(upbeat music) >> Welcome to transforming ideas into insights, presented with theCUBE and made possible by Cloudera. My name is Dave Vellante from theCUBE and I'll be your host for today. In the next hundred minutes, you're going to hear how to turn your best ideas into action using data and we're going to share the real-world examples of 12 industry use cases that apply modern data techniques to improve customer experience, reduce fraud, drive manufacturing efficiencies, better forecast retail demand, transform analytics, improve public sector service and so much more. How we use data is rapidly evolving. That is the language that we use to describe data. I mean, for example, we don't really use the term big data as often as we used to, rather we use terms like, digital transformation and digital business. But you think about it. What is a digital business? How is that different from just a business? Well, a digital business is a data business and it differentiates itself by the way it uses data to compete. So whether we call it data, big data or digital, our belief is we're entering the next decade of a world that puts data at the core of our organizations. And as such, the way we use insights is also rapidly evolving. You know, of course we get value from enabling humans to act with confidence on let's call it near perfect information or capitalize on non-intuitive findings, but increasingly insights are leading to the development of data products and services that can be monetized. Or as you'll hear in our industry examples, data is enabling machines to take cognitive actions on our behalf. Examples are everywhere in the forms of apps and products and services, all built on data. Think about a real time fraud detection, know your customer and finance, personal health apps that monitor our heart rates. Self-service investing, filing insurance claims on our smart phones and so many examples. IOT systems that communicate and act machine to machine. Real-time pricing actions, these are all examples of products and services that drive revenue, cut costs or create other value and they all rely on data. Now, while many business leaders sometimes express frustration that their investments in data, people and process and technologies haven't delivered the full results they desire. The truth is that the investments that they've made over the past several years should be thought of as a step on the data journey. Key learnings and expertise from these efforts are now part of the organizational DNA that can catapult us into this next era of data transformation and leadership. One thing is certain, the next 10 years of data and digital transformation won't be like the last 10. So let's into it. Please join us in the chat. You can ask questions. You can share your comments. Hit us up on Twitter. Right now, it's my pleasure to welcome Mick Holliston and he's the president of Cloudera. Mick, great to see you. >> Great to see you as well, Dave. >> Hey, so I call it the new abnormal, right? The world is kind of out of whack. Offices are reopening again. We're seeing travel coming back. There's all this pent up demand for cars and vacations, line cooks at restaurants. Everything that we consumers have missed, but here's the one thing, it seems like the algorithms are off. Whether it's retail's fulfillment capabilities, airline scheduling, their pricing algorithms, commodity prices, we don't know. Is inflation transitory? Is it a long-term threat, trying to forecast GDP? It seems like we have to reset all of our assumptions and Mick, I feel a quality data is going to be a key here. How do you see the current state of the industry in the role data plays to get us into a more predictable and stable future? >> Well, I can sure tell you this, Dave, out of whack is definitely right. I don't know if you know or not, but I happened to be coming to you live today from Atlanta and as a native of Atlanta, I can tell you there's a lot to be known about the airport here. It's often said that whether you're going to heaven or hell, you got to change planes in Atlanta and after 40 minutes waiting on an algorithm to be right for baggage claim last night, I finally managed to get some bag and to be able to show up, dressed appropriately for you today. Here's one thing that I know for sure though, Dave. Clean, consistent and safe data will be essential to getting the world and businesses as we know it back on track again. Without well-managed data, we're certain to get very inconsistent outcomes. Quality data will be the normalizing factor because one thing really hasn't changed about computing since the dawn of time, back when I was taking computer classes at Georgia Tech here in Atlanta and that's what we used to refer to as garbage in, garbage out. In other words, you'll never get quality data-driven insights from a poor dataset. This is especially important today for machine learning and AI. You can build the most amazing models and algorithms, but none of it will matter if the underlying data isn't rock solid. As AI is increasingly used in every business app, you must build a solid data foundation. >> Mick, let's talk about hybrid. Every CXO that I talked to, they're trying to get hybrid right. Whether it's hybrid work, hybrid events, which is our business, hybrid cloud. How are you thinking about the hybrid everything, what's your point of view? >> With all those prescriptions of hybrid and everything, there was one item you might not have quite hit on, Dave and that's hybrid data. >> Oh yeah, you're right, Mick, I did miss that. What do you mean by hybrid data? >> Well, Dave, in Cloudera, we think hybrid data is all about the juxtaposition of two things, freedom and security. Now, every business wants to be more agile. They want the freedom to work with their data, wherever it happens to work best for them, whether that's on premises, in a private cloud, in public cloud or perhaps even in a new open data exchange. Now, this matters to businesses because not all data applications are created equal. Some apps are best suited to be run in the cloud because of their transitory nature. Others may be more economical if they're running a private cloud. But either way, security, regulatory compliance and increasingly data sovereignty are playing a bigger and more important role in every industry. If you don't believe me, just watch or read a recent news story. Data breaches are at an all time high and the ethics of AI applications are being called into question everyday. And understanding lineage of machine learning algorithms is now paramount for every business. So how in the heck do you get both the freedom and security that you're looking for? Well, the answer is actually pretty straightforward. The key is developing a hybrid data strategy. And what do you know, Dave, that's the business Cloudera is in. On a serious note, from Cloudera's perspective, adopting a hybrid data strategy is central to every business' digital transformation. It will enable rapid adoption of new technologies and optimize economic models, while ensuring the security and privacy of every bit of data. >> Okay, Mick, I'm glad you brought in that notion of hybrid data because when you think about things, especially remote work, it really changes a lot of the assumptions. You talked about security, the data flows are going to change. You got the economics, the physics, the local laws come into play, so what about the rest of hybrid? >> Yeah, that's a great question, Dave and certainly, Cloudera itself as a business and all of our customers are feeling this in a big way. We now have the overwhelming majority of our workforce working from home. And in other words, we've got a much larger surface area from a security perspective to keep in mind, the rate and pace of data, just generating a report that might've happened very quickly and rapidly on the office ethernet may not be happening quite so fast in somebody's rural home in the middle of Nebraska somewhere. So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it, hybrid I think is going to play a more important role in how work is conducted and what percentage of people are working in the office and are not, I know our plans, Dave, involve us kind of slowly coming back to work, beginning this fall. And we're looking forward to being able to shake hands and see one another again for the first time, in many cases, for more than a year and a half. But yes, hybrid work and hybrid data are playing an increasingly important role for every kind of business. >> Thanks for that. I wonder if we could talk about industry transformation for a moment because it's a major theme of course, of this event. So, here's how I think about it. I mean, some industries have transformed. You think about retail, for example, it's pretty clear. Although, every physical retail brand I know has not only beefed up its online presence, but they also have an Amazon war room strategy because they're trying to take greater advantage of that physical presence. And reverse, we see Amazon building out physical assets, so there's more hybrid going on. But when you look at healthcare, for example, it's just starting with such highly regulated industry. It seems that there's some hurdles there. Financial services is always been data savvy, but you're seeing the emergence of FinTech and some other challenges there in terms of control of payment systems. In manufacturing, the pandemic highlighted, America's reliance on China as a manufacturing partner and supply chain. And so my point is, it seems at different industries, they're in different stages of transformation, but two things look really clear. One, you got to put data at the core of the business model, that's compulsory. It seems like embedding AI into the applications, the data, the business process, that's going to become increasingly important. So how do you see that? >> Wow, there's a lot packed into that question there, Dave. But yeah, at Cloudera, I happened to be leading our own digital transformation as a technology company and what I would tell you there that's been an interesting process. The shift from being largely a subscription-based model to a consumption-based model requires a completely different level of instrumentation in our products and data collection that takes place in real-time, both for billing for our customers and to be able to check on the health and wellness, if you will, of their Cloudera implementations. But it's clearly not just impacting the technology industry. You mentioned healthcare and we've been helping a number of different organizations in the life sciences realm, either speed the rate and pace of getting vaccines to market or we've been assisting with testing process that's taken place. Because you can imagine the quantity of data that's been generated as we've tried to study the efficacy of these vaccines on millions of people and try to ensure that they were going to deliver great outcomes and healthy and safe outcomes for everyone. And Cloudera has been underneath a great deal of that type of work. And the financial services industry you pointed out, we continue to be central to the large banks, meeting their compliance and regulatory requirements around the globe. And in many parts of the world, those are becoming more stringent than ever. And Cloudera solutions are helping those kinds of organizations get through those difficult challenges. You also happened to mention public sector and in public sector, we're also playing a key role in working with government entities around the world and applying AI to some of the most challenging missions that those organizations face. And while I've made the kind of pivot between the industry conversation and the AI conversation, what I'll share with you about AI, I touched upon a little bit earlier. You can't build great AI, you can't build great ML apps unless you've got a strong data foundation underneath. It's back to that garbage in, garbage out comment that I made previously. And so, in order to do that, you've got to have a great hybrid data management platform at your disposal to ensure that your data is clean and organized and up to date. Just as importantly from that, that's kind of the freedom side of things. On the security side of things, you've got to ensure that you can see who's touched not just the data itself, Dave, but actually the machine learning models. And organizations around the globe are now being challenged. It's kind of on the topic of the ethics of AI to produce model lineage in addition to data lineage. In other words, who's had access to the machine learning models? When and where and at what time and what decisions were made perhaps, by the humans, perhaps by the machines that may have led to a particular outcome? So, every kind of business that is deploying AI applications should be thinking long and hard about whether or not they can track the full lineage of those machine learning models, just as they can track the lineage of data. So, lots going on there across industries. Lots going on as those various industries think about how AI can be applied to their businesses. >> It's a pretty interesting concept you're bringing into the discussion, the hybrid data, sort of, I think new to a lot of people. And this idea of model lineage is a great point because people want to talk about AI ethics, transparency of AI. When you start putting those models into machines to do real-time inferencing at the edge, it starts to get really complicated. I wonder if we could talk, we're still on that theme of industry transformation. I felt like coming into the pre-pandemic, there was just a lot of complacency. Yeah, digital transformation and a lot of buzz words and then we had this forced march to digital, but people are now being more planful, but there's still a lot of sort of POC limbo going on. How do you see that? Can you help accelerate that and get people out of that state? >> There definitely is a lot of a POC limbo or I think some of us internally have referred to as POC purgatory, just getting in that phase, not being able to get from point A to point B in digital transformation. And for every industry, transformation, change in general, is difficult and it takes time and money and thoughtfulness. But like with all things, what we've found is small wins work best and done quickly. So trying to get to quick, easy successes where you can identify a clear goal and a clear objective and then accomplish it in rapid fashion is sort of the way to build your way towards those larger transformative efforts. To say it another way, Dave, it's not wise to try to boil the ocean with your digital transformation efforts, as it relates to the underlying technology here and to bring it home a little bit more practically, I guess I would say. At Cloudera, we tend to recommend that companies begin to adopt cloud infrastructure, for example, containerization. And they begin to deploy that on-prem and then they start to look at how they may move those containerized workloads into the public cloud. That'll give them an opportunity to work with the data and the underlying applications themselves, right close to home. In place, they can kind of experiment a little bit more safely and economically and then determine which workloads are best suited for the public cloud and which ones should remain on prem. That's a way in which a hybrid data strategy can help get a digital transformation accomplished, but kind of starting small and then drawing fast from there on customer's journey to the cloud. >> Well Mick, we've covered a lot of ground. Last question, what do you want people to leave this event, this session with and thinking about sort of the next era of data that we're entering? >> Well, it's a great question, but I think it could be summed up in two words. I want them to think about a hybrid data strategy. So, really hybrid data is a concept that we're bringing forward on this show really, for the first time, arguably. And we really do think that it enables customers to experience what we refer to, Dave, as the power of ANT. That is freedom and security and in a world where we're all still trying to decide whether each day when we walk out, each building we walk into, whether we're free to come in and out with a mask, without a mask, that sort of thing, we all want freedom, but we also also want to be safe and feel safe for ourselves and for others. And the same is true of organization's IT strategies. They want the freedom to choose, to run workloads and applications in the best and most economical place possible, but they also want to do that with certainty that they're going to be able to deploy those applications in a safe and secure way that meets the regulatory requirements of their particular industry. So, hybrid data we think is key to accomplishing both freedom and security for your data and for your business as a whole. >> Nick, thanks so much, great conversation. I really appreciate the insights that you're bringing to this event, into the industry, really. Thank you for your time. >> You bet, Dave, pleasure being with you.

(lively peaceful music) >> Live, from New York, it's The Cube. Covering "The Cube New York City 2018." Brought to you by SiliconANGLE Media and its ecosystem partners. >> Well, everyone, welcome back to The Cube special conversation here in New York City. We're live for Cube NYC. This is our ninth year covering the big data ecosystem, now evolved into AI, machine learning, cloud. All things data in conjunction with Strata Conference, which is going on right around the corner. This is the Cube studio. I'm John Furrier. Dave Vellante. Our next guest is Mick Hollison, who is the CMO, Chief Marketing Officer, of Cloudera. Welcome to The Cube, thanks for joining us. >> Thanks for having me. >> So Cloudera, obviously we love Cloudera. Cube started in Cloudera's office, (laughing) everyone in our community knows that. I keep, keep saying it all the time. But we're so proud to have the honor of working with Cloudera over the years. And, uh, the thing that's interesting though is that the new building in Palo Alto is right in front of the old building where the first Palo Alto office was. So, a lot of success. You have a billboard in the airport. Amr Awadallah is saying, hey, it's a milestone. You're in the airport. But your business is changing. You're reaching new audiences. You have, you're public. You guys are growing up fast. All the data is out there. Tom's doing a great job. But, the business side is changing. Data is everywhere, it's a big, hardcore enterprise conversation. Give us the update, what's new with Cloudera. >> Yeah. Thanks very much for having me again. It's, it's a delight. I've been with the company for about two years now, so I'm officially part of the problem now. (chuckling) It's been a, it's been a great journey thus far. And really the first order of business when I arrived at the company was, like, welcome aboard. We're going public. Time to dig into the S-1 and reimagine who Cloudera is going to be five, ten years out from now. And we spent a good deal of time, about three or four months, actually crafting what turned out to be just 38 total words and kind of a vision and mission statement. But the, the most central to those was what we were trying to build. And it was a modern platform for machine learning analytics in the cloud. And, each of those words, when you unpack them a little bit, are very, very important. And this week, at Strata, we're really happy on the modern platform side. We just released Cloudera Enterprise Six. It's the biggest release in the history of the company. There are now over 30 open-source projects embedded into this, something that Amr and Mike could have never imagined back in the day when it was just a couple of projects. So, a very very large and meaningful update to the platform. The next piece is machine learning, and Hilary Mason will be giving the kickoff tomorrow, and she's probably forgotten more about ML and AI than somebody like me will ever know. But she's going to give the audience an update on what we're doing in that space. But, the foundation of having that data management platform, is absolutely fundamental and necessary to do good machine learning. Without good data, without good data management, you can't do good ML or AI. Sounds sort of simple but very true. And then the last thing that we'll be announcing this week, is around the analytics space. So, on the analytic side, we announced Cloudera Data Warehouse and Altus Data Warehouse, which is a PaaS flavor of our new data warehouse offering. And last, but certainly not least, is just the "optimize for the cloud" bit. So, everything that we're doing is optimized not just around a single cloud but around multi-cloud, hybrid-cloud, and really trying to bridge that gap for enterprises and what they're doing today. So, it's a new Cloudera to say the very least, but it's all still based on that core foundation and platform that, you got to know it, with very early on. >> And you guys have operating history too, so it's not like it's a pivot for Cloudera. I know for a fact that you guys had very large-scale customers, both with three letter, letters in them, the government, as well as just commercial. So, that's cool. Question I want to ask you is, as the conversation changes from, how many clusters do I have, how am I storing the data, to what problems am I solving because of the enterprises. There's a lot of hard things that enterprises want. They want compliance, all these, you know things that have either legacy. You guys work on those technical products. But, at the end of the day, they want the outcomes, they want to solve some problems. And data is clearly an opportunity and a challenge for large enterprises. What problems are you guys going after, these large enterprises in this modern platform? What are the core problems that you guys knock down? >> Yeah, absolutely. It's a great question. And we sort of categorize the way we think about addressing business problems into three broad categories. We use the terms grow, connect, and protect. So, in the "grow" sense, we help companies build or find new revenue streams. And, this is an amazing part of our business. You see it in everything from doing analytics on clickstreams and helping people understand what's happening with their web visitors and the like, all the way through to people standing up entirely new businesses based simply on their data. One large insurance provider that is a customer of ours, as an example, has taken on the challenge and asked us to engage with them on building really, effectively, insurance as a service. So, think of it as data-driven insurance rates that are gauged based on your driving behaviors in real time. So no longer simply just using demographics as the way that you determine, you know, all 18-year old young men are poor drivers. As it turns out, with actual data you can find out there's some excellent 18 year olds. >> Telematic, not demographics! >> Yeah, yeah, yeah, exactly! >> That Tesla don't connect to the >> Exactly! And Parents will love this, love this as well, I think. So they can find out exactly how their kids are really behaving by the way. >> They're going to know I rolled through the stop signs in Palo Alto. (laughing) My rates just went up. >> Exactly, exactly. So, so helping people grow new businesses based on their data. The second piece is "Connect". This is not just simply connecting devices, but that's a big part of it, so the IOT world is a big engine for us there. One of our favorite customer stories is a company called Komatsu. It's a mining manufacturer. Think of it as the ones that make those, just massive mines that are, that are all over the world. They're particularly big in Australia. And, this is equipment that, when you leave it sit somewhere, because it doesn't work, it actually starts to sink into the earth. So, being able to do predictive maintenance on that level and type and expense of equipment is very valuable to a company like Komatsu. We're helping them do that. So that's the "Connect" piece. And last is "Protect". Since data is in fact the new oil, the most valuable resource on earth, you really need to be able to protect it. Whether that's from a cyber security threat or it's just meeting compliance and regulations that are put in place by governments. Certainly GDPR is got a lot of people thinking very differently about their data management strategies. So we're helping a number of companies in that space as well. So that's how we kind of categorize what we're doing. >> So Mick, I wonder if you could address how that's all affected the ecosystem. I mean, one of the misconceptions early on was that Hadoop, Big Data, is going to kill the enterprise data warehouse. NoSQL is going to knock out Oracle. And, Mike has always said, "No, we are incremental". And people are like, "Yeah, right". But that's really, what's happened here. >> Yes. >> EDW was a fundamental component of your big data strategies. As Amr used to say, you know, SQL is the killer app for, for big data. (chuckling) So all those data sources that have been integrated. So you kind of fast forward to today, you talked about IOT and The Edge. You guys have announced, you know, your own data warehouse and platform as a service. So you see this embracing in this hybrid world emerging. How has that affected the evolution of your ecosystem? >> Yeah, it's definitely evolved considerably. So, I think I'd give you a couple of specific areas. So, clearly we've been quite successful in large enterprises, so the big SI type of vendors want a, want a piece of that action these days. And they're, they're much more engaged than they were early days, when they weren't so sure all of this was real. >> I always say, they like to eat at the trough and then the trough is full, so they dive right in. (all laughing) They're definitely very engaged, and they built big data practices and distinctive analytics practices as well. Beyond that, sort of the developer community has also begun to shift. And it's shifted from simply people that could spell, you know, Hive or could spell Kafka and all of the various projects that are involved. And it is elevated, in particular into a data science community. So one of additional communities that we sort of brought on board with what we're doing, not just with the engine and SPARK, but also with tools for data scientists like Cloudera Data Science Workbench, has added that element to the community that really wasn't a part of it, historically. So that's been a nice add on. And then last, but certainly not least, are the cloud providers. And like everybody, they're, those are complicated relationships because on the one hand, they're incredibly valuable partners to it, certainly both Microsoft and Amazon are critical partners for Cloudera, at the same time, they've got competitive offerings. So, like most successful software companies there's a lot of coopetition to contend with that also wasn't there just a few years ago when we didn't have cloud offerings, and they didn't have, you know, data warehouse in the cloud offerings. But, those are things that have sort of impacted the ecosystem. >> So, I've got to ask you a marketing question, since you're the CMO. By the way, great message UL. I like the, the "grow, connect, protect." I think that's really easy to understand. >> Thank you. >> And the other one was modern. The phrase, say the phrase again. >> Yeah. It's the "Cloudera builds the modern platform for machine learning analytics optimized for the cloud." >> Very tight mission statement. Question on the name. Cloudera. >> Mmhmm. >> It's spelled, it's actually cloud with ERA in the letters, so "the cloud era." People use that term all the time. We're living in the cloud era. >> Yes. >> Cloud-native is the hottest market right now in the Linux foundation. The CNCF has over two hundred and forty members and growing. Cloud-native clearly has indicated that the new, modern developers here in the renaissance of software development, in general, enterprises want more developers. (laughs) Not that you want to be against developers, because, clearly, they're going to hire developers. >> Absolutely. >> And you're going to enable that. And then you've got the, obviously, cloud-native on-premise dynamic. Hybrid cloud and multi-cloud. So is there plans to think about that cloud era, is it a cloud positioning? You see cloud certainly important in what you guys do, because the cloud creates more compute, more capabilities to move data around. >> Sure. >> And (laughs) process it. And make it, make machine learning go faster, which gives more data, more AI capabilities, >> It's the flywheel you and I were discussing. >> It's the flywheel of, what's the innovation sandwich, Dave? You know? (laughs) >> A little bit of data, a little bit of machine itelligence, in the cloud. >> So, the innovation's in play. >> Yeah, Absolutely. >> Positioning around Cloud. How are you looking at that? >> Yeah. So, it's a fascinating story. You were with us in the earliest days, so you know that the original architecture of everything that we built was intended to be run in the public cloud. It turns out, in 2008, there were exactly zero customers that wanted all of their data in a public cloud environment. So the company actually pivoted and re-architected the original design of the offerings to work on-prim. And, no sooner did we do that, then it was time to re-architect it yet again. And we are right in the midst of doing that. So, we really have offerings that span the whole gamut. If you want to just pick up you whole current Cloudera environment in an infrastructure as a service model, we offer something called Altus Director that allows you to do that. Just pick up the entire environment, step it up onto AWUS, or Microsoft Azure, and off you go. If you want the convenience and the elasticity and the ease of use of a true platform as a service, just this past week we announced Altus Data Warehouse, which is a platform as a service kind of a model. For data warehousing, we have the data engineering module for Altus as well. Last, but not least, is everybody's not going to sign up for just one cloud vendor. So we're big believers in multi-cloud. And that's why we support the major cloud vendors that are out there. And, in addition to that, it's going to be a hybrid world for as far out as we can see it. People are going to have certain workloads that, either for economics or for security reasons, they're going to continue to want to run in-house. And they're going to have other workloads, certainly more transient workloads, and I think ML and data science will fall into this camp, that the public cloud's going to make a great deal of sense. And, allowing companies to bridge that gap while maintaining one security compliance and management model, something we call a Shared Data Experience, is really our core differentiator as a business. That's at the very core of what we do. >> Classic cloud workload experience that you're bringing, whether it's on-prim or whatever cloud. >> That's right. >> Cloud is an operating environment for you guys. You look at it just as >> The delivery mechanism. In effect. Awesome. All right, future for Cloudera. What can you share with us. I know you're a public company. Can't say any forward-looking statements. Got to do all those disclaimers. But for customers, what's the, what's the North Star for Cloudera? You mentioned going after a much more hardcore enterprise. >> Yes. >> That's clear. What's the North Star for you guys when you talk to customers? What's the big pitch? >> Yeah. I think there's a, there's a couple of really interesting things that we learned about our business over the course of the past six, nine months or so here. One, was that the greatest need for our offerings is in very, very large and complex enterprises. They have the most data, not surprisingly. And they have the most business gain to be had from leveraging that data. So we narrowed our focus. We have now identified approximately five thousand global customers, so think of it as kind of Fortune or Forbes 5000. That is our sole focus. So, we are entirely focused on that end of the market. Within that market, there are certain industries that we play particularly well in. We're incredibly well-positioned in financial services. Very well-positioned in healthcare and telecommunications. Any regulated industry, that really cares about how they govern and maintain their data, is really the great target audience for us. And so, that continues to be the focus for the business. And we're really excited about that narrowing of focus and what opportunities that's going to build for us. To not just land new customers, but more to expand our existing ones into a broader and broader set of use cases. >> And data is coming down faster. There's more data growth than ever seen before. It's never stopping.. It's only going to get worse. >> We love it. >> Bring it on. >> Any way you look at it, it's getting worse or better. Mick, thanks for spending the time. I know you're super busy with the event going on. Congratulations on the success, and the focus, and the positioning. Appreciate it. Thanks for coming on The Cube. >> Absolutely. Thank you gentlemen. It was a pleasure. >> We are Cube NYC. This is our ninth year doing all action. Everything that's going on in the data world now is horizontally scaling across all aspects of the company, the society, as we know. It's super important, and this is what we're talking about here in New York. This is The Cube, and John Furrier. Dave Vellante. Be back with more after this short break. Stay with us for more coverage from New York City. (upbeat music)