Muglia, former CEO of Snowflake and Microsoft Exec was kind enough to spend some time looking at the community's supercloud definition and he felt that it needed to be simplified. So in near real time he came up with the following definition that we're showing here. I'll read it. "A Supercloud is a platform that provides programmatically consistent services hosted on heterogeneous cloud providers." So not only did Bob simplify the initial definition he's stressed that the Supercloud is a platform versus an architecture implying that the platform provider eg Snowflake, VMware, Databricks, Cohesity, et cetera is responsible for determining the architecture. Now interestingly in the shared Google doc that the working group uses to collaborate on the supercloud de definition, Dr. Nelu Mihai who is actually building a Supercloud responded as follows to Bob's assertion "We need to avoid creating many Supercloud platforms with their own architectures. If we do that, then we create other proprietary clouds on top of existing ones. We need to define an architecture of how Supercloud interfaces with all other clouds. What is the information model? What is the execution model and how users will interact with Supercloud?" What does this seemingly nuanced point tell us and why does it matter? Well, history suggests that de facto standards will emerge more quickly to resolve real world practitioner problems and catch on more quickly than consensus-based architectures and standards-based architectures. But in the long run, the ladder may serve customers better. So we'll be exploring this topic in more detail in Supercloud 2, and of course we'd love to hear what you think platform, architecture, both? Now one of the real technical gurus that we'll have in studio at Supercloud two is David Flynn. He's one of the people behind the the movement that enabled enterprise flash adoption, that craze. And he did that with Fusion IO and he is now working on a system to enable read write data access to any user in any application in any data center or on any cloud anywhere. So think of this company as a Supercloud enabler. Allow me to share an excerpt from a conversation David Flore and I had with David Flynn last year. He as well gave a lot of thought to the Supercloud definition and was really helpful with an opinionated point of view. He said something to us that was, we thought relevant. "What is the operating system for a decentralized cloud? The main two functions of an operating system or an operating environment are one the process scheduler and two, the file system. The strongest argument for supercloud is made when you go down to the platform layer and talk about it as an operating environment on which you can run all forms of applications." So a couple of implications here that will be exploring with David Flynn in studio. First we're inferring from his comment that he's in the platform camp where the platform owner is responsible for the architecture and there are obviously trade-offs there and benefits but we'll have to clarify that with him. And second, he's basically saying, you kill the concept the further you move up the stack. So the weak, the further you move the stack the weaker the supercloud argument becomes because it's just becoming SaaS. Now this is something we're going to explore to better understand is thinking on this, but also whether the existing notion of SaaS is changing and whether or not a new breed of Supercloud apps will emerge. Which brings us to this really interesting fellow that George Gilbert and I RIFed with ahead of Supercloud two. Tristan Handy, he's the founder and CEO of DBT Labs and he has a highly opinionated and technical mind. Here's what he said, "One of the things that we still don't know how to API-ify is concepts that live inside of your data warehouse inside of your data lake. These are core concepts that the business should be able to create applications around very easily. In fact, that's not the case because it involves a lot of data engineering pipeline and other work to make these available. So if you really want to make it easy to create these data experiences for users you need to have an ability to describe these metrics and then to turn them into APIs to make them accessible to application developers who have literally no idea how they're calculated behind the scenes and they don't need to." A lot of implications to this statement that will explore at Supercloud two versus Jamma Dani's data mesh comes into play here with her critique of hyper specialized data pipeline experts with little or no domain knowledge. Also the need for simplified self-service infrastructure which Kit Colbert is likely going to touch upon. Veronica Durgin of SAKS and her ideal state for data shearing along with Harveer Singh of Western Union. They got to deal with 200 locations around the world in data privacy issues, data sovereignty how do you share data safely? Same with Nick Taylor of Ionis Pharmaceutical. And not to blow your mind but Thomas Hazel and Bob Muglia deposit that to make data apps a reality across the Supercloud you have to rethink everything. You can't just let in memory databases and caching architectures take care of everything in a brute force manner. Rather you have to get down to really detailed levels even things like how data is laid out on disk, ie flash and think about rewriting applications for the Supercloud and the MLAI era. All of this and more at Supercloud two which wouldn't be complete without some data. So we pinged our friends from ETR Eric Bradley and Darren Bramberm to see if they had any data on Supercloud that we could tap. And so we're going to be analyzing a number of the players as well at Supercloud two. Now, many of you are familiar with this graphic here we show some of the players involved in delivering or enabling Supercloud-like capabilities. On the Y axis is spending momentum and on the horizontal accesses market presence or pervasiveness in the data. So netscore versus what they call overlap or end in the data. And the table insert shows how the dots are plotted now not to steal ETR's thunder but the first point is you really can't have supercloud without the hyperscale cloud platforms which is shown on this graphic. But the exciting aspect of Supercloud is the opportunity to build value on top of that hyperscale infrastructure. Snowflake here continues to show strong spending velocity as those Databricks, Hashi, Rubrik. VMware Tanzu, which we all put under the magnifying glass after the Broadcom announcements, is also showing momentum. Unfortunately due to a scheduling conflict we weren't able to get Red Hat on the program but they're clearly a player here. And we've put Cohesity and Veeam on the chart as well because backup is a likely use case across clouds and on-premises. And now one other call out that we drill down on at Supercloud two is CloudFlare, which actually uses the term supercloud maybe in a different way. They look at Supercloud really as you know, serverless on steroids. And so the data brains at ETR will have more to say on this topic at Supercloud two along with many others. Okay, so why should you attend Supercloud two? What's in it for me kind of thing? So first of all, if you're a practitioner and you want to understand what the possibilities are for doing cross-cloud services for monetizing data how your peers are doing data sharing, how some of your peers are actually building out a Supercloud you're going to get real world input from practitioners. If you're a technologist, you're trying to figure out various ways to solve problems around data, data sharing, cross-cloud service deployment there's going to be a number of deep technology experts that are going to share how they're doing it. We're also going to drill down with Walmart into a practical example of Supercloud with some other examples of how practitioners are dealing with cross-cloud complexity. Some of them, by the way, are kind of thrown up their hands and saying, Hey, we're going mono cloud. And we'll talk about the potential implications and dangers and risks of doing that. And also some of the benefits. You know, there's a question, right? Is Supercloud the same wine new bottle or is it truly something different that can drive substantive business value? So look, go to Supercloud.world it's January 17th at 9:00 AM Pacific. You can register for free and participate directly in the program. Okay, that's a wrap. I want to give a shout out to the Supercloud supporters. VMware has been a great partner as our anchor sponsor Chaos Search Proximo, and Alura as well. For contributing to the effort I want to thank Alex Myerson who's on production and manages the podcast. Ken Schiffman is his supporting cast as well. Kristen Martin and Cheryl Knight to help get the word out on social media and at our newsletters. And Rob Ho is our editor-in-chief over at Silicon Angle. Thank you all. Remember, these episodes are all available as podcast. Wherever you listen we really appreciate the support that you've given. We just saw some stats from from Buzz Sprout, we hit the top 25% we're almost at 400,000 downloads last year. So really appreciate your participation. All you got to do is search Breaking Analysis podcast and you'll find those I publish each week on wikibon.com and siliconangle.com. Or if you want to get ahold of me you can email me directly at David.Vellante@siliconangle.com or dm me DVellante or comment on our LinkedIn post. I want you to check out etr.ai. They've got the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. We'll see you next week at Supercloud two or next time on breaking analysis. (light music)

>>From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from the cube and E T R. This is breaking analysis with Dave Valante. >>Making technology predictions in 2022 was tricky business, especially if you were projecting the performance of markets or identifying I P O prospects and making binary forecast on data AI and the macro spending climate and other related topics in enterprise tech 2022, of course was characterized by a seesaw economy where central banks were restructuring their balance sheets. The war on Ukraine fueled inflation supply chains were a mess. And the unintended consequences of of forced march to digital and the acceleration still being sorted out. Hello and welcome to this week's weekly on Cube Insights powered by E T R. In this breaking analysis, we continue our annual tradition of transparently grading last year's enterprise tech predictions. And you may or may not agree with our self grading system, but look, we're gonna give you the data and you can draw your own conclusions and tell you what, tell us what you think. >>All right, let's get right to it. So our first prediction was tech spending increases by 8% in 2022. And as we exited 2021 CIOs, they were optimistic about their digital transformation plans. You know, they rushed to make changes to their business and were eager to sharpen their focus and continue to iterate on their digital business models and plug the holes that they, the, in the learnings that they had. And so we predicted that 8% rise in enterprise tech spending, which looked pretty good until Ukraine and the Fed decided that, you know, had to rush and make up for lost time. We kind of nailed the momentum in the energy sector, but we can't give ourselves too much credit for that layup. And as of October, Gartner had it spending growing at just over 5%. I think it was 5.1%. So we're gonna take a C plus on this one and, and move on. >>Our next prediction was basically kind of a slow ground ball. The second base, if I have to be honest, but we felt it was important to highlight that security would remain front and center as the number one priority for organizations in 2022. As is our tradition, you know, we try to up the degree of difficulty by specifically identifying companies that are gonna benefit from these trends. So we highlighted some possible I P O candidates, which of course didn't pan out. S NQ was on our radar. The company had just had to do another raise and they recently took a valuation hit and it was a down round. They raised 196 million. So good chunk of cash, but, but not the i p O that we had predicted Aqua Securities focus on containers and cloud native. That was a trendy call and we thought maybe an M SS P or multiple managed security service providers like Arctic Wolf would I p o, but no way that was happening in the crummy market. >>Nonetheless, we think these types of companies, they're still faring well as the talent shortage in security remains really acute, particularly in the sort of mid-size and small businesses that often don't have a sock Lacework laid off 20% of its workforce in 2022. And CO C e o Dave Hatfield left the company. So that I p o didn't, didn't happen. It was probably too early for Lacework. Anyway, meanwhile you got Netscope, which we've cited as strong in the E T R data as particularly in the emerging technology survey. And then, you know, I lumia holding its own, you know, we never liked that 7 billion price tag that Okta paid for auth zero, but we loved the TAM expansion strategy to target developers beyond sort of Okta's enterprise strength. But we gotta take some points off of the failure thus far of, of Okta to really nail the integration and the go to market model with azero and build, you know, bring that into the, the, the core Okta. >>So the focus on endpoint security that was a winner in 2022 is CrowdStrike led that charge with others holding their own, not the least of which was Palo Alto Networks as it continued to expand beyond its core network security and firewall business, you know, through acquisition. So overall we're gonna give ourselves an A minus for this relatively easy call, but again, we had some specifics associated with it to make it a little tougher. And of course we're watching ve very closely this this coming year in 2023. The vendor consolidation trend. You know, according to a recent Palo Alto network survey with 1300 SecOps pros on average organizations have more than 30 tools to manage security tools. So this is a logical way to optimize cost consolidating vendors and consolidating redundant vendors. The E T R data shows that's clearly a trend that's on the upswing. >>Now moving on, a big theme of 2020 and 2021 of course was remote work and hybrid work and new ways to work and return to work. So we predicted in 2022 that hybrid work models would become the dominant protocol, which clearly is the case. We predicted that about 33% of the workforce would come back to the office in 2022 in September. The E T R data showed that figure was at 29%, but organizations expected that 32% would be in the office, you know, pretty much full-time by year end. That hasn't quite happened, but we were pretty close with the projection, so we're gonna take an A minus on this one. Now, supply chain disruption was another big theme that we felt would carry through 2022. And sure that sounds like another easy one, but as is our tradition, again we try to put some binary metrics around our predictions to put some meat in the bone, so to speak, and and allow us than you to say, okay, did it come true or not? >>So we had some data that we presented last year and supply chain issues impacting hardware spend. We said at the time, you can see this on the left hand side of this chart, the PC laptop demand would remain above pre covid levels, which would reverse a decade of year on year declines, which I think started in around 2011, 2012. Now, while demand is down this year pretty substantially relative to 2021, I D C has worldwide unit shipments for PCs at just over 300 million for 22. If you go back to 2019 and you're looking at around let's say 260 million units shipped globally, you know, roughly, so, you know, pretty good call there. Definitely much higher than pre covid levels. But so what you might be asking why the B, well, we projected that 30% of customers would replace security appliances with cloud-based services and that more than a third would replace their internal data center server and storage hardware with cloud services like 30 and 40% respectively. >>And we don't have explicit survey data on exactly these metrics, but anecdotally we see this happening in earnest. And we do have some data that we're showing here on cloud adoption from ET R'S October survey where the midpoint of workloads running in the cloud is around 34% and forecast, as you can see, to grow steadily over the next three years. So this, well look, this is not, we understand it's not a one-to-one correlation with our prediction, but it's a pretty good bet that we were right, but we gotta take some points off, we think for the lack of unequivocal proof. Cause again, we always strive to make our predictions in ways that can be measured as accurate or not. Is it binary? Did it happen, did it not? Kind of like an O K R and you know, we strive to provide data as proof and in this case it's a bit fuzzy. >>We have to admit that although we're pretty comfortable that the prediction was accurate. And look, when you make an hard forecast, sometimes you gotta pay the price. All right, next, we said in 2022 that the big four cloud players would generate 167 billion in IS and PaaS revenue combining for 38% market growth. And our current forecasts are shown here with a comparison to our January, 2022 figures. So coming into this year now where we are today, so currently we expect 162 billion in total revenue and a 33% growth rate. Still very healthy, but not on our mark. So we think a w s is gonna miss our predictions by about a billion dollars, not, you know, not bad for an 80 billion company. So they're not gonna hit that expectation though of getting really close to a hundred billion run rate. We thought they'd exit the year, you know, closer to, you know, 25 billion a quarter and we don't think they're gonna get there. >>Look, we pretty much nailed Azure even though our prediction W was was correct about g Google Cloud platform surpassing Alibaba, Alibaba, we way overestimated the performance of both of those companies. So we're gonna give ourselves a C plus here and we think, yeah, you might think it's a little bit harsh, we could argue for a B minus to the professor, but the misses on GCP and Alibaba we think warrant a a self penalty on this one. All right, let's move on to our prediction about Supercloud. We said it becomes a thing in 2022 and we think by many accounts it has, despite the naysayers, we're seeing clear evidence that the concept of a layer of value add that sits above and across clouds is taking shape. And on this slide we showed just some of the pickup in the industry. I mean one of the most interesting is CloudFlare, the biggest supercloud antagonist. >>Charles Fitzgerald even predicted that no vendor would ever use the term in their marketing. And that would be proof if that happened that Supercloud was a thing and he said it would never happen. Well CloudFlare has, and they launched their version of Supercloud at their developer week. Chris Miller of the register put out a Supercloud block diagram, something else that Charles Fitzgerald was, it was was pushing us for, which is rightly so, it was a good call on his part. And Chris Miller actually came up with one that's pretty good at David Linthicum also has produced a a a A block diagram, kind of similar, David uses the term metacloud and he uses the term supercloud kind of interchangeably to describe that trend. And so we we're aligned on that front. Brian Gracely has covered the concept on the popular cloud podcast. Berkeley launched the Sky computing initiative. >>You read through that white paper and many of the concepts highlighted in the Supercloud 3.0 community developed definition align with that. Walmart launched a platform with many of the supercloud salient attributes. So did Goldman Sachs, so did Capital One, so did nasdaq. So you know, sorry you can hate the term, but very clearly the evidence is gathering for the super cloud storm. We're gonna take an a plus on this one. Sorry, haters. Alright, let's talk about data mesh in our 21 predictions posts. We said that in the 2020s, 75% of large organizations are gonna re-architect their big data platforms. So kind of a decade long prediction. We don't like to do that always, but sometimes it's warranted. And because it was a longer term prediction, we, at the time in, in coming into 22 when we were evaluating our 21 predictions, we took a grade of incomplete because the sort of decade long or majority of the decade better part of the decade prediction. >>So last year, earlier this year, we said our number seven prediction was data mesh gains momentum in 22. But it's largely confined and narrow data problems with limited scope as you can see here with some of the key bullets. So there's a lot of discussion in the data community about data mesh and while there are an increasing number of examples, JP Morgan Chase, Intuit, H S P C, HelloFresh, and others that are completely rearchitecting parts of their data platform completely rearchitecting entire data platforms is non-trivial. There are organizational challenges, there're data, data ownership, debates, technical considerations, and in particular two of the four fundamental data mesh principles that the, the need for a self-service infrastructure and federated computational governance are challenging. Look, democratizing data and facilitating data sharing creates conflicts with regulatory requirements around data privacy. As such many organizations are being really selective with their data mesh implementations and hence our prediction of narrowing the scope of data mesh initiatives. >>I think that was right on J P M C is a good example of this, where you got a single group within a, within a division narrowly implementing the data mesh architecture. They're using a w s, they're using data lakes, they're using Amazon Glue, creating a catalog and a variety of other techniques to meet their objectives. They kind of automating data quality and it was pretty well thought out and interesting approach and I think it's gonna be made easier by some of the announcements that Amazon made at the recent, you know, reinvent, particularly trying to eliminate ET t l, better connections between Aurora and Redshift and, and, and better data sharing the data clean room. So a lot of that is gonna help. Of course, snowflake has been on this for a while now. Many other companies are facing, you know, limitations as we said here and this slide with their Hadoop data platforms. They need to do new, some new thinking around that to scale. HelloFresh is a really good example of this. Look, the bottom line is that organizations want to get more value from data and having a centralized, highly specialized teams that own the data problem, it's been a barrier and a blocker to success. The data mesh starts with organizational considerations as described in great detail by Ash Nair of Warner Brothers. So take a listen to this clip. >>Yeah, so when people think of Warner Brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of H B O, you think of t n t, you think of C N N. We have 30 plus brands in our portfolio and each have their own needs. So the, the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against, as an example, HBO if Game of Thrones is going on. >>So it's often the case that data mesh is in the eyes of the implementer. And while a company's implementation may not strictly adhere to Jamma Dani's vision of data mesh, and that's okay, the goal is to use data more effectively. And despite Gartner's attempts to deposition data mesh in favor of the somewhat confusing or frankly far more confusing data fabric concept that they stole from NetApp data mesh is taking hold in organizations globally today. So we're gonna take a B on this one. The prediction is shaping up the way we envision, but as we previously reported, it's gonna take some time. The better part of a decade in our view, new standards have to emerge to make this vision become reality and they'll come in the form of both open and de facto approaches. Okay, our eighth prediction last year focused on the face off between Snowflake and Databricks. >>And we realized this popular topic, and maybe one that's getting a little overplayed, but these are two companies that initially, you know, looked like they were shaping up as partners and they, by the way, they are still partnering in the field. But you go back a couple years ago, the idea of using an AW w s infrastructure, Databricks machine intelligence and applying that on top of Snowflake as a facile data warehouse, still very viable. But both of these companies, they have much larger ambitions. They got big total available markets to chase and large valuations that they have to justify. So what's happening is, as we've previously reported, each of these companies is moving toward the other firm's core domain and they're building out an ecosystem that'll be critical for their future. So as part of that effort, we said each is gonna become aggressive investors and maybe start doing some m and a and they have in various companies. >>And on this chart that we produced last year, we studied some of the companies that were targets and we've added some recent investments of both Snowflake and Databricks. As you can see, they've both, for example, invested in elation snowflake's, put money into Lacework, the Secur security firm, ThoughtSpot, which is trying to democratize data with ai. Collibra is a governance platform and you can see Databricks investments in data transformation with D B T labs, Matillion doing simplified business intelligence hunters. So that's, you know, they're security investment and so forth. So other than our thought that we'd see Databricks I p o last year, this prediction been pretty spot on. So we'll give ourselves an A on that one. Now observability has been a hot topic and we've been covering it for a while with our friends at E T R, particularly Eric Bradley. Our number nine prediction last year was basically that if you're not cloud native and observability, you are gonna be in big trouble. >>So everything guys gotta go cloud native. And that's clearly been the case. Splunk, the big player in the space has been transitioning to the cloud, hasn't always been pretty, as we reported, Datadog real momentum, the elk stack, that's open source model. You got new entrants that we've cited before, like observe, honeycomb, chaos search and others that we've, we've reported on, they're all born in the cloud. So we're gonna take another a on this one, admittedly, yeah, it's a re reasonably easy call, but you gotta have a few of those in the mix. Okay, our last prediction, our number 10 was around events. Something the cube knows a little bit about. We said that a new category of events would emerge as hybrid and that for the most part is happened. So that's gonna be the mainstay is what we said. That pure play virtual events are gonna give way to hi hybrid. >>And the narrative is that virtual only events are, you know, they're good for quick hits, but lousy replacements for in-person events. And you know that said, organizations of all shapes and sizes, they learn how to create better virtual content and support remote audiences during the pandemic. So when we set at pure play is gonna give way to hybrid, we said we, we i we implied or specific or specified that the physical event that v i p experience is going defined. That overall experience and those v i p events would create a little fomo, fear of, of missing out in a virtual component would overlay that serves an audience 10 x the size of the physical. We saw that really two really good examples. Red Hat Summit in Boston, small event, couple thousand people served tens of thousands, you know, online. Second was Google Cloud next v i p event in, in New York City. >>Everything else was, was, was, was virtual. You know, even examples of our prediction of metaverse like immersion have popped up and, and and, and you know, other companies are doing roadshow as we predicted like a lot of companies are doing it. You're seeing that as a major trend where organizations are going with their sales teams out into the regions and doing a little belly to belly action as opposed to the big giant event. That's a definitely a, a trend that we're seeing. So in reviewing this prediction, the grade we gave ourselves is, you know, maybe a bit unfair, it should be, you could argue for a higher grade, but the, but the organization still haven't figured it out. They have hybrid experiences but they generally do a really poor job of leveraging the afterglow and of event of an event. It still tends to be one and done, let's move on to the next event or the next city. >>Let the sales team pick up the pieces if they were paying attention. So because of that, we're only taking a B plus on this one. Okay, so that's the review of last year's predictions. You know, overall if you average out our grade on the 10 predictions that come out to a b plus, I dunno why we can't seem to get that elusive a, but we're gonna keep trying our friends at E T R and we are starting to look at the data for 2023 from the surveys and all the work that we've done on the cube and our, our analysis and we're gonna put together our predictions. We've had literally hundreds of inbounds from PR pros pitching us. We've got this huge thick folder that we've started to review with our yellow highlighter. And our plan is to review it this month, take a look at all the data, get some ideas from the inbounds and then the e t R of January surveys in the field. >>It's probably got a little over a thousand responses right now. You know, they'll get up to, you know, 1400 or so. And once we've digested all that, we're gonna go back and publish our predictions for 2023 sometime in January. So stay tuned for that. All right, we're gonna leave it there for today. You wanna thank Alex Myerson who's on production and he manages the podcast, Ken Schiffman as well out of our, our Boston studio. I gotta really heartfelt thank you to Kristen Martin and Cheryl Knight and their team. They helped get the word out on social and in our newsletters. Rob Ho is our editor in chief over at Silicon Angle who does some great editing for us. Thank you all. Remember all these podcasts are available or all these episodes are available is podcasts. Wherever you listen, just all you do Search Breaking analysis podcast, really getting some great traction there. Appreciate you guys subscribing. I published each week on wikibon.com, silicon angle.com or you can email me directly at david dot valante silicon angle.com or dm me Dante, or you can comment on my LinkedIn post. And please check out ETR AI for the very best survey data in the enterprise tech business. Some awesome stuff in there. This is Dante for the Cube Insights powered by etr. Thanks for watching and we'll see you next time on breaking analysis.

(upbeat music) >> Hello friends and welcome back to theCUBE's Live coverage of AWS re:Invent 2022 from the Venetian Expo in Vegas, baby. This show is absolutely packed. Lisa Martin with Dave Vellante, Dave this is day two, but really full day one of our wall to wall coverage on theCUBE. We've had great conversations the last half day this morning already, we've been talking with a lot of companies, a lot of Amazonians and some Amazonians that have left and gone on to interesting more things, which is what we're going to talk about next. >> Well, I'm excited about this segment because it's a really interesting space. You've got a search company who's gotten into observability and security and through our ETR partner our research, we do quarterly research and Elastic off the charts. Obviously they're the public company, so you can see how well they're doing. But the spending momentum on this platform is very, very strong and it has been consistently for quite some time. So really excited to learn more. >> The voice of the customer speaking loudly, from Elastic, its Chief Product Officer joins us, Ken Exner. Ken, welcome to the program. Hi, thank you, good to be here. >> Dave Vellante: Hey Ken. >> So a lot of us know about Elastic from Elastic Search but it's so much more than that these days. Talk about Elastic, what's going on now? What's the current product strategy? What's your vision? >> Yeah. So people know Elastic from the ELK Stack, you know Elastic Search, Logstash, Kibana. Very, very popular open source projects. They've been used by millions of developers for years and years. But one of the things that we started noticing over the years is that people were using it for all kinds of different use cases beyond just traditional search. So people started using Elastic Search to search through operational data, search through logs, search through all kinds of other types of data just to find different answers. And what we started realizing is the customers were taking us into different spaces. They took us into log analytics they started building log management solutions. And we said, cool, we can actually help these customers by providing solutions that already do this for them. So it took us into observability, they took us into security, and we started building solutions for security and observability based on what customers were starting to do with the platform. So customers can still use the platform for any number of different use cases for how do you get answers added data or they can use our pre-built packaged solutions for observability and security. >> So you were a longtime Amazonian. >> I was. I was. >> Talk a little bit about some of the things that you did there and what attracted you to Elastic? 'Cause it's only been a couple months, right? >> I've been here three months, I think three months as of yesterday. And I was at AWS for 16 years. So I was there a long, long time. I was there pretty much from the beginning. I was hired as one of the first product managers in AWS. Adam Selipsky hired me. And it was a great run. I had a ton of fun, I learned a lot. But you know, after 16 years I was kind of itching to do something new and it was going to take something special because I had a great gig and enjoyed the team at AWS. But I saw in Elastic sort of a great foundational technology they had a lot of momentum, a huge community behind it. I saw the business opportunity where they were going. I saw, you know the business opportunity of observability and security. These are massive industries with tons of business problems. Customers are excited about trying to get more answers out of data about their operational environment. And I saw, you know, that customers were struggling with their operating environments and things were becoming increasingly complicated. We used to talk in AWS about, you know how customers want to move from monolithic applications to monoliths, but one of the secrets was that things were increasingly complicated. Suddenly people had all these different microservices they had all these different managed services and their operating environment got complicated became this constellation of different systems, all emitting data. So companies like Elastic were helping people find answers in that data, find the problems with their systems so helping tame that complexity. So I saw that opportunity and I said I want to jump on that. Great foundational technology, good community and building solutions that actually helped solve real problems. >> Right. >> So, before you joined you probably looked back, and said, let think about the market, what's happening in the market space. What were the big trends that you saw that sort of informed your decision? >> Well, just sort of the mountain of data that was sort of emerging. Adam Selipsky in his talk this morning began by talking about how data is just multiplying constant. And I saw this, I saw how much data businesses were drowning in. Operational data, security data. You know, if you're trying to secure your business you have all these different endpoints you have all these different devices, you have different systems that you need to monitor all tons of data. And companies like Elastic were helping companies sort of manage that complexity, helping them find answers in that. So, when you're trying to track intruders or trying to track you know, malicious activity, there's a ton of different systems you need to pay attention to. And you know, there's a bunch of data. It's different devices, laptops and phone devices and stuff that you need to pay attention to. And you find correlations across that to figure out what is going on in your network, what is going on in your business. And that was exciting to me. This is a company sort of tackling one of the hardest problems which is helping you understand your operating environment, helping you understand and secure your business. >> So everybody's getting into observability. >> Yep. >> Right, it's a very crowded space right now. First of all, you know it's like overnight it just became the hottest thing going. VCs were throwing money at it. Why was that and how were you guys different? >> Well, we began by focusing on log analytics because that was the core of what we were doing. But customers started using it beyond log analytics and started using it for APM and started using it for performance data. And what we realized is that we could do all this for customers. So we ended up, sort of overnight over the course of three years building that a complete observe observability suite. So you can do APM, you can do profiling, you can do tracing, sort of distributed tracing, you can do synthetic monitoring everything you want to do, wheel user wondering. >> Metrics? >> All of it, metrics, all of it. And you can use the same system for this. So this was sort of a powerful concept, not only is it the best in leading log system, it also provides everything you need for complete observability. And because it's based on this open platform you can extend it to a number of different scenarios. So this is important, a lot of the different observability companies provide you something that's sort of packaged and as long as you're trying to do what it wants to support, it's great. But with Elastic, you have this flexible data architecture that you can use for anything. So companies use it to monitor assembly lines, they use it to monitor dish networks, for example use it to not only manage their fleet of servers they also use it to manage all their devices. So 25 million desktop devices. So, you know, observability systems like that that can do a number of different scenarios, I think that's a powerful thing. It's not just about how do you manage your servers how do you manage the things that are simple. It's how do you manage anything? How do you get observability into anything. >> Multiple use cases. >> Sorry, when you say complete, okay you talked about all the different APM, log analytics tracing, metrics, and also end-to-end. >> Ken Exner: End-to-end, yeah. >> Could you talk about that component of complete? >> So, if you're trying to find an issue like you have some metric that goes into alarm. You want to have a metric system that has alarming. Once that metric goes in alarm you're going to want to dig into your log. So you're going to want it to take you to the area of your logs that has that issue. Once you gets to there, you're going to want to find the trace ID that takes you to your traces and looks at sort of profiling, distributed tracing information. So a system that can do all of that end-to-end is a powerful solution. So it not only helps you track things end-to-end across the different signals that you're monitoring, but it actually helps you remediate more quickly. And the other thing that Elastic does that is unique is a lot of ML in this. So not only helping you find the information but surfacing things before you even know of them. So anomaly detection for example, helps you know about something before you even realize that there was an issue. So you should pay attention to this because it's anomalous. So a lot of systems help you find something if you know what to look for. But we're trying to help you not only find the things that you know to look for, but help you find the things that you didn't even think to know about. >> And it's fair to say one of your differentiators is you're open, open source. I mean, maybe talk about the ELK stack a little bit and how that plays. >> Yeah, well, so the great thing about this is we've extended that openness to both security and to observability. An example of this on the security side is all the detection rules that you use for looking for intrusion all the detection rules are open source and there's an entire community around this. So if you wanted to create a detection rule you can publish an open source, there's a bunch in GitHub you can benefit from what the community is doing as well. So in the world of security you want to be supported by the entire community, everyone looking for the same kind of issues. And there's an entire community around Elastic that is helping support these detection rules. So that approach, you know wanting to focus on community is differentiating for us. Not just, we got you covered as long you use things from us you can use it from the entire community. >> Well there implies the name Elastic. >> Yeah >> Talk a little bit about the influence that the customer has in the product roadmap and the direction. You've talked a little bit in the beginning about customers were leading us in different directions. It sounds very Amazonian in terms of following the customers where they go. >> It does, it actually does, it was one of the things that resonated for me personally is the journey that Elastic took to observability and security was customer led. So, we started looking at what customers were doing and realized that they were taking us into log analytics they were taking us into APM, they were taking us into these different solutions, and yeah, it is an Amazonian thing, so it resonated for me personally. And they're going to continue taking us in new places. Like we love seeing all the novel things that customers do with the platform and it's sort of one of the hallmarks of a great platform is you can have all kinds of novel things that, novel use cases for how people use your platform and we'll continue to see things and we may get taken into other solutions as well as we start seeing things emerge, like common patterns. But for now we're really excited about security and observability. >> So what do you see, so security's a big space, right? >> Yep. >> You see the optiv taxonomy and it makes your eyes bleed 'cause there's so many tools in there. Where do you fit in that taxonomy? How do you see and think about the security space and the opportunity for your customers? >> Yeah, so we began with logs in the security space as well. So SIEM, which is intrusion detection is based on aggregating a bunch of logs and helping you do threat hunting on those logs. So looking for patterns of malicious behavior or intrusion. So we started there and we did both detections as well as just ad hoc threat hunting. But then we started expanding into endpoint protection. So if we were going to have agents on all these different devices they were gathering logs, what if we also started providing remediation. So if you had malicious activity that was happening on one of the servers, don't just grab the information quarantine it, isolate it. So that took us into sort of endpoint protection or XDR. And then beyond that, we recently got into cloud security as well. So similar to observability, we started with logs but expanded to a full suite so that you can do everything. You can have both endpoint protection, you can have cloud security, all of it from one solution. >> Security is a very crowded market as well. What's your superpower? >> Ken Exner: What's our super power? >> Yeah. >> I think it, a lot of it is just the openness. It's the open platform, there's the community around it. People know and love the, the Elastic Search ELK stack and use it, we go into businesses all the time and they're familiar, their security engineers are using our product for searching through logs. So they're familiar with the product already and the community behind it. So they were excited about being able to use detection rules from other businesses and stay on top of that and be part of that community. The transparency of that is important to the customers. So if you're trying to be the most secure place, the most secure business, you want to basically invest in a community that's going to support that and not be alone in that. >> Right, absolutely, so much that rides on that. Favorite customer example that you think really articulates the value of Elastic, its openness, its transparency. >> Well, there's a customer Dish Media Dish Networks that's going to present here at re:Invent tomorrow at 1:45 at Mandalay Bay. I'm excited about their example because they use it to manage, I think it's 10 billion records a day across 25 million devices. So it illustrates the scale that we can support for managing observability for a company but also just sort of the unique use cases. We can use this for set top boxes for all their customers and they can track the performance that those customers are having. It's a unique case that a lot of vendors couldn't support but we can support because of the openness of the platform, the open data architecture that we have. So I think it illustrates the scale that we support, the elasticity, but also the openness of the data platform. >> Awesome and folks can catch that tomorrow, 1:45 PM at the Mandalay Bay. Last question for you, Ken, is you have a bumper sticker. >> Ken Exner: A bumper sticker? >> A bumper sticker you're going to put it on your fancy sexy new car and it's about elastic, what does it say? >> Helping you get answers out of data. So yeah. >> Love it, love it. Brilliant. >> Ken Exner: Thank you. >> Short and sweet. Ken, it's been a pleasure. >> It's been a pleasure being here, thank you. >> Thank you so much for sharing your journey with us as an Amazonian now into Elastic what Elastic is doing from a product perspective. We will keep our eyes peeled as Dave was saying. >> Ken Exner: Fantastic. >> The data show is really strong spending momentum so well done. >> Thank you very much, good to meet you. >> Our pleasure. For our guest and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (upbeat music)

(upbeat music) >> Hey everyone, welcome to this CUBE conversation. I'm John Furrier, your host of theCUBE here in Palo Alto, California. We've got Sam Kassoumeh, co-founder and chief operating office at SecurityScorecard here remotely coming in. Thanks for coming on Sam. Security, Sam. Thanks for coming on. >> Thank you, John. Thanks for having me. >> Love the security conversations. I love what you guys are doing. I think this idea of managed services, SaaS. Developers love it. Operation teams love getting into tools easily and having values what you guys got with SecurityScorecard. So let's get into what we were talking before we came on. You guys have a unique solution around ratings, but also it's not your grandfather's pen test want to be security app. Take us through what you guys are doing at SecurityScorecard. >> Yeah. So just like you said, it's not a point in time assessment and it's similar to a traditional credit rating, but also a little bit different. You can really think about it in three steps. In step one, what we're doing is we're doing threat intelligence data collection. We invest really heavily into R&D function. We never stop investing in R&D. We collect all of our own data across the entire IPV force space. All of the different layers. Some of the data we collect is pretty straightforward. We might crawl a website like the example I was giving. We might crawl a website and see that the website says copyright 2005, but we know it's 2022. Now, while that signal isn't enough to go hack and break into the company, it's definitely a signal that someone might not be keeping things up to date. And if a hacker saw that it might encourage them to dig deeper. To more complex signals where we're running one of the largest DNS single infrastructures in the world. We're monitoring command and control malware and its behaviors. We're essentially collecting signals and vulnerabilities from the entire IPV force space, the entire network layer, the entire web app player, leaked credentials. Everything that we think about when we talk about the security onion, we collect data at each one of those layers of the onion. That's step one. And we can do all sorts of interesting insights and information and reports just out of that thread intel. Now, step two is really interesting. What we do is we go identify the attack surface area or what we call the digital footprint of any company in the world. So as a customer, you can simply type in the name of a company and we identify all of the domains, sub domains, subsidiaries, organizations that are identified on the internet that belong to that organization. So every digital asset of every company we go out and we identify that and we update that every 24 hours. And step three is the rating. The rating is probabilistic and it's deterministic. The rating is a benchmark. We're looking at companies compared to their peers of similar size within the same industry and we're looking at how they're performing. And it's probabilistic in the sense that companies that have an F are about seven to eight times more likely to experience a breach. We're an A through F scale, universally understood. Ds and Fs, more likely to experience a breach. A's we see less breaches now. Like I was mentioning before, it doesn't mean that an F is always going to get hacked or an A can never get hacked. If a nation state targets an A, they're going to eventually get in with enough persistence and budget. If the pizza shop on the corner has an F, they may never get hacked because no one cares, but natural correlation, more doors open to the house equals higher likelihood someone unauthorized is going to walk in. So it's really those three steps. The collection, we map it to the surface area of the company and then we produce a rating. Today we're rating about 12 million companies every single day. >> And how many people do you have as customers? >> We have 50,000 organizations using us, both free and paid. We have a freemium tier where just like Yelp or a LinkedIn business profile. Any company in the world has a right to go claim the score. We never extort companies to fix the score. We never charge a company to see the score or fix it. Any company in a world without paying us a cent can go in. They can understand what we're seeing about them, what a hacker could see about their environment. And then we empower them with the tools to fix it and they can fix it and the score will go up. Now companies pay us because they want enterprise capabilities. They want additional modules, insights, which we can talk about. But in total, there's about 50,000 companies that at any given point in time, they're monitoring about a million and a half organizations of the 12 million that we're rating. It sounds like Google. >> If you want to look at it. >> Sounds like Google Search you got going on there. You got a lot of search and then you create relevance, a score, like a ranking. >> That's precisely it. And that's exactly why Google ventures invested in us in our Series B round. And they're on our board. They looked and they said, wow, you guys are building like a Google Search engine over some really impressive threat intelligence. And then you're distilling it into a score which anybody in the world can easily understand. >> Yeah. You obviously have page rank, which changed the organic search business in the late 90s, early 2000s and the rest is history. AdWords. >> Yeah. >> So you got a lot of customer growth there potentially with the opt-in customer view, but you're looking at this from the outside in. You're looking at companies and saying, what's your security posture? Getting a feel for what they got going on and giving them scores. It sounds like it's not like a hacker proof. It's just more of a indicator for management and the team. >> It's an indicator. It's an indicator. Because today, when we go look at our vendors, business partners, third parties were flying blind. We have no idea how they're doing, how they're performing. So the status quo for the last 20 years has been perform a risk assessments, send a questionnaire, ask for a pen test and an audit evidence. We're trying to break that cycle. Nobody enjoys it. They're long tail. It's a trust without verification. We don't really like that. So we think we can evolve beyond this point in time assessment and give a continuous view. Now, today, historically, we've been outside in. Not intrusive, and we'll show you what a hacker can see about an environment, but we have some cool things percolating under the hood that give more of a 360 view outside, inside, and also a regulatory compliance view as well. >> Why is the compliance of the whole third party thing that you're engaging with important? Because I mean, obviously having some sort of way to say, who am I dealing with is important. I mean, we hear all kinds of things in the security landscape, oh, zero trust, and then we hear trust, supply chain, software risk, for example. There's a huge trust factor there. I need to trust this tool or this container. And then you got the zero trust, don't trust anything. And then you've got trust and verify. So you have all these different models and postures, and it just seems hard to keep up with. >> Sam: It's so hard. >> Take us through what that means 'cause pen tests, SOC reports. I mean the clouds help with the SOC report, but if you're doing agile, anything DevOps, you basically would need to do a pen test like every minute. >> It's impossible. The market shifted to the cloud. We watched and it still is. And that created a lot of complexity, not to date myself. But when I was starting off as a security practitioner, the data center used to be in the basement and I would have lunch with the database administrator and we talk about how we were protecting the data. Those days are long gone. We outsource a lot of our key business practices. We might use, for example, ADP for a payroll provider or Dropbox to store our data. But we've shifted and we no longer no who that person is that's protecting our data. They're sitting in another company in another area unknown. And I think about 10, 15 years ago, CISOs had the realization, Hey, wait a second. I'm relying on that third party to function and operate and protect my data, but I don't have any insight, visibility or control of their program. And we were recommended to use questionnaires and audit forms, and those are great. It's good hygiene. It's good practice. Get to know the people that are protecting your data, ask them the questions, get the evidence. The challenge is it's point in time, it's limited. Sometimes the information is inaccurate. Not intentionally, I don't think people intentionally want to go lie, but Hey, if there's a $50 million deal we're trying to close and it's dependent on checking this one box, someone might bend a rule a little bit. >> And I said on theCUBE publicly that I think pen test reports are probably being fudged and dates being replicated because it's just too fast. And again, today's world is about velocity on developers, trust on the code. So you got all kinds of trust issues. So I think verification, the blue check mark on Twitter kind of thing going on, you're going to see a lot more of that and I think this is just the beginning. I think what you guys are doing is scratching the surface. I think this outside in is a good first step, but that's not going to solve the internal problem that still coming and have big surface areas. So you got more surface area expanding. I mean, IOT's coming in, the Edge is coming fast. Never mind hybrid on-premise cloud. What's your organizations do to evaluate the risk and the third party? Hands shaking, verification, scorecards. Is it like a free look here or is it more depth to it? Do you double click on it? Take us through how this evolves. >> John it's become so disparate and so complex, Because in addition to the market moving to the cloud, we're now completely decentralized. People are working from home or working hybrid, which adds more endpoints. Then what we've learned over time is that it's not just a third party problem, because guess what? My third parties behind the scenes are also using third parties. So while I might be relying on them to process my customer's payment information, they're relying on 20 vendors behind the scene that I don't even know about. I might have an A, they might have an A. It's really important that we expand beyond that. So coming out of our innovation hub, we've developed a number of key capabilities that allow us to expand the value for the customer. One, you mentioned, outside in is great, but it's limited. We can see what a hacker sees and that's helpful. It gives us pointers where to maybe go ask double click, get comfort, but there's a whole nother world going on behind the firewall inside of an organization. And there might be a lot of good things going on that CISO security teams need to be rewarded for. So we built an inside module and component that allows teams to start plugging in the tools, the capabilities, keys to their cloud environments. And that can show anybody who's looking at the scorecard. It's less like a credit score and more like a social platform where we can go and look at someone's profile and say, Hey, how are things going on the inside? Do they have two-factor off? Are there cloud instances configured correctly? And it's not a point in time. This is a live connection that's being made. This is any point in time, we can validate that. The other component that we created is called an evidence locker. And an evidence locker, it's like a secure vault in my scorecard and it allows me to upload things that you don't really stand for or check for. Collateral, compliance paperwork, SOC 2 reports. Those things that I always begrudgingly email. I don't want to share with people my trade secrets, my security policies, and have it sit on their exchange server. So instead of having to email the same documents out, 300 times a month, I just upload them to my evidence locker. And what's great is now anybody following my scorecard can proactively see all the great things I'm doing. They see the outside view. They see the inside view. They see the compliance view. And now they have the holy grail view of my environment and can have a more intelligent conversation. >> Access to data and access methods are an interesting innovation area around data lineage. Tracing is becoming a big thing. We're seeing that. I was just talking with the Snowflake co-founder the other day here in theCUBE about data access and they're building a proprietary mesh on top of the clouds to figure out, Hey, I don't want to give just some tool access to data because I don't know what's on the other side of those tools. Now they had a robust ecosystem. So I can see this whole vendor risk supply chain challenge around integration as a huge problem space that you guys are attacking. What's your reaction to that? >> Yeah. Integration is tricky because we want to be really particular about who we allow access into our environment or where we're punching holes in the firewall and piping data out out of the environment. And that can quickly become unwieldy just with the control that we have. Now, if we give access to a third party, we then don't have any control over who they're sharing our information with. When I talk to CISOs today about this challenge, a lot of folks are scratching their head, a lot of folks treat this as a pet project. Like how do I control the larger span beyond just the third parties? How do I know that their software partners, their contractors that they're working with building their tools are doing a good job? And even if I know, meaning, John, you might send me a list of all of your vendors. I don't want to be the bad guy. I don't really have the right to go reach out to my vendors' vendors knocking on their door saying, hi, I'm Sam. I'm working with John and he's your customer. And I need to make sure that you're protecting my data. It's an awkward chain of conversation. So we're building some tools that help the security teams hold the entire ecosystem accountable. We actually have a capability called automatic vendor discovery. We can go detect who are the vendors of a company based on the connections that we see, the inbound and outbound connections. And what often ends up happening John is we're bringing to the attention to our customers, awareness about inbound and outbound connections. They had no idea existed. There were the shadow IT and the ghost vendors that were signed without going through an assessment. We detect those connections and then they can go triage and reduce the risk accordingly. >> I think that risk assessment of vendors is key. I was just reading a story about this, about how a percentage, I forget the number. It was pretty large of applications that aren't even being used that are still on in companies. And that becomes a safe haven for bad actors to hang out and penetrate 'cause they get overlooked 'cause no one's using them, but they're still online. And so there's a whole, I called cleaning up the old dead applications that are still connected. >> That happens all the time. Those applications also have applications that are dead and applications that are alive may also have users that are dead as well. So you have that problem at the application level, at the user level. We also see a permutation of what you describe, which is leftover artifacts due to configuration mistakes. So a company just put up a new data center, a satellite office in Singapore and they hired a team to go install all the hardware. Somebody accidentally left an administrative portal exposed to the public internet and nobody knew the internet works, the lights are on, the office is up and running, but there was something that was supposed to be turned off that was left turned on. So sometimes we bring to company's attention and they say, that's not mine. That doesn't belong to me. And we're like, oh, well, we see some reason why. >> It's his fault. >> Yeah and they're like, oh, that was the contractor set up the thing. They forgot to turn off the administrative portal with the default login credentials. So we shut off those doors. >> Yeah. Sam, this is really something that's not talked about a lot in the industry that we've become so reliant on managed services and other people, CISOs, CIOs, and even all departments that have applications, even marketing departments, they become reliant on agencies and other parties to do stuff for them which inherently just increases the risk here of what they have. So there inherently could be as secure as they could be, but yet exposed completely on the other side. >> That's right. We have so many virtual touch points with our partners, our vendors, our managed service providers, suppliers, other third parties, and all the humans that are involved in that mix. It creates just a massive ripple effect. So everybody in a chain can be doing things right. And if there's one bad link, the whole chain breaks. I know it's like the cliche analogy, but it rings true. >> Supply chain trust again. Trust who you trust. Let's see how those all reconcile. So Sam, I have to ask you, okay, you're a former CISO. You've seen many movies in the industry. Co-founded this company. You're in the front lines. You've got some cool things happening. I can almost imagine the vision is a lot more than just providing a rating and score. I'm sure there's more vision around intelligence, automation. You mentioned vault, wallet capabilities, exchanging keys. We heard at re:Inforce automated reasoning, metadata reasoning. You got all kinds of crypto and quantum. I mean, there's a lot going on that you can tap into. What's your vision where you see SecurityScorecard going? >> When we started the company, the rating was the thing that we sold and it was a language that helped technical and non-technical folks alike level the playing field and talk about risk and use it to drive their strategy. Today, the rating just opens the door to that discussion and there's so much additional value. I think in the next one to two years, we're going to see the rating becomes standardized. It's going to be more frequently asked or even required or leveraged by key decision makers. When we're doing business, it's going to be like, Hey, show me your scorecard. So I'm seeing the rating get baked more and more the lexicon of risk. But beyond the rating, the goal is really to make a world a safer place. Help transform and rise the tide. So all ships can lift. In order to do that, we have to help companies, not only identify the risk, but also rectify the risk. So there's tools we build to really understand the full risk. Like we talked about the inside, the outside, the fourth parties, fifth parties, the real ecosystem. Once we identified where are all the Fs and bad things, will then what? So couple things that we're doing. We've launched a pro serve arm to help companies. Now companies don't have to pay to fix the score. Anybody, like I said, can fix the score completely free of charge, but some companies need help. They ask us and they say, Hey, I'm looking for a trusted advisor. A Sherpa, a guide to get me to a better place or they'll say, Hey, I need some pen testing services. So we've augmented a service arm to help accelerate the remediation efforts. We're also partnered with different industries that use the rating as part of a larger picture. The cyber rating isn't the end all be all. When companies are assessing risk, they may be looking at a financial ratings, ESG ratings, KYC AML, cyber security, and they're trying to form a complete risk profile. So we go and we integrate into those decision points. Insurance companies, all the top insurers, re-insurers, brokers are leveraging SecurityScorecard as an ingredient to help underwrite for cyber liability insurance. It's not the only ingredient, but it helps them underwrite and identify the help and price the risk so they can push out a policy faster. First policy is usually the one that's signed. So time to quote is an important metric. We help to accelerate that. We partner with credit rating agencies like Fitch, who are talking to board members, who are asking, Hey, I need a third party, independent verification of what my CISO is saying. So the CISO is presenting the rating, but so are the proxy advisors and the ratings companies to the board. So we're helping to inform the boards and evolve how they're thinking about cyber risk. We're helping with the insurance space. I think that, like you said, we're only scratching the surface. I can see, today we have about 50,000 companies that are engaging a rating and there's no reason why it's not going to be in the millions in just the next couple years here. >> And you got the capability to bring in more telemetry and see the new things, bring that into the index, bring that into the scorecard and then map that to potential any vulnerabilities. >> Bingo. >> But like you said, the old days, when you were dating yourself, you were in a glass room with a door lock and key and you can see who's two folks in there having lunch, talking database. No one's going to get hurt. Now that's gone, right? So now you don't know who's out there and machines. So you got humans that you don't know and you got machines that are turning on and off services, putting containers out there. Who knows what's in those payloads. So a ton of surface area and complexity to weave through. I mean only is going to get done with automation. >> It's the only way. Part of our vision includes not attempting to make a faster questionnaire, but rid ourselves of the process all altogether and get more into the continuous assessment mindset. Now look, as a former CISO myself, I don't want another tool to log into. We already have 50 tools we log into every day. Folks don't need a 51st and that's not the intent. So what we've done is we've created today, an automation suite, I call it, set it and forget it. Like I'm probably dating myself, but like those old infomercials. And look, and you've got what? 50,000 vendors business partners. Then behind there, there's another a hundred thousand that they're using. How are you going to keep track of all those folks? You're not going to log in every day. You're going to set rules and parameters about the things that you care about and you care depending on the nature of the engagement. If we're exchanging sensitive data on the network layer, you might care about exposed database. If we're doing it on the app layer, you're going to look at application security vulnerabilities. So what our customers do is they go create rules that say, Hey, if any of these companies in my tier one critical vendor watch list, if they have any of these parameters, if the score drops, if they drop below a B, if they have these issues, pick these actions and the actions could be, send them a questionnaire. We can send the questionnaire for you. You don't have to send pen and paper, forget about it. You're going to open your email and drag the Excel spreadsheet. Those days are over. We're done with that. We automate that. You don't want to send a questionnaire, send a report. We have integrations, notify Slack, create a Jira ticket, pipe it to ServiceNow. Whatever system of record, system of intelligence, workflow tools companies are using, we write in and allow them to expedite the whole. We're trying to close the window. We want to close the window of the attack. And in order to do that, we have to bring the attention to the people as quickly as possible. That's not going to happen if someone logs in every day. So we've got the platform and then that automation capability on top of it. >> I love the vision. I love the utility of a scorecard, a verification mark, something that could be presented, credential, an image, social proof. To security and an ongoing way to monitor it, observe it, update it, add value. I think this is only going to be the beginning of what I would see as much more of a new way to think about credentialing companies. >> I think we're going to reach a point, John, where and some of our customers are already doing this. They're publishing their scorecard in the public domain, not with the technical details, but an abstracted view. And thought leaders, what they're doing is they're saying, Hey, before you send me anything, look at my scorecard securityscorecard.com/securityrating, and then the name of their company, and it's there. It's in the public domain. If somebody Googles scorecard for certain companies, it's going to show up in the Google Search results. They can mitigate probably 30, 40% of inbound requests by just pointing to that thing. So we want to give more of those tools, turn security from a reactive to a proactive motion. >> Great stuff, Sam. I love it. I'm going to make sure when you hit our site, our company, we've got camouflage sites so we can make sure you get the right ones. I'm sure we got some copyright dates. >> We can navigate the decoys. We can navigate the decoys sites. >> Sam, thanks for coming on. And looking forward to speaking more in depth on showcase that we have upcoming Amazon Startup Showcase where you guys are going to be presenting. But I really appreciate this conversation. Thanks for sharing what you guys are working on. We really appreciate. Thanks for coming on. >> Thank you so much, John. Thank you for having me. >> Okay. This is theCUBE conversation here in Palo Alto, California. Coming in from New York city is the co-founder, chief operating officer of securityscorecard.com. I'm John Furrier. Thanks for watching. (gentle music)

(introductory riff) >> Hey everyone. Welcome to theCUBE's presentation of the "AWS Startup Showcase." This is Season 2, Episode 3 of our ongoing series that features great partners in the massive AWS partner ecosystem. This series is focused on, "MarTech, Emerging Cloud-Scale Customer Experiences." I'm Lisa Martin, and I've got two guests here with me to talk about this. Please welcome Daisy Urfer, Cloud Alliance Sales Director at Algolia, and Jason Lang, the Head of Product for Apply Digital. These folks are here to talk with us today about how Algolia's Search and Discovery enables customers to create dynamic realtime user experiences for those oh so demanding customers. Daisy and Jason, it's great to have you on the program. >> Great to be here. >> Thanks for having us. >> Daisy, we're going to go ahead and start with you. Give the audience an overview of Algolia, what you guys do, when you were founded, what some of the gaps were in the market that your founders saw and fixed? >> Sure. It's actually a really fun story. We were founded in 2012. We are an API first SaaS solution for Search and Discovery, but our founders actually started off with a search tool for mobile platforms, so just for your phone and it quickly expanded, we recognize the need across the market. It's been a really fun place to grow the business. And we have 11,000 customers today and growing every day, with 30 billion searches a week. So we do a lot of business, it's fun. >> Lisa: 30 billion searches a week and I saw some great customer brands, Locost, NBC Universal, you mentioned over 11,000. Talk to me a little bit about some of the technologies, I see that you have a search product, you have a recommendation product. What are some of those key capabilities that the products deliver? 'Cause as we know, as users, when we're searching for something, we expect it to be incredibly fast. >> Sure. Yeah. What's fun about Algolia is we are actually the second largest search engine on the internet today to Google. So we are right below the guy who's made search of their verb. So we really provide an overall search strategy. We provide a dashboard for our end users so they can provide the best results to their customers and what their customers see. Customers want to see everything from Recommend, which is our recommended engine. So when you search for that dress, it shows you the frequently bought together shoes that match, things like that, to things like promoted items and what's missing in the search results. So we do that with a different algorithm today. Most in the industry rank and they'll stack what you would want to see. We do kind of a pair for pair ranking system. So we really compare what you're looking for and it gives a much better result. >> And that's incredibly critical for users these days who want results in milliseconds. Jason, you, Apply Digital as a partner of Algolia, talk to us about Apply Digital, what it is that you guys do, and then give us a little bit of insight on that partnership. >> Sure. So Apply Digital was originally founded in 2016 in Vancouver, Canada. And we have offices in Vancouver, Toronto, New York, LA, San Francisco, Mexico city, Sao Paulo and Amsterdam. And we are a digital experiences agency. So brands and companies, and startups, and all the way from startups to major global conglomerates who have this desire to truly create these amazing digital experiences, it could be a website, it could be an app, it could be a full blown marketing platform, just whatever it is. And they lack either the experience or the internal resources, or what have you, then they come to us. And and we are end-to-end, we strategy, design, product, development, all the way through the execution side. And to help us out, we partner with organizations like Algolia to offer certain solutions, like an Algolia's case, like search recommendation, things like that, to our various clients and customers who are like, "Hey, I want to create this experience and it's going to require search, or it's going to require some sort of recommendation." And we're like, "Well, we highly recommend that you use Algolia. They're a partner of ours, they've been absolutely amazing over the time that we've had the partnership. And that's what we do." And honestly, for digital experiences, search is the essence of the internet, it just is. So, I cannot think of a single digital experience that doesn't require some sort of search or recommendation engine attached to it. So, and Algolia has just knocked it out of the park with their experience, not only from a customer experience, but also from a development experience. So that's why they're just an amazing, amazing partner to have. >> Sounds like a great partnership. Daisy, let's point it back over to you. Talk about some of those main challenges, Jason alluded to them, that businesses are facing, whether it's e-commerce, SaaS, a startup or whatnot, where search and recommendations are concerned. 'Cause we all, I think I've had that experience, where we're searching for something, and Daisy, you were describing how the recommendation engine works. And when we are searching for something, if I've already bought a tent, don't show me more tent, show me things that would go with it. What are some of those main challenges that Algolia solution just eliminates? >> Sure. So I think, one of the main challenges we have to focus on is, most of our customers are fighting against the big guides out there that have hundreds of engineers on staff, custom building a search solution. And our consumers expect that response. You expect the same search response that you get when you're streaming video content looking for a movie, from your big retailer shopping experiences. So what we want to provide is the ability to deliver that result with much less work and hassle and have it all show up. And we do that by really focusing on the results that the customers need and what that view needs to look like. We see a lot of our customers just experiencing a huge loss in revenue by only providing basic search. And because as Jason put it, search is so fundamental to the internet, we all think it's easy, we all think it's just basic. And when you provide basic, you don't get the shoes with the dress, you get just the text response results back. And so we want to make sure that we're providing that back to our customers. What we see average is even, and everybody's going mobile. A lot of times I know I do all my shopping on my phone a lot of the time, and 40%-50% better relevancy results for our customers for mobile users. That's a huge impact to their use case. >> That is huge. And when we talked about patients wearing quite thin the last couple of years. But we have this expectation in our consumer lives and in our business lives if we're looking for SaaS or software, or whatnot, that we're going to be able to find what we want that's relevant to what we're looking for. And you mentioned revenue impact, customer churn, brand reputation, those are all things that if search isn't done well, to your point, Daisy, if it's done in a basic fashion, those are some of the things that customers are going to experience. Jason, talk to us about why Algolia, what was it specifically about that technology that really led Apply Digital to say, "This is the right partner to help eliminate some of those challenges that our customers could face?" >> Sure. So I'm in the product world. So I have the wonderful advantage of not worrying about how something's built, that is left, unfortunately, to the poor, poor engineers that have to work with us, mad scientist, product people, who are like, "I want, make it do this. I don't know how, but make it do this." And one of the big things is, with Algolia is the lift to implement is really, really light. Working closely with our engineering team, and even with our customers/users and everything like that, you kind of alluded to it a little earlier, it's like, at the end of the day, if it's bad search, it's bad search. It just is. It's terrible. And people's attention span can now be measured in nanoseconds, but they don't care how it works, they just want it to work. I push a button, I want something to happen, period. There's an entire universe that is behind that button, and that's what Algolia has really focused on, that universe behind that button. So there's two ways that we use them, on a web experience, there's the embedded Search widget, which is really, really easy to implement, documentation, and I cannot speak high enough about documentation, is amazing. And then from the web aspect, I'm sorry, from the mobile aspect, it's very API fort. And any type of API implementation where you can customize the UI, which obviously you can imagine our clients are like, "No we want to have our own front end. We want to have our own custom experience." We use Algolia as that engine. Again, the documentation and the light lift of implementation is huge. That is a massive, massive bonus for why we partnered with them. Before product, I was an engineer a very long time ago. I've seen bad documentation. And it's like, (Lisa laughing) "I don't know how to imple-- I don't know what this is. I don't know how to implement this, I don't even know what I'm looking at." But with Algolia and everything, it's so simple. And I know I can just hear the Apply Digital technology team, just grinding sometimes, "Why is a product guy saying that (mumbles)? He should do it." But it is, it just the lift, it's the documentation, it's the support. And it's a full blown partnership. And that's why we went with it, and that's what we tell our clients. It's like, listen, this is why we chose Algolia, because eventually this experience we're creating for them is theirs, ultimately it's theirs. And then they are going to have to pick it up after a certain amount of time once it's theirs. And having that transition of, "Look this is how easy it is to implement, here is all the documentation, here's all the support that you get." It just makes that transition from us to them beautifully seamless. >> And that's huge. We often talk about hard metrics, but ease of use, ease of implementation, the documentation, the support, those are all absolutely business critical for the organization who's implementing the software, the fastest time to value they can get, can be table stakes, and it can be on also a massive competitive differentiator. Daisy, I want to go back to you in terms of hard numbers. Algolia has a recent force or Total Economic Impact, or TEI study that really has some compelling stats. Can you share some of those insights with us? >> Yeah. Absolutely. I think that this is the one of the most fun numbers to share. We have a recent report that came out, it shared that there's a 382% Return on Investment across three years by implementing Algolia. So that's increase to revenue, increased conversion rate, increased time on your site, 382% Return on Investment for the purchase. So we know our pricing's right, we know we're providing for our customers. We know that we're giving them the results that we need. I've been in the search industry for long enough to know that those are some amazing stats, and I'm really proud to work for them and be behind them. >> That can be transformative for a business. I think we've all had that experience of trying to search on a website and not finding anything of relevance. And sometimes I scratch my head, "Why is this experience still like this? If I could churn, I would." So having that ability to easily implement, have the documentation that makes sense, and get such high ROI in a short time period is hugely differentiated for businesses. And I think we all know, as Jason said, we measure response time in nanoseconds, that's how much patience and tolerance we all have on the business side, on the consumer side. So having that, not just this fast search, but the contextual search is table stakes for organizations these days. I'd love for you guys, and on either one of you can take this, to share a customer example or two, that really shows the value of the Algolia product, and then also maybe the partnership. >> So I'll go. We have a couple of partners in two vastly different industries, but both use Algolia as a solution for search. One of them is a, best way to put this, multinational biotech health company that has this-- We built for them this internal portal for all of their healthcare practitioners, their HCPs, so that they could access information, data, reports, wikis, the whole thing. And it's basically, almost their version of Wikipedia, but it's all internal, and you can imagine the level of of data security that it has to be, because this is biotech and healthcare. So we implemented Algolia as an internal search engine for them. And the three main reasons why we recommended Algolia, and we implemented Algolia was one, HIPAA compliance. That's the first one, it's like, if that's a no, we're not playing. So HIPAA compliance, again, the ease of search, the whole contextual search, and then the recommendations and things like that. It was a true, it didn't-- It wasn't just like a a halfhearted implementation of an internal search engine to look for files thing, it is a full blown search engine, specifically for the data that they want. And I think we're averaging, if I remember the numbers correctly, it's north of 200,000 searches a month, just on this internal portal specifically for their employees in their company. And it's amazing, it's absolutely amazing. And then conversely, we work with a pretty high level adventure clothing brand, standard, traditional e-commerce, stable mobile application, Lisa, what you were saying earlier. It's like, "I buy everything on my phone," thing. And so that's what we did. We built and we support their mobile application. And they wanted to use for search, they wanted to do a couple of things which was really interesting. They wanted do traditional search, search catalog, search skews, recommendations, so forth and so on, but they also wanted to do a store finder, which was kind of interesting. So, we'd said, all right, we're going to be implementing Algolia because the lift is going to be so much easier than trying to do everything like that. And we did, and they're using it, and massively successful. They are so happy with it, where it's like, they've got this really contextual experience where it's like, I'm looking for a store near me. "Hey, I've been looking for these items. You know, I've been looking for this puffy vest, and I'm looking for a store near me." It's like, "Well, there's a store near me but it doesn't have it, but there's a store closer to me and it does have it." And all of that wraps around what it is. And all of it was, again, using Algolia, because like I said earlier, it's like, if I'm searching for something, I want it to be correct. And I don't just want it to be correct, I want it to be relevant. >> Lisa: Yes. >> And I want it to feel personalized. >> Yes. >> I'm asking to find something, give me something that I am looking for. So yeah. >> Yeah. That personalization and that relevance is critical. I keep saying that word "critical," I'm overusing it, but it is, we have that expectation that whether it's an internal portal, as you talked about Jason, or it's an adventure clothing brand, or a grocery store, or an e-commerce site, that what they're going to be showing me is exactly what I'm looking for, that magic behind there that's almost border lines on creepy, but we want it. We want it to be able to make our lives easier whether we are on the consumer side, whether we on the business side. And I do wonder what the Go To Market is. Daisy, can you talk a little bit about, where do customers go that are saying, "Oh, I need to Algolia, and I want to be able to do that." Now, what's the GTM between both of these companies? >> So where to find us, you can find us on AWS Marketplace which another favorite place. You can quickly click through and find, but you can connect us through Apply Digital as well. I think, we try to be pretty available and meet our customers where they are. So we're open to any options, and we love exploring with them. I think, what is fun and I'd love to talk about as well, in the customer cases, is not just the e-commerce space, but also the content space. We have a lot of content customers, things about news, organizations, things like that. And since that's a struggle to deliver results on, it's really a challenge. And also you want it to be relevant, so up-to-date content. So it's not just about e-commerce, it's about all of your solution overall, but we hope that you'll find us on AWS Marketplace or anywhere else. >> Got it. And that's a great point, that it's not just e-commerce, it's content. And that's really critical for some industry, businesses across industries. Jason and Daisy, thank you so much for joining me talking about Algolia, Apply Digital, what you guys are doing together, and the huge impact that you're making to the customer user experience that we all appreciate and know, and come to expect these days is going to be awesome. We appreciate your insights. >> Thank you. >> Thank you >> For Daisy and Jason, I'm Lisa Martin. You're watching "theCUBE," our "AWS Startup Showcase, MarTech Emerging Cloud-Scale Customer Experiences." Keep it right here on "theCUBE" for more great content. We're the leader in live tech coverage. (ending riff)

(upbeat music) >> Welcome back. Day two of theCUBE's coverage of re:MARS in Las Vegas. Amazon re:MARS, it's part of the Re Series they call it at Amazon. re:Invent is their big show, re:Inforce is a security show, re:MARS is the new emerging machine learning automation, robotics, and space. The confluence of machine learning powering a new industrial age and inflection point. I'm John Furrier, host of theCUBE. We're here to break it down for another wall to wall coverage. We've got a great guest here, CUBE alumni from our AWS startup showcase, Krishna Gade, founder and CEO of fiddler.ai. Welcome back to theCUBE. Good to see you. >> Great to see you, John. >> In person. We did the remote one before. >> Absolutely, great to be here, and I always love to be part of these interviews and love to talk more about what we're doing. >> Well, you guys have a lot of good street cred, a lot of good word of mouth around the quality of your product, the work you're doing. I know a lot of folks that I admire and trust in the AI machine learning area say great things about you. A lot going on, you guys are growing companies. So you're kind of like a startup on a rocket ship, getting ready to go, pun intended here at the space event. What's going on with you guys? You're here. Machine learning is the centerpiece of it. Swami gave the keynote here at day two and it really is an inflection point. Machine learning is now ready, it's scaling, and some of the examples that they were showing with the workloads and the data sets that they're tapping into, you know, you've got CodeWhisperer, which they announced, you've got trust and bias now being addressed, we're hitting a level, a new level in ML, ML operations, ML modeling, ML workloads for developers. >> Yep, yep, absolutely. You know, I think machine learning now has become an operational software, right? Like you know a lot of companies are investing millions and billions of dollars and creating teams to operationalize machine learning based products. And that's the exciting part. I think the thing that that is very exciting for us is like we are helping those teams to observe how those machine learning applications are working so that they can build trust into it. Because I believe as Swami was alluding to this today, without actually building trust into AI, it's really hard to actually have your business users use it in their business workflows. And that's where we are excited about bringing their trust and visibility factor into machine learning. >> You know, a lot of us all know what you guys are doing here in the ecosystem of AWS. And now extending here, take a minute to explain what Fiddler is doing for the folks that are in the space, that are in discovery mode, trying to understand who's got what, because like Swami said on stage, it's a full-time job to keep up on all the machine learning activities and tool sets and platforms. Take a minute to explain what Fiddler's doing, then we can get into some, some good questions. >> Absolutely. As the enterprise is taking on operationalization of machine learning models, one of the key problems that they run into is lack of visibility into how those models perform. You know, for example, let's say if I'm a bank, I'm trying to introduce credit risk scoring models using machine learning. You know, how do I know when my model is rejecting someone's loan? You know, when my model is accepting someone's loan? And why is it doing it? And I think this is basically what makes machine learning a complex thing to implement and operationalize. Without this visibility, you cannot build trust and actually use it in your business. With Fiddler, what we provide is we actually open up this black box and we help our customers to really understand how those models work. You know, for example, how is my model doing? Is it accurately working or not? You know, why is it actually rejecting someone's loan application? We provide these both fine grain as well as coarse grain insights. So our customers can actually deploy machine learning in a safe and trustworthy manner. >> Who is your customer? Who you're targeting? What persona is it, the data engineer, is it data science, is it the CSO, is it all the above? >> Yeah, our customer is the data scientist and the machine learning engineer, right? And we usually talk to teams that have a few models running in production, that's basically our sweet spot, where they're trying to look for a single pane of glass to see like what models are running in their production, how they're performing, how they're affecting their business metrics. So we typically engage with like head of data science or head of machine learning that has a few machine learning engineers and data scientists. >> Okay, so those people that are watching, you're into this, you can go check it out. It's good to learn. I want to get your thoughts on some trends that I see emerging, and I want to get your reaction to those. Number one, we're seeing the cloud scale now and integration a big part of things. So the time to value was brought up on stage today, Swami kind of mentioned time to value, showed some benchmark where they got four hours, some other teams were doing eight weeks. Where are we on the progression of value, time to value, and on the scale side. Can you scope that for me? >> I mean, it depends, right? You know, depending upon the company. So for example, when we work with banks, for them to time to operationalize a model can take months actually, because of all the regulatory procedures that they have to go through. You know, they have to get the models reviewed by model validators, model risk management teams, and then they audit those models, they have to then ship those models and constantly monitor them. So it's a very long process for them. And even for non-regulated sectors, if you do not have the right tools and processes in place, operationalizing machine learning models can take a long time. You know, with tools like Fiddler, what we are enabling is we are basically compressing that life cycle. We are helping them automate like model monitoring and explainability so that they can actually ship models more faster. Like you get like velocity in terms of shipping models. For example, one of the growing fintech companies that started with us last year started with six models in production, now they're running about 36 models in production. So it's within a year, they were able to like grow like 10x. So that is basically what we are trying to do. >> At other things, we at re:MARS, so first of all, you got a great product and a lot of markets that grow onto, but here you got space. I mean, anyone who's coming out of college or university PhD program, and if they're into aero, they're going to be here, right? This is where they are. Now you have a new core companies with machine learning, not just the engineering that you see in the space or aerospace area, you have a new engineering. Now I go back to the old days where my parents, there was Fortran, you used Fortran was Lingua Franca to manage the equipment. Little throwback to the old school. But now machine learning is companion, first class citizen, to the hardware. And in fact, and some will say more important. >> Yep, I mean, machine learning model is the new software artifact. It is going into production in a big way. And I think it has two different things that compare to traditional software. Number one, unlike traditional software, it's a black box. You cannot read up a machine learning model score and see why it's making those predictions. Number two, it's a stochastic entity. What that means is it's predictive power can wane over time. So it needs to be constantly monitored and then constantly refreshed so that it's actually working in tech. So those are the two main things you need to take care. And if you can do that, then machine learning can give you a huge amount of ROI. >> There is some practitioner kind of like craft to it. >> Correct. >> As you said, you got to know when to refresh, what data sets to bring in, which to stay away from, certainly when you get to the bias, but I'll get to that in a second. My next question is really along the lines of software. So if you believe that open source will dominate the software business, which I do, I mean, most people won't argue. I think you would agree with that, right? Open source is driving everything. If everything's open source, where's the differentiation coming from? So if I'm a startup entrepreneur or I'm a project manager working on the next Artemis mission, I got to open source. Okay, there's definitely security issues here. I don't want to talk about shift left right now, but like, okay, open source is everything. Where's the differentiation, where do I have the proprietary edge? >> It's a great question, right? So I used to work in tech companies before Fiddler. You know, when I used to work at Facebook, we would build everything in house. We would not even use a lot of open source software. So there are companies like that that build everything in house. And then I also worked at companies like Twitter and Pinterest, which are actually used a lot of open source, right? So now, like the thing is, it depends on the maturity of the organization. So if you're a Facebook or a Google, you can build a lot of things in house. Then if you're like a modern tech company, you would probably leverage open source, but there are lots of other companies in the world that still don't have the talent pool to actually build, take things from open source and productionize it. And that's where the opportunity for startups comes in so that we can commercialize these things, create a great enterprise experience, so actually operationalize things for them so that they don't have to do it in house for them. And that's the advantage working with startups. >> I don't want to get all operating systems with you on theory here on the stage here, but I will have to ask you the next question, which I totally agree with you, by the way, that's the way to go. There's not a lot of people out there that are peaked. And that's just statistical and it'll get better. Data engineering is really narrow. That is like the SRE of data. That's a new role emerging. Okay, all the things are happening. So if open source is there, integration is a huge deal. And you start to see the rise of a lot of MSPs, managed service providers. I run Kubernetes clusters, I do this, that, and the other thing. So what's your reaction to the growth of the integration side of the business and this role of new services coming from third parties? >> Yeah, absolutely. I think one of the big challenges for a chief data officer or someone like a CTO is how do they devise this infrastructure architecture and with components, either homegrown components or open source components or some vendor components, and how do they integrate? You know, when I used to run data engineering at Pinterest, we had to devise a data architecture combining all of these things and create something that actually flows very nicely, right? >> If you didn't do it right, it would break. >> Absolutely. And this is why it's important for us, like at Fiddler, to really make sure that Fiddler can integrate to all varies of ML platforms. Today, a lot of our customers use machine learning, build machine learning models on SageMaker. So Fiddler nicely integrate with SageMaker so that data, they get a seamless experience to monitor their models. >> Yeah, I mean, this might not be the right words for it, but I think data engineering as a service is really what I see you guys doing, as well other things, you're providing all that. >> And ML engineering as a service. >> ML engineering as a- Well it's hard. I mean, it's like the hard stuff. >> Yeah, yeah. >> Hear, hear. But that has to enable. So you as a business entrepreneur, you have to create a multiple of value proposition to your customers. What's your vision on that? What is that value? It has to be a multiple, at least 5 to 10. >> I mean, the value is simple, right? You know, if you have to operationize machine learning, you need visibility into how these things work. You know, if you're CTO or like chief data officer is asking how is my model working and how is it affecting my business? You need to be able to show them a dashboard, how it's working, right? And so like a data scientist today struggles to do this. They have to manually generate a report, manually do this analysis. What Fiddler is doing them is basically reducing their work so that they can automate these things and they can still focus on the core aspect of model building and data preparation and this boring aspect of monitoring the model and creating reports around the models is automated for them. >> Yeah, you guys got a great business. I think it's a lot of great future there and it's only going to get bigger. Again, the TAM's going to expand as the growth rising tide comes in. I want to ask you on while we're on that topic of rising tides, Dave Malik and I, since re:Invent last year have been kind of kicked down around this term that we made up called supercloud. And supercloud was a word that came out of these clouds that were not Amazon hyperscalers. So Snowflake, Buildman Sachs, Capital One, you name it, they're building massive proprietary value on top of the CapEx of Amazon. Jerry Chen at Greylock calls it castles in the cloud. You can create these moats. >> Yeah, right. >> So this is a phenomenon, right? And you land on one, and then you go to the others. So the strategies, everyone goes to Amazon first, and then hits Azure and GCP. That then creates this kind of multicloud so, okay, so super cloud's kind of happening, it's a thing. Charles Fitzgerald will disagree, he's a platformer, he says he's against the term. I get why, but he's off base a little. We can't wait to debate him on that. So superclouds are happening, but now what do I do about multicloud, because now I understand multicloud, I have this on that cloud, integrating across clouds is a very difficult thing. >> Krishna: Right, right, right. >> If I'm Snowflake or whatever, hey, I'll go to Azure, more TAM expansion, more market. But are people actually working together? Are we there yet? Where it's like, okay, I'm going to re-operationalize this code base over here. >> I mean, the reality of it, enterprise wants optionality, right? I think they don't want to be locked in into one particular cloud vendor on one particular software. And therefore you actually have in a situation where you have a multicloud scenario where they want to have some workloads in Amazon, some workloads in Azure. And this is an opportunity for startups like us because we are cloud agnostic. We can monitor models wherever you have. So this is where a lot of our customers, they have some of their models are running in their data centers and some of their models running in Amazon. And so we can provide a universal single pan of glass, right? So we can basically connect all of those data and actually showcase. I think this is an opportunity for startups to combine the data streams come from various different clouds and give them a single pain of experience. That way, the sort of the where is your data, where are my models running, which cloud are there, is all abstracted out from the customer. Because at the end of the day, enterprises will want optionality. And we are in this multicloud. >> Yeah, I mean, this reminds me of the interoperability days back when I was growing into the business. Everything was interoperability and OSI and the standards came out, but what's your opinion on openness, okay? There's a kneejerk reaction right now in the market to go silo on your data for governance or whatever reasons, but yet machine learning gurus and experts will say, "Hey, you want to horizon horizontal scalability and have the best machine learning models, you've got to have access to data and fast in real time or near real time." And the antithesis is siloing. >> Krishna: Right, right, right. >> So what's the solution? Customers control the data plane and have a control plane that's... What do customers do? It's a big challenge. >> Yeah, absolutely. I think there are multiple different architectures of ML, right, you know? We've seen like where vendors like us used to deploy completely on-prem, right? And they still do it, we still do it in some customers. And then you had this managed cloud experience where you just abstract out the entire operations from the customer. And then now you have this hybrid experience where you split the control plane and data plane. So you preserve the privacy of the customer from the data perspective, but you still control the infrastructure, right? I don't think there's a right answer. It depends on the product that you're trying to solve. You know, Databricks is able to solve this control plane, data plane split really well. I've seen some other tools that have not done this really well. So I think it all depends upon- >> What about Snowflake? I think they a- >> Sorry, correct. They have a managed cloud service, right? So predominantly that's their business. So I think it all depends on what is your go to market? You know, which customers you're talking to? You know, what's your product architecture look like? You know, from Fiddler's perspective today, we actually have chosen, we either go completely on-prem or we basically provide a managed cloud service and that's actually simpler for us instead of splitting- >> John: So it's customer choice. >> Exactly. >> That's your position. >> Exactly. >> Whoever you want to use Fiddler, go on-prem, no problem, or cloud. >> Correct, or cloud, yeah. >> You'll deploy and you'll work across whatever observability space you want to. >> That's right, that's right. >> Okay, yeah. So that's the big challenge, all right. What's the big observation from your standpoint? You've been on the hyperscaler side, your journey, Facebook, Pinterest, so back then you built everything, because no one else had software for you, but now everybody wants to be a hyperscaler, but there's a huge CapEx advantage. What should someone do? If you're a big enterprise, obviously I could be a big insurance, I could be financial services, oil and gas, whatever vertical, I want a supercloud, what do I do? >> I think like the biggest advantage enterprise today have is they have a plethora of tools. You know, when I used to work on machine learning way back in Microsoft on Bing Search, we had to build everything. You know, from like training platforms, deployment platforms, experimentation platforms. You know, how do we monitor those models? You know, everything has to be homegrown, right? A lot of open source also did not exist at the time. Today, the enterprise has this advantage, they're sitting on this gold mine of tools. You know, obviously there's probably a little bit of tool fatigue as well. You know, which tools to select? >> There's plenty of tools available. >> Exactly, right? And then there's like services available for you. So now you need to make like smarter choices to cobble together this, to create like a workflow for your engineers. And you can really get started quite fast, and actually get on par with some of these modern tech companies. And that is the advantage that a lot of enterprises see. >> If you were going to be the CTO or CEO of a big transformation, knowing what you know, 'cause you just brought up the killer point about why it's such a great time right now, you got platform as a service and the tooling essentially reset everything. So if you're going to throw everything out and start fresh, you're basically brewing the system architecture. It's a complete reset. That's doable. How fast do you think you could do that for say a large enterprise? >> See, I think if you set aside the organization processes and whatever kind of comes in the friction, from a technology perspective, it's pretty fast, right? You can devise a data architecture today with like tools like Kafka, Snowflake and Redshift, and you can actually devise a data architecture very clearly right from day one and actually implement it at scale. And then once you have accumulated enough data and you can extract more value from it, you can go and implement your MLOps workflow as well on top of it. And I think this is where tools like Fiddler can help as well. So I would start with looking at data, do we have centralization of data? Do we have like governance around data? Do we have analytics around data? And then kind of get into machine learning operations. >> Krishna, always great to have you on theCUBE. You're great masterclass guest. Obviously great success in your company. Been there, done that, and doing it again. I got to ask you, since you just brought that up about the whole reset, what is the superhero persona right now? Because it used to be the full stack developer, you know? And then it's like, then I call them, it didn't go over very well in theCUBE, the half stack developer, because nobody wants to be a half stack anything, a half sounds bad, worse than full. But cloud is essentially half a stack. I mean, you got infrastructure, you got tools. Now you're talking about a persona that's going to reset, look at tools, make selections, build an architecture, build an operating environment, distributed computing operating. Who is that person? What's that persona look like? >> I mean, I think the superhero persona today is ML engineering. I'm usually surprised how much is put on an ML engineer to do actually these days. You know, when I entered the industry as a software engineer, I had three or four things in my job to do, I write code, I test it, I deploy it, I'm done. Like today as an ML engineer, I need to worry about my data. How do I collect it? I need to clean the data, I need to train my models, I need to experiment with what it is, and to deploy them, I need to make sure that they're working once they're deployed. >> Now you got to do all the DevOps behind it. >> And all the DevOps behind it. And so I'm like working halftime as a data scientist, halftime as a software engineer, halftime as like a DevOps cloud. >> Cloud architect. >> It's like a heroic job. And I think this is why this is why obviously these jobs are like now really hard jobs and people want to be more and more machine learning >> And they get paid. >> engineering. >> Commensurate with the- >> And they're paid commensurately as well. And this is where I think an opportunity for tools like Fiddler exists as well because we can help those ML engineers do their jobs better. >> Thanks for coming on theCUBE. Great to see you. We're here at re:MARS. And great to see you again. And congratulations on being on the AWS startup showcase that we're in year two, episode four, coming up. We'll have to have you back on. Krishna, great to see you. Thanks for coming on. Okay, This is theCUBE's coverage here at re:MARS. I'm John Furrier, bringing all the signal from all the noise here. Not a lot of noise at this event, it's very small, very intimate, a little bit different, but all on point with space, machine learning, robotics, the future of industrial. We'll back with more coverage after the short break. >> Man: Thank you John. (upbeat music)

(gentle music) >> Okay, we're back with Mark Hill who's the director of IT operations at Digital River. Mark, Welcome to "The Cube." Good to see you. >> Thanks for having me. I really appreciate it. >> Hey, tell us a little bit more about Digital River, people know you as a payment platform. >> You've got marketing expertise. >> Yeah. >> How do you differentiate from other e-commerce platforms? >> Well, I don't think people realize it, but Digital River was founded about 27 years ago primarily as a one-stop shop for e-commerce, right? And so we offered site development, hosting, order management, fraud, expert controls, tax, physical and digital fulfillment as well as multilingual customer service, advanced reporting and email marketing campaigns, right? So it was really just kind of a broad base for e-commerce. People could just go there. Didn't have to worry about anything. What we found over time as e-commerce has matured, we've really pivoted to a more focused API offering specializing in just a global seller services. And to us that means payment, fraud, tax and compliance management. So our global footprint allows companies to outsource that risk management and expand their markets internationally very quickly and with the low cost of entry. >> Yeah, it's an awesome business. And, you know, to your point, you were founded way before there was such a thing as the modern cloud, and yet you're a cloud native business. >> Yeah. >> Which I think talks to the fact that incumbents can evolve, they can reinvent themselves from a technology perspective. I wonder if you could first paint a picture of how you use the cloud, you use AWS, you know, I'm sure you got S3 in there. Maybe we could talk about that a little bit. >> Yeah, exactly. So when I think of a cloud native business, you kind of go back to the history. Well, 27 years ago, there wasn't a cloud, right? There wasn't any public infrastructure. We basically started our own data center up in a warehouse. And so over our history, we've managed our own infrastructure and co-located data centers over time through acquisitions and just how things works, you know, those are over 10 data centers globally for us. For us it was expensive, well from a software, hardware perspective, as well as, you know, getting the operational teams and expertise up to speed too. And it was really difficult to maintain and ultimately not core to our business, right? Nowhere in our mission statement does it say that our goal is to manage data centers. (laughing) So, about five years ago we started the journey from our host into AWS. It was a hundred percent lift and shift plan and we were able to complete that migration a little over two years, right? Amazon really just fit for us, it was a natural, a natural place for us to land in and they made it really easy here for us to, not to say it wasn't difficult, but once in the public cloud, we really adopted a cloud first vision, meaning that we'll not only consume their infrastructure as the service, but we'll also purposely evaluate and migrate to software as a service. So, I come from a database background. So an example would be migrating from self deployed and manage relational databases over to AWS RDS, relational database service. You know, you're able to utilize the backups, the standby and the patching tools auto magically, you know, with a click of a button. And that's pretty cool. And so we moved away from the time consuming operational task and really put our resources into revenue and generating the products, you know, like pivoting to an API offering. I always like to say that we stopped being busy and started being productive. (laughing) >> I love that. >> And that's really what the cloud has done for us. >> Is that what you mean by cloud native? I mean, being able to take advantage of those primitives and native API. So what does that mean for your business? >> Yeah, exactly. I think, well, the first step for us was just to consume the infrastructure, right? But now we're looking at targeted services that they have in there too. So, you know, we have our data stream of services. So log analytics, for example, we used to put it locally on the machine. Now we're just dumping into an S3 bucket the way you're using Kinesis to consume that data and put it in elastic and go from there. And none of the services are managed by Digital River. We're just realizing the capabilities that AWS has there too. >> And as an e-commerce player, retail company, were you ever concerned about moving to AWS as a possible competitor, or did you look at other clouds? What can you tell us about that? >> Yeah, and so, I think e-commerce is really mature, right? And so we got squeezed out by the Amazons of the world. It's just not something that we were doing, but we had really a good area of expertise with our global seller services. So we evaluated Microsoft, we evaluated AWS as well as Google and, you know, back when we did that, Microsoft was Windows-based. Google was just coming into the picture, really didn't fit for what we're doing, but Amazon was just a natural fit. So, we made a business decision, right? It was financially really the best decision for us. And so we didn't really put our feelings into it, right? We just had to move forward and it's better than where we're at and we've been delighted actually. >> Yeah, makes sense, best cloud, the best tech. >> Yeah. >> You know, I want to talk about Chaos Search. A lot of people describe it as a data lake for log analytics. Do you agree with that? You know, what does that even mean? >> Yeah, well, from our perspective because the self-managed solutions are costly and difficult to maintain. You know, we had older versions of self deployed using Splunk, other things like that too. So over time, we made a conscious decision to limit our data retention in generally seven days. But in a lot of cases, it was zero. We just couldn't consume that log data because of the cost, intimidating in itself, because of this limit, you know, we've lost important data points, use for incident triage problem management, trending and other things too. So, Chaos Search has offered us a manageable and cost-effective opportunity to store months or even years of data that we can use for operations as well as trending automation. And really the big thing that we're pushing into is in the event of an architecture so that we can proactively manage our services. >> Yeah, you mentioned elastic. So I know I've talked to people who use the Elk Stack. They say, yes, this is exponential growth in the amount of data. So you have to cut it off at whatever. I think you said seven days, >> Yeah. >> Or less, you're saying you're not finding that with Chaos Search? >> Yeah, yeah, exactly. And that was one of the huge benefits here too. So, you know, we we're losing out if there was, you know, a lower priority incident for example and people didn't get to it until eight, nine days later. Well, all the bread crumbs are gone. So it was really just kind of a best guess or the incident really wasn't resolved. We didn't find a root cause. >> Yeah, like my video camera's down you know, by your other house, is that when somebody breaks in, I don't find out for two weeks and then the video's gone, kind of like same thing. >> Yeah. >> So, how do you, can you give us some more detail on how you use your data lake and Chaos Search specifically? >> Yeah, yeah. Yep and so there's many different areas, but what we found is we were able to easily consolidate data from multiple regions into a single pane of glass to our customers. So internal and externally, you know, it really does serve that operational support for the data extract transformation load process, right? It offered us also a seamless transition for the users who were familiar with elastic search, right? It wasn't difficult to move over. And so all these are a lot of selling points benefits. And so now that we have all this data that we're able to capture and utilize, it gives us an opportunity to use machine learning, predictive analysis. And like I said, you know, driving to an event driven architecture. >> Okay. >> So that's really what is offered and it's been a huge benefit. >> So you're saying you can speak the language of elastic. You don't have to move the data out of an S3 bucket and you can scale more easily. Is that right? >> Yeah, yeah, absolutely. And it is so for us just because running in multiple regions to drive more high availability, having that data available from multiple regions in a single pane of glass or a single way to utilize it is a huge benefit as well, just to, you know, not to mention actually having the data. >> What was the initial catalyst to sort of rethink what you were doing with log analytics? Was it cost, was it flexibility scale? >> There was, I think all of those went into it. One of the main drivers, so last year we had a huge project, so we have our Elk Stack and it's probably from a decade ago, right? And, you know, a version point or two or something, you know, anyways, it's very old and we went through a whole project to get that upgraded and migrated over. And it was just, we found it impossible internally to do, right? And so this was a method for us to get out of that business, to get rid of the security risks and support risk and have a way for people to easily migrate over. And it was just a nightmare here consolidating the data across regions. And so that was a huge thing. But yeah, it has also been the cost, right? We're finding that cheaper to use Chaos Search and have more data available versus what we were doing currently in AWS. >> Got it, I wonder if you could share maybe any stories that you have or examples that underscore the impact that this approach to analytics, >> Yeah >> Is having on your business, maybe your team's everyday activities, any metrics you can provide, >> Yeah. >> Or even just anecdotal information? >> Yeah, yeah. And and I think, you know, one, coming from an Oracle background here, so Digital River historically has been an Oracle shop, right? And we've been developing a reporting and analytics environment on Oracle and that's complicated and expensive, right? We had to use advanced features in Oracle like partitioning materialized views and bringing other supporting software like Informatic, Hyperion, Essbase, right? And all of these require a large team with a wide set of expertise into the separate focus areas, right? And the amount of data that we were pushing at the KF search would simply have overwhelmed this legacy method for data analysis than a relational database, right? In that dimension, the human toll of the stress of supporting that Oracle environment than a 24 by seven by 365 environment, you know, which requires literal or no downtime. So just that alone, it was a huge thing. So, it's allowed us to break away from Oracle, it's allowed us to use new technologies that make sense to solve business solutions. >> You know, Chaos Search is just a really interesting company to me, I'm sure like me, you see a lot of startups. I'm sure they're knocking on your door every day. And I always like to say, "Okay, where are they going after? "Are they going after a big market? "How are they getting product market fit?" And it seems like Chaos Search has really looked that hard at log analytics and sort of maybe disrupting the Elk Stack. But I see, you know, other potential use cases, you know, beyond analyzing logs. I wonder if you agree, are there other use cases that you see in your future? >> Yeah, exactly. So, I think there's one area would be Splunk for example. We have that here too. So we use Splunk versus, you know, flat file analysis or other ways to capture that data just because from a PCI perspective, it needs to be secured for our compliance and certification, right? So Chaos Search allows us to do that. There's different types of authentication, really a hodgepodge of authentication that we used in our old environment, but Chaos Search has a more easily usable one, one that we could set up, one that can really segregate the data and allows to satisfy our PCR requirements too. But Splunk, I think really, deprecating all of our elastic search environments are homegrown ones, but then also taking a hard look at what we're doing with relational databases, right? 27 years ago, there was only relational databases, Oracle and SQL server. So we've been logging into those types of databases and that's not cost-effective, it's not supportable. And so really getting away from that and putting the data where it belongs and that is easily accessible in a secure environment and allowing us to push our business forward. >> And when you say where the data belongs, it sounds like you're putting it in the bit bucket S3, leaving it there, >> Yeah. >> And this is the most cost-effective way to do it and then sort of adding value on top of it. That's what's interesting about Chaos Search to me. >> Yeah, exactly, yup, yup versus the high price storage, you know, that you have to use for a relational database, you know, and not to mention the standbys, the backups. So, you know, you're duplicating, triplicating all this data in here too in expensive manner. So yeah. >> Yeah, copy creating, moving data around and it gets expensive. It's funny when you say about databases, it's true. But database used to be such a boring market now it's exploded. Then you had the whole no SQL movement and SQL became the killer app, you know, it's like full circle. (laughing) >> Yeah, yeah, exactly. >> Well, anyway, good stuff Mark, really, I really appreciate you coming on "The Cube" and sharing your perspectives. We'd love to have you back in the future. >> Oh yeah, yeah, no problem. Thanks for having me. I really appreciate it. >> Yeah, our pleasure. Okay, in a moment, I'll have some closing thoughts on getting more value out of your growing data lakes. You're watching "The Cube," you're leader in high-tech coverage. (gentle music)

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is braking analysis with Dave Vellante. >> Techniques to protect sensitive data have evolved over thousands of years, literally. The pace of modern data protection is rapidly accelerating and presents both opportunities and threats for organizations. In particular, the amount of data stored in the cloud combined with hybrid work models, the clear and present threat of cyber crime, regulatory edicts, and the ever expanding edge and associated use cases should put CXOs on notice that the time is now to rethink your data protection strategies. Hello, and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we're going to explore the evolving world of data protection and share some information on how we see the market changing in the competitive landscape for some of the top players. Steve Kenniston, AKA the Storage Alchemist, shared a story with me, and it was pretty clever. Way back in 4000 BC, the Sumerians invented the first system of writing. Now, they used clay tokens to represent transactions at that time. Now, to prevent messing with these tokens, they sealed them in clay jars to ensure that the tokens, i.e the data, would remain secure with an accurate record that was, let's call it quasi, immutable, and lived in a clay vault. And since that time, we've seen quite an evolution of data protection. Tape, of course, was the main means of protecting data and backing data up during most of the mainframe era. And that carried into client server computing, which really accentuated and underscored the issues around backup windows and challenges with RTO, recovery time objective and RPO recovery point objective. And just overall recovery nightmares. Then in the 2000's data reduction made disk-based backup more popular and pushed tape into an archive last resort media. Data Domain, then EMC, now Dell still sell many purpose-built backup appliances as do others as a primary backup target disc-based. The rise of virtualization brought more changes in backup and recovery strategies, as a reduction in physical resources squeezed the one application that wasn't under utilizing compute, i.e, backup. And we saw the rise of Veem, the cleverly-named company that became synonymous with data protection for virtual machines. Now, the cloud has created new challenges related to data sovereignty, governance, latency, copy creep, expense, et cetera. But more recently, cyber threats have elevated data protection to become a critical adjacency to information security. Cyber resilience to specifically protect against attacks is the new trend being pushed by the vendor community as organizations are urgently looking for help with this insidious threat. Okay, so there are two major disruptors that we're going to talk about today, the cloud and cyber crime, especially around ransoming your data. Every customer is using the cloud in some way, shape, or form. Around 76% are using multiple clouds, that's according to a recent study by Hashi Corp. We've talked extensively about skill shortages on theCUBE, and data protection and security concerns are really key challenges to address, given that skill shortage is a real talent gap in terms of being able to throw people at solving this problem. So what customers are doing, they're either building out or they're buying really mostly building abstraction layers to hide the underlying cloud complexity. So what this does... The good news is it's simplifies provisioning and management, but it creates problems around opacity. In other words, you can't see sometimes what's going on with the data. These challenges fundamentally become data problems, in our view. Things like fast, accurate, and complete backup recovery, compliance, data sovereignty, data sharing. I mentioned copy creep, cyber resiliency, privacy protections. These are all challenges brought to fore by the cloud, the advantages, the pros, and the cons. Now, remote workers are especially vulnerable. And as clouds span rapidly, data protection technologies are struggling to keep pace. So let's talk briefly about the rapidly-expanding public cloud. This chart shows worldwide revenue for the big four hyperscalers. As you can see, we projected that they're going to surpass $115 billion in revenue in 2021. That's up from 86 billion last year. So it's a huge market, it's growing in the 35% range. The interesting thing is last year, 80-plus billion dollars in revenue, but 100 billion dollars was spent last year by these firms in cap ex. So they're building out infrastructure for the industry. This is a gift to the balance of the industry. Now to date, legacy vendors and the surrounding community have been pretty defensive around the cloud. Oh, not everything's going to move to the cloud. It's not a zero sum game we hear. And while that's all true, the narrative was really kind of a defensive posture, and that's starting to change as large tech companies like Dell, IBM, Cisco, HPE, and others see opportunities to build on top of this infrastructure. You certainly see that with Arvind Krishna comments at IBM, Cisco obviously leaning in from a networking and security perspective, HPE using language that is very much cloud-like with its GreenLake strategy. And of course, Dell is all over this. Let's listen to how Michael Dell is thinking about this opportunity when he was questioned on the queue by John Furrier about the cloud. Play the clip. So in my view, Michael nailed it. The cloud is everywhere. You have to make it easy. And you have to admire the scope of his comments. We know this guy, he thinks big. He said, "Enables everything." He's basically saying is that technology is at the point where it has the potential to touch virtually every industry, every person, every problem, everything. So let's talk about how this informs the changing world of data protection. Now, we all know, we've seen with the pandemic, there's an acceleration in toward digital, and that has caused an escalation, if you will, in the data protection mandate. So essentially what we're talking about here is the application of Michael Dell's cloud everywhere comments. You've got on-prem, private clouds, hybrid clouds. You've got public clouds across AWS, Azure, Google, Alibaba. Really those are the big four hyperscalers. You got many clouds that are popping up all their place. But multi-cloud, to that Hashi Corp data point, 75, 70 6%. And then you now see the cloud expanding out to the edge, programmable infrastructure heading out to the edge. So the opportunity here to build the data protection cloud is to have the same experiences across all these estates with automation and orchestration in that cloud, that data protection cloud, if you will. So think of it as an abstraction layer that hides that underlying complexity, you log into that data protection cloud, it's the same experience. So you've got backup, you've got recovery, you can handle bare metal. You can do virtualized backups and recoveries, any cloud, any OS, out to the edge, Kubernetes and container use cases, which is an emerging data protection requirement. And you've got analytics, perhaps you've got PII, personally identifiable information protection in there. So the attributes of this data protection cloud, again, abstracts the underlying cloud primitives, takes care of that. It also explodes cloud native technologies. In other words, it takes advantage of whether it's machine learning, which all the big cloud players have expertise in, new processor models, things like graviton, and other services that are in the cloud natively. It doesn't just wrap it's on-prem stack in a container and shove it into the cloud, no. It actually re architects or architects around those cloud native services. And it's got distributed metadata to track files and volumes and any organizational data irrespective of location. And it enables sets of services to intelligently govern in a federated governance manner while ensuring data integrity. And all this is automated and an orchestrated to help with the skills gap. Now, as it relates to cyber recovery, air-gap solutions must be part of the portfolio, but managed outside of that data protection cloud that we just briefly described. The orchestration and the management must also be gaped, if you will. Otherwise, (laughs) you don't have an air gap. So all of this is really a cohort to cyber security or your cybersecurity strategy and posture, but you have to be careful here because your data protection strategy could get lost in this mess. So you want to think about the data protection cloud as again, an adjacency or maybe an overlay to your cybersecurity approach. Not a bolt on, it's got to be fundamentally architectured from the bottom up. And yes, this is going to maybe create some overheads and some integration challenges, but this is the way in which we think you should think about it. So you'll likely need a partner to do this. Again, we come back to the skill skills gap if we're seeing the rise of MSPs, managed service providers and specialist service providers. Not public cloud providers. People are concerned about lock-in, and that's really not their role. They're not high-touch services company. Probably not your technology arms dealer, (clear throat) excuse me, they're selling technology to these MSPs. So the MSPs, they have intimate relationships with their customers. They understand their business and specialize in architecting solutions to handle these difficult challenges. So let's take a look at some of the risk factors here, dig a little bit into the cyber threat that organizations face. This is a slide that, again, the Storage Alchemists, Steve Kenniston, shared with me. It's based on a study that IBM funds with the Panmore Institute, which is a firm that studies these things like cost of breaches and has for many, many, many years. The slide shows the total cost of a typical breach within each dot and on the Y axis and the frequency in percentage terms on the horizontal axis. Now, it's interesting. The top two compromise credentials and phishing, which once again proves that bad user behavior trumps good security every time. But the point here is that the adversary's attack vectors are many. And specific companies often specialize in solving these problems often with point products, which is why the slide that we showed from Optiv earlier, that messy slide, looks so cluttered. So there's a huge challenge for companies. And that's why we've seen the emergence of cyber recovery solutions from virtually all the major players. Ransomware and the solar winds hack have made trust the number one issue for CIOs and CISOs and boards of directors. Shifting CISO spending patterns are clear. They're shifting largely because they're catalyzed by the work from home. But outside of the moat to endpoint security, identity and access management, cloud security, the horizontal network security. So security priorities and spending are changing. And that's why you see the emergence of disruptors like we've covered extensively, Okta, CrowdStrike, Zscaler. And cyber resilience is top of mind, and robust solutions are required. And that's why companies are building cyber recovery solutions that are most often focused on the backup corpus because that's a target for the bad guys. So there is an opportunity, however, to expand from just the backup corpus to all data and protect this kind of 3, 2, 1, or maybe it's 3, 2, 1, 1, three copies, two backups, a backup in the cloud and one that's air gaped. So this can be extended to primary storage, copies, snaps, containers, data in motion, et cetera, to have a comprehensive data protection strategy. And customers, as I said earlier, are increasingly looking to manage service providers and specialists because of that skills gap. And that's a big reason why automation is so important in orchestration. And automation and orchestration, I'll emphasize, on the air gap solutions should be separated physically and logically. All right, now let's take a look at some of the ETR data and some of the players. This is a chart that we like to show often. It's a X-Y axis. And the Y axis is net score, which is a measure of spending momentum. And the horizontal axis is market share. Now, market share is an indicator of pervasiveness in the survey. It's not spending market share, it's not market share of the overall market, it's a term that ETR uses. It's essentially market share of the responses within the survey set. Think of it as mind share. Okay, you've got the pure plays here on this slide, in the storage category. There is no data protection or backup category. So what we've done is we've isolated the pure plays or close to pure plays in backup and data protection. Now notice that red line, that red is kind of our subjective view of anything that's over that 40% line is elevated. And you can see only Rubrik, and the July survey is over that 40% line. I'll show you the ends in a moment. Smaller ends, but still, Rubrik is the only one. Now, look at Cohesity and Rubrik in the January 2020. So last year, pre-pandemic, Cohesity and Rubrik, they've come well off their peak for net score. Look at Veeam. Veeam, having studied this data for the last say 24 hours months, Veeam has been steady Eddy. It is really always in the mid to high 30s, always shows a large shared end, so it's coming up in the survey. Customers are mentioning Veeam. And it's got a very solid net score. It's not above that 40% line, but it's hovering just below consistently. That's very impressive. Commvault has steadily been moving up. Sanjay Mirchandani has made some acquisitions. He did the Hedvig acquisition. They launched Metallic, that's driving cloud affinity within Commvault's large customer base. So it's good example of a legacy player pivoting and evolving and transforming itself. Veritas, it continues to under perform in the ETR surveys relative to the other players. Now, for context, let's add IBM and Dell to the chart. Now just note, this is IBM and Dell's full storage portfolio. The category in the taxonomy at ETR is all storage. Just previous slide, I isolated on the pure plays. But this now adds in IBM and Dell. It probably representative of where they would be. Probably Dell larger on the horizontal axis than IBM, of course. And you could see the spending momentum accordingly. So you can see that in the data chart that we've inserted. So some smaller ends for Rubrik and Cohesity. But still enough to pay attention, it's not like one or two. When you're 20-plus, 15-plus 25-plus, you can start to pay attention to trends. Veeam, again, is very impressive. It's net score is solid, it's got a consistent presence in the dataset, it's clear leader here. SimpliVity is small, but it's improving relative to last several surveys. And we talked about Convolt. Now, I want to emphasize something that we've been hitting on for quite some time now. And that's the Renaissance that's coming in compute. Now, we all know about Moore's Law, the doubling of transistor density every two years, 18 to 24 months. And that leads to a doubling of performance in that timeframe. X86, that x86 curve is in the blue. And if you do the math, this is expressed in trillions of operations per second. The orange line is representative of Apples A series, culminating in the A15, most recently. The A series is what Apple is now... Well, it's the technology basis for what's inside M1, the new Apple laptops, which is replacing Intel. That's that that orange line there, we'll come back to that. So go back to the blue line for a minute. If you do the math on doubling performance every 24 months, it comes out to roughly 40% annual improvement in processing power per year. That's now moderated. So Moore's Law is waning in one sense, so we wrote a piece Moore's Law is not dead. So I'm sort of contradicting myself there. But the traditional Moore's Law curve on x86 is waning. It's probably now down to around 30%, low 30s. But look at the orange line. Again, using the A series as an indicator, if you combine then the CPU, the NPU, which neuro processing unit, XPU, pick whatever PU you want, the accelerators, the DSPs, that line is growing at 100% plus per year. It's probably more accurately around 110% a year. So there's a new industry curve occurring, and it's being led by the Arm ecosystem. The other key factor there, and you're seeing this in a lot of use cases, a lot of consumer use cases, Apple is an example, but you're also seeing it in things like Tesla, Amazon with AWS graviton, the Annapurna acquisition, building out graviton and nitro, that's based on Arm. You can get from design to tape out in less than two years. Whereas the Intel cycles, we know, they've been running it four to five years now. Maybe Pat Gelsinger is compressing those. But Intel is behind. So organizations that are on that orange curve are going to see faster acceleration, lower cost, lower power, et cetera. All right, so what's the tie to data protection. I'm going to leave you with this chart. Arm has introduced it's confidential, compute architecture and is ushering in a new era of security and data protection. Zero trust is the new mandate. And what Arm has it's done with what they call realms is create physical separation of the vulnerable components by creating essentially physical buckets to put code in and to put data in, separate from the OS. Remember, the OS is the most valuable entry point for hackers or one of them because it contains privileged access, and it's a weak link because of things like memory leakages and vulnerabilities. And malicious code can be placed by bad guys within data in the OS and appear benign, even though it's anything but. So in this, all the OS does is create API calls to the realm controller. That's the only interaction. So it makes it much harder for bad actors to get access to the code and the data. And importantly, very importantly, it's an end-to-end architecture. So there's protection throughout. If you're pulling data from the edge and bringing it back to the on-prem or the cloud, you've got that end to end architecture and protection throughout. So the link to data protection is that backup software vendors need to be the most trusted of applications. Backup software needs to be the most trusted of applications because it's one of the most targeted areas in a cyber attack. Realms provide an end-to-end separation of data and code from the OS and it's a better architectural construct to support zero trust and confidential computing and critical use cases like data protection/backup and other digital business apps. So our call to action is backup software vendors, you can lead the charge. Arm is several years ahead at the moment, ahead of Intel, in our view. So you've got to pay attention to that, research that. We're not saying over rotate, but go investigate that. And use your relationships with Intel to accelerate its version of this architecture. Or ideally, the industry should agree on common standards and solve this problem together. Pat Gelsinger told us in theCUBE that if it's the last thing he's going to do in his industry life, he's going to solve this security problem. That's when he was at VMware. Well, Pat, you're even in a better place to do it now. You don't have to solve it yourself, you can't, and you know that. So while you're going about your business saving Intel, look to partner with Arm. I know it sounds crazy to use these published APIs and push to collaborate on an open source architecture that addresses the cyber problem. If anyone can do it, you can. Okay, that's it for today. Remember, these episodes are all available as podcasts. All you got to do is search Braking Analysis Podcast. I publish weekly on wikibond.com and siliconangle.com. Or you can reach me @dvellante on Twitter, email me at david.vellante@siliconangle.com. And don't forget to check out etr.plus for all the survey and data action. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching, everybody. Be well, and we'll see you next time. (gentle music)

(cheery music) >> Thanks, Adam. Thanks for everyone in the studio. Dave, we've got some great main stage CUBE interviews. Normally we'll sit at the desk, and do a remote, but since it's a virtual event, and a physical event, it's a hybrid event. We've got two amazing Google leaders to talk with us. I had a chance to sit down with Amol who was gone yesterday during our breaking news segment. They had the big news. We had two great guests, Amol Phadke. He's our first interview. He's the head of Google's telecom industry. Again, he came in, broke into our segment yesterday with breaking news. Obviously released with Ericsson, and the O-RAN Alliance. I had a great chance to chat with him. A wide ranging conversation for 13 minutes. Enjoy my interview with Amol, right now. (cheery music) Well welcome to the CUBE's coverage for Mobile World Congress, 2021. I'm John Furrier, your host of the CUBE. We're here in person as well as remote. It's a hybrid event. We're on the ground at Mobile World Congress, bringing all the action here. We're remote with Amol Phadke, who's the Managing Director of the Telecom Industry Solutions team at Google Cloud, a big leader, and driving a lot of the change. Amol, thank you for coming on theCUBE here in the hybrid event from Mobile World Congress. >> Thank you, John. Thank you, John. Thank you for having me, So, hybrid event, which means it's in person, we're on the floor, as well as doing remote interviews and people are virtual. This is the new normal. Kind of highlights where we are in this telecom world, because the last time, Mobile World Congress actually had a physical event was winter of 2019. A ton has changed in the industry. Look at the momentum at the Edge. Hybrid cloud is now standard. Multi-cloud is being set up as we speak. This is all now the new normal, what is your take? And so it's pretty active in your industry. Tell us your opinion. >> Yes, John I mean the last two years have been seismic to say the least, right? I mean, in terms of the change that the CSP industries had had to do. You know, John, in the last two years, the importance of a CSP infrastructure has never become so important, right? The infrastructure is paramount. I'm talking to you remotely over the CSP infrastructure right now, and everything that we are doing in the last two years, whether it's working, or studying, or entertaining ourselves, all on that CSP infrastructure. So from that perspective, they are really becoming a critical national global information fabric on which the society is actually depending on. And that we see at Google as well, in the sense that we have seen up to 60% increase in demand, John, in the last two years, for that infrastructure. And then when we look at the industry itself, unfortunately all of that huge demand is not translating into revenue, because as an industry, the revenue is still flat-lining. In fact, the forecasted revenue for globally, for all the industry over the next 12 months is three to five per cent negative on revenue, right? So one starts to think, how come there is so much demand over the last two years, post-pandemic, and that's not translating to revenue? Having said that, the other thing that's happening is this demand is driving significant CapEx and OPEX investments in the infrastructure, as much as eight to $900 billion over the next decade is going to get spent in this infrastructure, from our perspective, Which means it's really a perfect storm. John, We have massive demand, massive need to invest to meet that demand, yet not translating to revenue, and the crux of all this is customer experience, because ultimately all of that translates into not having that kind of radically disruptive or transformational customer experience, right? So that's a backdrop that we find ourselves in the industry, and that really sets the stage for us to look at these challenges in terms of how does the CSP industry as a whole, grow top line, radically transform CSPCO, at the same time, reinventing the customer experience and finding those capital efficiencies. It's almost an impossible problem to find solution. >> It's a perfect storm. The waves are kind of coming together to form one big wave. You mentioned CapEx and OPEX. That's obviously changing the investments of their post-pandemic growth, and change in user behavior and expectations. The modern applications are being built on top of the infrastructure, that's changing. All of this is being driven by Cloud Native, and that's clear. You're seeing a lot more open kind of approaches, IT and OT coming together, whatever you want to do, this is just, it's a collision, right? It's a collision of many things. And this positive innovation coming out of it. So I have to ask you, what are you seeing as a solution that are showing the most promise for these telco industry leaders, because they're digitally transforming, so they got to re-factor their platforms while enabling innovation, which is a key growth for the revenue. >> Yes. So John, from a solution standpoint, what we actually did first and foremost as Google Cloud, was look at ourselves. So just like the transformation we just talked about in the CSP industry, we are seeing Google being transformed over the last two decades or so, right. And it's important to understand that there's a lot Google data over the last two decades that we can actually not externalize all of that innovation, all of that open source, all of that multicloud, was originally built for all the Google applications that all of us use daily, whether it's YouTube, or email or maps, you know. Same infrastructure, same open source, same multicloud. And we decided to sort of use the same paradigm to build the telecom solutions that I'm going to talk about next, right. So that's important to bear in mind, that those assets were there, and we wanted to externalize those assets, right. There are really four big solutions that are resonating really well with our CSP partners, John. You know, number one to your point, is how can they monetize the Edge? All of this happens at the Edge. All of this gets converged at the Edge. We believe with 5G acting as the brilliant catalyst to really drive this Edge deployment. CSPs would be in a very strong position, partnering with Cloud players like ourselves to drive growth, not just for their top line, but also to add value to the actual end enterprises that are seeking to use that Edge. Let me give you a couple of examples. We've been working with industries like retail and manufacturing, to create end solutions in a post-pandemic world. Solutions like contact-less shopping, or visual inspection of an assembly line in a manufacturing plant, without the need for having a human there, because of the digitalization of workforce. Which meant these kinds of solutions, can actually work well at the Edge driven by 5G. But of course they can't be done in isolation. So what we do is we partner with CSPs. We bring our set of solutions, and we actually launch in December 30 partners that are already on our Google Cloud Solutions. And then we partner with the CSPs based on our infrastructure, and their infrastructure to ultimately bring this all to life at the end customer, which often tends to be an enterprise, whether it's a manufacturing, plant, or a retail chain. >> Yeah, you guys got some great examples there. I love that Edge story. I think it's huge. I think it's only going to get bigger. I got to ask you while I got you here, because again, you're in the industry, you're the managing director, so you have to oversee this whole telecom industry. But it's bigger, it's beyond Telecom, where it's now Telecom's just one other Edge network, piece of the pie of the surety computing, as we say. So I got to ask you, one of the big things that Google brings to the table is the developer mojo, and opensource, and scale obviously. Scale's unprecedented, everyone knows that. But ecosystems are super important, and Telco's kind of really aren't good at that, right? So, you know, the Telco ecosystem was, I mean, okay, I'd say, okay, but mostly driven by carriers and moving bits from point A to point B. But now you've got a developer mindset, public cloud, developer ecosystem. How is this changing the landscape of the CSPs and how is it changing this cloud service provider's ability to execute, because that's the key in this new world? What's your opinion? >> Absolutely, John. So, there are two things, there are two dimensions to look at. One is when we came to market a couple of years ago with AnToks, we recognized exactly what you said, John, which is the world is moving to multi-cloud, hybrid cloud. We needed to provide a common platform that the developer community can utilize through microservices and API. And that platform had to by definition, work not just from Google Cloud, but any cloud. It could work on any public cloud, can work on CSP's private cloud. And of course, supports on some Google Cloud, right? The reason was, once you deploy and cause, once as a seamless application development platform, you could put all kinds of developer apps on top. So I just talked about 5G Edge John, a minute ago, those apps can sit on Antoks, but at the same time, IT to your point, John, IT apps could also sit on the same AnToks paradigm, and network apps. So as networks start becoming Cloud Native, whether it's SRAN, whether it's O-Ran, whether it's 5G core, same principle. And that's why we believe when we partner with CSPs, we are saying, "Hey, you give this AnToks to an ecosystem of community, whether that community is network, whether that community is IT, whether the communities Edge apps, all of those can reside seamlessly on this sort of AnToks fabric, John. >> Yeah, and that's going to set the table for multicloud, which is basically cloud words for multi-vendor, multi app. Amol, I've got to ask you while I have you here, first of all, thank you for coming on and sharing your insights. It's really great industry perspective. And obviously Google Cloud's got huge scale, and great leadership. And again, you know, the big, cloud players are moving in and helping out, and enabling a lot of value. I got to ask you, if you don't mind sharing, if someone asked you, "Amol, tell me about the impact that public cloud is having on the Telco industry." What would you say? What's the answer to that? Because a lot of people are like, okay, public cloud, I get it. I know what it looks like, but now everyone's knows it's going hybrid. So everyone will ask you the question, "What is public cloud doing for the telecom sector?" >> Yeah, I think it's doing three things, John, and great question by the way. Number one, we are actually providing unprecedented amount of insights on data that the CSPs traditionally already had, but have never looked at it from the angle we have looked at it. Whether that insights are at the network layer, whether those insights are to personalize customer experiences on the front-end systems. Or whether those insights are to drive care solutions in contact centers, and so on, and so forth. So it's a massive uplift of customer experience that we can help with, right. So that's a very important point, because we do have a significant amount of leadership, John at Google Cloud on analytics and data and insights, right? So, and we offer those roads to these people. Number two, is really what I talked about, which is helping them build an ecosystem, because let's take retail as an example. As a minimum, there are five constituents in that ecosystem, John. There is a CSP, there is Google Cloud, there's an actual retail store. There is a hardware supplier, there's a software developer. All of them as a minimum, have to work together to build that ecosystem, which is where we give those solutions, right? So that's the second part. And then the third part is, as they move towards Cloud Native, we are really helping them change their business model to become a DevOps, a Cloud Native mindset, not just a Cloud Native network or IP. But a Cloud Native mindset that creates unparalleled agility and flexibility in how they work as a business. So those are the three things I would say, as a response to that question. >> And also the retail's a great vertical for Google to go in there, given the Amazon fear out there. People want this for certainly low hanging fruit. I think the DevOps piece is going to be a big, winning opportunity to see how the developers get driven into the landscape. I think that's a huge point. Amol, that's really great insight. A final question for you, while I got you here. If someone says, "Hey, what's happened in the industry since 2019?" Last time we had Mobile World Congress, they were talking speeds and feeds. Now the world has changed. We're coming out of the pandemic. California is opening up. There's going to be a physical event. The world's going hybrid, certainly on the event, and certainly cloud. What's different in the telecom industry, from, you know, many, many months ago, over a year and a half ago, from 2019? >> I would say primarily, it's the adoption of digital everywhere, which previously, you know, there were all these inhibitions and oh, would this work? Would my customer systems become fully digital? Would I be able to offer AR VR experiences? Ah, that's a futuristic thing, you know. And suddenly the pandemic has created this acceleration that says, "Oh, even post-pandemic, half my customers are always going to talk to me, via our digital channel only." Which means the way they experience us, has to be through these new experiences whether it's AR VR, whether it's some other thing or applications. So that has been accelerated John, and the CSPs have therefore really started to go to the application, and to the services. Which is why you are seeing less on, you know, speeds and feeds because 5G is here, 5G's been deployed. Now, how do we monetize 5G? How can we leverage that biggest number? So that's the biggest- >> There's down stack, and then there's a top of the stack for applications. And certainly there's a lot of assets in the telecom landscape, a lot of value, a lot of refactoring going on, and new opportunities that are out there. Great, great conversation. Well, thank you, Amol Phadka, Managing Director, Telecom Industry Solutions. Thanks for comin' on the CUBE, appreciate it. >> Thank you, John. Thank you having me. >> Okay, Mobile World Congress here, in person, and hybrid, and remote. I'm John Furrier, host of theCUBE. Thank you for watching. We are here in person at the Cloud City Expo Community Area. Thanks for watching. Okay, that was us. That was me, online. Now, I'm here in person, as you can see Dave. That's a lot of fun. I love doing those interviews. So we had a chance to grab Google's top people when we could. They're not here, obviously. Amazon Web Services, Microsoft, and Google, the three hyperscalers, Dave, didn't make it out here. They didn't have a booth, but we had a chance to grab them. And that was head of the industry marketing, and I mean the industry group. So he's like the managing door. He runs the business side. >> It's an important sector for Google. You know, Amazon was really first, with that push into telco. Thomas Curran last March, laid out Google strategy for Telco. It's a huge sector. They know it. They understand how the cloud can disrupt it, and play a massive role there. >> Yeah. >> And Google, of course. >> They're not going to object to the public cloud narrative that Danielle Royston- >> No. >> I think they like it open source, Android coming to telco. Who knows what it's going to look like? >> That's what we call digital- >> So the next interview I did was with Shailesh Shukla. He is the Senior Vice-president. He's the Senior Leader at Google Cloud for Networking. And if you know, Google, Dave, Google's networking is really well known in the industry for being really awesome, because they power obviously Google Search, and a variety of other things. They pioneered the concept of SRE, Site Reliability Engineer, which is now a de facto position for DevOps, which is a cloud now persona inside almost every company, and certainly a very important position. And so- >> Probably the biggest global network, right? Undersea cables, and- >> I mean, Microsoft's got a big hyper-scale, because they've had MSN, and bunch of other stuff, infrastructure globally. But Amazon, Google and Microsoft all have massive scale, and Google again, very well engineered. They're total, and they're as we know, I live in Palo Alto, so I can attest that they're very strong. So this next interview is really from a networking perspective, because as infrastructure, as code gets more prolific and more penetrated, it's going to be programmable. And that's really going to be a key new enabler. So let's hear from Shailesh, Head of Networking at Google Cloud, and my interview with him. (cheery music) Welcome to theCUBE's coverage of Mobile World Congress, 2021. We are here in person in Barcelona, as well as remote. It's a hybrid event. You're going to have the physical space, in Barcelona for the first time, since 2019, and virtual worlds connecting. I've got a great guest here from Google, Shailesh Shukla, Vice-president and General Manager of the Networking Team, Google Cloud. Shailesh, it's great to see you. Thank you for coming on theCUBE for the special presentation from Mobile World Congress. Obviously, the Edge networking core, Edge human devices, all coming together. Thanks for coming on. >> Thank you so much, John. It's great to see you again. And it's always a pleasure talking to theCUBE. And I want to say hello to everybody, from, you know, in Mobile World Congress. >> Yeah, and people don't know your background. You have a great history in networking. You've been there, many ways of innovation. You've been part of directly, big companies that were now known. Big names are all there. But now we haven't had a Mobile World Congress, since 2019. Think about that. That's, you know, many months, 20 something months gone by, since the world has changed in telco. I got to ask you, what is the disruption happening? Because think about that. Since 2019, a lot's changed in telco. Cloud-scale has happened. You've got the Edge developing. It's IT like now. What's your take? Shailesh, tell us. >> Yeah, John, as you correctly pointed out the last 18 months have been very difficult. And you know, I'll acknowledge that right up front, for a number of people around the world. I empathize with that. Now in the telecom, and kind of the broader Edge world, I would say that the last 18, 24 months have actually been transformative. O-RAN, it turns out was a very interesting sort of, you know, driver of completely new ways of both living, as well as working, right, as we all have experienced. I don't think that I've had a chance to see you live in 24 months. So, what we are seeing is the following. Number one, a number of telecom carriers around the world have started the investment process for 5G, right, and deployment process. And that actually changes the game, as you know, due to latency, due to all of the capabilities around kind of incalculable bandwidth, right. Much lower latency, as well as, much higher kind of enterprise oriented capabilities, right? So network's licensing, as an example, quality of service, you know, by a traffic type, and for a given enterprise. So that's number one. Number two, I would say that the cloud is becoming a lot more kind of mainstream in the world, broader world of telecom. What we are seeing is an incredible amount of partnerships between telecom carriers and cloud providers, right? So instead of thinking of those two as separate universes, those are starting to come together. So I believe that over a period of time, you will see the notion of kind of Cloud Native capability for both the IT side of the house, as well as the network side of the house is becoming, you know, kind of mainstream, right. And then the third thing is that increasingly it's a lot more about enabling new markets, new applications, in the enterprise world, right. So certainly it opens up a new kind of revenue stream for service providers and carriers around the world. But it also does something unique, which is brings together the cloud capabilities right, around elasticity, flexibility, intelligence, and so on, with the enterprise customer base that most of the cloud providers already have. And with the combination of 5G, brings it to the telecom world. And those, you know, I started to call it, as a kind of the triad, right? The triad of an enterprise, the telecom service provider, and the cloud provider, all working together to solve real business problems. >> Yeah, and it's totally a great call out there on the pandemic. I think the pandemic has shown us, coming out of it now, that cloud-scale matters. And you look at all the successes between work, play, and how we've all kind of adjusted, the cloud technologies were a big part of that, those solutions that got us through it. Now you've got the Edge developing with 5G. And I got to ask you this question, because when we have CUBE interviews with all the leaders of engineering teams, whether it's in the industry, or customers in the enterprise, and even in the telcos, the modern application teams have end-to-end visibility into the workload. You're starting to see more and more of that. You starting to see more open source in everything, right. So okay, I buy that. You got an SRE on the team, you got some modern developers, you're shifting left, you've got Devs set up. All good, all cloud. However, you're a networking guy. You know this. Routing packets across multiple networks is difficult. So if you're going to have end-to-end visibility, you got to have end-to-end intelligence on the networking. How is that being solved? Because this is a critical discussion here at Mobile World Congress. Okay, I buy Cloud Native, I buy observability, I buy open source, but I got to have end-to-end visibility for security, and workload management and managing all the data. What's the answer on the network side? >> Yeah, so that's a great question. And the simple way to think about this, is first and foremost, you need kind of global infrastructure, right? So that's a given, and of course, you know, Google with its kind of global infrastructure, and some of the largest networks in the world, we have that present, right. So that's important. Second is, to be able to abstract a way that underlying infrastructure, and make it available to applications, to a set of APIs. Right, so I'll give an analogy here. Just as you know, say 10 years ago, around 10 years ago, Android came into the market from Google, in the following way. What it did, was that it abstracted away the underlying devices with a simple kind of layer on top of operating system, which exposed APIs northbound. So then application developers can write new applications. And that actually unleashed, you know, a ton of kind of creativity right, around the world. And that's precisely what we believe is kind of the next step, as you said, on an end-to-end observability basis, right? If you can do an abstraction away from all of the underlying kind of core infrastructure, provide the right APIs, the right kind of information around observability, around telemetric, instead of making, you know, cloud and the infrastructure, the black box. Make it open, make it kind of visible to the applications. Bring that to the applications, and let the thousand flowers bloom, right? The creativity in each vertical area is so significant, because there are independent software vendors. There are systems integrators. There are individual developers. So one of the things that we are doing right now, is utilizing open source technologies, such as Kubernetes, right? Which is something that Google actually brought into the market. And it has become kind of the de facto standard for all of the container and modernization of applications. So by leveraging those open technologies, creating this common control plane, exposing APIs, right, for everything from application development, to observability, you certainly have the ability to solve business problems through a large number of entities in the systems integrator and the ISC and the developer community. So that's the approach that we are taking, John. >> I love the Android analogy of the abstraction layer, because at that time, the iPhone was closed. It still is. And they got their own little strategy there. Android went the other way. They went open, went open abstraction. Now abstraction layers are good. And now I want to get your thoughts on this, because anyone in operating systems knows abstractions are great for innovation. How does that apply to the real world on telco? Because I get how it could add some programmability in there. I get the control plane piece. Putting it into the operator's hands, how do you guys see, and how do you guys talk about the Edge service offering? What does it mean for the telco? Because if they get this right, this is going to be in telco cloud developer play. It's going to be a telco cloud ecosystem play. It's an opportunity for a new kind of telco system. How do you see that rolling out in real world? >> Great question, John. So the way I look at it, actually even we should take a step back, right? So the confluence of 5G, the kind of cloud capabilities and the Edge is, you know, very clear to me that it's going to unleash a significant amount of innovation. We are in early stages, no question, but it's going to drive innovation. So one almost has to start by saying what exactly is Edge, right? So the way I look at it, is that the Edge can be a continuum all the way from kind of an IOT device in automobiles, right? Or an enterprise Edge, like a factory location, or a retail store, or kind of a bank branch. To the telecom Edge, which is where the service providers have, not only their points of presence, and central offices, but increasingly a very large amount of intelligent RAN sites as well, right. And then the, kind of public cloud Edge, right. Where, for example, Google has, you know, 25 plus kind of regions around the world. 144, you know, PoPS, lots of CDN locations. We have, you know, few thousand nodes deployed deep inside service provider networks for caching of content, and so on. So if you think about these as different places in the network that you can deploy, compute, storage and intelligence act, right. And do that in a smart way, right? For example, if you were to run the learning algorithms in the cloud with its flexibility and elasticity, and run the inferencing at the Edge, very Edge, at the point of sort of a sale, or a point, a very consumer standing. Now you suddenly have the ability to create a variety of Edge applications. So going back to the new question, what have we seen, right? So what we are seeing, is depending on the vertical, there are different types of Edge applications, okay. So let's take a few examples. And I'll give you some, a favorite example of mine, which is in the sports arena, right? So in baseball, when you are in a stadium, and soon there are people sort of starting to be in stadiums, right? And a pitcher is throwing the pitch, right, the trajectory of the ball, the speed of the pitch, where the batter is, you know, what the strike zone is, and all of these things, if they can be in a stadium in real time, analyzed, and presented to the consumer as additional intelligence, and additional insight, suddenly it actually creates kind of a immersive experience. Even though you may be in the stadium, looking at the real thing, you are also seeing an immersive experience. And of course at home, you get a completely different experience, right? So the idea is that in sports, in media and entertainment, the power of Edge compute, and the power of AI ML, right, can be utilized to create completely new immersive experiences. Similarly, in a factory or an automotive environment, you have the ability to use AI ML, and the power of the Edge and 5G coming together, to find where the defects are, in a manufacturing environment, right? So every vertical, what we're finding is, there are very specific applications, which you can call as kind of killer apps, right in the Edge world, that over time will become prevalent and mainstream. And they will drive the innovation. They will drive deployment, and they also will drive ultimately, kind of the economics of all of this. >> You're laying out, essentially the role of the public cloud in the telco market. I'd love to get your thoughts, because a lot of people are saying, "Oh, the cloud, it's all Edge now. It's going back to on-premises." This is not the case. I mean, I've been really vocal on this. The public cloud and cloud operations is now the new normal. So developers are there. So I want you to explain real quick, the role of the public cloud in the telecom market and the Telecom Edge, because now they're working together. You've got abstraction, you mentioned that Android-like environment coming, there's going to be an Android-like effect, that abstraction. You got O-RAN out there, creating these connection points, for interoperability, for radio signals, and the End Transceivers or the Edge of the radios. All of this is happening. How is Google powering this? What is the role of public cloud in this? >> Yeah, so let me first talk about genetically the role of public cloud. Then I'll talk about Google, okay, in particular. So, if at the end of the day, the goal here is to create applications in a very simple and efficient manner, right? So what do you like, if you look for that as the goal, then the public cloud brings, you know, three fundamental things. Number one, is what I would call as elasticity and flexibility, right? So why is this important? Because as we discussed earlier, Edge is not one place, it's a variety of kind of different locations. If there is a mechanism to create this common control plane, and have the ability to kind of have elastic compute, elastic networking, elastic storage, and have this deployed in a flexible manner. Literally if you think, think about it like an effortless Edge is what we are starting to call it. You can move workload and capability, and run it precisely where it makes sense, right? Like I said, earlier, training and learning algorithms in the deep cloud. Inferencing, at the very edge, right? So if you can make that decision, then it becomes very powerful. So that's the first point, you know, elasticity and flexibility that cloud can bring. Second is, intelligence. The whole notion of leveraging the power of data, and the power of AI and ML is extremely crucial for creation of new services. So that's something that the public cloud brings. And the third is this notion of, write once, deploy anywhere, right? This notion of kind of a full stack capability that when open, kind of developer ecosystem can be brought in, right? Like we talked about Kubernetes earlier. So if there's a way in which you can bring in those developer and ISV ecosystem, which is already present in the world of public cloud, that's something that is the third thing that public cloud brings. And Google strategy very simply, is to play on all of these, right? Because we, you know, Google has incredibly rich deployment experience around the world for some of the largest services on the planet, right? With some of the biggest infrastructure in the networking world. Second, is we have a very open and flexible approach, right? So open as you know, we not only leverage kind of the Kubernetes environment, but also there are many other areas, Key Native, and so on where Google has brought a lot of open kind of capabilities to the broader market. And the third, is the enablement of the ecosystem. So last year we actually announced 200 applications, you know, from 30 ISVs in multiple verticals that we're now going to be deployed on Google Cloud, in order to solve specific business pain points, right. And building out that ecosystem, working with telecom service providers, with systems integrators, with equipment players, is the way that we believe Google Cloud can make a difference in this world of developing Edge applications. We are seeing great traction, John, you know, whether it is in the carrier world. Carrier such as Orange, Telecom Italia, TELUS, SK Telecom, Vodafone. These have all publicly announced their work with Google Cloud, leveraging the power of data, analytics, AI ML, and our very flexible infrastructure. And then a variety of kind of partners and OEM players, in the industry. As an example, Nokia, right, Amdocs, and Netcracker, and many others. So we are really excited in the traction that we are getting. And we believe that public cloud is going to be a key part of the evolution of the telecom industry. >> Shailesh, it's great to have you on. Shailesh Shukla, VP and GM of Networking at Google Cloud. And I would just add to that final point there, that open and this Android-like open environment is going to create a thousand flowers to bloom. Those are new applications, new modern applications, new companies, a new ecosystem in the Telco Cloud. So congratulations. Thanks for coming on and sharing your insights. Google Cloud, you guys are about the data, and being open. Thanks for comin' on. >> Thank you, John. Good to talk to you. >> Okay, so keeps coverage of Mobile World Congress. Google Cloud, featured interview here on theCUBE. Really a big part of the public cloud is going to be a big driver. Call it public cloud, hybrid cloud, whatever you want to call it. It's the cloud, cloud and Edge with 5G, making a big difference and changing the landscape, and trying innovation for the telco space. I'm John Furrier, your CUBE host. Thanks for watching. Okay, Dave, that's the Google support. They are obviously singing the same song as Danielle Royston, every vertical. >> Two great interviews, John. Really nice job. We can see the tech. The strategy is becoming more clear. You know, one of the big four. >> Yeah, I just love, these guys are so smart. Every vertical is going to be impacted by elastic infrastructure, AI, machine learning, and this new code deployment, write once, deploy anywhere. That's theCUBE. We love being here it's a cloud show now. Mobile World Congress, back to the studio for more awesome Cloud City content.

(upbeat music) >> Hey everyone, I'm John Furrier with The Cube, we're here in Palo Alto, California for a remote interview and session for The Cube presents AWS startup showcase, the next big thing in AI security in life sciences. I'm John Furrier. We're here with a great segment on cloud. Next big thing in Cloud with Chaos Search, Thomas Hazel, Chief Technology and Science Officer of Chaos Search joined by Jeremy Foran, the head of data analytics, the bad boy of data analyst as they say, but BAI communications, Jeremy Thomas, great to have you on. >> Great to be here. >> Pleasure to be here. >> So we're going to be talking about applying large scale log analytics to building the future of the transit industry. Obviously Telco's a big part of that, smart cities, you name the use case self-driving trucks, cars, you name it, everything's now edge. That the edge is super valuable, it's a new kind of last mile if you will, it's moving fast, it's mobile. This is a huge deal. Let's get into it, Thomas. What's this big story around this, this session? >> Well, we provide unique ability to take all that edge data and drive it into a data lake offering that we provide data analytics, both in logs, BI and coming out with ML there this year into next. So our unique play is transforming customers' cloud outer storage into an analytical platform. And really, I think with BIA is a log analytics specifically where, you know there's a lot of data streams from all those devices going into a lake that we transform their lake into analytics for driving, I guess, operational analysis. >> You know, Jeremy, I remember back in the day, I'm old enough to remember when the edge was the remote switch or campus hub or something. And then even on the Telco side, there was no wifi back in 2000 and you know, someone was driving in a car and you got any signal, you're lucky. Now you got, you know, no perimeter you have unlimited connectivity everywhere. This has opened up more of an Omni channel data problem. How do you see that world? Because you still got more devices pushing out at this edge and it's getting super local, right? Even on the body, even on people in the car. So certainly a lot of change on the infrastructure side. What does that pose for data challenge? >> Yeah, I, I would say that, you know users always want more, more bandwidth, more performance and that requires us to create more systems that require more complexity to deliver that user experience that we're, we're very proud of. And with that complexity means, you know exponentially more data. And so one of the wifi networks we offer in the Toronto subway system, T-connect, you know we see a 100-200,000 unique users a day and you can imagine just the amount of infrastructure to support that so that everyone has a seamless experience and can get their news and emails and even stream media while they're waiting for the subway. >> So you guys provide state of the art infrastructure for cell, wifi, broadcast, radio, IP networks, basically I mean, I call it the smart city kind of go-to. But that's basically anything involving kind of that edge piece. This is a huge thing. So as smart cities are on the table, which and you seeing 5G being called more of an enterprise app where there's feeding large dense areas of people this is now a new modern version of what I would call the, the smart city blueprint. What's changed in your mind on this whole modernization of this smart city infrastructure concept? What's new? What's cutting edge? >> Yeah. I would say that, you know there was an explosion of data and a lot of our insights aren't coming from one system anymore. It's coming from collecting data from all of the different pieces, the different infrastructure whether that's your fiber infrastructure or your wireless infrastructure, and then to solve problems you need to correlate data across those systems. So we're seeing more and more technologies that allow you to do that correlation. And that's really where we're finding tons of value, right? >> Thomas, take us through what you guys do as a, as a, as a product, a value proposition, the secret sauce, and and why I'm here with Jeremy? Why is this conversation important for the folks watching? What's the connection between Chaos Search and BAI communication? >> Well, it's data, right? And lots of it. So our unique platform allows people like Jeremy to stream all this data, right? In you know, today's world terabytes go to petabytes really easily, billions go to trillion really easily, and so providing the analysis of that data for their operations is challenging particularly based on technology and architectures that have been around for a long time. So what we do here at Chaos Search is the ability for BIA to stream all these devices, all these services into one centralized data lake on their cloud outer storage, where we connect to that cloud outer storage and transform it into an analytical database to do, in this case log analytics and do it seamlessly, easily where a new workload a new stream just streams into that lake. And we, as a service take over, we discover we index it and publish well-known open API and visualization so that they can focus on their business, not all the operational data pipeline, database and data engineering type work that again, at these types of scales is is frankly a nightmare. >> You know, one of the things that we've always observed on The Cube when you see new things come out that are really cool groundbreaking products like you guys are doing it's always a challenge to manage the cost and complexity of bringing in the new. So Jeremy, take us through this tech stack here because you know, it's, sometimes it might be unwieldy just in from a tech stack perspective, nevermind the business logic or the business processes that got to be either unwound or changed. Can you take us through the IT stack that's critical to support your, your area? >> Yeah, absolutely. So with all the various different equipment you know, to provide our public wifi and and our desks, carrier agnostic, LT and 5G networks, you know, we need to be able to adhere to PCI compliance and ISO 27,000, so that, you know, requires us to keep a tremendous amount of our data. And the challenge we were facing is how do we do that cost effectively, and not have to make any sort of compromises on how we do that? A lot of times you'll find you don't know the value of your data today until tomorrow. An example would be COVID. You know, we, when we were storing data two years ago we weren't planning for a pandemic, but now that we were able to retain that data and look back we can see a tremendous amount of value with trying to forecast how our systems will recover when things get back to normal. And so when I met Thomas and we were sort of talking about how we were going to solve some of these data retention problems, he started explaining to me their compression in some of the performance metrics of their profession. And, you know, I said, oh, middle out compression. And it was a bit, it's been a bit of a running joke between me and him and I'm sure others, but it's incredibly impressive the amount of data we're able to store at the kind of cost, right? >> What, what problem does, did he solve for you? Because I mean, these guys, honestly, you know the startups have a lot and the Cloud's enabling more value now, we're seeing this, but when you look at this what was your, what was your core problem that you had? >> Yeah, so we, when you we want to be able to, I mean, primarily this is for our CIS log server. And CIS long servers today aren't what they were 10, 15 years ago where you just sort of had a machine and if something broke you went and looked, right? Now, they're very complex, that data is feeding to various systems and third-party software. So, you know, we're actively looking for changes in patterns and we have our, you know security teams auditing these from, for penetration testing and such. And then the getting that data to S3 so that we could have it in case, you know, for two, three years of storage. Well, the problem we were facing is all of that all of these different systems we needed to feed and retain data, we couldn't do that on site. We wanted to do use S3 but when we were doing some projections, it's like, we, we don't really have the budget for all of these places. Meeting Thomas and, and working with Chaos Search, you know, using their compression brought those costs down drastically. And then as we've been working with them the really exciting thing is they we're bringing more and more features to that surface or offering. So, you know, first it was just storing that data away. And now we're starting to build solutions off of that sitting in storage. So that's where it gets really exciting because you know, there, it's nothing to start getting anomaly detection off those logs, which, you know originally it was just, we need to store them in case somebody needs them two, three years from now. >> So Thomas Thomas, if I get this right then what I'm hearing is obviously I've put aside the complexity and the governing side the regulations for a minute just generally. Data retention as, as a key value proposition and having data available when you need it and then to do that and doing it in a very cost-effective simple way. It sounds like what you guys are offering. Is that right? >> Yeah, I mean, one key aspect of our solution is retention, right? Those are a lot of the challenges, but at the same time we provide real time notification like a classic log analytic type platform, alerting, monitoring. The key thing is to bringing both those worlds together and solving that problem. And so this, you know, middle in middle out, well, to be frank, we created a new technology called what we call Chaos Index that is a database index that is wonderfully small as as we're indicating, but also provides all the features that makes Cloud object storage, high performance. And so the idea is that use this lake offering to store all your data in a cost effective way but our service allows you to analyze it both in a long retention perspective as well as real-time perspective and bringing those two worlds together is so key because typically you have Silo Solutions and whether it's real-time at scale or retention scale the cost complexity and time to build out those solutions I know Jeremy knows also, well, a lot of folks come to us to solve those problems because you know when you're dealing with, you know terabytes and up, you know these things get complicated and to be frank, fall over quite often. >> Yeah. Let me, let me just ask you the question that's probably on everyone's mind who's watching and you guys probably have both heard this many times, because a lot of people just throw the data lake solution around like it's, you know why they whitewash their kind of old legacy solutions with data lake, store it on data lake. It's been called a data swamp. So people are fearful that, okay. I love this idea of a data lake, who doesn't like throwing data into a repository, having it available at will with notifications, all this secret magic beans that just magically create value. But I doubt that, I don't want to turn into a data swamp. So Thomas and Jeremy, talk about that, that concern. How do you mitigate that? How do you talk to that? Because if done properly, there's huge value in having a control plane or some sort of data system that is going to be tied in with signals and just storage retention. So I see the value. How do you manage the concern that people might say, Hey, I don't want to date a swamp? >> Yeah, I'll jump into that. So, you know, let's just be frank, Hadoop was a great tool for a very narrow scenario. I think that data swamp came out because people were using the tooling in an incorrect way. I've always had the belief that data lakes are the future. You just have the right to have the right service the right philosophy to leverage it. So what we do here at Chaos Search is we allow you to organize it, discover it, automatically index that data so that swamp doesn't get swampy. You know, when you stream data into your lake how do you organize it, such that it's has a nice stream? How do you transform that data into a value? So with our service we actually start where the storage begins, not a end point, not an archive. So we have tooling and services that keep your lake from being swampy to be, to be clear. And, but the key value is the benefits of the lake, the cost effectiveness, the reliability, security, the scale, those are all the benefits. The problem was that no one really made cloud offer storage a first-class citizen and we've done that. We've dressed the swamp nature but provided all the value of analysis. And that cost metrics, that scale. No one can touch cloud outer storage, it just, you can't. But what we've done is cracked the code of how you make it analytical. >> Jeremy, I want to get your thoughts on this too, on your side I mean, as a practitioner and customer of, of of these solutions, you know, the concern is am I missing anything? And I've been a big proponent of data retention for many, many years. You know, Dave Alondra in our Cube knows all know that I bang on the table all the time, store your data, be a data hoarder, because it's going to come back and be valuable. Costs are going down so I'm a big fan of data retention. But the fear might be on, what am I missing? Because machine learning starts to come in down the road you got AI, the more data you have that's accessible in real time, the more machine learning is effective. Do you, do you worry about missing anything or do you just store everything? >> We, we store everything. Sometimes it's, it's interesting where the value and insights come from your data. Something that see, might seem trivial today down the road offers tremendous, tremendous value. So one of the things we do is provide because we have wifi in the subway infrastructure, you know taking that wifi data, we can start to understand the flow of people in and out of the subway network. And we can take that and provide insights to the rail operators, which get them from A to B quicker. You know, when we built the wifi it wasn't with the intention of getting Torontonians across the city faster. But that was one of the values that we were able to get from the data in terms of, you know, Thomas's solution, I think one of the reasons we we engaged him in the first place is because I didn't believe his compression. It sounded a little too good to be true. And so when it was time to try them out, you know all we had to do was ship data to an S3 bucket. You know, there's tons of, of solutions to do that. And, and data shippers right out of the box. It took a few, you know, a few minutes and then to start exploring the data was in Cabana, which is or their dashboard, which is, you know, an interface that's easy to use. So we were, you know, within a two days getting the value out of that data that we were looking for which is, you know, phenomenal. We've been very happy. >> Thomas, sounds like you've got a great, great testimonial here and it's not like an easy problem that he's living in there. I mean, I think, you know, I was mentioning this earlier and we're going to get into it now. There's regulations and there's certain compliance issues. First of all, everyone has this now problem now, it's not just within that space. But just the technical complexities of packets moving around I got on my wifi and the stop here, I'm jumping over here, and there's a ton of data it's all over the place, it's totally unstructured. So it's a tough, tough test for you guys, Chaos Search. So yeah, it's almost like the Mount Everest of customer testimonials. You've got to, it's a big, it's a big use case here. How does this translate to other clients? And talk about this governance and security controls because I know this highly regulated and you got there's penalties involved on his side of the world and Telco, the providers that have these edge devices there's actually penalties and, and whatnot so, not just commercial, it's maybe a, you know risk management, but here there's actually penalties. >> Absolutely. So, you know centralizing your data has a real benefit of of not getting in trouble, right? So you have one place, you store one place that's a good thing, but what we've done and this was a key aspect to our offering is we as Chaos, Chaos Search folks, we don't own the customer's data. We don't own BIA's data. They own the data. They give us access rights, very standard way with Cloud App storage roll on policies from Amazon, read only access rights to their data. And so not owning a customer's data is a big selling point not only for them, but for us for compliance regulatory perspective. So, you know, unlike a lot of solutions where you move the data into them and now they are responsible, actually BIA owns everything. We, they provide access so that we could provide an analysis that they could turn off at any point in time. We're also SOC 2 type 1 and type 2 compliant you got to do it, you know, in this, this world, you know when we were young we ran at this because of all of these compliance scenarios that we will be in, but, you know, the long as short of it is, we're transient service. The storage, cloud storage is the source of truth where all data resides and, you know, think about it, it's architecturally smart, it's cost effective, it's secure, it's reliable, it's durable. But from a security perspective, having the customer own their own data is a big differentiation in the market, a big differentiation. >> Jeremy, talk about on your end the security controls surrounding the log management environments that span across countries with different regulations. Now you've got all kinds of policy dimensions and technical dimensions and topology dimensions. >> Yeah, absolutely. So how we approach it is we look at where we have offerings across the globe and we figure out what the sort of highest watermark level of adherence we need to hit. And then we standardize across that. And by shipping to S3, it allows us to enforce that governance really easily and right to Tom's point you know, we manage the data, which is very important to us and we don't have to be worried about a third party or if we want to change providers years down the road. Although I don't think anyone's coming out with 81% compression anytime soon (laughs). But yeah, so that's, for us, it's about meeting those high standards and having the technologies that enable us to do it. And Chaos Search is a very big part of that right now. >> All right let me ask you a question, for the folks watching that are like really interested in this topic, what would you say to them when evaluating Chaos Search obviously, your use case is complex, but so are others as enterprises start to have an edge, obviously the security posture shifts, everything shifts. There's no more perimeter and the data problem becomes acute to them. So the enterprises are going to start seeing what you've been living for in your world. What's your advice to people watching? >> My advice would be to give them a try. You know, it's it's has been really quite impressive. The customer service has been hands-on and we've been getting, you know, they've been under-promising and over-delivering, which when you have the kind of requirements to manage solutions in these very complex environment, cloud local, you know various data centers and such, you know that kind of customer service is very important, right? It enables us to continue to deliver those high quality solutions. >> So Thomas give us the, the overview of the secret sauce. You've got a great testimonial here. You got people watching, what's different now in the world that you're going after, what wave are you on? Talk to the people who are watching this and saying, okay why Chaos Search? Why are you relevant? Obviously there's some cool things you're doing. I love that. What's cool, and what's relevant and why what's in it for them if they work with you? >> Yeah. So you know, that that whole Silicon Valley reference actually got that from my patent attorney when we were talking. But yeah, no, we, we, you know, focus on if we can crack this code of making data, one a face small, store small, moves small, process small. But then make it multimodal access make it virtual transformation. If we could do that, and we could transform cloud outer storage into a high-performance medical database all these heavy, heavy problems, all that complexity that scaffolding that you build to do these type of scales would be solved. Now what we had to focus on and this has been my, I guess you say life passion is working on a new data representation. And that's our secret sauce that enables a new architecture a new service that where the customer folks on their tooling, their APIs, their visualizations that they know and love, what we focus is on taking that data lake, and again, to transform it into an analytical database, both for log analytics think of like elastic search replacement, as well as a BI replacement for your SQL warehousing database. And coming out later this year into 2022, ML support on one representation. You don't have the silo your information you don't have to re index your data, both. So elastic search CQL and actually ML TensorFlow actions on the exact same representation. So think about the data retention, doing some post analysis on all those logs of data, months, years, and then maybe set up some triggers if you see some anomaly that's happening within your service. So you think about it, the hunt with BI reporting, with predictive analysis on one platform. Again, it sounds a little unicorn, I agree with Jeremy, maybe it didn't sound true but it's been a life's work. So it didn't happen overnight. And you know, it's eight years, at least in the in the making, but I guess the life journey in the end. >> Well, you know, the timing is great. You know, all the database geeks out there who have been following the data industry know that, you know there's a good point for structured data but when you start getting into mechanisms and they become a bottleneck or a blocker to innovation, you know you starting to see this idea of a data lake being let the data kind of form, let it be. You know, I hate the word control plane but more of a, a connective tissue between systems is become an interesting thing. So now you can store everything so you know, no worries there, no blind spots and then let the magic of machine learning in the future, come around. So Jeremy, with that, I got to ask you since you're the bad boy of data analytics at BAI communications head of data analytics, what does that, what do you look for in the future as you start to set this up because I can almost imagine and connecting the dots here in the interview, you got the data lake you're storing everything, which is good. Now you have to create more insights and get ahead of the curve and provide some prescriptive and automated ways to do things better. What's your vision? >> First I would just like to say that, you know when astrophysicists talk about, you know, dark dark energy, dark matter, I'm convinced that's where Thomas is hiding the ones and zeros to get that compression, right? I don't don't know that to be fact but I know it to be true. And then in terms of machine learning and these sort of future technologies, which are becoming available you know, starting from scratch and trying to build out you know, models that have value, you know that takes a fair amount of work. And that landscape keeps changing, right? Being able to push our data into an S3 bucket and then you know, retain that data and then get anomaly detection on top of it. That's, I mean, that's something special and that unlocks a lot of ability for you know, our teams to very easily deliver anomaly detection, machine learning to our customers, without having to take on a lot of work to understand the latest and greatest in machine learning. So, I mean, it's really empowering to our team, right? And, and a tool that we're going to. >> Yeah, I love and I love the name, Chaos Search, Thomas. I got to say, you know it brings up the inside baseball around chaos monkey which everyone knows was a DevOps tool to create kind of day two simulate day two operations and disruptions in DevOps. But what you're really getting at is your whole new architecture that's beyond DevOps movement, it's like next gen architecture. Talk about that to the people watching who have a lot of legacy and want to transform over to a more enabling platform that's going to give them some headroom for their data. What, what do you say to them? How do they get started? What, how should they, how what's their mindset? What they, what are some first principles you can share? >> Well, you know, I always start with first principles but you know, I like to say we're the next next gen. The key thing with the Chaos Search offering is you can start today with B, without even Chaos Search. Stream your data to S3. We're going to make hip and cool data lakes again. And actually it's a, Google it now, data lakes are hip and cool. So start streaming now, start managing your data in a well-formed centralized viewpoint with security governance and cost effectiveness. Then call Chaos Search shop, and we'll make access to it easily, simply to ultimately solve your problems. The bug whether your security issue, the bug, whether it's more performance issues at scale, right? And so when workloads can be added instantaneously in your data lake it's, it's game changing it's mind changing. So from the DevOps folks where, you know, you're up all night trying to say, how am I going to scale from terabyte, you know one today to 50 terabytes, don't. Stream it to S3. We'll take over, we'll worry about that scale pain. You worry about your job of security, performance, operations, integrity. >> That really highlights the cloud scale the value proposition as, as apps start to be using data as an input, not just as a a part of a repo repo, so great stuff. Thomas, thanks for sharing your life's work and your technology magic. Jeremy, thanks for coming on and sharing your use cases with us and how you are making it all work. Appreciate it. >> Thank you. >> My pleasure. >> Okay. This is The Cubes, coverage and presenting AWS this time showcase the next big thing here with Chaos Search. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by Silicon angle. The cube on cloud continues. We're here with Anna Pinza, who is the chief development officer and Anna Plan. We've been unpacking the future of Cloud. We've heard from a number of CEOs how they're thinking about Cloud in the coming decade. And first of all, Anna, welcome back to the Cube. Thanks for participating. It's great to see you again. >>It's great to see you, Dave. And I'm so excited to be here with you again, so hopefully we'll be doing this soon. >>I hope in 2021 will be able to be face to face everybody. Oh, no. A lot of respect. You think about the CEO role, something that you're intimately familiar with its unique because she or he has a very wide observation space across the company. You know, where is the GM or a business line manager there, You know, most concerned with their respective business, the CEO, they're gonna worry about the whole enchilada. And we've heard a lot in this program about digital transformation. We've heard a lot, of course, in the past couple of years, a lot of it was lip service, but but digital transformation, it's no longer optional. What's changed, in your view, in the way that businesses air going about it? >>You know, Dave, I mean, from my perspective, it's interesting. And this year in particular has been really telling for us, right? So I think before many companies were thinking about Hey, I wanna be online, I wanna grow my revenues, you know, with with digital I wanna have a presence. But what's happened actually this year with covert in particular, is that it's gone from being kind of a good to have, you know, to really ah, fundamental necessity. We must have it. And so when I talked to CEOs today, they're really thinking about different kinds of things than before, not just going digital, but how do I enable um, my people toe work remotely right? I've got to enable that how doe I bring the agility and the flexibility that I need in our business, especially with these new ways of working right? How do I look at business resiliency? You know, not just from a you know, something happens, and then how do I recover from it? But also how do I help our, You know, our company and our people then actually spring forward and grow from where we are. So it's gone from a a topic that was happening at the CEO, maybe at the business level. But now it's really also a fundamental CEO and board conversation. And so now we're seeing the CEO is having to present two boards. You know, what is our digital transformation? Are our digital strategy. So I wonder what >>you've seen in that regard. I'm interested in what role cloud plays and supporting those digital initiatives. But more specifically, you know, cloud migration came, you know, off the charts in terms of interest because of co vid. But you had those that that were, you know, deep into cloud had a lot of experience of those maybe not as much. Are you seeing any kind of schism in the marketplace where there's maybe a great advantage to those who really had years of experience on may be a disadvantage to those who didn't or is there kind of an equilibrium you're seeing in the market place? How do you see that playing out? >>Yeah. I mean, you know, What I'm seeing is that I think there used to be a spectrum of CEOs and effect, you know, the ones that were kind of a little bit, you know, you know, forward, ahead on the cloud, both on cloud infrastructure as well, Assassin. Right. And what are the services that we have? And then there were some that were really, um, you know, trying to think about what's the security, you know, implications of the cloud. And, you know, is it more expensive? And you know, So there was this spectrum of CEOs and I think now what's happened is there's such a business imperative that I think CEO s air saying, Look, I'm either gonna survive, you know, in this new world with the agility and the flexibility that I need And so cloud, you know, I'm seeing a lot of CEO is really saying Okay, Cloud is not just fashionable, but it z in and a necessity, right? And we must on we must do it. And I think frankly, the c e. O. S that don't embrace the cloud and that level of agility are going to struggle, right? It's a it's really a personal imperative. for a CEO in addition to sort of for the company. So >>a lot of times we talk about, you know, the three dimensions of people, process and technology, and I'm interested in your thoughts on how cloud has affected those traditional structures and the value chains. I mean, you've got some people are really good a text. Some people are really good at people. Some people are really good at process. Has the cloud affected that is, it upended? It changed it in any way. >>Yeah. I mean, let's let's, like, unpack that a little bit. You know, Dave, because if you think about process, I mean, one of the interesting things about the cloud is that And if you think about the cloud as going all the way from, like I as their sort of infrastructure all the way up the stack toe, actually providing business processes embedded, you know, in in a fast service, then from a process perspective and for CEOs, it's really upended how they think about business process reengineering in their companies. Um, if I think even, you know, five years ago, where you would have ah whole organization, that's, you know, focused on business process reengineering You do that? It takes a long time. You know, you get a consultant, maybe to help you, and then you work through that process. If you look at a SAS service like Anna plan today, where we our goal is, for example, toe orchestrate business performance. We were assassin business planning platform. We've incorporated into our platform that business process. Right. So the role of the CEO relative to business process and effect changes Right now, it's about how the leverage, ah, cloud infrastructure, and then how do you enable the customization is on top of that. But generally speaking, that's a lot easier than having to think about re engineering the whole company. Um, if you think about the technology stock, obviously the cloud, uh, embeds a lot of technology, you know, in the cloud. Right. So you have a lot of native services that are available to you. Um, that is awesome from a talent perspective, you know, because before, maybe you need to have, you know, needed to have database experts or, you know, kubernetes experts. And not that we don't need those today. But many of those capabilities come native in the cloud today. So, in effect, how it helps the CEO is to provide sort of this ecosystem of talent kind of embedded in what the cloud provider does. Right? So >>I wondered. So stay on that for a minute. So remember, before Amazon announced a W s and whether 2006 it was CEO said to me, >>Yeah, I'm thinking >>about maybe I don't need to run my own email, right? So because you have to have seen the SAS ification of of of businesses, which to your point, you know, makes things, uh, simpler and that I can focus on other areas and not to worry about, you know, managing infrastructure to support APS. At the same time you've had this proliferation of cloud you mentioned, of course, that you're with Anna Plan. You see, you got work day, you got Salesforce. You've got service now Oracle, APS and and people struggle. Okay, how do I get these things Talking to whether there's that worried about that data layer. So there's this new level of complexity. How do you see that playing out in the next decade? >>Yeah. You know, we used to say that, you know, we sort of, um, shift. What we do at a certain level and now is an organization we start to look at kind of higher value outcomes, right. And so I see that happening. And you're absolutely right. The conversations that I have with customers now are Hey, um, you know, there's things that are enabled by the cloud, and then on top of that, you need a set of a P i s or connectors or ways to get data in and out, you know, in and out of a particular system or ways to link. In our case, we're linking with Salesforce toe, Anna plan, toe workday or other tools, right? And so you start to think more about the business outcome that you want. The CEO needs to be focused on that, um, instead of maybe, uh, sort of the fundamentals of the technology. Those come, you know, those come for you, and then it's really more about the partnership with the business side. Right to say Okay, what is it that you're trying to do and can I enable that through my you know, cloud architectures, the work days, you know, the adobes or or the sales forces of the world. So I think the conversation is changing. And from my perspective, what's really cool about that is, um it brings the CEO Thio, you know, really makes the CEO of business and thought leader a strategic leader, right, Because, uh, the I t shop is not just talking tech, you know, the top shop has toe talk a lot more about the outcome that they're trying to deliver. >>So I mean, in the early days of cloud, I just wanna pick up on what you just said. I mean, a lot of people in I t's saw the cloud is a threat to their livelihood. And e think I'm inferring from your statements that were largely through that dynamic. And the CEO is now really trying to make the cloud platform for transformation and monetization or whatever other organizational goal might be saving lives or better government. Is >>that sort >>of how you see it, that the role has changed to that? >>I know. I mean, I talked to so many companies, and it's still we're still going through that transition, so I don't think we're completely over the hump of, you know, cloud all day everywhere but a same time. Um, I think what the CEO so really focused on these days is really around business, agility and business outcomes for their partners. By the way, that's one of the things. The second thing, especially these days, is around people, you know, collaboration, communication. How do we, you know, facilitate interaction of people, whether inside or outside of the company on DSO? You know, that's, um that's a very different conversation for the CEO. It doesn't mean that we're not still having the basic conversation of how safe is the cloud. What security do you have built into the cloud, Right, Andi? But I think, frankly, Dave, that we've across the chasm where before it used to be. Hey, I'm a lot more secure on Prem and, you know, given the tremendous focus of the cloud providers and says companies have put on security, um, I see many more companies, you know, feeling very at ease and in fact, telling their organizations right, we actually need to switch to the cloud, including large. Um, you know, large companies that have compliance issues, you know, or like large financial companies. Many of those are making that switch as well. Well, >>it's interesting talk about security, but I think it's kind of a two edged sword, right? Because I think a lot of frankly, I think a lot of executives early days used security as a way to sort of kick the can >>down the road. But >>the reality was cloud, you know better. Worse you could make that argument is different. And so, you know, different concerns people. But it's still a the end of the day. Bad security practices Trump, >>you >>know, good security. And so that's what we've seen so many times that shared responsibility model on DSO. People are still >>learning there, so >>so security is almost this beast in and of itself. I'm interested in your thoughts on on the priorities. I mean, >>our >>customers are they streamlining their their tech investments? I mean, the major focus, as you pointed out on Cloud, has been it's a driver of agility and shifting. Resource is as we talked about. But there's this constant cost pressure, you know, the procurement. Looking at the Amazon Bill, Uh, do you see ah lot of the same going forward? Or do you think the value equation is shifting such that there'll be Maybe, you know, I t is less cost pressure is always gonna be cost pressure. I know, but But more value producer, >>I think I think you're right. I mean, I see it and I see it. Over the last six months, I've seen it really accelerate where CEOs are thinking about three things and one is business resiliency. When I talk about business resiliency, I talk about the ability to recover from crap that happens. You know, where you know, whether it's pandemics or, you know, global events and shifts that companies have to accommodate. Right? So that's one thing that I see them thinking about. The second one that we talked about a little bit is just agility. You know, I see them really focused on that. And the cloud enables that. And, you know, the third one in conversations is really speed innovation, because, um, you know, when companies air talking to cloud providers and particularly SAS cos what I see them talking about is Look, I've got this particular need and it would take me, you know, two years to do it with a legacy player because of, you know, I've got to do this on Prem. But you have the fundamentals built in. And I think I could do it with you in three months. So I think, you know, business Resiliency both to grow and toe recover from stuff. Um, agility and innovation are really three fundamental levers that I see for movement, uh, movement to the cloud. Right? Andi, any one of those that these days I mean, it's funny, uh, depending on who you talk Thio. Any one of those can propel a CEO to make a choice to make that choice. And when they have all of that together, um, they have a lot more, um, lift in effect As a CEO, they have a lot more leverage, right in terms of what they could do for their companies. Well, >>let's stay on innovation. I mean innovation. I've said many times in tech, >>you >>know, for decades it came, came from Moore's Law, it seems, seems so nineties to even say that it's true. So what's going to drive innovation in the in the coming years? I'm interested in your perspective on how machine intelligence and a I n m l on cloud, of course, play into that innovation agenda. >>Yeah. I mean, it's it's interesting, You know, I see it a lot in our business with Anna plan. Um, innovation comes from the ability to bring instead of what you do internally and match it with what's available in the external world. Right? And you mentioned it earlier. Data, You know, data is like the new currency. That's that's, like software, you know, eats the world. Now we talk about data, right? And, um and I think what's really going to drive innovation is being able to have access to the world's data once the company builds this digital DNA, You know, this digital foundation and puts, you know and is able to have access to that data, Then you start to make decisions. You know, you start Thio offer services. Um, you start thio, bring intelligence. Um, that wasn't available before, right? And, um, that's a really powerful thing for any company, whether you're doing, you know, forecasting. And you need to sort of bring the world's data. Whether you're a agricultural company, we talking. And in these days, um, innovation comes in the form of speed, you know, being able to just deliver something new to an audience faster. So to me, the cloud enables, You know, all of that the ability Thio bring in data. And then on top of that, I mean, think about all the A i m l innovation that's happening around the world. We we just launched an offer, actually, um, to be able to dio forecasting intelligent forecasting on top of the cloud we partner with with a W s forecast for that, Um, if we didn't have a cloud platform, you know, to do that and instead of a p i s you know, being digital that way really enables us, uh, the opportunity Thio toe match. You know, one plus one equals one, you know, 100. Really? And bringing the power of that to get to companies together to be ableto enable that type of innovation. >>Well, that that that's interesting. It reminds me of my friends. Ed Walsh is the CEO of a startup called Chaos Search. And you use the statement. He said, where we're standing on the shoulders of the giants, you know what you know, trying not trying to recreate it. And I think you know, you got what you just said is the same thing. You're sort of relying on others to build out cloud infrastructure. So there's a totally left field question. When you hear all the talks about breaking up big tech I >>want Is that a >>relevant to you? Because you figured okay, the clouds gonna be there. It's maybe more about search or it's about, you know, Facebook or, you know, Amazon's dominance. Interestingly, Microsoft's really not in those discussions anymore. They were the center of it >>back. No, no. >>But as a head of development for a company, does that even factor into the equation? And you're kind of not worry about that? >>No. I mean, I'll be honest for me personally. What I do is I compartmentalize my world, right. In a sense, I view I view the partnerships and we have partnerships with Google and AWS and Microsoft and others, Right? So, um, I view those as part of a non opportunity to really provide on ecosystem set of solutions right to customers and those air very powerful. I think those partnerships enable companies like ours, like Sasse companies, to innovate faster, right? And so I compartmentalize and I say those things are are wonderful. I don't know why you would want to break up those companies at the same time. Um, you know, part of what you're referring Thio, you know, has to do with, um more the social and the consumer elements of what's going on. But as a business leader, um, I really I really focused on what the power is, particularly in the enterprise. What is it that we can do for global enterprise companies? And at least in my mind, those two things tend to be separate. >>Couple of things, you said they're triggered my mind. One was ecosystems. We've been talking about data. One of our guests on this program, Alan Nance, has been talking about ecosystems and the power of ecosystems. And I definitely see Cloud is a platform to allow data sharing across those those clouds and then to form ecosystems and share data in ways that we really couldn't have, you know, half a decade or even you no longer ago. And that seems to be where ah lot of the innovation is going to occur. Some of the people talk about the flywheel effect, but it's the power of many versus the resource is of, you know, a few. >>And I'm such a big believer in the ecosystem play. And part of that is because, um, frankly, even over the last 20 years, that the skills that are required and the knowledge that required that is required is so specialized. Dave, you know, if you think about, you know, a I m l and all the algorithms that we need to know when the innovation that's happening there. And so I really don't think that there's any one company that can serve a customer alone, right? And if you think about it from a customer perspective, you know they're made up of their business is made up of needs from a lot of different parties that they're putting together, you know, to accommodate their business outcome. And so the only way to play right now in tech is is in a collaborative way in an ecosystem way. I think the mawr that companies like ours worked with other companies on these partnerships. And frankly, by the way, I think in the past, many companies that have made bold announcements and they would say, Oh, you know, I'm partnering with so and so and I've got this great partner, you know, partnership. And then nothing would happen. You know, like it was just a lot of, you know, talk. But I think what's actually happening now and it's enabled by the cloud, is, um, we have much more of a show me culture, right? We can we can actually say. Okay, well, let's say, uh, Anna plan is partnering with Google. Show me. You know, show me what you're actually doing. And I see our customers, um, asking for references of how these ecosystem partnerships air playing. Um, and, uh, because these stories air out there mawr, I think partnerships are actually much more feasible and and really and pragmatic. Yeah. >>Anna, we call those Barney deals, you know, I love you. You love me, would do a press release, and then nothing ever happens. >>That's right. That's right. And I think that Z that's not gonna work. Going forward day, right? People are asking for a lot more transparency. And so when we think about ecosystems, they really want the meat on the bone, right? They don't want just, uh, announcements that don't really help their business move forward. Yeah, >>And you know the other thing to the come back to data. It's always comes back to data, right? Every conversation. But the data that's created out of that ecosystem is gonna throw off, you know, new capabilities and new data products, data services. And that, to me, is a really exciting, you know, new chapter, I think of cloud. >>Yeah, and it's interesting. You know, the conversations I'm having now are are about data and believe it or not, also about metadata, right? Because people are trying to analyze what's happening with the cloud. You know, among cloud providers what our customers doing with the data, right? How are they using data? How often are they accessing data? Um, security. You know, from that perspective, looking at who's accessing? Accessing what? So, um, the data conversation in the metadata conversation are truly enabled by the cloud and their their key. And they weren't that easy to do in a prior, you know, legacy sort of environment. There's >>a great point. I'm glad you brought that up, because legacy, environment, all the all that metadata that data about the data is locked inside of these systems. And if you're gonna go across clouds and you're gonna have it secure and govern. You've gotta have that metadata visibility and a point of control that actually can see that and and can manage it. So thank you for that at that point. And thank you for coming on the on the Cuban participating. The Cuban cloud has been great having you. >>Thank you so much for having me. It's been a pleasure. >>Alright, Keep it right there. Everybody mawr from the Cuban cloud right after this short break.

>>Yeah. >>All right. Welcome back to our third session, which is all about administering analytics at Global Scale. We're gonna be discussing how you can implement security data compliance and governance across the globe at for large numbers of users to ensure thoughts. What is open for everyone across your organization? So coming right up is Cheryl Zang, who is a senior director of product management of Thought spot, and Kendrick. He threw the sports sports director of Systems Engineering. So, Cheryl and Kendrick, the floor is yours. >>Thank you, Tina, for the introduction. So let's talk about analytics scale on. Let's understand what that is. It's really three components. It's the access to not only data but its technology, and we start looking at the intersection of that is the value that you get as an organization. When you start thinking about analytics scale, a lot of times we think of analysts at scale and we look at the cloud as the A seven m for it, and that's a That's an accurate statement because people are moving towards the cloud for a variety of reasons. And if you think about what's been driving, it has been the applications like Salesforce, Forcados, Mongo, DB, among others. And it's actually part of where we're seeing our market go where 64% of the company's air planning to move their analytics to the cloud. And if you think of stock spotted specifically, we see that vast majority of our customers are already in the cloud with one of the Big Four Cloud Data warehouses, or they're evaluated. And what we found, though, is that even though companies are moving their analytics to the cloud, we have not solved. The problem of accessing the data is a matter of fact. Our customers. They're telling us that 10 to 25% of that data warehouse that they're leveraging, they've moved and I'm utilizing. And if you look at in General, Forrester says that 60 to 73% of data that you have is not being leveraged, and if we think about why you go through, you have this process of taking enterprise data, moving it into these cubes and aggregates and building these reports dashboards. And there's this bottleneck typically of that be I to and at the end of the day, the people that are getting that data on the right hand side or on Lee. Anywhere from 20 to 30% of the population when companies want to be data driven is 20 to 30% of the population. Really what you're looking for now it's something north of that. And if you think of Cloud data, warehouse is being the the process and you bring Cloud Data Warehouse and it's still within the same framework. You know? Why invest? Why invest and truly not fix the problem? And if you take that out and your leverage okay, you don't necessarily have the You could go directly against the warehouse, but you're still not solving the reports and dashboards. Why investing truly not scale? It's the three pillars. It's technology, it's data, and it's a accessibility. So if we look at analytics at scale, it truly is being able to get to that north of the 20 to 30% have that be I team become enablers, often organization. Have them be ableto work with the data in the Cloud Data warehouse and allow the cells marking finding supplies and then hr get direct access to that. Ask their own questions to be able to leverage that to be able to do that. You really have to look at your modern data architecture and figure out where you are in this maturity, and then they'll be able to build that out. So you look at this from the left to right and sources. It's ingestion transformation. It's the storage that the technology brains e. It's the data from a historical predictive perspective. And then it's the accessibility. So it's technology. It's data accessibility. And how do you build that? Well, if you look at for a thought to spot perspective, it truly is taking and driving and leveraging the cloud data warehouse architectures, interrogated, essay behind it. And then the accessibility is the search answers pen boards and embedded analytics. If you take that and extend it where you want to augment it, it's adding our partners from E T L R E L t. Perspective like al tricks talent Matile Ian Streaming data from data brings or if you wanna leverage your cloud, data warehouses of Data Lake and then leverage the Martin capability of your child data warehouse. The augmentation leveraging out through its data bricks and data robot. And that's where your data side of that pillar gets stronger, the technologies are enabling it. And then the accessibility from the output. This thought spot. Now, if you look at the hot spots, why and how do we make this technology accessible? What's the user experience we are? We allow an organization to go from 20 to 30% population, having access to data to what it means to be truly data driven by our users. That user experience is enabled by our ability to lead a person through the search process. There are search index and rankings. This is built for search for corporate data on top of the Cloud Data Warehouse. On top of the data that you need to be able to allow a person who doesn't understand analytics to get access to the data and the questions they need to answer, Arcuri Engine makes it simple for customers to take. Ask those questions and what you might think are not complex business questions. But they turn into complex queries in the back end that someone who typically needs to know that's that power user needs to know are very engine. Isolate that from an end user and allows them to ask that question and drive that query. And it's built on an architecture that allows us to change and adapt to the types of things. It's micro services architecture, that we've not only gone from a non grim system to our cloud offering, in a matter of of really true these 23 years. And it's amazing the reason why we can do that, do that and in a sense, future proof your investment. It's because of the way we've developed this. It's wild. First, it's Michael Services. It's able to drive. So what this architecture ER that we've talked about. We've seen different conversations of beyond its thought spot everywhere, which allows us to take that spot. One. Our ability to for search for search data for auto analyzed the Monitor with that govern security in the background and being able to leverage that not only internally but externally and then being able to take thought spot modeling language for that analysts and that person who just really good at creating and let them create these models that it could be deployed anywhere very, very quickly and then taking advantage off the Cloud Data warehouse or the technology that you have and really give you accessibility the technology that you need as well as the data that you need. That's what you need to be able to administer, uh, to take analytics at scale. So what I'm gonna do now is I'm gonna turn it over to Cheryl and she's gonna talk about administration in thought spot. Cheryl, >>thank you very much Can take. Today. I'm going to show you how you can administrator and manage South Spot for your organization >>covering >>streaming topics, the user management >>data management and >>also user adoption and performance monitoring. Let's jump into the demo. >>I think the Southport Application The Admin Council provides all the core functions needed for system level administration. Let's start with user management and authentication. With the user tab. You can add or delete a user, or you can modify the setting for an existing user. For example, user name, password email. Or you can add the user toe a different group with the group's tab. You can add or delete group, or you can manage the group setting. For example, Privileges associated with all the group members, for example, can administrate a soft spot can share data with all users or can manage data this can manage data privilege is very important. It grants a user the privileges to add data source added table and worksheet, manage data for different organizations or use cases without being an at me. There is also a field called Default Pin Board. You can select a set of PIN board that will be shown toe all of the users in that group on their homepage in terms off authentication. Currently, we support three different methods local active directory and samel By default. Local authentication is enabled and you can also choose to have several integration with an external identity provider. Currently, we support actor Ping Identity, Seaside Minor or a T. F. S. The third method is integration with active directory. You can configure integration with L DAP through active directory, allowing you to authenticate users against an elder up server. Once the users and groups are added to the system, we can share pin board wisdom or they can search to ask and answer their own questions. To create a searchable data, we first need to connect to our data warehouses with embraced. You can directly query the data as it exists in the data warehouse without having to move or transfer the data. In this page, you can add a connection to any off the six supported data warehouses. Today we will be focusing on the administrative aspect off the data management. So I will close the tap here and we will be using the connections that are already being set up. Under the Data Objects tab, we can see all of the tables from the connections. Sometimes there are a lot of tables, and it may be overwhelming for the administrator to manage the data as a best practice. We recommend using stickers toe organize your data sets here, we're going to select the Salesforce sticker. This will refined a list off tables coming from Salesforce only. This helps with data, lineage and the traceability because worksheets are curated data that's based on those tables. Let's take a look at this worksheet. Here we can see the joints between tables that created a schema. Once the data analyst created the table and worksheet, the data is searchable by end users. Let's go to search first, let's select the data source here. We can see all of the data that we have been granted access to see Let's choose the Salesforce sticker and we will see all of the tables and work ship that's available to us as a data source. Let's choose this worksheet as a data source. Now we're ready to search the search Insight can be saved either into a PIN board or an answer. Okay, it's important to know that the sticker actually persist with PIN board and answers. So when the user logging, they will be able to see all of the content that's available to them. Let's go to the Admin Council and check out the User Adoption Pin board. The User Adoption Pin board contains essential information about your soft spot users and their adoption off the platform. Here, you can see daily active user, weekly, active user and monthly active user. Count that in the last 30 days you can also see the total count off the pin board and answers that saved in the system. Here, you can see that unique count off users. Now. You can also find out the top 10 users in the last 30 days. The top 10 PIN board consumers and top 10 ad hoc searchers here, you can see that trending off weekly, active users, daily, active users and hourly active users over time. You can also get information about popular pin boards and user actions in the last one month. Now let's zoom in into this chart. With this chart, you can see weekly active users and how they're using soft spot. In this example, you can see 60% of the time people are doing at Hawk search. If you would like to see what people are searching, you can do a simple drill down on quarry tax. Here we can find out the most popular credit tax that's being used is number off the opportunities. At last, I would like to show you assistant performance Tracking PIN board that's available to the ad means this PIN board contains essential information about your soft spot. Instance performance You this pimple. To understand the query, Leighton see user traffic, how users are interacting with soft spot, most frequently loaded tables and so on. The last component toe scowling hundreds of users, is a great on boarding experience. A new feature we call Search Assist helps automate on boarding while ensuring new users have the foundation. They need to be successful on Day one, when new users logging for the first time, they're presented with personalized sample searches that are specific to their data set. In this example, someone in a sales organization would see questions like What were sales by product? Type in 2020. From there are guided step by step process helps introduce new users with search ensuring a successful on boarding experience. The search assist. The coach is a customized in product Walk through that uses your own data and your own business vocabulary to take your business users from unfamiliar to near fluent in minutes. Instead of showing the entire end user experience today, I will focus on the set up and administration side off the search assist. Search Assist is easy to set up at worksheet level with flexible options for multiple guided lessons. Using preview template, we help you create multiple learning path based on department or based on your business. Users needs to set up a learning path. You're simply feeling the template with relevant search examples while previewing what the end user will see and then increase the complexity with each additional question toe. Help your users progress >>in summary. It is easy to administrator user management, data management, management and the user adoption at scale Using soft spot Admin Council Back to you, Kendrick. >>Thank you, Cheryl. That was great. Appreciate the demo there. It's awesome. It's real life data, real life software. You know what? Enclosing here? I want to talk a little bit about what we've seen out in the marketplace and some of them when we're talking through prospects and customers, what they talk a little bit about. Well, I'm not quite area either. My data is not ready or I've got I don't have a file data warehouse. That's this process. In this thinking on, we have examples and three different examples. We have a company that actually had never I hadn't even thought about analytics at scale. We come in, we talked to them in less than a week. They're able to move their data thought spot and ask questions of the billion rose in less than a week now. We've also had customers that are early adoption. They're sticking their toes in the water around the technology, so they have a lot of data warehouse and they put some data at it, and with 11 minute within 11 minutes, we were able to search on a billion rows of their data. Now they're adding more data to combine to, to be able to work with. And then we have customers that are more mature in their process. Uh, they put large volumes of data within nine minutes. We're asking questions of their data, their business users air understanding. What's going on? A second question we get sometimes is my data is not clean. We'll talk Spot is very, very good at finding that type of data. If you take, you start moving and becomes an inner door process, and we can help with that again. Within a week, we could take data, get it into your system, start asking business questions of that and be ready to go. You know, I'm gonna turn it back to you and thank you for your time. >>Kendrick and Carol thank you for joining us today and bringing all of that amazing inside for our audience at home. Let's do a couple of stretches and then join us in a few minutes for our last session of the track. Insides for all about how Canadian Tire is delivering Korean making business outcomes would certainly not in a I. So you're there