>>We're back with Jess Borgman of Starburst and Richard Jarvis of EVAs health. Okay. We're gonna get into 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'll, you'll never get performance because you need to be column. You need to store data in a column format. And then, you know, column formats were introduced to, to data lake. 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 hoote that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a quote from, you know, Kurt Monash many years ago where he said, you 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 lie 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, the clothes 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 done is backed itself into a corner that then prevents it from innovating. So if you have chosen the 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 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 JAK about this and you know, obviously her vision is there's an open source that, that data mesh is 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 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 hit 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 hit 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, it's interesting remind of when I, you know, I see the, the gas price, the TSR 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, 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. 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 wanna 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 and, 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 there, but, but a lot of Oracle customers and they, you know, they'll admit yeah, you know, the Jammin us on price and the license cost, 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 an ROI? >>I think the answer to that is it can depend a bit. It depends on your business's 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 always 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 IE, 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 command a 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 the 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, it, it 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 data 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 understanding 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.

>>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. And what if this also included enterprise security and fast performance with Starburst enterprise, you can provide your data consumers with a single point of secure access to all of your data, no matter where it lives with features like strict, fine grained, access control, end to end data encryption and data masking Starburst meets the security standards of the largest companies. Starburst enterprise can easily be deployed anywhere and managed with insights where data teams holistically view their clusters operation and query execution. So they can reach meaningful business decisions faster, all this with the support of the largest team of Trino experts in the world, delivering fully tested stable releases and available to support you 24 7 to unlock the value in all of your data. You need a solution that easily fits with what you have today and can adapt to your architecture. Tomorrow. 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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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>>We're back with Jess Borgman of Starburst and Richard Jarvis of emus health. Okay. We're gonna get into 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'll, you'll never get performance because you need to be column. You need to store data in a column format. And then, you know, column formats were introduced to, to data lakes. 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 DY 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, 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 lie 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, the 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 the 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, but 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 Justin, let me play devil's advocate here a little bit, and I've talked to JAK about this and you know, obviously her vision is there's an open source that, that data mesh is 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 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 hit 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 had back then. And I think, 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, it's interesting reminded when I, you know, I see the, the gas price, the TSR 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, 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 amazing quote. He said, it buys us the ability to be unsure of the future. 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 smart to train a machine learning model and you wanna use Starbust to query be a 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 and, and locks you in. >>So 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 Jimin some 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 an ROI? >>I think the answer to that is it can depend a bit. It depends on your business's 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 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 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 P Sanji 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 command a 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 the 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, it 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 data 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 control 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.

>>Welcome back to the big apple, everybody. The Cube's continuous coverage here of MongoDB world 2022. We're at the new Javet center. It's it's quite nice. It was built during the pandemic. I believe on top of a former bus terminal. I'm told by our next guest Tony bear, who's the principal at DB insight of data and database expert, longtime analyst, Tony. Good to see you. Thanks for coming >>On. Thanks >>For having us. You face to face >>And welcome to New York. >>Yeah. Right. >>New York is open for business. >>So, yeah. And actually, you know, it's interesting. We've been doing a lot of these events lately and, and especially the ones in Vegas, it's the first time everybody's been out, you know, face to face, not so much here, you know, people have been out and about a lot of masks >>In, >>In New York city, but, but it's good. And, and this new venue is fantastic >>Much nicer than the old Javits. >>Yeah. And I would say maybe 3000 people here. >>Yeah. Probably, but I think like most conferences right now are kind of, they're going through like a slow ramp up. And like for instance, you know, sapphires had maybe about one third, their normal turnout. So I think that you're saying like one third to one half seems to be the norm right now are still figuring out how we're, how and where we're gonna get back together. Yeah. >>I think that's about right. And, and I, but I do think that that in most of the cases that we've seen, it's exceeded people's expectations at tenants, but anyway sure. Let's talk about Mongo, very interesting company. You know, we've been kind of been watching their progression from just sort of document database and all the features and functions they're adding, you just published a piece this morning in venture beat is time for Mongo to get into analytics. Yes. You know? Yes. One of your favorite topics. Well, can they expand analytics? They seem to be doing that. Let's dig into it. Well, >>They're taking, they've been taking slow. They've been taking baby steps and there's good reason for that because first thing is an operational database. The last thing you wanna do is slow it down with very complex analytics. On the other hand, there's huge value to be had if you would, if you could, you know, turn, let's say a smart, if you can turn, let's say an operational database or a transaction database into a smart transaction database. In other words, for instance, you know, let's say if you're, you're, you're doing, you know, an eCommerce site and a customer has made an order, that's basically been out of the norm. Whether it be like, you know, good or bad, it would be nice. Basically, if at that point you could then have a next best action, which is where analytics comes in. But it's a very lightweight form of analytics. It's not gonna, it's actually, I think probably the best metaphor for this is real time credit scoring. It's not that they're doing your scoring you in real time. It's that the model has been computed offline so that when you come on in real time, it can make a smart decision. >>Got it. Okay. So, and I think it was your article where I, I wrote down some examples. Sure. Operational, you know, use cases, patient data. There's certainly retail. We had Forbes on earlier, right? Obviously, so very wide range of, of use cases for operational will, will Mongo, essentially, in your view, is it positioned to replace traditional R D BMS? >>Well, okay. That's a long that's, that's much, it's >>Sort of a loaded question, but >>That's, that's a very loaded question. I think that for certain cases, I think it will replace R D BMS, but I still, I mean, where I, where I depart from Mongo is I do not believe that they're going to replace all R D D BMSs. I think, for instance, like when you're doing financial transactions, you know, the world has been used to table, you know, you know, columns and rows and tables. That's, it's a natural form for something that's very structured like that. On the other hand, when you take a look, let's say OT data, or you're taking a look at home listings that tends to more naturally represent itself as documents. And so there's a, so it's kind of like documents are the way that let's say you normally see the world. Relational is the way that you would structure the world. >>Okay. Well, I like that. So, but I mean, in the early days, obviously, and even to this day, it's like the target for Mongo has been Oracle. Yeah. Right, right. And so, and then, you know, you talk to a lot of Oracle customers as do I sure. And they are running the most mission, critical applications in the world, and it's like banking and financial and so many. And, and, and, you know, they've kind of carved out that space, but are we, should we be rethinking the definition of, of mission critical? Is that changing? >>Well, number one, I think what we've traditionally associated mission critical systems with is our financial transaction systems and to a less, and also let's say systems that schedule operations. But the fact is there are many forms of operations where for instance, let's say you're in a social network, do you need to have that very latest update? Or, you know, basically, can you go off, let's say like, you know, a server that's eventually consistent. In other words, the, do you absolutely have, you know, it's just like when you go on Twitter, do you naturally see all the latest tweets? It's not the system's not gonna crash for that reason. Whereas let's say if you're doing it, you know, let's say an ATM banking ATM system, that system better be current. So I think there's a delineation. The fact is, is that in a social network, arguably that operational system is mission critical, but it's mission critical in a different way from a, you know, from, let's say a banking system. >>So coming back to this idea of, of this hybrid, I think, you know, I think Gartner calls it H tab hybrid, transactional analytics >>Is changed by >>The minute, right. I mean, you mentioned that in, in your article, but basically it's bringing analytics to transactions bringing those, those roles together. Right. Right. And you're saying with Mongo, it's, it's lightweight now take, you use two other examples in your article, my SQL heat wave. Right. I think you had a Google example as well, DB, those are, you're saying much, much heavier analytics, is that correct? Or >>I we'll put it this way. I think they're because they're coming from a relational background. And because they also are coming from companies that already have, you know, analytic database or data warehouses, if you will, that their analytic, you know, capabilities are gonna be much more fully rounded than what Mongo has at this point. It's not a criticism of a Mongo MongoDB per >>Per, is that by design though? Or ne not necessarily. Is that a function of maturity? >>I think it's function of maturity. Oh, okay. I mean, look, to a certain extent, it's also a function of design in terms of that the document model is a little, it's not impossible to basically model it for analytics, but it takes more, you know, transformation to, to decide which, you know, let's say field in that document is gonna be a column. >>Now, the big thing about some of these other, these hybrid systems is, is eliminating the need for two databases, right? Eliminating the need for, you know, complex ETL. Is, is that a value proposition that will emerge with, with Mongo in your view? >>You know, I, I mean, put it this way. I think that if you take a look at how they've, how Mongo is basically has added more function to its operations, someone talking about analytics here, for instance, adding streaming, you know, adding, adding, search, adding time series, that's a matter of like where they've eliminated the need to do, you know, transformation ETL, but that's not for analytics per se for analytics. I think through, you know, I mean through replication, there's still gonna be some transformation in terms of turning, let's say data, that's, that's formed in a document into something that's represented by columns. There is a form of transformation, you know, so that said, and Mongo is already, you know, it has some NA you know, nascent capability there, but it's all, but this is still like at a rev 1.0 level, you know, I expect a lot more >>Of so refin you, how Amazon says in the fullness of time, all workloads will be in the cloud. And we could certainly debate that. What do we mean by cloud? So, but there's a sort of analog for Mongo that I'll ask you in the fullness of time, will Mongo be in a position to replace data warehouses or data lakes? No. Or, or, or, and we know the answer is no. So that's of course, yeah. But are these two worlds on a quasi collision course? I think they >>More on a convergence course or the collision course, because number one is I said, the first principle and operational database is the last thing you wanna do is slow it down. And to do all this complex modeling that let's say that you would do in a data bricks, or very complex analytics that you would do in a snowflake that is going to get, you know, you know, no matter how much you partition the load, you know, in Atlas, and yes, you can have separate nodes. The fact is you really do not wanna burden the operational database with that. And that's not what it's meant for, but what it is meant for is, you know, can I make a smart decision on the spot? In other words, kinda like close the loop on that. And so therefore there's a, a form of lightweight analytic that you can perform in there. And actually that's also the same principle, you know, on which let's say for instance, you know, my SQL heat wave and Allo DBR based on, they're not, they're predicated on, they're not meant to replace, you know, whether it be exit data or big query, the idea there is to do more of the lightweight stuff, you know, and keep the database, you know, keep the operations, you know, >>Operating. And, but from a practitioner's standpoint, I, I, I can and should isolate you're saying that node, right. That's what they'll do. Sure. How does that affect cuz my understanding is that that the Mon Mongo specifically, but I think document databases generally will have a primary node. Right? And then you can set up secondary nodes, which then you have to think about availability, but, but would that analytic node be sort of fenced off? Is that part of the >>Well, that's actually what they're, they've already, I mean, they already laid the groundwork for it last year, by saying that you can set up separate nodes and dedicate them to analytics and what they've >>As, as a primary, >>Right? Yes, yes. For analytics and what they've added, what they're a, what they are adding this year is the fact to say like that separate node does not have to be the same instance class, you know, as, as, as, as the, >>What, what does that mean? Explain >>That in other words, it's a, you know, you could have BA you know, for instance, you could have a node for operations, that's basically very eye ops intensive, whereas you could have a node let's say for analytics that might be more compute intensive or, or more he, or, or more heavily, you know, configured with, with memory per se. And so the idea here is you can tailor in a node to the workload. So that's, you know what they're saying with, you know, and I forget what they're calling it, but the idea that you can have a different type, you can specify a different type of node, a different type of instance for the analytic node, I think is, you know, is a major step forward >>And that, and that that's enabled by the cloud and architecture. >>Of course. Yes. I mean, we're separating, compute from data is, is, is the starter. And so yeah. Then at that point you can then start to, you know, you know, to go less vanilla. I think, you know, the re you know, the, you know, the, I guess the fruition of this is going to be when they say, okay, you can run your, let's say your operational nodes, you know, dedicated, but we'll let you run your analytic nodes serverless. Can't do it yet, but I've gotta believe that's on the roadmap. >>Yeah. So seq brings a lot of overhead. So you get MQL, but now square this circle for me, cuz now you got Mago talking sequel. >>They had to start doing that some time. I mean, and I it's been a court take I've had from them from the, from the get go, which I said, I understand that you're looking at this as an alternative to SQL and that's perfectly valid, but don't deny the validity of SQL or the reason why we, you know, we need it. The fact is that you have, okay, the number, you know, according to Ty index, JavaScript is the seventh, most popular language. Most SQL follows closely behind at the ninth, most popular language you don't want to cl. And the fact is those people exist in the enterprise and they're, and they're disproportionately concentrated in analytics. I mean, you know, it's getting a little less, so now we're seeing like, you know, basically, you know, Python, the programmatic, but still, you know, a lot of sequel expertise there. It does not make, it makes no sense for Mongo to, to, to ignore or to overlook that audience. I think now they're, you know, you know, they're taking baby steps to start, you know, reaching out to them. >>It's interesting. You see it going both ways. See Oracle announces a Mongo, DB, Mongo. I mean, it's just convergence. You called it not, I love collisions, you know, >>I know it's like, because you thrive on drama and I thrive on can't. We all love each other, but you know, act. But the thing is actually, I've been, I wrote about this. I forget when I think it was like 2014 or 2016. It's when we, I was noticed I was noting basically the, you know, the rise of all these specialized databases and probably Amazon, you know, AWS is probably the best exemplar of that. I've got 15 or 16 or however, number of databases and they're all dedicated purpose. Right. But I also was, you know, basically saw that inevitably there was gonna be some overlap. It's not that all databases were gonna become one and the same we're gonna be, we're gonna become back into like the, you know, into a pan G continent or something like that. But that you're gonna have a relational database that can do JSON and, and a, and a document database that can do relational. I mean, you know, it's, to me, that's a no brainer. >>So I asked Andy Ja one time, I'd love to get your take on this, about those, you know, multiple data stores at the time. They probably had a thousand. I think they're probably up to 15 now, right? Different APIs, different S et cetera. And his response. I said, why don't you make it easier for, for customers and maybe build an abstraction or converge these? And he said, well, it's by design. What if you buy this? And, and what your thoughts are, cuz I, you know, he's a pretty straight shooter. Yeah. It's by design because it allows us as the market moves, we can move with it. And if we, if we give developers access to those low level primitives and APIs, then they can move with, with at market speed. Right. And so that again, by design, now we heard certainly Mongo poo pooing that today they didn't mention, they didn't call out Amazon. Yeah. Oracle has no compunction about specifically calling out Amazon. They do it all the time. What do you make of that? Can't Amazon have its cake and eat it too. In other words, extend some of the functionality of those specific databases without going to the Swiss army. >>I I'll put it this way. You, you kind of tapped in you're, you're sort of like, you know, killing me softly with your song there, which is that, you know, I was actually kind of went on a rant about this, actually know in, you know, come, you know, you know, my year ahead sort of out predictions. And I said, look, cloud folks, it's great that you're making individual SAS, you know, products easy to use. But now that I have to mix and match SAS products, you know, the burden of integration is on my shoulders. Start making my life easier. I think a good, you know, a good example of this would be, you know, for instance, you could take something like, you know, let's say like a Google big query. There's no reason why I can't have a piece of that that might, you know, might be paired, say, you know, say with span or something like that. >>The idea being is that if we're all working off a common, you know, common storage, we, you know, it's in cloud native, we can separate the computer engines. It means that we can use the right engine for the right part of the task. And the thing is that maybe, you know, myself as a consumer, I should not have to be choosing between big query and span. But the thing is, I should be able to say, look, I want to, you know, globally distribute database, but I also wanna do some analytics and therefore behind the scenes, you know, new microservices, it could connect the two wouldn't >>Microsoft synapse be an example of doing that. >>It should be an example. I wish I, I would love to hear more from Microsoft about this. They've been radio silent for about the past two or three years in data. You hardly hear about it, but synapse is actually those actually one of the ideas I had in mind now keep in mind that with synapse, you're not talking about, let's say, you know, I mean, it's, it's obviously a sequel data warehouse. It's not pure spark. It's basically their, it was their curated version of spark, but that's fine. But again, I would love to hear Microsoft talk more about that. They've been very quiet. >>Yeah. You, you, the intent is there to >>Simplify >>It exactly. And create an abstraction. Exactly. Yeah. They have been quiet about it. Yeah. Yeah. You would expect that, that maybe they're still trying to figure it out. So what's your prognosis from Mongo? I mean, since this company IP, you know, usually I, I tell and I tell everybody this, especially my kids, like don't buy a stock at IPO. You'll always get a better chance at a cheaper price to buy it. Yeah. And even though that was true with Mongo, you didn't have a big window. No. Like you did, for instance, with, with Facebook, certainly that's been the case with snowflake and sure. Alibaba, I mean, I name a zillion style was almost universal. Yeah. But, but since that, that, that first, you know, few months, period, this, this company has been on a roll. Right. And it, it obviously has been some volatility, but the execution has been outstanding. >>No question about that. I mean, the thing is, look what I, what I, and I'm just gonna talk on the product side on the sales side. Yeah. But on the product side, from the get go, they made a product that was easy for developers. Whereas let's say someone's giving an example, for instance, Cosmo CB, where to do certain operations. They had to go through multiple services in, you know, including Azure portal with Atlas, it's all within Atlas. So they've really, it's been kinda like design thinking from the start initially with, with the core Mongo DB, you know, you, the on premise, both this predates Atlas, I mean, part of it was that they were coming with a language that developers knew was just Javas script. The construct that they knew, which was JS on. So they started with that home core advantage, but they weren't the only ones doing that. But they did it with tooling that was very intuitive to developers that met developers, where they lived and what I give them, you know, then additional credit for is that when they went to the cloud and it wasn't an immediate thing, Atlas was not an overnight success, but they employed that same design thinking to Atlas, they made Atlas a good cloud experience. They didn't just do a lift and shift the cloud. And so that's why today basically like five or six years later, Atlas's most of their business. >>Yeah. It's what, 60% of the business now. Yeah. And then Dave, on the, on the earning scholar, maybe it wasn't Dave and somebody else in response to question said, yeah, ultimately this is the future will be be 90% of the business. I'm not gonna predict when. So my, my question is, okay, so let's call that the midterm midterm ATLA is gonna be 90% of the business with some exceptions that people just won't move to the cloud. What's next is the edge. A new opportunity is Mongo architecturally suited for the, I mean, it's certainly suited for the right, the home Depot store. Sure. You know, at the edge. Yeah. If you, if you consider that edge, which I guess it is form of edge, but how about the far edge EVs cell towers, you know, far side, real time, AI inferencing, what's the requirement there, can Mongo fit there? Any thoughts >>On that? I think the AI and the inferencing stuff is interesting. It's something which really Mongo has not tackled yet. I think we take the same principle, which is the lightweight stuff. In other words, you'll say, do let's say a classification or a prediction or some sort of prescriptive action in other words, where you're not doing some convolution, neural networking and trying to do like, you know, text, text to voice or, or, or vice versa. Well, you're not trying to do all that really fancy stuff. I think that's, you know, if you're keeping it SIM you know, kinda like the kiss principle, I think that's very much within Mongo's future. I think with the realm they have, they basically have the infrastructure to go out to the edge. I think with the fact that they've embraced GraphQL has also made them a lot more extensible. So I think they certainly do have, you know, I, I do see the edge as being, you know, you know, in, in, you know, in their, in their pathway. I do see basically lightweight analytics and lightweight, let's say machine learning definitely in their >>Future. And, but, and they would, would you agree that they're in a better position to tap that opportunity than say a snowflake or an Oracle now maybe M and a can change that. R D can maybe change that, but fundamentally from an architectural standpoint yeah. Are they in a better position? >>Good question. I think that that Mongo snowflake by virtual fact, I mean that they've been all, you know, all cloud start off with, I think makes it more difficult, not impossible to move out to the edge, but it means that, and I, and know, and I, and I said, they're really starting to making some tentative moves in that direction. I'm looking forward to next week to, you know, seeing what, you know, hearing what we're gonna, what they're gonna be saying about that. But I do think, right. You know, you know, to answer your question directly, I'd say like right now, I'd say Mongo probably has a, you know, has a head start there. >>I'm losing track of time. I could go forever with you. Tony bear DB insight with tons of insights. Thanks so much for coming back with. >>It's only one insight insight, Dave. Good to see you again. All >>Right. Good to see you. Thank you. Okay. Keep it right there. Right back at the Java center, Mongo DB world 2022, you're watching the cube.