 Welcome back to the Javits in the Big Apple, New York City. This is theCUBE's coverage of MongoDB World 2022. We're here for a full day of coverage. We're talking to customers, partners, executives, and analysts as well. Peter Ulander is here. He's the Chief Marketing Officer of MongoDB, and he's joined by Radhika Krishnan, who's the Chief Product Officer at Hitachi Ventara. Folks, welcome back to theCUBE. Great to see you both again. Thank you for having us. Good to see you. It's good to be back again. Peter, first time since 2019, we've been doing a lot of these conferences, and many of them, it's the first time people have been out in a physical event in three years. Amazing. I mean, after three years to come back here in our hometown of New York and get together with a few thousand of our favorite customers, partners, analysts and such, to have real good discussions around where we're taking the world with regards to our developer data platform. It's been great. Any big part of that story, of course, is ecosystem and partnerships and Radhika. I remember I was at an event when Hitachi announced its strategy and its name change, and really tried to understand why and what's behind that. And of course, Hitachi's a company that looks out over the long term. And of course, it has to perform tactically, but it thinks about the future. So give us the update on what's new at Hitachi Ventara, especially as it relates to data. Sure thing, Dave. You know, there's many, many folks might be aware. There's a very strong heritage that Hitachi has had in the data space, right? By virtue of our products and our presence in the data storage market, which dates back to many decades, right? And then on the industrial side, the parent company, Hitachi, has been heavily focused on the OT sector. And as you know, there is a pretty significant digital transformation underway in the OT arena, which is all being led by data. So if you look at our mission statement for instance, it's actually engineering the data driven because we do believe that data is the fundamental platform that's going to drive that digital transformation irrespective of what industry you're in. So one of the themes that you guys both talk about is modernization, of course. I mean, you can take a cloud. I remember Alan Nancy was at the time, he was a CIO at Philips. He said, look, you could take a cloud workload, you can, or on-prem workload, stick it into the cloud and lift it and shift it. And in your case, you could just put it on, run it on an RDBMS, but you're not going to affect the operational model. It's just your mess for less, man. If you do that, it's your mess for less. And so he goes, you'll get a couple of zeros out of that. But if you want to have, in his case, billion dollar impact to the business, you have to modernize. So what does modernize mean to each of you? Maybe Peter, you can start. Yeah, no, I'm happy to start. I think it comes down to what's going on in the industry. I mean, we are truly moving from a world of data centers to centers of data. And the centers of data are happening further and further out along the network all the way down to the edges. And if you look at the transformation of infrastructure or software that has enabled us to get there, we've seen apps go from monoliths to microservices. We've seen compute go from physical to serverless. We've seen network can go from old wireline copper to high-powered 5G networks. They've all transformed. What's the one layer that hasn't completely transformed yet? Data. So if we do see this world where things are getting further and further out, you've got to rethink your data architecture and how you basically support this move to modernization. And we feel that MongoDB with our partners, especially with Etachi, we're best suited to really kind of help with this transition for our customers as they move from data centers to centers of data. So architecture and the failure, I will say this, and you tell me if you agree or not, a lot of the failures of the big data architectures of today are there's everything's in this monolithic database. You've got to go through a series of hyper-specialized professionals to get to the data. If you're a business individual, you're so frustrated because the market's changing faster than you can get answers. So you guys I know use this concept of data fabric. People talk about data mesh. So how do you think, Radhika, about modernization and the future of data, which by its very nature is distributed? Yeah, so everybody talks about the hybrid cloud, right? And so the reality is every one of our customers is having to deal with data that's straddled across, on-prem as well as the public cloud and many other places as well. And so it becomes incredibly important that you have a fairly seamless framework that's relatively low friction that allows you to go from the capture of the data, which could be happening at the edge, it could be happening at the core, any number of places, all the way to publish, right? Which is ultimately what you want to do with data because data exists to deliver insights, right? And therefore you dramatically want to minimize the friction in the process. And that is exactly what we're attempting to do with our data fabric construct, right? You know, we're essentially saying customers don't have to worry about, like you mentioned, they may have federated data structures, architectures, data lakes, fitting in multiple locations. How do you ensure that you're not having to develop custom code in order to drive the pipelines in order to drive the data movement from one location to the other and so forth? And so essentially what we're providing is a mechanism whereby they can be confident about the quality of the data at the end of the day, right? And this is so paramount. Every customer that I talk to is most worried about ensuring that they have data that is trustworthy. So this is a really important point because I've always felt like from a data quality standpoint, you know, you got the data engineers who might not have any business context trying to figure out the quality problem. If you can put the data responsibility in the hands of the business owner who he or she has context, that maybe starts to solve this problem. There's some buts though. So infrastructure becomes an operational detail. Let's hide that, don't worry about it. Figure it out, okay? So the business can run. But you need self-service infrastructure and you have to figure out how to have federated governance so that the right people can have access. So how do you guys think about that problem in the future? Because it's almost like this vision creates those two challenges. Oh, by the way, you got to get your organization behind it, right? Because there's an organizational construct as well. But those are, to me, wonderful opportunities but they create technology challenges. So how are you guys thinking about that and how are you working on it? Yeah, that's exactly right, Dave. As we talk to data practitioners, the recurring theme that we keep hearing is there is just a lot of use cases that require you to have deep understanding of data and requires you to have that background and data sciences and so on, such as data governance and various other use cases. But ultimately, the reason that data exists is to be able to drive those insights for the end customer, for the domain expert, for the end user, and therefore, it becomes incredibly important that we be able to bridge that cast on that exists today between the data universe and the end customer. And that is what we essentially are focused on by virtue of leaning into capabilities like publishing, right? Like self ad hoc reporting and things that allow citizen data scientists to be able to take advantage of the plethora of data that exists. Peter, I'm interested in this notion of IT and OT, of course, Atachi is a partner established in both. Talk about Mongo's position and thinking because you've got on-prem customers, you're running now across all clouds, you know, I call it super cloud, connecting all these things, but part of that is the edge. Is Mongo running there? Can Mongo run there? Sort of a lightweight version? How do you see that evolve, Bob? Give us some details here. So I think first and foremost, we were born on-prem, obviously, with the origins of MongoDB. About five, a little over five years ago, we introduced Atlas and today we run across a hundred different availability zones around the globe. So we're pretty well covered there. The third bit that I think people miss is we also picked up a product called Realm. Realm is an embedded database for mobile devices. So if you think about car companies, Toyota, for example, building connected cars, they'll have Realm in the car for the telemetry, connects back into an Atlas system for the bigger operational side of things. So there's this seamless kind of, or consistency that runs between data center to cloud to edge to device that MongoDB plays across all the way through. And then taking that to the next level, we talked about this before we sat down, we're also building in the security elements of that because obviously you not only have that data and rest and data in motion, but what happens when you have that data in use and announced I think today, we purchased a little company, Arochi, experts in encryption, some of the smartest security minds on the planet. And today we introduced Quariable Encryption which basically enables developers without any security background to be able to build searchable capabilities into their applications to access data and do it in a way where the security rules and the privacy all remain constant regardless of whether that developer or the end user actually knows how that works. This is a great example of people talking about shift left, designing security in for the developer right from the start, not as a bolt-on. It's a great example. And I'm actually going to ground that with an example, a real life customer example if that's okay Dave. So we actually have a utility company in North Carolina that's responsible for energy and water. And so you can imagine, I mean you alluded to the IoT use case, the industrial use case and this particular customer has to contend with millions of sensors that are constantly streaming data back, right? And now think about the challenge that they were encountering. They had all this data streaming in and in large quantities and they were actually resident on numerous databases, right? And so they had this very real challenge of getting to that quality data, data quality that I talked about earlier, as well, they had this challenge of being able to consolidate all of it and make sense of it. And so that's where our partnership with MongoDB really paid off, where we were able to leverage Pentaho to integrate all of the data, have that be resident on MongoDB. And now they're leveraging some of the data capabilities, the data fabric capabilities that we bring to the table to actually deliver meaningful insights to their customers. Now their customers are actually able to save on their electricity and water bills. So great success story right there. So I love the business impact there, but also you mentioned Pentaho. I remember that acquisition was transformative for Hitachi because it was the beginning of sort of your new vector which became Hitachi Ventaro. What is Lumata? That's, I presume the evolution of Pentaho? Could you brought in organic that added capabilities on top of that, bringing in your knowledge of IoT and OT? Explain what Lumata is. Yeah, that's a great question, Dave. And I'll say this, I mentioned this early on. We fundamentally believe that data is the backbone for all digital transformation. And so to that end, Hitachi has actually been making a series of acquisitions and as well as investing organically to build up these data capabilities. And so Pentaho, give us some of that front end capability in terms of integrations and so forth. And the Lumata platform, the umbrella brand name is really connoting everything that we do in the data space that allow customers to go through that, to derive those meaningful insights. Lumata literally stands for illuminating data. And so that's exactly what we do irrespective of what vertical, what use case we're talking about. As you know very well, Hitachi is very prominent and just about every vertical we're in like 90% of the Fortune 500 customers across banking and financial, retail, telecom. And as you know very well, very, very strong in the industrial space as well. You know, it's interesting, Peter and Radhika were both talking about this sort of edge model. And so if I understand it correctly and maybe you could bring in sort of the IoT requirements as well, you know, you think about AI. Most of the AI that's done today is modeling in the cloud but in the future, and we're seeing this, it's real time inferencing at the edge and it's massive amounts of data. But you're probably not, you're going to persist some I'm hearing, you're probably not going to persist all of it. Some of it's going to be throw away and then you're going to send some back to the cloud. I think of EVs or, you know, a deer runs in front of the vehicle and they capture that. Okay, send that back. And the amounts of data is just massive. Is that the right way to think about this new model? Is that going to require new architectures and a hearing that Mongo fits in? Yeah, so this is a little bit what we talked about earlier where, you know, historically there've been three silos of data, whether it's a classic system of record, system of engagement or system of intelligence and they've each operated independently but as applications are pushing in further and further to the edge and real time becomes more and more important, you need to be able to take all three types of workloads or models, data models and actually incorporate it into a single platform. That's the vision we have behind our developer data platform and it enables us to handle those transactional, operational and analytical workloads in real time, right? One of the things that we've announced here this week was our columnar indexing, which enables some of that, you know, step into the analytics so that we can actually do in-app analytics for those things that are not going back into the data warehouse or not going back into the cloud, real time happening with the application itself. As you add, this is interesting, basically Mongo is becoming this all in one database, as you add those capabilities, are you able to preserve, it sounds like you're still focused on simplicity, developer productivity, are there trade-offs, as you add, does it detract from those things or are you able to architecturally preserve those? And I think it comes down to how we're thinking through the use case and what's going to be important for the developers. So if you look at the model today, the legacy model was, let's put it all in one big monolith. We recognize that that doesn't work for everyone, but the counter to that was this explosion of niche databases, right? You go to certain cloud providers, you get to choose between 15 different databases for whatever workload you want. Time series here, graph here, in memory here, it becomes a big mess that is pushed back on the company to glue back together and figure out how to work within those systems. We're focused on really kind of embracing the document model. We obviously believe that's a great general purpose model for all types of workloads. And then focusing in on not taking a full search platform that's doing everything from log management all the way through in-app. We're optimizing for in-app experiences. We're optimizing analytics for in-app experiences. We're optimizing all of the different things we're doing for what the developer is trying to go accomplish. That helps us maintain consistency on the architectural design. It helps us maintain consistency in the model by which we're engaging with our customers. And I think it helps us innovate as quickly as we've been able to innovate. Great, thank you. Radhika, we'll give you the last word. We're seeing this convergence of function in the database, data models. But at the same time, we're seeing the distribution of data. We're not, you're clearly not fighting that. You're embracing that. What does the future look like from Hitachi Ventara's standpoint over the next half decade or even further out? So, you know, we're trying to lean into what customers are trying to solve for Dave. And so that fundamentally comes down to use cases and the approaches just make look dramatically different with every customer and every use case, right? And that's perfectly fine. We're leaning into those models, whether that is data refining on the edge or the core or the cloud. We're leaning into it. And our intent really is to ensure that we're providing that frictionless experience from end to end, right? And I'll give a couple of examples. We had this very large bank, one of the top 10 banks here in the US, that essentially had multiple data catalogs that they were using to essentially sort through their metadata and make sense of all of this data that was coming into their systems. And we were able to essentially, dramatically simplify it, cut down on the amount of time that it takes to deliver insights to them, right? And it was like, you know, the metric shade was 600% improvement. And so this is the kind of thing that we're maniacally focused on is how do we deliver that quantifiable end customer improvement, right? Whether it's in terms of, you know, shortening the amount to derive the insights, whether it's in terms of the number of data practitioners that they have to throw at a problem, the level of manual intervention that is required. So, you know, we're automating everything. We're trying to build in a lot of security, as Peter talked about, that is a common goal for both sides, you know, where we're trying to address it through a combination of security solutions at varying ends of the spectrum. And then finally as well, delivering that resiliency and scale that is required, because again, the one thing we know for sure that we can take for granted is data is exploding, right? And so, you know, you need that scale, you need that resiliency, you need for customers to feel like, you know, there is high quality, it's not dirty, it's not dark, and it's something that they can rely upon. Yeah, if it's not trusted, they're not going to use it. The interesting thing about the partnership, especially with Hitachi is you're in so many different examples and use cases. You've got IT, you've got OT, you've got industrial, and so many different examples. And if Mongo can truly fit into all those, it's just the rocket ship's going to continue. Peter, Radhika, thank you so much for coming back to theCUBE. It's great to see you both. All right, keep it right there. This is Dave Vellante from the Javits Center in New York City at MongoDB World 2022. We'll be right back.