>> From The Cube Studios in Palo Alto and Boston, bringing you data driven insights from The Cube at ETR. This is "Breaking Analysis" with Dave Vellante. >> The reverse momentum in tech stocks caused by rising interest rates, less attractive discounted cash flow models, and more tepid forward guidance, can be easily measured by public market valuations. And while there's lots of discussion about the impact on private companies and cash runway and 409A valuations, measuring the performance of non-public companies isn't as easy. IPOs have dried up and public statements by private companies, of course, they accentuate the good and they kind of hide the bad. Real data, unless you're an insider, is hard to find. Hello and welcome to this week's "Wikibon Cube Insights" powered by ETR. In this "Breaking Analysis", we unlock some of the secrets that non-public, emerging tech companies may or may not be sharing. And we do this by introducing you to a capability from ETR that we've not exposed you to over the past couple of years, it's called the Emerging Technologies Survey, and it is packed with sentiment data and performance data based on surveys of more than a thousand CIOs and IT buyers covering more than 400 companies. And we've invited back our colleague, Erik Bradley of ETR to help explain the survey and the data that we're going to cover today. Erik, this survey is something that I've not personally spent much time on, but I'm blown away at the data. It's really unique and detailed. First of all, welcome. Good to see you again. >> Great to see you too, Dave, and I'm really happy to be talking about the ETS or the Emerging Technology Survey. Even our own clients of constituents probably don't spend as much time in here as they should. >> Yeah, because there's so much in the mainstream, but let's pull up a slide to bring out the survey composition. Tell us about the study. How often do you run it? What's the background and the methodology? >> Yeah, you were just spot on the way you were talking about the private tech companies out there. So what we did is we decided to take all the vendors that we track that are not yet public and move 'em over to the ETS. And there isn't a lot of information out there. If you're not in Silicon (indistinct), you're not going to get this stuff. So PitchBook and Tech Crunch are two out there that gives some data on these guys. But what we really wanted to do was go out to our community. We have 6,000, ITDMs in our community. We wanted to ask them, "Are you aware of these companies? And if so, are you allocating any resources to them? Are you planning to evaluate them," and really just kind of figure out what we can do. So this particular survey, as you can see, 1000 plus responses, over 450 vendors that we track. And essentially what we're trying to do here is talk about your evaluation and awareness of these companies and also your utilization. And also if you're not utilizing 'em, then we can also figure out your sales conversion or churn. So this is interesting, not only for the ITDMs themselves to figure out what their peers are evaluating and what they should put in POCs against the big guys when contracts come up. But it's also really interesting for the tech vendors themselves to see how they're performing. >> And you can see 2/3 of the respondents are director level of above. You got 28% is C-suite. There is of course a North America bias, 70, 75% is North America. But these smaller companies, you know, that's when they start doing business. So, okay. We're going to do a couple of things here today. First, we're going to give you the big picture across the sectors that ETR covers within the ETS survey. And then we're going to look at the high and low sentiment for the larger private companies. And then we're going to do the same for the smaller private companies, the ones that don't have as much mindshare. And then I'm going to put those two groups together and we're going to look at two dimensions, actually three dimensions, which companies are being evaluated the most. Second, companies are getting the most usage and adoption of their offerings. And then third, which companies are seeing the highest churn rates, which of course is a silent killer of companies. And then finally, we're going to look at the sentiment and mindshare for two key areas that we like to cover often here on "Breaking Analysis", security and data. And data comprises database, including data warehousing, and then big data analytics is the second part of data. And then machine learning and AI is the third section within data that we're going to look at. Now, one other thing before we get into it, ETR very often will include open source offerings in the mix, even though they're not companies like TensorFlow or Kubernetes, for example. And we'll call that out during this discussion. The reason this is done is for context, because everyone is using open source. It is the heart of innovation and many business models are super glued to an open source offering, like take MariaDB, for example. There's the foundation and then there's with the open source code and then there, of course, the company that sells services around the offering. Okay, so let's first look at the highest and lowest sentiment among these private firms, the ones that have the highest mindshare. So they're naturally going to be somewhat larger. And we do this on two dimensions, sentiment on the vertical axis and mindshare on the horizontal axis and note the open source tool, see Kubernetes, Postgres, Kafka, TensorFlow, Jenkins, Grafana, et cetera. So Erik, please explain what we're looking at here, how it's derived and what the data tells us. >> Certainly, so there is a lot here, so we're going to break it down first of all by explaining just what mindshare and net sentiment is. You explain the axis. We have so many evaluation metrics, but we need to aggregate them into one so that way we can rank against each other. Net sentiment is really the aggregation of all the positive and subtracting out the negative. So the net sentiment is a very quick way of looking at where these companies stand versus their peers in their sectors and sub sectors. Mindshare is basically the awareness of them, which is good for very early stage companies. And you'll see some names on here that are obviously been around for a very long time. And they're clearly be the bigger on the axis on the outside. Kubernetes, for instance, as you mentioned, is open source. This de facto standard for all container orchestration, and it should be that far up into the right, because that's what everyone's using. In fact, the open source leaders are so prevalent in the emerging technology survey that we break them out later in our analysis, 'cause it's really not fair to include them and compare them to the actual companies that are providing the support and the security around that open source technology. But no survey, no analysis, no research would be complete without including these open source tech. So what we're looking at here, if I can just get away from the open source names, we see other things like Databricks and OneTrust . They're repeating as top net sentiment performers here. And then also the design vendors. People don't spend a lot of time on 'em, but Miro and Figma. This is their third survey in a row where they're just dominating that sentiment overall. And Adobe should probably take note of that because they're really coming after them. But Databricks, we all know probably would've been a public company by now if the market hadn't turned, but you can see just how dominant they are in a survey of nothing but private companies. And we'll see that again when we talk about the database later. >> And I'll just add, so you see automation anywhere on there, the big UiPath competitor company that was not able to get to the public markets. They've been trying. Snyk, Peter McKay's company, they've raised a bunch of money, big security player. They're doing some really interesting things in developer security, helping developers secure the data flow, H2O.ai, Dataiku AI company. We saw them at the Snowflake Summit. Redis Labs, Netskope and security. So a lot of names that we know that ultimately we think are probably going to be hitting the public market. Okay, here's the same view for private companies with less mindshare, Erik. Take us through this one. >> On the previous slide too real quickly, I wanted to pull that security scorecard and we'll get back into it. But this is a newcomer, that I couldn't believe how strong their data was, but we'll bring that up in a second. Now, when we go to the ones of lower mindshare, it's interesting to talk about open source, right? Kubernetes was all the way on the top right. Everyone uses containers. Here we see Istio up there. Not everyone is using service mesh as much. And that's why Istio is in the smaller breakout. But still when you talk about net sentiment, it's about the leader, it's the highest one there is. So really interesting to point out. Then we see other names like Collibra in the data side really performing well. And again, as always security, very well represented here. We have Aqua, Wiz, Armis, which is a standout in this survey this time around. They do IoT security. I hadn't even heard of them until I started digging into the data here. And I couldn't believe how well they were doing. And then of course you have AnyScale, which is doing a second best in this and the best name in the survey Hugging Face, which is a machine learning AI tool. Also doing really well on a net sentiment, but they're not as far along on that access of mindshare just yet. So these are again, emerging companies that might not be as well represented in the enterprise as they will be in a couple of years. >> Hugging Face sounds like something you do with your two year old. Like you said, you see high performers, AnyScale do machine learning and you mentioned them. They came out of Berkeley. Collibra Governance, InfluxData is on there. InfluxDB's a time series database. And yeah, of course, Alex, if you bring that back up, you get a big group of red dots, right? That's the bad zone, I guess, which Sisense does vis, Yellowbrick Data is a NPP database. How should we interpret the red dots, Erik? I mean, is it necessarily a bad thing? Could it be misinterpreted? What's your take on that? >> Sure, well, let me just explain the definition of it first from a data science perspective, right? We're a data company first. So the gray dots that you're seeing that aren't named, that's the mean that's the average. So in order for you to be on this chart, you have to be at least one standard deviation above or below that average. So that gray is where we're saying, "Hey, this is where the lump of average comes in. This is where everyone normally stands." So you either have to be an outperformer or an underperformer to even show up in this analysis. So by definition, yes, the red dots are bad. You're at least one standard deviation below the average of your peers. It's not where you want to be. And if you're on the lower left, not only are you not performing well from a utilization or an actual usage rate, but people don't even know who you are. So that's a problem, obviously. And the VCs and the PEs out there that are backing these companies, they're the ones who mostly are interested in this data. >> Yeah. Oh, that's great explanation. Thank you for that. No, nice benchmarking there and yeah, you don't want to be in the red. All right, let's get into the next segment here. Here going to look at evaluation rates, adoption and the all important churn. First new evaluations. Let's bring up that slide. And Erik, take us through this. >> So essentially I just want to explain what evaluation means is that people will cite that they either plan to evaluate the company or they're currently evaluating. So that means we're aware of 'em and we are choosing to do a POC of them. And then we'll see later how that turns into utilization, which is what a company wants to see, awareness, evaluation, and then actually utilizing them. That's sort of the life cycle for these emerging companies. So what we're seeing here, again, with very high evaluation rates. H2O, we mentioned. SecurityScorecard jumped up again. Chargebee, Snyk, Salt Security, Armis. A lot of security names are up here, Aqua, Netskope, which God has been around forever. I still can't believe it's in an Emerging Technology Survey But so many of these names fall in data and security again, which is why we decided to pick those out Dave. And on the lower side, Vena, Acton, those unfortunately took the dubious award of the lowest evaluations in our survey, but I prefer to focus on the positive. So SecurityScorecard, again, real standout in this one, they're in a security assessment space, basically. They'll come in and assess for you how your security hygiene is. And it's an area of a real interest right now amongst our ITDM community. >> Yeah, I mean, I think those, and then Arctic Wolf is up there too. They're doing managed services. You had mentioned Netskope. Yeah, okay. All right, let's look at now adoption. These are the companies whose offerings are being used the most and are above that standard deviation in the green. Take us through this, Erik. >> Sure, yet again, what we're looking at is, okay, we went from awareness, we went to evaluation. Now it's about utilization, which means a survey respondent's going to state "Yes, we evaluated and we plan to utilize it" or "It's already in our enterprise and we're actually allocating further resources to it." Not surprising, again, a lot of open source, the reason why, it's free. So it's really easy to grow your utilization on something that's free. But as you and I both know, as Red Hat proved, there's a lot of money to be made once the open source is adopted, right? You need the governance, you need the security, you need the support wrapped around it. So here we're seeing Kubernetes, Postgres, Apache Kafka, Jenkins, Grafana. These are all open source based names. But if we're looking at names that are non open source, we're going to see Databricks, Automation Anywhere, Rubrik all have the highest mindshare. So these are the names, not surprisingly, all names that probably should have been public by now. Everyone's expecting an IPO imminently. These are the names that have the highest mindshare. If we talk about the highest utilization rates, again, Miro and Figma pop up, and I know they're not household names, but they are just dominant in this survey. These are applications that are meant for design software and, again, they're going after an Autodesk or a CAD or Adobe type of thing. It is just dominant how high the utilization rates are here, which again is something Adobe should be paying attention to. And then you'll see a little bit lower, but also interesting, we see Collibra again, we see Hugging Face again. And these are names that are obviously in the data governance, ML, AI side. So we're seeing a ton of data, a ton of security and Rubrik was interesting in this one, too, high utilization and high mindshare. We know how pervasive they are in the enterprise already. >> Erik, Alex, keep that up for a second, if you would. So yeah, you mentioned Rubrik. Cohesity's not on there. They're sort of the big one. We're going to talk about them in a moment. Puppet is interesting to me because you remember the early days of that sort of space, you had Puppet and Chef and then you had Ansible. Red Hat bought Ansible and then Ansible really took off. So it's interesting to see Puppet on there as well. Okay. So now let's look at the churn because this one is where you don't want to be. It's, of course, all red 'cause churn is bad. Take us through this, Erik. >> Yeah, definitely don't want to be here and I don't love to dwell on the negative. So we won't spend as much time. But to your point, there's one thing I want to point out that think it's important. So you see Rubrik in the same spot, but Rubrik has so many citations in our survey that it actually would make sense that they're both being high utilization and churn just because they're so well represented. They have such a high overall representation in our survey. And the reason I call that out is Cohesity. Cohesity has an extremely high churn rate here about 17% and unlike Rubrik, they were not on the utilization side. So Rubrik is seeing both, Cohesity is not. It's not being utilized, but it's seeing a high churn. So that's the way you can look at this data and say, "Hm." Same thing with Puppet. You noticed that it was on the other slide. It's also on this one. So basically what it means is a lot of people are giving Puppet a shot, but it's starting to churn, which means it's not as sticky as we would like. One that was surprising on here for me was Tanium. It's kind of jumbled in there. It's hard to see in the middle, but Tanium, I was very surprised to see as high of a churn because what I do hear from our end user community is that people that use it, like it. It really kind of spreads into not only vulnerability management, but also that endpoint detection and response side. So I was surprised by that one, mostly to see Tanium in here. Mural, again, was another one of those application design softwares that's seeing a very high churn as well. >> So you're saying if you're in both... Alex, bring that back up if you would. So if you're in both like MariaDB is for example, I think, yeah, they're in both. They're both green in the previous one and red here, that's not as bad. You mentioned Rubrik is going to be in both. Cohesity is a bit of a concern. Cohesity just brought on Sanjay Poonen. So this could be a go to market issue, right? I mean, 'cause Cohesity has got a great product and they got really happy customers. So they're just maybe having to figure out, okay, what's the right ideal customer profile and Sanjay Poonen, I guarantee, is going to have that company cranking. I mean they had been doing very well on the surveys and had fallen off of a bit. The other interesting things wondering the previous survey I saw Cvent, which is an event platform. My only reason I pay attention to that is 'cause we actually have an event platform. We don't sell it separately. We bundle it as part of our offerings. And you see Hopin on here. Hopin raised a billion dollars during the pandemic. And we were like, "Wow, that's going to blow up." And so you see Hopin on the churn and you didn't see 'em in the previous chart, but that's sort of interesting. Like you said, let's not kind of dwell on the negative, but you really don't. You know, churn is a real big concern. Okay, now we're going to drill down into two sectors, security and data. Where data comprises three areas, database and data warehousing, machine learning and AI and big data analytics. So first let's take a look at the security sector. Now this is interesting because not only is it a sector drill down, but also gives an indicator of how much money the firm has raised, which is the size of that bubble. And to tell us if a company is punching above its weight and efficiently using its venture capital. Erik, take us through this slide. Explain the dots, the size of the dots. Set this up please. >> Yeah. So again, the axis is still the same, net sentiment and mindshare, but what we've done this time is we've taken publicly available information on how much capital company is raised and that'll be the size of the circle you see around the name. And then whether it's green or red is basically saying relative to the amount of money they've raised, how are they doing in our data? So when you see a Netskope, which has been around forever, raised a lot of money, that's why you're going to see them more leading towards red, 'cause it's just been around forever and kind of would expect it. Versus a name like SecurityScorecard, which is only raised a little bit of money and it's actually performing just as well, if not better than a name, like a Netskope. OneTrust doing absolutely incredible right now. BeyondTrust. We've seen the issues with Okta, right. So those are two names that play in that space that obviously are probably getting some looks about what's going on right now. Wiz, we've all heard about right? So raised a ton of money. It's doing well on net sentiment, but the mindshare isn't as well as you'd want, which is why you're going to see a little bit of that red versus a name like Aqua, which is doing container and application security. And hasn't raised as much money, but is really neck and neck with a name like Wiz. So that is why on a relative basis, you'll see that more green. As we all know, information security is never going away. But as we'll get to later in the program, Dave, I'm not sure in this current market environment, if people are as willing to do POCs and switch away from their security provider, right. There's a little bit of tepidness out there, a little trepidation. So right now we're seeing overall a slight pause, a slight cooling in overall evaluations on the security side versus historical levels a year ago. >> Now let's stay on here for a second. So a couple things I want to point out. So it's interesting. Now Snyk has raised over, I think $800 million but you can see them, they're high on the vertical and the horizontal, but now compare that to Lacework. It's hard to see, but they're kind of buried in the middle there. That's the biggest dot in this whole thing. I think I'm interpreting this correctly. They've raised over a billion dollars. It's a Mike Speiser company. He was the founding investor in Snowflake. So people watch that very closely, but that's an example of where they're not punching above their weight. They recently had a layoff and they got to fine tune things, but I'm still confident they they're going to do well. 'Cause they're approaching security as a data problem, which is probably people having trouble getting their arms around that. And then again, I see Arctic Wolf. They're not red, they're not green, but they've raised fair amount of money, but it's showing up to the right and decent level there. And a couple of the other ones that you mentioned, Netskope. Yeah, they've raised a lot of money, but they're actually performing where you want. What you don't want is where Lacework is, right. They've got some work to do to really take advantage of the money that they raised last November and prior to that. >> Yeah, if you're seeing that more neutral color, like you're calling out with an Arctic Wolf, like that means relative to their peers, this is where they should be. It's when you're seeing that red on a Lacework where we all know, wow, you raised a ton of money and your mindshare isn't where it should be. Your net sentiment is not where it should be comparatively. And then you see these great standouts, like Salt Security and SecurityScorecard and Abnormal. You know they haven't raised that much money yet, but their net sentiment's higher and their mindshare's doing well. So those basically in a nutshell, if you're a PE or a VC and you see a small green circle, then you're doing well, then it means you made a good investment. >> Some of these guys, I don't know, but you see these small green circles. Those are the ones you want to start digging into and maybe help them catch a wave. Okay, let's get into the data discussion. And again, three areas, database slash data warehousing, big data analytics and ML AI. First, we're going to look at the database sector. So Alex, thank you for bringing that up. Alright, take us through this, Erik. Actually, let me just say Postgres SQL. I got to ask you about this. It shows some funding, but that actually could be a mix of EDB, the company that commercializes Postgres and Postgres the open source database, which is a transaction system and kind of an open source Oracle. You see MariaDB is a database, but open source database. But the companies they've raised over $200 million and they filed an S-4. So Erik looks like this might be a little bit of mashup of companies and open source products. Help us understand this. >> Yeah, it's tough when you start dealing with the open source side and I'll be honest with you, there is a little bit of a mashup here. There are certain names here that are a hundred percent for profit companies. And then there are others that are obviously open source based like Redis is open source, but Redis Labs is the one trying to monetize the support around it. So you're a hundred percent accurate on this slide. I think one of the things here that's important to note though, is just how important open source is to data. If you're going to be going to any of these areas, it's going to be open source based to begin with. And Neo4j is one I want to call out here. It's not one everyone's familiar with, but it's basically geographical charting database, which is a name that we're seeing on a net sentiment side actually really, really high. When you think about it's the third overall net sentiment for a niche database play. It's not as big on the mindshare 'cause it's use cases aren't as often, but third biggest play on net sentiment. I found really interesting on this slide. >> And again, so MariaDB, as I said, they filed an S-4 I think $50 million in revenue, that might even be ARR. So they're not huge, but they're getting there. And by the way, MariaDB, if you don't know, was the company that was formed the day that Oracle bought Sun in which they got MySQL and MariaDB has done a really good job of replacing a lot of MySQL instances. Oracle has responded with MySQL HeatWave, which was kind of the Oracle version of MySQL. So there's some interesting battles going on there. If you think about the LAMP stack, the M in the LAMP stack was MySQL. And so now it's all MariaDB replacing that MySQL for a large part. And then you see again, the red, you know, you got to have some concerns about there. Aerospike's been around for a long time. SingleStore changed their name a couple years ago, last year. Yellowbrick Data, Fire Bolt was kind of going after Snowflake for a while, but yeah, you want to get out of that red zone. So they got some work to do. >> And Dave, real quick for the people that aren't aware, I just want to let them know that we can cut this data with the public company data as well. So we can cross over this with that because some of these names are competing with the larger public company names as well. So we can go ahead and cross reference like a MariaDB with a Mongo, for instance, or of something of that nature. So it's not in this slide, but at another point we can certainly explain on a relative basis how these private names are doing compared to the other ones as well. >> All right, let's take a quick look at analytics. Alex, bring that up if you would. Go ahead, Erik. >> Yeah, I mean, essentially here, I can't see it on my screen, my apologies. I just kind of went to blank on that. So gimme one second to catch up. >> So I could set it up while you're doing that. You got Grafana up and to the right. I mean, this is huge right. >> Got it thank you. I lost my screen there for a second. Yep. Again, open source name Grafana, absolutely up and to the right. But as we know, Grafana Labs is actually picking up a lot of speed based on Grafana, of course. And I think we might actually hear some noise from them coming this year. The names that are actually a little bit more disappointing than I want to call out are names like ThoughtSpot. It's been around forever. Their mindshare of course is second best here but based on the amount of time they've been around and the amount of money they've raised, it's not actually outperforming the way it should be. We're seeing Moogsoft obviously make some waves. That's very high net sentiment for that company. It's, you know, what, third, fourth position overall in this entire area, Another name like Fivetran, Matillion is doing well. Fivetran, even though it's got a high net sentiment, again, it's raised so much money that we would've expected a little bit more at this point. I know you know this space extremely well, but basically what we're looking at here and to the bottom left, you're going to see some names with a lot of red, large circles that really just aren't performing that well. InfluxData, however, second highest net sentiment. And it's really pretty early on in this stage and the feedback we're getting on this name is the use cases are great, the efficacy's great. And I think it's one to watch out for. >> InfluxData, time series database. The other interesting things I just noticed here, you got Tamer on here, which is that little small green. Those are the ones we were saying before, look for those guys. They might be some of the interesting companies out there and then observe Jeremy Burton's company. They do observability on top of Snowflake, not green, but kind of in that gray. So that's kind of cool. Monte Carlo is another one, they're sort of slightly green. They are doing some really interesting things in data and data mesh. So yeah, okay. So I can spend all day on this stuff, Erik, phenomenal data. I got to get back and really dig in. Let's end with machine learning and AI. Now this chart it's similar in its dimensions, of course, except for the money raised. We're not showing that size of the bubble, but AI is so hot. We wanted to cover that here, Erik, explain this please. Why TensorFlow is highlighted and walk us through this chart. >> Yeah, it's funny yet again, right? Another open source name, TensorFlow being up there. And I just want to explain, we do break out machine learning, AI is its own sector. A lot of this of course really is intertwined with the data side, but it is on its own area. And one of the things I think that's most important here to break out is Databricks. We started to cover Databricks in machine learning, AI. That company has grown into much, much more than that. So I do want to state to you Dave, and also the audience out there that moving forward, we're going to be moving Databricks out of only the MA/AI into other sectors. So we can kind of value them against their peers a little bit better. But in this instance, you could just see how dominant they are in this area. And one thing that's not here, but I do want to point out is that we have the ability to break this down by industry vertical, organization size. And when I break this down into Fortune 500 and Fortune 1000, both Databricks and Tensorflow are even better than you see here. So it's quite interesting to see that the names that are succeeding are also succeeding with the largest organizations in the world. And as we know, large organizations means large budgets. So this is one area that I just thought was really interesting to point out that as we break it down, the data by vertical, these two names still are the outstanding players. >> I just also want to call it H2O.ai. They're getting a lot of buzz in the marketplace and I'm seeing them a lot more. Anaconda, another one. Dataiku consistently popping up. DataRobot is also interesting because all the kerfuffle that's going on there. The Cube guy, Cube alum, Chris Lynch stepped down as executive chairman. All this stuff came out about how the executives were taking money off the table and didn't allow the employees to participate in that money raising deal. So that's pissed a lot of people off. And so they're now going through some kind of uncomfortable things, which is unfortunate because DataRobot, I noticed, we haven't covered them that much in "Breaking Analysis", but I've noticed them oftentimes, Erik, in the surveys doing really well. So you would think that company has a lot of potential. But yeah, it's an important space that we're going to continue to watch. Let me ask you Erik, can you contextualize this from a time series standpoint? I mean, how is this changed over time? >> Yeah, again, not show here, but in the data. I'm sorry, go ahead. >> No, I'm sorry. What I meant, I should have interjected. In other words, you would think in a downturn that these emerging companies would be less interesting to buyers 'cause they're more risky. What have you seen? >> Yeah, and it was interesting before we went live, you and I were having this conversation about "Is the downturn stopping people from evaluating these private companies or not," right. In a larger sense, that's really what we're doing here. How are these private companies doing when it comes down to the actual practitioners? The people with the budget, the people with the decision making. And so what I did is, we have historical data as you know, I went back to the Emerging Technology Survey we did in November of 21, right at the crest right before the market started to really fall and everything kind of started to fall apart there. And what I noticed is on the security side, very much so, we're seeing less evaluations than we were in November 21. So I broke it down. On cloud security, net sentiment went from 21% to 16% from November '21. That's a pretty big drop. And again, that sentiment is our one aggregate metric for overall positivity, meaning utilization and actual evaluation of the name. Again in database, we saw it drop a little bit from 19% to 13%. However, in analytics we actually saw it stay steady. So it's pretty interesting that yes, cloud security and security in general is always going to be important. But right now we're seeing less overall net sentiment in that space. But within analytics, we're seeing steady with growing mindshare. And also to your point earlier in machine learning, AI, we're seeing steady net sentiment and mindshare has grown a whopping 25% to 30%. So despite the downturn, we're seeing more awareness of these companies in analytics and machine learning and a steady, actual utilization of them. I can't say the same in security and database. They're actually shrinking a little bit since the end of last year. >> You know it's interesting, we were on a round table, Erik does these round tables with CISOs and CIOs, and I remember one time you had asked the question, "How do you think about some of these emerging tech companies?" And one of the executives said, "I always include somebody in the bottom left of the Gartner Magic Quadrant in my RFPs. I think he said, "That's how I found," I don't know, it was Zscaler or something like that years before anybody ever knew of them "Because they're going to help me get to the next level." So it's interesting to see Erik in these sectors, how they're holding up in many cases. >> Yeah. It's a very important part for the actual IT practitioners themselves. There's always contracts coming up and you always have to worry about your next round of negotiations. And that's one of the roles these guys play. You have to do a POC when contracts come up, but it's also their job to stay on top of the new technology. You can't fall behind. Like everyone's a software company. Now everyone's a tech company, no matter what you're doing. So these guys have to stay in on top of it. And that's what this ETS can do. You can go in here and look and say, "All right, I'm going to evaluate their technology," and it could be twofold. It might be that you're ready to upgrade your technology and they're actually pushing the envelope or it simply might be I'm using them as a negotiation ploy. So when I go back to the big guy who I have full intentions of writing that contract to, at least I have some negotiation leverage. >> Erik, we got to leave it there. I could spend all day. I'm going to definitely dig into this on my own time. Thank you for introducing this, really appreciate your time today. >> I always enjoy it, Dave and I hope everyone out there has a great holiday weekend. Enjoy the rest of the summer. And, you know, I love to talk data. So anytime you want, just point the camera on me and I'll start talking data. >> You got it. I also want to thank the team at ETR, not only Erik, but Darren Bramen who's a data scientist, really helped prepare this data, the entire team over at ETR. I cannot tell you how much additional data there is. We are just scratching the surface in this "Breaking Analysis". So great job guys. I want to thank Alex Myerson. Who's on production and he manages the podcast. Ken Shifman as well, who's just coming back from VMware Explore. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE. Does some great editing for us. Thank you. All of you guys. Remember these episodes, they're all available as podcast, wherever you listen. All you got to do is just search "Breaking Analysis" podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me to get in touch david.vellante@siliconangle.com. You can DM me at dvellante or comment on my LinkedIn posts and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for Erik Bradley and The Cube Insights powered by ETR. Thanks for watching. Be well. And we'll see you next time on "Breaking Analysis". (upbeat music)

(upbeat music) >> Welcome back everyone, theCUBE's three days of wall to wall coverage of Snowflake Summit '22 is coming to an end, but Dave Vellante and I, Lisa Martin are so pleased to have our final guest as none other than the co-founder and president of products at Snowflake, Benoit Dageville. Benoit, thank you so much for joining us on the program. Welcome. >> Thank you. Thank you, thank you. >> So this is day four, 'cause you guys started on Monday. This is Thursday. The amount of people that are still here speaks volumes. We've had close to 10,000 people here. >> Yeah. >> Could you ever have imagined back in the day, 10 years ago that it would come to something like this in such a short period of time? >> Absolutely not. And I always say if I had imagined that I might not have started Snowflake, right. This is somehow scary. I mean and yeah, it's huge. And you can feel the excitement of everyone. It is like mind boggling and the fact that so many people are still there after four days is great. >> Your keynote on Tuesday was fantastic. Your energy was off the charts. It was standing room only. There were overflow rooms. Like we just mentioned, a lot of people are still here. Talk about the evolution of Snowflake, this week's announcements and what it means for the future of the data cloud. >> Yeah, so evolution, I mean, I will start with the evolution. It's true that that's what we have announced. This week is not where we started necessarily. So we started really very quickly with big data combined with data warehouse as one thing. We saw that the world was moving into fragmented siloing data and we thought with Thierry, we are going to combine big data and data warehouse in one system for the cloud with this elasticity and this service simplicity. So simplicity, amazing elasticity, which is this multi workload architecture that I was explaining during the keynotes and really extreme simplicity with the service. Then we realized that there is one other attribute in the cloud, which is unique, which doesn't exist on-premise, which is collaboration. How you can connect different tenets of the platform together. And Google showed that with Google Docs. I always say to me, it was amazing that you could share document and have direct access to document that you didn't produce and you can collaborate on this document. So we wanted to do the same thing for data and this is where we created the data cloud and the marketplace where you can have all these data sets available and really the next evolution I would say is really about applications that are (indistinct) by that data, but are way simpler to use for all the tenets of the data cloud. And this is the way you can share expertise also, including, ML model, everyone talks about ML and the democratization of ML. How are you going to democratize ML? It's not by making necessary training super easy. Such that everyone can train their ML for themselves. It's by having very specialized application where data and ML is at the core, which are shared, through the marketplace and we shall leverage by many tenets of this marketplace that have no necessary knowledge about building this ML models. So that's where, yeah. >> When you and Thierry started the company, I go back to the improbable rise of Kubernetes and there were other more sophisticated container management systems back then, but they chose to focus on simplicity. And you've told me before, that was our main tenet. We are not going to worry about all the complex database stuff. You knew how to do that, but you chose not to. So my question is, did you envision solving those complex problems over time yourselves or through an ecosystem? Was this by design or did you... As you started to get into it, say let's not even try to go there let's partner to go there. >> Yeah, I mean, it's both. It's a combination of both. Snowflake, the simplicity of the platform is really important because if our partners are struggling to put their solution and build solution on top of Snowflake they will not build it. So it's very important that number one, our platform is really easy to use from day one. And that really has to be built inside the platform. You cannot build simplicity on top. You cannot have a complex solution and all of a sudden realize that, oh, this is complex. I need to build another layer on top of it to make it simpler, that will not work. So it had to be built from day one, but you're right. What is going to be Snowflake? I always say in 10 years from now, we just turn 10 years old or we are going to turn 10 years old in few months. Actually a few months, yes. >> Right. >> So for the next 10 years I really believe that most of Snowflake will not be built by Snowflake. And that's the power of the partners and these applications. When you are going to say I'm using Snowflake, actually, probably you are not going to use directly code developed by Snowflake. That code will leverage our platform, but you will use a solution that has been built on top of Snowflake. And this is the way we are going to decouple, the effort of Snowflake and multiply it. >> It's an interesting balance, isn't it? When I think of what you did with Apache Iceberg, if I use Iceberg and I'm not going to get as much functionality, but I may want that openness, but I'm going to get more functionality inside of the data cloud. And I don't know, but if you know the answer to what's going to happen. >> No, that's a super good question. So to explain what we did with Apache Iceberg, and the fact that now it's a native format for us. So everything that you can do with our internal formats, you can do it with Apache Iceberg, including security, defining masking, data masking all the governors that we have, fine grain security aspects, the replications you can define you can use (indistinct) on top of... >> But there's a but, right? But if I do that with native Snowflake tools, I'm going to get an even greater advantage, am I not? >> Yes. So that's what I'm saying. So that's why we embraced Iceberg, because I think we can bring all the benefit of Snowflake to people who have decided to use Iceberg, I mean open formats. Iceberg is a table format. So and why it was important because people had massive investments in open source in Hadoop. And we had a lot of companies saying, we love Snowflake. We want to be a Snowflake customer, but we cannot really migrate all our data. I mean, it will be really costly. And we have a lot of tools that need access, direct access. So this is why we created Iceberg because we can really... I mean, we really think that we can bring the benefit of Snowflake to this data. >> Gives customers optionality. Okay. I use this term super cloud. You don't use the term, but that's okay. And I get a lot of heat for it. But to me, what you're doing is quite a bit different than multicloud because you're creating that abstraction layer. You're bringing value above it. My question to you is, the most of the heat I get is, oh, that's just SaaS. Are you just SaaS? >> No. I mean, no, absolutely not. I mean, you're right we are a super cloud. I mean it's a much better word than saying we are multicloud. Multicloud is often viewed as oh, I have my system and now I can run this system in the different cloud providers. Snowflake is different. We have one single platform for the world, which happens to have some regions are AWS region, some regions are Azure, some regions are GCP, Google and we merge them together. We have this Snowgrid technology that connects all our regions together so that we have really one platform for the world. And that's very important because when you talk about connections of data and expertise applications you want to have global reach, right. It doesn't exist. We are not siloed by region of the world, right? You have a lot of companies which are multinational that have presence everywhere. And you want to have this global reach. The world is not a independent set of regions and countries, right. And that's the realization. So we had to create this global platform for our customers. >> And now you have people building clouds on top of your data cloud, well that to me is the next signal. In your keynote, you talked about seven pillars, all data, all workloads, global architecture, self-managed, programmable, marketplace, governance, which ones are the most important? >> All of them. It's like when you have kids, you don't want to pick and say, this one is my preferred one, so they are really important. All of them, as I said without data, there is no Snowflake, right? So all data is so important that we can reach every data, wherever it is. And Iceberg is a part of that, but all workload is really important because you don't want to put your data in one platform, if you cannot run all your workloads and workloads are much broader than just data warehousing, there is data engineering, data science, ML engineering, (indistinct) all these workloads applications. So that's critical. Programmable is where we are moving, right. We want to be the place where data applications are built. And we think we have a lot of advantages because data application needs to use many workloads at once, right? It's not that that application will do only data warehousing, they need to store their states, they need to use this new workload that we define, which is Unistore. They need to do data engineering because they need to get data, right. They have to save this data. So they need to combine many workload and if they have to stitch this workload, because the platform was not designed as one single product where everything is consistent and works together, that you have to stitch, it's complicated for this application to make it work. So Snowflake is we believe an ideal platform to run these data applications. So all workloads, programmable, obviously, so that you can program. And programmable has two aspects, which is big part of our announcement. Is both data programmability, which is running Python against petabyte, terabytes of data at scale and doing it scale out. So that's what we call data programmability. So both Java, Python and (indistinct), but also running applications like UI. And we had this acquisition of Streamlit. Streamlit now has been fully integrated in Snowflake. We announced that such that not only you can have this data programmability, but you can expose your data through this nice UIs, interactive UI to business users potentially. So it goes all the way there. Global is super important. As we say, we want to be one platform for the world. And of course, as I said, the last pillar, which is somehow critical for us, because we are cloud, we need to have governance. We need to have security of our data. And why it took us so long to do Python is not because it's out to run Python, right? Everyone can run Python it's because we had to secure it. And I talk about it creating this amazing sandboxing technology, such that when you include third party libraries and third party codes, you are guaranteed that this third party code will not reach to infiltrate your data, right. We control the environment that Snowflake provides. >> Can you share us some of the feedback from the customer? You probably had many customer conversations over the last four days. >> Look at that smile. (interviewer laughing) (Lisa laughing) >> Actually not because I was so busy everywhere. Unfortunately, I didn't speak to many customers. Saying that, I had everyone stopping me and talking about what they heard and yeah, there is a huge excitement about all of this. >> What's been the feedback around the theme of the event? The world of data collaboration. Data collaboration is so critical as every company these days must be a data company to compete, to win. What's been from just some of the feedback that you've had customers really embracing data collaboration, what Snowflake is enabling. >> Yeah. I mean, almost every company which is using Snowflake, is collaborating with data. You have heard, the number of stable edges that we have, and there is a real need for that because your data alone... You cannot make sense of your data if it is just alone. It needs to be connected with other data. You haven't not generated. So all data, when you say the first pillar of Snowflake is all data is not only about your data, but is about all the data that's created around you. That puts perspective on your own data. And that's critical and it's so painful to get. I mean, even your data is difficult to have access to your data, but imagine data that you didn't produce. And so yes, so the data collaboration is critical, and then now we expanded it to application and expertise, sharing models, for example, That's going to have a huge impact. >> All data includes now transaction data, right? >> Yes. >> That's a big part of the announcements that you guys made. >> Yeah. So and that's the motivation for that was really, if we want to run application, full application, we announced native applications, which are fully executed and run inside the (indistinct) data cloud, right. They need all the services that application need and in particular managing their states. And so we created Unistore, which is a new workload, which allows you to combine transactional data, which are generated by this application. And at the same time being able to do analytics directly on this data. So we call it Hybrid Table because it has this hybrid aspect. You can do both transactional access to this data and at the same time analytic here without having data pipeline and moving data and transforming it from the transactional system to the analytical system, right. Snowflake is one system. Again, in the spirit of simplifying everything, this is the Snowflake (indistinct). >> I can ask the same question I ask at first, (indistinct) when was the aha moment that you and Thierry had that said, this is not just a better data warehouse, it's actually more than that. You probably didn't call it a data cloud until later on, but did you know that from the beginning or was that something you kind of stumbled into? >> No. So as I said, we founded Snowflake in 2012 and Thierry and I, we locked in my apartment and we were doing the blueprint of Snowflake and trying to find what is the revolution with the cloud for this data warehouse system and analytical system, both big data and data warehouse. And the aha moment was but of course cloud, okay. What is cloud? It's elasticity, it's service and later collaboration. So in the elasticity aspect, when you ask database people, what is elasticity, they will tell you, oh, you have a cluster of nodes. Like if it is Oracle, it would be a (indistinct) cluster. And the elasticities that you can add one node, two node to this cluster without having too much impact on the existing workload, because you need to shuffle data, right. It's hard and doing it online, right, that's elasticity. If you can do that, you are elastic. We thought that that was not very interesting to do that. What is interesting with elasticity is to plug new workloads. You can plug a workload like that and that workload is running without having any impact on other workloads, which are running on the platform. So elasticity for us was having dedicated computer resources to workloads. And these computer resources could start and be part as soon as the workload starts and will shut down when the workload finishes and they will be sized exactly for the demand of that workload. And we thought the aha moment was, okay if we can do that, now we can run a workload with, let's say 10X more computer resources than what you would have used or 100X more. Okay, let's say 100X more because we paralyzed things. Now this workload can run 100X faster, right? That's assuming we do a good job in the scale, which is our IP. And if we can do that, now the computer resources that you have used, you have used them for 100 times less. So you have used 100 times more resources because you have more nodes, but because you go fast, you use them for less time, right? So if you multiply the two it's constant. So you can run and accelerate workload dramatically 10X, 100X for the same price. Even if we are not better in efficiency than competition, just having that was the magic, right? >> You know how Google founders originally had trouble raising money because who needs another search engine? Did you get from original, like when you started going to raise money, Amazon's got a database, so who needs another cloud database? Did you get that early on or was it just obvious Speiser and companies as well. >> Speiser is a little bit on the crazy side and ambitious and so Speiser is Speiser. And of course he had no doubt, but even him was saying Benoit, Thierry, Hadoop, right. Everyone is saying Hadoop is going to be the revolution. And you guys are betting actually against Hadoop because we told Speiser, Hadoop is a bad system, it's going to fail, but at the time everyone was so bullish about Hadoop, everyone was implementing Hadoop that it didn't look like it was going to fail and we were probably wrong. So there was a lot of skepticism about not leveraging Hadoop and not being an Hadoop. Okay, something being on top of Hadoop. That was number one. There was no cloud warehouse at the time we started. Redshift was not started. It was the pioneer somewhere when Snowflake was founded. So creating a data warehouse in the cloud sounded crazy to people. How am I going to move my data over there? And security and what about security, the cloud is not secure. So that was another... >> So you guys predated that Parexel move by... >> Yes. >> Okay, so that's interesting. And I thought when Redshift... I mean, Amazon announced Redshift, I was sure that Mike Speiser will come and say, guys it's too sad, but they beat you guys and they build something and actually it was the reverse. Mike Speiser was super excited and so it was interesting to me. >> Wow, that's amazing. 'Cause John Furrier and I, we were early with theCUBE. when theCUBE started it was like the beginning of Hadoop. And so we brought theCUBE to, I think it was the second Hadoop World and we was rubbing nickels together at the time. And I was so excited bring compute to storage and it made so much sense. But I remember and I won't say who it was, but an early Hadoop committer told me this is going to fail. And I'm like, what? And he started going age basis crap and all this stuff. And I was sad because I was so excited, but it turned out that you had the same (indistinct). >> Because of complexity. Okay, Hadoop failed for two reasons. One is because they decided that, oh, a lot of this database thing, you don't need transaction, you don't need SQL, you don't necessarily, you don't need to go fast. It'll be batch, normal real time interaction with data, no one needs that. >> Cheap storage. >> So a lot of compromise on the very important technology. And at the same time, extreme complexity and complexity for me was, where I was I knew that it was going to fail big time and we bet Snowflake on the failure of Hadoop indeed. >> And there was no cloud early on in Hadoop. >> And there was no cloud too. >> And that was what killed it. That was like... >> You're right. And the model that Hadoop had for data didn't work on block storage. Block storage is not as efficient as HGFS. So that was also another figure. >> Do you ever sit back and think about... So you think about how much money has poured in to separating compute from storage and cloud databases and you started it all. (interviewer laughing) >> Yeah. No, this is... >> Pretty amazing. >> Yeah. >> Right, so that's good. That means that you're onto a good idea, but a lot of people get confused that again, they think that you're a cloud data warehouse and you're not, I mean, you're much more than that. >> Yeah, I hate that. I have to say, because from day one we were not a cloud data warehouse. As I said, it was all about combining the big data, massive amount of unstructured data, petabytes stored as files. Okay, that's very important, store as files where it's very easy to drop data in the system without... Very low cost to combine with data warehouse, full multi statement transaction when people will tell you today, oh, now we are a data warehouse. They don't have multi statement transaction, right. So we had from day one multi statement transaction really efficient SQL. You could run your dashboard. So combining these two worlds was I think the crazy thing, that's the crazy innovation that Snowflake did initially. >> Yeah. >> And I know it's really easy to build data warehouse somewhere, because if you don't think about big data, petabytes, extremely structured data, you remove a lot of complexity. >> This is why Lisa, when you get excited about technology, but you always have to have a, somebody who really deeply understands technology to stink test it, all right so awesome. Thank you for sharing that story. >> Yeah. >> Fantastic. So over 5,900 customers now. I saw over 500 in the Forbes G2K, over almost 10,000 people here this year. If we think back to 2019, there was about what? Less than 2000 people. >> Yeah. >> What do you think is going to happen next year? >> I don't know. I don't like to think about next year. I mean, I always say, Snowflake is so exciting to me because it is like a TV show, right. Where you wait the next season and we have one season every year. So I'm really excited to know what is going to happen next year. And I don't want to project what I think will happen, but all these movements to the Snowflake being the platform for data application. I want to see what people are going to build on our platform. I mean, that's the excitement. >> Season 11 coming up. >> Yes. Season 11. Yes. >> No binge watching here. Benoit, it's been a pleasure to have you on the program. >> Thank you. >> Congratulations on incredible success, the momentum, the energy is contagious. We love it. (Benoit laughing) >> Thank you so much. >> Thank you. >> Bye bye. >> For Benoit Dageville and Dave Vellante, I'm Lisa Martin. You're watching theCUBE's coverage of Snowflake Summit '22. Dave and I will be right back with a wrap. (upbeat music)

(bright music) >> Hello everyone and welcome back to theCUBE's live coverage here in San Francisco, California for AWS Summit 2022. I'm John Furrier, your host of theCUBE. Two days of coverage, AWS Summit 2022 in New York city's coming up this summer, we'll be there as well. Events are back. theCUBE is back. Of course, with theCUBE virtual, CUBE hybrid, the cube.net. Check it out, a lot of content this year more than ever. A lot more cloud data, cloud native, modern applications, all happening. Got a great guest here. Jeremy Burton, CUBE alumni, CEO of Observe, Inc. in the middle of all the cloud scale, big data, observability. Jeremy, great to see you. Thanks for coming on. >> Always great to come and talk to you on theCUBE man. It's been a few years. >> Well, you got your hands. You're in the trenches with great startup, good funding, great board, great people involved in the observability space, hot area, but also you've been a senior executive. President of Dell, EMC, 11 years ago you had a vision and you actually had an event called cloud meets big data. >> Jeremy: Yeah. >> And it's here. You predicted it 11 years ago. Look around, it's cloud meets big data. >> Yeah, the cloud thing I think was probably already a thing, but the big data thing I do claim credit for sort of catching that bus early, We were on the bus early and I think it was only inevitable. Like if you could bring the economics and the compute of cloud to big data, you could find out things you could never possibly imagine. >> So you're close to a lot of companies that we've been covering deeply. Snowflake obviously are involved. The board level, the founders, the people there, cloud, Amazon, what's going on here? You're doing a startup as the CEO at the helm, chief of Observe, Inc., which is an observability, which is to me in the center of this confluence of data, engineering, large scale integrations, data as code, integrating into applications. It's a whole another world developing, like you see with Snowflake, it means Snowflake is super cloud as we call it. So a whole nother wave is here. What's this wave we're on? How would you describe the wave? >> Well, a couple of things. People are, I think, riding more software than ever before. Why? Because they've realized that if you don't take your business online and offer a service, then you become largely irrelevant. And so you you've got a whole set of new applications. I think more applications now than any point, not just ever, but the mid nineties. I always looked at as the golden age of application development. Now, back then people were building for Windows. Well now they're building for things like, AWS is now the platform. So you've got all of that going on. And then at the same time, the side effect of these applications is they generate data and lots of data and the transactions, what you bought today or something like that. But then there's what we do, which is all the telemetry data, all the exhaust fumes. And I think people really are realizing that their differentiation is not so much their application. It's their understanding of the data. Can I understand who my best customers are? What I sell today? If people came to my website and didn't buy, then why not? Where did they drop off? All of that they want to analyze. And the answers are all in the data. The question is, can you understand it? >> In our last startup showcase, we featured data as code. One of the insights that we got out of that, and I want to get your opinion on or reaction to is, is that data used to be put into a data lake and turns into a data swamp or throw into the data warehouse, and then we'll do some queries, maybe a report once in a while. And so data, once it was done, unless it was real time, even real time was not good anymore after real time. That was the old way. Now you're seeing more and more effort to say, let's go look at the data, 'cause now machine learning is getting better. Not just train once, they're iterating. This notion of iterating and then pivoting, iterating and pivoting That's a Silicon Valley story. That's like how startups were, but now you're seeing data being treated the same way. So now you have this data concept that's now part of a new way to create more value for the apps. So this whole new cycle of data being reused and repurposed, then figure it out. >> Yeah, yeah, I'm a big fan of, years ago, just an amazing guy, Andy McAfee, at the MIT labs. I spent time with and he had this line, which still sticks to me this day, which is look, he said, I'm part of a body, which believes that everything is a matter of data. Like if you have enough data, you can answer any question. And this has going back 10 years when he was saying these kind of things and certainly, research is on the forefront. But I think starting to see that mindset of the MIT research be mainstream in enterprises. They're realizing that, yeah, it is about the data. If I can better understand my data better than competitor, then I've got an advantage. And so the question is how? What technologies and what skills do I need in my organization to allow me to do that? >> So let's talk about Observe, Inc. You're the CEO. Given you've seen the waves before, you're in the front lines of observability, which again is in the center of all this action. What's going on with the company? Give a quick minute to explain Observe for the folks who don't know what you guys do. What's the company doing? What's the funding status? What's the product status? And what's the customer status? >> Yeah, so we realized, a handful of years ago, let's say five years ago. Look, the way people are building applications is different. They're way more functional. They change every day. But in some respects there are a lot more complicated. They're distributed, microservices architectures. And when something goes wrong, the old way of troubleshooting and solving problems was not going to fly because you had so much change going into production on a daily basis. It was hard to tell like where the problem was. And so we thought, okay, it's about time. Somebody looks at the exhaust fumes from this application and all the telemetry data and helps people troubleshoot and make sense of the problems that they're seeing. So that's observability. It's actually a term that goes back to the 1960s. It was, a guy called, like everything in tech, it's a reinvention of something from years gone by, but there's a guy called Rudy Coleman in 1960s, kind of term. And the term was been able to determine the state of a system by looking at its external outputs. And so we've been going on this for the best part of four years now. It took us three years just to build the product. I think what people don't appreciate these days often is the barrier to entry in a lot of these markets is quite high. You need a lot of functionality to have something that's credible with a customer. So yeah, this last year, we did our first year selling. We've got about 40 customers now. We got great investors Sutter Hill Ventures. Mike Speiser who was really the first guy in the Snowflake and the initial investor. We're fortunate enough to have Mike on our board. And part of the Observe story is closely knit with Snowflake because all of that telemetry data, we store in there. >> So I want to pivot to that. Mike Speiser, Snowflake, Jeremy Burton, theCUBE kind of same thinking. This idea of a super cloud or what Snowflake became. >> Jeremy: Yeah. >> Snowflake is massively successful on top of AWS. And now you're seeing startups and companies build on top of Snowflake. >> Jeremy: Yeah. >> So that's become an entrepreneurial story that we think that to go big in the cloud, you can have a cloud on a cloud, like as Jerry Chen in Greylock calls it, castles in the cloud where there are moats in the cloud. So you're close to it. I know you're doing some stuff with Snowflake's. So as a startup, what's your view on building on top of say a Snowflake or an AWS, because again, you got to go where the data is. You need all the data. >> Jeremy: Yeah. >> What's your take on that? >> Having enough gray hair now. Again, in tech, I think if you want to predict the future, look at the past. And 20 years ago, 25 years ago, I was at a smaller company called Oracle. And an Oracle was the database company and their ambition was to manage all of the world's transactional data. And they built on a platform or a couple of platforms. One, Windows, and the other main one was Solaris. And so at that time, the operating system was the platform. And then that was the ecosystem that you would compete on top of. And then there were companies like SAP that built applications on top of Oracle. So then wind the clock forward 25 years, gray hairs, the platform isn't the operating system anymore. The platform is AWS, Google cloud. I probably look around if I say that in. >> It's okay. But Hyperscale. >> Yeah. >> CapEx built out. >> That is the new platform. And then Snowflake comes along. Well, their aspiration is to manage all of the, not just human generated data, but machine generated data in the world of cloud. And I think they they've done an amazing job doing for the, I'd say the big data world, what Oracle did for the relational data world way back 25 years ago. And then there are folks like us come along and of course my ambition would be, look, if we can be as successful as an SAP building on top of Snowflake, as they were on top of Oracle, then we'd probably be quite happy. >> So you're building on top of Snowflake? >> We're building on top of Snowflake a hundred percent. And I've had folks say to me, well, aren't you worried about that? Isn't that a risk? It's like, well, that's a risk. >> Are you still on the board? >> Yeah, I'm still on the board. Yeah. That's a risk I'm prepared to take. I am long on Snowflake. >> It sounds, well, you're in a good spot. Stay on the board then you'll know as going on. Okay, seriously, this is a real dynamic. >> Jeremy: It is. >> It's not a one off. >> Well, and I do believe as well that the platform that you see now with AWS, if you look at the revenues of AWS, it is an order of magnitude more than Microsoft was 25 years ago with windows. And so I believe the opportunity for folks like Snowflake and folks like Observe, it's an order magnitude more than it was for the Oracle and the SAPs of the old world. >> Yeah, and I think this is something that this next generation of entrepreneurship is the go big scenario is you got to be on a platform. >> Yeah and it's quite easy. >> Or be the platform, but it's hard. There's only like how many seats are at that table left. >> Well, value migrates up over time. So when the cloud thing got going, there were probably 10, 20, 30, rack space and there's 1,000,001 infrastructure for service, platform as a service. My old employee EMC, we had Pivotal. Pivotal was a platform as a service. You don't hear so much about it these days, but initially there's a lot of players and then it consolidates. And then to extract a real business, you got to move up, you got to add value, you got to build databases, then you got to build applications. >> It's interesting. Moving from the data center to the cloud was a dream for starters 'cause they didn't have to provision the CapEx. Now the CapEx is in the cloud. Then you build on top of that, you got Snowflake. Now you got on top of that. >> The assumption is almost that compute and storage is free. I know it's not quite free. >> Yeah, it's almost free. >> But as an application vendor, you think, well, what can I do if I assume compute and storage is free, that's the mindset you've got to get into. >> And I think the platform enablement to value. So if I'm an entrepreneur, I'm going to get a serious multiple of value in what I'm paying. Most people don't even blink at their AWS bills unless they're like massively huge. Then it's a repatriation question or whatever discount question. But for most startups or any growing company, the Amazon bill should be a small factor. >> Yeah, a lot of people ask me like, look, you're building on Snowflake. You're going to be paying their money. How does that work with your business model? If you're paying them money, do you have a viable business? And it's like, well, okay. We could build a database as well in Observe, but then I've got half the development team working on something that will never be as good as Snowflake. And so we made the call early on that, no, we want to innovate above the database. Snowflake are doing a great job of innovating on the database and the same is true with something like Amazon, like Snowflake could have built their own cloud and their own platform, but they didn't. >> Yeah and what's interesting is that Dave Vellante and I have been pointing this out and he's obviously more on Snowflake. I've been looking at Databricks and the same dynamics happening. The proof is the ecosystem. >> Yeah. >> If you look at Snowflake's ecosystem right now and Databricks, it's exploding. The shows are selling out. This floor space is booked. That's the old days at VMware. The old days at AWS. >> One and for Snowflake and any platform provider, it's a beautiful thing because we build on Snowflake and we pay their money. They don't have to sell to us. And we do a lot of the support. And so the economics work out really, really well if you're a platform provider and you've got a lot of ecosystems. >> And then also you get a trajectory of economies of scale with the institutional knowledge of Snowflake, integrations, new products, you're scaling and step function with them. >> Yeah, we manage 10 petabytes of data right now. When I arrived at EMC in 2010, we had one petabyte customer. And so at Observe, we've been only selling the product for a year. We have 10 petabytes of data under management. And so being able to rely on a platform that can manage that is invaluable. >> Well, Jeremy, great conversation. Thanks for sharing your insights on the industry. We got a couple minutes left, put a plug in for Observe. What do you guys do? You got some good funding, great partners. I don't know if you can talk about your POC customers, but you got a lot of high ends folks that are working with you. You get in traction. >> Yeah >> Scales around the corner sounds like. Is that where you at? Pre-scale? >> We've got a big announcement coming up in two or three weeks. We've got new funding, which is always great. The product is really, really close. I think, as a startup, you always strive for market fit, at which point can you just start hiring salespeople and the revenue keeps going. We're getting pretty close to that right now. We've got about 40 SaaS companies that run on the platform. They're almost all AWS Kubernetes, which is our sweet spot to begin with, but we're starting to get some really interesting enterprise type customers. We're F5 networks. We're POC in right now with Capital One. We've got some interesting news around Capital One coming up. I can't share too much, but it's going to be exciting. And like I said, Sutter Hill continue to stick. >> And I think Capital One's a big Snowflake customer as well, right? >> They were early and one of the things that attracted me to Capital One was they were very, very good with Snowflake early on and they put Snowflake in a position in the bank where they thought that snowflake could be successful. And today that is one of Snowflake's biggest accounts. >> Capital One, very innovative cloud. Obviously, AWS customer and very innovative. certainly in the CISO and CIO. On another point on where you're at. So you're pre-scale meaning you're about to scale. >> Jeremy: Right. >> So you got POCs. What's that trajectory look like? And you see around the corner, what's going on? What's around the corner that you're going to hit the straight and narrow and gas it fast? >> Yeah, the key thing for us is we got to get the product right. The nice thing about having a guy like Mike Speiser on the board is he doesn't obsess about revenue at this stage. His questions at the board are always about like, is the product right? Is the product right? Have you got the product right? 'Cause we know when the product's right, we can then scale the sales team and the revenue will take care of itself. So right now all the attention is on the product. This year, the exciting thing is we're adding all the tracing visualizations. So people will be able to the kind of things that back in the day you could do with the New Relics and AppDynamics, the last generation of APM tools. You're going to be able to do that within Observe. And we've already got the logs and the metrics capability in there. So for us this year is a big one 'cause we complete the trifecta, the logs. >> What's the secret sauce of observe if you put it into a sentence, what's the secret sauce? >> I think, an amazing founding engineering team, number one. At the end of the day, you have to build an amazing product and you have to solve a problem in a different way and we've got great long term investors. And the biggest thing our investors give is, actually it's not just money, it gives us time to get the product right. Because if we get the product right, then we can get the growth. >> Got it. Final question while I got you here. You've been on the enterprise business for a long time. What's the buyer landscape out there? You got people doing POCs, Capital One scale. So we know that goes on. What's the appetite at the buyer side for startups and what are their requirements that you're seeing? Obviously, we're seeing people go in and dip into the startup pool because new ways to refactor their business, restructure. So a lot of happening in cloud. What's the criteria? How are enterprises engaging in with startups? >> Yeah, enterprises, they know they've got to spend money transforming the business. I almost feel like my old Dell or EMC self there, but what we were saying five years ago is happening. Everybody needs to figure out a way to take their business to this digital world. Everybody has to do it. So the nice thing from a startup standpoint is they know at times they need to risk or take a bet on new technology in order to help them do that. So I think you've got buyers that A, have money, B, are prepared to take risks, and it's a race against time to get their offerings in this new digital footprint. >> Final, final question. What's the state of AWS? Where do you see them going next? Obviously, they're continuing to be successful. How does cloud 3.0? Or they always say it's day one, but it's maybe more like day 10, but what's next for AWS? Where do they go from here? Obviously, they're doing well and they're getting bigger and bigger. >> Yeah, it's an amazing story. We are on AWS as well. And so I think if they keep nurturing the builders and the ecosystem, then that is their superpower. They have an early leads. And if you look at where, maybe the likes of Microsoft lost the plot in the late nineties, it was they stopped really caring about developers and the folks who are building on top of their ecosystem. In fact, they started buying up their ecosystem and competing with people in their ecosystem. And I see with AWS, they have an amazing head start. And if they did more, if they do more than that, that's what's going to keep this juggernaut rolling for many years to come. >> They got the Silicon and they got the Stack developing. Jeremy Burton inside theCUBE, great resource for commentary, but also founding with the CEO of a company called Observe, Inc. In the middle of all the action and the board of Snowflake as well. Great startup. Thanks for coming on theCUBE. >> Always a pleasure. >> Live from San Francisco's theCUBE. I'm John Furrier, your host. Stay with us. More coverage from San Francisco, California after the short break. (soft music)

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE in ETR. This is Breaking Analysis with Dave Vellante. >> Despite the more than $100 billion spent each year fighting Cyber-crime. When we do an end-of-the year look back and ask "How did we do?" The answer is invariably the same, "Worse than last year." Pre pandemic, the picture was disheartening, but since March of 2020 the situation has only worsened as cyber-criminals have become increasingly sophisticated, better funded and more brazen. SecOps pros continue to fight, but unlike conventional wars, this one has no end. Now the flip side of course, is that markets continue to value cybersecurity firms at significant premiums. Because this huge market will continue to grow by double digits for the foreseeable future. Hello and welcome to this week's Wikibon theCUBE Insights powered by ETR. In this Breaking Analysis, we look at the state of cybersecurity in 2021 and beyond. We'll update you with the latest survey data from enterprise technology research and share the fundamentals that have investors piling into the security space like never before. Let's start with the customer view. Cybersecurity remains the number one priority for CIOs and CSOs. This latest ETR survey, once again asked IT buyers to rank their top priorities for the next 12 months. Now the last three polling period dating back to last March. Cybersecurity has outranked every top spending category, including cloud, data analytics, productivity software, networking, AI, and automation or RPA. Now this shouldn't surprise anybody, but it underscores the challenges that organizations face. Not only are they in the midst of a non-optional digital transformation, but they have to also fund a cyber war that has no ceasefires, no truces, and no exit path. Now there's much more going on in cybersecurity than ransomware, but certainly that has the attention of executives. And it's becoming more and more lucrative for attackers. Here's a snapshot of some of the more well-documented attacks this decade many which have occurred in very recent months. CNA Financial, they got hit earlier this year and paid a $40 million ransom. The Ireland Health Service also got hit this year and refused to pay the ransom, but it's estimated that the cost to recover and the damage to the organization exceeded half a billion dollars. The request was for a $20 million ransom. The JBS meat company hack, they paid $11 million. CWT travel paid $5 million. The disruption from the Colonial Pipeline company, was widely reported they paid more than $4 million, as the Brenntag, the chemical company. The NBA got hit. Computer makers, Quanta and Acer also. More than 2,000 random attacks were reported to the FBI in the first seven months of 2021. Up more than 60% from 2020. Now, as I've said many times, you don't have to be a genius to be a ransomware as today. Anyone can go on the dark web, tap into ransomware as a service. Attackers, they have insidious names like darkside, evil, the cobalt, crime gang, wizard spider, the Lazarus gang, and numerous others. Criminals they have negotiation services is most typically the attackers, they'll demand a specific amount of money but they're willing to compromise in an exchange of cryptocurrency for decryption keys. And as mentioned, it's not just ransomware supply chain attacks like the solar winds hack hit organizations within the U.S government and companies like Mimecast this year. Now, while these attacks often do end up in a ransom situation. The attackers sometimes find it more lucrative to live off the land and stealth fashion and ex filtrates sensitive data that can be sold or in the case of many financial institution attacks they'll steal information from say a chief investment officer that signals an upcoming trading strategy and then the attackers will front run that trade in the stock market. Now, of course phishing, remains one of the most prominent threats. Only escalated by the work from home trend as users bring their own devices and of course home networks are less secure. So it's bad, worse than ever before. But you know, if there's a problem, entrepreneurs and investors, they're going to be there to solve it. So here's a LinkedIn post from one of the top investors in the business, Mike Speiser. He was a founding investor in Snowflake. He helped get pure storage to escape velocity and many, many other successes. This hit my LinkedIn feed the other day, his company Sutter Hill Ventures is co-leading a 1.3 Series D on an $8.3 billion valuation. They're putting in over $200 million. Now Lacework is a threat detection software company that looks at security as a data problem and they monitor exposures across clouds. So very timely. So watch that company. They're going to soar. Now the right hand chart shows venture investments in cybersecurity over the past several years. You can see it exploded in 2019 to $7.6 billion. And people thought the market was peaking at that time, if you recall. But then investments rose a little bit to $7.8 billion in 2020 right in the middle of lockdown. And then the hybrid work, the cloud, the new normal thesis kicked in big time. It's in full gear this year. You can see nearly $12 billion invested in cybersecurity in the first half of 2021 alone. So the money keeps coming in as the problem gets worse and the market gets more crowded. Now we'd like to show this slide from Optiv, it's their security taxonomy. It'll make your eyes cross. It's so packed with companies in different sectors. We'll put a link in our posts, so you can stare at this. We've used this truck before. It's pretty good. It's comprehensive and it's worth spending some time to see what that landscape looks like. But now let's reduce this down a bit and bring in some of the ETR data. This is survey data from October that shows net score or spending momentum on the vertical axis and market share or pervasiveness in the dataset on the horizontal axis. That's a measure of mentioned share if you will. Now this is just isolated on the information security sector within the ETR taxonomies. No filters in terms of the number of responses. So it's every company that ETR picks up in cybersecurity from its buyer surveys. Now companies above that red line, we consider them to have a highly elevated spending momentum for their products and services. And you can see, there are a lot of companies that are in this map first of all, and several above that magic mark. So you can see the momentum of Microsoft and Palo Alto. That's most impressive because of their size, their pervasiveness in the study, Cisco and Splunk are also quite prominent. They don't have as much spending momentum, but they're pretty respectable. And you can see the companies that have been real movers in this market that we've been reporting on for a while. Okta, CrowdStrike, Zscaler, CyberArk, SailPoint, Authzero, all companies that we've extensively covered in previous breaking analysis episodes as the up and comers. And isn't it interesting that Datadog is now showing up in the vertical axis. You see that in the left-hand side up high, they're becoming more and more competitive to Splunk in this space as an alternative and lines are blurring between observability, log analytics, security, and as we previously reported even backup and recovery. But now let's simplify this picture a bit more and filter down a little bit further. This chart shows the same X, Y view. Same data construct and framework, but we required more than a hundred responses to hit the chart. So the companies, they have to have a notable market presence in the ETR survey. It's perhaps a bit less crowded, but still very packed. Isn't it? You can see firms that are less prominent in the space like Datadog fell off. The big companies we mentioned, obviously still prominent Microsoft, Palo Alto, Cisco and Splunk and then those with real momentum, they stand out a little bit. There's somewhat smaller, but they're gaining traction in the market. As we felt they would Okta and Auth zero, which Okta acquired as we reported on earlier this year, both showing strength as our CrowdStrike, Zscaler, CyberArk, which does identity and competition with Okta and SentinelOne, which went public mid this year. The company SentinelOne uses AI to do threat detection and has been doing quite well. SalePoint and Proofpoint are right on that red elevated line and then there's a big pack in the middle. Look, this is not an easy market to track. It's virtually every company plays in security. Look, AWS says some of the most advanced security in the business but they're not in the chart specifically, but you see Microsoft is. Because much of AWS security is built into services. Amazon customers heavily rely on the Amazon ecosystem which is in the Amazon marketplace for security products. And often they associate their security spend with those partners and not necessarily Amazon. And you'll see networking companies you see right there, like Juniper and the bottom there and in the ETR data set and the players like VMware in the middle of the pack. They've been really acquisitive for example, with carbon black. And the, of course, you've got a lot of legacy players like McAfee and RSA and IBM. Look, virtually every company has a security story and that will only become more common in the coming years. Now here's another look at the ETR data it's in the raw form, but it'll give you a sense of two things; One is how the data from the previous chart is plotted. And two, it gives you a time series of the data. So the data lists the top companies in the ETR data sets sorted by the October net score in the right most column. Again, that measures spending momentum. So to make the cut here, you had to have more than a hundred mentions which is shown on the left-hand side of the chart that shared N, IE that's shared accounts in the dataset. And you can track the data from last October, July of this year and the most recent October, 2021 survey. So we, drew that red line just about at the 40% net score market coincidentally, there are 10 companies that are over that figure over that bar. We sometimes call out the four star companies. We give four stars to those companies that both are in the top 10 and spending momentum and the top in prominence are shared N in the dataset. So some of these 10 would fit into that profile by that methodology, specifically, Microsoft, Okta, CrowdStrike, and Palo Alto networks. They would be the four star companies. Now a couple of other things to point out here, DDoS attacks, they're still relevant, and they're real threat. So a company like CloudFlare which is just above that red line they play in that space. Now we've also shaded the companies in the fat middle. A lot of these companies like Cisco and Splunk for example, they're major players in the security space with very strong offerings and customer affinity. We sometimes give them two stars. So this is what makes this market so interesting. It's not like the high end discourage market where literally every vendor in the Gartner magic quadrant is up in the right, okay. And there's only five or four or five, six vendors there. This market is diverse with many, many segments and sub segments, and it's such a vital space. And there's so many holes to fill with an ever changing threat landscape as we've seen in the last two years. So this is in part which makes it such a good market for investors. There's a lot of room for growth and not just from stealing market share. That's certainly an opportunity there, but things like cloud, multi-cloud, shifting end points, the edge ,and so forth make this space really ripe for investments. And to underscore this, we put together this little chart of some of the pure play security firms to see how their stock performance has done recently. So you can see that here, you know, it's a little hard to read, but it's not hard to see that Okta, CrowdStrike, Zscaler on the left have been big movers. These charts where possible all show a cross here, starting at the lockdown last year. The only exception is SentinelOne which IPO mid this year. So that's the point March, 2020 when the whole world changed and security priorities really started to shift to accommodate the work from home. But it's quite obvious that since the pandemic, these six companies have been on a tear for the fundamental reason that hybrid work has created a shift in spending priorities for CSOs. No longer are organizations just spending on hardening a perimeter, that perimeter has been blown away. The network is flattening. Work is what you do, it's no longer a place. As such threats are on the rise and cloud, endpoint security, identity access tools there become increasingly vital and the vendors who provide them are on the rise. So it's no surprise that the players that we've listed here which play quite prominently in those markets are all on fire. So now in summary, I want to stress that while the picture is sometimes discouraging. The entire world is becoming more and more tuned in to the cyber threat. And that's a good thing. Money is pouring in. Look, technology got us into this problem and technology is a defensive weapon that will help us continue this fight. But it's going to take more than technology. And I want to share something. We get dozens and dozens of in bounds this time of the year because we do an annual predictions posts. So folks and they want to help us out. So now most of the in bounds and the predictions that we get, they're just kind of observations or frankly, non predictions that can't really be measured as like where you right, or where you're wrong. So for the most part I like predictions that are binary. For example, last December we predicted their IT spending in 2021 would rebound and grow at 4% relative to 2020. Well, it did rebound but that prediction really wasn't as accurate as I'd like. It was frankly wrong. We think it's actually the market's going to actually grow. Spending's going to grow more like 7% this year. Not to worry plenty of our predictions came true, but we'll leave that for another day. Anyway, I got an email from Dean Fisk of Fisk partners. It's a PR firm representing an individual named Lyndon Brown chief of strategy officer of Pondurance. Pondurance is a security consultancy. And the email had the standard, Hey, in case you're working on a predictions post this year end, blah, blah, blah. But instead of sharing with me, a bunch of non predictions, the notes said here's some trends in cybersecurity that might be worth thinking about. And there were a few predictions sprinkled in there, but I wanted to call it a couple of the comments from Linden Brown, whom I don't know, I never met the guy, but I really thought his trends were spot on. The first was a stat I'll share that the United Nations report cyber crime is up 600% due to the pandemic. If as if I couldn't feel worse already. His first point though was that the hybrid workplace will be the new frontier for cyber. Yes, we totally agree. There are permanent shifts taking place. And we actually predicted that last year, but he further cited that many companies went from zero to full digital transformation overnight and many are still on that journey. And his point is that hybrid work is going to require a complete overhaul of how we think about security. We think this is very true. Now the other point that stood out is that governments are going to crack down on this behavior. And we've seen this where criminals have had their critical infrastructure dismantled by governments. No doubt the U.S government has the capabilities to do so. And it is very much focused on this issue. But it's tricky as Robert Gates, who was the former defense secretary, told me a few years back in theCUBE. He said, well, we have the best offense. We also have the most to lose. So we have to be very careful, but Linden's key point was you are going to see a much more forward and aggressive public policy and new laws that give crime fighters more latitude . Again, it's tricky kind of like the Patriot act was tricky but it's coming. Now, another call-out from Linden shares his assertion that natural disasters will bring increased cyber risk. And I thought this was a really astute point because natural disasters they're on the rise. And when there's chaos, there's cash opportunities for criminals. And I'll add to this that the supply chain risk is far from over. This is going to be continuing theme this coming year and beyond. And one of the things that Linden Brown said in his note to me is essentially you can't take humans out of the equation. Automation alone can't solve the problem, but some companies operate as though they can. Just as bad human behavior, can tramp good security, Good human education and behavior is going to be a key weapon in this endless war. Now the last point is we're going to see continued escalation government crackdowns are going to bring retaliation and to Gates' point. The U.S has a lot at stake. So expect insurance premiums are going to go through the roof. That's assuming you can even get cyber insurance. And so we got to hope for the best, but for sure, we have to plan for the worst because it's coming. Deploy technology aggressively but people in process will ultimately be the other ingredients that allow us to live to battle for another day. Okay. That's a wrap for today. Remember these episodes they're all available as podcasts, wherever you listen just search "breaking analysis" podcast. Check out ETR his website at ETR.plus. We also publish a full report every week on Wikibond.com and siliconangle.com. You can get in touch. Email me @david.volante@tsiliconangle.com or you can DM me @dvellante. Comment on our LinkedIn posts. This is Dave Vellante for theCUBE insights powered by ETR. Have a great week. everybody stay safe, be well. And we'll see you next time. (techno music)

(upbeat music) >> Hello, welcome to this CUBE conversation. I'm John Furrier your host of theCUBE here in our Palo Alto studio. We got a great conversation with the CEO of Lacework, David Hatfield. Who's in on theCUBE remote. David great to see you guys, a security platform at Lacework, you're at the helm as CEO. Welcome to theCUBE conversation. >> Thank you, John. Great to see you congrats to you and the team and all the success. I think what you guys are doing is really important so happy to be part of it. >> Great to have you in the community and you guys are doing great work. I know about Lacework I've done some due diligence on you guys. I love your business model, but for the folks who don't know what you guys do, take a minute to explain who is Lacework? What do you guys do? What's your positioning? And what's your focus? >> Yeah, well, we're a modern data security platform for the cloud. And so I think data science meets cloud security ultimately. The company has been around since 2015. We received one of the largest financing rounds that we're aware of I think in history in security business, $525 million in January. Led by Sutter Hill Ventures which many people may know about they founded PureStorage with the notion that we're going to go fundamentally change and revamp the ownership model for a high speed data storage using flash versus using spinning disc drives. I spent eight years with that company. Love with what we built there. Then Mike Speiser considered an investment in a company called Snowflake computing. I think you're aware of what Snowflake does which is bringing data warehousing into the cloud. And the third big investment that Sutter Hill made is really to help disrupt security, and that's in Lacework. So north of a billion dollar valuation a 300% year over year growth and have a ton of momentum. So at the core of what we do, it's really trying to merge, when we look at we look at security as a data problem, security and compliance the data problem. And when you apply that to the cloud, it's a massive data problem. you literally have trillions of data points across shared infrastructure that we need to be able to ingest and capture and then you need to be able to process efficiently and provide context back to the end-user. And so we approached it very differently than how legacy approaches have been in place, you know largely rules-based engines that are written to be able to try and stop the bad guys. And they miss a lot of things. And so our data-driven approach that we patented is called a polygraph. It's a, it's a security architecture and there are three primary benefits. It does a lot of things, but the three things that we think are most profound first is it eliminates the need for, you know dozens of point solutions. I was shocked when I, you know kind of learned about security. I was at Symantec back in the day. And just to see how fragmented this market is, it's one of the biggest markets in tech. $124 billion in annual spend growing at, to $300 billion in the next three years. And it's massively fragmented. And the average number of point solutions that customers have to deal with is dozens. Like literally 75 is the average number. And so we wanted to take a platform approach to solve this problem where the larger the attack surface that you put in the more data that you put into our machine learning algorithms the smarter it gets and the higher, the efficacy. So eliminating point solutions is his value proposition one. Point two is that we have to be 10 X better than everybody else in the business. Otherwise the merchant companies don't get a breakout and become long and during companies. And so there's a number of different dimensions. The first dimension that I think is probably the most important is efficacy, you know in anomaly detection or in, you know threat detection where you're trying to identify what risks we have in the business. It's, it's generally a very noisy activity. And so rules-based approaches on average will produce a hundred alerts to our one or two. Those, the signal to noise ratio, is, is, you know is a massive a 100x, but call it 10x a reduction. And so we're actually delivering the needle versus the haystack for security administrators and dev developers to actually solve the problem. So it's 10x, higher efficacy it's 10x faster to be able to resolve the problems. And obviously the ROI is, is a no-brainer because you're eliminating all these points which is in having to manage it. And the third, and probably the thing that I'm most excited about what we're doing and what our customers are already realizing is that we're transforming security and compliance teams from kind of compliance into business enablers. when you automate all these processes and you build it into, you know the CICD platforms for the developers you actually enable the developers to write code to differentiate their business, you know to create new customer experiences to get competitive advantage and drive revenue for their businesses. And, and you know that's not what security has done up to this point. We oftentimes, they're the ones we're the ones having to say, no, you know we're slow down or it's too risky, etc. But when you automate that and you increase the efficacy you can enable the developers to do their thing. And it allows the CSOs and allows the security professionals to up level their responsibility into selling and driving revenue. And that is increasingly going to become more and more important for supply chains and partners of these cloud native businesses of how secure am I working with you, etc. And so we think that that transformation of the role of security is going to be as, as meaningful as the technology that we're providing the business. So we're super excited about it. >> I could tell you have so much going on this investment team Sutter Hill, you mentioned big time players huge success track record. Just saw them written up in the wall street journal as one of the best venture capital firms and returns. It's just that the bets are all coming home, but their bet strategy is simple. Disrupting the market that's growing and changing PureStorage, you mentioned company you've worked for, you know people were saying, oh, they'll never get escape velocity. They disrupted an existing, boring storage market changed the game there, security, right for change. A lot of tools, a lot of people have buying tools off the shelf, you know and everyone fighting for the platform. That seems to be the conversation. So I have to ask you, you guys want to be the player that that platform you are, that platform what's different in this platform where everyone's trying to be a security platform, what's makes you different. >> Yeah. So I mean, I think the platform wars are, are clearly, upon us, you know I think what's different about our approach is that we were built on the cloud, for the cloud so we're a cloud native business that, you know runs our business on AWS and everything that we do. We don't have hardware, we don't own data centers. we don't have any of the legacy elements that are there. we use software run on the cloud to enable this. So that's point number one point number two is we did the hard work of mapping the data elements that are out there and adjusting them in and then have this polygraph, you know behavioral anomaly detection, that is it can be applied to today. It's being applied to vulnerability and discovery management and containers and Kubernetes. But over time we believe it extends very naturally to a larger part of the attack server. So we don't have to rewrite the data engine to develop solutions across broader attack services. We already have that, you know so I think our time to develop and innovate will be profound. And I think the third thing that we're seeing companies do and largely the legacy bigger companies is that they're just acquiring their way there. And, it's very, very difficult to acquire 8 to 10 to 20, 30 companies, 30 different CTOs 30 different code bases and try and integrate them to provide a delightful customer experience. And, the parallels, you know in the storage business are, are are pretty similar actually, Dell bought EMC, EMC bought a hundred companies. And, we went after a platform approach to be able to go attack them with a unified file system in a in a unified customer experience that was native for the media that we're working with. We're doing the same playbook here, you know which is you have to have the hard work of the foundation elements in place to be cloud native to deliver great outcomes, great efficacy and and a really great customer experience. So when we get head to head with any of these points coming out and trying to solve something for containers or Kubernetes, or just vulnerability discovery and management, etc, or we're competing with the legacy companies that have, a hodgepodge of acquisitions that they're trying to pull together we went North of 95% of the time. our POC win rates are phenomenal better than anything I've ever seen. We had a pretty good one to appear too. And the, the product and the experience and the efficacy kind of stand on their own once we're in those fights. So part of why we enjoy working with AWS and are really focused on building the partnership together is that it creates awareness of what could be and what possibilities all we want is a shot. And, our approach is such that you can be up and running in minutes, you know and every single one of our customers does a POC. So we'll stand behind our technology as our real differentiator compared to anybody else that's out there. >> Great. You guys had great traction going on with the company certainly saw the investment news that you mentioned earlier at the top. Why did you come on as CEO? And when did you come on and join the team? And what was the reason? What, what, what attracted you to join as the CEO of Lacework? >> Well, I've been involved in the company for since the beginning actually I invested in the early rounds participated on the board and I've always bought into this. The thesis that security is fundamentally a data problem. And if we can get the data problem and the data processing right, you know you can fundamentally change the industry but you need to have a major inflection. And that inflection is people moving to the cloud. And we all have seen it during the pandemic. things are accelerating. AWS just did their earnings yesterday. I think they increased their top-line guidance from 46 billion to 56 billion this year. I mean, it's a machine that is continuing to move forward. They have 30% market share. Azure's investing at 20% GCP still investing people are moving their businesses online aggressively. And as they shift to the cloud the rules-based approach just doesn't work. It doesn't scale. And so a new approach needs to be done. And so by being cloud native and best of breed and solving the thorny problem of this data processing problem first, you know it gives us an opportunity to use that to then extend and build a business, you know at an enduring level over the next 10 to 20 years. And that's Sutter's model, that's their playbook. They don't invest in 400 companies and kind of spray and pray, which is what most venture funds do. And I love them. They're great. And we appreciate the investment in tech, but Sutter's focus is find a really big market find a catalyst for change. In our case, it's moving to the cloud and then build a modern approach. that is 10x better in every dimension. And that attracted to me. I mean, it's, it's a, it's one of the biggest markets in tech and it's one of the most important things that we can do is a digital business is to ensure that we're secure and we're safe and the threats are becoming much more skilled much more deliberate, much better funded. And so the importance for us to ensure that company's security is really tight is, is increasingly critical. So the combination of those factors, and then as I dove back into it and talked to a bunch of customers and talk to partners and seeing the outcomes and enthusiasm that they had and the, the team is phenomenal. And so talking to them, and I just kind of got energized by the opportunity to go build a really important company that really delivers great outcomes. So I'm having a ball great to be back into it. >> Yeah. It's great to have leadership that has experienced that you have and go to the next level because this is classic next level. When you talk about Amazon's earnings and cloud scale and hybrid and edge right around the corner at scale as well. So you start to see that transformation really hit the tipping point, which is changing the landscape on the developer side, which I think is super valuable. I think you hit that. You mentioned core problem. You guys look at that through the lens of data problem. How does this trend of everything going hybrid and soon to be, you know edge core to edge impact your businesses of tailwind? How do you see you capturing that next level of scale from a business perspective for lease work? >> Well, I think that the trend, you know from core to edge, you know, hybrid and, you know ultimately cloud a hundred percent, there we've started with the cloud native businesses. Like, we've been focusing on those companies that are already there, you know and so now we're we just had finished a phenomenal record-breaking Q1 and multiple seven figure deals, you know with very complex global environments where they do have a hybrid environment and they are leveraging the edge. And we're perfect for that. I mean, as you think about what we deliver in its most simplistic context, you know we're effectively delivering a security solution from the container to control plane, right. You know we want to be able to have a granular understanding of operated trillions of data points coming in and those can be collected in the core. They can be collected on-prem. They can be collected in the cloud. Ultimately they need to be collected and then contextualized so, you know and this is where our behavioral polygraph technology transitions data into information that's useful via the polygraph. And so we think that, the complexity that's added with environments that are hybrid environments that are leveraging the edge environments that are leveraging the cloud native all need a control plane to run across that to deliver efficacy, you know, for our customers. And, we work with, you know AWS has their own security tools. Azure has some security tools UCPs security tools, but ultimately, our, our challenge and opportunity is to be best of breed to deliver incremental value on top of that and that horizontal value across it. so customers have choice but they know that their security posture is, is, is secure. And so we, we see it as a tailwind for our businesses as we go forward. >> I always said the companies that have the horizontal scalability with cloud and then have that vertical AI kind of vibe where you can get in the context of the data is there to win it all. And I think that you guys have a great solution potentially there. I want to get more information if you don't mind double clicking on that with me, this is kind of a different take on cloud security because you've got the scalability, which gives you the observation space. And then you got to get the context to get the right patterns or whatever magic you guys have in the, in the secret sauce. But you doing that on top of massive exponential velocity. >> Yeah. >> Where's that secret sauce? Is it in the compute? Is it in the software? What's different about what you guys have in security to give us a- >> It's all in the, it's all in the software. Ultimately, it's the intelligence of how you capture it how you ingest it, how you, you process it but then ultimately how you, how you contextualize it and then how you apply it to different problems. and so the attack surface area and security is a very broad, that's why there's so many point solutions that are out there. And so the breadth of solutions, you know we just want to continue to add solutions and capabilities on top of this polygraph security architecture that allows for the same kind of simple experience, the same kind of 10x value proposition, but, but, but wider. And so we can eliminate more and more of those of those point solutions. So, our, our thinking on it is that, you know we can participate once we have a customer the land and expand motion of what we have. We want to make it really really frictionless for customers to try our technology. And so that's why we do POC. That's why it only takes a couple of minutes and you can do it for just Kubernetes or just containers or just vulnerability discovery and managed like wherever your specific pain point is. We want to help identify what that is, you know give you a chance to try it. And then once we prove ourselves it's very easy to extend that across the board. So we get natural growth in velocity from people moving to cloud and just, you know more usage of, of compute and storage and sort of etc, but breadth of actually the security or posture or a tax service that they have as well. So, you know so I think we have an opportunity to benefit from, from both the depth and the breadth, you know but the value that we're delivering is ultimately the software that we're running on top of the infrastructure. And you mentioned observability, there's a number of companies that are leveraging the data and insights collected in different ways to converge security and observability over time. And, we see that, you know that ultimately there's a very very big security company that needs to be built. That really is best of breed, but the data and the insights that we're providing to our primary customer, which is really DevOps. I mean, it's really the development communities and the builders or who we're changing security for and enabling, in addition to the security teams, you know we think that we're going to continue to drive software that adds value on that data set and it can be applied to multiple problems in the future. So today security is a massive market. We're going to focus there, but it does. It does extend pretty naturally to other markets >> It's a hot market security. Everyone needs to have the latest and greatest and also has to be effective. I got to ask you specifically around startup transition to a rapidly growing company to now you're going to the next level where you're starting to having to get into some serious, big complex enterprise go to market sales motions. So what's in it for the customer. What's the, what's the pain point? What's the customer orientation. What do you marketing into as a solution? Is it the developer? Is it the CSO? Is it the CXO, what's in it for the enterprise? Why Lacework, why are they engaging? You guys get record numbers. What's the, what's in it for them. What's the, if I'm the customer what's in it for me? >> Ultimately efficacy, which is your security posture is it goes up significantly, simplicity, which is makes it easier for you to do your other jobs, you know and I'll have to look for those needles in a haystack and ROI, you know which is it's just compelling, and much, much more efficient than what, what you're doing today. So that that's a pretty universal value proposition and applies to cloud native businesses that are high growth that applies to government agencies. It applies to a large complex enterprises. We have a wonderful kind of go to market motion right now. I think Andy Byron and the team who've been here have really done a wonderful job of really making the customer buying experience and the journey really efficient, you know and help them quantify the impact and the risks and then deliver value. And I think, that that applies in sort of the commercial mid-market and cloud native space. And like I mentioned, we had, a number of deals in the quarter that were seven figure deals, you know in very complex organizations with massive demands. And, you know it ultimately selling is a team sport and, you know and still having the process and the rigor, that's there fine tuning that to make sure you have the people and the partnerships, you know, that deliver solutions in the way that customers want to buy them and then ultimately deliver a value proposition that is just unquestionably better. And I think we have all of those elements, you know we'll be entering the, the large enterprise very aggressively in the quarters to come. I that's where I've come from, you know running a multi-tool, you know, kind of go to market engines where you've got mid-market commercial enterprise large enterprise government across all geographies is, is really fun to expand. And, we're we're hiring as fast as we can maintain quality, you know? And so we're out of that startup phase now and entering into real scale. And, I think that, you know in the AWS marketplace I think we're the number one startup vendor. If I, if I got my facts, right. for, for private offers, we're one of the top security players and top 50 ISBs in the marketplace overall. And so in order for us to get the motion we need to make sure that we're delivering our value in the context of how companies want to buy it. And people want to use AWS credits, you know to apply to their solutions. And so it's really important for us to make that frictionless buying experience occur. And so we're excited about it. I think we've got a really nice start and it's the fun part of building companies, which is how do you attune things to make sure you're making it really really easy for the market to absorb your technology. And then once you're there, delight the hell out of them and just make sure that, that there's that they're excited in our, our net retention rates are the best I've seen in the marketplace. Our net promoter scores, you know, are in the high fifties low sixties, which, which is fantastic in this space. I think it's best in class by order of magnitude some players, big SIM players that are out there, you know have a customer in net promoter score of four. You know that means 96% of the people or 96 boats that says they wouldn't recommend the solution to their, to their peers. So, at pure, we've got this at scale. So from 70 to, in the, in the low eighties I think we have the opportunity to do the same thing here. So, combination of tailoring the motion that we have making it really easy for the buyer to buy what they want with whom they want from whom they want, you know and then just spreading a value proposition. That is a no brainer is, is I think the secret recipe >> If anything, it's interesting, you know you're so much experience in the enterprise and tech with cloud native you're basically laying out the success formula, which is if you have a value proposition you should be able to get it in quickly. You don't need the top down. win everything you can have a value proposition that can be enabled for usage and then grow rapidly when it's successful and that's cloud, that's the cloud business model. So it's not so much about organic versus this. It's really what the preferred motion is. >> It's speed, and I think developers in particular it's why the cloud happened, right? I.T wasn't delivering services in, in the speed and the efficacy that, that, that the developers wanted. And so in order to appeal to the developer community you need to deliver something that's frictionless and easy and fits into JIRA and fits into their workflow processes and speaks their language. And so we built our platform and our solutions for builders because that's where the money is. That's where the pain point is and that's and they want to build secure code. They just don't want to be told no. And so, we want to automate that process and make code secure and do that, you know in the build phase and then do it in the runtime. And then across the CICD pipeline we want to continuously be adding value across that. And, and the developers, candidly when pure bought the solution, many years ago and I introduced him to the company, it was it was the general manager of our software business unit that bought it not the security team. And I think that's a trend that is continuing that we're going to focus on. >> A lot of people realize that security and compliance and automation kind of all go together where you don't want to disrupt developers to kind of engineer something just to do an integration, for instance. So there's a real business model impact that you're hitting on here. That's not just a technical solution. It's really how the business is operating. And I think that to me is super interesting use case. What's your reaction to that? Do you see this as a, as a- >> No it's, that's that's that third part that I was talking about, you know which is that's most exciting is that, you know people are calling shift left, right. so moving, you know security into the development pipeline as it's happening and in integrating security architects as value added into the development organizations themselves and leveraging automated machine learning tools like ours to be able to simplify and automate the process versus slowing it down. So we think that shift left is, is super exciting and, and will continue. And we actually think we're the leaders in that space. We want to continue to be the leaders in that. >> Congratulations, great insight. Awesome to have you on and to hear from your experience and also the great venture that your scaling up and to the next level. Lacework, David thanks for coming on, but I'll give you the last minute to close us out. Give us a quick plug for the company vitals, what you're working on now, what you're looking for, you're obviously hiring give a quick plug for Lacework. What you, what are you working on? >> So, number one, we love our partnership with AWS. And so we're going to continue to invest, invest there. Two the businesses growing North of 300% year over year. That means that we've got record breaking growth and lots of hiring. So we're hiring across all functions. And three give us an opportunity. I, I think that, you know, you can fundamentally we want to be the bar of what you define all other security companies and all the technology companies. So it's a high bar. We want to make it frictionless, frictionless to try give us a shot, give us some feedback. And I'm grateful and privileged to be part of this, this wonderful team. So look forward to spending more time with you, John, in the future. >> Man, looking forward to a lot lots of talk about David Hatfield CEO of Lacework great company scaling up again. Another success story in cloud, cloud native as Po, COVID comes to a close, if you will for this phase and people get back to real life. The scale of cloud is going to be leading it and a new technology is going to be powering it. This is theCube conversation. I'm John Furrier. Thanks for watching. (soft music playing) (music fades)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCube and ETR, this is Breaking Analysis with Dave Vellante. >> When a piece of digital art sells for $69.3 million, more than has ever been paid for works, by Gauguin or Salvador Dali, making it created the third most expensive living artists in the world. One can't help but take notice and ask, what is going on? The latest craze around NFTs may feel a bit bubblicious, but it's yet another sign, that the digital age is now fully upon us. Hello and welcome to this week's Wikibon's CUBE insights, powered by ETR. In this Breaking Analysis, we want to take a look at some of the trends, that may be difficult for observers and investors to understand, but we think offer significant insights to the future and possibly some opportunities for young investors many of whom are fans of this program. And how the trends may relate to enterprise tech. Okay, so this guy Beeple is now the hottest artist on the planet. That's his Twitter profile. That picture on the inset. His name is Mike Winkelmann. He is actually a normal looking dude, but that's the picture he chose for his Twitter. This collage reminds me of the Million Dollar Homepage. You may already know the story, but many of you may not. Back in 2005 a college kid from England named Alex Tew, T-E-W created The Million Dollar Homepage to fund his education. And his idea was to create a website with a million pixels, and sell ads at a dollar for each pixel. Guess how much money he raised. A million bucks, right? No, wrong. He raised $1,037,100. How so you ask? Well, he auctioned off the last 1000 pixels on eBay, which fetched an additional $38,000. Crazy, right? Well, maybe not. Pretty creative in a way, way early sign of things to come. Now, I'm not going to go deep into NFTs, and explain the justification behind them. There's a lot of material that's been published that can do justice to the topic better than I can. But here are the basics, NFTs stands for Non-Fungible Tokens. They are digital representations of assets that exist in a blockchain. Now, each token as a unique and immutable identifier, and it uses cryptography to ensure its authenticity. NFTs by the name, they're not fungible. So, unlike Bitcoin, Ethereum or other cryptocurrencies, which can be traded on a like-for-like basis, in other words, if you and I each own one bitcoin we know exactly how much each of our bitcoins is worth at any point of time. Non-Fungible Tokens each have their own unique values. So, they're not comparable on a like-to-like basis. But what's the point of this? Well, NFTs can be applied to any property, identities tweets, videos, we're seeing collectables, digital art, pretty much anything. And it's really. The use cases are unlimited. And NFTs can streamline transactions, and they can be bought and sold very efficiently without the need for a trusted third party involved. Now, the other benefit is the probability of fraud, is greatly reduced. So where do NFTs fit as an asset class? Well, they're definitely a new type of asset. And again, I'm not going to try to justify their existence, but I want to talk about the choices, that investors have in the market today. The other day, I was on a call with Jay Po. He is a VC and a Principal at a company called Stage 2 Capital. He's a former Bessemer VC and one of the sharper investors around. And he was talking about the choices that investors have and he gave a nice example that I want to share with you and try to apply here. Now, as an investor, you have alternatives, of course we're showing here a few with their year to date charts. Now, as an example, you can buy Amazon stock. Now, if you bought just about exactly a year ago you did really well, you probably saw around an 80% return or more. But if you want to jump in today, your mindset might be, hmm, well, okay. Amazon, they're going to be around for a long time, so it's kind of low risk and I like the stock, but you're probably going to get, well let's say, maybe a 10% annual return over the longterm, 15% or maybe less maybe single digits, but, maybe more than that but it's unlikely that any kind of reasonable timeframe within any reasonable timeframe you're going to get a 10X return. In order to get that type of return on invested capital, Amazon would have to become a $16 trillion valued company. So, you sit there, you asked yourself, what's the probability that Amazon goes out of business? Well, that's pretty low, right? And what are the chances it becomes a $16 trillion company over the next several years? Well, it's probably more likely that it continues to grow at that more stable rate that I talked about. Okay, now let's talk about Snowflake. Now, as you know, we've covered the company quite extensively. We watched this company grow from an early stage startup and then saw its valuation increase steadily as a private company, but you know, even early last year it was valued around $12 billion, I think in February, and as late as mid September right before the IPO news hit that Marc Benioff and Warren Buffett were going to put in $250 million each at the IPO or just after the IPO and it was projected that Snowflake's valuation could go over $20 billion at that point. And on day one after the IPO Snowflake, closed worth more than $50 billion, the stock opened at 120, but unless you knew a guy, you had to hold your nose and buy on day one. And you know, maybe got it at 240, maybe you got it at 250, you might have got it at higher and at the time you might recall, I said, You're likely going to get a better price than on day one, which is usually the case with most IPOs, stock today's around 230. But you look at Snowflake today and if you want to buy in, you look at it and say, Okay, well I like the company, it's probably still overvalued, but I can see the company's value growing substantially over the next several years, maybe doubling in the near to midterm [mumbles] hit more than a hundred billion dollar valuation back as recently as December, so that's certainly feasible. The company is not likely to flame out because it's highly valued, I have to probably be patient for a couple of years. But you know, let's say I liked the management, I liked the company, maybe the company gets into the $200 billion range over time and I can make a decent return, but to get a 10X return on Snowflake you have to get to a valuation of over a half a trillion. Now, to get there, if it gets there it's going to become one of the next great software companies of our time. And you know, frankly if it gets there I think it's going to go to a trillion. So, if that's what your bet is then you know, you would be happy with that of course. But what's the likelihood? As an investor you have to evaluate that, what's the probability? So, it's a lower risk investment in Snowflake but maybe more likely that Snowflake, you know, they run into competition or the market shifts, maybe they get into the $200 billion range, but it really has to transform the industry execute for you to get in to that 10 bagger territory. Okay, now let's look at a different asset that is cryptocurrency called Compound, way more risky. But Compound is a decentralized protocol that allows you to lend and borrow cryptocurrencies. Now, I'm not saying go out and buy compound but just as a thought exercise is it's got an asset here with a lower valuation, probably much higher upside, but much higher risk. But so for Compound to get to 10X return it's got to get to $20 billion valuation. Now, maybe compound isn't the right asset for your cup of tea, but there are many cryptos that have made it that far and if you do your research and your homework you could find a project that's much, much earlier stage that yes, is higher risk but has a much higher upside that you can participate in. So, this is how investors, all investors really look at their choices and make decisions. And the more sophisticated investors, they're going to use detailed metrics and analyze things like MOIC, Multiple on Invested Capital and IRR, which is Internal Rate of Return, do TAM analysis, Total Available Market. They're going to look at competition. They're going to look at detailed company models in ARR and Churn rates and so forth. But one of the things we really want to talk about today and we brought this up at the snowflake IPO is if you were Buffet or Benioff and you had to, you know, quarter of a dollars to put in you could get an almost guaranteed return with your late in the game, but pre IPO money or a look if you were Mike Speiser or one of the earlier VCs or even someone like Jeremy Burton who was part of the inside network you could get stock or options, much cheaper. You get a 5X, 10X, 50X or even North of a hundred X return like the early VCs who took a big risk. But chances are, you're not one of these in one of these categories. So how can you as a little guy participate in something big and you might remember at the time of the snowflake IPO we showed you this picture, who are these people, Olaf Carlson-Wee, Chris Dixon, this girl Sono. And of course Tim Berners-Lee, you know, that these are some of the folks that inspired me personally to pay attention to crypto. And I want to share the premise that caught my attention. It was this. Think about the early days of the internet. If you saw what Berners-Lee was working on or Linus Torvalds, in one to invest in the internet, you really couldn't. I mean, you couldn't invest in Linux or TCP/IP or HTTP. Suppose you could have invested in Cisco after its IPO that would have paid off pretty big time, for sure. You know, he could have waited for the Netscape IPO but the core infrastructure of the internet was fundamentally not directly a candidate for investment by you or really, you know, by anybody. And Satya Nadella said the other day we have reached maximum centralization. The main protocols of the internet were largely funded by the government and they've been co-opted by the giants. But with crypto, you actually can invest in core infrastructure technologies that are building out a decentralized internet, a new internet, you know call it web three Datto. It's a big part of the investment thesis behind what Carlson-wee is doing. And Andreessen Horowitz they have two crypto funds. They've raised more than $800 million to invest and you should read the firm's crypto investment thesis and maybe even take their crypto startup classes and some great content there. Now, one of the people that I haven't mentioned in this picture is Camila Russo. She's a journalist she's turned into hardcore crypto author is doing great job explaining the white hot defining space or decentralized finance. If you're just at read her work and educate yourself and learn more about the future and be happy perhaps you'll find some 10X or even hundred X opportunities. So look, there's so much innovation going around going on around blockchain and crypto. I mean, you could listen to Warren Buffet and Janet Yellen who implied this is all going to end badly. But while look, these individuals they're smart people. I don't think they would be my go-to source on understanding the potential of the technology and the future of what it could bring. Now, we've talked earlier at the, at the start here about NFTs. DeFi is one of the most interesting and disruptive trends to FinTech, names like Celsius, Nexo, BlockFi. BlockFi let's actually the average person participate in liquidity pools is actually quite interesting. Crypto is going mainstream Tesla, micro strategy putting Bitcoin on their balance sheets. We have a 2017 Jamie diamond. He called Bitcoin a tulip bulb like fraud, yet just the other day JPM announced a structured investment vehicle to give its clients a basket of stocks that have exposure to crypto, PayPal allowing customers to buy, sell, and Hodl crypto. You can trade crypto on Robin Hood. Central banks are talking about launching digital currencies. I talked about the Fedcoin for a number of years and why not? Coinbase is doing an IPO will give it a value of over a hundred billion. Wow, that sounds frothy, but still big names like Mark Cuban and Jamaat palliate Patiala have been active in crypto for a while. Gronk is getting into NFTs. So it goes to have a little bit of that bubble feel to it. But look often when tech bubbles burst they shake out the pretenders but if there's real tech involved, some contenders emerge. So, and they often do so as dominant players. And I really believe that the innovation around crypto is going to be sustained. Now, there is a new web being built out. So if you want to participate, you got to do some research figure out things like how PolkaWorks, make a call on whether you think avalanche is an Ethereum killer dig in and find out about new projects and form a thesis. And you may, as a small player be able to find some big winners, but look you do have to be careful. There was a lot of fraud during the ICO. Craze is your risk. So understand the Tokenomics and maybe as importantly the Pump-a-nomics, because they certainly loom as dangers. This is not for the faint of heart but because I believe it involves real tech. I like it way better than Reddit stocks like GameStop for example, now not to diss Reddit. There's some good information on Reddit. If you're patient, you can find it. And there's lots of good information flowing on Discord. There's people flocking to Telegram as a hedge against big tech. Maybe there's all sounds crazy. And you know what, if you've grown up in a privileged household and you have a US Education you know, maybe it is nuts and a bit too risky for you. But if you're one of the many people who haven't been able to participate in these elite circles there are things going on, especially outside of the US that are democratizing investment opportunities. And I think that's pretty cool. You just got to be careful. So, this is a bit off topic from our typical focus and ETR survey analysis. So let's bring this back to the enterprise because there's a lot going on there as well with blockchain. Now let me first share some quotes on blockchain from a few ETR Venn Roundtables. First comment is from a CIO to diversified holdings company who says correctly, blockchain will hit the finance industry first but there are use cases in healthcare given the privacy and security concerns and logistics to ensure provenance and reduce fraud. And to that individual's point about finance. This is from the CTO of a major financial platform. We're really taking a look at payments. Yeah. Do you think traditional banks are going to lose control of the payment systems? Well, not without a fight, I guess, but look there's some real disruption possibilities here. And just last comment from a government CIO says, we're going to wait until the big platform players they get into their software. And so that is happening Oracle, IBM, VMware, Microsoft, AWS Cisco, they all have blockchain initiatives going on, now by the way, none of these tech companies wants to talk about crypto. They try to distance themselves from that topic which is understandable, I guess, but I'll tell you there's far more innovation going on in crypto than there is in enterprise tech companies at this point. But I predict that the crypto innovations will absolutely be seeping into enterprise tech players over time. But for now the cloud players, they want to support developers who are building out this new internet. The database is certainly a logical place to support a mutable transactions which allow people to do business one-on-one and have total confidence that the source hasn't been hacked or changed and infrastructure to support smart contracts. We've seen that. The use cases in the enterprise are endless asset tracking data access, food, tracking, maintenance, KYC or know your customer, there's applications in different industries, telecoms, oil and gas on and on and on. So look, think of NFTs as a signal crypto craziness is a signal. It's a signal as to how IT in other parts of companies and their data might be organized, managed and tracked and protected, and very importantly, valued. Look today. There's a lot of memes. Crypto kitties, art, of course money as well. Money is the killer app for blockchain, but in the future the underlying technology of blockchain and the many percolating innovations around it could become I think will become a fundamental component of a new digital economy. So get on board, do some research and learn for yourself. Okay, that's it for today. Remember all of these episodes they're available as podcasts, wherever you listen. I publish weekly on wikibon.com and siliconangle.com. Please feel free to comment on my LinkedIn post or tweet me @dvellante or email me at david.vellante@siliconangle.com. Don't forget to check out etr.plus for all the survey action and data science. This is Dave Vellante for theCUBE Insights powered by ETR. Be well, be careful out there in crypto land. Thanks for watching. We'll see you next time. (soft music)