(upbeat music) >> Welcome back everyone to The Cubes coverage here in Seattle, Washington. For AWS's Marketplace Seller Conference. It's the big news within the Amazon partner network, combining with marketplace, forming the Amazon partner organization. Part of a big reorg as they grow to the next level, NextGen cloud, mid-game on the chessboard. Cube's got it covered. I'm John Furry, your host at Cube. Great guests here from Data bricks. Both cube alumni's. Jack Anderson, GM and VP of the Databricks partnership team for AWS. You handle that relationship and Joel Minick vice president of product and partner marketing. You guys have the keys to the kingdom with Databricks and AWS. Thanks for joining. Good to see you again. >> Thanks for having us back. >> Yeah, John, great to be here. >> So I feel like we're at Reinvent 2013. Small event, no stage, but there's a real shift happening with procurement. Obviously it's a no brainer on the micro, you know, people should be buying online. Self-service, Cloud Scale. But Amazon's got billions being sold through their marketplace. They've reorganized their partner network. You can see kind of what's going on. They've kind of figured it out. Like let's put everything together and simplify and make it less of a website, marketplace. Merge our partner organizations, have more synergy and frictionless experiences so everyone can make more money and customer's are going to be happier. >> Yeah, that's right. >> I mean, you're running relationship. You're in the middle of it. >> Well, Amazon's mental model here is that they want the world's best ISVs to operate on AWS so that we can collaborate and co architect on behalf of customers. And that's exactly what the APO and marketplace allow us to do, is to work with Amazon on these really, you know, unique use cases. >> You know, I interviewed Ali many times over the years. I remember many years ago, maybe six, seven years ago, we were talking. He's like, "we're all in on AWS." Obviously now the success of Databricks, you've got multiple clouds, see that. Customers have choice. But I remember the strategy early on. It was like, we're going to be deep. So this is, speaks volumes to the relationship you have. Years. Jack, take us through the relationship that Databricks has with AWS from a partner perspective. Joel, and from a product perspective. Because it's not like you guys are Johnny come lately, new to the scene. >> Right. >> You've been there, almost president creation of this wave. What's the relationship and how does it relate to what's going on today? >> So most people may not know that Databricks was born on AWS. We actually did our first $100 million of revenue on Amazon. And today we're obviously available on multiple clouds. But we're very fond of our Amazon relationship. And when you look at what the APN allows us to do, you know, we're able to expand our reach and co-sell with Amazon, and marketplace broadens our reach. And so, we think of marketplace in three different aspects. We've got the marketplace private offer business, which we've been doing for a number of years. Matter of fact, we were driving well over a hundred percent year over year growth in private offers. And we have a nine figure business. So it's a very significant business. And when a customer uses a private offer, that private offer counts against their private pricing agreement with AWS. So they get pricing power against their private pricing. So it's really important it goes on their Amazon bill. In may we launched our pay as you go, on demand offering. And in five short months, we have well over a thousand subscribers. And what this does, is it really reduces the barriers to entry. It's low friction. So anybody in an enterprise or startup or public sector company can start to use Databricks on AWS, in a consumption based model, and have it go against their monthly bill. And so we see customers, you know, doing rapid experimentation, pilots, POCs. They're really learning the value of that first, use case. And then we see rapid use case expansion. And the third aspect is the consulting partner, private offer, CPPO. Super important in how we involve our partner ecosystem of our consulting partners and our resellers that are able to work with Databricks on behalf of customers. >> So you got the big contracts with the private offer. You got the product market fit, kind of people iterating with data, coming in with the buyers you get. And obviously the integration piece all fitting in there. >> Exactly. >> Okay, so those are the offers, that's current, what's in marketplace today. Is that the products... What are people buying? >> Yeah. >> I mean, I guess what's the... Joel, what are people buying in the marketplace? And what does it mean for them? >> So fundamentally what they're buying is the ability to take silos out of their organization. And that is the problem that Databricks is out there to solve. Which is, when you look across your data landscape today, you've got unstructured data, you've got structured data, you've got real time streaming data. And your teams are trying to use all of this data to solve really complicated problems. And as Databricks, as the Lakehouse Company, what we're helping customers do is, how do they get into the new world? How do they move to a place where they can use all of that data across all of their teams? And so we allow them to begin to find, through the marketplace, those rapid adoption use cases where they can get rid of these data warehousing, data lake silos they've had in the past. Get their unstructured and structured data onto one data platform, an open data platform, that is no longer adherent to any proprietary formats and standards and something they can, very much, very easily, integrate into the rest of their data environment. Apply one common data governance layer on top of that. So that from the time they ingest that data, to the time they use that data, to the time they share that data, inside and outside of their organization, they know exactly how it's flowing. They know where it came from. They know who's using it. They know who has access to it. They know how it's changing. And then with that common data platform, with that common governance solution, they'd being able to bring all of those use cases together. Across their real time streaming, their data engineering, their BI, their AI. All of their teams working on one set of data. And that lets them move really, really fast. And it also lets them solve challenges they just couldn't solve before. A good example of this, you know, one of the world's now largest data streaming platforms runs on Databricks with AWS. And if you think about what does it take to set that up? Well, they've got all this customer data that was historically inside of data warehouses. That they have to understand who their customers are. They have all this unstructured data, they've built their data science model, so they can do the right kinds of recommendation engines and forecasting around. And then they've got all this streaming data going back and forth between click stream data, from what the customers are doing with their platform and the recommendations they want to push back out. And if those teams were all working in individual silos, building these kinds of platforms would be extraordinarily slow and complex. But by building it on Databricks, they were able to release it in record time and have grown at a record pace to now be the number one platform. >> And this product, it's impacting product development. >> Absolutely. >> I mean, this is like the difference between lagging months of product development, to like days. >> Yes. >> Pretty much what you're getting at. >> Yes. >> So total agility. >> Mm-hmm. >> I got that. Okay, now, I'm a customer I want to buy in the marketplace, but you got direct Salesforce up there. So how do you guys look at this? Is there channel conflict? Are there comp programs? Because one of the things I heard today in on the stage from AWS's leadership, Chris, was up there speaking, and Mona was, "Hey, he's a CRO conference chief revenue officer" conversation. Which means someone's getting compensated. So, if I'm the sales rep at Databricks, what's my motion to the customer? Do I get paid? Does Amazon sell it? Take us through that. Is there channel conflict? Or, how do you handle it? >> Well, I'd add what Joel just talked about with, you know, with the solution, the value of the solution our entire offering is available on AWS marketplace. So it's not a subset, it's the entire Data Bricks offering. And- >> The flagship, all the, the top stuff. >> Everything, the flagship, the complete offering. So it's not segmented. It's not a sub segment. >> Okay. >> It's, you know, you can use all of our different offerings. Now when it comes to seller compensation, we view this two different ways, right? One is that AWS is also incented, right? Versus selling a native service to recommend Databricks for the right situation. Same thing with Databricks, our sales force wants to do the right thing for the customer. If the customer wants to use marketplace as their procurement vehicle. And that really helps customers because if you get Databricks and five other ISVs together, and let's say each ISV is spending, you're spending a million dollars. You have $5 million of spend. You put that spend through the flywheel with AWS marketplace, and then you can use that in your negotiations with AWS to get better pricing overall. So that's how we view it. >> So customers are driving. This sounds like. >> Correct. For sure. >> So they're looking at this as saying, Hey, I'm going to just get purchasing power with all my relationships. Because it's a solution architectural market, right? >> Yeah. It makes sense. Because if most customers will have a primary and secondary cloud provider. If they can consolidate, you know, multiple ISV spend through that same primary provider, you get pricing power. >> Okay, Joel, we're going to date ourselves. At least I will. So back in the old days, (group laughter) It used to be, do a Barney deal with someone, Hey, let's go to market together. You got to get paper, you do a biz dev deal. And then you got to say, okay, now let's coordinate our sales teams, a lot of moving parts. So what you're getting at here is that the alternative for Databricks, or any company is, to go find those partners and do deals, versus now Amazon is the center point for the customer. So you can still do those joint deals, but this seems to be flipping the script a little bit. >> Well, it is, but we still have vars and consulting partners that are doing implementation work. Very valuable work, advisory work, that can actually work with marketplace through the CPPO offering. So the marketplace allows multiple ways to procure your solution. >> So it doesn't change your business structure. It just makes it more efficient. >> That's correct. >> That's a great way to say it. >> Yeah, that's great. >> Okay. So, that's it. So that's just makes it more efficient. So you guys are actually incented to point customers to the marketplace. >> Yes. >> Absolutely. >> Economically. >> Economically, it's the right thing to do for the customer. It's the right thing to do for our relationship with Amazon. Especially when it comes back to co-selling, right? Because Amazon now is leaning in with ISVs and making recommendations for, you know, an ISV solution. And our teams are working backwards from those use cases, you know, to collaborate and land them. >> Yeah. I want to get that out there. Go ahead, Joel. >> So one of the other things I might add to that too, you know, and why this is advantageous for companies like Databricks to work through the marketplace. Is it makes it so much easier for customers to deploy a solution. It's very, literally, one click through the marketplace to get Databricks stood up inside of your environment. And so if you're looking at how do I help customers most rapidly adopt these solutions in the AWS cloud, the marketplace is a fantastic accelerator to that. >> You know, it's interesting. I want to bring this up and get your reaction to it because to me, I think this is the future of procurement. So from a procurement standpoint, I mean, again, dating myself, EDI back in the old days, you know, all that craziness. Now this is all the internet, basically through the console. I get the infrastructure side, you know, spin up and provision some servers, all been good. You guys have played well there in the marketplace. But now as we get into more of what I call the business apps, and they brought this up on stage. A little nuanced. Most enterprises aren't yet there of integrating tech, on the business apps, into the stack. This is where I think you guys are a use case of success where you guys have been successful with data integration. It's an integrators dilemma, not an innovator's dilemma. So like, I want to integrate. So now I have integration points with Databricks, but I want to put an app in there. I want to provision an application, but it has to be built. It's not, you don't buy it. You build, you got to build stuff. And this is the nuance. What's your reaction to that? Am I getting this right? Or am I off because, no one's going to be buying software like they used to. They buy software to integrate it. >> Yeah, no- >> Because everything's integrated. >> I think AWS has done a great job at creating a partner ecosystem, right? To give customers the right tools for the right jobs. And those might be with third parties. Databricks is doing the same thing with our partner connect program, right? We've got customer partners like Five Tran and DBT that, you know, augment and enhance our platform. And so you're looking at multi ISV architectures and all of that can be procured through the AWS marketplace. >> Yeah. It's almost like, you know, bundling and un bundling. I was talking about this with, with Dave Alante about Supercloud. Which is why wouldn't a customer want the best solution in their architecture? Period. In its class. If someone's got API security or an API gateway. Well, you know, I don't want to be forced to buy something because it's part of a suite. And that's where you see things get sub optimized. Where someone dominates a category and they have, oh, you got to buy my version of this. >> Joel and I were talking, we were actually saying, what's really important about Databricks, is that customers control the data, right? You want to comment on that? >> Yeah. I was going to say, you know, what you're pushing on there, we think is extraordinarily, you know, the way the market is going to go. Is that customers want a lot of control over how they build their data stack. And everyone's unique in what tools are the right ones for them. And so one of the, you know, philosophically, I think, really strong places, Databricks and AWS have lined up, is we both take an approach that you should be able to have maximum flexibility on the platform. And as we think about the Lakehouse, one thing we've always been extremely committed to, as a company, is building the data platform on an open foundation. And we do that primarily through Delta Lake and making sure that, to Jack's point, with Databricks, the data is always in your control. And then it's always stored in a completely open format. And that is one of the things that's allowed Databricks to have the breadth of integrations that it has with all the other data tools out there. Because you're not tied into any proprietary format, but instead are able to take advantage of all the innovation that's happening out there in the open source ecosystem. >> When you see other solutions out there that aren't as open as you guys, you guys are very open by the way, we love that too. We think that's a great strategy, but what am I foreclosing if I go with something else that's not as open? What's the customer's downside as you think about what's around the corner in the industry? Because if you believe it's going to be open, open source, which I think open source software is the software industry, and integration is a big deal. Because software's going to be plentiful. >> Sure. >> Let's face it. It's a good time to be in software business. But Cloud's booming. So what's the downside, from your Databricks perspective? You see a buyer clicking on Databricks versus that alternative. What's potentially should they be a nervous about, down the road, if they go with a more proprietary or locked in approach? >> Yeah. >> Well, I think the challenge with proprietary ecosystems is you become beholden to the ability of that provider to both build relationships and convince other vendors that they should invest in that format. But you're also, then, beholden to the pace at which that provider is able to innovate. >> Mm-hmm. >> And I think we've seen lots of times over history where, you know, a proprietary format may run ahead, for a while, on a lot of innovation. But as that market control begins to solidify, that desire to innovate begins to degrade. Whereas in the open formats- >> So extract rents versus innovation. (John laughs) >> Exactly. Yeah, exactly. >> I'll say it. >> But in the open world, you know, you have to continue to innovate. >> Yeah. >> And the open source world is always innovating. If you look at the last 10 to 15 years, I challenge you to find, you know, an example where the innovation in the data and AI world is not coming from open source. And so by investing in open ecosystems, that means you are always going to be at the forefront of what is the latest. >> You know, again, not to date myself again, but you look back at the eighties and nineties, the protocol stacked with proprietary. >> Yeah. >> You know, SNA and IBM, deck net was digital. You know the rest. And then TCPIP was part of the open systems interconnect. >> Mm-hmm. >> Revolutionary (indistinct) a big part of that, as well as my school did. And so like, you know, that was, but it didn't standardize the whole stack. It stopped at IP and TCP. >> Yeah. >> But that helped inter operate, that created a nice defacto. So this is a big part of this mid game. I call it the chessboard, you know, you got opening game and mid-game, then you get the end game. You're not there at the end game yet at Cloud. But Cloud- >> There's, always some form of lock in, right? Andy Jazzy will address it, you know, when making a decision. But if you're going to make a decision you want to reduce- You don't want to be limited, right? So I would advise a customer that there could be limitations with a proprietary architecture. And if you look at what every customer's trying to become right now, is an AI driven business, right? And so it has to do with, can you get that data out of silos? Can you organize it and secure it? And then can you work with data scientists to feed those models? >> Yeah. >> In a very consistent manner. And so the tools of tomorrow will, to Joel's point, will be open and we want interoperability with those tools. >> And choice is a matter too. And I would say that, you know, the argument for why I think Amazon is not as locked in as maybe some other clouds, is that they have to compete directly too. Redshift competes directly with a lot of other stuff. But they can't play the bundling game because the customers are getting savvy to the fact that if you try to bundle an inferior product with something else, it may not work great at all. And they're going to be, they're onto it. This is the- >> To Amazon's credit by having these solutions that may compete with native services in marketplace, they are providing customers with choice, low price- >> And access to the core value. Which is the hardware- >> Exactly. >> Which is their platform. Okay. So I want to get you guys thought on something else I see emerging. This is, again, kind of Cube rumination moment. So on stage, Chris unpacked a lot of stuff. I mean this marketplace, they're touching a lot of hot buttons here, you know, pricing, compensation, workflows, services behind the curtain. And one of those things he mentioned was, they talk about resellers or channel partners, depending upon what you talk about. We believe, Dave and I believe on the Cube, that the entire indirect sales channel of the industry is going to be disrupted radically. Because those players were selling hardware in the old days and software. That game is going to change. You mentioned you guys have a program, let me get your thoughts on this. We believe that once this gets set up, they can play in this game and bring their services in. Which means that the old reseller channels are going to be rewritten. They're going to be refactored with this new kinds of access. Because you've got scale, you've got money and you've got product. And you got customers coming into the marketplace. So if you're like a reseller that sold computers to data centers or software, you know, a value added reseller or VAB or business. >> You've got to evolve. >> You got to, you got to be here. >> Yes. >> Yeah. >> How are you guys working with those partners? Because you say you have a product in your marketplace there. How do I make money if I'm a reseller with Databricks, with Amazon? Take me through that use case. >> Well I'll let Joel comment, but I think it's pretty straightforward, right? Customers need expertise. They need knowhow. When we're seeing customers do mass migrations to the cloud or Hadoop specific migrations or data transformation implementations. They need expertise from consulting and SI partners. If those consulting and SI partners happen to resell the solution as well. Well, that's another aspect of their business. But I really think it is the expertise that the partners bring to help customers get outcomes. >> Joel, channel big opportunity for Amazon to reimagine this. >> For sure. Yeah. And I think, you know, to your comment about how do resellers take advantage of that, I think what Jack was pushing on is spot on. Which is, it's becoming more and more about the expertise you bring to the table. And not just transacting the software. But now actually helping customers make the right choices. And we're seeing, you know, both SIs begin to be able to resell solutions and finding a lot of opportunity in that. >> Yeah. And I think we're seeing traditional resellers begin to move into that SI model as well. And that's going to be the evolution that this goes. >> At the end of the day, it's about services, right? >> For sure. Yeah. >> I mean... >> You've got a great service. You're going to have high gross profits. >> Yeah >> Managed service provider business is alive and well, right? Because there are a number of customers that want that type of a service. >> I think that's going to be a really hot, hot button for you guys. I think being the way you guys are open, this channel, partner services model coming in, to the fold, really kind of makes for kind of that Supercloud like experience, where you guys now have an ecosystem. And that's my next question. You guys have an ecosystem going on, within Databricks. >> For sure. >> On top of this ecosystem. How does that work? This is kind of like, hasn't been written up in business school and case studies yet. This is new. What is this? >> I think, you know, what it comes down to is, you're seeing ecosystems begin to evolve around the data platforms. And that's going to be one of the big, kind of, new horizons for us as we think about what drives ecosystems. It's going to be around, well, what's the data platform that I'm using? And then all the tools that have to encircle that to get my business done. And so I think there's, you know, absolutely ecosystems inside of the AWS business on all of AWS's services, across data analytics and AI. And then to your point, you are seeing ecosystems now arise around Databricks in its Lakehouse platform as well. As customers are looking at well, if I'm standing these Lakehouses up and I'm beginning to invest in this, then I need a whole set of tools that help me get that done as well. >> I mean you think about ecosystem theory, we're living a whole nother dream. And I'm not kidding. It hasn't yet been written up and for business school case studies is that, we're now in a whole nother connective tissue, ecology thing happening. Where you have dependencies and value proposition. Economics, connectedness. So you have relationships in these ecosystems. >> And I think one of the great things about the relationships with these ecosystems, is that there's a high degree of overlap. >> Yeah. >> So you're seeing that, you know, the way that the cloud business is evolving, the ecosystem partners of Databricks, are the same ecosystem partners of AWS. And so as you build these platforms out into the cloud, you're able to really take advantage of best of breed, the broadest set of solutions out there for you. >> Joel, Jack, I love it because you know what it means? The best ecosystem will win, if you keep it open. >> Sure, sure. >> You can see everything. If you're going to do it in the dark, you know, you don't know the outcome. I mean, this is really kind of what we're talking about. >> And John, can I just add that when I was at Amazon, we had a theory that there's buyers and builders, right? There's very innovative companies that want to build things themselves. We're seeing now that that builders want to buy a platform. Right? >> Yeah. >> And so there's a platform decision being made and that ecosystem is going to evolve around the platform. >> Yeah, and I totally agree. And the word innovation gets kicked around. That's why, you know, when we had our Supercloud panel, it was called the innovators dilemma, with a slash through it, called the integrater's dilemma. Innovation is the digital transformation. So- >> Absolutely. >> Like that becomes cliche in a way, but it really becomes more of a, are you open? Are you integrating? If APIs are connective tissue, what's automation, what's the service messages look like? I mean, a whole nother set of, kind of thinking, goes on in these new ecosystems and these new products. >> And that thinking is, has been born in Delta Sharing, right? So the idea that you can have a multi-cloud implementation of Databricks, and actually share data between those two different clouds, that is the next layer on top of the native cloud solution. >> Well, Databricks has done a good job of building on top of the goodness of, and the CapEx gift from AWS. But you guys have done a great job taking that building differentiation into the product. You guys have great customer base, great growing ecosystem. And again, I think a shining example of what every enterprise is going to do. Build on top of something, operating model, get that operating model, driving revenue. >> Mm-hmm. >> Yeah. >> Whether, you're Goldman Sachs or capital one or XYZ corporation. >> S and P global, NASDAQ. >> Yeah. >> We've got, you know, the biggest verticals in the world are solving tough problems with Databricks. I think we'd be remiss because if Ali was here, he would really want to thank Amazon for all of the investments across all of the different functions. Whether it's the relationship we have with our engineering and service teams. Our marketing teams, you know, product development. And we're going to be at Reinvent. A big presence at Reinvent. We're looking forward to seeing you there, again. >> Yeah. We'll see you guys there. Yeah. Again, good ecosystem. I love the ecosystem evolutions happening. This NextGen Cloud is here. We're seeing this evolve, kind of new economics, new value propositions kind of scaling up. Producing more. So you guys are doing a great job. Thanks for coming on the Cube and taking the time. Joel, great to see you at the check. >> Thanks for having us, John. >> Okay. Cube coverage here. The world's changing as APN comes together with the marketplace for a new partner organization at Amazon web services. The Cube's got it covered. This should be a very big, growing ecosystem as this continues. Billions of being sold through the marketplace. And of course the buyers are happy as well. So we've got it all covered. I'm John Furry. your host of the cube. Thanks for watching. (upbeat music)

(upbeat electronic music) >> Welcome back to everyone, to "theCUBE's" coverage here in Seattle, Washington for Amazon Web Services Partner Marketplace Seller Conference, combining their partner network with Marketplace forming a new organization called AWS Partner Organization. This is "theCUBE" coverage. I'm John Furrier, your host. We've got great "Cube" alumni here from Zscaler, a very successful cloud company doing great work. Stelio D'Alo, senior director of cloud business development and Raveesh Chugh, VP of Public Cloud Partnerships at Zscaler. Welcome back to "theCUBE." Good to see you guys. Thanks for coming on. >> Thank you. >> Thanks having us, John. >> So we've been doing a lot of coverage of Zscaler, what a great success story. I mean, the numbers are great. The business performance, it's in the top two, three, one, two, three in all metrics on public companies, SaaS. So you guys, check. Good job. >> Yes, thank you. >> So you guys have done a good job. Now you're here, selling through the Marketplace. You guys are a world class performing company in cloud SaaS, so you're in the front lines doing well. Now, Marketplace is a procurement front end opportunity for people to buy. Hey, self-service, buy and put things together. Sounds novel, what a great concept. Great cloud life. >> Yes. >> You guys are participating and now sellers are coming together. The merger of the public, the partner network with Marketplace. It feels like this is a second act for AWS to go to the next level. They got their training wheels done with partners. Now they're going to the next level. What do you guys think about this? >> Well, I think you're right, John. I think it is very much something that is in keeping with the way AWS does business. Very Amazonian, they're working back from the customer. What we're seeing is, our customers and in general, the market is gravitating towards purchase mechanisms and route to market that just are lower friction. So in the same way that companies are going through their digital transformations now, really modernizing the way they host applications and they reach the internet. They're also modernizing on the purchasing side, which is super exciting, because we're all motivated to help customers with that agility. >> You know, it's fun to watch and again I'm being really candid and props to you guys as a company. Now, everyone else is kind of following that. Okay, lift and shift, check, doing some things. Now they go, whoa, I can really build on this. People are building their own apps for their companies. Going to build their own stuff. They're going to use piece parts. They're going to put it together in a really scalable way. That's the new normal. Okay, so now they go okay, I'm going to just buy through the market, I get purchasing power. So you guys have been a real leader with AWS. Can you share what you guys are doing in the Marketplace? I think you guys are a nice example of how to execute the Marketplace. Take us through. What are you guys offering there? What's the contract look like? Is it multi-pronged? What's the approach? What do customers get if they go to the marketplace for Zscaler? >> Yeah, so it's been a very exciting story and been a very pleasing one for us with AWS marketplace. We see a huge growth potentially. There are more than 350,000 customers that are actively buying through Marketplace today. We expect that number to grow to around a million customers by the next, I would say, five to ten years and we want to be part of this wave. We see AWS Marketplace to be a channel where not only our resalers or our channel partners can come and transact, but also our GSIs like Accenture want to transact through this channel. We are doing a lot, in terms of bringing new customers through Marketplace, who want to not only close their deals, but close it in the next few hours. That's the beauty of Marketplace, the agility, the flexibility in terms of pricing that it provides to ISVs like us. If a customer wants to delay their payments by a couple of quarters, Marketplace supports that. If a customer wants to do monthly payments, Marketplace supports that. We are seeing lot of customers, big customers, that have signed EDPs, enterprise discount plans with AWS. These are multi-year cloud commits coming to us and saying we can retire our EDPs with AWS if we transact your solution through AWS Marketplace. So what we have done, as of today, we have all of our production services enabled through AWS Marketplace. What that means for customers, they can now retire their EDPs by buying Zscaler products through AWS Marketplace and in return get the full benefit of maximizing their EDP commits with AWS. >> So you guys are fully committed, no toe on the water, as we heard. You guys are all in. >> Absolutely, that's exactly the way to put it. We're all in, all of our solutions are available in the marketplace. As you mentioned, we're a SaaS provider. So we're one of the vendors in the Marketplace that have SaaS solutions. So unlike a lot of customers and even the market in general, associate the Marketplace for historical reasons, the way it started with a lot of monthly subscriptions and just dipping your toe in it from a consumer perspective. Whereas we're doing multimillion dollar, multi-year SaaS contracts. So the most complicated kinds of transactions you'd normally associate with enterprise software, we're doing in very low friction ways. >> On the Zscaler side going in low friction. >> Yep, yeah, that's right. >> How about the customer experience? >> So it is primarily the the customer that experiences. >> Driving it? >> Yeah, they're driving it and it's because rather than traditional methods of going through paperwork, purchase orders- >> What are some of the things that customers are saying about this, bcause I see two benefits, I'll say that. The friction, it's a channel, okay, for Zscaler. Let's be clear, but now you have a customer who's got a lot of Amazon. They're a trusted partner too. So why wouldn't they want to have one point of contact to use their purchasing power and you guys are okay with that. >> We're absolutely okay with it. The reason being, we're still doing the transaction and we can do the transaction with our... We're a channel first company, so that's another important distinction of how people tend to think of the Marketplace. We go through channel. A lot of our transactions are with traditional channel partners and you'd be surprised the kinds of, even the Telcos, carrier providers, are starting to embrace Marketplace. So from a customer perspective, it's less paperwork, less legal work. >> Yeah, I'd love to get your reaction to something, because I think this highlights to me what we've been reporting on with "theCUBE" with super cloud and other trends that are different in a good way. Taking it to the next level and that is that if you look at Zscaler, SaaS, SaaS is self-service, the scale, there's efficiencies. Marketplace first started out as a self-service catalog, a website, you know, click and choose, but now it's a different. He calls it a supply chain, like the CICD pipeline of buying software. He mentions that, there's also services. He put the Channel partners can come in. The GSIs, global system integrators can come in. So it's more than just a catalog now. It's kind of self-service procurement more than it is just a catalog of buy stuff. >> Yes, so yeah, I feel CEOs, CSOs of today should understand what Marketplace brings to the bear in terms of different kinds of services or Zscaler solutions that they can acquire through Marketplace and other ISV solutions, for that matter. I feel like we are at a point, after the pandemic, where there'll be a lot of digital exploration and companies can do more in terms of not just Marketplace, but also including the channel partners as part of deals. So you talked about channel conflict. AWS addressed this by bringing a program called CPPO in the picture, Channel Partner Private Offers. What that does is, we are not only bringing all our channel partners into deals. For renewals as well, they're the partner of record and they get paid alongside with the customer. So AWS does all the heavy lifting, in terms of disbursements of payments to us, to the channel partner, so it's a win-win situation for all. >> I mean, private offers and co-sale has been very popular. >> It has been, and that is our bread and butter in the Marketplace. Again, we do primarily three year contracts and so private offers work super well. A nice thing for us as a vendor is it provides a great amount of flexibility. Private Offer gives you a lot of optionality, in terms of how the constructs of the deal and whether or not you're working with a partner, how the partner is utilizing as well to resell to the end user. So, we've always talked about AWS giving IT agility. This gives purchasing and finance business agility. >> Yeah, and I think this comes up a lot. I just noticed this happening a lot more, where you see dedicated sessions, not just on DevOps and all the goodies of the cloud, financial strategy. >> Yeah. >> Seeing a lot more conversation around how to operationalize the business transactions in the cloud. >> Absolutely. >> This is the new, I mean it's not new, it's been thrown around, but not at a tech conference. You don't see that. So I got to ask you guys, what's the message to the CISOs and executives watching the business people about Zscaler in the Marketplace? What should they be looking at? What is the pitch for Zscaler for the Marketplace buyer? >> So I would say that we are a cloud-delivered network security service. We have been in this game for more than a decade. We have years of early head start with lots of features and functionality versus our competitors. If customers were to move into AWS Cloud, they can get rid of their next-gen firewalls and just have all the traffic routed through our Zscaler internet access and use Zscaler private access for accessing their private applications. We feel we have done everything in our capacity, in terms of enabling customers through Marketplace and will continue to participate in more features and functionality that Marketplace has to offer. We would like these customers to take advantage of their EDPs as well as their retirement and spend for the multi-commit through AWS Marketplace. Learn about what we have to offer and how we can really expedite the motion for them, if they want to procure our solutions through Marketplace >> You know, we're seeing an ability for them to get more creative, more progressive in terms of the purchasing. We're also doing, we're really excited about the ability to serve multiple markets. So we've had an immense amount of success in commercial. We also are seeing increasing amount of public sector, US federal government agencies that want to procure this way as well for the same reasons. So there's a lot of innovation going on. >> So you have the FedRAMP going on, you got all those certifications. >> Exactly right. So we are the first cloud-native solution to provide IL5 ATO, as well as FedRAMP pie and we make that all available, GSA schedule pricing through the AWS Marketplace, again through FSIs and other resellers. >> Public private partnerships have been a big factor, having that span of capability. I got to ask you about, this is a cool conversation, because now you're like, okay, I'm selling through the Marketplace. Companies themselves are changing how they operate. They don't just buy software that we used to use. So general purpose, bundled stuff. Oh yeah, I'm buying this product, because this has got a great solution and I have to get forced to use this firewall, because I bought this over here. That's not how companies are architecting and developing their businesses. It's no longer buying IT. They're building their company digitally. They have to be the application. So they're not sitting around, saying hey, can I get a solution? They're building and architecting their solution. This is kind of like the new enterprise that no one's talking about. They kind of, got to do their own work. >> Yes. >> There's no general purpose solution that maps every company. So they got to pick the best piece parts and integrate them. >> Yes and I feel- >> Do you guys agree with that? >> Yeah, I agree with that and customers don't want to go for point solutions anymore. They want to go with a platform approach. They want go with a vendor that can not only cut down their vendors from multi-dozens to maybe a dozen or less and that's where, you know, we kind of have pivoted to the platform-centric approach, where we not only help customers with Cloud Network Security, but we also help customers with Cloud Native Application Protection Platform that we just recently launched. It's going by the name of the different elements, including Cloud Security Posture Management, Cloud Identity Event Management and so we are continuously doing more and more on the configuration and vulnerability side space. So if a customer has an AWS S3 bucket that is opened it can be detected and can be remediated. So all of those proactive steps we are taking, in terms of enhancing our portfolio, but we have come a long way as a company, as a platform that we have evolved in the Marketplace. >> What's the hottest product? >> The hottest product? >> In Marketplace right now. >> Well, the fastest growing products include our digital experience products and we have new Cloud Protection. So we've got Posture and Workload Protection as well and those are the fastest growing. For AWS customers a strong affinity also for ZPA, which provides you zero trust access to your workloads on AWS. So those are all the most popular in Marketplace. >> Yeah. >> So I would like to add that we recently launched and this has been a few years, a couple of years. We launched a product called Zscaler Digital X, the ZDX. >> Mm-hmm. >> What that product does is, let's say you're making a Zoom call and your WiFi network is laggy or it's a Zoom server that's laggy. It kind of detects where is the problem and it further tells the IT department you need to fix either the server on which Zoom is running, or fix your home network. So that is the beauty of the product. So I think we are seeing massive growth with some of our new editions in the portfolio, which is a long time coming. >> Yeah and certainly a lot of growth opportunities for you guys, as you come in. Where do you see Zscaler's big growth coming from product-wise? What's the big push? Actually, this is great upside for you here. >> Yeah. >> On the go to market side. Where's the big growth for Zscaler right now? So I think we are focused as a company on zero trust architecture. We want to securely connect users to apps, apps to apps, workloads to workloads and machines to machines. We want to give customers an experience where they have direct access to the apps that's hidden from the outside world and they can securely connect to the apps in a very succinct fashion. The user experience is second to none. A lot of customers use us on the Microsoft Office 365 side, where they see a lag in connecting to Microsoft Office 365 directly. They use the IE service to securely connect. >> Yeah, latency kills. >> Microsoft Office 365. >> Latency kills, as we always say, you know and security, you got to look at the pattern, you want to see that data. >> Yeah, and emerging use cases, there is an immense amount of white space and upside for us as well in emerging use cases, like OT, 5G, IOT. >> Yeah. >> Federal government, DOD. >> Oh god, tactical edge government. >> Security at the edge, absolutely, yeah. >> Where's the big edge? What's the edge challenge right now, if you have to put your finger on the edge, because right now that's the hot area, we're watching that. It's going to be highly contested. It's not yet clear, I mean certainly hybrid is the operating model, cloud, distributing, computing, but edge has got unique things that you can't really point to on premises that's the same. It's highly dynamic, you need high bandwidth, low latency, compute at the edge. The data has to be processed right there. What's the big thing at the edge right now? >> Well, so that's probably an emerging answer. I mean, we're working with our customers, they're inventing and they're kind of finding the use cases for those edge, but one of the good things about Zscaler is that we are able to, we've got low latency at the edge. We're able to work as a computer at the edge. We work on Outpost, Snowball, Snowcone, the Snow devices. So we can be wherever our customers need us. Mobile devices, there are a lot of applications where we've got to be either on embedded devices, on tractors, providing security for those IOT devices. So we're pretty comfortable with where we are being the- >> So that's why you guys are financially doing so well, performance wise. I got to ask you though, because I think that brings up the great point. If this is why I like the Marketplace, if I'm a customer, the edge is highly dynamic. It's changing all the time. I don't want to wait to buy something. If I got my solution architects on a product, I need to know I'm going to have zero trust built in and I need to push the button on Zscaler. I don't want to wait. So how does the procurement side impact? What have you guys seen? Share your thoughts on how Marketplace is working from the procurement standpoint, because it seems to me to be fast. Is that right, or is it still slow on their side? On the buyer side, because this to me would be a benefit to developers, if we say, hey, the procurement can just go really fast. I don't want to go through a bunch of PO approvals or slow meetings. >> It can be, that manifests itself in several ways, John. It can be, for instance, somebody wants to do a POC and traditionally you could take any amount of time to get budget approval, take it through. What if you had a pre-approved cloud budget and that was spent primarily through AWS Marketplace, because it's consolidated data on your AWS invoice. The ability to purchase a POC on the Marketplace could be done literally within minutes of the decision being made to go forward with it. So that's kind of a front end, you know, early stage use case. We've got examples we didn't talk about on our recent earnings call of how we have helped customers bring in their procurement with large million dollar, multimillion dollar deals. Even when a resaler's been involved, one of our resaler partners. Being able to accelerate deals, because there's so much less legal work and traditional bureaucratic effort. >> Agility. >> That agility purchasing process has allowed our customers to pull into the quarter, or the end of month, or end of quarter for them, deals that would've otherwise not been able to be done. >> So this is a great example of where you can set policy and kind of create some guard rails around innovation and integration deals, knowing if it's something that the edge is happening, say okay, here's some budget. We approved it, or Amazon gives credits and partnership going on. Then I'd say, hey, well green light this, not to exceed a million dollars, or whatever number in their range and then let people have the freedom to execute. >> You're absolutely right, so from the purchasing side, it does give them that agility. It eliminates a lot of the processes that would push out a purchase in actual execution past when the business decision is made and quite frankly, to be honest, AWS has been very accommodative. They're a great partner. They've invested a lot in Marketplace, Marketplace programs, to help customers do the right thing and do it more quickly as well as vendors like us to help our customers make the decisions they need to. >> Rising tide, a rising tide floats all boats and you guys are a great example of an independent company. Highly successful on your own. >> Yep. >> Certainly the numbers are clear. Wall Street loves Zscaler and economics are great. >> Our customer CSAT numbers are off the scale as well. >> Customers are great and now you've got the Marketplace. This is again, a new normal. A new kind of ecosystem is developing where it's not like the old monolithic ecosystems. The value creation and extraction is happening differently now. It's kind of interesting. >> Yes and I feel we have a long way to go, but what I can tell you is that Zscaler is in this for the long run. We are seeing some of the competitors erupt in the space as well, but they have a long way to go. What we have built requires years worth of R&D and features and thousands of customer's use cases which kind of lead to something what Zscaler has come up with today. What we have is very unique and is going to continuously be an innovation in the market in the years to come. In terms of being more cloud-savvy or more cloud-focused or more cloud-native than what the market has seen so far in the form of next-gen firewalls. >> I know you guys have got a lot of AI work. We've had many conversations with Howie over there. Great stuff and really appreciate you guys participating in our super cloud event we had and we'll see more of that where we're talking about the next generation clouds, really enabling that new disruptive, open-spanning capabilities across multiple environments to run cloud-native modern applications at scale and secure. Appreciate your time to come on "theCUBE". >> Thank you. >> Thank you very much. >> Thanks for having us. >> Thanks, I totally appreciate it. Zscaler, leading company here on "theCUBE" talking about their relationship with Marketplace as they continue to grow and succeed as technology goes to the next level in the cloud. Of course "theCUBE's" covering it here in Seattle. I'm John Furrier, your host. Thanks for watching. (peaceful electronic music)

>>Okay, welcome back everyone. It's the cubes coverage here in Seattle, Washington for Amazon web services, marketplace seller event. Really the big news here is the combination of the partner network with marketplace to one organization called the Amazon web services partner organization. Again, great news. Things are coming together, getting simplified and I'm John furry host of the cube. You've got a great guest here. Trish TRO head of worldwide Alliance at Wiz the fastest growing software company in history. Congratulations. Welcome to the cube. >>Thank you so much. And thanks for having us. >>So we were talking on camera. You had a little insight to a AWS. You jumped on this company. Oh my God. Amazing team. Take us through the story real quick. It's worth noting Wiz the company fastest growth. We're seeing take us through the quick soundbite. >>Sure. So the quick soundbite. So I was at AWS and my husband shared an article with me on cnbc.com about Wiz. They just done a big funding raise and he's like, you really have to read this. And I read it. And I said, oh my God, every single customer that I've met with the last year and a half has this problem. I have to find a way to be there. I don't care if I have to sweep the floors, lucky enough, they needed someone to run channels and alliances. So I did not have to sweep the floors, but for me, you know, when I think about our success, it's really this convergence of a series of things it's it's right time. Right? COVID forced everybody to the cloud, probably a little faster than they were ready to, you know, right market. And we have this convergence of the incredible product market fit, helping customers accelerate their cloud journey securely. And then I can't say enough about the team. You know, I thought it was fascinating, you know, as great as our product is when I got on board, everyone kept telling me, you know, they bought our product because of the team. And I was like, okay, cool. What about the product? And then I met the team and I understood. So jumped >>On one off one rocket ship. Yeah. To go onto another one. Yeah. You like the rocket, you like to ride those big, fast growth companies. You >>Know, I, I wish I was the kind of person where, you know, I just, I need excitement. Right? I'm I love to build. And I've had really good luck that I've always been able to find myself in a place, whether it's at a massive company or a startup to find myself as a builder, which has always been awesome. >>Well, tr it's great to have you on the cube. And a little fun fact is your sister was interviewed here on the cube in 2019 by myself. And so we have the first sisters, both cube alumni. Congratulations. >>I think that's, you know, honestly of all the accomplishments in my career, that's definitely one. I gotta make sure I get a plaque for that. You >>Will get a VIP sticker too. Yes, we, we all >>Sticker. Let's not get crazy now. >>All right. We'll designate in the front page. We'll have a very big story. L fund all good. We'd love the queue. We'd love to get the insight. So I wanna get your thoughts. Okay. You you've seen the Amazon side. You've been on that side. Now you're another side of the table with a partner growing. We're here to seller our conference. Big mission here is let's make things simpler and easier to procure software since you're already fast growing, what's in it for the customer to work through AWS, to get Wiz. Obviously you guys got a lot of demand. Yeah. A lot of money flowing through. You guys have a direct sales force. Are you going through the marketplace? What's the relationship between Wiz and Aish marketplace. >>So huge, honestly, and it's been a huge contributor to our success. We were lucky because we're, we were born during COVID, we're born in the cloud company. We got to build it from the ground up. This wasn't something that we had to go and figure out how to integrate into our existing ecosystem. Our ecosystem is actually built around the marketplace motion. You know, it's, it's interesting as you know, coming from AWS and now being on the other side, you know, something we really put a focus on is, you know, I see a lot of the companies that I was working with, you know, cloud was very much this thing. That's kind of in a silo and it's its own box and it competes internally. And really when you, you get deeper and deeper into the marketplace, it becomes about how do I use the cloud to really accelerate what I'm doing and to integrate it across my different channels. And for us, you know, AWS is our deepest relationship on the partner side. We invested heavily early and often, and it's been amazing. You >>Know, tr I was talking one of the data brick guys as well, and other companies that are big successes. This is a unique time here at the marketplace. We're on the ground floor. You can see here, we're at the, there's no stage. It's the smaller Q small venue, very intimate event. But it reminds me of 2013 when reinvent was starting to get traction second year, small, intimate, little bit bigger, obviously, but this is gonna feel like it's gonna explode. And you mentioned that you guys are building emotions around the ecosystem of the marketplace because you were born, born in the cloud. And COVID, so it's almost like if you're a startup today, why wouldn't you be in the marketplace first? Why even have that motion? So reminds me of the old days of you're a startup. Why not use the cloud? Why build a data center? >>No, and I think that's a really great analogy, you know, at least from what I've seen, it's, it's super interesting as a startup, because part of when you come out with a new technology in a perfect world, customers would already know what you were gonna make and have funding allocated for it. And we would all have this much easier sales cycle. That's not how it works. The customers, you know, as much as they might wanna get your solution, they have real things like budgets to deal with. And so it's really cool because when you work with the marketplace, it's a pool of funding that the customer has allocated on the customer side. It burns down their commit with the, with their different contracts. So that's usually powerful for them, right? Being able to consolidate your it, spend, reduce your overall total cost of ownership is, is usually powerful to the customer. And it on our side is a startup. So not only are they the financial benefits, it also helps you elevate the conversation. You know, a lot of times in the security industry, it's really all about like speeds and beads. That's how we sell cyber crime is 300% on the rise and stuff like that. Right. But being able to kind of get above that and help the customer, you know, have that financial conversation is, is really helpful too. >>So if I'm a startup, I'm a company, what would be the playbook for me and say, you know what, I'm gonna go all in, in the marketplace, I'm just gonna build the best kick ass product. Okay. I got product market fit. I'm gonna focus all my creative energy on building the best tech with the best, best team. All my friends and colleagues, and none of this non says go to market direct Salesforce, go all in on AWS. I know the product market fits there. What's the playbook. What do I do? Do just list it. >>So list, I think this is one of the mistakes that a lot of companies make when, when they first start out with the marketplace, right? They're like I will get to the marketplace and then AWS will sell my solution. I'm done the marketplace really? >>Where's the money back up the truck, come on. >>Exactly. Right? Like they have all these customers, they should just all come to me. Right. And I think that's one of the mistakes that organizations stumble on initially, cuz they go to the marketplace and then AWS is not selling their solution for them immediately. And they're like, the marketplace is a failure and it's really not. It's just the beginning of that. Being able to go into the marketplace, being able, honestly, to set expectations internally and understanding the journey that really comes into play here. You know, building, you know, one of the things that I talk to a lot about my team with is like building success within the sales reps and helping them be big advocates and champions for the marketplace. And the other thing is like, don't assume people know, I can't tell you. I feel like my, my real job at Wiz is I'm like the marketplace evangelist and cheap cuz that's all I do is talk about why they should use the marketplace and how it can solve all these different problems. Don't assume that people know how to do these things. Like you have to keep reiterating the message. You have to find sellers that are ready for it. And then you have to really, you have to teach them how to do it and then align your sales process accordingly. Like confidentiality come up a whole bunch at this conference today. It's important. You need it. >>It's huge. How big is your sales force right now? >>On >>The direct side. >>On the direct side, I think we're like a hundred or something like >>That. So you have, you have people out there on the streets knocking on doors selling. How's that comp decision go internally as you guys have that, what's the, what's the uptake in the marketplace for you guys right now? Is it high? Is it it's >>Been really high honestly. Yeah. It's and we've been really great. We have some incredible champions internally who are really great about sharing their experience, helping other sellers understand like we've, we've honestly had amazing co-sell stories at AWS where they've been so supportive and helpful. And it's amazing. Like we've had so many sellers that have done their first marketplace transaction ever. And now it's like for some of our sellers, they're at the point where they're like, I don't wanna, I don't wanna not do a marketplace transaction. It's just, it's so much easier. Take us >>For the procurement benefits. Take, walk me through what happens on the procurement side. What's the benefits for using the marketplace as you, as the procurement process goes through? >>Oh, from a, from a procurement side, right? It's like, it's simple, right? Like you, you essentially click a button and it's done like from the seller's side, like imagine not having to like chase down 15 different signatures and make sure nobody's on vacation. Right? So it just takes this really convoluted ti process that they would normally deal with. It makes it a lot simpler on the customer side. Right. Being able to have one consolidated is super powerful, burning down against commit, super powerful. And I think that's something that's really helped. Our sellers too, is being able, like we, we spend a tremendous amount of resources on educating our sellers. Not only about how it's gonna help them, but also how it's gonna help the customer too, >>Too. So good internally for you guys frictionless easier, better, better. Sounds like a better path >>On that. Oh, I won't say frictionless. I mean we're, we're about a year into this, but it wasn't so much frictionless, but it's not a hassle itself. Right. It's not a hassle. And it's all about >>On scale one to 10, 10 being frictionless. Would you get a, an eight or >>I'd say like an eight. Yeah. Okay. Okay. Cool. But it's important for organizations to understand that, right? Like that just because there's a little bit of friction at first. Like the most important thing I told my team is they were like, look like, well, why doesn't everybody wanna do this? This is so easy. And a, a good seller will take the hard time every way when they know what the defined outcome is. Yeah. The marketplace to them feels like a shortcut at first. Yeah. So a very much helps them become like, Hey look, this isn't a shortcut. This is gonna help you. Like, this is a good thing. And once you get that adoption like that, that's where the primary friction is. They almost go, is this, is this too good to be true? This can't be real. >>It, it, it almost sounds too good to be true when you think about, okay, so lemme take, I'm gonna put them a sales rep for a second. Like I'm selling WIS and I go and knock on a door and there's a company and I get an, a champion inside the company and says, oh, I love this product. I wanna buy it. I gotta get my PO approved and I gotta go get, I tell my boss about it. Does it go through that kind of normal kind of normal sales motion where you got buy in and now they gotta commit and close and get contract or they just go to the person who runs the account, click the button, like, like, is there, I mean, I'd like to see that shortcut happen. Like so on the customer side, what, what do you see as the process? Is it just go to the console and hit by and >>You know, depends on the customer honestly, and kind of where they are in their cloud journey. You know, really mature customers tend to have a little bit more of a mature process, you know, earlier customers, it tends to be a little less, let's say structured, but no, it's definitely not. The customer just clicks the button and it's done. That would be quite nice. We're just not there yet, but it's definitely a much simpler process cuz you know, you think about it on the customer side when they decide they wanna buy something, especially something new, they don't have allocated funding for us. They have to go build all this justification for funding. They still have to do that. Right. But then now there's a pot of money that they can go to and be able to retire against. There, there, it does help in that sense. A >>Lot. Chris, Chris grew has talked about on his keynote, the buyer journey survey. That seems to be on the, on the customer side. Yeah. Having those processes where they can forecast against it, they kind of know what they're getting. That's that's that's sounds like a great thing that's happening. I wanna get back to this comp issue again. Cause this came up. I heard that a lot. We talked with Chris about the competing thing. That's not an issue in my mind, but I think the factor to me, if I'm looking at this is that if you get the comp right, they can sell it at Amazon. You get comped, your sales people get comped goes through the marketplace. How do you look at that? How do company her look? How do they look at the comp what's what's the deciding factor or is it a non-issue what's the, what's the core. >>So I'm opportunity. I'm gonna be honest. I think I got a little lucky because I think the getting alignment at the executive level that this was something we should do to be totally honest here. Wasn't wasn't super hard. When we presented a clear plan, how we were gonna do it, what other companies were doing, what it did for their business to our executives. We do, we get some pushback. Sure. Healthy questions. Sure. But like it, it really >>Was it margin related or more like operational costs. >>It wasn't even margin related. It was again, more of like, is this, this feels too good to be true kind of thing. So it was more like proving it to them. Like no, like it really can be that easy. Yeah. And then on the, the comp side, right. For us, we look at it as like cost of sales. So yeah. You know, we, we treat it the same way. We treat all other channels and we wanted to make sure for our reps that, you know, when we think about the channel, whether, you know, from, especially with marketplace, like it can't be harder for them to do a marketplace transaction or less incentive for them to do that than a direct one that doesn't incentivize the right behaviors. >>So it's more of an indirect channel play. >>Yeah. So it's all for us. It was about aligning the right incentives to drive the right behaviors. It wasn't, it actually was a pretty short discussion on the confidentiality. Everyone was like, no, this, this makes sense. We should do that. >>Yeah. I mean, I think it's, I think it's an easy, easy, but you have to be organized for it. Like, like Chris said, don't put the toe in the water. Right. Put your flagship offering in there, make it valuable. And then the flag wheel gets going, the Amazon sales people can sell it. Right. They get calm. That's always a good thing. >>Yeah. And I think that's something that was really interesting. Like when we started on the marketplace journey, like I said, it's not just, you get in a marketplace and you're done, you know, Chris talked a lot about ISV accelerate and you know, how you elevate yourself within that program, doing things with ACE, like putting in different opportunities to, to start to essentially build that groundswell to drive co-sell it's, it's gets that first step into it. But there's so much more that, that we're still discovering and learning today is we're building it >>Out. And you said you had some good co-sell examples. >>Oh yeah. So we've had some great Cosell. >>What's your best one. Best one to >>Share. Oh, so my favorite one, I won't say the customer name, but we were in the final stages and a customer was really like, oh, like this is a lot of money. I'm really nervous. And the, they, I think what's crazy is that at AWS you have a different relationship with customers. Like you are truly a trusted advisor and rightfully so. Yeah. AWS really does a great job with making sure their account teams do what's best for the customer. And so an AWS seller or technical resource on an account says, Hey, no, this is the right thing for your business. That is huge for the customer. So we at Wiz actually spend a lot of time investing in enabling and educating the AWS account teams. So they feel comfortable when they get into that situation where the customers nervous of being saying like, no, this is you need to do this. This is >>Gonna be, you carry a lot of weight with the customers. >>Absolutely. >>And so you almost have to treat them like a lunch and learn, get 'em up, find, share. So it's kind of like an indirect relationship for you, but for them it's a part, you know, this is basically a channel. >>Yeah. And I think that's the thing that, that really is something we we've really heavily invested in is, is building. I like call the ground game within AWS. Right? Yeah. Making sure we spend time with enabling their reps. We enable their technical teams lunch and learns, right? Like there's so much energy at AWS to really invest in technical solutions that help their customers. Awesome. Which you don't always find that a lot of partners honestly. >>Well, Trish, great. Great to have you on sharing the AWS relationship story with WIS, gotta ask you, what's it like to be working for the fastest growing startup? What's it like? It's, it's, it's pretty fun. >>You know, it's, let's say I don't ever wake up on a day and say, man, I just wish I had more things to do. No, it's, it's been an incredible journey. The people, you know, my favorite part of a startup is, you know, getting to do this with a bunch of really incredible, awesome people. It's, it's the most fun thing in the world. We've, I've learned more in the last, you know, we like to joke that we're a five year old company and a one year old company at the exact same time. Yeah. And what's cool is we get to learn and, and I I've learned so much this year. >>When was the company officially >>Formed? It was officially formed before. Like, so it was officially formed in February, 2020. We started officially operating in the January following 21. So 21. Yep. >>Yeah. So one and a half years, >>One and a half years. Isn't that crazy? Great. >>And a hundred million ARR already. Yeah. Hitting that. >>Yep. It's been a, a wild journey. I I'll put it that way >>Is the, what's the success of the businesses? It, the onboarding the, is it the business model of freemium? What's the product market fit dynamic. Why is so fast? I mean, that's the needs there? Pandemic fresh, clean piece, piece of paper doing it, right. What's the, why is it? Why is that going so fast? >>Well, I think about this, I've been in the security industry for too many years. And when you think about normal security products, like there's so much time to value, you have to deploy all this infrastructure and then you gotta wait till something happens that you find that's scary, that will excite the customer. Right? It's, it's, it's a lot of time to show value. What blew my mind is the way that we approach our, the problem that we're solving is essentially immediate time to value. So the customer connects within minutes, they're immediately presented with here's your, your top risks. And then they can take action on them. Right? Like it's not just, here's these big threats and detecting, it's actually giving, empowering the customer to go and, and fix things. That's that's powerful for them. Yeah. Yeah. >>So, and the renewals are there coming in, people like the product, >>I mean, we've only been around for a year and a half, so there aren't that many renewals yet, but let's say we have extremely strong renewal rate from our customer base. >>Yeah. I mean you can have when you have a great product. Yeah. Well, thanks for coming on sharing. What's your assessment so far of the database marketplace kind of reorg with APN partner network to have one organization. What does that mean to the, to the market? What does that what's that tell you? >>So I was really excited. So we're actually built this way. So I run both our channels and alliances organization and it was, it was great because it allows these two things to work together and, and very well. And AWS, I think, is realizing the power of bringing those two groups together. So when I saw that, I was like, that's gonna be great. It's gonna make it simpler, easier. And at least for us, it's been really powerful. >>Awesome. Thanks for coming on the cube. Really appreciate it. We'll get you that plaque shortly. >>I thought I was getting a sticker too. >>Don't forget the sticker. Oh, the sticker definitely guaranteed. And we'll give you a VIP icon on our cube alumni network. All >>Right. I like that. >>Thanks for coming out. Alls great stuff. Thanks. Awesome. Thanks for having all best growing company history here on the cube, bringing all the action again, the new flywheel is gonna be procured through the marketplaces. This is obvious how it all kind of works and forms. It's kind of happening in real time. Cube's got you covered on the ground floor here in Seattle with more coverage after the short break.

>>Hello everyone. I'm John furry with the cube. We're here, live on the ground in Seattle, Washington at the Bellevue Hilton for thes marketplace seller conference. It's kind of like the one and a half inaugural event. They have their first event in 2019, and now with the pandemic, they're re rebooting it, but it's really all about AWS's marketplace and partner network coming together, creating an experience for how people will be buying software and how people will be selling through with their ecosystem. I'm Jennifer, the cube we're here with Megan. Fontain, who's the VP of cloud seek. Who's a seller and partner of AWS making great to see you. Thanks for coming on the cube. >>Thank you so much. It's, it's nice to be back in person and it's great to be with you. >>So watching the progression of how Amazon web services is evolving the marketplace and the partner network, you're starting to see some patterns. One is, I'll say they have their own stuff, and they're addressing that in the room, but they're really letting the thousand flowers bloom in the ecosystem. You hear that every year reinvent, even when Andy Jesse who's now the CEO of Amazon would say, no, we want the best of breed. Best product wins. Adam. Celeste's the same view, new leadership here, the combination of APN partner network with the marketplace now partner organization, APO is the big news. They're open. They're building an API service layer between their old marketplace to create this new model here. What's your, what's your, what's your take? What's your seller view? >>Yeah, so our marketplace and APN journey started with AWS about three years ago. And I think something that was the most profound to me out of the keynote this morning was that Chris Gus, who runs the API organization for ISVs talked about marketplace as the automation layer for how AWS will partner going forward. So an independent software vendor likes, we see that as opening up the door for two things. One, we get to leverage the great global scale and platform of AWS, but then secondly, it really brings together this idea that we will sell together to the end customer through the marketplace. And we will also sell as partners through co-sell and APM. >>You know, I love these kind of new, new development models around channel partners, ISVs at the end of the day, buyers are buying software. Yes. And they're cloud they're on a cloud journey. You're the VP of cloud at the company, your company seek take a minute to explain what your company's known for, what you guys do, your relationship with the market. You're an ISV. Yeah. Where are you guys? Cuz you guys ha have a good thing going on here. What do you guys do? What are you known for >>Sure. So seek is market leading software for advanced analytics for the manufacturing industry. So we're squarely in that industry. ISV, we sell SAS solutions to business buyers who want two things. One is they want technology that they can deploy quickly in their organizations drive that great business value ROI that drives the next level of investment in technology seeks unique offering in marketplace is that we've solved a lot of the challenges around that operational data in manufacturing. So manufacturing the industry, it's going through massive transformation, supply chain, disruption, or coming out of that, the globalization of manufacturing. And yet they have data that they've stored for 20, 30 years, that they're still in the first generation of trying to gain insights from. So that's why seek exists. It's really to bring the insights outta that data and then help the manufacturing customers we work with. Get to the cloud. >>What's interesting. I like your perspective and I want to follow up on that because data analytics used to be this thing. Well, I got a database. Yeah. You hosted on some storage and you got structured data, unstructured data. Okay. You got scale. But now you've got data platforms. You've got data mesh. I think Gardner actually has a different term, but gets a whole nother conversation. Data platforms are diverse. Yeah. They're pervasive. They're part of core infrastructure in cloud. It's not like a point solution anymore. It's gotta be integrated and customers are trying to work on, this is one of the hardest problems today. Yeah. In cloud transformation is the data layer, the relationship to other services. Yeah. >>So the Dataverse common data models. How APIs will interact with data. The trend there though is something that it is the ecosystem that will bring value to customers because no database is gonna serve every need. Right. And you think about the data layer. It really has to solve the problems whereby any application, any user, any insight can be generated almost seamlessly. And we're really on the first wave of that journey. But I think a, an element for seek that we certainly understand with our customers is that data alone is not an end objective, right? If it doesn't lead to a decision and an action and a workflow that humans can take to go drive and improvement in their business process, then you haven't tapped into the, you know, value of that technology >>When a buyer comes to the marketplace. Yeah. And they see your listing and solutions. Yes. What are they getting? What are they, what, what are they buying? >>So for seek, we've radically simplified that we, we really embrace this idea of simplification. We just sell, seek. So we have one seat listing in the AWS marketplace, all applications of seek they're all available there. We really leaned into the enterprise procurement models. So private offers are how we do the most of our business on marketplace. And it really went from a stage of experimentation where couple of customers, you know, what is this marketplace? Maybe we'll buy a few of our business applications there all the way through to now we're starting to see the demand side come through for customers where it's not just their security software or their DevOps or infrastructure software. They wanna buy solutions like seek including line of business buyers through a common catalog in the marketplace. >>Great. So I wanna ask you, cuz I want to give you the opportunity to give the pitch, the customer watching right now. Yeah. What's the pitch. Why seek, why this listing? Why should they hit the purchase button? I wish it was that easy. Why should they, why should they what's the pitch? Sure. >>So the first thing is seek through marketplace is a five clicks on three screens procurement experience. So compare that to months and months of back and forth with contracts and purchase orders and vendor set up, this is five less than five minutes, few screens, couple of clicks. And you can buy a multi-year subscription of seek to cover your entire enterprise. The second pitch is that it's a SaaS application that now can be deployed within hours. And then your users, your insights, your value is starting within the first couple hours. This is not a heavy lift it project. That's gonna take months. And then lastly seek specifically. So seek, because we're validated in the marketplace has been well architected for AWS cloud. We have that, you know, stamp of credibility. And we are leading in this space for manufacturing organizations who want cloud native secure software for analytics on their operational data. >>That's awesome. And customers have the challenge when they think about data, the use case security, yes governance, there's a variety of different use cases. What are you seeing as the top three use cases for C? >>So on the there's two lines of that question. The first is really the line of business use cases. And those are all about what outcome are we gonna drive? Are we gonna approve efficiency in your factory? Are we gonna reduce greenhouse gas emissions? Those are the kinds of use cases on the business side that that seek works with our customers on, on the it side. They wanna know that we can access data securely, that we can be part of an ecosystem where they can bring in aerations and algorithms and machine learning and new applications. And they also wanna know that we are sustainable. So meaning that we're driving constant innovation that is easy for them to consume and to gain access, to, to drive the next level of >>Improvement. My final AWS marketplace seller question is, yeah. How does the procurement process through marketplace help you and your customers what's in it for them? What value do the, does the customer get going through AWS procuring? >>So there's really really three. The first is you get a validated set of a catalog of solutions, right? That AWS says, you know, we undergo a rigorous process technically and commercially to be in the marketplace. The second thing for procurement effect of for procurement professionals is that they can leverage their cloud committed spend with AWS. So as they commit more expense and spend with AWS, now these marketplace purchases can be credited to that committed expense. We found that brings it and the business together with procurement to really work more collectively on that. And then the third piece is, imagine buying software where you don't need legal, you know, back and forth, back and forth because we're using a standard doula that thousands of other software companies are using in the marketplace today. >>I thought the keynote had a great line. We are not just a website of a catalog. We are a API service layer. Yes. With automation, more like a C I C D pipe lining. Yes. Of software. Yeah. And we are hearing more and more about software supply chain, more about scaling. This is kind of the future of procurement. Why wouldn't you buy direct, pick a few buttons and assemble your solutions at scale. >>There's some amount of tenant consequences that we've really learned as well. It brings it and the business closer together. So the it person wants to know, well, what is this seek, you know, piece of my AWS invoice. And so they get more engaged earlier in the process with procurement, with the business. And we've actually found that it brings internally for our customers, more people to the seat at the table around what are the applications and how will they govern them across the enterprise. >>Megan, I really appreciate you taking the time to speak with me here at the, at the conference, the seller S marketplace. I have to ask you, we were talking before we came on camera, you made a comment. I'd like you to share this comment with some commentary. You said I'm the VP of cloud transformation. And in the future that might title might not exist. Explain what you mean there, cuz I think this is kind of a telling moment about where we are at this point in the industry. >>Sure. So maybe it's, maybe it's funny to sort of envision a future where your role doesn't exist. But I think, you know, it's a to innovators do that, right? And for us we're a software company. That's going through the transition on-prem to SAS, you know, cloud native sets of applications, but in the pretty near term fore, really the next two years, all of our business will be SaaS and cloud. And so we won't need a separate VP or a separate team or separate function. It will just be how the business operates. >>Megan, thanks for running cue, Meghan bine, who is SI, she's a cloud VP of cloud transformation, VP of cloud, and she's successful. The title will go away and she'll move on to some other great valuable things like running the business. Thanks for coming on. Thank you so much. Okay. This is a cube here in Seattle. We're covering the eights marketplace seller conference. Part of APN merging with Amazon marketplace now called the APO Amazon partner organization. I'm John ER, with the cube. Thanks for watching.

>>Hello. And welcome back to the cubes live coverage here in Seattle for the cubes coverage of AWS marketplace seller conference. Now part of really big move and news, Amazon partner network combines with AWS marketplace to form one organization, the Amazon partner organization, APO where the efficiencies, the next iteration, as they say in Amazon language, where they make things better, simpler, faster, and, and for customers is happening. We're here with Chris Cruz, who's the general manager, worldwide leader of ISV alliances and marketplace, which includes all the channel partners and the buyer and seller relationships all now under one partner organization, bringing together years of work. Yes. If you work with AWS and are a partner and, or sell with them, all kind of coming together, kind of in a new way for the next generation, Chris, congratulations on the new role and the reor. >>Thank you. Yeah, it's very exciting. We're we think it invent, simplifies the process on how we work with our partners and we're really optimistic so far. The feedback's been great. And I think it's just gonna get even better as we kind of work out the final details. >>This is huge news because one, we've been very close to the partner that we've been working with and we talking to, we cover them. We cover the news, the startups from startups, channel partners, big ISVs, big and small from the dorm room to the board room. You guys have great relationships. So check marketplace, the future of procurement, how software will be bought, implemented and deployed is also changed. So you've got the confluence of two worlds coming together, growth in the ecosystem. Yep. NextGen cloud on the horizon for AWS and the customers as digital transformation goes from lift and shift to refactoring businesses. Yep. This is really a seminal moment. Can you share what you talked about on the keynote stage here, around why this is happening now? Yeah. What's the guiding principle. What's the north star where, why what's what's the big news. >>Yeah. And so, you know, a lot of reasons on why we kind of, we pulled the two teams together, but you know, a lot of it kind gets centered around co-sell. And so if you take a look at marketplace where we started off, where it was really a machine image business, and it was a great self-service model and we were working with ISVs that wanted to have this new delivery mechanism on how to bring in at the time was Amazon machine images and you fast forward, we started adding more product types like SAS and containers. And the experience that we saw was that customers would use marketplace for kind of up to a certain limit on a self-service perspective. But then invariably, they wanted by a quantity discount, they wanted to get an enterprise discount and we couldn't do that through marketplace. And so they would exit us and go do a direct deal with a, an ISV. >>And, and so to remedy that we launched private offers, you know, four years ago. And private offers now allowed ISVs to do these larger deals, but do 'em all through marketplace. And so they could start off doing self-service business. And then as a customer graduated up to buying for a full department or an organization, they can now use private offers to execute that larger agreement. And it, we started to do more and more private offers, really kind of coincided with a lot of the initiatives that were going on within Amazon partner network at the time around co-sell. And, and so we started to launch programs like ISV accelerate that really kind of focused on our co-sell relationship with ISVs. And what we found was that marketplace private offers became this awesome way to automate how we co-sell with ISV. And so we kinda had these two organizations that were parallel. We said, you know what, this is gonna be better together. If we put together, it's gonna invent simplify and we can use marketplace private offers as part of that co-sell experience and really feed that automation layer for all of our ISVs as they interacted with native >>Discussions. Well, I gotta give you props, you and Mona work on stage. You guys did a great job and it reminds me of the humble nature of AWS and Amazon. I used to talk to Andy jazzy about this all the time. That reminds me of 2013 here right now, because you're in that mode where Amazon reinvent was in 2013. Yeah. Where you knew it was breaking out. Yeah. Everyone's it was kind of small, but we haven't made it yet. Yeah. But you guys are doing billions of vows in transactions. Yeah. But this event is really, I think the beginning of what we're seeing as the change over from securing and deploying applications in the cloud, because there's a lot of nuanced things I want to get your reaction on one. I heard making your part product as an ISV, more native to AWS's stack. That was one major call out. I heard the other one was, Hey, if you're a channel partner, you can play too. And by the way, there's more choice. There's a lot going on here. That's about to kind of explode in a good way for customers. Yeah. Buyers get more access to assemble their solutions. Yeah. And you got all kinds of like business logic, compensation, integration, and scale. Yeah. This is like unprecedented. >>Yeah. It's, it's exciting to see what's going on. I mean, I think we kind of saw the tipping point probably about two years ago, which, you know, prior to that, you know, we would be working with ISVs and customers and it was really much more of an evangelism role where we were just getting people to try it. Just, just list a product. We think this is gonna be a good idea. And if you're a buyer, it's like just try out a private offer, try out a self, you know, service subscription. And, and what's happened now is there's no longer a lot of that convincing that needs to happen. It's really become accepted. And so a lot of the conversations I have now with ISVs, it's not about, should I do marketplace it's how do I do it better? And how do I really leverage marketplace as part of my co-sell initiatives as, as part of my go to market strategy. >>And so you've, you've really kind of passed this tipping point where marketplaces are now becoming very accepted ways to buy third party software. And so that's really exciting. And, and we see that we, you know, we can really enhance that experience, you know, and what we saw on the machine image side is we had this awesome integrated experience where you would buy it. It was tied right into the EC two control plane. And you could go from buying to deploying in one single motion. SAS is a little bit different, you know, we can do all the buying in a very simple motion, but then deploying it. There's a whole bunch of other stuff that our customers have to do. And so we see all kinds of ways that we can simplify that. You know, recently we launched the ability to put third party solutions outta marketplace, into control tower, which is how we deploy all of our landing zones for AWS. And now it's like, instead of having to go wire that up as you're adding new AWS environments, why not just use that third party solution that you've already integrated to you and have it there as you're span those landing zones through >>Control towers, again, back to humble nature, you guys have dominated the infrastructure as a service layer. You kind of mentioned it. You didn't really kind of highlight it other than saying you're doing pretty good. Yeah. On the IAS or the technology partners as you call or infrastructure as you guys call it. Okay. I can see how the, the, the pan, the control panel is great for those customers. But outside that, when you get into like CRM, you mentioned E R P these business apps, these horizontal and verticals have data they're gonna have SageMaker, they're gonna have edge. They might have, you know, other services that are coming online from Amazon. How do I, as an ISV, get my stuff in there. Yeah. And how do I succeed? And what are you doing to make that better? Cause I know it's kind of new, but not new. Yeah, >>No, it's not. I mean, that's one of the things that we've really invested on is how do we make it really easy to list marketplace? And, you know, again, when we first start started, it was a big, huge spreadsheet that you had to fill out. It was very cumbersome and we've really automated all those aspects. So now we've exposed an API as an example. So you can go straight out of your own build process and you might have your own C I CD pipeline. And then you have a build step at the end. And now you can have that execute marketplace update from your build script, right across that API all the way over to AWS marketplace. So it's taking that effectively, a C CD pipeline from an ISV and extending it all the way to AWS and then eventually to a customer, because now it's just an automated supply chain for that software coming into their environment. And we see that being super powerful. There's nowhere manual steps >>Along. Yeah. I wanna dig into that because you made a comment and I want you to clarify it here in the cube. Some have said, even us on the cube. Oh, marketplace. Just the website's a catalog. Yeah. Feels old school. Yeah. Feels like 1995 database. I'm kind of just, you know, saying no offense sake. And now you're saying, you're now looking at this and, and implementing more of a API based. Why is that relevant? I'm I know the answer. You already set up with APIs, but explain the transition from the mindset of it's a website. Yeah. Buy stuff on a catalog to full blown API layer. Yeah. Services. >>Absolutely. Well, when you look at all AWS services, you know, our customers will interface, you know, they'll interface them through a console initially, but when they're using them in production, they're, it's all about APIs and marketplace, as you mentioned, did start off as a website. And so we've kind of taken the opposite approach. We've got this great website experience, which is great for demand gen and, you know, highlighting those listings. But what we want to do is really have this API service layer that you're interfacing with so that an ISV effectively is not even in our marketplace. They interfacing over APIs to do a variety of their high, you know, value functions, whether it's listing soy, private offers. We don't have that all available through APIs and the same thing on the buyer side. So it's integrating directly into their AWS environment and then they can view all their third party spend within things like our cost management suites. They can look at things like cost Explorer, see third party software, right next to first party software, and have that all integrated this nice as seamless >>For the customer. That's a nice cloud native kind of native experience. I think that's a huge advantage. I'm gonna track that closer. We're we're gonna follow that. I think that's gonna be the killer killer feature. All right. Now let's get to the killer feature and the business logic. Okay. Yeah. All partners all wanna know what's in it for me. Yeah. How do I make more cash? Yeah. How do I compensate my sales people? Yeah. What do you guys don't compete with me? Give me leads. Yeah. Can I get MDF market development funds? Yeah. So take me through the, how you're thinking about supporting the partners that are leaning in that, you know, the parachute will open when they jump outta the plane. Yeah. It's gonna be, they're gonna land safely with you. Yeah. MDF marketing to leads. What are you doing to support the partners to help them serve their >>Customers? It's interesting. Market marketplace has become much more of an accepted way to buy, you know, our customers are, are really defaulting to that as the way to go get that third party software. So we've had some industry analysts do some studies and in what they found, they interviewed a whole cohort of ISVs across various categories within marketplace, whether it was security or network or even line of business software. And what they've found is that on average, our ISVs will see a 24% increased close rate by using marketplace. Right. So when I go talk to a CRO and say, do you want to close, you know, more deals? Yes. Right. And we've got data to show that we're also finding that customers on average, when an ISV sales marketplace, they're seeing an 80% uplift in the actual deal size. And so if your ASP is a hundred K 180 K has a heck of a lot better, right? >>So we're seeing increased deal sizes by going through marketplace. And then the third thing that we've seen, that's a value prop for ISVs is speed of closure. And so on average, what we're finding is that our ISVs are closing deals 40% faster by using marketplace. So if you've got a 10 month sales cycle, shaving four months off of a sales cycle means you're bringing deals in, in an earlier calendar year, earlier quarter. And for ISVs getting that cash flow early is very important. So those are great metrics that we're seeing. And, and, you know, we think that they're only >>Gonna improve and from startups who also want, they don't have a lot of cash ISVs that are rich and doing well. Yeah. They have good, good, good, good, good to market funding. Yeah. You got the range of partners and you know, the next startup could be the next Figma could be in that batch startups. Exactly. Yeah. You don't know the game is changing. Yeah. The next brand could be one of those batch of startups. Yeah. What's the message to the startup community. Yeah. >>I mean, marketplace in a lot of ways becomes a level in effect, right. Because, you know, if, if you look at pre marketplace, if you were a startup, you were having to go generate sales, have a sales force, go compete, you know, kind of hand to hand with these largest ISVs marketplace is really kind of leveling that because now you can both list in marketplace. You have the same advantage of putting that directly in the AWS bill, taking advantage of all the management go features that we offer all the automation that we bring to the table. And so >>A lot of us joint selling >>And joint selling, right? When it goes through marketplace, you know, it's gonna feed into a number of our APN programs like ISV accelerate, our sales teams are gonna get recognized for those deals. And so, you know, it brings nice co-sell behavior to how we work with our, our field sales teams together. It brings nice automation that, you know, pre marketplaces, they would have to go build all that. And that was a heavy lift that really now becomes just kind of table stakes for any kind of ISV selling to an, any of >>Customer. Well, you know, I'm a big fan of the marketplace. I've always have been, even from the early days, I saw this as a procurement game changer. It makes total sense. It's so obvious. Yeah. Not obvious to everyone, but there's a lot of moving parts behind the scenes behind the curtain. So to speak that you're handling. Yeah. What's your message to the audience out there, both the buyers and the sellers. Yeah. About what your mission is, what you're you wake up every day thinking about. Yeah. And what's your promise to them and what you're gonna work on. Cause it's not easy. You're building a, an operating model. That's not a website. It's a full on cloud service. Yeah. What's your promise. And what's >>Your goals. No. And like, you know, ultimately we're trying to do from an Aus market perspective is, is provide that selection experience to the ABUS customer, right? There's the infamous flywheel that Jeff put together that had the concepts of why Amazon is successful. And one are the concepts he points to is the concept of selection. And, and what we mean by that is if you come to Amazon it's is effectively that everything stored. And when you come across, AWS marketplace becomes that selection experience. And so that's what we're trying to do is provide whatever our AWS customers wanna buy, whatever form factor, whatever software type, whatever data type it's gonna be available in AWS marketplace for consumption. And that ultimately helps our customers because now they can get whatever technologies that they need to use alongside Avis. >>And I want, wanna give you props too. You answered the hard question on stage. I've asked Andy EY this on the cube when he was the CEO, Adam Celski last year, I asked him the same question and the answer has been consistent. We have some solutions that people want a AWS end to end, but your ecosystem, you want people to compete yes. And build a product and mostly point to things like snowflake, new Relic. Yeah. Other people that compete with Amazon services. Yeah. You guys want that. You encourage that. Yeah. You're ratifying that same statement. >>Absolutely. Right. Again, it feeds into that selection experience. Right. If a customer wants something, we wanna make sure it's gonna be a great experience. Right. And so a lot of these ISVs are building on top of AWS. We wanna make sure that they're successful. And, you know, while we have a number of our first party services, we have a variety of third party technologies that run very well in a AWS. And ultimately the customer's gonna make their decision. We're customer obsessed. And if they want to go with a third party product, we're absolutely gonna support them in every way shape we can and make sure that's a successful experience for our customers. >>I, I know you referenced two studies check out the website's got buyer and seller surveys on there for Boer. Yeah. I don't want to get into that. I want to just end on one. Yeah. Kind of final note, you got a lot of successful buyers and a lot of successful sellers. The word billions, yes. With an S was and the slide. Can you say the number, how much, how many billions are sold yeah. Through the marketplace. Yeah. And the buyer experience future what's those two things. >>Yeah. So we went on record at reinvent last year, so it's approaching it birthday, but it was the first year that we've in our 10 year history announced how much was actually being sold to the marketplace. And, you know, we are now selling billions of dollars to our marketplace and that's with an S so you can assume, at least it's two, but it's, it's a, it's a large number and it's going >>Very quickly. Yeah. Can't disclose, you know, >>But it's a, it's been a very healthy part of our business. And you know, we look at this, the experience that we >>Saw, there's a lot of headroom. I mean, oh yeah, you have infrastructure nailed down. That's long, you get better, but you have basically growth up upside with these categor other categories. What's the hot categories. You >>Know, we, we started off with infrastructure related products and we've kind of hit critical mass there. Right? We've, there's very few ISVs left that are in that infrastructure related space that are not in our marketplace. And what's happened now is our customers are saying, well, I've been buying infrastructure products for years. I'm gonna buy everything. I wanna buy my line of business software. I wanna buy my vertical solutions. I wanna buy my data and I wanna buy all my services alongside of that. And so there's tons of upside. We're seeing all of these either horizontal business applications coming to our marketplace or vertical specific solutions. Yeah. Which, you know, when we first designed our marketplace, we weren't sure if that would ever happen. We're starting to see that actually really accelerate because customers are now just defaulting to buying everything through their marketplace. >>Chris, thanks for coming on the queue. I know we went a little extra long. There wanted to get that clarification on the new role. Yeah. New organization. Great, great reorg. It makes a lot of sense. Next level NextGen. Thanks for coming on the cube. Okay. >>Thank you for the opportunity. >>All right here, covering the new big news here of AWS marketplace and the AWS partner network coming together under one coherent organization, serving fires and sellers, billions sold the future of how people are gonna be buying software, deploying it, managing it, operating it. It's all happening in the marketplace. This is the big trend. It's the cue here in Seattle with more coverage here at Davis marketplace sellers conference. After the short break.

>>Welcome back everyone to the cubes coverage here in Seattle, Washington for the Avis marketplace seller conference, part of the APN partner network merging with the marketplace to form the Amazon partner organization. I'm John furrier, host of the cube Walter Wall coverage today, Christian Gor cash, who is the VP of alliances at Kong Inc. Welcome to the cube. Thanks for coming on. >>Thank you. Thank you, John. Really glad to be here. Corke exactly. Yeah. It's awesome. >>So Kong we've been following you guys for while Docker Kong cube. You've been part of our cube conversation. Also part of our, our startup showcase fast growing startup, you know, working on stuff that everyone loves APIs. I mean, APIs are so popular now that they now a security concern, right? Yeah. So like it gets squat there everywhere. I won't say API sprawl, but APIs are the connections and that are, is the web. That is the cloud. Okay. Now with cloud native developers who are now in the front lines have taken over it, everyone knows DevOps dev SecOps is now the new it and it's the developers security and data they're below they're the new ops, right? So, so this is where microservices come in, open source service MES new automation is coming down the pike. That's super valuable to businesses as they look at cloud native architecture, what are you guys doing in there? Take a minute to explain Kong's value proposition, the hot products, and then why you're here. >>Yeah. So, you know, I joined Kong now or three years ago, you know, we were still just reaching our hundred employees, mark, which is very important, very startup, but even back then, you know, Kong was relatively well known in industry, you know, so we have one of the most, well the most popular open source project in API gateway area. So con API gateway, you know, we cross now 300 million downloads, even more important is just the scale it, which the product's been used. So between our open source community and enterprise customers, we are now crossing like 11 trillion transactions per month. Now just give you comparison. Like this is like 18, 19 times more than Netflix per month. You know? So for any company that has a technology that operates it at scale, you need to hit few things outta the park. You know, as he mentions cloud data developers, they want simplicity. You know, they want automation. They also want performance and scale and security, which are all critical, you know, to how Kong, you know, start as opensource project. Now, of course we have the whole suite of enterprise products. We also have our con service mesh offering as well as our cloud offerings. >>Yeah. And this is how open source is doing it now, obviously, you know, I, I still remember, I still tell the story to the young startups. Hey, I, there was proprietary software when I was in college. Open source is now everything. Now you've got, got cloud scale. So the dynamic between open source, which has become the software industry open source success doesn't mean it's it's game over. It's the beginning. The commercialization that you guys have gone through is super important. Trillions of transactions. Now you have enterprises working with you. What's the big advantage of the seller relationship that you have with Amazon? Why are customers using it? What are they buying it for? Give the pitch of con for the marketplace customer. >>Yeah, it's actually, we are relatively new in AWS marketplace. You know, so our first transaction that we ever done was actually in July and 2021. So we are just over a year, you know, that journey, you know, when I look what Chris gross talked today, he was talking about, you know, Hey, just publishing marketplace, not enough. You know, you need to understand what's your value proposition. You need to make sure your operations already, your sales is ready. Everything is, is set. And we kind of did this for the first year and a half is spend a lot of time improving our integration with AWS overall, all the first party services relevant to con we also understood, well, what does it take to kind of fine tune our value proposition? We have like three specific sales place. And you know, when we launch our flagship product con connect enterprise and got our first transaction, that was great milestone for, for star like Kong. But then what we've seen is just that work that we've done before really paid off. I mean right now, >>Like what we'll give example. >>Yeah. So, you know, we are focusing on as measure three sales place. Money is we are focused, specific on helping customers who are modernizing and, and their application going to the cloud. And you have a lot of these, you know, lifting shift and are rearchitect and modernized, but most of the attentions on the workloads, what about the connections? You know, so a monolith application had to authentic all the users understand wheres the network and so on. When you build those, when you now decouple this built like 1,000 thousand microservices, you don't want to repeat this for every microservice. So that's where K brings the whole suite from, you know, service match to the API gate to help manage the journey and really support this environment. And we spend a lot of time to just fine tune that message. So that customers understood where, you know, how can we help them on their journey beyond what, for instance, cloud native or AWS API gateway offers them. So we can really help them from day one on the journey and accelerate. And >>I think I it's a no, it's a no braining for a customer to buyer or to come into the marketplace and say, click, I'm gonna buy some data analytics services. I'm gonna buy gateway through Kong. But when they start getting into these microservices, this automation opportunity there, there's more behind the curtain for them with Kong. So I have to ask you with the keynote we heard from Chris, the leader of the marketplace. Now he said that he wants the ISVs to be more native in the cloud. That probably resonates with you. You, >>You guys well with con's relatively simple because we were built at cloud native, you know, so very briefly the whole story of Congo. This is before Ajo, our founders were actually running the, the very popular API exchange col mesh shape. And they had to build their own gateway just to handle the scale and was built on cloud native technologies. And then when everybody's calling you, what are you using to running? This are using PGS. And so else, no, we built ourselves, oh, how can we get our hands on? That's how con actually >>Came to. And that's how the big winners usually happen too. They start build their own, solve their own problem because it's a big scale problem. Exactly. No one's had that problem. >>Yeah. And what we have seen, especially what was very, you know, through, through the pandemic, what we have seen. And it's interesting, you know, being in a startup doing pandemic is like, whoa, will the life just shut down or what we're doing? You know? But actually what we have seen customers prioritize the new business capability. For instance, you have a large parental companies that overnight, they have to understand where the assets are. Yeah. Or banks who are like 45 days of, you know, approving process for the loans. They need to reduce it for a day or two. >>Yeah. And they're adding more developers, too, exactly. To build the modern application. So they need to have that infrastructure as code aspect. Correct. >>And they >>Need in place. >>Yeah. I need to like you have, you know, I don't think that many customers still have waterfall cycles, but they have, have pre pretty long developers development cycles. And now you need to, you know, do this multiple times a day. That's >>Interesting. We talked to a lot of cloud architects and C CIO C says, and you know, the executive just hire more developers take that hill, build. It just don't build a new app. It's not that easy boss. When, when the cloud architect says we have to be fully operationally ready with cloud native infrastructure's code. So with that, you're seeing a lot more enterprises come in now that are more savvy. They getting better. We're seeing Kubernetes more and more. You're seeing containerization. You're seeing that cloud native enterprise acceptance. What does that mean for you guys in the marketplace, as you look at the value proposition, how are you guys working with the marketplace today and where do you see customers buying in the future? >>Yeah, so we as mentioned, you know, we, we are now a year into that journey. We already seen tremendous benefits just in terms of reducing the friction. You know, the whole procurement, you know, you come as a startup with some, some of the largest companies in the world, they used to buy five, 10 billion in software and they have all these processes and you're like, well, but we only have like two people in finance. Sorry. How can you, and where marketplace can really, really helps us is, you know, improve this experience, both sides because they understand like we are fast moving company. They, they want us because of our speed and, and innovation that we, the product's strong. Yeah. They don't want us to get bogged down in all these pro procurement processes either. And so, so that's the first benefit. We also are working very hard to make sure that the customers can provision Kong in AWS and automate across the board. So essentially reducing their time to value dramatically. Yeah. And another thing that we found tremendously beneficial for us is a startup is the whole concept of a standard marketplace contract. Yeah. So instead of us coming with our little MSA or come like 50 page MSA from companies, we now have a middle ground. So we can just agree. You know, there's some differences, some specifics to qu software and it's tremendously reduced costs on both sides. >>Great. For you guys great for the buyers. Yeah. You get deployed services. They're not just buying, they're managing and deploying. Yeah, >>Exactly. Great. >>Quick, final question. Put a plugin for the company. What are you working on now? What's the big news. What's the con update? >>Well, that's an interesting part because I can't tell you because next week we have our con summit. Oh right. In San Francisco. The cubes not so 28, 20 ninth. Yeah. We, we we'll, I think we are gonna fix that in the future. But anyway, this is the first time after pandemic to do this in person, we have number of very exciting announcement, our Kong products, as well as you may hear some news about our AWS partnership, >>We like con we believe that DevOps has happened. Dev sec ops, whatever you gonna call it, dev is now the developers they're in the front lines. They're in the C I CD pipeline. They're shifting left. That's the new they took over it. That's what DevOps does. It's not a title. Now you have security and data ops behind the scenes. That's gonna be middleware. That's gonna have tons of microservices. So more, more, more action coming, all API based. >>Exactly. And the more, you know, the more complexity we can take away from that, the better we, you know, the >>Whole community. Thank you. Spending the time to come on the cube here at the, a us marketplace seller conference. What do you think about the APN merging with the marketplace formed the P the Amazon partner organization. Thumbs up, thumbs down. What's your heard? >>It's excellent. We have a great friend in AP, a great friend, us marketplace. Now both of them work together with huge. >>Fantastic. Yes. Thanks for okay. Cube coverage here in Seattle. I'm John furier APN marketplace together. APOs the new organization making it easier. Of course, we got all the coverage here. Thanks for watching.

>>Welcome back to the cubes coverage here in Seattle for AWS marketplace seller conference, the combination of the Amazon partner network, combined with the marketplace from the AWS partner organization, the APO and John Forer host of the queue, bringing you all the action and what it all means. Our next guest is Trisha kata, Malva, chief strategy officer at DataRobot. Great to have you. Thanks for coming on. >>Thank you, John. Great to be here. >>So DataRobot obviously in the big data business data is the big theme here. A lot of companies are in the marketplace selling data solutions. I just ran into snowflake person. I ran into another data analyst company, lot of, lot of data everywhere. You're seeing security. You're seeing insights a lot more going on with data than ever before. It's one of the most popular categories in the marketplace. Talk about DataRobot what you guys are doing. What's your product in there? Yeah, >>Absolutely. John. So we are an artificial intelligence machine learning platform company. We have been around for 10 years. This is this year marks our 10th anniversary and we provide a platform for data scientists and also citizen data scientists. So essentially wanna be data scientists on the business side to rapidly experiment with data and to get insights and then productionize ML models. So the 100% workflow that goes into identifying the data that you need for machine learning and then building models on top of that and operationalizing a, >>How big is the company, roughly employee count? What's the number in >>General general, about a thousand employees. And we have customers all over the world. Our biggest verticals are financial services, insurance, manufacturing, healthcare pharma, all the highly regulated, as well as our tech presence is also growing. And we have people spread across multiple geographies and I can't disclose a customer number, but needless to say, we have hundreds of customers across the >>World. A lot of customers. Yeah, yeah. You guys are well known in the industry have been following some of the recent news lately as well. Yeah. Obviously data's exploding. What in the marketplace are you guys offering? What's the pitch, someone hits the marketplace that wants to buy DataRobot what's the pitch. >>The pitch is if you're looking to get real value from your data science, personal investments and your data, then you have DataRobot that you can download from your AWS marketplace. You can do a free trial and essentially get from, get value from data in a matter of minutes and not months or quarters, that's generally associated with IML. And after that, if you want to purchase you, it's a private offer on, in the marketplace. So you need to call DataRobot representative, but AWS marketplace offers a fantastic distribution channel for us. >>Yeah. I mean, one of the things I heard Chris say, who's now heading up the marketplace and the partner network was the streamlining, a lot of the benefits for the sellers and for the buyers to have a great experience buyers. Clearly we see this as a macro trend, that's gonna only get stronger in terms of self-service buying bundling, having the console on AWS for low level services like infrastructure. But now you've got other business applications that like analytics applies to. You're seeing that work. Now he said things like than the keynote, I wanna get your reaction to like, we're gonna make this more like a C I C D pipeline. We're gonna have more native services built into AWS. What that means to me is that sounds like, oh, if I have a solution, like DataRobot, that can be more native into AWS level services. How do you see that working out for you guys is that play well for your strategy and your customers? What's the, what's the what's resonating with the >>Customers. It plays extremely well with the strategy. So I call this as a win, win, win strategy, win for DataRobot win for customers and win for AWS, which is our partner. And it's a win for DataRobot because the amount of people, the number of eyeballs that look at AWS marketplace, a significantly higher than, than the doors that we can go knock on. So it's a distribution multiplier for us. And the integration into AWS services that you're talking about. It is very important because in this day and age, we need to be interoperable with cloud player services that they offer, whether it is with SageMaker or Redshift, we support all of those. And it's a win for customers because customers, it is a very important growing buyer persona for DataRobot. Yeah. And they already have pre-committed spend with AWS and they can use the, those spend dollars for DataRobot to procure DataRobot. So it eases their procurement life cycle as >>Well. It's a forced multiplier on, on the revenue side, correct? I mean, as well as, as on the business front cost of sales, go down the cost of order dollar. Correct. This is good. Goodness. >>It's it's definitely sorry, just to finish my thought on the win for the partner for AWS. It's great win for them because they're getting the consumption from the partner side, to your point on the force multiplier. Absolutely. It is a force multiplier on the revenue side, and it's great for customers and us, because for us, we have seen that the deal size increases when there is the cloud commit that we can draw down for, for our customers, the procurement cycle shortens. And also we have multiple constituencies within the customers working together in a very seamless fashion. >>How has the procurement going through AWS helped your customers? What specific things are you seeing that are popping out as benefits to the customer? >>So from a procurement standpoint, we, we are early in our marketplace journey. We got listed about a year ago, but the amount of revenue that has gone through marketplace is pretty significant at DataRobot. We experienced like just in, by, I think this quarter until this quarter, we got like about 20 to 30 transactions that went through AWS marketplace. And that is significant within just a year of us operating on the marketplace. And the procurement becomes easier for our customers. Yeah. Because they trust AWS and we can put our legal paperwork through the AWS machine as well, which we haven't done yet. But if we do that, that'll be a further force multiplier because that's the, the less friction there is. >>I like how you say that it's a machine. Yeah. And if you think about the benefits too, like one of the things that I see happening, and I love to get your thoughts because I think this is what's happening here. Infrastructure services, I get that IAS done hardware I'm oversimplifying, but all the, all the goodness, but as customers have business apps and vertical market solutions, you got more AI involved. You need more data that's specialized for that use case. Or you need a business application. Those, you don't hear words like let's provision that app. I mean, your provision hardware and, and infrastructure, but the, the new net cloud native is that you provision turn on the apps. So you're seeing the wave of building apps are composing Lego blocks, if you will. So it seems like the customers are starting to assemble the solution, almost like deploying a service, correct. And just pressing a button. And it happens. This seems to be where the, the business apps are going. >>Yeah, absolutely. You agree for us? We are, we are a data science platform and for us being very close to the data that the customers have is very important. And where if, if the customer's data is in Redshift, we are close to there. So being very close to the hyperscale or ecosystem in that entire C I C D pipeline, and also the data platform pipeline is very important. >>You know, what's interesting is, is the data is such a big part of, I mean, DevOps infrastructure has code has been the movement for decade. Yeah. So throw security in there. It's dev SecOps. Yeah. That is the developer now. Yeah. They're running essentially what used to be it now the new ops is security and data. Yeah. You see, in those teams really level up to be highly high velocity data meshes, semantic layer. These are words I'm hearing in the industry around the big waves of data, having this mesh. Yeah. Having it connected. So you're starting to see data availability become more pervasive. And, and we see this as a way that's powering this next gen data science revolution where it's like the business person is now the data science person. >>That's exactly. That is, that is what DataRobot does the best. We were founded with the vision that we wanted to democratize the access to AI within enterprises. It shouldn't be restricted to a small group of people don't get me wrong. Data scientists also love DataRobot. They use DataRobot. But the mission is to enhance many, many hundreds of people within an organization to use data science, like how you use Tableau on a regular basis, how you use Microsoft Excel on a regular basis. We want to democratize AI. And when you want to democratize AI, you need to democratize access to data, which is, which could be stored in data marketplaces, which could be stored in data warehouses and push all the intelligence that we grab from that data into the E R P into the apps layer. Because at the end of the day, business users, customers consume predictions through applications layer. >>You know, it's interesting, you mentioned that comment about, you know, trying not to, to offend data scientists, it's actually a rising tide that the tsunami of data is actually making that population bigger too. Right. So correct. You also have data engineering, which has come out of the woodwork. We covered a lot on the cube, which is, you know, we call data as code. So infrastructure as code kind of a spoof on that. But the reality is that there's a lot more data engineering. I call that the smallest population. Those are the, those are the alphas, the alpha geeks. Yeah. Hardcore data operating systems, kind of education, data science, big pool growing. And then the users yeah. Are the new data science practitioners. Correct? Exactly. So kind of a, the landscape is you see that picture too, right? >>For sure. I mean, we, we have presence in all of those, right? Like data engineers are very important. Data scientists. Those are core users of DataRobot like, how can you develop thousands and hundreds of thousands of models without having to hand code? If you have to hand code, it takes months and years to solve one problem for one customer in one location. I mean, see how fast the microeconomic conditions are moving. And data engineers are very important because at the end of the day, yes, you do. You create the model, but you need to operationalize that model. You need to monitor that model for data drift. You need to monitor how the model is performing and you need to productionize the insights that you gain. And for that engineering effort is very important behind the scenes. Yeah. And the users at the end of the day, they are the ones who consume the predictions. >>Yeah. I mean the volume and, and the scale and scope of the data requires a lot of automation as well. Correct. Cause you had that on top of it. You gotta have a platform that's gonna do the heavy lifting. >>Correct. Exactly. The platform is we call it as an augmented platform. It augments data scientists by eliminating the tedious work that they don't want to do in their everyday life, which some of which is like feature engineering, right? It's a very high value add work. However, it takes like multiple iterations to understand which features in your data actually impact the outcome. >>This is where the SAS platform is a service is evolved and we call that super cloud, right. This new model where people can scale it out and up. So horizontally, scalable cloud, but vertically integrated into the applications. It's an integrator dilemma. Not so much correct innovators dilemma, as we say in the queue. Yeah. So I have to ask you, I'm a, I'm a buyer I'm gonna come to the marketplace. I want DataRobot why should they buy DataRobot what's in it for them? What's the key features of DataRobot for a company to hit the subscribe, buy button. >>Absolutely. Do you want to scale your data science to multiple projects? Do you want to be ahead of your competition? Do you want to make AI real? That is our pitch. We are not about doing data science for the sake of data science. We are about generating business value out of data science. And we have done it for hundreds of customers in multiple different verticals across the world, whether it is investment banks or regional banks or insurance companies or healthcare companies, we have provided real value out of data for them. And we have the knowhow in how to solve, whether it is your supply chain, forecasting, problem, demand, forecasting problem, whether it is your foreign exchange training problem, how to solve all these use cases with AI, with DataRobot. So if you want to be in the business of using your data and being ahead of your competitors, DataRobot is your tool log choice. >>Sure. Great to have you on the cube as a strategy officer, you gotta look at the chess board, right. And we're kind of in the mid game, I call it the cloud opening game was, you know, happened. Now we're in the mid game of cloud computing where you're seeing a lot of refactoring of opportunities where technologies and data is the key to success, being things secure and operationally, scalable, etcetera, et cetera. What's the key right now for the ecosystem as a strategy, look at the chessboard for data robots. Obviously marketplace is important strategy. Yeah. And bet for, for DataRobot. What else do you see for your company to be successful? And you could share with, with customers watching. >>Yeah. For us, we are in the intelligence layer, the data, the layer below us is the data layer. The layer about us is the applications and the engagement layer. DataRobot I mean, interoperability and ecosystem is important for every company, but for DataRobot it's extra important because we are in that middle of middle layer of intelligence. And we, we have to integrate with all different data warehouses out there enable our customers to pull the data out in a very, very faster way and then showcase all the predictions into, into their tool of choice. And from a chessboard perspective, I like your phrase of we are in the mid cycle of the cloud revolution. Yeah. And every cloud player has a data science platform, whether it is simple one or more complex one, or whether it has been around for quite some time or it's been latent features. And it is important for us that we have complimentary value proposition with all of them, because at the end of the day, we want to maximize our customer's choice. And DataRobot wants to be a neutral platform in supporting all the different vendors out there from a complementary standpoint, because you don't want to have a vendor lock in for your customers. So you create models in SageMaker. For example, you monitor those in DataRobot or you create models in DataRobot and monitor those in AWS so that you have to provide like a very flexible >>That's a solution architecture. >>Correct? Exactly. You have to provide a very flexible tech stack for your customers. >>Yeah. That's the choice. That's the choice. It's all good. Thank you for coming on the cube, sharing the data robot. So I really appreciate it. Thank >>You for coming. Thank you very much for the opportunity. >>Okay. Breaking it all down with the partners here, the marketplace, it's the future, obviously where people are gonna buy the buyers and sellers coming together, the partner network and marketplace, the big news here at 80 seller conference. I'm John ferry with the cube will be right back with more coverage after this short break.

>>Welcome back everyone to the cubes coverage here in Seattle, Washington, AWS's marketplace seller conference. It's the big news within the Amazon partner network, combining with marketplaces, forming the Amazon partner organization, part of a big reorg as they grow the next level NextGen cloud mid-game on the chessboard. Cube's got cover. I'm John fur, host of Cub, a great guests here from data bricks, both cube alumnis, Jack Anderson, GM of the and VP of the data bricks partnership team. For ADOS, you handle that relationship and Joel Minick vice president of product and partner marketing. You guys are the, have the keys to the kingdom with data, bricks, and AWS. Thanks for joining. Thanks for good to see you again. Thanks for >>Having us back. Yeah, John, great to be here. >>So I feel like we're at reinvent 2013 small event, no stage, but there's a real shift happening with procurement. Obviously it makes it's a no brainer on the micro, you know, people should be buying online self-service cloud scale, but Amazon's got billions being sold to their marketplace. They've reorganized their partner network. You can see kind of what's going on. They've kind of figured it out. Like let's put everything together and simplify and make it less of a website marketplace merge our partner to have more synergy and friction, less experiences so everyone can make more money and customer's gonna be happier. >>Yeah, that's right. >>I mean, you're run relationship. You're in the middle of it. >>Well, Amazon's mental model here is that they want the world's best ISVs to operate on AWS so that we can collaborate and co architect on behalf of customers. And that's exactly what the APO and marketplace allow us to do is to work with Amazon on these really, you know, unique use cases. >>You know, I interviewed Ali many times over the years. I remember many years ago, I think six, maybe six, seven years ago, we were talking. He's like, we're all in ons. Obviously. Now the success of data bricks, you've got multiple clouds. See that customers have choice, but I remember the strategy early on. It was like, we're gonna be deep. So this is speaks volumes to the, the relationship you have years. Jack take us through the relationship that data bricks has with AWS from a, from a partner perspective, Joel, and from a product perspective, because it's not like you got to Johnny come lately new to the new, to the scene, right? We've been there almost president creation of this wave. What's the relationship and has it relate to what's going on today? >>So, so most people may not know that data bricks was born on AWS. We actually did our first 100 million of revenue on Amazon. And today we're obviously available on multiple clouds, but we're very fond of our Amazon relationship. And when you look at what the APN allows us to do, you know, we're able to expand our reach and co-sell with Amazon and marketplace broadens our reach. And so we think of marketplace in three different aspects. We've got the marketplace, private offer business, which we've been doing for a number of years. Matter of fact, we we're driving well over a hundred percent year over year growth in private offers and we have a nine figure business. So it's a very significant business. And when a customer uses a private offer that private offer counts against their private pricing agreement with AWS. So they get pricing power against their, their private pricing. >>So it's really important. It goes on their Amazon bill in may. We launched our pay as you go on demand offering. And in five short months, we have well over a thousand subscribers. And what this does is it really reduces the barriers to entry it's low friction. So anybody in an enterprise or startup or public sector company can start to use data bricks on AWS and pay consumption based model and have it go against their monthly bill. And so we see customers, you know, doing rapid experimentation pilots, POCs, they're, they're really learning the value of that first use case. And then we see rapid use case expansion. And the third aspect is the consulting partner, private offers C P O super important in how we involve our partner ecosystem of our consulting partners and our resellers that are able to work with data bricks on behalf of customers. >>So you got the big contracts with the private offer. You got the product market fit, kind of people iterating with data coming in with, with the buyers you go. And obviously the integration piece all fitting in there. Exactly. Exactly. Okay. So that's that those are the offers that's current and what's in marketplace today. Is that the products, what are, what are people buying? I mean, I guess what's the Joel, what are, what are people buying in the marketplace and what does it mean for >>Them? So fundamentally what they're buying is the ability to take silos out of their organization. And that's, that is the problem that data bricks is out there to solve, which is when you look across your data landscape today, you've got unstructured data, you've got structured data, you've got real time streaming data, and your teams are trying to use all of this data to solve really complicated problems. And as data bricks as the lake house company, what we're helping customers do is how do they get into the new world? How do they move to a place where they can use all of that data across all of their teams? And so we allow them to begin to find through the marketplace, those rapid adoption use cases where they can get rid of these data, warehousing data lake silos they've had in the past, get their unstructured and structured data onto one data platform and open data platform that is no longer adherent to any proprietary formats and standards and something. >>They can very much, very easily integrate into the rest of their data environment, apply one common data governance layer on top of that. So that from the time they ingest that data to the time they use that data to the time they share that data inside and outside of their organization, they know exactly how it's flowing. They know where it came from. They know who's using it. They know who has access to it. They know how it's changing. And then with that common data platform with that common governance solution, they'd being able to bring all of those use cases together across their real time, streaming their data engineering, their BI, their AI, all of their teams working on one set of data. And that lets them move really, really fast. And it also lets them solve challenges. They just couldn't solve before a good example of this, you know, one of the world's now largest data streaming platforms runs on data bricks with AWS. >>And if you think about what does it take to set that up? Well, they've got all this customer data that was historically inside of data warehouses, that they have to understand who their customers are. They have all this unstructured data, they've built their data science model, so they can do the right kinds of recommendation engines and forecasting around. And then they've got all this streaming data going back and forth between click stream data from what the customers are doing with their platform and the recommendations they wanna push back out. And if those teams were all working in individual silos, building these kinds of platforms would be extraordinarily slow and complex, but by building it on data bricks, they were able to release it in record time and have grown at, at record pace >>To not be that's product platform that's impacting product development. Absolutely. I mean, this is like the difference between lagging months of product development to like days. Yes. Pretty much what you're getting at. Yeah. So total agility. I got that. Okay. Now I'm a customer I wanna buy in the marketplace, but I also, you got direct Salesforce up there. So how do you guys look at this? Is there channel conflict? Are there comp programs? Because one of the things I heard today in on the stage from a Davis's leadership, Chris was up there speaking and, and, and moment I was, Hey, he's a CRO conference, chief revenue officer conversation, which means someone's getting compensated. So if I'm the sales rep at data bricks, what's my motion to the customer. Do I get paid? Does Amazon sell it? Take us through that. Is there channel conflict? Is there or an audio lift? >>Well, I I'd add what Joel just talked about with, with, you know, what the solution, the value of the solution our entire offering is available on AWS marketplace. So it's not a subset, the entire data bricks offering and >>The flagship, all the, the top, >>Everything, the flagship, the complete offering. So it's not, it's not segmented. It's not a sub segment. It's it's, you know, you can use all of our different offerings. Now when it comes to seller compensation, we, we, we view this two, two different ways, right? One is that AWS is also incented, right? Versus selling a native service to recommend data bricks for the right situation. Same thing with data bricks. Our Salesforce wants to do the right thing for the customer. If the customer wants to use marketplace as their procurement vehicle. And that really helps customers because if you get data bricks and five other ISVs together, and let's say each ISV is spending, you're spending a million dollars, you have $5 million of spend, you put that spend through the flywheel with AWS marketplace. And then you can use that in your negotiations with AWS to get better pricing overall. So that's how we, >>We do it. So customers are driving. This sounds like, correct. For sure. So they're looking at this as saying, Hey, I'm gonna just get purchasing power with all my relationships because it's a solution architectural market, right? >>Yeah. It makes sense. Because if most customers will have a primary and secondary cloud provider, if they can consolidate, you know, multiple ISV spend through that same primary provider, you get pricing >>Power, okay, Jill, we're gonna date ourselves. At least I will. So back in the old days, it used to be, do a Barney deal with someone, Hey, let's go to market together. You gotta get paper, you do a biz dev deal. And then you gotta say, okay, now let's coordinate our sales teams, a lot of moving parts. So what you're getting at here is that the alternative for data bricks or any company is to go find those partners and do deals versus now Amazon is the center point for the customer so that you can still do those joint deals. But this seems to be flipping the script a little bit. >>Well, it is, but we still have VAs and consulting partners that are doing implementation work very valuable work advisory work that can actually work with marketplace through the C PPO offering. So the marketplace allows multiple ways to procure your >>Solution. So it doesn't change your business structure. It just makes it more efficient. That's >>Correct. >>That's a great way to say it. Yeah, >>That's great. So that's so that's it. So that's just makes it more efficient. So you guys are actually incented to point customers to the marketplace. >>Yes, >>Absolutely. Economically. Yeah. >>E economically it's the right thing to do for the customer. It's the right thing to do for our relationship with Amazon, especially when it comes back to co-selling right? Because Amazon now is leaning in with ISVs and making recommendations for, you know, an ISV solution and our teams are working backwards from those use cases, you know, to collaborate, land them. >>Yeah. I want, I wanna get that out there. Go ahead, Joel. >>So one of the other things I might add to that too, you know, and why this is advantageous for, for companies like data bricks to, to work through the marketplace, is it makes it so much easier for customers to deploy a solution. It's, it's very, literally one click through the marketplace to get data bricks stood up inside of your environment. And so if you're looking at how do I help customers most rapidly adopt these solutions in the AWS cloud, the marketplace is a fantastic accelerator to that. You >>Know, it's interesting. I wanna bring this up and get your reaction to it because to me, I think this is the future of procurement. So from a procurement standpoint, I mean, again, dating myself EDI back in the old days, you know, all that craziness. Now this is all the, all the internet, basically through the console, I get the infrastructure side, you know, spin up and provision. Some servers, all been good. You guys have played well there in the marketplace. But now as we get into more of what I call the business apps, and they brought this up on stage little nuance, most enterprises aren't yet there of integrating tech on the business apps, into the stack. This is where I think you guys are a use case of success where you guys have been successful with data integration. It's an integrator's dilemma, not an innovator's dilemma. So like, I want to integrate, so now I have integration points with data bricks, but I want to put an app in there. I want to provision an application, but it has to be built. It's not, you don't buy it. You build, you gotta build stuff. And this is the nuance. What's your reaction to that? Am I getting this right? Or, or am I off because no, one's gonna be buying software. Like they used to, they buy software to integrate it. >>Yeah, >>No, I, cause everything's integrated. >>I think AWS has done a great job at creating a partner ecosystem, right. To give customers the right tools for the right jobs. And those might be with third parties, data bricks is doing the same thing with our partner connect program. Right. We've got customer, customer partners like five tra and D V T that, you know, augment and enhance our platform. And so you, you're looking at multi ISV architectures and all of that can be procured through the AWS marketplace. >>Yeah. It's almost like, you know, bundling and unbundling. I was talking about this with, with Dave ante about Supercloud, which is why wouldn't a customer want the best solution in their architecture period. And it's class. If someone's got API security or an API gateway. Well, you know, I don't wanna be forced to buy something because it's part of a suite and that's where you see things get suboptimized where someone dominates a category and they have, oh, you gotta buy my version of this. Yeah. >>Joel, Joel. And that's Joel and I were talking, we're actually saying what what's really important about Databricks is that customers control the data. Right? You wanna comment on that? >>Yeah. I was say the, you know what you're pushing on there we think is extraordinarily, you know, the way the market is gonna go is that customers want a lot of control over how they build their data stack. And everyone's unique in what tools are the right ones for them. And so one of the, you know, philosophically I think really strong places, data, bricks, and AWS have lined up is we both take an approach that you should be able to have maximum flexibility on the platform. And as we think about the lake house, one thing we've always been extremely committed to as a company is building the data platform on an open foundation. And we do that primarily through Delta lake and making sure that to Jack's point with data bricks, the data is always in your control. And then it's always stored in a completely open format. And that is one of the things that's allowed data bricks to have the breadth of integrations that it has with all the other data tools out there, because you're not tied into any proprietary format, but instead are able to take advantage of all the innovation that's happening out there in the open source ecosystem. >>When you see other solutions out there that aren't as open as you guys, you guys are very open by the way, we love that too. We think that's a great strategy, but what's the, what am I foreclosing? If I go with something else that's not as open what what's the customer's downside as you think about what's around the corner in the industry. Cuz if you believe it's gonna be open, open source, which I think opens our software is the software industry and integration is a big deal, cuz software's gonna be plentiful. Let's face it. It's a good time to be in software business, but cloud's booming. So what's the downside from your data bricks perspective, you see a buyer clicking on data bricks versus that alternative what's potentially is should they be a nervous about down the road if they go with a more proprietary or locked in approach? Well, >>I think the challenge with proprietary ecosystems is you become beholden to the ability of that provider to both build relationships and convince other vendors that they should invest in that format. But you're also then beholden to the pace at which that provider is able to innovate. And I think we've seen lots of times over history where, you know, a proprietary format may run ahead for a while on a lot of innovation. But as that market control begins to solidify that desire to innovate begins to, to degrade, whereas in the open format. So >>Extract rents versus innovation. Exactly. >>Yeah, exactly. >>But >>I'll say it in the open world, you know, you have to continue to innovate. Yeah. And the open source world is always innovating. If you look at the last 10 to 15 years, I challenge you to find, you know, an example where the innovation in the data and AI world is not coming from open source. And so by investing in open ecosystems, that means you were always going to be at the forefront of what is the >>Latest, you know, again, not to date myself again, but you look back at the eighties and nineties, the protocol stacked for proprietary. Yeah. You know, SNA at IBM deck net was digital, you know, the rest is, and then TCP, I P was part of the open systems, interconnect, revolutionary Oly, a big part of that as well as my school did. And so like, you know, that was, but it didn't standardize the whole stack. It stopped at IP and TCP. Yeah. But that helped interoperate, that created a nice defacto. So this is a big part of this mid game. I call it the chessboard, you know, you got opening game and mid game. Then you got the end game and we're not there. The end game yet cloud the cloud. >>There's, there's always some form of lock in, right. Andy jazzy will, will address it, you know, when making a decision. But if you're gonna make a decision you want to reduce as you don't wanna be limited. Right. So I would advise a customer that there could be limitations with a proprietary architecture. And if you look at what every customer's trying to become right now is an AI driven business. Right? And so it has to do with, can you get that data outta silos? Can you, can you organize it and secure it? And then can you work with data scientists to feed those models? Yeah. In a, in a very consistent manner. And so the tools of tomorrow will to Joel's point will be open and we want interoperability with those >>Tools and, and choice is a matter too. And I would say that, you know, the argument for why I think Amazon is not as locked in as maybe some other clouds is that they have to compete directly too. Redshift competes directly with a lot of other stuff, but they can't play the bundling game because the customers are getting savvy to the fact that if you try to bundle an inferior product with something else, it may not work great at all. And they're gonna be they're onto it. This is >>The Amazon's credit by having these, these solutions that may compete with native services in marketplace, they are providing customers with choice, low >>Price and access to the S and access to the core value. Exactly. Which the >>Hardware, which is their platform. Okay. So I wanna get you guys thought on something else. I, I see emerging, this is again kind of cube rumination moment. So on stage Chris unpacked, a lot of stuff. I mean this marketplace, they're touching a lot of hot buttons here, you know, pricing compensation, workflows services behind the curtain. And one of the things he mentioned was they talk about resellers or channel partners, depending upon what you talk about. We believe Dave and I believe on the cube that the entire indirect sales channel of the industry is gonna be disrupted radically because those players were selling hardware in the old days and software, that game is gonna change. You know, you mentioned you guys have a program, want to get your thoughts on this. We believe that once this gets set up, they can play in this game and bring their services in which means that the old reseller channels are gonna be rewritten. They're gonna be refactored with this new kinds of access. Cuz you've got scale, you've got money and you've got product and you got customers coming into the marketplace. So if you're like a reseller that sold computers to data centers or software, you know, value added reseller or V or business, >>You've gotta evolve. >>You gotta, you gotta be here. Yes. How are you guys working with those partners? Cuz you say you have a part in your marketplace there. How do I make money? If I'm a reseller with data bricks with eight Amazon, take me through that use case. >>Well I'll let Joel comment, but I think it's, it's, it's pretty straightforward, right? Customers need expertise. They need knowhow. When we're seeing customers do mass migrations to the cloud or Hadoop specific migrations or data transformation implementations, they need expertise from consulting and SI partners. If those consulting SI partners happen to resell the solution as well. Well, that's another aspect of their business, but I really think it is the expertise that the partners bring to help customers get outcomes. >>Joel, channel big opportunity for re re Amazon to reimagine this. >>For sure. Yeah. And I think, you know, to your comment about how to resellers take advantage of that, I think what Jack was pushing on is spot on, which is it's becoming more about more and more about the expertise you bring to the table and not just transacting the software, but now actually helping customers make the right choices. And we're seeing, you know, both SI begin to be able to resell solutions and finding a lot of opportunity in that. Yeah. And I think we're seeing traditional resellers begin to move into that SI model as well. And that's gonna be the evolution that >>This gets at the end of the day. It's about services for sure, for sure. You've got a great service. You're gonna have high gross profits. And >>I think that the managed service provider business is alive and well, right? Because there are a number of customers that want that, that type of a service. >>I think that's gonna be a really hot, hot button for you guys. I think being the way you guys are open this channel partner services model coming in to the fold really kind of makes for kind of that super cloudlike experience where you guys now have an ecosystem. And that's my next question. You guys have an ecosystem going on within data bricks for sure. On top of this ecosystem, how does that work? This is kinda like hasn't been written up in business school and case studies yet this is new. What is this? >>I think, you know, what it comes down to is you're seeing ecosystems begin to evolve around the data platforms and that's gonna be one of the big kind of new horizons for us as we think about what drives ecosystems it's going to be around. Well, what is the, what's the data platform that I'm using and then all the tools that have to encircle that to get my business done. And so I think there's, you know, absolutely ecosystems inside of the AWS business on all of AWS's services, across data analytics and AI. And then to your point, you are seeing ecosystems now arise around data bricks in its Lakehouse platform, as well as customers are looking at well, if I'm standing these Lakehouse up and I'm beginning to invest in this, then I need a whole set of tools that help me get that done as well. >>I mean you think about ecosystem theory, we're living a whole nother dream and I'm, and I'm not kidding. It hasn't yet been written up and for business school case studies is that we're now in a whole nother connective tissue ecology thing happening where you have dependencies and value proposition economics connectedness. So you have relationships in these ecosystems. >>And I think one of the great things about relationships with these ecosystems is that there's a high degree of overlap. Yeah. So you're seeing that, you know, the way that the cloud business is evolving, the, the ecosystem partners of data bricks are the same ecosystem partners of AWS. And so as you build these platforms out into the cloud, you're able to really take advantage of best of breed, the broadest set of solutions out there for >>You. Joel, Jack, I love it because you know what it means the best ecosystem will win. If you keep it open. Sure. You can see everything. If you're gonna do it in the dark, you know, you don't know the outcome. I mean, this is really kind we're talking about. >>And John, can I just add that when I was in Amazon, we had a, a theory that there's buyers and builders, right? There's very innovative companies that want to build things themselves. We're seeing now that that builders want to buy a platform. Right? Yeah. And so there's a platform decision being made and that ecosystem gonna evolve around the >>Platform. Yeah. And I totally agree. And, and, and the word innovation get kicks around. That's why, you know, when we had our super cloud panel was called the innovators dilemma with a slash through it called the integrated dilemma, innovation is the digital transformation. So absolutely like that becomes cliche in a way, but it really becomes more of a, are you open? Are you integrating if APIs are the connective tissue, what's automation, what's the service message look like. I mean, a whole nother set of kind of thinking goes on and these new ecosystems and these new products >>And that, and that thinking is, has been born in Delta sharing. Right? So the idea that you can have a multi-cloud implementation of data bricks, and actually share data between those two different clouds, that is the next layer on top of the native cloud >>Solution. Well, data bricks has done a good job of building on top of the goodness of, and the CapEx gift from AWS. But you guys have done a great job taking that building differentiation into the product. You guys have great customer base, great grow ecosystem. And again, I think in a shining example of what every enterprise is going to do, build on top of something operating model, get that operating model, driving revenue. >>Yeah. >>Well we, whether whether you're Goldman Sachs or capital one or XYZ corporation >>S and P global NASDAQ, right. We've got, you know, these, the biggest verticals in the world are solving tough problems with data breaks. I think we'd be remiss cuz if Ali was here, he would really want to thank Amazon for all of the investments across all of the different functions, whether it's the relationship we have with our engineering and service teams. Yeah. Our marketing teams, you know, product development and we're gonna be at reinvent the big presence of reinvent. We're looking forward to seeing you there again. >>Yeah. We'll see you guys there. Yeah. Again, good ecosystem. I love the ecosystem evolutions happening this next gen cloud is here. We're seeing this evolve kind of new economics, new value propositions kind of scaling up, producing more so you guys are doing a great job. Thanks for coming on the Cuban, taking time. Chill. Great to see you at the check. Thanks for having us. Thanks. Going. Okay. Cube coverage here. The world's changing as APN comes to give the marketplace for a new partner organization at Amazon web services, the Cube's got a covered. This should be a very big growing ecosystem as this continues, billions of being sold through the marketplace. Of course the buyers are happy as well. So we've got it all covered. I'm John furry, your host of the cube. Thanks for watching.

>>Welcome back everyone to the cubes coverage here in Seattle, Washington for AWS's marketplace seller conference. The big news here is that the Amazon partner network and marketplace coming together and reorganizing into one organization, the AIST partner organization, APO bringing together the best of the partnership and the marketplace to sell through. It's a sellers company. This is the second year, but technically with COVID, I call it a year and a half. This is the cube. I'm John for your host. Got a great guest, Leah for sale vice president of business development at four square. Leah, thanks for coming on the cube. Look great. Yeah. >>Hey, thanks. Thanks for having me here. >>So four square, everyone, and that has internet history knows you. You check in you'd become the mayor of a place right back in the day, all fun. It was a great app and I think it was competitor go sold the Facebook, but that was the beginning of location data. Now you got Uber apps, you got all apps, location, everywhere. Data is big here in the marketplace. They sell data, they got a data exchange, Chris head of marketplaces. Like we have all these things we're gonna bring 'em together, make it simpler. So you're on the data side. I'm assuming you're selling data and you're participating at the data exchange. What is Foursquare doing right now? Yeah, >>Exactly. So we are part of the data exchange. And you mentioned checking in. So we, we are really proud of our roots, the, the four square app, and that's kind of the basis still of our business. We have a hundred million data points, which are actually places of interest across the world 200 countries. And we are we're in the business of understanding whereplace are and how people move through those places over time. And >>What's the value proposition for that data. You're selling the data. >>We are selling the data and we're selling it. You can think about use cases. Like how can I improve the engagement with my app through location data? So for example, next door, as a customer of ours, everyone knows next door. When a new business comes online, they wanna make sure that business is a real business. So they use our places to ensure that the address of that business is accurate. >>So how did you, how do you guys get your data? Because if you don't have the first party app, you probably had critical mass of data. Yeah. But then do other people use your data and then re contribute back in kinda like, well, Stripe is for financial. You guys are plugging in yeah. To >>Apps. A great question. So we still do have our consumer apps. We're still proud of those. It's still a basis of our company really. Okay. So, but we take that data. So our first party data, we also, for all the web, we have some partners integrate our SDK. And so we're pulling in all that data from various sources and then scrubbing it and making sure we have the most unique. >>So you guys still have a business where the app's working. Yep. Okay. But also let's just say, I wanna have a cube app. Yeah. And I want to do a check in button. Yep. So rather than build checking in, could I OEM you could four square is that you >>Could, and we could help you understand where people are checking in. So we know someone's here at the Hilton and Bellevue, we know exactly where that place is. You building the Cub app. You could say, I'm gonna check in here and we are verified. We know that that's the >>Right place. So that's a good for developer if they're building an app. >>Absolutely. So we have an SDK that any developer can integrate. >>Great. Okay. So what's the relationship with the marketplace? Take us through how Foursquare works with AWS marketplace. >>Sure. So we are primarily integrated with ADX, which is sort of a piece of marketplace it's for data specifically, we have both of our main products, which are places that POI database and visits, which is how people move through those places over time. So we're able to say these are the top chains in the country. Here's how people move throughout those. And both those products are listed on ADX. >>So if I'm in Palo Alto and I go to Joe in the juice yeah. You know that I kind of hang in one spot or is it privacy there? I mean, how do you know like what goes on? Well, >>We know somebody does that. We don't >>Know that you do that. So >>We ensure, you know, we're very privacy centric and privacy focused. We're not gonna, we don't tell anybody at you >>Yourself it's pattern data. It is. >>Okay. So it's normalized data, right? Over time groups of people, >>How they, how are people using the data to improve processes, user experience? What are some of the use cases? >>So that example, nextdoor, that's really a use case that we see a lot and that's improving their application. So that nextdoor app to ensure that the ACC, the data's accurate and that as you, as a user, you know, that that business is real. Cuz it's verified by four wear. Another one is you can use our data to make business decisions around where you're gonna place your next loca. You know, your next QSR. So young brands is a customer of ours. Those are, those guys are pizza hut KFC. They work with us to figure out where they should put their next KFC. Yeah. >>I mean retail location, location, location. Yeah. >>Right. Yeah. People are still, even though e-commerce right. People still go into stores >>And still are. Yeah. There's, there's, there's probably lot, a lot of math involved in knowing demographics patterns. Volume. >>Yeah. Some of our key customers are really data scientists. Like the think about cus with businesses that have true data science companies. They're really looking at that. >>Yeah. I mean in, and out's on the exit for a reason. Right. They want in and out. Yeah. So they wanna put it inland. >>Right. And we can actually tell you where that customer from in and out where they go next. Right. So then, you know, oh, they go to this park or they go somewhere and we can help you place your next in and out based on that visitation. >>Yeah. And so it's real science involved. So take us through the customers. You said data scientists, >>Mostly data scientists is kind of a key customer data science at a large corporation, like a QSR that's >>Somebody. Okay. So how is the procurement process on the marketplace? What does the buyer get? >>So what we see the real value is, is because they're already a customer of Amazon. That procurement is really easy, right? All the fulfillment goes through Amazon, through ADX. And what you're buying is either at API. So you can, that API can make real time calls or you're buying a flat file, like an actual database of those hundred points of interest. >>And then they integrate into their tool set. Right. They can do it. So it's pretty data friendly in terms of format. >>You can kind of do whatever you want with it. We're gonna give you that as long as you're smart enough to figure out what to do. Do we have a >>Lot of, so what's your experience with AWS marketplace? I mean, obviously we, we see a lot of changes. They had a reorg partner network merging with marketplace. You've been more on the data exchange, Chris kind of called that out. It's yeah. It's kind of a new thing. And, and he was hinting at a lot of confusion, but simplifying things. Yeah. What's your take of the current AWS marketplace >>Religions? I actually think ADX because our experience has primarily been ADX. I think they've done a really good job. They've really focused on the data and they understand how CU, how, you know, people like us sell our data. It hasn't been super confusing. We've had a lot of support. I think that's what Amazon gives you. You have to put a lot of effort into it, but they're also, they also give you a lot of support. >>Yeah. And, and I think data exchange is pretty significant to the strategic. It is >>Mission. It is. We feel that. Yeah. You know, we feel like they really value us as a partner. >>What's the big thing you're seeing out there right now in data, because like you're seeing a lot more data exchanges going on. There's always been data exchange, but you're seeing a lot more exchanges between companies. So let's just take partners. You're seeing a lot more people handle front end of a, a supply chain and you got more data exchanges. What's the future of data exchanges. If you had to kind of, you know, guess given your history in, in the industry. Yeah. What's the next around the corner trend? >>I think. Well, I think there's a, has to be consolidation. I know everyone's building one, but there's probably too many. I know from our experience, we can't support all of them. We're not a huge company. We can't support Amazon and X and Y and Z. Like it's just too many. So we kind of put all of our eggs in a couple baskets. So I think there'll be consolidation. I think there has to be just some innovation on what data products are, you know, for us, we have these two, it's an API and a flat file. I think as exchanges think about, you know, expanding what are the other types of data products that can help us build? >>Yeah. I mean, one of the things that's, you know, we see, we cover a lot of on the cube is edge. You know, you got, yeah. Amazon putting out new products in regions, you got new wavelength out there, you got regions, you got city level connectivity, data coming from cars. So a lot more IOT data. How do you guys see that folding into your vision of data acquisition and data usage, leverage, reuse, durability. These >>Are, yeah. I mean, we're, we are keeping an eye on all of that. You know, I think we haven't quite figured out how we wanna allocate resources against it, but you know, it's definitely, it's a really interesting space to be in. Like, I don't think data's going anywhere and I think it's really just gonna grow and how people use it's >>Gonna expand. Okay. So if I'm a customer, I go to the marketplace, I wanna buy four square data. What's the pitch. >>We can help you improve your business decisions or your applications through location data. We know where places are and how people move through the world over time. So we can tell you we're, we're sure that this is the Hilton in Bellevue. We know that, that we know how many people are moving through here and that's really the pitch. >>And they use that for whatever their needs are, business improvement, user experience. Yeah. >>Those are really the primary. I mean, we also have some financial use cases. So hedge funds, maybe they're thinking about yeah. How they wanna invest their money. They're gonna look at visits over time to understand what people are doing. Right. The pandemic made that super important. >>Yeah. That's awesome. Well, this is great. Great success story. Congratulations. And thanks for sharing on the cube. Really appreciate you coming on. Thank you. My final question is more about kind of the future. I wanna get your thoughts because your season pro, when you have the confluence of physical and digital coming together. Yeah. You know, I was just talking with a friend about FedEx's earnings, comparing that to say, AWS has a fleet of delivery too. Right? Amazon, Amazon nots. So, but physical world only products location matters. But then what about the person when they're walking around the real world? What happens when they get to the metaverses or, you know, they get to digital, they tend an event. Yeah. How do you see that crossroad? Cuz you have foot in both camps. We do, you got the app and you got the physical world it's gonna come together. Is there thoughts around, you can take your course care hat off and put your industry hat on. Yeah. You wanna answer that? Not officially on behalf of Foursquare, but I'm just curious, this is a, this is the confluence of like the blending of physical and digital. >>Yeah. I know. Wow. I admittedly haven't thought a whole lot about that. I think it would be really weird if I could track myself over time and the metaverse I mean, I think, yeah, as you said, it's >>It's, by the way, I'm not Bo on the metaverse when it's blocked diagrams, when you have gaming platforms that are like the best visual experience possible, right? >>Yeah. I mean, I think it, I think we'll see, I don't, I don't know that I have a >>Prediction, well hybrid we've seeing a lot of hybrid events. Like this event is still intimate VIP, but next year I guarantee it's gonna be larger, much larger and it's gonna be physical and face to face, but, but digital right as well. Yeah. Not people experiencing the, both that first party, physical, digital hybrid. Yeah. And it's interesting something that we track a lot >>Of. Yeah, for sure. Yeah. I think we'll have a, well, I think we'll, there's something there for us. I think that those there's a play there as we watch kind >>Of things change. All right, Leah, thank you for coming on the Q appreciate so much it all right. With four Graham, John fur a year checking in with four square here on the cube here at the Amazon web services marketplace seller conference. Second year back from the pandemic in person, more coverage after this break.

(upbeat electronic music) >> Welcome back to everyone, to "theCUBE's" coverage here in Seattle, Washington for Amazon Web Services Partner Marketplace Seller Conference, combining their partner network with Marketplace forming a new organization called AWS Partner Organization. This is "theCUBE" coverage. I'm John Furrier, your host. We've got great "Cube" alumni here from Zscaler, a very successful cloud company doing great work. Stelio D'Alo, senior director of cloud business development and Raveesh Chugh, VP of Public Cloud Partnerships at Zscaler. Welcome back to "theCUBE." Good to see you guys. Thanks for coming on. >> Thank you. >> Thanks having us, John. >> So we've been doing a lot of coverage of Zscaler, what a great success story. I mean, the numbers are great. The business performance, it's in the top two, three, one, two, three in all metrics on public companies, SaaS. So you guys, check. Good job. >> Yes, thank you. >> So you guys have done a good job. Now you're here, selling through the Marketplace. You guys are a world class performing company in cloud SaaS, so you're in the front lines doing well. Now, Marketplace is a procurement front end opportunity for people to buy. Hey, self-service, buy and put things together. Sounds novel, what a great concept. Great cloud life. >> Yes. >> You guys are participating and now sellers are coming together. The merger of the public, the partner network with Marketplace. It feels like this is a second act for AWS to go to the next level. They got their training wheels done with partners. Now they're going to the next level. What do you guys think about this? >> Well, I think you're right, John. I think it is very much something that is in keeping with the way AWS does business. Very Amazonian, they're working back from the customer. What we're seeing is, our customers and in general, the market is gravitating towards purchase mechanisms and route to market that just are lower friction. So in the same way that companies are going through their digital transformations now, really modernizing the way they host applications and they reach the internet. They're also modernizing on the purchasing side, which is super exciting, because we're all motivated to help customers with that agility. >> You know, it's fun to watch and again I'm being really candid and props to you guys as a company. Now, everyone else is kind of following that. Okay, lift and shift, check, doing some things. Now they go, whoa, I can really build on this. People are building their own apps for their companies. Going to build their own stuff. They're going to use piece parts. They're going to put it together in a really scalable way. That's the new normal. Okay, so now they go okay, I'm going to just buy through the market, I get purchasing power. So you guys have been a real leader with AWS. Can you share what you guys are doing in the Marketplace? I think you guys are a nice example of how to execute the Marketplace. Take us through. What are you guys offering there? What's the contract look like? Is it multi-pronged? What's the approach? What do customers get if they go to the marketplace for Zscaler? >> Yeah, so it's been a very exciting story and been a very pleasing one for us with AWS marketplace. We see a huge growth potentially. There are more than 350,000 customers that are actively buying through Marketplace today. We expect that number to grow to around a million customers by the next, I would say, five to ten years and we want to be part of this wave. We see AWS Marketplace to be a channel where not only our resalers or our channel partners can come and transact, but also our GSIs like Accenture want to transact through this channel. We are doing a lot, in terms of bringing new customers through Marketplace, who want to not only close their deals, but close it in the next few hours. That's the beauty of Marketplace, the agility, the flexibility in terms of pricing that it provides to ISVs like us. If a customer wants to delay their payments by a couple of quarters, Marketplace supports that. If a customer wants to do monthly payments, Marketplace supports that. We are seeing lot of customers, big customers, that have signed EDPs, enterprise discount plans with AWS. These are multi-year cloud commits coming to us and saying we can retire our EDPs with AWS if we transact your solution through AWS Marketplace. So what we have done, as of today, we have all of our production services enabled through AWS Marketplace. What that means for customers, they can now retire their EDPs by buying Zscaler products through AWS Marketplace and in return get the full benefit of maximizing their EDP commits with AWS. >> So you guys are fully committed, no toe on the water, as we heard. You guys are all in. >> Absolutely, that's exactly the way to put it. We're all in, all of our solutions are available in the marketplace. As you mentioned, we're a SaaS provider. So we're one of the vendors in the Marketplace that have SaaS solutions. So unlike a lot of customers and even the market in general, associate the Marketplace for historical reasons, the way it started with a lot of monthly subscriptions and just dipping your toe in it from a consumer perspective. Whereas we're doing multimillion dollar, multi-year SaaS contracts. So the most complicated kinds of transactions you'd normally associate with enterprise software, we're doing in very low friction ways. >> On the Zscaler side going in low friction. >> Yep, yeah, that's right. >> How about the customer experience? >> So it is primarily the the customer that experiences. >> Driving it? >> Yeah, they're driving it and it's because rather than traditional methods of going through paperwork, purchase orders- >> What are some of the things that customers are saying about this, bcause I see two benefits, I'll say that. The friction, it's a channel, okay, for Zscaler. Let's be clear, but now you have a customer who's got a lot of Amazon. They're a trusted partner too. So why wouldn't they want to have one point of contact to use their purchasing power and you guys are okay with that. >> We're absolutely okay with it. The reason being, we're still doing the transaction and we can do the transaction with our... We're a channel first company, so that's another important distinction of how people tend to think of the Marketplace. We go through channel. A lot of our transactions are with traditional channel partners and you'd be surprised the kinds of, even the Telcos, carrier providers, are starting to embrace Marketplace. So from a customer perspective, it's less paperwork, less legal work. >> Yeah, I'd love to get your reaction to something, because I think this highlights to me what we've been reporting on with "theCUBE" with super cloud and other trends that are different in a good way. Taking it to the next level and that is that if you look at Zscaler, SaaS, SaaS is self-service, the scale, there's efficiencies. Marketplace first started out as a self-service catalog, a website, you know, click and choose, but now it's a different. He calls it a supply chain, like the CICD pipeline of buying software. He mentions that, there's also services. He put the Channel partners can come in. The GSIs, global system integrators can come in. So it's more than just a catalog now. It's kind of self-service procurement more than it is just a catalog of buy stuff. >> Yes, so yeah, I feel CEOs, CSOs of today should understand what Marketplace brings to the bear in terms of different kinds of services or Zscaler solutions that they can acquire through Marketplace and other ISV solutions, for that matter. I feel like we are at a point, after the pandemic, where there'll be a lot of digital exploration and companies can do more in terms of not just Marketplace, but also including the channel partners as part of deals. So you talked about channel conflict. AWS addressed this by bringing a program called CPPO in the picture, Channel Partner Private Offers. What that does is, we are not only bringing all our channel partners into deals. For renewals as well, they're the partner of record and they get paid alongside with the customer. So AWS does all the heavy lifting, in terms of disbursements of payments to us, to the channel partner, so it's a win-win situation for all. >> I mean, private offers and co-sale has been very popular. >> It has been, and that is our bread and butter in the Marketplace. Again, we do primarily three year contracts and so private offers work super well. A nice thing for us as a vendor is it provides a great amount of flexibility. Private Offer gives you a lot of optionality, in terms of how the constructs of the deal and whether or not you're working with a partner, how the partner is utilizing as well to resell to the end user. So, we've always talked about AWS giving IT agility. This gives purchasing and finance business agility. >> Yeah, and I think this comes up a lot. I just noticed this happening a lot more, where you see dedicated sessions, not just on DevOps and all the goodies of the cloud, financial strategy. >> Yeah. >> Seeing a lot more conversation around how to operationalize the business transactions in the cloud. >> Absolutely. >> This is the new, I mean it's not new, it's been thrown around, but not at a tech conference. You don't see that. So I got to ask you guys, what's the message to the CISOs and executives watching the business people about Zscaler in the Marketplace? What should they be looking at? What is the pitch for Zscaler for the Marketplace buyer? >> So I would say that we are a cloud-delivered network security service. We have been in this game for more than a decade. We have years of early head start with lots of features and functionality versus our competitors. If customers were to move into AWS Cloud, they can get rid of their next-gen firewalls and just have all the traffic routed through our Zscaler internet access and use Zscaler private access for accessing their private applications. We feel we have done everything in our capacity, in terms of enabling customers through Marketplace and will continue to participate in more features and functionality that Marketplace has to offer. We would like these customers to take advantage of their EDPs as well as their retirement and spend for the multi-commit through AWS Marketplace. Learn about what we have to offer and how we can really expedite the motion for them, if they want to procure our solutions through Marketplace >> You know, we're seeing an ability for them to get more creative, more progressive in terms of the purchasing. We're also doing, we're really excited about the ability to serve multiple markets. So we've had an immense amount of success in commercial. We also are seeing increasing amount of public sector, US federal government agencies that want to procure this way as well for the same reasons. So there's a lot of innovation going on. >> So you have the FedRAMP going on, you got all those certifications. >> Exactly right. So we are the first cloud-native solution to provide IL5 ATO, as well as FedRAMP pie and we make that all available, GSA schedule pricing through the AWS Marketplace, again through FSIs and other resellers. >> Public private partnerships have been a big factor, having that span of capability. I got to ask you about, this is a cool conversation, because now you're like, okay, I'm selling through the Marketplace. Companies themselves are changing how they operate. They don't just buy software that we used to use. So general purpose, bundled stuff. Oh yeah, I'm buying this product, because this has got a great solution and I have to get forced to use this firewall, because I bought this over here. That's not how companies are architecting and developing their businesses. It's no longer buying IT. They're building their company digitally. They have to be the application. So they're not sitting around, saying hey, can I get a solution? They're building and architecting their solution. This is kind of like the new enterprise that no one's talking about. They kind of, got to do their own work. >> Yes. >> There's no general purpose solution that maps every company. So they got to pick the best piece parts and integrate them. >> Yes and I feel- >> Do you guys agree with that? >> Yeah, I agree with that and customers don't want to go for point solutions anymore. They want to go with a platform approach. They want go with a vendor that can not only cut down their vendors from multi-dozens to maybe a dozen or less and that's where, you know, we kind of have pivoted to the platform-centric approach, where we not only help customers with Cloud Network Security, but we also help customers with Cloud Native Application Protection Platform that we just recently launched. It's going by the name of the different elements, including Cloud Security Posture Management, Cloud Identity Event Management and so we are continuously doing more and more on the configuration and vulnerability side space. So if a customer has an AWS S3 bucket that is opened it can be detected and can be remediated. So all of those proactive steps we are taking, in terms of enhancing our portfolio, but we have come a long way as a company, as a platform that we have evolved in the Marketplace. >> What's the hottest product? >> The hottest product? >> In Marketplace right now. >> Well, the fastest growing products include our digital experience products and we have new Cloud Protection. So we've got Posture and Workload Protection as well and those are the fastest growing. For AWS customers a strong affinity also for ZPA, which provides you zero trust access to your workloads on AWS. So those are all the most popular in Marketplace. >> Yeah. >> So I would like to add that we recently launched and this has been a few years, a couple of years. We launched a product called Zscaler Digital X, the ZDX. >> Mm-hmm. >> What that product does is, let's say you're making a Zoom call and your WiFi network is laggy or it's a Zoom server that's laggy. It kind of detects where is the problem and it further tells the IT department you need to fix either the server on which Zoom is running, or fix your home network. So that is the beauty of the product. So I think we are seeing massive growth with some of our new editions in the portfolio, which is a long time coming. >> Yeah and certainly a lot of growth opportunities for you guys, as you come in. Where do you see Zscaler's big growth coming from product-wise? What's the big push? Actually, this is great upside for you here. >> Yeah. >> On the go to market side. Where's the big growth for Zscaler right now? So I think we are focused as a company on zero trust architecture. We want to securely connect users to apps, apps to apps, workloads to workloads and machines to machines. We want to give customers an experience where they have direct access to the apps that's hidden from the outside world and they can securely connect to the apps in a very succinct fashion. The user experience is second to none. A lot of customers use us on the Microsoft Office 365 side, where they see a lag in connecting to Microsoft Office 365 directly. They use the IE service to securely connect. >> Yeah, latency kills. >> Microsoft Office 365. >> Latency kills, as we always say, you know and security, you got to look at the pattern, you want to see that data. >> Yeah, and emerging use cases, there is an immense amount of white space and upside for us as well in emerging use cases, like OT, 5G, IOT. >> Yeah. >> Federal government, DOD. >> Oh god, tactical edge government. >> Security at the edge, absolutely, yeah. >> Where's the big edge? What's the edge challenge right now, if you have to put your finger on the edge, because right now that's the hot area, we're watching that. It's going to be highly contested. It's not yet clear, I mean certainly hybrid is the operating model, cloud, distributing, computing, but edge has got unique things that you can't really point to on premises that's the same. It's highly dynamic, you need high bandwidth, low latency, compute at the edge. The data has to be processed right there. What's the big thing at the edge right now? >> Well, so that's probably an emerging answer. I mean, we're working with our customers, they're inventing and they're kind of finding the use cases for those edge, but one of the good things about Zscaler is that we are able to, we've got low latency at the edge. We're able to work as a computer at the edge. We work on Outpost, Snowball, Snowcone, the Snow devices. So we can be wherever our customers need us. Mobile devices, there are a lot of applications where we've got to be either on embedded devices, on tractors, providing security for those IOT devices. So we're pretty comfortable with where we are being the- >> So that's why you guys are financially doing so well, performance wise. I got to ask you though, because I think that brings up the great point. If this is why I like the Marketplace, if I'm a customer, the edge is highly dynamic. It's changing all the time. I don't want to wait to buy something. If I got my solution architects on a product, I need to know I'm going to have zero trust built in and I need to push the button on Zscaler. I don't want to wait. So how does the procurement side impact? What have you guys seen? Share your thoughts on how Marketplace is working from the procurement standpoint, because it seems to me to be fast. Is that right, or is it still slow on their side? On the buyer side, because this to me would be a benefit to developers, if we say, hey, the procurement can just go really fast. I don't want to go through a bunch of PO approvals or slow meetings. >> It can be, that manifests itself in several ways, John. It can be, for instance, somebody wants to do a POC and traditionally you could take any amount of time to get budget approval, take it through. What if you had a pre-approved cloud budget and that was spent primarily through AWS Marketplace, because it's consolidated data on your AWS invoice. The ability to purchase a POC on the Marketplace could be done literally within minutes of the decision being made to go forward with it. So that's kind of a front end, you know, early stage use case. We've got examples we didn't talk about on our recent earnings call of how we have helped customers bring in their procurement with large million dollar, multimillion dollar deals. Even when a resaler's been involved, one of our resaler partners. Being able to accelerate deals, because there's so much less legal work and traditional bureaucratic effort. >> Agility. >> That agility purchasing process has allowed our customers to pull into the quarter, or the end of month, or end of quarter for them, deals that would've otherwise not been able to be done. >> So this is a great example of where you can set policy and kind of create some guard rails around innovation and integration deals, knowing if it's something that the edge is happening, say okay, here's some budget. We approved it, or Amazon gives credits and partnership going on. Then I'd say, hey, well green light this, not to exceed a million dollars, or whatever number in their range and then let people have the freedom to execute. >> You're absolutely right, so from the purchasing side, it does give them that agility. It eliminates a lot of the processes that would push out a purchase in actual execution past when the business decision is made and quite frankly, to be honest, AWS has been very accommodative. They're a great partner. They've invested a lot in Marketplace, Marketplace programs, to help customers do the right thing and do it more quickly as well as vendors like us to help our customers make the decisions they need to. >> Rising tide, a rising tide floats all boats and you guys are a great example of an independent company. Highly successful on your own. >> Yep. >> Certainly the numbers are clear. Wall Street loves Zscaler and economics are great. >> Our customer CSAT numbers are off the scale as well. >> Customers are great and now you've got the Marketplace. This is again, a new normal. A new kind of ecosystem is developing where it's not like the old monolithic ecosystems. The value creation and extraction is happening differently now. It's kind of interesting. >> Yes and I feel we have a long way to go, but what I can tell you is that Zscaler is in this for the long run. We are seeing some of the competitors erupt in the space as well, but they have a long way to go. What we have built requires years worth of R&D and features and thousands of customer's use cases which kind of lead to something what Zscaler has come up with today. What we have is very unique and is going to continuously be an innovation in the market in the years to come. In terms of being more cloud-savvy or more cloud-focused or more cloud-native than what the market has seen so far in the form of next-gen firewalls. >> I know you guys have got a lot of AI work. We've had many conversations with Howie over there. Great stuff and really appreciate you guys participating in our super cloud event we had and we'll see more of that where we're talking about the next generation clouds, really enabling that new disruptive, open-spanning capabilities across multiple environments to run cloud-native modern applications at scale and secure. Appreciate your time to come on "theCUBE". >> Thank you. >> Thank you very much. >> Thanks for having us. >> Thanks, I totally appreciate it. Zscaler, leading company here on "theCUBE" talking about their relationship with Marketplace as they continue to grow and succeed as technology goes to the next level in the cloud. Of course "theCUBE's" covering it here in Seattle. I'm John Furrier, your host. Thanks for watching. (peaceful electronic music)

>>Hey, everyone. Welcome to the cubes coverage of women in data science, 2022. I'm Lisa Martin. And I'm here with one of the key featured keynotes for this year is with events. So the Aragon, the professor and department of human centered design and engineering at the university of Washington Cecilia, it's a pleasure to have you on the cube. >>Thank you so much, Lisa Lisa, it's a pleasure to be here as well. >>You got an amazing background that I want to share with the audience. You are a professor, you are a data scientist, an aerobatic pilot, and an author with expertise in human centered, data science, visual analytics, aviation safety, and analysis of extremely large and complex data sets. That's quite the background. >>Well, thank you so much. It's it's all very interesting and fun. So, >>And as a professor, you study how people make sense of vast data sets, including a combination of computer science and art, which I love. And as an author, you write about interesting things. You write about how to overcome fear, which is something that everybody can benefit from and how to expand your life until it becomes amazing. I need to take a page out of your book. You were also honored by president Obama a few years back. My goodness. >>Thank you so much. Yes. I I've had quite a journey to come here, but I feel really fortunate to be here today. >>Talk about that journey. I'd love to understand if you were always interested in stem, if it was something that you got into later, I know that you are the co-founder of Latinas in computing, a passionate advocate for girls and women in stem. Were you always interested in stem or was it something that you got into in a kind of a non-linear path? >>I was always interested in it when I was a young girl. I grew up in a small Midwestern town and my parents are both immigrants and I was one of the few Latinas in a mostly white community. And I was, um, I loved math, but I also wanted to be an astronaut. And I remember I, when we were asked, I think it was in second grade. What would you like to be when you grow up? I said, oh, I want to be an astronaut. And my teacher said, oh, you can't do that. You're a girl pick something else. And um, so I picked math and she was like, okay. >>Um, so I always wanted to, well, maybe it would be better to say I never really quite lost my love of being up in the air and potentially space. But, um, but I ended up working in math and science and, um, I, I loved it because one of the great advantages of math is that it's kind of like a magic trick for young people, especially if you're a girl or if you are from an underrepresented group, because if you get the answers right on a math test, no one can mark you wrong. It doesn't matter what the color of your skin is or what your gender is. Math is powerful that way. And I will say there's nothing like standing in a room in front of a room of people who think little of you and you silence them with your love with numbers. >>I love that. I never thought about math as power before, but it clearly is. But also, you know, and, and I wish we had more time because I would love to get into how you overcame that fear. And you write books about that, but being told you can't be an astronaut. You're a girl and maybe laughing at you because you liked Matt. How did you overcome that? And so nevermind I'm doing it anyway. >>Well, that's a, it's a, okay. The short answer is I had incredible imposter syndrome. I didn't believe that I was smart enough to get a PhD in math and computer science. But what enabled me to do that was becoming a pilot and I B I learned how to fly small airplanes. I learned how to fly them upside down and pointing straight at the ground. And I know this might sound kind of extreme. So this is not what I recommend to everybody. But if you are brought up in a way where everybody thinks little of you, one of the best things you can possibly do is take on a challenge. That's scary. I was afraid of everything, but by learning to fly and especially learning to fly loops and rolls, it gave me confidence to do everything else because I thought I appointed the airplane at the ground at 250 miles an hour and waited, why am I afraid to get a PhD in computer science? >>Wow. How empowering is that? >>Yeah, it really was. So that's really how I overcame the fear. And I will say that, you know, I encountered situations getting my PhD in computer science where I didn't believe that I was good enough to finish the degree. I didn't believe that I was smart enough. And what I've learned later on is that was just my own emotional, you know, residue from my childhood and from people telling me that they, you know, that they, that I couldn't achieve >>As I look what, look what you've achieved so far. It's amazing. And we're going to be talking about some of the books that you've written, but I want to get into data science and AI and get your thoughts on this. Why is it necessary to think about human issues and data science >>And what are your thoughts there? So there's been a lot of work in data science recently looking at societal impacts. And if you just address data science as a purely technical field, and you don't think about unintended consequences, you can end up with tremendous injustices and societal harms and harms to individuals. And I think any of us who has dealt with an inflexible algorithm, even if you just call up, you know, customer service and you get told, press five for this press four for that. And you say, well, I don't fit into any of those categories, you know, or have the system hang up on you after an hour. I think you'll understand that any type of algorithmic approach, especially on very large data sets has the risk of impacting people, particularly from low income or marginalized groups, but really any of us can be impacted in a negative way. >>And so, as a developer of algorithms that work over very large data sets, I've always found it really important to consider the humans on the other end of the algorithm. And that's why I believe that all data science is truly human centered or should be human centered, should be human centered and also involves both technical issues as well as social issues. Absolutely correct. So one example is that, um, many of us who started working in data science, including I have to admit me when I started out assume that data is unbiased. It's scrubbed of human influence. It is pure in some ways, however, that's really not true as I've started working with datasets. And this is generally known in the field that data sets are touched by humans everywhere. As a matter of fact, in our, in the recent book that we're, that we're coming out with human centered data science, we talk about five important points where humans touch data, no matter how scrubbed of human influence it's support it's supposed to be. >>Um, so the first one is discovery. So when a human encounters, a data set and starts to use it, it's a human decision. And then there's capture, which is the process of searching for a data set. So any data that has to be selected and chosen by an individual, um, then once that data set is brought in there's curation, a human will have to select various data sets. They'll have to decide what is, what is the proper set to use. And they'll be making judgements on this the time. And perhaps one of the most important ways the data is changed and touched by humans is what we call the design of data. And what that means is whenever you bring in a data set, you have to categorize it. No, for example, let's suppose you are, um, a geologist and you are classifying soil data. >>Well, you don't just take whatever the description of the soil data is. You actually may put it into a previously established taxonomy and you're making human judgments on that. So even though you think, oh, geology data, that's just rocks. You know, that's soil. It has nothing to do with people, but it really does. Um, and finally, uh, people will label the data that they have. And this is especially critical when humans are making subjective judgments, such as what race is the person in this dataset. And they may judge it based on looking at the individual skin color. They may try to apply an algorithm to it, but you know what? We all have very different skin colors, categorizing us into race boxes, really diminishes us and makes us less than we truly are. So it's very important to realize that humans touch the data. We interpret the data. It is not scrubbed of bias. And when we make algorithmic decisions, even the very fact of having an algorithm that makes a judgment say on whether a prisoner's likely to offend again, the judge just by having an algorithm, even if the algorithm makes a recommended statement, they are impacted by that algorithms recommendation. And that has obviously an impact on that human's life. So we consider all of this. >>So you just get given five solid reasons why data science and AI are inevitably human centric should be, but in the past, what's led to the separation between data science and humans. >>Well, I think a lot of it simply has to do with incorrect mental models. So many of us grew up thinking that, oh, humans have biases, but computers don't. And so if we just take decision-making out of people's hands and put it into the hands of an algorithm, we will be having less biased results. However, recent work in the field of data science and artificial intelligence has shown that that's simply not true that algorithmic algorithms reinforce human biases. They amplify them. So algorithmic biases can be much worse than human biases and can greater impact. >>So how do we pull ethics into all of this data science and AI and that ethical component, which seems to be that it needs to be foundational. >>It absolutely has to be foundational. And this is why we believe. And what we teach at the university of Washington in our data science courses is that ethical and human centered approaches and ideas have to be brought in at the very beginning of the algorithm. It's not something you slap on at the end or say, well, I'll wait for the ethicists to weigh in on this. Now we are all human. We can all make human decisions. We can all think about the unintended consequences of our algorithms as we develop them. And we should do that at the very beginning. And all algorithm designers really need to spend some time thinking about the impact that their algorithm may have. >>Right. Do you, do you find that people are still in need of convincing of that or is it generally moving in that direction of understanding? We need to bring ethics in from the beginning, >>It's moving in that direction, but there are still people who haven't modified their mental models yet. So we're working on it. And we hope that with the publication of our book, that it will be used as a supplemental textbook in many data science courses that are focused exclusively on the algorithms and that they can open up the idea that considering the human centered approaches at the beginning of learning about algorithms and data science and the mathematical and statistical techniques, that the next generation of data scientists and artificial intelligence developers will be able to mitigate some of the potentially harmful effects. And we're very excited about this. This is why I'm a professor, because I want to teach the next generation of data scientists and artificial intelligence experts, how to make sure that their work really achieves what they intended it to, which is to make the world a better place, not a worse place, but to enable humans to do better and to mitigate biases and really to lead us into this century in a positive way. >>So the book, human centered data science, you can see it there over Sicily, his right shoulder. When does this come out and how can folks get a copy of it? >>So it came out March 1st and it's available in bookstores everywhere. It was published by MIT press, and you can go online or you can go to your local independent bookstore, or you can order it from your university bookstore as well. >>Excellent. Got to, got to get a copy of, get my hands on that. Got cut and get a copy and dig into that. Cause it sounds so interesting, but also so thoughtful and, um, clear in the way that you described that. And also all the opportunities that, that AI data science and humans are gonna unlock for the world and humans and jobs and, and great things like that. So I'm sure there's lots of great information there. Last question I mentioned, you are keynoting at this year's conference. Talk to me about like the top three takeaways that the audience is going to get from your keynote. >>So I'm very excited to have been invited to wins this year, which of course is a wonderful conference to support women in data science. And I've been a big fan of the conference since it was first developed here, uh, here at Stanford. Um, the three, the three top takeaways I would say is to really consider the data. Science can be rigorous and mathematical and human centered and ethical. It's not a trade-off, it's both at the same time. And that's really the, the number one that, that I'm hoping to keynote will bring to, to the entire audience. And secondly, I hope that it will encourage women or people who've been told that maybe you're not a science person or this isn't for you, or you're not good at math. I hope it will encourage them to disbelieve those views. And to realize that if you, as a member of any type of unread, underrepresented group have ever felt, oh, I'm not good enough for this. >>I'm not smart enough. It's not for me that you will reconsider because I firmly believe that everyone can be good at math. And it's a matter of having the information presented to you in a way that honors your, the background you had. So when I started out my, my high school didn't have AP classes and I needed to learn in a somewhat different way than other people around me. And it's really, it's really something. That's what I tell young people today is if you are struggling in a class, don't think it's because you're not good enough. It might just be that the teacher is not presenting it in a way that is best for someone with your particular background. So it doesn't mean they're a bad teacher. It doesn't mean you're unintelligent. It just means the, maybe you need to find someone else that can explain it to you in a simple and clear way, or maybe you need to get some scaffolding that is Tate, learn extra, take extra classes that will help you. Not necessarily remedial classes. I believe very strongly as a teacher in giving students very challenging classes, but then giving them the scaffolding so that they can learn that difficult material. And I have longer stories on that, but I think I've already talked a bit too long. >>I love that. The scaffolding, I th I think the, the one, one of the high level takeaways that we're all going to get from your keynote is inspiration. Thank you so much for sharing your path to stem, how you got here, why humans, data science and AI are, have to be foundationally human centered, looking forward to the keynote. And again, Cecilia, Aragon. Thank you so much for spending time with me today. >>Thank you so much, Lisa. It's been a pleasure, >>Likewise versus silly Aragon. I'm Lisa Martin. You're watching the cubes coverage of women in data science, 2022.

>> Joy: Welcome to the Virtual Big Data Conference. Vertica is so excited to host this event. I'm Joy King, and I'll be your host for today's Big Data Conference Keynote Session. It's my honor and my genuine pleasure to lead Vertica's product and go-to-market strategy. And I'm so lucky to have a passionate and committed team who turned our Vertica BDC event, into a virtual event in a very short amount of time. I want to thank the thousands of people, and yes, that's our true number who have registered to attend this virtual event. We were determined to balance your health, safety and your peace of mind with the excitement of the Vertica BDC. This is a very unique event. Because as I hope you all know, we focus on engineering and architecture, best practice sharing and customer stories that will educate and inspire everyone. I also want to thank our top sponsors for the virtual BDC, Arrow, and Pure Storage. Our partnerships are so important to us and to everyone in the audience. Because together, we get things done faster and better. Now for today's keynote, you'll hear from three very important and energizing speakers. First, Colin Mahony, our SVP and General Manager for Vertica, will talk about the market trends that Vertica is betting on to win for our customers. And he'll share the exciting news about our Vertica 10 announcement and how this will benefit our customers. Then you'll hear from Amy Fowler, VP of strategy and solutions for FlashBlade at Pure Storage. Our partnership with Pure Storage is truly unique in the industry, because together modern infrastructure from Pure powers modern analytics from Vertica. And then you'll hear from John Yovanovich, Director of IT at AT&T, who will tell you about the Pure Vertica Symphony that plays live every day at AT&T. Here we go, Colin, over to you. >> Colin: Well, thanks a lot joy. And, I want to echo Joy's thanks to our sponsors, and so many of you who have helped make this happen. This is not an easy time for anyone. We were certainly looking forward to getting together in person in Boston during the Vertica Big Data Conference and Winning with Data. But I think all of you and our team have done a great job, scrambling and putting together a terrific virtual event. So really appreciate your time. I also want to remind people that we will make both the slides and the full recording available after this. So for any of those who weren't able to join live, that is still going to be available. Well, things have been pretty exciting here. And in the analytic space in general, certainly for Vertica, there's a lot happening. There are a lot of problems to solve, a lot of opportunities to make things better, and a lot of data that can really make every business stronger, more efficient, and frankly, more differentiated. For Vertica, though, we know that focusing on the challenges that we can directly address with our platform, and our people, and where we can actually make the biggest difference is where we ought to be putting our energy and our resources. I think one of the things that has made Vertica so strong over the years is our ability to focus on those areas where we can make a great difference. So for us as we look at the market, and we look at where we play, there are really three recent and some not so recent, but certainly picking up a lot of the market trends that have become critical for every industry that wants to Win Big With Data. We've heard this loud and clear from our customers and from the analysts that cover the market. If I were to summarize these three areas, this really is the core focus for us right now. We know that there's massive data growth. And if we can unify the data silos so that people can really take advantage of that data, we can make a huge difference. We know that public clouds offer tremendous advantages, but we also know that balance and flexibility is critical. And we all need the benefit that machine learning for all the types up to the end data science. We all need the benefits that they can bring to every single use case, but only if it can really be operationalized at scale, accurate and in real time. And the power of Vertica is, of course, how we're able to bring so many of these things together. Let me talk a little bit more about some of these trends. So one of the first industry trends that we've all been following probably now for over the last decade, is Hadoop and specifically HDFS. So many companies have invested, time, money, more importantly, people in leveraging the opportunity that HDFS brought to the market. HDFS is really part of a much broader storage disruption that we'll talk a little bit more about, more broadly than HDFS. But HDFS itself was really designed for petabytes of data, leveraging low cost commodity hardware and the ability to capture a wide variety of data formats, from a wide variety of data sources and applications. And I think what people really wanted, was to store that data before having to define exactly what structures they should go into. So over the last decade or so, the focus for most organizations is figuring out how to capture, store and frankly manage that data. And as a platform to do that, I think, Hadoop was pretty good. It certainly changed the way that a lot of enterprises think about their data and where it's locked up. In parallel with Hadoop, particularly over the last five years, Cloud Object Storage has also given every organization another option for collecting, storing and managing even more data. That has led to a huge growth in data storage, obviously, up on public clouds like Amazon and their S3, Google Cloud Storage and Azure Blob Storage just to name a few. And then when you consider regional and local object storage offered by cloud vendors all over the world, the explosion of that data, in leveraging this type of object storage is very real. And I think, as I mentioned, it's just part of this broader storage disruption that's been going on. But with all this growth in the data, in all these new places to put this data, every organization we talk to is facing even more challenges now around the data silo. Sure the data silos certainly getting bigger. And hopefully they're getting cheaper per bit. But as I said, the focus has really been on collecting, storing and managing the data. But between the new data lakes and many different cloud object storage combined with all sorts of data types from the complexity of managing all this, getting that business value has been very limited. This actually takes me to big bet number one for Team Vertica, which is to unify the data. Our goal, and some of the announcements we have made today plus roadmap announcements I'll share with you throughout this presentation. Our goal is to ensure that all the time, money and effort that has gone into storing that data, all the data turns into business value. So how are we going to do that? With a unified analytics platform that analyzes the data wherever it is HDFS, Cloud Object Storage, External tables in an any format ORC, Parquet, JSON, and of course, our own Native Roth Vertica format. Analyze the data in the right place in the right format, using a single unified tool. This is something that Vertica has always been committed to, and you'll see in some of our announcements today, we're just doubling down on that commitment. Let's talk a little bit more about the public cloud. This is certainly the second trend. It's the second wave maybe of data disruption with object storage. And there's a lot of advantages when it comes to public cloud. There's no question that the public clouds give rapid access to compute storage with the added benefit of eliminating data center maintenance that so many companies, want to get out of themselves. But maybe the biggest advantage that I see is the architectural innovation. The public clouds have introduced so many methodologies around how to provision quickly, separating compute and storage and really dialing-in the exact needs on demand, as you change workloads. When public clouds began, it made a lot of sense for the cloud providers and their customers to charge and pay for compute and storage in the ratio that each use case demanded. And I think you're seeing that trend, proliferate all over the place, not just up in public cloud. That architecture itself is really becoming the next generation architecture for on-premise data centers, as well. But there are a lot of concerns. I think we're all aware of them. They're out there many times for different workloads, there are higher costs. Especially if some of the workloads that are being run through analytics, which tend to run all the time. Just like some of the silo challenges that companies are facing with HDFS, data lakes and cloud storage, the public clouds have similar types of siloed challenges as well. Initially, there was a belief that they were cheaper than data centers, and when you added in all the costs, it looked that way. And again, for certain elastic workloads, that is the case. I don't think that's true across the board overall. Even to the point where a lot of the cloud vendors aren't just charging lower costs anymore. We hear from a lot of customers that they don't really want to tether themselves to any one cloud because of some of those uncertainties. Of course, security and privacy are a concern. We hear a lot of concerns with regards to cloud and even some SaaS vendors around shared data catalogs, across all the customers and not enough separation. But security concerns are out there, you can read about them. I'm not going to jump into that bandwagon. But we hear about them. And then, of course, I think one of the things we hear the most from our customers, is that each cloud stack is starting to feel even a lot more locked in than the traditional data warehouse appliance. And as everybody knows, the industry has been running away from appliances as fast as it can. And so they're not eager to get locked into another, quote, unquote, virtual appliance, if you will, up in the cloud. They really want to make sure they have flexibility in which clouds, they're going to today, tomorrow and in the future. And frankly, we hear from a lot of our customers that they're very interested in eventually mixing and matching, compute from one cloud with, say storage from another cloud, which I think is something that we'll hear a lot more about. And so for us, that's why we've got our big bet number two. we love the cloud. We love the public cloud. We love the private clouds on-premise, and other hosting providers. But our passion and commitment is for Vertica to be able to run in any of the clouds that our customers choose, and make it portable across those clouds. We have supported on-premises and all public clouds for years. And today, we have announced even more support for Vertica in Eon Mode, the deployment option that leverages the separation of compute from storage, with even more deployment choices, which I'm going to also touch more on as we go. So super excited about our big bet number two. And finally as I mentioned, for all the hype that there is around machine learning, I actually think that most importantly, this third trend that team Vertica is determined to address is the need to bring business critical, analytics, machine learning, data science projects into production. For so many years, there just wasn't enough data available to justify the investment in machine learning. Also, processing power was expensive, and storage was prohibitively expensive. But to train and score and evaluate all the different models to unlock the full power of predictive analytics was tough. Today you have those massive data volumes. You have the relatively cheap processing power and storage to make that dream a reality. And if you think about this, I mean with all the data that's available to every company, the real need is to operationalize the speed and the scale of machine learning so that these organizations can actually take advantage of it where they need to. I mean, we've seen this for years with Vertica, going back to some of the most advanced gaming companies in the early days, they were incorporating this with live data directly into their gaming experiences. Well, every organization wants to do that now. And the accuracy for clickability and real time actions are all key to separating the leaders from the rest of the pack in every industry when it comes to machine learning. But if you look at a lot of these projects, the reality is that there's a ton of buzz, there's a ton of hype spanning every acronym that you can imagine. But most companies are struggling, do the separate teams, different tools, silos and the limitation that many platforms are facing, driving, down sampling to get a small subset of the data, to try to create a model that then doesn't apply, or compromising accuracy and making it virtually impossible to replicate models, and understand decisions. And if there's one thing that we've learned when it comes to data, prescriptive data at the atomic level, being able to show end of one as we refer to it, meaning individually tailored data. No matter what it is healthcare, entertainment experiences, like gaming or other, being able to get at the granular data and make these decisions, make that scoring applies to machine learning just as much as it applies to giving somebody a next-best-offer. But the opportunity has never been greater. The need to integrate this end-to-end workflow and support the right tools without compromising on that accuracy. Think about it as no downsampling, using all the data, it really is key to machine learning success. Which should be no surprise then why the third big bet from Vertica is one that we've actually been working on for years. And we're so proud to be where we are today, helping the data disruptors across the world operationalize machine learning. This big bet has the potential to truly unlock, really the potential of machine learning. And today, we're announcing some very important new capabilities specifically focused on unifying the work being done by the data science community, with their preferred tools and platforms, and the volume of data and performance at scale, available in Vertica. Our strategy has been very consistent over the last several years. As I said in the beginning, we haven't deviated from our strategy. Of course, there's always things that we add. Most of the time, it's customer driven, it's based on what our customers are asking us to do. But I think we've also done a great job, not trying to be all things to all people. Especially as these hype cycles flare up around us, we absolutely love participating in these different areas without getting completely distracted. I mean, there's a variety of query tools and data warehouses and analytics platforms in the market. We all know that. There are tools and platforms that are offered by the public cloud vendors, by other vendors that support one or two specific clouds. There are appliance vendors, who I was referring to earlier who can deliver package data warehouse offerings for private data centers. And there's a ton of popular machine learning tools, languages and other kits. But Vertica is the only advanced analytic platform that can do all this, that can bring it together. We can analyze the data wherever it is, in HDFS, S3 Object Storage, or Vertica itself. Natively we support multiple clouds on-premise deployments, And maybe most importantly, we offer that choice of deployment modes to allow our customers to choose the architecture that works for them right now. It still also gives them the option to change move, evolve over time. And Vertica is the only analytics database with end-to-end machine learning that can truly operationalize ML at scale. And I know it's a mouthful. But it is not easy to do all these things. It is one of the things that highly differentiates Vertica from the rest of the pack. It is also why our customers, all of you continue to bet on us and see the value that we are delivering and we will continue to deliver. Here's a couple of examples of some of our customers who are powered by Vertica. It's the scale of data. It's the millisecond response times. Performance and scale have always been a huge part of what we have been about, not the only thing. I think the functionality all the capabilities that we add to the platform, the ease of use, the flexibility, obviously with the deployment. But if you look at some of the numbers they are under these customers on this slide. And I've shared a lot of different stories about these customers. Which, by the way, it still amaze me every time I talk to one and I get the updates, you can see the power and the difference that Vertica is making. Equally important, if you look at a lot of these customers, they are the epitome of being able to deploy Vertica in a lot of different environments. Many of the customers on this slide are not using Vertica just on-premise or just in the cloud. They're using it in a hybrid way. They're using it in multiple different clouds. And again, we've been with them on that journey throughout, which is what has made this product and frankly, our roadmap and our vision exactly what it is. It's been quite a journey. And that journey continues now with the Vertica 10 release. The Vertica 10 release is obviously a massive release for us. But if you look back, you can see that building on that native columnar architecture that started a long time ago, obviously, with the C-Store paper. We built it to leverage that commodity hardware, because it was an architecture that was never tightly integrated with any specific underlying infrastructure. I still remember hearing the initial pitch from Mike Stonebreaker, about the vision of Vertica as a software only solution and the importance of separating the company from hardware innovation. And at the time, Mike basically said to me, "there's so much R&D in innovation that's going to happen in hardware, we shouldn't bake hardware into our solution. We should do it in software, and we'll be able to take advantage of that hardware." And that is exactly what has happened. But one of the most recent innovations that we embraced with hardware is certainly that separation of compute and storage. As I said previously, the public cloud providers offered this next generation architecture, really to ensure that they can provide the customers exactly what they needed, more compute or more storage and charge for each, respectively. The separation of compute and storage, compute from storage is a major milestone in data center architectures. If you think about it, it's really not only a public cloud innovation, though. It fundamentally redefines the next generation data architecture for on-premise and for pretty much every way people are thinking about computing today. And that goes for software too. Object storage is an example of the cost effective means for storing data. And even more importantly, separating compute from storage for analytic workloads has a lot of advantages. Including the opportunity to manage much more dynamic, flexible workloads. And more importantly, truly isolate those workloads from others. And by the way, once you start having something that can truly isolate workloads, then you can have the conversations around autonomic computing, around setting up some nodes, some compute resources on the data that won't affect any of the other data to do some things on their own, maybe some self analytics, by the system, etc. A lot of things that many of you know we've already been exploring in terms of our own system data in the product. But it was May 2018, believe it or not, it seems like a long time ago where we first announced Eon Mode and I want to make something very clear, actually about Eon mode. It's a mode, it's a deployment option for Vertica customers. And I think this is another huge benefit that we don't talk about enough. But unlike a lot of vendors in the market who will dig you and charge you for every single add-on like hit-buy, you name it. You get this with the Vertica product. If you continue to pay support and maintenance, this comes with the upgrade. This comes as part of the new release. So any customer who owns or buys Vertica has the ability to set up either an Enterprise Mode or Eon Mode, which is a question I know that comes up sometimes. Our first announcement of Eon was obviously AWS customers, including the trade desk, AT&T. Most of whom will be speaking here later at the Virtual Big Data Conference. They saw a huge opportunity. Eon Mode, not only allowed Vertica to scale elastically with that specific compute and storage that was needed, but it really dramatically simplified database operations including things like workload balancing, node recovery, compute provisioning, etc. So one of the most popular functions is that ability to isolate the workloads and really allocate those resources without negatively affecting others. And even though traditional data warehouses, including Vertica Enterprise Mode have been able to do lots of different workload isolation, it's never been as strong as Eon Mode. Well, it certainly didn't take long for our customers to see that value across the board with Eon Mode. Not just up in the cloud, in partnership with one of our most valued partners and a platinum sponsor here. Joy mentioned at the beginning. We announced Vertica Eon Mode for Pure Storage FlashBlade in September 2019. And again, just to be clear, this is not a new product, it's one Vertica with yet more deployment options. With Pure Storage, Vertica in Eon mode is not limited in any way by variable cloud, network latency. The performance is actually amazing when you take the benefits of separate and compute from storage and you run it with a Pure environment on-premise. Vertica in Eon Mode has a super smart cache layer that we call the depot. It's a big part of our secret sauce around Eon mode. And combined with the power and performance of Pure's FlashBlade, Vertica became the industry's first advanced analytics platform that actually separates compute and storage for on-premises data centers. Something that a lot of our customers are already benefiting from, and we're super excited about it. But as I said, this is a journey. We don't stop, we're not going to stop. Our customers need the flexibility of multiple public clouds. So today with Vertica 10, we're super proud and excited to announce support for Vertica in Eon Mode on Google Cloud. This gives our customers the ability to use their Vertica licenses on Amazon AWS, on-premise with Pure Storage and on Google Cloud. Now, we were talking about HDFS and a lot of our customers who have invested quite a bit in HDFS as a place, especially to store data have been pushing us to support Eon Mode with HDFS. So as part of Vertica 10, we are also announcing support for Vertica in Eon Mode using HDFS as the communal storage. Vertica's own Roth format data can be stored in HDFS, and actually the full functionality of Vertica is complete analytics, geospatial pattern matching, time series, machine learning, everything that we have in there can be applied to this data. And on the same HDFS nodes, Vertica can actually also analyze data in ORC or Parquet format, using External tables. We can also execute joins between the Roth data the External table holds, which powers a much more comprehensive view. So again, it's that flexibility to be able to support our customers, wherever they need us to support them on whatever platform, they have. Vertica 10 gives us a lot more ways that we can deploy Eon Mode in various environments for our customers. It allows them to take advantage of Vertica in Eon Mode and the power that it brings with that separation, with that workload isolation, to whichever platform they are most comfortable with. Now, there's a lot that has come in Vertica 10. I'm definitely not going to be able to cover everything. But we also introduced complex types as an example. And complex data types fit very well into Eon as well in this separation. They significantly reduce the data pipeline, the cost of moving data between those, a much better support for unstructured data, which a lot of our customers have mixed with structured data, of course, and they leverage a lot of columnar execution that Vertica provides. So you get complex data types in Vertica now, a lot more data, stronger performance. It goes great with the announcement that we made with the broader Eon Mode. Let's talk a little bit more about machine learning. We've been actually doing work in and around machine learning with various extra regressions and a whole bunch of other algorithms for several years. We saw the huge advantage that MPP offered, not just as a sequel engine as a database, but for ML as well. Didn't take as long to realize that there's a lot more to operationalizing machine learning than just those algorithms. It's data preparation, it's that model trade training. It's the scoring, the shaping, the evaluation. That is so much of what machine learning and frankly, data science is about. You do know, everybody always wants to jump to the sexy algorithm and we handle those tasks very, very well. It makes Vertica a terrific platform to do that. A lot of work in data science and machine learning is done in other tools. I had mentioned that there's just so many tools out there. We want people to be able to take advantage of all that. We never believed we were going to be the best algorithm company or come up with the best models for people to use. So with Vertica 10, we support PMML. We can import now and export PMML models. It's a huge step for us around that operationalizing machine learning projects for our customers. Allowing the models to get built outside of Vertica yet be imported in and then applying to that full scale of data with all the performance that you would expect from Vertica. We also are more tightly integrating with Python. As many of you know, we've been doing a lot of open source projects with the community driven by many of our customers, like Uber. And so now with Python we've integrated with TensorFlow, allowing data scientists to build models in their preferred language, to take advantage of TensorFlow. But again, to store and deploy those models at scale with Vertica. I think both these announcements are proof of our big bet number three, and really our commitment to supporting innovation throughout the community by operationalizing ML with that accuracy, performance and scale of Vertica for our customers. Again, there's a lot of steps when it comes to the workflow of machine learning. These are some of them that you can see on the slide, and it's definitely not linear either. We see this as a circle. And companies that do it, well just continue to learn, they continue to rescore, they continue to redeploy and they want to operationalize all that within a single platform that can take advantage of all those capabilities. And that is the platform, with a very robust ecosystem that Vertica has always been committed to as an organization and will continue to be. This graphic, many of you have seen it evolve over the years. Frankly, if we put everything and everyone on here wouldn't fit on a slide. But it will absolutely continue to evolve and grow as we support our customers, where they need the support most. So, again, being able to deploy everywhere, being able to take advantage of Vertica, not just as a business analyst or a business user, but as a data scientists or as an operational or BI person. We want Vertica to be leveraged and used by the broader organization. So I think it's fair to say and I encourage everybody to learn more about Vertica 10, because I'm just highlighting some of the bigger aspects of it. But we talked about those three market trends. The need to unify the silos, the need for hybrid multiple cloud deployment options, the need to operationalize business critical machine learning projects. Vertica 10 has absolutely delivered on those. But again, we are not going to stop. It is our job not to, and this is how Team Vertica thrives. I always joke that the next release is the best release. And, of course, even after Vertica 10, that is also true, although Vertica 10 is pretty awesome. But, you know, from the first line of code, we've always been focused on performance and scale, right. And like any really strong data platform, the execution engine, the optimizer and the execution engine are the two core pieces of that. Beyond Vertica 10, some of the big things that we're already working on, next generation execution engine. We're already actually seeing incredible early performance from this. And this is just one example, of how important it is for an organization like Vertica to constantly go back and re-innovate. Every single release, we do the sit ups and crunches, our performance and scale. How do we improve? And there's so many parts of the core server, there's so many parts of our broader ecosystem. We are constantly looking at coverages of how we can go back to all the code lines that we have, and make them better in the current environment. And it's not an easy thing to do when you're doing that, and you're also expanding in the environment that we are expanding into to take advantage of the different deployments, which is a great segue to this slide. Because if you think about today, we're obviously already available with Eon Mode and Amazon, AWS and Pure and actually MinIO as well. As I talked about in Vertica 10 we're adding Google and HDFS. And coming next, obviously, Microsoft Azure, Alibaba cloud. So being able to expand into more of these environments is really important for the Vertica team and how we go forward. And it's not just running in these clouds, for us, we want it to be a SaaS like experience in all these clouds. We want you to be able to deploy Vertica in 15 minutes or less on these clouds. You can also consume Vertica, in a lot of different ways, on these clouds. As an example, in Amazon Vertica by the Hour. So for us, it's not just about running, it's about taking advantage of the ecosystems that all these cloud providers offer, and really optimizing the Vertica experience as part of them. Optimization, around automation, around self service capabilities, extending our management console, we now have products that like the Vertica Advisor Tool that our Customer Success Team has created to actually use our own smarts in Vertica. To take data from customers that give it to us and help them tune automatically their environment. You can imagine that we're taking that to the next level, in a lot of different endeavors that we're doing around how Vertica as a product can actually be smarter because we all know that simplicity is key. There just aren't enough people in the world who are good at managing data and taking it to the next level. And of course, other things that we all hear about, whether it's Kubernetes and containerization. You can imagine that that probably works very well with the Eon Mode and separating compute and storage. But innovation happens everywhere. We innovate around our community documentation. Many of you have taken advantage of the Vertica Academy. The numbers there are through the roof in terms of the number of people coming in and certifying on it. So there's a lot of things that are within the core products. There's a lot of activity and action beyond the core products that we're taking advantage of. And let's not forget why we're here, right? It's easy to talk about a platform, a data platform, it's easy to jump into all the functionality, the analytics, the flexibility, how we can offer it. But at the end of the day, somebody, a person, she's got to take advantage of this data, she's got to be able to take this data and use this information to make a critical business decision. And that doesn't happen unless we explore lots of different and frankly, new ways to get that predictive analytics UI and interface beyond just the standard BI tools in front of her at the right time. And so there's a lot of activity, I'll tease you with that going on in this organization right now about how we can do that and deliver that for our customers. We're in a great position to be able to see exactly how this data is consumed and used and start with this core platform that we have to go out. Look, I know, the plan wasn't to do this as a virtual BDC. But I really appreciate you tuning in. Really appreciate your support. I think if there's any silver lining to us, maybe not being able to do this in person, it's the fact that the reach has actually gone significantly higher than what we would have been able to do in person in Boston. We're certainly looking forward to doing a Big Data Conference in the future. But if I could leave you with anything, know this, since that first release for Vertica, and our very first customers, we have been very consistent. We respect all the innovation around us, whether it's open source or not. We understand the market trends. We embrace those new ideas and technologies and for us true north, and the most important thing is what does our customer need to do? What problem are they trying to solve? And how do we use the advantages that we have without disrupting our customers? But knowing that you depend on us to deliver that unified analytics strategy, it will deliver that performance of scale, not only today, but tomorrow and for years to come. We've added a lot of great features to Vertica. I think we've said no to a lot of things, frankly, that we just knew we wouldn't be the best company to deliver. When we say we're going to do things we do them. Vertica 10 is a perfect example of so many of those things that we from you, our customers have heard loud and clear, and we have delivered. I am incredibly proud of this team across the board. I think the culture of Vertica, a customer first culture, jumping in to help our customers win no matter what is also something that sets us massively apart. I hear horror stories about support experiences with other organizations. And people always seem to be amazed at Team Vertica's willingness to jump in or their aptitude for certain technical capabilities or understanding the business. And I think sometimes we take that for granted. But that is the team that we have as Team Vertica. We are incredibly excited about Vertica 10. I think you're going to love the Virtual Big Data Conference this year. I encourage you to tune in. Maybe one other benefit is I know some people were worried about not being able to see different sessions because they were going to overlap with each other well now, even if you can't do it live, you'll be able to do those sessions on demand. Please enjoy the Vertica Big Data Conference here in 2020. Please you and your families and your co-workers be safe during these times. I know we will get through it. And analytics is probably going to help with a lot of that and we already know it is helping in many different ways. So believe in the data, believe in data's ability to change the world for the better. And thank you for your time. And with that, I am delighted to now introduce Micro Focus CEO Stephen Murdoch to the Vertica Big Data Virtual Conference. Thank you Stephen. >> Stephen: Hi, everyone, my name is Stephen Murdoch. I have the pleasure and privilege of being the Chief Executive Officer here at Micro Focus. Please let me add my welcome to the Big Data Conference. And also my thanks for your support, as we've had to pivot to this being virtual rather than a physical conference. Its amazing how quickly we all reset to a new normal. I certainly didn't expect to be addressing you from my study. Vertica is an incredibly important part of Micro Focus family. Is key to our goal of trying to enable and help customers become much more data driven across all of their IT operations. Vertica 10 is a huge step forward, we believe. It allows for multi-cloud innovation, genuinely hybrid deployments, begin to leverage machine learning properly in the enterprise, and also allows the opportunity to unify currently siloed lakes of information. We operate in a very noisy, very competitive market, and there are people, who are in that market who can do some of those things. The reason we are so excited about Vertica is we genuinely believe that we are the best at doing all of those things. And that's why we've announced publicly, you're under executing internally, incremental investment into Vertica. That investments targeted at accelerating the roadmaps that already exist. And getting that innovation into your hands faster. This idea is speed is key. It's not a question of if companies have to become data driven organizations, it's a question of when. So that speed now is really important. And that's why we believe that the Big Data Conference gives a great opportunity for you to accelerate your own plans. You will have the opportunity to talk to some of our best architects, some of the best development brains that we have. But more importantly, you'll also get to hear from some of our phenomenal Roth Data customers. You'll hear from Uber, from the Trade Desk, from Philips, and from AT&T, as well as many many others. And just hearing how those customers are using the power of Vertica to accelerate their own, I think is the highlight. And I encourage you to use this opportunity to its full. Let me close by, again saying thank you, we genuinely hope that you get as much from this virtual conference as you could have from a physical conference. And we look forward to your engagement, and we look forward to hearing your feedback. With that, thank you very much. >> Joy: Thank you so much, Stephen, for joining us for the Vertica Big Data Conference. Your support and enthusiasm for Vertica is so clear, and it makes a big difference. Now, I'm delighted to introduce Amy Fowler, the VP of Strategy and Solutions for FlashBlade at Pure Storage, who was one of our BDC Platinum Sponsors, and one of our most valued partners. It was a proud moment for me, when we announced Vertica in Eon mode for Pure Storage FlashBlade and we became the first analytics data warehouse that separates compute from storage for on-premise data centers. Thank you so much, Amy, for joining us. Let's get started. >> Amy: Well, thank you, Joy so much for having us. And thank you all for joining us today, virtually, as we may all be. So, as we just heard from Colin Mahony, there are some really interesting trends that are happening right now in the big data analytics market. From the end of the Hadoop hype cycle, to the new cloud reality, and even the opportunity to help the many data science and machine learning projects move from labs to production. So let's talk about these trends in the context of infrastructure. And in particular, look at why a modern storage platform is relevant as organizations take on the challenges and opportunities associated with these trends. The answer is the Hadoop hype cycles left a lot of data in HDFS data lakes, or reservoirs or swamps depending upon the level of the data hygiene. But without the ability to get the value that was promised from Hadoop as a platform rather than a distributed file store. And when we combine that data with the massive volume of data in Cloud Object Storage, we find ourselves with a lot of data and a lot of silos, but without a way to unify that data and find value in it. Now when you look at the infrastructure data lakes are traditionally built on, it is often direct attached storage or data. The approach that Hadoop took when it entered the market was primarily bound by the limits of networking and storage technologies. One gig ethernet and slower spinning disk. But today, those barriers do not exist. And all FlashStorage has fundamentally transformed how data is accessed, managed and leveraged. The need for local data storage for significant volumes of data has been largely mitigated by the performance increases afforded by all Flash. At the same time, organizations can achieve superior economies of scale with that segregation of compute and storage. With compute and storage, you don't always scale in lockstep. Would you want to add an engine to the train every time you add another boxcar? Probably not. But from a Pure Storage perspective, FlashBlade is uniquely architected to allow customers to achieve better resource utilization for compute and storage, while at the same time, reducing complexity that has arisen from the siloed nature of the original big data solutions. The second and equally important recent trend we see is something I'll call cloud reality. The public clouds made a lot of promises and some of those promises were delivered. But cloud economics, especially usage based and elastic scaling, without the control that many companies need to manage the financial impact is causing a lot of issues. In addition, the risk of vendor lock-in from data egress, charges, to integrated software stacks that can't be moved or deployed on-premise is causing a lot of organizations to back off the all the way non-cloud strategy, and move toward hybrid deployments. Which is kind of funny in a way because it wasn't that long ago that there was a lot of talk about no more data centers. And for example, one large retailer, I won't name them, but I'll admit they are my favorites. They several years ago told us they were completely done with on-prem storage infrastructure, because they were going 100% to the cloud. But they just deployed FlashBlade for their data pipelines, because they need predictable performance at scale. And the all cloud TCO just didn't add up. Now, that being said, well, there are certainly challenges with the public cloud. It has also brought some things to the table that we see most organizations wanting. First of all, in a lot of cases applications have been built to leverage object storage platforms like S3. So they need that object protocol, but they may also need it to be fast. And the said object may be oxymoron only a few years ago, and this is an area of the market where Pure and FlashBlade have really taken a leadership position. Second, regardless of where the data is physically stored, organizations want the best elements of a cloud experience. And for us, that means two main things. Number one is simplicity and ease of use. If you need a bunch of storage experts to run the system, that should be considered a bug. The other big one is the consumption model. The ability to pay for what you need when you need it, and seamlessly grow your environment over time totally nondestructively. This is actually pretty huge and something that a lot of vendors try to solve for with finance programs. But no finance program can address the pain of a forklift upgrade, when you need to move to next gen hardware. To scale nondestructively over long periods of time, five to 10 years plus is a crucial architectural decisions need to be made at the outset. Plus, you need the ability to pay as you use it. And we offer something for FlashBlade called Pure as a Service, which delivers exactly that. The third cloud characteristic that many organizations want is the option for hybrid. Even if that is just a DR site in the cloud. In our case, that means supporting appplication of S3, at the AWS. And the final trend, which to me represents the biggest opportunity for all of us, is the need to help the many data science and machine learning projects move from labs to production. This means bringing all the machine learning functions and model training to the data, rather than moving samples or segments of data to separate platforms. As we all know, machine learning needs a ton of data for accuracy. And there is just too much data to retrieve from the cloud for every training job. At the same time, predictive analytics without accuracy is not going to deliver the business advantage that everyone is seeking. You can kind of visualize data analytics as it is traditionally deployed as being on a continuum. With that thing, we've been doing the longest, data warehousing on one end, and AI on the other end. But the way this manifests in most environments is a series of silos that get built up. So data is duplicated across all kinds of bespoke analytics and AI, environments and infrastructure. This creates an expensive and complex environment. So historically, there was no other way to do it because some level of performance is always table stakes. And each of these parts of the data pipeline has a different workload profile. A single platform to deliver on the multi dimensional performances, diverse set of applications required, that didn't exist three years ago. And that's why the application vendors pointed you towards bespoke things like DAS environments that we talked about earlier. And the fact that better options exists today is why we're seeing them move towards supporting this disaggregation of compute and storage. And when it comes to a platform that is a better option, one with a modern architecture that can address the diverse performance requirements of this continuum, and allow organizations to bring a model to the data instead of creating separate silos. That's exactly what FlashBlade is built for. Small files, large files, high throughput, low latency and scale to petabytes in a single namespace. And this is importantly a single rapid space is what we're focused on delivering for our customers. At Pure, we talk about it in the context of modern data experience because at the end of the day, that's what it's really all about. The experience for your teams in your organization. And together Pure Storage and Vertica have delivered that experience to a wide range of customers. From a SaaS analytics company, which uses Vertica on FlashBlade to authenticate the quality of digital media in real time, to a multinational car company, which uses Vertica on FlashBlade to make thousands of decisions per second for autonomous cars, or a healthcare organization, which uses Vertica on FlashBlade to enable healthcare providers to make real time decisions that impact lives. And I'm sure you're all looking forward to hearing from John Yavanovich from AT&T. To hear how he's been doing this with Vertica and FlashBlade as well. He's coming up soon. We have been really excited to build this partnership with Vertica. And we're proud to provide the only on-premise storage platform validated with Vertica Eon Mode. And deliver this modern data experience to our customers together. Thank you all so much for joining us today. >> Joy: Amy, thank you so much for your time and your insights. Modern infrastructure is key to modern analytics, especially as organizations leverage next generation data center architectures, and object storage for their on-premise data centers. Now, I'm delighted to introduce our last speaker in our Vertica Big Data Conference Keynote, John Yovanovich, Director of IT for AT&T. Vertica is so proud to serve AT&T, and especially proud of the harmonious impact we are having in partnership with Pure Storage. John, welcome to the Virtual Vertica BDC. >> John: Thank you joy. It's a pleasure to be here. And I'm excited to go through this presentation today. And in a unique fashion today 'cause as I was thinking through how I wanted to present the partnership that we have formed together between Pure Storage, Vertica and AT&T, I want to emphasize how well we all work together and how these three components have really driven home, my desire for a harmonious to use your word relationship. So, I'm going to move forward here and with. So here, what I'm going to do the theme of today's presentation is the Pure Vertica Symphony live at AT&T. And if anybody is a Westworld fan, you can appreciate the sheet music on the right hand side. What we're going to what I'm going to highlight here is in a musical fashion, is how we at AT&T leverage these technologies to save money to deliver a more efficient platform, and to actually just to make our customers happier overall. So as we look back, and back as early as just maybe a few years ago here at AT&T, I realized that we had many musicians to help the company. Or maybe you might want to call them data scientists, or data analysts. For the theme we'll stay with musicians. None of them were singing or playing from the same hymn book or sheet music. And so what we had was many organizations chasing a similar dream, but not exactly the same dream. And, best way to describe that is and I think with a lot of people this might resonate in your organizations. How many organizations are chasing a customer 360 view in your company? Well, I can tell you that I have at least four in my company. And I'm sure there are many that I don't know of. That is our problem because what we see is a repetitive sourcing of data. We see a repetitive copying of data. And there's just so much money to be spent. This is where I asked Pure Storage and Vertica to help me solve that problem with their technologies. What I also noticed was that there was no coordination between these departments. In fact, if you look here, nobody really wants to play with finance. Sales, marketing and care, sure that you all copied each other's data. But they actually didn't communicate with each other as they were copying the data. So the data became replicated and out of sync. This is a challenge throughout, not just my company, but all companies across the world. And that is, the more we replicate the data, the more problems we have at chasing or conquering the goal of single version of truth. In fact, I kid that I think that AT&T, we actually have adopted the multiple versions of truth, techno theory, which is not where we want to be, but this is where we are. But we are conquering that with the synergies between Pure Storage and Vertica. This is what it leaves us with. And this is where we are challenged and that if each one of our siloed business units had their own stories, their own dedicated stories, and some of them had more money than others so they bought more storage. Some of them anticipating storing more data, and then they really did. Others are running out of space, but can't put anymore because their bodies aren't been replenished. So if you look at it from this side view here, we have a limited amount of compute or fixed compute dedicated to each one of these silos. And that's because of the, wanting to own your own. And the other part is that you are limited or wasting space, depending on where you are in the organization. So there were the synergies aren't just about the data, but actually the compute and the storage. And I wanted to tackle that challenge as well. So I was tackling the data. I was tackling the storage, and I was tackling the compute all at the same time. So my ask across the company was can we just please play together okay. And to do that, I knew that I wasn't going to tackle this by getting everybody in the same room and getting them to agree that we needed one account table, because they will argue about whose account table is the best account table. But I knew that if I brought the account tables together, they would soon see that they had so much redundancy that I can now start retiring data sources. I also knew that if I brought all the compute together, that they would all be happy. But I didn't want them to tackle across tackle each other. And in fact that was one of the things that all business units really enjoy. Is they enjoy the silo of having their own compute, and more or less being able to control their own destiny. Well, Vertica's subclustering allows just that. And this is exactly what I was hoping for, and I'm glad they've brought through. And finally, how did I solve the problem of the single account table? Well when you don't have dedicated storage, and you can separate compute and storage as Vertica in Eon Mode does. And we store the data on FlashBlades, which you see on the left and right hand side, of our container, which I can describe in a moment. Okay, so what we have here, is we have a container full of compute with all the Vertica nodes sitting in the middle. Two loader, we'll call them loader subclusters, sitting on the sides, which are dedicated to just putting data onto the FlashBlades, which is sitting on both ends of the container. Now today, I have two dedicated storage or common dedicated might not be the right word, but two storage racks one on the left one on the right. And I treat them as separate storage racks. They could be one, but i created them separately for disaster recovery purposes, lashing work in case that rack were to go down. But that being said, there's no reason why I'm probably going to add a couple of them here in the future. So I can just have a, say five to 10, petabyte storage, setup, and I'll have my DR in another 'cause the DR shouldn't be in the same container. Okay, but I'll DR outside of this container. So I got them all together, I leveraged subclustering, I leveraged separate and compute. I was able to convince many of my clients that they didn't need their own account table, that they were better off having one. I eliminated, I reduced latency, I reduced our ticketing I reduce our data quality issues AKA ticketing okay. I was able to expand. What is this? As work. I was able to leverage elasticity within this cluster. As you can see, there are racks and racks of compute. We set up what we'll call the fixed capacity that each of the business units needed. And then I'm able to ramp up and release the compute that's necessary for each one of my clients based on their workloads throughout the day. And so while they compute to the right before you see that the instruments have already like, more or less, dedicated themselves towards all those are free for anybody to use. So in essence, what I have, is I have a concert hall with a lot of seats available. So if I want to run a 10 chair Symphony or 80, chairs, Symphony, I'm able to do that. And all the while, I can also do the same with my loader nodes. I can expand my loader nodes, to actually have their own Symphony or write all to themselves and not compete with any other workloads of the other clusters. What does that change for our organization? Well, it really changes the way our database administrators actually do their jobs. This has been a big transformation for them. They have actually become data conductors. Maybe you might even call them composers, which is interesting, because what I've asked them to do is morph into less technology and more workload analysis. And in doing so we're able to write auto-detect scripts, that watch the queues, watch the workloads so that we can help ramp up and trim down the cluster and subclusters as necessary. There has been an exciting transformation for our DBAs, who I need to now classify as something maybe like DCAs. I don't know, I have to work with HR on that. But I think it's an exciting future for their careers. And if we bring it all together, If we bring it all together, and then our clusters, start looking like this. Where everything is moving in harmonious, we have lots of seats open for extra musicians. And we are able to emulate a cloud experience on-prem. And so, I want you to sit back and enjoy the Pure Vertica Symphony live at AT&T. (soft music) >> Joy: Thank you so much, John, for an informative and very creative look at the benefits that AT&T is getting from its Pure Vertica symphony. I do really like the idea of engaging HR to change the title to Data Conductor. That's fantastic. I've always believed that music brings people together. And now it's clear that analytics at AT&T is part of that musical advantage. So, now it's time for a short break. And we'll be back for our breakout sessions, beginning at 12 pm Eastern Daylight Time. We have some really exciting sessions planned later today. And then again, as you can see on Wednesday. Now because all of you are already logged in and listening to this keynote, you already know the steps to continue to participate in the sessions that are listed here and on the previous slide. In addition, everyone received an email yesterday, today, and you'll get another one tomorrow, outlining the simple steps to register, login and choose your session. If you have any questions, check out the emails or go to www.vertica.com/bdc2020 for the logistics information. There are a lot of choices and that's always a good thing. Don't worry if you want to attend one or more or can't listen to these live sessions due to your timezone. All the sessions, including the Q&A sections will be available on demand and everyone will have access to the recordings as well as even more pre-recorded sessions that we'll post to the BDC website. Now I do want to leave you with two other important sites. First, our Vertica Academy. Vertica Academy is available to everyone. And there's a variety of very technical, self-paced, on-demand training, virtual instructor-led workshops, and Vertica Essentials Certification. And it's all free. Because we believe that Vertica expertise, helps everyone accelerate their Vertica projects and the advantage that those projects deliver. Now, if you have questions or want to engage with our Vertica engineering team now, we're waiting for you on the Vertica forum. We'll answer any questions or discuss any ideas that you might have. Thank you again for joining the Vertica Big Data Conference Keynote Session. Enjoy the rest of the BDC because there's a lot more to come

absolutely I think if I were to net it out Jeff what I'm sensing is there is a whole movement to shift security left which is this whole idea of IT stepping up as the first line of defense reduce cyber exposure take care of patching multi-factor authentication reduce their tax surface intrinsic security right so you know DevOps active ops take care of it right up front with all the apps even get built right then there is another movement to shift things right which is take care of the new new aspects of the attack surface right what the hackers always take advantage of of other areas where in a sense we are unprepared and for a long time they've seen us being unprepared in terms of reducing the attack surface and then they go after the new aspects of the tak surface and what are those IT I ot ot data as as an attack surface and the edge right so so these are areas where there's a lot of activity a lot of innovation you know on the on the air on the floor here if you walk the corners shifting left shifting right as in all the new aspects of the tax F is I'm seeing a lot of conversations a lot of innovation in that area I think it also boils down to real-world examples we've been really understand the demographics that we're working for I think today it's the first time really in history that we have four generations working side-by-side in the workforce so we have to understand that people learn differently training should be adjusted to the type of people that we're teaching but phishing doesn't just oil down to clicking on links phishing teaches also it boils down to tricking somebody getting someone's trust and it can come in different forms for example think of social media how do people connect we're connecting across social media on many different platforms I'll give a very easy example LinkedIn LinkedIn is for business have form we're all connected on LinkedIn why we connect on LinkedIn because that's a social platform that people feel safe on because we're able to connect to each other in a business form I want to think of the person who's getting the first job with an organization their first job in maybe their project manager and they're working for Bank a excited to be working for Bank a hey I'm gonna list all the projects I'm working for so here's now my resume on LinkedIn I'm working on project ABCD and this is my manager I report to perfect there's some information sitting there on LinkedIn now what else I will tell you is that you might have somebody who's looking to get into that Bank what will they do let's look for the lowest hanging fruit who this new project manager oh I see they're working on these projects and they're reporting in to someone well I'm not a project manager I'm a senior project manager from a competing bank I'm gonna befriend them and tell them that I'm really excited about the work they're doing here there's social engineering their way into their friendship into the good graces into their trust once done the video becomes a trusted source people share information freely so people are putting too much information out there on social trusting too easily opening the door for more than a phishing attack and things are just rapidly going out of control right so my co-founder and I both came from the world of being practitioners and we saw how limited the space wasn't actually changing human behavior I was given some animated powerpoints that use this to keep the Russians out of your Network which is a practical joke unless your job is on the line I took a huge step back and I said there are other fields that have figured this out behavioral science being one of them they use positive reinforcement gamification marketing and advertisement has figured out how to engage this human element just look around the RSA floor and there are so many learnings of how we make decisions as human beings that can be applied into changing people's behaviors and security so that's what we did adventure so this is my first early stage company we're still seeking series a we're a young company but our mantras we are the data value company so they have had this very robust analytics engine that goes into the heart of data I can track it and map it and make it beautiful and Along Came McNeely who actually sits on our board Oh does he and they said we need someone who's this week it's all happening so they asked Scott McNealy who is the craziest person in privacy and data that you know and he said oh my god get the done any woman so they got the den of a woman and that's what I do now so I'm taking this analytics value engine I'm pointing it to the board as I've always said Grace Hopper said data value and data risk has to be on the corporate balance sheet and so that's what we're building is a data balance sheet for everyone to use to actually value data for me it starts with technology that takes look we've only got so many security practitioners in the company actually defend your email example we've got to defend every user from those kinds of problems and so how do I find technology solutions that help take that load off the security practitioners so they can focus on the niche examples that are really really well-crafted emails and and and help take that load off the user because users just you're not going to be able to handle that right it's not fair to ask them and like you said it was just poorly timed that helps protect it so how do we help make sure that we're taking that technology load off identify the threats in advance and and protect them and so I think one of the biggest things that Chris and I talk a lot about is how do our solutions help make it easier for people to secure themselves instead of just providing only a technology technology advantage so the virtual analyst is able to sit on premises so it's localized learning collector has to understand the nature of those strats collect to be able to look at the needles of the needles if you will make sense of that and then automatically generate reports based off of that right so it's really an assist tool that a network in min or a security analyst was able to pick up and virtually save hours and hours of time so we have this we call it a thread research group within the company and their job is to take all the data from the sensors we have I mean we have we look at about 25 petabytes of data every day all our solutions are cloud solutions as well as on forum so we get the benefit of basically seeing all the data's that are hitting our customers every day I mean we block about 1 million attacks every minutes like every minute 1 billion attacks every minute minute right we protect over 3 million databases and you know we've mitigated some of the largest DDoS attacks that's ever been reported so we have a lot of date right that we're seen and the interesting thing is that you're right we are having to always we're using that threat research data to see what's happening how the threat landscape is changing therefore guiding us on how we need to augment and add to our products to prevent that but interestingly we're also consuming AI and machine learning as well on our products because we're able to use those solutions to actually do a lot of attack analytics and do a lot of predictive and research for our customers that can kind of guide them about you know where things are happening because what's happening is that before a lot of the tacks were just sort of fast and furious now we're seeing a pattern towards snow snow and continuous if that makes sense we're seeing all these patterns and threats coming in so we're fighting against those technologies like AI Barossa using those technologies to help us soon you know decide where we need to continue to add capabilities to stop it you know the whole bad box thing wasn't a problem right a number of years ago and so it's it's ever-changing your world which frankly speaking makes it an interesting place to be yes who wants to be in a static in a boring place right well I mean we do you're a good package or a bad package you have to traverse the network to be interesting we've all you know put our phones in airplane mode at blackhat or events like that but we don't want to be on it they're really boring when they're offline but they're also really boring too attackers when they're offline as soon as you turn them on you have a problem or could have a problem but as things traverse the network what better place to see who and what's on your network and on the gear and end of the day we're able to provide that visibility we're able to provide that enforcement so as you mentioned 2020 is now the year of awareness for us so the threat aware network we're able to do things like look at encrypted traffic do heuristics and analysis to figure out should that even be on my network because as you bring it into a network and you have to decrypt it a there's privacy concerns of that in these times but also it's computationally expensive to do that so it becomes a challenge from a both a financial perspective as well as a compliance perspective so we're helping solve s even kind of offset that traffic and be able to ensure your network secure so when we started developing our cyber recovery solution about five years ago we used the NIST cybersecurity framework which is a very well known standard that defines really five pillars of how organizations can think about building a cyber resilience strategy a cyber resilience strategy really encompasses everything from perimeter threat detection and response all the way through incident response after an attack and everything that happens in between protecting the data and recovering the data right and critical systems so I think of cyber resilience is that holistic strategy of protecting an organization and its data from a cyberattack yeah I think the human element is the hardest part you know in mind of this conference and its theme the human element the hardest part about this job is that it's not just mechanical issues and routing issues and networking issues but is about dealing with all types of humans innocent humans that do strange and bad things unknowingly and it's in malicious people who do very bad things that is by design and so the research suggests that no matter what we do in security awareness training some four percent of our employee base will continually bail security awareness that's what we fished and actively and so one of the things that we need to do is use automation and intelligence so that you can comb through all of that data and make a better informed decision about what risks are going to mitigate right and for this four percent are habitually abusing the system and can't be retrained well you can isolate them right and make sure that they're separated and then they're not able to to do things that may harm the organization you

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Hi! And welcome to the Cube. I'm your host, Sonia, to guard. And we're live at Stanford University covering Woods Women in Data Science Conference The fifth annual one And joining us today is Daphne Koller, who is the co founder who sorry is the CEO and founder of In Citro that Daphne. Welcome to the Cube. >>Nice to be here, Sonia. Thank you for having me. So >>tell us a little bit about in Citro how you how you got founded and more about your >>role. So I've been working in the intersection of machine learning and biology and health for quite a while, and it was always a bit of an interesting journey and that the data sets were quite small and limited. We're now in a different world where there's tools that are allowing us to create massive biological data sense that I think can help us solve really significant societal problems. And one of those problems that I think is really important is drug discovery and development, where despite many important advancements, the costs just keep going up and up and up. And the question is, can we use machine learning to solve that problem >>better? And you talk about this more in your keynote, so give us a few highlights of what you talked about. So in the last, you can think of >>drug discovery development in the last 50 to 70 years as being a bit of a glass half full glass, half empty. The glass half full is the fact that there's diseases that used to be a death sentence or of sentenced, a lifelong of pain and suffering that >>are now >>addressed by some of the modern day medicines. And I think that's absolutely amazing. The >>other side of >>it is that the cost of developing new drugs has been growing exponentially and what's come to be known as the Rooms law being the inverse of Moore's law, which is the one we're all familiar with because the number of drugs approved per 1,000,000,000 U. S. Dollars just keeps going down exponentially. So the question is, can we change that curve? >>And you talked in your keynote about the interdisciplinary culture to tell us more about that? I think in >>order to address some of the critical problems that we're facing. One needs to really build a culture of people who work together at from different disciplines, each bringing their own insights and their own ideas into the mix. So and in Citro, we actually have a company. That's half life scientists, many of whom are producing data for the purpose of driving machine learning models and the other Halford machine learning people in data scientists who are working on those. But it's not a handoff where one group produces that they then the other one consumes and interpreted. But really, they start from the very beginning to understand. What are the problems that one could solve together? How do you design the experiment? How do you build the model and how do you derive insights from that that can help us make better medicines for people? >>And, um, I also wanted to ask you the you co founded coursera, so tell us a little bit more about that platform. So I found that >>coursera as a result of work that I've been doing at Stanford, working on how technology can make education better and more accessible. This was a project that I did here, number of my colleagues as well. And at some point in the fall of 2011 there was an experiment of Let's take some of the content that we've been we've been developing within within Stanford and put it out there for people to just benefit from, and we didn't know what would happen. Would it be a few 1000 people, but within a matter of weeks with minimal advertising Other than one New York Times article that went viral, we had 100,000 people in each of those courses. And that was a moment in time where, you know, we looked at it at this and said, Can we just go back to writing more papers or is there an incredible opportunity to transform access to education to people all over the world? And so I ended up taking a what was supposed to be to really absence from Stanford to go and co found coursera, and I thought I'd go back after two years, but the But at the end of that two year period, the there was just so much more to be done and so much more impact that we could bring to people all over the world, people of both genders, people of different social economic status, every single country around the world. We just felt like this was something that I couldn't not dio. >>And how did you Why did you decide to go from an educational platform to then going into machine learning and biomedicine? >>So I've been doing Corsair for about five years in 2016 and the company was on a great trajectory. But it's primarily >>a >>a content company, and around me, machine learning was transforming the world, and I wanted to come back and be part of that. And when I looked around, I saw machine learning being applied to e commerce and the natural language and to self driving cars. But there really wasn't a lot of impact being made on the life science area. I wanted to be part of making that happen, partly because I felt like coming back to your earlier comment that in order to really have that impact, you need to have someone who speaks both languages. And while there's a new generation of researchers who are bilingual in biology and machine learning, there's still a small group in there, very few of those in kind of my age cohort and I thought that I would be able to have a real impact by bullying company in the space. >>So it sounds like your background is pretty varied. What advice would you give to women who are just starting college now who may be interested in the similar field? Would you tell them they have to major in math? Or or do you think that maybe, like there's some other majors that may be influential as well? I think >>there is a lot of ways to get into data science. Math is one of them. But there's also statistics or physics. And I would say that especially for the field that I'm currently in, which is at the intersection of machine learning data science on the one hand, and biology and health on the other one can, um, get there from biology or medicine as well. But what I think is important is not to shy away from the more mathematically oriented courses in whatever major you're in, because that foundation is a really strong one. There is ah lot of people out there who are basically lightweight consumers of data science, and they don't really understand how the methods that they're deploying, how they work and that limits thumb in their ability to advance the field and come up with new methods that are better suited, perhaps, of the problems of their tackling. So I think it's totally fine. And in fact, there's a lot of value to coming into data science from fields other than now third computer science. But I think taking courses in those fields, even while you're majoring in whatever field you're interested in, is going to make you a much better person who lives at that intersection. >>And how do you think having a technology background has helped you in in founding your companies and has helped you become a successful CEO in companies >>that are very strongly R and D, focused like like in Citro and others? Having a technical co founder is absolutely essential because it's fine to have and understanding of whatever the user needs and so on and come from the business side of it. And a lot of companies have a business co founder. But not understanding what the technology can actually do is highly limiting because you end up hallucinating. Oh, if we could only do this and that would be great. But you can't and people end up often times making ridiculous promises about what's technology will or will not do because they just don't understand where the land mines sit. And, um, and where you're going to hit reels, obstacles in the path. So I think it's really important to have a strong technical foundation in these companies. >>And that being said, Where do you see in Teacher in the future? And how do you see it solving, Say, Nash, that you talked about in your keynote. >>So we hope that in Citro will be a fully integrated drug discovery and development company that is based on a completely different foundation than a traditional pharma company where they grew up. In the old approach of that is very much a bespoke scientific um, analysis of the biology of different diseases and then going after targets are ways of dealing with the disease that are driven by human intuition. Where I think we have the opportunity to go today is to build a very data driven approach that collects massive amounts of data and then let analysis of those data really reveal new hypotheses that might not be the ones that accord with people's preconceptions of what matters and what doesn't. And so hopefully we'll be able to overtime create enough data and applying machine learning to address key bottlenecks in the drug discovery development process that we can bring better drugs to people, and we can do it faster and hopefully it much lower cost. >>That's great. And you also mention in your keynote that you think the 20 twenties is like a digital biology era, so tell us more about that. So I think if >>you look, if you take a historical perspective on science and think back, you realize that there's periods in history where one discipline has made a tremendous amount of progress in relatively short amount of time because of a new technology or a new way of looking at things in the 18 seventies, that discipline was chemistry with the understanding of the periodic table, and that you actually couldn't turn lead into gold in the 19 hundreds. That was physics with understanding the connection between matter and energy in between space and time. In the 19 fifties that was computing where silicon chips were suddenly able to perform calculations that up until that point, only people have been able to >>dio. And then in 19 nineties, >>there was an interesting bifurcation. One was three era of data, which is related to computing but also involves elements, statistics and optimization of neuroscience. And the other one was quantitative biology. In which file do you move from a descriptive signs of taxonomy izing phenomenon to really probing and measuring biology in a very detailed on high throughput way, using techniques like micro arrays that measure the activity of 20,000 genes at once, or the human genome sequencing of the human genome and many others. But >>these two fields kind of >>evolved in parallel, and what I think is coming now, 30 years later, is the convergence of those two fields into one field that I like to think of a digital biology where we are able using the tools that have and continue to be developed, measure biology, an entirely new levels of detail, of fidelity of scale. We can use the techniques of machine learning and data signs to interpret what we're seeing and then use some of the technologies that are also emerging to engineer biology to do things that it otherwise wouldn't do. And that will have implications and bio materials in energy and the environment in agriculture. And I think also in human health. And it's a incredibly exciting space toe to be in right now, because just so much is happening in the opportunities to make a difference and make the world a better place or just so large. >>That sounds awesome. Stephanie. Thank you for your insight. And thanks for being on the Cube. Thank you. I'm Sonia. Taqueria. Thanks for watching. Stay tuned for more. Okay? Great. Yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in Data Science 2020. Brought to you by Silicon Angle Media >>and welcome to the Cube. I'm your host Sonia category, and we're live at Stanford University, covering the fifth annual Woods Women in Data Science conference. Joining us today is Tilapia Williams, who's the associate professor of mathematics at Harvey Mudd College and host of Nova Wonders at PBS to leave a welcome to the Cappy to be here. Thanks for having me. So you have a lot of rules. So let's first tell us about being an associate professor at Harvey Mudd. >>Yeah, I've been at Harvey Mudd now for 11 years, so it's been really a lot of fun in the math department, but I'm a statistician by training, so I teach a lot of courses and statistics and data science and things like that. >>Very cool. And you're also a host of API s show called Novo Wonders. >>Yeah, that came about a couple of years ago. Folks at PBS reached out they had seen my Ted talk, and they said, Hey, it looks like you could be fund host of this science documentary shows So, Nova Wonders, is a six episode Siri's. It kind of takes viewers on a journey of what the cutting edge questions and science are. Um, so I got to host the show with a couple other co host and really think about like, you know, what are what are the animals saying? And so we've got some really fun episodes to do. What's the universe made of? Was one of them what's living inside of us. That was definitely a gross win. Todo figure out all the different micro organisms that live inside our body. So, yeah, it's been funded in hopes that show as well. >>And you talk about data science and AI and all that stuff on >>Yeah. Oh, yeah, yeah, one of the episodes. Can we build a Brain was dealt with a lot of data, big data and artificial intelligence, and you know, how good can we get? How good can computers get and really sort of compared to what we see in the movies? We're a long way away from that, but it seems like you know we're getting better every year, building technology that is truly intelligent, >>and you gave a talk today about mining for your own personal data. So give us some highlights from your talk. Yeah, >>so that talks sort of stemmed out of the Ted talk that I gave on owning your body's data. And it's really challenging people to think about how they can use data that they collect about their bodies to help make better health decisions on DSO ways that you can use, like your temperature data or your heart rate. Dina. Or what is data say over time? What does it say about your body's health and really challenging the audience to get excited about looking at that data? We have so many devices that collect data automatically for us, and often we don't pause on enough to actually look at that historical data. And so that was what the talk was about today, like, here's what you can find when you actually sit down and look at that data. >>What's the most important data you think people should be collecting about themselves? >>Well, definitely not. Your weight is. I don't >>want to know what that >>is. Um, it depends, you know, I think for women who are in the fertile years of life taking your daily waking temperature can tell you when your body's fertile. When you're ovulating, it can. So that information could give women during that time period really critical information. But in general, I think it's just a matter of being aware of of how your body is changing. So for some people, maybe it's your blood pressure or your blood sugar. You have high blood pressure or high blood sugar. Those things become really critical to keep an eye on. And, um, and I really encourage people whatever data they take, too, the active in the understanding of an interpretation of the data. It's not like if you take this data, you'll be healthy radio. You live to 100. It's really a matter of challenging people to own the data that they have and get excited about understanding the data that they are taking. So >>absolutely put putting people in charge of their >>own bodies. That's >>right. >>And actually speaking about that in your Ted talk, you mentioned how you were. Your doctor told you to have a C section and you looked at the data and he said, No, I'm gonna have this baby naturally. So tell us more about that. >>Yes, you should always listen to your medical pressures. But in this case, I will say that it was It was definitely more of a dialogue. And so I wasn't just sort of trying to lean on the fact that, like, I have a PhD in statistics and I know data, he was really kind of objectively with the on call doctor at the time, looking at the data >>and talking about it. >>And this doctor was this is his first time seeing me. And so I think it would have been different had my personal midwife or my doctor been telling me that. But this person would have only looked at this one chart and was it was making a decision without thinking about my historical data. And so I tried to bring that to the conversation and say, like, let me tell you more about you know, my body and this is pregnancy number three like, here's how my body works. And I think this person in particular just wasn't really hearing any of that. It was like, Here's my advice. We just need to do this. I'm like, >>Oh, >>you know, and so is gently as possible. I tried to really share that data. Um, and then it got to the point where it was sort of like either you're gonna do what I say or you're gonna have to sign a waiver. And we were like, Well, to sign the waiver that cost quite a buzz in the hospital that day. But we came back and had a very successful labor and delivery. And so, yeah, >>I think >>that at the time, >>But, >>you know, with that caveat that you should listen to what, your doctors >>Yeah. I mean, there's really interesting, like, what's the boundary between, Like what the numbers tell you and what professional >>tells me Because I don't have an MD. Right. And so, you know, I'm cautious not to overstep that, but I felt like in that case, the doctor wasn't really even considering the data that I was bringing. Um, I was we were actually induced with our first son, but again, that was more of a conversation, more of a dialogue. Here's what's happening here is what we're concerned about and the data to really back it up. And so I felt like in that case, like Yeah, I'm happy to go with your suggestion, but I could number three. It was just like, No, this isn't really >>great. Um, so you also wrote a book called Power In Numbers. The Rebel Women of Mathematics. So what inspired you to write this book? And what do you hope readers take away from it? >>A couple different things. I remember when I saw the movie hidden figures. And, um, I spent three summers at NASA working at JPL, the Jet Propulsion Laboratory. And so I had this very fun connection toe, you know, having worked at NASA. And, um, when this movie came out and I'm sitting there watching it and I'm, like ball in just crying, like I didn't know that there were black women who worked at NASA like, before me, you know, um and so it felt it felt it was just so transformative for me to see these stories just sort of unfold. And I thought, like, Well, why didn't I learn about these women growing up? Like imagine, Had I known about Katherine Johnsons of the world? Maybe that would have really inspired Not just me, but, you know, thinking of all the women of color who aren't in mathematics or who don't see themselves working at at NASA. And so for me, the book was really a way to leave that legacy to the generation that's coming up and say, like, there have been women who've done mathematics, um, and statistics and data science for years, and they're women who are doing it now. So a lot of the about 1/3 of the book are women who were still here and, like, active in the field and doing great things. And so I really wanted to highlight sort of where we've been, where we've been, but also where we're going and the amazing women that are doing work in it. And it's very visual. So some things like, Oh my gosh, >>women in math >>It is really like a very picturesque book of showing this beautiful images of the women and their mathematics and their work. And yes, I'm really proud of it. >>That's awesome. And even though there is like greater diversity now in the tech industry, there's still very few African American women, especially who are part of this industry. So what advice would you give to those women who who feel like they don't belong. >>Yeah, well, a they really do belong. Um, and I think it's also incumbent of people in the industry to sort of recognize ways that they could be advocate for women, and especially for women of color, because often it takes someone who's already at the table to invite other people to the table. And I can't just walk up like move over, get out the way I'm here now. But really being thoughtful about who's not representative, how do we get those voices here? And so I think the onus is often mawr on. People who occupy those spaces are ready to think about how they can be more intentional in bringing diversity in other spaces >>and going back to your talk a little bit. Um uh, how how should people use their data? >>Yeah, so I mean, I think, um, the ways that we've used our data, um, have been to change our lifestyle practices. And so, for example, when I first got a Fitbit, um, it wasn't really that I was like, Oh, I have a goal. It was just like I want something to keep track of my steps And then I look at him and I feel like, Oh, gosh, I didn't even do anything today. And so I think having sort of even that baseline data gave me a place to say, Okay, let me see if I hit 10 stuff, you know, 10,000 >>steps in a day or >>and so, in some ways, having the data allows you to set goals. Some people come in knowing, like, I've got this goal. I want to hit it. But for me, it was just sort of like, um and so I think that's also how I've started to use additional data. So when I take my heart rate data or my pulse, I'm really trying to see if I can get lower than how it was before. So the push is really like, how is my exercise and my diet changing so that I can bring my resting heart rate down? And so having the data gives me a gold up, restore it, and it also gives me that historical information to see like, Oh, this is how far I've come. Like I can't stop there, you know, >>that's a great social impact. >>That's right. Yeah, absolutely. >>and, um, Do you think that so in terms of, like, a security and privacy point of view, like if you're recording all your personal data on these devices, how do you navigate that? >>Yeah, that's a tough one. I mean, because you are giving up that data privacy. Um, I usually make sure that the data that I'm allowing access to this sort of data that I wouldn't care if it got published on the cover of you know, the New York Times. Maybe I wouldn't want everyone to see what my weight is, but, um, and so in some ways, while it is my personal data, there's something that's a bit abstract from it. Like it could be anyone's data as opposed to, say, my DNA. Like I'm not going to do a DNA test. You know, I don't want my data to be mapped it out there for the world. Um, but I think that that's increasingly become a concern because people are giving access to of their information to different companies. It's not clear how companies would use that information, so if they're using my data to build a product will make a product better. You know we don't see any world from that way. We don't have the benefit of it, but they have access to our data. And so I think in terms of data, privacy and data ethics, there's a huge conversation to have around that. We're only kind >>of at the beginning of understanding what that is. Yeah, >>well, thank you so much for being on the Cube. Really having you here. Thank you. Thanks. So I'm Sonia to Gary. Thanks so much for watching the cube and stay tuned for more. Yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Yeah, yeah, and welcome to the Cube. I'm your host Sonia Category and we're live at Stanford University, covering the fifth annual Woods Women in Data Science Conference. Joining us today is new Sha Ajami, who's the director of urban water policy for Stanford. You should welcome to the Cube. Thank you for having me. Absolutely. So tell us a little bit about your role. So >>I directed around water policy program at Stanford. We focused on building solutions for resilient cities to try to use data science and also the mathematical models to better understand how water use is changing and how we can build a future cities and infrastructure to address the needs of the people in the US, in California and across the world. >>That's great. And you're gonna give a talk today about how to build water security using big data. So give us a preview of your talk. >>Sure. So the 20th century water infrastructure model was very much of a >>top down model, >>so we built solutions or infrastructure to bring water to people, but people were not part of the loop. They were not the way that they behaved their decision making process. What they used, how they use it wasn't necessarily part of the process and the assume. There's enough water out there to bring water to people, and they can do whatever they want with it. So what we're trying to do is you want to change this paradigm and try to make it more bottom up at to engage people's decision making process and the uncertainty associated with that as part of the infrastructure planning process. Until I'll be talking, I'll talk a little bit about that. >>And where is the most water usage coming from? So, >>interestingly enough, in developed world, especially in the in the western United States, 50% of our water is used outdoors for grass and outdoor spacing, which we don't necessarily are dependent on. Our lives depend on it. I'll talk about the statistics and my talk, but grass is the biggest club you're going in the US while you're not really needing it for food consumption and also uses four times more water >>than than >>corn, which is which is a lot of water. And in California alone, if you just think about some of the spaces that we have grass or green spaces, we have our doors in the in. The in the malls are institutional buildings or different outdoor spaces. We have some of that water. If we can save, it can provide water for about a 1,000,000 or two million people a year. So that's a lot of water that we can be able to we can save and use, or you are actually a repurpose for needs that you really half. >>So does that also boil down to like people of watering their own lawns? Or is the problem for a much bigger grass message? >>Actually, interestingly enough, that's only 10% of that water out the water use. The rest of it is actually the residential water use, which is what you and I, the grass you and I have in our backyard and watering it so that water is even more than that amount that I mentioned. So we use a lot of water outdoors and again. Some of these green spaces are important for community building for making sure everybody has access to green spaces and people. Kids can play soccer or play outdoors, but really our individual lawns and outdoor spaces. If there are not really a native you know landscaping, it's not something that views enough to justify the amount of water you use for that purpose. >>So taking longer showers and all the stuff is very minimal compared to no, not >>at all. Sure, those are also very, very important. That's another 50% of our water. They're using that urban areas. It is important to be mindful the baby wash dishes. Maybe take shower the baby brush rt. They're not wasting water while you're doing that. And a lot of other individual decisions that we make that can impact water use on a daily basis. >>Right, So So tell us a little bit more about right now in California, We just had a dry February was the 1st 150 years, and you know, this is a huge issue for cities, agriculture and for potential wildfires. So tell us about your opinion about that. So, >>um, the 20th century's infrastructure model I mentioned at the beginning One of the flaws in that system is that it assumes that we will have enough snow in the mountains that would melt during the spring and summer time and would provide us water. The problem is, climate change has really, really impacted that assumption, and now you're not getting as much snow, which is comes back to the fact that this February we have not received any snow. We're still in the winter and we have spring weather and we don't really have much snow on the mountain. Which means that's going to impact the amount of water we have for summer and spring time this year. We had a great last year. We got enough water in our reservoirs, which means that you can potentially make it through. But then you have consecutive years that are dry and they don't receive a lot of water precipitation in form of snow or rain. That will become a very problematic issue to meet future water demands in California. >>And do you think this issue is along with not having enough rainfall, but also about how we store water, or do you think there should be a change in that policy? >>Sure, I think that it definitely has something also in the way we store water and be definitely you're in the 21st century. We have different problems and challenges. It's good to think about alternative ways off a storing water, including using groundwater sources. Groundwater as a way off, storing excess water or moving water around faster and making sure we use every drop of water that falls on the ground and also protecting our water supplies from contamination or pollution. >>And you see it's ever going to desalination or to get clean water. So, interestingly >>enough, I think desalination definitely has worth in other parts of the world, and then they have. Then you have smaller population or you have already tapped out of all the other options that are available to you. Desalination is expensive. Solution costs a lot of money to build this infrastructure and also again depends on you know, this centralized approach that we will build something and provide resources to people from from that location. So it's very costly to build this kind of solutions. I think for for California we still have plenty of water that we can save and repurpose, I would say, and also we still can do recycling and reuse. We can capture our stone water and reuse it, so there's so many other, cheaper, more accessible options available before you go ahead and build a desalination plants >>and you're gonna be talking about sustainable water resource management. So tell us a little bit more about that, too. So the thing with >>water mismanagement and occasionally I use also the word like building resilient water. Future is all about diversifying our water supply and being mindful of how they use our water, every drop of water that use its degraded on. It needs to be cleaned up and put back in the environment, so it always starts from the bottom. The more you save, the less impact you have on the environment. The second thing is you want to make sure every trouble wanted have used. We can use it as many times possible and not make it not not. Take it, use it, lose its right away, but actually be able to use it multiple times for different purposes. Another point that's very important, as actually majority of the water they've used on a daily basis is it doesn't need to be extremely clean drinking water quality. For example, if you tell someone that you're flushing down our toilets. Drinkable water would surprise you that we would spend this much time and resources and money and energy to clean that water to flush it down the toilet video using it. So So basically rethinking the way we built this infrastructure model is very important, being able to tailor water to the needs that we have and also being mindful of Have you use that resource? >>So is your research focus mainly on California or the local community? We actually >>are solutions that we built on our California focus. Actually, we try to build solutions that can be easily applied to different places. Having said that, because you're working from the bottom up, wavy approach water from the bottom up, you need to have a local collaboration and local perspective to bring to their to this picture on. A lot of our collaborators have been so far in California, we have had data from them. We were able to sort of demonstrate some of the assumptions we had in California. But we work actually all over the world. We have collaborators in Europe in Asia and they're all trying to do the same thing that we dio on. You're trying to sort of collaborate with them on some of the projects in other parts of the world. >>That's awesome. So going forward, what do you hope to see with sustainable water management? So, to >>be honest with you, I would often we think about technology as a way that would solve all our problems and move us out of the challenges we have. I would say technology is great, but we need to really rethink the way we manager resource is on the institutions that we have on there. We manage our data and information that we have. And I really hope that became revolutionized that part of the water sector and disrupt that part because as we disrupt this institutional part >>on the >>system, provide more system level thinking to the water sector, I'm hoping that that would change the way we manage our water and then actually opens up space for some of these technologies to come into play as >>we go forward. That's awesome. So before we leave here, you're originally from Tehran. Um and and now you're in this data science industry. What would you say to a kid who's abroad, who wants to maybe move here and have a career in data science? >>I would say Study hard, Don't let anything to disk or do you know we're all equal? Our brains are all made the same way. Doesn't matter what's on the surface. So, um so I and encourage all the girls study hard and not get discouraged and fail as many times as you can, because failing is an opportunity to become more resilient and learn how to grow. And, um and I have, and I really hope to see more girls and women in this in these engineering and stem fields, to be more active on, become more prominent. >>Have you seen a large growth within the past few years? Definitely, >>the conversation is definitely there, and there are a lot more women, and I love how Margot and her team are sort of trying to highlight the number of people who are out there. And working on these issues because that demonstrates that the field wasn't necessarily empty was just not not highlighted as much. So for sure, it's very encouraging to see how much growth you have seen over the years for sure >>you shed. Thank you so much. It's really inspiring all the work you do. Thank you for having me. So no, Absolutely nice to meet you. I'm Senator Gary. Thanks for watching the Cube and stay tuned for more. Yeah, yeah, yeah.

>> Reporter: Live from Stanford University, it's theCUBE, covering Stanford Women in Data Science 2020. Brought to you by SiliconANGLE media. >> Hi, and welcome to theCUBE. I'm your host, Sonia Tagare, and we're live at Stanford University covering the fifth annual WiDs, Women in Data Science conference. Joining us today is Emily Glassberg Sands, the Head of Data Science at Coursera, Emily, welcome to theCUBE. >> Thanks, so great to be on. >> So, tell us a little bit more about what you do at Coursera. >> Yeah, absolutely, so Coursera is the world's largest platform for higher education. We partner with about 160 universities and 20 industry partners and we provide top learning content from data science to child nutrition to about 50 million learners around the world. I lead the end to end data team so spanning data engineering, data science and machine learning. >> Wow, and we just had Daphne Koller on earlier this morning who is the co-founder of Coursera and she's also the one who hired you. >> Yeah. >> So tell us more about that relationship. >> Well, I love Daphne, I think the world of her, as I will talk about shortly, she actually didn't hire me from the start. The first answer I got one from Coursera was a no, that the company wasn't quite ready for someone who wasn't a full blown coder. But I eventually talked to her into bringing me on board, and she's been an inspiration ever since. I think one of my first memories of Daphne was when she was painting the vision of what's possible with online education, and she said, "think about the first movie." The first movie was literally just filming a play on stage. You'll appreciate this, given your background in film, and then fast forward to today and think about what's possible in movies that could never be possible on the brick-and-mortar stage. And the analog she was creating was the first MOOC, the first Massive Open Online Course was very simply filming a professor in a classroom. But she was thinking forward to today and tomorrow and five years from now, and what's possible in terms of how data and technology can transform, how educators teach and how learners learn. >> That's very cool. So, how has Coursera changed from when she started it to now? >> So, it's evolved a lot. So, I've been at Coursera about six years, when I joined the company, it had less than 50 people. Today we're 10 times that size, we have 500. I think there have been obviously dramatic growth in the platform over all the three main changes to our business model. The first is we've moved from partnering exclusively with universities to recognizing that actually, a lot of the most important education for folks in the labor market is being taught within companies. So, Google is super incentivized to train people in Google Cloud, Amazon and AWS. Folks need to learn Tableau and a whole host of other software's. So, we've expanded to including education that's provided not just by top institutions like Stanford, but also by top institutions that are companies like Amazon and Google. The second big change is we've recognized that while for many learners and individual course or a MOOC is sufficient, some learners need access to full degree, a diploma bearing credential. So we've moved to the degree space we now have 14 degrees live on the platform masters in computer science and data science but also in business, accounting, and so on. And the third major changes, I think just sort of as the world has evolved to recognize that folks need to be learning throughout their lives. There's also general consensus that it's not just on the individuals to learn, but also on their companies to train them and governments as well, and so we launched Coursera enterprise, which is about providing learning content through employers and through governments so we can reach a wider swath of individuals who might not be able to afford it themselves. >> And how are you able to use data science to track individual, user preferences and user behavior? >> Yeah, that's a great question so you can imagine right? 50 million learners, they're from almost every country in the world from a range of different backgrounds have a bunch of different goals, And so I think what you're getting out is that so much of creating the right learning experience for each person is about personalizing that experience. And we personalized throughout the learner journey so in discovery up-front, when you first joined the platform, we ask you, what's your career goal? What role are you in today? And then we help you find the right content to close the gap. As you're moving through courses we predict whether or not you need some additional support. Whether it's a fully automated intervention like a behavioral nudge, emphasizing growth mindset, or a pedagogical nudge like recommending the right review material and provide it to you, and then we also do the same to accelerate support staff on campus. So, we identify for each individual what type of human touch might they need, and we serve up to support staff recommendations for who they should reach out to, whether it's a counselor reaching out to degree student who hasn't logged in for a while, or a TA reaching out to a degree student who's struggling with an assignment. So, data really powers all of that, understanding someone's goals, their backgrounds, the content that's going to close the gap, as well as understanding where they need additional support and what type of help we can provide. >> And how are you able to track this data, are you using AV testing? >> Yeah, great question, so the, we call it a venting level data, which basically tracks what every learner is doing as they're moving through the platform. And then we use AV testing to understand the influence of kind of our big feature. So, say we roll out a new search ranking algorithm or a new learning experience we would AV-Test that, yes to understand how learners in the new variant compared to learners in the old variant. But for many of our machine learn systems, we're actually doing more of a multi-armed bandit approach where on the margin, we're changing a little bit the experience people have to understand what effect that has on their downstream behavior, separate from this mass hold-in or hold-out AV-Test. >> And so today, you're giving a talk about Coursera's latest data products so give us a little insight about that. >> So, I'm covering three data products that we've launched over the last couple of years. The first two are oriented around really helping learners be successful in the learning experience. So the first is predicting when learners are going to need additional nudges and intervening in fully automated ways to get them back on track. The second is about identifying learners who need human support and serving up really easily interpretable insights to support staff so they can reach out to the right learner with the right help. And then the third is a little bit different. It's about once learners are out in the labor market, how can they credibly signal what they know, so that they can be rewarded for that learning on the job. And this is a product called skill scoring, where we're actually measuring what skills each learner has up to what level so I can for example, compare that to the skills required in my target career or show it to my employer so I can be rewarded for what I know. >> That can be really helpful when people are creating resumes, by ranking how much of a skill that they have. >> Absolutely. So, it's really interesting when you talk about resumes, so many of what, so much of what's shown on resumes are traditional credentials, things like What school did you go to? what did you major in? what jobs have you had? And as you and I both know, there's unequal access to the school you go to or the early jobs you get. And so, part of the motivation behind skill scoring is to create more equitable or fair or accessible signals for the labor market. So, we're really excited about that direction. >> And do you think companies are taking that into consideration when they're hiring people who say have like a five out of five skills in computer science, but they didn't go to Stanford? >> Yeah. >> Think they're taking that >> Absolutely, I think companies are hungry to find more diverse talent and the biggest challenge is, when you look at people from diverse backgrounds, it's hard to know who has what skills. And so skill scoring provides a really valuable input, we're actually seeing it in use already by many of our enterprise customers who are using it to identify who have their internal employees is well positioned for new opportunities or new roles. For example, I may have a bunch of backend engineers, if I know who's good in math and machine learning and statistics, I can actually tap those folks to transition over to machine learning roles. And so it's used both as an external signal and external labor market, as well as an internal signal within companies. >> And just our last question here, what advice would you give to young women who are either out of college or just starting college who are interested in data science? Who maybe, don't haven't majored in a typical data science major? What advice would you give to them? >> So, I love that you asked you haven't made it, majored in a typical data science major. I'm actually an economist by training. And I think that's probably the reason why I was at first rejected from Coursera because an economist is a very strange background to go into data science. I think my primary advice to those young women would be to really not get too lost in the data science, in the math, in the algorithms and instead to remember that those are a means to an end, and the end is impact. So, think about the problems in the world that you care about. For me, it's education. For others, it's health care, or personal finance or a range of other issues. And remember that data science provides this vast set of tools that you can use to solve the problems you care about most. >> That's great, thank you so much for being on theCUBE. >> Thank you. I'm Sonia Tagare, thank you so much for watching theCUBE and stay tuned for more. (upbeat music)