(bright music) >> Hi, everyone. Welcome to theCUBE's coverage of WiDS 2022. I'm Lisa Martin, very happy to be covering this conference. I've got Vidya Setlur here with me, the director of Tableau Research. Vidya, welcome to the program. >> Thanks, Lisa. It's great to be here. >> So this is one of my favorite events. You're a keynote this year. You're going to be talking about what makes intelligent visual analytics tools really intelligent. Talk to me a little bit about some of the key takeaways that the audience is going to glean from your conversation. >> Yeah, definitely. I think we've reached a point where everybody understands that data is important, trying to understand that data is equally important. And we're also getting to that point where technology and AI is really picking up. Algorithms are getting better, computers are getting faster. And so there's a lot of dialogue and conversation around how AI can help with visual analysis to make our jobs easier, help us glean insights. So I thought it was a really timely point where we can really actually talk about it, and distilling into the specifics of how these tools can actually be intelligent beyond just the general buzz of AI. >> And that's a great point that you bring up. There's been a lot of buzz around AI for a long time. The organizations talk about it, software vendors talk about it being integrated into their technologies, but how can AI really help to make visual analytics interpretable in a way that makes sense for the data enthusiast and the business? >> Yeah, so to me, I think my point of view, which tends to be the general agreement among the research community, is AI is getting better. And there are certain types of algorithms, especially these repetitive tasks. We see this with even Instagram, right? You put a picture on Instagram, there are filters that can maybe make the image look better, some fun backgrounds. And those, generally speaking, are AI algorithms at work. So there are these simple, either fun ways or tasks that reduce friction where AI can play a role, and they tend to be really good with these repetitive tasks, right? If I had to upload a picture and constantly edit the background manually, that's a pain. So AI algorithms are really good at figuring out where people tend to do a particular task often, and that's a good place for these algorithms to come into play. But that being said, I think fundamentally speaking, there are going to be tasks where AI can't simply replace a human. Humans have a really strong visual system. We have a very highly cognitive system where we can glean insights and takeaways beyond just the pixels, or just the text. And so how do we actually design systems where algorithms augment a human, where a human can stay in the driver's seat, stay creative, but defer all these mundane or repetitive tasks that simply add friction to the computer? And that's what the keynote is about. >> And talk to me about when you're talking with organizations, where are they in terms of appetite to understand the benefits that natural language processing, AI and humans together, can have on visual analytics, and being able to interpret that data? >> Yeah. So I would say it's really moving fast. So three years ago, organizations were like AI, it's a great buzzword, we're weary because when rubber hits the road, it's really hard to take that into action. But now we're slowly seeing places where it can actually work. So organizations are really thirsty to figure out how do we actually add customer value? How do we actually build products where AI can move from a simple, cute proof of concept working in a lab to actual production? And that is where organizations are right now. And we've already seen that with various types of examples, like machine translation. You open up a Google page in Spanish, and you can hit auto translate and it will convert it into English. Now, is it perfect? Not, but is it good enough? Yes. And I think that's where AI algorithms are heading, and organizations are really trying to figure out what's in it for us, and what's in it for our customers. >> What are some of the cultural, anytime we talk about AI, we always talk about ethics. But what are some of the cultural, or the language specific challenges with respect to natural language techniques that organizations need to be aware of? >> Yeah, that's a great question, and it's a common question, and really important. So as I've said, these AI algorithms are only as good as the data that they're often trained on. And so it's really important, in addition to the cultural aspects of incorporating those into the techniques, is to really figure out what sort of biases come into play, right? So a simple example is there's sarcasm in language, and different cultures have different ways of interpreting it. There are subtleties in language, jokes. My kids have a certain type of language when they're talking with each other that I may not understand. So there's a whole complexity around cultural appropriation generations that, where language constantly evolves, as well as biases. For example, we've had conversations in the news where AI algorithms are trained on a particular data set for detecting crime. And there are hidden biases that go into play with that sort of data. So we're really, it's important to be acknowledged of where the data is, and what sorts of cultural biases come into play. But translation, simple language translation is already more or less a solved problem. But beyond the simple language translation, we also have to account for language subtleties as well. >> Right, and the subtleties can be very dramatic. When you're talking with organizations that are really looking to become data driven. Everybody talks about being data driven, and we hear it on the news all the time, it's mainstream. But what that actually really means, and how an organization actually delivers on that are two different things. When you're talking with customers that are, okay, we've got to talk about ethics. We know that there's biases and data. How do you help them get around that so that they can actually adopt that technology, and make it useful and impactful to the business? >> Yeah. So just as important as figuring out how AI algorithms can help an organization's business, it's equally important for an organization to be more data literate about the data that feeds into these algorithms. So making data as a first class citizen, and figuring out are there hidden biases? Is the data comprehensive enough? Acknowledging where there are limitations in the data and being completely transparent about that. And sharing that with customers, I think, is really key. And coming back to humans being in the driver's seat. If these experiences are designed where humans are, in fact, in the driver's seat, as a human, they can intervene and correct and repair the system if they do see certain types of oddities that come into play with these algorithms. >> Going to ask you in our final few minutes here, I know that you have a PhD in computer graphics from Northwestern, is it? >> Yep. >> Northwestern. >> Go Wildcats, yep. >> Were you always interested in STEM and data? Talk to me a little bit about your background. >> Yeah. I grew up in a family full of academics and female academics. And now, yes, I have boys, including my dog. Everybody's male, but I have a really strong vested interest in supporting women in STEM. And I actually would go further and say, STEAM. I think arts and science are both equally important. In fact, I would say that on our research team, there's a good representation of minorities and women. And data analysis and visual analysis, in particular, is a field that is very conducive for women in the field, because women tend to be naturally meticulous. They're very good at distilling what they're seeing. So I would argue that there are a host of disciplines in this space that make it equally exciting and conducive for women to jump in. >> I'm glad that you said that. That's actually quite exciting, and that's a real positive thing that's going on in the industry, and what you're seeing. So I'm looking forward to your keynote, and I'm sure the audience is as well. Vidya, it was a pleasure to have you on the program talking about intelligent visual analytics tools, and the opportunities that they bring to organizations. Thanks for your time. >> Thanks, Lisa. >> For Vidya Setlur, I'm Lisa Martin. You're watching theCUBE's coverage of WiDS conference 2022. Stick around, more great content coming up next. (bright music)

(upbeat music) >> Live from Las Vegas, it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World. I'm your host, Rebecca Knight. We have two guests for this segment. We have Ronen Schwartz. He is the senior vice-president and general manager, Big Data Cloud and Data Integration at Informatica. Welcome Ronen, Welcome back, Ronen. >> Yes, pleasure to be here. Welcome to Informatica World. >> Thank you. And we have Daniel Jewett, VP Product Management at Tableau. Thank you so much for coming on theCUBE. >> Thank you for the welcome, Rebecca. Happy to be here. >> Yes So there's some big news that's going to be announced later today. Tell us about the partnership with Tableau and Informatica. I Want to start with you, Ronen. >> Yes, So Tableau been an amazing innovator in the area of data visualization, analytics. I think more than all they've actually opened the ability for more people to use data. And Informatica have been very excited to partner with Tableau on this journey of how do we empower more users, more company, to become data driven. So I think very exciting partnership. A lot of innovation. A lot of great capabilities. >> So we hear so much about the explosion of data and how much its use is being just across the enterprise. More and more functions are using data to make their decisions. How does this impact the strategic importance of data? >> Yeah, absolutely. Well, the relationship with Informatica for us has become important over the years as that data has exploded. Right, it used to start off, you had a spreadsheet of some numbers and you wanted to try and understand what was in there and Tableau helped you with that. But then as data lake started coming on the scene and not just a single data lake but multiple feeds of data and streaming data and data's here, and data all over in Europe, and data's wherever it happens to be, that becomes a real challenge for the individuals who have some questions about data. So Tableau's only as good as the data that we can get our hands on. So to have a great partner like Informatica, who can marshal and rationalize where all that data is is a valuable partnership for us to have. >> And it's really about data governance but then also about democratization of data and analytics. Want to talk about that a little bit, Ronen? >> Yes, so I think democratization of data actually depends on your ability to have built-in governance. So that the users are using the right data at the right time. And the organization actually understands what is available where. I think this is actually one of the sweet spots for the partnership. >> Right. >> Actually, the ability of Tableau with a very easy interface to allow everybody to really work with data and the ability of Informatica to enable everybody to get the data in a governed way when you can actually control the quality and the availability of the data is actually our sweet spot as partners. >> There's some real tension there between the democratization and the governance side, right? So from a business user's perspective, democratization means, I want to use that data and I want to start working with it. From a business user's perspective governance, typically means no. IT says you can't use that data or you can't have it or it's too complicated for you. So to be able to break that down and say no. Data catalog and some of the tools from Informatica make the data available in an accessible and friendly manner and understandable manner, is what enables the democratization to happen. So it's kind of turning that "no" into a "yes, let me help you", which is a big difference. >> And how is that relationship between IT and the business side? How would you say that it has evolved in recent years as there is more of a push and pull between these two functions. >> Yes, it's definitely evolved over the years. So as Ronen said, we have been working for a long time. I think we officially became partners back in 2011. There was probably some tension out of a lot of accounts between the IT camp and the business camp and we were always the flag bearers of the business users As we've seen over the years, business users get frustrated by untrusted data and not being able to find data. So as the IT organizations have helped bridge that gap I would like to think we're helping put that olive branch in between the two. The two camps have companies with the products working together. >> I think, imagine that instead of IT actually being on the way of people using data. IT is really giving the power to find the right data to the business users. And this is actually, instead of, like, the user really, working really really hard to get the data, now it's in their fingertip. They can find it. And when they find it, they can use it all the way from the source into Tableau in a very very easy way. >> And trust it. >> And trust it. >> The value add >> The veracity, exactly. >> I can find a lot of data easily but most of it is not trustworthy and I don't know if I want to do my analysis on untrustworthy data. So to be able to trust that data that I've come across is really important. >> We're talking a lot about AI and machine learning here. How do those two concepts, ideas, approaches, methodologies play into Tableau's vision? >> For Tableau, we've always been the company that wants the human as part of the process, right? We think people are curious and we want them to explore that data and work with that. So, at first glance you might think AI and machine learning doesn't fit in with that but we think there's really a powerful way for it to do it. Instead of a machine learning solution handing you the answer, we want the machine solution to say, we think there's something interesting here that you should go explore more. So that's the angle that we're putting our investment in. >> So putting the human into these tech >> Human still needs to be >> Human centered >> in the loop >> machine learning. >> and the machine can help coach you along the right way to make those inferences around the data. >> Final question. We're talking a lot about the skills gap. It is a pressing problem in the technology industry. Ronen, I'm going to start with you. How much does this keep you up at night? And what are you doing to ensure that you have the right technical and business talent to fill the open roles you have on your team? I think, I don't know if, I probably answer it in a relatively unique way. I think one of our job as a vendor is actually to empower more users to do more complex tasks, actually without the necessity to build a huge skill set. And I think today, especially in this event, a lot of the clear AI technologies really coming to give user that are less skill a lot of power. And this is actually a critical thing in order to address the new needs, right? So the needs will continue to grow. The demand is going to continue to grow. We believe that a big part of answering the demand versus supply is by empowering new users to participate in an effective way within the integration, data management analytics space. So we're making a major major effort there. But we're also adding a lot of guided, a lot of advice, a lot of optimization that is done for the users automatically so the users are more effective. I still think that the need for talent is only going to grow. It's not just a growth in the data. It's the growth in the demand for data and the growth in the demand for good data. So I think a lot of enablement, a lot of investment in people, and the technology to actually empower more users. >> Daniel? >> Yeah so for us part of the onus is on us to make the software easy enough to use and understandable for the audiences that are coming across it. So there's really no reason why everybody can't be an analyst. They might be afraid of that title but you're all working with data. You're looking at your phone, You're looking at your steps, You're looking at everything. Data. It's as simple as that. But data comes across your landscape in a lot of ways. So it's up to us to make the analytic flow as easy as we can and understandable as we can. But it's also up to us to help grow the skills. You can only make it so easy 'cause sometimes doing analytic task and working with data is just hard. There are complicated things. So what can we do to uplift the skills? We do a lot with Tableau for teaching and trying to nurture education programs all the way from K to 12, and up in universities to try and seed the universities' and elementary school instructors to start introducing the concepts of working with data at early ages. And then in college, there's whole classes that people use Tableau in to help understand the analytic process. So it's a little step and it's a forward looking step. The payoff won't be for many years until those people get into the workforce. >> We're starting them young. (laughing) >> But you have to. >> Mommas, teach your babies data science. >> Absolutely. (laughing) >> Daniel, Ronen, Thank you both so much for coming on theCUBE. It's been a great conversation. >> Excellent, >> Thank you. >> thank you, Rebecca. >> I'm Rebecca Knight, we will have much more of theCUBE's live coverage of Informatica World 2019. Stay tuned. (upbeat music)

>>Live from Seattle, Washington. It's the queue at Tableau conference 2014 brought to you by headline sponsor Tableau.. >>Here are your hosts, John furrier and Jeff Kelly. >>Okay, welcome back. And when we hear live in Seattle, Washington for the cube, this is our flagship program. We go out to the events, expect to see with the noise. I'm John furrier, my coach Jeff Kelly, analysts that we bond.org and we'd love to go talk to the senior leaders of the companies that are hosting the event, the Tablo data 14 conference and Kelly, right EVP of sales for Tableau software. Welcome to the cube. >>Thank you. Thank you for having me. >>So, uh, you're under the, you're in the pressure cooker seat. So sales is everything, right? You know, you guys are a public company and you have to perform. Performance is happy customers, they pay you money, you collect the cash, you put it in the bank and invested into your business and do it again and again. Um, you've done very well as a company. You guys have been great. So I got to ask you, um, about where Chad blow is today. Share with the folks a little bit of the history. Um, you know, we've been big fans of the company actually. We are, uh, you know, me personally being an entrepreneur, I love when companies get built by the founders and don't have to raise money to start the company. They get critical mass and take the extra growth capital. And you guys have done that. You've been in real big success story is an entrepreneurial venture. So share the culture and kind of where you guys are now and with the customer base, the culture. >>Oh, that's a lot of questions all in one. Uh, well thank you for having me. It's a pleasure being here. You know, you asked about what it's been like on this whole journey and a lot of the people that were here at the beginning, we're all still here, right? So I was the first salesperson at Tableau. I joined a month before we started version one. And I've seen how things have changed and evolved. And the truth of the matter is we have a lot more people. We have more customers, but the culture of the company has stayed really sound from the beginning. We were a bunch of people who were very, very passionate about this mission to help people see and understand data. And that's still our mission today. So from the day I started to now, it's all been focused on empowering people to answer their questions more. And so the culture of the people that started were very passionate, really excited about the mission, really a group of company builders who wanted to roll up their sleeves and go make things happen. And yes, we're a bigger company now. Now we're a public company, but we're still just barely, barely scratching the surface. I mean, they're 55 million companies out there in the world. We have 20,000 customers. So we have a long, long way to go. >>I love that you're a senior lead as a company. You've been there as the first is awesome. So I've got to ask you, I mean there's always a moment in time where you go, Oh, will we make it? Or that moment where you going? We've the flywheels going. Could you share just some color around because startups are very hard. Think they're easy all yet. Anyone can do that. So share with a moment where you go, Oh my God, it's gonna be tough shipping where they're shipping a product or hiring or personnel or, and an aha moment where you said, Oh my God, we're doing it. Well, >>when, when you're in this company building mode, it's just you put your head down and you go and you're just go, go, go. And it's always about going and finding the next customer, making sure that customer is excited, ecstatic, hiring more people on the team, making sure that culture is still vibing. And we really just took the focus of doing things one day at a time and treating each customer like their goals. And that's still what we do. Our customers are our lifeblood, right? And that's what's keeping us going. So there were certain times at during during the whole journey, I mean, I remember 2009 when the economy was slowing down. Tableau actually still grew at a really healthy clip, but it was harder. But there was really no time that I felt, Oh, this is a huge uphill battle. I, it was an uphill battle all the time. >>We're still kind of the underdogs, right, where there's tons of customers to help. We haven't helped tons of them yet. And it's just doing things to make sure that we're building good products, empowering people to you go, wow, we're really doing this well. Did you take a break and pause and say, Hey, we're doing it, we're making it. Well, you know, I think one of the moments that really resonated for me is we worked so long to say is Tao, is Tablo gonna make it just keep doing what we're doing and believe in what we're doing. Believe in that mission. And for a long time it was, can we make it to be a public company? Can we ever get to that moment? And I remember the day, it was May 17th last year, 2013 when we were on the floor of the New York stock exchange. And we had brought tons of customers. I mean not customers. We had a lot of employees. So we had over a hundred employees filling out the floor. And in that moment when we had the management team and Christian was ringing the bell, just looking out at all these people who had helped us build Tableau and get to that day. I think that was a moment of real. A lot of pride. And it's funny talking about it right now because where I just came from is gesturing in the bell again at the, at the closing bell. So >>cause that's a lot of those steps are very hard. I mean Jeff and I talked to special all the time. We'll get a big pile of money from the VCs. Four or five guys. >>Well we didn't get a big pile of, >>I know, I just, why I was thinking why it's such a great story because the pilot money could complicate it. Being hungry actually is motivating. So, and then having that customer product successes is a great testimony. So we, I mean I think you guys are a great testimonial to successful startups. Thank you. So let's dig into the sales strategy a little bit. So as you've grown up Tableau, when you started off you really, this is you know, this very nimble underdog. You were kind of going in there with really disrupting the old guard BI players. A lot of, more of a kind of I think a desktop focus, a single user kind of focus. You've expanded, you've got enterprise licenses, now you've got cloud, now you've got mobile. How has the sales strategy evolved over that time period to, to adopt or to adjust to these new, uh, Kevin, the new ways of reaching your customer? >>Well, you know, our model is actually really quite simple. I'll go back to what I had talked about before. We help people see and understand data. So everything about what we're trying to do is to help people to be able to answer their own questions and to empower them with flexibility and agility and self service. And as we add additional products, it's really just extending the number of people that we can help. Some people want to work in the cloud, so Tableau online's better. Some people want to do it on their desktop so they're doing it more with tablet, desktop, some people out in the server and so as long as our salespeople are are looking for what is the best way that I can help this customer to be able to be more self sufficient in answering their own question and then we really hear what's the customer's use case. >>Then to answer that we have different products that actually fit that in. So in terms of how our sales strategy is working, the sales strategy is the same as it always is so we don't really focus on what to do with this product line versus that product line or this product line or small customers versus big customers. It's really all in this landed expand, let the customer buy as big or as little as they want to get started. We'll work with them very closely to make them successful and then as they're successful, they'll come back to buy more. And we have all these different ways that they can buy software and types of software that they can buy to be able to address their needs of self service agility and answering their own questions. >>The buyer, the profile of the buyer changed at all. So I know obviously Tableau is all about the end user, the person who's interacting with the software interact with the data as you'd like to focus on. But as you move to larger accounts, larger enterprises, are you still dealing directly with that user when you sell? Are you dealing with essential it more often? Right, right. >>And I guess that was kind of my question. You evolve to that, you know, I think that's a great, it's a great question because if I were to roll back the clock to almost 10 years ago when I was starting, we were, we were actually interacting mostly with the business user. So the end user and over time we're interacting with the C level, the C suite, we're interacting with the VP of it, we're interacting with the business users. And actually we're, we're working with both groups a lot. So what happened early on was we'd start with the business and over time as they bought more and more and more, they would bring us into it. And now actually we're seeing a shift that sometimes it's the it and the C suite that's coming to us and they're saying, Hey, we want to be able to empower our user community answered their own questions, but we need to be able to do that in a more secure governed control type of way. >>And is there a way that we can balance with Tableau? So we see it happening in both. I think one of the interesting changes that we're seeing is there is a cultural shift that's going on right now and companies are now starting to realize that the way that the past is very different than the wave of the future. So the wave of the past was if you had a question, you threw it over the fence to this central group that was report writers and these report writers knew how to code and they were very, very specialized. And the user that had the question, they had absolutely no idea how to operate those systems well. Now that companies are saying as data's coming in at such a fast clip, it just takes too long. They have to empower people to be able to answer their own questions, otherwise they end up being at a standstill. And so as we start having more discussions with the enterprise in the C suite, those folks who are in it and the CIO who realize, Hey, there's a shift that's going on and we need to be doing things in the way of where the world is going, not the way that we've done it in the past. It makes that conversation quite a bit easier. And so now we're seeing more and more conversations that are along those lines of how are we going to keep our organization to be competitive going into the. >>So I've got to ask you about the international expansion. We were talking earlier with your colleague Dave Martin, um, and also move at the HP big data event. And I had also had a conversation with Dave, CEO firearm, huge international. He says, John, my big growth happened. He's public company. You got you guys, he says international huge growth opportunity for us. So you have a Tam, then you have 55 million customers. You have one of those unique products at all customers need. So that's good. Check growth is on the horizon. How are you going to attack that new territory? I mean international and to grow, I mean channel strategy, indirect big part of it. I mean you guys are enabling people to create value. That seems to be the formula for a great indirect strategy. You've built a successful direct sales force graduations, but that's can take time. >>Yeah. Well you know, our model for international international is a huge opportunity for us. So we are putting a lot of resources and time into expanding internationally. We have our headquarters over in AMEA, we have headquarters over an APAC. We're now just w we opened up offices in Japan and in Germany we opened up operations in India. We are opening up another, a bigger office in, in Australia and even in Latin America, Brazil and Mexico. There's a fair amount going on now as we're going to market. It actually is pretty similar, so we're building direct sales force in all of those regions. But international, as you start doing more international, the channel becomes even increasingly important and it is, we're focusing a lot of time and energy on the channel here in the States. But in places like AMEA and certain locations over an APAC and and certainly in Latin America there is just the way of doing business tends to be more around the channel. >>Equalization has always been a nice thing of having in country operations. So that's always been kind of the international playbook. But with data I can be complicated. So having people in country, in a channel delivering value, is that the preferred way you guys, is that what you're saying? Is that, is that kind of? >>You know what I th th well the interesting part about Tableau is as we talked about, it's agnostic. Anyone can use it. And so when we go into a new country, there's two ways that we can go in. We can go on with our directing and we can go in with empowering our channel. And we actually have customers in over a hundred countries throughout the world, right? And we have partners operating in a large number of those. So our partners often are the ones that are the local feet on the street. They're going and they're having the conversations and, and they're providing the local support in the language and in the culture that it is now. When we actually open up offices in those different regions, we try to be very aligned, not only just putting our salespeople in, but having our entire company all lined up behind it. So we have our sales team, we have our marketing, we have our product. So when we go into Japan, for instance, we want to be able to have the website in Japanese. We want to be able to have the product localized in Japanese, we want to be able to have support staff that can help. And, and then of course having the partner ecosystem where the partners are able to help us make those customers all realistic. >>Flip yet in the U S I mean, as you guys get the channel going, has there been some channel conflict on order orders and who owns the accounts? >>Yeah, well you know what, our channel, we were developing a lot in the channel, but we're still pretty early in the, in our channel development and we're spending a lot of time to make sure that our channel is really successful as well as our, as well as our customers being successful. And the truth of the matter is we can't, we can't go and help all the people that we want to help without embracing the channel. And they're system integrators that they're in there and they're doing huge multi-year projects and we're working closely with them. And when we talk about the channel, we're working with resellers but also OEM and technology partners and system integrators. So lots and lots of channel activity going on. >>Yeah, I think you just touched on, well I think is one of the going to be one of the challenges for Tableau is that you can't, as you expand so fast, you can't keep your finger or your pulse on the customer quite as quite as closely as maybe you'd like. You've got to, you've got to count on the channel to do some of that. So that, and Tableau is of course known for being very customer focused. I mean the show here, you know, the crowds are cheering and Christian as he's giving his keynote and different visualizations are being demoed on stage and the crowds standing on their feet, you know, to keep that kind of customer focus as you expand. I think it's a challenge. It sounds like you really got to focus on those relationships with your partners and your OEM partners, et cetera. So they kind of understand that the Tableau approach is that, yeah, >>I I, I totally agree. Actually. I think you can even see at the show today, if you go down to that partner expo hall, there are so many partners, you're way more partners than we've ever had before. And when I was checking in with them, even yesterday where the show hadn't even started, they're getting a huge number of leads that are coming in and they're, there's so many opportunities for us to work together with our partners. In fact, this year, not only did we build of being really growing our partner sales team, but we had a whole series of partner summits this year and we traveled around the world. We had one in AMEA, one in APAC, one here in the States of being able to really train and enable our partners not only how to sell Tableau, but to work with them in a conversation of what's the best way that we can engage with them and make them really successful. So when we think about our ecosystem, it's not just about our customers, it's now about our customers and about our partners. And we're all part of the Tableau >>here. So obviously one of the things that you guys have done, you do a great job because you're such walking testimonials as customers. Um, what channel partners do you have as customers and that are top references now that you're showcasing and what end users are you showcasing here at this event? Can you name names and? >>Yeah, well I think you can, you can actually go downstairs and look in the partners of who we are and we're doing Watson, lots of, uh, partner with, with whether it's Vertica or with Alteryx or with data, uh, where we're doing joint sales and a lot of those, a lot of the that you'll see here, they're using Tableau internally in a pretty big way. And then in terms of customers, and we have showcases all over the place. I think we have a hundred customer speakers that are here. So there are there hospitals, we have Barnes, Jewish and Seattle children's who are talking about how they're using Tableau actually in the operating rooms and with nurses. And to be able to help save lives. We have education institutions who are using Tableau for how they can teach better in school, how the teachers can have their administration going. Uh, and we also have a number of corporate customers who are helping with that as well. >>So one of the things that we always talk about when we talk about startups, you guys want to start certainly, but company building is a great team. You guys are on that next generation of building out. Um, you always get the question, um, high touch sales, indirect low cost, our automated self-service if you're, you know, kind of a platform, um, inside sales is a great strategy for expanding out growth. Um, but it's hard. Um, do you guys have an inside sales organization? You, are you building it out? Is that a big part of your increase in your customer service? Cause a lot of you got great fans. Loyalties, high products is good. So are you building out? >>Yeah. You know, we actually, we got predominantly with inside sales, so we started with inside sales and then enterprise sales came later. And with our inside sales, we still have a very, very robust inside sales. We have kind of both models, some customers prefer to be interacted with field, face to face. And so we have field folks that are all over, uh, in our, all our major regions and we have a lot of inside folks. And the same is true when we look at how we're going to support them. So we have technical folks and services folks in training folks that will go out and meet the customer on their site, help to enable them setting up center of excellence, all that. And then we have a large number of that is that is done remotely. The benefit we have at Tableau is actually tablets, pretty easy to use. >>And so we don't always have to sit down and do it beside them. So how about sales compensation, if you will? Not with numbers, but like, I mean culturally is it, is it, we're hiring you killed like in the early days of Cisco sales guys were making zillions of dollars. Um, there's Tableau have, um, the kind of product pricing mix where you guys have a lot of like huge compensation, uh, rewards. So how does that work? You know, what we focus on having our salespeople be really excited about working here, having it be a very good as you know, right. I mean, compensation drives behavior. How do you guys, we have a lot of salespeople that have been here for a very long period of time. So we have a huge opportunity and we focus on the opportunity to help more customers and then the opportunity to have a really good career progression path. >>You know? Yes. I'm not going to answer your question, but you can keep on top a little bit about the competitive landscape. So, and again, maybe you know, because you've been with Tableau since the beginning, how has it evolved again, when you guys started, you were very much the disruptor going in. Yeah. Let's name some names, the disruptor, SAP business objects. You had Cognos, Hyperion, you guys are going in there and say, no, that's the old way. This is the new way. Um, since then you've now that some of those old players are started, they're focusing now on you know, being very self service, kind of emulating a lot of the things top load yet now you've got also kind of even newer companies, newer startups out there that are coming, even some are maybe mobile focused or cloud focused. What's the competitive landscape look like for you and from a sales perspective, again, how do you adapt as you got to come in from, you know, from the, from the new guys, you've got to come in from the old guard, you guys are targeted. >>When you're this successful you're always going to be a target. What it's like from your perspective. You know what, one of the things that we actually really focused on at Tableau, cause we talk about this a lot internally with our team is we can only control what we can control. We can control what our products are, we can control what our customer success is, we can control how we engage with our customers. And so we spend a lot of time just focusing on what it is that Tableau can do. And as we're now talking more about data discovery and agile and analytics and self-service, there's a lot of noise out there. A lot of other players who are saying that they can do the same thing and that they can do it as well. And our strategy is really, if you think you can use that, so why don't you go download their product and download our product and see how long it takes. And we actually encourage people to go out and test it out and try. And what we find is when someone is really interested in self service and helping people to answer their own questions, then the answer to them becomes really clear when it is an a question of we just want traditional old pixel perfect reporting you have. There are a lot of people that can play in that game. Uh, but we're finding the conversations changing quite a bit when they really want self-service. Then we actually feel like we're, we're pretty well positioned competitively. >>So are your lottery, your deals going up in, you know, competitive environments where you've got Tableau lined up against business objects against, I don't know. Good data against whoever. Is it a lot of that or do you have a lot of, you know, people who are trying the product love it and just say, Hey, we want to go with Tableau. >>You know, there's both, but the majority of our deals are actually when we're competing against the status quo, they actually aren't even looking at other business intelligence. They might have it in their company but it's not solving their need and their requirement. So a lot of people are just using what is already commissioned on their computer. Now there are situations where there is a competitive bake-off and we love competition. I mess with salespeople. Do we go and compete? Uh, but we're finding that the conversation is shifting and where we tend to really focus our time and energy is with those companies that are really looking for the new way. >>Kelly, you got to get the, I got to get the hook here, but I want to ask you two final questions. One is an easy one. What's it like working with Christian? >>It's great working with Christen. You know what? We've worked together all for so long and it's, it's really, we say it's like we're a family, right? We, we know each other, we know each other's families, we know each other's kids and it's pretty much the same as it was when I started almost 10 years ago. Nothing's really >>the second question. Share with the folks out there watching what is the culture of Tablo, if you could. Every culture has their own little weird tweak that makes them so unique. Intel, it's Moore's law. What's Tableau's cultural? >>Well, you have to go ask all the Tablo people if they think our culture is weird, probably not like a unique tweak that makes them so successful. The Moore's law was first called the weird, you know, people that work here are really, really passionate about what we do. We're passionate, we're mission focus and people have a lot of fun at what they do. They work hard and they play hard and it's, it's a very fun place to be. But we go fast. Yeah, certainly not weird, that's for sure. I didn't mean that, but I want a good way, a good thing. And it's usually the, it's the ones that the best deals are the ones that no one sees that doesn't look like it's going to be. And you guys were certainly a great winner of our hiring, so everyone in the world were hiring. We couldn't get the sales comp out of her, but we, you know, we tried our best, uh, Kelly, seriously, thanks for coming on cue. Really appreciate it. We know the journey you've been on has fantastic. It's a >>whirlwind now. You just got to go to the next leg of the journey, which is build a global 50 million customer business. Congratulations. Thank you for having me. We'll be right back with our next guest after this short break live in Seattle, Washington to the cube. Thank you.

okay we're back this is Dave Volante with Jeff Kelly we're with Ricky bond on organ this is the cubes silicon angles flagship product we go out to the events we extract the signal from the noise we bring you the tech athletes who are really changing the industry and we have one here today christiane sabo is the CEO the leader the spiritual leader of of this conference and of Tablo Kristin welcome to the cube thanks for having me yeah it's our pleasure great keynote the other day I just got back from Italy so I'm full of superlatives right it really was magnificent I was inspired I think the whole audience was inspired by your enthusiasm and what struck me is I'm a big fan of simon Sinek who says that people don't buy what you do they buy why you do it and your whole speech was about why you're here everybody can talk about their you know differentiators they can talk about what they sell you talked about why you're here was awesome so congratulations I appreciate that yeah so um so why did you start then you and your colleagues tableau well it's how below really started with a series of breakthrough research innovations that was this seed there are three co-founders of tableau myself dr. crystal T and professor Pat Hanrahan and those two are brilliant inventors and designers and researchers and the real hero of the tableau story and the company formed when they met on entrepreneur and a customer I had spent several years as a data analyst when I first came out of college and I understood the problems making sense of data and so when I encountered the research advancements they had made I saw a vision of the future a much better world that could bring the power of data to a vastly larger number of people yeah and it's really that simple isn't it and and so you gave some fantastic examples them in the way in which penicillin you know was discovered you know happenstance and many many others so those things inspire you to to create this innovation or was it the other way around you've created this innovation and said let's look around and see what others have done well I think the thing that we're really excited about is simply put as making databases and spreadsheets easy for people to use I can talk to someone who knows nothing about business intelligence technology or databases or anything but if I say hey do you have any spreadsheets or data files or databases you you just feel like it could it could get in there and answer some questions and put it all together and see the big picture and maybe find a thing or two everyone not everyone has been in that situation if nothing else with the spreadsheet full of stuff like your readership or the linkage the look the the traffic flow on on the cube website everyone can relate to that idea of geez why can't I just have a google for databases and that's what tableau is doing right right so you've kind of got this it's really not a war it's just two front two vectors you know sometimes I did I did tweet out they have a two-front war yeah what'd you call it the traditional BI business I love how you slow down your kids and you do that and then Excel but the point I made on Twitter in 140 characters was you it will be longer here I'm a little long-winded sometimes on the cube but you've got really entrenched you know bi usage and you've got Excel which is ubiquitous so it sounds easy to compete with those it's not it's really not you have to have a 10x plus value problem solutely talked about that a little bit well I think the most important thing we're doing is we're bringing the power of data and analytics to a much broader population of people so the reason the answer that way is that if you look at these traditional solutions that you described they have names like and these are the product brand names forget who owns them but the product brand names people are used to hearing when it comes to enterprise bi technology our names like Business Objects and Cognos and MicroStrategy and Oracle Oh bi and big heavy complicated develop intensive platforms and surprise surprise they're not in the hands of very many people they're just too complicated and development heavy to use so when we go into the worlds even the world's biggest companies this was a shocker for us even when we go into the world's most sophisticated fortune 500 companies and the most cutting-edge industries with the top-notch people most of the people in their organization aren't using those platforms because of theirs their complication and expense and development pull and so usually what we end up doing is just bringing the power of easy analytics and dashboards and visualization and easy QA with data to people who have nothing other than maybe a spreadsheet on their desk so in that sense it's actually a little easier than it sounds well you know I have to tell you I just have a cio consultancy and back in the day and we used to go in and do application portfolio analysis and we would look at the applications and we always advise the CIOs that the value of an application is a function of its use how much is being adopted and the impact of that use you know productivity of the users right and you'd always find that this is the dss system the decision support system like you said there were maybe 3 to 15 users yeah and an organization of tens of thousands of people yeah if they were very productive so imagine if you can you can permeate the other you know hundreds of thousands of users that are out there do you see that kind of impact that productivity impact as the potential for your marketplace absolutely I you know the person who I think said it best was the CEO of Cisco John Chambers and I'll paraphrase him here but he has this great thing he said which is he said you know if I can get each of the people on my team consulting data say oh I don't know twice per day before making a decision and they do the same thing with their people and their people and so you know that's a million decisions a month you did the math better made than my competition I don't want people waiting around for top top management to consult some data before making a decision I want all of our people all the time Consulting data before making a decision and that's the real the real spirit of this new age of BI for too long it's been in the hands of a high priesthood of people who know how to operate these complicated convoluted enterprise bi systems and the revolution is here people are fed up with it they're taking power into their hands and they're driving their organizations forward with the power of data thanks to the magic of an easy-to-use suite like tableau well it's a perfect storm right because everybody wants to be a data-driven organization absolutely data-driven if you don't have the tools to be able to visualize the data absolutely so Jeff if you want to jump in well Christian so in your keynote you talked for the majority of the keynote about human intuition and the human element talk a little bit about that because when we hear about in the press these days about big data it's oh well the the volume of data will tell you what the answer is you don't need much of the human element talk about why you think the human element is so important to data-driven decision-making and how you incorporate that into your design philosophy when you're building the product and you're you know adding new features how does the human element play in that scenario yeah I mean it's funny dated the data driven moniker is coming these days and we're tableaus a big big believer in the power of data we use our tools internally but of course no one really wants to be data driven if you drive your company completely based on data say hello to the cliff wall you will drive it off a cliff you really want people intelligent domain experts using a combination of act and intuition and instinct to make data informed decisions to make great decisions along the way so although pure mining has some role in the scheme of analytics frankly it's a minor role what we really need to do is make analytic software that as I said yesterday is like a bicycle for our minds this was the great Steve Jobs quote about computers that their best are like bicycles for our mind effortless machines that just make us go so much faster than any other species with no more effort expended right that's the spirit of computers when they're at our best Google Google is effortless to use and makes my brain a thousand times smarter than it is right unfortunately over an analytic software we've never seen software that does tap in business intelligence software there's so much development weight and complexity and expense and slow rollout schedules that were never able to get that augmentation of the brain that can help lead to better decisions so at tableau in terms of design we value our product requirements documents say things like intuition and feel and design and instinct and user experience they're focused on the journey of working with data not just some magic algorithm that's gonna spit out some answer that tells you what to do yeah I mean I've often wondered where that bi business would be that traditional decision support business if it weren't for sarbanes-oxley I mean it gave it a new life right because you had to have a single version of the truth that was mandated by by the government here we had Bruce Boston on yesterday who works over eight for a company that shall not be named but anyway he was talking about okay Bruce in case you're watching we're sticking to our promise but he was talking about intent desire and satisfaction things those are three things intent desire and satisfaction that machines can't do like the point being you just you know it was the old bromide you can't take the humans in the last mile yeah I guess yeah do you see that ever changing no I mean I think you know I I went to a friend a friend of mine I just haven't seen in a while a friend of mine once said he was an he was an artificial intelligence expert had Emilie's PhD in a professorship in AI and once I naively asked him I said so do we have artificial intelligence do we have it or not and we've been talking about for decades like is it here and he said you're asking the wrong question the question is how smart our computers right so I just think we're analytics is going is we want to make our computers smarter and smarter and smarter there'll be no one day we're sudden when we flip a switch over and the computer now makes the decision so in that sense the answer to your question is I keep I see things going is there is it going now but underneath the covers of human human based decision making it are going to be fantastic advancements and the technology to support good decision making to help people do things like feel and and and chase findings and shift perspectives on a problem and actually be creative using data I think there's I think it's gonna be a great decade ahead ahead of us so I think part of the challenge Christian in doing that and making that that that evolution is we've you know in the way I come the economy and and a lot of jobs work over the last century is you know you're you're a cog in a wheel your this is how you do your job you go you do it the same way every day and it's more of that kind of almost assembly line type of thinking and now we're you know we're shifting now we're really the to get ahead in your career you've got to be as good but at an artist you've got to create B you've got to make a difference is the challenge do you see a challenge there in terms of getting people to embrace this new kind of creativity and again how do you as a company and as a you know provider of data visualization technology help change some of those attitudes and make people kind of help people make that shift to more of less of a you know a cog in a larger organization to a creative force inside that organ well mostly I feel like we support what people natively want to do so there are there are some challenges but I mostly see opportunity there in category after category of human activity we're seeing people go from consumers to makers look at publishing from 20 years ago to now self-publishing come a few blogs and Twitter's Network exactly I mean we've gone from consumers to makers everyone's now a maker and we have an ecosystem of ideas that's so positive people naturally want to go that way I mean people's best days on the job are when they feel they're creating something and have that sense of achievement of having had an idea and seeing some progress their hands made on that idea so in a sense we're just fueling the natural human desire to have more participation with data to id8 with data to be more involved with data then they've been able to in the past and again like other industries what we're seeing in this category of technology which is the one I know we're going from this very waterfall cog in a wheel type process is something that's much more agile and collaborative and real-time and so it's hard to be creative and inspired when you're just a cog stuck in a long waterfall development process so it's mostly just opportunity and really we're just fueling the fire that I think is already there yeah you talked about that yesterday in your talk you gave a great FAA example the Mayan writing system example was fantastic so I just really loved that story you in your talk yesterday basically told the audience first of all you have very you know you have clarity of vision you seem to have certainty in your vision of passion for your vision but the same time you said you know sometimes data can be confusing and you're not really certain where it's going don't worry about that it's no it's okay you know I was like all will be answered eventually what but what about uncertainty you know in your minds as the you know chief executive of this organization as a leader in a new industry what things are uncertain to you what are the what are the potential blind spots for you that you worry about do you mean for tableau as a company for people working with data general resource for tableau as a company oh I see well I think there's always you know I got a trip through the spirit of the question but we're growing a company we're going a disruptive technology company and we want to embrace all the tall the technologies that exist around us right we want to help to foster day to day data-driven decision-making in all of its places in forms and it seems to me that virtually every breakthrough technology company has gone through one or two major Journal technology transformations or technology shocks to the industry that they never anticipated when they founded the company okay probably the most recent example is Facebook and mobile I mean even though even though mobile the mobile revolution was well in play when when Facebook was founded it really hadn't taken off and that was a blind Facebook was found in oh seven right and look what happened to them right after and here's that here's new was the company you can get it was founded in oh seven yeah right so most companies I mean look how many companies were sort of shocked by the internet or shocked by the iPod or shocked by the emergence of a tablet right or shocked by the social graph you know I think for us in tableaus journey if this was the spirit of the thought of the question we will have our own shocks happen the first was the tablet I mean when we founded tableau like the rest of the world we never would have anticipated that that a brilliant company would finally come along and crack the tablet opportunity wide open and before in a blink of an eye hundreds of millions of people are walking around with powerful multi-touch graphic devices in their I mean who would have guessed people wouldn't have guessed it no six let alone oh three know what and so luckily that's what that's I mean so this is the good kind of uncertainty we've been able to really rally around that there are our developers love to work on this area and today we have probably the most innovative mobile analytics offering on the market but it's one we never could have anticipated so I think the biggest things in terms of big categories of uncertainty that we'll see going forward are similar shocks like that and our success will be determined by how well we're able to adapt to those so why is it and how is it that you're able to respond so quickly as an organization to some of those tectonic shifts well I think the most important thing is having a really fleet-footed R&D team we have just an exceptional group of developers who we have largely not hired from business technology companies we have something very distributed going a tableau yeah one of the amazing things about R&D key our R&D team is when we decided to build just this amazing high-wattage cutting-edge R&D team and focus them on analytics and data we decided not to hire from other business intelligence companies because we didn't think those companies made great products so we've actually been hiring from places like Google and Facebook and Stanford and MIT and computer gaming companies if you look at the R&D engineers who work on gaming companies in terms of the graphic displays and the response times and the high dimensional data there are actually hundreds of times more sophisticated in their thinking and their engineering then some engineer who was working for an enterprise bi reporting company so this incredible horsepower this unique team of inspired zealots and high wattage engineers we have in our R&D team like Apple that's the key to being able to respond to these disruptive shocks every once in a while and rule and really sees them as an opportunity well they're fun to I mean think of something on the stage yesterday and yeah we're in fucky hats and very comfortable there's never been an R&D team like ours assembled in analytics it's been done in other industries right Google and Facebook famously but in analytics there's never been such an amazing team of engineers and Christian what struck me one of the things that struck me yesterday during your keynote or the second half of the keynote was bringing up the developers and talking about the specific features and functions you're gonna add to the product and hearing the crowd kind of erupt at different different announcements different features that you're adding and it's clear that you're very customer focused at this at tableau of you I mean you're responding to the the needs and the requests of your customers and I that's clearly evident again in the in the passion that these customers have for your for your product for your company how do you know first I'm happy how do you maintain that or how do you get get to that point in the first place where you're so customer focused and as you go forward being a public company now you're gonna get pressure from Wall Street and quarter results and all that that you know that comes with that kind of comes with the territory how do you remain that focused on the customer kind of as your you know you're going to be under a lot of pressure to grow and and you know drive revenue yeah I keep that focus well there's two things we do it's a it's always a challenge to stay really connected to your customers as you get big but it's what we pride ourselves on doing and there's two specific things we do to foster it the first is that we really try to focus the company and we try to make a positive aspect of the culture the idea of impact what is the impact of the work we're having and in fact a great example of how we foster that is we bring our entire support and R&D team to this conference no matter where it is we take we fly I mean in this case we literally flew the entire R&D team and product management team and whatnot across country and the time they get here face to face face to face with customers and hearing the customer stories and the victories and actually seeing the feedback you just described really inspires them it gives them specific ideas literally to go back and start working on but it also just gives them a sense of who comes first in a way that if you don't leave the office and you don't focus on that really doesn't materialize and the way you want it the second thing we do is we are we are big followers of I guess what's called the dog food philosophy of eat your own dog so drink your own champagne and so one of our core company values that tableau is we use our products facility a stated value of the company we use our products and into an every group at tableau in tests in bug regressions in development in sales and marketing and planning and finance and HR every sip marketing marketing is so much data these these every group uses tableau to run our own business and make decisions and what happens Matt what's really nice about a company because you know we're getting close to a thousand people now and so it's keeping the spirit you just described alive is really important it becomes quite challenging vectors leagues for it because when that's one of your values and that's the way the culture has been built every single person in the company is a customer everyone understands the customer's situation and the frustrations and the feature requests and knows how to support them when they meet them and can empathize with them when they're on the phone and is a tester automatically by virtue of using the product so we just try to focus on a few very authentic things to keep our connection with the customer as close as possible I'll say christen your company is a rising star we've been talking all this week of the similarities that we were talking off about the similarities with with ServiceNow just in terms of the passion within the customer base we're tracking companies like workday you know great companies that are that are that are being built new emerging disruptive companies we put you in that in that category and we're very excited for different reasons you know different different business altogether but but there are some similar dynamics that we're watching so as observers it's independent observers what kinds of things do you want us to be focused on watching you over the next 12 18 24 months what should we be paying attention to well I think the most important thing is tableau ultimately is a product company and we view ourselves very early in our product development lifecycle I think people who don't really understand tableau think it's a visualization company or a visualization tool I don't I don't really understand that when you talk about the vision a lot but okay sure we can visualization but there's just something much bigger I mean you asked about people watching the company I think what's important to watch is that as I spoke about makino yesterday tableau believes what is called the business intelligence industry what's called the business analytics technology stack needs to be completely rewritten from scratch that's what we believe to do over it's a do-over it's based on technology from a prior hair prior era of computing there's been very little innovation the R&D investment ratios which you can look up online of the companies in this space are pathetically low and have been for decades and this industry needs a Google it needs an apple it's a Facebook an RD machine that is passionate and driven and is leveraging the most recent advances in computing to deliver products that people actually love using so that people start to enjoy doing analytics and have fun with it and make data-driven driven decision in a very in a very in a way that's just woven into their into their into their enjoyment and work style every every single day so the big series of product releases you're going to see from us over the next five years that's the thing to watch and we unveiled a few of them yesterday but trust me there's a lot more that's you a lot of applause christina is awesome you can see you know the passion that you're putting forth your great vision so congratulations in the progress you've made I know I know you're not done we'll be watching it thanks very much for coming to me I'm really a pleasure thank you all right keep right there everybody we're going wall to wall we got a break coming up next and then we'll be back this afternoon and this is Dave Volante with Jeff Kelly this is the cube we'll be right back

>>Hi buddy, we're back. This is Dave Volante with the cube goes out to the shows. We extract the signal from the noise. Nate Silver's here. Nate, we've been saying that since 2010, rip you off. Hey Marcus feeder. Oh, you have that trademarks. Okay. So anyway, welcome to the cube. You man who needs no introduction, but in case you don't know Nate, uh, he's a very famous author, five 30 eight.com. Statistician influence, influential individual predictor of a lot of things including presidential elections. And uh, great to have you here. Great to be here. So we listened to your keynote this morning. We asked earlier if some of our audience, can you tweet it and you know, what would you ask Nate silver? So of course we got the predictable, how the red Sox going to do this year? Who's going to be in the world series? Are we going to attack Syria? >>Uh, will the fed E's or tightened? Of course we're down here. Who'd you vote for? Or they, you know, they all want to know. And of course, a lot of these questions you can't answer because it's too far out. But, uh, but anyway, again, welcome, welcome to the cube. Um, so I want to start by, uh, picking up on some of the themes in your keynote. Uh, you're here at the Tableau conference. Obviously it's all about about data. Uh, and you, your basic, one of your basic premises was that, um, people will misinterpret data, they'll just use data for their own own biases. You have been a controversial figure, right? A lot of people have accused you of, of bias. Um, how, what do you F how do you feel about that as a person who's, uh, you know, statistician, somebody who loves data? >>I think everyone has bias in the sense that we all have one relatively narrow perspective as compared to a big set of problems that we all are trying to analyze or solve or understand together. Um, you know, but I do think some of this actually comes down to, uh, not just bias, but kind of personal morality and ethics really. It seems weird to talk about it that way, but there are a lot of people involved in the political world who are operating to manipulate public opinion, um, and that don't really place a lot of value on the truth. Right. And I consider that kind of immoral. Um, but people like that I think don't really understand that someone else might act morally by actually just trying to discover the way the objective world is and trying to use science and research to, to uncover things. >>And so I think it's hard people to, because if they were in your shoes, they would try and manipulate the forecast and they would cheat and put their finger on their scale. They assume that anyone else would do the same thing cause they, they don't own any. Yeah. So will you, you've made some incredibly accurate predictions, uh, in the face of, of, of others that clearly had bias that, that, that, you know mispredicted um, so how did you feel when you got those, those attacks? Were you flabbergasted? Were you pissed? Were you hurt? I mean, all of the above having you move houses for, for you? I mean you get used to them with a lot of bullshit, right? You're not too surprised. Um, I guess it surprised me how, but how much the people who you know are pretty intelligent are willing to, to fool themselves and how specious arguments where meet and by the way, people are always constructing arguments for, for outcomes they happen to be rooting for. >>Right? It'd be one thing if you said, well I'm a Republican, but boy I think Obama's going to crush Romney electoral college or vice versa. But you should have an extra layer of scrutiny when you have a view that diverges from the consensus or what kind of the markets are saying. And by the way, you can go and they're betting Margaret's, you can go and you could have bet on the outcome of election bookies in the UK, other countries. Right. And they kind of had forecast similar to ours. We were actually putting their money where their mouth was. Agree that Obama was a. Not a lot, but a pretty heavy favorite route. Most of the last two months in the election. I wanted to ask you about prediction markets cause as you probably know, I mean the betting public are actually very efficient. Handicappers right over. >>So I'll throw a two to one shot is going to be to three to one is going to be a four to one, you know, more often than not. But what are your thoughts on, on prediction markets? I mean you just sort of betting markets, you'd just alluded it to them just recently or is that a, is that a good, well there a lot there then then I think the punditry right. I mean, you know, so with, with prediction markets you have a couple of issues. Number one is do you have enough, uh, liquidity, um, and my volume in the markets for them to be, uh, uh, optimal. Right. And I think the answer right now is maybe not exactly. And like these in trade type markets, knowing trade has been, has been shut down. In fact, it was pretty light trading volumes. It might've had people who stood to gain or lose, um, you know, thousands of dollars. >>Whereas in quote, unquote real markets, uh, the stakes are, are several orders of magnitude higher. If you look at what happened to, for example, just prices of common stocks a day after the election last year, um, oil and gas stocks lost billions of dollars of market capitalization after Romney lost. Uh, conversely, some, you know, green tech stocks or certain types of healthcare socks at benefit from Obamacare going into play gain hundreds of millions, billions of dollars in market capitalization. So real investors have to price in these political risks. Um, anyway, I would love to have see fully legal, uh, trading markets in the U S people can get bet kind of proper sums of money where you have, um, a lot of real capital going in and people can kind of hedge their economic risk a little bit more. But you know, they're, they're bigger and it's very hard to beat markets. They're not flawless. And there's a whole chapter in the book about how, you know, the minute you assume that markets are, are clairvoyant and perfect, then that's when they start to fail. >>Ironically enough. But they're very good. They're very tough to beat and they certainly provide a reality check in terms of providing people with, with real incentives to actually, you know, make a bet on, on their beliefs and people when they have financial incentives, uh, uh, to be accurate then a lot of bullshit. There's a tax on bullshit is one way. That's okay. I've got to ask him for anyway that you're still a baseball fan, right? Is that an in Detroit fan? Right. I'm a tiger. There's my bias. You remember the bird? It's too young to remember a little too. I, so I grew up, I was born in 78, so 84, the Kirk Gibson, Alan Trammell teams are kind of my, my earliest. So you definitely don't remember Mickey Lola cha. I used to be a big guy. That's right fan as well. But so, but Sony, right when Moneyball came out, we just were at the Vertica conference. >>We saw Billy being there and, and uh, when, when, when, when, when that book came out, I said Billy Bean's out of his mind for releasing all these secrets. And you alluded to in your talk today that other teams like the rays and like the red Sox have sort of started to adopt those techniques. At the same time, I feel like culturally when another one of your V and your Venn diagram, I don't want you vectors, uh, that, that Oakland's done a better job of that, that others may S they still culturally so pushing back, even the red Sox themselves, it can be argued, you know, went out and sort of violated the, the principles were of course Oakland A's can't cause they don't have a, have a, have a budget to do. So what's your take on Moneyball? Is the, is the strategy that he put forth sustainable or is it all going to be sort of level playing field eventually? >>I mean, you know, the strategy in terms of Oh fine guys that take a lot of walks, right? Um, I mean everyone realizes that now it's a fairly basic conclusion and it was kind of the sign of, of how far behind how many biases there were in the market for that, you know, use LBP instead of day. And I actually like, but that, that was arbitrage, you know, five or 10 years ago now, um, put butts in the seat, right? Man, if they win, I guess it does, but even the red Sox are winning and nobody goes to the games anymore. The red Sox, tons of empty seats, even for Yankees games. Well, it's, I mean they're also charging 200 bucks a ticket or something. you can get a ticket for 20, 30 bucks. But, but you know, but I, you know, I, I, I mean, first of all, the most emotional connection to baseball is that if your team is in pennant races, wins world series, right then that produces multimillion dollar increases in ticket sales and, and TV contracts down the road. >>So, um, in fact, you know, I think one thing is, is looking at the financial side, like modeling the martial impact of a win, but also kind of modeling. If you do kind of sign a free agent, then, uh, that signaling effect, how much does that matter for season ticket sales? So you could do some more kind of high finance stuff in baseball. But, but some of the low hanging fruit, I mean, you know, almost every team now has a Cisco analyst on their payroll or increasingly the distinctions aren't even as relevant anymore. Right? Where someone who's first in analytics is also listening to what the Scouts say. And you have organizations that you know, aren't making these kind of distinctions between stat heads and Scouts at all. They all kind of get along and it's all, you know, finding better ways, more responsible ways to, to analyze data. >>And basically you have the advantage of a very clear way of measure, measure success where, you know, do you win? That's the bottom line. Or do you make money or, or both. You can isolate guys Marshall contribution. I mean, you know, I am in the process now of hiring a bunch of uh, writers and editors and developers for five 38 right? So someone has a column and they do really well. How much of that is on the, the writer versus the ed or versus the brand of the site versus the guy at ESPN who promoted it or whatever else. Right. That's hard to say. But in baseball, everyone kind of takes their turn. It's very easy to measure each player's kind of marginal contribution to sort of balance and equilibrium and, and, and it's potentially achieved. But, and again, from your talk this morning modeling or volume of data doesn't Trump modeling, right? >>You need both. And you need culture. You need, you need, you know, you need volume of data, you need high quality data. You need, uh, a culture that actually has the right incentives align where you really do want to find a way to build a better product to make more money. Right? And again, they'll seem like, Oh, you know, how difficult should it be for a company to want to make more money and build better products. But, um, when you have large organizations, you have a lot of people who are, uh, who are thinking very short term or only about only about their P and L and not how the whole company as a whole is doing or have, you know, hangups or personality conflicts or, or whatever else. So, you know, a lot of success I think in business. Um, and certainly when it comes to use of analytics, it's just stripping away the things that, that get in the way from understanding and distract you. >>It's not some wave a magic wand and have some formula where you uncover all the secrets in the world. It's more like if you can strip away the noise there and you're going to have a much clearer understanding of, of what's really there. Uh, Nate, again, thanks so much for joining us. So kind of wanna expand on that a little bit. So when people think of Nate silver, sometimes they, you know, they think Nate silver analytics big data, but you're actually a S some of your positions are kind of, you take issue with some of the core notions of big data really around the, the, the importance of causality versus correlation. So, um, so we had Kenneth kookier on from, uh, the economist who wrote a book about big data a while back, the strata conference. And you know, he, in that book, they talk a lot about it really doesn't matter how valid anymore, if you know that your customers are gonna buy more products based on this dataset or this correlation that it doesn't really matter why. >>You just try to try to try to exploit that. Uh, but in your book you talk about, well and in the keynote today you talked about, well actually hypothesis testing coming in with some questions and actually looking for that causality is also important. Um, so, so what is your, what is your opinion of kind of, you know, all this hype around big data? Um, you know, you mentioned volume is important, but it's not the only thing. I mean, like, I mean, I'll tell you I'm, I'm kind of an empiricist about anything, right? So, you know, if it's true that merely finding a lot of correlations and kind of very high volume data sets will improve productivity. And how come we've had, you know, kind of such slow economic growth over the past 10 years, where is the tangible increase in patent growth or, or different measures of progress. >>And obviously there's a lot of noise in that data set as well. But you know, partly why both in the presentation today and in the book I kind of opened up with the, with the history is saying, you know, let's really look at the history of technology. It's a kind of fascinating, an understudied feel, the link between technology and progress and growth. But, um, it doesn't always go as planned. And I certainly don't think we've seen any kind of paradigm shift as far as, you know, technological, economic productivity in the world today. I mean, the thing to remember too is that, uh, uh, technology is always growing in and developing and that if you have roughly 3% economic growth per year exponential, that's a lot of growth, right? It's not even a straight line growth. It's like exponential growth. And to have 3% exponential growth compounding over how many years is a lot. >>So you're always going to have new technologies developing. Um, but what I, I'm suspicious that as people will say this one technology is, is a game changer relative to the whole history of civilization up until now. Um, and also, you know, again, a lot of technologies you look at kind of economic models where you have different factors or productivity. It's not usually an additive relationship. It's more a multiplicative relationships. So if you have a lot of data, but people who aren't very good at analyzing it, you have a lot of data but it's unstructured and unscrutinised you know, you're not going to get particularly good results by and large. Um, so I just want to talk a little bit about the, the kind of the, the cultural issue of adopting kind of analytics and, and becoming a data driven organization. And you talk a lot about, um, you know, really what you do is, is setting, um, you know, try to predict the probabilities of something happening, not really predicting what's going to happen necessarily. >>And you talked to New York, you know, today about, you know, knowledging where, you know, you're not, you're not 100% sure acknowledging that this is, you know, this is our best estimate based on the data. Um, but of course in business, you know, a lot of people, a lot of, um, importance is put on kind of, you know, putting on that front that you're, you know, what you're talking about. It's, you know, you be confident, you go in, this is gonna happen. And, and sometimes that can actually move markets and move decision-making. Um, how do you balance that in a, in a business environment where, you know, you want to keep, be realistic, but you want to, you know, put forth a confident, uh, persona. Well, you know, I mean, first of all, everyone, I think the answer is that you have to, uh, uh, kind of take a long time to build the narrative correctly and kind of get back to the first principles. >>And so at five 38, it's kind of a case where you have a dialogue with the readers of the site every day, right? But it's not that you can solve in one conversation. If you come in to a boss who you never talked to you before, you have to present some PowerPoint and you're like, actually this initiative has a, you know, 57% chance of succeeding and the baseline is 50% and it's really good cause the upside's high, right? Like you know, that's going to be tricky if you don't have a good and open dialogue. And it's another barrier by the way to success is that uh, you know, none of this big data stuff is going to be a solution for companies that have poor corporate cultures where you have trouble communicating ideas where you don't everyone on the same page. Um, you know, you need buy in from, from all throughout the organization, which means both you need senior level people who, uh, who understand the value of analytics. >>You also need analysts or junior level people who understand what business problems the company is trying to solve, what organizational goals are. Um, so I mean, how do you communicate? It's tricky, you know, maybe if you can't communicate it, then you find another firm or go, uh, go trade stocks and, and uh, and short that company if you're not violating like insider trading rules of, of various kinds. Um, you know, I mean, the one thing that seems to work better is if you can, uh, depict things visually. People intuitively grasp uncertainty. If you kind of portray it to them in a graphic environment, especially with interactive graphics, uh, more than they might've just kind of put numbers on a page. You know, one thing we're thinking about doing with the new 580 ESPN, we're hiring a lot of designers and developers is in case where there is uncertainty, then you can press a button, kind of like a slot, Michigan and simulate and outcome many times, then it'll make sense to people. Right? And they do that already for, you know, NCAA tournament stuff or NFL playoffs. Um, but that can help. >>So Nate, I asked you my, my partner John furry, who's often or normally the cohost of this show, uh, just just tweeted me asking about crowd spotting. So he's got this notion that there's all this exhaust out there, the social exhaustive social data. How do you, or do you, or do you see the potential to use that exhaust that's thrown off from the connected consumer to actually make predictions? Um, so I'm >>a, I guess probably mildly pessimistic about this for the reason being that, uh, a lot of this data is very new and so we don't really have a way to kind of calibrate a model based on it. So you can look and say, well, you know, let's say Twitter during the Republican primaries in 2016 that, Oh, Paul Ryan is getting five times as much favorable Twitter sentiment as Rick Santorum or whatever among Republicans. But, but what's that mean? You know, to put something into a model, you have to have enough history generally, um, where you can translate X into Y by means of some function or some formula. And a lot of data is so new where you don't have enough history to do that. And the other thing too is that, um, um, the demographics of who is using social media is changing a lot. Where we are right now you come to conference like this and everyone has you know, all their different accounts but, but we're not quite there yet in terms of the broader population. >>Um, you have a lot of kind of thought leaders now a lot of, you know, kind of young, smart urban tech geeks and they're not necessarily as representative of the population as a whole. That will over time the data will become more valuable. But if you're kind of calibrating expectations based on the way that at Twitter or Facebook were used in 2013 to expect that to be reliable when you want a high degree of precision three years from now, even six months from now is, is I think a little optimistic. Some sentiment though, we would agree with that. I mean sentiment is this concept of how many people are talking about a thumbs up, thumbs down. But to the extent that you can get metadata and make it more stable, longer term, you would see potential there is, I mean, there are environments where the terrain is shifting so fast that by the time you know, the forecast that you'd be interested in, right? >>Like things have already changed enough where like it's hard to do, to make good forecast. Right? And I think one of the kind of fundamental themes here, one of my critiques is some of the, uh, of, uh, the more optimistic interpretations of big data is that fundamentally people are, are, most people want a shortcut, right? Most people are, are fairly lazy like labor. What's the hot stock? Yeah. Right. Um, and so I'm worried whenever people talk about, you know, biased interpretations of, of the data or information, right? Whenever people say, Oh, this is going to solve my problems, I don't have to work very hard. You know, not usually true. Even if you look at sports, even steroids, performance enhancing drugs, the guys who really get the benefits of the steroids, they have to work their butts off, right? And then you have a synergy which hell. >>So they are very free free meal tickets in life when they are going to be gobbled up in competitive environments. So you know, uh, bigger datasets, faster data sets are going to be very powerful for people who have the right expertise and the right partners. But, but it's not going to make, uh, you know anyone to be able to kind of quit their job and go on the beach and sip my ties. So ne what are you working on these days as it relates to data? What's exciting you? Um, so with the, with the move to ESPN, I'm thinking more about, uh, you know, working with them on sports type projects, which is something having mostly cover politics. The past four or five years I've, I've kind of a lot of pent up ideas. So you know, looking at things in basketball for example, you have a team of five players and solving the problem of, of who takes the shot, when is the guy taking a good shot? >>Cause the shot clock's running out. When does a guy stealing a better opportunity from, from one of his teammates. Question. We want to look at, um, you know, we have the world cup the summer, so soccer is an interest of mine and we worked in 2010 with ESPN on something called the soccer power index. So continuing to improve that and roll that out. Um, you know, obviously baseball is very analytics rich as well, but you know, my near term focus might be on some of these sports projects. Yeah. So that the, I have to ask you a followup on the, on the soccer question. Is that an individual level? Is that a team level of both? So what we do is kind of uh, uh, one problem you have with the national teams, the Italian national team or Brazilian or the U S team is that they shift their personnel a lot. >>So they'll use certain guys for unimportant friendly matches for training matches that weren't actually playing in Brazil next year. So the system soccer power next we developed for ESPN actually it looks at the rosters and tries to make inferences about who is the a team so to speak and how much quality improvement do you have with them versus versus, uh, guys that are playing only in the marginal and important games. Okay. So you're able to mix and match teams and sort of predict on your flow state also from club league play to make inferences about how the national teams will come together. Um, but soccer is a case where, where we're going into here where we had a lot more data than we used to. Basically you had goals and bookings, I mean, and yellow cards and red cards and now you've collected a lot more data on how guys are moving throughout the field and how many passes there are, how much territory they're covering, uh, tackles and everything else. So that's becoming a lot smarter. Excellent. All right, Nate, I know you've got to go. I really appreciate the time. Thanks for coming on. The cube was a pleasure to meet you. Great. Thank you guys. All right. Keep it right there, everybody. We'll be back with our next guest. Dave Volante and Jeff Kelly. We're live at the Tableau user conference. This is the cube.

(upbeat music beginning) >> From the Cube Studios in Palo Alto and Boston, bringing you data-driven insights from the Cube and ETR, this is "Breaking Analysis" with Dave Vellante. >> Making predictions about the future of enterprise tech is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say, with some degree of certainty, whether the prediction came true or not, with evidence to back that up. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we aim to do just that, with predictions about the macro IT spending environment, cost optimization, security, lots to talk about there, generative AI, cloud, and of course supercloud, blockchain adoption, data platforms, including commentary on Databricks, snowflake, and other key players, automation, events, and we may even have some bonus predictions around quantum computing, and perhaps some other areas. To make all this happen, we welcome back, for the third year in a row, my colleague and friend Eric Bradley from ETR. Eric, thanks for all you do for the community, and thanks for being part of this program. Again. >> I wouldn't miss it for the world. I always enjoy this one. Dave, good to see you. >> Yeah, so let me bring up this next slide and show you, actually come back to me if you would. I got to show the audience this. These are the inbounds that we got from PR firms starting in October around predictions. They know we do prediction posts. And so they'll send literally thousands and thousands of predictions from hundreds of experts in the industry, technologists, consultants, et cetera. And if you bring up the slide I can show you sort of the pattern that developed here. 40% of these thousands of predictions were from cyber. You had AI and data. If you combine those, it's still not close to cyber. Cost optimization was a big thing. Of course, cloud, some on DevOps, and software. Digital... Digital transformation got, you know, some lip service and SaaS. And then there was other, it's kind of around 2%. So quite remarkable, when you think about the focus on cyber, Eric. >> Yeah, there's two reasons why I think it makes sense, though. One, the cybersecurity companies have a lot of cash, so therefore the PR firms might be working a little bit harder for them than some of their other clients. (laughs) And then secondly, as you know, for multiple years now, when we do our macro survey, we ask, "What's your number one spending priority?" And again, it's security. It just isn't going anywhere. It just stays at the top. So I'm actually not that surprised by that little pie chart there, but I was shocked that SaaS was only 5%. You know, going back 10 years ago, that would've been the only thing anyone was talking about. >> Yeah. So true. All right, let's get into it. First prediction, we always start with kind of tech spending. Number one is tech spending increases between four and 5%. ETR has currently got it at 4.6% coming into 2023. This has been a consistently downward trend all year. We started, you know, much, much higher as we've been reporting. Bottom line is the fed is still in control. They're going to ease up on tightening, is the expectation, they're going to shoot for a soft landing. But you know, my feeling is this slingshot economy is going to continue, and it's going to continue to confound, whether it's supply chains or spending. The, the interesting thing about the ETR data, Eric, and I want you to comment on this, the largest companies are the most aggressive to cut. They're laying off, smaller firms are spending faster. They're actually growing at a much larger, faster rate as are companies in EMEA. And that's a surprise. That's outpacing the US and APAC. Chime in on this, Eric. >> Yeah, I was surprised on all of that. First on the higher level spending, we are definitely seeing it coming down, but the interesting thing here is headlines are making it worse. The huge research shop recently said 0% growth. We're coming in at 4.6%. And just so everyone knows, this is not us guessing, we asked 1,525 IT decision-makers what their budget growth will be, and they came in at 4.6%. Now there's a huge disparity, as you mentioned. The Fortune 500, global 2000, barely at 2% growth, but small, it's at 7%. So we're at a situation right now where the smaller companies are still playing a little bit of catch up on digital transformation, and they're spending money. The largest companies that have the most to lose from a recession are being more trepidatious, obviously. So they're playing a "Wait and see." And I hope we don't talk ourselves into a recession. Certainly the headlines and some of their research shops are helping it along. But another interesting comment here is, you know, energy and utilities used to be called an orphan and widow stock group, right? They are spending more than anyone, more than financials insurance, more than retail consumer. So right now it's being driven by mid, small, and energy and utilities. They're all spending like gangbusters, like nothing's happening. And it's the rest of everyone else that's being very cautious. >> Yeah, so very unpredictable right now. All right, let's go to number two. Cost optimization remains a major theme in 2023. We've been reporting on this. You've, we've shown a chart here. What's the primary method that your organization plans to use? You asked this question of those individuals that cited that they were going to reduce their spend and- >> Mhm. >> consolidating redundant vendors, you know, still leads the way, you know, far behind, cloud optimization is second, but it, but cloud continues to outpace legacy on-prem spending, no doubt. Somebody, it was, the guy's name was Alexander Feiglstorfer from Storyblok, sent in a prediction, said "All in one becomes extinct." Now, generally I would say I disagree with that because, you know, as we know over the years, suites tend to win out over, you know, individual, you know, point products. But I think what's going to happen is all in one is going to remain the norm for these larger companies that are cutting back. They want to consolidate redundant vendors, and the smaller companies are going to stick with that best of breed and be more aggressive and try to compete more effectively. What's your take on that? >> Yeah, I'm seeing much more consolidation in vendors, but also consolidation in functionality. We're seeing people building out new functionality, whether it's, we're going to talk about this later, so I don't want to steal too much of our thunder right now, but data and security also, we're seeing a functionality creep. So I think there's further consolidation happening here. I think niche solutions are going to be less likely, and platform solutions are going to be more likely in a spending environment where you want to reduce your vendors. You want to have one bill to pay, not 10. Another thing on this slide, real quick if I can before I move on, is we had a bunch of people write in and some of the answer options that aren't on this graph but did get cited a lot, unfortunately, is the obvious reduction in staff, hiring freezes, and delaying hardware, were three of the top write-ins. And another one was offshore outsourcing. So in addition to what we're seeing here, there were a lot of write-in options, and I just thought it would be important to state that, but essentially the cost optimization is by and far the highest one, and it's growing. So it's actually increased in our citations over the last year. >> And yeah, specifically consolidating redundant vendors. And so I actually thank you for bringing that other up, 'cause I had asked you, Eric, is there any evidence that repatriation is going on and we don't see it in the numbers, we don't see it even in the other, there was, I think very little or no mention of cloud repatriation, even though it might be happening in this in a smattering. >> Not a single mention, not one single mention. I went through it for you. Yep. Not one write-in. >> All right, let's move on. Number three, security leads M&A in 2023. Now you might say, "Oh, well that's a layup," but let me set this up Eric, because I didn't really do a great job with the slide. I hid the, what you've done, because you basically took, this is from the emerging technology survey with 1,181 responses from November. And what we did is we took Palo Alto and looked at the overlap in Palo Alto Networks accounts with these vendors that were showing on this chart. And Eric, I'm going to ask you to explain why we put a circle around OneTrust, but let me just set it up, and then have you comment on the slide and take, give us more detail. We're seeing private company valuations are off, you know, 10 to 40%. We saw a sneak, do a down round, but pretty good actually only down 12%. We've seen much higher down rounds. Palo Alto Networks we think is going to get busy. Again, they're an inquisitive company, they've been sort of quiet lately, and we think CrowdStrike, Cisco, Microsoft, Zscaler, we're predicting all of those will make some acquisitions and we're thinking that the targets are somewhere in this mess of security taxonomy. Other thing we're predicting AI meets cyber big time in 2023, we're going to probably going to see some acquisitions of those companies that are leaning into AI. We've seen some of that with Palo Alto. And then, you know, your comment to me, Eric, was "The RSA conference is going to be insane, hopping mad, "crazy this April," (Eric laughing) but give us your take on this data, and why the red circle around OneTrust? Take us back to that slide if you would, Alex. >> Sure. There's a few things here. First, let me explain what we're looking at. So because we separate the public companies and the private companies into two separate surveys, this allows us the ability to cross-reference that data. So what we're doing here is in our public survey, the tesis, everyone who cited some spending with Palo Alto, meaning they're a Palo Alto customer, we then cross-reference that with the private tech companies. Who also are they spending with? So what you're seeing here is an overlap. These companies that we have circled are doing the best in Palo Alto's accounts. Now, Palo Alto went and bought Twistlock a few years ago, which this data slide predicted, to be quite honest. And so I don't know if they necessarily are going to go after Snyk. Snyk, sorry. They already have something in that space. What they do need, however, is more on the authentication space. So I'm looking at OneTrust, with a 45% overlap in their overall net sentiment. That is a company that's already existing in their accounts and could be very synergistic to them. BeyondTrust as well, authentication identity. This is something that Palo needs to do to move more down that zero trust path. Now why did I pick Palo first? Because usually they're very inquisitive. They've been a little quiet lately. Secondly, if you look at the backdrop in the markets, the IPO freeze isn't going to last forever. Sooner or later, the IPO markets are going to open up, and some of these private companies are going to tap into public equity. In the meantime, however, cash funding on the private side is drying up. If they need another round, they're not going to get it, and they're certainly not going to get it at the valuations they were getting. So we're seeing valuations maybe come down where they're a touch more attractive, and Palo knows this isn't going to last forever. Cisco knows that, CrowdStrike, Zscaler, all these companies that are trying to make a push to become that vendor that you're consolidating in, around, they have a chance now, they have a window where they need to go make some acquisitions. And that's why I believe leading up to RSA, we're going to see some movement. I think it's going to pretty, a really exciting time in security right now. >> Awesome. Thank you. Great explanation. All right, let's go on the next one. Number four is, it relates to security. Let's stay there. Zero trust moves from hype to reality in 2023. Now again, you might say, "Oh yeah, that's a layup." A lot of these inbounds that we got are very, you know, kind of self-serving, but we always try to put some meat in the bone. So first thing we do is we pull out some commentary from, Eric, your roundtable, your insights roundtable. And we have a CISO from a global hospitality firm says, "For me that's the highest priority." He's talking about zero trust because it's the best ROI, it's the most forward-looking, and it enables a lot of the business transformation activities that we want to do. CISOs tell me that they actually can drive forward transformation projects that have zero trust, and because they can accelerate them, because they don't have to go through the hurdle of, you know, getting, making sure that it's secure. Second comment, zero trust closes that last mile where once you're authenticated, they open up the resource to you in a zero trust way. That's a CISO of a, and a managing director of a cyber risk services enterprise. Your thoughts on this? >> I can be here all day, so I'm going to try to be quick on this one. This is not a fluff piece on this one. There's a couple of other reasons this is happening. One, the board finally gets it. Zero trust at first was just a marketing hype term. Now the board understands it, and that's why CISOs are able to push through it. And what they finally did was redefine what it means. Zero trust simply means moving away from hardware security, moving towards software-defined security, with authentication as its base. The board finally gets that, and now they understand that this is necessary and it's being moved forward. The other reason it's happening now is hybrid work is here to stay. We weren't really sure at first, large companies were still trying to push people back to the office, and it's going to happen. The pendulum will swing back, but hybrid work's not going anywhere. By basically on our own data, we're seeing that 69% of companies expect remote and hybrid to be permanent, with only 30% permanent in office. Zero trust works for a hybrid environment. So all of that is the reason why this is happening right now. And going back to our previous prediction, this is why we're picking Palo, this is why we're picking Zscaler to make these acquisitions. Palo Alto needs to be better on the authentication side, and so does Zscaler. They're both fantastic on zero trust network access, but they need the authentication software defined aspect, and that's why we think this is going to happen. One last thing, in that CISO round table, I also had somebody say, "Listen, Zscaler is incredible. "They're doing incredibly well pervading the enterprise, "but their pricing's getting a little high," and they actually think Palo Alto is well-suited to start taking some of that share, if Palo can make one move. >> Yeah, Palo Alto's consolidation story is very strong. Here's my question and challenge. Do you and me, so I'm always hardcore about, okay, you've got to have evidence. I want to look back at these things a year from now and say, "Did we get it right? Yes or no?" If we got it wrong, we'll tell you we got it wrong. So how are we going to measure this? I'd say a couple things, and you can chime in. One is just the number of vendors talking about it. That's, but the marketing always leads the reality. So the second part of that is we got to get evidence from the buying community. Can you help us with that? >> (laughs) Luckily, that's what I do. I have a data company that asks thousands of IT decision-makers what they're adopting and what they're increasing spend on, as well as what they're decreasing spend on and what they're replacing. So I have snapshots in time over the last 11 years where I can go ahead and compare and contrast whether this adoption is happening or not. So come back to me in 12 months and I'll let you know. >> Now, you know, I will. Okay, let's bring up the next one. Number five, generative AI hits where the Metaverse missed. Of course everybody's talking about ChatGPT, we just wrote last week in a breaking analysis with John Furrier and Sarjeet Joha our take on that. We think 2023 does mark a pivot point as natural language processing really infiltrates enterprise tech just as Amazon turned the data center into an API. We think going forward, you're going to be interacting with technology through natural language, through English commands or other, you know, foreign language commands, and investors are lining up, all the VCs are getting excited about creating something competitive to ChatGPT, according to (indistinct) a hundred million dollars gets you a seat at the table, gets you into the game. (laughing) That's before you have to start doing promotion. But he thinks that's what it takes to actually create a clone or something equivalent. We've seen stuff from, you know, the head of Facebook's, you know, AI saying, "Oh, it's really not that sophisticated, ChatGPT, "it's kind of like IBM Watson, it's great engineering, "but you know, we've got more advanced technology." We know Google's working on some really interesting stuff. But here's the thing. ETR just launched this survey for the February survey. It's in the field now. We circle open AI in this category. They weren't even in the survey, Eric, last quarter. So 52% of the ETR survey respondents indicated a positive sentiment toward open AI. I added up all the sort of different bars, we could double click on that. And then I got this inbound from Scott Stevenson of Deep Graham. He said "AI is recession-proof." I don't know if that's the case, but it's a good quote. So bring this back up and take us through this. Explain this chart for us, if you would. >> First of all, I like Scott's quote better than the Facebook one. I think that's some sour grapes. Meta just spent an insane amount of money on the Metaverse and that's a dud. Microsoft just spent money on open AI and it is hot, undoubtedly hot. We've only been in the field with our current ETS survey for a week. So my caveat is it's preliminary data, but I don't care if it's preliminary data. (laughing) We're getting a sneak peek here at what is the number one net sentiment and mindshare leader in the entire machine-learning AI sector within a week. It's beating Data- >> 600. 600 in. >> It's beating Databricks. And we all know Databricks is a huge established enterprise company, not only in machine-learning AI, but it's in the top 10 in the entire survey. We have over 400 vendors in this survey. It's number eight overall, already. In a week. This is not hype. This is real. And I could go on the NLP stuff for a while. Not only here are we seeing it in open AI and machine-learning and AI, but we're seeing NLP in security. It's huge in email security. It's completely transforming that area. It's one of the reasons I thought Palo might take Abnormal out. They're doing such a great job with NLP in this email side, and also in the data prep tools. NLP is going to take out data prep tools. If we have time, I'll discuss that later. But yeah, this is, to me this is a no-brainer, and we're already seeing it in the data. >> Yeah, John Furrier called, you know, the ChatGPT introduction. He said it reminded him of the Netscape moment, when we all first saw Netscape Navigator and went, "Wow, it really could be transformative." All right, number six, the cloud expands to supercloud as edge computing accelerates and CloudFlare is a big winner in 2023. We've reported obviously on cloud, multi-cloud, supercloud and CloudFlare, basically saying what multi-cloud should have been. We pulled this quote from Atif Kahn, who is the founder and CTO of Alkira, thanks, one of the inbounds, thank you. "In 2023, highly distributed IT environments "will become more the norm "as organizations increasingly deploy hybrid cloud, "multi-cloud and edge settings..." Eric, from one of your round tables, "If my sources from edge computing are coming "from the cloud, that means I have my workloads "running in the cloud. "There is no one better than CloudFlare," That's a senior director of IT architecture at a huge financial firm. And then your analysis shows CloudFlare really growing in pervasion, that sort of market presence in the dataset, dramatically, to near 20%, leading, I think you had told me that they're even ahead of Google Cloud in terms of momentum right now. >> That was probably the biggest shock to me in our January 2023 tesis, which covers the public companies in the cloud computing sector. CloudFlare has now overtaken GCP in overall spending, and I was shocked by that. It's already extremely pervasive in networking, of course, for the edge networking side, and also in security. This is the number one leader in SaaSi, web access firewall, DDoS, bot protection, by your definition of supercloud, which we just did a couple of weeks ago, and I really enjoyed that by the way Dave, I think CloudFlare is the one that fits your definition best, because it's bringing all of these aspects together, and most importantly, it's cloud agnostic. It does not need to rely on Azure or AWS to do this. It has its own cloud. So I just think it's, when we look at your definition of supercloud, CloudFlare is the poster child. >> You know, what's interesting about that too, is a lot of people are poo-pooing CloudFlare, "Ah, it's, you know, really kind of not that sophisticated." "You don't have as many tools," but to your point, you're can have those tools in the cloud, Cloudflare's doing serverless on steroids, trying to keep things really simple, doing a phenomenal job at, you know, various locations around the world. And they're definitely one to watch. Somebody put them on my radar (laughing) a while ago and said, "Dave, you got to do a breaking analysis on CloudFlare." And so I want to thank that person. I can't really name them, 'cause they work inside of a giant hyperscaler. But- (Eric laughing) (Dave chuckling) >> Real quickly, if I can from a competitive perspective too, who else is there? They've already taken share from Akamai, and Fastly is their really only other direct comp, and they're not there. And these guys are in poll position and they're the only game in town right now. I just, I don't see it slowing down. >> I thought one of your comments from your roundtable I was reading, one of the folks said, you know, CloudFlare, if my workloads are in the cloud, they are, you know, dominant, they said not as strong with on-prem. And so Akamai is doing better there. I'm like, "Okay, where would you want to be?" (laughing) >> Yeah, which one of those two would you rather be? >> Right? Anyway, all right, let's move on. Number seven, blockchain continues to look for a home in the enterprise, but devs will slowly begin to adopt in 2023. You know, blockchains have got a lot of buzz, obviously crypto is, you know, the killer app for blockchain. Senior IT architect in financial services from your, one of your insight roundtables said quote, "For enterprises to adopt a new technology, "there have to be proven turnkey solutions. "My experience in talking with my peers are, "blockchain is still an open-source component "where you have to build around it." Now I want to thank Ravi Mayuram, who's the CTO of Couchbase sent in, you know, one of the predictions, he said, "DevOps will adopt blockchain, specifically Ethereum." And he referenced actually in his email to me, Solidity, which is the programming language for Ethereum, "will be in every DevOps pro's playbook, "mirroring the boom in machine-learning. "Newer programming languages like Solidity "will enter the toolkits of devs." His point there, you know, Solidity for those of you don't know, you know, Bitcoin is not programmable. Solidity, you know, came out and that was their whole shtick, and they've been improving that, and so forth. But it, Eric, it's true, it really hasn't found its home despite, you know, the potential for smart contracts. IBM's pushing it, VMware has had announcements, and others, really hasn't found its way in the enterprise yet. >> Yeah, and I got to be honest, I don't think it's going to, either. So when we did our top trends series, this was basically chosen as an anti-prediction, I would guess, that it just continues to not gain hold. And the reason why was that first comment, right? It's very much a niche solution that requires a ton of custom work around it. You can't just plug and play it. And at the end of the day, let's be very real what this technology is, it's a database ledger, and we already have database ledgers in the enterprise. So why is this a priority to move to a different database ledger? It's going to be very niche cases. I like the CTO comment from Couchbase about it being adopted by DevOps. I agree with that, but it has to be a DevOps in a very specific use case, and a very sophisticated use case in financial services, most likely. And that's not across the entire enterprise. So I just think it's still going to struggle to get its foothold for a little bit longer, if ever. >> Great, thanks. Okay, let's move on. Number eight, AWS Databricks, Google Snowflake lead the data charge with Microsoft. Keeping it simple. So let's unpack this a little bit. This is the shared accounts peer position for, I pulled data platforms in for analytics, machine-learning and AI and database. So I could grab all these accounts or these vendors and see how they compare in those three sectors. Analytics, machine-learning and database. Snowflake and Databricks, you know, they're on a crash course, as you and I have talked about. They're battling to be the single source of truth in analytics. They're, there's going to be a big focus. They're already started. It's going to be accelerated in 2023 on open formats. Iceberg, Python, you know, they're all the rage. We heard about Iceberg at Snowflake Summit, last summer or last June. Not a lot of people had heard of it, but of course the Databricks crowd, who knows it well. A lot of other open source tooling. There's a company called DBT Labs, which you're going to talk about in a minute. George Gilbert put them on our radar. We just had Tristan Handy, the CEO of DBT labs, on at supercloud last week. They are a new disruptor in data that's, they're essentially making, they're API-ifying, if you will, KPIs inside the data warehouse and dramatically simplifying that whole data pipeline. So really, you know, the ETL guys should be shaking in their boots with them. Coming back to the slide. Google really remains focused on BigQuery adoption. Customers have complained to me that they would like to use Snowflake with Google's AI tools, but they're being forced to go to BigQuery. I got to ask Google about that. AWS continues to stitch together its bespoke data stores, that's gone down that "Right tool for the right job" path. David Foyer two years ago said, "AWS absolutely is going to have to solve that problem." We saw them start to do it in, at Reinvent, bringing together NoETL between Aurora and Redshift, and really trying to simplify those worlds. There's going to be more of that. And then Microsoft, they're just making it cheap and easy to use their stuff, you know, despite some of the complaints that we hear in the community, you know, about things like Cosmos, but Eric, your take? >> Yeah, my concern here is that Snowflake and Databricks are fighting each other, and it's allowing AWS and Microsoft to kind of catch up against them, and I don't know if that's the right move for either of those two companies individually, Azure and AWS are building out functionality. Are they as good? No they're not. The other thing to remember too is that AWS and Azure get paid anyway, because both Databricks and Snowflake run on top of 'em. So (laughing) they're basically collecting their toll, while these two fight it out with each other, and they build out functionality. I think they need to stop focusing on each other, a little bit, and think about the overall strategy. Now for Databricks, we know they came out first as a machine-learning AI tool. They were known better for that spot, and now they're really trying to play catch-up on that data storage compute spot, and inversely for Snowflake, they were killing it with the compute separation from storage, and now they're trying to get into the MLAI spot. I actually wouldn't be surprised to see them make some sort of acquisition. Frank Slootman has been a little bit quiet, in my opinion there. The other thing to mention is your comment about DBT Labs. If we look at our emerging technology survey, last survey when this came out, DBT labs, number one leader in that data integration space, I'm going to just pull it up real quickly. It looks like they had a 33% overall net sentiment to lead data analytics integration. So they are clearly growing, it's fourth straight survey consecutively that they've grown. The other name we're seeing there a little bit is Cribl, but DBT labs is by far the number one player in this space. >> All right. Okay, cool. Moving on, let's go to number nine. With Automation mixer resurgence in 2023, we're showing again data. The x axis is overlap or presence in the dataset, and the vertical axis is shared net score. Net score is a measure of spending momentum. As always, you've seen UI path and Microsoft Power Automate up until the right, that red line, that 40% line is generally considered elevated. UI path is really separating, creating some distance from Automation Anywhere, they, you know, previous quarters they were much closer. Microsoft Power Automate came on the scene in a big way, they loom large with this "Good enough" approach. I will say this, I, somebody sent me a results of a (indistinct) survey, which showed UiPath actually had more mentions than Power Automate, which was surprising, but I think that's not been the case in the ETR data set. We're definitely seeing a shift from back office to front soft office kind of workloads. Having said that, software testing is emerging as a mainstream use case, we're seeing ML and AI become embedded in end-to-end automations, and low-code is serving the line of business. And so this, we think, is going to increasingly have appeal to organizations in the coming year, who want to automate as much as possible and not necessarily, we've seen a lot of layoffs in tech, and people... You're going to have to fill the gaps with automation. That's a trend that's going to continue. >> Yep, agreed. At first that comment about Microsoft Power Automate having less citations than UiPath, that's shocking to me. I'm looking at my chart right here where Microsoft Power Automate was cited by over 60% of our entire survey takers, and UiPath at around 38%. Now don't get me wrong, 38% pervasion's fantastic, but you know you're not going to beat an entrenched Microsoft. So I don't really know where that comment came from. So UiPath, looking at it alone, it's doing incredibly well. It had a huge rebound in its net score this last survey. It had dropped going through the back half of 2022, but we saw a big spike in the last one. So it's got a net score of over 55%. A lot of people citing adoption and increasing. So that's really what you want to see for a name like this. The problem is that just Microsoft is doing its playbook. At the end of the day, I'm going to do a POC, why am I going to pay more for UiPath, or even take on another separate bill, when we know everyone's consolidating vendors, if my license already includes Microsoft Power Automate? It might not be perfect, it might not be as good, but what I'm hearing all the time is it's good enough, and I really don't want another invoice. >> Right. So how does UiPath, you know, and Automation Anywhere, how do they compete with that? Well, the way they compete with it is they got to have a better product. They got a product that's 10 times better. You know, they- >> Right. >> they're not going to compete based on where the lowest cost, Microsoft's got that locked up, or where the easiest to, you know, Microsoft basically give it away for free, and that's their playbook. So that's, you know, up to UiPath. UiPath brought on Rob Ensslin, I've interviewed him. Very, very capable individual, is now Co-CEO. So he's kind of bringing that adult supervision in, and really tightening up the go to market. So, you know, we know this company has been a rocket ship, and so getting some control on that and really getting focused like a laser, you know, could be good things ahead there for that company. Okay. >> One of the problems, if I could real quick Dave, is what the use cases are. When we first came out with RPA, everyone was super excited about like, "No, UiPath is going to be great for super powerful "projects, use cases." That's not what RPA is being used for. As you mentioned, it's being used for mundane tasks, so it's not automating complex things, which I think UiPath was built for. So if you were going to get UiPath, and choose that over Microsoft, it's going to be 'cause you're doing it for more powerful use case, where it is better. But the problem is that's not where the enterprise is using it. The enterprise are using this for base rote tasks, and simply, Microsoft Power Automate can do that. >> Yeah, it's interesting. I've had people on theCube that are both Microsoft Power Automate customers and UiPath customers, and I've asked them, "Well you know, "how do you differentiate between the two?" And they've said to me, "Look, our users and personal productivity users, "they like Power Automate, "they can use it themselves, and you know, "it doesn't take a lot of, you know, support on our end." The flip side is you could do that with UiPath, but like you said, there's more of a focus now on end-to-end enterprise automation and building out those capabilities. So it's increasingly a value play, and that's going to be obviously the challenge going forward. Okay, my last one, and then I think you've got some bonus ones. Number 10, hybrid events are the new category. Look it, if I can get a thousand inbounds that are largely self-serving, I can do my own here, 'cause we're in the events business. (Eric chuckling) Here's the prediction though, and this is a trend we're seeing, the number of physical events is going to dramatically increase. That might surprise people, but most of the big giant events are going to get smaller. The exception is AWS with Reinvent, I think Snowflake's going to continue to grow. So there are examples of physical events that are growing, but generally, most of the big ones are getting smaller, and there's going to be many more smaller intimate regional events and road shows. These micro-events, they're going to be stitched together. Digital is becoming a first class citizen, so people really got to get their digital acts together, and brands are prioritizing earned media, and they're beginning to build their own news networks, going direct to their customers. And so that's a trend we see, and I, you know, we're right in the middle of it, Eric, so you know we're going to, you mentioned RSA, I think that's perhaps going to be one of those crazy ones that continues to grow. It's shrunk, and then it, you know, 'cause last year- >> Yeah, it did shrink. >> right, it was the last one before the pandemic, and then they sort of made another run at it last year. It was smaller but it was very vibrant, and I think this year's going to be huge. Global World Congress is another one, we're going to be there end of Feb. That's obviously a big big show, but in general, the brands and the technology vendors, even Oracle is going to scale down. I don't know about Salesforce. We'll see. You had a couple of bonus predictions. Quantum and maybe some others? Bring us home. >> Yeah, sure. I got a few more. I think we touched upon one, but I definitely think the data prep tools are facing extinction, unfortunately, you know, the Talons Informatica is some of those names. The problem there is that the BI tools are kind of including data prep into it already. You know, an example of that is Tableau Prep Builder, and then in addition, Advanced NLP is being worked in as well. ThoughtSpot, Intelius, both often say that as their selling point, Tableau has Ask Data, Click has Insight Bot, so you don't have to really be intelligent on data prep anymore. A regular business user can just self-query, using either the search bar, or even just speaking into what it needs, and these tools are kind of doing the data prep for it. I don't think that's a, you know, an out in left field type of prediction, but it's the time is nigh. The other one I would also state is that I think knowledge graphs are going to break through this year. Neo4j in our survey is growing in pervasion in Mindshare. So more and more people are citing it, AWS Neptune's getting its act together, and we're seeing that spending intentions are growing there. Tiger Graph is also growing in our survey sample. I just think that the time is now for knowledge graphs to break through, and if I had to do one more, I'd say real-time streaming analytics moves from the very, very rich big enterprises to downstream, to more people are actually going to be moving towards real-time streaming, again, because the data prep tools and the data pipelines have gotten easier to use, and I think the ROI on real-time streaming is obviously there. So those are three that didn't make the cut, but I thought deserved an honorable mention. >> Yeah, I'm glad you did. Several weeks ago, we did an analyst prediction roundtable, if you will, a cube session power panel with a number of data analysts and that, you know, streaming, real-time streaming was top of mind. So glad you brought that up. Eric, as always, thank you very much. I appreciate the time you put in beforehand. I know it's been crazy, because you guys are wrapping up, you know, the last quarter survey in- >> Been a nuts three weeks for us. (laughing) >> job. I love the fact that you're doing, you know, the ETS survey now, I think it's quarterly now, right? Is that right? >> Yep. >> Yep. So that's phenomenal. >> Four times a year. I'll be happy to jump on with you when we get that done. I know you were really impressed with that last time. >> It's unbelievable. This is so much data at ETR. Okay. Hey, that's a wrap. Thanks again. >> Take care Dave. Good seeing you. >> All right, many thanks to our team here, Alex Myerson as production, he manages the podcast force. Ken Schiffman as well is a critical component of our East Coast studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hoof is our editor-in-chief. He's at siliconangle.com. He's just a great editing for us. Thank you all. Remember all these episodes that are available as podcasts, wherever you listen, podcast is doing great. Just search "Breaking analysis podcast." Really appreciate you guys listening. I publish each week on wikibon.com and siliconangle.com, or you can email me directly if you want to get in touch, david.vellante@siliconangle.com. That's how I got all these. I really appreciate it. I went through every single one with a yellow highlighter. It took some time, (laughing) but I appreciate it. You could DM me at dvellante, or comment on our LinkedIn post and please check out etr.ai. Its data is amazing. Best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights, powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis." (upbeat music beginning) (upbeat music ending)

>>The Cube presents Ignite 22, brought to you by Palo Alto Networks. >>Hey guys, welcome back to the Cube, the leader in live enterprise and emerging tech coverage. We are live in Las Vegas at MGM Grand Hotel, Lisa Martin with Dave Valante, covering our first time covering Palo Alto Ignite. 22 in person. Dave, we've had some great conversations so far. We've got two days of wall to wall coverage. We're gonna be talking with Palo Alto execs, leaders, customers, partners, and we're gonna be talking about the partner ecosystem >>Next. Wow. Super important. You know, it's funny you talk about for a minute, you didn't know where we were. I, I came to Vegas in May. I feel like I never left two weeks ago reinvent, which was I, I thought the most awesome reinvent ever. And it was really all about the ecosystem and the marketplace. So super excited to have that >>Conversation. Yeah, we've got Wet Whit Krump joining us, director of America's business development worldwide channels and customer programs at AWS marketplace. Wet, welcome to the Cube. Great to have >>You. Thanks for having me. Give >>Us a, you got a big title there. Give us a little bit of flavor of your scope of work at aws. >>Yeah, sure. So I, I've been with the marketplace team now almost eight years and originally founded our channel programs. And my scope has expanded to not just cover channels, but all things related to customers. So if you think about marketplace having sort of two sides, one being very focused on the isv, I tend to manage all things related to our in customer and our, our channel partners. >>What are some of the feedback that you're getting from customers and channel partners as the marketplace has has evolved so much? >>Yeah. You know, it's, it's, it's been interesting to watch over the course of the years, getting to see it start its infancy and grow up. One of the things that we hear often from customers and from our channel partners, and maybe not so directly, is it's not about finding the things they necessarily want to buy, although that's important, but it's the actual act of how they're able to purchase things and making that a much more streamlined process, especially in large enterprises where there's a lot of complexity. We wanna make that a lot simple, simpler for our customers. >>I mean, vendor management is such a hassle, right? But, so when I come into the marketplace, it's all there. I gotta console, it's integrated, I choose what I want. The billing is simplified. How has that capability evolved since the time that you've been at aws and where do you, where do you want to take it? >>Yeah, so when we, we first started Marketplace, it was really a pay as you go model customer come, they buy whatever, you know, whatever the, the whatever the solution was. And then it was, you know, charged by the hour and then the year. And one of the things that we discovered through customer and partner feedback was especially when they're dealing with large enterprise purchases, you know, they want to be able to instantiate those custom price and terms, you know, into that contract while enjoying the benefits of, of marketplace. And that's been, I think the biggest evolution started in 2017 with private offers, 2018 with consulting partner private offers. And then we've added things on over time to streamline procurement for, for >>Customers. So one of the hottest topics right now, everybody wants to talk about the macro and the headwinds and everything else, but when you talk to customers like, look, I gotta do more with less, less, that's the big theme. Yeah. And, and I wanna optimize my spend. Cloud allows me to do that because I can dial down, I can push storage to, to lower tiers. There's a lot of different things that I can do. Yeah. What are the techniques that people are using in the ecosystem Yeah. To bring in the partner cost optimization. Yeah. >>And so one of the key things that, that partners are, are, are doing for customers, they act as that trusted advisor. And, you know, when using marketplace either directly or through a partner, you know, customers are able to really save money through a licensing flexibility. They're also able to streamline their procurement. And then if there's an at-risk spin situation, they're able to, to manage that at-risk spend by combining marketplace and AWS spin into into one, you know, basically draws down their commitments to, to the company. >>And we talk about ask at-risk spend, you might talk about user or lose IT type of spend, right? Yeah. And so you, you increase the optionality in terms of where you can get value from your cloud spend. That's >>All right. Customers are thinking about their, their IT spend more strategically now more than ever. And so they're not just thinking about how do I buy infrastructure here and then software here, data services, they wanna combine this into one place. It's a lot less to keep up with a lot, a lot less overhead for them. But also just the simplification that you alluded to earlier around, you know, all the billing and vendor management is, and now in one, one streamlined, one streamlined process. Talk >>About that as a facilitator of organizations being able to reduce their risk profile. >>Yeah, so, you know, one of the things that, that came out earlier this year with Forrester was a to were total economic impact studies for both an ISV and for the end customer. But there was also a thought leadership study done where they surveyed over 700 customers worldwide to sort of get their thoughts on procurement and risk profile management. And, and one of the things that was really, you know, really surprising was is was that, you know, I guess it was like over 78% of of respondents DEF stated that they didn't feel like their, their companies had a really well-defined governance model and that over half of software and data purchases actually went outside of procurement. And so the companies aren't really able to, don't, they don't really have eyes on all of this spin and it's substantial >>And that's a, a huge risk for the organization. >>Yeah. Huge risk for the organization. And, and you know, half of the respondents stated outright that like they viewed marketplaces a way for them to reduce their risk profile because they, they were able to have a better governance model around that. >>So what's the business case can take us through that. How, how should a customer think about that? So, okay, I get that the procurement department likes it and the CFO probably likes it, but how, what, what's the dynamic around the business? So if I'm a, let's say I'm, I'm a bus, I'm a business person, I'm a, and running the process, I got my little, I get my procurement reach around. Yeah. What does the data suggest that what's in it from me, right? From a company wide standpoint, you know, what are the, maybe the Forester guys address this. So yeah, that overall business case I think is important. >>Yeah, I think, I think one of the big headlines for the end customer is because of license flexibility is that is is about a 10% cost savings in, in license cost. They're able to right size their purchases to buy the things they actually need. They're not gonna have these big overarching ELAs. There's gonna be a lot of other things in there that, that they don't, they don't really aren't gonna really directly use. You're talking about shelfware, you know, that sort of the classic term buy something, it never gets used, you know, also from just a, a getting things done perspective, big piece of feedback from customers is the contracting process takes a long time. It takes several months, especially for a large purchase. And a lot of those discussions are very repetitive. You know, you're talking about the same things over and over again. And we actually built a feature called standardized contract where we talked to a number of customers and ISVs distilled a contract down into a, a largely a set of terms that both sides already agreed to. And it cuts that, that contract time down by 90%. So if you're a legal team in a company, there's only so many of you and you have a lot of things to get done. If you can shave 90% off your time, that that's, that's now you can now work on a lot of other things for the, the corporation. Right. >>A lot of business impact there. You think faster time to value, faster time to market workforce optimization. >>Yeah. Yeah. I mean, it, it, you know, from an ISV standpoint, the measurement is they're, they're able to close deals about 40% faster, which is great for the isv. I mean obviously they love that. But if you're a customer, you're actually getting the innovative technologies you need 40% faster. So you can actually do the work you want to take it to your customers and drive the business. >>You guys recently launched, what is it, vendor Insights? Yeah. Talk a little bit about that, the value. What are some of the things that you're seeing with that? >>Yeah, so that goes into the, the onboarding value add of marketplaces. The number of things that go into, to cutting that time according to Forrester by 75%. But Vendor Insights was based on a key piece, offa impact from customers. So, you know, marketplace is used for, one of the reasons is discoverability by customers, Hey, what is the broader landscape? Look for example of security or storage partners, you know, trying to, trying to understand what is even available. And then the double click is, alright, well how does that company, or how does that vendor fit into my risk profile? You know, understanding what their compliance metrics are, things of that nature. And so historically they would have to, a customer would've to go to an ISV and say, all right, I want you to fill out this form, you know that my questionnaire. And so they would trade this back and forth as they have questions. Now with vendor insights, a customer can actually subscribe to this and they're able to actually see the risk profile of that vendor from the inside out, you know, from the inside of their SaaS application, what does it look like on a real time basis? And they can go back and look at that whenever they want. And you know, the, the, the feedback since the launch has been fantastic. And that, and I think that helps us double down on the already the, the onboarding benefits that we are providing customers. >>This, this, I wanna come back to this idea of cost optimization and, and try to tie it into predictability. You know, a lot of people, you know, complain, oh, I got surprised at the end of the month. So if I understand it wit by, by leveraging the marketplace and the breadth that you have in the marketplace, I can say, okay, look, I'm gonna spend X amount on tech. Yeah. And, and this approach allows me to say, all right, because right now procurement or historically procurement's been a bunch of stove pipes, I can't take from here and easily put it over there. Right. You're saying that this not only addresses the sort of cost optimization, does it also address the predictability challenge? >>Yeah, and I, I think another way to describe that is, is around cost controls. And you know, just from a reporting perspective, you know, we, we have what are called cost utilization reports or curve files. And we provide those to customers anytime they want and they can load those into Tableau, use whatever analysis tools that they want to be able to use. And so, and then you can actually tag usage in those reports. And what we're really talking about is helping customers adopt thin op practices. So, you know, develop directly for the cloud customers are able to understand, okay, who's using what, when and where. So everyone's informed that creates a really collaborative environment. It also holds people accountable for their spin. So that, you know, again, talking about shelfware, we bought things we're not gonna use or we're overusing people are using software that they probably don't really need to. And so that's, that adds to that predictable is everyone has great visibility into what's happening. And there's >>Another, I mean, of course saving money is, is, is in vogue right now because you know, the headwinds and the economics, et cetera. But there's also another side of the equation, which is, I mean, I see this a lot. You know, the CFO says financial people, why is our cloud bill so high? Well it's because we're actually driving all this revenue. And so, you know, you've seen it so many so often in companies, you know, the, the spreadsheet analysis says, oh, cut that. Well, what happens to revenue if you cut that? Right? Yeah. So with that visibility, the answer may be, well actually if we double down on that, yeah, we're actually gonna make more money cuz we actually have a margin on this and it's, it's got operating leverage. So if we double that, you know, we could, so that kind of cross organization communication to make better decisions, I think is another key factor. Yeah. >>Huge impact there. Talk ultimately about how the buyer's journey seems to have been really transformed >>The >>Correct. Right? So if you're, if you're a buyer, you know, initially to your point is, you know, I'm just looking for a point solution, right? And then you move on to the next one and the next one. And now, you know, working with our teams and using the platform, you know, and frankly customers are thinking more strategically about their IT spend holistically. The conversations that we're having with us is, it's not about how do I find the solution today, but here's my forward looking software spend, or I'm going through a migration, I wanna rationalize the software portfolio I have today as I'm gonna lift and shift it to aws. You know, what is going to make the trip? What are we gonna discard entirely because it's not really optimized for the cloud. Or there's that shelf wheel component, which is, hey, you know, maybe 15 to 25% of my portfolio, it's just not even getting utilized. And that, and that's a sunk cost to your point, which is, you know, that's, that's money I could be using on something that really impacts the bottom line in various areas of the business. Right. >>What would you say is the number one request you get or feedback you get from the end customers? And how is that different from what you hear from the channel partners? How aligned or Yeah. Are those >>Vectors? I would say from a customer perspective, one of the key things I hear about is around visibility of spin, right? And I was just talking about these reports and you know, using cost optimization tools, being able to use features like identity and access management, managing entitlements, private marketplaces. Basically them being able to have a stronger governance model in the cloud. For one thing, it's, it's, you know, keeping everybody on track like some of the points I was talking about earlier, but also cost, cost optimization around, you know, limiting vendor sprawl. Are we actually really using all the things that we need? And then from a channel partner perspective, you know, some of the things I talked about earlier about that 40% faster sales cycle, you know, that that TEI or the total economic impact study that was done by Forrester was, was built for the isv. >>But if you're a channel partner sitting between the customer and the isv, you kind of get to, you get a little bit of the best of both worlds, right? You're acting as that, you're acting as that that advisor. And so if you're a channel partner, the procurement streamlining is a huge benefit because the, you know, like you said, saving money is in vogue right now. You're trying to do more with less. So if you're thinking about 20, 27% faster win rates, 40% faster time to close, and you're the customer who's trying to impact the bottom line by, by innovating more, more quickly, those two pieces of feedback are really coming together and meeting in, in the middle >>Throughout 2021, or sorry, 2022, our survey partner, etr Enterprise Technology Research has asked their panel a question is what's your strategy for, you know, doing more with less? By far the number one response has been consolidating redundant vendors. Yes. And then optimizing cloud was, you know, second, but, but way, way lower than that. The number from last survey went from 34%. It's now up to 44% in the January survey, which is in the field, which they gave me a glimpse to last night. So you're seeing dramatic uptick Yeah. In that point. Yeah. And then you guys are helping, >>We, we definitely are. I mean, it, there's the reporting piece so they have a better visibility of what they're doing. And then you think about a, a feature like private marketplace and manage entitlements. So private marketplace enables a customer to create their own private marketplace as the name states where they can limit access to it for certain types of software to the actual in customer who needs to use that software. And so, you know, not everybody needs a license to software X, right? And so that helps with the sprawl comment to your point, that's, that's on the increase, right? Am I actually spending money on things that we need to use? >>But also on the consolidation front, you, we, we talked with nikesh an hour or so ago, he was mentioning on stage, if you, if you just think of this number of security tools or cybersecurity tools that an organization has on its network, 30 to 50. And we were talking about, well, how does Palo Alto Networks what's realistic in terms of consolidation? But it sounds like what you're doing in the marketplace is giving organizations the visibility, correct, for sure. Into what they're running, usage spend, et cetera, to help facilitate ultimately at some point facilitate a strategic consolidation. >>It's, that's exactly right. And if you, you think about cost optimization, our procurement features, you know, the, the practice that we're trying to help customers around, around finops, it's all about helping customers build a, a modern procurement practice and supply chain. And so that helps with, with that point exactly. The keynotes >>Point. Exactly. So last question for you. What, what's next? What can we expect? >>Oh, so what's next for me is, you know, I, I really want to, you know, my channel business for example, you know, I want to think about enabling new types of partners. So if we've worked really heavily with resellers, we worked very heavily with Palo Alto on the reseller community, how are we bringing in more services partners of various types? You know, the gsi, the distributors, cloud service providers, managed security service providers was in a keynote yesterday listening to Palo Alto talk about their five routes to market. And, you know, they had these bubbles. And so I was like, gosh, that's exactly how I'm thinking about the business is how am I expanding my own footprint to customers that have deeper, I mean, excuse me, to partners that have deeper levels of cloud knowledge, can be more of that advisor, help customers really understand how to maximize their business on aws. And, and you know, my job is to really help facilitate that, that innovative technology through those partners. >>So sounds like powerful force, that ecosystem. Exactly. Great alignment. AWS and Palo Alto, thank you so much for joining us with, we >>Appreciate, thanks for having >>With what's going on at aws, the partner network, the mp, and all that good stuff. That's really the value in it for customers, ISVs and channel partners. I like. We appreciate your insights. >>Thank you. Thanks for having me. Thank you. >>Our guests and Dave Valante. I'm Lisa Martin. You're watching the Cube Lee Leer in live enterprise and emerging tech coverage.

(upbeat music) (air whooshing) >> Good afternoon, cloud nerds, and welcome back to beautiful Las Vegas, Nevada. We are at AWS re:invent day four. Afternoon of day four here on theCUBE. I'm Savannah Peterson, joined by my fabulous cohost, Paul Gillin. Paul, you look sharp today. How you doing? >> Oh, you're just as fabulous, Savannah. You always look sharp. >> I appreciate that. They pay you enough to keep me buttered up over here at- (Paul laughing) It's wonderful. >> You're holding up well. >> Yeah, thank you. I am excited about our next conversation. Two fabulous gentlemen. Please welcome Sam and Monty, welcome to the show. >> Thank you. >> And it was great. Of the PR 2%, the most interesting man alive. (Paul and Savannah laughing) >> In person. Yeah, yeah. >> In the flesh. Our favorite guests so far. So how's the show been for you guys? >> Sam: It's been phenomenal. >> Just spending a lot of time with customers and partners and AWS. It's been great. It's been great. >> It is great. It's really about the community. It feels good to be back. >> Monty: Eating good food, getting my steps in above goals. >> I feel like the balance is good. We walk enough of these convention centers that you can enjoy the libations and the delicious food that's in Las Vegas and still not go home feeling like a cow. It is awesome. It's a win-win. >> To Sam's point though, meeting with customers, meeting with other technology providers that we may be able to partner with. And most importantly, in my role especially, meeting with all of our AWS key stakeholders in the partnership. So yeah, it's been great. >> Everyone's here. It's just different having a conversation in person. Even like us right now. So just in case folks aren't familiar, tell me about Talend. >> Yeah. Well, Talend is a data integration company. We've been around for a while. We have tons of different ways to get data from point A to point B, lots of different sources, lots of different connectors, and it's all about creating accessibility to that data. And then on top of that, we also have a number of solutions around governance, data health, data quality, data observability, which I think is really taking off. And so that's kind of how we're changing the business here. >> Casual change, data and governance. I don't know if anyone's talking about that at all on the snow floor. >> Been on big topic here. We've had a lot of conversations with the customers about that. >> So governance, what new dynamics has the cloud introduced into data governance? >> Well, I think historically, customers have been able to have their data on-prem. They put it into things like data lakes. And now having the flexibility to be able to bring that data to the clouds, it opens up a lot of doors, but it also opens up a lot of risks. So if you think about the chief data officer role, where you have, okay, I want to be able to bring my data to the users. I want to be able to do that at scale, operationally. But at the same time you have a tension then between the governance and the rules that really restrict the way that you can do that. Very strong tension between those two things. >> It really is a delicate balance. And especially as people are trying to accelerate and streamline their cloud projects, a lot to consider. How do you all help them do that? Monty, let's go to you. >> Yeah, we keep saying data, data, what is it really? It's ones and zeros. In this day and age, everything we see, we touch, we do, we either use data, or we create data, and then that... >> Savannah: We are data quite literally. >> We literally are data. And so then what you end up with is all these disparate data silos and different applications with different data, and how do you bring all that together? And that's where customers really struggle. And what we do is we bring it all together, and we make it actionable for the customer. We make it very simple for them to take the data, use it for the outcomes that they're looking for in their business initiatives. >> Expand on that. What do you mean make it actionable? Do you tag it? Do you organize it in some way? What's different about your approach? >> I mean, it's a really flexible platform. And I think we're part of a broader ecosystem. Even internally, we are a data driven company. Coming into the company in April, I was able to come in and get this realtime view of like, "Hey, here's where our teams are." And it's all in front of me in a Tableau dashboard that's populated from Talend integration, bringing data out of our different systems, different systems like Workday where we're giving offers out to people. And so everything from managing headcount to where our AWS spend is, all of that stuff. >> Now, we've heard a lot of talk about data and in fact the keynote yesterday that was focused mainly on data and getting data out of silos. How do you play with AWS in that role? Because AWS has other data integration partners. >> Sam: For sure. >> What's different about your relationship? Yeah. >> Go ahead. >> Yeah, we've had a strong relationship with AWS for many years now. We've got more than 80 connectors into the different AWS services. So we're not new to the AWS game. We align with the sales teams, we align with the partner teams, and then of course, we align with all the different business units and verticals so that we can enact that co-sell motion together with AWS. >> Sam: Yeah. And I think from our product standpoint, again, just being a hyper flexible platform, being able to put, again, any different type of source of data, to any type of different destination, so things like Redshift, being able to bring data into those cloud data warehouses is really how we do that. And then I think we have between bringing data from A to B, we're also able to do that along a number of different dimensions. Whether that's just like, "Hey, we just need to do this once a day to batch, all the way down to event driven things, streaming and the like. >> That customization must be really valuable for your customers as well. So one of the big themes of the show has been cost reduction. Obviously with the economic times as we're potentially dipping our toes into as well, is just in general, always wanting to increase margins. How do you help customers cut cost? >> Well, it's cost cutting, but it's also speed to market. The faster you can get a product to market, the faster you can help your customers. Let's say healthcare life sciences, pharmaceutical companies, patient outcomes. >> Great and timely example there. >> Patient outcomes, how do they get drugs to market quicker? Well, AstraZeneca leveraged our platform along with AWS. And they even said >> Cool. >> for every dollar that they spend on data initiatives, they get $40 back. That's a billion dollars >> Wow. >> savings by getting a drug to market one month faster. >> Everybody wins. >> How do you accelerate that process? >> Well, by giving them the right data, taking all the massive data that I mentioned, siloed in everywhere, and making it so that the data scientists can take all of this data and make use of it, makes sense of it, and move their drug production along much quicker. >> Yeah. And I think there's other things too like being very flexible in the way that it's deployed. Again, I think like you have this historical story of like, it takes forever for data to get updated, to get put together. >> Savannah: I need it now. And in context. >> And I think where we're coming from is almost more of a developer focus where your jobs are able to be deployed in any way you want. If you want to containerize those, you want to scale them, you need to schedule them that way. We plug into a lot of different ecosystems. I think that's a differentiation as well. >> I want to hang out on this one just for a second 'cause it's such a great customer success story and so powerful. I mean, in VC land, if you can take a dollar and make two, they'll give you a 10x valuation, 40. That is so compelling. I mean, do you think other customers could expect that kind of savings? A billion dollars is nothing to laugh at especially when we're talking about developing a vaccine. Yeah, go for it, Sam. >> It really depends on the use case. I think what we're trying to do is being able to say, "Hey, it's not just about cost cutting, but it's about tailoring the offerings." We have other customers like major fast food vendors. They have mobile apps and when you pull up that mobile app and you're going to do a delivery, they want to be able to have a customized offering. And it's not like mass market, 20% off. It's like, they want to have a very tailored offer to that customer or to that person that's pulling open that app. And so we're able to help them architect and bring that data together so that it's immediately available and reliable to be able to give those promotions. >> We had ARP on the show yesterday. We're talking about 50 million subscribers and how they customize each one of their experiences. We all want it to be about us. We don't want that generic at... Yeah, go for it, Paul. >> Oh, okay. >> Yeah. >> Well, I don't want to break break the rhythm here, but one area where you have differentiated, about two years ago you introduced something called the trust score. >> Sam: Yeah. >> Can you explain what that is and how that has resonated with your customers? >> Yeah, let's talk about this. >> Yeah, the thing about the trust score is, how many times have you gotten a set of data? And you look at it and you say, "Where did you get this data? Something doesn't look right here." And with the trust score, what we're able to do is quantify and value the different attributes of the data. Whether it's how much this is being used. We can profile the data, and we have a trust score that runs over time where you can actually then look at each of these data sets. You can look at aggregates of data sets to then say... If you're the data engineer, you can say, "Oh my, something has gone wrong with this particular dataset." Go in, quickly pull up the data. You can see if some third party integration has polluted your data source. I mean, this happens all the time. And I think if you sort of compare this to the engineering world, you're always looking to solve those problems sooner, earlier in the chain. You don't want your consumer calling you saying, "Hey, I've got a problem with the data, or I've got a problem- >> You don't want them to know there was ever a problem in theory. >> Yeah, the trust score helps those data engineers and those people that are taking care of the data address those problems sooner. >> How much data does somebody need to be able to get to the point where they can have a trust score? If you know what I'm trying to say. How do we train that? >> I mean, it can be all the way from just like a single data source that's getting updated, all the way to very large complex ones. That's where we've introduced this hierarchy of data sets. So it's not just like, "Hey, you've got a billion data sources here and here are the trust scores." But it's like, you can actually architect this to say like, "Okay, well, I have these data sets that belong to finance." And then finance will actually get, "Here's the trust score for these data sets that they rely on." >> What causes datasets to become untrustworthy? >> Yeah. Yeah. I mean, it happens all the time. >> A of different things, right? >> In my history, in the different companies that I've been at, on the product side, we have seen different integrations that maybe somebody changes something. In upstream, some of those integrations can actually be quite brittle. And as a consumer of that data, it's not necessarily your fault, but that data ends up getting put into your production database. All of a sudden your data engineering team is spending two days unwinding those transactions, fixing the data that's in there. And all the while, that bad data that's in your production system, is causing a problem for somebody that is ultimately relying on that. >> Is that usually a governance problem? >> I think governance is probably a separate set of constraints. This is sort of the tension between wanting to get all of the data available to your consumers versus wanting to have the quality around it as well. >> It's tough balance. And I think that it's really interesting. Everybody wants great data, and you could be making decisions that affect people's wellness, quite frankly. >> For sure. >> Very dramatically if you're ill-informed. So that's very exciting. >> To your point, we are all data. So if the data is bad, we're not going to get the outcomes that we want ultimately, >> I know. We certainly want the best outcomes for ourselves. >> We track that data health for its entire life cycle throughout the process. >> That's cool. And that probably increases your confidence in the trust score as well 'cause you're looking at so much data all the time. You got a smart thing going on over here. I like it. I like it a lot. >> We believe in it and so does AWS because they are a strong partner of ours, and so do customers. I think we mentioned we've had some phenomenal customer conversations along with- >> What a success story and case study. I want to dust your shoulders off right now if I wasn't tethered in. That's super impressive. So what's next for you all? >> Yeah, so I think we're going to continue down this path of data health and data governance. Again, I kind of talked about the... you're talking about data health being this differentiator on top of just moving the data around and being really good at that. I think you're also going to have different things around country level or state level governance, literal laws that you need to comply with. And so like- >> Savannah: CCPA- >> I mean, a long list- >> Oodles. Yeah. Yeah, yeah, yeah. >> I think we're going to be doing some interesting things there. We are continuing to proliferate the sources of data that we connect to. We're always looking for the latest and greatest things to put the data into. I think you're going to see some interesting things come out of that too. >> And we continue to grow our relationship with AWS, our already strong relationship. So you can procure Talend products to the AWS marketplace. We just announced Redshift serverless support for Talend. >> All their age. >> Which sounds amazing, but because we've been doing this for so long with AWS, dirty little secret, that was easy for us to do because we're already doing all this stuff. So we made the announcement and everyone was like, "Congratulations." Like, "Thanks." >> Look at you all. Full of the humble brags. I love it. >> Talend has gone through some twists and turns over the last couple of years. Company went private, was purchased by Thoma Bravo about a year and a half ago. At that time, your CEO said that it was a chance to really refocus the company on some core strategic initiatives and move forward. Both of you joined obviously after that happened. But what did you see about sort of the new Talend that attracted you, made you want to come over here? >> For sure. Yeah. I think, when I got a chance to talk to the board and talk to Chris, our chair, we talked about there being the growth thesis behind it. So I think Thoma been a great partner to Talend. I think we're able to do some things internally that would be I think, fairly challenging for companies that are in the public markets right now. I think especially, just a lot of pressure on different prices and the cost capital and all of that. >> Right now. >> That was a really casual way of stating that. But yeah, just a little pressure. >> Little bit of pressure. And who knows? Who knows how long that's going to last, right? But I think we've got a great board in place. They've been very strong strategic partner for us talking about all the different ways that we can grow. I think it's been a good partner for us- >> One of the strengths of Thoma's strategy is synergy between the companies they've acquired. >> Oh, for sure. >> They've acquired about 40 software companies. Are you seeing synergy? You talk to those other companies a lot? >> Yeah, so I have an operating partner. I talk with him on a weekly, sometimes daily basis. If we have questions or like, "Hey, what are you seeing in this space?" We can get plugged in to advisors very quickly. I think it's been a very helpful thing where... otherwise, you're relying on your personal network or things like that. >> This is why Monty was saying it was easy for you guys to go serverless. >> And we keep talking about trust, but in this case, Thoma Bravo really trusts our senior leadership team to make the right decisions that Sam and I are here making as we move forward. It's a great relationship. >> Sam: A good team. >> It sounds like it. All the love. I can feel the love even from you guys talking about it, it's genuine. You're not just getting paid to show this. That's fantastic. >> Are we getting paid for this or... >> Yeah. (Savannah giggling) (Paul laughing) I mean, some folks in the audience are probably going to want your autograph after this, although you get that a lot- >> Pictures are available after- >> Yeah, selfies are 10 bucks. That's how I get my boos budget. So last question for you. We have a challenge here on the theCUBE re:invent. We're looking for your 32nd hot take. Think of it as your thought leadership sizzle reel. Biggest takeaway, key themes from the show or looking forward into 2023? Sam, you're ready to rock, go. >> Yeah, totally. >> I think you're going to continue to hear the tension between being able to bring the data to the masses versus the simplicity and being able to do that in a way that is compliant with all the different laws, and then clean data. It's like a lot of different challenges that arise when you do this at scale. And so I think if you look at the things that AWS is announcing, I think you look at any sort of vendor in the data space are announcing, you see them sort of coming around to that set of ideas. Gives me a lot of confidence in the direction that we're going that we're doing the right stuff and we're meeting customers and prospects and partners, and everybody is like... We kind of get into this conversation and I'll say, "Yeah, that's it. We want to get involved in that." >> You can really feel the momentum. Yeah, it's true. It's great. What about you, Monty? >> I mean, I don't need 30 seconds. I mentioned it. >> Great. >> Between Talend and AWS, we're aligned from the sales teams to the product teams, the partner teams and the alliances. We're just moving forward and growing this relationship. >> I love it. That was perfect. And on that note, Sam, Monty, thank you so much for joining us. >> Yeah, thanks for having us. >> I'm sure your careers are going to continue to be rad at Talend and I can't wait to continue the conversation. >> Sam: Yeah, it's a great team. >> Yeah, clearly. I mean, look at you two. If you're any representation of the culture over there, they're doing something great. (Monty laughing) I thank all of you for tuning in to our nearly... Well, shoot. I think now over 100 interviews at AWS Reinvent in Sin City. We are hanging out here. Paul and I've got a couple more for you. So we hope to see you tuning in with Paul Gillin. I'm Savannah Peterson. You're watching theCUBE, the leader in high tech coverage. (upbeat music)

(upbeat music) >> Hey, everyone, welcome back to Las Vegas, "theCube" is on our fourth day of covering AWS re:Invent, live from the Venetian Expo Center. This week has been amazing. We've created a ton of content, as you know, 'cause you've been watching. But, there's been north of 55,000 people here, hundreds of thousands online. We've had amazing conversations across the AWS ecosystem. Lisa Martin, Paul Gillan. Paul, what's your, kind of, take on day four of the conference? It's still highly packed. >> Oh, there's lots of people here. (laughs) >> Yep. Unusual for the final day of a conference. I think Werner Vogels, if I'm pronouncing it right kicked things off today when he talked about asymmetry and how the world is, you know, asymmetric. We build symmetric software, because it's convenient to do so, but asymmetric software actually scales and evolves much better. And I think that that was a conversation starter for a lot of what people are talking about here today, which is how the cloud changes the way we think about building software. >> Absolutely does. >> Our next guest, Holger Mueller, that's one of his key areas of focus. And Holger, welcome, thanks for joining us on the "theCube". >> Thanks for having me. >> What did you take away from the keynote this morning? >> Well, how do you feel on the final day of the marathon, right? We're like 23, 24 miles. Hit the ball yesterday, right? >> We are going strong Holger. And, of course, >> Yeah. >> you guys, we can either talk about business transformation with cloud or the World Cup. >> Or we can do both. >> The World Cup, hands down. World Cup. (Lisa laughs) Germany's out, I'm unbiased now. They just got eliminated. >> Spain is out now. >> What will the U.S. do against Netherlands tomorrow? >> They're going to win. What's your forecast? U.S. will win? >> They're going to win 2 to 1. >> What do you say, 2:1? >> I'm optimistic, but realistic. >> 3? >> I think Netherlands. >> Netherlands will win? >> 2 to nothing. >> Okay, I'll vote for the U.S.. >> Okay, okay >> 3:1 for the U.S.. >> Be optimistic. >> Root for the U.S.. >> Okay, I like that. >> Hope for the best wherever you work. >> Tomorrow you'll see how much soccer experts we are. >> If your prediction was right. (laughs) >> (laughs) Ja, ja. Or yours was right, right, so. Cool, no, but the event, I think the event is great to have 50,000 people. Biggest event of the year again, right? Not yet the 70,000 we had in 2019. But it's great to have the energy. I've never seen the show floor going all the way down like this, right? >> I haven't either. >> I've never seen that. I think it's a record. Often vendors get the space here and they have the keynote area, and the entertainment area, >> Yeah. >> and the food area, and then there's an exposition, right? This is packed. >> It's packed. >> Maybe it'll pay off. >> You don't see the big empty booths that you often see. >> Oh no. >> Exactly, exactly. You know, the white spaces and so on. >> No. >> Right. >> Which is a good thing. >> There's lots of energy, which is great. And today's, of course, the developer day, like you said before, right now Vogels' a rockstar in the developer community, right. Revered visionary on what has been built, right? And he's becoming a little professorial is my feeling, right. He had these moments before too, when it was justifying how AWS moved off the Oracle database about the importance of data warehouses and structures and why DynamoDB is better and so on. But, he had a large part of this too, and this coming right across the keynotes, right? Adam Selipsky talking about Antarctica, right? Scott against almonds and what went wrong. He didn't tell us, by the way, which often the tech winners forget. Scott banked on technology. He had motorized sleds, which failed after three miles. So, that's not the story to tell the technology. Let everything down. Everybody went back to ponies and horses and dogs. >> Maybe goes back to these asynchronous behavior. >> Yeah. >> The way of nature. >> And, yesterday, Swami talking about the bridges, right? The root bridges, right? >> Right. >> So, how could Werner pick up with his video at the beginning. >> Yeah. >> And then talk about space and other things? So I think it's important to educate about event-based architecture, right? And we see this massive transformation. Modern software has to be event based, right? Because, that's how things work and we didn't think like this before. I see this massive transformation in my other research area in other platforms about the HR space, where payrolls are being rebuilt completely. And payroll used to be one of the three peaks of ERP, right? You would size your ERP machine before the cloud to financial close, to run the payroll, and to do an MRP manufacturing run if you're manufacturing. God forbid you run those three at the same time. Your machine wouldn't be able to do that, right? So it was like start the engine, start the boosters, we are running payroll. And now the modern payroll designs like you see from ADP or from Ceridian, they're taking every payroll relevant event. You check in time wise, right? You go overtime, you take a day of vacation and right away they trigger and run the payroll, so it's up to date for you, up to date for you, which, in this economy, is super important, because we have more gig workers, we have more contractors, we have employees who are leaving suddenly, right? The great resignation, which is happening. So, from that perspective, it's the modern way of building software. So it's great to see Werner showing that. The dirty little secrets though is that is more efficient software for the cloud platform vendor too. Takes less resources, gets less committed things, so it's a much more scalable architecture. You can move the events, you can work asynchronously much better. And the biggest showcase, right? What's the biggest transactional showcase for an eventually consistent asynchronous transactional application? I know it's a mouthful, but we at Amazon, AWS, Amazon, right? You buy something on Amazon they tell you it's going to come tomorrow. >> Yep. >> They don't know it's going to come tomorrow by that time, because it's not transactionally consistent, right? We're just making every ERP vendor, who lives in transactional work, having nightmares of course, (Lisa laughs) but for them it's like, yes we have the delivery to promise, a promise to do that, right? But they come back to you and say, "Sorry, we couldn't make it, delivery didn't work and so on. It's going to be a new date. We are out of the product.", right? So these kind of event base asynchronous things are more and more what's going to scale around the world. It's going to be efficient for everybody, it's going to be better customer experience, better employee experience, ultimately better user experience, it's going to be better for the enterprise to build, but we have to learn to build it. So big announcement was to build our environment to build better eventful applications from today. >> Talk about... This is the first re:Invent... Well, actually, I'm sorry, it's the second re:Invent under Adam Selipsky. >> Right. Adam Selipsky, yep. >> But his first year. >> Right >> We're hearing a lot of momentum. What's your takeaway with what he delivered with the direction Amazon is going, their vision? >> Ja, I think compared to the Jassy times, right, we didn't see the hockey stick slide, right? With a number of innovations and releases. That was done in 2019 too, right? So I think it's a more pedestrian pace, which, ultimately, is good for everybody, because it means that when software vendors go slower, they do less width, but more depth. >> Yeah. >> And depth is what customers need. So Amazon's building more on the depth side, which is good news. I also think, and that's not official, right, but Adam Selipsky came from Tableau, right? >> Yeah. So he is a BI analytics guy. So it's no surprise we have three data lake offerings, right? Security data lake, we have a healthcare data lake and we have a supply chain data lake, right? Where all, again, the epigonos mentioned them I was like, "Oh, my god, Amazon's coming to supply chain.", but it's actually data lakes, which is an interesting part. But, I think it's not a surprise that someone who comes heavily out of the analytics BI world, it's off ringside, if I was pitching internally to him maybe I'd do something which he's is familiar with and I think that's what we see in the major announcement of his keynote on Tuesday. >> I mean, speaking of analytics, one of the big announcements early on was Amazon is trying to bridge the gap between Aurora. >> Yep. >> And Redshift. >> Right. >> And setting up for continuous pipelines, continuous integration. >> Right. >> Seems to be a trend that is common to all database players. I mean, Oracle is doing the same thing. SAP is doing the same thing. MariaDB. Do you see the distinction between transactional and analytical databases going away? >> It's coming together, right? Certainly coming together, from that perspective, but there's a fundamental different starting point, right? And with the big idea part, right? The universal database, which does everything for you in one system, whereas the suite of specialized databases, right? Oracle is in the classic Oracle database in the universal database camp. On the other side you have Amazon, which built a database. This is one of the first few Amazon re:Invents. It's my 10th where there was no new database announced. Right? >> No. >> So it was always add another one specially- >> I think they have enough. >> It's a great approach. They have enough, right? So it's a great approach to build something quick, which Amazon is all about. It's not so great when customers want to leverage things. And, ultimately, which I think with Selipsky, AWS is waking up to the enterprise saying, "I have all this different database and what is in them matters to me." >> Yeah. >> "So how can I get this better?" So no surprise between the two most popular database, Aurora and RDS. They're bring together the data with some out of the box parts. I think it's kind of, like, silly when Swami's saying, "Hey, no ETL.". (chuckles) Right? >> Yeah. >> There shouldn't be an ETL from the same vendor, right? There should be data pipes from that perspective anyway. So it looks like, on the overall value proposition database side, AWS is moving closer to the universal database on the Oracle side, right? Because, if you lift, of course, the universal database, under the hood, you see, well, there's different database there, different part there, you do something there, you have to configure stuff, which is also the case but it's one part of it, right, so. >> With that shift, talk about the value that's going to be in it for customers regardless of industry. >> Well, the value for customers is great, because when software vendors, or platform vendors, go in depth, you get more functionality, you get more maturity you get easier ways of setting up the whole things. You get ways of maintaining things. And you, ultimately, get lower TCO to build them, which is super important for enterprise. Because, here, this is the developer cloud, right? Developers love AWS. Developers are scarce, expensive. Might not be want to work for you, right? So developer velocity getting more done with same amount of developers, getting less done, less developers getting more done, is super crucial, super important. So this is all good news for enterprise banking on AWS and then providing them more efficiency, more automation, out of the box. >> Some of your customer conversations this week, talk to us about some of the feedback. What's the common denominator amongst customers right now? >> Customers are excited. First of all, like, first event, again in person, large, right? >> Yeah. >> People can travel, people meet each other, meet in person. They have a good handle around the complexity, which used to be a huge challenge in the past, because people say, "Do I do this?" I know so many CXOs saying, "Yeah, I want to build, say, something in IoT with AWS. The first reference built it like this, the next reference built it completely different. The third one built it completely different again. So now I'm doubting if my team has the skills to build things successfully, because will they be smart enough, like your teams, because there's no repetitiveness and that repetitiveness is going to be very important for AWS to come up with some higher packaging and version numbers.", right? But customers like that message. They like that things are working better together. They're not missing the big announcement, right? One of the traditional things of AWS would be, and they made it even proud, as a system, Jassy was saying, "If we look at the IT spend and we see something which is, like, high margin for us and not served well and we announced something there, right?" So Quick Start, Workspaces, where all liaisons where AWS went after traditional IT spend and had an offering. We haven't had this in 2019, we don't have them in 2020. Last year and didn't have it now. So something is changing on the AWS side. It's a little bit too early to figure out what, but they're not chewing off as many big things as they used in the past. >> Right. >> Yep. >> Did you get the sense that... Keith Townsend, from "The CTO Advisor", was on earlier. >> Yep. >> And he said he's been to many re:Invents, as you have, and he said that he got the sense that this is Amazon's chance to do a victory lap, as he called it. That this is a way for Amazon to reinforce the leadership cloud. >> Ja. >> And really, kind of, establish that nobody can come close to them, nobody can compete with them. >> You don't think that- >> I don't think that's at all... I mean, love Keith, he's a great guy, but I don't think that's the mindset at all, right? So, I mean, Jassy was always saying, "It's still the morning of the day in the cloud.", right? They're far away from being done. They're obsessed over being right. They do more work with the analysts. We think we got something right. And I like the passion, from that perspective. So I think Amazon's far from being complacent and the area, which is the biggest bit, right, the biggest. The only thing where Amazon truly has floundered, always floundered, is the AI space, right? So, 2018, Werner Vogels was doing more technical stuff that "Oh, this is all about linear regression.", right? And Amazon didn't start to put algorithms on silicon, right? And they have a three four trail and they didn't announce anything new here, behind Google who's been doing this for much, much longer than TPU platform, so. >> But they have now. >> They're keen aware. >> Yep. >> They now have three, or they own two of their own hardware platforms for AI. >> Right. >> They support the Intel platform. They seem to be catching up in that area. >> It's very hard to catch up on hardware, right? Because, there's release cycles, right? And just the volume that, just talking about the largest models that we have right now, to do with the language models, and Google is just doing a side note of saying, "Oh, we supported 50 less or 30 less, not little spoken languages, which I've never even heard of, because they're under banked and under supported and here's the language model, right? And I think it's all about little bit the organizational DNA of a company. I'm a strong believer in that. And, you have to remember AWS comes from the retail side, right? >> Yeah. >> Their roll out of data centers follows their retail strategy. Open secret, right? But, the same thing as the scale of the AI is very very different than if you take a look over at Google where it makes sense of the internet, right? The scale right away >> Right. >> is a solution, which is a good solution for some of the DNA of AWS. Also, Microsoft Azure is good. There has no chance to even get off the ship of that at Google, right? And these leaders with Google and it's not getting smaller, right? We didn't hear anything. I mean so much focused on data. Why do they focus so much on data? Because, data is the first step for AI. If AWS was doing a victory lap, data would've been done. They would own data, right? They would have a competitor to BigQuery Omni from the Google side to get data from the different clouds. There's crickets on that topic, right? So I think they know that they're catching up on the AI side, but it's really, really hard. It's not like in software where you can't acquire someone they could acquire in video. >> Not at Core Donovan. >> Might play a game, but that's not a good idea, right? So you can't, there's no shortcuts on the hardware side. As much as I'm a software guy and love software and don't like hardware, it's always a pain, right? There's no shortcuts there and there's nothing, which I think, has a new Artanium instance, of course, certainly, but they're not catching up. The distance is the same, yep. >> One of the things is funny, one of our guests, I think it was Tuesday, it was, it was right after Adam's keynote. >> Sure. >> Said that Adam Selipsky stood up on stage and talked about data for 52 minutes. >> Yeah. Right. >> It was timed, 52 minutes. >> Right. >> Huge emphasis on that. One of the things that Adam said to John Furrier when they were able to sit down >> Yeah >> a week or so ago at an event preview, was that CIOs and CEOs are not coming to Adam to talk about technology. They want to talk about transformation. They want to talk about business transformation. >> Sure, yes, yes. >> Talk to me in our last couple of minutes about what CEOs and CIOs are coming to you saying, "Holger, help us figure this out. We have to transform the business." >> Right. So we advise, I'm going quote our friends at Gartner, once the type A company. So we'll use technology aggressively, right? So take everything in the audience with a grain of salt, followers are the laggards, and so on. So for them, it's really the cusp of doing AI, right? Getting that data together. It has to be in the cloud. We live in the air of infinite computing. The cloud makes computing infinite, both from a storage, from a compute perspective, from an AI perspective, and then define new business models and create new best practices on top of that. Because, in the past, everything was fine out on premise, right? We talked about the (indistinct) size. Now in the cloud, it's just the business model to say, "Do I want to have a little more AI? Do I want a to run a little more? Will it give me the insight in the business?". So, that's the transformation that is happening, really. So, bringing your data together, this live conversation data, but not for bringing the data together. There's often the big win for the business for the first time to see the data. AWS is banking on that. The supply chain product, as an example. So many disparate systems, bring them them together. Big win for the business. But, the win for the business, ultimately, is when you change the paradigm from the user showing up to do something, to software doing stuff for us, right? >> Right. >> We have too much in this operator paradigm. If the user doesn't show up, doesn't find the click, doesn't find where to go, nothing happens. It can't be done in the 21st century, right? Software has to look over your shoulder. >> Good point. >> Understand one for you, autonomous self-driving systems. That's what CXOs, who're future looking, will be talked to come to AWS and all the other cloud vendors. >> Got it, last question for you. We're making a sizzle reel on Instagram. >> Yeah. >> If you had, like, a phrase, like, or a 30 second pitch that would describe re:Invent 2022 in the direction the company's going. What would that elevator pitch say? >> 30 second pitch? >> Yeah. >> All right, just timing. AWS is doing well. It's providing more depth, less breadth. Making things work together. It's catching up in some areas, has some interesting offerings, like the healthcare offering, the security data lake offering, which might change some things in the industry. It's staying the course and it's going strong. >> Ah, beautifully said, Holger. Thank you so much for joining Paul and me. >> Might have been too short. I don't know. (laughs) >> About 10 seconds left over. >> It was perfect, absolutely perfect. >> Thanks for having me. >> Perfect sizzle reel. >> Appreciate it. >> We appreciate your insights, what you're seeing this week, and the direction the company is going. We can't wait to see what happens in the next year. And, yeah. >> Thanks for having me. >> And of course, we've been on so many times. We know we're going to have you back. (laughs) >> Looking forward to it, thank you. >> All right, for Holger Mueller and Paul Gillan, I'm Lisa Martin. You're watching "theCube", the leader in live enterprise and emerging tech coverage. (upbeat music)

>>Good morning fellow nerds and welcome back to AWS Reinvent. We are live from the show floor here in Las Vegas, Nevada. My name is Savannah Peterson, joined with my fabulous co-host John Furrier. Day two keynotes are rolling. >>Yeah. What do you thinking this? This is the day where everything comes, so the core gets popped off the bottle, all the announcements start flowing out tomorrow. You hear machine learning from swee lot more in depth around AI probably. And then developers with Verner Vos, the CTO who wrote the seminal paper in in early two thousands around web service that becames. So again, just another great year of next level cloud. Big discussion of data in the keynote bulk of the time was talking about data and business intelligence, business transformation easier. Is that what people want? They want the easy button and we're gonna talk a lot about that in this segment. I'm really looking forward to this interview. >>Easy button. We all want the >>Easy, we want the easy button. >>I love that you brought up champagne. It really feels like a champagne moment for the AWS community as a whole. Being here on the floor feels a bit like the before times. I don't want to jinx it. Our next guest, Scott Castle, from Si Sense. Thank you so much for joining us. How are you feeling? How's the show for you going so far? Oh, >>This is exciting. It's really great to see the changes that are coming in aws. It's great to see the, the excitement and the activity around how we can do so much more with data, with compute, with visualization, with reporting. It's fun. >>It is very fun. I just got a note. I think you have the coolest last name of anyone we've had on the show so far, castle. Oh, thank you. I'm here for it. I'm sure no one's ever said that before, but I'm so just in case our audience isn't familiar, tell us about >>Soy Sense is an embedded analytics platform. So we're used to take the queries and the analysis that you can power off of Aurora and Redshift and everything else and bring it to the end user in the applications they already know how to use. So it's all about embedding insights into tools. >>Embedded has been a, a real theme. Nobody wants to, it's I, I keep using the analogy of multiple tabs. Nobody wants to have to leave where they are. They want it all to come in there. Yep. Now this space is older than I think everyone at this table bis been around since 1958. Yep. How do you see Siente playing a role in the evolution there of we're in a different generation of analytics? >>Yeah, I mean, BI started, as you said, 58 with Peter Lu's paper that he wrote for IBM kind of get became popular in the late eighties and early nineties. And that was Gen one bi, that was Cognos and Business Objects and Lotus 1 23 think like green and black screen days. And the way things worked back then is if you ran a business and you wanted to get insights about that business, you went to it with a big check in your hand and said, Hey, can I have a report? And they'd come back and here's a report. And it wasn't quite right. You'd go back and cycle, cycle, cycle and eventually you'd get something. And it wasn't great. It wasn't all that accurate, but it's what we had. And then that whole thing changed in about two, 2004 when self-service BI became a thing. And the whole idea was instead of going to it with a big check in your hand, how about you make your own charts? >>And that was totally transformative. Everybody started doing this and it was great. And it was all built on semantic modeling and having very fast databases and data warehouses. Here's the problem, the tools to get to those insights needed to serve both business users like you and me and also power users who could do a lot more complex analysis and transformation. And as the tools got more complicated, the barrier to entry for everyday users got higher and higher and higher to the point where now you look, look at Gartner and Forester and IDC this year. They're all reporting in the same statistic. Between 10 and 20% of knowledge workers have learned business intelligence and everybody else is just waiting in line for a data analyst or a BI analyst to get a report for them. And that's why the focus on embedded is suddenly showing up so strong because little startups have been putting analytics into their products. People are seeing, oh my, this doesn't have to be hard. It can be easy, it can be intuitive, it can be native. Well why don't I have that for my whole business? So suddenly there's a lot of focus on how do we embed analytics seamlessly? How do we embed the investments people make in machine learning in data science? How do we bring those back to the users who can actually operationalize that? Yeah. And that's what Tysons does. Yeah. >>Yeah. It's interesting. Savannah, you know, data processing used to be what the IT department used to be called back in the day data processing. Now data processing is what everyone wants to do. There's a ton of data we got, we saw the keynote this morning at Adam Lesky. There was almost a standing of vision, big applause for his announcement around ML powered forecasting with Quick Site Cube. My point is people want automation. They want to have this embedded semantic layer in where they are not having all the process of ETL or all the muck that goes on with aligning the data. All this like a lot of stuff that goes on. How do you make it easier? >>Well, to be honest, I, I would argue that they don't want that. I think they, they think they want that, cuz that feels easier. But what users actually want is they want the insight, right? When they are about to make a decision. If you have a, you have an ML powered forecast, Andy Sense has had that built in for years, now you have an ML powered forecast. You don't need it two weeks before or a week after in a report somewhere. You need it when you're about to decide do I hire more salespeople or do I put a hundred grand into a marketing program? It's putting that insight at the point of decision that's important. And you don't wanna be waiting to dig through a lot of infrastructure to find it. You just want it when you need it. What's >>The alternative from a time standpoint? So real time insight, which is what you're saying. Yep. What's the alternative? If they don't have that, what's >>The alternative? Is what we are currently seeing in the market. You hire a bunch of BI analysts and data analysts to do the work for you and you hire enough that your business users can ask questions and get answers in a timely fashion. And by the way, if you're paying attention, there's not enough data analysts in the whole world to do that. Good luck. I am >>Time to get it. I really empathize with when I, I used to work for a 3D printing startup and I can, I have just, I mean, I would call it PTSD flashbacks of standing behind our BI guy with my list of queries and things that I wanted to learn more about our e-commerce platform in our, in our marketplace and community. And it would take weeks and I mean this was only in 2012. We're not talking 1958 here. We're talking, we're talking, well, a decade in, in startup years is, is a hundred years in the rest of the world life. But I think it's really interesting. So talk to us a little bit about infused and composable analytics. Sure. And how does this relate to embedded? Yeah. >>So embedded analytics for a long time was I want to take a dashboard I built in a BI environment. I wanna lift it and shift it into some other application so it's close to the user and that is the right direction to go. But going back to that statistic about how, hey, 10 to 20% of users know how to do something with that dashboard. Well how do you reach the rest of users? Yeah. When you think about breaking that up and making it more personalized so that instead of getting a dashboard embedded in a tool, you get individual insights, you get data visualizations, you get controls, maybe it's not even actually a visualization at all. Maybe it's just a query result that influences the ordering of a list. So like if you're a csm, you have a list of accounts in your book of business, you wanna rank those by who's priorities the most likely to churn. >>Yeah. You get that. How do you get that most likely to churn? You get it from your BI system. So how, but then the question is, how do I insert that back into the application that CSM is using? So that's what we talk about when we talk about Infusion. And SI started the infusion term about two years ago and now it's being used everywhere. We see it in marketing from Click and Tableau and from Looker just recently did a whole launch on infusion. The idea is you break this up into very small digestible pieces. You put those pieces into user experiences where they're relevant and when you need them. And to do that, you need a set of APIs, SDKs, to program it. But you also need a lot of very solid building blocks so that you're not building this from scratch, you're, you're assembling it from big pieces. >>And so what we do aty sense is we've got machine learning built in. We have an LQ built in. We have a whole bunch of AI powered features, including a knowledge graph that helps users find what else they need to know. And we, we provide those to our customers as building blocks so that they can put those into their own products, make them look and feel native and get that experience. In fact, one of the things that was most interesting this last couple of couple of quarters is that we built a technology demo. We integrated SI sensee with Office 365 with Google apps for business with Slack and MS teams. We literally just threw an Nlq box into Excel and now users can go in and say, Hey, which of my sales people in the northwest region are on track to meet their quota? And they just get the table back in Excel. They can build charts of it and PowerPoint. And then when they go to their q do their QBR next week or week after that, they just hit refresh to get live data. It makes it so much more digestible. And that's the whole point of infusion. It's bigger than just, yeah. The iframe based embedding or the JavaScript embedding we used to talk about four or five years >>Ago. APIs are very key. You brought that up. That's gonna be more of the integration piece. How does embedable and composable work as more people start getting on board? It's kind of like a Yeah. A flywheel. Yes. What, how do you guys see that progression? Cause everyone's copying you. We see that, but this is a, this means it's standard. People want this. Yeah. What's next? What's the, what's that next flywheel benefit that you guys coming out with >>Composability, fundamentally, if you read the Gartner analysis, right, they, when they talk about composable, they're talking about building pre-built analytics pieces in different business units for, for different purposes. And being able to plug those together. Think of like containers and services that can, that can talk to each other. You have a composition platform that can pull it into a presentation layer. Well, the presentation layer is where I focus. And so the, so for us, composable means I'm gonna have formulas and queries and widgets and charts and everything else that my, that my end users are gonna wanna say almost minority report style. If I'm not dating myself with that, I can put this card here, I can put that chart here. I can set these filters here and I get my own personalized view. But based on all the investments my organization's made in data and governance and quality so that all that infrastructure is supporting me without me worrying much about it. >>Well that's productivity on the user side. Talk about the software angle development. Yeah. Is your low code, no code? Is there coding involved? APIs are certainly the connective tissue. What's the impact to Yeah, the >>Developer. Oh. So if you were working on a traditional legacy BI platform, it's virtually impossible because this is an architectural thing that you have to be able to do. Every single tool that can make a chart has an API to embed that chart somewhere. But that's not the point. You need the life cycle automation to create models, to modify models, to create new dashboards and charts and queries on the fly. And be able to manage the whole life cycle of that. So that in your composable application, when you say, well I want chart and I want it to go here and I want it to do this and I want it to be filtered this way you can interact with the underlying platform. And most importantly, when you want to use big pieces like, Hey, I wanna forecast revenue for the next six months. You don't want it popping down into Python and writing that yourself. >>You wanna be able to say, okay, here's my forecasting algorithm. Here are the inputs, here's the dimensions, and then go and just put it somewhere for me. And so that's what you get withy sense. And there aren't any other analytics platforms that were built to do that. We were built that way because of our architecture. We're an API first product. But more importantly, most of the legacy BI tools are legacy. They're coming from that desktop single user, self-service, BI environment. And it's a small use case for them to go embedding. And so composable is kind of out of reach without a complete rebuild. Right? But with SI senses, because our bread and butter has always been embedding, it's all architected to be API first. It's integrated for software developers with gi, but it also has all those low code and no code capabilities for business users to do the minority report style thing. And it's assemble endless components into a workable digital workspace application. >>Talk about the strategy with aws. You're here at the ecosystem, you're in the ecosystem, you're leading product and they have a strategy. We know their strategy, they have some stuff, but then the ecosystem goes faster and ends up making a better product in most of the cases. If you compare, I know they'll take me to school on that, but I, that's pretty much what we report on. Mongo's doing a great job. They have databases. So you kind of see this balance. How are you guys playing in the ecosystem? What's the, what's the feedback? What's it like? What's going on? >>AWS is actually really our best partner. And the reason why is because AWS has been clear for many, many years. They build componentry, they build services, they build infrastructure, they build Redshift, they build all these different things, but they need, they need vendors to pull it all together into something usable. And fundamentally, that's what Cient does. I mean, we didn't invent sequel, right? We didn't invent jackal or dle. These are not, these are underlying analytics technologies, but we're taking the bricks out of the briefcase. We're assembling it into something that users can actually deploy for their use cases. And so for us, AWS is perfect because they focus on the hard bits. The the underlying technologies we assemble those make them usable for customers. And we get the distribution. And of course AWS loves that. Cause it drives more compute and it drives more, more consumption. >>How much do they pay you to say that >>Keynote, >>That was a wonderful pitch. That's >>Absolutely, we always say, hey, they got a lot of, they got a lot of great goodness in the cloud, but they're not always the best at the solutions and that they're trying to bring out, and you guys are making these solutions for customers. Yeah. That resonates with what they got with Amazon. For >>Example, we, last year we did a, a technology demo with Comprehend where we put comprehend inside of a semantic model and we would compile it and then send it back to Redshift. And it takes comprehend, which is a very cool service, but you kind of gotta be a coder to use it. >>I've been hear a lot of hype about the semantic layer. What is, what is going on with that >>Semantec layer is what connects the actual data, the tables in your database with how they're connected and what they mean so that a user like you or me who's saying I wanna bar chart with revenue over time can just work with revenue and time. And the semantic layer translates between what we did and what the database knows >>About. So it speaks English and then they converts it to data language. It's >>Exactly >>Right. >>Yeah. It's facilitating the exchange of information. And, and I love this. So I like that you actually talked about it in the beginning, the knowledge map and helping people figure out what they might not know. Yeah. I, I am not a bi analyst by trade and I, I don't always know what's possible to know. Yeah. And I think it's really great that you're doing that education piece. I'm sure, especially working with AWS companies, depending on their scale, that's gotta be a big part of it. How much is the community play a role in your product development? >>It's huge because I'll tell you, one of the challenges in embedding is someone who sees an amazing experience in outreach or in seismic. And to say, I want that. And I want it to be exactly the way my product is built, but I don't wanna learn a lot. And so you, what you want do is you want to have a community of people who have already built things who can help lead the way. And our community, we launched a new version of the SES community in early 2022 and we've seen a 450% growth in the c in that community. And we've gone from an average of one response, >>450%. I just wanna put a little exclamation point on that. Yeah, yeah. That's awesome. We, >>We've tripled our organic activity. So now if you post this Tysons community, it used to be, you'd get one response maybe from us, maybe from from a customer. Now it's up to three. And it's continuing to trend up. So we're, it's >>Amazing how much people are willing to help each other. If you just get in the platform, >>Do it. It's great. I mean, business is so >>Competitive. I think it's time for the, it's time. I think it's time. Instagram challenge. The reels on John. So we have a new thing. We're gonna run by you. Okay. We just call it the bumper sticker for reinvent. Instead of calling it the Instagram reels. If we're gonna do an Instagram reel for 30 seconds, what would be your take on what's going on this year at Reinvent? What you guys are doing? What's the most important story that you would share with folks on Instagram? >>You know, I think it's really what, what's been interesting to me is the, the story with Redshift composable, sorry. No, composable, Redshift Serverless. Yeah. One of the things I've been >>Seeing, we know you're thinking about composable a lot. Yes. Right? It's, it's just, it's in there, it's in your mouth. Yeah. >>So the fact that Redshift Serverless is now kind becoming the defacto standard, it changes something for, for my customers. Cuz one of the challenges with Redshift that I've seen in, in production is if as people use it more, you gotta get more boxes. You have to manage that. The fact that serverless is now available, it's, it's the default means it now people are just seeing Redshift as a very fast, very responsive repository. And that plays right into the story I'm telling cuz I'm telling them it's not that hard to put some analysis on top of things. So for me it's, it's a, maybe it's a narrow Instagram reel, but it's an >>Important one. Yeah. And that makes it better for you because you get to embed that. Yeah. And you get access to better data. Faster data. Yeah. Higher quality, relevant, updated. >>Yep. Awesome. As it goes into that 80% of knowledge workers, they have a consumer great expectation of experience. They're expecting that five ms response time. They're not waiting 2, 3, 4, 5, 10 seconds. They're not trained on theola expectations. And so it's, it matters a lot. >>Final question for you. Five years out from now, if things progress the way they're going with more innovation around data, this front end being very usable, semantic layer kicks in, you got the Lambda and you got serverless kind of coming in, helping out along the way. What's the experience gonna look like for a user? What's it in your mind's eye? What's that user look like? What's their experience? >>I, I think it shifts almost every role in a business towards being a quantitative one. Talking about, Hey, this is what I saw. This is my hypothesis and this is what came out of it. So here's what we should do next. I, I'm really excited to see that sort of scientific method move into more functions in the business. Cuz for decades it's been the domain of a few people like me doing strategy, but now I'm seeing it in CSMs, in support people and sales engineers and line engineers. That's gonna be a big shift. Awesome. >>Thank >>You Scott. Thank you so much. This has been a fantastic session. We wish you the best at si sense. John, always pleasure to share the, the stage with you. Thank you to everybody who's attuning in, tell us your thoughts. We're always eager to hear what, what features have got you most excited. And as you know, we will be live here from Las Vegas at reinvent from the show floor 10 to six all week except for Friday. We'll give you Friday off with John Furrier. My name's Savannah Peterson. We're the cube, the the, the leader in high tech coverage.

(bright music) >> Hello, everyone. Welcome back to live coverage day two or day one, day two for theCUBE, day one for the event. I'm John Furrier, host of theCUBE. It's the keynote analysis segment. Adam just finished coming off stage. I'm here with Dave Vellante and Zeus Kerravala, with principal analyst at ZK Research, Zeus, it's great to see you. Dave. Guys, the analysis is clear. AWS is going NextGen. You guys had a multi-day analyst sessions in on the pre-briefs. We heard the keynote, it's out there. Adam's getting his sea legs, so to speak, a lot of metaphors around ocean. >> Yeah. >> Space. He's got these thematic exploration as he chunked his keynote out into sections. Zeus, a lot of networking in there in terms of some of the price performance, specialized instances around compute, this end-to-end data services. Dave, you were all over this data aspect going into the keynote and obviously, we had visibility into this business transformation theme. What's your analysis? Zeus, we'll start with you. What's your take on what Amazon web service is doing this year and the keynote? What's your analysis? >> Well, I think, there was a few key themes here. The first one is I do think we're seeing better integration across the AWS portfolio. Historically, AWS makes a lot of stuff and it's not always been easy to use say, Aurora and Redshift together, although most customers buy them together. So, they announce the integration of that. It's a lot tighter now. It's almost like it could be one product, but I know they like to keep the product development separately. Also, I think, we're seeing a real legitimization of AWS in a bunch of areas where people said it wasn't possible before. Last year, Nasdaq said they're running in the cloud. The Options Exchange today announced that they're going to be moving to the cloud. Contact centers running the cloud for a lot of real time voice. And so, things that we looked at before and said those will never move to the cloud have now moved to the cloud. And I think, my third takeaway is just AWS is changing and they're now getting into areas to allow customers to do things they couldn't do before. So, if you look at what they're doing in the area of AI, a lot of their AI and ML services before were prediction. And I'm not saying you need an AI, ML to do prediction, was certainly a lot more accurate, but now they're getting into generative data. So, being able to create data where data didn't exist before and that's a whole new use case for 'em. So, AWS, I think, is actually for all the might and power they've had, it's actually stepping up and becoming a much different company now. >> Yeah, I had wrote that post. I had a one-on-one day, got used of the transcript with Adam Selipsky. He went down that route of hey, we going to change NextGen. Oh, that's my word. AWS Classic my word. The AWS Classic, the old school cloud, which a bunch of Lego blocks, and you got this new NextGen cloud with the ecosystems emerging. So, clearly, it's Amazon shifting. >> Yeah. >> But Dave, your breaking analysis teed out the keynote. You went into the whole cost recovery. We heard Adam talk about macro at the beginning of his keynote. He talked about economic impact, sustainability, big macro issues. >> Yeah. >> And then, he went into data and spent most of the time on the keynote on data. Tools, integration, governance, insights. You're all over that. You had that, almost your breaking analysis almost matched the keynote, >> Yeah. >> thematically, macro, cost savings right-sizing with the cloud. And last night, I was talking to some of the marketplace people, we think that the marketplace might be the center where people start managing their cost better. This could have an impact on the ecosystem if they're not in in the marketplace. So, again, so much is going on. >> What's your analogy? >> Yeah, there's so much to unpack, a couple things. One is we get so much insight from theCUBE community plus your sit down 101 with Adam Selipsky allowed us to gather some nuggets, and really, I think, predict pretty accurately. But the number one question I get, if I could hit the escape key a bit, is what's going to be different in the Adam Selipsky era that was different from the Jassy era. Jassy was all about the primitives. The best cloud. And Selipsky's got to double down on that. So, he's got to keep that going. Plus, he's got to do that end-to-end integration and he's got to do the deeper business integration, up the stack, if you will. And so, when you're thinking about the keynote and the spirit of keynote analysis, we definitely heard, hey, more primitives, more database features, more Graviton, the network stuff, the HPC, Graviton for HPC. So, okay, check on that. We heard some better end-to-end integration between the elimination of ETL between Aurora and Redshift. Zeus and I were sitting next to each other. Okay, it's about time. >> Yeah. >> Okay, finally we got that. So, that's good. Check. And then, they called it this thing, the Amazon data zones, which was basically extending Redshift data sharing within your organization. So, you can now do that. Now, I don't know if it works across regions. >> Well, they mentioned APIs and they have the data zone. >> Yep. And so, I don't know if it works across regions, but the interesting thing there is he specifically mentioned integration with Snowflake and Tableau. And so, that gets me to your point, at the end of the day, in order for Amazon, and this is why they win, to succeed, they've got to have this ecosystem really cranking. And that's something that is just the secret sauce of the business model. >> Yeah. And it's their integration into that ecosystem. I think, it's an interesting trend that I've seen for customers where everybody wanted best of breed, everybody wanted disaggregated, and their customers are having trouble now putting those building blocks together. And then, nobody created more building blocks than AWS. And so, I think, under Adam, what we're seeing is much more concerted effort to make it easier for customers to consume those building blocks in an easy way. And the AWS execs >> Yeah. >> I talked to yesterday all committed to that. It's easy, easy, easy. And I think that's why. (Dave laughing) Yeah, there's no question they've had a lead in cloud for a long time. But if they're going to keep that, that needs to be upfront. >> Well, you're close to this, how easy is it? >> Yeah. >> But we're going to have Adrian Cockcroft (Dave laughing) on at the end of the day today, go into one analysis. Now, that- >> Well, less difficult. >> How's that? (indistinct) (group laughing) >> There you go. >> Adrian retired from Amazon. He's a CUBE analyst retiree, but he had a good point. You can buy the bag of Lego blocks if you want primitives >> Yeah. >> or you can buy the toy that's glued together. And it works, but it breaks. And you can't really manage it, and you buy a new one. So, his metaphor was, okay, if the primitives allow you to construct a durable solutions, a lot harder relative to rolling your own, not like that, but also the simplest out-of-the box capability is what people want. They want solutions. We call Adam the solutions CEO. So, I think, you're going to start to see this purpose built specialized services allow the ecosystem to build those toys, so that the customers can have an out-of-the box experience while having the option for the AWS Classic, which is if you want durability, you want to tune it, you want to manage it, that's the way to go for the hardcore. Now, can be foundational, but I just see the solutions things being very much like an out-of-the-box. Okay, throw away, >> Yeah. >> buy a new toy. >> More and more, I'm saying less customers want to be that hardcore assembler of building blocks. And obviously, the really big companies do, but that line is moving >> Yeah. >> and more companies, I think, just want to run their business and they want those prebuilt solutions. >> We had to cut out of the keynote early. But I didn't hear a lot about... The example that they often use is Amazon Connect, the call center solution. >> Yeah. >> I didn't hear a lot to that in the keynote. Maybe it's happening right now, but look, at the end of the day, suites always win. The best of breed does well, (John laughing) takes off, generate a couple billion, Snowflake will grow, they'll get to 10 billion. But you look at Oracle, suites work. (laughs) >> Yeah. >> What I found interesting about the keynote is that he had this thematic exploration themes. First one was space that was like connect the dot, the nebula, different (mumbles) lens, >> Ocean. >> ask the right questions. (Dave laughing) >> Ocean was security which bears more, >> Yeah. >> a lot more needed to manage that oxygen going deep. Are you snorkeling? Are you scuba diving? Barely interesting amount of work. >> In Antarctica. >> Antarctica was the performance around how you handle tough conditions and you've got to get that performance. >> Dave: We're laughing, but it was good. >> But the day, the Ocean Day- >> Those are very poetic. >> I tweeted you, Dave, (Dave laughing) because I sit on theCUBE in 2011. I hate hail. (Dave laughing) It's the worst term ever. It's the day the ocean's more dynamic. It's a lot more flowing. Maybe 10 years too soon, Dave. But he announces the ocean theme and then says we have a Security Lake. So, like lake, ocean, little fun on words- >> I actually think the Security Lake is pretty meaningful, because we were listening to talk, coming over here talking about it, where I think, if you look at a lot of the existing solutions, security solutions there, I describe 'em as a collection of data ponds that you can view through one map, but they're not really connected. And the amount of data that AWS holds now, arguably more than any other company, if they're not going to provide the Security Lake, who is? >> Well, but staying >> Yeah. >> on security for a second. To me, the big difference between Azure and Amazon is the ecosystem. So, CrowdStrike, Okta, Zscaler, name it, CyberArk, Rapid7, they're all part of this ecosystem. Whereas Microsoft competes with all of those guys. >> Yes. Yeah. >> So it's a lot more white space than the Amazon ecosystem. >> Well, I want to get you guys to take on, so in your reaction, because I think, my vision of what what's happening here is that I think that whole data portion's going to be data as code. And I think, the ecosystem harvests the data play. If you look at AWS' key announcements here, Security Lake, price performance, they're going to optimize for those kinds of services. Look at security, okay, Security Lake, GuardDuty, EKS, that's a Docker. Docker has security problems. They're going inside the container and looking at threat detection inside containers with Kubernetes as the runtime. That's a little nuance point, but that's pretty significant, Dave. And they're now getting into, we're talking in the weeds on the security piece, adding that to their large scale security footprint. Security is going to be one of those things where if you're not on the inside of their security play, you're probably going to be on the outside. And of course, the price performance is going to be the killer. The networking piece surprise me. Their continuing to innovate on the network. What does that mean for Cisco? So many questions. >> We had Ajay Patel on yesterday for VMware. He's an awesome middleware guy. And I was asking about serverless and architectures. And he said, "Look, basically, serverless' great for stateless, but if you want to run state, you got to have control over the run time." But the point he made was that people used to think of running containers with straight VMs versus Fargate or Knative, if you choose, or serverless. They used to think of those as different architectures. And his point was they're all coming together. And it's now you're architecting and calling, which service you need. And that's how people are thinking about future architectures, which I think, makes a lot of sense. >> If you are running managed Kubernetes, which everyone's doing, 'cause no one's really building it in-house themselves. >> No. >> They're running it as managed service, skills gaps and a variety of other reasons. This EKS protection is very interesting. They're managing inside and outside the container, which means that gives 'em visibility on both sides, under the hood and inside the application layer. So, very nuanced point, Zeus. What's your reaction to this? And obviously, the networking piece, I'd love to get your thought. >> Well, security, obviously, it's becoming a... It's less about signatures and more of an analytics. And so, things happen inside the container and outside the container. And so, their ability to look on both sides of that allows you to happen threats in time, but then also predict threats that could happen when you spin the container up. And the difficulty with the containers is they are ephemeral. It's not like a VM where it's a persistent workload that you can do analysis on. You need to know what's going on with the container almost before it spins up. >> Yeah. >> And that's a much different task. So, I do think the amount of work they're doing with the containers gives them that entry into that and I think, it's a good offering for them. On the network side, they provide a lot of basic connectivity. I do think there's a role still for the Ciscos and the Aristas and companies like that to provide a layer of enhanced network services that connects multicloud. 'Cause AWS is never going to do that. But they've certainly, they're as legitimate network vendor as there is today. >> We had NetApp on yesterday. They were talking about latency in their- >> I'll tell you this, the analyst session, Steven Armstrong said, "You are going to hear us talk about multicloud." Yes. We're not going to necessarily lead with it. >> Without a mention. >> Yeah. >> But you said it before, never say never with Amazon. >> Yeah. >> We talk about supercloud and you're like, Dave, ultimately, the cloud guys are going to get into supercloud. They have to. >> Look, they will do multicloud. I predict that they will do multicloud. I'll tell you why. Just like in networking- >> Well, customers are asking for it. >> Well, one, they have the, not by design, but by defaulter and multiple clouds are in their environment. They got to deal with that. I think, the supercloud and sky cloud visions, there will be common services. Remember networking back in the old days when Cisco broke in as a startup. There was no real shortest path, first thinking. Policy came in after you connected all the routers together. So, right now, it's going to be best of breed, low latency, high performance. But I think, there's going to be a need in the future saying, hey, I want to run my compute on the slower lower cost compute. They already got segmentation by their announcements today. So, I think, you're going to see policy-based AI coming in where developers can look at common services across clouds and saying, I want to lock in an SLA on latency and compute services. It won't be super fast compared to say, on AWS, with the next Graviton 10 or whatever comes out. >> Yeah. >> So, I think, you're going to start to see that come in. >> Actually, I'm glad you brought Graviton up too, because the work they're doing in Silicon, actually I think, is... 'Cause I think, the one thing AWS now understands is some things are best optimized in Silicon, some at software layers, some in cloud. And they're doing work on all those layers. And Graviton to me is- >> John: Is a home run. >> Yeah. >> Well- >> Dave, they've got more instances, it's going to be... They already have Gravitons that's slower than the other versions. So, what they going to do, sunset them? >> They don't deprecate anything ever. So, (John laughing) Amazon paid $350 million. People believe that it's a number for Annapurna, which is like one of the best acquisitions in history. (group laughing) And it's given them, it's put them on an arm curve for Silicon that is blowing away Intel. Intel's finally going to get Sapphire Rapids out in January. Meanwhile, Amazon just keeps spinning out new Gravitons and Trainiums. >> Yeah. >> And so, they are on a price performance curve. And like you say, no developer ever wants to run on slower hardware, ever. >> Today, if there's a common need for multicloud, they might say, hey, I got the trade off latency and performance on common services if that's what gets me there. >> Sure. >> If there's maybe a business case to do that. >> Well, that's what they're- >> Which by the way, I want to.... Selipsky had strong quote I thought was, "If you're looking to tighten your belt, the cloud is the place >> Yeah. >> to do it." I thought >> I tweeted that. >> that was very strong. >> Yeah. >> Yeah. >> And I think, he's right. And then, the other point I want to make on that is, I think, I don't have any data on this, but I believe believe just based on some of the discussions I've had that most of Amazon's revenue is on demand. Paid by the drink. Those on demand customers are at risk, 'cause they can go somewhere else. So, they're trying to get you into optimized pricing, whether it's reserved instances or one year or three-year subscriptions. And so, they're working really hard at doing that. >> My prediction on that is that's a great point you brought up. My prediction is that the cost belt tightening is going to come in the marketplace, is going to be a major factor as companies want to get their belts tighten. How they going to do that, Dave? They're going to go in the marketplace saying, hey, I already overpaid a three-year commitment. Can I get some cohesively in there? Can I get some of this or that and the other thing? >> Yep. >> You're going to start to see the vendors and the ecosystem. If they're not in the marketplace, that's where I think, the customers will go. There are other choices to either cut their supplier base or renegotiate. I think, it's going to happen in the marketplace. Let's watch. I think, we're going to watch that grow. >> I actually think the optimization services that AWS has to help customers lower spend is a secret sauce for them that they... Customers tell me all the time, AWS comes in, they'll bring their costs down and they wind up spending more with them. >> Dave: Yeah. >> And the other cloud providers don't do that. And that has been almost a silver bullet for them to get customers to stay with them. >> Okay. And this is always the way. You drop the price of storage, you drop the price of memory, you drop the price of compute, people buy more. And in the question, long term is okay. And does AWS get commoditized? Is that where they're going? Or do they continue to thrive up the stack? John, you're always asking people about the bumper sticker. >> Hold on. (John drowns out Dave) Before we get the bumper sticker, I want to get into what we missed, what they missed on the keynote. >> Yeah, there are some blind spots. >> I think- >> That's good call. >> Let's go around the horn and think what did they miss? I'll start, I think, they missed the developer productivity angle. Supply chain software was not talked about at all. We see that at all the other conferences. I thought that could have been weaved in. >> Dave: You mean security in the supply chain? >> Just overall developer productivity has been one of the most constant themes I've seen at events. Who are building the apps? Who are the builders? What are they actually doing? Maybe Werner will bring that up on his last day, but I didn't hear Adam talk about it all, developer productivity. What's your take in this? >> Yeah, I think, on the security side, they announced security data lake. I think, the other cloud providers do a better job of providing insights on how they do security. With AWS, it's almost a black hole. And I know there's a careful line they walk between what they do, what their partners do. But I do think they could be a little clearer on how they operate, much like Azure and GCP. They announce a lot of stuff on how their operations works and things like that. >> I think, platform across cloud is definitely a blind spot for these guys. >> Yeah. >> I think, look at- >> But none of the cloud providers have embraced that, right? >> It's true. >> Yeah. >> Maybe Google a little bit >> Yeah. >> and Microsoft a little bit. Certainly, AWS hasn't at this point in time, but I think, they perceive the likes of Mongo and Snowflake and Databricks, and others as ISVs and they're not. They're platform players that are building across clouds. They're leveraging, they're building superclouds. So, I think that's an opportunity for the ecosystem. And very curious to see how Amazon plays there down the stream. So, John, what do you think is the bumper sticker? We're only in day one and a half here. What do you think so far the bumper sticker is for re:Invent 2022? >> Well, to me, the day one is about infrastructure performance with the whole what's in the data center? What's at the chip level? Today was about data, specialized services, and security. I think that was the key theme here. And then, that's going to sequence into how they're going to reorganize their ecosystem. They have a new leader, Ruba Borno, who's going to be leading the charge. They've integrated all their bespoke fragmented partner network pieces into one leadership. That's going to be really important to hear that. And then, finally, Werner for developers and event-based services, micro services. What that world's going on, because that's where the developers are. And ultimately, they build the app. So, you got infrastructure, data, specialized services, and security. Machine learning with Swami is going to be huge. And again, how do developers code it all up is going to be key. And is it the bag of Legos or the glued toy? (Dave chuckles) So, what do you want? Out-of-the-box or you want to build your own? >> And that's the bottom line is connecting those dots. All they got to be is good enough. I think, Zeus, to your point, >> Yep. >> if they're just good enough, less complicated, the will keep people on the base. >> Yeah. I think, the bumper stickers, the more you buy, the more you're saving. (John laughing) Because from an operational perspective, they are trying to bring down the complexity level. And with their optimization services and the way their credit model works, I do think they're trending down that path. >> And my bumper sticker's ecosystem, ecosystem, ecosystem. This company has 100,000 partners and that is a business model secret weapon. >> All right, there it is. The keynote announced. More analysis coming up. We're going to have the leader of (indistinct) coming up next, here on to break down their perspective, you got theCUBE's analyst perspective here. Thanks for watching. Day two, more live coverage for the next two more days, so stay with us. I'm John Furrier with Dave Vellante and Zeus Kerravala here on theCUBE. Be right back. (bright music)

>>Hey everyone. Welcome to Las Vegas. The Cube is here, live at the Venetian Expo Center for AWS Reinvent 2022. Amazing attendance. This is day one of our coverage. Lisa Martin here with Day Ante. David is great to see so many people back. We're gonna be talk, we've been having great conversations already. We have a wall to wall coverage for the next three and a half days. When we talk to companies, customers, every company has to be a data company. And one of the things I think we learned in the pandemic is that access to real time data and real time analytics, no longer a nice to have that is a differentiator and a competitive all >>About data. I mean, you know, I love the topic and it's, it's got so many dimensions and such texture, can't get enough of data. >>I know we have a great guest joining us. One of our alumni is back, Evan Kaplan, the CEO of Influx Data. Evan, thank you so much for joining us. Welcome back to the Cube. >>Thanks for having me. It's great to be here. So here >>We are, day one. I was telling you before we went live, we're nice and fresh hosts. Talk to us about what's new at Influxed since the last time we saw you at Reinvent. >>That's great. So first of all, we should acknowledge what's going on here. This is pretty exciting. Yeah, that does really feel like, I know there was a show last year, but this feels like the first post Covid shows a lot of energy, a lot of attention despite a difficult economy. In terms of, you know, you guys were commenting in the lead into Big data. I think, you know, if we were to talk about Big Data five, six years ago, what would we be talking about? We'd been talking about Hadoop, we were talking about Cloudera, we were talking about Hortonworks, we were talking about Big Data Lakes, data stores. I think what's happened is, is this this interesting dynamic of, let's call it if you will, the, the secularization of data in which it breaks into different fields, different, almost a taxonomy. You've got this set of search data, you've got this observability data, you've got graph data, you've got document data and what you're seeing in the market and now you have time series data. >>And what you're seeing in the market is this incredible capability by developers as well and mostly open source dynamic driving this, this incredible capability of developers to assemble data platforms that aren't unicellular, that aren't just built on Hado or Oracle or Postgres or MySQL, but in fact represent different data types. So for us, what we care about his time series, we care about anything that happens in time, where time can be the primary measurement, which if you think about it, is a huge proportion of real data. Cuz when you think about what drives ai, you think about what happened, what happened, what happened, what happened, what's going to happen. That's the functional thing. But what happened is always defined by a period, a measurement, a time. And so what's new for us is we've developed this new open source engine called IOx. And so it's basically a refresh of the whole database, a kilo database that uses Apache Arrow, par K and data fusion and turns it into a super powerful real time analytics platform. It was already pretty real time before, but it's increasingly now and it adds SQL capability and infinite cardinality. And so it handles bigger data sets, but importantly, not just bigger but faster, faster data. So that's primarily what we're talking about to show. >>So how does that affect where you can play in the marketplace? Is it, I mean, how does it affect your total available market? Your great question. Your, your customer opportunities. >>I think it's, it's really an interesting market in that you've got all of these different approaches to database. Whether you take data warehouses from Snowflake or, or arguably data bricks also. And you take these individual database companies like Mongo Influx, Neo Forge, elastic, and people like that. I think the commonality you see across the volume is, is many of 'em, if not all of them, are based on some sort of open source dynamic. So I think that is an in an untractable trend that will continue for on. But in terms of the broader, the broader database market, our total expand, total available tam, lots of these things are coming together in interesting ways. And so the, the, the wave that will ride that we wanna ride, because it's all big data and it's all increasingly fast data and it's all machine learning and AI is really around that measurement issue. That instrumentation the idea that if you're gonna build any sophisticated system, it starts with instrumentation and the journey is defined by instrumentation. So we view ourselves as that instrumentation tooling for understanding complex systems. And how, >>I have to follow quick follow up. Why did you say arguably data bricks? I mean open source ethos? >>Well, I was saying arguably data bricks cuz Spark, I mean it's a great company and it's based on Spark, but there's quite a gap between Spark and what Data Bricks is today. And in some ways data bricks from the outside looking in looks a lot like Snowflake to me looks a lot like a really sophisticated data warehouse with a lot of post-processing capabilities >>And, and with an open source less >>Than a >>Core database. Yeah. Right, right, right. Yeah, I totally agree. Okay, thank you for that >>Part that that was not arguably like they're, they're not a good company or >>No, no. They got great momentum and I'm just curious. Absolutely. You know, so, >>So talk a little bit about IOx and, and what it is enabling you guys to achieve from a competitive advantage perspective. The key differentiators give us that scoop. >>So if you think about, so our old storage engine was called tsm, also open sourced, right? And IOx is open sourced and the old storage engine was really built around this time series measurements, particularly metrics, lots of metrics and handling those at scale and making it super easy for developers to use. But, but our old data engine only supported either a custom graphical UI that you'd build yourself on top of it or a dashboarding tool like Grafana or Chronograph or things like that. With IOCs. Two or three interventions were important. One is we now support, we'll support things like Tableau, Microsoft, bi, and so you're taking that same data that was available for instrumentation and now you're using it for business intelligence also. So that became super important and it kind of answers your question about the expanded market expands the market. The second thing is, when you're dealing with time series data, you're dealing with this concept of cardinality, which is, and I don't know if you're familiar with it, but the idea that that it's a multiplication of measurements in a table. And so the more measurements you want over the more series you have, you have this really expanding exponential set that can choke a database off. And the way we've designed IIS to handle what we call infinite cardinality, where you don't even have to think about that design point of view. And then lastly, it's just query performance is dramatically better. And so it's pretty exciting. >>So the unlimited cardinality, basically you could identify relationships between data and different databases. Is that right? Between >>The same database but different measurements, different tables, yeah. Yeah. Right. Yeah, yeah. So you can handle, so you could say, I wanna look at the way, the way the noise levels are performed in this room according to 400 different locations on 25 different days, over seven months of the year. And that each one is a measurement. Each one adds to cardinality. And you can say, I wanna search on Tuesdays in December, what the noise level is at 2:21 PM and you get a very quick response. That kind of instrumentation is critical to smarter systems. How are >>You able to process that data at at, in a performance level that doesn't bring the database to its knees? What's the secret sauce behind that? >>It's AUM database. It's built on Parque and Apache Arrow. But it's, but to say it's nice to say without a much longer conversation, it's an architecture that's really built for pulling that kind of data. If you know the data is time series and you're looking for a time measurement, you already have the ability to optimize pretty dramatically. >>So it's, it's that purpose built aspect of it. It's the >>Purpose built aspect. You couldn't take Postgres and do the same >>Thing. Right? Because a lot of vendors say, oh yeah, we have time series now. Yeah. Right. So yeah. Yeah. Right. >>And they >>Do. Yeah. But >>It's not, it's not, the founding of the company came because Paul Dicks was working on Wall Street building time series databases on H base, on MyQ, on other platforms and realize every time we do it, we have to rewrite the code. We build a bunch of application logic to handle all these. We're talking about, we have customers that are adding hundreds of millions to billions of points a second. So you're talking about an ingest level. You know, you think about all those data points, you're talking about ingest level that just doesn't, you know, it just databases aren't designed for that. Right? And so it's not just us, our competitors also build good time series databases. And so the category is really emergent. Yeah, >>Sure. Talk about a favorite customer story they think really articulates the value of what Influx is doing, especially with IOx. >>Yeah, sure. And I love this, I love this story because you know, Tesla may not be in favor because of the latest Elon Musker aids, but, but, but so we've had about a four year relationship with Tesla where they built their power wall technology around recording that, seeing your device, seeing the stuff, seeing the charging on your car. It's all captured in influx databases that are reporting from power walls and mega power packs all over the world. And they report to a central place at, at, at Tesla's headquarters and it reports out to your phone and so you can see it. And what's really cool about this to me is I've got two Tesla cars and I've got a Tesla solar roof tiles. So I watch this date all the time. So it's a great customer story. And actually if you go on our website, you can see I did an hour interview with the engineer that designed the system cuz the system is super impressive and I just think it's really cool. Plus it's, you know, it's all the good green stuff that we really appreciate supporting sustainability, right? Yeah. >>Right, right. Talk about from a, what's in it for me as a customer, what you guys have done, the change to IOCs, what, what are some of the key features of it and the key values in it for customers like Tesla, like other industry customers as well? >>Well, so it's relatively new. It just arrived in our cloud product. So Tesla's not using it today. We have a first set of customers starting to use it. We, the, it's in open source. So it's a very popular project in the open source world. But the key issues are, are really the stuff that we've kind of covered here, which is that a broad SQL environment. So accessing all those SQL developers, the same people who code against Snowflake's data warehouse or data bricks or Postgres, can now can code that data against influx, open up the BI market. It's the cardinality, it's the performance. It's really an architecture. It's the next gen. We've been doing this for six years, it's the next generation of everything. We've seen how you make time series be super performing. And that's only relevant because more and more things are becoming real time as we develop smarter and smarter systems. The journey is pretty clear. You instrument the system, you, you let it run, you watch for anomalies, you correct those anomalies, you re instrument the system. You do that 4 billion times, you have a self-driving car, you do that 55 times, you have a better podcast that is, that is handling its audio better, right? So everything is on that journey of getting smarter and smarter. So >>You guys, you guys the big committers to IOCs, right? Yes. And how, talk about how you support the, develop the surrounding developer community, how you get that flywheel effect going >>First. I mean it's actually actually a really kind of, let's call it, it's more art than science. Yeah. First of all, you you, you come up with an architecture that really resonates for developers. And Paul Ds our founder, really is a developer's developer. And so he started talking about this in the community about an architecture that uses Apache Arrow Parque, which is, you know, the standard now becoming for file formats that uses Apache Arrow for directing queries and things like that and uses data fusion and said what this thing needs is a Columbia database that sits behind all of this stuff and integrates it. And he started talking about it two years ago and then he started publishing in IOCs that commits in the, in GitHub commits. And slowly, but over time in Hacker News and other, and other people go, oh yeah, this is fundamentally right. >>It addresses the problems that people have with things like click cows or plain databases or Coast and they go, okay, this is the right architecture at the right time. Not different than original influx, not different than what Elastic hit on, not different than what Confluent with Kafka hit on and their time is you build an audience of people who are committed to understanding this kind of stuff and they become committers and they become the core. Yeah. And you build out from it. And so super. And so we chose to have an MIT open source license. Yeah. It's not some secondary license competitors can use it and, and competitors can use it against us. Yeah. >>One of the things I know that Influx data talks about is the time to awesome, which I love that, but what does that mean? What is the time to Awesome. Yeah. For developer, >>It comes from that original story where, where Paul would have to write six months of application logic and stuff to build a time series based applications. And so Paul's notion was, and this was based on the original Mongo, which was very successful because it was very easy to use relative to most databases. So Paul developed this commitment, this idea that I quickly joined on, which was, hey, it should be relatively quickly for a developer to build something of import to solve a problem, it should be able to happen very quickly. So it's got a schemaless background so you don't have to know the schema beforehand. It does some things that make it really easy to feel powerful as a developer quickly. And if you think about that journey, if you feel powerful with a tool quickly, then you'll go deeper and deeper and deeper and pretty soon you're taking that tool with you wherever you go, it becomes the tool of choice as you go to that next job or you go to that next application. And so that's a fundamental way we think about it. To be honest with you, we haven't always delivered perfectly on that. It's generally in our dna. So we do pretty well, but I always feel like we can do better. >>So if you were to put a bumper sticker on one of your Teslas about influx data, what would it >>Say? By the way, I'm not rich. It just happened to be that we have two Teslas and we have for a while, we just committed to that. The, the, so ask the question again. Sorry. >>Bumper sticker on influx data. What would it say? How, how would I >>Understand it be time to Awesome. It would be that that phrase his time to Awesome. Right. >>Love that. >>Yeah, I'd love it. >>Excellent time to. Awesome. Evan, thank you so much for joining David, the >>Program. It's really fun. Great thing >>On Evan. Great to, you're on. Haven't Well, great to have you back talking about what you guys are doing and helping organizations like Tesla and others really transform their businesses, which is all about business transformation these days. We appreciate your insights. >>That's great. Thank >>You for our guest and Dave Ante. I'm Lisa Martin, you're watching The Cube, the leader in emerging and enterprise tech coverage. We'll be right back with our next guest.

from the cube studios in Palo Alto in Boston bringing you data-driven insights from the cube and ETR this is breaking analysis with Dave vellante the ascendancy of AWS under the leadership of Andy jassy was marked by a tsunami of data and corresponding cloud services to leverage that data now those Services they mainly came in the form of Primitives I.E basic building blocks that were used by developers to create more sophisticated capabilities AWS in the 2020s being led by CEO Adam solipski will be marked by four high-level Trends in our opinion one A Rush of data that will dwarf anything we've previously seen two a doubling or even tripling down on the basic elements of cloud compute storage database security Etc three a greater emphasis on end-to-end integration of AWS services to simplify and accelerate customer adoption of cloud and four significantly deeper business integration of cloud Beyond it as an underlying element of organizational operations hello and welcome to this week's wikibon Cube insights powered by ETR in this breaking analysis we extract and analyze nuggets from John furrier's annual sit-down with the CEO of AWS we'll share data from ETR and other sources to set the context for the market and competition in cloud and we'll give you our glimpse of what to expect at re invent in 2022. now before we get into the core of our analysis Alibaba has announced earnings they always announced after the big three you know a month later and we've updated our Q3 slash November hyperscale Computing forecast for the year as seen here and we're going to spend a lot of time on this as most of you have seen the bulk of it already but suffice to say alibaba's cloud business is hitting that same macro Trend that we're seeing across the board but a more substantial slowdown than we expected and more substantial than its peers they're facing China headwinds they've been restructuring its Cloud business and it's led to significantly slower growth uh in in the you know low double digits as opposed to where we had it at 15 this puts our year-end estimates for 2022 Revenue at 161 billion still a healthy 34 growth with AWS surpassing 80 billion in 2022 Revenue now on a related note one of the big themes in Cloud that we've been reporting on is how customers are optimizing their Cloud spend it's a technique that they use and when the economy looks a little shaky and here's a graphic that we pulled from aws's website which shows the various pricing plans at a high level as you know they're much more granular than that and more sophisticated but Simplicity we'll just keep it here basically there are four levels first one here is on demand I.E pay by the drink now we're going to jump down to what we've labeled as number two spot instances that's like the right place at the right time I can use that extra capacity in the moment the third is reserved instances or RIS where I pay up front to get a discount and the fourth is sort of optimized savings plans where customers commit to a one or three year term and for a better price now you'll notice we labeled the choices in a different order than AWS presented them on its website and that's because we believe that the order that we chose is the natural progression for customers this started on demand they maybe experiment with spot instances they move to reserve instances when the cloud bill becomes too onerous and if you're large enough you lock in for one or three years okay the interesting thing is the order in which AWS presents them we believe that on-demand accounts for the majority of AWS customer spending now if you think about it those on-demand customers they're also at risk customers yeah sure there's some switching costs like egress and learning curve but many customers they have multiple clouds and they've got experience and so they're kind of already up to a learning curve and if you're not married to AWS with a longer term commitment there's less friction to switch now AWS here presents the most attractive plan from a financial perspective second after on demand and it's also the plan that makes the greatest commitment from a lock-in standpoint now In fairness to AWS it's also true that there is a trend towards subscription-based pricing and we have some data on that this chart is from an ETR drill down survey the end is 300. pay attention to the bars on the right the left side is sort of busy but the pink is subscription and you can see the trend upward the light blue is consumption based or on demand based pricing and you can see there's a steady Trend toward subscription now we'll dig into this in a later episode of Breaking analysis but we'll share with you a little some tidbits with the data that ETR provides you can select which segment is and pass or you can go up the stack Etc but so when you choose is and paths 44 of customers either prefer or are required to use on-demand pricing whereas around 40 percent of customers say they either prefer or are required to use subscription pricing again that's for is so now the further mu you move up the stack the more prominent subscription pricing becomes often with sixty percent or more for the software-based offerings that require or prefer subscription and interestingly cyber security tracks along with software at around 60 percent that that prefer subscription it's likely because as with software you're not shutting down your cyber protection on demand all right let's get into the expectations for reinvent and we're going to start with an observation in data in this 2018 book seeing digital author David michella made the point that whereas most companies apply data on the periphery of their business kind of as an add-on function successful data companies like Google and Amazon and Facebook have placed data at the core of their operations they've operationalized data and they apply machine intelligence to that foundational element why is this the fact is it's not easy to do what the internet Giants have done very very sophisticated engineering and and and cultural discipline and this brings us to reinvent 2022 in the future of cloud machine learning and AI will increasingly be infused into applications we believe the data stack and the application stack are coming together as organizations build data apps and data products data expertise is moving from the domain of Highly specialized individuals to Everyday business people and we are just at the cusp of this trend this will in our view be a massive theme of not only re invent 22 but of cloud in the 2020s the vision of data mesh We Believe jamachtagani's principles will be realized in this decade now what we'd like to do now is share with you a glimpse of the thinking of Adam solipsky from his sit down with John Furrier each year John has a one-on-one conversation with the CEO of AWS AWS he's been doing this for years and the outcome is a better understanding of the directional thinking of the leader of the number one Cloud platform so we're now going to share some direct quotes I'm going to run through them with some commentary and then bring in some ETR data to analyze the market implications here we go this is from solipsky quote I.T in general and data are moving from departments into becoming intrinsic parts of how businesses function okay we're talking here about deeper business integration let's go on to the next one quote in time we'll stop talking about people who have the word analyst we inserted data he meant data data analyst in their title rather will have hundreds of millions of people who analyze data as part of their day-to-day job most of whom will not have the word analyst anywhere in their title we're talking about graphic designers and pizza shop owners and product managers and data scientists as well he threw that in I'm going to come back to that very interesting so he's talking about here about democratizing data operationalizing data next quote customers need to be able to take an end-to-end integrated view of their entire data Journey from ingestion to storage to harmonizing the data to being able to query it doing business Intelligence and human-based Analysis and being able to collaborate and share data and we've been putting together we being Amazon together a broad Suite of tools from database to analytics to business intelligence to help customers with that and this last statement it's true Amazon has a lot of tools and you know they're beginning to become more and more integrated but again under jassy there was not a lot of emphasis on that end-to-end integrated view we believe it's clear from these statements that solipsky's customer interactions are leading him to underscore that the time has come for this capability okay continuing quote if you have data in one place you shouldn't have to move it every time you want to analyze that data couldn't agree more it would be much better if you could leave that data in place avoid all the ETL which has become a nasty three-letter word more and more we're building capabilities where you can query that data in place end quote okay this we see a lot in the marketplace Oracle with mySQL Heatwave the entire Trend toward converge database snowflake [ __ ] extending their platforms into transaction and analytics respectively and so forth a lot of the partners are are doing things as well in that vein let's go into the next quote the other phenomenon is infusing machine learning into all those capabilities yes the comments from the michelleographic come into play here infusing Ai and machine intelligence everywhere next one quote it's not a data Cloud it's not a separate Cloud it's a series of broad but integrated capabilities to help you manage the end-to-end life cycle of your data there you go we AWS are the cloud we're going to come back to that in a moment as well next set of comments around data very interesting here quote data governance is a huge issue really what customers need is to find the right balance of their organization between access to data and control and if you provide too much access then you're nervous that your data is going to end up in places that it shouldn't shouldn't be viewed by people who shouldn't be viewing it and you feel like you lack security around that data and by the way what happens then is people overreact and they lock it down so that almost nobody can see it it's those handcuffs there's data and asset are reliability we've talked about that for years okay very well put by solipsky but this is a gap in our in our view within AWS today and we're we're hoping that they close it at reinvent it's not easy to share data in a safe way within AWS today outside of your organization so we're going to look for that at re invent 2022. now all this leads to the following statement by solipsky quote data clean room is a really interesting area and I think there's a lot of different Industries in which clean rooms are applicable I think that clean rooms are an interesting way of enabling multiple parties to share and collaborate on the data while completely respecting each party's rights and their privacy mandate okay again this is a gap currently within AWS today in our view and we know snowflake is well down this path and databricks with Delta sharing is also on this curve so AWS has to address this and demonstrate this end-to-end data integration and the ability to safely share data in our view now let's bring in some ETR spending data to put some context around these comments with reference points in the form of AWS itself and its competitors and partners here's a chart from ETR that shows Net score or spending momentum on the x-axis an overlap or pervasiveness in the survey um sorry let me go back up the net scores on the y-axis and overlap or pervasiveness in the survey is on the x-axis so spending momentum by pervasiveness okay or should have share within the data set the table that's inserted there with the Reds and the greens that informs us to how the dots are positioned so it's Net score and then the shared ends are how the plots are determined now we've filtered the data on the three big data segments analytics database and machine learning slash Ai and we've only selected one company with fewer than 100 ends in the survey and that's databricks you'll see why in a moment the red dotted line indicates highly elevated customer spend at 40 percent now as usual snowflake outperforms all players on the y-axis with a Net score of 63 percent off the charts all three big U.S cloud players are above that line with Microsoft and AWS dominating the x-axis so very impressive that they have such spending momentum and they're so large and you see a number of other emerging data players like rafana and datadog mongodbs there in the mix and then more established players data players like Splunk and Tableau now you got Cisco who's gonna you know it's a it's a it's a adjacent to their core networking business but they're definitely into you know the analytics business then the really established players in data like Informatica IBM and Oracle all with strong presence but you'll notice in the red from the momentum standpoint now what you're going to see in a moment is we put red highlights around databricks Snowflake and AWS why let's bring that back up and we'll explain so there's no way let's bring that back up Alex if you would there's no way AWS is going to hit the brakes on innovating at the base service level what we call Primitives earlier solipsky told Furrier as much in their sit down that AWS will serve the technical user and data science Community the traditional domain of data bricks and at the same time address the end-to-end integration data sharing and business line requirements that snowflake is positioned to serve now people often ask Snowflake and databricks how will you compete with the likes of AWS and we know the answer focus on data exclusively they have their multi-cloud plays perhaps the more interesting question is how will AWS compete with the likes of Specialists like Snowflake and data bricks and the answer is depicted here in this chart AWS is going to serve both the technical and developer communities and the data science audience and through end-to-end Integrations and future services that simplify the data Journey they're going to serve the business lines as well but the Nuance is in all the other dots in the hundreds or hundreds of thousands that are not shown here and that's the AWS ecosystem you can see AWS has earned the status of the number one Cloud platform that everyone wants to partner with as they say it has over a hundred thousand partners and that ecosystem combined with these capabilities that we're discussing well perhaps behind in areas like data sharing and integrated governance can wildly succeed by offering the capabilities and leveraging its ecosystem now for their part the snowflakes of the world have to stay focused on the mission build the best products possible and develop their own ecosystems to compete and attract the Mind share of both developers and business users and that's why it's so interesting to hear solipski basically say it's not a separate Cloud it's a set of integrated Services well snowflake is in our view building a super cloud on top of AWS Azure and Google when great products meet great sales and marketing good things can happen so this will be really fun to watch what AWS announces in this area at re invent all right one other topic that solipsky talked about was the correlation between serverless and container adoption and you know I don't know if this gets into there certainly their hybrid place maybe it starts to get into their multi-cloud we'll see but we have some data on this so again we're talking about the correlation between serverless and container adoption but before we get into that let's go back to 2017 and listen to what Andy jassy said on the cube about serverless play the clip very very earliest days of AWS Jeff used to say a lot if I were starting Amazon today I'd have built it on top of AWS we didn't have all the capability and all the functionality at that very moment but he knew what was coming and he saw what people were still able to accomplish even with where the services were at that point I think the same thing is true here with Lambda which is I think if Amazon were starting today it's a given they would build it on the cloud and I think we with a lot of the applications that comprise Amazon's consumer business we would build those on on our serverless capabilities now we still have plenty of capabilities and features and functionality we need to add to to Lambda and our various serverless services so that may not be true from the get-go right now but I think if you look at the hundreds of thousands of customers who are building on top of Lambda and lots of real applications you know finra has built a good chunk of their market watch application on top of Lambda and Thompson Reuters has built you know one of their key analytics apps like people are building real serious things on top of Lambda and the pace of iteration you'll see there will increase as well and I really believe that to be true over the next year or two so years ago when Jesse gave a road map that serverless was going to be a key developer platform going forward and so lipsky referenced the correlation between serverless and containers in the Furrier sit down so we wanted to test that within the ETR data set now here's a screen grab of The View across 1300 respondents from the October ETR survey and what we've done here is we've isolated on the cloud computing segment okay so you can see right there cloud computing segment now we've taken the functions from Google AWS Lambda and Microsoft Azure functions all the serverless offerings and we've got Net score on the vertical axis we've got presence in the data set oh by the way 440 by the way is highly elevated remember that and then we've got on the horizontal axis we have the presence in the data center overlap okay that's relative to each other so remember 40 all these guys are above that 40 mark okay so you see that now what we're going to do this is just for serverless and what we're going to do is we're going to turn on containers to see the correlation and see what happens so watch what happens when we click on container boom everything moves to the right you can see all three move to the right Google drops a little bit but all the others now the the filtered end drops as well so you don't have as many people that are aggressively leaning into both but all three move to the right so watch again containers off and then containers on containers off containers on so you can see a really major correlation between containers and serverless okay so to get a better understanding of what that means I call my friend and former Cube co-host Stu miniman what he said was people generally used to think of VMS containers and serverless as distinctly different architectures but the lines are beginning to blur serverless makes things simpler for developers who don't want to worry about underlying infrastructure as solipsky and the data from ETR indicate serverless and containers are coming together but as Stu and I discussed there's a spectrum where on the left you have kind of native Cloud VMS in the middle you got AWS fargate and in the rightmost anchor is Lambda AWS Lambda now traditionally in the cloud if you wanted to use containers developers would have to build a container image they have to select and deploy the ec2 images that they or instances that they wanted to use they have to allocate a certain amount of memory and then fence off the apps in a virtual machine and then run the ec2 instances against the apps and then pay for all those ec2 resources now with AWS fargate you can run containerized apps with less infrastructure management but you still have some you know things that you can you can you can do with the with the infrastructure so with fargate what you do is you'd build the container images then you'd allocate your memory and compute resources then run the app and pay for the resources only when they're used so fargate lets you control the runtime environment while at the same time simplifying the infrastructure management you gotta you don't have to worry about isolating the app and other stuff like choosing server types and patching AWS does all that for you then there's Lambda with Lambda you don't have to worry about any of the underlying server infrastructure you're just running code AS functions so the developer spends their time worrying about the applications and the functions that you're calling the point is there's a movement and we saw in the data towards simplifying the development environment and allowing the cloud vendor AWS in this case to do more of the underlying management now some folks will still want to turn knobs and dials but increasingly we're going to see more higher level service adoption now re invent is always a fire hose of content so let's do a rapid rundown of what to expect we talked about operate optimizing data and the organization we talked about Cloud optimization there'll be a lot of talk on the show floor about best practices and customer sharing data solipsky is leading AWS into the next phase of growth and that means moving beyond I.T transformation into deeper business integration and organizational transformation not just digital transformation organizational transformation so he's leading a multi-vector strategy serving the traditional peeps who want fine-grained access to core services so we'll see continued Innovation compute storage AI Etc and simplification through integration and horizontal apps further up to stack Amazon connect is an example that's often cited now as we've reported many times databricks is moving from its stronghold realm of data science into business intelligence and analytics where snowflake is coming from its data analytics stronghold and moving into the world of data science AWS is going down a path of snowflake meet data bricks with an underlying cloud is and pass layer that puts these three companies on a very interesting trajectory and you can expect AWS to go right after the data sharing opportunity and in doing so it will have to address data governance they go hand in hand okay price performance that is a topic that will never go away and it's something that we haven't mentioned today silicon it's a it's an area we've covered extensively on breaking analysis from Nitro to graviton to the AWS acquisition of Annapurna its secret weapon new special specialized capabilities like inferential and trainium we'd expect something more at re invent maybe new graviton instances David floyer our colleague said he's expecting at some point a complete system on a chip SOC from AWS and maybe an arm-based server to eventually include high-speed cxl connections to devices and memories all to address next-gen applications data intensive applications with low power requirements and lower cost overall now of course every year Swami gives his usual update on machine learning and AI building on Amazon's years of sagemaker innovation perhaps a focus on conversational AI or a better support for vision and maybe better integration across Amazon's portfolio of you know large language models uh neural networks generative AI really infusing AI everywhere of course security always high on the list that reinvent and and Amazon even has reinforce a conference dedicated to it uh to security now here we'd like to see more on supply chain security and perhaps how AWS can help there as well as tooling to make the cio's life easier but the key so far is AWS is much more partner friendly in the security space than say for instance Microsoft traditionally so firms like OCTA and crowdstrike in Palo Alto have plenty of room to play in the AWS ecosystem we'd expect of course to hear something about ESG it's an important topic and hopefully how not only AWS is helping the environment that's important but also how they help customers save money and drive inclusion and diversity again very important topics and finally come back to it reinvent is an ecosystem event it's the Super Bowl of tech events and the ecosystem will be out in full force every tech company on the planet will have a presence and the cube will be featuring many of the partners from the serial floor as well as AWS execs and of course our own independent analysis so you'll definitely want to tune into thecube.net and check out our re invent coverage we start Monday evening and then we go wall to wall through Thursday hopefully my voice will come back we have three sets at the show and our entire team will be there so please reach out or stop by and say hello all right we're going to leave it there for today many thanks to Stu miniman and David floyer for the input to today's episode of course John Furrier for extracting the signal from the noise and a sit down with Adam solipski thanks to Alex Meyerson who was on production and manages the podcast Ken schiffman as well Kristen Martin and Cheryl Knight helped get the word out on social and of course in our newsletters Rob hoef is our editor-in-chief over at siliconangle does some great editing thank thanks to all of you remember all these episodes are available as podcasts wherever you listen you can pop in the headphones go for a walk just search breaking analysis podcast I published each week on wikibon.com at siliconangle.com or you can email me at david.valante at siliconangle.com or DM me at di vallante or please comment on our LinkedIn posts and do check out etr.ai for the best survey data in the Enterprise Tech business this is Dave vellante for the cube insights powered by ETR thanks for watching we'll see it reinvent or we'll see you next time on breaking analysis [Music]

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> This past week we saw two of the Big 3 cloud providers present the latest update on their respective cloud visions, their business progress, their announcements and innovations. The content at these events had many overlapping themes, including modern cloud infrastructure at global scale, applying advanced machine intelligence, AKA AI, end-to-end data platforms, collaboration software. They talked a lot about the future of work automation. And they gave us a little taste, each company of the Metaverse Web 3.0 and much more. Despite these striking similarities, the differences between these two cloud platforms and that of AWS remains significant. With Microsoft leveraging its massive application software footprint to dominate virtually all markets and Google doing everything in its power to keep up with the frenetic pace of today's cloud innovation, which was set into motion a decade and a half ago by AWS. Hello and welcome to this week's Wikibon CUBE Insights, powered by ETR. In this Breaking Analysis, we unpack the immense amount of content presented by the CEOs of Microsoft and Google Cloud at Microsoft Ignite and Google Cloud Next. We'll also quantify with ETR survey data the relative position of these two cloud giants in four key sectors: cloud IaaS, BI analytics, data platforms and collaboration software. Now one thing was clear this past week, hybrid events are the thing. Google Cloud Next took place live over a 24-hour period in six cities around the world, with the main gathering in New York City. Microsoft Ignite, which normally is attended by 30,000 people, had a smaller event in Seattle, in person with a virtual audience around the world. AWS re:Invent, of course, is much different. Yes, there's a virtual component at re:Invent, but it's all about a big live audience gathering the week after Thanksgiving, in the first week of December in Las Vegas. Regardless, Satya Nadella keynote address was prerecorded. It was highly produced and substantive. It was visionary, energetic with a strong message that Azure was a platform to allow customers to build their digital businesses. Doing more with less, which was a key theme of his. Nadella covered a lot of ground, starting with infrastructure from the compute, highlighting a collaboration with Arm-based, Ampere processors. New block storage, 60 regions, 175,000 miles of fiber cables around the world. He presented a meaningful multi-cloud message with Azure Arc to support on-prem and edge workloads, as well as of course the public cloud. And talked about confidential computing at the infrastructure level, a theme we hear from all cloud vendors. He then went deeper into the end-to-end data platform that Microsoft is building from the core data stores to analytics, to governance and the myriad tooling Microsoft offers. AI was next with a big focus on automation, AI, training models. He showed demos of machines coding and fixing code and machines automatically creating designs for creative workers and how Power Automate, Microsoft's RPA tooling, would combine with Microsoft Syntex to understand documents and provide standard ways for organizations to communicate with those documents. There was of course a big focus on Azure as developer cloud platform with GitHub Copilot as a linchpin using AI to assist coders in low-code and no-code innovations that are coming down the pipe. And another giant theme was a workforce transformation and how Microsoft is using its heritage and collaboration and productivity software to move beyond what Nadella called productivity paranoia, i.e., are remote workers doing their jobs? In a world where collaboration is built into intelligent workflows, and he even showed a glimpse of the future with AI-powered avatars and partnerships with Meta and Cisco with Teams of all firms. And finally, security with a bevy of tools from identity, endpoint, governance, et cetera, stressing a suite of tools from a single provider, i.e., Microsoft. So a couple points here. One, Microsoft is following in the footsteps of AWS with silicon advancements and didn't really emphasize that trend much except for the Ampere announcement. But it's building out cloud infrastructure at a massive scale, there is no debate about that. Its plan on data is to try and provide a somewhat more abstracted and simplified solutions, which differs a little bit from AWS's approach of the right database tool, for example, for the right job. Microsoft's automation play appears to provide simple individual productivity tools, kind of a ground up approach and make it really easy for users to drive these bottoms up initiatives. We heard from UiPath that forward five last month, a little bit of a different approach of horizontal automation, end-to-end across platforms. So quite a different play there. Microsoft's angle on workforce transformation is visionary and will continue to solidify in our view its dominant position with Teams and Microsoft 365, and it will drive cloud infrastructure consumption by default. On security as well as a cloud player, it has to have world-class security, and Azure does. There's not a lot of debate about that, but the knock on Microsoft is Patch Tuesday becomes Hack Wednesday because Microsoft releases so many patches, it's got so much Swiss cheese in its legacy estate and patching frequently, it becomes a roadmap and a trigger for hackers. Hey, patch Tuesday, these are all the exploits that you can go after so you can act before the patches are implemented. And so it's really become a problem for users. As well Microsoft is competing with many of the best-of-breed platforms like CrowdStrike and Okta, which have market momentum and appear to be more attractive horizontal plays for customers outside of just the Microsoft cloud. But again, it's Microsoft. They make it easy and very inexpensive to adopt. Now, despite the outstanding presentation by Satya Nadella, there are a couple of statements that should raise eyebrows. Here are two of them. First, as he said, Azure is the only cloud that supports all organizations and all workloads from enterprises to startups, to highly regulated industries. I had a conversation with Sarbjeet Johal about this, to make sure I wasn't just missing something and we were both surprised, somewhat, by this claim. I mean most certainly AWS supports more certifications for example, and we would think it has a reasonable case to dispute that claim. And the other statement, Nadella made, Azure is the only cloud provider enabling highly regulated industries to bring their most sensitive applications to the cloud. Now, reasonable people can debate whether AWS is there yet, but very clearly Oracle and IBM would have something to say about that statement. Now maybe it's not just, would say, "Oh, they're not real clouds, you know, they're just going to hosting in the cloud if you will." But still, when it comes to mission-critical applications, you would think Oracle is really the the leader there. Oh, and Satya also mentioned the claim that the Edge browser, the Microsoft Edge browser, no questions asked, he said, is the best browser for business. And we could see some people having some questions about that. Like isn't Edge based on Chrome? Anyway, so we just had to question these statements and challenge Microsoft to defend them because to us it's a little bit of BS and makes one wonder what else in such as awesome keynote and it was awesome, it was hyperbole. Okay, moving on to Google Cloud Next. The keynote started with Sundar Pichai doing a virtual session, he was remote, stressing the importance of Google Cloud. He mentioned that Google Cloud from its Q2 earnings was on a $25-billion annual run rate. What he didn't mention is that it's also on a 3.6 billion annual operating loss run rate based on its first half performance. Just saying. And we'll dig into that issue a little bit more later in this episode. He also stressed that the investments that Google has made to support its core business and search, like its global network of 22 subsea cables to support things like, YouTube video, great performance obviously that we all rely on, those innovations there. Innovations in BigQuery to support its search business and its threat analysis that it's always had and its AI, it's always been an AI-first company, he's stressed, that they're all leveraged by the Google Cloud Platform, GCP. This is all true by the way. Google has absolutely awesome tech and the talk, as well as his talk, Pichai, but also Kurian's was forward thinking and laid out a vision of the future. But it didn't address in our view, and I talked to Sarbjeet Johal about this as well, today's challenges to the degree that Microsoft did and we expect AWS will at re:Invent this year, it was more out there, more forward thinking, what's possible in the future, somewhat less about today's problem, so I think it's resonates less with today's enterprise players. Thomas Kurian then took over from Sundar Pichai and did a really good job of highlighting customers, and I think he has to, right? He has to say, "Look, we are in this game. We have customers, 9 out of the top 10 media firms use Google Cloud. 8 out of the top 10 manufacturers. 9 out of the top 10 retailers. Same for telecom, same for healthcare. 8 out of the top 10 retail banks." He and Sundar specifically referenced a number of companies, customers, including Avery Dennison, Groupe Renault, H&M, John Hopkins, Prudential, Minna Bank out of Japan, ANZ bank and many, many others during the session. So you know, they had some proof points and you got to give 'em props for that. Now like Microsoft, Google talked about infrastructure, they referenced training processors and regions and compute optionality and storage and how new workloads were emerging, particularly data-driven workloads in AI that required new infrastructure. He explicitly highlighted partnerships within Nvidia and Intel. I didn't see anything on Arm, which somewhat surprised me 'cause I believe Google's working on that or at least has come following in AWS's suit if you will, but maybe that's why they're not mentioning it or maybe I got to do more research there, but let's park that for a minute. But again, as we've extensively discussed in Breaking Analysis in our view when it comes to compute, AWS via its Annapurna acquisition is well ahead of the pack in this area. Arm is making its way into the enterprise, but all three companies are heavily investing in infrastructure, which is great news for customers and the ecosystem. We'll come back to that. Data and AI go hand in hand, and there was no shortage of data talk. Google didn't mention Snowflake or Databricks specifically, but it did mention, by the way, it mentioned Mongo a couple of times, but it did mention Google's, quote, Open Data cloud. Now maybe Google has used that term before, but Snowflake has been marketing the data cloud concept for a couple of years now. So that struck as a shot across the bow to one of its partners and obviously competitor, Snowflake. At BigQuery is a main centerpiece of Google's data strategy. Kurian talked about how they can take any data from any source in any format from any cloud provider with BigQuery Omni and aggregate and understand it. And with the support of Apache Iceberg and Delta and Hudi coming in the future and its open Data Cloud Alliance, they talked a lot about that. So without specifically mentioning Snowflake or Databricks, Kurian co-opted a lot of messaging from these two players, such as life and tech. Kurian also talked about Google Workspace and how it's now at 8 million users up from 6 million just two years ago. There's a lot of discussion on developer optionality and several details on tools supported and the open mantra of Google. And finally on security, Google brought out Kevin Mandian, he's a CUBE alum, extremely impressive individual who's CEO of Mandiant, a leading security service provider and consultancy that Google recently acquired for around 5.3 billion. They talked about moving from a shared responsibility model to a shared fate model, which is again, it's kind of a shot across AWS's bow, kind of shared responsibility model. It's unclear that Google will pay the same penalty if a customer doesn't live up to its portion of the shared responsibility, but we can probably assume that the customer is still going to bear the brunt of the pain, nonetheless. Mandiant is really interesting because it's a services play and Google has stated that it is not a services company, it's going to give partners in the channel plenty of room to play. So we'll see what it does with Mandiant. But Mandiant is a very strong enterprise capability and in the single most important area security. So interesting acquisition by Google. Now as well, unlike Microsoft, Google is not competing with security leaders like Okta and CrowdStrike. Rather, it's partnering aggressively with those firms and prominently putting them forth. All right. Let's get into the ETR survey data and see how Microsoft and Google are positioned in four key markets that we've mentioned before, IaaS, BI analytics, database data platforms and collaboration software. First, let's look at the IaaS cloud. ETR is just about to release its October survey, so I cannot share the that data yet. I can only show July data, but we're going to give you some directional hints throughout this conversation. This chart shows net score or spending momentum on the vertical axis and overlap or presence in the data, i.e., how pervasive the platform is. That's on the horizontal axis. And we've inserted the Wikibon estimates of IaaS revenue for the companies, the Big 3. Actually the Big 4, we included Alibaba. So a couple of points in this somewhat busy data chart. First, Microsoft and AWS as always are dominant on both axes. The red dotted line there at 40% on the vertical axis. That represents a highly elevated spending velocity and all of the Big 3 are above the line. Now at the same time, GCP is well behind the two leaders on the horizontal axis and you can see that in the table insert as well in our revenue estimates. Now why is Azure bigger in the ETR survey when AWS is larger according to the Wikibon revenue estimates? And the answer is because Microsoft with products like 365 and Teams will often be considered by respondents in the survey as cloud by customers, so they fit into that ETR category. But in the insert data we're stripping out applications and SaaS from Microsoft and Google and we're only isolating on IaaS. The other point is when you take a look at the early October returns, you see downward pressure as signified by those dotted arrows on every name. The only exception was Dell, or Dell and IBM, which showing slightly improved momentum. So the survey data generally confirms what we know that AWS and Azure have a massive lead and strong momentum in the marketplace. But the real story is below the line. Unlike Google Cloud, which is on pace to lose well over 3 billion on an operating basis this year, AWS's operating profit is around $20 billion annually. Microsoft's Intelligent Cloud generated more than $30 billion in operating income last fiscal year. Let that sink in for a moment. Now again, that's not to say Google doesn't have traction, it does and Kurian gave some nice proof points and customer examples in his keynote presentation, but the data underscores the lead that Microsoft and AWS have on Google in cloud. And here's a breakdown of ETR's proprietary net score methodology, that vertical axis that we showed you in the previous chart. It asks customers, are you adopting the platform new? That's that lime green. Are you spending 6% or more? That's the forest green. Is you're spending flat? That's the gray. Is you're spending down 6% or worse? That's the pinkest color. Or are you replacing the platform, defecting? That's the bright red. You subtract the reds from the greens and you get a net score. Now one caveat here, which actually is really favorable from Microsoft, the Microsoft data that we're showing here is across the entire Microsoft portfolio. The other point is, this is July data, we'll have an update for you once ETR releases its October results. But we're talking about meaningful samples here, the ends. 620 for AWS over a thousand from Microsoft in more than 450 respondents in the survey for Google. So the real tell is replacements, that bright red. There is virtually no churn for AWS and Microsoft, but Google's churn is 5x, those two in the survey. Now 5% churn is not high, but you'd like to see three things for Google given it's smaller size. One is less churn, two is much, much higher adoption rates in the lime green. Three is a higher percentage of those spending more, the forest green. And four is a lower percentage of those spending less. And none of these conditions really applies here for Google. GCP is still not growing fast enough in our opinion, and doesn't have nearly the traction of the two leaders and that shows up in the survey data. All right, let's look at the next sector, BI analytics. Here we have that same XY dimension. Again, Microsoft dominating the picture. AWS very strong also in both axes. Tableau, very popular and respectable of course acquired by Salesforce on the vertical axis, still looking pretty good there. And again on the horizontal axis, big presence there for Tableau. And Google with Looker and its other platforms is also respectable, but it again, has some work to do. Now notice Streamlit, that's a recent Snowflake acquisition. It's strong in the vertical axis and because of Snowflake's go-to-market (indistinct), it's likely going to move to the right overtime. Grafana is also prominent in the Y axis, but a glimpse at the most recent survey data shows them slightly declining while Looker actually improves a bit. As does Cloudera, which we'll move up slightly. Again, Microsoft just blows you away, doesn't it? All right, now let's get into database and data platform. Same X Y dimensions, but now database and data warehouse. Snowflake as usual takes the top spot on the vertical axis and it is actually keeps moving to the right as well with again, Microsoft and AWS is dominant in the market, as is Oracle on the X axis, albeit it's got less spending velocity, but of course it's the database king. Google is well behind on the X axis but solidly above the 40% line on the vertical axis. Note that virtually all platforms will see pressure in the next survey due to the macro environment. Microsoft might even dip below the 40% line for the first time in a while. Lastly, let's look at the collaboration and productivity software market. This is such an important area for both Microsoft and Google. And just look at Microsoft with 365 and Teams up into the right. I mean just so impressive in ubiquitous. And we've highlighted Google. It's in the pack. It certainly is a nice base with 174 N, which I can tell you that N will rise in the next survey, which is an indication that more people are adopting. But given the investment and the tech behind it and all the AI and Google's resources, you'd really like to see Google in this space above the 40% line, given the importance of this market, of this collaboration area to Google's success and the degree to which they emphasize it in their pitch. And look, this brings up something that we've talked about before on Breaking Analysis. Google doesn't have a tech problem. This is a go-to-market and marketing challenge that Google faces and it's up against two go-to-market champs and Microsoft and AWS. And Google doesn't have the enterprise sales culture. It's trying, it's making progress, but it's like that racehorse that has all the potential in the world, but it's just missing some kind of key ingredient to put it over at the top. It's always coming in third, (chuckles) but we're watching and Google's obviously, making some investments as we shared with earlier. All right. Some final thoughts on what we learned this week and in this research: customers and partners should be thrilled that both Microsoft and Google along with AWS are spending so much money on innovation and building out global platforms. This is a gift to the industry and we should be thankful frankly because it's good for business, it's good for competitiveness and future innovation as a platform that can be built upon. Now we didn't talk much about multi-cloud, we haven't even mentioned supercloud, but both Microsoft and Google have a story that resonates with customers in cross cloud capabilities, unlike AWS at this time. But we never say never when it comes to AWS. They sometimes and oftentimes surprise you. One of the other things that Sarbjeet Johal and John Furrier and I have discussed is that each of the Big 3 is positioning to their respective strengths. AWS is the best IaaS. Microsoft is building out the kind of, quote, we-make-it-easy-for-you cloud, and Google is trying to be the open data cloud with its open-source chops and excellent tech. And that puts added pressure on Snowflake, doesn't it? You know, Thomas Kurian made some comments according to CRN, something to the effect that, we are the only company that can do the data cloud thing across clouds, which again, if I'm being honest is not really accurate. Now I haven't clarified these statements with Google and often things get misquoted, but there's little question that, as AWS has done in the past with Redshift, Google is taking a page out of Snowflake, Databricks as well. A big difference in the Big 3 is that AWS doesn't have this big emphasis on the up-the-stack collaboration software that both Microsoft and Google have, and that for Microsoft and Google will drive captive IaaS consumption. AWS obviously does some of that in database, a lot of that in database, but ISVs that compete with Microsoft and Google should have a greater affinity, one would think, to AWS for competitive reasons. and the same thing could be said in security, we would think because, as I mentioned before, Microsoft competes very directly with CrowdStrike and Okta and others. One of the big thing that Sarbjeet mentioned that I want to call out here, I'd love to have your opinion. AWS specifically, but also Microsoft with Azure have successfully created what Sarbjeet calls brand distance. AWS from the Amazon Retail, and even though AWS all the time talks about Amazon X and Amazon Y is in their product portfolio, but you don't really consider it part of the retail organization 'cause it's not. Azure, same thing, has created its own identity. And it seems that Google still struggles to do that. It's still very highly linked to the sort of core of Google. Now, maybe that's by design, but for enterprise customers, there's still some potential confusion with Google, what's its intentions? How long will they continue to lose money and invest? Are they going to pull the plug like they do on so many other tools? So you know, maybe some rethinking of the marketing there and the positioning. Now we didn't talk much about ecosystem, but it's vital for any cloud player, and Google again has some work to do relative to the leaders. Which brings us to supercloud. The ecosystem and end customers are now in a position this decade to digitally transform. And we're talking here about building out their own clouds, not by putting in and building data centers and installing racks of servers and storage devices, no. Rather to build value on top of the hyperscaler gift that has been presented. And that is a mega trend that we're watching closely in theCUBE community. While there's debate about the supercloud name and so forth, there little question in our minds that the next decade of cloud will not be like the last. All right, we're going to leave it there today. Many thanks to Sarbjeet Johal, and my business partner, John Furrier, for their input to today's episode. Thanks to Alex Myerson who's on production and manages the podcast and Ken Schiffman as well. Kristen Martin and Cheryl Knight helped get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE, who does some wonderful editing. And check out SiliconANGLE, a lot of coverage on Google Cloud Next and Microsoft Ignite. Remember, all these episodes are available as podcast wherever you listen. Just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. And you can always get in touch with me via email, david.vellante@siliconangle.com or you can DM me at dvellante or comment on my LinkedIn posts. And please do check out etr.ai, the best survey data in the enterprise tech business. This is Dave Vellante for the CUBE Insights, powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (gentle music)

>>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.

>>Hey everyone. Welcome to this cube conversation. I'm your host, Lisa Martin Rajko same joins me now the chief product officer at elation. Raj. Great to have you on the cube. Welcome. >>It's great to be here, Lisa. And I've been a fan for a while and excited to have a chance to talk with you live. >>And we've got some exciting stuff to talk about elation in terms of the success in the enterprise market. I see more than 25% of the fortune 100 doing great. There is customers elation and snowflake. Before we get into your exciting news. Talk to me a little bit about the evolution of the partnership. >>Yeah, no, absolutely. So, you know, we've always been a, a close partner and integrator with snowflake and last year snowflake became an investor in elation and they participated in our series D round. And the thing I'm most excited about beyond that is we were announced in the snowflake summit back in June to be their data governance partner of the year for the second year running. And so we've always had a closer relationship with snowflake, both at the go to market level and at the product level. And you know, the stuff that we're about to talk about is a Testament to that. >>Absolutely. It is. So talk to us before we get into the announcement. What you're seeing in the market as organizations are really becoming much more serious about being data driven and building a data culture. What are you seeing with respect to enterprises as well as those smaller folks? >>Yeah, no, it, it, it's, it's a great question. I mean, you, you hear the T tropes data is the new oil data is like water it's essential. And we're seeing that very consistently across every customer, every segment, every geo that we, that we talk to, I, I think the challenges that organizations are seeing that are leading to the amazing growth that we've seen at elation are there's so much data. They don't know where it resides. You've got silos or islands of knowledge that exist across the, the enterprise. And they need a data intelligence platform to bring it all together, to help them make sense of it and ultimately build a data culture that, you know, it lets their employees make data driven decisions as opposed to relying on gut. And so those are some of the macro trends that we're seeing and with the migration of data to the cloud and in particular snowflake, it seemed like a huge opportunity for us to partner even more closely with, with snowflake. And we're, we're excited about the progress that we've seen with them thus far. >>All right, let's get right into it. So first of all, define a data culture and then talk to us about how elation and snowflake are helping organizations to really achieve that. >>Yeah. You know, it, it's interesting. The, the company vision that we have at elation is to empower a curious and rational world. And you know, what that really means is we want to deliver solutions that drive curiosity and drive rational behavior. So making, making decisions based on data and insights, as opposed to gut, or, you know, the, the highest paid, you know, person's opinion or what have you. And so delivering a data culture, building a data culture, which is something we hear from all the CDOs that we talk to is, Hey, elation, help us drive data literacy across the organization, provide that single source of reference. So if anybody has a question about, do we have data that answers this, or, you know, what kind of performance are we seeing in this product area? Give me a starting point for my data exploration journey. And that's really where elation and our data intelligence solutions kind of come into the play. >>So unpack elation cloud service for snowflake. Talk to us about what it is, why you're doing it, what the significance of this partnership and this solution is delivering. >>Absolutely. So the elation cloud service for snowflake is a brand new offering that we just brought to market. And the intent really was, you know, we've had massive success in the global 2000. You mentioned the, the progress that we've had with fortune 100 customers, we see the need for data, culture, and data literacy and governance in organizations, you know, that are massive global multinational enterprises all the way down to divisions of an organization, or even, you know, mid-market and SMB companies. And so we thought there was a huge opportunity to really drive data culture for those organizations that are adopting snowflake, but still need that data intelligence overlay across the, the data that's in the snowflake cloud. And so what we did is we launched the elation cloud service for snowflake as a free trial, and then, you know, low cost purchase solution that, you know, can be adopted for less than a hundred thousand dollars a year. >>Got it. So tar from a target market perspective that lower end of the market for, of course, you know, these days, Raj, as we talk about every company, regardless of size, regardless of industry and location has to be a data company getting there and, and, and, and really defining and going on a journey to get there is really complex. So you're going now down market to meet those customers where they are, how will elation cloud service for snowflake help those customers, those smaller customers really become data driven and, and, and adopt a data culture. >>Yeah. Yeah. It's, it's a great question. I, I think the biggest goal that we had was making it really simple and easy for them to begin this journey. So, you know, we are now live in the snowflake partner connect portal. And if someone wants to experience the power of elation cloud service for snowflake, they just need to go to that portal, click the elation tile. And literally within less than two minutes, a brand new instance of elation is spun up. Their snowflake data is automatically being cataloged as part of this trial. And they have 14 days to go through this experience and, and get a sense of the power of elation to give them insights into what's in their snowflake platform, what governance options they can layer on top of their snowflake data cloud and how the data is transforming across their organization. >>So talk to me about who you're talking to within a customer. I was looking at some data that elation provided to me, and I see that according to Gartner data culture is priority number one for chief data officers, but for those smaller organizations, do they have chief data officers? Is that responsibility line still with the CIO? Who are you engaging with? >>Yeah, it's very, very, really great question. I, I think the larger organizations that we sell to definitely have a, a CDO and, you know, CDO sometimes is the chief data and analytics officer in smaller organizations, or even in divisions of big companies that, that, you know, might be target customers for ACS, for snowflake could be a, a VP of analytics could be head of marketing. Operations could be a data engineering function, so that might roll up into the it. And so I think that's, what's interesting is we, we wanted to take the friction out of the, the experience process and the trial process, and whoever is responsible for the snowflake instance and, and leveraging snowflake for, for data and analytics, they can explore and understand what the, a power elation layered on top of snowflake can provide for them. >>Okay. So another, another thing that I uncovered in researching for this segment is McKenzie says data, culture is decision culture. I thought that was a really profound statement, but it's also such a challenge to get there is organizations of all sizes are on various points in their journey to become data driven. What does that mean? How, how well, how do elation and help customers really achieve that data culture so that they can really have that decision culture so they can make faster, better data based decisions? >>Yeah, it, so I, I think a huge part of it, like if we think about our, our, our big area of focus, how do we enable users to find, understand trust, govern, and use data within snowflake in this instance? And so step one to drive data culture is how, how do you provide a single source of reference a, a, a search box, frankly, you know, Google for your, for your data environment, so that you can actually find data, then how do you understand it? You know, what's in there. What does it mean? What are the relationships between these data objects? Can I trust this? Is this sandbox data, or is this production data that can be used for reporting and analytics? How do I govern the data? So I know who's using it, who should use it, what policies are there. And so if, if we go through the set of features that we've built into ation cloud service for snowflake, it enables us to deliver on that promise result at the very end, resulting in the ability to explore the data that exists in the snowflake platform as well. >>Let's go ahead and unpack that. Now, talk to me about some of the key capabilities of the solution and what it's enabling organizations to achieve. >>Yeah, so, you know, it starts with cataloging the data itself. You know, we, we, we are the data catalog company. We basically define that category. And so step one is how do we connect to snowflake and automatically ingest all the metadata that exists within that snowflake cloud, as well as extract the lineage relationships between tables. So you can understand how the data is transforming within the snowflake data cloud. And so that provides visibility to, to begin that fine journey. You know, how, how do I actually discover data on the understand and trust front? I think where things get really interesting is we've integrated deeply with Snowflake's new data governance features. So they've got data policies that provide things like row level security and, and data masking. We integrate directly with those policies, extract them, ingest them into elation so that they can be discovered, can be easily applied or added to other data sets within snowflake directly from the elation UI. >>So now you've got policies layered on top of your data environment. Snowflake's introduced, tagging and classification capabilities. We automatically extract and ingest those tags. They're surfaced in inhalation. So if somebody looks for a data set that they're not familiar with, they can see, oh, here are the policies that this data set is applied to. Here are the tags that are applied. And so snow elation actually becomes almost like a user interface to the data that exists within that snowflake platform. And then maybe just two other things with the lineage that we extract. One of the most important things that you can deliver for users is impact analysis. Hey, if I'm gonna deprecate this table, or if I'm gonna make a change to what this table definition is, what are the downstream objects and users that should know about that? So, Hey, if this table's going away and my Tableau report over here is gonna stop working, boy, it'd be great to be able to get visibility into that before that change is made, we can do that automatically within the elation UI and, and really just make it easier for somebody to govern and manage the data that exists within the snowflake data cloud. >>So easier to govern and manage the data. Let's go up a level or two. Sure. Talk to me about some of the business outcomes that this solution is gonna help organizations to achieve. We talked about every company these days has to be a data company. Consumers expect this very personalized, relevant experience. What are you thinking? Some of the outcomes are gonna be that this technology and this partnership is gonna unlock. >>Yeah, no, I, I, I think step one, and this has always been a huge area of focus for us is just simply driving business productivity. So, you know, the, the data that we see in talking to CDOs and CDOs is the onboarding and, and getting productive the time it takes to onboard and, and get a data analyst productive. It, it can be nine to 12 months. And, you know, we all know the battle for talent these days is significant. And so if we can provide a solution, and this is exactly what we do that enables an organization to get a data analyst productive in weeks instead of months, or, or, you know, potentially even a year, the value that that analyst can deliver to the organization goes up dramatically because they're spending less time looking for data and figuring out who knows what about the data. >>They can go to elation, get those insights and start answering business questions, as opposed to trying to wrangle or figure out does the data exist. And, and, and where does it exist? So that's, that's one key dimension. I'd say the other one that, that I'd highlight is just being able to have a governance program that is monitored managed and well understood. So that, you know, whether it's dealing with CCPA or GDPR, you know, some of the regulatory regimes, the, the ability for an organization to feel like they have control over their data, and they understand where it is who's using it and how it's being used. Those are hugely important business outcomes that CIOs and CDOs tell us they need. And that's why we built the lation cloud service for snowflake >>On the first front. One of the things that popped into my mind in terms of really enabling workforce productivity, workforce efficiency, getting analysts ramped up dramatically faster also seems to me to be something that your customers can leverage from a talent attraction and retention perspective, which in today's market is critical. >>I, I so glad you mentioned that that's, that's actually one of the key pillars that we highlight as well is like, if you give great tools to employees, they're gonna be happier. And, and you'll be a, a preferred employer and people are gonna feel like, oh, this is an organization that I wanna work at because they're making my job easier and they're making it easier for me to deliver value and be productive to the organization. And that's, it's absolutely critical this, this, this war for talent that everybody talks about. It's real and great self-service tools that are empowering to employees are the things that are gonna differentiate companies and allow them to, to unleash the power of data, >>Unleash the power of data, really use it to the competitive advantage that it can and should be used for. When we look at, when you look at customers that are on that journey, that data catalog journey, they, you probably see such a variety of, of locations about where they are in that journey. Do you see a common thread when you're in customer conversations? Is there kind of a common denominator that you, you speak to where you, you really know elation and snowflake here is absolutely the right thing. >>Yeah, no, it, it, it's a good question. I would actually say the fact that a customer is on snowflake. I they're already, you know, a step up on that maturity curve. You know, one of the big use cases that we see with customers that is, is leading to the need for data intelligence solutions that, you know, like that elation can deliver is digital transformation and, and, and cloud migration, you know, we've got legacy data. On-prem, we know we need to move to the cloud to get better agility, better scaling, you know, perhaps, you know, reduced costs, et cetera. And so I think step one, on that, that qualification criteria or that maturity journey is, Hey, if you're already in snowflake, that's a great sign because you're, you're recognizing the power of a data cloud platform and, and, and warehouse like snowflake. And so that's a, a, a great signal to us that this is a customer that wants to, you know, really better understand how they can get value out of, out of their solution. I think the next step on that journey is a recognition that they're not utilizing the data that they have as effectively as they can and should be, and they're not, and, and their employees are still struggling with, you know, where does the data exist? Can I trust it? It, you know, it, who do I know tends to be more important than do I have a tool that will help me understand the data. And so customers that are asking those sorts of questions are ideal customers for the elation cloud service for snowflake solution. >>So enabling those customers to get their hands on it, there's a free trial. Talk to us about that. And where can the audience go to actually click and try? >>Absolutely. So, you know, we'll, we'll be doing our usual marketing and, and promotion of this, but what I'm super excited about, you know, again, I mentioned earlier, you know, this is part of our, our cloud native multi 10 and architecture. We are live in the snowflake partner connect portal. And so if you are logged into snowflake and are an admin, you can go to the partner connect portal and you will see a tile. I think it's alphabetically, sorted and elation starts with a so pretty easy to find. I don't think you'll have to do too much searching. And literally all you have to do is click on that tile, answer a couple quick questions. And in the background in about two minutes, your elation instance will get spun up. We'll we will have sample data sets in there. There's some guided tours that you can walk through to kind of get a feel for the power of snowflake. >>So policy center lineage, you know, tags, our intelligent SQL tool that allows you to smartly query the snowflake data cloud and publish queries, share queries with others, collaborate on them for, for greater insights. And there's, you know, as you would expect with any, you know, online free trial, you know, we've got a built in chat bot. So if you have a question, wanna get a better sense of how a particular feature works or curious about how elation might work. In other areas, you can, you know, ask a question to the chat bot and we've got product specialists on the back end that can answer questions. So we really wanna make that journey as, as seamless and easy as, as possible. And hopefully that results in enough interests that the customer wants to, to, or the, the trial user wants to become a customer. And, and that's where our great sales organization will kind of take the Baton from there. >>And there's the, there's the objective there, and I'm sure Raj folks can find out about the free trial and access it. You, you mentioned through the marketplace, more information on elian.com. I imagine they can go there to access it as well, >>A hundred percent elation.com. We're on Twitter, we're on LinkedIn, but yeah, if you have any questions, you know, you can just search for elation cloud service for snowflake, or just go to the elation.com website. Absolutely. >>All right. Elation cloud service for snowflake. Congratulations on the launch to you and the entire elation team. We look forward to hearing customer success stories and really seeing those business outcomes realize in the next few months, Raj, thanks so much for your time. >>Thank you so much, Lisa. It's great to talk to you. >>Likewise, Raj gin. I'm Lisa Martin. Thank you for watching this cube conversation. Stay right here for more great action on the cube, the leader in live tech coverage.