(upbeat music) >> Morning guys and girls, welcome back to theCUBE's live coverage of Women in Data Science WIDS 2023 live at Stanford University. Lisa Martin here with my co-host for this segment, Tracy Zhang. We're really excited to be talking with a great female rockstar. You're going to learn a lot from her next, Jacqueline Kuo, solutions engineer at Dataiku. Welcome, Jacqueline. Great to have you. >> Thank you so much. >> Thank for being here. >> I'm so excited to be here. >> So one of the things I have to start out with, 'cause my mom Kathy Dahlia is watching, she's a New Yorker. You are a born and raised New Yorker and I learned from my mom and others. If you're born in New York no matter how long you've moved away, you are a New Yorker. There's you guys have like a secret club. (group laughs) >> I am definitely very proud of being born and raised in New York. My family immigrated to New York, New Jersey from Taiwan. So very proud Taiwanese American as well. But I absolutely love New York and I can't imagine living anywhere else. >> Yeah, yeah. >> I love it. >> So you studied, I was doing some research on you you studied mechanical engineering at MIT. >> Yes. >> That's huge. And you discovered your passion for all things data-related. You worked at IBM as an analytics consultant. Talk to us a little bit about your career path. Were you always interested in engineering STEM-related subjects from the time you were a child? >> I feel like my interests were ranging in many different things and I ended up landing in engineering, 'cause I felt like I wanted to gain a toolkit like a toolset to make some sort of change with or use my career to make some sort of change in this world. And I landed on engineering and mechanical engineering specifically, because I felt like I got to, in my undergrad do a lot of hands-on projects, learn every part of the engineering and design process to build products which is super-transferable and transferable skills sort of is like the trend in my career so far. Where after undergrad I wanted to move back to New York and mechanical engineering jobs are kind of few and fall far in between in the city. And I ended up landing at IBM doing analytics consulting, because I wanted to understand how to use data. I knew that data was really powerful and I knew that working with it could allow me to tell better stories to influence people across different industries. And that's also how I kind of landed at Dataiku to my current role, because it really does allow me to work across different industries and work on different problems that are just interesting. >> Yeah, I like the way that, how you mentioned building a toolkit when doing your studies at school. Do you think a lot of skills are still very relevant to your job at Dataiku right now? >> I think that at the core of it is just problem solving and asking questions and continuing to be curious or trying to challenge what is is currently given to you. And I think in an engineering degree you get a lot of that. >> Yeah, I'm sure. >> But I think that we've actually seen that a lot in the panels today already, that you get that through all different types of work and research and that kind of thoughtfulness comes across in all different industries too. >> Talk a little bit about some of the challenges, that data science is solving, because every company these days, whether it's an enterprise in manufacturing or a small business in retail, everybody has to be data-driven, because the end user, the end customer, whoever that is whether it's a person, an individual, a company, a B2B, expects to have a personalized custom experience and that comes from data. But you have to be able to understand that data treated properly, responsibly. Talk about some of the interesting projects that you're doing at Dataiku or maybe some that you've done in the past that are really kind of transformative across things climate change or police violence, some of the things that data science really is impacting these days. >> Yeah, absolutely. I think that what I love about coming to these conferences is that you hear about those really impactful social impact projects that I think everybody who's in data science wants to be working on. And I think at Dataiku what's great is that we do have this program called Ikig.AI where we work with nonprofits and we support them in their data and analytics projects. And so, a project I worked on was with the Clean Water, oh my goodness, the Ocean Cleanup project, Ocean Cleanup organization, which was amazing, because it was sort of outside of my day-to-day and it allowed me to work with them and help them understand better where plastic is being aggregated across the world and where it appears, whether that's on beaches or in lakes and rivers. So using data to help them better understand that. I feel like from a day-to-day though, we, in terms of our customers, they're really looking at very basic problems with data. And I say basic, not to diminish it, but really just to kind of say that it's high impact, but basic problems around how do they forecast sales better? That's a really kind of, sort of basic problem, but it's actually super-complex and really impactful for people, for companies when it comes to forecasting how much headcount they need to have in the next year or how much inventory to have if they're retail. And all of those are going to, especially for smaller companies, make a huge impact on whether they make profit or not. And so, what's great about working at Dataiku is you get to work on these high-impact projects and oftentimes I think from my perspective, I work as a solutions engineer on the commercial team. So it's just, we work generally with smaller customers and sometimes talking to them, me talking to them is like their first introduction to what data science is and what they can do with that data. And sort of using our platform to show them what the possibilities are and help them build a strategy around how they can implement data in their day-to-day. >> What's the difference? You were a data scientist by title and function, now you're a solutions engineer. Talk about the ascendancy into that and also some of the things that you and Tracy will talk about as those transferable, those transportable skills that probably maybe you learned in engineering, you brought data science now you're bringing to solutions engineering. >> Yeah, absolutely. So data science, I love working with data. I love getting in the weeds of things and I love, oftentimes that means debugging things or looking line by line at your code and trying to make it better. I found that on in the data science role, while those things I really loved, sometimes it also meant that I didn't, couldn't see or didn't have visibility into the broader picture of well like, well why are we doing this project? And who is it impacting? And because oftentimes your day-to-day is very much in the weeds. And so, I moved into sales or solutions engineering at Dataiku to get that perspective, because what a sales engineer does is support the sale from a technical perspective. And so, you really truly understand well, what is the customer looking for and what is going to influence them to make a purchase? And how do you tell the story of the impact of data? Because oftentimes they need to quantify well, if I purchase a software like Dataiku then I'm able to build this project and make this X impact on the business. And that is really powerful. That's where the storytelling comes in and that I feel like a lot of what we've been hearing today about connecting data with people who can actually do something with that data. That's really the bridge that we as sales engineers are trying to connect in that sales process. >> It's all about connectivity, isn't it? >> Yeah, definitely. We were talking about this earlier that it's about making impact and it's about people who we are analyzing data is like influencing. And I saw that one of the keywords or one of the biggest thing at Dataiku is everyday AI, so I wanted to just ask, could you please talk more about how does that weave into the problem solving and then day-to-day making an impact process? >> Yes, so I started working on Dataiku around three years ago and I fell in love with the product itself. The product that we have is we allow for people with different backgrounds. If you're coming from a data analyst background, data science, data engineering, maybe you are more of like a business subject matter expert, to all work in one unified central platform, one user interface. And why that's powerful is that when you're working with data, it's not just that data scientist working on their own and their own computer coding. We've heard today that it's all about connecting the data scientists with those business people, with maybe the data engineers and IT people who are actually going to put that model into production or other folks. And so, they all use different languages. Data scientists might use Python and R, your business people are using PowerPoint and Excel, everyone's using different tools. How do we bring them all in one place so that you can have conversations faster? So the business people can understand exactly what you're building with the data and can get their hands on that data and that model prediction faster. So that's what Dataiku does. That's the product that we have. And I completely forgot your question, 'cause I got so invested in talking about this. Oh, everyday AI. Yeah, so the goal of of Dataiku is really to allow for those maybe less technical people with less traditional data science backgrounds. Maybe they're data experts and they understand the data really well and they've been working in SQL for all their career. Maybe they're just subject matter experts and want to get more into working with data. We allow those people to do that through our no and low-code tools within our platform. Platform is very visual as well. And so, I've seen a lot of people learn data science, learn machine learning by working in the tool itself. And that's sort of, that's where everyday AI comes in, 'cause we truly believe that there are a lot of, there's a lot of unutilized expertise out there that we can bring in. And if we did give them access to data, imagine what we could do in the kind of work that they can do and become empowered basically with that. >> Yeah, we're just scratching the surface. I find data science so fascinating, especially when you talk about some of the real world applications, police violence, health inequities, climate change. Here we are in California and I don't know if you know, we're experiencing an atmospheric river again tomorrow. Californians and the rain- >> Storm is coming. >> We are not good... And I'm a native Californian, but we all know about climate change. People probably don't associate all of the data that is helping us understand it, make decisions based on what's coming what's happened in the past. I just find that so fascinating. But I really think we're truly at the beginning of really understanding the impact that being data-driven can actually mean whether you are investigating climate change or police violence or health inequities or your a grocery store that needs to become data-driven, because your consumer is expecting a personalized relevant experience. I want you to offer me up things that I know I was doing online grocery shopping, yesterday, I just got back from Europe and I was so thankful that my grocer is data-driven, because they made the process so easy for me. And but we have that expectation as consumers that it's going to be that easy, it's going to be that personalized. And what a lot of folks don't understand is the data the democratization of data, the AI that's helping make that a possibility that makes our lives easier. >> Yeah, I love that point around data is everywhere and the more we have, the actually the more access we actually are providing. 'cause now compute is cheaper, data is literally everywhere, you can get access to it very easily. And so, I feel like more people are just getting themselves involved and that's, I mean this whole conference around just bringing more women into this industry and more people with different backgrounds from minority groups so that we get their thoughts, their opinions into the work is so important and it's becoming a lot easier with all of the technology and tools just being open source being easier to access, being cheaper. And that I feel really hopeful about in this field. >> That's good. Hope is good, isn't it? >> Yes, that's all we need. But yeah, I'm glad to see that we're working towards that direction. I'm excited to see what lies in the future. >> We've been talking about numbers of women, percentages of women in technical roles for years and we've seen it hover around 25%. I was looking at some, I need to AnitaB.org stats from 2022 was just looking at this yesterday and the numbers are going up. I think the number was 26, 27.6% of women in technical roles. So we're seeing a growth there especially over pre-pandemic levels. Definitely the biggest challenge that still seems to be one of the biggest that remains is attrition. I would love to get your advice on what would you tell your younger self or the previous prior generation in terms of having the confidence and the courage to pursue engineering, pursue data science, pursue a technical role, and also stay in that role so you can be one of those females on stage that we saw today? >> Yeah, that's the goal right there one day. I think it's really about finding other people to lift and mentor and support you. And I talked to a bunch of people today who just found this conference through Googling it, and the fact that organizations like this exist really do help, because those are the people who are going to understand the struggles you're going through as a woman in this industry, which can get tough, but it gets easier when you have a community to share that with and to support you. And I do want to definitely give a plug to the WIDS@Dataiku team. >> Talk to us about that. >> Yeah, I was so fortunate to be a WIDS ambassador last year and again this year with Dataiku and I was here last year as well with Dataiku, but we have grown the WIDS effort so much over the last few years. So the first year we had two events in New York and also in London. Our Dataiku's global. So this year we additionally have one in the west coast out here in SF and another one in Singapore which is incredible to involve that team. But what I love is that everyone is really passionate about just getting more women involved in this industry. But then also what I find fortunate too at Dataiku is that we have a strong female, just a lot of women. >> Good. >> Yeah. >> A lot of women working as data scientists, solutions engineer and sales and all across the company who even if they aren't doing data work in a day-to-day, they are super-involved and excited to get more women in the technical field. And so. that's like our Empower group internally that hosts events and I feel like it's a really nice safe space for all of us to speak about challenges that we encounter and feel like we're not alone in that we have a support system to make it better. So I think from a nutrition standpoint every organization should have a female ERG to just support one another. >> Absolutely. There's so much value in a network in the community. I was talking to somebody who I'm blanking on this may have been in Barcelona last week, talking about a stat that showed that a really high percentage, 78% of people couldn't identify a female role model in technology. Of course, Sheryl Sandberg's been one of our role models and I thought a lot of people know Sheryl who's leaving or has left. And then a whole, YouTube influencers that have no idea that the CEO of YouTube for years has been a woman, who has- >> And she came last year to speak at WIDS. >> Did she? >> Yeah. >> Oh, I missed that. It must have been, we were probably filming. But we need more, we need to be, and it sounds like Dataiku was doing a great job of this. Tracy, we've talked about this earlier today. We need to see what we can be. And it sounds like Dataiku was pioneering that with that ERG program that you talked about. And I completely agree with you. That should be a standard program everywhere and women should feel empowered to raise their hand ask a question, or really embrace, "I'm interested in engineering, I'm interested in data science." Then maybe there's not a lot of women in classes. That's okay. Be the pioneer, be that next Sheryl Sandberg or the CTO of ChatGPT, Mira Murati, who's a female. We need more people that we can see and lean into that and embrace it. I think you're going to be one of them. >> I think so too. Just so that young girls like me like other who's so in school, can see, can look up to you and be like, "She's my role model and I want to be like her. And I know that there's someone to listen to me and to support me if I have any questions in this field." So yeah. >> Yeah, I mean that's how I feel about literally everyone that I'm surrounded by here. I find that you find role models and people to look up to in every conversation whenever I'm speaking with another woman in tech, because there's a journey that has had happen for you to get to that place. So it's incredible, this community. >> It is incredible. WIDS is a movement we're so proud of at theCUBE to have been a part of it since the very beginning, since 2015, I've been covering it since 2017. It's always one of my favorite events. It's so inspiring and it just goes to show the power that data can have, the influence, but also just that we're at the beginning of uncovering so much. Jacqueline's been such a pleasure having you on theCUBE. Thank you. >> Thank you. >> For sharing your story, sharing with us what Dataiku was doing and keep going. More power to you girl. We're going to see you up on that stage one of these years. >> Thank you so much. Thank you guys. >> Our pleasure. >> Our pleasure. >> For our guests and Tracy Zhang, this is Lisa Martin, you're watching theCUBE live at WIDS '23. #EmbraceEquity is this year's International Women's Day theme. Stick around, our next guest joins us in just a minute. (upbeat music)

(bright music) >> Welcome back to Vegas, guys and girls. We're pleased that you're watching theCUBE. We know you've been with us. This is our fourth day. We know you've been with us since day one. Why wouldn't you be? Lisa Martin, here. As I mentioned, day four of theCUBE's coverage of AWS re:Invent. There are north of 55,000 people that have been at this event this week. We're hearing hundreds of thousands online. It really feels like old times, which is awesome. We're pleased to welcome back a gentleman from Dataiku who's actually new to theCUBE but Dataiku is not. Jed Dougherty is here, the VP of Platform Strategy. Thanks to joining me today, Jed. >> Oh, I'm so happy to be here. >> Talk a little bit, for anybody that isn't familiar with Dataiku, tell the audience a little bit about the technology, what you guys do. >> Dataiku is an end-to-end data science machine learning platform. We take everything from data ingestion, piplining of that data, bringing it all together, something that's useful for building models, deploying those models and then managing your ML ops workflow. So, really all the way across. And we sit on top of, basically, tons of different AWS stack as well as lots of the partners that are here today. >> Okay, got it. >> Snowflake, Databricks, all that. >> Got it, so one of the things that, it was funny, I think it was Adam's keynote Tuesday morning. I didn't time it, I watched it, but one of my guests said to me earlier this week that Adam spent exactly 52 minutes talking about data. >> Yeah. >> 52 minutes. Obviously, we can't come to an event like this without talking about data. Every company these days has to be a data company. Whether it's my grocery store or a retailer, a hospital, and so- >> Jed: It is the lifeblood of every modern company. >> It is, but you have to be able to access it. You have to be able to harness it, access it, derive insights from it, and be able to act on that faster than the competitors that are waiting, like, right back here. One of the things Adam Selipsky talked about with our boss, John Furrier, who's the co-CEO of theCUBE, they had a sit-down about a week before re:Invent. John always gets a preview of the show and Adam said, you know, he thinks the role of data analyst is going to go away. Or at least the term, because with data democratization that needs to happen. Putting data in the hands of all the business users, that every business user, whether you're in technology or marketing or ops or finance, it's going to have to analyze data to do their jobs. >> Could not agree more. >> Are you hearing that from customers? >> 100% >> Yeah. >> I was just at the CTO Summit of Bank of America two weeks ago out in California, and they told, their CTO had a statistic, 60,000 technologists in Bank of America, all asking data-type questions. You can have the best team of data scientists in the world, and they do. They have some of the best data scientists in the world there. And this team of data scientists could answer any one of the questions that those 60,000 people might have but they can't answer all of them, right? You need those people to be able to answer their own questions. I don't know if the term data analysts are going away. I think, yeah, everybody's just going to have to become a bit more of one. Just like how Excel taught everybody how to use the spreadsheet, in the future, in the next five, 10 years, the democratization of AI means that tools like Dataiku and other data science tools are going to teach everybody how to analyze data. >> Talk about Dataiku as a facilitator of that, of that democratization. Giving, like the citizen technologist who might be in finance, the ability to do that. >> So, a lot of data science tools are aimed at your hardcore coder, right? Somebody who wants to be sitting at a notebook writing (indistinct) or something like that and running models on some big fancy Spark server. Dataiku is still going to be running models on some big fancy Spark server but we're really obfuscating the challenge of writing code away from the user. So we target low code, no code, and high code users all working together in a collaborative platform. So we really do, we believe that there is always going to be a place for data scientists. That role is not going away. You will always need hardcore coders to take on those moonshot very challenging topics. But for every day AI, anybody should be able to do this and it should be open to anybody. >> Right. >> Jed: Really aim to facilitate that. >> I would love to hear some feedback, you know, this is day four of the show as I was saying, and day four is packed. I mean, this is energy-level-wise, guys, it is the same as it was when we started here on Friday night. But I'd love to hear, Jed, from your perspective some of the customer conversations that you've had, what are some of the challenges? They're coming to you saying, "Jed, Dataiku, help us eradicate these challenges so we can transform our business." >> What I'm hearing from customers and partners and AWS here is, over and over, we don't want to buy tools anymore. We want to buy solutions. We want a vertical solution that's pre-built for our industry. And we want it to be, not necessarily click and run out of the box, but we want a template that we can build off of quickly. And I've heard that customers are also looking to understand how tools can be packaged together. You got how many booths are here? 1000 booths? >> Yes, easily. >> You have 1000 different products being talked about, right behind us. Customers need to know which of these products are friends with each other and how they fit together so that they are making sure that when they purchase a set, a suite of tools to do their jobs, it's all going to work naturally together. So, being able, I think this is a really vital concept for GSIs as well. GSIs needs to understand how to package sets of tools together to deliver a full solution to clients. People don't want to be, you know, I think 10 years ago, five years ago, AWS was in the business of selling servers in the cloud. But basically what you do is, you would buy an EC two instance and you install whatever software you wanted on it. I don't know that they're in that business still but customers don't want to buy servers from AWS anymore. They want to buy solutions. >> Right. >> Rent, whatever. >> Yeah. (chuckles) >> That is the big repeated message that I've heard here. >> So you brought up a good point that there are probably 1000 booths here. You could be here every day and not get to see everything that's going on. Plus this show was going on across the strip. We're only getting a fraction of the people that are here. But with that said, to your point, there are so many tools out there. Customers are looking for solutions. One of the things that we say about theCUBE is, we extract the signal from the noise. How does Dataiku get past the noise? How do you get up the stack to really impact customers so they understand the value that you're delivering? >> I think that Data science and ML sound like a very complicated topic but our value prop is relatively simple. And we appeal both to your end users who are excited to learn about how data science works and how they can leverage these tools in their day-to-day jobs, as well as appealing to IT. IT, right now, at major organizations they want to be able to build a full stack that makes sense. And the big choices they're making right now are around infrastructure. Where am I going to run my compute? So, they're choosing between Snowflake or Databricks or a native AWS compute solution, right? And so they make this big choice around compute and then they realize, "Oh, how many of our users across our organization are actually able to leverage this big compute choice?" Oh, maybe 100, maybe 200. That's not incredibly useful for what we've just decided to completely stand behind. Dataiku, all of a sudden, opens that up to 1000s of users across your organization. So it makes IT feel empowered by being able to help more people. And it makes users feel empowered by being able to use a great tool and start answering their own questions. >> And where are your customer conversations these days? As we look at AI and ML, emerging technologies, so many customers and companies, knowing we have to go in this direction. We have to have AI to speed the business. Are you seeing more of the conversations are still in IT or are they actually going up the stack? >> (chuckles) It's a great question. When you're going into large organizations, there's two sales motions, right? There's convincing the business users that this is a great thing and then convincing IT that it's not going to be too painful. You always have to go to both places. IT doesn't want to take on a boondoggler, or there's an albatross, I don't remember the word, but, something that they're going to have to deal with for the next 10 years and then eventually dismantle and pull apart. I think a lot of IT got very scared about big data platforms and solutions because of Hadoop. To be honest, Hadoop was incredibly powerful but maybe not as mature of technology as IT would've liked it to be. From a maintenance and administration standpoint. So yes, you will always have to sell to IT and help IT feel comfortable with the platform. But no, the conversations that I want to have are the use case conversations with a Chief Data Officer, Chief Revenue Officer, Chief Marketing Officer. That's who I really want to convince that this is going to be a worthwhile opportunity. >> And what are some of the key, sorry. What are some of the key use cases that Dataiku is tackling in the market these days? >> So we work a lot. Two of the biggest organizations, or verticals, that I work with personally are finance and pharmaceuticals. In finance, we are closely embedded with wealth management organizations. So, a lot of that is around customer entertainment, churn, relatively obvious, simple concepts but ones where it's worth a lot of money. In pharma, we work both on the supply side. So, doing supply chain optimization, ensuring the right drugs get to the right places at the right time. As well as on the business and marketing side. So, ensuring that your ad spend is correctly distributed across different advertising platforms. >> So if you're working with a financial organization, I want to understand from a consumer, from the end user's perspective, although obviously this technology impacts the end user who's trying to do a transaction. What's in it for me? And I don't know as the end user that Dataiku is under the hood. >> You'd never know. >> Which is good. I shouldn't have to worry about the technology. >> Jed: You shouldn't have to worry about that at all. >> What's in it for the end user customer? What are they gaining from this? >> So, from a very end user perspective, if you think about when you logged onto maybe your Bank of America, your Chase app, five or 10 years ago, maybe you didn't even have it on your phone five years ago. Or when you logged into your account online. We do 95% of our banking online right now, right? I go into a physical location, what? I don't know, once every six months or something? Get a cashier's check? I don't know. The experience that you're getting and the amount of information you're getting back about your spending habits, where your money is going, what your credit score is, all of these things are being driven by these big data organizations inside the banks. Also, any type, this is a little creepier, but any type of promotional emails or the types of things that you get feedback on when you use your credit card and the offers that you get through that, are all being personalized to you through the information that these banks are collecting about your spending habits. >> Yeah, but we want that as a consumer, we want the personalized. >> Yeah, of course. We want it to be magic slash not creepy. (laughs) >> Right, I want them to recommend the best card for me. >> Right. >> The next best thing. >> It's good for me, it's good for them. >> Don't serve me up something that I've already bought. That always bugs me when I'm like, I already bought that. >> I get that all the time. I'm like, yeah, I have that card already. It's in my wallet. Why are you telling me? >> We only have a couple of minutes left Jed, but talk to me about from a platform strategy perspective, what's next for Dataiku and AWS? >> So we are making a matrix transition right now and it's core to our platform. For a long time, the way that we've installed Dataiku is, we help our customers install it on their AWS account so it runs inside their tenant. This is very comfortable for, for example, large banking clients, pharma clients that have personally identifiable information, all that kind of thing. They own everything. However, as we were talking about before, we're really moving from providing a tool to providing solutions. And part of that is obviously a move to SaaS. So two years ago we released a SaaS offering. We've been expanding it more and more to, this year, we want to be pushing SaaS first. So Dataiku online should be the first option when new customers move on. And that is a huge platform shift. It means making sure that we have the right security in place. It means making sure that we have the right scaling in place, that we have 24-7 support. All this has been a big challenge. A big fascinating challenge, actually, to put together. >> Awesome. Last question for you. Say you get a brand new DeLorean, I hear they're coming back, and you want to put, you really, really want to put a bumper sticker on it, 'cause why not? And it's about Dataiku and it's like a sizzle reel kind of thing. >> A sizzle real, alright. >> Yeah. What does it say? >> Extraordinary people, everyday AI. >> Wow. Drop the mic, Jed. That was awesome. Thank you so much for coming on the program. We really appreciate the update on Dataiku. What you guys are doing for customers, your specialization and solutions for verticals. Awesome stuff, we'll have to have you back. >> Thank you so much. >> Alright, my pleasure. >> Bye-Bye. >> For my guest, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (bright music)

(upbeat music) >> Hello, welcome back to theCUBE's AWS RE:Invent 2022 Coverage. I'm John Furrier, host of theCUBE. Got a great lineup here, Itamar Ankorion SVP Technology Alliance at Qlik and Peter McDonald, vice President, cloud partnerships and business development Snowflake. We're going to talk about bringing SAP data to life, for joint Snowflake, Qlik and AWS Solution. Gentlemen, thanks for coming on theCUBE Really appreciate it. >> Thank you. >> Thank you, great meeting you John. >> Just to get started, introduce yourselves to the audience, then going to jump into what you guys are doing together, unique relationship here, really compelling solution in cloud. Big story about applications and scale this year. Let's introduce yourselves. Peter, we'll start with you. >> Great. I'm Peter MacDonald. I am vice president of Cloud Partners and business development here at Snowflake. On the Cloud Partner side, that means I manage AWS relationship along with Microsoft and Google Cloud. What we do together in terms of complimentary products, GTM, co-selling, things like that. Importantly, working with other third parties like Qlik for joint solutions. On business development, it's negotiating custom commercial partnerships, large companies like Salesforce and Dell, smaller companies at most for our venture portfolio. >> Thanks Peter and hi John. It's great to be back here. So I'm Itamar Ankorion and I'm the senior vice president responsible for technology alliances here at Qlik. With that, own strategic alliances, including our key partners in the cloud, including Snowflake and AWS. I've been in the data and analytics enterprise software market for 20 plus years, and my main focus is product management, marketing, alliances, and business development. I joined Qlik about three and a half years ago through the acquisition of Attunity, which is now the foundation for Qlik data integration. So again, we focus in my team on creating joint solution alignment with our key partners to provide more value to our customers. >> Great to have both you guys, senior executives in the industry on theCUBE here, talking about data, obviously bringing SAP data to life is the theme of this segment, but this reinvent, it's all about the data, big data end-to-end story, a lot about data being intrinsic as the CEO says on stage around in the organizations in all aspects. Take a minute to explain what you guys are doing as from a company standpoint. Snowflake and Qlik and the solutions, why here at AWS? Peter, we'll start with you at Snowflake, what you guys do as a company, your mission, your focus. >> That was great, John. Yeah, so here at Snowflake, we focus on the data platform and until recently, data platforms required expensive on-prem hardware appliances. And despite all that expense, customers had capacity constraints, inexpensive maintenance, and had limited functionality that all impeded these organizations from reaching their goals. Snowflake is a cloud native SaaS platform, and we've become so successful because we've addressed these pain points and have other new special features. For example, securely sharing data across both the organization and the value chain without copying the data, support for new data types such as JSON and structured data, and also advance in database data governance. Snowflake integrates with complimentary AWS services and other partner products. So we can enable holistic solutions that include, for example, here, both Qlik and AWS SageMaker, and comprehend and bring those to joint customers. Our customers want to convert data into insights along with advanced analytics platforms in AI. That is how they make holistic data-driven solutions that will give them competitive advantage. With Snowflake, our approach is to focus on customer solutions that leverage data from existing systems such as SAP, wherever they are in the cloud or on-premise. And to do this, we leverage partners like Qlik native US to help customers transform their businesses. We provide customers with a premier data analytics platform as a result. Itamar, why don't you talk about Qlik a little bit and then we can dive into the specific SAP solution here and some trends >> Sounds great, Peter. So Qlik provides modern data integration and analytics software used by over 38,000 customers worldwide. Our focus is to help our customers turn data into value and help them close the gap between data all the way through insight and action. We offer click data integration and click data analytics. Click data integration helps to automate the data pipelines to deliver data to where they want to use them in real-time and make the data ready for analytics and then Qlik data analytics is a robust platform for analytics and business intelligence has been a leader in the Gartner Magic Quadrant for over 11 years now in the market. And both of these come together into what we call Qlik Cloud, which is our SaaS based platform. So providing a more seamless way to consume all these services and accelerate time to value with customer solutions. In terms of partnerships, both Snowflake and AWS are very strategic to us here at Qlik, so we have very comprehensive investment to ensure strong joint value proposition to we can bring to our mutual customers, everything from aligning our roadmaps through optimizing and validating integrations, collaborating on best practices, packaging joint solutions like the one we'll talk about today. And with that investment, we are an elite level, top level partner with Snowflake. We fly that our technology is Snowflake-ready across the entire product set and we have hundreds of joint customers together and with AWS we've also partnered for a long time. We're here to reinvent. We've been here with the first reinvent since the inaugural one, so it kind of gives you an idea for how long we've been working with AWS. We provide very comprehensive integration with AWS data analytics services, and we have several competencies ranging from data analytics to migration and modernization. So that's our focus and again, we're excited about working with Snowflake and AWS to bring solutions together to market. >> Well, I'm looking forward to unpacking the solutions specifically, and congratulations on the continued success of both your companies. We've been following them obviously for a very long time and seeing the platform evolve beyond just SaaS and a lot more going on in cloud these days, kind of next generation emerging. You know, we're seeing a lot of macro trends that are going to be powering some of the things we're going to get into real quickly. But before we get into the solution, what are some of those power dynamics in the industry that you're seeing in trends specifically that are impacting your customers that are taking us down this road of getting more out of the data and specifically the SAP, but in general trends and dynamics. What are you hearing from your customers? Why do they care? Why are they going down this road? Peter, we'll start with you. >> Yeah, I'll go ahead and start. Thanks. Yeah, I'd say we continue to see customers being, being very eager to transform their businesses and they know they need to leverage technology and data to do so. They're also increasingly depending upon the cloud to bring that agility, that elasticity, new functionality necessary to react in real-time to every evolving customer needs. You look at what's happened over the last three years, and boy, the macro environment customers, it's all changing so fast. With our partnerships with AWS and Qlik, we've been able to bring to market innovative solutions like the one we're announcing today that spans all three companies. It provides a holistic solution and an integrated solution for our customer. >> Itamar let's get into it, you've been with theCUBE, you've seen the journey, you have your own journey, many, many years, you've seen the waves. What's going on now? I mean, what's the big wave? What's the dynamic powering this trend? >> Yeah, in a nutshell I'll call it, it's all about time. You know, it's time to value and it's about real-time data. I'll kind of talk about that a bit. So, I mean, you hear a lot about the data being the new oil, but it's definitely, we see more and more customers seeing data as their critical enabler for innovation and digital transformation. They look for ways to monetize data. They look as the data as the way in which they can innovate and bring different value to the customers. So we see customers want to use more data so to get more value from data. We definitely see them wanting to do it faster, right, than before. And we definitely see them looking for agility and automation as ways to accelerate time to value, and also reduce overall costs. I did mention real-time data, so we definitely see more and more customers, they want to be able to act and make decisions based on fresh data. So yesterday's data is just not good enough. >> John: Yeah. >> It's got to be down to the hour, down to the minutes and sometimes even lower than that. And then I think we're also seeing customers look to their core business systems where they have a lot of value, like the SAP, like mainframe and thinking, okay, our core data is there, how can we get more value from this data? So that's key things we see all the time with customers. >> Yeah, we did a big editorial segment this year on, we called data as code. Data as code is kind of a riff on infrastructure as code and you start to see data becoming proliferating into all aspects, fresh data. It's not just where you store it, it's how you share it, it's how you turn it into an application intrinsically involved in all aspects. This is the big theme this year and that's driving all the conversations here at RE:Invent. And I'm guaranteeing you, it's going to happen for another five and 10 years. It's not stopping. So I got to get into the solution, you guys mentioned SAP and you've announced the solution by Qlik, Snowflake and AWS for your customers using SAP. Can you share more about this solution? What's unique about it? Why is it important and why now? Peter, Itamar, we'll start with you first. >> Let me jump in, this is really, I'll jump because I'm excited. We're very excited about this solution and it's also a solution by the way and again, we've seen proven customer success with it. So to your point, it's ready to scale, it's starting, I think we're going to see a lot of companies doing this over the next few years. But before we jump to the solution, let me maybe take a few minutes just to clarify the need, why we're seeing, why we're seeing customers jump to do this. So customers that use SAP, they use it to manage the core of their business. So think order processing, management, finance, inventory, supply chain, and so much more. So if you're running SAP in your company, that data creates a great opportunity for you to drive innovation and modernization. So what we see customers want to do, they want to do more with their data and more means they want to take SAP with non-SAP data and use it together to drive new insights. They want to use real-time data to drive real-time analytics, which they couldn't do to date. They want to bring together descriptive with predictive analytics. So adding machine learning in AI to drive more value from the data. And naturally they want to do it faster. So find ways to iterate faster on their solutions, have freedom with the data and agility. And I think this is really where cloud data platforms like Snowflake and AWS, you know, bring that value to be able to drive that. Now to do that you need to unlock the SAP data, which is a lot of also where Qlik comes in because typical challenges these customers run into is the complexity, inherent in SAP data. Tens of thousands of tables, proprietary formats, complex data models, licensing restrictions, and more than, you have performance issues, they usually run into how do we handle the throughput, the volumes while maintaining lower latency and impact. Where do we find knowledge to really understand how to get all this done? So these are the things we've looked at when we came together to create a solution and make it unique. So when you think about its uniqueness, because we put together a lot, and I'll go through three, four key things that come together to make this unique. First is about data delivery. How do you have the SAP data delivery? So how do you get it from ECC, from HANA from S/4HANA, how do you deliver the data and the metadata and how that integration well into Snowflake. And what we've done is we've focused a lot on optimizing that process and the continuous ingestion, so the real-time ingestion of the data in a way that works really well with the Snowflake system, data cloud. Second thing is we looked at SAP data transformation, so once the data arrives at Snowflake, how do we turn it into being analytics ready? So that's where data transformation and data worth automation come in. And these are all elements of this solution. So creating derivative datasets, creating data marts, and all of that is done by again, creating an optimized integration that pushes down SQL based transformations, so they can be processed inside Snowflake, leveraging its powerful engine. And then the third element is bringing together data visualization analytics that can also take all the data now that in organizing inside Snowflake, bring other data in, bring machine learning from SageMaker, and then you go to create a seamless integration to bring analytic applications to life. So these are all things we put together in the solution. And maybe the last point is we actually took the next step with this and we created something we refer to as solution accelerators, which we're really, really keen about. Think about this as prepackaged templates for common business analytic needs like order to cash, finance, inventory. And we can either dig into that a little more later, but this gets the next level of value to the customers all built into this joint solution. >> Yeah, I want to get to the accelerators, but real quick, Peter, your reaction to the solution, what's unique about it? And obviously Snowflake, we've been seeing the progression data applications, more developers developing on top of Snowflake, data as code kind of implies developer ecosystem. This is kind of interesting. I mean, you got partnering with Qlik and AWS, it's kind of a developer-like thinking real solution. What's unique about this SAP solution that's, that's different than what customers can get anywhere else or not? >> Yeah, well listen, I think first of all, you have to start with the idea of the solution. This are three companies coming together to build a holistic solution that is all about, you know, creating a great opportunity to turn SAP data into value this is Itamar was talking about, that's really what we're talking about here and there's a lot of technology underneath it. I'll talk more about the Snowflake technology, what's involved here, and then cover some of the AWS pieces as well. But you know, we're focusing on getting that value out and accelerating time to value for our joint customers. As Itamar was saying, you know, there's a lot of complexity with the SAP data and a lot of value there. How can we manage that in a prepackaged way, bringing together best of breed solutions with proven capabilities and bringing this to market quickly for our joint customers. You know, Snowflake and AWS have been strong partners for a number of years now, and that's not only on how Snowflake runs on top of AWS, but also how we integrate with their complementary analytics and then all products. And so, you know, we want to be able to leverage those in addition to what Qlik is bringing in terms of the data transformations, bringing data out of SAP in the visualization as well. All very critical. And then we want to bring in the predictive analytics, AWS brings and what Sage brings. We'll talk about that a little bit later on. Some of the technologies that we're leveraging are some of our latest cutting edge technologies that really make things easier for both our partners and our customers. For example, Qlik leverages Snowflakes recently released Snowpark for Python functionality to push down those data transformations from clicking the Snowflake that Itamar's mentioning. And while we also leverage Snowpark for integrations with Amazon SageMaker, but there's a lot of great new technology that just makes this easy and compelling for customers. >> I think that's the big word, easy button here for what may look like a complex kind of integration, kind of turnkey, really, really compelling example of the modern era we're living in, as we always say in theCUBE. You mentioned accelerators, SAP accelerators. Can you give an example of how that works with the technology from the third party providers to deliver this business value Itamar, 'cause that was an interesting comment. What's the example? Give an example of this acceleration. >> Yes, certainly. I think this is something that really makes this truly, truly unique in the industry and again, a great opportunity for customers. So we kind talked earlier about there's a lot of things that need to be done with SP data to turn it to value. And these accelerator, as the name suggests, are designed to do just that, to kind of jumpstart the process and reduce the time and the risk involved in such project. So again, these are pre-packaged templates. We basically took a lot of knowledge, and a lot of configurations, best practices about to get things done and we put 'em together. So think about all the steps, it includes things like data extraction, so already knowing which tables, all the relevant tables that you need to get data from in the contexts of the solution you're looking for, say like order to cash, we'll get back to that one. How do you continuously deliver that data into Snowflake in an in efficient manner, handling things like data type mappings, metadata naming conventions and transformations. The data models you build all the way to data mart definitions and all the transformations that the data needs to go through moving through steps until it's fully analytics ready. And then on top of that, even adding a library of comprehensive analytic dashboards and integrations through machine learning and AI and put all of that in a way that's in pre-integrated and tested to work with Snowflake and AWS. So this is where again, you get this entire recipe that's ready. So take for example, I think I mentioned order to cash. So again, all these things I just talked about, I mean, for those who are not familiar, I mean order to cash is a critical business process for every organization. So especially if you're in retail, manufacturing, enterprise, it's a big... This is where, you know, starting with booking a sales order, following by fulfilling the order, billing the customer, then managing the accounts receivable when the customer actually pays, right? So this all process, you got sales order fulfillment and the billing impacts customer satisfaction, you got receivable payments, you know, the impact's working capital, cash liquidity. So again, as a result this order to cash process is a lifeblood for many businesses and it's critical to optimize and understand. So the solution accelerator we created specifically for order to cash takes care of understanding all these aspects and the data that needs to come with it. So everything we outline before to make the data available in Snowflake in a way that's really useful for downstream analytics, along with dashboards that are already common for that, for that use case. So again, this enables customers to gain real-time visibility into their sales orders, fulfillment, accounts receivable performance. That's what the Excel's are all about. And very similarly, we have another one for example, for finance analytics, right? So this will optimize financial data reporting, helps customers get insights into P&L, financial risk of stability or inventory analytics that helps with, you know, improve planning and inventory management, utilization, increased efficiencies, you know, so in supply chain. So again, these accelerators really help customers get a jumpstart and move faster with their solutions. >> Peter, this is the easy button we just talked about, getting things going, you know, get the ball rolling, get some acceleration. Big part of this are the three companies coming together doing this. >> Yeah, and to build on what Itamar just said that the SAP data obviously has tremendous value. Those sales orders, distribution data, financial data, bringing that into Snowflake makes it easily accessible, but also it enables it to be combined with other data too, is one of the things that Snowflake does so well. So you can get a full view of the end-to-end process and the business overall. You know, for example, I'll just take one, you know, one example that, that may not come to mind right away, but you know, looking at the impact of weather conditions on supply chain logistics is relevant and material and have interest to our customers. How do you bring those different data sets together in an easy way, bringing the data out of SAP, bringing maybe other data out of other systems through Qlik or through Snowflake, directly bringing data in from our data marketplace and bring that all together to make it work. You know, fundamentally organizational silos and the data fragmentation exist otherwise make it really difficult to drive modern analytics projects. And that in turn limits the value that our customers are getting from SAP data and these other data sets. We want to enable that and unleash. >> Yeah, time for value. This is great stuff. Itamar final question, you know, what are customers using this? What do you have? I'm sure you have customers examples already using the solution. Can you share kind of what these examples look like in the use cases and the value? >> Oh yeah, absolutely. Thank you. Happy to. We have customers across different, different sectors. You see manufacturing, retail, energy, oil and gas, CPG. So again, customers in those segments, typically sectors typically have SAP. So we have customers in all of them. A great example is like Siemens Energy. Siemens Energy is a global provider of gas par services. You know, over what, 28 billion, 30 billion in revenue. 90,000 employees. They operate globally in over 90 countries. So they've used SAP HANA as a core system, so it's running on premises, multiple locations around the world. And what they were looking for is a way to bring all these data together so they can innovate with it. And the thing is, Peter mentioned earlier, not just the SAP data, but also bring other data from other systems to bring it together for more value. That includes finance data, these logistics data, these customer CRM data. So they bring data from over 20 different SAP systems. Okay, with Qlik data integration, feeding that into Snowflake in under 20 minutes, 24/7, 365, you know, days a year. Okay, they get data from over 20,000 tables, you know, over million, hundreds of millions of records daily going in. So it is a great example of the type of scale, scalability, agility and speed that they can get to drive these kind of innovation. So that's a great example with Siemens. You know, another one comes to mind is a global manufacturer. Very similar scenario, but you know, they're using it for real-time executive reporting. So it's more like feasibility to the production data as well as for financial analytics. So think, think, think about everything from audit to texts to innovate financial intelligence because all the data's coming from SAP. >> It's a great time to be in the data business again. It keeps getting better and better. There's more data coming. It's not stopping, you know, it's growing so fast, it keeps coming. Every year, it's the same story, Peter. It's like, doesn't stop coming. As we wrap up here, let's just get customers some information on how to get started. I mean, obviously you're starting to see the accelerators, it's a great program there. What a great partnership between the two companies and AWS. How can customers get started to learn about the solution and take advantage of it, getting more out of their SAP data, Peter? >> Yeah, I think the first place to go to is talk to Snowflake, talk to AWS, talk to our account executives that are assigned to your account. Reach out to them and they will be able to educate you on the solution. We have packages up very nicely and can be deployed very, very quickly. >> Well gentlemen, thank you so much for coming on. Appreciate the conversation. Great overview of the partnership between, you know, Snowflake and Qlik and AWS on a joint solution. You know, getting more out of the SAP data. It's really kind of a key, key solution, bringing SAP data to life. Thanks for coming on theCUBE. Appreciate it. >> Thank you. >> Thank you John. >> Okay, this is theCUBE coverage here at RE:Invent 2022. I'm John Furrier, your host of theCUBE. Thanks for watching. (upbeat music)

(upbeat music) >> It's theCUBE live in Las Vegas, covering AWS re:Invent 2022. This is the first full day of coverage. We will be here tomorrow and Thursday but we started last night. So hopefully you've caught some of those interviews. Lisa Martin here in Vegas with Paul Gillin. Paul, it's great to be back. We just saw a tweet from a very reliable source saying that there are upwards of 70,000 people here at rei:Invent '22 >> I think there's 70,000 people just in that aisle right there. >> I think so. It's been great so far we've gotten, what are some of the things that you have been excited about today? >> Data, I just see data everywhere, which very much relates to our next guest. Companies realizing the value of data and the strategic value of data, beginning to treat it as an asset rather than just exhaust. I see a lot of focus on app development here and building scalable applications now. Developers have to get over that, have to sort of reorient themselves toward building around the set of cloud native primitives which I think we'll see some amazing applications come out of that. >> Absolutely, we will. We're pleased to welcome back one of our alumni to the program. Shinji Kim joins us, the CEO and founder of Select Star. Welcome back Shinji. It's great to have you. >> Thanks Lisa, great to be back. >> So for the audience who may not know much about Select Star before we start digging into all of the good stuff give us a little overview about what the company does and what differentiates you. >> Sure, so Select Star is an automated data discovery platform. We act like it's Google for data scientists, data analysts and data engineers to help find and understand their data better. Lot of companies today, like what you mentioned, Paul, have 100s and 1000s of database tables now swimming through large volumes of data and variety of data today and it's getting harder and harder for people that wants to utilize data make decisions around data and analyze data to truly have the full context of where this data came from, who do you think that's inside the company or what other analysis might have been done? So Select Star's role in this case is we connect different data warehouses BI tools, wherever the data is actually being used inside the company, bringing out all the usage analytics and the pipeline and the models in one place so anyone can search through what's available and how the data has been created, used and being analyzed within the company. So that's why we call it it's kind of like your Google for data. >> What are some of the biggest challenges to doing that? I mean you've got data squirreled away in lots of corners of the organization, Excel spreadsheets, thumb drives, cloud storage accounts. How granular do you get and what's the difficulty of finding all this data? >> So today we focus primarily on lot of cloud data warehouses and data lakes. So this includes data warehouses like Redshift, Snowflake (indistinct), Databricks, S3 buckets, where a lot of the data from different sources are arriving. Because this is a one area where a lot of analysis are now being done. This is a place where you can join other data sets within the same infrastructural umbrella. And so that is one portion that we always integrate with. The other part that we also integrate a lot with are the BI tools. So whether that's (indistinct) where you are running analysis, building reports, and dashboards. We will pull out how those are, which analysis has been done and which business stakeholders are consuming that data through those tools. So you also mentioned about the differentiation. I would say one of the biggest differentiation that we have in the market today is that we are more in the cloud. So it's very cloud native, fully managed SaaS service and it's really focused on user experience of how easily anyone can really search and understand data through Select Star. In the past, data catalogs as a sector has been primarily focused on inventorizing all your enterprise data which are in many disciplinary forces. So it was more focused on technical aspect of the metadata. At the same time now this enterprise data catalog is important and is needed for even smaller companies because they are dealing with ton of data. Another part that we also see is more of democratization of data. Many different types of users are utilizing data whether they are fully technical or not. So we had basically emphasis around how to make our user interface as intuitive as possible for business users or non-technical users but also bring out as much context as possible from the metadata and the laws that we have access to, to bring out these insights for our customers. >> Got it. What was the impetus or the catalyst to launch the business just a couple of years ago? >> Yeah, so prior to this I had another data startup called Concord Systems. We focused on distributed stream processing framework. I sold the company to Akamai which is now called ... and the product is now called IoT Edge Connect. Through Akamai I started working with a lot of enterprises in automotive and consumer electronics and this is where I saw lot of the issues starting to happen when enterprises are starting to try to use the data. Collection of data, storage of data, processing of data with the help of lot of cloud providers, scaling that is not going to be a challenge as much anymore. At the same time now lot of enterprises, what I realized is a lot of enterprises were sitting on top of ton of data that they may not know how to utilize it or know even how to give the access to because they are not 100% sure what's really inside. And more and more companies, as they are building up their cloud data warehouse infrastructure they're starting to run into the same issue. So this is a part that I felt like was missing gap in the market that I wanted to fulfill and that's why I started the company. >> I'm fascinated with some of the mechanics of doing that. In March of 2020 when lockdowns were happening worldwide you're starting new a company, you have to get funding, you have to hire people, you don't have a team in place presumably. So you have to build that as free to core. How did you do all that? (Shinji laughs) >> Yeah, that was definitely a lot of work just starting from scratch. But I've been brewing this idea, I would say three four months prior. I had a few other ideas. Basically after Akamai I took some time off and then when I decided I wanted to start another company there were a number of ideas that I was toying around with. And so late 2019 I was talking to a lot of different potential customers and users to learn a little bit more about whether my hypothesis around data discovery was true or not. And that kind of led into starting to build prototypes and designs and showing them around to see if there is an interest. So it's only after all those validations and conversations in place that I truly decided that I was going to start another company and it just happened to be at the timing of end of February, early March. So that's kind of how it happened. At the same time, I'm very lucky that I was able to have had number of investors that I kept in touch with and I kept them posted on how this process was going and that's why I think during the pandemic it was definitely not an easy thing to raise our initial seed round but we were able to close it and then move on to really start building the product in 2020. >> Now you were also entering a market that's there's quite a few competitors already in that market. What has been your strategy for getting a foot in the door, getting some name recognition for your company other than being on the queue? >> Yes, this is certainly part of it. So I think there are a few things. One is when I was doing my market research and even today there are a lot of customers out there looking for an easier, faster, time to value solution. >> Yes. >> In the market. Today, existing players and legacy players have a whole suite of platform. However, the implementation time for those platforms take six months or longer and they don't necessarily are built for lot of users to use. They are built for database administrators or more technical people to use so that they end up finding their data governance project not necessarily succeeding or getting as much value out of it as they were hoping for. So this is an area that we really try to fill the gaps in because for us from day one you will be able to see all the usage analysis, how your data models look like, and the analysis right up front. And this is one part that a lot of our customers really like and also some of those customers have moved from the legacy players to Select Star's floor. >> Interesting, so you're actually taking business from some of the legacy guys and girls that may not be able to move as fast and quickly as you can. But I'd love to hear, every company these days has to be a data company, whether it's a grocery store or obviously a bank or a car dealership, there's no choice anymore. As consumers, we have this expectation that we're going to be able to get what we want, self-service. So these companies have to figure out where all the data is, what's the insides, what does it say, how can they act on that quickly? And that's a big challenge to enable organizations to be able to see what it is that they have, where's the value, where's the liability as well. Give me a favorite customer story example that you think really highlights the value of what Select Star is delivering. >> Sure, so one customer that we helped and have been working with closely is Pitney Bowes. It's one of the oldest companies, 100 year old company in logistics and manufacturing. They have ton of IoT data they collect from parcels and all the tracking and all the manufacturing that they run. They have recently, I would say a couple years ago moved to a cloud data warehouse. And this is where their challenge around managing data have really started because they have many different teams accessing the data warehouses but maybe different teams creating different things that might have been created before and it's not clear to the other teams and there is no single source of truth that they could manage. So for them, as they were starting to look into implementing data mesh architecture they adopted Select Star. And they have a, as being a very large and also mature company they have considered a lot of other legacy solutions in the market as well. But they decided to give it a try with select Star mainly because all of the automated version of data modeling and the documentation that we were able to provide upfront. And with all that, with the implementation of Select Star now they claim that they save more than 30 hours a month of every person that they have in the data management team. And we have a case study about that. So this is like one place where we see it save a lot of time for the data team as well as all the consumers that data teams serve. >> I have to ask you this as a successful woman in technology, a field that has not been very inviting to women over the years, what do you think this industry has to do better in terms of bringing along girls and young women, particularly in secondary school to encourage them to pursue careers in science and technology? >> Like what could they do better? >> What could this industry do? What is this industry, these 70,000 people here need to do better? Of which maybe 15% are female. >> Yeah, so actually I do see a lot more women and minority in data analytics field which is always great to see, also like bridging the gap between technology and the business point of view. If anything as a takeaway I feel like just making more opportunities for everyone to participate is always great. I feel like there has been, or you know just like being in the industry, a lot of people tends to congregate with people that they know or more closed groups but having more inclusive open groups that is inviting regardless of the level or gender I think is definitely something that needs to be encouraged more just overall in the industry. >> I agree. I think the inclusivity is so important but it also needs to be intentional. We've done a lot of chatting with women in tech lately and we've been talking about this very topic and that they all talk about the inclusivity, diversity, equity but it needs to be intentional by companies to be able to do that. >> Right, and I think in a way if you were to put it as like women in tech then I feel like that's also making it more explosive. I think it's better when it's focused on the industry problem or like the subject matter, but then intentionally inviting more women and minority to participate so that there's more exchange with more diverse attendees in the AWS. >> That's a great point and I hope to your 0.1 day that we're able to get there, but we don't have to call out women in tech but it is just so much more even playing field. And I hope like you that we're on our way to doing that but it's amazing that Paul brought up that you started the company during the pandemic. Also as a female founder getting funding is incredibly difficult. So kudos to you. >> Thank you. >> For all the successes that you've had. Tell us what's next for Select Star before we get to that last question. >> Yeah, we have a lot of exciting features that have been recently released and also coming up. First and foremost we have an auto documentation feature that we recently released. We have a fairly sophisticated data lineage function that parses through activity log and sequel queries to give you what the data pipeline models look like. This allows you to tell what is the dependency of different tables and dashboards so you can plan what your migration or any changes that might happen in the data warehouse so that nothing breaks whenever these changes happen. We went one step further to that to understand how the data replication actually happens and based on that we are now able to detect which are the duplicated data sets and how each different field might have changed their data values. And if the data actually stays the same then we can also propagate the same documentation as well as tagging. So this is particularly useful if you are doing like a PII tagging, you just mark one thing once and based on the data model we will also have the rest of the PII that it's associated with. So that's one part. The second part is more on the security and data governance front. So we are really seeing policy based access control where you can define who can see what data in the catalog based on their team tags and how you want to define the model. So this allows more enterprises to be able to have different teams to work together. And last one at least we have more integrations that we are releasing. We have an upgraded integration now with Redshift so that there's an easy cloud formation template to get it set up, but we now have not added Databricks, and power BI as well. So there are lots of stuff coming up. >> Man, you have accomplished a lot in two and a half years Shinji, my goodness! Last question for you, describing Select Star in a bumper sticker, what would that bumper sticker say? >> So this is on our website, but yes, automated data catalog in 15 minutes would be what I would call. >> 15 minutes. That's awesome. Thank you so much for joining us back on the program reintroducing our audience to Select Star. And again, congratulations on the successes that you've had. You have to come back because what you're creating is a flywheel and I can't wait to see where it goes. >> Awesome, thanks so much for having me here. >> Oh, our pleasure. Shinji Kim and Paul Gillin, 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.

(outro music) >> Hey, I'm John Furrier, here with an exclusive interview with Ali Ghodsi, who's the CEO of Databricks. Ali, great to see you. Preview for reinvent. We're going to launch this story, exclusive Databricks material on the notes, after the keynotes prior to the keynotes and after the keynotes that reinvent. So great to see you. You know, you've been a partner of AWS for a very, very long time. I think five years ago, I think I first interviewed you, you were one of the first to publicly declare that this was a place to build a company on and not just post an application, but refactor capabilities to create, essentially a platform in the cloud, on the cloud. Not just an ISV; Independent Software Vendor, kind of an old term, we're talking about real platform like capability to change the game. Can you talk about your experience as an AWS partner? >> Yeah, look, so we started in 2013. I swiped my personal credit card on AWS and some of my co-founders did the same. And we started building. And we were excited because we just thought this is a much better way to launch a company because you can just much faster get time to market and launch your thing and you can get the end users much quicker access to the thing you're building. So we didn't really talk to anyone at AWS, we just swiped a credit card. And eventually they told us, "Hey, do you want to buy extra support?" "You're asking a lot of advanced questions from us." "Maybe you want to buy our advanced support." And we said, no, no, no, no. We're very advanced ourselves, we know what we're doing. We're not going to buy any advanced support. So, you know, we just built this, you know, startup from nothing on AWS without even talking to anyone there. So at some point, I think around 2017, they suddenly saw this company with maybe a hundred million ARR pop up on their radar and it's driving massive amounts of compute, massive amounts of data. And it took a little bit in the beginning just us to get to know each other because as I said, it's like we were not on their radar and we weren't really looking, we were just doing our thing. And then over the years the partnership has deepened and deepened and deepened and then with, you know, Andy (indistinct) really leaning into the partnership, he mentioned us at Reinvent. And then we sort of figured out a way to really integrate the two service, the Databricks platform with AWS . And today it's an amazing partnership. You know, we directly connected with the general managers for the services. We're connected at the CEO level, you know, the sellers get compensated for pushing Databricks, we're, we have multiple offerings on their marketplace. We have a native offering on AWS. You know, we're prominently always sort of marketed and you know, we're aligned also vision wise in what we're trying to do. So yeah, we've come a very, very long way. >> Do you consider yourself a SaaS app or an ISV or do you see yourself more of a platform company because you have customers. How would you categorize your category as a company? >> Well, it's a data platform, right? And actually the, the strategy of the Databricks is take what's otherwise five, six services in the industry or five, six different startups, but do them as part of one data platform that's integrated. So in one word, the strategy of data bricks is "unification." We call it the data lake house. But really the idea behind the data lake house is that of unification, or in more words it's, "The whole is greater than the sum of its parts." So you could actually go and buy five, six services out there or actually use five, six services from the cloud vendors, stitch it together and it kind of resembles Databricks. Our power is in doing those integrated, together in a way in which it's really, really easy and simple to use for end users. So yeah, we're a data platform. I wouldn't, you know, ISV that's a old term, you know, Independent Software Vendor. You know, I think, you know, we have actually a whole slew of ISVs on top of Databricks, that integrate with our platform. And you know, in our marketplace as well as in our partner connect, we host those ISVs that then, you know, work on top of the data that we have in the Databricks, data lake house. >> You know, I think one of the things your journey has been great to document and watch from the beginning. I got to give you guys credit over there and props, congratulations. But I think you're the poster child as a company to what we see enterprises doing now. So go back in time when you guys swiped a credit card, you didn't need attending technical support because you guys had brains, you were refactoring, rethinking. It wasn't just banging out software, you had, you were doing some complex things. It wasn't like it was just write some software hosted on server. It was really a lot more. And as a result your business worth billions of dollars. I think 38 billion or something like that, big numbers, big numbers of great revenue growth as well, billions in revenue. You have customers, you have an ecosystem, you have data applications on top of Databricks. So in a way you're a cloud on top of the cloud. So is there a cloud on top of the cloud? So you have ISVs, Amazon has ISVs. Can you take us through what this means and at this point in history, because this seems to be an advanced version of benefits of platforming and refactoring, leveraging say AWS. >> Yeah, so look, when we started, there was really only one game in town. It was AWS. So it was one cloud. And the strategy of the company then was, well Amazon had this beautiful set of services that they're building bottom up, they have storage, compute, networking, and then they have databases and so on. But it's a lot of services. So let us not directly compete with AWS and try to take out one of their services. Let's not do that because frankly we can't. We were not of that size. They had the scale, they had the size and they were the only cloud vendor in town. So our strategy instead was, let's do something else. Let's not compete directly with say, a particular service they're building, let's take a different strategy. What if we had a unified holistic data platform, where it's just one integrated service end to end. So think of it as Microsoft office, which contains PowerPoint, and Word, and Excel and even Access, if you want to use it. What if we build that and AWS has this really amazing knack for releasing things, you know services, lots of them, every reinvent. And they're sort of a DevOps person's dream and you can stitch these together and you know you have to be technical. How do we elevate that and make it simpler and integrate it? That was our original strategy and it resonated with a segment of the market. And the reason it worked with AWS so that we wouldn't butt heads with AWS was because we weren't a direct replacement for this service or for that service, we were taking a different approach. And AWS, because credit goes to them, they're so customer obsessed, they would actually do what's right for the customer. So if the customer said we want this unified thing, their sellers would actually say, okay, so then you should use Databricks. So they truly are customer obsessed in that way. And I really mean it, John. Things have changed over the years. They're not the only cloud anymore. You know, Azure is real, GCP is real, there's also Alibaba. And now over 70% of our customers are on more than one cloud. So now what we hear from them is, not only want, do we want a simplified, unified thing, but we want it also to work across the clouds. Because those of them that are seriously considering multiple clouds, they don't want to use a service on cloud one and then use a similar service on cloud two. But it's a little bit different. And now they have to do twice the work to make it work. You know, John, it's hard enough as it is, like it's this data stuff and analytics. It's not a walk in the park, you know. You hire an administrator in the back office that clicks a button and its just, now you're a data driven digital transformed company. It's hard. If you now have to do it again on the second cloud with different set of services and then again on a third cloud with a different set of services. That's very, very costly. So the strategy then has changed that, how do we take that unified simple approach and make it also the same and standardize across the clouds, but then also integrate it as far down as we can on each of the clouds. So that you're not giving up any of the benefits that the particular cloud has. >> Yeah, I think one of the things that we see, and I want get your reaction to this, is this rise of the super cloud as we call it. I think you were involved in the Sky paper that I saw your position paper came out after we had introduced Super Cloud, which is great. Congratulations to the Berkeley team, wearing the hat here. But you guys are, I think a driver of this because you're creating the need for these things. You're saying, okay, we went on one cloud with AWS and you didn't hide that. And now you're publicly saying there's other clouds too, increased ham for your business. And customers have multiple clouds in their infrastructure for the best of breed that they have. Okay, get that. But there's still a challenge around the innovation, growth that's still around the corner. We still have a supply chain problem, we still have skill gaps. You know, you guys are unique at Databricks as other these big examples of super clouds that are developing. Enterprises don't have the Databricks kind of talent. They need, they need turnkey solutions. So Adam and the team at Amazon are promoting, you know, more solution oriented approaches higher up on the stack. You're starting to see kind of like, I won't say templates, but you know, almost like application specific headless like, low code, no code capability to accelerate clients who are wanting to write code for the modern error. Right, so this kind of, and then now you, as you guys pointed out with these common services, you're pushing the envelope. So you're saying, hey, I need to compete, I don't want to go to my customers and have them to have a staff or this cloud and this cloud and this cloud because they don't have the staff. Or if they do, they're very unique. So what's your reaction? Because this kind is the, it kind of shows your leadership as a partner of AWS and the clouds, but also highlights I think what's coming. But you share your reaction. >> Yeah, look, it's, first of all, you know, I wish I could take credit for this but I can't because it's really the customers that have decided to go on multiple clouds. You know, it's not Databricks that you know, push this or some other vendor, you know, that, Snowflake or someone who pushed this and now enterprises listened to us and they picked two clouds. That's not how it happened. The enterprises picked two clouds or three clouds themselves and we can get into why, but they did that. So this largely just happened in the market. We as data platforms responded to what they're then saying, which is they're saying, "I don't want to redo this again on the other cloud." So I think the writing is on the wall. I think it's super obvious what's going to happen next. They will say, "Any service I'm using, it better work exactly the same on all the clouds." You know, that's what's going to happen. So in the next five years, every enterprise will say, "I'm going to use the service, but you better make sure that this service works equally well on all of the clouds." And obviously the multicloud vendors like us, are there to do that. But I actually think that what you're going to see happening is that you're going to see the cloud vendors changing the existing services that they have to make them work on the other clouds. That's what's goin to happen, I think. >> Yeah, and I think I would add that, first of all, I agree with you. I think that's going to be a forcing function. Because I think you're driving it. You guys are in a way, one, are just an actor in the driving this because you're on the front end of this and there are others and there will be people following. But I think to me, I'm a cloud vendor, I got to differentiate. Adam, If I'm Adam Saleski, I got to say, "Hey, I got to differentiate." So I don't wan to get stuck in the middle, so to speak. Am I just going to innovate on the hardware AKA infrastructure or am I going to innovate at the higher level services? So what we're talking about here is the tail of two clouds within Amazon, for instance. So do I innovate on the silicon and get low level into the physics and squeeze performance out of the hardware and infrastructure? Or do I focus on ease of use at the top of the stack for the developers? So again, there's a channel of two clouds here. So I got to ask you, how do they differentiate? Number one and number two, I never heard a developer ever say, "I want to run my app or workload on the slower cloud." So I mean, you know, back when we had PCs you wanted to go, "I want the fastest processor." So again, you can have common level services, but where is that performance differentiation with the cloud? What do the clouds do in your opinion? >> Yeah, look, I think it's pretty clear. I think that it's, this is, you know, no surprise. Probably 70% or so of the revenue is in the lower infrastructure layers, compute, storage, networking. And they have to win that. They have to be competitive there. As you said, you can say, oh you know, I guess my CPUs are slower than the other cloud, but who cares? I have amazing other services which only work on my cloud by the way, right? That's not going to be a winning recipe. So I think all three are laser focused on, we going to have specialized hardware and the nuts and bolts of the infrastructure, we can do it better than the other clouds for sure. And you can see lots of innovation happening there, right? The Graviton chips, you know, we see huge price performance benefits in those chips. I mean it's real, right? It's basically a 20, 30% free lunch. You know, why wouldn't you, why wouldn't you go for it there? There's no downside. You know, there's no, "got you" or no catch. But we see Azure doing the same thing now, they're also building their own chips and we know that Google builds specialized machine learning chips, TPU, Tenor Processing Units. So their legs are focused on that. I don't think they can give up that or focused on higher levels if they had to pick bets. And I think actually in the next few years, most of us have to make more, we have to be more deliberate and calculated in the picks we do. I think in the last five years, most of us have said, "We'll do all of it." You know. >> Well you made a good bet with Spark, you know, the duke was pretty obvious trend that was, everyone was shut on that bandwagon and you guys picked a big bet with Spark. Look what happened with you guys? So again, I love this betting kind of concept because as the world matures, growth slows down and shifts and that next wave of value coming in, AKA customers, they're going to integrate with a new ecosystem. A new kind of partner network for AWS and the other clouds. But with aws they're going to need to nurture the next Databricks. They're going to need to still provide that SaaS, ISV like experience for, you know, a basic software hosting or some application. But I go to get your thoughts on this idea of multiple clouds because if I'm a developer, the old days was, old days, within our decade, full stack developer- >> It was two years ago, yeah (John laughing) >> This is a decade ago, full stack and then the cloud came in, you kind had the half stack and then you would do some things. It seems like the clouds are trying to say, we want to be the full stack or not. Or is it still going to be, you know, I'm an application like a PC and a Mac, I'm going to write the same application for both hardware. I mean what's your take on this? Are they trying to do full stack and you see them more like- >> Absolutely. I mean look, of course they're going, they have, I mean they have over 300, I think Amazon has over 300 services, right? That's not just compute, storage, networking, it's the whole stack, right? But my key point is, I think they have to nail the core infrastructure storage compute networking because the three clouds that are there competing, they're formidable companies with formidable balance sheets and it doesn't look like any of them is going to throw in the towel and say, we give up. So I think it's going to intensify. And given that they have a 70% revenue on that infrastructure layer, I think they, if they have to pick their bets, I think they'll focus it on that infrastructure layer. I think the layer above where they're also placing bets, they're doing that, the full stack, right? But there I think the demand will be, can you make that work on the other clouds? And therein lies an innovator's dilemma because if I make it work on the other clouds, then I'm foregoing that 70% revenue of the infrastructure. I'm not getting it. The other cloud vendor is going to get it. So should I do that or not? Second, is the other cloud vendor going to be welcoming of me making my service work on their cloud if I am a competing cloud, right? And what kind of terms of service are I giving me? And am I going to really invest in doing that? And I think right now we, you know, most, the vast, vast, vast majority of the services only work on the one cloud that you know, it's built on. It doesn't work on others, but this will shift. >> Yeah, I think the innovators dilemma is also very good point. And also add, it's an integrators dilemma too because now you talk about integration across services. So I believe that the super cloud movement's going to happen before Sky. And I think what explained by that, what you guys did and what other companies are doing by representing advanced, I call platform engineering, refactoring an existing market really fast, time to value and CAPEX is, I mean capital, market cap is going to be really fast. I think there's going to be an opportunity for those to emerge that's going to set the table for global multicloud ultimately in the future. So I think you're going to start to see the same pattern of what you guys did get in, leverage the hell out of it, use it, not in the way just to host, but to refactor and take down territory of markets. So number one, and then ultimately you get into, okay, I want to run some SLA across services, then there's a little bit more complication. I think that's where you guys put that beautiful paper out on Sky Computing. Okay, that makes sense. Now if you go to today's market, okay, I'm betting on Amazon because they're the best, this is the best cloud win scenario, not the most robust cloud. So if I'm a developer, I want the best. How do you look at their bet when it comes to data? Because now they've got machine learning, Swami's got a big keynote on Wednesday, I'm expecting to see a lot of AI and machine learning. I'm expecting to hear an end to end data story. This is what you do, so as a major partner, how do you view the moves Amazon's making and the bets they're making with data and machine learning and AI? >> First I want to lift off my hat to AWS for being customer obsessed. So I know that if a customer wants Databricks, I know that AWS and their sellers will actually help us get that customer deploy Databricks. Now which of the services is the customer going to pick? Are they going to pick ours or the end to end, what Swami is going to present on stage? Right? So that's the question we're getting. But I wanted to start with by just saying, their customer obsessed. So I think they're going to do the right thing for the customer and I see the evidence of it again and again and again. So kudos to them. They're amazing at this actually. Ultimately our bet is, customers want this to be simple, integrated, okay? So yes there are hundreds of services that together give you the end to end experience and they're very customizable that AWS gives you. But if you want just something simply integrated that also works across the clouds, then I think there's a special place for Databricks. And I think the lake house approach that we have, which is an integrated, completely integrated, we integrate data lakes with data warehouses, integrate workflows with machine learning, with real time processing, all these in one platform. I think there's going to be tailwinds because I think the most important thing that's going to happen in the next few years is that every customer is going to now be obsessed, given the recession and the environment we're in. How do I cut my costs? How do I cut my costs? And we learn this from the customers they're adopting the lake house because they're thinking, instead of using five vendors or three vendors, I can simplify it down to one with you and I can cut my cost. So I think that's going to be one of the main drivers of why people bet on the lake house because it helps them lower their TCO; Total Cost of Ownership. And it's as simple as that. Like I have three things right now. If I can get the same job done of those three with one, I'd rather do that. And by the way, if it's three or four across two clouds and I can just use one and it just works across two clouds, I'm going to do that. Because my boss is telling me I need to cut my budget. >> (indistinct) (John laughing) >> Yeah, and I'd rather not to do layoffs and they're asking me to do more. How can I get smaller budgets, not lay people off and do more? I have to cut, I have to optimize. What's happened in the last five, six years is there's been a huge sprawl of services and startups, you know, you know most of them, all these startups, all of them, all the activity, all the VC investments, well those companies sold their software, right? Even if a startup didn't make it big, you know, they still sold their software to some vendors. So the ecosystem is now full of lots and lots and lots and lots of different software. And right now people are looking, how do I consolidate, how do I simplify, how do I cut my costs? >> And you guys have a great solution. You're also an arms dealer and a innovator. So I have to ask this question, because you're a professor of the industry as well as at Berkeley, you've seen a lot of the historical innovations. If you look at the moment we're in right now with the recession, okay we had COVID, okay, it changed how people work, you know, people working at home, provisioning VLAN, all that (indistinct) infrastructure, okay, yeah, technology and cloud health. But we're in a recession. This is the first recession where the Amazon and the other cloud, mainly Amazon Web Services is a major economic puzzle in the piece. So they were never around before, even 2008, they were too small. They're now a major economic enabler, player, they're serving startups, enterprises, they have super clouds like you guys. They're a force and the people, their customers are cutting back but also they can also get faster. So agility is now an equation in the economic recovery. And I want to get your thoughts because you just brought that up. Customers can actually use the cloud and Databricks to actually get out of the recovery because no one's going to say, stop making profit or make more profit. So yeah, cut costs, be more efficient, but agility's also like, let's drive more revenue. So in this digital transformation, if you take this to conclusion, every company transforms, their company is the app. So their revenue is tied directly to their technology deployment. What's your reaction and comment to that because this is a new historical moment where cloud and scale and data, actually could be configured in a way to actually change the nature of a business in such a short time. And with the recession looming, no one's got time to wait. >> Yeah, absolutely. Look, the secular tailwind in the market is that of, you know, 10 years ago it was software is eating the world, now it's AI's going to eat all of software software. So more and more we're going to have, wherever you have software, which is everywhere now because it's eaten the world, it's going to be eaten up by AI and data. You know, AI doesn't exist without data so they're synonymous. You can't do machine learning if you don't have data. So yeah, you're going to see that everywhere and that automation will help people simplify things and cut down the costs and automate more things. And in the cloud you can also do that by changing your CAPEX to OPEX. So instead of I invest, you know, 10 million into a data center that I buy, I'm going to have headcount to manage the software. Why don't we change this to OPEX? And then they are going to optimize it. They want to lower the TCO because okay, it's in the cloud. but I do want the costs to be much lower that what they were in the previous years. Last five years, nobody cared. Who cares? You know what it costs. You know, there's a new brave world out there. Now there's like, no, it has to be efficient. So I think they're going to optimize it. And I think this lake house approach, which is an integration of the lakes and the warehouse, allows you to rationalize the two and simplify them. It allows you to basically rationalize away the data warehouse. So I think much faster we're going to see the, why do I need the data warehouse? If I can get the same thing done with the lake house for fraction of the cost, that's what's going to happen. I think there's going to be focus on that simplification. But I agree with you. Ultimately everyone knows, everybody's a software company. Every company out there is a software company and in the next 10 years, all of them are also going to be AI companies. So that is going to continue. >> (indistinct), dev's going to stop. And right sizing right now is a key economic forcing function. Final question for you and I really appreciate you taking the time. This year Reinvent, what's the bumper sticker in your mind around what's the most important industry dynamic, power dynamic, ecosystem dynamic that people should pay attention to as we move from the brave new world of okay, I see cloud, cloud operations. I need to really make it structurally change my business. How do I, what's the most important story? What's the bumper sticker in your mind for Reinvent? >> Bumper sticker? lake house 24. (John laughing) >> That's data (indistinct) bumper sticker. What's the- >> (indistinct) in the market. No, no, no, no. You know, it's, AWS talks about, you know, all of their services becoming a lake house because they want the center of the gravity to be S3, their lake. And they want all the services to directly work on that, so that's a lake house. We're Bumper see Microsoft with Synapse, modern, you know the modern intelligent data platform. Same thing there. We're going to see the same thing, we already seeing it on GCP with Big Lake and so on. So I actually think it's the how do I reduce my costs and the lake house integrates those two. So that's one of the main ways you can rationalize and simplify. You get in the lake house, which is the name itself is a (indistinct) of two things, right? Lake house, "lake" gives you the AI, "house" give you the database data warehouse. So you get your AI and you get your data warehousing in one place at the lower cost. So for me, the bumper sticker is lake house, you know, 24. >> All right. Awesome Ali, well thanks for the exclusive interview. Appreciate it and get to see you. Congratulations on your success and I know you guys are going to be fine. >> Awesome. Thank you John. It's always a pleasure. >> Always great to chat with you again. >> Likewise. >> You guys are a great team. We're big fans of what you guys have done. We think you're an example of what we call "super cloud." Which is getting the hype up and again your paper speaks to some of the innovation, which I agree with by the way. I think that that approach of not forcing standards is really smart. And I think that's absolutely correct, that having the market still innovate is going to be key. standards with- >> Yeah, I love it. We're big fans too, you know, you're doing awesome work. We'd love to continue the partnership. >> So, great, great Ali, thanks. >> Take care (outro music)

(outro music) >> Hey, I'm John Furrier, here with an exclusive interview with Ali Ghodsi, who's the CEO of Databricks. Ali, great to see you. Preview for reinvent. We're going to launch this story, exclusive Databricks material on the notes, after the keynotes prior to the keynotes and after the keynotes that reinvent. So great to see you. You know, you've been a partner of AWS for a very, very long time. I think five years ago, I think I first interviewed you, you were one of the first to publicly declare that this was a place to build a company on and not just post an application, but refactor capabilities to create, essentially a platform in the cloud, on the cloud. Not just an ISV; Independent Software Vendor, kind of an old term, we're talking about real platform like capability to change the game. Can you talk about your experience as an AWS partner? >> Yeah, look, so we started in 2013. I swiped my personal credit card on AWS and some of my co-founders did the same. And we started building. And we were excited because we just thought this is a much better way to launch a company because you can just much faster get time to market and launch your thing and you can get the end users much quicker access to the thing you're building. So we didn't really talk to anyone at AWS, we just swiped a credit card. And eventually they told us, "Hey, do you want to buy extra support?" "You're asking a lot of advanced questions from us." "Maybe you want to buy our advanced support." And we said, no, no, no, no. We're very advanced ourselves, we know what we're doing. We're not going to buy any advanced support. So, you know, we just built this, you know, startup from nothing on AWS without even talking to anyone there. So at some point, I think around 2017, they suddenly saw this company with maybe a hundred million ARR pop up on their radar and it's driving massive amounts of compute, massive amounts of data. And it took a little bit in the beginning just us to get to know each other because as I said, it's like we were not on their radar and we weren't really looking, we were just doing our thing. And then over the years the partnership has deepened and deepened and deepened and then with, you know, Andy (indistinct) really leaning into the partnership, he mentioned us at Reinvent. And then we sort of figured out a way to really integrate the two service, the Databricks platform with AWS . And today it's an amazing partnership. You know, we directly connected with the general managers for the services. We're connected at the CEO level, you know, the sellers get compensated for pushing Databricks, we're, we have multiple offerings on their marketplace. We have a native offering on AWS. You know, we're prominently always sort of marketed and you know, we're aligned also vision wise in what we're trying to do. So yeah, we've come a very, very long way. >> Do you consider yourself a SaaS app or an ISV or do you see yourself more of a platform company because you have customers. How would you categorize your category as a company? >> Well, it's a data platform, right? And actually the, the strategy of the Databricks is take what's otherwise five, six services in the industry or five, six different startups, but do them as part of one data platform that's integrated. So in one word, the strategy of data bricks is "unification." We call it the data lake house. But really the idea behind the data lake house is that of unification, or in more words it's, "The whole is greater than the sum of its parts." So you could actually go and buy five, six services out there or actually use five, six services from the cloud vendors, stitch it together and it kind of resembles Databricks. Our power is in doing those integrated, together in a way in which it's really, really easy and simple to use for end users. So yeah, we're a data platform. I wouldn't, you know, ISV that's a old term, you know, Independent Software Vendor. You know, I think, you know, we have actually a whole slew of ISVs on top of Databricks, that integrate with our platform. And you know, in our marketplace as well as in our partner connect, we host those ISVs that then, you know, work on top of the data that we have in the Databricks, data lake house. >> You know, I think one of the things your journey has been great to document and watch from the beginning. I got to give you guys credit over there and props, congratulations. But I think you're the poster child as a company to what we see enterprises doing now. So go back in time when you guys swiped a credit card, you didn't need attending technical support because you guys had brains, you were refactoring, rethinking. It wasn't just banging out software, you had, you were doing some complex things. It wasn't like it was just write some software hosted on server. It was really a lot more. And as a result your business worth billions of dollars. I think 38 billion or something like that, big numbers, big numbers of great revenue growth as well, billions in revenue. You have customers, you have an ecosystem, you have data applications on top of Databricks. So in a way you're a cloud on top of the cloud. So is there a cloud on top of the cloud? So you have ISVs, Amazon has ISVs. Can you take us through what this means and at this point in history, because this seems to be an advanced version of benefits of platforming and refactoring, leveraging say AWS. >> Yeah, so look, when we started, there was really only one game in town. It was AWS. So it was one cloud. And the strategy of the company then was, well Amazon had this beautiful set of services that they're building bottom up, they have storage, compute, networking, and then they have databases and so on. But it's a lot of services. So let us not directly compete with AWS and try to take out one of their services. Let's not do that because frankly we can't. We were not of that size. They had the scale, they had the size and they were the only cloud vendor in town. So our strategy instead was, let's do something else. Let's not compete directly with say, a particular service they're building, let's take a different strategy. What if we had a unified holistic data platform, where it's just one integrated service end to end. So think of it as Microsoft office, which contains PowerPoint, and Word, and Excel and even Access, if you want to use it. What if we build that and AWS has this really amazing knack for releasing things, you know services, lots of them, every reinvent. And they're sort of a DevOps person's dream and you can stitch these together and you know you have to be technical. How do we elevate that and make it simpler and integrate it? That was our original strategy and it resonated with a segment of the market. And the reason it worked with AWS so that we wouldn't butt heads with AWS was because we weren't a direct replacement for this service or for that service, we were taking a different approach. And AWS, because credit goes to them, they're so customer obsessed, they would actually do what's right for the customer. So if the customer said we want this unified thing, their sellers would actually say, okay, so then you should use Databricks. So they truly are customer obsessed in that way. And I really mean it, John. Things have changed over the years. They're not the only cloud anymore. You know, Azure is real, GCP is real, there's also Alibaba. And now over 70% of our customers are on more than one cloud. So now what we hear from them is, not only want, do we want a simplified, unified thing, but we want it also to work across the clouds. Because those of them that are seriously considering multiple clouds, they don't want to use a service on cloud one and then use a similar service on cloud two. But it's a little bit different. And now they have to do twice the work to make it work. You know, John, it's hard enough as it is, like it's this data stuff and analytics. It's not a walk in the park, you know. You hire an administrator in the back office that clicks a button and its just, now you're a data driven digital transformed company. It's hard. If you now have to do it again on the second cloud with different set of services and then again on a third cloud with a different set of services. That's very, very costly. So the strategy then has changed that, how do we take that unified simple approach and make it also the same and standardize across the clouds, but then also integrate it as far down as we can on each of the clouds. So that you're not giving up any of the benefits that the particular cloud has. >> Yeah, I think one of the things that we see, and I want get your reaction to this, is this rise of the super cloud as we call it. I think you were involved in the Sky paper that I saw your position paper came out after we had introduced Super Cloud, which is great. Congratulations to the Berkeley team, wearing the hat here. But you guys are, I think a driver of this because you're creating the need for these things. You're saying, okay, we went on one cloud with AWS and you didn't hide that. And now you're publicly saying there's other clouds too, increased ham for your business. And customers have multiple clouds in their infrastructure for the best of breed that they have. Okay, get that. But there's still a challenge around the innovation, growth that's still around the corner. We still have a supply chain problem, we still have skill gaps. You know, you guys are unique at Databricks as other these big examples of super clouds that are developing. Enterprises don't have the Databricks kind of talent. They need, they need turnkey solutions. So Adam and the team at Amazon are promoting, you know, more solution oriented approaches higher up on the stack. You're starting to see kind of like, I won't say templates, but you know, almost like application specific headless like, low code, no code capability to accelerate clients who are wanting to write code for the modern error. Right, so this kind of, and then now you, as you guys pointed out with these common services, you're pushing the envelope. So you're saying, hey, I need to compete, I don't want to go to my customers and have them to have a staff or this cloud and this cloud and this cloud because they don't have the staff. Or if they do, they're very unique. So what's your reaction? Because this kind is the, it kind of shows your leadership as a partner of AWS and the clouds, but also highlights I think what's coming. But you share your reaction. >> Yeah, look, it's, first of all, you know, I wish I could take credit for this but I can't because it's really the customers that have decided to go on multiple clouds. You know, it's not Databricks that you know, push this or some other vendor, you know, that, Snowflake or someone who pushed this and now enterprises listened to us and they picked two clouds. That's not how it happened. The enterprises picked two clouds or three clouds themselves and we can get into why, but they did that. So this largely just happened in the market. We as data platforms responded to what they're then saying, which is they're saying, "I don't want to redo this again on the other cloud." So I think the writing is on the wall. I think it's super obvious what's going to happen next. They will say, "Any service I'm using, it better work exactly the same on all the clouds." You know, that's what's going to happen. So in the next five years, every enterprise will say, "I'm going to use the service, but you better make sure that this service works equally well on all of the clouds." And obviously the multicloud vendors like us, are there to do that. But I actually think that what you're going to see happening is that you're going to see the cloud vendors changing the existing services that they have to make them work on the other clouds. That's what's goin to happen, I think. >> Yeah, and I think I would add that, first of all, I agree with you. I think that's going to be a forcing function. Because I think you're driving it. You guys are in a way, one, are just an actor in the driving this because you're on the front end of this and there are others and there will be people following. But I think to me, I'm a cloud vendor, I got to differentiate. Adam, If I'm Adam Saleski, I got to say, "Hey, I got to differentiate." So I don't wan to get stuck in the middle, so to speak. Am I just going to innovate on the hardware AKA infrastructure or am I going to innovate at the higher level services? So what we're talking about here is the tail of two clouds within Amazon, for instance. So do I innovate on the silicon and get low level into the physics and squeeze performance out of the hardware and infrastructure? Or do I focus on ease of use at the top of the stack for the developers? So again, there's a channel of two clouds here. So I got to ask you, how do they differentiate? Number one and number two, I never heard a developer ever say, "I want to run my app or workload on the slower cloud." So I mean, you know, back when we had PCs you wanted to go, "I want the fastest processor." So again, you can have common level services, but where is that performance differentiation with the cloud? What do the clouds do in your opinion? >> Yeah, look, I think it's pretty clear. I think that it's, this is, you know, no surprise. Probably 70% or so of the revenue is in the lower infrastructure layers, compute, storage, networking. And they have to win that. They have to be competitive there. As you said, you can say, oh you know, I guess my CPUs are slower than the other cloud, but who cares? I have amazing other services which only work on my cloud by the way, right? That's not going to be a winning recipe. So I think all three are laser focused on, we going to have specialized hardware and the nuts and bolts of the infrastructure, we can do it better than the other clouds for sure. And you can see lots of innovation happening there, right? The Graviton chips, you know, we see huge price performance benefits in those chips. I mean it's real, right? It's basically a 20, 30% free lunch. You know, why wouldn't you, why wouldn't you go for it there? There's no downside. You know, there's no, "got you" or no catch. But we see Azure doing the same thing now, they're also building their own chips and we know that Google builds specialized machine learning chips, TPU, Tenor Processing Units. So their legs are focused on that. I don't think they can give up that or focused on higher levels if they had to pick bets. And I think actually in the next few years, most of us have to make more, we have to be more deliberate and calculated in the picks we do. I think in the last five years, most of us have said, "We'll do all of it." You know. >> Well you made a good bet with Spark, you know, the duke was pretty obvious trend that was, everyone was shut on that bandwagon and you guys picked a big bet with Spark. Look what happened with you guys? So again, I love this betting kind of concept because as the world matures, growth slows down and shifts and that next wave of value coming in, AKA customers, they're going to integrate with a new ecosystem. A new kind of partner network for AWS and the other clouds. But with aws they're going to need to nurture the next Databricks. They're going to need to still provide that SaaS, ISV like experience for, you know, a basic software hosting or some application. But I go to get your thoughts on this idea of multiple clouds because if I'm a developer, the old days was, old days, within our decade, full stack developer- >> It was two years ago, yeah (John laughing) >> This is a decade ago, full stack and then the cloud came in, you kind had the half stack and then you would do some things. It seems like the clouds are trying to say, we want to be the full stack or not. Or is it still going to be, you know, I'm an application like a PC and a Mac, I'm going to write the same application for both hardware. I mean what's your take on this? Are they trying to do full stack and you see them more like- >> Absolutely. I mean look, of course they're going, they have, I mean they have over 300, I think Amazon has over 300 services, right? That's not just compute, storage, networking, it's the whole stack, right? But my key point is, I think they have to nail the core infrastructure storage compute networking because the three clouds that are there competing, they're formidable companies with formidable balance sheets and it doesn't look like any of them is going to throw in the towel and say, we give up. So I think it's going to intensify. And given that they have a 70% revenue on that infrastructure layer, I think they, if they have to pick their bets, I think they'll focus it on that infrastructure layer. I think the layer above where they're also placing bets, they're doing that, the full stack, right? But there I think the demand will be, can you make that work on the other clouds? And therein lies an innovator's dilemma because if I make it work on the other clouds, then I'm foregoing that 70% revenue of the infrastructure. I'm not getting it. The other cloud vendor is going to get it. So should I do that or not? Second, is the other cloud vendor going to be welcoming of me making my service work on their cloud if I am a competing cloud, right? And what kind of terms of service are I giving me? And am I going to really invest in doing that? And I think right now we, you know, most, the vast, vast, vast majority of the services only work on the one cloud that you know, it's built on. It doesn't work on others, but this will shift. >> Yeah, I think the innovators dilemma is also very good point. And also add, it's an integrators dilemma too because now you talk about integration across services. So I believe that the super cloud movement's going to happen before Sky. And I think what explained by that, what you guys did and what other companies are doing by representing advanced, I call platform engineering, refactoring an existing market really fast, time to value and CAPEX is, I mean capital, market cap is going to be really fast. I think there's going to be an opportunity for those to emerge that's going to set the table for global multicloud ultimately in the future. So I think you're going to start to see the same pattern of what you guys did get in, leverage the hell out of it, use it, not in the way just to host, but to refactor and take down territory of markets. So number one, and then ultimately you get into, okay, I want to run some SLA across services, then there's a little bit more complication. I think that's where you guys put that beautiful paper out on Sky Computing. Okay, that makes sense. Now if you go to today's market, okay, I'm betting on Amazon because they're the best, this is the best cloud win scenario, not the most robust cloud. So if I'm a developer, I want the best. How do you look at their bet when it comes to data? Because now they've got machine learning, Swami's got a big keynote on Wednesday, I'm expecting to see a lot of AI and machine learning. I'm expecting to hear an end to end data story. This is what you do, so as a major partner, how do you view the moves Amazon's making and the bets they're making with data and machine learning and AI? >> First I want to lift off my hat to AWS for being customer obsessed. So I know that if a customer wants Databricks, I know that AWS and their sellers will actually help us get that customer deploy Databricks. Now which of the services is the customer going to pick? Are they going to pick ours or the end to end, what Swami is going to present on stage? Right? So that's the question we're getting. But I wanted to start with by just saying, their customer obsessed. So I think they're going to do the right thing for the customer and I see the evidence of it again and again and again. So kudos to them. They're amazing at this actually. Ultimately our bet is, customers want this to be simple, integrated, okay? So yes there are hundreds of services that together give you the end to end experience and they're very customizable that AWS gives you. But if you want just something simply integrated that also works across the clouds, then I think there's a special place for Databricks. And I think the lake house approach that we have, which is an integrated, completely integrated, we integrate data lakes with data warehouses, integrate workflows with machine learning, with real time processing, all these in one platform. I think there's going to be tailwinds because I think the most important thing that's going to happen in the next few years is that every customer is going to now be obsessed, given the recession and the environment we're in. How do I cut my costs? How do I cut my costs? And we learn this from the customers they're adopting the lake house because they're thinking, instead of using five vendors or three vendors, I can simplify it down to one with you and I can cut my cost. So I think that's going to be one of the main drivers of why people bet on the lake house because it helps them lower their TCO; Total Cost of Ownership. And it's as simple as that. Like I have three things right now. If I can get the same job done of those three with one, I'd rather do that. And by the way, if it's three or four across two clouds and I can just use one and it just works across two clouds, I'm going to do that. Because my boss is telling me I need to cut my budget. >> (indistinct) (John laughing) >> Yeah, and I'd rather not to do layoffs and they're asking me to do more. How can I get smaller budgets, not lay people off and do more? I have to cut, I have to optimize. What's happened in the last five, six years is there's been a huge sprawl of services and startups, you know, you know most of them, all these startups, all of them, all the activity, all the VC investments, well those companies sold their software, right? Even if a startup didn't make it big, you know, they still sold their software to some vendors. So the ecosystem is now full of lots and lots and lots and lots of different software. And right now people are looking, how do I consolidate, how do I simplify, how do I cut my costs? >> And you guys have a great solution. You're also an arms dealer and a innovator. So I have to ask this question, because you're a professor of the industry as well as at Berkeley, you've seen a lot of the historical innovations. If you look at the moment we're in right now with the recession, okay we had COVID, okay, it changed how people work, you know, people working at home, provisioning VLAN, all that (indistinct) infrastructure, okay, yeah, technology and cloud health. But we're in a recession. This is the first recession where the Amazon and the other cloud, mainly Amazon Web Services is a major economic puzzle in the piece. So they were never around before, even 2008, they were too small. They're now a major economic enabler, player, they're serving startups, enterprises, they have super clouds like you guys. They're a force and the people, their customers are cutting back but also they can also get faster. So agility is now an equation in the economic recovery. And I want to get your thoughts because you just brought that up. Customers can actually use the cloud and Databricks to actually get out of the recovery because no one's going to say, stop making profit or make more profit. So yeah, cut costs, be more efficient, but agility's also like, let's drive more revenue. So in this digital transformation, if you take this to conclusion, every company transforms, their company is the app. So their revenue is tied directly to their technology deployment. What's your reaction and comment to that because this is a new historical moment where cloud and scale and data, actually could be configured in a way to actually change the nature of a business in such a short time. And with the recession looming, no one's got time to wait. >> Yeah, absolutely. Look, the secular tailwind in the market is that of, you know, 10 years ago it was software is eating the world, now it's AI's going to eat all of software software. So more and more we're going to have, wherever you have software, which is everywhere now because it's eaten the world, it's going to be eaten up by AI and data. You know, AI doesn't exist without data so they're synonymous. You can't do machine learning if you don't have data. So yeah, you're going to see that everywhere and that automation will help people simplify things and cut down the costs and automate more things. And in the cloud you can also do that by changing your CAPEX to OPEX. So instead of I invest, you know, 10 million into a data center that I buy, I'm going to have headcount to manage the software. Why don't we change this to OPEX? And then they are going to optimize it. They want to lower the TCO because okay, it's in the cloud. but I do want the costs to be much lower that what they were in the previous years. Last five years, nobody cared. Who cares? You know what it costs. You know, there's a new brave world out there. Now there's like, no, it has to be efficient. So I think they're going to optimize it. And I think this lake house approach, which is an integration of the lakes and the warehouse, allows you to rationalize the two and simplify them. It allows you to basically rationalize away the data warehouse. So I think much faster we're going to see the, why do I need the data warehouse? If I can get the same thing done with the lake house for fraction of the cost, that's what's going to happen. I think there's going to be focus on that simplification. But I agree with you. Ultimately everyone knows, everybody's a software company. Every company out there is a software company and in the next 10 years, all of them are also going to be AI companies. So that is going to continue. >> (indistinct), dev's going to stop. And right sizing right now is a key economic forcing function. Final question for you and I really appreciate you taking the time. This year Reinvent, what's the bumper sticker in your mind around what's the most important industry dynamic, power dynamic, ecosystem dynamic that people should pay attention to as we move from the brave new world of okay, I see cloud, cloud operations. I need to really make it structurally change my business. How do I, what's the most important story? What's the bumper sticker in your mind for Reinvent? >> Bumper sticker? lake house 24. (John laughing) >> That's data (indistinct) bumper sticker. What's the- >> (indistinct) in the market. No, no, no, no. You know, it's, AWS talks about, you know, all of their services becoming a lake house because they want the center of the gravity to be S3, their lake. And they want all the services to directly work on that, so that's a lake house. We're Bumper see Microsoft with Synapse, modern, you know the modern intelligent data platform. Same thing there. We're going to see the same thing, we already seeing it on GCP with Big Lake and so on. So I actually think it's the how do I reduce my costs and the lake house integrates those two. So that's one of the main ways you can rationalize and simplify. You get in the lake house, which is the name itself is a (indistinct) of two things, right? Lake house, "lake" gives you the AI, "house" give you the database data warehouse. So you get your AI and you get your data warehousing in one place at the lower cost. So for me, the bumper sticker is lake house, you know, 24. >> All right. Awesome Ali, well thanks for the exclusive interview. Appreciate it and get to see you. Congratulations on your success and I know you guys are going to be fine. >> Awesome. Thank you John. It's always a pleasure. >> Always great to chat with you again. >> Likewise. >> You guys are a great team. We're big fans of what you guys have done. We think you're an example of what we call "super cloud." Which is getting the hype up and again your paper speaks to some of the innovation, which I agree with by the way. I think that that approach of not forcing standards is really smart. And I think that's absolutely correct, that having the market still innovate is going to be key. standards with- >> Yeah, I love it. We're big fans too, you know, you're doing awesome work. We'd love to continue the partnership. >> So, great, great Ali, thanks. >> Take care (outro music)

(upbeat music) >> Hello, welcome back to theCUBE's AWS RE:Invent 2022 Coverage. I'm John Furrier, host of theCUBE. Got a great lineup here, Itamar Ankorion SVP Technology Alliance at Qlik and Peter McDonald, vice President, cloud partnerships and business development Snowflake. We're going to talk about bringing SAP data to life, for joint Snowflake, Qlik and AWS Solution. Gentlemen, thanks for coming on theCUBE Really appreciate it. >> Thank you. >> Thank you, great meeting you John. >> Just to get started, introduce yourselves to the audience, then going to jump into what you guys are doing together, unique relationship here, really compelling solution in cloud. Big story about applications and scale this year. Let's introduce yourselves. Peter, we'll start with you. >> Great. I'm Peter MacDonald. I am vice president of Cloud Partners and business development here at Snowflake. On the Cloud Partner side, that means I manage AWS relationship along with Microsoft and Google Cloud. What we do together in terms of complimentary products, GTM, co-selling, things like that. Importantly, working with other third parties like Qlik for joint solutions. On business development, it's negotiating custom commercial partnerships, large companies like Salesforce and Dell, smaller companies at most for our venture portfolio. >> Thanks Peter and hi John. It's great to be back here. So I'm Itamar Ankorion and I'm the senior vice president responsible for technology alliances here at Qlik. With that, own strategic alliances, including our key partners in the cloud, including Snowflake and AWS. I've been in the data and analytics enterprise software market for 20 plus years, and my main focus is product management, marketing, alliances, and business development. I joined Qlik about three and a half years ago through the acquisition of Attunity, which is now the foundation for Qlik data integration. So again, we focus in my team on creating joint solution alignment with our key partners to provide more value to our customers. >> Great to have both you guys, senior executives in the industry on theCUBE here, talking about data, obviously bringing SAP data to life is the theme of this segment, but this reinvent, it's all about the data, big data end-to-end story, a lot about data being intrinsic as the CEO says on stage around in the organizations in all aspects. Take a minute to explain what you guys are doing as from a company standpoint. Snowflake and Qlik and the solutions, why here at AWS? Peter, we'll start with you at Snowflake, what you guys do as a company, your mission, your focus. >> That was great, John. Yeah, so here at Snowflake, we focus on the data platform and until recently, data platforms required expensive on-prem hardware appliances. And despite all that expense, customers had capacity constraints, inexpensive maintenance, and had limited functionality that all impeded these organizations from reaching their goals. Snowflake is a cloud native SaaS platform, and we've become so successful because we've addressed these pain points and have other new special features. For example, securely sharing data across both the organization and the value chain without copying the data, support for new data types such as JSON and structured data, and also advance in database data governance. Snowflake integrates with complimentary AWS services and other partner products. So we can enable holistic solutions that include, for example, here, both Qlik and AWS SageMaker, and comprehend and bring those to joint customers. Our customers want to convert data into insights along with advanced analytics platforms in AI. That is how they make holistic data-driven solutions that will give them competitive advantage. With Snowflake, our approach is to focus on customer solutions that leverage data from existing systems such as SAP, wherever they are in the cloud or on-premise. And to do this, we leverage partners like Qlik native US to help customers transform their businesses. We provide customers with a premier data analytics platform as a result. Itamar, why don't you talk about Qlik a little bit and then we can dive into the specific SAP solution here and some trends >> Sounds great, Peter. So Qlik provides modern data integration and analytics software used by over 38,000 customers worldwide. Our focus is to help our customers turn data into value and help them close the gap between data all the way through insight and action. We offer click data integration and click data analytics. Click data integration helps to automate the data pipelines to deliver data to where they want to use them in real-time and make the data ready for analytics and then Qlik data analytics is a robust platform for analytics and business intelligence has been a leader in the Gartner Magic Quadrant for over 11 years now in the market. And both of these come together into what we call Qlik Cloud, which is our SaaS based platform. So providing a more seamless way to consume all these services and accelerate time to value with customer solutions. In terms of partnerships, both Snowflake and AWS are very strategic to us here at Qlik, so we have very comprehensive investment to ensure strong joint value proposition to we can bring to our mutual customers, everything from aligning our roadmaps through optimizing and validating integrations, collaborating on best practices, packaging joint solutions like the one we'll talk about today. And with that investment, we are an elite level, top level partner with Snowflake. We fly that our technology is Snowflake-ready across the entire product set and we have hundreds of joint customers together and with AWS we've also partnered for a long time. We're here to reinvent. We've been here with the first reinvent since the inaugural one, so it kind of gives you an idea for how long we've been working with AWS. We provide very comprehensive integration with AWS data analytics services, and we have several competencies ranging from data analytics to migration and modernization. So that's our focus and again, we're excited about working with Snowflake and AWS to bring solutions together to market. >> Well, I'm looking forward to unpacking the solutions specifically, and congratulations on the continued success of both your companies. We've been following them obviously for a very long time and seeing the platform evolve beyond just SaaS and a lot more going on in cloud these days, kind of next generation emerging. You know, we're seeing a lot of macro trends that are going to be powering some of the things we're going to get into real quickly. But before we get into the solution, what are some of those power dynamics in the industry that you're seeing in trends specifically that are impacting your customers that are taking us down this road of getting more out of the data and specifically the SAP, but in general trends and dynamics. What are you hearing from your customers? Why do they care? Why are they going down this road? Peter, we'll start with you. >> Yeah, I'll go ahead and start. Thanks. Yeah, I'd say we continue to see customers being, being very eager to transform their businesses and they know they need to leverage technology and data to do so. They're also increasingly depending upon the cloud to bring that agility, that elasticity, new functionality necessary to react in real-time to every evolving customer needs. You look at what's happened over the last three years, and boy, the macro environment customers, it's all changing so fast. With our partnerships with AWS and Qlik, we've been able to bring to market innovative solutions like the one we're announcing today that spans all three companies. It provides a holistic solution and an integrated solution for our customer. >> Itamar let's get into it, you've been with theCUBE, you've seen the journey, you have your own journey, many, many years, you've seen the waves. What's going on now? I mean, what's the big wave? What's the dynamic powering this trend? >> Yeah, in a nutshell I'll call it, it's all about time. You know, it's time to value and it's about real-time data. I'll kind of talk about that a bit. So, I mean, you hear a lot about the data being the new oil, but it's definitely, we see more and more customers seeing data as their critical enabler for innovation and digital transformation. They look for ways to monetize data. They look as the data as the way in which they can innovate and bring different value to the customers. So we see customers want to use more data so to get more value from data. We definitely see them wanting to do it faster, right, than before. And we definitely see them looking for agility and automation as ways to accelerate time to value, and also reduce overall costs. I did mention real-time data, so we definitely see more and more customers, they want to be able to act and make decisions based on fresh data. So yesterday's data is just not good enough. >> John: Yeah. >> It's got to be down to the hour, down to the minutes and sometimes even lower than that. And then I think we're also seeing customers look to their core business systems where they have a lot of value, like the SAP, like mainframe and thinking, okay, our core data is there, how can we get more value from this data? So that's key things we see all the time with customers. >> Yeah, we did a big editorial segment this year on, we called data as code. Data as code is kind of a riff on infrastructure as code and you start to see data becoming proliferating into all aspects, fresh data. It's not just where you store it, it's how you share it, it's how you turn it into an application intrinsically involved in all aspects. This is the big theme this year and that's driving all the conversations here at RE:Invent. And I'm guaranteeing you, it's going to happen for another five and 10 years. It's not stopping. So I got to get into the solution, you guys mentioned SAP and you've announced the solution by Qlik, Snowflake and AWS for your customers using SAP. Can you share more about this solution? What's unique about it? Why is it important and why now? Peter, Itamar, we'll start with you first. >> Let me jump in, this is really, I'll jump because I'm excited. We're very excited about this solution and it's also a solution by the way and again, we've seen proven customer success with it. So to your point, it's ready to scale, it's starting, I think we're going to see a lot of companies doing this over the next few years. But before we jump to the solution, let me maybe take a few minutes just to clarify the need, why we're seeing, why we're seeing customers jump to do this. So customers that use SAP, they use it to manage the core of their business. So think order processing, management, finance, inventory, supply chain, and so much more. So if you're running SAP in your company, that data creates a great opportunity for you to drive innovation and modernization. So what we see customers want to do, they want to do more with their data and more means they want to take SAP with non-SAP data and use it together to drive new insights. They want to use real-time data to drive real-time analytics, which they couldn't do to date. They want to bring together descriptive with predictive analytics. So adding machine learning in AI to drive more value from the data. And naturally they want to do it faster. So find ways to iterate faster on their solutions, have freedom with the data and agility. And I think this is really where cloud data platforms like Snowflake and AWS, you know, bring that value to be able to drive that. Now to do that you need to unlock the SAP data, which is a lot of also where Qlik comes in because typical challenges these customers run into is the complexity, inherent in SAP data. Tens of thousands of tables, proprietary formats, complex data models, licensing restrictions, and more than, you have performance issues, they usually run into how do we handle the throughput, the volumes while maintaining lower latency and impact. Where do we find knowledge to really understand how to get all this done? So these are the things we've looked at when we came together to create a solution and make it unique. So when you think about its uniqueness, because we put together a lot, and I'll go through three, four key things that come together to make this unique. First is about data delivery. How do you have the SAP data delivery? So how do you get it from ECC, from HANA from S/4HANA, how do you deliver the data and the metadata and how that integration well into Snowflake. And what we've done is we've focused a lot on optimizing that process and the continuous ingestion, so the real-time ingestion of the data in a way that works really well with the Snowflake system, data cloud. Second thing is we looked at SAP data transformation, so once the data arrives at Snowflake, how do we turn it into being analytics ready? So that's where data transformation and data worth automation come in. And these are all elements of this solution. So creating derivative datasets, creating data marts, and all of that is done by again, creating an optimized integration that pushes down SQL based transformations, so they can be processed inside Snowflake, leveraging its powerful engine. And then the third element is bringing together data visualization analytics that can also take all the data now that in organizing inside Snowflake, bring other data in, bring machine learning from SageMaker, and then you go to create a seamless integration to bring analytic applications to life. So these are all things we put together in the solution. And maybe the last point is we actually took the next step with this and we created something we refer to as solution accelerators, which we're really, really keen about. Think about this as prepackaged templates for common business analytic needs like order to cash, finance, inventory. And we can either dig into that a little more later, but this gets the next level of value to the customers all built into this joint solution. >> Yeah, I want to get to the accelerators, but real quick, Peter, your reaction to the solution, what's unique about it? And obviously Snowflake, we've been seeing the progression data applications, more developers developing on top of Snowflake, data as code kind of implies developer ecosystem. This is kind of interesting. I mean, you got partnering with Qlik and AWS, it's kind of a developer-like thinking real solution. What's unique about this SAP solution that's, that's different than what customers can get anywhere else or not? >> Yeah, well listen, I think first of all, you have to start with the idea of the solution. This are three companies coming together to build a holistic solution that is all about, you know, creating a great opportunity to turn SAP data into value this is Itamar was talking about, that's really what we're talking about here and there's a lot of technology underneath it. I'll talk more about the Snowflake technology, what's involved here, and then cover some of the AWS pieces as well. But you know, we're focusing on getting that value out and accelerating time to value for our joint customers. As Itamar was saying, you know, there's a lot of complexity with the SAP data and a lot of value there. How can we manage that in a prepackaged way, bringing together best of breed solutions with proven capabilities and bringing this to market quickly for our joint customers. You know, Snowflake and AWS have been strong partners for a number of years now, and that's not only on how Snowflake runs on top of AWS, but also how we integrate with their complementary analytics and then all products. And so, you know, we want to be able to leverage those in addition to what Qlik is bringing in terms of the data transformations, bringing data out of SAP in the visualization as well. All very critical. And then we want to bring in the predictive analytics, AWS brings and what Sage brings. We'll talk about that a little bit later on. Some of the technologies that we're leveraging are some of our latest cutting edge technologies that really make things easier for both our partners and our customers. For example, Qlik leverages Snowflakes recently released Snowpark for Python functionality to push down those data transformations from clicking the Snowflake that Itamar's mentioning. And while we also leverage Snowpark for integrations with Amazon SageMaker, but there's a lot of great new technology that just makes this easy and compelling for customers. >> I think that's the big word, easy button here for what may look like a complex kind of integration, kind of turnkey, really, really compelling example of the modern era we're living in, as we always say in theCUBE. You mentioned accelerators, SAP accelerators. Can you give an example of how that works with the technology from the third party providers to deliver this business value Itamar, 'cause that was an interesting comment. What's the example? Give an example of this acceleration. >> Yes, certainly. I think this is something that really makes this truly, truly unique in the industry and again, a great opportunity for customers. So we kind talked earlier about there's a lot of things that need to be done with SP data to turn it to value. And these accelerator, as the name suggests, are designed to do just that, to kind of jumpstart the process and reduce the time and the risk involved in such project. So again, these are pre-packaged templates. We basically took a lot of knowledge, and a lot of configurations, best practices about to get things done and we put 'em together. So think about all the steps, it includes things like data extraction, so already knowing which tables, all the relevant tables that you need to get data from in the contexts of the solution you're looking for, say like order to cash, we'll get back to that one. How do you continuously deliver that data into Snowflake in an in efficient manner, handling things like data type mappings, metadata naming conventions and transformations. The data models you build all the way to data mart definitions and all the transformations that the data needs to go through moving through steps until it's fully analytics ready. And then on top of that, even adding a library of comprehensive analytic dashboards and integrations through machine learning and AI and put all of that in a way that's in pre-integrated and tested to work with Snowflake and AWS. So this is where again, you get this entire recipe that's ready. So take for example, I think I mentioned order to cash. So again, all these things I just talked about, I mean, for those who are not familiar, I mean order to cash is a critical business process for every organization. So especially if you're in retail, manufacturing, enterprise, it's a big... This is where, you know, starting with booking a sales order, following by fulfilling the order, billing the customer, then managing the accounts receivable when the customer actually pays, right? So this all process, you got sales order fulfillment and the billing impacts customer satisfaction, you got receivable payments, you know, the impact's working capital, cash liquidity. So again, as a result this order to cash process is a lifeblood for many businesses and it's critical to optimize and understand. So the solution accelerator we created specifically for order to cash takes care of understanding all these aspects and the data that needs to come with it. So everything we outline before to make the data available in Snowflake in a way that's really useful for downstream analytics, along with dashboards that are already common for that, for that use case. So again, this enables customers to gain real-time visibility into their sales orders, fulfillment, accounts receivable performance. That's what the Excel's are all about. And very similarly, we have another one for example, for finance analytics, right? So this will optimize financial data reporting, helps customers get insights into P&L, financial risk of stability or inventory analytics that helps with, you know, improve planning and inventory management, utilization, increased efficiencies, you know, so in supply chain. So again, these accelerators really help customers get a jumpstart and move faster with their solutions. >> Peter, this is the easy button we just talked about, getting things going, you know, get the ball rolling, get some acceleration. Big part of this are the three companies coming together doing this. >> Yeah, and to build on what Itamar just said that the SAP data obviously has tremendous value. Those sales orders, distribution data, financial data, bringing that into Snowflake makes it easily accessible, but also it enables it to be combined with other data too, is one of the things that Snowflake does so well. So you can get a full view of the end-to-end process and the business overall. You know, for example, I'll just take one, you know, one example that, that may not come to mind right away, but you know, looking at the impact of weather conditions on supply chain logistics is relevant and material and have interest to our customers. How do you bring those different data sets together in an easy way, bringing the data out of SAP, bringing maybe other data out of other systems through Qlik or through Snowflake, directly bringing data in from our data marketplace and bring that all together to make it work. You know, fundamentally organizational silos and the data fragmentation exist otherwise make it really difficult to drive modern analytics projects. And that in turn limits the value that our customers are getting from SAP data and these other data sets. We want to enable that and unleash. >> Yeah, time for value. This is great stuff. Itamar final question, you know, what are customers using this? What do you have? I'm sure you have customers examples already using the solution. Can you share kind of what these examples look like in the use cases and the value? >> Oh yeah, absolutely. Thank you. Happy to. We have customers across different, different sectors. You see manufacturing, retail, energy, oil and gas, CPG. So again, customers in those segments, typically sectors typically have SAP. So we have customers in all of them. A great example is like Siemens Energy. Siemens Energy is a global provider of gas par services. You know, over what, 28 billion, 30 billion in revenue. 90,000 employees. They operate globally in over 90 countries. So they've used SAP HANA as a core system, so it's running on premises, multiple locations around the world. And what they were looking for is a way to bring all these data together so they can innovate with it. And the thing is, Peter mentioned earlier, not just the SAP data, but also bring other data from other systems to bring it together for more value. That includes finance data, these logistics data, these customer CRM data. So they bring data from over 20 different SAP systems. Okay, with Qlik data integration, feeding that into Snowflake in under 20 minutes, 24/7, 365, you know, days a year. Okay, they get data from over 20,000 tables, you know, over million, hundreds of millions of records daily going in. So it is a great example of the type of scale, scalability, agility and speed that they can get to drive these kind of innovation. So that's a great example with Siemens. You know, another one comes to mind is a global manufacturer. Very similar scenario, but you know, they're using it for real-time executive reporting. So it's more like feasibility to the production data as well as for financial analytics. So think, think, think about everything from audit to texts to innovate financial intelligence because all the data's coming from SAP. >> It's a great time to be in the data business again. It keeps getting better and better. There's more data coming. It's not stopping, you know, it's growing so fast, it keeps coming. Every year, it's the same story, Peter. It's like, doesn't stop coming. As we wrap up here, let's just get customers some information on how to get started. I mean, obviously you're starting to see the accelerators, it's a great program there. What a great partnership between the two companies and AWS. How can customers get started to learn about the solution and take advantage of it, getting more out of their SAP data, Peter? >> Yeah, I think the first place to go to is talk to Snowflake, talk to AWS, talk to our account executives that are assigned to your account. Reach out to them and they will be able to educate you on the solution. We have packages up very nicely and can be deployed very, very quickly. >> Well gentlemen, thank you so much for coming on. Appreciate the conversation. Great overview of the partnership between, you know, Snowflake and Qlik and AWS on a joint solution. You know, getting more out of the SAP data. It's really kind of a key, key solution, bringing SAP data to life. Thanks for coming on theCUBE. Appreciate it. >> Thank you. >> Thank you John. >> Okay, this is theCUBE coverage here at RE:Invent 2022. I'm John Furrier, your host of theCUBE. Thanks for watching. (upbeat music)

(upbeat electronic music) >> Hello, and welcome to this Cube Conversation. My name is Dave Vellante, and we're here with Raj Gossain, who's the Chief Product Officer at Alation. We have some news. Hello, Raj. Thanks for coming on. >> Dave, it's great to be with you on theCUBE again. >> Yeah, good to see you. So, okay, we're going to talk about Alation Connected Sheets. You know, what is that? Talk to us about what it is, what it does, what it brings to customers. >> So we recognize, spreadsheets are really the dark matter of the data universe. And they're used by, over 78 million people use spreadsheets on a regular basis to drive critical business analysis. But there's a lot of challenges with spreadsheet usage. It brings risk to the organization. There's no visibility into where data comes from. And so we wanted to bring the power of the Alation Data Intelligence Platform to business users where they spend most of their time. And that's in a tool that they love, and that's spreadsheets. And so we're launching a brand new product next week called Alation Connected Sheets. >> So talk more about that. So yes, I get the lineage issue, like where did-- who did this, where's this data come from? I got different data. But talk more about the problems that Alation Connected Sheets solves, specifically for customers. >> Yeah, so the big challenges that we see when we talk to data organizations is how do they understand where the data came from? Is it trusted? Is it reusable? Should it be used in this format? And if you look at where most users that use spreadsheets get the data to power their spreadsheets, maybe it's a CSV download from a database, and then you have no idea where the data came from and where it's going. Or even worse, it's copying and pasting data from other spreadsheets. And so if you take those problems, how can we bring trusted data from governed sources like Snowflake and Redshift and put it in the hands of spreadsheet users, and give them the power and flexibility of Google Sheets or Microsoft Excel, but use trusted, reliable, well-governed data so that the data office feels great about them using spreadsheets and the end users, the business users, can take advantage of the tool that they know and love and do the work that they need to do quickly. >> So, okay. So I'm inferring from your comments there that you've got the ability to take data from you mentioned a couple, Snowflake and Redshift, other popular data warehouses. >> Yep. >> So talk about the key capabilities that you have, any specific features that we should know about. >> Sure. So, we built the leading data intelligence platform and the leading data catalog. And one of the benefits of that catalog is where you have visibility into all of the trusted, governed data sources that a data organization cares about, whether it's enterprise warehouses like Snowflake or Redshift, databases like SQL Server, Google BigQuery, what have you. So what we've done is we've brought the power of that data catalog directly into both Google Sheets as well as Excel. And the idea there is a user can log into their application, authenticate to Alation using the Alation Connected Sheets plugin into their spreadsheet tool, and browse those trusted data sets that are surfaced in the Alation catalog. They get trust signals, they get visibility into where this data came from. So lineage, insights, descriptive information. And then with one or two clicks, they can choose a data set from their warehouse, basically apply filtering conditions. So let's say I'm looking for customer data in Snowflake. I can find the right customer table. If I only want it for say, 2022, I can apply some filter conditions, I can reorder columns, push one button, authenticate to that data source. We want to maintain and ensure security is being applied, so only those users that have access to the warehouse can actually download that data set. But once they've authenticated, that data gets downloaded into their spreadsheet and there's a live connection that's maintained to that spreadsheet. So anytime you need to refresh the data, one push of a button and that data set gets updated. I can schedule the updates. So, you know, if I have to produce a report every Monday morning, I could have that data set refreshed at 8:00 a.m. Monday morning, or whatever schedule the user wants. And so it gives the user the data set they need, but the data organization, they can see where that data came from and they understand the lineage of the data as it is used in analysis in those spreadsheets themselves. >> So Raj, I know you're at the Super Bowl this week, a.k.a. re:Invent. >> Yes. >> And I know you got very close relationships with Snowflake, you've mentioned them a couple times with the data summit last spring. And I know you've done some integration work with those platforms and I'm sure others. So should we think of this as you're extending that sort of trust and governance out to spreadsheets, is that right? And stretching that out? >> That's exactly right. The way we talk about it is how do we bring data intelligence to business users in the tool that they know and love, which is the spreadsheet. And so, the data catalog and data intelligence platforms in general have really primarily been focused on servicing the needs of data users: data analysts, data scientists, data engineers. But you know, our vision, our aspiration at Alation is to really bring data intelligence to any business user. And so it's a big part of our strategy to make sure that the insights from the Alation catalog and platform can find their way into tools like Excel and Google Sheets. And so that's, what you highlighted, Dave, is exactly correct. We want to maximize the likelihood that a business user can have self-service access to trusted, governed data, do the work that they need to do, and ensure that the organization has a set of data assets in spreadsheets, frankly as opposed to liabilities, which is the way most data organizations look at spreadsheets is it's almost like a risk factor. We want to convert that risk, that liability, into an asset so that people can reuse data sets and they understand where this analysis is actually coming from. >> It's something that we've talked about for well over a decade on theCUBE. Is data an asset or is it a liability? >> Yeah, yeah. >> You obviously want to get value out of it, but if you can't share it, it's not trusted. So what people do is they lock it down and then that constricts value creation. >> Exactly. >> My understanding is this tech came out of an acquisition from a company, Kloudio. >> That's correct. >> Tell us about Kloudio. Why Kloudio? What's the fit there? >> Yeah, so Kloudio is a company, it's about five years old. We closed the acquisition of the company in March of this past year. And they had about 20 customers, 10 engineers. And we saw an opportunity with the spreadsheet tool that they'd created to really compliment our data intelligence strategy. And as you said, Dave, extend the value of data intelligence to business users. And so, we brought the Kloudio team into the fold. The thing I'm most excited about as a product guy, is within seven months of them joining Alation, we're actually shipping a brand new product that's going to drive revenue and meet the needs of tens of millions of users, ultimately. Like that's really our aspiration. And so, the tech they had was extremely modern. It reinforces the platform position that we have. You know, this microservices architecture that we've built Alation around, made it easy for that new team to come in and leverage existing APIs and capabilities from our platform and the tech that they brought into Alation to essentially connect the dots and deliver a brand new set of capabilities to an entirely new audience, to help our customers achieve their business objectives, which is really creating a data culture across their entire organization, inclusive of business users, not just, like I said, the data X users that are already taking advantage of solutions like Alation and cloud warehouses, et cetera. >> So I have two questions, follow up questions by me, and I think you might have answered the second one. The first one is what's the secret sauce behind Kloudio? How does the tech work? The second question is how does it fit into the Alation portfolio? How were you able to integrate it so quickly? Maybe that's the microservices architecture. But start with the secret sauce. What is it, what can you share with me? >> I think the thing that we saw with Kloudio that got us excited, and the fact that they, even though it was a small company, they had 20 customers, they were generating revenue, and they were delivering real value to business users, by really enabling business users to tap into the value of trusted, governed data, and frankly, get IT out of the way. You know, we almost refer to it as like smart self-service, which is, they could find a data asset and connect to that source, and just with a couple quick clicks, almost a low-code, no-code type of an experience, bring that sort of data into their spreadsheet so they could do the work that they needed to do. That opportunity, that tech that the Kloudio team had built out, the big gap that they had is, my goodness, what does it take to actually be aware of all the data sources that exist across an organization and connect to them? And that's what Alation does, right? That's why we built the platform that we built, so that we can basically understand all of a customer's data assets, whether they're on-prem or in the cloud. And so it was a little bit of, you know, that Reese's Peanut Butter Cup analogy. The chocolate and the peanut butter coming together. The Alation platform, the Alation catalog, coupled with the technology that Kloudio brought to us really was sort of a match made in heaven. And it's allowed us to bring this new capability to market that really is value-add on top of the platform and catalog investments that our customers have already made. >> Yeah, so they had this magic pixie dust, but it was sort of isolated, and then you've integrated it into your catalog. And that's the second part of my question. How were you able to do that so quickly? >> So, we've been on this evolution, enhancing the the Alation data intelligence platform. We've moved to a microservices architecture, we're fully multi-tenant in the cloud. And the fact that we'd made those investments over the past few years gave us the opportunity to make it easy for an acquired business like Kloudio, or you know, perhaps a future acquisition, or third party developers leveraging APIs that we expose to make it easy for them to integrate into the Alation platform. And so, I think it's a bit of foresight. We recognize that in starting with the catalog, the opportunity was much bigger than just providing a data catalog. We've added data governance, we've built out this platform and we recognize that more and more users can and should be benefiting from data intelligence. And so I think those platform investments have paid significant dividends and accelerated our ability to deliver Alation Connected Sheets as quickly as we have. >> Sounds like a great acquisition, like a diamond in the rough. I mean, I love big these big mega acquisitions 'cause the media company can write about 'em, but I really love the high, high return. You know, low denominator, high value. So, congratulations. >> Thank you. >> Where can people learn more about this? Maybe play around a little bit with it? >> Yeah, so we're going to be demoing Alation Connected Sheets at AWS re:Invent next week. And it's going to be available starting next week, so the 28th of November. And obviously you'll see it online, on social media, on our website as well. But folks that are going to be in Las Vegas next week, come to the Alation booth and you'll get a chance to see it directly. >> Awesome. Okay, Raj. Hey, thanks for spending some time with us today. Really appreciate it. >> Great, thanks so much, Dave. Great to see you. >> Hey, you're very welcome. And thank you for watching. This is Dave Vellante for theCUBE, your leader in enterprise and emerging tech coverage.

(Intro music) >> Welcome back. We're LIVE here from SuperComputing 22 in Dallas Paul Gillin, for Silicon Angle in theCUBE with my guest host Dave... excuse me. And our, our guest today, this segment is Kim Leyenaar who is a storage performance architect at Broadcom. And the topic of this conversation is, is is networking, it's connectivity. I guess, how does that relate to the work of a storage performance architect? >> Well, that's a really good question. So yeah, I have been focused on storage performance for about 22 years. But even, even if we're talking about just storage the entire, all the components have a really big impact on ultimately how quickly you can access your data. So, you know, the, the switches the memory bandwidth, the, the expanders the just the different protocols that you're using. And so, and the big part of is actually ethernet because as you know, data's not siloed anymore. You have to be able to access it from anywhere in the world. >> Dave: So wait, so you're telling me that we're just not living in a CPU centric world now? >> Ha ha ha >> Because it is it is sort of interesting. When we talk about supercomputing and high performance computing we're always talking about clustering systems. So how do you connect those systems? Isn't that, isn't that kind of your, your wheelhouse? >> Kim: It really is. >> Dave: At Broadcom. >> It's, it is, it is Broadcom's wheelhouse. We are all about interconnectivity and we own the interconnectivity. You know, you know, years ago it was, 'Hey, you know buy this new server because, you know, we we've added more cores or we've got better memory.' But now you've got all this siloed data and we've got you know, we've got this, this stuff or defined kind of environment now this composable environments where, hey if you need more networking, just plug this in or just go here and just allocate yourself more. So what we're seeing is these silos really of, 'hey here's our compute, here's your networking, here's your storage.' And so, how do you put those all together? The thing is interconnectivity. So, that's really what we specialize in. I'm really, you know, I'm really happy to be here to talk about some of the things that that we do to enable high performance computing. >> Paul: Now we're seeing, you know, new breed of AI computers being built with multiple GPUs very large amounts of data being transferred between them. And the internet really has become a, a bottleneck. The interconnect has become a bottle, a bottleneck. Is that something that Broadcom is working on alleviating? >> Kim: Absolutely. So we work with a lot of different, there's there's a lot of different standards that we work with to define so that we can make sure that we work everywhere. So even if you're just a dentist's office that's deploying one server, or we're talking about these hyperscalers that are, you know that have thousands or, you know tens of thousands of servers, you know, we're working on making sure that the next generation is able to outperform the previous generation. Not only that, but we found that, you know with these siloed things, if, if you add more storage but that means we're going to eat up six cores using that it's not really as useful. So Broadcom's really been focused on trying to offload the CPU. So we're offloading it from, you know data security, data protection, you know, we're we do packet sniffing ourselves and things like that. So no longer do we rely on the CPU to do that kind of processing for us but we become very smart devices all on our own so that they work very well in these kind of environments. >> Dave: So how about, give, give us an example. I know a lot of the discussion here has been around using ethernet as the connectivity layer. >> Yes. >> You know, in in, in the past, people would think about supercomputing as exclusively being InfiniBand based. >> Ha ha ha. >> But give, give us an idea of what Broadcom is doing in the ethernet space. What, you know, what's what are the advantages of using ethernet? >> Kim: So we've made two really big announcements. The first one is our Tomahawk five ethernet switch. So it's a 400 gigi ethernet switch. And the other thing we announced too was our Thor. So we have, these are our network controllers that also support up to 400 gigi each as well. So, those two alone, it just, it's amazing to me how much data we're able to transfer with those. But not only that, but they're super super intelligent controllers too. And then we realized, you know, hey, we're we're managing all this data, let's go ahead and offload the CPU. So we actually adopted the Rocky Standards. So that's one of the things that puts us above InfiniBand is that ethernet is ubiquitous, it's everywhere. And InfiniBand is primarily just owned by one or two companies. And, and so, and it's also a lot more expensive. So ethernet is just, it's everywhere. And now with the, with the Rocky standards, we're working along with, it's, it's, it does what you're talking about much better than, you know predecessors. >> Tell us about the Rocky Standards. I'm not familiar with it. I'm sure some of our listeners are not. What is the Rocky standard? >> Kim: Ha ha ha. So it's our DNA over converged to ethernet. I'm not a Rocky expert myself but I am an expert on how to offload the CPU. And so one of the things it does is instead of using the CPU to transfer the data from, you know the user space over to the next, you know server when you're transferring it we actually will do it ourselves. So we'll handle it ourselves. We will take it, we will move it across the wire and we will put it in that remote computer. And we don't have to ask the CPU to do anything to get involved in that. So big, you know, it's a big savings. >> Yeah, I mean in, in a nutshell, because there are parts of the InfiniBand protocol that are essentially embedded in RDMA over converged ethernet. So... >> Right. >> So if you can, if you can leverage kind of the best of both worlds, but have it in an ethernet environment which is already ubiquitous, it seems like it's, kind of democratizing supercomputing and, and HPC and I know you guys are big partners with Dell as an example, you guys work with all sorts of other people. >> Kim: Yeah. >> But let's say, let's say somebody is going to be doing ethernet for connectivity, you also offer switches? >> Kim: We do, actually. >> So is that, I mean that's another piece of the puzzle. >> That's a big piece of the puzzle. So we just released our, our Atlas 2 switch. It is a PCIE Gen Five switch. And... >> Dave: What does that mean? What does Gen five, what does that mean? >> Oh, Gen Five PCIE, it's it's a magic connectivity right now. So, you know, we talk about the Sapphire Rapids release as well as the GENUWA release. I know that those, you know those have been talked about a lot here. I've been walking around and everybody's talking about it. Well, those enable the Gen Five PCIE interfaces. So we've been able to double the bandwidth from the Gen Four up to the Gen Five. So, in order to, to support that we do now have our Atlas two PCIE Gen Five switch. And it allows you to connect especially around here we're talking about, you know artificial intelligence and machine learning. A lot of these are relying on the GPU and the DPU that you see, you know a lot of people talking about enabling. So by in, you know, putting these switches in the servers you can connect multitudes of not only NVME devices but also these GPUs and these, these CPUs. So besides that we also have the storage component of it too. So to support that, we we just recently have released our 9,500 series HBAs which support 24 gig SAS. And you know, this is kind of a, this is kind of a big deal for some of our hyperscalers that say, Hey, look our next generation, we're putting a hundred hard drives in. So we're like, you know, so a lot of it is maybe for cold storage, but by giving them that 24 gig bandwidth and by having these mass 24 gig SAS expanders that allows these hyperscalers to build up their systems. >> Paul: And how are you supporting the HPC community at large? And what are you doing that's exclusively for supercomputing? >> Kim: Exclusively for? So we're doing the interconnectivity really for them. You know, you can have as, as much compute power as you want, but these are very data hungry applications and a lot of that data is not sitting right in the box. A lot of that data is sitting in some other country or in some other city, or just the box next door. So to be able to move that data around, you know there's a new concept where they say, you know do the compute where the data is and then there's another kind of, you know the other way is move the data around which is a lot easier kind of sometimes, but so we're allowing us to move that data around. So for that, you know, we do have our our tomahawk switches, we've got our Thor NICS and of course we got, you know, the really wide pipe. So our, our new 9,500 series HBA and RAID controllers not only allow us to do, so we're doing 28 gigabytes a second that we can trans through the one controller, and that's on protected data. So we can actually have the high availability protected data of RAID 5 or RAID 6, or RAID 10 in the box giving in 27 gigabytes a second. So it's, it's unheard of the latency that we're seeing even off of this too, we have a right cash latency that is sub 8 microseconds that is lower than most of the NVME drives that you see, you know that are available today. So, so you know we're able to support these applications that require really low latency as well as data protection. >> Dave: So, so often when we talk about the underlying hardware, it's a it's a game of, you know, whack-a-mole chase the bottleneck. And so you've mentioned PCIE five, a lot of folks who will be implementing five, gen five PCIE five are coming off of three, not even four. >> Kim: I know. >> So make, so, so they're not just getting a last generation to this generation bump but they're getting a two generations, bump. >> Kim: They are. >> How does that, is it the case that it would never make sense to use a next gen or a current gen card in an older generation bus because of the mismatch and performance? Are these things all designed to work together? >> Uh... That's a really tough question. I want to say, no, it doesn't make sense. It, it really makes sense just to kind of move things forward and buy a card that's made for the bus it's in. However, that's not always the case. So for instance, our 9,500 controller is a Gen four PCIE but what we did, we doubled the PCIE so it's a by 16, even though it's a gen four, it's a by 16. So we're getting really, really good bandwidth out of it. As I said before, you know, we're getting 28, 27.8 or almost 28 gigabytes a second bandwidth out of that by doubling the PCIE bus. >> Dave: But they worked together, it all works together? >> All works together. You can put, you can put our Gen four and a Gen five all day long and they work beautifully. Yeah. We, we do work to validate that. >> We're almost out our time. But I, I want to ask you a more, nuts and bolts question, about storage. And we've heard for, you know, for years of the aerial density of hard disk has been reached and there's really no, no way to excel. There's no way to make the, the dish any denser. What is the future of the hard disk look like as a storage medium? >> Kim: Multi actuator actually, we're seeing a lot of multi-actuator. I was surprised to see it come across my desk, you know because our 9,500 actually does support multi-actuator. And, and, and so it was really neat after I've been working with hard drives for 22 years and I remember when they could do 30 megabytes a second, and that was amazing. That was like, wow, 30 megabytes a second. And then, about 15 years ago, they hit around 200 to 250 megabytes a second, and they stayed there. They haven't gone anywhere. What they have done is they've increased the density so that you can have more storage. So you can easily go out and buy 15 to 30 terabyte drive, but you're not going to get any more performance. So what they've done is they've added multiple actuators. So each one of these can do its own streaming and each one of these can actually do their own seeking. So you can get two and four. And I've even seen a talk about, you know eight actuator per disc. I, I don't think that, I think that's still theory, but but they could implement those. So that's one of the things that we're seeing. >> Paul: Old technology somehow finds a way to, to remain current. >> It does. >> Even it does even in the face of new alternatives. Kim Leyenaar, Storage Architect, Storage Performance Architect at Broadcom Thanks so much for being here with us today. Thank you so much for having me. >> This is Paul Gillin with Dave Nicholson here at SuperComputing 22. We'll be right back. (Outro music)

>> 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. >> A better than expected earnings report in late August got people excited about Snowflake again, but the negative sentiment in the market is weighed heavily on virtually all growth tech stocks and Snowflake is no exception. As we've stressed many times the company's management is on a long term mission to dramatically simplify the way organizations use data. Snowflake is tapping into a multi hundred billion dollar total available market and continues to grow at a rapid pace. In our view, Snowflake is embarking on its third major wave of innovation data apps, while its first and second waves are still bearing significant fruit. Now for short term traders focused on the next 90 or 180 days, that probably doesn't matter. But those taking a longer view are asking, "Should we still be optimistic about the future of this high flyer or is it just another over hyped tech play?" Hello and welcome to this week's Wiki Bond Cube Insights powered by ETR. Snowflake's Quarter just ended. And in this breaking analysis we take a look at the most recent survey data from ETR to see what clues and nuggets we can extract to predict the near term future in the long term outlook for Snowflake which is going to announce its earnings at the end of this month. Okay, so you know the story. If you've been investor in Snowflake this year, it's been painful. We said at IPO, "If you really want to own this stock on day one, just hold your nose and buy it." But like most IPOs we said there will be likely a better entry point in the future, and not surprisingly that's been the case. Snowflake IPOed a price of 120, which you couldn't touch on day one unless you got into a friends and family Delio. And if you did, you're still up 5% or so. So congratulations. But at one point last year you were up well over 200%. That's been the nature of this volatile stock, and I certainly can't help you with the timing of the market. But longer term Snowflake is targeting 10 billion in revenue for fiscal year 2028. A big number. Is it achievable? Is it big enough? Tell you what, let's come back to that. Now shorter term, our expert trader and breaking analysis contributor Chip Simonton said he got out of the stock a while ago after having taken a shot at what turned out to be a bear market rally. He pointed out that the stock had been bouncing around the 150 level for the last few months and broke that to the downside last Friday. So he'd expect 150 is where the stock is going to find resistance on the way back up, but there's no sign of support right now. He said maybe at 120, which was the July low and of course the IPO price that we just talked about. Now, perhaps earnings will be a catalyst, when Snowflake announces on November 30th, but until the mentality toward growth tech changes, nothing's likely to change dramatically according to Simonton. So now that we have that out of the way, let's take a look at the spending data for Snowflake in the ETR survey. Here's a chart that shows the time series breakdown of snowflake's net score going back to the October, 2021 survey. Now at that time, Snowflake's net score stood at a robust 77%. And remember, net score is a measure of spending velocity. It's a proprietary network, and ETR derives it from a quarterly survey of IT buyers and asks the respondents, "Are you adopting the platform new? Are you spending 6% or more? Is you're spending flat? Is you're spending down 6% or worse? Or are you leaving the platform decommissioning?" You subtract the percent of customers that are spending less or churning from those that are spending more and adopting or adopting and you get a net score. And that's expressed as a percentage of customers responding. In this chart we show Snowflake's in out of the total survey which ranges... The total survey ranges between 1,200 and 1,400 each quarter. And the very last column... Oh sorry, very last row, we show the number of Snowflake respondents that are coming in the survey from the Fortune 500 and the Global 2000. Those are two very important Snowflake constituencies. Now what this data tells us is that Snowflake exited 2021 with very strong momentum in a net score of 82%, which is off the charts and it was actually accelerating from the previous survey. Now by April that sentiment had flipped and Snowflake came down to earth with a 68% net score. Still highly elevated relative to its peers, but meaningfully down. Why was that? Because we saw a drop in new ads and an increase in flat spend. Then into the July and most recent October surveys, you saw a significant drop in the percentage of customers that were spending more. Now, notably, the percentage of customers who are contemplating adding the platform is actually staying pretty strong, but it is off a bit this past survey. And combined with a slight uptick in planned churn, net score is now down to 60%. That uptick from 0% and 1% and then 3%, it's still small, but that net score at 60% is still 20 percentage points higher than our highly elevated benchmark of 40% as you recall from listening to earlier breaking analysis. That 40% range is we consider a milestone. Anything above that is actually quite strong. But again, Snowflake is down and coming back to churn, while 3% churn is very low, in previous quarters we've seen Snowflake 0% or 1% decommissions. Now the last thing to note in this chart is the meaningful uptick in survey respondents that are citing, they're using the Snowflake platform. That's up to 212 in the survey. So look, it's hard to imagine that Snowflake doesn't feel the softening in the market like everyone else. Snowflake is guiding for around 60% growth in product revenue against the tough compare from a year ago with a 2% operating margin. So like every company, the reaction of the street is going to come down to how accurate or conservative the guide is from their CFO. Now, earlier this year, Snowflake acquired a company called Streamlit for around $800 million. Streamlit is an open source Python library and it makes it easier to build data apps with machine learning, obviously a huge trend. And like Snowflake, generally its focus is on simplifying the complex, in this case making data science easier to integrate into data apps that business people can use. So we were excited this summer in the July ETR survey to see that they added some nice data and pick on Streamlit, which we're showing here in comparison to Snowflake's core business on the left hand side. That's the data warehousing, the Streamlit pieces on the right hand side. And we show again net score over time from the previous survey for Snowflake's core database and data warehouse offering again on the left as compared to a Streamlit on the right. Snowflake's core product had 194 responses in the October, 22 survey, Streamlit had an end of 73, which is up from 52 in the July survey. So significant uptick of people responding that they're doing business in adopting Streamlit. That was pretty impressive to us. And it's hard to see, but the net scores stayed pretty constant for Streamlit at 51%. It was 52% I think in the previous quarter, well over that magic 40% mark. But when you blend it with Snowflake, it does sort of bring things down a little bit. Now there are two key points here. One is that the acquisition seems to have gained exposure right out of the gate as evidenced by the large number of responses. And two, the spending momentum. Again while it's lower than Snowflake overall, and when you blend it with Snowflake it does pull it down, it's very healthy and steady. Now let's do a little pure comparison with some of our favorite names in this space. This chart shows net score or spending velocity in the Y-axis, an overlap or presence, pervasiveness if you will, in the data set on the X-axis. That red dotted line again is that 40% highly elevated net score that we like to talk about. And that table inserted informs us as to how the companies are plotted, where the dots set up, the net score, the ins. And we're comparing a number of database players, although just a caution, Oracle includes all of Oracle including its apps. But we just put it in there for reference because it is the leader in database. Right off the bat, Snowflake jumps out with a net score of 64%. The 60% from the earlier chart, again included Streamlit. So you can see its core database, data warehouse business actually is higher than the total company average that we showed you before 'cause the Streamlit is blended in. So when you separate it out, Streamlit is right on top of data bricks. Isn't that ironic? Only Snowflake and Databricks in this selection of names are above the 40% level. You see Mongo and Couchbase, they know they're solid and Teradata cloud actually showing pretty well compared to some of the earlier survey results. Now let's isolate on the database data platform sector and see how that shapes up. And for this analysis, same XY dimensions, we've added the big giants, AWS and Microsoft and Google. And notice that those three plus Snowflake are just at or above the 40% line. Snowflake continues to lead by a significant margin in spending momentum and it keeps creeping to the right. That's that end that we talked about earlier. Now here's an interesting tidbit. Snowflake is often asked, and I've asked them myself many times, "How are you faring relative to AWS, Microsoft and Google, these big whales with Redshift and Synapse and Big Query?" And Snowflake has been telling folks that 80% of its business comes from AWS. And when Microsoft heard that, they said, "Whoa, wait a minute, Snowflake, let's partner up." 'Cause Microsoft is smart, and they understand that the market is enormous. And if they could do better with Snowflake, one, they may steal some business from AWS. And two, even if Snowflake is winning against some of the Microsoft database products, if it wins on Azure, Microsoft is going to sell more compute and more storage, more AI tools, more other stuff to these customers. Now AWS is really aggressive from a partnering standpoint with Snowflake. They're openly negotiating, not openly, but they're negotiating better prices. They're realizing that when it comes to data, the cheaper that you make the offering, the more people are going to consume. At scale economies and operating leverage are really powerful things at volume that kick in. Now Microsoft, they're coming along, they obviously get it, but Google is seemingly resistant to that type of go to market partnership. Rather than lean into Snowflake as a great partner Google's field force is kind of fighting fashion. Google itself at Cloud next heavily messaged what they call the open data cloud, which is a direct rip off of Snowflake. So what can we say about Google? They continue to be kind of behind the curve when it comes to go to market. Now just a brief aside on the competitive posture. I've seen Slootman, Frank Slootman, CEO of Snowflake in action with his prior companies and how he depositioned the competition. At Data Domain, he eviscerated a company called Avamar with their, what he called their expensive and slow post process architecture. I think he actually called it garbage, if I recall at one conference I heard him speak at. And that sort of destroyed BMC when he was at ServiceNow, kind of positioning them as the equivalent of the department of motor vehicles. And so it's interesting to hear how Snowflake openly talks about the data platforms of AWS, Microsoft, Google, and data bricks. I'll give you this sort of short bumper sticker. Redshift is just an on-prem database that AWS morphed to the cloud, which by the way is kind of true. They actually did a brilliant job of it, but it's basically a fact. Microsoft Excel, a collection of legacy databases, which also kind of morphed to run in the cloud. And even Big Query, which is considered cloud native by many if not most, is being positioned by Snowflake as originally an on-prem database to support Google's ad business, maybe. And data bricks is for those people smart enough to get it to Berkeley that love complexity. And now Snowflake doesn't, they don't mention Berkeley as far as I know. That's my addition. But you get the point. And the interesting thing about Databricks and Snowflake is a while ago in the cube I said that there was a new workload type emerging around data where you have AWS cloud, Snowflake obviously for the cloud database and Databricks data for the data science and EML, you bring those things together and there's this new workload emerging that's going to be very powerful in the future. And it's interesting to see now the aspirations of all three of these platforms are colliding. That's quite a dynamic, especially when you see both Snowflake and Databricks putting venture money and getting their hooks into the loyalties of the same companies like DBT labs and Calibra. Anyway, Snowflake's posture is that we are the pioneer in cloud native data warehouse, data sharing and now data apps. And our platform is designed for business people that want simplicity. The other guys, yes, they're formidable, but we Snowflake have an architectural lead and of course we run in multiple clouds. So it's pretty strong positioning or depositioning, you have to admit. Now I'm not sure I agree with the big query knockoffs completely. I think that's a bit of a stretch, but snowflake, as we see in the ETR survey data is winning. So in thinking about the longer term future, let's talk about what's different with Snowflake, where it's headed and what the opportunities are for the company. Snowflake put itself on the map by focusing on simplifying data analytics. What's interesting about that is the company's founders are as you probably know from Oracle. And rather than focusing on transactional data, which is Oracle's sweet spot, the stuff they worked on when they were at Oracle, the founder said, "We're going to go somewhere else. We're going to attack the data warehousing problem and the data analytics problem." And they completely re-imagined the database and how it could be applied to solve those challenges and reimagine what was possible if you had virtually unlimited compute and storage capacity. And of course Snowflake became famous for separating the compute from storage and being able to completely shut down compute so you didn't have to pay for it when you're not using it. And the ability to have multiple clusters hit the same data without making endless copies and a consumption/cloud pricing model. And then of course everyone on the planet realized, "Wow, that's a pretty good idea." Every venture capitalist in Silicon Valley has been funding companies to copy that move. And that today has pretty much become mainstream in table stakes. But I would argue that Snowflake not only had the lead, but when you look at how others are approaching this problem, it's not necessarily as clean and as elegant. Some of the startups, the early startups I think get it and maybe had an advantage of starting later, which can be a disadvantage too. But AWS is a good example of what I'm saying here. Is its version of separating compute from storage was an afterthought and it's good, it's... Given what they had it was actually quite clever and customers like it, but it's more of a, "Okay, we're going to tier to storage to lower cost, we're going to sort of dial down the compute not completely, we're not going to shut it off, we're going to minimize the compute required." It's really not true as separation is like for instance Snowflake has. But having said that, we're talking about competitors with lots of resources and cohort offerings. And so I don't want to make this necessarily all about the product, but all things being equal architecture matters, okay? So that's the cloud S-curve, the first one we're showing. Snowflake's still on that S-curve, and in and of itself it's got legs, but it's not what's going to power the company to 10 billion. The next S-curve we denote is the multi-cloud in the middle. And now while 80% of Snowflake's revenue is AWS, Microsoft is ramping up and Google, well, we'll see. But the interesting part of that curve is data sharing, and this idea of data clean rooms. I mean it really should be called the data sharing curve, but I have my reasons for calling it multi-cloud. And this is all about network effects and data gravity, and you're seeing this play out today, especially in industries like financial services and healthcare and government that are highly regulated verticals where folks are super paranoid about compliance. There not going to share data if they're going to get sued for it, if they're going to be in the front page of the Wall Street Journal for some kind of privacy breach. And what Snowflake has done is said, "Put all the data in our cloud." Now, of course now that triggers a lot of people because it's a walled garden, okay? It is. That's the trade off. It's not the Wild West, it's not Windows, it's Mac, it's more controlled. But the idea is that as different parts of the organization or even partners begin to share data that they need, it's got to be governed, it's got to be secure, it's got to be compliant, it's got to be trusted. So Snowflake introduced the idea of, they call these things stable edges. I think that's the term that they use. And they track a metric around stable edges. And so a stable edge, or think of it as a persistent edge is an ongoing relationship between two parties that last for some period of time, more than a month. It's not just a one shot deal, one a done type of, "Oh guys shared it for a day, done." It sent you an FTP, it's done. No, it's got to have trajectory over time. Four weeks or six weeks or some period of time that's meaningful. And that metric is growing. Now I think sort of a different metric that they track. I think around 20% of Snowflake customers are actively sharing data today and then they track the number of those edge relationships that exist. So that's something that's unique. Because again, most data sharing is all about making copies of data. That's great for storage companies, it's bad for auditors, and it's bad for compliance officers. And that trend is just starting out, that middle S-curve, it's going to kind of hit the base of that steep part of the S-curve and it's going to have legs through this decade we think. And then finally the third wave that we show here is what we call super cloud. That's why I called it multi-cloud before, so it could invoke super cloud. The idea that you've built a PAS layer that is purpose built for a specific objective, and in this case it's building data apps that are cloud native, shareable and governed. And is a long-term trend that's going to take some time to develop. I mean, application development platforms can take five to 10 years to mature and gain significant adoption, but this one's unique. This is a critical play for Snowflake. If it's going to compete with the big cloud players, it has to have an app development framework like Snowpark. It has to accommodate new data types like transactional data. That's why it announced this thing called UniStore last June, Snowflake a summit. And the pattern that's forming here is Snowflake is building layer upon layer with its architecture at the core. It's not currently anyway, it's not going out and saying, "All right, we're going to buy a company that's got to another billion dollars in revenue and that's how we're going to get to 10 billion." So it's not buying its way into new markets through revenue. It's actually buying smaller companies that can complement Snowflake and that it can turn into revenue for growth that fit in to the data cloud. Now as to the 10 billion by fiscal year 28, is that achievable? That's the question. Yeah, I think so. Would the momentum resources go to market product and management prowess that Snowflake has? Yes, it's definitely achievable. And one could argue to $10 billion is too conservative. Indeed, Snowflake CFO, Mike Scarpelli will fully admit his forecaster built on existing offerings. He's not including revenue as I understand it from all the new stuff that's in the pipeline because he doesn't know what it's going to look like. He doesn't know what the adoption is going to look like. He doesn't have data on that adoption, not just yet anyway. And now of course things can change quite dramatically. It's possible that is forecast for existing businesses don't materialize or competition picks them off or a company like Databricks actually is able in the longer term replicate the functionality of Snowflake with open source technologies, which would be a very competitive source of innovation. But in our view, there's plenty of room for growth, the market is enormous and the real key is, can and will Snowflake deliver on the promises of simplifying data? Of course we've heard this before from data warehouse, the data mars and data legs and master data management and ETLs and data movers and data copiers and Hadoop and a raft of technologies that have not lived up to expectations. And we've also, by the way, seen some tremendous successes in the software business with the likes of ServiceNow and Salesforce. So will Snowflake be the next great software name and hit that 10 billion magic mark? I think so. Let's reconnect in 2028 and see. Okay, we'll leave it there today. I want to thank Chip Simonton for his input to today's episode. Thanks to Alex Myerson who's on production and manages the podcast. Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hove is our Editor in Chief over at Silicon Angle. He does some great editing for us. Check it out for all the news. Remember all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me to get in touch David.vallante@siliconangle.com. DM me @dvellante or comment on our LinkedIn post. And please do check out etr.ai, they've got the best survey data in the enterprise tech business. This is Dave Vellante for the CUBE Insights, powered by ETR. Thanks for watching, thanks for listening and we'll see you next time on breaking analysis. (upbeat music)

(upbeat music) >> Hey everyone, welcome back to theCUBE live in Miami on the floor of IFS Unleashed. I'm your host, Lisa Martin. Had some great conversations. Have more great conversations coming your way. I have two guests joining me. Please welcome Martin Schirmer, the President of Enterprise Service Management, IFS Assyst. And Parminder Khosa, the Senior IT Manager at Parexel. Guys, it's great to have you on the program. >> Lovely to be here. >> It's good to be here. >> Martin, talk to me a little bit... tell the audience a little bit about Assyst so that that get that context before we start asking questions. >> Yeah. Absolutely. So IFS Assyst is a recent acquisition. It's an acquisition we made about a year ago. And fundamentally, it's a platform that takes care of IT service management, enterprise service management and IT operations management. So think of it, of managing sort of the ERP for IT and then broadening that out into the sort of enterprise where you're driving enterprise use cases for all lines of businesses like HR, finance, facilities, so on and so forth. >> Got it. And then Parminder, give the audience just a little bit of a flavor of Parexel, who you guys are, what you do. >> Sure. >> Maybe the impact that you make. >> Yeah, so Parexel is a clinical research organization. And what that means is that we manage drug trials for big pharmaceutical companies. So we're a big company. We're 25,000 people. We have offices in 150 locations all the way from Japan and the east through to the West Coast of the USA. >> Big company. >> Yeah, we are. We are a lot of people. >> And let's start chatting now Martin with some of the questions that you have so we get the understanding of how IFS and Parexel are working together. >> Yeah. Absolutely. I suppose... I mean the first thing is and thank you for traveling here all the way from the UK. (Lisa chuckles) Appreciate it and great energy and vibe. So just what the first question I had really was, you're customer of ours for the last 15 years plus. Maybe just give the audience a bit of context into your journey and how you've evolved from the sort of early years to where you're going into the future. >> Sure. So our history, I was part of a company that Parexel acquired that was already using Assyst. And as Parexel acquired us, they were in the process of also buying Assyst. So it became a kind of natural fit where I carried on with Assyst. And we started relatively small, sort of just the service desktop. And throughout the ongoing 15 years or so, we've just grown and expanded into kind of being a critical tool for Parexel right now. >> Okay, that's fantastic. I mean part of that journey, I know you started in sort of the more they call a ticketing space or IT service management space. Expand a little bit how you've expanded out of that and really moved into the enterprise. >> Sure. So yeah. So when we first rolled Assyst out, it was as I say, purely IT. And eventually we reached out to other business units to say asking questions like, Are you managing your workload through email? Are you managing your workload through Excel spreadsheets? In which case, if you are, we've got a solution for you that will make it a much better experience for your customers. They're all internal. It'll make it much easier for you because you will have official tracking going on through our system. I'll make it better for your management because we can drive metrics from all of the data that we're getting. So if you imagine finance we're getting, kind of 200 miles a day because of the size of our company. And they were just working through them one by one responding, and they becomes just a mess. So we developed forms for them to say, "Okay, Larry raise all your requests here. We will pick it up. We will manage it. We will communicate with you. And once the piece of work that you've asked for is done, we will let you know." And as we go through that process, we'll make it better for us because as I say we're getting those metrics. And we'll make it better for you because we can spot where our gaps are. If a request is taking three days, and of that three days, two days is waiting for someone on our end to respond to you or is waiting for us waiting for a customer to respond, we can iron those out and make it a much better experience for everyone. >> That's fantastic. It's really music to my ears because we always pushing the industry to say move away from just the IT side and really get into the enterprise. And it sounds like you've really gotten a lot of sort of productivity and efficiency gains out of that. >> Definitely, definitely. And it becomes kind of a happy circle. So the finance guys will work with the procurement guys. And they also look... Well, we're doing all of our work through Assyst now. So procurement's a little turnaround. So, well we're using this big spreadsheet to manage all of ours. Can we do the same? And they'll reach out to us and we'll say, "Of course we can. What is your process?" For example, they will say, okay, if someone asks for a new laptop, we need to get the approval from their line manager, from the supplier. We need to do our own internal work and then we will send it out. So imagine if you're doing that in a an email chain. It just becomes chaos. >> Yeah. >> So we will build all of that out for them. And then procurement will talk to HR and it just becomes a snowball. And before you know it, we are doing about 4,000 tickets per day in our Assyst system. And of those, 50% perhaps maybe more than 50% now will be non IT related. >> Oh, that's fantastic. Really music to my ears. And it really breaking down the boundaries or silos within an organization. It's really good. Let the teams work together. Right? >> Definitely. And that's one of the key things that we've learned is that we have to engage completely with our business partners. And our business partners are becoming more and more IT literate as well. So for example, we had a recent big HR solution provided to us. And as part of that, we know there are going to be questions, and queries and perhaps even issues to do with our HR system. So we have to work with us guys, the Assyst front end, the IT HR guys who look after the databases, all of the technology in the background. Then there'll be IT HR who are Workday experts. And then kind of not necessarily at the bottom of the chain will be the HR people themselves who are in their own way, experts in their area, experts in IT in a certain way. So all of those people have to work together. We become the front end, but we have to work with all of those parts of the business. >> That's really great. It's basically what you just said is taking business, IT processes and underpinning solutions. Effectively digital transformation, right? >> Exactly. Yeah. So HR is a great example. They used to have paper flying around with leave request, with sickness requests, with all of those kind of issues. And you said, well if you have an issue with your HR system, you can't raise a leave request, or you can't raise a sickness request, tell us. We will take care of it. We will fix it for you. We will give you the instructions. And we will get rid of all of that paper. >> That's brilliant. Just sort of turning the attention. And all of that, how do you drive the sort of, we'll talk about the autonomous enterprise. How do you drive automation in that process? >> Yeah. Of course, we have to map all of those processes out. Because we're not the experts in HR or procurement or whatever the business area may be. We have to really dig into their work methods, their working areas. What is necessary for them? What is a must have? What is a like to have? What is we don't really need? So we really drive into that processes. Once we've got those, we will automate them. We will build them out in Assyst with the process designer. It's very intuitive now. The latest version is really good to work with. We will do some pretty clever stuff in there. We'll say, okay the manager approval. If the manager is not there, then escalate it to the next person. Then we go to HR and say, okay HR have taken two days to do this. We're not particularly okay with that. So we will escalate it to the next person. And all of that process is completely automated, completely in Assyst. >> Brilliant. I mean obviously, we have a codeless workflow engine with a designer. And if you look at one of the trends from post covid is a war in talent in particular developers. The IDC says there's going to be around 4 million shortage of developers. What is your view on, how easy... Do I need developers? Is it easy, is it difficult to do these workflow extensions and automations? >> Definitely not, no. So the two key areas that you mentioned that with the customizer to develop the forms to make them available to our end users, drag and drop. Really easy to do. You can put some nice filters in there. You can put some nice variables in there. You can drive intelligent drive the forms from there as well. So if option A is correct, then don't show me option B, show me option C. And all of that is codeless, entirely codeless. I don't need to type any code. And when we move on to a process designer that hooks in nicely with the form customizer because we can say, "Okay, if option B on that form is selected, then runs this process." And all of that process is entirely codeless as well. Drag and drop. Creates some tasks. Create some decisions. >> Fantastic. >> Brilliant. >> Sounds really good. Switching gears a little bit. You spoke about experience, and that's also obviously very topical post, well, Covid becoming a remote workforce. Clearly, we need to be digitally connected to our business and organization because the hybrid workforce, as we all know, is here to stay. And that employee experience is fundamental because it is their sort of channel to the engagement of the organization. Of course, that has retention impacts and productivity impact. So just from your perspective, how was Covid, from your perspective, and how easy or difficult was it to get your employees engaged and productive and working? >> Yeah. And for us, it's a double edged sword Covid was. Because of the nature of our business. We do covid stuff. We do drug stuff. So we may have issues with some trials that are related to that. So we need to escalate those. We need to be aware of them and move them to the top of the chain as soon as possible. And then Assyst becomes a source of truth. Everybody knows that if I've got an issue with the current environment that we're living in, I can raise it in Assyst. And everybody knows that's where that information is. There's no need to have huge conference calls or huge email chains to try and follow those around. So with our Assyst platform, with our employees as well, everybody knew that this is where the source of truth was. We didn't have any dropouts. We didn't have any concerns with our system or performance. We knew it was there. We had to do some work like, as I say, around covid issues just to make sure they get pushed up to the top of the chain. But otherwise, we were fine. And great credit to our IT operations team as well who managed that pretty much seamlessly. >> That's brilliant. That's good news. >> Yeah. >> It really is. Just taking a little bit further and talking a little bit about what next. My team has been, I know, talking to your team about the whole area of asset management. Maybe talk to us a little bit about that journey. >> Sure, sure. So we're an ITOM customer as well. So all of our hardware data is stored within the ITOM platform. So we've pushed out the agents to all of our end user machines, so 25,000 agents. And we're in the process of integrating that into our Assyst platform to make that the single source of truth. And that part of that we're working on the software asset management side as well. So we've got a really good idea of where our software assets are. It comes to all license auditing, we know exactly how much we've got there. And the more complex side of it is of course server. So software management management as well. So we're in the process of getting all of that data as well. So once we've done all that, there is other all as the next step. The next step will be to perhaps do monitoring or pushing out software using the ITOM platform and getting rid of some of the disparate systems that we have right now. >> Well that's good news. And I think I saw a study. I think, every single person as an employee carries around 15 or 20 assets with him at any one time. Be it from a PC, phone, physical software licenses, so on and so forth. In that context, I can imagine the business case around it. >> Definitely. Yeah. And every, again, we map every user to their assets and (indistinct) their assets. And again Assyst as a source of truth for that. So if you want to look at my record, so, all right. Pam's got a laptop. He's got a mobile phone. We're thinking about giving him a tablet, but we'll find out. That he's in the process of getting a tablet as well. So I can have a look at my user record and know exactly what I've got with all of the asset tags and the various links that it has to the software pieces so it becomes a big tree of my assets. >> That's wonderful. Just the question I had was, we spoke about breaking down silos and the enterprise use cases and the effect that has. Do you envisage that Assyst can really get to being enterprisewide as, when I say enterprisewide, everybody in the organization effectively using this tool as their sort of source of experience, and level of automation of process? >> Definitely, definitely. As I say, we're getting... We're really pushing to get to that. As I say, 4,000 tickets a day with a user base of 25,000 kind of means that everybody will interact with the system perhaps every two weeks or so. So we're getting to that point and with the new functionality that's coming out with the Assyst product, with the team's integration, and the bot and everything that will bring to us because we are a big. We use teams. We use bots. We use that kind of technology. It will just fit in seamlessly. And trying to break down the silos, as I say finance, procurement, all of the big beasts within our company already are using the Assyst tool. And we want to bring in more and more of those processes as we mature. >> Brilliant. I think Omnichannel's critical. We want to connect from any device from anywhere. It's just the way we work. So I think that's critical. Teams is of course a a tool that most of us have become too familiar with. >> Yup. (chuckles) >> To be fair. (chuckles) It's better to be here in person finally, right? >> Yeah. >> So I think, that's all exciting news. And it's really fantastic. >> Great. >> So I suppose maybe in the time that we have left, what's next? >> What's next for us is that we're in the process of migrating our solution to the cloud, to the IFS cloud. That will open up a huge new user base for us. If we think all of our customers, all of our people who work on studies will have the ability to connect to Assyst and ask questions. That's a lot of it is just ask a question, or raise an issue or ask for something. So we're talking, it could be expanded by hundreds of thousands of new users that will meet more people on the backend to manage those requests as well. So yeah. It's just going to get bigger and bigger. And as you say, with the CMDB work that we're doing as well, that's another big ongoing stream for us. >> It's great because as you know, with Assyst we have a disruptive licensing model. >> Yeah. >> We have a t-shirt size pricing. All you can need based a number of employees. So there's no barriers to entry for you. >> There really is. And that really helps us because as I said initially, particularly when finance came on board and now they're expanding, there is no cost implication for it. The more that we use it, the better it is for. The more bang for buck that we get. >> Yep. That's our mantra. Enterprise users, right? For the price of a cup of coffee, for the price of a user. That's our mantra. >> I love it. You guys have done such a great job of articulating the synergies in the relationship that IFS Assyst has with Paraxel. You talked about the great outcomes that you're achieving. And it's all about Martin, I know, from IFS Assyst perspective, it's all about helping customers achieve those outcomes and those moments of service that are so critical to your customers on the other end staying with you, doing more business. Whether it's the end user customer, whether it's the actual employee. You talked a lot about the customer experience, the employee experience, and what you guys are doing together to enable that. And I always think that the employee experience and the customer experience are like this. They're inextricably linked. You can't, you shouldn't. Otherwise you're going to have problems. >> Yeah, no, absolutely. And there's actually a study on that saying that, 70% of customers generally don't feel they get what they want from organizations. >> 70. Wow! >> And if you take that one step further to what you said, the interconnectivity between customer employee, employee shops on Amazon, right? It's on those websites. So you can't be rolling out and digitally connect to the employee with something that is clunky and has the wrong experience. Like I said, it really affects that level of engagement the employee has with the company which happens to be largely these days remote. >> It does. Last question Martin, is for you. Talk to us about what's next for IFS Assyst. Obviously, we're back in person. There's a lot of momentum about the company. I was talking with Darren, the growth and first half was great. He kind of gave us some teaser about second half, but what's next from your perspective? >> Yeah. So what's next for us is achieving our goal. We are here to disrupt the industry. It's an industry that's dominated by one player and a fair amount of legacy players. We've disrupted the business model as I've told you. We here to do more because it's a simple thing. And that's the word simple. We want to keep things simple. We're going to keep engineering and driving our product forward, right? We've made sure that our platform is up there with the best. Yeah. We've just been certified by pink. Pink is a verification of ITIL four they call it. So it's a body. And the top level is you can get 20 out of 20. We got 17 out of 20. There's only one other vendor that has more than us and it's only by little. And after it's a big white space, the next one is 14. So we on the right track. We are going to of course drive and capture the market. So watch this space. We here to grow. >> We will watch this space. Congratulations on being that disrupter. >> Thank you. >> Parminder great work with what you guys are doing. You did a great job of articulating, as I said, the customers tour here. We appreciate your insights, your time. >> Thank you very much. >> Pleasure. >> All right, my pleasure. >> Thank you. For my guests, I'm Lisa Martin. You're watching The Cube live from Miami on the show floor of IFS Unleashed. We'll be back after a short break.

>> From every ISV to solve the problems. You want there to be tools in place that you can use, either open source tools or whatever it is that help you build it. And slowly over time, that building will become easier and easier. So my question to you was, where do you see you playing? Do you see yourself playing to ISVs as a set of tools, which will make their life a lot easier and provide that work? >> Absolutely. >> If they don't have, so they don't have to do it. Or you're providing this for the end users? Or both? >> So it's a progression. If you go to the ISVs first, you're doomed to starved before you have time for that other option. >> Yeah. >> Right? So it's a question of phase, the phasing of it. And also if you go directly to end users, you can demonstrate the power of it and get the attention of the ISVs. I believe that the ISVs, especially those with the biggest footprints and the most, you know, coveted estates, they have already made massive investments at trying to solve decentralization of their software stack. And I believe that they have used it as a hook to try to move to a software as a service model and rope people into leasing their infrastructure. So if you look at the clouds that have been propped up by Autodesk or by Adobe, or you name the company, they are building proprietary makeshift solutions for decentralizing or hybrid clouding. Or maybe they're not even doing that at all and all they're is saying hey, if you want to get location agnosticness, then what you should just, is just move into our cloud. >> Right. >> And then they try to solve on the background how to decentralize it between different regions so they can have decent offerings in each region. But those who are more advanced have already made larger investments and will be more averse to, you know, throwing that stuff away, all of their makeshift machinery away, and using a platform that gives them high performance parallel, low level file system access, while at the same time having metadata-driven, you know, policy-based, intent-based orchestration to manage the diffusion of data across a decentralized infrastructure. They are not going to be as open because they've made such an investment and they're going to look at how do they monetize it. So what we have found with like the movie studios who are using us already, many of the app they're using, many of those software offerings, the ISVs have their own cloud that offers that software for the cloud. But what we got when I asked about this, 'cause I was dealt specifically into this question because I'm very interested to know how we're going to make that leap from end user upstream into the ISVs where I believe we need to, and they said, look, we cannot use these software ISV-specific SAS clouds for two reasons. Number one is we lose control of the data. We're giving it to them. That's security and other issues. And here you're talking about we're doing work for Disney, we're doing work for Netflix, and they're not going to let us put our data on those software clouds, on those SAS clouds. Secondly, in any reasonable pipeline, the data is shared by many different applications. We need to be agnostic as to the application. 'Cause the inputs to one application, you know, the output for one application provides the input to the next, and it's not necessarily from the same vendor. So they need to have a data platform that lets them, you know, go from one software stack, and you know, to run it on another. Because they might do the rendering with this and yet, they do the editing with that, and you know, et cetera, et cetera. So I think the further you go up the stack in the structured data and dedicated applications for specific functions in specific verticals, the further up the stack you go, the harder it is to justify a SAS offering where you're basically telling the end users you need to park all your data with us and then you can run your application in our cloud and get this. That ultimately is a dead end path versus having the data be open and available to many applications across this supercloud layer. >> Okay, so-- >> Is that making any sense? >> Yes, so if I could just ask a clarifying question. So, if I had to take Snowflake as an example, I think they're doing exactly what you're saying is a dead end, put everything into our proprietary system and then we'll figure out how to distribute it. >> Yeah. >> And and I think if you're familiar with Zhamak Dehghaniis' data mesh concept. Are you? >> A little bit, yeah. >> But in her model, Snowflake, a Snowflake warehouse is just a node on the mesh and that mesh is-- >> That's right. >> Ultimately the supercloud and you're an enabler of that is what I'm hearing. >> That's right. What they're doing up at the structured level and what they're talking about at the structured level we're doing at the underlying, unstructured level, which by the way has implications for how you implement those distributed database things. In other words, implementing a Snowflake on top of Hammerspace would have made building stuff like in the first place easier. It would allow you to easily shift and run the database engine anywhere. You still have to solve how to shard and distribute at the transaction layer above, so I'm not saying we're a substitute for what you need to do at the app layer. By the way, there is another example of that and that's Microsoft Office, right? It's one thing to share that, to have a file share where you can share all the docs. It's something else to have Word and PowerPoint, Excel know how to allow people to be simultaneously editing the same doc. That's always going to happen in the app layer. But not all applications need that level of, you know, in-app decentralization. You know, many of them, many workflows are pipelined, especially the ones that are very data intensive where you're doing drug discovery or you're doing rendering, or you're doing machine learning training. These things are human in the loop with large stages of processing across tens of thousands of cores. And I think that kind of data processing pipeline is what we're focusing on first. Not so much the Microsoft Office or the Snowflake, you know, parking a relational database because that takes a lot of application layer stuff and that's what they're good at. >> Right. >> But I think... >> Go ahead, sorry. >> Later entrance in these markets will find Hammerspace as a way to accelerate their work so they can focus more narrowly on just the stuff that's app-specific, higher level sharing in the app. >> Yes, Snowflake founders-- >> I think it might be worth mentioning also, just keep this confidential guys, but one of our customers is Blue Origin. And one of the things that we have found is kind of the point of what you're talking about with our customers. They're needing to build this and since it's not commercially available or they don't know where to look for it to be commercially available, they're all building themselves. So this layer is needed. And Blue is just one of the examples of quite a few we're now talking to. And like manufacturing, HPC, research where they're out trying to solve this problem with their own scripting tools and things like that. And I just, I don't know if there's anything you want to add, David, but you know, but there's definitely a demand here and customers are trying to figure out how to solve it beyond what Hammerspace is doing. Like the need is so great that they're just putting developers on trying to do it themselves. >> Well, and you know, Snowflake founders, they didn't have a Hammerspace to lean on. But, one of the things that's interesting about supercloud is we feel as though industry clouds will emerge, that as part of company's digital transformations, they will, you know, every company's a software company, they'll begin to build their own clouds and they will be able to use a Hammerspace to do that. >> A super pass layer. >> Yes. It's really, I don't know if David's speaking, I don't want to speak over him, but we can't hear you. May be going through a bad... >> Well, a regional, regional talks that make that possible. And so they're doing these render farms and editing farms, and it's a cloud-specific to the types of workflows in the median entertainment world. Or clouds specifically to workflows in the chip design world or in the drug and bio and life sciences exploration world. There are large organizations that are kind of a blend of end users, like the Broad, which has their own kind of cloud where they're asking collaborators to come in and work with them. So it starts to even blur who's an end user versus an ISV. >> Yes. >> Right? When you start talking about the massive data is the main gravity is to having lots of people participate. >> Yep, and that's where the value is. And that's where the value is. And this is a megatrend that we see. And so it's really important for us to get to the point of what is and what is not a supercloud and, you know, that's where we're trying to evolve. >> Let's talk about this for a second 'cause I want to, I want to challenge you on something and it's something that I got challenged on and it has led me to thinking differently than I did at first, which Molly can attest to. Okay? So, we have been looking for a way to talk about the concept of cloud of utility computing, run anything anywhere that isn't addressed in today's realization of cloud. 'Cause today's cloud is not run anything anywhere, it's quite the opposite. You park your data in AWS and that's where you run stuff. And you pretty much have to. Same with with Azure. They're using data gravity to keep you captive there, just like the old infrastructure guys did. But now it's even worse because it's coupled back with the software to some degree, as well. And you have to use their storage, networking, and compute. It's not, I mean it fell back to the mainframe era. Anyhow, so I love the concept of supercloud. By the way, I was going to suggest that a better term might be hyper cloud since hyper speaks to the multidimensionality of it and the ability to be in a, you know, be in a different dimension, a different plane of existence kind of thing like hyperspace. But super and hyper are somewhat synonyms. I mean, you have hyper cars and you have super cars and blah, blah, blah. I happen to like hyper maybe also because it ties into the whole Hammerspace notion of a hyper-dimensional, you know, reality, having your data centers connected by a wormhole that is Hammerspace. But regardless, what I got challenged on is calling it something different at all versus simply saying, this is what cloud has always meant to be. This is the true cloud, this is real cloud, this is cloud. And I think back to what happened, you'll remember, at Fusion IO we talked about IO memory and we did that because people had a conceptualization of what an SSD was. And an SSD back then was low capacity, low endurance, made to go military, aerospace where things needed to be rugged but was completely useless in the data center. And we needed people to imagine this thing as being able to displace entire SAND, with the kind of capacity density, performance density, endurance. And so we talked IO memory, we could have said enterprise SSD, and that's what the industry now refers to for that concept. What will people be saying five and 10 years from now? Will they simply say, well this is cloud as it was always meant to be where you are truly able to run anything anywhere and have not only the same APIs, but you're same data available with high performance access, all forms of access, block file and object everywhere. So yeah. And I wonder, and this is just me throwing it out there, I wonder if, well, there's trade offs, right? Giving it a new moniker, supercloud, versus simply talking about how cloud is always intended to be and what it was meant to be, you know, the real cloud or true cloud, there are trade-offs. By putting a name on it and branding it, that lets people talk about it and understand they're talking about something different. But it also is that an affront to people who thought that that's what they already had. >> What's different, what's new? Yes, and so we've given a lot of thought to this. >> Right, it's like you. >> And it's because we've been asked that why does the industry need a new term, and we've tried to address some of that. But some of the inside baseball that we haven't shared is, you remember the Web 2.0, back then? >> Yep. >> Web 2.0 was the same thing. And I remember Tim Burners Lee saying, "Why do we need Web 2.0? "This is what the Web was always supposed to be." But the truth is-- >> I know, that was another perfect-- >> But the truth is it wasn't, number one. Number two, everybody hated the Web 2.0 term. John Furrier was actually in the middle of it all. And then it created this groundswell. So one of the things we wrote about is that supercloud is an evocative term that catalyzes debate and conversation, which is what we like, of course. And maybe that's self-serving. But yeah, HyperCloud, Metacloud, super, meaning, it's funny because super came from Latin supra, above, it was never the superlative. But the superlative was a convenient byproduct that caused a lot of friction and flack, which again, in the media business is like a perfect storm brewing. >> The bad thing to have to, and I think you do need to shake people out of their, the complacency of the limitations that they're used to. And I'll tell you what, the fact that you even have the terms hybrid cloud, multi-cloud, private cloud, edge computing, those are all just referring to the different boundaries that isolate the silo that is the current limited cloud. >> Right. >> So if I heard correctly, what just, in terms of us defining what is and what isn't in supercloud, you would say traditional applications which have to run in a certain place, in a certain cloud can't run anywhere else, would be the stuff that you would not put in as being addressed by supercloud. And over time, you would want to be able to run the data where you want to and in any of those concepts. >> Or even modern apps, right? Or even modern apps that are siloed in SAS within an individual cloud, right? >> So yeah, I guess it's twofold. Number one, if you're going at the high application layers, there's lots of ways that you can give the appearance of anything running anywhere. The ISV, the SAS vendor can engineer stuff to have the ability to serve with low enough latency to different geographies, right? So if you go too high up the stack, it kind of loses its meaning because there's lots of different ways to make due and give the appearance of omni-presence of the service. Okay? As you come down more towards the platform layer, it gets harder and harder to mask the fact that supercloud is something entirely different than just a good regionally-distributed SAS service. So I don't think you, I don't think you can distinguish supercloud if you go too high up the stack because it's just SAS, it's just a good SAS service where the SAS vendor has done the hard work to give you low latency access from different geographic regions. >> Yeah, so this is one of the hardest things, David. >> Common among them. >> Yeah, this is really an important point. This is one of the things I've had the most trouble with is why is this not just SAS? >> So you dilute your message when you go up to the SAS layer. If you were to focus most of this around the super pass layer, the how can you host applications and run them anywhere and not host this, not run a service, not have a service available everywhere. So how can you take any application, even applications that are written, you know, in a traditional legacy data center fashion and be able to run them anywhere and have them have their binaries and their datasets and the runtime environment and the infrastructure to start them and stop them? You know, the jobs, the, what the Kubernetes, the job scheduler? What we're really talking about here, what I think we're really talking about here is building the operating system for a decentralized cloud. What is the operating system, the operating environment for a decentralized cloud? Where you can, and that the main two functions of an operating system or an operating environment are the process scheduler, the thing that's scheduling what is running where and when and so forth, and the file system, right? The thing that's supplying a common view and access to data. So when we talk about this, I think that the strongest argument for supercloud is made when you go down to the platform layer and talk of it, talk about it as an operating environment on which you can run all forms of applications. >> Would you exclude--? >> Not a specific application that's been engineered as a SAS. (audio distortion) >> He'll come back. >> Are you there? >> Yeah, yeah, you just cut out for a minute. >> I lost your last statement when you broke up. >> We heard you, you said that not the specific application. So would you exclude Snowflake from supercloud? >> Frankly, I would. I would. Because, well, and this is kind of hard to do because Snowflake doesn't like to, Frank doesn't like to talk about Snowflake as a SAS service. It has a negative connotation. >> But it is. >> I know, we all know it is. We all know it is and because it is, yes, I would exclude them. >> I think I actually have him on camera. >> There's nothing in common. >> I think I have him on camera or maybe Benoit as saying, "Well, we are a SAS." I think it's Slootman. I think I said to Slootman, "I know you don't like to say you're a SAS." And I think he said, "Well, we are a SAS." >> Because again, if you go to the top of the application stack, there's any number of ways you can give it location agnostic function or you know, regional, local stuff. It's like let's solve the location problem by having me be your one location. How can it be decentralized if you're centralizing on (audio distortion)? >> Well, it's more decentralized than if it's all in one cloud. So let me actually, so the spectrum. So again, in the spirit of what is and what isn't, I think it's safe to say Hammerspace is supercloud. I think there's no debate there, right? Certainly among this crowd. And I think we can all agree that Dell, Dell Storage is not supercloud. Where it gets fuzzy is this Snowflake example or even, how about a, how about a Cohesity that instantiates its stack in different cloud regions in different clouds, and synchronizes, however magic sauce it does that. Is that a supercloud? I mean, so I'm cautious about having too strict of a definition 'cause then only-- >> Fair enough, fair enough. >> But I could use your help and thoughts on that. >> So I think we're talking about two different spectrums here. One is the spectrum of platform to application-specific. As you go up the application stack and it becomes this specific thing. Or you go up to the more and more structured where it's serving a specific application function where it's more of a SAS thing. I think it's harder to call a SAS service a supercloud. And I would argue that the reason there, and what you're lacking in the definition is to talk about it as general purpose. Okay? Now, that said, a data warehouse is general purpose at the structured data level. So you could make the argument for why Snowflake is a supercloud by saying that it is a general purpose platform for doing lots of different things. It's just one at a higher level up at the structured data level. So one spectrum is the high level going from platform to, you know, unstructured data to structured data to very application-specific, right? Like a specific, you know, CAD/CAM mechanical design cloud, like an Autodesk would want to give you their cloud for running, you know, and sharing CAD/CAM designs, doing your CAD/CAM anywhere stuff. Well, the other spectrum is how well does the purported supercloud technology actually live up to allowing you to run anything anywhere with not just the same APIs but with the local presence of data with the exact same runtime environment everywhere, and to be able to correctly manage how to get that runtime environment anywhere. So a Cohesity has some means of running things in different places and some means of coordinating what's where and of serving diff, you know, things in different places. I would argue that it is a very poor approximation of what Hammerspace does in providing the exact same file system with local high performance access everywhere with metadata ability to control where the data is actually instantiated so that you don't have to wait for it to get orchestrated. But even then when you do have to wait for it, it happens automatically and so it's still only a matter of, well, how quick is it? And on the other end of the spectrum is you could look at NetApp with Flexcache and say, "Is that supercloud?" And I would argue, well kind of because it allows you to run things in different places because it's a cache. But you know, it really isn't because it presumes some central silo from which you're cacheing stuff. So, you know, is it or isn't it? Well, it's on a spectrum of exactly how fully is it decoupling a runtime environment from specific locality? And I think a cache doesn't, it stretches a specific silo and makes it have some semblance of similar access in other places. But there's still a very big difference to the central silo, right? You can't turn off that central silo, for example. >> So it comes down to how specific you make the definition. And this is where it gets kind of really interesting. It's like cloud. Does IBM have a cloud? >> Exactly. >> I would say yes. Does it have the kind of quality that you would expect from a hyper-scale cloud? No. Or see if you could say the same thing about-- >> But that's a problem with choosing a name. That's the problem with choosing a name supercloud versus talking about the concept of cloud and how true up you are to that concept. >> For sure. >> Right? Because without getting a name, you don't have to draw, yeah. >> I'd like to explore one particular or bring them together. You made a very interesting observation that from a enterprise point of view, they want to safeguard their store, their data, and they want to make sure that they can have that data running in their own workflows, as well as, as other service providers providing services to them for that data. So, and in in particular, if you go back to, you go back to Snowflake. If Snowflake could provide the ability for you to have your data where you wanted, you were in charge of that, would that make Snowflake a supercloud? >> I'll tell you, in my mind, they would be closer to my conceptualization of supercloud if you can instantiate Snowflake as software on your own infrastructure, and pump your own data to Snowflake that's instantiated on your own infrastructure. The fact that it has to be on their infrastructure or that it's on their, that it's on their account in the cloud, that you're giving them the data and they're, that fundamentally goes against it to me. If they, you know, they would be a pure, a pure plate if they were a software defined thing where you could instantiate Snowflake machinery on the infrastructure of your choice and then put your data into that machinery and get all the benefits of Snowflake. >> So did you see--? >> In other words, if they were not a SAS service, but offered all of the similar benefits of being, you know, if it were a service that you could run on your own infrastructure. >> So did you see what they announced, that--? >> I hope that's making sense. >> It does, did you see what they announced at Dell? They basically announced the ability to take non-native Snowflake data, read it in from an object store on-prem, like a Dell object store. They do the same thing with Pure, read it in, running it in the cloud, and then push it back out. And I was saying to Dell, look, that's fine. Okay, that's interesting. You're taking a materialized view or an extended table, whatever you're doing, wouldn't it be more interesting if you could actually run the query locally with your compute? That would be an extension that would actually get my attention and extend that. >> That is what I'm talking about. That's what I'm talking about. And that's why I'm saying I think Hammerspace is more progressive on that front because with our technology, anybody who can instantiate a service, can make a service. And so I, so MSPs can use Hammerspace as a way to build a super pass layer and host their clients on their infrastructure in a cloud-like fashion. And their clients can have their own private data centers and the MSP or the public clouds, and Hammerspace can be instantiated, get this, by different parties in these different pieces of infrastructure and yet linked together to make a common file system across all of it. >> But this is data mesh. If I were HPE and Dell it's exactly what I'd be doing. I'd be working with Hammerspace to create my own data. I'd work with Databricks, Snowflake, and any other-- >> Data mesh is a good way to put it. Data mesh is a good way to put it. And this is at the lowest level of, you know, the underlying file system that's mountable by the operating system, consumed as a real file system. You can't get lower level than that. That's why this is the foundation for all of the other apps and structured data systems because you need to have a data mesh that can at least mesh the binary blob. >> Okay. >> That hold the binaries and that hold the datasets that those applications are running. >> So David, in the third week of January, we're doing supercloud 2 and I'm trying to convince John Furrier to make it a data slash data mesh edition. I'm slowly getting him to the knothole. I would very much, I mean you're in the Bay Area, I'd very much like you to be one of the headlines. As Zhamak Dehghaniis going to speak, she's the creator of Data Mesh, >> Sure. >> I'd love to have you come into our studio as well, for the live session. If you can't make it, we can pre-record. But you're right there, so I'll get you the dates. >> We'd love to, yeah. No, you can count on it. No, definitely. And you know, we don't typically talk about what we do as Data Mesh. We've been, you know, using global data environment. But, you know, under the covers, that's what the thing is. And so yeah, I think we can frame the discussion like that to line up with other, you know, with the other discussions. >> Yeah, and Data Mesh, of course, is one of those evocative names, but she has come up with some very well defined principles around decentralized data, data as products, self-serve infrastructure, automated governance, and and so forth, which I think your vision plugs right into. And she's brilliant. You'll love meeting her. >> Well, you know, and I think.. Oh, go ahead. Go ahead, Peter. >> Just like to work one other interface which I think is important. How do you see yourself and the open source? You talked about having an operating system. Obviously, Linux is the operating system at one level. How are you imagining that you would interface with cost community as part of this development? >> Well, it's funny you ask 'cause my CTO is the kernel maintainer of the storage networking stack. So how the Linux operating system perceives and consumes networked data at the file system level, the network file system stack is his purview. He owns that, he wrote most of it over the last decade that he's been the maintainer, but he's the gatekeeper of what goes in. And we have leveraged his abilities to enhance Linux to be able to use this decentralized data, in particular with decoupling the control plane driven by metadata from the data access path and the many storage systems on which the data gets accessed. So this factoring, this splitting of control plane from data path, metadata from data, was absolutely necessary to create a data mesh like we're talking about. And to be able to build this supercloud concept. And the highways on which the data runs and the client which knows how to talk to it is all open source. And we have, we've driven the NFS 4.2 spec. The newest NFS spec came from my team. And it was specifically the enhancements needed to be able to build a spanning file system, a data mesh at a file system level. Now that said, our file system itself and our server, our file server, our data orchestration, our data management stuff, that's all closed source, proprietary Hammerspace tech. But the highways on which the mesh connects are actually all open source and the client that knows how to consume it. So we would, honestly, I would welcome competitors using those same highways. They would be at a major disadvantage because we kind of built them, but it would still be very validating and I think only increase the potential adoption rate by more than whatever they might take of the market. So it'd actually be good to split the market with somebody else to come in and share those now super highways for how to mesh data at the file system level, you know, in here. So yeah, hopefully that answered your question. Does that answer the question about how we embrace the open source? >> Right, and there was one other, just that my last one is how do you enable something to run in every environment? And if we take the edge, for example, as being, as an environment which is much very, very compute heavy, but having a lot less capability, how do you do a hold? >> Perfect question. Perfect question. What we do today is a software appliance. We are using a Linux RHEL 8, RHEL 8 equivalent or a CentOS 8, or it's, you know, they're all roughly equivalent. But we have bundled and a software appliance which can be instantiated on bare metal hardware on any type of VM system from VMware to all of the different hypervisors in the Linux world, to even Nutanix and such. So it can run in any virtualized environment and it can run on any cloud instance, server instance in the cloud. And we have it packaged and deployable from the marketplaces within the different clouds. So you can literally spin it up at the click of an API in the cloud on instances in the cloud. So with all of these together, you can basically instantiate a Hammerspace set of machinery that can offer up this file system mesh. like we've been using the terminology we've been using now, anywhere. So it's like being able to take and spin up Snowflake and then just be able to install and run some VMs anywhere you want and boom, now you have a Snowflake service. And by the way, it is so complete that some of our customers, I would argue many aren't even using public clouds at all, they're using this just to run their own data centers in a cloud-like fashion, you know, where they have a data service that can span it all. >> Yeah and to Molly's first point, we would consider that, you know, cloud. Let me put you on the spot. If you had to describe conceptually without a chalkboard what an architectural diagram would look like for supercloud, what would you say? >> I would say it's to have the same runtime environment within every data center and defining that runtime environment as what it takes to schedule the execution of applications, so job scheduling, runtime stuff, and here we're talking Kubernetes, Slurm, other things that do job scheduling. We're talking about having a common way to, you know, instantiate compute resources. So a global compute environment, having a common compute environment where you can instantiate things that need computing. Okay? So that's the first part. And then the second is the data platform where you can have file block and object volumes, and have them available with the same APIs in each of these distributed data centers and have the exact same data omnipresent with the ability to control where the data is from one moment to the next, local, where all the data is instantiate. So my definition would be a common runtime environment that's bifurcate-- >> Oh. (attendees chuckling) We just lost them at the money slide. >> That's part of the magic makes people listen. We keep someone on pin and needles waiting. (attendees chuckling) >> That's good. >> Are you back, David? >> I'm on the edge of my seat. Common runtime environment. It was like... >> And just wait, there's more. >> But see, I'm maybe hyper-focused on the lower level of what it takes to host and run applications. And that's the stuff to schedule what resources they need to run and to get them going and to get them connected through to their persistence, you know, and their data. And to have that data available in all forms and have it be the same data everywhere. On top of that, you could then instantiate applications of different types, including relational databases, and data warehouses and such. And then you could say, now I've got, you know, now I've got these more application-level or structured data-level things. I tend to focus less on that structured data level and the application level and am more focused on what it takes to host any of them generically on that super pass layer. And I'll admit, I'm maybe hyper-focused on the pass layer and I think it's valid to include, you know, higher levels up the stack like the structured data level. But as soon as you go all the way up to like, you know, a very specific SAS service, I don't know that you would call that supercloud. >> Well, and that's the question, is there value? And Marianna Tessel from Intuit said, you know, we looked at it, we did it, and it just, it was actually negative value for us because connecting to all these separate clouds was a real pain in the neck. Didn't bring us any additional-- >> Well that's 'cause they don't have this pass layer underneath it so they can't even shop around, which actually makes it hard to stand up your own SAS service. And ultimately they end up having to build their own infrastructure. Like, you know, I think there's been examples like Netflix moving away from the cloud to their own infrastructure. Basically, if you're going to rent it for more than a few months, it makes sense to build it yourself, if it's at any kind of scale. >> Yeah, for certain components of that cloud. But if the Goldman Sachs came to you, David, and said, "Hey, we want to collaborate and we want to build "out a cloud and essentially build our SAS system "and we want to do that with Hammerspace, "and we want to tap the physical infrastructure "of not only our data centers but all the clouds," then that essentially would be a SAS, would it not? And wouldn't that be a Super SAS or a supercloud? >> Well, you know, what they may be using to build their service is a supercloud, but their service at the end of the day is just a SAS service with global reach. Right? >> Yeah. >> You know, look at, oh shoot. What's the name of the company that does? It has a cloud for doing bookkeeping and accounting. I forget their name, net something. NetSuite. >> NetSuite. NetSuite, yeah, Oracle. >> Yeah. >> Yep. >> Oracle acquired them, right? Is NetSuite a supercloud or is it just a SAS service? You know? I think under the covers you might ask are they using supercloud under the covers so that they can run their SAS service anywhere and be able to shop the venue, get elasticity, get all the benefits of cloud in the, to the benefit of their service that they're offering? But you know, folks who consume the service, they don't care because to them they're just connecting to some endpoint somewhere and they don't have to care. So the further up the stack you go, the more location-agnostic it is inherently anyway. >> And I think it's, paths is really the critical layer. We thought about IAS Plus and we thought about SAS Minus, you know, Heroku and hence, that's why we kind of got caught up and included it. But SAS, I admit, is the hardest one to crack. And so maybe we exclude that as a deployment model. >> That's right, and maybe coming down a level to saying but you can have a structured data supercloud, so you could still include, say, Snowflake. Because what Snowflake is doing is more general purpose. So it's about how general purpose it is. Is it hosting lots of other applications or is it the end application? Right? >> Yeah. >> So I would argue general purpose nature forces you to go further towards platform down-stack. And you really need that general purpose or else there is no real distinguishing. So if you want defensible turf to say supercloud is something different, I think it's important to not try to wrap your arms around SAS in the general sense. >> Yeah, and we've kind of not really gone, leaned hard into SAS, we've just included it as a deployment model, which, given the constraints that you just described for structured data would apply if it's general purpose. So David, super helpful. >> Had it sign. Define the SAS as including the hybrid model hold SAS. >> Yep. >> Okay, so with your permission, I'm going to add you to the list of contributors to the definition. I'm going to add-- >> Absolutely. >> I'm going to add this in. I'll share with Molly. >> Absolutely. >> We'll get on the calendar for the date. >> If Molly can share some specific language that we've been putting in that kind of goes to stuff we've been talking about, so. >> Oh, great. >> I think we can, we can share some written kind of concrete recommendations around this stuff, around the general purpose, nature, the common data thing and yeah. >> Okay. >> Really look forward to it and would be glad to be part of this thing. You said it's in February? >> It's in January, I'll let Molly know. >> Oh, January. >> What the date is. >> Excellent. >> Yeah, third week of January. Third week of January on a Tuesday, whatever that is. So yeah, we would welcome you in. But like I said, if it doesn't work for your schedule, we can prerecord something. But it would be awesome to have you in studio. >> I'm sure with this much notice we'll be able to get something. Let's make sure we have the dates communicated to Molly and she'll get my admin to set it up outside so that we have it. >> I'll get those today to you, Molly. Thank you. >> By the way, I am so, so pleased with being able to work with you guys on this. I think the industry needs it very bad. They need something to break them out of the box of their own mental constraints of what the cloud is versus what it's supposed to be. And obviously, the more we get people to question their reality and what is real, what are we really capable of today that then the more business that we're going to get. So we're excited to lend the hand behind this notion of supercloud and a super pass layer in whatever way we can. >> Awesome. >> Can I ask you whether your platforms include ARM as well as X86? >> So we have not done an ARM port yet. It has been entertained and won't be much of a stretch. >> Yeah, it's just a matter of time. >> Actually, entertained doing it on behalf of NVIDIA, but it will absolutely happen because ARM in the data center I think is a foregone conclusion. Well, it's already there in some cases, but not quite at volume. So definitely will be the case. And I'll tell you where this gets really interesting, discussion for another time, is back to my old friend, the SSD, and having SSDs that have enough brains on them to be part of that fabric. Directly. >> Interesting. Interesting. >> Very interesting. >> Directly attached to ethernet and able to create a data mesh global file system, that's going to be really fascinating. Got to run now. >> All right, hey, thanks you guys. Thanks David, thanks Molly. Great to catch up. Bye-bye. >> Bye >> Talk to you soon.

(upbeat music) >> TheCUBE presents UiPath Forward 5. Brought to you by UiPath. >> Hello and welcome back to TheCUBE's coverage of UiPath Forward 5. We're here in Las Vegas at the Venetian Convention Center. This is day two. We're wrapping up Dave Nicholson and Dave Vellante. This is the fourth time theCUBE has been at UiPath Forward. And we've seen the transformation of the company from, essentially, what was a really interesting and easy to adopt point product to now one through acquisitions, IPO, has made a number of enhancements to its platform. David Cardenas is here. Deputy Director of Operations for County of Los Angeles, the Department of Public Health. David, good to see you. Thanks for coming on theCUBE. >> Thanks for having me on guys. Appreciate it. >> So what is your role? What does it have to do with automation? >> So I had been, actually started off in the IT space within the public health. Had served as a CIO previously, but now been moving into broader operations. And I basically manage all of the back office operations for the department, HR, IT, finance, all that. >> So you've had a wild ride in the last couple of years. >> Yeah, I think, like I've been talking earlier, it's just been, the last two years have just been horrendous. It's been a really difficult experience for us. >> Yeah, and I mean, the scars are there, and maybe permanently. But it also had major effects on organizations, on operations that, again, seem to be permanent. How would you describe the situation in your organization? >> So I think it, the urgency that came along with the pandemic response, kind of required us to look at things, you know, differently. We had to be, realize we had to be a lot more nimble than when we were and try to figure out how to enhance our operations. But really look at the core of what we're doing and figure out how it is to be more efficient. So I think we've kind of seen it as an opportunity to really examine ourselves a little bit more deeply and see what things we need to do to kind of, to fix our operations and get things on a better path. >> You know, I think a lot of organizations we talked to say that. But I want to understand how you handle this is, you didn't have time to sit back in the middle of the pandemic. >> Yeah. >> And then as you exit, what I call the isolation economy, people are so burned out, you know? So how do you deal with that organizational trauma? Say, okay now, let's sit back and think about this. Do people, are they eager to do so? Do they have the appetite for it? What's that dynamic like? >> So I think certainly there's a level of exhaustion inside the organization. I can't say that there isn't because it's just been, you know, two years of 24/7/365 kind of work. And that's tough on any organization. But I think what we realize is that there's, you know, we need to move into action quickly 'cause we don't know what's going to come next, right? And we're expecting that this is just a sign of what's to come and that we're just at the start of that stage of, we're just going to see a lot more outbreaks, we're going to see a lot more conditions kind of hitting us. And if we're not prepared for that, we're not going to be able to respond for the, and preserve the health and safety of our citizens, right? So I think we're taking a very active, like, look at these opportunities and see what we've done and say how do we now make the changes that we made in response to the pandemic permanent so that the next time this comes at us, we won't have to be struggling the way that we were to try to figure things out because we'll have such a better foundation in place to be able to move things forward. >> I mean, I've never served in the military, but I imagine that when you're in the military, you're always prepared for some kind of, you know, in your world, code red, right? >> Yeah. >> So it's like this code red culture. And that seems to have carried through, right? People are, you know, constantly aware that, wow. We got caught off guard and we don't want that to happen again. Because that was a big part of the trauma was just the unknown- >> Right. >> and the lack of preparedness. So thinking about technology and its role in helping you to prepare for that type of uncertainty. Can you describe how you're applying technology to prepare for the next unknown? >> So I think, so that first part of what you said, I think the difficulty we've always had in the public health side is that there's the, generally the approach to healthcare is very reactionary, right? Your first interface with the healthcare system is, "I'm going to go see my doctor; I'm going to go to the hospital." The work that we do in public health is to try to do everything we can to keep you out of that, right? So it's broad-based messaging, social media now is going to put us out there. But also, to be able to surveil disease in a different way. And so the holy grail for us in healthcare has always been, at least on the public health side, has been to try to see how can we tap in more actively that when you go see the doctor or when you go to the hospital, how can I get access to that information very, very quickly so that I know, and can see, and surveil my entire county in my jurisdiction and know, oh, there's an outbreak of disease happening in this section of the county. We're 10 million people with, you know, hundreds of square miles inside of LA. There are places where we can see very, you know, specific targets that we know we have to hit. But the data's a little stale and we find out several months after. We need to figure out a way to do that more actively. Technology's going to be our path to be able to capture that information more actively and come up on something a little bit, so we can track things faster and be able to respond more quickly. So that's our focus for all our technology implementations, automation like UiPath has offered us and other things, is around how to gather that information more quickly and put that into action so we can do quick interventions. >> People have notoriously short memories. Please tell me (chuckles) any of the friction that you may have experienced in years past before the pandemic. That those friction points where people are thinking, "Eh, what are the odds?" >> Yeah. "Eh, I've got finite budget, I think I'm going to spend it on this thing over here." Do you, are you able to still ride sort of the wave of mind share at this point when putting programs together for the future? >> So whatever friction was there during the pandemic wiped away. I mean, we had amazing collaboration with the medical provider community, our hospital partners. The healthcare system in LA was working very closely with us to make sure that we were responding. And there is that wave that we are trying to make sure that we use this as an opportunity to kind of ride it so that we can implement all the things that we want. 'Cause we don't know how long that's going to last us. The last time that I saw anything this large was after the anthrax attacks and the bioterrorism attacks that we had after 9/11. >> How interesting. >> Public health was really in lens at that point. And we had a huge infusion of funding, a lot of support from stakeholders, both politically and within the healthcare system. And we were able to make some large steps in movement at that point. This feels the same but in a larger scale because now it touched every part of the infrastructure. And we saw how society really had to react to what was going on in a different way than anyone has ever prepared for. And so now is we think is a time where we know that people are making more investments. And our success is going to be their success in the longterm. >> And you have to know that expectations are now set- >> Extremely high. >> at a completely different level, right? >> Yes, absolutely. >> There is no, "Oh, we don't have enough PPE." >> Correct. >> Right? >> David: Correct. >> The the expectation level is, hey, you should have learned from all of- >> We should have it; we can deliver it, We'll have it at the ready when we need to provide it. Yes, absolutely. >> Okay, so I sort of mentioned, we're, David cubed on theCUBE (all laughing). So three Daves. You spoke today at the conference? >> Actually I'm speaking later actually in the session in an hour or so. >> Oh Okay. My understanding is that you've got this concept of putting humans at the center of the automation. What does that mean? Why is that important? Help us understand that. >> So I think what we found in the crisis is that the high demand for information was something we hadn't seen before, right? We're one of the largest media markets in the United States. And what we really had trouble with is trying to figure out how to serve the residents, to provide them the information that we needed to provide to them. And so what we had traditionally done is press releases, you know, just general marketing campaigns, billboards, trying to send our message out. And when you're talking about a pandemic where on a daily basis, hour-by-hour people wanted to know what was going on in their local communities. Like, we had to change the way that we focused on. So we started thinking about, what is the information that the residents of our county need? And how can we set up an infrastructure to sustain the feeding of that? Because if we can provide more information, people will make their own personal decisions around their personal risk, their personal safety measures they need to take, and do so more actively. More so than, you know, one of us going on camera to say, "This is what you should do." They can look for themselves and look at the data that's in front of them and be able to make those choices for themselves, right? And so we needed to make sure that everything that we were doing wasn't built around feeding it to our political stakeholders, which are important stakeholders. We needed to make sure that they're aware and are messaging out, and our leadership are aware. But it's what could we give the public to be able to make them have access to information that we were collecting on an every single day basis to be able to make the decisions for their lives. And so the automation was key to that. We were at the beginning of the pandemic just had tons and tons of resources that we were throwing at the problem that was, our systems were slow, we didn't have good ability to move data back and forth between our systems, and we needed a stop-gap solution to really fill that need and be able to make the data cycles to meet the data cycles. We had basically every day had to deliver reports and analytics and dashboards by like 10 o'clock in the morning because we knew that the 12 an hour and the five-hour news cycles were going to hit and the press were going to then take those and message out. And the public started to kind of come in at that same time and look at 10 and 11 o'clock and 12 o'clock. >> Yeah. >> We could see it from how many hits were hitting our website, looking for that information. So when we failed and had a cycle where that data cycle didn't work and we couldn't deliver, the public would let us know, the press would let us know, the stakeholders would let us know. We had never experienced anything like that before, right. Where people had like this voracious appetite for the information. So we needed to have a very bulletproof process to make sure that every single 24 hours we were delivering that data, making it available at the ready. >> Software robots enabled that. >> Exactly. >> Okay. And so how were you able to implement that so quickly within such a traumatic environment? >> So I think, I guess necessity is always the mother of invention. It kind of drove us to go real quickly to look at what we had. We had data entry operations set up where we had dozens and dozens of people whose sole job in life on a 24-hour cycle was to receive medical reports that we we're getting, interview data that's coming from our case interviews, hospitalization data that was coming in through all these different channels. And it was all coming in in various forms. And they were entering that into our systems of record. And that's what we were using, extracts from that system of record, what was using to generate the data analyses in our systems and our dashboards. And so we couldn't rely on those after a while because the data was coming in at such high volume. There wasn't enough data entry staff to be able to fit the need, right? And so we needed to replace those humans and take them out of that data entry cycle, pop in the bots. And so what we started to look at is, let's pick off the, where it is that that data entry cycle starts and see what we could do to kind of replace that cycle. And we started off with a very discreet workload that was focused on some of our case interview data that was being turned into PDFs that somebody was using to enter into our systems. And we said, "Well before you do that," since we can't import into the systems 'cause it wasn't working, the import utilities weren't working. We got 'em into simple Excel spreadsheets, mapped those to the fields in our systems and let the bots do that over and over again. And we just started off with that one-use case and just tuned it and went cycle after cycle. The bots just got better and better to the point where we had almost like 95% success rates on each submission of data transactions that we did every day. >> Okay, and you applied that automation, I don't know, how many bots was it roughly? >> We're now at like 30; we started with about five. >> Okay, oh, interesting. So you started with five and you applied 'em to this specific use case to handle the velocity and volume of data- >> Correct. >> that was coming in. But that's obviously dynamic and it's changed. >> Absolutely. >> I presume it's shifted to other areas now. So how did you take what you learned there and then apply it to other use cases in other parts of the organization? >> So, fortunately for us, the process that was being used to capture the information to generate the dashboards and the analyses for the case interview data, which is what we started with- >> Yeah. >> Was essentially being used the same for the hospitalization data that we were getting and for tracking deaths as they were coming in as well. And so the bots essentially were just, we just took one process, take the same bots, copy them over essentially, and had them follow the very same process. We didn't try to introduce any different workflow than what was being done for the first one so we could replicate quickly. So I think it was lucky for us a lot- >> Dave V.: I was going to say, was that luck or by design? >> It was the same people doing the same analyses, right? So in the end they were thinking about how to be efficient themselves. So they kind of had coalesced around a similar process. And so it was kind of like fortunate, but it was by design in terms of how they- >> Dave V.: It was logical to them. >> Logical to them to make it. >> Interesting. >> So for us to be able to insert the bots became pretty easy on the front end. It's just now as we're trying to now expand to other areas that were now encountering like unique processes that we just can't replicate that quickly. We're having to like now dig into. >> So how are you handling that? First of all, how are you determining which processes? Is it sort of process driven? Is it data driven? How do you determine that? >> So obviously right now the focus still is COVID. So the the priorities scale that we've set internally for analyzing those opportunities really is centered around, you know, which things are really going to help our pandemic response, right? We're expecting another surge that's going to happen probably in the next couple of weeks. That'll probably take us through December. Hopefully, at that point, things start to calm down. But that means high-data volume again; these same process. So we're looking at optimizing the processes that we have, what can we do to make those cycles better, faster, you know, what else can we add? The data teams haven't stopped to try to figure out how else can they turn out new data reports, new data analysis, to give us a different perspective on the new variants and the new different outbreaks and hotspots that are popping up. And so we also have to kind of keep up with where they're going on these data dashboards. So they're adding more data into these reports so we know we have to optimize that. And then there's these kind of tangential work. So for example, COVID brought about, unfortunately, a lot of domestic violence reports. And so we have a lot of domestic violence agencies that we work with and that we have interactions with and to monitor their work, we have certain processes. So that's kind of like COVID-adjacent. But it's because it's such a very critical task, we're looking at how we can kind of help in those processes and areas. Same thing in like in our substance abuse area. We have substance use disorder treatment services that we provide. And we're delivering those at a higher rate because COVID kind of created more of a crisis than we would've liked. And so that's how we're prioritizing. It's really about what is the social need, what does the community need, and how can we put the technology work in those areas? >> So how do you envision the future of automation in your organization and the future of your organization? What does that look like? Paint a picture for us. >> So I'm hoping that it really does, you know, so we're going to take everything that's COVID related in the disease control areas, both in terms of our laboratory operations, in terms of our clinic operations, the way we respond, vaccination campaigns, things of that nature. And we're going to look at it to see what can efficiencies can we do there because it's a natural outgrowth of everything we've done on COVID up to this point. So, you know, it's almost like it's as simple as you're just replicating it with another disease. The disease might have different characteristics, but the work process that we follow is very similar. It's not like we're going to change everything and do something completely different for a respiratory condition as we would for some other type of foodborne condition or something else that might happen. So we certainly see very easy opportunities to just to grow out what we've already done in terms of the processes is to do that. So that's wave one, is really focus on that grow out. The second piece I think is to look at these kind of other general kind of community-based type of operations and see what operations we can do there to kind of implement some improvements there. And then I'm certainly in my new role of, in Deputy Director of Operation, I'm a CIO before. Now that I'm in this operations role, I have access to the full administrative apparatus for the department. And believe me, there's enough to keep me busy there. (Dave V. Laughing) And so that's going to be kind of my third prong is to kind of look at the implement there. >> Awesome. Go ahead, Dave. >> Yeah, so, this is going to be taking a step back, kind of a higher level view. If we could direct the same level of rigor and attention towards some other thing that we've directed towards COVID, if you could snap your fingers and make that happen, what would that thing be in the arena of public health in LA County in particular, or if you want California, United States. What is something that you feel maybe needs more attention that it's getting right now? >> So I think I touched on it a little bit earlier, but I think it's the thing we've been always been trying to get to is how to really become just very intentional about how we share data more actively, right? I don't have to know everything about you, but there are certain things I care about when you go to the doctor for that doctor and that physician to tell me. Our physicians, our healthcare system as you know, is always under a lot of pressure. Doctors don't have the time to sit down and write a form out for me and tell me everything that's going on. During COVID they did because they were, they cared about their patients so much and knew, I need to know what's going on at every single moment. And if I don't tell you what's going on in my office, you'll never know and can't tell us what's going on in the community. So they had a vested interest in telling us. But on a normal day-to-day, they don't have the time for that. I got to replace that. We got to make sure that when we get to, not me only, but everyone in this public health community has to be focused and working with our healthcare partners to automate the dissemination and the distribution of information so that I have the information at my fingers, that I can then tell you, "Here's what's going on in your local community," down to your neighborhood, down to your zip code, your census tracked, down to your neighbors' homes. We'll be able to tell you, "This is your risk. Here are the things that are going on. This is what you have to watch out for." And the more that we can be more that focused and laser-focused on meeting that goal, we will be able to do our job more effectively. >> And you can do that while preserving people's privacy. >> Privacy, absolutely. >> Yeah, absolutely. But if people are informed then they can make their own decisions. >> Correct. >> And they're not frustrated at the systems. David, we got to wrap. >> Sure. >> But maybe you can help us. What's your impression of the, first of all, is this your first Forward? You've been to others? >> This is my first time. >> Okay. >> My first time. >> What's your sort of takeaway when you go back to the office or home and people say, "Hey, how was the show? What, what'd you learn?" What are you going to say? >> Well, from just seeing all the partners here and kind of seeing all the different events I've been able to go to and the sessions there's, you don't know many times I've gone to and say, "We've got to be doing that." And so there's certainly these opportunities for, you know, more AI, more automation opportunities that we have not, we just haven't even touched on really. I think that we really need to do that. I have to be able to, as a public institution at some point our budgets get capped. We only have so much that we're going to receive. Even riding this wave, there's only so much we're going to be able to get. So we have to be very efficient and use our resources more. There's a lot more that we can do with AI, a lot more with the tools that we saw, some of the work product that are coming out at this conference that we think we can directly apply to kind of take the humans out of that, their traditional roles, get them doing higher level work so I can get the most out of them and have this other more mundane type of work, just have the systems just do it. I don't need anybody doing that necessarily, that work. I need to be able to leverage them for other higher level capabilities. >> Well thank you for that. Thanks for coming on theCUBE and really appreciate. Dave- >> It's been great talking to you guys, thank you. >> Dave, you know, I love software shows because the business impact is so enormous and I especially love cool software shows. You know, this first of all, the venue. 3,500 people here. Very cool venue. I like the fact that it's not like booth in your face, booth competition. I mean I love VMware, VMworld, VMware Explore. But it's like, "My booth is bigger than your booth." This is really nice and clean, and it's all about the experience. >> A lot of steak, not as much sizzle. >> Yeah, definitely. >> A lot of steak. >> And the customer content at the UiPath events is always outstanding. But we are entering a new era for UiPath, and we're talking. We heard a lot about the Enterprise platform. You know, the big thing is this company's been in this quarterly shock-lock since last April when it went public. And it hasn't all been pretty. And so new co-CEO comes in, they've got, you know, resetting priorities around financials, go to market, they've got to have profitable growth. So watching that that closely. But also product innovation so the co-CEOs will be able to split that up, split their duties up. Daniel Dines the product visionary, product guru. Rob Enslin, you know- making the operations work. >> Operations execution business, yeah. >> We heard that Carl Eschenbach did the introduction. Carl's a major operator, wanted that DNA into the company. 'Cause they got to keep product innovation. And I want to, I want to see R&D spending, stay relatively high. >> Product innovation, but under the heading of platform. And that's the key thing is just not being that tool set. The positioning has been, I think, accurate that, you know, over history, we started with these RPA tools and now we've moved into business process automation and now we're moving into new frontiers where, where truly, AI and ML are being leveraged. I love the re-infer story about going in and using natural national (chuckles) national, natural language processing. I can't even say it, to go through messaging. That's sort of a next-level of intelligence to be able to automate things that couldn't be automated before. So that whole platform story is key. And they seem to have made a pretty good case for their journey into platform as far as I'm concerned. >> Well, yeah, to me again. So it's always about the customers, want to come to an event like this, you listen to what they say in the keynotes and then you listen to what the customers say. And there's a very strong alignment in the UiPath community between, you know, the marketing and the actual implementation. You know, marketing's always going to be ahead. But, we saw this a couple of years ago with platform. And now we're seeing it, you know, throughout the customer base, 10,000+ customers. I think this company could have, you know, easily double, tripled, maybe even 10x that. All right, we got to wrap. Dave Nicholson, thank you. Two weeks in a row. Good job. And let's see. Check out siliconangle.com for all the news. Check out thecube.net; wikibon.com has the research. We'll be on the road as usual. theCUBE, you can follow us. UiPath Forward 5, Dave Vellante for Dave Nicholson. We're out and we'll see you next time. Thanks for watching. (gentle music)

(intro upbeat music) >> TheCUBE presents UiPath Forward5, Brought to you by UiPath. >> Welcome to day two of Forward5 UiPath Customer Conference. You're watching theCUBE. My name is Dave Vellante. My co-host is David Nicholson. Yesterday, Dave, we heard about the extension into an enterprise platform. We heard about, from the two CEOs, a new go-to-market strategy. We heard from a lot of customers how they're implementing UiPath generally and automation, specifically, scaling, hyper-automation, and all the buzzwords you hear. Todd Foley is the CDO and CSO of Lydonia Technologies and Devika Saharya is the director of ERP and RPA at MongoDB. Folks, welcome to theCUBE. Thanks for taking time out of your busy day and coming on. >> Thank you Dave. >> Thank you so much. >> So let's start with the roles. So Devika, ERP and RPA. >> Yes. >> It's like peanut butter and jelly, or how do those things relate? What's your, what's your role? >> Absolutely. So I started at Mongo as an ERP manager, and you know, as we were growing, the one thing that came out of, you know, the every year goals for the company, one big goal that came out was how we have to scale. There are so many barriers to scale. How can we become a billion dollar company? What do we need to do? And when we started drilling down into, you know, different areas, we figured it out that people do a lot of stuff manually. It's like comparing sheets, you know, copying data from one place to the other, and so on and so forth. So one thing that we realized was we definitely need some kind of automation. At that time, we didn't know about automation, but we did our own market research and here we are. >> Let's automate. Yeah, right. (Devika laughs) Sounds easy. All right, thank you. Todd, CDO, Chief Data or Chief Dig, and CSO, I'm assuming Chief Data? >> Chief Data. >> And the Chief Information Security Officer. Tell us about Lydonia and also your role. >> Sure, Lydonia, we started just over three years ago. We looked at the RPA market. We saw great opportunity, but we also saw a challenge. We saw that a lot of people had deployed RPA but weren't getting the promised, you know, immediate ROI, rapid deployment that was out there. And when we looked at it, we saw that it really wasn't a technical challenge. Sometimes it was how technology was applied, but there were a lot of things that people were doing in their process and how they were treating RPA, often as if it were traditional technology that slowed them down. So we built our practice, our company, around the idea of being able to help people scale very quickly and drive that faster. And we're finding now with the RPA being pretty ubiquitous, that it's the one thing that's in the greatest demand among our clients. >> Okay, so you're the implementation partner for Mongo, is that right? >> We are. >> Okay, so relatively new. Very new actually, but a specialist. Why'd you choose Lydonia? >> So, that's an interesting question. When we came last year to UiPath Forward, we were looking for, you know, the right kind of people who can, you know, put us on track. We had the technology, we had everything in place, we did the POC, everybody liked it, but we didn't know how to, you know, basically go in that direction. We were missing that direction. And then we, you know, we were doing our homework here, we found, we accidentally stumbled with Lydonia, and I had follow up conversations with Todd, and they were just so tapered. I knew exactly what Todd was explaining me, and we knew we are, we are in safe hands. >> So, where did you start? >> So we, the first thing that we did was a POC for the finance side of business. And right after that POC, we realized that, you know, how much time people were actually investing manually, like things that were done in three to four days was turning into a 30 minute process. And that gave us, you know, the idea that we should start drilling down into different departments and try to find where there are, you know, areas where we can improve. And we did all of that. And then we met with Todd, and Todd explained that how his Reignite process works. So we took Reignite as our first step and, you know, took it from there. We chose one department, we worked with them. We had about 10 processes highlighted, thanks to Todd, he worked with them, and he literally drilled and nailed it down that what we need to do. And as of today, all those 10 are automated. >> Wow. Okay. >> Todd, does this interaction between Lydonia and MongoDB, as a customer, apply equally in the field when you're going out and talking to clients that might be running MongoDB, they might be customers of MongoDB, they may have financial applications that are backended with MongoDB, is there a synergy there that you've been able to gain? >> I think there is. I think there's one thing that's kind of unique about RPA, and that the traditional questions around integration and applicability aren't as important when you have a platform that can work with anything that people can use. I think also, you know, when we look at what we typically do with people, some of the things we see at Mongo are very common use cases you know, across all of our clients. So I, there's definitely the ability for us to take things we've done and have clients get leverage out of them. At the same time, the platform itself is, makes it different than a traditional model where, you know if somebody has worked in a particular area or built an automation for a particular application, there's some kind of utility to do it faster for another client. What we find is that that's not really the case. And that oftentimes we'll compete with people who use different tool sets than UiPath who have that kind of value story around having done it before, we come in and we do it twice as fast as they could. >> So you've, you're a veteran of complex integrations. >> Oh yeah. (Todd laughs) >> I know that from our paths have crossed in the past. So you're saying that in this world of RPA, that this tool set like UiPath as a platform, we've been talking a lot about the difference between being a tool set and being a platform. >> Right. >> That this platform can sort of hover above things without that same layer of complexity, or level of complexity, that you've experienced in the past. Because that speaks to the idea that UiPath, as a platform, is going to work moving forward in a big way. >> Exactly, right. I think we've seen for years and years that regardless of the type of development environment you're using, a developer's value sometimes is based on what reusable libraries they've created, what they have to cut and paste from their old code to be able to do things faster. The challenge with that is it has to be maintained, when things change, they've got to update those libraries. It's a value prop that's very high touch. With UiPath, they've created the ultimate in reusability. The platform, especially since they acquired cloud elements and built all of those API integrations into their platform. The platform maintains the reusability and the libraries in such a way where they're drag and drop from a development standpoint and you don't have to maintain them. It's the ultimate expression of reusability as a platform. >> Yeah, cloud elements, API automation, obviously a key pick by UiPath. Devika, what's the scale of your operation today? Like how many bots and where do you see it going? >> Yes. So we, we started with one bot. Last year we experimented a lot that, you know, we were just trying to make our footprint in the company, trying to understand that, you know, people understand what RPA is, what UiPath is. Initially we got a lot of pushback. We got a pushback from our security team as well, because they could not understand, you know, that what UiPath is and how secure it is. And we had to explain them that how we would host it over AWS, how we will work, how we will not save passwords, et cetera. When we did all of that and they got comfort, we started picking, you know, very small processes around to show, you know, people the capability of RPA and UiPath per se. When we did that, people started just coming with bigger processes, and one specific team that I can think of came that we do, you know, fuzzy logic in Excel, and we do it twice a week, but it takes a lot of time. We automated it, they run it daily, every single day, two times now. And the exponential growth that we saw just with that one automation was mind boggling. I couldn't believe that, you know. We were tracking our insights and we were like, oh my God, what happened? It just blew out of proportion. >> Okay. So then did you need more bots? Are you still running one bot, or? >> Nope. Now at the moment we have nine. >> Okay. >> And we are still looking to grow. >> Okay. So the initial friction, you said there was some, you know, concern, it was primarily security or were there others, people afraid they're going to lose their jobs? Was there any of that? >> There was no risk of losing the job. The major, you know, pushback was, one was from security, the other one was from different system owners because a lot of people were not sure why we want UI access, or why we want API access, and why are we accessing their systems? What type of information we are trying to gather out of their systems. Are we writing into their system? Because a lot of people have issues when we start saying that we will write or override data. So most of the processes that we are working around are either writing, comparing, and reading and comparing, and if it is writing, we take special permission that this is what we are going to do. >> So what did you have to do to get through the security mottle, a AWS SOC 2 report, did you have to show them the UiPath pen test? >> Absolutely. >> Did you have to change any of your processes? What was that sort of punch list like? >> Everything. >> Yeah. >> So we had to start from pen test. We had to start, we had to explain that UiPath is in the process of, you know, acquiring SOC. We also explained that how things are hosted on AWS. We had to, you know, bring our consultants in who explained that how on, on AWS, this will be a very secured way of doing things. And when we did our first process, which was actually for the auditors, which is, you know, interesting. >> Yeah. >> What we did was we did segregation of duties, which I think is very important in every field and every sphere we work in. So for example, the the writeup that we were building for auditors, we made sure that it is approved by a physical or a human, you know, and not everything is done by the bot. The biggest piece of the puzzle was writing, you know, because it was taking a lot of time. People were going into different systems, gathering information, putting it on Excel, and then you know, comparing and submitting it to PWC. >> When you say write, you mean any update to a system of record? >> Correct. >> Required some scrutiny? >> Some scrutiny, yes, yes. >> Okay, initially by a human until there was comfort level and then it's like these bots know what they're doing. >> Correct, correct. >> Okay. And now you're a NetSuite customer, correct? >> Yes. >> That's your ERP? >> That's right. >> Now we were talking about Oracle is going to acquire OCR capabilities. Will that, and we've been talking, Dave and I, a week about, okay well ServiceNow has, you know, RPA, and Salesforce, and SAP, et cetera. How will that affect your thinking about adopting UiPath? >> I don't think it should matter because I think all these systems kind of coexist in a bigger ecosystem, you know, and I also feel that all these systems have their own plus points and minus points. Not one system in, per se, can do everything within a company. So it could be that, for example, NetSuite might be very strong for financials in the space we are in, but not extremely good around sales and marketing. So for that company chose Salesforce. So you know, you have those smaller smaller multiple systems that build into a bigger ecosystem, right. And I think the other piece of the puzzle is that UiPath helps bridge that gap between these systems. You know, it could happen that certain things can get integrated, certain things cannot because of the nature of business, the nature of work that the teams are trying to do. And I think UiPath is leveraging that gap, you know, and putting, you know, those strings together. >> As you scale - >> Mm hmm. >> How will, and Todd I presume you're going to assist in this process, but how will you decide what processes to prioritize, and is that a process driven decision? Is it data led? Both? If so, what kind of data? Can you describe how you guys are going to approach that? >> Yep. Todd, would you like to take that first before I start? >> Sure, yeah. >> Maybe some best practices and then we can maybe get specific to Mongo. >> Absolutely. Our guidance is always that it should be a business decision, right? And it should be data driven, based on a business defined metric around the business case for that particular automation. Our guidance to customers is don't automate it unless you know why you're automating it, and what the value is. We see sometimes there are challenges with people being able to articulate the business case for an automation, and it can almost always be resolved by having that business case be the first step, and qualifying and identifying an automation candidate. >> And how does that apply to Mongo? Do you, where are you thinking about scaling, in your opinion? >> It's interesting because, you know, initially we thought that we will, you know, explore one area in MongoDB. And the other thing that we did was we did road shows. So because we had to create some awareness in the company that we have UiPath there's something called bots. There's something called, you know, automation that we can do, so we created a presentation with small demos inside it and, you know, circulated it within the company. Different departments tried to explain what we can achieve. And based off of that, you know, we came up with a laundry list of all the automations that different departments needed. And out of that, you know, we started doing the business case, the value, you know, trying to come up with complexity, effort. We did a full estimation matrix and based off of that we came, okay, these are the top 20 that we should build first. And as soon as we built those top 20, we saw a skyrocket, you know, growth and - >> And you're looking for hard dollars, right? >> Yes, yes. Absolutely. >> Okay, just to be clear. >> Devika, I think Mongo also is great at taking a data driven approach to looking at their program. Do you want to share how you do that? >> Yes, absolutely. So one thing that we were very sure was we have to talk in terms of numbers because that's the only solid way to see growth. And what we did was, you know, we got insights, we started doing full metrics in terms of dollar saved, hour saved, and we are trying to track how every process is impacting, you know, in the grand scheme of things. Like say for example, for finance, are we shortening the close cycle in any shape or form by doing these two or three automations that we are doing? And I'm happy to report that we have really shortened our close cycle from where we started. >> Your quarter end or month end close. >> Correct, yes. >> Daily? You at the daily close yet, (all laugh) or the "John Chambers"? >> Drive everyone nuts. First I have to say, I could feel the audience sort of smiling as they see, as they hear from MongoDB, disruptor of legacy databases being cautious in their internal approach to change. As everyone else is. >> Exactly, yeah. >> But Todd, just sort of, double clicking on this idea of kind of stove pipes of capabilities in the RPA space. I mean OCR, being added to NetSuite, I'm not sure if that's the greatest example, but the point is Lydonia will work with all of those technologies to synthesize something. Is that correct? Or are you a UiPath only? >> Both. So we exclusively use UiPath with our customers. We don't use other RPA platforms. >> Okay. >> And we don't because, not because we can't, but because we don't believe that anything else is going to be as quick or as effective. Also, it's the only platform that is as broad and comprehensive as it needs to be to deliver outcomes to our customers. We have partnerships with other companies that have gaps where UiPath isn't currently playing, but the number of companies and the number of gaps has shrunk down to almost nothing these days. And we're well placed as UiPath continues to grow their platform to take advantage of that and leverage that to deliver outcomes to customers. >> It was a great story of starting small, being careful. >> Yes. >> And prudent, from a security standpoint, especially as a public company. And then it sounds like there's virtually unlimited opportunity. >> Yes, absolutely, absolutely. >> For you guys. Great story, thank you very much for sharing it. Appreciate it. >> Thank you. >> All right, good luck. All right, thank you for watching. Keep it right there. Dave Nicholson and Dave Vellante will be back from UiPath Forward5 from the Venetian in Las Vegas. Be right back. (upbeat music playing)