 From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Hello everyone, this is Dave Vellante. Welcome to this CUBE Conversation. We've got a really cool company that we're going to introduce you to, and Anthony Brooks Williams is here. He's the CEO of that company, HVR. Anthony, good to see you. Thanks for coming on. I'm good to see you again, I appreciate it. Yeah, cheers. So tell us a little bit about HVR. Give us the background of the company. We'll get into a little bit of the history. Yeah, sure. So at HVR, we are changing the way companies route and access their data. And as we know, data really is the lifeblood of organizations today. And if that stops moving or stops circulating, well, there's a problem. And people want to make decisions on the freshest data. And so what we do is we move critical business data around these organizations. The most predominant place today is to the cloud into a platform such as Snowflake where we've seen massive traction. Yeah, boy, have we ever. I mean, of course, last week we saw the Snowflake IPO. The industry is a buzz with that. So tell us a little bit more about the history of the company. What's the background of you guys? Where'd y'all come from? Sure, the company originated out of the Netherlands, out of Amsterdam, founded in 2012, helping solve the issue that customers had of moving data efficiently at scale across a wide-area network. And obviously, the cloud is one of those endpoints and did that for a company such as the Dutch Postal Service personnel, where today we now move the data to Asia and to AWS. But it was really around how you can efficiently move data at scale across these networks. And I have a bit of a background in this, dating back from early 2000s, when I found out a company that did auditing recovery of SQL Server databases. And we did that through reading the logs. And so, they sold that company to Golden Gate and had that sort of foundation there in those early days. So we've been getting HVR human moving data efficiently as we can across these organizations with the key aim of allowing customers to make decisions on the freshest data, which today is really table stakes. Yeah, so, okay. So we should think about you as, I want to invoke Einstein here. Move as much data as you need to, but no more, right? Because it's hard to move data. So you're a high-speed data mover at efficiency at scale. Is that how we should think about you? Absolutely. I mean, at our core, we are CDC, trying to capture moving incremental workloads of data, moving the updates across the network. You mean combined with the distributed architecture that's highly flexible and extensible. These days, just at one point, customers want to make decisions on as much data as they can get. We have companies that we're doing this for a large, parallel company that's taking some of their, not only their core sales data, but some of that IoT data that they get and sort of blending that together and giving the ability to have a full view of the organization so they can make better decisions. So it's moving as much data as they can, but also you need to do that in a very efficient way. Yeah, I mean, you mentioned Snowflake. So what I'd like to do is take my old data warehouse and whatever, let it do what it does, reporting and compliance, stuff like that, but then bring as much data as I need into my Snowflake or whatever modern cloud database I'm using and then apply whatever, machine intelligence and really analyze it. So really that is kind of the problem that you're solving is getting all that data to a place where it actually can be acted on and turned it to insights, is that right? Absolutely, I mean, part of what we need to do is there's a whole story around multi-cloud and that's obviously where Snowflake fit in as well, but from our point of view it's supporting over 30 different platforms. I mean, data is generated, data is created in a number of different source systems and so our ability to support each of those in this very efficient way using these techniques such as CDC is going to capture that data at source and then moving it together into some consolidated platform where they can do the type of analysis they need to do on that and obviously the cloud is the predominant target system of choice with something like a Snowflake there in either of these clouds. I mean, we support a number of different technologies in there, but yeah, it's about getting all that data together so they can make decisions on all areas of the business. So I'd love to get into the secret sauce a little bit. I mean, we've heard luminaries like Andy Jassy stand up last year at Reinvent. He talked about nitro and the big pipes and how hard it is to move data at scale. So what's the secret sauce that you guys have that allow you to be so effective at this? Absolutely. I mean, it starts with how you go acquire that data and you want to do that in the least obtrusive way to the database. So we'll actually go in and we read the transaction logs of each of these databases. They all generate logs and we go read those log systems of these different source systems and then put it through our, we have some secret sauce in how we move the data and how we compress that data as well. So I mean, if you want to move data across a wide area network, you mean the technique that a few companies use such as ourselves is change how to capture. And you're moving incremental updates, incremental workloads, you know, the change data across that network. But then combine that with the ability that we have around some of the compression techniques that we use and then just into a very distributed architecture that was one of the things that made me join A2 of my previous experiences and seeing that how that really fits in today's world of real time and cloud. I mean, those are all table stakes things. Okay, so it's that change data capture. Now, of course, you've got to initially seed that target. So you do that, if I understand it, you use data reduction techniques so that you're minimizing the amount of data and then what? You sort of, do you use asynchronous methodologies, dial it down, dial it up, you know, in off hours? How does that work? Absolutely, exactly what you've said that you mean. So we'll go in and we'll go, initially there's an initial instantiation or an initial concept where you're taking a copy of all of that data that sits in that source system and replicating that over to that target system. You turn on that CDC mechanism which is then moving that change data at the same time you're compressing it, you're encrypting it, you're making sure it's highly secure and loading that in the most efficient way into that target systems. And so we either do a lot of that or we've also worked with, if there's a ETL vendor involved that's doing some level of transformations and they take over the transformation capabilities of loading, we obviously do a fair amount of that ourselves as well, but depending what is the architecture that's in there for the customer as well. The key thing is that what we also have is we have this compare and repair ability that's built into the product. So we will move data across and we make sure that that data that gets moved from A to B is absolutely accurate. You make people want to know that their data can move fast. They want it to be efficient, but those want it to be secure. They want to know that they have a piece of mind to make decisions on accurate data. And that's some stuff that we have built into the products as well, supported across all of the different platforms as well. So something else that just sets us apart in that as well. So I want to understand the business case, if you will. I mean, is it as simple as, hey, we can move way more data faster. We can do it at a lower cost. So what's the business case for you guys and the business impact? Absolutely. So I mean, the key thing is the business case is, moving that data as efficiently as we can across there so they can make these decisions. So biggest online retail in the US uses us on their biggest busiest system. They have some other standard vendors that in there, but they use us because of the scalability that we can achieve there of, making decisions on that financial data and all the transactions that happen between the main e-commerce side and all the third-party vendors. That's us moving that data across there as efficiently as they can. And for us, we look at it as a, I mean, they pretty much, it's a subscription-based and it's all connection-based type pricing as well. Okay, I want to ask you about pricing. Yeah. I mean, pricing transparency is a big topic in the industry today. So how do you price? Let's start there. Yeah, we charge a simple per connection price. So what are the number of source systems? A connection is a source system or a target system. And we charge them very simply, but we try and keep it as simple as possible and charge them on the connection. So they will buy a bucket of five connections. They have three source systems, two target systems and it's pretty much as simple as that. You mentioned security before. So you're encrypting the data. So data in motions, encrypted. What else do we need to know about security? Yeah, you mean that we have this concept in how we handle and we have this wallet concept and how we integrate with the standard security systems that those customers have already in those architectures. So it's something that we constantly doing. I mean, there's a data encryption at rest. And initially, the whole aim is to make sure that the customer feels safe, that this data that is moving is highly secure. Let's talk a little bit about cloud. You know, maybe the architecture. Are you running in the cloud? Are you running on-prem, both across clouds? Well, how does that work? Yeah, yeah, all of the above. So I mean, what we see today is the majority of the data is still generated on-prem and then the majority of the talk systems we see are in the cloud. And this is not a one-time thing. This is continuous. I mean, they've moved the analytical workload into the cloud. I mean, they have these large events a few times a year and they want their ability to scale up and scale down. So we typically see, I mean, right now, you know, analytics, you know, data warehouses, that type of workload is sitting in the cloud because of the elasticity and the scalability and the reasons that cloud was brought on. So absolutely, you know, we can support the cloud to cloud, we can support on-prem to cloud. I think, I mean, a lot of companies are adopting this hybrid strategy that we've seen certainly for the foreseeable next, you know, five years. But yeah, absolutely, the source and target systems can sit on-prem or in the cloud. And where's the point of control? Is it wherever I want it to be? Is it in one of the clouds on-prem? Yeah, absolutely, you can put that point of control where you want it to be. You know, we have a concept of agents. These agents sit on the source and target systems and they may have the, so the HPR brain, the hub that is controlling what is happening, this data movement, that can be sitting with the source system separately or on target system. So it's highly extensible and flexible architecture there as well. So if something goes wrong, it's the HPR brain that helps me recover, right? And make sure that I don't have all kinds of data corruption. Maybe you could explain that a little bit. What happens when something goes wrong? Yeah, absolutely. I mean, we have things that are built into the product that help us highlight what has gone wrong and how we can correct those. And then there's alerts that get sent back, you know, to us, to the end customer. And there's been a whole bunch of training and stuff that's taken place for them, what actions they can take. But there's a lot of it is controlled, you know, through the HPR core system that happens that. And we are working, you know, next step. So as we move more into, as a service, into more of an autonomous data integration by ourselves, whichever a bunch of exciting things coming up, that just takes that all to the next levels. Right. Well, you guys, golden gate heritage, you sold that to Oracle. They're pretty hardcore about things like recovery. Anthony, how do you think about the market, the total available market, you know, kind of, can you take us through your opportunity broadly? Yeah, absolutely. You mean, there's the core opportunity, the space that we play in is where customers want to move data, they want to do data integration, they want to move data from A to B. There's those that are then branching out more to moving a lot of their business workloads to the cloud on a continuous basis. And then where we're seeing, you know, a lot of traction around is particularly data that resides in these critical business systems, such as SAP. That is something you're asking early about, what are some core things in our product? We have the ability to unpack, to unlock that data that sits in some of these SAP environments. So we can go and then decode this data that sits in these cluster and pool tables and combine that with our CDC techniques and move that data across the network. And so particularly, you know, sort of bringing it back a little bit, what we're seeing today, people are adopting the cloud, the massive adoption of Snowflake. I mean, as we see their growth, a lot of that is driven through consumption, why it's these big, large enterprises that are now really to consume more, we've seen that tailwind from our perspective as well. It's just taking these workloads, such as SAP, and moving that into something like these cloud platforms, such as the Snowflake. And so that's where we see the immediate opportunity for us and then branching out from there further. But I mean, that is the core immediate, you know, area of focus right now. Okay, so we've talked about Snowflake a couple of times in other platforms and not the only one, but they're the hot one right now. But when you think about what organizations are doing, they're trying to really streamline their data pipeline to get, to turn raw data into insights. So, you know, you're seeing, you know, that emerge in organizations, that data pipeline, we've been talking about it for quite some time. I mean, Snowflake obviously is one piece of that. You're, where's your value in that pipeline? Is it all about getting the data into that stream? Yeah, you just mentioned something there that, I mean, we have an initiative internally that's called raw data to ready data. And that's about capturing this data, moving that across. And that's where we're building value on that data as well, particularly around some of our SAP type initiatives and solutions related to that, that we're bringing out as well. So one, it's absolutely going and acquiring that data. It's then moving it as efficiently as we can at scale, which a lot of people talk about, we truly operate at scale. The biggest companies in the world use us to do that across there and giving them that ability to make decisions on the freshest data. They're in lies, the value of them being able to make decisions on data. That is a few seconds, few minutes old versus some other technologies that they may be using that take hours, days of, you mean, that is it. Keeping, you know, large companies that we work with today. You mean, you know, keeping toilet paper on shelves. You mean, one thing that happened after COVID. You mean, one of our big customers was making them their performer process and making the shelves are full. Another healthcare provider being able to do analysis on what was happening on supplies from the hospital and the other providers during this COVID crisis. So that's where it's a lot of that value, helping them, you know, reinvent their businesses, drive down that digital transformation strategy is the key areas there, you know, no data. They can't make those type of decisions. Yeah, so I mean, your vision really, I mean, you're betting on data. I always say, don't bet against the data. But really that's kind of the premise here is the data is going to continue to grow. And data, I often say data is plentiful, insights aren't. And you know, we use the bromide you said before. So really maybe you summarize the vision for us and where you want to take this thing. Yeah, absolutely. So we're going to continue building on what we have, making it easier to use. Certainly as we move, as more customers move into the cloud. And then from there, I mean, we have some strategic initiatives of looking at some acquisitions as well, just to build on around offering in some of the other core areas, but ultimately it's getting closer to the business user. You know, in today's world, you know, there is many IT tech savvy people sitting in the business side of organization as they are in IT, if not more. And so as we go down that flow, you know, with our product, it's getting closer to those end users because they are at the forefront of wanting this data. As we said, the data is the lifeblood of organization. And so given the ability to drive the actual power that they need to from the data is a core part of that vision. So I mean, we have some strategic initiatives around some acquisitions as well, but also continue to build on the product. I mean, there's, as I say, you mean, sources and targets come and go. There's new ones that are created each week and new adoptions. And so we've got to support those. That's our table stakes. And then continue to make it easier to use, you know, scale even quicker, more autonomous, those type of things. And you're working with a lot of big companies. The company's well-funded. If CrunchBase is up to date, over $50 million in funding, give us the update there. Yeah, absolutely. I mean, a company's well-funded. You know, we're on a good footing. Obviously there's a lot of, it's a very hot space to be in. You know, it's with COVID this year, like everybody, we sat down and looked in sort of for a bit and said, okay, well, let's have a look how this whole thing's going to shake out and get plan A, B and C in action. And we sort of ended up with plan A plus. You know, we've done an annual budget for the year. We had our best quarter ever in Q2. You know, 193% year-over-year growth. And it's just, I mean, the momentum is just there. I think large. I mean, obviously it sounds cliche a lot of people say it around digital transformation. Like COVID, absolutely. You know, we've been building this engine for a few years now, and it's really clicked into gear. And I think projects due to COVID and things that would have taken nine, 12 months to happen, that sort of taking a month or two now, it's been getting driven down from the top. So all of that's come together for us very fortunately. You know, the timing has been ideal. And then tie in something like a snowflake traction. As you said, we support many other platforms, but all of that together, it's just, it's set up really nicely for us fortunately. Yeah, that's amazing. I mean, with all the turmoil that's going on in the world right now, and all the pain in many businesses, it's, I can't, I tell you, I interview people all day every day and the technology business is really humming. So that's awesome to hear that you guys, I mean, especially if you're in the right place and data is the place to be. Anthony, thanks so much for coming on theCUBE and summarizing your thoughts and give us the update on HVR, really interesting. Absolutely, I appreciate the time and opportunity. All right. And thank you for watching everybody. This is Dave Vellante for theCUBE and we'll see you next time.