>> Narrator: Live from Washington D.C. it's the Cube covering AWS Public Sector Summit. Brought to you by Amazon Web Services. >> Welcome back everyone to the Cube's live coverage of the AWS Public Sector Summit here in our nation's capital, I'm your host Rebecca Knight. We are joined by Aileen Gemma Smith, the CEO and co-founder of Vizalytics Technology. Thank you so much for coming on the cube. >> Thank you for having me, it's a pleasure to be here. >> Let's start by telling our viewers a little bit about Vizalytics, there's a story there about how you founded it. >> Thank you, the mission of Vizalytics is enabling change with data and we saw tremendous opportunity in open and public available data to say, let's make a difference for communities and the whole reason why we started was in 2012 Hurricane Sandy hit my home town of Staten Island and I saw firsthand digital divide, people need access to information, it's not put together in a format that they can use, but it actually is there, so I said, we've got to do something to make a difference. Our first product was a mobile app for shopkeepers. We had thousands of users throughout New York City and then that led on to out first enterprise client being the City of New York. >> The mobile app for shopkeepers could do what? What did it do? >> It let you know everything that was going on outside and around your business that could make a difference to your bottom line, so imagine all you had to do is business name, business address, I'm going to tell you here's your risk for fines, here's when there's going to be public works, here's when someone's filed for a different permit, et cetera, and shopkeepers loved it because we didn't have to do anything to get that information, you told me exactly what I needed to know and you made it really easy to share. >> And now you are a woman founder, a female founder with a she builds t-shirt on and an AWS Hero medallion. Tell me more about this. >> Absolutely, it is a distinct privilege to be an AWS Community Hero. Community Heroes are evangelists for the community where we're talking about how can we build and create more diverse and inclusive communities. I'm privileged and honored to be the only female hero in the Australia and New Zealand region, so I'm determined to say, how can we support more women, how can we support more underestimated founders and tech developers? We have this whole series called She Builds on AWS. We've got events in Sydney, Melbourne, soon to be in Perth, et cetera and that's how we're doing more for our community and as a Community Hero how can I find more voices who aren't me, give them a platform to say, we need to hear what you're building and what you're doing and how can we all support one another as we want to build on on AWS. >> What is it to be like at event like this, where as you said you're the only female Community Hero here, how often are you getting together, collaborating, learning, and how are best practices emerging and what are those best practices? >> First off I want to mention that we have the first ever developer's lounge here in the main hall which is great because we need to see that here in public sector and having those opportunities to meet and greet and talk with folks, hey, you're working on this as well? Tell me more about what you're doing, let me surface out what kind of solutions you're doing, that's where all of the energy and the excitement happens because then you start to discover, oh, I didn't know. Folks are working on this and this, hey we've got the same problem and especially in public sector where folks so often have the challenge of different siloes. I didn't know what I didn't know, how can we bring them all together, so seeing that here in public sector where we can champion, you've got all of these different folks who are working together, it's just a wonderful opportunity. >> And what are you hearing? The big theme here is about IT modernization in the public sector, the public sector, for better or for worse has a reputation of being a little slow or a little more antiquated, there's certain divisions of the government in particular and educational institutions that are incredibly innovative. >> Absolutely. >> Rebecca: Where do you think things stand right now? >> There's absolutely positive change and I like to celebrate here are the leaders and here are the folks that are doing more, yes, public sector does, for good reasons in some cases take a long time to say, how do we want to change, do we feel safe for this change, et cetera, but then you see pockets of excellence. I'm currently based in Sydney, Australia. Transport for New South Wales is one of our clients and I am honored and excited by all that they're doing where at the executive level you have buy-in and you have support. You have support for saying we need organizational change. You have support for saying, let's do proof of concept, let's do these explorations, let's actually have a startup accelerator hub so we as public sector can interact with startups and early-stage founders or university students to make that kind of a difference. When you see that, that's part of why, okay great, we're in Australia now because there's this energy and action and a willingness to move so that's where I think look to those centers of excellence and say, how can we do that within our organization and what can we do better. >> But not saying that we're not seeing quite that energy in the US or how did you think about the differences? >> Again, it depends district by district. Different municipalities have different challenges, different size, et cetera. When you look at this, for example, in San Francisco where you have the Startup in Residence program, started off small, cohort, five or six companies, great, now how can we scale that program and make it national where they had something like 700 applications for maybe a cohort of 50 or 60 companies that are working. That's where you start to see there's an energy that's flowing through, so I think the opportunity for change comes in that kind of cross collaboration and if you have an event like this where you've got public sector folks from all over the world saying, really interesting, you feel my pain, how can we work together on this, what's your team doing, how can I learn from that, how can I take that back to my teams or where can we think about some of the harder problems of organizational change and what do we do if we don't have that executive champion, how can we start to get there? I think that's the kind of energy and opportunity of all the things we're seeing here at Public Sector Summit. >> But as you said, it's also looking for the rest of us, looking at these centers of excellence, see what they're doing, see how they're experimenting, getting those proofs of concept and then saying, hey, we've got something there, let's see if we can replicate this. >> Absolutely, and within public sector, when you have that opportunity to say, and look at how we're doing this in London, look at how we're doing this in Toronto, look at how we're doing this in Sydney and how we're doing this in Melbourne then you can suddenly go back to New York and say, okay great, we do have these other examples, it is being done so we can use that as a guide for what we wanted to do as we continue to innovate. >> What are some of the most exciting things that you're seeing here, some new public sector initiatives, technology, services that you think are really going to be game changers. >> How much time do we have? (laughing) First off, the energy to we want to collaborate, we want to be more agile, we want to make a difference. The sense that this event has grown from just a small cohort to 1,000, couple of thousand, now I believe there's something like 15,000 attendees. >> 18,000 according to Theresa Carlson. >> Think about the fact that we're all willing to be here together, that's a line in the sand that we need to be able to do more, so it's not about a particular technology per se, but willingness to say, we need to be here, we need to face these problems. We've got this challenge of should we bring these legacy systems over, should we think about how we want to work together in public product partnerships that we can all come together and start to work at this and also think about, we've got Public Sector Summits throughout the world, please join us at Canberra Summit that's going to be going on in late August. We've got Tokyo Summit going on right now, so it's not just all here in D.C., you're starting to see these clusters move out and that's really wonderful and exciting for us. >> It's wonderful and exciting on the one hand and yet this summit is taking place against a backdrop where we're seeing a real backlash against technology. The public sentiment has really soured, regulators and lawmakers are sharpening their blades and saying, hey, maybe we should pay attention more to what these technology companies are doing and just how powerful they've become in all of our daily lives. What's the sentiment that you're hearing on the ground, particularly as the founder yourself. >> I think that's where knowledge can be powerful. Can we empathize with some of the challenges? I hope that all companies choose to act with integrity, not necessarily that they do, but there are a lot of folks saying, we need to be able to do more. From a policy perspective, how can tech companies partner with policymakers who may not understand how all of these technologies work and what they're capable of or not capable of, we need more clarity on that because I think that's where it becomes a black box of conflict and if you can change it to say, this is challenges that you have with facial recognition or sentiment analysis or what have you, let's really think about do the systems today do, what are the guard rails that we need to put in and how can we work as partners with policymakers so it's not just driven by lobbyists but there's actually an understanding of, this is the implication of these systems. >> Here are the unintended consequences. >> Absolutely and if I can come back to New York for a second, New York City has one of the strongest open data logs in the nation. Part of that is because Gale Brewer, the Borough President of Manhattan said we need to formalize this. How do we put this together? She didn't come from a tech background, but she saw a problem that needed to be solved and she said, how do we put this together and how do we get the right folks to the table to think about doing this in a really scalable, meaningful way, so the more that we see those opportunities in that backdrop of tensions and concerns, that's how we move forward, facing those hard questions. It's not Rome was built in a day, it's not. It's going to take us a lot of time and there's a lot of unanswered ethical questions as well that we have to start really thinking deeply about. >> But it starts, as you said, with making the data visible and then getting more voices who-- >> Making it visible and also understanding what's not included in the data. Coming back to when I started my company, there was a lot of, but this isn't being counted and what happens when you're saying, I'm making a bias based on this particular dataset that leaves out this whole community over here. Can we think about what's not included in that data or how the data collection itself or the organization itself is changing things, so that's why, coming back to, you need more female founders, you need more underrepresented populations to have those voices of have you considered this, have you given representation to this particular group, to this population. Without doing that, then you're just reinforcing the same siloes and the same biases and we have an obligation to our community and to one another to change that. >> I know you have a keen interest in diversity issues and, as you're talking about, bringing in more women and more underrepresented minorities to lend their perspective to these very important issues that are shaping our lives. How do we solve this problem? Technology has such a bro culture and we're seeing the problems with that. >> First off, from a founder's point of view, you have to know when not to listen, you have to know when not to let someone shut you down because they'll say-- >> The noise. >> Oh my goodness, the noise of, we've got ageism, we've got sexism, we've got racism, we've got elitism. I went to Brooklyn College, I'm very proud of that fact. I had venture capitalists say, I don't want to invest in you, you're too old and you didn't go to a pedigree school, well guess what, my company's still here, some of the folks you've invested in, they folded a long time ago, so part of it is a willingness to drive forward but it's also building networks of support. Coming back to being the community hero, how can I elevate these voices and say, we need to give them an opportunity to be here, we need to change this, so part of it is we want more seats at the table, but if that table's not going to welcome me, I'm creating a whole 'nother table over here where we can start to have that cluster effect and that's where the dedication, the tenacity and you see things like we power tech, where we're really looking to elevate those voices. That change can't happen unless we keep doing that and unless the folks who are like, but this is how we've always done it, are willing to say, actually, shortcoming here, let's think about changing this and broadening the conversation. >> Is that changing though? >> We were talking a lot about how there's a new generation of workers coming up who do think differently and they do grow up with this stuff and they say, we don't need this red tape, why is this taking so long? They're impatient and maybe a more willingness to listen to other voices, are you seeing a difference? >> Absolutely, I'm seeing a difference for sure. That doesn't mean sexism, ageism, elitism has gone away. It has not, but you're starting to see, again, clusters of excellence and I think if you really want to make change you focus on where that traction is, use that as your foothold to build and scale and then start to be able to do more because that's the only way. We've got some barriers that for other founders I empathize with how insurmountable it can be, but if you've got that dedication, if you refuse to be defined by what someone else says you are or what your company is capable of being and then you find those great partners to say, let's do this together, the whole conversation changes. >> Aileen Gemma Smith those are great words to end on. Thank you so much for coming on the Cube. >> Absolute pleasure, thank you. >> I'm Rebecca Knight, we will have much more of the Cube's live coverage of the AWS Public Sector Summit here in Washington D.C. coming up in just a bit. (techno music)

(upbeat music) >> Announcer: Live from Las Vegas. It's theCUBE. Covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. >> Welcome back to theCUBE's live coverage of ServiceNow Knowledge 18, #know18. I'm your host, Rebecca Knight along with Dave Vellante. We're joined by Gemma Kyle. She's the head of management assurance at MLC. She's straight from Sydney. So welcome Gemma. >> Thank you. Thank you very much. >> Let's start off by having you tell our viewers a little bit about MLC. >> Sure. MLC is a life insurance company. It's interestingly Australia's oldest and newest life insurance company. We were recently sold by National Australia Bank to Nipon Life, a Japanese life insurance company. And we are now, thanks to the investment of capital from Nipon into MLC Life Insurance, Australia's newest stand-alone life insurance company. So we come as a 130 year old company with 1.4 million customers. But also we're investing in new technology, new infrastructure, new processes, new business operations. >> So, insurance is one of those industries that hasn't been radically disrupted. And I wonder what the conversation is like internally. Is there a complacency? "Not in our industry," "Not in my lifetime," "I'll be retired by then." Or is there paranoia? >> It's a great question. Look, it hasn't been disrupted, but it will be. I can't talk for the American market, but certainly in the Australian market we have 17 players right now and we know that they are going to consolidate down to eight or nine and we want to be one of those eight or nine. The disruption is going to come from the fact that previously there had been complacency. Customers had not been communicated with, invariably because their life insurance sits within a broader wealth management product called superannuation. Now, what's happening is customers are becoming more informed, more demanding and want more access to more flexible and innovative products that can follow them through their life cycles of marriage and children and mortgage. We really need to be on the front foot to offer the right kind of products and life insurance products to meet our customers' need. >> So, superannuation was not a term that I was familiar with. And I don't want to go too deep into it, but it's basically Australia's version of Social Security, except you can see your money. You can invest, you have control over where it goes and it's yours. >> Gemma: That's correct. >> It's not just some black hole. >> Yeah, that's correct. >> Okay, nice. >> I was just thinking about when you were talking about how customers are starting to demand more. So, when you think about digital transformation, is that what's leading the charge, would you say? >> Absolutely, absolutely. When we talk about digital transformation, it's really about breaking down the silos that previously existed within companies. And it's not just life insurance companies, it's all types of financial services. So previously, we would have the actuaries who do the pricing of our products and the advisors who sell our products, were the gods of the insurance industry. Now, when we look towards digital transformation, it becomes technology, it becomes process and it becomes risk management and control that are in the ascendancy. This is really critical because customers are looking to self-serve. They want to make their own choices. So that's where we need to meet them. We need to break down the traditional barriers between the silos and have a single platform that we can use a data analytics to better serve our customers. >> So, the advisors are getting disrupted by the whole self service trend. The actuaries. Are we getting robo-actuaries now with machines? >> Well, I don't see why not. >> Right, the data's there. >> Yeah, absolutely. You know, we are actually required under regulation to have a chief actuary, and that absolutely makes sense. But what we're starting to have now is automatic underwriting engines. So, previously you would apply for an insurance policy and the underwriter would come in, who has an actuarial background, and they'd price the risk you present to the business. These days we have sufficient data, that as soon as you put your information into the system, we can automatically approve a policy for you. That automated underwriting is an example of the type of disruption we're starting to see. >> But humans are still the last mile, if necessary. Is that right? >> Gemma: Absolutely, absolutely. Advisors are important because they help our customers understand their financial need. And advisors are very strongly regulated within the Australian market. They're required to have a qualification. And we've recently seen changes to our legislation around the requirement to, evidence that they are treating customers honestly, efficiently and fairly. And selling them products that they need, not that they have been encouraged to sell by another supplier. >> What's your biggest challenge? (laughs) Top three. >> Yeah, yeah, top three. Look, without a doubt, it's cultural change. Cultural change. By cultural change, I mean the behaviors and the beliefs that surround not just internally how we manage risk and compliance, but also externally around how customers perceive the insurance industry. We definitely suffer from a lack of trust. Now, the disruption that we're facing is that customers are saying "We don't trust you," and it's not well founded. It actually doesn't bear out in the data in terms of how we pay our claims and how we service our customers. But there's certainly an image problem there. So, we think we need to service, we think we need to address this cultural issue from the inside out. We need to fix ourselves and make sure that we, can with integrity, defend the decisions we make around how we service our customers and then in turn have customers really trust us. See what we do. Trust us by how we behave, not just what we say. >> When you're talking about the behaviors, changing the behaviors, how is security, risk, compliance, how is that all perceived within your company? >> Yeah, yeah. Well, like I said, the actuaries are king. They have sophisticated data models through which they can price policies. And, where we've traditionally been with risk and compliance is very much in a qualitative space around actions that are undertaken, or senses that things aren't working as well as they should do. What we've started to do, and this service now has been absolutely critical to this journey, we're starting to shift the conversation away from risk and compliance and towards business process and control. We're simply shifting the conversation away from a focus on risk exposures, or the things you must do and onto the cost benefit analysis of control investment options. That allows our executive and our board to start to use data and analytics to drive decision making around where we need to focus our efforts. >> So you're turning all of this kind of back office risk oriented stuff into a value proposition for the organization. >> Gemma: Exactly right. >> Can you talk a little bit more on how ServiceNow participates in that process? >> Sure. Ultimately, the value proposition. It is about behaviors, but the value proposition is all around being able to defend the decisions that you make. Being able to demonstrate with data and analytics. And being able to put a quantified amount of money on the bottom line around what's the value to actually changing our behaviors, or changing the way we manage your process. ServiceNow is obviously critical to that, because they have this amazing performance analytics engine that enables us to draw data out of the system as it relates to business process. As it relates to operational loss events. As it relates to customer complaints. As it relates to asset management. And integrate it to tell a story of where we're most exposed. To loss today and potential loss tomorrow. It's a very powerful tool that even our, surprisingly, our CEO, not only does he now use his app that we've created for him, but he personally calls people in the office to say "Hey, I see you've got "an overdue action here, what are you doing about it?" Now, I know, nobody wants that call. (laughs) Nobody wants that call. So consequently, we've got this incredible tone from the top that reinforces how important it is to pay attention to your controls, to your obligations, to genuinely own them. That's when you start to see the cultural change. >> You don't do business in Europe, do you? >> Gemma: No. >> Is there a GDPR equivalent in Australia, if you're familiar with GDPR? >> Gemma: No, sorry. >> Okay, so, it's all about privacy. So, the GDPR? >> Gemma: Oh, oh. Yes, yes. >> The fines go into effect this month and that doesn't affect you because you're not doing business in Europe. But is there something similar in Australia where if a customer says "I want to know "what data you have on me." Or, "I want you to delete that data." You have to prove that. It's quite onerous. But, is there anything similar for you guys? >> Gemma: Absolutely, absolutely. So, we've actually got two things. First of all, we do have the privacy act. And under the privacy act that's been in place for quite some time, all individuals, whether you're an employee or a customer, you have access to your data. And, you also have the right to be taken off lists and call trees and the like. The government's just recently introduced CPS147, which is a prudential standard around data breaches. Now previously, if there was a breach of data. Say for example, we accidentally send a letter to the wrong customer, and in that letter it has personal details about somebody's medical history. Now previously that was not okay from a privacy perspective, but it wasn't notifiable to the regulator. With CPS147, we now have a notifiable data breach system. It's just come in place. And we have to notify the regulator when we breach somebody's privacy, somebody's data. And we could be subject to fines. >> And does the ServiceNow platform play a role in that, in terms of just tracking the notification, or compliance, or? >> Absolutely, absolutely. So, when the change in legislation was introduced, we simply added literally another little tick box into our operational loss event module to say, "Is this a data breach?" And just simply by doing that, now when you log a loss event, and you tick that little box, we can see from across the company where are our data breaches happening? And if they're a cluster. Is there something here that's telling us that we've got a systemic problem that we need to fix? As soon as it came in, we were automatically reporting on it. >> One of the things we're hearing is that there's so much great customer-on-customer learning that takes place at Knowledge. Are you finding that? Are you talking with a lot of customers, and about how you use the platform? And success? >> Absolutely. This is a really exciting conference. I'm really having such a good time and sort of overcoming my jet lag, to tell the truth. It is very exciting. In Australia, we've already started some groups. So, we work with other, myself and my systems manager, Greg Dominich, the two of us tend to go to a lot of companies to talk about our experiences with the implementation of ServiceNow. What worked well, what didn't work well and what we would do better. Because we want to create a community of practice. We want to lift the practice of risk management above where it currently sits. And so, walking around here there's so many networking events, and this hall in particular, is wonderful. So yes, talking to lots of other customers. Just sharing innovations. It's very exciting. >> How long have you, when did you go live with ServiceNow? >> Okay, so, we went live, let's see, probably in, the first GIC module went live in June, 2017. We then had another iteration in September, 2017. So, we've basically spread it out to make sure. The next modules we're looking to introduce are business continuity management. Each time, we follow the same, we use the PPM tool, that ServiceNow provides to actually implement the modules. But then we have a process that we follow ourselves in terms of putting in the data, cleaning it, categorizing it, making sure we've got the analytics right and then we step to the next module. Interestingly enough, cultural change doesn't happen once you've implemented the system. Cultural change starts at the very point where you recognize there's an opportunity to do better. So, as we implement each module, we're also maturing our practices. And we're also changing the culture of how the business approaches risk and compliance. >> What's your relationship with IT in all of this? How does that all work? >> Look, it's very close. It's part of the transformation journey that those silos still exist. And they exist because we all create our own languages for understanding our world and how we engage with a business. Now, it's about breaking down those barriers. So, we work very closely with them on security management, on business continuity management and on incident management. And we're going through the process now of aligning our language so that once we have that shared language, we have the shared data and we can really become quite powerful. >> Rebecca: Great. Well Gemma, thanks so much for coming on theCUBE. It's been a really fun conversation. >> Thank you very much. It's been nice to hear. >> I'm Rebecca Knight for Dave Vellante. We will have more from.

>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data-driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting cost could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data marts, data hubs, and yes, even data lakes were broken and left us wanting from more welcome to the data doesn't lie, or doesn't a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have featured parody with the data lake or vice versa is the so-called modern data stack, simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for hit that was acquired by Teradata. And when I got to Teradata, of course, Teradata is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience show? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know, right. You actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like star Oxley, for things like security for certainly very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited Jamma, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come. I think that's the story at Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenants of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about SAR, brain Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but what does that mean? Does that mean the E D w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's going to be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems, maybe either those that either source systems for the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to use all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got to, you know, domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue are two, you know, challenges, self-serve infrastructure let's park that for a second. And then in your industry, the one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at emus is we have a single security layer that sits on top of our data match, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin, I mean, Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, do an analytic queries and with data that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah. I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively a almost eCommerce like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself, >>Okay. G guys grab a sip of water. After this short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence, keep it right there. >>Your company has more data than ever, and more people trying to understand it, but there's a problem. Your data is stored across multiple systems. It's hard to access and that delays analytics and ultimately decisions. The old method of moving all of your data into a single source of truth is slow and definitely not built for the volume of data we have today or where we are headed while your data engineers spent over half their time, moving data, your analysts and data scientists are left, waiting, feeling frustrated, unproductive, and unable to move the needle for your business. But what if you could spend less time moving or copying data? What if your data consumers could analyze all your data quickly? >>Starburst helps your teams run fast queries on any data source. We help you create a single point of access to your data, no matter where it's stored. And we support high concurrency, we solve for speed and scale, whether it's fast, SQL queries on your data lake or faster queries across multiple data sets, Starburst helps your teams run analytics anywhere you can't afford to wait for data to be available. Your team has questions that need answers. Now with Starburst, the wait is over. You'll have faster access to data with enterprise level security, easy connectivity, and 24 7 support from experts, organizations like Zolando Comcast and FINRA rely on Starburst to move their businesses forward. Contact our Trino experts to get started. >>We're back with Jess Borgman of Starburst and Richard Jarvis of EVAs health. Okay, we're gonna get to lie. Number two, and that is this an open source based platform cannot give you the performance and control that you can get with a proprietary system. Is that a lie? Justin, the enterprise data warehouse has been pretty dominant and has evolved and matured. Its stack has mature over the years. Why is it not the default platform for data? >>Yeah, well, I think that's become a lie over time. So I, I think, you know, if we go back 10 or 12 years ago with the advent of the first data lake really around Hudu, that probably was true that you couldn't get the performance that you needed to run fast, interactive, SQL queries in a data lake. Now a lot's changed in 10 or 12 years. I remember in the very early days, people would say, you you'll never get performance because you need to be column there. You need to store data in a column format. And then, you know, column formats we're introduced to, to data apes, you have Parque ORC file in aro that were created to ultimately deliver performance out of that. So, okay. We got, you know, largely over the performance hurdle, you know, more recently people will say, well, you don't have the ability to do updates and deletes like a traditional data warehouse. >>And now we've got the creation of new data formats, again like iceberg and Delta and Hodi that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a, a quote from, you know, Kurt Monash many years ago where he said, you know, know it takes six or seven years to build a functional database. I think that's that's right. And now we've had almost a decade go by. So, you know, these technologies have matured to really deliver very, very close to the same level performance and functionality of, of cloud data warehouses. So I think the, the reality is that's become a line and now we have large giant hyperscale internet companies that, you know, don't have the traditional data warehouse at all. They do all of their analytics in a data lake. So I think we've, we've proven that it's very much possible today. >>Thank you for that. And so Richard, talk about your perspective as a practitioner in terms of what open brings you versus, I mean, look closed is it's open as a moving target. I remember Unix used to be open systems and so it's, it is an evolving, you know, spectrum, but, but from your perspective, what does open give you that you can't get from a proprietary system where you are fearful of in a proprietary system? >>I, I suppose for me open buys us the ability to be unsure about the future, because one thing that's always true about technology is it evolves in a, a direction, slightly different to what people expect. And what you don't want to end up is done is backed itself into a corner that then prevents it from innovating. So if you have chosen a technology and you've stored trillions of records in that technology and suddenly a new way of processing or machine learning comes out, you wanna be able to take advantage and your competitive edge might depend upon it. And so I suppose for us, we acknowledge that we don't have perfect vision of what the future might be. And so by backing open storage technologies, we can apply a number of different technologies to the processing of that data. And that gives us the ability to remain relevant, innovate on our data storage. And we have bought our way out of the, any performance concerns because we can use cloud scale infrastructure to scale up and scale down as we need. And so we don't have the concerns that we don't have enough hardware today to process what we want to do, want to achieve. We can just scale up when we need it and scale back down. So open source has really allowed us to maintain the being at the cutting edge. >>So Jess, let me play devil's advocate here a little bit, and I've talked to Shaak about this and you know, obviously her vision is there's an open source that, that the data meshes open source, an open source tooling, and it's not a proprietary, you know, you're not gonna buy a data mesh. You're gonna build it with, with open source toolings and, and vendors like you are gonna support it, but to come back to sort of today, you can get to market with a proprietary solution faster. I'm gonna make that statement. You tell me if it's a lie and then you can say, okay, we support Apache iceberg. We're gonna support open source tooling, take a company like VMware, not really in the data business, but how, the way they embraced Kubernetes and, and you know, every new open source thing that comes along, they say, we do that too. Why can't proprietary systems do that and be as effective? >>Yeah, well, I think at least with the, within the data landscape saying that you can access open data formats like iceberg or, or others is, is a bit dis disingenuous because really what you're selling to your customer is a certain degree of performance, a certain SLA, and you know, those cloud data warehouses that can reach beyond their own proprietary storage drop all the performance that they were able to provide. So it is, it reminds me kind of, of, again, going back 10 or 12 years ago when everybody had a connector to Haddo and that they thought that was the solution, right? But the reality was, you know, a connector was not the same as running workloads in Haddo back then. And I think similarly, you know, being able to connect to an external table that lives in an open data format, you know, you're, you're not going to give it the performance that your customers are accustomed to. And at the end of the day, they're always going to be predisposed. They're always going to be incentivized to get that data ingested into the data warehouse, cuz that's where they have control. And you know, the bottom line is the database industry has really been built around vendor lockin. I mean, from the start, how, how many people love Oracle today, but our customers, nonetheless, I think, you know, lockin is, is, is part of this industry. And I think that's really what we're trying to change with open data formats. >>Well, that's interesting reminded when I, you know, I see the, the gas price, the tees or gas price I, I drive up and then I say, oh, that's the cash price credit card. I gotta pay 20 cents more, but okay. But so the, the argument then, so let me, let me come back to you, Justin. So what's wrong with saying, Hey, we support open data formats, but yeah, you're gonna get better performance if you, if you keep it into our closed system, are you saying that long term that's gonna come back and bite you cuz you're gonna end up, you mentioned Oracle, you mentioned Teradata. Yeah. That's by, by implication, you're saying that's where snowflake customers are headed. >>Yeah, absolutely. I think this is a movie that, you know, we've all seen before. At least those of us who've been in the industry long enough to, to see this movie play over a couple times. So I do think that's the future. And I think, you know, I loved what Richard said. I actually wrote it down. Cause I thought it was an amazing quote. He said, it buys us the ability to be unsure of the future. Th that that pretty much says it all the, the future is unknowable and the reality is using open data formats. You remain interoperable with any technology you want to utilize. If you want to use spark to train a machine learning model and you want to use Starbust to query via sequel, that's totally cool. They can both work off the same exact, you know, data, data sets by contrast, if you're, you know, focused on a proprietary model, then you're kind of locked in again to that model. I think the same applies to data, sharing to data products, to a wide variety of, of aspects of the data landscape that a proprietary approach kind of closes you in and locks you in. >>So I, I would say this Richard, I'd love to get your thoughts on it. Cause I talked to a lot of Oracle customers, not as many te data customers, but, but a lot of Oracle customers and they, you know, they'll admit, yeah, you know, they're jamming us on price and the license cost they give, but we do get value out of it. And so my question to you, Richard, is, is do the, let's call it data warehouse systems or the proprietary systems. Are they gonna deliver a greater ROI sooner? And is that in allure of, of that customers, you know, are attracted to, or can open platforms deliver as fast in ROI? >>I think the answer to that is it can depend a bit. It depends on your businesses skillset. So we are lucky that we have a number of proprietary teams that work in databases that provide our operational data capability. And we have teams of analytics and big data experts who can work with open data sets and open data formats. And so for those different teams, they can get to an ROI more quickly with different technologies for the business though, we can't do better for our operational data stores than proprietary databases. Today we can back off very tight SLAs to them. We can demonstrate reliability from millions of hours of those databases being run at enterprise scale, but for an analytics workload where increasing our business is growing in that direction, we can't do better than open data formats with cloud-based data mesh type technologies. And so it's not a simple answer. That one will always be the right answer for our business. We definitely have times when proprietary databases provide a capability that we couldn't easily represent or replicate with open technologies. >>Yeah. Richard, stay with you. You mentioned, you know, you know, some things before that, that strike me, you know, the data brick snowflake, you know, thing is, oh, is a lot of fun for analysts like me. You've got data bricks coming at it. Richard, you mentioned you have a lot of rockstar, data engineers, data bricks coming at it from a data engineering heritage. You get snowflake coming at it from an analytics heritage. Those two worlds are, are colliding people like PJI Mohan said, you know what? I think it's actually harder to play in the data engineering. So I E it's easier to for data engineering world to go into the analytics world versus the reverse, but thinking about up and coming engineers and developers preparing for this future of data engineering and data analytics, how, how should they be thinking about the future? What, what's your advice to those young people? >>So I think I'd probably fall back on general programming skill sets. So the advice that I saw years ago was if you have open source technologies, the pythons and Javas on your CV, you commander 20% pay, hike over people who can only do proprietary programming languages. And I think that's true of data technologies as well. And from a business point of view, that makes sense. I'd rather spend the money that I save on proprietary licenses on better engineers, because they can provide more value to the business that can innovate us beyond our competitors. So I think I would my advice to people who are starting here or trying to build teams to capitalize on data assets is begin with open license, free capabilities, because they're very cheap to experiment with. And they generate a lot of interest from people who want to join you as a business. And you can make them very successful early, early doors with, with your analytics journey. >>It's interesting. Again, analysts like myself, we do a lot of TCO work and have over the last 20 plus years. And in world of Oracle, you know, normally it's the staff, that's the biggest nut in total cost of ownership, not an Oracle. It's the it's the license cost is by far the biggest component in the, in the blame pie. All right, Justin, help us close out this segment. We've been talking about this sort of data mesh open, closed snowflake data bricks. Where does Starburst sort of as this engine for the data lake data lake house, the data warehouse fit in this, in this world? >>Yeah. So our view on how the future ultimately unfolds is we think that data lakes will be a natural center of gravity for a lot of the reasons that we described open data formats, lowest total cost of ownership, because you get to choose the cheapest storage available to you. Maybe that's S3 or Azure data lake storage, or Google cloud storage, or maybe it's on-prem object storage that you bought at a, at a really good price. So ultimately storing a lot of data in a deal lake makes a lot of sense, but I think what makes our perspective unique is we still don't think you're gonna get everything there either. We think that basically centralization of all your data assets is just an impossible endeavor. And so you wanna be able to access data that lives outside of the lake as well. So we kind of think of the lake as maybe the biggest place by volume in terms of how much data you have, but to, to have comprehensive analytics and to truly understand your business and understand it holistically, you need to be able to go access other data sources as well. And so that's the role that we wanna play is to be a single point of access for our customers, provide the right level of fine grained access controls so that the right people have access to the right data and ultimately make it easy to discover and consume via, you know, the creation of data products as well. >>Great. Okay. Thanks guys. Right after this quick break, we're gonna be back to debate whether the cloud data model that we see emerging and the so-called modern data stack is really modern, or is it the same wine new bottle? When it comes to data architectures, you're watching the cube, the leader in enterprise and emerging tech coverage. >>Your data is capable of producing incredible results, but data consumers are often left in the dark without fast access to the data they need. Starers makes your data visible from wherever it lives. Your company is acquiring more data in more places, more rapidly than ever to rely solely on a data centralization strategy. Whether it's in a lake or a warehouse is unrealistic. A single source of truth approach is no longer viable, but disconnected data silos are often left untapped. We need a new approach. One that embraces distributed data. One that enables fast and secure access to any of your data from anywhere with Starburst, you'll have the fastest query engine for the data lake that allows you to connect and analyze your disparate data sources no matter where they live Starburst provides the foundational technology required for you to build towards the vision of a decentralized data mesh Starburst enterprise and Starburst galaxy offer enterprise ready, connectivity, interoperability, and security features for multiple regions, multiple clouds and everchanging global regulatory requirements. The data is yours. And with Starburst, you can perform analytics anywhere in light of your world. >>Okay. We're back with Justin Boardman. CEO of Starbust Richard Jarvis is the CTO of EMI health and Theresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie it's it is it's is that it's not modern. Justin, what do you say? >>Yeah. I mean, I think new isn't modern, right? I think it's the, it's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exist just yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess, the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, a microservices layer on top of leg legacy apps. How do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more, more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Well thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. De-risked experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that. I'm a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and, and principles there >>Of, of how it should look like or, or how >>It's yeah. What it should be. >>Yeah. Yeah. Well, I think, you know, in, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of it was starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go buy a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, my takeaway there is it's inclusive, whether it's a data Mar data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include there on Preem data? O obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on Preem? I mean, most implementations I've seen in data mesh, frankly really aren't, you know, adhering to the philosophy. They're maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data. Me, the fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both worlds. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds. That, again, going back to Richard's point, that is meaningful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, talking about data as product, wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight. And in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured, managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to researchers. >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned you, you appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have, and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use case? >>Yeah, it makes sense. It's got the context. If the, if the domains own the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's send Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Theresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave Valante for the cube, and we'll see you next time. >>The explosion of data sources has forced organizations to modernize their systems and architecture and come to terms with one size does not fit all for data management today. Your teams are constantly moving and copying data, which requires time management. And in some cases, double paying for compute resources. Instead, what if you could access all your data anywhere using the BI tools and SQL skills your users already have. 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>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data-driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting cost could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data marts, data hubs, and yes, even data lakes were broken and left us wanting from more welcome to the data doesn't lie, or doesn't a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have featured parody with the data lake or vice versa is the so-called modern data stack, simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for hit that was acquired by Teradata. And when I got to Teradata, of course, Teradata is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience show? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know, right. You actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like star Oxley, for things like security for certainly very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited Jamma, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come. I think that's the story at Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenants of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about SAR, brain Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but what does that mean? Does that mean the E D w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's going to be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems, maybe either those that either source systems for the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to use all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got to, you know, domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue are two, you know, challenges, self-serve infrastructure let's park that for a second. And then in your industry, the one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at emus is we have a single security layer that sits on top of our data match, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin, I mean, Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, do an analytic queries and with data that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah. I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively a almost eCommerce like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself, >>Okay. G guys grab a sip of water. After this short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence, keep it right there. >>Your company has more data than ever, and more people trying to understand it, but there's a problem. Your data is stored across multiple systems. It's hard to access and that delays analytics and ultimately decisions. The old method of moving all of your data into a single source of truth is slow and definitely not built for the volume of data we have today or where we are headed while your data engineers spent over half their time, moving data, your analysts and data scientists are left, waiting, feeling frustrated, unproductive, and unable to move the needle for your business. But what if you could spend less time moving or copying data? What if your data consumers could analyze all your data quickly? >>Starburst helps your teams run fast queries on any data source. We help you create a single point of access to your data, no matter where it's stored. And we support high concurrency, we solve for speed and scale, whether it's fast, SQL queries on your data lake or faster queries across multiple data sets, Starburst helps your teams run analytics anywhere you can't afford to wait for data to be available. Your team has questions that need answers. Now with Starburst, the wait is over. You'll have faster access to data with enterprise level security, easy connectivity, and 24 7 support from experts, organizations like Zolando Comcast and FINRA rely on Starburst to move their businesses forward. Contact our Trino experts to get started. >>We're back with Jess Borgman of Starburst and Richard Jarvis of EVAs health. Okay, we're gonna get to lie. Number two, and that is this an open source based platform cannot give you the performance and control that you can get with a proprietary system. Is that a lie? Justin, the enterprise data warehouse has been pretty dominant and has evolved and matured. Its stack has mature over the years. Why is it not the default platform for data? >>Yeah, well, I think that's become a lie over time. So I, I think, you know, if we go back 10 or 12 years ago with the advent of the first data lake really around Hudu, that probably was true that you couldn't get the performance that you needed to run fast, interactive, SQL queries in a data lake. Now a lot's changed in 10 or 12 years. I remember in the very early days, people would say, you you'll never get performance because you need to be column there. You need to store data in a column format. And then, you know, column formats we're introduced to, to data apes, you have Parque ORC file in aro that were created to ultimately deliver performance out of that. So, okay. We got, you know, largely over the performance hurdle, you know, more recently people will say, well, you don't have the ability to do updates and deletes like a traditional data warehouse. >>And now we've got the creation of new data formats, again like iceberg and Delta and Hodi that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a, a quote from, you know, Kurt Monash many years ago where he said, you know, know it takes six or seven years to build a functional database. I think that's that's right. And now we've had almost a decade go by. So, you know, these technologies have matured to really deliver very, very close to the same level performance and functionality of, of cloud data warehouses. So I think the, the reality is that's become a line and now we have large giant hyperscale internet companies that, you know, don't have the traditional data warehouse at all. They do all of their analytics in a data lake. So I think we've, we've proven that it's very much possible today. >>Thank you for that. And so Richard, talk about your perspective as a practitioner in terms of what open brings you versus, I mean, look closed is it's open as a moving target. I remember Unix used to be open systems and so it's, it is an evolving, you know, spectrum, but, but from your perspective, what does open give you that you can't get from a proprietary system where you are fearful of in a proprietary system? >>I, I suppose for me open buys us the ability to be unsure about the future, because one thing that's always true about technology is it evolves in a, a direction, slightly different to what people expect. And what you don't want to end up is done is backed itself into a corner that then prevents it from innovating. So if you have chosen a technology and you've stored trillions of records in that technology and suddenly a new way of processing or machine learning comes out, you wanna be able to take advantage and your competitive edge might depend upon it. And so I suppose for us, we acknowledge that we don't have perfect vision of what the future might be. And so by backing open storage technologies, we can apply a number of different technologies to the processing of that data. And that gives us the ability to remain relevant, innovate on our data storage. And we have bought our way out of the, any performance concerns because we can use cloud scale infrastructure to scale up and scale down as we need. And so we don't have the concerns that we don't have enough hardware today to process what we want to do, want to achieve. We can just scale up when we need it and scale back down. So open source has really allowed us to maintain the being at the cutting edge. >>So Jess, let me play devil's advocate here a little bit, and I've talked to Shaak about this and you know, obviously her vision is there's an open source that, that the data meshes open source, an open source tooling, and it's not a proprietary, you know, you're not gonna buy a data mesh. You're gonna build it with, with open source toolings and, and vendors like you are gonna support it, but to come back to sort of today, you can get to market with a proprietary solution faster. I'm gonna make that statement. You tell me if it's a lie and then you can say, okay, we support Apache iceberg. We're gonna support open source tooling, take a company like VMware, not really in the data business, but how, the way they embraced Kubernetes and, and you know, every new open source thing that comes along, they say, we do that too. Why can't proprietary systems do that and be as effective? >>Yeah, well, I think at least with the, within the data landscape saying that you can access open data formats like iceberg or, or others is, is a bit dis disingenuous because really what you're selling to your customer is a certain degree of performance, a certain SLA, and you know, those cloud data warehouses that can reach beyond their own proprietary storage drop all the performance that they were able to provide. So it is, it reminds me kind of, of, again, going back 10 or 12 years ago when everybody had a connector to Haddo and that they thought that was the solution, right? But the reality was, you know, a connector was not the same as running workloads in Haddo back then. And I think similarly, you know, being able to connect to an external table that lives in an open data format, you know, you're, you're not going to give it the performance that your customers are accustomed to. And at the end of the day, they're always going to be predisposed. They're always going to be incentivized to get that data ingested into the data warehouse, cuz that's where they have control. And you know, the bottom line is the database industry has really been built around vendor lockin. I mean, from the start, how, how many people love Oracle today, but our customers, nonetheless, I think, you know, lockin is, is, is part of this industry. And I think that's really what we're trying to change with open data formats. >>Well, that's interesting reminded when I, you know, I see the, the gas price, the tees or gas price I, I drive up and then I say, oh, that's the cash price credit card. I gotta pay 20 cents more, but okay. But so the, the argument then, so let me, let me come back to you, Justin. So what's wrong with saying, Hey, we support open data formats, but yeah, you're gonna get better performance if you, if you keep it into our closed system, are you saying that long term that's gonna come back and bite you cuz you're gonna end up, you mentioned Oracle, you mentioned Teradata. Yeah. That's by, by implication, you're saying that's where snowflake customers are headed. >>Yeah, absolutely. I think this is a movie that, you know, we've all seen before. At least those of us who've been in the industry long enough to, to see this movie play over a couple times. So I do think that's the future. And I think, you know, I loved what Richard said. I actually wrote it down. Cause I thought it was an amazing quote. He said, it buys us the ability to be unsure of the future. Th that that pretty much says it all the, the future is unknowable and the reality is using open data formats. You remain interoperable with any technology you want to utilize. If you want to use spark to train a machine learning model and you want to use Starbust to query via sequel, that's totally cool. They can both work off the same exact, you know, data, data sets by contrast, if you're, you know, focused on a proprietary model, then you're kind of locked in again to that model. I think the same applies to data, sharing to data products, to a wide variety of, of aspects of the data landscape that a proprietary approach kind of closes you in and locks you in. >>So I, I would say this Richard, I'd love to get your thoughts on it. Cause I talked to a lot of Oracle customers, not as many te data customers, but, but a lot of Oracle customers and they, you know, they'll admit, yeah, you know, they're jamming us on price and the license cost they give, but we do get value out of it. And so my question to you, Richard, is, is do the, let's call it data warehouse systems or the proprietary systems. Are they gonna deliver a greater ROI sooner? And is that in allure of, of that customers, you know, are attracted to, or can open platforms deliver as fast in ROI? >>I think the answer to that is it can depend a bit. It depends on your businesses skillset. So we are lucky that we have a number of proprietary teams that work in databases that provide our operational data capability. And we have teams of analytics and big data experts who can work with open data sets and open data formats. And so for those different teams, they can get to an ROI more quickly with different technologies for the business though, we can't do better for our operational data stores than proprietary databases. Today we can back off very tight SLAs to them. We can demonstrate reliability from millions of hours of those databases being run at enterprise scale, but for an analytics workload where increasing our business is growing in that direction, we can't do better than open data formats with cloud-based data mesh type technologies. And so it's not a simple answer. That one will always be the right answer for our business. We definitely have times when proprietary databases provide a capability that we couldn't easily represent or replicate with open technologies. >>Yeah. Richard, stay with you. You mentioned, you know, you know, some things before that, that strike me, you know, the data brick snowflake, you know, thing is, oh, is a lot of fun for analysts like me. You've got data bricks coming at it. Richard, you mentioned you have a lot of rockstar, data engineers, data bricks coming at it from a data engineering heritage. You get snowflake coming at it from an analytics heritage. Those two worlds are, are colliding people like PJI Mohan said, you know what? I think it's actually harder to play in the data engineering. So I E it's easier to for data engineering world to go into the analytics world versus the reverse, but thinking about up and coming engineers and developers preparing for this future of data engineering and data analytics, how, how should they be thinking about the future? What, what's your advice to those young people? >>So I think I'd probably fall back on general programming skill sets. So the advice that I saw years ago was if you have open source technologies, the pythons and Javas on your CV, you commander 20% pay, hike over people who can only do proprietary programming languages. And I think that's true of data technologies as well. And from a business point of view, that makes sense. I'd rather spend the money that I save on proprietary licenses on better engineers, because they can provide more value to the business that can innovate us beyond our competitors. So I think I would my advice to people who are starting here or trying to build teams to capitalize on data assets is begin with open license, free capabilities, because they're very cheap to experiment with. And they generate a lot of interest from people who want to join you as a business. And you can make them very successful early, early doors with, with your analytics journey. >>It's interesting. Again, analysts like myself, we do a lot of TCO work and have over the last 20 plus years. And in world of Oracle, you know, normally it's the staff, that's the biggest nut in total cost of ownership, not an Oracle. It's the it's the license cost is by far the biggest component in the, in the blame pie. All right, Justin, help us close out this segment. We've been talking about this sort of data mesh open, closed snowflake data bricks. Where does Starburst sort of as this engine for the data lake data lake house, the data warehouse fit in this, in this world? >>Yeah. So our view on how the future ultimately unfolds is we think that data lakes will be a natural center of gravity for a lot of the reasons that we described open data formats, lowest total cost of ownership, because you get to choose the cheapest storage available to you. Maybe that's S3 or Azure data lake storage, or Google cloud storage, or maybe it's on-prem object storage that you bought at a, at a really good price. So ultimately storing a lot of data in a deal lake makes a lot of sense, but I think what makes our perspective unique is we still don't think you're gonna get everything there either. We think that basically centralization of all your data assets is just an impossible endeavor. And so you wanna be able to access data that lives outside of the lake as well. So we kind of think of the lake as maybe the biggest place by volume in terms of how much data you have, but to, to have comprehensive analytics and to truly understand your business and understand it holistically, you need to be able to go access other data sources as well. And so that's the role that we wanna play is to be a single point of access for our customers, provide the right level of fine grained access controls so that the right people have access to the right data and ultimately make it easy to discover and consume via, you know, the creation of data products as well. >>Great. Okay. Thanks guys. Right after this quick break, we're gonna be back to debate whether the cloud data model that we see emerging and the so-called modern data stack is really modern, or is it the same wine new bottle? When it comes to data architectures, you're watching the cube, the leader in enterprise and emerging tech coverage. >>Your data is capable of producing incredible results, but data consumers are often left in the dark without fast access to the data they need. Starers makes your data visible from wherever it lives. Your company is acquiring more data in more places, more rapidly than ever to rely solely on a data centralization strategy. Whether it's in a lake or a warehouse is unrealistic. A single source of truth approach is no longer viable, but disconnected data silos are often left untapped. We need a new approach. One that embraces distributed data. One that enables fast and secure access to any of your data from anywhere with Starburst, you'll have the fastest query engine for the data lake that allows you to connect and analyze your disparate data sources no matter where they live Starburst provides the foundational technology required for you to build towards the vision of a decentralized data mesh Starburst enterprise and Starburst galaxy offer enterprise ready, connectivity, interoperability, and security features for multiple regions, multiple clouds and everchanging global regulatory requirements. The data is yours. And with Starburst, you can perform analytics anywhere in light of your world. >>Okay. We're back with Justin Boardman. CEO of Starbust Richard Jarvis is the CTO of EMI health and Theresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie it's it is it's is that it's not modern. Justin, what do you say? >>Yeah. I mean, I think new isn't modern, right? I think it's the, it's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exist just yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess, the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, a microservices layer on top of leg legacy apps. How do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more, more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Well thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. De-risked experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that. I'm a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and, and principles there >>Of, of how it should look like or, or how >>It's yeah. What it should be. >>Yeah. Yeah. Well, I think, you know, in, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of it was starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go buy a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, my takeaway there is it's inclusive, whether it's a data Mar data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include there on Preem data? O obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on Preem? I mean, most implementations I've seen in data mesh, frankly really aren't, you know, adhering to the philosophy. They're maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data. Me, the fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both worlds. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds. That, again, going back to Richard's point, that is meaningful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, talking about data as product, wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight. And in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured, managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to researchers. >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned you, you appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have, and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use case? >>Yeah, it makes sense. It's got the context. If the, if the domains own the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's send Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Theresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave Valante for the cube, and we'll see you next time. >>The explosion of data sources has forced organizations to modernize their systems and architecture and come to terms with one size does not fit all for data management today. Your teams are constantly moving and copying data, which requires time management. And in some cases, double paying for compute resources. Instead, what if you could access all your data anywhere using the BI tools and SQL skills your users already have. 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>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting costs could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data, Mars, data hubs, and yes, even data lakes were broken and left us wanting for more welcome to the data doesn't lie, or does it a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have feature parody with the data lake or vice versa is the so-called modern data stack simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for IDU that was acquired by Teradata. And when I got to Teradata, of course, Terada is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on-prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience, Joe? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know? Right. But you actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like swans Oxley, for things like security, for certain very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited JAK, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come, I think that's the story of Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenets of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about, so Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and con contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but the, what does that mean? Does that mean the ed w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's gonna be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems. Maybe either those that either source systems, the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to lose all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got, you know, the domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue or two, you know, challenges self-serve infrastructure let's park that for a second. And then in your industry, one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And, and I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at EMI is we have a single security layer that sits on top of our data mesh, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. >>And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin mean Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, doing analytic queries and with data, that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah, I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively, almost eCommerce, like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself. >>Okay. G guys grab a sip of water. After the short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence. Keep it right there.

>>Good morning live from Las Vegas, Lisa Martin and Dave Lanta here covering snowflake summit 22. Dave, it's great to be here in person. The keynote we just came from was standing room only. In fact, there was overflow. People are excited to be back and to hear from the company in person the first time, since the IPO, >>Lots of stuff, lots of deep technical dives, uh, you know, they took the high end of the pyramid and then dove down deep in the keynotes. It >>Was good. They did. And we've got Doug Hench with us to break this down in the next eight to 10 minutes, VP and principle analyst at constellation research. Doug, welcome to the cube. >>Great to be here. >>All right, so guys, I was telling Dave, as we were walking back from the keynote, this was probably the most technical keynote I've seen in a very long time. Obviously in person let's break down some of the key announcements. What were some of the things Dave that stood out to you and what they announced just in the last hour and a half alone? >>Well, I, you know, we had a leave before they did it, but the unit store piece was really interesting to me cuz you know, the big criticism is, oh, say snowflake, that doesn't do transaction data. It's just a data warehouse. And now they're sort of reaching out. We're seeing the evolution of the ecosystem. Uh, sluman said it was by design. It was one of the questions I had for them. Is this just kind of happen or is it by design? So that's one of many things that, that we can unpack. I mean the security workload, uh, the, the Apache tables, we were just talking about thatt, which not a lot of hands went up when they said, who uses Apache tables, but, but a lot of the things they're doing seem to me anyway, to be trying to counteract the narrative, that snow, I mean that data bricks is put out there about you guys. Aren't open, you're a walled garden and now they're saying, Hey, we're we're as open as anybody, but what are your thoughts, Doug? >>Well, that's the, the iceberg announcement, uh, also, uh, the announcement of, of uni store being able to reach out to, to any source. Uh, you know, I think the big theme here was this, this contrast you constantly see with snowflake between their effort to democratize and simplify and disrupt the market by bringing in a great big tent. And you saw that great big tent here today, 7,000 people, 2,007,000 plus I'm told 2000 just three years ago. So this company is growing hugely quickly, >>Unprecedented everybody. >>Yeah. Uh, fastest company to a billion in revenue is Frank Salman said in his keynote today. Um, you know, and I think that there's, there's that great big tent. And then there's the innovations they're delivering. And a lot of their announcements are way ahead of the J general availability. A lot of the things they talked about today, Python support and some, some other aspects they're just getting into public preview. And many of the things that they're announcing today are in private preview. So it could be six, 12 months be before they're generally available. So they're here educating a lot of these customers. What is iceberg? You know, they're letting them know about, Hey, we're not just the data warehouse. We're not just letting you migrate your old workloads into the cloud. We're helping you innovate with things like the data marketplace. I see the data marketplace is really crucial to a lot of the announcements they're making today. Particularly the native apps, >>You know, what was interesting sluman in his keynote said we don't use the term data mesh, cuz that means has meaning to the people, lady from Geico stood up and said, we're building a data mesh. And when you think about, you know, the, those Gemma Dani's definition of data mesh, Snowflake's actually ticking a lot of boxes. I mean, it's it's is it a decentralized architecture? You could argue that it's sort of their own wall garden, but things like data as product we heard about building data products, uh, uh, self-serve infrastructure, uh, computational governance, automated governance. So those are all principles of Gemma's data mesh. So I there's close as anybody that, that I've seen with the exception of it's all in the data cloud. >>Why do you think he was very particular in saying we're not gonna call it a data mesh? I, >>I think he's respecting the principles that have been put forth by the data mesh community generally and specifically Jamma Dani. Uh, and they don't want to, you know, they don't want to data mesh wash. I mean, I, I, I think that's a good call. >>Yeah, that's it's a little bit out there and, and it, they didn't talk about data mesh so much as Geico, uh, the keynote or mentioned their building one. So again, they have this mix of the great big tent of customers and then very forward looking very sophisticated customers. And that's who they're speaking to with some of these announcements, like the native apps and the uni store to bring transactional data, bring more data in and innovate, create new apps. And the key to the apps is that they're made available through the marketplace. Things like data sharing. That's pretty simple. A lot of, uh, of their competitors are talking about, Hey, we can data share, but they don't have the things that make it easy, like the way to distribute the data, the way to monetize the data. So now they're looking forward monetizing apps, they changed the name from the data marketplace to the, to the snowflake marketplace. So it'll be apps. It will be data. It'll be all sorts of innovative products. >>We talk about Geico, uh, JPMC is speaking at this conference, uh, and the lead technical person of their data mesh initiative. So it's like, they're some of their customers that they're putting forth. So it's kind of interesting. And then Doug, something else that you and I have talked about on the, some of the panels that we've done is you've got an application development stack, you got the database over there and then you have the data analytics stack and we've, I've said, well, those things come together. Then people have said, yeah, they have to. And this is what snowflake seems to be driving towards. >>Well with uni store, they're reaching out and trying to bring transactional data in, right? Hey, don't limit this to analytical information. And there's other ways to do that, like CDC and streaming, but they're very closely tying that again to that marketplace, with the idea of bring your data over here and you can monetize it. Don't just leave it in that transactional database. So a, another reach to a broader play across a big community that they're >>Building different than what we saw last week at Mongo, different than what you know, Oracle does with, with heat wave. A lot of ways to skin a cat. >>That was gonna be my next question to both of you is talk to me about all the announcements that we saw. And, and like we said, we didn't actually get to see the entire keynote had come back here. Where are they from a differentiation perspective in terms of the competitive market? You mentioned Doug, a lot of the announcements in either private preview or soon to be public preview early. Talk to me about your thoughts where they are from a competitive standpoint. >>Again, it's that dichotomy between their very forward looking announcements. They're just coming on with things like Python support. That's just becoming generally available. They're just introducing, uh, uh, machine learning algorithms, like time series built into the database. So in some ways they're catching up while painting this vision of future capabilities and talking about things that are in development or in private preview that won't be here for a year or two, but they're so they're out there, uh, talking about a BLE bleeding edge story yet the reality is the product sometimes are lagging behind. Yeah, >>It's interesting. I mean, they' a lot of companies choose not to announce anything until it's ready to ship. Yeah. Typically that's a technique used by the big whales to try to freeze the market, but I think it's different here. And the strategy is to educate customers on what's possible because snowflake really does have, you know, they're trying to differentiate from, Hey, we're not just a data warehouse. We have a highly differentiatable strategy from whether it's Oracle or certainly, you know, Mongo is more transactional, but, but you know, whether it's couch base or Redis or all the other databases out there, they're saying we're not a database, we're a data cloud. <laugh> right. Right. Okay. What is that? Well, look at all the things that you can do with the data cloud, but to me, the most interesting is you can actually build data products and you can monetize that. And their, the emphasis on ecosystem, you, they look at Salman's previous company would ServiceNow took a long time for them to build an ecosystem. It was a lot of SI in smaller SI and they finally kind of took off, but this is exceeding my expectations and ecosystem is critical because they can't do it all. You know, they're gonna O otherwise they're gonna spread themselves to >>That. That's what I think some competitors just don't get about snowflake. They don't get that. It's all about the community, about their network that they're building and the relationships between these customers. And that they're facilitating that with distribution, with monetization, things that are hard. So you can't just add sharing, or you can share data from one of their, uh, legacy competitors, uh, in, in somebody else's marketplace that doesn't facilitate the transaction that doesn't, you know, build on the community. Well, >>And you know, one of the criticisms too, of the criticism on snowflake goes, they don't, you know, they can't do complex joins. They don't do workload management. And I think their answer to that is, well, we're gonna look to the ecosystem to do that. Or you, you saw some kind of, um, cost governance today in the, in the keynote, we're gonna help you optimize your spend, um, a little different than workload management, but related >>Part of their governance was having a, a, a node, uh, for every workload. So workload isolation in that way, but that led to the cost problems, you know, like too many nodes with not enough optimization. So here too, you saw a lot of, uh, announcements around cost controls, budgets, new features, uh, user groups that you could bring, uh, caps and guardrails around those costs. >>In the last couple minutes, guys talk about their momentum. Franks Lutman showed a slide today that showed over 5,900 customers. I was looking at some stats, uh, in the last couple of days that showed that there is an over 1200% increase in the number of customers with a million plus ARR. Talk about their momentum, what you expect to see here. A lot of people here, people are ready to hear what they're doing in person. >>Well, I think this, the stats say it all, uh, fastest company to a, to a billion in revenue. Uh, you see the land and expand experience that many companies have and in the cost control, uh, announcements they were making, they showed the typical curve like, and he talked about it being a roller coaster, and we wanna help you level that out. Uh, so that's, uh, a matter of maturation. Uh, that's one of the downsides of this rapid growth. You know, you have customers adding new users, adding new clusters, multi clusters, and the costs get outta control. They want to help customers even that out, uh, with reporting with these budget and cost control measures. So, uh, one of the growing pains that comes with, uh, adding so many customers so quickly, and those customers adding so many users and new, uh, workloads quickly, >>I know we gotta break, but last point I'll make about the key. Uh, keynote is SL alluded to the fact that they're not taking the foot off the gas. They don't see any reason to, despite the narrative in the press, they have inherent profitability. If they want to be more profitable, they could be, but they're going for growth >>Going for growth. There is so much to unpack in the next three days. You won't wanna miss it. The Cube's wall to oil coverage, Lisa Martin for Dave Valenti, Doug hen joined us in our keynote analysis. Thanks so much for walking, watching stick around. Our first guest is up in just a few minutes.

>> The Cube presents Dell Technologies World brought to you by Dell. >> Good evening, welcome back to the Cube's coverage of Dell Technologies World, live from the show floor in Las Vegas. Lisa Martin, Dave Vellante. We've been here two and a half days. We've unpacked a lot of announcements in the last couple days, and we're going to be doing a little bit more of that for our final segment. We've got a couple of guests joining us. Itzik Reich, the VP of the Technologist ISG at Dell and Magi Kapoor Director of Storage Product Management at Dell. Guys, welcome. >> Thank you for having us. >> So great to be back in person. I'm sure great for all of you to see customers and partners and your team that you probably haven't seen in quite a while. But Itzik we want to, we want to start with you VP of the Technologists. That sounds like a, like you need to wear a cape or something. >> Right? Yeah. I wish I do sometimes >> Talk about that role and what you do. >> Right, so our role, we have an outbound part and an inbound part. From an outbound perspective, our role is to ensure that our customers are knowing where we going from a technology perspective. And we do it via conferences or customer calls or via blogs, and think of that nature. But as important, we also have an inbound role to ensure that our employees are knowing where we're going. You can imagine they're a very large company. Not every engineer or any other role knows exactly what we are doing in that space, especially around innovation. So we also ensure that they understand it internally about where we going into that nature. And as a side role, I also have a side job which is to be responsible for our container strategy which has started couple of years ago which I'm sure we're going to talk about today. >> Yeah, that's-- >> Got a side gig. My goodness. >> That's right. >> Maggie, lots of announcements in the last couple of days. Great attendance here. Seven to 8,000 people. Dell's coming off its best year ever, north of 100 billion in revenue and FY 22, 17% year on year growth. What are some of the things that excite you about the strategic direction that Dell is going in with its partners, with the hyperscalers storage bringing it to the hyperscalers? >> Yeah. No lots of great announcements. It's been an exciting week. Like you said, it's been great to be back in person, have these face to face meetings and, you know, see the customers, have presentations in person. Like I feel like we haven't done that in forever. So it's felt really, really great. And announcements, it's been incredible. Like the two keynotes that we had on Monday and Tuesday were both incredible. And so I'd like to talk about a couple of key ones, you know, so just to let you know, I'm a director of product management and I'm responsible for a bunch of pan-ISG initiatives, DevOps and our container strategy being one of those items. And so, you know, we're at this cusp where there are, you know, customers that are on this journey of, you know, developers coming up to speed with multicloud being one of the key areas. We've heard that a lot this week, right? And what I loved about Chuck's keynote when he talked about, you know, a multicloud by default and how we're working to change that to be multicloud for design by design, right? And so what we mean by that is, and DevOps plays a very key role there, right? In the last few years developers have had this opportunity to pick different multi from different multi clouds, right? And develop the applications wherever they find the right tool sets. But that's creating havoc with IT operations because IT has worked in it in different ways, right? So what we're trying to do with DevOps is really bridge the gap between the developers and the IT ops and make it more frictionless. And project Alpine is one of the key ones to make that, you know, to bring that bridge together. Really bring that operational consistency across on-prem and the public clouds and colo facilities and Edge and everything that we've talked about. So project Alpine is really key to the success of DevOps that we're driving across. And then the other thing that I would like to call out in terms of announce and Chuck brought that up on Monday was our focus on developers. And we have a portal called developer.dell.com which we announced and launched in January of this year. Right? It's think of that as our one stop shop for all of our APIs. You heard from Caitlin, you heard from a lot of our leaders that we have been on this journey of having a API first approach to everything we're doing be it products, be it features, functionality. And so the developer portal is the place where we're putting all of our ISG APIs and not just having a one stop shop but standardizing on APIs, which is really key. >> We just spoke to Shannon Champion and Gemma from Salesforce. And we talked about how we entered last decade for visioning lungs. And now we're programming infrastructure. So really interested in your container strategy, your DevOps strategy. How did it start? How was it evolving? Where are you in the spectrum? You know, where are customers in that maturity? Let's dig in >> 2015, I believe was the year when DockerCon their CTO went on stage and they explained their customer that they shouldn't care about storage. They should design their applications running in containers in the 12 factor way, designed to fail, storage doesn't matter. And I remember scratching my head because I was hearing this one before. If there's one thing that I've learned both as a customer and later on as an employee of a storage company at the time, is that customers care about data and they care a lot about their data. Especially if it's not available. It's a bad day for the customer and possibly a very bad day for me as well. And so we actually, at the time, work with a startup called Cluster HQ to offer persistent volumes for Kubernetes. That startup eventually went down of business. But Google took over the some part of the intellectual property and came with an API called CSI. Which does not stand for your famous TV show. It's actually an acronym for container storage interface. And the CSI role in life is to be able to provide persistent volume from a storage array to Kubernetes. So we start working with Google, just like many other vendors in order to ensure that our stands outs are part of the CSI stand out. And we start to providing CSI interfaces for our storage arrays. And that's how all of these things started. We started to get more and more customers telling us I'm going all in with Kubernetes and I need you to support me in that journey. But what we've also learned is that Kubernetes similarly in a way to the open stock days is very fragmented. There are many distributions that are running on the top of Kubernetes. So seed side itself is not just the end of it. Many customer wants day to be working with VMware (indistinct) with zoo or with red OpenShift or with Rancher. So we need to do different adjustments for each one of these distributions in order to ensure that we are meeting the customer where they are today but also in the future as well. >> Yeah, and Kubernetes back in 2015 was, you know, pretty immature. We were focused on simplicity. You had Mesos doing, you know, more sophisticated things, you know, cluster HQ, obvious. And now you see Kubernetes moving into that realm tackling all those, a lot of those problems. So where does storage fit into that resilient resiliency equation? >> Yeah, so, you know, I think storages are key. What we're hearing a lot from customers is they have infrastructure in place already and they want to take advantage of cloud native and modernizing their applications whether they're the legacy applications or as they're building new applications. So how do really take advantage of the infrastructure that they have invested in? And they love, and they need. I mean, the reason why our customers love our products is because of the enterprise and the data management capabilities that we provide, right? Be it PowerMax for our gold standards on SRDF replication, for instance, they want to make sure that they leverage all of that as they are containerizing their applications. So the piece that Itzik talked about with the CSI plugins, that gives customers the opportunity to take advantage of the infrastructure that's already in place, take advantage of all the enterprise capabilities that it provides but yet take advantage of cloudifying, if I can say, the applications that they're doing, right? And then on top of that we also have what we call our CSM modules which is the container storage modules which is so, you know, going back again, we, CSI industry stack spec standards, you know, customers started to use it. And what we heard from our customers was, this is great but it has very minimum capabilities, right? Very basic ones. And we love your enterprise products. We want enterprise capabilities with it. So we've been working with CNCF very closely on, you know, working on contributions. But what we have realized is that they're, the community is still far from delivering some of these enterprise capabilities. So we came up with container storage modules which is an extension of CSI modules but to add those enterprise capabilities, you know, be it observability, be it replication, authorization, resiliency. These are the things that customers wanted to use enterprise storage when it comes to containers. And that's what we've been delivering on with our container storage modules. I do want to call out that all of our CSM modules just like CSI are all open source. That's what developers want. They don't want it closed source. And so we're listening to them and we're creating all of this in open source waiting, you know, and wanting them to contribute to the court. So it's not just us doing, you know and writing what we want but we also want the community to contribute. >> You're committing resources there, publishing them, it's all open source? >> Exactly. >> That's the contribution. >> And working with CNCF to see if they can be standardized across the board not just for Dell customers. >> Is that a project going, is that your ideal? It that becomes a project within CNCF or is it? >> That's our goal. Yes. We're definitely working and influencing. We'll see how it goes. >> More committers. Just keep throwing committers at it. >> Support these day is done via slack channel. So if we're changing the way that we run interacting with our customers that are now the developers themselves via slack channel. You don't need to call 100, 800 Dell to get a support case. >> So I'm interested in, you mentioned project Alpine, and it was very interesting to me to see that. You know, you guys talk about multicloud. I try to take it to another level. I call it super cloud and that's this abstraction layer. You know, some people laugh at that, but it has meaning. Multi-cloud is going to multivendor by default. And my premise is data ultimately is going to stay where it belongs in place. And then this mesh evolves, not my word, Jamoc Degani kind of invented. And there needs to be standards to be able to share data and govern that data. And it's wide open now. There are no standards there. And I think open sources has an opportunity as opposed to a defacto standard that would emerge. It seems to be real white space there. I think a company like Dell could provide that self-service infrastructure to those data points on the mesh and standards or software that governs that in a computational way. Is that something that's, you know, that super cloud idea is a reality from a technologist perspective? >> I think it is. So for example, Katie Gordon, which I believe you interviewed earlier this week, was demonstrating the Kubernetes data mobility aspect, which is another project. That's exactly power part of the its rational, the rationale of customers being able to move some of their Kubernetes workloads to the cloud and back and between different clouds. Why we doing it? Because customers wants to have the ability to move between different cloud providers using a common API that will be able to orchestrate all of those things with a self-service that may be offered via the apex console itself. So it's all around enabling developers and meeting them where they are today and also meeting them in tomorrow's world where they actually may have changed their mind to do those things. So, yes, we are working on all of those different aspects. >> Dell meeting the developers where they are. Guys thank you so much for joining David and me and unpacking that. We appreciate your insights and your time. >> Thank you so much for having us. >> Thank you. >> Thank you. Speaking of unpacking, Lisa. We're unpacking Dell tech world. >> They're packing up around us. Exactly. We better go. We want to thank you for watching The Cube's two and a half days of live coverage of Dell Technologies world. Dave it's been great to co-host with you, be back in person. >> Thank you Lisa. It was really a pleasure. >> Of course. My pleasure too. >> Let's do more of this. >> Let's do it! >> All right. >> We want to thank you again for watching. You can catch all of this on replay on thecube.net. We look forward to seeing you next time. (soft music)

>> live from Washington, D. C. It's the Cube covering a ws public sector summit by Amazon Web services. >> Welcome back, everyone. You are watching the Cuban. We're kicking off our day two of our live coverage a ws public sector summit here in our nation's capital. I'm Rebecca Knight co hosting with John Fer Yer John. It's great to be here. 18,000 people having important conversations around around governments and cloud computing. Let's extract the signal from the noise. Let's do with the Cube. Does best, >> Yeah, I mean, this is to me a really exciting event because it's got the confluence of what we love tech and cloud computing and all the awesomeness of that and that enables. But even in Washington, D. C. With the backdrop against tech clash on this, you know, narrative run tech for illah tech for bad, bad check whatever you want to call it. Anti trust is a lot of narratives around that there's a huge story around check for good. So I think there's an interesting balance there around the conversations, but this is world of heavy hitters are this week You've got senior people at the government level here, you have senior tech people hear all kind of meddling and trying to figure out howto let the tail winds of cloud computing Dr Change within government against this backdrop of tech for ill as Jay Carney, whose the global marketing policy guy for Amazon on reports to Jeff Bezos, former Obama press secretary. He's super savvy on policy, super savvy on tech. But this is a really big point in time where the future's gonna be determined by some key people and some key decisions around the role of technology for society, for the citizens, United States, for nation states as people start to figure out the role of data and all the impact of this so super exciting at that level, but also dangerous and people are telling a little bit. But I also want to run hard. That's pretty much the big story. >> So let's let's let's get into this tech backlash because you're absolutely right. Through the public, sentiment about technology and the tech behemoths has really soured. The regulators are sharpening their blades and really paying much more attention, uh, particularly because so many people say, Hey, wait a minute, why? How does Google and Facebook know all this stuff about me, but what do you think? What are we hearing on the ground in terms of where regulation is going? Before, before the cameras were rolling, you were talking about this idea of regulators working closely with the innovators, observing but not meddling. I mean, do you think that that's that's That's these dollars underwears We're going in? >> Well, not really. I think that that's where people wanted to go in. I think right now the the surprise attack of tech taking over, if you will in the minds of people and or without Israel or not, it's happened, right? So I was talking yesterday around how the Internet, when Bill Clinton was president, really grew a little bit slower than the pace of this today. But they did a good job of managing that they had private sectors take over the domain name system. We saw that grow that created in the open Web and the Web was open. Today it's different. It's faster in terms of technology innovation, and it's not as open. You have Facebook, LinkedIn and these companies that have silos of data, and they're not sharing it with cyber security General Keith Alexander, former head of the NSA and the first commander of cyber command in the U. S. The United States under Obama. He pointed out that visibility into the cyber attacks aren't there because there's no sharing of data. We heard about open data and knishes from a think tank. The role of data and information is going to be a critical conversation, and I don't think the government officials are smart enough and educated enough yet to understand that So regulatory groups want to regulate they don't know how to. They're reaching out the Amazons, Google's and the Facebook to try to figure out what's going on. And then from there they might get a path. But they're still in the early stages. Amazon feels like they're not harming anyone there. Lower prices, fast delivery, more options. They're creating an enablement environment for tons of startups, so they feel like they're not harming anyone. You're the antitrust, but if they're going to being monopolizing the market place, that's another issue. But I still think Amazon still an enabling mode, and I think you know, they're just running so hard. It's going so fast, I think there's gonna be a big challenge. And if industry doesn't step up and partner with government, it's going to be a real mess. And I think it's just moving too fast. It's very complicated. Digital is nuanced. Now. You get the role of data all this place into into into effect there. >> Well, you're absolutely right that it's going fast. Teresa Carlson on the other day talking about eight of US growth, UH, 41% year over year and she said, Cloud is the new normal. The cloud cloud is here more and more governments on state and local, really recognizing and obviously international countries to recognize that this, this is they're adopting these cloud first approach is, >> yeah, I mean, I think the first approach is validated 100%. There's no debate. I think it's not an ah ha moment. Cloud Israel. Amazon has absolutely proven since the CIA deal in 2013 that this is a viable strategy for government to get to value fast, and that is the whole speed of cloud game. It's all about time to value with agility. Eccentric center. We've been talking about that with Dev Ops for a long, long time. The real thing that I think's happening that's going on. That's kind of, you know, to read the tea leaves and we'll hear from Corey Quinn. Our host at large will go on later. This is a new generation of talent coming on board and this new generation. It feels like a counterculture mindset. These are Dev ops, mindset, people not necessarily Dev ops like in the Cloud Computing Way. They're younger, they're thinking differently, and they think like Amazon not because they love Amazon, because that's their nature. Their got their getting content in a digital way, their digital natives. They're born into that kind of cultural mindset. Of what is all this nonsense red tape? What's the bottlenecked in solving these problems? There's really not a good answer anymore, because with cloud computing and machine learning an A I, you can solve things faster. So if you expose the data, smart people go well. That's a problem that could be song. Let's solve it. So I think there's going to be a resurgence is going to be a renaissance of of younger people, kind of in a counter culture way that's going to move fast and an impact society and I think it's gonna happen pretty quickly over the next 10 years. >> Well, that's one of the things that's so inspiring about being at a conference like this one a ws public sector summit, Because we are hearing getting back to what you just said. We're solving problems and these air problems about not just selling more widgets. This's actually about saving lives, helping people, delivery of healthcare, finding Mr Missing Persons and POWs who are missing in action. >> I mean, the problems could be solved with technology now for goodwill, I think will outweigh the technology for Ayla's Jay Carney calls it. So right now, unfortunately, was talking about Facebook and all this nonsense that happened with the elections. I think that's pretty visible. That's painful for people to kind of deal with. But in the reality that never should have happened, I think you're going to see a resurgence of people that's going to solve problems. And if you look at the software developer persona over the past 10 to 15 years, it went from hire. Some developers build a product ship it market. It makes some money to developers being the frontlines. Power players in software companies there on the front lines. They're making changes. They're moving fast, creating value. I see that kind of paradigm hitting normal people where they can impact change like a developer would foran application in society. I think you're gonna have younger people solving all kinds of crisis around. Whether it's open opioid crisis, healthcare, these problems will be solved. I think cloud computing with a I and machine learning and the role of data will be a big catalyst. >> But money, the money, the money is the thing we're going to have Cory Quinn on later talking about this this talent gap because there are people who are, As you said, they're young people who are motivated to solve these problems, and they want to work for mission driving institutions. What better mission, then helping the United States government >> just heard in the hallway? This has been the I've heard this multiple times here. This show I just heard someone saying Yeah, but that person's great. I can't keep them. What's happening is with the talent is the people that they need for cloud computing. Khun, get a job that pays three times Mohr orm or at the private sector. So, you know, Governor doesn't have stock options, >> right? All right, all right. If >> you're, ah, machine learning, >> people call girls in the lounge. >> Eso all kinds of different diners. But I think this mission driven culture of working for society for good might be that currency. That will be the equivalent stock option that I think is something that we were watching. Not haven't seen anything yet, But maybe that will happen. >> Paid in good feelings way. We've got a lot of great guests. Wave already teed up. We've got your E. Quinn. Bill Britain from Cal Poly to talk more about ground station. We have alien Gemma Smith of YSL Itics, uh, and Jameel Jaffer. >> Think ground station. But the biggest surprise for me and the show so far has been ground station that that product has got so much traction. That's ridiculous. I thought it would be kind of cool. Spacey. I like it, but it's turning into a critical need for a I ot I mean, I was just talking with you. Came on about the airplane having WiFi on the plane. We all like Wow, we expected now, but you go back years ago is like, Oh, my God. I got WiFi on the plane. That's a ground station, like dynamic people going. Oh, my God. I can provision satellite and get data back, all for io ti anywhere in the world. So that is pretty killer. >> Excellently. I'm looking forward to digging in with you with many guests today. >> Good. >> I'm Rebecca Knight. For John. For your stay tuned, you are watching the Cube.