>> From the Silicon Angle Media office, in Boston Massachusetts, it's the Cube. Now, here's your host, Dave Vellante. >> Hi everybody, welcome to this Cube Conversation here in our studios, outside of Boston. My name is Dave Vellante. I'm here with Matt Carroll, who's the CEO of Immuta. Matt, good to see ya. >> Good, nice to have me on. >> So we're going to talk about governance, how to automate governance, data privacy, but let me start with Immuta. What is Immuta, why did you guys start this company? >> Yeah, Immuta is an automated data governance platform. We started this company back in 2014 because we saw a gap in the market to be able to control data. What's happened in the market as changes is that every enterprise wants to leverage their data. Data's the new app. But, governments want to regulate it and consumers want to protect it. These were at odds with one another, so we saw a need of creating a platform that could meet the needs of everyone. To democratize access to data and in the enterprise, but at the same time, provide the necessary controls on the data to enforce any regulation, and ensure that there was transparency as to who is using it and why. >> So let's unpack that a little bit. Just try to dig into the problem here. So we all know about the data explosion, of course, and I often say data used to be a liability, now it's turned into an asset. People used to say get rid of the data, now everybody wants to mine it, and they want to take advantage of it, but that causes privacy concerns for individuals. We've seen this with Facebook and many others. Regulations now come into play, GDPR, different states applying different regulations, so you have all these competing forces. The business guys just want to go and get out to the market, but then the lawyers and the compliance officers and others. So are you attacking that problem? Maybe you could describe that problem a little further and talk about how you guys... >> Yeah, absolutely. As you described, there's over 150 privacy regulations being proposed over 25 states, just in 2019 alone. GDPR has created or opened the flood gates if you will, for people to start thinking about how do we want to insert our values into data? How should people use it? And so, the challenge now is, you're right, your most sensitive data in an enterprise is most likely going to give you the most insight into driving your business forward, creating new revenue channels, and be able to optimize your operational expenses. But the challenge is that consumers have awoken to, we're not exactly sure we're okay with that, right? We signed a YULU with you to just use our data for marketing, but now you're using it for other revenue channels? Why? And so, where Immuta is trying to play in there is how do we give the line of business the ability to access that instantaneously? But also give the CISO, the Chief Information Security Officer, and the governance seems the ability to take control back. So it's a delicate balance between speed and safety. And I think what's really happening in the market is we used to think about security from building firewalls, we invested in physical security controls around managing external adversaries from stealing our data. But now it's not necessarily someone trying to steal it, it's just potentially misusing it by accident in the enterprise. And the CISO is having to step in and provide that level of control. And it's also the collision of the cloud and these privacy regulations. Cause now, we have data everywhere, it's not just in our firewalls. And that's the big challenge. That's the opportunity at hand, democratization of data in the enterprise. The problem is data's not all in the enterprise. Data's in the cloud, data's in SaaS, data's in the infrastructure. >> It's distributed by it's very nature. All right, so there's a lot of things I want to follow up on. So first, there's GDPR. When GDPR came out of course, it was May of 2018 I think. It went into effect. It actually came out in 2017, but the penalties didn't take effect till '18. And I thought, okay, maybe this can be a framework for governments around the world and states. It sounds like yeah sort of, but not really. Maybe there's elements of GDPR that people are adopting, but then it sounds like they're putting in their own twists, which is going to be a nightmare for companies. So, are you not seeing a sort of, GDPR becoming this global standard? It sounds like, no. >> I don't think it's going to be necessarily global standard, but I do think the spirit of the GDPR, and at the core of it is, why are you using my data? What was the purpose? So traditionally, when we think about using data, we think about all right, who's the user, and what authorizations do they have, right? But now, there's a third question. Sure, you're authorized to see this data, depending on your role or organization right? But why are you using it? Are you using it for certain business use? Are you using it for personal use? Why are you using this? That's the spirit of GDPR that everyone is adopting across the board. And then of course, each state, or each federal organization is thinking about their unique lens on it, right? And so you're right. This is going to be incredibly complex. And the amount of policies being enforced at query time. I'm in my favorite, let's just say I'm in Tableau or Looker right? I'm just some simple analyst, I'm a young kid, I'm 22, my first job right? And I'm running these queries, I don't know where the data is, right? I don't know what I'm combining. And what we found is on average in these large enterprises, any query at any moment in time, might have over 500 thousand policies that need to be enforced in real time. >> Wow. >> And it's only getting worse. We have to automate it. No human can handle all those edge cases. We have to automate. >> So, I want to get into how you guys actually do that. Before I do, there seems to be... There's a lot of confusion in the marketplace. Take the word data management, data protection. All the backup guys are using that term, the database guys use that term, GOC folks use that term, so there's a lot of confusion there. You have all these adjacent markets coming together. You've got the whole governance risk and compliance space, you've got cyber security, there's privacy concerns, which is kind of two sides of the same coin. How do you see these adjacencies coming together? It seems like you sit in the middle of all that. >> Yeah, welcome to why my marketing budget is getting bigger and bigger. The challenge we're facing now is I think, who owns the problem right? The Chief Data Officer is taking on a much larger role in these organizations, the CISO is taking a much more larger role in reporting up to the board. You have the line of business who now is almost self-sustaining, they don't have to depend on IT as much any longer because of the cloud and because of the new compute layers to make it easier. So who owns it? At the end of the day, where we see it is we think there's a next generation of cyber tools that are coming out. We think that the CISO has to own this. And the reason is that the CISO's job is to protect the enterprise from cyber risk. And at the core of cyber risk is data. And they must own the data problem. The CDO must find the data, and explain what that data is, and make sure it's quality, but it is the CISO that must protect the enterprise from these threats. And so, I see us as part of this next wave of cyber tools that are coming out. There's other companies that are equally in our stratosphere, like BigID, we're seeing AWS with Macy doing sensitive data discovery, Google has their data loss prevention service. So the cloud players are starting to see, hey, we've got to identify sensitive data. There's other startups that are saying hey, we got to identify and catalog sensitive data. And for us, we're saying hey, we need to be able to consume all that cataloging, understand what's sensitive, and automatically apply policies to ensure that any regulation in that environment is met. >> I want to ask you about the cloud too. So much to talk to you about here, Matt. So, I also wanted to get your perspective on variances within industries. So you mentioned Chief Data Officers. The ascendancy of the Chief Data Officers started in financial services, healthcare, and government where we had highly regulation industries. And now it's sort of seeped into more commercial. But it terms of those regulated industries, take healthcare for example. There are specific nuances. Can you talk about what you're seeing in terms of industry variance. >> Yeah, it's a great point. Starting with like, healthcare. What does it mean to be HIPPA compliant anymore? There are different types of devices now where I can point it at your heartbeat from a distance away and I can have 99 percent accuracy of identifying you, right? It takes three data points in any data set to identify 87 percent of US citizens. If I have your age, sex, location, I can identify you. So, what does it mean anymore to be HIPPA compliant? So the challenge is how do we build guarantees of trust that we've de-identified these DESA's, cause we have to use it, right? No one's going to go into a hospital and say, "You know what, I don't want you to say my life. "Cause I want my data protected," right? No one's ever going to say that. So the challenges we face now across these regulated industries is the most sensitive data sets are critical for those businesses to operate. So there has to be a compromise. So, what we're trying to do in these organizations is help them leverage their data and build levels of proportionality, to access that right? So, the key isn't to stop people from using data. The key is to build the controls necessary to leverage a small bit of the data. Let's just say, we've made it indistinguishable. You can only ask Agriculture and Statistics the question. Well, you know what, we actually found some really interesting things there, we need to be a little bit more useful, it's this trade-off between privacy and utility. It's a pendulum that swings back and forth. As someone proves I need more of this, you can swing it, or just mask it. I need more of it? All right, we'll just redact some of the certain things. Nope, this is really important, it's going to save someone's life. Okay, completely unmasked, you have the raw data. But it's that control that's necessary in these environments, that's what's missing. You know, we came out of the US Intelligence community. We understood this better than anyone. Because highly regulated, very sensitive data, but we knew we needed the ability to rapidly control. Well is this just a hunch, or is this a 9-11 event? And you need the ability to switch like that. That's the difference and so, healthcare is going through a change of, we have all these new algorithms. Like Facebook the other day said, hey, we have machine learning algorithms that can look at MRI scans, and we're going to be better than anyone in the world at identifying these. Do you feel good about giving your data to Facebook? I don't know, but we can maybe provide guaranteed anonymization to them, to prove to the world they're going to do right. That's where we have to get to. >> Well, this is huge, especially for the consumer, cause you just gave several examples. Facebook's going to know a lot about me, a mobile device, a Fit Bit, and yet, if I want to get access to my own medical records, it's like Fort Knox to try to get, please, give this to my insurance company. You know, you got to go through all these forms. So, you've got those diverging objectives and so, as a consumer, I want to be able to trust that when I say yes you can use it, go, and I can get access to it, and other can get access to it. I want to understand exactly what it is that you guys do, what you sell. Is it software, is it SAS, and then let's get into how it works. So what is it? >> Yeah, so we're a software platform. We deploy into any infrastructure, but it is not multi-tenant so, we can deploy on any cloud, or on premises for any customer, and we do that with customers across the world. But if you think about at the core of what is Immuta, think of Immuta as a system of record for the CISO or the line of business where I can connect to any data, on any infrastructure, on any compute layer, and we connect into over 61 different storage platforms. We then have built a UI where lawyers... We actually have three lawyers as employees that act as product managers to help any lawyer of any stature take what's on paper, these regulations, these rules and policies, and they digitize it essentially, in active code. So they can build any policy they want on any data in the ecosystem, in the enterprise, and enforce it globally without having to write any code. And then because we're this plane where you can connect any tool to this data, and enforce any regulation because we're the man in the middle, we can audit who is using what data and why. In every action, in any change in policy. So, if you think about it, it's connect any tool to any data, control it, any regulation, and prove compliance in a court of law. >> So you can set the policy at the data set level? >> Correct. >> And so, how does one do that? Can you automate that on the creation of that data set? I mean you've got you know, dependencies. How does that all work? >> Yeah, what's a really interesting part of our secret sauce is that one, we could do that at the column level, we can do it at the row level, we can do it at the cell level. >> So very granular. >> Very, very granular. This is something again, we learned from the US Intelligence community, that we have to have very fine grained access to every little bit of the data. The reason is that, especially in the age of data, is people are going to combine many data sets together. The challenge isn't enforcing the policy on a static data set, the challenge is enforcing the policy across three data sets where you merge three pieces of data together, who have conflicting policies. What do you do then? That's the beauty of our system. We deal with that policy inheritance, we manage that lineage of the policy, and can tell you here's what the policy will be. >> In other words, you can manage to the highest common denominator as an example. >> Or we can automate it to the lowest common denominator, where you can work in projects together recognizing hey, we're going to bring someone into the project that's not going to have the level of access. Everyone else will automatically change it to the lowest common denominator. But then you share that work with another team and it'll automatically be brought to the highest common denominator. And we've built all these work flows in. That was what was missing and that's why I call it a system of record. It's really a symbiotic relationship between IT, the data owner, governance, the CISO, who are trying to protect the data, and the consumer, and all they want to do is access the data as fast as possible to make better, more informed decisions. >> So the other mega-trend you have is obviously, the super power of machine intelligence, or artificial intelligence, and then you've got edge devices and machine to machine communication, where it's just an explosion of IP addresses and data, and so, it sounds like you guys can attack that problem as well. >> Any of this data coming in on any system, the idea is that eventually it's going to land somewhere, right? And you got to protect it. We call that like rogue data, right? This is why I said earlier, when we talk about data, we have to start thinking about it as it's not in some building anymore. Data's everywhere. It's going to be on a cloud infrastructure, it's going to be on premises, and it's likely, in the future, going to be on many distributed data centers around the world cause business is global. And so, what's interesting to us is no matter where the data's sitting, we can protect it, we can connect to it, and we allow people to access it. And that's the key thing is not worrying about how to lock down your physical infrastructure, it's about logically separating it. And that's why what differentiates us from other people is one, we don't copy the data, right? That's the always the barrier for these types of platforms. We leave the data where it is. The second is we take all those regulations and we can actually, at query time, push it down to where that data is. So rather than bring it to us, we push the policy to the data. And what that does is that's what allows us, what differentiates us from everyone else is, it allows us to guarantee that protection, no matter where the data's living. >> So you're essentially virtualizing the data? >> Yeah, yeah. It's virtual views of data, but it's not all the data. What people have to realize is in the day of apps, we cared about storage. We put all the data into a database, we built some services on top of it and a UI, and it was controlled that way, right? You had all the nice business logic to control it. In the age of data, right? Data is the new app, right? We have all these automation tools, Data Robot, and H20, and Domino, and Tableau's building all these automation work flows. >> The robotic process automation. >> Yeah, RPA, UI Path, the Work Fusion, right? They're making it easier and easier for any user to connect to any data and then automate the process around it. They don't need an app to build a unique work flows, these new tools do that for them. The key is getting to the data. And the challenge with the supply chain of data is time to data is the most critical aspect of that. Cause, the time to insight is perishable. And so, what I always tell people, a little story, I came from the government, I worked in Baghdad, we had 42 minutes to know whether or not a bad guy in the environment, we could go after him. After that, that data was perishable, right? We didn't know where he was. It's the same thing in the real world. It's like imagine if Google told you, well, in 42 minutes it might be a good time to go 495. (laughter) It's not very useful, I need to know the information now. That's the key. What we see is policy enforcement and regulations are the key barrier of entry. So our ability to rapidly, with no latency, be able to connect anyone to that data and enforce those policies where the data lives, that's the critical nature. >> Okay, so you can apply the policies and you do it quickly, and so now you can help solve the problem. You mentioned a cloud before, or on prem. What is the strategy there with regard to various clouds and how do you approach multi-clouds? >> I think cloud is what used to be an infrastructure as a service game, is now becoming a compute game. I think large, regulated enterprises, government, healthcare, financial services, insurance, are all moving to cloud now in a different way. >> What do you mean by that? Cause people think infrastructure as service, they'll say oh that's compute storage and some networking. What do you mean by that? >> I think there's a whole new age of software that's being laid on top of the availability of compute and the availability of storage. That's companies like Databricks, companies like Snowflake, and what they're doing is dramatically changing how people interact with data. The availability zones, the different types of features, the ability to rip and replace legacy warehouses and main frames. It's changing the ability to not just access, but also the types of users that could even come on to leverage this data. And so these enterprises are now thinking through, "How do I move my entire infrastructure of data to them? "And what are these new capabilities "that I could get out of that?" Which, that is just happening now. A lot of people have been thinking, "Oh, this has been happening over the past five years," no, the compute game is now the new war. I used to think of like, Big Data, right? Big Data created, everyone started to understand, "Ah, if we've got our data assets together, "we can get value." Now they're thinking, "All right, let's move beyond that." The new cloud at our currents works is Snowflake and Databricks. What they're thinking about is, "How do I take all your meta-data "and allow anyone to connect any BI tool, "any data science tool, and provide highly performance, "and highly dependable compute services "to process petabytes of data?" It's pretty fantastic. >> And very cost efficient and being able to scale, compute independent of storage, from an architectural perspective. A lot of people claim they can do that, but it doesn't scale the same way. >> Yeah, when you're talking about... Cause that's the thing is you got to remember, these financial systems especially, they depend on these transactions. They cannot go down and they're processing petabytes of data. That's what the new war is over, is that data in the compute layer. >> And the opportunity for you is that data that can come from anywhere, it's not sitting in a God box, where you can enforce policies on that corpus. You don't know where it's coming from. >> We want to be invisible to that right? You're using Snowflake, it's just automatically enforced. You're using Databricks, it's automatically enforced. All these policies are enforced in flight. No one should even truly care about us. We just want to allow you to use the data the way you're used to using it. >> And you do this, this secret sauce you talked about is math, it's artificial intelligence? >> It's math. I wish I could say it was like super fancy, unsupervised neural nets or what not, it's 15 years of working in the most regulated, sticky environments. We learned about very simple novel ways of pushing it down. Great engineering's always simple. But what we've done is... At query time, what's really neat is we figured a way to take user attributes from identity management system and combine that with a purpose, and then what we do is we've built all these libraries to connect into all these dispert storage and compute systems, to push it in there. The nice thing about that is prior to this what people were doing, was making copies. They'd go to the data engineering team and they'd say hey, "I need to ETL this "and get a copy and it'll be anatomized." Think about that for a second. One, the load on your production systems, of all these copies, all the time, right? The second is CISO, the surface area. Now you've got all this data that in a snapshot in time, is legal and ethical, might change tomorrow. And so, now you've got an increase surface area of risk. Like that no-copy aspect. So the pushing it down and then the no-copy aspect really changed the game for enterprises. >> And you've got providence issues, like you say. You've got governance and compliance. >> And imagine trying, if someone said to you, imagine Congress said hey, "Any data source that you've processed "over the past five years, I want to know if "there was these three people in any of these data sources "and if there were, who touched that data "and why did they touch it?" >> Yeah and storage is cheap, but there's unintended consequences. People are, management isn't. >> We just don't have a unified way to look at all of the logs cross listed. >> So we started to talk about cloud and then I took you down a different path. But you offer your software on any cloud, is that right? >> Yeah, so right now, we are in production on Immuta's Marketplace. And that is a managed service, so you can go deploy in there, it'll go into your VPC, and we can manage the updates for you, we have no insight into your infrastructure, but we can push those updates, it'll automatically update, so you're getting our quarterly releases, we release every season. But yeah, we started with AWBS, and then we will grow out. We see cloud is just too ubiquitous. Currently, we still support though, Bigquery, Data Praq, we support Azure, Data Light Storage version two, as well as Azure Databricks. But you can get us through Immuta's Marketplace. We're also investing in ReInvent, we'll be out there in Vegas in a couple weeks. It's a big event for us just because obviously, the government has a very big stake in AWBS, but also commercial customers. It's been a massive endeavor to move. We've seen lots of infrastructure. Most of our deals now are on cloud infrastructure. >> Great, so tell us about the company. You've raised, I think in a Series B, about 28 million to date. Maybe you could give us the head count, and whatever you can share about momentum, maybe customer examples. >> Yeah, so we've raised 32 million to date. >> 32 million. >> From some great investors. The company's about 70 people now. So not too big, but not small anymore. Just this year, at this point, I haven't closed my fiscal year, so I don't want to give too much, but we've doubled our ARR and we've tripled our LOGO count this year alone and we've still got one more quarter here. We just started our fourth quarter. And some customer cases, the way I think about our business is I love healthcare, I love government, I love finance. To give you some examples is like, COGNO is a really great example. COGNO and what they're trying to solve is can they predict where a child is on the autism spectrum? And they're trying to use machine learning to be able to narrow these children down so that they can see patterns as to how a provider, a therapist is helping these families give these kids the skills to operate in the real world. And so it's like this symbiotic relationship utilizing software, surveys and video and what not, to help connect these kids that are in similar areas of the spectrum, to help say hey, this is a successful treatment, right? The problem with that is we need lots of training data. And this is children, one, two, this is healthcare, and so, how do you guarantee HIPPA compliance? How do you get through FDA trials, through third party, blind testing? And still continue to validate and retrain your models, while protecting the identity of these children? So we provide a platform where we can anonymize all the data for them, we can guarantee that there's blind studies, where the company doesn't have access to certain subsets of the data. We can also then connect providers to gain access to the HIPPA data as needed. We can automate the whole thing for them. And they're a startup too, there are 100 people. But imagine if you were a startup in this health-tech industry and you had to invest in the backend infrastructure to handle all of that. It's too expensive. What we're unlocking for them, I mean yes, it's great that they're HIPPA compliant and all that, that's what we want right? But the more important thing is like, we're providing a value add to innovate in areas utilizing machine learning, that regulations would've stymied, right? We're allowing startups in that ecosystem to really push us forward and help those families. >> Cause HIPPA compliance is table stay compulsory. But now you're talking about enabling new business models. >> Yeah, yeah exactly. >> How did you get into all this? You're CEO, you're business savvy, but it sounds like you're pretty technical as well. What's your background? >> Yeah I mean, so I worked in the intelligence community before this. And most of my focus was on how do we take data and be able to leverage it, either for counter-terrorism missions, to different non-kinetic operations. And so, where I kind of grew up in is in this age of, think about billions of dollars in Baghdad. Where I learned is that through the computing infrastructure there, everything changed. 2006 Baghdad created this boom of technology. We had drones, right? We had all these devices on our trucks that were collecting information in real time and telling us things. And then we started building computing infrastructure and it burst Hadoop. So, I kind of grew up in this era of Big Data. We were collecting it all, we had no idea what to do with it. We had nowhere to process it. And so, I kind of saw like, there's a problem here. If we can find the unique little, you know, nuggets of information out of that, we can make some really smart decisions and save lives. So once I left that community, I kind of dedicated myself to that. The birth of this company again, was spun out of the US Intelligence community and it was really a simple problem. It was, they had a bunch of data scientists that couldn't access data fast enough. So they couldn't solve problems at the speed they needed to. It took four to six months to get to data, the mission said they needed it in less than 72 hours. So it was orthogonal to one another, and so it was very clear we had to solve that problem fast. So that weird world of very secure, really sensitive, but also the success that we saw of using data. It was so obvious that we need to democratize access to data, but we need to do it securely and we need to be able to prove it. We work with more lawyers in the intelligence community than you could ever imagine, so the goal was always, how do we make a lawyer happy? If you figure that problem out, you have some success and I think we've done it. >> Well that's awesome in applying that example to the commercial business world. Scott McNeely's famous for saying there is no privacy in the internet, get over it. Well guess what, people aren't going to get over it. It's the individuals that are much more concerned with it after the whole Facebook and fake news debacle. And as well, organizations putting data in the cloud. They need to govern their data, they need that privacy. So Matt, thanks very much for sharing with us your perspectives on the market, and the best of luck with Immuta. >> Thanks so much, I appreciate it. Thanks for having me out. >> All right, you're welcome. All right and thank you everybody for watching this Cube Conversation. This is Dave Vellante, we'll see ya next time. (digital music)

>>Hello, everyone. Welcome to the cubes coverage here. New York city for AWS Amazon web services summit 2022. I'm John furrier, host of the cube with Dave ante. My co-host. We are breaking it down, getting an update on the ecosystem. As the GDP drops, inflations up gas prices up the enterprise continues to grow. We're seeing exceptional growth. We're here on the ground floor. Live at the Summit's packed house, 10,000 people. Eric Bradley's here. Chief STR at ETR, one of the premier enterprise research firms out there, partners with the cube and powers are breaking analysis that Dave does check that out as the hottest podcast in enterprise. Eric. Great to have you on the cube. Thanks for coming on. >>Thank you so much, John. I really appreciate the collaboration always. >>Yeah. Great stuff. Your data's amazing ETR folks watching check out ETR. They have a unique formula, very accurate. We love it. It's been moving the market. Congratulations. Let's talk about the market right now. This market is booming. Enterprise is the hottest thing, consumers kind of in the toilet. Okay. I said that all right, back out devices and, and, and consumer enterprise is still growing. And by the way, this first downturn, the history of the world where hyperscalers are on full pumping on all cylinders, which means they're still powering the revolution. >>Yeah, it's true. The hyperscalers were basically at this two sun system when Microsoft and an AWS first came around and everything was orbiting around it. And we're starting to see that sun cool off a little bit, but we're talking about a gradient here, right? When we say cool off, we're not talking to shutdown, it's still burning hot. That's for sure. And I can get it to some of the macro data in a minute, if that's all right. Or do you want me to go right? No, go go. Right. Yeah. So right now we just closed our most recent survey and that's macro and vendor specific. We had 1200 people talk to us on the macro side. And what we're seeing here is a cool down in spending. We originally had about 8.5% increase in budgets. That's cool down is 6.5 now, but I'll say with the doom and gloom and the headlines that we're seeing every day, 6.5% growth coming off of what we just did the last couple of years is still pretty fantastic as a backdrop. >>Okay. So you, you started to see John mentioned consumer. We saw that in Snowflake's earnings. For example, we, we certainly saw, you know, Walmart, other retailers, the FA Facebooks of the world where consumption was being dialed down, certain snowflake customers. Not necessarily, they didn't have mentioned any customers, but they were able to say, all right, we're gonna dial down, consumption this quarter, hold on until we saw some of that in snowflake results and other results. But at the same time, the rest of the industry is booming. But your data is showing softness within the fortune 500 for AWS, >>Not only AWS, but fortune 500 across the board. Okay. So going back to that larger macro data, the biggest drop in spending that we captured is fortune 500, which is surprising. But at the same time, these companies have a better purview into the economy. In general, they tend to see things further in advance. And we often remember they spend a lot of money, so they don't need to play catch up. They'll easily more easily be able to pump the brakes a little bit in the fortune 500. But to your point, when we get into the AWS data, the fortune 500 decrease seems to be hitting them a little bit more than it is Azure and GCP. I >>Mean, we're still talking about a huge business, right? >>I mean, they're catching up. I mean, Amazon has been transforming from owning the developer cloud startup cloud decade ago to really putting a dent on the enterprise as being number one cloud. And I still contest that they're number one by a long ways, but Azure kicking ass and catching up. Okay. You seeing people move to Azure, you got Charlie bell over there, Sean, by former Amazonians, Theresa Carlson, people are going over there, there there's lift over at Azure. >>There certainly is. >>Is there kinks in the arm or for AWS? There's >>A couple of kinks, but I think your point is really good. We need to take a second there. If you're talking about true pass or infrastructure is a service true cloud compute. I think AWS still is the powerhouse. And a lot of times the, the data gets a little muddied because Azure is really a hosted platform for applications. And you're not really sure where that line is drawn. And I think that's an important caveat to make, but based on the data, yes, we are seeing some kinks in the armor for AWS. Yes. Explain. So right now, a first of all caveat, 40% net score, which is our proprietary spending metric across the board. So we're not like raising any alarms here. It's still strong that said there are declines and there are declines pretty much across the board. The only spot we're not seeing a decline at all is in container, spend everything else is coming down specifically. We're seeing it come down in data analytics, data warehousing, and M I, which is a little bit of a concern because that, that rate of decline is not the same with Azure. >>Okay. So I gotta ask macro, I see the headwinds on the macro side, you pointed that out. Is there any insight into any underlying conditions that might be there on AWS or just a chronic kind of situational thing >>Right now? It seems situational. Other than that correlation between their big fortune 500, you know, audience and that being our biggest decline. The other aspect of the macro survey is we ask people, if you are planning to decline spend, how do you plan on doing it? And the number two answer is taking a look at our cloud spend and auditing it. So they're kind say, all right, you know, for the last 10 years it's been drunken, sail or spend, I >>Was gonna use that same line, you know, >>Cloud spend, just spend and we'll figure it out later, who cares? And then right now it's time to tighten the belts a little bit, >>But this is part of the allure of cloud at some point. Yeah. You, you could say, I'm gonna, I'm gonna dial it down. I'm gonna rein it in. So that's part of the reason why people go to the cloud. I want to, I wanna focus in on the data side of things and specifically the database. Let, just to give some context if, and correct me if I'm, I'm a little off here, but snowflake, which hot company, you know, on the planet, their net score was up around 80% consistently. It it's dropped down the last, you know, quarter, last survey to 60%. Yeah. So still highly, highly elevated, but that's relative to where Amazon is much larger, but you're saying they're coming down to the 40% level. Is that right? >>Yeah, they are. And I remember, you know, when I first started doing this 10 years ago, AWS at a 70%, you know, net score as well. So what's gonna happen over time is those adoptions are gonna get less and you're gonna see more flattening of spend, which ultimately is going to lower the score because we're looking for expansion rates. We wanna see adoption and increase. And when you see flattening a spend, it starts to contract a little bit. And you're right. Snowflake also was in the stratosphere that cooled off a little bit, but still, you know, very strong and AWS is coming down. I think the reason why it's so concerning is because a it's within the fortune 500 and their rate of decline is more than Azure right >>Now. Well, and, and one of the big trends you're seeing in database is this idea of converging function. In other words, bringing transaction and analytics right together at snowflake summit, they added the capability to handle transaction data, Mongo DB, which is largely mostly transactions added the capability in June to bring in analytic data. You see data bricks going from data engineering and data science now getting into snowflake space and analytics. So you're seeing that convergence Oracle is converging with my SQL heat wave and their core databases, couch base couch base is doing the same. Maria do virtually all these database companies are, are converging their platforms with the exception of AWS. AWS is still the right tool for the right job. So they've got Aurora, they've got RDS, they've got, you know, a dynamo DV, they've got red, they've got, you know, going on and on and on. And so the question everybody's asking is will that change? Will they start to sort of cross those swim lanes? We haven't seen it thus far. How is that affecting the data >>Performance? I mean, that's fantastic analysis. I think that's why we're seeing it because you have to be in the AWS ecosystem and they're really not playing nicely with others in the sandbox right now that now I will say, oh, Amazon's not playing nicely. Well, no, no. Simply to your point though, that there, the other ones are actually bringing in others at consolidating other different vendor types. And they're really not. You know, if you're in AWS, you need to stay within AWS. Now I will say their tools are fantastic. So if you do stay within AWS, they have a tool for every job they're advanced. And they're incredible. I think sometimes the complexity of their tools hurts them a little bit. Cause to your point earlier, AWS started as a developer-centric type of cloud. They have moved on to enterprise cloud and it's a little bit more business oriented, but their still roots are still DevOps friendly. And unless you're truly trained, AWS can be a little scary. >>So a common use case is I'm gonna be using Aurora for my transaction system and then I'm gonna ETL it into Redshift. Right. And, and I, now I have two data stores and I have two different sets of APIs and primitives two different teams of skills. And so that is probably causing some friction and complexity in the customer base that again, the question is, will they begin to expand some of those platforms to minimize some of that friction? >>Well, yeah, this is the question I wanted to ask on that point. So I've heard from people inside Amazon don't count out Redshift, we're making, we're catching up. I think that's my word, but they were kind of saying that right. Cuz Redshift is good, good database, but they're adding a lot more. So you got snowflake success. I think it's a little bit of a jealousy factor going on there within Redshift team, but then you got Azure synapse with the Synap product synapse. Yep. And then you got big query from Google big >>Query. Yep. >>What's the differentiation. What are you seeing for the data for the data warehouse or the data clouds that are out there for the customers? What's the data say, say to us? >>Yeah, unfortunately the data's showing that they're dropping a little bit whose day AWS is dropping a little bit now of their data products, Redshift and RDS are still the two highest of them, but they are starting to decline. Now I think one of the great data points that we have, we just closed the survey is we took a comparison of the legacy data. Now please forgive me for the word legacy. We're gonna anger a few people, but we Gotter data Oracle on-prem, we've got IBM. Some of those more legacy data warehouse type of names. When we look at our art survey takers that have them where their spend is going, that spends going to snowflake first, and then it's going to Google and then it's going to Microsoft Azure and, and AWS is actually declining in there. So when you talk about who's taking that legacy market share, it's not AWS right now. >>So legacy goes to legacy. So Microsoft, >>So, so let's work through in a little context because Redshift really was the first to take, you know, take the database to the cloud. And they did that by doing a one time license deal with par XL, which was an on-prem database. And then they re-engineered it, they did a fantastic job, but it was still engineered for on-prem. Then you along comes snowflake a couple years later and true cloud native, same thing with big query. Yep. True cloud native architecture. So they get a lot of props. Now what, what Amazon did, they took a page outta of the snowflake, for example, separating compute from storage. Now of course what's what, what Amazon did is actually not really completely separating like snowflake did they couldn't because of the architecture, they created a tearing system that you could dial down the compute. So little nuances like that. I understand. But at the end of the day, what we're seeing from snowflake is the gathering of an ecosystem in this true data cloud, bringing in different data types, they got to the public markets, data bricks was not able to get to the public markets. Yeah. And think is, is struggling >>And a 25 billion evaluation. >>Right. And so that's, that's gonna be dialed down, struggling somewhat from a go to market standpoint where snowflake has no troubles from a go to market. They are the masters at go to market. And so now they've got momentum. We talked to Frank sluman at the snowflake. He basically said, I'm not taking the foot off the gas, no way. Yeah. We, few of our large, you know, consumer customers dialed things down, but we're going balls to the >>Wall. Well, if you look at their show before you get in the numbers, you look at the two shows. Snowflake had their summit in person in Vegas. Data bricks has had their show in San Francisco. And if you compare the two shows, it's clear, who's winning snowflake is blew away from a, from a market standpoint. And we were at snowflake, but we weren't at data bricks, but there was really nothing online. I heard from sources that it was like less than 3000 people. So >>Snowflake was 1900 people in 2019, nearly 10,000. Yeah. In 2020, >>It's gonna be fun to sort of track that as a, as an odd caveat to say, okay, let's see what that growth is. Because in fairness, data, bricks, you know, a little bit younger, Snowflake's had a couple more years. So I'd be curious to see where they are. Their, their Lakehouse paradigm is interesting. >>Yeah. And I think it's >>And their product first company, yes. Their go to market might be a little bit weak from our analysis, but that, but they'll figure it out. >>CEO's pretty smart. But I think it's worth pointing out. It's like two different philosophies, right? It is. Snowflake is come into our data cloud. That's their proprietary environment. They're the, they think of the iPhone, right? End to end. We, we guarantee it's all gonna work. And we're in control. Snowflake is like, Hey, open source, no, bring in data bricks. I mean data bricks, open source, bring in this tool that too, now you are seeing snowflake capitulate a little bit. They announce, for instance, Apache iceberg support at their, at the snowflake summit. So they're tipping their cap to open source. But at the end of the day, they're gonna market and sell the fact that it's gonna run better in native snowflake. Whereas data bricks, they're coming at it from much more of an open source, a mantra. So that's gonna, you know, we'll see who look at, you had windows and you had apple, >>You got, they both want, you got Cal and you got Stanford. >>They both >>Consider, I don't think it's actually there yet. I, I find the more interesting dynamic right now is between AWS and snowflake. It's really a fun tit for tat, right? I mean, AWS has the S three and then, you know, snowflake comes right on top of it and announces R two, we're gonna do one letter, one number better than you. They just seem to have this really interesting dynamic. And I, and it is SLT and no one's betting against him. I mean, this guy's fantastic. So, and he hasn't used his war chest yet. He's still sitting on all that money that he raised to your point, that data bricks five, their timing just was a little off >>5 billion in >>Capital when Slootman hasn't used that money yet. So what's he gonna do? What can he do when he turns that on? He finds the right. >>They're making some acquisitions. They did the stream lit acquisitions stream. >>Fantastic >>Problem. With data bricks, their valuation is underwater. Yes. So they're recruiting and their MNAs. Yes. In the toilet, they cannot make the moves because they don't have the currency until they refactor the multiple, let the, this market settle. I I'm, I'm really nervous that they have to over factor the >>Valuation. Having said that to your point, Eric, the lake house architecture is definitely gaining traction. When you talk to practitioners, they're all saying, yeah, we're building data lakes, we're building lake houses. You know, it's a much, much smaller market than the enterprise data warehouse. But nonetheless, when you talk to practitioners that are actually doing things like self serve data, they're building data lakes and you know, snow. I mean, data bricks is right there. And as a clear leader in, in ML and AI and they're ahead of snowflake, right. >>And I was gonna say, that's the thing with data bricks. You know, you're getting that analytics at M I built into it. >>You know, what's ironic is I remember talking to Matt Carroll, who's CEO of auDA like four or five years ago. He came into the office in ma bro. And we were in temporary space and we were talking about how there's this new workload emerging, which combines AWS for cloud infrastructure, snowflake for the simple data warehouse and data bricks for the ML AI, and then all now all of a sudden you see data bricks yeah. And snowflake going at it. I think, you know, to your point about the competition between AWS and snowflake, here's what I think, I think the Redshift team is, you know, doesn't like snowflake, right. But I think the EC two team loves it. Loves it. Exactly. So, so I think snowflake is driving a lot of, >>Yeah. To John's point, there is plenty to go around. And I think I saw just the other day, I saw somebody say less than 40% of true global 2000 organizations believe that they're at real time data analytics right now. They're not really there yet. Yeah. Think about how much runway is left and how many tools you need to get to real time streaming use cases. It's complex. It's not easy. >>It's gonna be a product value market to me, snowflake in data bricks. They're not going away. Right. They're winning architectures. Yeah. In the cloud, what data bricks did would spark and took over the Haddo market. Yeah. To your point. Now that big data, market's got two players, in my opinion, snow flicking data, bricks converging. Well, Redshift is sitting there behind the curtain, their wild card. Yeah. They're wild card, Dave. >>Okay. I'm gonna give one more wild card, which is the edge. Sure. Okay. And that's something that when you talk about real time analytics and AI referencing at the edge, there aren't a lot of database companies in a position to do that. You know, Amazon trying to put outposts out there. I think it runs RDS. I don't think it runs any other database. Right. Snowflake really doesn't have a strong edge strategy when I'm talking the far edge, the tiny edge. >>I think, I think that's gonna be HPE or Dell's gonna own the outpost market. >>I think you're right. I'll come back to that. Couch base is an interesting company to watch with Capella Mongo. DB really doesn't have a far edge strategy at this point, but couch base does. And that's one to watch. They're doing some really interesting things there. And I think >>That, but they have to leapfrog bongo in my >>Opinion. Yeah. But there's a new architecture emerging at the edge and it's gonna take a number of years to develop, but it could eventually from an economic standpoint, seep back into the enterprise arm base, low end, take a look at what couch base is >>Doing. They hired an Amazon guard system. They have to leapfrog though. They need to, they can't incrementally who's they who >>Couch >>Base needs to needs to make a big move in >>Leap frog. Well, think they're trying to, that's what Capella is all about was not only, you know, their version of Atlas bringing to the cloud couch base, but it's also stretching it out to the edge and bringing converged database analytics >>Real quick on the numbers. Any data on CloudFlare, >>I was, I've been sitting here trying to get the word CloudFlare out my mouth the whole time you guys were talking, >>Is this another that's innovated in the ecosystem. So >>Platform, it was really simple for them early on, right? They're gonna get that edge network out there and they're gonna steal share from Akamai. Then they started doing exactly what Akamai did. We're gonna start rolling out some security. Their security is fantastic. Maybe some practitioners are saying a little bit too much, cuz they're not focused on one thing or another, but they are doing extremely well. And now they're out there in the cloud as well. You >>Got S3 compare. They got two, they got an S3 competitor. >>Exactly. So when I'm listening to you guys talk about, you know, a, a couch base I'm like, wow, those two would just be an absolute fantastic, you know, combination between the two of them. You mean >>CloudFlare >>Couch base. Yeah. >>I mean you got S3 alternative, right? You got a Mongo alternative basically in my >>Opinion. And you're going and you got the edge and you got the edge >>Network with security security, interesting dynamic. This brings up the super cloud date. I wanna talk about Supercloud because we're seeing a trend on we're reporting this since last year that basically people don't have to spend the CapEx to be cloud scale. And you're seeing Amazon enable that, but snowflake has become a super cloud. They're on AWS. Now they're on Azure. Why not tan expansion expand the market? Why not get that? And then it'll be on Google next, all these marketplaces. So the emergence of this super cloud, and then the ability to make that across a substrate across multiple clouds is a strategy we're seeing. What do you, what do you think? >>Well, honestly, I'm gonna be really Frank here. The, everything I know about the super cloud I know from this guy. So I've been following his lead on this and I'm looking forward to you guys doing that conference and that summit coming up from a data perspective. I think what you're saying is spot on though, cuz those are the areas we're seeing expansion in without a doubt. >>I think, you know, when you talk about things like super cloud and you talk about things like metaverse, there's, there's a, there, there look every 15 or 20 years or so this industry reinvents itself and a new disruption comes out and you've got the internet, you've got the cloud, you've got an AI and VR layer. You've got, you've got machine intelligence. You've got now gaming. There's a new matrix, emerging, super cloud. Metaverse there's something happening out there here. That's not just your, your father's SAS or is or pass. Well, >>No, it's also the spend too. Right? So if I'm a company like say capital one or Goldman Sachs, my it spend has traditionally been massive every year. Yes. It's basically like tons of CapEx comes the cloud. It's an operating expense. Wait a minute, Amazon has all the CapEx. So I'm not gonna dial down my budget. I want a competitive advantage. So next thing they know they have a super cloud by default because they just pivoted their, it spend into new capabilities that they then can sell to the market in FinTech makes total sense. >>Right? They're building out a digital platform >>That would, that was not possible. Pre-cloud >>No, it wasn't cause you weren't gonna go put all that money into CapEx expenditure to build that out. Not knowing whether or not the market was there, but the scalability, the ability to spend, reduce and be flexible with it really changes that paradigm entire. >>So we're looking at this market now thinking about, okay, it might be Greenfield in every vertical. It might have a power law where you have a head of the long tail. That's a player like a capital one, an insurance. It could be Liberty mutual or mass mutual that has so much it and capital that they're now gonna scale it into a super cloud >>And they have data >>And they have the data tools >>And the tools. And they're gonna bring that to their constituents. Yes, yes. And scale it using >>Cloud. So that means they can then service the entire vertical as a service provider. >>And the industry cloud is becoming bigger and bigger and bigger. I mean, that's really a way that people are delivering to market. So >>Remember in the early days of cloud, all the banks thought they could build their own cloud. Yeah. Yep. Well actually it's come full circle. They're like, we can actually build a cloud on top of the cloud. >>Right. And by the way, they can have a private cloud in their super cloud. Exactly. >>And you know, it's interesting cause we're talking about financial services insurance, all the people we know spend money in our macro survey. Do you know the, the sector that's spending the most right now? It's gonna shock you energy utilities. Oh yeah. I was gonna, the energy utilities industry right now is the one spending the most money I saw largely cuz they're playing ketchup. But also because they don't have these type of things for their consumers, they need the consumer app. They need to be able to do that delivery. They need to be able to do metrics. And they're the they're, they're the one spending right >>Now it's an arms race, but the, the vector shifts to value creation. So >>It's it just goes back to your post when it was a 2012, the trillion dollar baby. Yeah. It's a multi-trillion dollar baby that they, >>The world was going my chassis post on Forbes, headline trillion dollar baby 2012. You know, I should add it's happening. That's >>On the end. Yeah, exactly. >>Trillions of babies, Eric. Great to have you on the key. >>Thank you so much guys. >>Great to bring the data. Thanks for sharing. Check out ETR. If you're into the enterprise, want to know what's going on. They have a unique approach, very accurate in their survey data. They got a great market basket of, of, of, of, of data questions and people and community. Check it out. Thanks for coming on and sharing with. >>Thank you guys. Always enjoy. >>We'll be back with more coverage here in the cube in New York city live at summit 22. I'm John fur with Dave ante. We'll be right back.

>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel, along with it's ecosystem partners. >> Welcome inside Live here at the Sands as we continue our coverage of AWS re:Invent 2019 on theCUBE, day three. Always an exciting time I think to get a summary of what's happened here. Dave Vellante, John Walls, we're joined by a couple of gentlemen from Immuta, Steven Touw who's a co-founder and CTO. Steve, good to see you. >> Yeah thanks for having me. >> John Walls: And Rob Lancaster, who's the GM of Cloud at Immuta. Rob, thanks for joining us as well. >> Great to be here. >> First off, let's talk about Immuta a little bit. You're all about governance right? You're trying to make it simple, easy, taking out the complexity. But for those at home who might not be too familiar with your company, tell us a little bit about you. >> Yeah so the company started out, our roots are in the U.S. intelligence community. So we had been dealing with access and control issues for data for years and we said to ourselves, "Hey this product has to be useful for non-IC customers. "This problem has to exist." And with the advent of all these privacy regulations like CCPA, GDPR and of course HIPPA's been around for a long time, really our goal was to bring a product to the market that makes it easy to govern access to data in a way that you don't have to be technical to do it, you don't have to understand how to write SQL statements, you don't have to be a system administrator. We really bring together three personas, the users that want to get access to the data, legal compliance that needs to understand how the rules are being enforced or even enforce them themselves, and then of course the data owners and the DBAs who need to expose the data. So usually those three personas are at odds with one another, we bring them together in our platform and allow them to work together in a way that's compliant and also accelerates their data analytics. >> Could we talk a little bit about why this is such a problem? Because it is a big problem and especially today and in the cloud and we'll get into that, but you've got data lakes, data oceans now, you got data coming in, all types of data. Might be internal transaction data, it might be stuff in your data warehouse. And the organization say, "Well I want some other data. "I want to bring in maybe some social data." So certain data is, everybody can have access to. Certain data not everybody can have access to. And it's not necessarily just a security problem, edicts of my organization that need to be enforced. So first of all, is that sort of, the problem that you're solving? And maybe you can double-click on that a little bit. >> Yeah sure, so the market has evolved and is evolving. You allude to data lakes, I think you can point to the immersion of Hadoop, as a distributed infrastructure as kind of the original data lakes, or the most recent data lakes, where you can store all your data and run analytics on all your data, and now with the advent, with the emergence of Cloud you've effectively got very low, if not zero cost storage, and the ability to throw an unlimited amount of compute at the data. That, kind of in conjunction with heightened awareness for consumer data privacy and risk associated with data, has created a market for data governance beyond kind of the course-grained access controls that people have been using on their databases for decades now. >> Yeah I mean Hadoop really got it all started. You're right and despite all it's problems, it had some real epiphany-like technical innovations, but one of the things that it didn't worry about at the time was governance. So whose responsibility is this? Is it the CISO? That is essentially trying to build out a new cloud stack to provide security, privacy, governance and what does that stack look like? >> Rob: Go ahead. >> Yeah so it depends, it's actually pretty interesting that different organizations have tackled this different ways. So we have CISOs that maintain this. In other organizations we've got the legal compliance teams that want to do this but maybe don't have the technical chops. And the CISO doesn't necessarily know all the privacy rules that need to be enforced, so it's kind of moving into this world where security is about keeping the bad guys out and black or white access, like you either can see the data or you won't, but with privacy controls it gets into this gray area where there's a lot of technical complexity and there's a lot of legal complexity. So the organizations struggle with this 'cause you've got to play in that gray area where it's not just like I said, black and white. The analogy we use is, security is like a light switch, you're either in or you're out. With privacy controls you need to anonymize the data, you need to do privacy by design. It's like a dimmer switch where you want to play in that gray area and allow some utility out of the data but also protect privacy at differing levels of whatever you're doing analytically. So this can be challenging for an organization to wrestle with because it's not as, I would argue it's not as black and white as it is with security. >> Your question is in many cases it's the business that's running really fast and that is building these data lakes because they want to get value out of their data and the CISO or the compliance or risk officers are the ones that are telling them to slow down. So our product that Steve set up caters to both parties. It checks the boxes for risk, but it also enable the business to get utility out of their data lake. >> It's a very complicated situation because you've got this corpus of data that's organic and constantly changing and you have, you mentioned GDPR, you've got California now, every state's going to have it's own regulations so you've got to be able to sort of adjudicate that. And can you talk about, I mean obviously I've interviewed Matt Carroll, we covered you guys so I know a little bit about you, but can you talk about your tech in terms of it's ability? You've got a capability to do really granular level understanding and governance policies, can you describe that a little bit? >> Yeah sure, so when we talk about privacy controls, these are things like way beyond just table-level access. So instead of saying, "Hey you have access to this table or not," or even, "You have access to this column or not," you've got to go deeper than that, you've got to be able to make rows disappear based on what people are doing. So for example, we have financial institution customers that are using us for all their trading data and only some traders can see some trade desks and we manage all that dynamically. We're not making anonymized copies of data. Everything happens at query time, and depending on what compute you're using that all works differently, but then at the column level we're able to do these anonymization techniques like we could make numeric data less specific, we could use techniques like k-anonymization that allows analysts to analyze the data but ensures that small groups that exist in that data won't reveal someone's true identity. And we have techniques like differential privacy, which provides mathematical guarantees of privacy. So for example, one of our manufacturing customers set aside, these are the four analytical use cases that we're using our data for and under GDPR we want different levels of privacy associated to those use cases. So they could do that all with Immuta. So they could say, "When I'm doing this "I want these columns to be anonymized to this level "and these rows to disappear, but if I'm doing something, "maybe more critical, which our consumers have consented to "you know there's less privacy controls." And that all happens dynamically so the analysts could actually switch context of what they're doing and get a different view of the data and all of that is audited so we understand why someone's doing what they're doing and when they're running queries we can associate those queries to purpose. >> We've talked about customers of course and they're adapting right, to a new world? How are you adapting? I mean what are you learning about, in terms of policy regulation and governance, what have you, you said you came out of the intelligence community, high bar there right? >> Steven Touw: Yeah. >> So what have you done to evolve as a company and what are you, as the headlights basically for these folks, what are you seeing change that is going to require a lot of shift on the other side? >> Yeah so, I don't know if you have thoughts. >> I mean it's a great question but there's really two parts to it, there's what are we doing? But, what is the market doing as well, right? So if you think about when we got started, even a year ago people understood the technology, they thought it was cool but maybe a little nichey for government or financial services or maybe healthcare because there's well understood regulation, these vertical regulation. Even over the past year with kind of this increasing or heightened awareness for consumer data privacy, not just driven by CCPA and GDPR but kind of this, call it the Facebook Effect right? Cambridge Analytica has created this awareness within the general population for what are these organizations actually doing with my data? Before it was okay 'cause you give your data to Google and you get a better search result and you're okay with that but now they may be using your data for their own profit in different ways so this has created this rising tides effect for the overall market and we talk a lot about organizations using something like Immuta to protect their highly sensitive data. I like to think of it is their most valuable data, which may be highly sensitive but it also could be the crown jewels, trading data for a bank for example. So it's become about extracting value and operational benefit from data, whereas the risk offices are trying to lock it down in many cases. >> So, there's definitely a big problem and people are becoming more aware of it. I want to talk about where you guys fit into this whole cloud ecosystem. There's a sea change now, there's this sort of, this new cloud coming into play. It's not just about infrastructure anymore. I'll give you some examples, you got all these data lakes, maybe you got Redshift running, Snowflake's another one, you've now got this data exchange where you can bring data right in the Cloud bring in all different types of data, you're bringing in some AML and AI and it's all, really again, a complicated situation. So I see you guys as fitting in there and real need but can you describe where you fit in the ecosystem, what your relationship is with AWS, how do I engage with you? >> Yeah absolutely, so a core part of our value is that we are heterogeneous in terms of the environment that we support. We support a hybrid estate so the architecture of the product is fully microservices based so we can run on PRIM as well as on Cloud, on any Cloud, we support effectively any popular database system or analytical tool. So think of us as a data abstraction layer across a hybrid environment, so we're here because AWS is obviously the big boy in the market, they have market share, this is a strategic relationship for us. We're working very deeply with AWS field teams, particularly around some of their verticals, the verticals that align to our business and at the end of the day we're trying to define a category. It's a similar category that we've had for decades but with all the changes that are happening in data and regulation and infrastructure what we're trying to do is raise the level of awareness for the fact that Immuta has actually solved the problem that many of these risk officers are struggling with today. >> Yeah and from a, diving a little on the technical side of that answer is that we are, think of us as the way to enforce policy in the Cloud. We consider ourselves a Cloud-first software vendor. And you don't necessarily want one point solution in Redshift or another point solution on your on-premise Cloudera instance, whatever it may be where you're using your data and running analytics, you need to abstract the policies out into a consistent layer and then have them be enforced across whatever you're using. So you might be using Cloudera today and then you switch to Databricks tomorrow, that shouldn't be a hard change from you from a policy perspective. You just re-point Immuta at Databricks and all your policies are still working like they used to so it gives you this flexibility now to use all these different services that AWS provides 'cause as was stated in the keynote on Tuesday, there's no one database solves all. You're always going to be using a heterogenous set of compute to do your job in analytics so you need a consistent way to enforce policies across all of that. >> That's a great point. I mean I don't know if you saw the Vanguard guy today in the keynote, he basically said, "We rip down, or tore down our big data infrastructure "moved it to the Cloud, spun up EMR." I mean there's a perfect example of, you got to bring your governance with you. You can't have to rebuild that whole stack. Are you in the Marketplace yet? >> Steve and Rob: Yes. >> You are, great, awesome. >> Yeah we launched a managed version of Immuta over the summer on AWS Marketplace. We'll be launching a second one shortly and it's really, the offering that we have out there is really geared toward, for lack of a better term, democratizing data governance. It's actually free up to the fifth user so any organization can deploy Immuta in under 30 minutes through Marketplace and start protecting their data. >> That's great, we had Dave McCann on yesterday, he runs the Marketplace, he was telling us just now, private offers for every marketplace, so ICV, so that's from. Last question I have is, how do you see this all playing out? You got GDPR, remember you talked about California regulations, there's a technology component, any predictions you guys want to share? What's your telescope say? >> All data will be regulated data eventually. So if you're not thinking about that now you need to. So, at least that's our theory, obviously, so we think it's critical that you're doing that from day one instead of day 365 and in your migration strategy. And if you're not thinking about that it's going to potentially bite you in the ass. >> Yeah you're right, I mean Web 2.0 was the wild, wild west, there was no privacy, there was no regulation, GDPR started to get people focused on that and it's now a whole new world. >> Gentlemen thank you, appreciate the time and best of luck. I know you said you had the big launch this summer but good things are ahead no doubt. >> For sure, thank you. >> Thank you. >> Dave Vellante: Thanks guys. >> Back with more coverage here on theCUBE. You're watching AWS re:Invent 2019. We are live and we're in Las Vegas. (upbeat tones)