(energetic music) >> Okay, we're back with Justin Borgman, CEO of Starburst, Richard Jarvis is the CTO of EMIS Health, and Teresa Tung is the cloud first technologist from Accenture. We're on to lie number three. And that is the claim that today's "Modern Data Stack" is actually modern. So (chuckles), I guess that's the lie. Or, is that it's not modern. Justin, what do you say? >> Yeah, I think new isn't modern. Right? I think 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 Teradata, you have Snowflake. Rather than Informatica, you have Fivetran. So, it's the same general stack, just, y'know, a cloud version of it. And I think a lot of the challenges that have plagued us for 40 years still maintain. >> So, let me come back to you Justin. Okay, but there are differences, right? You can scale. You can throw resources at the problem. You can separate compute from storage. You really, there's a lot of money being thrown at that by venture capitalists, and Snowflake you mentioned, its competitors. So that's different. Is it not? Is that not at least an aspect of modern dial it up, dial it down? So what do you say to that? >> Well, it is. It's certainly taking, y'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's 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 structural constraints that exist with the old enterprise data warehouse model on-preem still exist. Just yes, a little bit more elastic now because the cloud offers that. >> So Teresa, let me go to you, 'cause you have cloud-first in your title. So, what's 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 use 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's going to very much fall the same thing. There was going to be more edge. There's going to 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 stack needs to be much more federated. >> Okay, so Richard, how do you deal with this? You've obviously got, you know, the technical debt, the existing infrastructure, it's on the books. You don't want to just throw it out. A lot of conversation about modernizing applications, which a lot of times is, you know, of microservices layer on top of 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 'cause you can scale CPU and storage doesn't mean you can get more people to use your data to generate you 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. Derisked experimentation, you know that we talked about the ability to scale up scale down, but it's, 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 buy that. I'm you 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, the, 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 how it should look like or, or how >> 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 certainly agree with that. So by no means, are we suggesting that, you know Snowflake or what 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 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 by 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 future proof yourself from the inevitable change that you will you won't encounter over time. >> So thank you. So Theresa, based on what Justin just said, I I might take away there is it's inclusive whether it's a data mart, data hub, data lake, data warehouse, just a node on the mesh. Okay. I get that. Does that include Theresa on, on Preem data? 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 and data mesh, frankly really aren't, you know adhering to the philosophy there. Maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing, HelloFresh, 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 think it's a killer case for data mesh. The fact that you have valuable data sources on Preem, and then yet you still want to 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 world. You can start using the data products on Preem, 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 tapping into better analytics to get better insights, right? So you're going to 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 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 got to be governed as well. You mentioned 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 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 on the data, you know you got to cut through a lot of the, the centralized teams, the technical teams that that data agnostic, they don't really have that context. All right, let's end. 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 modern data stack that people have today. >> Excellent. Hey, I want to thank Justin, Teresa, and Richard for joining us today. You guys are great. 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 going to 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 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 Vellante for the Cube, and we'll see you next time. (upbeat music)

(bright upbeat music) >> In 2011, early Facebook employee and Cloudera co-founder Jeff Hammerbacher 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 and 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 marts, 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 theCUBE and made possible by Starburst Data. I'm your host, Dave Vellante, and joining me today are three industry experts. Justin Borgman is the co-founder and CEO of Starburst, Richard Jarvis is the CTO at EMIS Health, and Teresa Tung is cloud first technologist at Accenture. Today, we're going to 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 parity 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 an 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 theCUBE studios, about 30 miles outside of Boston. Folks, welcome to the program. Thanks for coming on. >> Thanks for having us. >> Okay, 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 Hadapt, which was an early SQL engine for IDU 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. Those companies were acquiring other companies and inheriting their data architecture. So 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, what are your thoughts? I mean, there's a lot of pressure to cut cost, keep things centralized, serve the business as best as possible from that standpoint. What does 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 a platform that's close to data experts people who really understand healthcare data from pharmacies or 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 businesses just don't grow up like that. And it's just really impossible to get that academic perfection of storing everything in one place. >> Teresa, I feel like Sarbanes-Oxley have kind of saved the data warehouse, right? (laughs) You actually did have to have a single version of the truth for certain financial data, but really for some of those other use cases I mentioned, I do feel like the industry has kind of 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 decentralize? >> I think you got to have centralized governance, right? So from the central team, for things like Sarbanes-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 going to 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 going to collaborate with your partners. So partners that are not within the company, right? External partners. We're going to see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >> So Justin, you guys last, jeez, I think it was about a year ago, had a session on data mesh. It was a great program. You invited Zhamak Dehghani. Of course, she's the creator of the data mesh. One of our fundamental premises is that you've got this hyper specialized team that you've got to go through if you want anything. But at the same time, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess, a question for you Richard. How do you deal with that? Do you organize so that there are a few sort of rock stars that build cubes and the like or have you had any success in sort of decentralizing with your constituencies that data model? >> Yeah. 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 the data that we use at EMIS is very private, it's healthcare information. And some of the rules and regulations around using the data are very complex 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 consulting type experience from a set of rock stars to help a more decentralized business who needs to understand the data and to generate some valuable output. >> Justin, what do you say to a customer or prospect that says, "Look, Justin. I got a centralized team and that's the most cost effective way to serve the business. Otherwise, I got duplication." What do you say to that? >> Well, I would argue it's probably not the most cost effective, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, 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 for many, many years to come. I think that's the story at Oracle or Teradata or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams, 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 mesh that Zhamak writes about is this idea of the domain owners actually know the data the best. And so by not only acknowledging that data is generally decentralized, and to your earlier point about Sarbanes-Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for those laws to be compliant. But I think the reality is the data mesh model basically says data's decentralized and we're going to turn that into an asset rather than a liability. And we're going to turn that into an asset by empowering the people that know the data the best to participate in the process of curating and creating data products 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 models comparing and contrasting. >> So do you think the demise of the data warehouse is inevitable? Teresa, you work with a lot of clients. They're not just going to rip and replace their existing infrastructure. Maybe they're going to build on top of it, but what does that mean? Does that mean the EDW just becomes 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. I would love it mastered, would love it owned by a central 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 going to be a new technology that's going to emerge that we're going to want to 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 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 with the domains that know it best, or the consumer-based systems or products that need to be packaged in a way that'd 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. Take Zhamak's principles back to those. You got the domain ownership and data as product. Okay, great. Sounds good. But it creates what I would argue are two challenges: self-serve infrastructure, let's park that for a second, and then in your industry, one of the most regulated, most sensitive, computational governance. How do you automate and ensure federated governance in that mesh model that Teresa was just talking about? >> Well, it absolutely depends on some of the tooling and processes that you put in place around those tools 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 EMIS 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 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 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 the data users. >> Yeah. So Justin, we always talk about data democratization, and 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 doing analytic queries with data that's all dispersed all over. How are you seeing your customers handle 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, the people who know the data the best, to create 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 you'd like. And so really trying to build on that notion of data democratization and self-service, and making it very easy to discover and start to use with whatever BI tool you may like or even just running SQL queries yourself. >> Okay 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. (bright upbeat music)