(upbeat music beginning) >> From the Cube Studios in Palo Alto and Boston, bringing you data-driven insights from the Cube and ETR, this is "Breaking Analysis" with Dave Vellante. >> Making predictions about the future of enterprise tech is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say, with some degree of certainty, whether the prediction came true or not, with evidence to back that up. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we aim to do just that, with predictions about the macro IT spending environment, cost optimization, security, lots to talk about there, generative AI, cloud, and of course supercloud, blockchain adoption, data platforms, including commentary on Databricks, snowflake, and other key players, automation, events, and we may even have some bonus predictions around quantum computing, and perhaps some other areas. To make all this happen, we welcome back, for the third year in a row, my colleague and friend Eric Bradley from ETR. Eric, thanks for all you do for the community, and thanks for being part of this program. Again. >> I wouldn't miss it for the world. I always enjoy this one. Dave, good to see you. >> Yeah, so let me bring up this next slide and show you, actually come back to me if you would. I got to show the audience this. These are the inbounds that we got from PR firms starting in October around predictions. They know we do prediction posts. And so they'll send literally thousands and thousands of predictions from hundreds of experts in the industry, technologists, consultants, et cetera. And if you bring up the slide I can show you sort of the pattern that developed here. 40% of these thousands of predictions were from cyber. You had AI and data. If you combine those, it's still not close to cyber. Cost optimization was a big thing. Of course, cloud, some on DevOps, and software. Digital... Digital transformation got, you know, some lip service and SaaS. And then there was other, it's kind of around 2%. So quite remarkable, when you think about the focus on cyber, Eric. >> Yeah, there's two reasons why I think it makes sense, though. One, the cybersecurity companies have a lot of cash, so therefore the PR firms might be working a little bit harder for them than some of their other clients. (laughs) And then secondly, as you know, for multiple years now, when we do our macro survey, we ask, "What's your number one spending priority?" And again, it's security. It just isn't going anywhere. It just stays at the top. So I'm actually not that surprised by that little pie chart there, but I was shocked that SaaS was only 5%. You know, going back 10 years ago, that would've been the only thing anyone was talking about. >> Yeah. So true. All right, let's get into it. First prediction, we always start with kind of tech spending. Number one is tech spending increases between four and 5%. ETR has currently got it at 4.6% coming into 2023. This has been a consistently downward trend all year. We started, you know, much, much higher as we've been reporting. Bottom line is the fed is still in control. They're going to ease up on tightening, is the expectation, they're going to shoot for a soft landing. But you know, my feeling is this slingshot economy is going to continue, and it's going to continue to confound, whether it's supply chains or spending. The, the interesting thing about the ETR data, Eric, and I want you to comment on this, the largest companies are the most aggressive to cut. They're laying off, smaller firms are spending faster. They're actually growing at a much larger, faster rate as are companies in EMEA. And that's a surprise. That's outpacing the US and APAC. Chime in on this, Eric. >> Yeah, I was surprised on all of that. First on the higher level spending, we are definitely seeing it coming down, but the interesting thing here is headlines are making it worse. The huge research shop recently said 0% growth. We're coming in at 4.6%. And just so everyone knows, this is not us guessing, we asked 1,525 IT decision-makers what their budget growth will be, and they came in at 4.6%. Now there's a huge disparity, as you mentioned. The Fortune 500, global 2000, barely at 2% growth, but small, it's at 7%. So we're at a situation right now where the smaller companies are still playing a little bit of catch up on digital transformation, and they're spending money. The largest companies that have the most to lose from a recession are being more trepidatious, obviously. So they're playing a "Wait and see." And I hope we don't talk ourselves into a recession. Certainly the headlines and some of their research shops are helping it along. But another interesting comment here is, you know, energy and utilities used to be called an orphan and widow stock group, right? They are spending more than anyone, more than financials insurance, more than retail consumer. So right now it's being driven by mid, small, and energy and utilities. They're all spending like gangbusters, like nothing's happening. And it's the rest of everyone else that's being very cautious. >> Yeah, so very unpredictable right now. All right, let's go to number two. Cost optimization remains a major theme in 2023. We've been reporting on this. You've, we've shown a chart here. What's the primary method that your organization plans to use? You asked this question of those individuals that cited that they were going to reduce their spend and- >> Mhm. >> consolidating redundant vendors, you know, still leads the way, you know, far behind, cloud optimization is second, but it, but cloud continues to outpace legacy on-prem spending, no doubt. Somebody, it was, the guy's name was Alexander Feiglstorfer from Storyblok, sent in a prediction, said "All in one becomes extinct." Now, generally I would say I disagree with that because, you know, as we know over the years, suites tend to win out over, you know, individual, you know, point products. But I think what's going to happen is all in one is going to remain the norm for these larger companies that are cutting back. They want to consolidate redundant vendors, and the smaller companies are going to stick with that best of breed and be more aggressive and try to compete more effectively. What's your take on that? >> Yeah, I'm seeing much more consolidation in vendors, but also consolidation in functionality. We're seeing people building out new functionality, whether it's, we're going to talk about this later, so I don't want to steal too much of our thunder right now, but data and security also, we're seeing a functionality creep. So I think there's further consolidation happening here. I think niche solutions are going to be less likely, and platform solutions are going to be more likely in a spending environment where you want to reduce your vendors. You want to have one bill to pay, not 10. Another thing on this slide, real quick if I can before I move on, is we had a bunch of people write in and some of the answer options that aren't on this graph but did get cited a lot, unfortunately, is the obvious reduction in staff, hiring freezes, and delaying hardware, were three of the top write-ins. And another one was offshore outsourcing. So in addition to what we're seeing here, there were a lot of write-in options, and I just thought it would be important to state that, but essentially the cost optimization is by and far the highest one, and it's growing. So it's actually increased in our citations over the last year. >> And yeah, specifically consolidating redundant vendors. And so I actually thank you for bringing that other up, 'cause I had asked you, Eric, is there any evidence that repatriation is going on and we don't see it in the numbers, we don't see it even in the other, there was, I think very little or no mention of cloud repatriation, even though it might be happening in this in a smattering. >> Not a single mention, not one single mention. I went through it for you. Yep. Not one write-in. >> All right, let's move on. Number three, security leads M&A in 2023. Now you might say, "Oh, well that's a layup," but let me set this up Eric, because I didn't really do a great job with the slide. I hid the, what you've done, because you basically took, this is from the emerging technology survey with 1,181 responses from November. And what we did is we took Palo Alto and looked at the overlap in Palo Alto Networks accounts with these vendors that were showing on this chart. And Eric, I'm going to ask you to explain why we put a circle around OneTrust, but let me just set it up, and then have you comment on the slide and take, give us more detail. We're seeing private company valuations are off, you know, 10 to 40%. We saw a sneak, do a down round, but pretty good actually only down 12%. We've seen much higher down rounds. Palo Alto Networks we think is going to get busy. Again, they're an inquisitive company, they've been sort of quiet lately, and we think CrowdStrike, Cisco, Microsoft, Zscaler, we're predicting all of those will make some acquisitions and we're thinking that the targets are somewhere in this mess of security taxonomy. Other thing we're predicting AI meets cyber big time in 2023, we're going to probably going to see some acquisitions of those companies that are leaning into AI. We've seen some of that with Palo Alto. And then, you know, your comment to me, Eric, was "The RSA conference is going to be insane, hopping mad, "crazy this April," (Eric laughing) but give us your take on this data, and why the red circle around OneTrust? Take us back to that slide if you would, Alex. >> Sure. There's a few things here. First, let me explain what we're looking at. So because we separate the public companies and the private companies into two separate surveys, this allows us the ability to cross-reference that data. So what we're doing here is in our public survey, the tesis, everyone who cited some spending with Palo Alto, meaning they're a Palo Alto customer, we then cross-reference that with the private tech companies. Who also are they spending with? So what you're seeing here is an overlap. These companies that we have circled are doing the best in Palo Alto's accounts. Now, Palo Alto went and bought Twistlock a few years ago, which this data slide predicted, to be quite honest. And so I don't know if they necessarily are going to go after Snyk. Snyk, sorry. They already have something in that space. What they do need, however, is more on the authentication space. So I'm looking at OneTrust, with a 45% overlap in their overall net sentiment. That is a company that's already existing in their accounts and could be very synergistic to them. BeyondTrust as well, authentication identity. This is something that Palo needs to do to move more down that zero trust path. Now why did I pick Palo first? Because usually they're very inquisitive. They've been a little quiet lately. Secondly, if you look at the backdrop in the markets, the IPO freeze isn't going to last forever. Sooner or later, the IPO markets are going to open up, and some of these private companies are going to tap into public equity. In the meantime, however, cash funding on the private side is drying up. If they need another round, they're not going to get it, and they're certainly not going to get it at the valuations they were getting. So we're seeing valuations maybe come down where they're a touch more attractive, and Palo knows this isn't going to last forever. Cisco knows that, CrowdStrike, Zscaler, all these companies that are trying to make a push to become that vendor that you're consolidating in, around, they have a chance now, they have a window where they need to go make some acquisitions. And that's why I believe leading up to RSA, we're going to see some movement. I think it's going to pretty, a really exciting time in security right now. >> Awesome. Thank you. Great explanation. All right, let's go on the next one. Number four is, it relates to security. Let's stay there. Zero trust moves from hype to reality in 2023. Now again, you might say, "Oh yeah, that's a layup." A lot of these inbounds that we got are very, you know, kind of self-serving, but we always try to put some meat in the bone. So first thing we do is we pull out some commentary from, Eric, your roundtable, your insights roundtable. And we have a CISO from a global hospitality firm says, "For me that's the highest priority." He's talking about zero trust because it's the best ROI, it's the most forward-looking, and it enables a lot of the business transformation activities that we want to do. CISOs tell me that they actually can drive forward transformation projects that have zero trust, and because they can accelerate them, because they don't have to go through the hurdle of, you know, getting, making sure that it's secure. Second comment, zero trust closes that last mile where once you're authenticated, they open up the resource to you in a zero trust way. That's a CISO of a, and a managing director of a cyber risk services enterprise. Your thoughts on this? >> I can be here all day, so I'm going to try to be quick on this one. This is not a fluff piece on this one. There's a couple of other reasons this is happening. One, the board finally gets it. Zero trust at first was just a marketing hype term. Now the board understands it, and that's why CISOs are able to push through it. And what they finally did was redefine what it means. Zero trust simply means moving away from hardware security, moving towards software-defined security, with authentication as its base. The board finally gets that, and now they understand that this is necessary and it's being moved forward. The other reason it's happening now is hybrid work is here to stay. We weren't really sure at first, large companies were still trying to push people back to the office, and it's going to happen. The pendulum will swing back, but hybrid work's not going anywhere. By basically on our own data, we're seeing that 69% of companies expect remote and hybrid to be permanent, with only 30% permanent in office. Zero trust works for a hybrid environment. So all of that is the reason why this is happening right now. And going back to our previous prediction, this is why we're picking Palo, this is why we're picking Zscaler to make these acquisitions. Palo Alto needs to be better on the authentication side, and so does Zscaler. They're both fantastic on zero trust network access, but they need the authentication software defined aspect, and that's why we think this is going to happen. One last thing, in that CISO round table, I also had somebody say, "Listen, Zscaler is incredible. "They're doing incredibly well pervading the enterprise, "but their pricing's getting a little high," and they actually think Palo Alto is well-suited to start taking some of that share, if Palo can make one move. >> Yeah, Palo Alto's consolidation story is very strong. Here's my question and challenge. Do you and me, so I'm always hardcore about, okay, you've got to have evidence. I want to look back at these things a year from now and say, "Did we get it right? Yes or no?" If we got it wrong, we'll tell you we got it wrong. So how are we going to measure this? I'd say a couple things, and you can chime in. One is just the number of vendors talking about it. That's, but the marketing always leads the reality. So the second part of that is we got to get evidence from the buying community. Can you help us with that? >> (laughs) Luckily, that's what I do. I have a data company that asks thousands of IT decision-makers what they're adopting and what they're increasing spend on, as well as what they're decreasing spend on and what they're replacing. So I have snapshots in time over the last 11 years where I can go ahead and compare and contrast whether this adoption is happening or not. So come back to me in 12 months and I'll let you know. >> Now, you know, I will. Okay, let's bring up the next one. Number five, generative AI hits where the Metaverse missed. Of course everybody's talking about ChatGPT, we just wrote last week in a breaking analysis with John Furrier and Sarjeet Joha our take on that. We think 2023 does mark a pivot point as natural language processing really infiltrates enterprise tech just as Amazon turned the data center into an API. We think going forward, you're going to be interacting with technology through natural language, through English commands or other, you know, foreign language commands, and investors are lining up, all the VCs are getting excited about creating something competitive to ChatGPT, according to (indistinct) a hundred million dollars gets you a seat at the table, gets you into the game. (laughing) That's before you have to start doing promotion. But he thinks that's what it takes to actually create a clone or something equivalent. We've seen stuff from, you know, the head of Facebook's, you know, AI saying, "Oh, it's really not that sophisticated, ChatGPT, "it's kind of like IBM Watson, it's great engineering, "but you know, we've got more advanced technology." We know Google's working on some really interesting stuff. But here's the thing. ETR just launched this survey for the February survey. It's in the field now. We circle open AI in this category. They weren't even in the survey, Eric, last quarter. So 52% of the ETR survey respondents indicated a positive sentiment toward open AI. I added up all the sort of different bars, we could double click on that. And then I got this inbound from Scott Stevenson of Deep Graham. He said "AI is recession-proof." I don't know if that's the case, but it's a good quote. So bring this back up and take us through this. Explain this chart for us, if you would. >> First of all, I like Scott's quote better than the Facebook one. I think that's some sour grapes. Meta just spent an insane amount of money on the Metaverse and that's a dud. Microsoft just spent money on open AI and it is hot, undoubtedly hot. We've only been in the field with our current ETS survey for a week. So my caveat is it's preliminary data, but I don't care if it's preliminary data. (laughing) We're getting a sneak peek here at what is the number one net sentiment and mindshare leader in the entire machine-learning AI sector within a week. It's beating Data- >> 600. 600 in. >> It's beating Databricks. And we all know Databricks is a huge established enterprise company, not only in machine-learning AI, but it's in the top 10 in the entire survey. We have over 400 vendors in this survey. It's number eight overall, already. In a week. This is not hype. This is real. And I could go on the NLP stuff for a while. Not only here are we seeing it in open AI and machine-learning and AI, but we're seeing NLP in security. It's huge in email security. It's completely transforming that area. It's one of the reasons I thought Palo might take Abnormal out. They're doing such a great job with NLP in this email side, and also in the data prep tools. NLP is going to take out data prep tools. If we have time, I'll discuss that later. But yeah, this is, to me this is a no-brainer, and we're already seeing it in the data. >> Yeah, John Furrier called, you know, the ChatGPT introduction. He said it reminded him of the Netscape moment, when we all first saw Netscape Navigator and went, "Wow, it really could be transformative." All right, number six, the cloud expands to supercloud as edge computing accelerates and CloudFlare is a big winner in 2023. We've reported obviously on cloud, multi-cloud, supercloud and CloudFlare, basically saying what multi-cloud should have been. We pulled this quote from Atif Kahn, who is the founder and CTO of Alkira, thanks, one of the inbounds, thank you. "In 2023, highly distributed IT environments "will become more the norm "as organizations increasingly deploy hybrid cloud, "multi-cloud and edge settings..." Eric, from one of your round tables, "If my sources from edge computing are coming "from the cloud, that means I have my workloads "running in the cloud. "There is no one better than CloudFlare," That's a senior director of IT architecture at a huge financial firm. And then your analysis shows CloudFlare really growing in pervasion, that sort of market presence in the dataset, dramatically, to near 20%, leading, I think you had told me that they're even ahead of Google Cloud in terms of momentum right now. >> That was probably the biggest shock to me in our January 2023 tesis, which covers the public companies in the cloud computing sector. CloudFlare has now overtaken GCP in overall spending, and I was shocked by that. It's already extremely pervasive in networking, of course, for the edge networking side, and also in security. This is the number one leader in SaaSi, web access firewall, DDoS, bot protection, by your definition of supercloud, which we just did a couple of weeks ago, and I really enjoyed that by the way Dave, I think CloudFlare is the one that fits your definition best, because it's bringing all of these aspects together, and most importantly, it's cloud agnostic. It does not need to rely on Azure or AWS to do this. It has its own cloud. So I just think it's, when we look at your definition of supercloud, CloudFlare is the poster child. >> You know, what's interesting about that too, is a lot of people are poo-pooing CloudFlare, "Ah, it's, you know, really kind of not that sophisticated." "You don't have as many tools," but to your point, you're can have those tools in the cloud, Cloudflare's doing serverless on steroids, trying to keep things really simple, doing a phenomenal job at, you know, various locations around the world. And they're definitely one to watch. Somebody put them on my radar (laughing) a while ago and said, "Dave, you got to do a breaking analysis on CloudFlare." And so I want to thank that person. I can't really name them, 'cause they work inside of a giant hyperscaler. But- (Eric laughing) (Dave chuckling) >> Real quickly, if I can from a competitive perspective too, who else is there? They've already taken share from Akamai, and Fastly is their really only other direct comp, and they're not there. And these guys are in poll position and they're the only game in town right now. I just, I don't see it slowing down. >> I thought one of your comments from your roundtable I was reading, one of the folks said, you know, CloudFlare, if my workloads are in the cloud, they are, you know, dominant, they said not as strong with on-prem. And so Akamai is doing better there. I'm like, "Okay, where would you want to be?" (laughing) >> Yeah, which one of those two would you rather be? >> Right? Anyway, all right, let's move on. Number seven, blockchain continues to look for a home in the enterprise, but devs will slowly begin to adopt in 2023. You know, blockchains have got a lot of buzz, obviously crypto is, you know, the killer app for blockchain. Senior IT architect in financial services from your, one of your insight roundtables said quote, "For enterprises to adopt a new technology, "there have to be proven turnkey solutions. "My experience in talking with my peers are, "blockchain is still an open-source component "where you have to build around it." Now I want to thank Ravi Mayuram, who's the CTO of Couchbase sent in, you know, one of the predictions, he said, "DevOps will adopt blockchain, specifically Ethereum." And he referenced actually in his email to me, Solidity, which is the programming language for Ethereum, "will be in every DevOps pro's playbook, "mirroring the boom in machine-learning. "Newer programming languages like Solidity "will enter the toolkits of devs." His point there, you know, Solidity for those of you don't know, you know, Bitcoin is not programmable. Solidity, you know, came out and that was their whole shtick, and they've been improving that, and so forth. But it, Eric, it's true, it really hasn't found its home despite, you know, the potential for smart contracts. IBM's pushing it, VMware has had announcements, and others, really hasn't found its way in the enterprise yet. >> Yeah, and I got to be honest, I don't think it's going to, either. So when we did our top trends series, this was basically chosen as an anti-prediction, I would guess, that it just continues to not gain hold. And the reason why was that first comment, right? It's very much a niche solution that requires a ton of custom work around it. You can't just plug and play it. And at the end of the day, let's be very real what this technology is, it's a database ledger, and we already have database ledgers in the enterprise. So why is this a priority to move to a different database ledger? It's going to be very niche cases. I like the CTO comment from Couchbase about it being adopted by DevOps. I agree with that, but it has to be a DevOps in a very specific use case, and a very sophisticated use case in financial services, most likely. And that's not across the entire enterprise. So I just think it's still going to struggle to get its foothold for a little bit longer, if ever. >> Great, thanks. Okay, let's move on. Number eight, AWS Databricks, Google Snowflake lead the data charge with Microsoft. Keeping it simple. So let's unpack this a little bit. This is the shared accounts peer position for, I pulled data platforms in for analytics, machine-learning and AI and database. So I could grab all these accounts or these vendors and see how they compare in those three sectors. Analytics, machine-learning and database. Snowflake and Databricks, you know, they're on a crash course, as you and I have talked about. They're battling to be the single source of truth in analytics. They're, there's going to be a big focus. They're already started. It's going to be accelerated in 2023 on open formats. Iceberg, Python, you know, they're all the rage. We heard about Iceberg at Snowflake Summit, last summer or last June. Not a lot of people had heard of it, but of course the Databricks crowd, who knows it well. A lot of other open source tooling. There's a company called DBT Labs, which you're going to talk about in a minute. George Gilbert put them on our radar. We just had Tristan Handy, the CEO of DBT labs, on at supercloud last week. They are a new disruptor in data that's, they're essentially making, they're API-ifying, if you will, KPIs inside the data warehouse and dramatically simplifying that whole data pipeline. So really, you know, the ETL guys should be shaking in their boots with them. Coming back to the slide. Google really remains focused on BigQuery adoption. Customers have complained to me that they would like to use Snowflake with Google's AI tools, but they're being forced to go to BigQuery. I got to ask Google about that. AWS continues to stitch together its bespoke data stores, that's gone down that "Right tool for the right job" path. David Foyer two years ago said, "AWS absolutely is going to have to solve that problem." We saw them start to do it in, at Reinvent, bringing together NoETL between Aurora and Redshift, and really trying to simplify those worlds. There's going to be more of that. And then Microsoft, they're just making it cheap and easy to use their stuff, you know, despite some of the complaints that we hear in the community, you know, about things like Cosmos, but Eric, your take? >> Yeah, my concern here is that Snowflake and Databricks are fighting each other, and it's allowing AWS and Microsoft to kind of catch up against them, and I don't know if that's the right move for either of those two companies individually, Azure and AWS are building out functionality. Are they as good? No they're not. The other thing to remember too is that AWS and Azure get paid anyway, because both Databricks and Snowflake run on top of 'em. So (laughing) they're basically collecting their toll, while these two fight it out with each other, and they build out functionality. I think they need to stop focusing on each other, a little bit, and think about the overall strategy. Now for Databricks, we know they came out first as a machine-learning AI tool. They were known better for that spot, and now they're really trying to play catch-up on that data storage compute spot, and inversely for Snowflake, they were killing it with the compute separation from storage, and now they're trying to get into the MLAI spot. I actually wouldn't be surprised to see them make some sort of acquisition. Frank Slootman has been a little bit quiet, in my opinion there. The other thing to mention is your comment about DBT Labs. If we look at our emerging technology survey, last survey when this came out, DBT labs, number one leader in that data integration space, I'm going to just pull it up real quickly. It looks like they had a 33% overall net sentiment to lead data analytics integration. So they are clearly growing, it's fourth straight survey consecutively that they've grown. The other name we're seeing there a little bit is Cribl, but DBT labs is by far the number one player in this space. >> All right. Okay, cool. Moving on, let's go to number nine. With Automation mixer resurgence in 2023, we're showing again data. The x axis is overlap or presence in the dataset, and the vertical axis is shared net score. Net score is a measure of spending momentum. As always, you've seen UI path and Microsoft Power Automate up until the right, that red line, that 40% line is generally considered elevated. UI path is really separating, creating some distance from Automation Anywhere, they, you know, previous quarters they were much closer. Microsoft Power Automate came on the scene in a big way, they loom large with this "Good enough" approach. I will say this, I, somebody sent me a results of a (indistinct) survey, which showed UiPath actually had more mentions than Power Automate, which was surprising, but I think that's not been the case in the ETR data set. We're definitely seeing a shift from back office to front soft office kind of workloads. Having said that, software testing is emerging as a mainstream use case, we're seeing ML and AI become embedded in end-to-end automations, and low-code is serving the line of business. And so this, we think, is going to increasingly have appeal to organizations in the coming year, who want to automate as much as possible and not necessarily, we've seen a lot of layoffs in tech, and people... You're going to have to fill the gaps with automation. That's a trend that's going to continue. >> Yep, agreed. At first that comment about Microsoft Power Automate having less citations than UiPath, that's shocking to me. I'm looking at my chart right here where Microsoft Power Automate was cited by over 60% of our entire survey takers, and UiPath at around 38%. Now don't get me wrong, 38% pervasion's fantastic, but you know you're not going to beat an entrenched Microsoft. So I don't really know where that comment came from. So UiPath, looking at it alone, it's doing incredibly well. It had a huge rebound in its net score this last survey. It had dropped going through the back half of 2022, but we saw a big spike in the last one. So it's got a net score of over 55%. A lot of people citing adoption and increasing. So that's really what you want to see for a name like this. The problem is that just Microsoft is doing its playbook. At the end of the day, I'm going to do a POC, why am I going to pay more for UiPath, or even take on another separate bill, when we know everyone's consolidating vendors, if my license already includes Microsoft Power Automate? It might not be perfect, it might not be as good, but what I'm hearing all the time is it's good enough, and I really don't want another invoice. >> Right. So how does UiPath, you know, and Automation Anywhere, how do they compete with that? Well, the way they compete with it is they got to have a better product. They got a product that's 10 times better. You know, they- >> Right. >> they're not going to compete based on where the lowest cost, Microsoft's got that locked up, or where the easiest to, you know, Microsoft basically give it away for free, and that's their playbook. So that's, you know, up to UiPath. UiPath brought on Rob Ensslin, I've interviewed him. Very, very capable individual, is now Co-CEO. So he's kind of bringing that adult supervision in, and really tightening up the go to market. So, you know, we know this company has been a rocket ship, and so getting some control on that and really getting focused like a laser, you know, could be good things ahead there for that company. Okay. >> One of the problems, if I could real quick Dave, is what the use cases are. When we first came out with RPA, everyone was super excited about like, "No, UiPath is going to be great for super powerful "projects, use cases." That's not what RPA is being used for. As you mentioned, it's being used for mundane tasks, so it's not automating complex things, which I think UiPath was built for. So if you were going to get UiPath, and choose that over Microsoft, it's going to be 'cause you're doing it for more powerful use case, where it is better. But the problem is that's not where the enterprise is using it. The enterprise are using this for base rote tasks, and simply, Microsoft Power Automate can do that. >> Yeah, it's interesting. I've had people on theCube that are both Microsoft Power Automate customers and UiPath customers, and I've asked them, "Well you know, "how do you differentiate between the two?" And they've said to me, "Look, our users and personal productivity users, "they like Power Automate, "they can use it themselves, and you know, "it doesn't take a lot of, you know, support on our end." The flip side is you could do that with UiPath, but like you said, there's more of a focus now on end-to-end enterprise automation and building out those capabilities. So it's increasingly a value play, and that's going to be obviously the challenge going forward. Okay, my last one, and then I think you've got some bonus ones. Number 10, hybrid events are the new category. Look it, if I can get a thousand inbounds that are largely self-serving, I can do my own here, 'cause we're in the events business. (Eric chuckling) Here's the prediction though, and this is a trend we're seeing, the number of physical events is going to dramatically increase. That might surprise people, but most of the big giant events are going to get smaller. The exception is AWS with Reinvent, I think Snowflake's going to continue to grow. So there are examples of physical events that are growing, but generally, most of the big ones are getting smaller, and there's going to be many more smaller intimate regional events and road shows. These micro-events, they're going to be stitched together. Digital is becoming a first class citizen, so people really got to get their digital acts together, and brands are prioritizing earned media, and they're beginning to build their own news networks, going direct to their customers. And so that's a trend we see, and I, you know, we're right in the middle of it, Eric, so you know we're going to, you mentioned RSA, I think that's perhaps going to be one of those crazy ones that continues to grow. It's shrunk, and then it, you know, 'cause last year- >> Yeah, it did shrink. >> right, it was the last one before the pandemic, and then they sort of made another run at it last year. It was smaller but it was very vibrant, and I think this year's going to be huge. Global World Congress is another one, we're going to be there end of Feb. That's obviously a big big show, but in general, the brands and the technology vendors, even Oracle is going to scale down. I don't know about Salesforce. We'll see. You had a couple of bonus predictions. Quantum and maybe some others? Bring us home. >> Yeah, sure. I got a few more. I think we touched upon one, but I definitely think the data prep tools are facing extinction, unfortunately, you know, the Talons Informatica is some of those names. The problem there is that the BI tools are kind of including data prep into it already. You know, an example of that is Tableau Prep Builder, and then in addition, Advanced NLP is being worked in as well. ThoughtSpot, Intelius, both often say that as their selling point, Tableau has Ask Data, Click has Insight Bot, so you don't have to really be intelligent on data prep anymore. A regular business user can just self-query, using either the search bar, or even just speaking into what it needs, and these tools are kind of doing the data prep for it. I don't think that's a, you know, an out in left field type of prediction, but it's the time is nigh. The other one I would also state is that I think knowledge graphs are going to break through this year. Neo4j in our survey is growing in pervasion in Mindshare. So more and more people are citing it, AWS Neptune's getting its act together, and we're seeing that spending intentions are growing there. Tiger Graph is also growing in our survey sample. I just think that the time is now for knowledge graphs to break through, and if I had to do one more, I'd say real-time streaming analytics moves from the very, very rich big enterprises to downstream, to more people are actually going to be moving towards real-time streaming, again, because the data prep tools and the data pipelines have gotten easier to use, and I think the ROI on real-time streaming is obviously there. So those are three that didn't make the cut, but I thought deserved an honorable mention. >> Yeah, I'm glad you did. Several weeks ago, we did an analyst prediction roundtable, if you will, a cube session power panel with a number of data analysts and that, you know, streaming, real-time streaming was top of mind. So glad you brought that up. Eric, as always, thank you very much. I appreciate the time you put in beforehand. I know it's been crazy, because you guys are wrapping up, you know, the last quarter survey in- >> Been a nuts three weeks for us. (laughing) >> job. I love the fact that you're doing, you know, the ETS survey now, I think it's quarterly now, right? Is that right? >> Yep. >> Yep. So that's phenomenal. >> Four times a year. I'll be happy to jump on with you when we get that done. I know you were really impressed with that last time. >> It's unbelievable. This is so much data at ETR. Okay. Hey, that's a wrap. Thanks again. >> Take care Dave. Good seeing you. >> All right, many thanks to our team here, Alex Myerson as production, he manages the podcast force. Ken Schiffman as well is a critical component of our East Coast studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hoof is our editor-in-chief. He's at siliconangle.com. He's just a great editing for us. Thank you all. Remember all these episodes that are available as podcasts, wherever you listen, podcast is doing great. Just search "Breaking analysis podcast." Really appreciate you guys listening. I publish each week on wikibon.com and siliconangle.com, or you can email me directly if you want to get in touch, david.vellante@siliconangle.com. That's how I got all these. I really appreciate it. I went through every single one with a yellow highlighter. It took some time, (laughing) but I appreciate it. You could DM me at dvellante, or comment on our LinkedIn post and please check out etr.ai. Its data is amazing. Best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights, powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis." (upbeat music beginning) (upbeat music ending)

(upbeat music) >> Hello, this is Dave Valente with theCUBE, and one of the most gratifying aspects of my role as a host of "theCUBE TV" is I get to cover a wide range of topics. And quite often, we're able to bring to our program a level of expertise that allows us to more deeply explore and unpack some of the topics that we cover throughout the year. And one of our favorite topics, of course, is data. Now, in 2021, after being in isolation for the better part of two years, a group of industry analysts met up at AWS re:Invent and started a collaboration to look at the trends in data and predict what some likely outcomes will be for the coming year. And it resulted in a very popular session that we had last year focused on the future of data management. And I'm very excited and pleased to tell you that the 2023 edition of that predictions episode is back, and with me are five outstanding market analyst, Sanjeev Mohan of SanjMo, Tony Baer of dbInsight, Carl Olofson from IDC, Dave Menninger from Ventana Research, and Doug Henschen, VP and Principal Analyst at Constellation Research. Now, what is it that we're calling you, guys? A data pack like the rat pack? No, no, no, no, that's not it. It's the data crowd, the data crowd, and the crowd includes some of the best minds in the data analyst community. They'll discuss how data management is evolving and what listeners should prepare for in 2023. Guys, welcome back. Great to see you. >> Good to be here. >> Thank you. >> Thanks, Dave. (Tony and Dave faintly speaks) >> All right, before we get into 2023 predictions, we thought it'd be good to do a look back at how we did in 2022 and give a transparent assessment of those predictions. So, let's get right into it. We're going to bring these up here, the predictions from 2022, they're color-coded red, yellow, and green to signify the degree of accuracy. And I'm pleased to report there's no red. Well, maybe some of you will want to debate that grading system. But as always, we want to be open, so you can decide for yourselves. So, we're going to ask each analyst to review their 2022 prediction and explain their rating and what evidence they have that led them to their conclusion. So, Sanjeev, please kick it off. Your prediction was data governance becomes key. I know that's going to knock you guys over, but elaborate, because you had more detail when you double click on that. >> Yeah, absolutely. Thank you so much, Dave, for having us on the show today. And we self-graded ourselves. I could have very easily made my prediction from last year green, but I mentioned why I left it as yellow. I totally fully believe that data governance was in a renaissance in 2022. And why do I say that? You have to look no further than AWS launching its own data catalog called DataZone. Before that, mid-year, we saw Unity Catalog from Databricks went GA. So, overall, I saw there was tremendous movement. When you see these big players launching a new data catalog, you know that they want to be in this space. And this space is highly critical to everything that I feel we will talk about in today's call. Also, if you look at established players, I spoke at Collibra's conference, data.world, work closely with Alation, Informatica, a bunch of other companies, they all added tremendous new capabilities. So, it did become key. The reason I left it as yellow is because I had made a prediction that Collibra would go IPO, and it did not. And I don't think anyone is going IPO right now. The market is really, really down, the funding in VC IPO market. But other than that, data governance had a banner year in 2022. >> Yeah. Well, thank you for that. And of course, you saw data clean rooms being announced at AWS re:Invent, so more evidence. And I like how the fact that you included in your predictions some things that were binary, so you dinged yourself there. So, good job. Okay, Tony Baer, you're up next. Data mesh hits reality check. As you see here, you've given yourself a bright green thumbs up. (Tony laughing) Okay. Let's hear why you feel that was the case. What do you mean by reality check? >> Okay. Thanks, Dave, for having us back again. This is something I just wrote and just tried to get away from, and this just a topic just won't go away. I did speak with a number of folks, early adopters and non-adopters during the year. And I did find that basically that it pretty much validated what I was expecting, which was that there was a lot more, this has now become a front burner issue. And if I had any doubt in my mind, the evidence I would point to is what was originally intended to be a throwaway post on LinkedIn, which I just quickly scribbled down the night before leaving for re:Invent. I was packing at the time, and for some reason, I was doing Google search on data mesh. And I happened to have tripped across this ridiculous article, I will not say where, because it doesn't deserve any publicity, about the eight (Dave laughing) best data mesh software companies of 2022. (Tony laughing) One of my predictions was that you'd see data mesh washing. And I just quickly just hopped on that maybe three sentences and wrote it at about a couple minutes saying this is hogwash, essentially. (laughs) And that just reun... And then, I left for re:Invent. And the next night, when I got into my Vegas hotel room, I clicked on my computer. I saw a 15,000 hits on that post, which was the most hits of any single post I put all year. And the responses were wildly pro and con. So, it pretty much validates my expectation in that data mesh really did hit a lot more scrutiny over this past year. >> Yeah, thank you for that. I remember that article. I remember rolling my eyes when I saw it, and then I recently, (Tony laughing) I talked to Walmart and they actually invoked Martin Fowler and they said that they're working through their data mesh. So, it takes a really lot of thought, and it really, as we've talked about, is really as much an organizational construct. You're not buying data mesh >> Bingo. >> to your point. Okay. Thank you, Tony. Carl Olofson, here we go. You've graded yourself a yellow in the prediction of graph databases. Take off. Please elaborate. >> Yeah, sure. So, I realized in looking at the prediction that it seemed to imply that graph databases could be a major factor in the data world in 2022, which obviously didn't become the case. It was an error on my part in that I should have said it in the right context. It's really a three to five-year time period that graph databases will really become significant, because they still need accepted methodologies that can be applied in a business context as well as proper tools in order for people to be able to use them seriously. But I stand by the idea that it is taking off, because for one thing, Neo4j, which is the leading independent graph database provider, had a very good year. And also, we're seeing interesting developments in terms of things like AWS with Neptune and with Oracle providing graph support in Oracle database this past year. Those things are, as I said, growing gradually. There are other companies like TigerGraph and so forth, that deserve watching as well. But as far as becoming mainstream, it's going to be a few years before we get all the elements together to make that happen. Like any new technology, you have to create an environment in which ordinary people without a whole ton of technical training can actually apply the technology to solve business problems. >> Yeah, thank you for that. These specialized databases, graph databases, time series databases, you see them embedded into mainstream data platforms, but there's a place for these specialized databases, I would suspect we're going to see new types of databases emerge with all this cloud sprawl that we have and maybe to the edge. >> Well, part of it is that it's not as specialized as you might think it. You can apply graphs to great many workloads and use cases. It's just that people have yet to fully explore and discover what those are. >> Yeah. >> And so, it's going to be a process. (laughs) >> All right, Dave Menninger, streaming data permeates the landscape. You gave yourself a yellow. Why? >> Well, I couldn't think of a appropriate combination of yellow and green. Maybe I should have used chartreuse, (Dave laughing) but I was probably a little hard on myself making it yellow. This is another type of specialized data processing like Carl was talking about graph databases is a stream processing, and nearly every data platform offers streaming capabilities now. Often, it's based on Kafka. If you look at Confluent, their revenues have grown at more than 50%, continue to grow at more than 50% a year. They're expected to do more than half a billion dollars in revenue this year. But the thing that hasn't happened yet, and to be honest, they didn't necessarily expect it to happen in one year, is that streaming hasn't become the default way in which we deal with data. It's still a sidecar to data at rest. And I do expect that we'll continue to see streaming become more and more mainstream. I do expect perhaps in the five-year timeframe that we will first deal with data as streaming and then at rest, but the worlds are starting to merge. And we even see some vendors bringing products to market, such as K2View, Hazelcast, and RisingWave Labs. So, in addition to all those core data platform vendors adding these capabilities, there are new vendors approaching this market as well. >> I like the tough grading system, and it's not trivial. And when you talk to practitioners doing this stuff, there's still some complications in the data pipeline. And so, but I think, you're right, it probably was a yellow plus. Doug Henschen, data lakehouses will emerge as dominant. When you talk to people about lakehouses, practitioners, they all use that term. They certainly use the term data lake, but now, they're using lakehouse more and more. What's your thoughts on here? Why the green? What's your evidence there? >> Well, I think, I was accurate. I spoke about it specifically as something that vendors would be pursuing. And we saw yet more lakehouse advocacy in 2022. Google introduced its BigLake service alongside BigQuery. Salesforce introduced Genie, which is really a lakehouse architecture. And it was a safe prediction to say vendors are going to be pursuing this in that AWS, Cloudera, Databricks, Microsoft, Oracle, SAP, Salesforce now, IBM, all advocate this idea of a single platform for all of your data. Now, the trend was also supported in 2023, in that we saw a big embrace of Apache Iceberg in 2022. That's a structured table format. It's used with these lakehouse platforms. It's open, so it ensures portability and it also ensures performance. And that's a structured table that helps with the warehouse side performance. But among those announcements, Snowflake, Google, Cloud Era, SAP, Salesforce, IBM, all embraced Iceberg. But keep in mind, again, I'm talking about this as something that vendors are pursuing as their approach. So, they're advocating end users. It's very cutting edge. I'd say the top, leading edge, 5% of of companies have really embraced the lakehouse. I think, we're now seeing the fast followers, the next 20 to 25% of firms embracing this idea and embracing a lakehouse architecture. I recall Christian Kleinerman at the big Snowflake event last summer, making the announcement about Iceberg, and he asked for a show of hands for any of you in the audience at the keynote, have you heard of Iceberg? And just a smattering of hands went up. So, the vendors are ahead of the curve. They're pushing this trend, and we're now seeing a little bit more mainstream uptake. >> Good. Doug, I was there. It was you, me, and I think, two other hands were up. That was just humorous. (Doug laughing) All right, well, so I liked the fact that we had some yellow and some green. When you think about these things, there's the prediction itself. Did it come true or not? There are the sub predictions that you guys make, and of course, the degree of difficulty. So, thank you for that open assessment. All right, let's get into the 2023 predictions. Let's bring up the predictions. Sanjeev, you're going first. You've got a prediction around unified metadata. What's the prediction, please? >> So, my prediction is that metadata space is currently a mess. It needs to get unified. There are too many use cases of metadata, which are being addressed by disparate systems. For example, data quality has become really big in the last couple of years, data observability, the whole catalog space is actually, people don't like to use the word data catalog anymore, because data catalog sounds like it's a catalog, a museum, if you may, of metadata that you go and admire. So, what I'm saying is that in 2023, we will see that metadata will become the driving force behind things like data ops, things like orchestration of tasks using metadata, not rules. Not saying that if this fails, then do this, if this succeeds, go do that. But it's like getting to the metadata level, and then making a decision as to what to orchestrate, what to automate, how to do data quality check, data observability. So, this space is starting to gel, and I see there'll be more maturation in the metadata space. Even security privacy, some of these topics, which are handled separately. And I'm just talking about data security and data privacy. I'm not talking about infrastructure security. These also need to merge into a unified metadata management piece with some knowledge graph, semantic layer on top, so you can do analytics on it. So, it's no longer something that sits on the side, it's limited in its scope. It is actually the very engine, the very glue that is going to connect data producers and consumers. >> Great. Thank you for that. Doug. Doug Henschen, any thoughts on what Sanjeev just said? Do you agree? Do you disagree? >> Well, I agree with many aspects of what he says. I think, there's a huge opportunity for consolidation and streamlining of these as aspects of governance. Last year, Sanjeev, you said something like, we'll see more people using catalogs than BI. And I have to disagree. I don't think this is a category that's headed for mainstream adoption. It's a behind the scenes activity for the wonky few, or better yet, companies want machine learning and automation to take care of these messy details. We've seen these waves of management technologies, some of the latest data observability, customer data platform, but they failed to sweep away all the earlier investments in data quality and master data management. So, yes, I hope the latest tech offers, glimmers that there's going to be a better, cleaner way of addressing these things. But to my mind, the business leaders, including the CIO, only want to spend as much time and effort and money and resources on these sorts of things to avoid getting breached, ending up in headlines, getting fired or going to jail. So, vendors bring on the ML and AI smarts and the automation of these sorts of activities. >> So, if I may say something, the reason why we have this dichotomy between data catalog and the BI vendors is because data catalogs are very soon, not going to be standalone products, in my opinion. They're going to get embedded. So, when you use a BI tool, you'll actually use the catalog to find out what is it that you want to do, whether you are looking for data or you're looking for an existing dashboard. So, the catalog becomes embedded into the BI tool. >> Hey, Dave Menninger, sometimes you have some data in your back pocket. Do you have any stats (chuckles) on this topic? >> No, I'm glad you asked, because I'm going to... Now, data catalogs are something that's interesting. Sanjeev made a statement that data catalogs are falling out of favor. I don't care what you call them. They're valuable to organizations. Our research shows that organizations that have adequate data catalog technologies are three times more likely to express satisfaction with their analytics for just the reasons that Sanjeev was talking about. You can find what you want, you know you're getting the right information, you know whether or not it's trusted. So, those are good things. So, we expect to see the capabilities, whether it's embedded or separate. We expect to see those capabilities continue to permeate the market. >> And a lot of those catalogs are driven now by machine learning and things. So, they're learning from those patterns of usage by people when people use the data. (airy laughs) >> All right. Okay. Thank you, guys. All right. Let's move on to the next one. Tony Bear, let's bring up the predictions. You got something in here about the modern data stack. We need to rethink it. Is the modern data stack getting long at the tooth? Is it not so modern anymore? >> I think, in a way, it's got almost too modern. It's gotten too, I don't know if it's being long in the tooth, but it is getting long. The modern data stack, it's traditionally been defined as basically you have the data platform, which would be the operational database and the data warehouse. And in between, you have all the tools that are necessary to essentially get that data from the operational realm or the streaming realm for that matter into basically the data warehouse, or as we might be seeing more and more, the data lakehouse. And I think, what's important here is that, or I think, we have seen a lot of progress, and this would be in the cloud, is with the SaaS services. And especially you see that in the modern data stack, which is like all these players, not just the MongoDBs or the Oracles or the Amazons have their database platforms. You see they have the Informatica's, and all the other players there in Fivetrans have their own SaaS services. And within those SaaS services, you get a certain degree of simplicity, which is it takes all the housekeeping off the shoulders of the customers. That's a good thing. The problem is that what we're getting to unfortunately is what I would call lots of islands of simplicity, which means that it leads it (Dave laughing) to the customer to have to integrate or put all that stuff together. It's a complex tool chain. And so, what we really need to think about here, we have too many pieces. And going back to the discussion of catalogs, it's like we have so many catalogs out there, which one do we use? 'Cause chances are of most organizations do not rely on a single catalog at this point. What I'm calling on all the data providers or all the SaaS service providers, is to literally get it together and essentially make this modern data stack less of a stack, make it more of a blending of an end-to-end solution. And that can come in a number of different ways. Part of it is that we're data platform providers have been adding services that are adjacent. And there's some very good examples of this. We've seen progress over the past year or so. For instance, MongoDB integrating search. It's a very common, I guess, sort of tool that basically, that the applications that are developed on MongoDB use, so MongoDB then built it into the database rather than requiring an extra elastic search or open search stack. Amazon just... AWS just did the zero-ETL, which is a first step towards simplifying the process from going from Aurora to Redshift. You've seen same thing with Google, BigQuery integrating basically streaming pipelines. And you're seeing also a lot of movement in database machine learning. So, there's some good moves in this direction. I expect to see more than this year. Part of it's from basically the SaaS platform is adding some functionality. But I also see more importantly, because you're never going to get... This is like asking your data team and your developers, herding cats to standardizing the same tool. In most organizations, that is not going to happen. So, take a look at the most popular combinations of tools and start to come up with some pre-built integrations and pre-built orchestrations, and offer some promotional pricing, maybe not quite two for, but in other words, get two products for the price of two services or for the price of one and a half. I see a lot of potential for this. And it's to me, if the class was to simplify things, this is the next logical step and I expect to see more of this here. >> Yeah, and you see in Oracle, MySQL heat wave, yet another example of eliminating that ETL. Carl Olofson, today, if you think about the data stack and the application stack, they're largely separate. Do you have any thoughts on how that's going to play out? Does that play into this prediction? What do you think? >> Well, I think, that the... I really like Tony's phrase, islands of simplification. It really says (Tony chuckles) what's going on here, which is that all these different vendors you ask about, about how these stacks work. All these different vendors have their own stack vision. And you can... One application group is going to use one, and another application group is going to use another. And some people will say, let's go to, like you go to a Informatica conference and they say, we should be the center of your universe, but you can't connect everything in your universe to Informatica, so you need to use other things. So, the challenge is how do we make those things work together? As Tony has said, and I totally agree, we're never going to get to the point where people standardize on one organizing system. So, the alternative is to have metadata that can be shared amongst those systems and protocols that allow those systems to coordinate their operations. This is standard stuff. It's not easy. But the motive for the vendors is that they can become more active critical players in the enterprise. And of course, the motive for the customer is that things will run better and more completely. So, I've been looking at this in terms of two kinds of metadata. One is the meaning metadata, which says what data can be put together. The other is the operational metadata, which says basically where did it come from? Who created it? What's its current state? What's the security level? Et cetera, et cetera, et cetera. The good news is the operational stuff can actually be done automatically, whereas the meaning stuff requires some human intervention. And as we've already heard from, was it Doug, I think, people are disinclined to put a lot of definition into meaning metadata. So, that may be the harder one, but coordination is key. This problem has been with us forever, but with the addition of new data sources, with streaming data with data in different formats, the whole thing has, it's been like what a customer of mine used to say, "I understand your product can make my system run faster, but right now I just feel I'm putting my problems on roller skates. (chuckles) I don't need that to accelerate what's already not working." >> Excellent. Okay, Carl, let's stay with you. I remember in the early days of the big data movement, Hadoop movement, NoSQL was the big thing. And I remember Amr Awadallah said to us in theCUBE that SQL is the killer app for big data. So, your prediction here, if we bring that up is SQL is back. Please elaborate. >> Yeah. So, of course, some people would say, well, it never left. Actually, that's probably closer to true, but in the perception of the marketplace, there's been all this noise about alternative ways of storing, retrieving data, whether it's in key value stores or document databases and so forth. We're getting a lot of messaging that for a while had persuaded people that, oh, we're not going to do analytics in SQL anymore. We're going to use Spark for everything, except that only a handful of people know how to use Spark. Oh, well, that's a problem. Well, how about, and for ordinary conventional business analytics, Spark is like an over-engineered solution to the problem. SQL works just great. What's happened in the past couple years, and what's going to continue to happen is that SQL is insinuating itself into everything we're seeing. We're seeing all the major data lake providers offering SQL support, whether it's Databricks or... And of course, Snowflake is loving this, because that is what they do, and their success is certainly points to the success of SQL, even MongoDB. And we were all, I think, at the MongoDB conference where on one day, we hear SQL is dead. They're not teaching SQL in schools anymore, and this kind of thing. And then, a couple days later at the same conference, they announced we're adding a new analytic capability-based on SQL. But didn't you just say SQL is dead? So, the reality is that SQL is better understood than most other methods of certainly of retrieving and finding data in a data collection, no matter whether it happens to be relational or non-relational. And even in systems that are very non-relational, such as graph and document databases, their query languages are being built or extended to resemble SQL, because SQL is something people understand. >> Now, you remember when we were in high school and you had had to take the... Your debating in the class and you were forced to take one side and defend it. So, I was was at a Vertica conference one time up on stage with Curt Monash, and I had to take the NoSQL, the world is changing paradigm shift. And so just to be controversial, I said to him, Curt Monash, I said, who really needs acid compliance anyway? Tony Baer. And so, (chuckles) of course, his head exploded, but what are your thoughts (guests laughing) on all this? >> Well, my first thought is congratulations, Dave, for surviving being up on stage with Curt Monash. >> Amen. (group laughing) >> I definitely would concur with Carl. We actually are definitely seeing a SQL renaissance and if there's any proof of the pudding here, I see lakehouse is being icing on the cake. As Doug had predicted last year, now, (clears throat) for the record, I think, Doug was about a year ahead of time in his predictions that this year is really the year that I see (clears throat) the lakehouse ecosystems really firming up. You saw the first shots last year. But anyway, on this, data lakes will not go away. I've actually, I'm on the home stretch of doing a market, a landscape on the lakehouse. And lakehouse will not replace data lakes in terms of that. There is the need for those, data scientists who do know Python, who knows Spark, to go in there and basically do their thing without all the restrictions or the constraints of a pre-built, pre-designed table structure. I get that. Same thing for developing models. But on the other hand, there is huge need. Basically, (clears throat) maybe MongoDB was saying that we're not teaching SQL anymore. Well, maybe we have an oversupply of SQL developers. Well, I'm being facetious there, but there is a huge skills based in SQL. Analytics have been built on SQL. They came with lakehouse and why this really helps to fuel a SQL revival is that the core need in the data lake, what brought on the lakehouse was not so much SQL, it was a need for acid. And what was the best way to do it? It was through a relational table structure. So, the whole idea of acid in the lakehouse was not to turn it into a transaction database, but to make the data trusted, secure, and more granularly governed, where you could govern down to column and row level, which you really could not do in a data lake or a file system. So, while lakehouse can be queried in a manner, you can go in there with Python or whatever, it's built on a relational table structure. And so, for that end, for those types of data lakes, it becomes the end state. You cannot bypass that table structure as I learned the hard way during my research. So, the bottom line I'd say here is that lakehouse is proof that we're starting to see the revenge of the SQL nerds. (Dave chuckles) >> Excellent. Okay, let's bring up back up the predictions. Dave Menninger, this one's really thought-provoking and interesting. We're hearing things like data as code, new data applications, machines actually generating plans with no human involvement. And your prediction is the definition of data is expanding. What do you mean by that? >> So, I think, for too long, we've thought about data as the, I would say facts that we collect the readings off of devices and things like that, but data on its own is really insufficient. Organizations need to manipulate that data and examine derivatives of the data to really understand what's happening in their organization, why has it happened, and to project what might happen in the future. And my comment is that these data derivatives need to be supported and managed just like the data needs to be managed. We can't treat this as entirely separate. Think about all the governance discussions we've had. Think about the metadata discussions we've had. If you separate these things, now you've got more moving parts. We're talking about simplicity and simplifying the stack. So, if these things are treated separately, it creates much more complexity. I also think it creates a little bit of a myopic view on the part of the IT organizations that are acquiring these technologies. They need to think more broadly. So, for instance, metrics. Metric stores are becoming much more common part of the tooling that's part of a data platform. Similarly, feature stores are gaining traction. So, those are designed to promote the reuse and consistency across the AI and ML initiatives. The elements that are used in developing an AI or ML model. And let me go back to metrics and just clarify what I mean by that. So, any type of formula involving the data points. I'm distinguishing metrics from features that are used in AI and ML models. And the data platforms themselves are increasingly managing the models as an element of data. So, just like figuring out how to calculate a metric. Well, if you're going to have the features associated with an AI and ML model, you probably need to be managing the model that's associated with those features. The other element where I see expansion is around external data. Organizations for decades have been focused on the data that they generate within their own organization. We see more and more of these platforms acquiring and publishing data to external third-party sources, whether they're within some sort of a partner ecosystem or whether it's a commercial distribution of that information. And our research shows that when organizations use external data, they derive even more benefits from the various analyses that they're conducting. And the last great frontier in my opinion on this expanding world of data is the world of driver-based planning. Very few of the major data platform providers provide these capabilities today. These are the types of things you would do in a spreadsheet. And we all know the issues associated with spreadsheets. They're hard to govern, they're error-prone. And so, if we can take that type of analysis, collecting the occupancy of a rental property, the projected rise in rental rates, the fluctuations perhaps in occupancy, the interest rates associated with financing that property, we can project forward. And that's a very common thing to do. What the income might look like from that property income, the expenses, we can plan and purchase things appropriately. So, I think, we need this broader purview and I'm beginning to see some of those things happen. And the evidence today I would say, is more focused around the metric stores and the feature stores starting to see vendors offer those capabilities. And we're starting to see the ML ops elements of managing the AI and ML models find their way closer to the data platforms as well. >> Very interesting. When I hear metrics, I think of KPIs, I think of data apps, orchestrate people and places and things to optimize around a set of KPIs. It sounds like a metadata challenge more... Somebody once predicted they'll have more metadata than data. Carl, what are your thoughts on this prediction? >> Yeah, I think that what Dave is describing as data derivatives is in a way, another word for what I was calling operational metadata, which not about the data itself, but how it's used, where it came from, what the rules are governing it, and that kind of thing. If you have a rich enough set of those things, then not only can you do a model of how well your vacation property rental may do in terms of income, but also how well your application that's measuring that is doing for you. In other words, how many times have I used it, how much data have I used and what is the relationship between the data that I've used and the benefits that I've derived from using it? Well, we don't have ways of doing that. What's interesting to me is that folks in the content world are way ahead of us here, because they have always tracked their content using these kinds of attributes. Where did it come from? When was it created, when was it modified? Who modified it? And so on and so forth. We need to do more of that with the structure data that we have, so that we can track what it's used. And also, it tells us how well we're doing with it. Is it really benefiting us? Are we being efficient? Are there improvements in processes that we need to consider? Because maybe data gets created and then it isn't used or it gets used, but it gets altered in some way that actually misleads people. (laughs) So, we need the mechanisms to be able to do that. So, I would say that that's... And I'd say that it's true that we need that stuff. I think, that starting to expand is probably the right way to put it. It's going to be expanding for some time. I think, we're still a distance from having all that stuff really working together. >> Maybe we should say it's gestating. (Dave and Carl laughing) >> Sorry, if I may- >> Sanjeev, yeah, I was going to say this... Sanjeev, please comment. This sounds to me like it supports Zhamak Dehghani's principles, but please. >> Absolutely. So, whether we call it data mesh or not, I'm not getting into that conversation, (Dave chuckles) but data (audio breaking) (Tony laughing) everything that I'm hearing what Dave is saying, Carl, this is the year when data products will start to take off. I'm not saying they'll become mainstream. They may take a couple of years to become so, but this is data products, all this thing about vacation rentals and how is it doing, that data is coming from different sources. I'm packaging it into our data product. And to Carl's point, there's a whole operational metadata associated with it. The idea is for organizations to see things like developer productivity, how many releases am I doing of this? What data products are most popular? I'm actually in right now in the process of formulating this concept that just like we had data catalogs, we are very soon going to be requiring data products catalog. So, I can discover these data products. I'm not just creating data products left, right, and center. I need to know, do they already exist? What is the usage? If no one is using a data product, maybe I want to retire and save cost. But this is a data product. Now, there's a associated thing that is also getting debated quite a bit called data contracts. And a data contract to me is literally just formalization of all these aspects of a product. How do you use it? What is the SLA on it, what is the quality that I am prescribing? So, data product, in my opinion, shifts the conversation to the consumers or to the business people. Up to this point when, Dave, you're talking about data and all of data discovery curation is a very data producer-centric. So, I think, we'll see a shift more into the consumer space. >> Yeah. Dave, can I just jump in there just very quickly there, which is that what Sanjeev has been saying there, this is really central to what Zhamak has been talking about. It's basically about making, one, data products are about the lifecycle management of data. Metadata is just elemental to that. And essentially, one of the things that she calls for is making data products discoverable. That's exactly what Sanjeev was talking about. >> By the way, did everyone just no notice how Sanjeev just snuck in another prediction there? So, we've got- >> Yeah. (group laughing) >> But you- >> Can we also say that he snuck in, I think, the term that we'll remember today, which is metadata museums. >> Yeah, but- >> Yeah. >> And also comment to, Tony, to your last year's prediction, you're really talking about it's not something that you're going to buy from a vendor. >> No. >> It's very specific >> Mm-hmm. >> to an organization, their own data product. So, touche on that one. Okay, last prediction. Let's bring them up. Doug Henschen, BI analytics is headed to embedding. What does that mean? >> Well, we all know that conventional BI dashboarding reporting is really commoditized from a vendor perspective. It never enjoyed truly mainstream adoption. Always that 25% of employees are really using these things. I'm seeing rising interest in embedding concise analytics at the point of decision or better still, using analytics as triggers for automation and workflows, and not even necessitating human interaction with visualizations, for example, if we have confidence in the analytics. So, leading companies are pushing for next generation applications, part of this low-code, no-code movement we've seen. And they want to build that decision support right into the app. So, the analytic is right there. Leading enterprise apps vendors, Salesforce, SAP, Microsoft, Oracle, they're all building smart apps with the analytics predictions, even recommendations built into these applications. And I think, the progressive BI analytics vendors are supporting this idea of driving insight to action, not necessarily necessitating humans interacting with it if there's confidence. So, we want prediction, we want embedding, we want automation. This low-code, no-code development movement is very important to bringing the analytics to where people are doing their work. We got to move beyond the, what I call swivel chair integration, between where people do their work and going off to separate reports and dashboards, and having to interpret and analyze before you can go back and do take action. >> And Dave Menninger, today, if you want, analytics or you want to absorb what's happening in the business, you typically got to go ask an expert, and then wait. So, what are your thoughts on Doug's prediction? >> I'm in total agreement with Doug. I'm going to say that collectively... So, how did we get here? I'm going to say collectively as an industry, we made a mistake. We made BI and analytics separate from the operational systems. Now, okay, it wasn't really a mistake. We were limited by the technology available at the time. Decades ago, we had to separate these two systems, so that the analytics didn't impact the operations. You don't want the operations preventing you from being able to do a transaction. But we've gone beyond that now. We can bring these two systems and worlds together and organizations recognize that need to change. As Doug said, the majority of the workforce and the majority of organizations doesn't have access to analytics. That's wrong. (chuckles) We've got to change that. And one of the ways that's going to change is with embedded analytics. 2/3 of organizations recognize that embedded analytics are important and it even ranks higher in importance than AI and ML in those organizations. So, it's interesting. This is a really important topic to the organizations that are consuming these technologies. The good news is it works. Organizations that have embraced embedded analytics are more comfortable with self-service than those that have not, as opposed to turning somebody loose, in the wild with the data. They're given a guided path to the data. And the research shows that 65% of organizations that have adopted embedded analytics are comfortable with self-service compared with just 40% of organizations that are turning people loose in an ad hoc way with the data. So, totally behind Doug's predictions. >> Can I just break in with something here, a comment on what Dave said about what Doug said, which (laughs) is that I totally agree with what you said about embedded analytics. And at IDC, we made a prediction in our future intelligence, future of intelligence service three years ago that this was going to happen. And the thing that we're waiting for is for developers to build... You have to write the applications to work that way. It just doesn't happen automagically. Developers have to write applications that reference analytic data and apply it while they're running. And that could involve simple things like complex queries against the live data, which is through something that I've been calling analytic transaction processing. Or it could be through something more sophisticated that involves AI operations as Doug has been suggesting, where the result is enacted pretty much automatically unless the scores are too low and you need to have a human being look at it. So, I think that that is definitely something we've been watching for. I'm not sure how soon it will come, because it seems to take a long time for people to change their thinking. But I think, as Dave was saying, once they do and they apply these principles in their application development, the rewards are great. >> Yeah, this is very much, I would say, very consistent with what we were talking about, I was talking about before, about basically rethinking the modern data stack and going into more of an end-to-end solution solution. I think, that what we're talking about clearly here is operational analytics. There'll still be a need for your data scientists to go offline just in their data lakes to do all that very exploratory and that deep modeling. But clearly, it just makes sense to bring operational analytics into where people work into their workspace and further flatten that modern data stack. >> But with all this metadata and all this intelligence, we're talking about injecting AI into applications, it does seem like we're entering a new era of not only data, but new era of apps. Today, most applications are about filling forms out or codifying processes and require a human input. And it seems like there's enough data now and enough intelligence in the system that the system can actually pull data from, whether it's the transaction system, e-commerce, the supply chain, ERP, and actually do something with that data without human involvement, present it to humans. Do you guys see this as a new frontier? >> I think, that's certainly- >> Very much so, but it's going to take a while, as Carl said. You have to design it, you have to get the prediction into the system, you have to get the analytics at the point of decision has to be relevant to that decision point. >> And I also recall basically a lot of the ERP vendors back like 10 years ago, we're promising that. And the fact that we're still looking at the promises shows just how difficult, how much of a challenge it is to get to what Doug's saying. >> One element that could be applied in this case is (indistinct) architecture. If applications are developed that are event-driven rather than following the script or sequence that some programmer or designer had preconceived, then you'll have much more flexible applications. You can inject decisions at various points using this technology much more easily. It's a completely different way of writing applications. And it actually involves a lot more data, which is why we should all like it. (laughs) But in the end (Tony laughing) it's more stable, it's easier to manage, easier to maintain, and it's actually more efficient, which is the result of an MIT study from about 10 years ago, and still, we are not seeing this come to fruition in most business applications. >> And do you think it's going to require a new type of data platform database? Today, data's all far-flung. We see that's all over the clouds and at the edge. Today, you cache- >> We need a super cloud. >> You cache that data, you're throwing into memory. I mentioned, MySQL heat wave. There are other examples where it's a brute force approach, but maybe we need new ways of laying data out on disk and new database architectures, and just when we thought we had it all figured out. >> Well, without referring to disk, which to my mind, is almost like talking about cave painting. I think, that (Dave laughing) all the things that have been mentioned by all of us today are elements of what I'm talking about. In other words, the whole improvement of the data mesh, the improvement of metadata across the board and improvement of the ability to track data and judge its freshness the way we judge the freshness of a melon or something like that, to determine whether we can still use it. Is it still good? That kind of thing. Bringing together data from multiple sources dynamically and real-time requires all the things we've been talking about. All the predictions that we've talked about today add up to elements that can make this happen. >> Well, guys, it's always tremendous to get these wonderful minds together and get your insights, and I love how it shapes the outcome here of the predictions, and let's see how we did. We're going to leave it there. I want to thank Sanjeev, Tony, Carl, David, and Doug. Really appreciate the collaboration and thought that you guys put into these sessions. Really, thank you. >> Thank you. >> Thanks, Dave. >> Thank you for having us. >> Thanks. >> Thank you. >> All right, this is Dave Valente for theCUBE, signing off for now. Follow these guys on social media. Look for coverage on siliconangle.com, theCUBE.net. Thank you for watching. (upbeat music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Recent conversations with IT decision makers show a stark contrast between exiting 2023 versus the mindset when we were leaving 2022. CIOs are generally funding new initiatives by pushing off or cutting lower priority items, while security efforts are still being funded. Those that enable business initiatives that generate revenue or taking priority over cleaning up legacy technical debt. The bottom line is, for the moment, at least, the mindset is not cut everything, rather, it's put a pause on cleaning up legacy hairballs and fund monetization. Hello, and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we tap recent discussions from two primary sources, year-end ETR roundtables with IT decision makers, and CUBE conversations with data, cloud, and IT architecture practitioners. The sources of data for this breaking analysis come from the following areas. Eric Bradley's recent ETR year end panel featured a financial services DevOps and SRE manager, a CSO in a large hospitality firm, a director of IT for a big tech company, the head of IT infrastructure for a financial firm, and a CTO for global travel enterprise, and for our upcoming Supercloud2 conference on January 17th, which you can register free by the way, at supercloud.world, we've had CUBE conversations with data and cloud practitioners, specifically, heads of data in retail and financial services, a cloud architect and a biotech firm, the director of cloud and data at a large media firm, and the director of engineering at a financial services company. Now we've curated commentary from these sources and now we share them with you today as anecdotal evidence supporting what we've been reporting on in the marketplace for these last couple of quarters. On this program, we've likened the economy to the slingshot effect when you're driving, when you're cruising along at full speed on the highway, and suddenly you see red brake lights up ahead, so, you tap your own brakes and then you speed up again, and traffic is moving along at full speed, so, you think nothing of it, and then, all of a sudden, the same thing happens. You slow down to a crawl and you start wondering, "What the heck is happening?" And you become a lot more cautious about the rate of acceleration when you start moving again. Well, that's the trend in IT spend right now. Back in June, we reported that despite the macro headwinds, CIOs were still expecting 6% to 7% spending growth for 2022. Now that was down from 8%, which we reported at the beginning of 2022. That was before Ukraine, and Fed tightening, but given those two factors, you know that that seemed pretty robust, but throughout the fall, we began reporting consistently declining expectations where CIOs are now saying Q4 will come in at around 3% growth relative to last year, and they're expecting, or should we say hoping that it pops back up in 2023 to 4% to 5%. The recent ETR panelists, when they heard this, are saying based on their businesses and discussions with their peers, they could see low single digit growth for 2023, so, 1%, 2%, 3%, so, this sort of slingshotting, or sometimes we call it a seesaw economy, has caught everyone off guard. Amazon is a good example of this, and there are others, but Amazon entered the pandemic with around 800,000 employees. It doubled that workforce during the pandemic. Now, right before Thanksgiving in 2022, Amazon announced that it was laying off 10,000 employees, and, Jassy, the CEO of Amazon, just last week announced that number is now going to grow to 18,000. Now look, this is a rounding error at Amazon from a headcount standpoint and their headcount remains far above 2019 levels. Its stock price, however, does not and it's back down to 2019 levels. The point is that visibility is very poor right now and it's reflected in that uncertainty. We've seen a lot of layoffs, obviously, the stock market's choppy, et cetera. Now importantly, not everything is on hold, and this downturn is different from previous tech pullbacks in that the speed at which new initiatives can be rolled out is much greater thanks to the cloud, and if you can show a fast return, you're going to get funding. Organizations are pausing on the cleanup of technical debt, unless it's driving fast business value. They're holding off on modernization projects. Those business enablement initiatives are still getting funded. CIOs are finding the money by consolidating redundant vendors, and they're stealing from other pockets of budget, so, it's not surprising that cybersecurity remains the number one technology priority in 2023. We've been reporting that for quite some time now. It's specifically cloud, cloud native security container and API security. That's where all the action is, because there's still holes to plug from that forced march to digital that occurred during COVID. Cloud migration, kind of showing here on number two on this chart, still a high priority, while optimizing cloud spend is definitely a strategy that organizations are taking to cut costs. It's behind consolidating redundant vendors by a long shot. There's very little evidence that cloud repatriation, i.e., moving workloads back on prem is a major cost cutting trend. The data just doesn't show it. What is a trend is getting more real time with analytics, so, companies can do faster and more accurate customer targeting, and they're really prioritizing that, obviously, in this down economy. Real time, we sometimes lose it, what's real time? Real time, we sometimes define as before you lose the customer. Now in the hiring front, customers tell us they're still having a hard time finding qualified site reliability engineers, SREs, Kubernetes expertise, and deep analytics pros. These job markets remain very tight. Let's stay with security for just a moment. We said many times that, prior to COVID, zero trust was this undefined buzzword, and the joke, of course, is, if you ask three people, "What is zero trust?" You're going to get three different answers, but the truth is that virtually every security company that was resisting taking a position on zero trust in an attempt to avoid... They didn't want to get caught up in the buzzword vortex, but they're now really being forced to go there by CISOs, so, there are some good quotes here on cyber that we want to share that came out of the recent conversations that we cited up front. The first one, "Zero trust is the highest ROI, because it enables business transformation." In other words, if I can have good security, I can move fast, it's not a blocker anymore. Second quote here, "ZTA," zero trust architecture, "Is more than securing the perimeter. It encompasses strong authentication and multiple identity layers. It requires taking a software approach to security instead of a hardware focus." The next one, "I'd love to have a security data lake that I could apply to asset management, vulnerability management, incident management, incident response, and all aspects for my security team. I see huge promise in that space," and the last one, I see NLP, natural language processing, as the foundation for email security, so, instead of searching for IP addresses, you can now read emails at light speed and identify phishing threats, so, look at, this is a small snapshot of the mindset around security, but I'll add, when you talk to the likes of CrowdStrike, and Zscaler, and Okta, and Palo Alto Networks, and many other security firms, they're listening to these narratives around zero trust. I'm confident they're working hard on skating to this puck, if you will. A good example is this idea of a security data lake and using analytics to improve security. We're hearing a lot about that. We're hearing architectures, there's acquisitions in that regard, and so, that's becoming real, and there are many other examples, because data is at the heart of digital business. This is the next area that we want to talk about. It's obvious that data, as a topic, gets a lot of mind share amongst practitioners, but getting data right is still really hard. It's a challenge for most organizations to get ROI and expected return out of data. Most companies still put data at the periphery of their businesses. It's not at the core. Data lives within silos or different business units, different clouds, it's on-prem, and increasingly it's at the edge, and it seems like the problem is getting worse before it gets better, so, here are some instructive comments from our recent conversations. The first one, "We're publishing events onto Kafka, having those events be processed by Dataproc." Dataproc is a Google managed service to run Hadoop, and Spark, and Flank, and Presto, and a bunch of other open source tools. We're putting them into the appropriate storage models within Google, and then normalize the data into BigQuery, and only then can you take advantage of tools like ThoughtSpot, so, here's a company like ThoughtSpot, and they're all about simplifying data, democratizing data, but to get there, you have to go through some pretty complex processes, so, this is a good example. All right, another comment. "In order to use Google's AI tools, we have to put the data into BigQuery. They haven't integrated in the way AWS and Snowflake have with SageMaker. Moving the data is too expensive, time consuming, and risky," so, I'll just say this, sharing data is a killer super cloud use case, and firms like Snowflake are on top of it, but it's still not pretty across clouds, and Google's posture seems to be, "We're going to let our database product competitiveness drive the strategy first, and the ecosystem is going to take a backseat." Now, in a way, I get it, owning the database is critical, and Google doesn't want to capitulate on that front. Look, BigQuery is really good and competitive, but you can't help but roll your eyes when a CEO stands up, and look, I'm not calling out Thomas Kurian, every CEO does this, and talks about how important their customers are, and they'll do whatever is right by the customer, so, look, I'm telling you, I'm rolling my eyes on that. Now let me also comment, AWS has figured this out. They're killing it in database. If you take Redshift for example, it's still growing, as is Aurora, really fast growing services and other data stores, but AWS realizes it can make more money in the long-term partnering with the Snowflakes and Databricks of the world, and other ecosystem vendors versus sub optimizing their relationships with partners and customers in order to sell more of their own homegrown tools. I get it. It's hard not to feature your own product. IBM chose OS/2 over Windows, and tried for years to popularize it. It failed. Lotus, go back way back to Lotus 1, 2, and 3, they refused to run on Windows when it first came out. They were running on DEC VAX. Many of you young people in the United States have never even heard of DEC VAX. IBM wanted to run every everything only in its cloud, the same with Oracle, originally. VMware, as you might recall, tried to build its own cloud, but, eventually, when the market speaks and reveals what seems to be obvious to analysts, years before, the vendors come around, they face reality, and they stop wasting money, fighting a losing battle. "The trend is your friend," as the saying goes. All right, last pull quote on data, "The hardest part is transformations, moving traditional Informatica, Teradata, or Oracle infrastructure to something more modern and real time, and that's why people still run apps in COBOL. In IT, we rarely get rid of stuff, rather we add on another coat of paint until the wood rots out or the roof is going to cave in. All right, the last key finding we want to highlight is going to bring us back to the cloud repatriation myth. Followers of this program know it's a real sore spot with us. We've heard the stories about repatriation, we've read the thoughtful articles from VCs on the subject, we've been whispered to by vendors that you should investigate this trend. It's really happening, but the data simply doesn't support it. Here's the question that was posed to these practitioners. If you had unlimited budget and the economy miraculously flipped, what initiatives would you tackle first? Where would you really lean into? The first answer, "I'd rip out legacy on-prem infrastructure and move to the cloud even faster," so, the thing here is, look, maybe renting infrastructure is more expensive than owning, maybe, but if I can optimize my rental with better utilization, turn off compute, use things like serverless, get on a steeper and higher performance over time, and lower cost Silicon curve with things like Graviton, tap best of breed tools in AI, and other areas that make my business more competitive. Move faster, fail faster, experiment more quickly, and cheaply, what's that worth? Even the most hard-o CFOs understand the business benefits far outweigh the possible added cost per gigabyte, and, again, I stress "possible." Okay, other interesting comments from practitioners. "I'd hire 50 more data engineers and accelerate our real-time data capabilities to better target customers." Real-time is becoming a thing. AI is being injected into data and apps to make faster decisions, perhaps, with less or even no human involvement. That's on the rise. Next quote, "I'd like to focus on resolving the concerns around cloud data compliance," so, again, despite the risks of data being spread out in different clouds, organizations realize cloud is a given, and they want to find ways to make it work better, not move away from it. The same thing in the next one, "I would automate the data analytics pipeline and focus on a safer way to share data across the states without moving it," and, finally, "The way I'm addressing complexity is to standardize on a single cloud." MonoCloud is actually a thing. We're hearing this more and more. Yes, my company has multiple clouds, but in my group, we've standardized on a single cloud to simplify things, and this is a somewhat dangerous trend, because it's creating even more silos and it's an opportunity that needs to be addressed, and that's why we've been talking so much about supercloud is a cross-cloud, unifying, architectural framework, or, perhaps, it's a platform. In fact, that's a question that we will be exploring later this month at Supercloud2 live from our Palo Alto Studios. Is supercloud an architecture or is it a platform? And in this program, we're featuring technologists, analysts, practitioners to explore the intersection between data and cloud and the future of cloud computing, so, you don't want to miss this opportunity. Go to supercloud.world. You can register for free and participate in the event directly. All right, thanks for listening. That's a wrap. I'd like to thank Alex Myerson, who's on production and manages our podcast, Ken Schiffman as well, Kristen Martin and Cheryl Knight, they helped get the word out on social media, and in our newsletters, and Rob Hof is our editor-in-chief over at siliconangle.com. He does some great editing. Thank you, all. Remember, all these episodes are available as podcasts wherever you listen. All you've got to do is search "breaking analysis podcasts." I publish each week on wikibon.com and siliconangle.com where you can email me directly at david.vellante@siliconangle.com or DM me, @Dante, or comment on our LinkedIn posts. By all means, check out etr.ai. They get the best survey data in the enterprise tech business. We'll be doing our annual predictions post in a few weeks, once the data comes out from the January survey. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, everybody, and we'll see you next time on "Breaking Analysis." (upbeat music)

(upbeat music) >> We're back talking about the path to sustainable IT and now we're going to get the perspective from Mattia Ballerio who is with Elmec Informatica, an IT services firm in the beautiful Lombardi region, of Italy, north of Milano. Mattia, welcome to theCUBE. Thanks so much for coming on. >> Thank you very much, Dave. Thank you. >> All right, before we jump in, tell us a little bit more about Elmec Informatica. What's your focus? Talk about your unique value add to customers. >> Yeah! So basically Elmec Informatica is middle company from the north part of Italy. And is managed service provider in the IT area. Okay, so the, the main focus area of Elmec is, rich digital transformation, and innovation to our clients with the focus on infrastructure services, workplace services, and also cybersecurity services, okay. And we try to follow the path of our clients to the digital transformation and innovation through technology and sustainability. >> Yeah, obviously very hot topics right now. Sustainability, environmental impact, they're growing areas of focus among leaders across all industries, particularly acute right now in, in Europe, with the, you know, the energy challenges. You've talked about things like sustainable business. What does that mean? What does that term, you know, speak to, and, and what can others learn from it? >> Yeah, at Elmec, our approach to sustainability is grounded in science and, and values. And also in a customer territory, but also employee centered. I mean, we conduct regular assessments to understand the most significant environment and social issues for our business with, with the goal of prioritizing what we do for a sustainability future. Our service delivery methodology, employee care, relationship with the local supplier, and local area and institution are a major factor for us to, to build a such a responsibility strategy. Specifically during the past year, we have been particularly focused on define sustainability governance in the company based on stakeholder engagement, defining material issues, establishing quantitative indicators, to monitor and setting medium to long term goals. >> Okay, so you have a lot of data. You can go into a customer, you can do an assessment, you can set a baseline, and then you have other data by which you can compare that and, and understand what's achievable. So what's your vision for sustainable business? You know, that strategy, you know, how has it affected your business in terms of the evolution? 'Cause this was, hasn't always been as hot a topic as it is today, and, and is it a competitive advantage for you? >> Yeah, yeah. For, for all intense and proposed sustainability is a competitive advantage for Elmec. I mean, it's so, because at the time of profound transformation in the work, in the world of work, CSR issues make a company more attractive when searching for new talent to enter in the workforce of our company. In addition, efforts to ensure people's proper work life balance are a strong retention factor. And, regarding our business proposition, Elmec's attempts is to meet high standard of sustainability and reliability. Our green data center, you said is a prime example of this approach, as at the same time, is there a conditioning activity that is done to give a second life to technology devices that come from, back from rental? I mean, our customer inquiries with respect to Elmec sustainability are increasingly frequent, and in depth. And which is why we monitor our performance, and invest in certification, such as, EcoVadis or ISO 14,001. Okay? >> Got it! So in a previous life, I actually did some work with, with power companies, and there were two big factors in IT, that affected the power consumption. Obviously virtualization was a big one, if you could consolidate servers, you know, that was huge. But the other was the advent of flash storage, and that was all we used to actually go in with the, the engineers and the power company put in alligator clips to measure of, of of an all flash array versus, you know, the spinning disk and it was a big impact. So, you want to talk about, your, your experience with Pure Storage. You use Flash Array, and the Evergreen architecture. Can you talk about your experience there? Why did you make that decision to select Pure Storage? How does that help you meet sustainability and operational requirements? Do those benefits scale as your customers grow? What's your experience been? >> Yeah! It was basically, an easy, an easy answer to our, to our business needs. Okay, because you said before that, in Elmec, we manage a lot of data, okay. And in the past we, we, we see, we see that, the constraints of managing so many, many data was very, very difficult to manage in terms of power consumption or simply for the, the space of storing the data. And, when, when Pure came to us and share our, their products, their vision, to the data management journey for Elmec Informatica, it was very easy to choose Pure, why? With values and the numbers, we, we create a business case and, we said, we see that our power consumption usage was much less, more than 90% of previous technology that we used in the past. Okay? And so of course you have to manage a gradual deploy of flash technology storage, but it was a good target. So we have tried to monitoring the adoption of flash technology, and monitor, monitoring also the power consumption, and the efficiency that the pure technology bring to our, to our IT systems, and of course the IT systems of our clients. And so this is one, the first part, the first good part of our trip with, with Pure. And after that, we approach also the sustainability in long term of choosing Pure technology storage. You mentioned the evergreen models of Pure, and of course this was, a game challenge for us because it allows, it allow us to extend the life cycle management of our data centers, but also the, it allows us to improve the facility, of the facilities of using technology from our technical side, okay. So we are much more efficient than in the past with the choose of Pure Storage Technologies, okay. Of course, this easy users, easy usage mode, let me say, it allow us to bring this value to our, to all our clients that put their data in our data centers. >> So, you talked about how you've seen, 90% improvement relative to previous technologies. I always, I haven't put you on the spot. Because I, I, I was on Pure's website, and I saw in their ESG report some com, you know, it was a comparison with a generic competitor. I'm presuming that competitor was not, you know 2010 spinning disk system. But, but, so I'm curious, as to the results that you're seeing with Pure, in terms of footprint and power usage. You, you're referencing some of that. We heard some metrics from Nicole and Ajay earlier in the program. Do you think, again I'm going to put you in the spot, do you think that Pure's architecture, and the way they've applied, whether it's machine intelligence or the Evergreen model, et cetera, is more competitive than other platforms, that you've seen? >> Yeah, of course. Is more competitor, more competitive. Because basically it allows to service provider to do much more efficient value proposition and offer services that are more that brings more values to, to the customers. Okay, so the customer is always at the center of a proposition of service provider. And the trying to adopt the methodology and also the, the value that Pure as inside, by design in the technology is, is for us very, very important and very, very strategic. Because, because, with like a glass, we can ourself transfer, try to transfer the values of Pure, Pure technologies to our service provider client. >> Okay Mattia, let's wrap and talk about sort of near term 2023 and then longer term. It looks like sustainability is a topic that's here to stay. Unlike when we were putting alligator clips on storage arrays, trying to help customers get rebates, that just didn't have legs. It was too complicated. Now it's a, a topic that everybody's measuring. What's next for Elmec, in its sustainability journey? What advice would you might have for sustainability leaders that want to make a meaningful impact on the environment but also on the bottom line? >> Okay. So, sustainability is fortunately a widely spread concept. And our role in, in this great game is to define a strategy, align with the common and fundamentals goals for the future of planet, and capable of expressing our inclination, and the particularities. Elmec sustainability goals in the near future, I can say that are will be basically free. One define sustainability plan, okay. It's fundamentals to define a sustainability plan. Then it's very important to monitor the, its emissions and we will calculate our carbon footprint, okay. And list, button list, produce a certifiable and comprehensive sustainability report, with respect to the demands of customers, suppliers, and also partners. Okay, so I can say that, this three target will be our direction in the, in the future. Okay? >> Yeah, so I mean, pretty straightforward. Make a plan. You got to monitor and measure. You can't improve what you can't measure. So you going to set a baseline, you're going to report on that. You're going to analyze the data and you're going to make continuous improvement. >> Yep. >> Mattia, thanks so much for joining us today and sharing your perspectives from the, the northern part of Italy. Really appreciate it. >> Yep. Thank you for having me on board. Thank you very much. >> It was really our pleasure. Okay, in a moment, I'm going to be back to wrap up the program, and share some resources , that could be valuable in your sustainability journey. Keep it right there. (upbeat music)

>>In the early part of this century, we're talking about the 2005 to 2007 timeframe. There was a lot of talk about so-called green it. And at that time there was some organizational friction. Like for example, the line was that the CIO never saw the power bill, so he or she didn't care, or that the facilities folks, they rarely talked to the IT department. So it was kind of that split brain. And, and then the oh 7 0 8 financial crisis really created an inflection point in a couple of ways. First, it caused organizations to kind of pump the brakes on it spending, and then they took their eye off the sustainability ball. And the second big trend, of course, was the cloud model, you know, kind of became a benchmark for it. Simplicity and automation and efficiency, the ability to dial down and dial up capacity as needed. >>And the third was by the end of the first decade of the, the two thousands, the technology of virtualization was really hitting its best stride. And then you had innovations like flash storage, which largely eliminated the need for these massive farms of spinning mechanical devices that sucked up a lot of power. And so really these technologies began their march to mainstream adoption. And as we progressed through the 2020s, the effect of climate change really come into focus as a critical component of esg. Environmental, social, and governance. Shareholders have come to demand metrics around sustainability. Employees are often choosing employers based on their ESG posture. And most importantly, companies are finding that savings on power cooling and footprint, it has a bottom line impact on the income statement. Now you add to that the energy challenges around the world, particularly facing Europe right now, the effects of global inflation and even more advanced technologies like machine intelligence. >>And you've got a perfect storm where technology can really provide some relief to organizations. Hello and welcome to the Path to Sustainable It Made Possible by Pure Storage and Collaboration with the Cube. My name is Dave Valante and I'm one of the host of the program, along with my colleague Lisa Martin. Now, today we're gonna hear from three leaders on the sustainability topic. First up, Lisa will talk to Nicole Johnson. She's the head of Social Impact and Sustainability at Pure Storage. Nicole will talk about the results from a study of around a thousand sustainability leaders worldwide, and she'll share some metrics from that study. And then next, Lisa will speak to AJ Singh. He's the Chief Product Officer at Pure Storage. We've had had him on the cube before, and not only will he share some useful stats in the market, I'll also talk about some of the technology innovations that customers can tap to address their energy consumption, not the least of which is ai, which is is entering every aspect of our lives, including how we deal with energy consumption. And then we'll bring it back to our Boston studio and go north of Italy with Mattia Ballero of Elec Informatica, a services provider with deep expertise on the topic of sustainability. We hope you enjoyed the program today. Thanks for watching. Let's get started >>At Pure Storage, the opportunity for change and our commitment to a sustainable future are a direct reflection of the way we've always operated and the values we live by every day. We are making significant and immediate impact worldwide through our environmental sustainability efforts. The milestones of change can be seen everywhere in everything we do. Pure's Evergreen Storage architecture delivers two key environmental benefits to customers, the reduction of wasted energy and the reduction of e-waste. Additionally, Pure's implemented a series of product packaging redesigns, promoting recycled and reuse in order to reduce waste that will not only benefit our customers, but also the environment. Pure is committed to doing what is right and leading the way with innovation. That has always been the pure difference, making a difference by enabling our customers to drive out energy usage and their data storage systems by up to 80%. Today, more than 97% of pure arrays purchased six years ago are still in service. And tomorrow our goal for the future is to reduce Scope three. Emissions Pure is committing to further reducing our sold products emissions by 66% per petabyte by 2030. All of this means what we said at the beginning, change that is simple and that is what it has always been about. Pure has a vision for the future today, tomorrow, forever. >>Hi everyone, welcome to this special event, pure Storage, the Path to Sustainable it. I'm your host, Lisa Martin. Very pleased to be joined by Nicole Johnson, the head of Social Impact and Sustainability at Pure Storage. Nicole, welcome to the Cube. Thanks >>For having me, Lisa. >>Sustainability is such an important topic to talk about and I understand that Pure just announced a report today about sustainability. What can you tell me what nuggets are in this report? >>Well, actually quite a few really interesting nuggets, at least for us. And I, I think probably for you and your viewers as well. So we actually commissioned about a thousand sustainability leaders across the globe to understand, you know, what are their sustainability goals, what are they working on, and what are the impacts of buying decisions, particularly around infrastructure when it comes to sustainable goals. I think one of the things that was really interesting for us was the fact that around the world we did not see a significant variation in terms of sustainability being a top priority. You've, I'm sure you've heard about the energy crisis that's happening across Europe. And so, you know, there was some thought that perhaps that might play into AMEA being a larger, you know, having sustainability goals that were more significant. But we actually did not find that we found sustainability to be really important no matter where the respondents were located. >>So very interesting at Pure sustainability is really at the heart of what we do and has been since our founding. It's interesting because we set out to make storage really simple, but it turns out really simple is also really sustainable. And the products and services that we bring to our customers have really powerful outcomes when it comes to decreasing their, their own carbon footprints. And so, you know, we often hear from customers that we've actually really helped them to significantly improve their storage performance, but also allow them to save on space power and cooling costs and, and their footprint. So really significant findings. One example of that is a company called Cengage, which is a global education technology company. They recently shared with us that they have actually been able to reduce their overall storage footprint by 80% while doubling to tripling the performance of their storage systems. So it's really critical for, for companies who are thinking about their sustainability goals, to consider the dynamic between their sustainability program and their IT teams who are making these buying decisions, >>Right? Those two teams need to be really inextricably linked these days. You talked about the fact that there was really consistency across the regions in terms of sustainability being of high priority for organizations. You had a great customer story that you shared that showed significant impact can be made there by bringing the sustainability both together with it. But I'm wondering why are we seeing that so much of the vendor selection process still isn't revolving around sustainability or it's overlooked? What are some of the things that you received despite so many people saying sustainability, huge priority? >>Well, in this survey, the most commonly cited challenge was really around the fact that there was a lack of management buy-in. 40% of respondents told us this was the top roadblock. So getting, I think getting that out of the way. And then we also just heard that sustainability teams were not brought into tech purchasing processes until after it's already rolling, right? So they're not even looped in. And that being said, you know, we know that it has been identified as one of the key departments to supporting a company sustainability goals. So we, we really want to ensure that these two teams are talking more to each other. When we look even closer at the data from the respondents, we see some really positive correlations. We see that 65% of respondents reported that they're on track to meet their sustainability goals. And the IT of those 65%, it is significantly engaged with reporting data for those sustainability initiatives. We saw that, that for those who did report, the sustainability is a top priority for vendor selection. They were twice as likely to be on track with their goals and their sustainability directors said that they were getting involved at the beginning of the tech purchasing program. Our process, I'm sorry, rather than towards the end. And so, you know, we know that to curb the impact of climate crisis, we really need to embrace sustainability from a cross-functional viewpoint. >>Definitely has to be cross-functional. So, so strong correlations there in the report that organizations that had closer alignment between the sustainability folks and the IT folks were farther along in their sustainability program development, execution, et cetera, those co was correlations, were they a surprise? >>Not entirely. You know, when we look at some of the statistics that come from the, you know, places like the World Economic Forum, they say that digitization generated 4% of greenhouse gas emissions in 2020. So, and that, you know, that's now almost three years ago, digital data only accelerates, and by 2025, we expect that number could be almost double. And so we know that that communication and that correlation is gonna be really important because data centers are taking up such a huge footprint of when companies are looking at their emissions. And it's, I mean, quite frankly, a really interesting opportunity for it to be a trailblazer in the sustainability journey. And, you know, perhaps people that are in IT haven't thought about how they can make an impact in this area, but there really is some incredible ways to help us work on cutting carbon emissions, both from your company's perspective and from the world's perspective, right? >>Like we are, we're all doing this because it's something that we know we have to do to drive down climate change. So I think when you, when you think about how to be a trailblazer, how to do things differently, how to differentiate your own department, it's a really interesting connection that IT and sustainability work together. I would also say, you know, I'll just note that of the respondents to the survey we were discussing, we do over half of those respondents expect to see closer alignment between the organization's IT and sustainability teams as they move forward. >>And that's really a, a tip a hat to those organizations embracing cultural change. That's always hard to do, but for those two, for sustainability in IT to come together as part of really the overall ethos of an organization, that's huge. And it's great to see the data demonstrating that, that those, that alignment, that close alignment is really on its way to helping organizations across industries make a big impact. I wanna dig in a little bit to here's ESG goals. What can you share with us about >>That? Absolutely. So as I mentioned peers kind of at the beginning of our formal ESG journey, but really has been working on the, on the sustainability front for a long time. I would, it's funny as we're, as we're doing a lot of this work and, and kind of building our own profile around this, we're coming back to some of the things that we have done in the past that consumers weren't necessarily interested in then but are now because the world has changed, becoming more and more invested in. So that's exciting. So we did a baseline scope one, two, and three analysis and discovered, interestingly enough that 70% of our emissions comes from use of sold products. So our customers work running our products in their data centers. So we know that we, we've made some ambitious goals around our Scope one and two emissions, which is our own office, our utilities, you know, those, they only account for 6% of our emissions. So we know that to really address the issue of climate change, we need to work on the use of sold products. So we've also made a, a really ambitious commitment to decrease our carbon emissions by 66% per bed per petabyte by 2030 in our product. So decreasing our own carbon footprint, but also affecting our customers as well. And we've also committed to a science-based target initiative and our road mapping how to achieve the ambitious goals set out in the Paris agreement. >>That's fantastic. It sounds like you really dialed in on where is the biggest opportunity for us as Pure Storage to make the biggest impact across our organization, across our customers organizations. There lofty goals that pure set, but knowing what I know about Pure, you guys are probably well on track to, to accomplish those goals in record time, >>I hope So. >>Talk a little bit about advice that you would give to viewers who might be at the very beginning of their sustainability journey and really wondering what are the core elements besides it, sustainability, team alignment that I need to bring into this program to make it actually successful? >>Yeah, so I think, you know, understanding that you don't have to pick between really powerful technology and sustainable technology. There are opportunities to get both and not just in storage right in, in your entire IT portfolio. We know that, you know, we're in a place in the world where we have to look at things from the bigger picture. We have to solve new challenges and we have to approach business a little bit differently. So adopting solutions and services that are environmentally efficient can actually help to scale and deliver more effective and efficient IT solutions over time. So I think that that's something that we need to, to really remind ourselves, right? We have to go about business a little bit differently and that's okay. We also know that data centers utilize an incredible amount of, of energy and, and carbon. And so everything that we can do to drive that down is going to address the sustainability goals for us individually as well as, again, drive down that climate change. So we, we need to get out of the mindset that data centers are, are about reliability or cost, et cetera, and really think about efficiency and carbon footprint when you're making those business decisions. I'll also say that, you know, the earlier that we can get sustainability teams into the conversation, the more impactful your business decisions are going to be and helping you to guide sustainable decision making. >>So shifting sustainability and IT left almost together really shows that the correlation between those folks getting together in the beginning with intention, the report shows and the successes that peers had demonstrate that that's very impactful for organizations to actually be able to implement even the cultural change that's needed for sustainability programs to be successful. My last question for you goes back to that report. You mentioned in there that the data show a lot of organizations are hampered by management buy-in, where sustainability is concerned. How can pure help its customers navigate around those barriers so that they get that management buy-in and they understand that the value in it for >>Them? Yeah, so I mean, I think that for me, my advice is always to speak to hearts and minds, right? And help the management to understand, first of all, the impact right on climate change. So I think that's the kind of hearts piece on the mind piece. I think it's addressing the sustainability goals that these companies have set for themselves and helping management understand how to, you know, how their IT buying decisions can actually really help them to reach these goals. We also, you know, we always run kind of TCOs for customers to understand what is the actual cost of, of the equipment. And so, you know, especially if you're in a, in a location in which energy costs are rising, I mean, I think we're seeing that around the world right now with inflation. Better understanding your energy costs can really help your management to understand the, again, the bigger picture and what that total cost is gonna be. Often we see, you know, that maybe the I the person who's buying the IT equipment isn't the same person who's purchasing, who's paying the, the electricity bills, right? And so sometimes even those two teams aren't talking. And there's a great opportunity there, I think, to just to just, you know, look at it from a more high level lens to better understand what total cost of ownership is. >>That's a great point. Great advice. Nicole, thank you so much for joining me on the program today, talking about the new report that on sustainability that Pure put out some really compelling nuggets in there, but really also some great successes that you've already achieved internally on your own ESG goals and what you're helping customers to achieve in terms of driving down their carbon footprint and emissions. We so appreciate your insights and your thoughts. >>Thank you, Lisa. It's been great speaking with you. >>AJ Singh joins me, the Chief Product Officer at Peer Storage. Aj, it's great to have you back on the program. >>Great to be back on, Lisa, good morning. >>Good morning. And sustainability is such an important topic to talk about. So we're gonna really unpack what PEER is doing, we're gonna get your viewpoints on what you're seeing and you're gonna leave the audience with some recommendations on how they can get started on their ESG journey. First question, we've been hearing a lot from pure AJ about the role that technology plays in organizations achieving sustainability goals. What's been the biggest environmental impact associated with, with customers achieving that given the massive volumes of data that keep being generated? >>Absolutely, Lisa, you can imagine that the data is only growing and exploding and, and, and, and there's a good reason for it. You know, data is the new currency. Some people call it the new oil. And the opportunity to go process this data gain insights is really helping customers drive an edge in the digital transformation. It's gonna make a difference between them being on the leaderboard a decade from now when the digital transformation kind of pans out versus, you know, being kind of somebody that, you know, quite missed the boat. So data is super critical and and obviously as part of that we see all these big benefits, but it has to be stored and, and, and that means it's gonna consume a lot of resources and, and the, and therefore data center usage has only accelerated, right? You can imagine the amount of data being generated, you know, recent study pointed to roughly by twenty twenty five, a hundred and seventy five zetabytes, which where each zettabyte is a billion terabytes. So just think of that size and scale of data. That's huge. And, and they also say that, you know, pretty soon, today, in fact in the developed world, every person is having an interaction with the data center literally every 18 seconds. So whether it's on Facebook or Twitter or you know, your email, people are constantly interacting with data. So you can imagine this data is only exploding. It has to be stored and it consumes a lot of energy. In fact, >>It, oh, go ahead. Sorry. >>No, I was saying in fact, you know, there's some studies have shown that data center usage literally consumes one to 2% of global energy consumption. So if there's one place we could really help climate change and, and all those aspects, if you can kind of really, you know, tamp down the data center, energy consumption, sorry, you were saying, >>I was just gonna say, it's, it's an incredibly important topic and the, the, the stats on data that you provided and also I, I like how you talked about, you know, every 18 seconds we're interacting with a data center, whether we know it or not, we think about the long term implications, the fact that data is growing massively. As you shared with the stats that you mentioned. If we think about though the responsibility that companies have, every company in today's world needs to be a data company, right? And we consumers expect it. We expect that you are gonna deliver these relevant, personalized experiences whether we're doing a transaction in our personal lives or in business. But what is the, what requirements do technology companies have to really start billing down their carbon footprints? >>No, absolutely. If you can think about it, just to kind of finish up the data story a little bit, the explosion is to the point where, in fact, if you just recently was in the news that Ireland went up and said, sorry, we can't have any more data centers here. We just don't have the power to supply them. That was big in the news and you know, all the hyperscale that was crashing the head. I know they've come around that and figured out a way around it, but it's getting there. Some, some organizations and and areas jurisdictions are saying pretty much no data center the law, you know, we're, we just can't do it. And so as you said, so companies like Pure, I mean, our view is that it has an opportunity here to really do our bit for climate change and be able to, you know, drive a sustainable environment. >>And, and at Pure we believe that, you know, today's data success really ultimately hinges on energy efficiency, you know, so to to really be energy efficient means you are gonna be successful long term with data. Because if you think of classic data infrastructures, the legacy infrastructures, you know, we've got disk infrastructures, hybrid infrastructures, flash infrastructures, low end systems, medium end systems, high end systems. So a lot of silos, you know, a lot of inefficiency across the silos. Cause the data doesn't get used across that. In fact, you know, today a lot of data centers are not really built with kind of the efficiency and environmental mindset. So there's a big opportunity there. >>So aj, talk to me about some of the steps that Pure is implementing as its chief product officer. Would love to get your your thoughts, what steps is it implementing to help Pures customers become more sustainable? >>No, absolutely. So essentially we are all inherently motivated, like pure and, and, and, and everybody else to solve problems for customers and really forward the status quo, right? You know, innovation, you know, that's what we are all about. And while we are doing that, the challenge is to how do you make technology and the data we feed into it faster, smarter, scalable obviously, but more importantly sustainable. And you can do all of that, but if you miss the sustainability bit, you're kind of missing the boat. And I also feel from an ethical perspective, that's really important for us. Not only you do all the other things, but also kind of make it sustainable. In fact, today 80% of the companies, the companies are realizing this, 80% today are in fact report out on sustainability, which is great. In fact, 80% of leadership at companies, you know, CEOs and senior executives say they've been impacted by some climate change event, you know, where it's a fire in the place they had to evacuate or floods or storms or hurricanes, you, you name it, right? >>So mitigating the carbon impact can in fact today be a competitive advantage for companies because that's where the puck is going and everybody's, you know, it's skating, wanting to skate towards the, and it's good, it's good business too to be sustainable and, and, and meet these, you know, customer requirements. In fact, the the recent survey that we released today is saying that more and more organizations are kickstarting, their sustainability initiatives and many take are aiming to make a significant progress against that over the next decade. So that's, that's really, you know, part of the big, the really, so our view is that that IT infrastructure, you know, can really make a big push towards greener it and not just kind of greenwash it, but actually, you know, you know, make things more greener and, and, and really take the, the lead in, in esg. And so it's important that organizations can reach alignment with their IT teams and challenge their IT teams to continue to lead, you know, for the organization, the sustainability aspects. >>I'm curious, aj, when you're in customer conversations, are you seeing that it's really the C-suite plus it coming together and, and how does peer help facilitate that? To your point, it needs to be able to deliver this, but it's, it's a board level objective these days. >>Absolutely. We're seeing increasingly, especially in Europe with the, you know, the war in Ukraine and the energy crisis that, you know, that's, that's, you know, unleashed. We definitely see it's becoming a bigger and bigger board level objective for, for a lot of companies. And we definitely see customers in starting to do that. So, so in particular, I do want to touch briefly on what steps we are taking as a company, you know, to to to make it sustainable. And obviously customers are doing all the things we talked about and, and we're also helping them become smarter with data. But the key difference is, you know, we have a big focus on efficiency, which is really optimizing performance per wat with unmatched storage density. So you can reduce the footprint and dramatically lower the power required. And and how efficient is that? You know, compared to other old flash systems, we tend to be one fifth, we tend to take one fifth the power compared to other flash systems and substantially lower compared to spinning this. >>So you can imagine, you know, cutting your, if data center consumption is a 2% of global consumption, roughly 40% of that tends to be storage cause of all the spinning disc. So you add about, you know, 0.8% to global consumption and if you can cut that by four fifths, you know, you can already start to make an impact. So, so we feel we can do that. And also we're quite a bit more denser, 10 times more denser. So imagine one fifth the power, one 10th the density, but then we take it a step further because okay, you've got the storage system in the data center, but what about the end of life aspect? What about the waste and reclamation? So we also have something called non-disruptive upgrades. We, using our AI technology in pure one, we can start to sense when a particular part is going to fail and just before it goes to failure, we actually replace it in a non-disruptive fashion. So customer's data is not impacted and then we recycle that so you get a full end to end life cycle, you know, from all the way from the time you deploy much lower power, much lower density, but then also at the back end, you know, reduction in e-waste and those kind of things. >>That's a great point you, that you bring up in terms of the reclamation process. It sounds like Pure does that on its own, the customer doesn't have to be involved in that. >>That's right. And we do that, it's a part of our evergreen, you know, service that we offer. A lot of customers sign up for that service and in fact they don't even, we tell them, Hey, you know, that part's about to go, we're gonna come in, we're gonna swap it out and, and then we actually recycle that part, >>The power of ai. Love that. What are some of the, the things that companies can do if they're, if they're early in this journey on sustainability, what are some of the specific steps companies can take to get started and maybe accelerate that journey as it's becoming climate change and things are becoming just more and more of a, of a daily topic on the news? >>No, absolutely. There's a lot of things companies can do. In fact, the four four item that we're gonna highlight, the first one is, you know, they can just start by doing a materiality assessment and a materiality assessment essentially engages all the stakeholders to find out which specific issues are important for the business, right? So you identify your key priorities that intersect with what the stakeholders want, you know, your different groups from sales, customers, partners, you know, different departments in the organization. And for example, for us, when we conducted our materiality assessment, for us, our product we felt was the biggest area of focus that could contribute a lot towards, you know, making an impact in, in, in from a sustainability standpoint. That's number one. I think number two companies can also think about taking an Azure service approach. The beauty of the Azure service approach is that you are buying a, your customer, they're buying outcomes with SLAs and, and when you are starting to buy outcomes with SLAs, you can start small and then grow as you consume more. >>So that way you don't have systems sitting idle waiting for you to consume more, right? And that's the beauty of the as service approach. And so for example, for us, you know, we have something called Evergreen one, which is our as service offer, where essentially customers are able to only use and have systems turned onto as much as they're consuming. So, so that reduces the waste associated with underutilized systems, right? That's number two. Number three is also you can optimize your supply chains end to end, right? Basically by making sure you're moving, recycling, packaging and eliminating waste in that thing so you can recycle it back to your suppliers. And you can also choose a sustainable supplier network that following sort of good practices, you know, you know, across the globe and such supply chains that are responsive and diverse can really help you. Also, the big business benefit benefited. >>You can also handle surges and demand, for example, for us during the pandemic with this global supply chain shortages, you know, whereas most of our competitors, you know, lead times went to 40, 50 weeks, our lead times went from three to six weeks cuz you know, we had this sustainable, you know, supply chain. And so all of these things, you know, the three things important, but the fourth thing I say more cultural and, and the cultural thing is how do you actually begin to have sustainability become a core part of your ethos at the company, you know, across all the departments, you know, and we've at Pure, definitely it's big for us, you know, you know, around sustainability starting with a product design, but all of the areas as well, if you follow those four items, they'll do the great place to start. >>That's great advice, great recommendations. You talk about the, the, the supply chain, sustainable supply chain optimization. We've been having a lot of conversations with businesses and vendors alike about that and how important it is. You bring up a great point too on supplier diversity, if we could have a whole conversation on that. Yes. But I'm also glad that you brought up culture that's huge to, for organizations to adopt an ESG strategy and really drive sustainability in their business. It has to become, to your point, part of their ethos. Yes. It's challenging. Cultural change management is challenging. Although I think with climate change and the things that are so public, it's, it's more on, on the top mindset folks. But it's a great point that the organization really as a whole needs to embrace the sustainability mindset so that it as a, as an organization lives and breathes that. Yes. And last question for you is advice. So you, you outlined the Four Steps organizations can take. I look how you made that quite simple. What advice would you give organizations who are on that journey to adopting those, those actions, as you said, as they look to really build and deploy and execute an ESG strategy? >>No, absolutely. And so obviously, you know, the advice is gonna come from, you know, a company like Pure, you know, our background kind of being a supplier of products. And so, you know, our advice is for companies that have products, usually they tend to be the biggest generator, the products that you sell to your, your customers, especially if they've got hardware components in it. But, you know, the biggest generator of e-waste and, and and, and, and, and kind of from a sustainability standpoint. So it's really important to have an intentional design approach towards your products with sustainability in mind. So it's not something that's, that you can handle at the very back end. You design it front in the product and so that sustainable design becomes very intentional. So for us, for example, doing these non-disruptive upgrades had to be designed up front so that, you know, a, you know, one of our repair person could go into a customer shop and be able to pull out a card and put in a new card without any change in the customer system. >>That non-receptive approach, it has to be designed into the hardware software systems to be able to pull that on. And that intentional design enables you to recover pieces just when they're about to fail and then putting them through a recovery, you know, waste recovery process. So that, that's kind of the one thing I would say that philosophy, again, it comes down to if that is, you know, seeping into the culture, into your core ethos, you will start to do, you know, you know, that type of work. So, so I mean it's important thing, you know, look, this year, you know, with the spike in energy prices, you know, you know, gas prices going up, it's super important that all of us, you know, do our bit in there and start to drive products that are fundamentally sustainable, not just at the initial, you know, install point, but from an end to end full life cycle standpoint. >>Absolutely. And I love that you brought up intention that is everything that peers doing is with, with such thought and intention and really for organizations and any industry to become more sustainable, to develop an ESG strategy. To your point, it all needs to start with intention. And of course that that cultural adoption, aj, it's been so great to have you on the program talking about what PEER is doing to help organizations really navigate that path to sustainable it. We appreciate your insights on your time. >>Thank you, Lisa. Pleasure being on board >>At Pure Storage. The opportunity for change and our commitment to a sustainable future are a direct reflection of the way we've always operated and the values we live by every day. We are making significant and immediate impact worldwide through our environmental sustainability efforts. The milestones of change can be seen everywhere in everything we do. Pures Evergreen storage architecture delivers two key environmental benefits to customers, the reduction of wasted energy and the reduction of e-waste. Additionally, pures implemented a series of product packaging redesigns, promoting recycle and reuse in order to reduce waste that will not only benefit our customers, but also the environment. Pure is committed to doing what is right and leading the way with innovation. That has always been the pure difference, making a difference by enabling our customers to drive out energy usage and their data storage systems by up to 80% today, more than 97% of Pure Array purchased six years ago are still in service. And tomorrow our goal for the future is to reduce Scope three emissions Pure is committing to further reducing our sold products emissions by 66% per petabyte by 2030. All of this means what we said at the beginning, change that is simple and that is what it has always been about. Pure has a vision for the future today, tomorrow, forever. >>We're back talking about the path to sustainable it and now we're gonna get the perspective from Mattia Valerio, who is with Elec Informatica and IT services firm and the beautiful Lombardi region of Italy north of Milano. Mattia, welcome to the Cube. Thanks so much for coming on. >>Thank you very much, Dave. Thank you. >>All right, before we jump in, tell us a little bit more about Elec Informatica. What's your focus, talk about your unique value add to customers. >>Yeah, so basically Alma Informatica is middle company from the north part of Italy and is managed service provider in the IT area. Okay. So the, the main focus area of Al Meca is reach digital transformation innovation to our clients with focus on infrastructure services, workplace services, and also cybersecurity services. Okay. And we try to follow the path of our clients to the digital transformation and the innovation through technology and sustainability. >>Yeah. Obviously very hot topics right now. Sustainability, environmental impact, they're growing areas of focus among leaders across all industries. A particularly acute right now in, in Europe with the, you know, the energy challenges you've talked about things like sustainable business. What does that mean? What does that term Yeah. You know, speak to and, and what can others learn from it? >>Yeah. At at, at our approach to sustainability is grounded in science and, and values and also in customer territory, but also employee centered. I mean, we conduct regular assessments to understand the most significant environment and social issues for our business with, with the goal of prioritizing what we do for a sustainability future. Our service delivery methodology, employee care relationship with the local supplier and local area and institution are a major factor for us to, to build a such a responsibility strategy. Specifically during the past year, we have been particularly focused on define sustainability governance in the company based on stakeholder engagement, defining material issues, establishing quantitative indicators to monitor and setting medium to long-term goals. >>Okay, so you have a lot of data. You can go into a customer, you can do an assessment, you can set a baseline, and then you have other data by which you can compare that and, and understand what's achievable. So what's your vision for sustainable business? You know, that strategy, you know, how has it affected your business in terms of the evolution? Cuz this wasn't, hasn't always been as hot a topic as it is today. And and is it a competitive advantage for you? >>Yeah, yeah. For, for, for all intense and proposed sustainability is a competitive advantage for elec. I mean, it's so, because at the time of profound transformation in the work, in the world of work, CSR issues make a company more attractive when searching for new talent to enter in the workforce of our company. In addition, efforts to ensure people's proper work life balance are a strong retention factor. And regarding our business proposition, ELEX attempts is to meet high standard of sustainability and reliability. Our green data center, you said is a prime example of this approach as at the same time, is there a conditioning activity that is done to give a second life to technology devices that come from back from rental? I mean, our customer inquiries with respect to sustainability are increasingly frequent and in depth and which is why we monitor our performance and invest in certification such as EcoVadis or ISO 14,001. Okay, >>Got it. So in a previous life I actually did some work with, with, with power companies and there were two big factors in it that affected the power consumption. Obviously virtualization was a big one, if you could consolidate servers, you know, that was huge. But the other was the advent of flash storage and that was, we used to actually go in with the, the engineers and the power company put in alligator clips to measure of, of, of an all flash array versus, you know, the spinning disc and it was a big impact. So you, I wanna talk about your, your experience with Pure Storage. You use Flash Array and the Evergreen architecture. Can you talk about what your experience there, why did you make that decision to select Pure Storage? How does that help you meet sustainability and operational requirements? Do those benefits scale as your customers grow? What's your experience been? >>Yeah, it was basically an easy and easy answer to our, to our business needs. Okay. Because you said before that in Elec we, we manage a lot of data, okay? And in the past we, we, we see it, we see that the constraints of managing so many, many data was very, very difficult to manage in terms of power consumption or simply for the, the space of storing the data. And when, when Pure came to us and share our products, their vision to the data management journey for Element Informatica, it was very easy to choose pure why with values and numbers. We, we create a business case and we said that we, we see that our power consumption usage was much less, more than 90% of previous technology that we used in the past. Okay. And so of course you have to manage a grade oil deploy of flash technology storage, but it was a good target. >>So we have tried to monitoring the adoption of flash technology and monitor monitoring also the power consumption and the efficiency that the pure technology bring to our, to our IT systems and of course the IT systems of our clients. And so this is one, the first part, the first good part of our trip with, with Pure. And after that we approach also the sustainability in long term of choosing pure technology storage. You mentioned the Evergreen models of Pure, and of course this was, again, challenge for us because it allows, it allow us to extend the life cycle management of our data centers, but also the, IT allows us to improve the facility of the facilities of using technology from our technical side. Okay. So we are much more efficient than in the past with the choose of Pure storage technologies. Okay. Of course, this easy users, easy usage mode, let me say it, allow us to bring this value to our, to all our clients that put their data in our data centers. >>So you talked about how you've seen a 90% improvement relative to previous technologies. I always, I haven't put you in the spot. Yeah, because I, I, I was on Pure's website and I saw in their ESG report some com, you know, it was a comparison with a generic competitor presuming that competitor was not, you know, a 2010 spinning disc system. But, but, so I'm curious as to the results that you're seeing with Pure in terms of footprint and power usage. You, you're referencing some of that. We heard some metrics from Nicole and AJ earlier in the program. Do you think, again, I'm gonna put you in the spot, do you think that Pure's architecture and the way they've applied, whether it's machine intelligence or the Evergreen model, et cetera, is more competitive than other platforms that you've seen? >>Yeah, of course. Is more competitor improve competitive because basically it allows to service provider to do much more efficient value proposition and offer services that are more, that brings more values to, to the customers. Okay. So the customer is always at the center of a proposition of a service provider and trying to adopt the methodology and also the, the value that pure as inside by design in the technology is, is for us very, very, very important and very, very strategic because, because with like a glass, we can, our self transfer try to transfer the values of pure, pure technologies to our service provider client. >>Okay. Matta, let's wrap and talk about sort of near term 2023 and then longer term it looks like sustainability is a topic that's here to stay. Unlike when we were putting alligator clips on storage arrays, trying to help customers get rebates that just didn't have legs. It was too complicated. Now it's a, a topic that everybody's measuring. What's next for elec in its sustainability journey? What advice would you might have? Sustainability leaders that wanna make a meaningful impact on the environment, but also on the bottom line. >>Okay, so sustainability is fortunately a widely spread concept. And our role in, in this great game is to define a strategy, align with the common and fundamentals goals for the future of planet and capable of expressing our inclination and the, and the particularities and accessibility goals in the near future. I, I say, I can say that are will be basically free one define sustainability plan. Okay? It's fundamentals to define a sustainability plan. Then it's very important to monitor the its emissions and we will calculate our carbon footprint. Okay? And least button list produces certifiable and comprehensive sustainability report with respect to the demands of customers, suppliers, and also partners. Okay. So I can say that this three target will be our direction in the, in the future. Okay. >>Yeah. So I mean, pretty straightforward. Make a plan. You gotta monitor and measure, you can't improve what you can't measure. So you gonna set a baseline, you're gonna report on that. Yep. You're gonna analyze the data and you're gonna make continuous improvement. >>Yep. >>Matea, thanks so much for joining us today in sharing your perspectives from the, the northern part of Italy. Really appreciate it. >>Yeah, thank you for having aboard. Thank you very >>Much. It was really our pleasure. Okay, in a moment, I'm gonna be back to wrap up the program and share some resources that could be valuable in your sustainability journey. Keep it right there. >>Sustainability is becoming increasingly important and is hitting more RFPs than ever before as a critical decision point for customers. Environmental benefits are not the only impetus. Rather bottom line cost savings are proving that sustainability actually means better business. You can make a strong business case around sustainability and you should, many more organizations are setting mid and long-term goals for sustainability and putting forth published metrics for shareholders and customers. Whereas early green IT initiatives at the beginning of this century, were met with skepticism and somewhat disappointing results. Today, vendor r and d is driving innovation in system design, semiconductor advancements, automation in machine intelligence that's really beginning to show tangible results. Thankfully. Now remember, all these videos are available on demand@thecube.net. So check them out at your convenience and don't forget to go to silicon angle.com for all the enterprise tech news of the day. You also want to check out pure storage.com. >>There are a ton of resources there. As an aside, pure is the only company I can recall to allow you to access resources like a Gartner Magic Quadrant without forcing you to fill out a lead gen form. So thank you for that. Pure storage, I love that. There's no squeeze page on that. No friction. It's kind of on brand there for pure well done. But to the topic today, sustainability, there's some really good information on the site around esg, Pure's Environmental, social and Governance mission. So there's more in there than just sustainability. You'll see some transparent statistics on things like gender and ethnic diversity, and of course you'll see that Pure has some work to do there. But kudos for publishing those stats transparently and setting goals so we can track your progress. And there's plenty on the sustainability topic as well, including some competitive benchmarks, which are interesting to look at and may give you some other things to think about. We hope you've enjoyed the path to Sustainable it made possible by Pure Storage produced with the Cube, your leader in enterprise and emerging tech, tech coverage.

from the cube studios in Palo Alto in Boston bringing you data-driven insights from the cube and ETR this is breaking analysis with Dave vellante the ascendancy of AWS under the leadership of Andy jassy was marked by a tsunami of data and corresponding cloud services to leverage that data now those Services they mainly came in the form of Primitives I.E basic building blocks that were used by developers to create more sophisticated capabilities AWS in the 2020s being led by CEO Adam solipski will be marked by four high-level Trends in our opinion one A Rush of data that will dwarf anything we've previously seen two a doubling or even tripling down on the basic elements of cloud compute storage database security Etc three a greater emphasis on end-to-end integration of AWS services to simplify and accelerate customer adoption of cloud and four significantly deeper business integration of cloud Beyond it as an underlying element of organizational operations hello and welcome to this week's wikibon Cube insights powered by ETR in this breaking analysis we extract and analyze nuggets from John furrier's annual sit-down with the CEO of AWS we'll share data from ETR and other sources to set the context for the market and competition in cloud and we'll give you our glimpse of what to expect at re invent in 2022. now before we get into the core of our analysis Alibaba has announced earnings they always announced after the big three you know a month later and we've updated our Q3 slash November hyperscale Computing forecast for the year as seen here and we're going to spend a lot of time on this as most of you have seen the bulk of it already but suffice to say alibaba's cloud business is hitting that same macro Trend that we're seeing across the board but a more substantial slowdown than we expected and more substantial than its peers they're facing China headwinds they've been restructuring its Cloud business and it's led to significantly slower growth uh in in the you know low double digits as opposed to where we had it at 15 this puts our year-end estimates for 2022 Revenue at 161 billion still a healthy 34 growth with AWS surpassing 80 billion in 2022 Revenue now on a related note one of the big themes in Cloud that we've been reporting on is how customers are optimizing their Cloud spend it's a technique that they use and when the economy looks a little shaky and here's a graphic that we pulled from aws's website which shows the various pricing plans at a high level as you know they're much more granular than that and more sophisticated but Simplicity we'll just keep it here basically there are four levels first one here is on demand I.E pay by the drink now we're going to jump down to what we've labeled as number two spot instances that's like the right place at the right time I can use that extra capacity in the moment the third is reserved instances or RIS where I pay up front to get a discount and the fourth is sort of optimized savings plans where customers commit to a one or three year term and for a better price now you'll notice we labeled the choices in a different order than AWS presented them on its website and that's because we believe that the order that we chose is the natural progression for customers this started on demand they maybe experiment with spot instances they move to reserve instances when the cloud bill becomes too onerous and if you're large enough you lock in for one or three years okay the interesting thing is the order in which AWS presents them we believe that on-demand accounts for the majority of AWS customer spending now if you think about it those on-demand customers they're also at risk customers yeah sure there's some switching costs like egress and learning curve but many customers they have multiple clouds and they've got experience and so they're kind of already up to a learning curve and if you're not married to AWS with a longer term commitment there's less friction to switch now AWS here presents the most attractive plan from a financial perspective second after on demand and it's also the plan that makes the greatest commitment from a lock-in standpoint now In fairness to AWS it's also true that there is a trend towards subscription-based pricing and we have some data on that this chart is from an ETR drill down survey the end is 300. pay attention to the bars on the right the left side is sort of busy but the pink is subscription and you can see the trend upward the light blue is consumption based or on demand based pricing and you can see there's a steady Trend toward subscription now we'll dig into this in a later episode of Breaking analysis but we'll share with you a little some tidbits with the data that ETR provides you can select which segment is and pass or you can go up the stack Etc but so when you choose is and paths 44 of customers either prefer or are required to use on-demand pricing whereas around 40 percent of customers say they either prefer or are required to use subscription pricing again that's for is so now the further mu you move up the stack the more prominent subscription pricing becomes often with sixty percent or more for the software-based offerings that require or prefer subscription and interestingly cyber security tracks along with software at around 60 percent that that prefer subscription it's likely because as with software you're not shutting down your cyber protection on demand all right let's get into the expectations for reinvent and we're going to start with an observation in data in this 2018 book seeing digital author David michella made the point that whereas most companies apply data on the periphery of their business kind of as an add-on function successful data companies like Google and Amazon and Facebook have placed data at the core of their operations they've operationalized data and they apply machine intelligence to that foundational element why is this the fact is it's not easy to do what the internet Giants have done very very sophisticated engineering and and and cultural discipline and this brings us to reinvent 2022 in the future of cloud machine learning and AI will increasingly be infused into applications we believe the data stack and the application stack are coming together as organizations build data apps and data products data expertise is moving from the domain of Highly specialized individuals to Everyday business people and we are just at the cusp of this trend this will in our view be a massive theme of not only re invent 22 but of cloud in the 2020s the vision of data mesh We Believe jamachtagani's principles will be realized in this decade now what we'd like to do now is share with you a glimpse of the thinking of Adam solipsky from his sit down with John Furrier each year John has a one-on-one conversation with the CEO of AWS AWS he's been doing this for years and the outcome is a better understanding of the directional thinking of the leader of the number one Cloud platform so we're now going to share some direct quotes I'm going to run through them with some commentary and then bring in some ETR data to analyze the market implications here we go this is from solipsky quote I.T in general and data are moving from departments into becoming intrinsic parts of how businesses function okay we're talking here about deeper business integration let's go on to the next one quote in time we'll stop talking about people who have the word analyst we inserted data he meant data data analyst in their title rather will have hundreds of millions of people who analyze data as part of their day-to-day job most of whom will not have the word analyst anywhere in their title we're talking about graphic designers and pizza shop owners and product managers and data scientists as well he threw that in I'm going to come back to that very interesting so he's talking about here about democratizing data operationalizing data next quote customers need to be able to take an end-to-end integrated view of their entire data Journey from ingestion to storage to harmonizing the data to being able to query it doing business Intelligence and human-based Analysis and being able to collaborate and share data and we've been putting together we being Amazon together a broad Suite of tools from database to analytics to business intelligence to help customers with that and this last statement it's true Amazon has a lot of tools and you know they're beginning to become more and more integrated but again under jassy there was not a lot of emphasis on that end-to-end integrated view we believe it's clear from these statements that solipsky's customer interactions are leading him to underscore that the time has come for this capability okay continuing quote if you have data in one place you shouldn't have to move it every time you want to analyze that data couldn't agree more it would be much better if you could leave that data in place avoid all the ETL which has become a nasty three-letter word more and more we're building capabilities where you can query that data in place end quote okay this we see a lot in the marketplace Oracle with mySQL Heatwave the entire Trend toward converge database snowflake [ __ ] extending their platforms into transaction and analytics respectively and so forth a lot of the partners are are doing things as well in that vein let's go into the next quote the other phenomenon is infusing machine learning into all those capabilities yes the comments from the michelleographic come into play here infusing Ai and machine intelligence everywhere next one quote it's not a data Cloud it's not a separate Cloud it's a series of broad but integrated capabilities to help you manage the end-to-end life cycle of your data there you go we AWS are the cloud we're going to come back to that in a moment as well next set of comments around data very interesting here quote data governance is a huge issue really what customers need is to find the right balance of their organization between access to data and control and if you provide too much access then you're nervous that your data is going to end up in places that it shouldn't shouldn't be viewed by people who shouldn't be viewing it and you feel like you lack security around that data and by the way what happens then is people overreact and they lock it down so that almost nobody can see it it's those handcuffs there's data and asset are reliability we've talked about that for years okay very well put by solipsky but this is a gap in our in our view within AWS today and we're we're hoping that they close it at reinvent it's not easy to share data in a safe way within AWS today outside of your organization so we're going to look for that at re invent 2022. now all this leads to the following statement by solipsky quote data clean room is a really interesting area and I think there's a lot of different Industries in which clean rooms are applicable I think that clean rooms are an interesting way of enabling multiple parties to share and collaborate on the data while completely respecting each party's rights and their privacy mandate okay again this is a gap currently within AWS today in our view and we know snowflake is well down this path and databricks with Delta sharing is also on this curve so AWS has to address this and demonstrate this end-to-end data integration and the ability to safely share data in our view now let's bring in some ETR spending data to put some context around these comments with reference points in the form of AWS itself and its competitors and partners here's a chart from ETR that shows Net score or spending momentum on the x-axis an overlap or pervasiveness in the survey um sorry let me go back up the net scores on the y-axis and overlap or pervasiveness in the survey is on the x-axis so spending momentum by pervasiveness okay or should have share within the data set the table that's inserted there with the Reds and the greens that informs us to how the dots are positioned so it's Net score and then the shared ends are how the plots are determined now we've filtered the data on the three big data segments analytics database and machine learning slash Ai and we've only selected one company with fewer than 100 ends in the survey and that's databricks you'll see why in a moment the red dotted line indicates highly elevated customer spend at 40 percent now as usual snowflake outperforms all players on the y-axis with a Net score of 63 percent off the charts all three big U.S cloud players are above that line with Microsoft and AWS dominating the x-axis so very impressive that they have such spending momentum and they're so large and you see a number of other emerging data players like rafana and datadog mongodbs there in the mix and then more established players data players like Splunk and Tableau now you got Cisco who's gonna you know it's a it's a it's a adjacent to their core networking business but they're definitely into you know the analytics business then the really established players in data like Informatica IBM and Oracle all with strong presence but you'll notice in the red from the momentum standpoint now what you're going to see in a moment is we put red highlights around databricks Snowflake and AWS why let's bring that back up and we'll explain so there's no way let's bring that back up Alex if you would there's no way AWS is going to hit the brakes on innovating at the base service level what we call Primitives earlier solipsky told Furrier as much in their sit down that AWS will serve the technical user and data science Community the traditional domain of data bricks and at the same time address the end-to-end integration data sharing and business line requirements that snowflake is positioned to serve now people often ask Snowflake and databricks how will you compete with the likes of AWS and we know the answer focus on data exclusively they have their multi-cloud plays perhaps the more interesting question is how will AWS compete with the likes of Specialists like Snowflake and data bricks and the answer is depicted here in this chart AWS is going to serve both the technical and developer communities and the data science audience and through end-to-end Integrations and future services that simplify the data Journey they're going to serve the business lines as well but the Nuance is in all the other dots in the hundreds or hundreds of thousands that are not shown here and that's the AWS ecosystem you can see AWS has earned the status of the number one Cloud platform that everyone wants to partner with as they say it has over a hundred thousand partners and that ecosystem combined with these capabilities that we're discussing well perhaps behind in areas like data sharing and integrated governance can wildly succeed by offering the capabilities and leveraging its ecosystem now for their part the snowflakes of the world have to stay focused on the mission build the best products possible and develop their own ecosystems to compete and attract the Mind share of both developers and business users and that's why it's so interesting to hear solipski basically say it's not a separate Cloud it's a set of integrated Services well snowflake is in our view building a super cloud on top of AWS Azure and Google when great products meet great sales and marketing good things can happen so this will be really fun to watch what AWS announces in this area at re invent all right one other topic that solipsky talked about was the correlation between serverless and container adoption and you know I don't know if this gets into there certainly their hybrid place maybe it starts to get into their multi-cloud we'll see but we have some data on this so again we're talking about the correlation between serverless and container adoption but before we get into that let's go back to 2017 and listen to what Andy jassy said on the cube about serverless play the clip very very earliest days of AWS Jeff used to say a lot if I were starting Amazon today I'd have built it on top of AWS we didn't have all the capability and all the functionality at that very moment but he knew what was coming and he saw what people were still able to accomplish even with where the services were at that point I think the same thing is true here with Lambda which is I think if Amazon were starting today it's a given they would build it on the cloud and I think we with a lot of the applications that comprise Amazon's consumer business we would build those on on our serverless capabilities now we still have plenty of capabilities and features and functionality we need to add to to Lambda and our various serverless services so that may not be true from the get-go right now but I think if you look at the hundreds of thousands of customers who are building on top of Lambda and lots of real applications you know finra has built a good chunk of their market watch application on top of Lambda and Thompson Reuters has built you know one of their key analytics apps like people are building real serious things on top of Lambda and the pace of iteration you'll see there will increase as well and I really believe that to be true over the next year or two so years ago when Jesse gave a road map that serverless was going to be a key developer platform going forward and so lipsky referenced the correlation between serverless and containers in the Furrier sit down so we wanted to test that within the ETR data set now here's a screen grab of The View across 1300 respondents from the October ETR survey and what we've done here is we've isolated on the cloud computing segment okay so you can see right there cloud computing segment now we've taken the functions from Google AWS Lambda and Microsoft Azure functions all the serverless offerings and we've got Net score on the vertical axis we've got presence in the data set oh by the way 440 by the way is highly elevated remember that and then we've got on the horizontal axis we have the presence in the data center overlap okay that's relative to each other so remember 40 all these guys are above that 40 mark okay so you see that now what we're going to do this is just for serverless and what we're going to do is we're going to turn on containers to see the correlation and see what happens so watch what happens when we click on container boom everything moves to the right you can see all three move to the right Google drops a little bit but all the others now the the filtered end drops as well so you don't have as many people that are aggressively leaning into both but all three move to the right so watch again containers off and then containers on containers off containers on so you can see a really major correlation between containers and serverless okay so to get a better understanding of what that means I call my friend and former Cube co-host Stu miniman what he said was people generally used to think of VMS containers and serverless as distinctly different architectures but the lines are beginning to blur serverless makes things simpler for developers who don't want to worry about underlying infrastructure as solipsky and the data from ETR indicate serverless and containers are coming together but as Stu and I discussed there's a spectrum where on the left you have kind of native Cloud VMS in the middle you got AWS fargate and in the rightmost anchor is Lambda AWS Lambda now traditionally in the cloud if you wanted to use containers developers would have to build a container image they have to select and deploy the ec2 images that they or instances that they wanted to use they have to allocate a certain amount of memory and then fence off the apps in a virtual machine and then run the ec2 instances against the apps and then pay for all those ec2 resources now with AWS fargate you can run containerized apps with less infrastructure management but you still have some you know things that you can you can you can do with the with the infrastructure so with fargate what you do is you'd build the container images then you'd allocate your memory and compute resources then run the app and pay for the resources only when they're used so fargate lets you control the runtime environment while at the same time simplifying the infrastructure management you gotta you don't have to worry about isolating the app and other stuff like choosing server types and patching AWS does all that for you then there's Lambda with Lambda you don't have to worry about any of the underlying server infrastructure you're just running code AS functions so the developer spends their time worrying about the applications and the functions that you're calling the point is there's a movement and we saw in the data towards simplifying the development environment and allowing the cloud vendor AWS in this case to do more of the underlying management now some folks will still want to turn knobs and dials but increasingly we're going to see more higher level service adoption now re invent is always a fire hose of content so let's do a rapid rundown of what to expect we talked about operate optimizing data and the organization we talked about Cloud optimization there'll be a lot of talk on the show floor about best practices and customer sharing data solipsky is leading AWS into the next phase of growth and that means moving beyond I.T transformation into deeper business integration and organizational transformation not just digital transformation organizational transformation so he's leading a multi-vector strategy serving the traditional peeps who want fine-grained access to core services so we'll see continued Innovation compute storage AI Etc and simplification through integration and horizontal apps further up to stack Amazon connect is an example that's often cited now as we've reported many times databricks is moving from its stronghold realm of data science into business intelligence and analytics where snowflake is coming from its data analytics stronghold and moving into the world of data science AWS is going down a path of snowflake meet data bricks with an underlying cloud is and pass layer that puts these three companies on a very interesting trajectory and you can expect AWS to go right after the data sharing opportunity and in doing so it will have to address data governance they go hand in hand okay price performance that is a topic that will never go away and it's something that we haven't mentioned today silicon it's a it's an area we've covered extensively on breaking analysis from Nitro to graviton to the AWS acquisition of Annapurna its secret weapon new special specialized capabilities like inferential and trainium we'd expect something more at re invent maybe new graviton instances David floyer our colleague said he's expecting at some point a complete system on a chip SOC from AWS and maybe an arm-based server to eventually include high-speed cxl connections to devices and memories all to address next-gen applications data intensive applications with low power requirements and lower cost overall now of course every year Swami gives his usual update on machine learning and AI building on Amazon's years of sagemaker innovation perhaps a focus on conversational AI or a better support for vision and maybe better integration across Amazon's portfolio of you know large language models uh neural networks generative AI really infusing AI everywhere of course security always high on the list that reinvent and and Amazon even has reinforce a conference dedicated to it uh to security now here we'd like to see more on supply chain security and perhaps how AWS can help there as well as tooling to make the cio's life easier but the key so far is AWS is much more partner friendly in the security space than say for instance Microsoft traditionally so firms like OCTA and crowdstrike in Palo Alto have plenty of room to play in the AWS ecosystem we'd expect of course to hear something about ESG it's an important topic and hopefully how not only AWS is helping the environment that's important but also how they help customers save money and drive inclusion and diversity again very important topics and finally come back to it reinvent is an ecosystem event it's the Super Bowl of tech events and the ecosystem will be out in full force every tech company on the planet will have a presence and the cube will be featuring many of the partners from the serial floor as well as AWS execs and of course our own independent analysis so you'll definitely want to tune into thecube.net and check out our re invent coverage we start Monday evening and then we go wall to wall through Thursday hopefully my voice will come back we have three sets at the show and our entire team will be there so please reach out or stop by and say hello all right we're going to leave it there for today many thanks to Stu miniman and David floyer for the input to today's episode of course John Furrier for extracting the signal from the noise and a sit down with Adam solipski thanks to Alex Meyerson who was on production and manages the podcast Ken schiffman as well Kristen Martin and Cheryl Knight helped get the word out on social and of course in our newsletters Rob hoef is our editor-in-chief over at siliconangle does some great editing thank thanks to all of you remember all these episodes are available as podcasts wherever you listen you can pop in the headphones go for a walk just search breaking analysis podcast I published each week on wikibon.com at siliconangle.com or you can email me at david.valante at siliconangle.com or DM me at di vallante or please comment on our LinkedIn posts and do check out etr.ai for the best survey data in the Enterprise Tech business this is Dave vellante for the cube insights powered by ETR thanks for watching we'll see it reinvent or we'll see you next time on breaking analysis [Music]

(energetic music) >> Okay, we're back with Justin Borgman, CEO of Starburst, Richard Jarvis is the CTO of EMIS Health, and Teresa Tung is the cloud first technologist from Accenture. We're on to lie number three. And that is the claim that today's "Modern Data Stack" is actually modern. So (chuckles), I guess that's the lie. Or, is that it's not modern. Justin, what do you say? >> Yeah, I think new isn't modern. Right? I think it's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually, are exactly the same as what we've had for 40 years. Rather than Teradata, you have Snowflake. Rather than Informatica, you have Fivetran. So, it's the same general stack, just, y'know, a cloud version of it. And I think a lot of the challenges that have plagued us for 40 years still maintain. >> So, let me come back to you Justin. Okay, but there are differences, right? You can scale. You can throw resources at the problem. You can separate compute from storage. You really, there's a lot of money being thrown at that by venture capitalists, and Snowflake you mentioned, its competitors. So that's different. Is it not? Is that not at least an aspect of modern dial it up, dial it down? So what do you say to that? >> Well, it is. It's certainly taking, y'know what the cloud offers and taking advantage of that. But it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data's still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same structural constraints that exist with the old enterprise data warehouse model on-preem still exist. Just yes, a little bit more elastic now because the cloud offers that. >> So Teresa, let me go to you, 'cause you have cloud-first in your title. So, what's say you to this conversation? >> Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud as we know it, maybe data lake, data warehouse in the central place, that's not even how the cloud providers are looking at it. They have use query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our- the future goes, right? That's going to very much fall the same thing. There was going to be more edge. There's going to be more on-premise, because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers, right? So, there's a lot of reasons why the modern, I guess, the next modern generation of the data stack needs to be much more federated. >> Okay, so Richard, how do you deal with this? You've obviously got, you know, the technical debt, the existing infrastructure, it's on the books. You don't want to just throw it out. A lot of conversation about modernizing applications, which a lot of times is, you know, of microservices layer on top of legacy apps. How do you think about the Modern Data Stack? >> Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just 'cause you can scale CPU and storage doesn't mean you can get more people to use your data to generate you more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business not just the technology itself. >> Well thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five seven years cloud obviously has given a different pricing model. Derisked experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm taking away that that's not enough. Based on what Richard just said, the modern data stack has to serve the business and enable the business to build data products. I buy that. I'm you a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about you know, the, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and, and principles there >> Of how it should look like or, or how >> Yeah. What it should be? >> Yeah. Yeah. Well, I think, you know, in, in Theresa mentioned this in in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I certainly agree with that. So by no means, are we suggesting that, you know Snowflake or what Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of of idealism. They had the benefit of starting with a clean slate that does not reflect the vast majority of enterprises. And even those companies, as they grow up, mature out of that ideal state, they go by a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really future proof yourself from the inevitable change that you will you won't encounter over time. >> So thank you. So Theresa, based on what Justin just said, I I might take away there is it's inclusive whether it's a data mart, data hub, data lake, data warehouse, just a node on the mesh. Okay. I get that. Does that include Theresa on, on Preem data? Obviously it has to. What are you seeing in terms of the ability to, to take that data mesh concept on Preem I mean most implementations I've seen and data mesh, frankly really aren't, you know adhering to the philosophy there. Maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing, HelloFresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >> I mean, I think it's a killer case for data mesh. The fact that you have valuable data sources on Preem, and then yet you still want to modernize and take the best of cloud. Cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both world. You can start using the data products on Preem, or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or or maybe just tapping into better analytics to get better insights, right? So you're going to be able to stretch and really have the best of both worlds. That, again, going back to Richard's point, that is meaningful by the business. Not everything has to have that one size fits all set a tool. >> Okay. Thank you. So Richard, you know, talking about data as product wonder if we could give us your perspectives here what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >> So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients, demographics about their their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight and in any business that's clearly not a desirable outcome but when that insight is so critical as it might be in healthcare or some security settings you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured managed way, even if that data comes from a variety of different sources in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to researchers. >> So that data product through whatever APIs is is accessible, it's discoverable, but it's obviously got to be governed as well. You mentioned appropriately provided to internally. >> Yeah. >> But also, you know, external folks as well. So the, so you've, you've architected that capability today? >> We have and because the data is standard it can generate value much more quickly and we can be sure of the security and value that that's providing, because the data product isn't just about formatting the data into the correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context what does this data mean, and what does it mean to process this data for a particular use case. >> Yeah, it makes sense. It's got the context. If the, if the domains on the data, you know you got to cut through a lot of the, the centralized teams, the technical teams that that data agnostic, they don't really have that context. All right, let's end. Justin. How does Starburst fit into this modern data stack? Bring us home. >> Yeah. So I think for us it's really providing our customers with, you know the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know and optionality provides the ability to reduce costs store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know incorporated into our offering as well you can really create and, and curate, you know data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know model and make that an appropriate compliment to you know, the modern data stack that people have today. >> Excellent. Hey, I want to thank Justin, Teresa, and Richard for joining us today. You guys are great. Big believers in the in the data mesh concept, and I think, you know we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are going to be available on the cube.net for on demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and they have awesome resources. Lots of data mesh conversations over there and really good stuff in, in the resource section. So check that out. Thanks for watching the "Data Doesn't Lie... or Does It?" made possible by Starburst data. This is Dave Vellante for the Cube, and we'll see you next time. (upbeat music)

>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data-driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting cost could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data marts, data hubs, and yes, even data lakes were broken and left us wanting from more welcome to the data doesn't lie, or doesn't a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have featured parody with the data lake or vice versa is the so-called modern data stack, simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for hit that was acquired by Teradata. And when I got to Teradata, of course, Teradata is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience show? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know, right. You actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like star Oxley, for things like security for certainly very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited Jamma, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come. I think that's the story at Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenants of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about SAR, brain Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but what does that mean? Does that mean the E D w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's going to be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems, maybe either those that either source systems for the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to use all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got to, you know, domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue are two, you know, challenges, self-serve infrastructure let's park that for a second. And then in your industry, the one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at emus is we have a single security layer that sits on top of our data match, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin, I mean, Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, do an analytic queries and with data that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah. I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively a almost eCommerce like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself, >>Okay. G guys grab a sip of water. After this short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence, keep it right there. >>Your company has more data than ever, and more people trying to understand it, but there's a problem. Your data is stored across multiple systems. It's hard to access and that delays analytics and ultimately decisions. The old method of moving all of your data into a single source of truth is slow and definitely not built for the volume of data we have today or where we are headed while your data engineers spent over half their time, moving data, your analysts and data scientists are left, waiting, feeling frustrated, unproductive, and unable to move the needle for your business. But what if you could spend less time moving or copying data? What if your data consumers could analyze all your data quickly? >>Starburst helps your teams run fast queries on any data source. We help you create a single point of access to your data, no matter where it's stored. And we support high concurrency, we solve for speed and scale, whether it's fast, SQL queries on your data lake or faster queries across multiple data sets, Starburst helps your teams run analytics anywhere you can't afford to wait for data to be available. Your team has questions that need answers. Now with Starburst, the wait is over. You'll have faster access to data with enterprise level security, easy connectivity, and 24 7 support from experts, organizations like Zolando Comcast and FINRA rely on Starburst to move their businesses forward. Contact our Trino experts to get started. >>We're back with Jess Borgman of Starburst and Richard Jarvis of EVAs health. Okay, we're gonna get to lie. Number two, and that is this an open source based platform cannot give you the performance and control that you can get with a proprietary system. Is that a lie? Justin, the enterprise data warehouse has been pretty dominant and has evolved and matured. Its stack has mature over the years. Why is it not the default platform for data? >>Yeah, well, I think that's become a lie over time. So I, I think, you know, if we go back 10 or 12 years ago with the advent of the first data lake really around Hudu, that probably was true that you couldn't get the performance that you needed to run fast, interactive, SQL queries in a data lake. Now a lot's changed in 10 or 12 years. I remember in the very early days, people would say, you you'll never get performance because you need to be column there. You need to store data in a column format. And then, you know, column formats we're introduced to, to data apes, you have Parque ORC file in aro that were created to ultimately deliver performance out of that. So, okay. We got, you know, largely over the performance hurdle, you know, more recently people will say, well, you don't have the ability to do updates and deletes like a traditional data warehouse. >>And now we've got the creation of new data formats, again like iceberg and Delta and Hodi that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a, a quote from, you know, Kurt Monash many years ago where he said, you know, know it takes six or seven years to build a functional database. I think that's that's right. And now we've had almost a decade go by. So, you know, these technologies have matured to really deliver very, very close to the same level performance and functionality of, of cloud data warehouses. So I think the, the reality is that's become a line and now we have large giant hyperscale internet companies that, you know, don't have the traditional data warehouse at all. They do all of their analytics in a data lake. So I think we've, we've proven that it's very much possible today. >>Thank you for that. And so Richard, talk about your perspective as a practitioner in terms of what open brings you versus, I mean, look closed is it's open as a moving target. I remember Unix used to be open systems and so it's, it is an evolving, you know, spectrum, but, but from your perspective, what does open give you that you can't get from a proprietary system where you are fearful of in a proprietary system? >>I, I suppose for me open buys us the ability to be unsure about the future, because one thing that's always true about technology is it evolves in a, a direction, slightly different to what people expect. And what you don't want to end up is done is backed itself into a corner that then prevents it from innovating. So if you have chosen a technology and you've stored trillions of records in that technology and suddenly a new way of processing or machine learning comes out, you wanna be able to take advantage and your competitive edge might depend upon it. And so I suppose for us, we acknowledge that we don't have perfect vision of what the future might be. And so by backing open storage technologies, we can apply a number of different technologies to the processing of that data. And that gives us the ability to remain relevant, innovate on our data storage. And we have bought our way out of the, any performance concerns because we can use cloud scale infrastructure to scale up and scale down as we need. And so we don't have the concerns that we don't have enough hardware today to process what we want to do, want to achieve. We can just scale up when we need it and scale back down. So open source has really allowed us to maintain the being at the cutting edge. >>So Jess, let me play devil's advocate here a little bit, and I've talked to Shaak about this and you know, obviously her vision is there's an open source that, that the data meshes open source, an open source tooling, and it's not a proprietary, you know, you're not gonna buy a data mesh. You're gonna build it with, with open source toolings and, and vendors like you are gonna support it, but to come back to sort of today, you can get to market with a proprietary solution faster. I'm gonna make that statement. You tell me if it's a lie and then you can say, okay, we support Apache iceberg. We're gonna support open source tooling, take a company like VMware, not really in the data business, but how, the way they embraced Kubernetes and, and you know, every new open source thing that comes along, they say, we do that too. Why can't proprietary systems do that and be as effective? >>Yeah, well, I think at least with the, within the data landscape saying that you can access open data formats like iceberg or, or others is, is a bit dis disingenuous because really what you're selling to your customer is a certain degree of performance, a certain SLA, and you know, those cloud data warehouses that can reach beyond their own proprietary storage drop all the performance that they were able to provide. So it is, it reminds me kind of, of, again, going back 10 or 12 years ago when everybody had a connector to Haddo and that they thought that was the solution, right? But the reality was, you know, a connector was not the same as running workloads in Haddo back then. And I think similarly, you know, being able to connect to an external table that lives in an open data format, you know, you're, you're not going to give it the performance that your customers are accustomed to. And at the end of the day, they're always going to be predisposed. They're always going to be incentivized to get that data ingested into the data warehouse, cuz that's where they have control. And you know, the bottom line is the database industry has really been built around vendor lockin. I mean, from the start, how, how many people love Oracle today, but our customers, nonetheless, I think, you know, lockin is, is, is part of this industry. And I think that's really what we're trying to change with open data formats. >>Well, that's interesting reminded when I, you know, I see the, the gas price, the tees or gas price I, I drive up and then I say, oh, that's the cash price credit card. I gotta pay 20 cents more, but okay. But so the, the argument then, so let me, let me come back to you, Justin. So what's wrong with saying, Hey, we support open data formats, but yeah, you're gonna get better performance if you, if you keep it into our closed system, are you saying that long term that's gonna come back and bite you cuz you're gonna end up, you mentioned Oracle, you mentioned Teradata. Yeah. That's by, by implication, you're saying that's where snowflake customers are headed. >>Yeah, absolutely. I think this is a movie that, you know, we've all seen before. At least those of us who've been in the industry long enough to, to see this movie play over a couple times. So I do think that's the future. And I think, you know, I loved what Richard said. I actually wrote it down. Cause I thought it was an amazing quote. He said, it buys us the ability to be unsure of the future. Th that that pretty much says it all the, the future is unknowable and the reality is using open data formats. You remain interoperable with any technology you want to utilize. If you want to use spark to train a machine learning model and you want to use Starbust to query via sequel, that's totally cool. They can both work off the same exact, you know, data, data sets by contrast, if you're, you know, focused on a proprietary model, then you're kind of locked in again to that model. I think the same applies to data, sharing to data products, to a wide variety of, of aspects of the data landscape that a proprietary approach kind of closes you in and locks you in. >>So I, I would say this Richard, I'd love to get your thoughts on it. Cause I talked to a lot of Oracle customers, not as many te data customers, but, but a lot of Oracle customers and they, you know, they'll admit, yeah, you know, they're jamming us on price and the license cost they give, but we do get value out of it. And so my question to you, Richard, is, is do the, let's call it data warehouse systems or the proprietary systems. Are they gonna deliver a greater ROI sooner? And is that in allure of, of that customers, you know, are attracted to, or can open platforms deliver as fast in ROI? >>I think the answer to that is it can depend a bit. It depends on your businesses skillset. So we are lucky that we have a number of proprietary teams that work in databases that provide our operational data capability. And we have teams of analytics and big data experts who can work with open data sets and open data formats. And so for those different teams, they can get to an ROI more quickly with different technologies for the business though, we can't do better for our operational data stores than proprietary databases. Today we can back off very tight SLAs to them. We can demonstrate reliability from millions of hours of those databases being run at enterprise scale, but for an analytics workload where increasing our business is growing in that direction, we can't do better than open data formats with cloud-based data mesh type technologies. And so it's not a simple answer. That one will always be the right answer for our business. We definitely have times when proprietary databases provide a capability that we couldn't easily represent or replicate with open technologies. >>Yeah. Richard, stay with you. You mentioned, you know, you know, some things before that, that strike me, you know, the data brick snowflake, you know, thing is, oh, is a lot of fun for analysts like me. You've got data bricks coming at it. Richard, you mentioned you have a lot of rockstar, data engineers, data bricks coming at it from a data engineering heritage. You get snowflake coming at it from an analytics heritage. Those two worlds are, are colliding people like PJI Mohan said, you know what? I think it's actually harder to play in the data engineering. So I E it's easier to for data engineering world to go into the analytics world versus the reverse, but thinking about up and coming engineers and developers preparing for this future of data engineering and data analytics, how, how should they be thinking about the future? What, what's your advice to those young people? >>So I think I'd probably fall back on general programming skill sets. So the advice that I saw years ago was if you have open source technologies, the pythons and Javas on your CV, you commander 20% pay, hike over people who can only do proprietary programming languages. And I think that's true of data technologies as well. And from a business point of view, that makes sense. I'd rather spend the money that I save on proprietary licenses on better engineers, because they can provide more value to the business that can innovate us beyond our competitors. So I think I would my advice to people who are starting here or trying to build teams to capitalize on data assets is begin with open license, free capabilities, because they're very cheap to experiment with. And they generate a lot of interest from people who want to join you as a business. And you can make them very successful early, early doors with, with your analytics journey. >>It's interesting. Again, analysts like myself, we do a lot of TCO work and have over the last 20 plus years. And in world of Oracle, you know, normally it's the staff, that's the biggest nut in total cost of ownership, not an Oracle. It's the it's the license cost is by far the biggest component in the, in the blame pie. All right, Justin, help us close out this segment. We've been talking about this sort of data mesh open, closed snowflake data bricks. Where does Starburst sort of as this engine for the data lake data lake house, the data warehouse fit in this, in this world? >>Yeah. So our view on how the future ultimately unfolds is we think that data lakes will be a natural center of gravity for a lot of the reasons that we described open data formats, lowest total cost of ownership, because you get to choose the cheapest storage available to you. Maybe that's S3 or Azure data lake storage, or Google cloud storage, or maybe it's on-prem object storage that you bought at a, at a really good price. So ultimately storing a lot of data in a deal lake makes a lot of sense, but I think what makes our perspective unique is we still don't think you're gonna get everything there either. We think that basically centralization of all your data assets is just an impossible endeavor. And so you wanna be able to access data that lives outside of the lake as well. So we kind of think of the lake as maybe the biggest place by volume in terms of how much data you have, but to, to have comprehensive analytics and to truly understand your business and understand it holistically, you need to be able to go access other data sources as well. And so that's the role that we wanna play is to be a single point of access for our customers, provide the right level of fine grained access controls so that the right people have access to the right data and ultimately make it easy to discover and consume via, you know, the creation of data products as well. >>Great. Okay. Thanks guys. Right after this quick break, we're gonna be back to debate whether the cloud data model that we see emerging and the so-called modern data stack is really modern, or is it the same wine new bottle? When it comes to data architectures, you're watching the cube, the leader in enterprise and emerging tech coverage. >>Your data is capable of producing incredible results, but data consumers are often left in the dark without fast access to the data they need. Starers makes your data visible from wherever it lives. Your company is acquiring more data in more places, more rapidly than ever to rely solely on a data centralization strategy. Whether it's in a lake or a warehouse is unrealistic. A single source of truth approach is no longer viable, but disconnected data silos are often left untapped. We need a new approach. One that embraces distributed data. One that enables fast and secure access to any of your data from anywhere with Starburst, you'll have the fastest query engine for the data lake that allows you to connect and analyze your disparate data sources no matter where they live Starburst provides the foundational technology required for you to build towards the vision of a decentralized data mesh Starburst enterprise and Starburst galaxy offer enterprise ready, connectivity, interoperability, and security features for multiple regions, multiple clouds and everchanging global regulatory requirements. The data is yours. And with Starburst, you can perform analytics anywhere in light of your world. >>Okay. We're back with Justin Boardman. CEO of Starbust Richard Jarvis is the CTO of EMI health and Theresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie it's it is it's is that it's not modern. Justin, what do you say? >>Yeah. I mean, I think new isn't modern, right? I think it's the, it's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exist just yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess, the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, a microservices layer on top of leg legacy apps. How do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more, more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Well thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. De-risked experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that. I'm a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and, and principles there >>Of, of how it should look like or, or how >>It's yeah. What it should be. >>Yeah. Yeah. Well, I think, you know, in, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of it was starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go buy a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, my takeaway there is it's inclusive, whether it's a data Mar data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include there on Preem data? O obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on Preem? I mean, most implementations I've seen in data mesh, frankly really aren't, you know, adhering to the philosophy. They're maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data. Me, the fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both worlds. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds. That, again, going back to Richard's point, that is meaningful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, talking about data as product, wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight. And in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured, managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to researchers. >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned you, you appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have, and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use case? >>Yeah, it makes sense. It's got the context. If the, if the domains own the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's send Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Theresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave Valante for the cube, and we'll see you next time. >>The explosion of data sources has forced organizations to modernize their systems and architecture and come to terms with one size does not fit all for data management today. Your teams are constantly moving and copying data, which requires time management. And in some cases, double paying for compute resources. Instead, what if you could access all your data anywhere using the BI tools and SQL skills your users already have. And what if this also included enterprise security and fast performance with Starburst enterprise, you can provide your data consumers with a single point of secure access to all of your data, no matter where it lives with features like strict, fine grained, access control, end to end data encryption and data masking Starburst meets the security standards of the largest companies. Starburst enterprise can easily be deployed anywhere and managed with insights where data teams holistically view their clusters operation and query execution. So they can reach meaningful business decisions faster, all this with the support of the largest team of Trino experts in the world, delivering fully tested stable releases and available to support you 24 7 to unlock the value in all of your data. You need a solution that easily fits with what you have today and can adapt to your architecture. Tomorrow. Starbust enterprise gives you the fastest path from big data to better decisions, cuz your team can't afford to wait. Trino was created to empower analytics anywhere and Starburst enterprise was created to give you the enterprise grade performance, connectivity, security management, and support your company needs organizations like Zolando Comcast and FINRA rely on Starburst to move their businesses forward. Contact us to get started.

>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data-driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting cost could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data marts, data hubs, and yes, even data lakes were broken and left us wanting from more welcome to the data doesn't lie, or doesn't a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have featured parody with the data lake or vice versa is the so-called modern data stack, simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for hit that was acquired by Teradata. And when I got to Teradata, of course, Teradata is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience show? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know, right. You actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like star Oxley, for things like security for certainly very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited Jamma, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come. I think that's the story at Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenants of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about SAR, brain Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but what does that mean? Does that mean the E D w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's going to be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems, maybe either those that either source systems for the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to use all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got to, you know, domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue are two, you know, challenges, self-serve infrastructure let's park that for a second. And then in your industry, the one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at emus is we have a single security layer that sits on top of our data match, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin, I mean, Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, do an analytic queries and with data that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah. I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively a almost eCommerce like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself, >>Okay. G guys grab a sip of water. After this short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence, keep it right there. >>Your company has more data than ever, and more people trying to understand it, but there's a problem. Your data is stored across multiple systems. It's hard to access and that delays analytics and ultimately decisions. The old method of moving all of your data into a single source of truth is slow and definitely not built for the volume of data we have today or where we are headed while your data engineers spent over half their time, moving data, your analysts and data scientists are left, waiting, feeling frustrated, unproductive, and unable to move the needle for your business. But what if you could spend less time moving or copying data? What if your data consumers could analyze all your data quickly? >>Starburst helps your teams run fast queries on any data source. We help you create a single point of access to your data, no matter where it's stored. And we support high concurrency, we solve for speed and scale, whether it's fast, SQL queries on your data lake or faster queries across multiple data sets, Starburst helps your teams run analytics anywhere you can't afford to wait for data to be available. Your team has questions that need answers. Now with Starburst, the wait is over. You'll have faster access to data with enterprise level security, easy connectivity, and 24 7 support from experts, organizations like Zolando Comcast and FINRA rely on Starburst to move their businesses forward. Contact our Trino experts to get started. >>We're back with Jess Borgman of Starburst and Richard Jarvis of EVAs health. Okay, we're gonna get to lie. Number two, and that is this an open source based platform cannot give you the performance and control that you can get with a proprietary system. Is that a lie? Justin, the enterprise data warehouse has been pretty dominant and has evolved and matured. Its stack has mature over the years. Why is it not the default platform for data? >>Yeah, well, I think that's become a lie over time. So I, I think, you know, if we go back 10 or 12 years ago with the advent of the first data lake really around Hudu, that probably was true that you couldn't get the performance that you needed to run fast, interactive, SQL queries in a data lake. Now a lot's changed in 10 or 12 years. I remember in the very early days, people would say, you you'll never get performance because you need to be column there. You need to store data in a column format. And then, you know, column formats we're introduced to, to data apes, you have Parque ORC file in aro that were created to ultimately deliver performance out of that. So, okay. We got, you know, largely over the performance hurdle, you know, more recently people will say, well, you don't have the ability to do updates and deletes like a traditional data warehouse. >>And now we've got the creation of new data formats, again like iceberg and Delta and Hodi that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a, a quote from, you know, Kurt Monash many years ago where he said, you know, know it takes six or seven years to build a functional database. I think that's that's right. And now we've had almost a decade go by. So, you know, these technologies have matured to really deliver very, very close to the same level performance and functionality of, of cloud data warehouses. So I think the, the reality is that's become a line and now we have large giant hyperscale internet companies that, you know, don't have the traditional data warehouse at all. They do all of their analytics in a data lake. So I think we've, we've proven that it's very much possible today. >>Thank you for that. And so Richard, talk about your perspective as a practitioner in terms of what open brings you versus, I mean, look closed is it's open as a moving target. I remember Unix used to be open systems and so it's, it is an evolving, you know, spectrum, but, but from your perspective, what does open give you that you can't get from a proprietary system where you are fearful of in a proprietary system? >>I, I suppose for me open buys us the ability to be unsure about the future, because one thing that's always true about technology is it evolves in a, a direction, slightly different to what people expect. And what you don't want to end up is done is backed itself into a corner that then prevents it from innovating. So if you have chosen a technology and you've stored trillions of records in that technology and suddenly a new way of processing or machine learning comes out, you wanna be able to take advantage and your competitive edge might depend upon it. And so I suppose for us, we acknowledge that we don't have perfect vision of what the future might be. And so by backing open storage technologies, we can apply a number of different technologies to the processing of that data. And that gives us the ability to remain relevant, innovate on our data storage. And we have bought our way out of the, any performance concerns because we can use cloud scale infrastructure to scale up and scale down as we need. And so we don't have the concerns that we don't have enough hardware today to process what we want to do, want to achieve. We can just scale up when we need it and scale back down. So open source has really allowed us to maintain the being at the cutting edge. >>So Jess, let me play devil's advocate here a little bit, and I've talked to Shaak about this and you know, obviously her vision is there's an open source that, that the data meshes open source, an open source tooling, and it's not a proprietary, you know, you're not gonna buy a data mesh. You're gonna build it with, with open source toolings and, and vendors like you are gonna support it, but to come back to sort of today, you can get to market with a proprietary solution faster. I'm gonna make that statement. You tell me if it's a lie and then you can say, okay, we support Apache iceberg. We're gonna support open source tooling, take a company like VMware, not really in the data business, but how, the way they embraced Kubernetes and, and you know, every new open source thing that comes along, they say, we do that too. Why can't proprietary systems do that and be as effective? >>Yeah, well, I think at least with the, within the data landscape saying that you can access open data formats like iceberg or, or others is, is a bit dis disingenuous because really what you're selling to your customer is a certain degree of performance, a certain SLA, and you know, those cloud data warehouses that can reach beyond their own proprietary storage drop all the performance that they were able to provide. So it is, it reminds me kind of, of, again, going back 10 or 12 years ago when everybody had a connector to Haddo and that they thought that was the solution, right? But the reality was, you know, a connector was not the same as running workloads in Haddo back then. And I think similarly, you know, being able to connect to an external table that lives in an open data format, you know, you're, you're not going to give it the performance that your customers are accustomed to. And at the end of the day, they're always going to be predisposed. They're always going to be incentivized to get that data ingested into the data warehouse, cuz that's where they have control. And you know, the bottom line is the database industry has really been built around vendor lockin. I mean, from the start, how, how many people love Oracle today, but our customers, nonetheless, I think, you know, lockin is, is, is part of this industry. And I think that's really what we're trying to change with open data formats. >>Well, that's interesting reminded when I, you know, I see the, the gas price, the tees or gas price I, I drive up and then I say, oh, that's the cash price credit card. I gotta pay 20 cents more, but okay. But so the, the argument then, so let me, let me come back to you, Justin. So what's wrong with saying, Hey, we support open data formats, but yeah, you're gonna get better performance if you, if you keep it into our closed system, are you saying that long term that's gonna come back and bite you cuz you're gonna end up, you mentioned Oracle, you mentioned Teradata. Yeah. That's by, by implication, you're saying that's where snowflake customers are headed. >>Yeah, absolutely. I think this is a movie that, you know, we've all seen before. At least those of us who've been in the industry long enough to, to see this movie play over a couple times. So I do think that's the future. And I think, you know, I loved what Richard said. I actually wrote it down. Cause I thought it was an amazing quote. He said, it buys us the ability to be unsure of the future. Th that that pretty much says it all the, the future is unknowable and the reality is using open data formats. You remain interoperable with any technology you want to utilize. If you want to use spark to train a machine learning model and you want to use Starbust to query via sequel, that's totally cool. They can both work off the same exact, you know, data, data sets by contrast, if you're, you know, focused on a proprietary model, then you're kind of locked in again to that model. I think the same applies to data, sharing to data products, to a wide variety of, of aspects of the data landscape that a proprietary approach kind of closes you in and locks you in. >>So I, I would say this Richard, I'd love to get your thoughts on it. Cause I talked to a lot of Oracle customers, not as many te data customers, but, but a lot of Oracle customers and they, you know, they'll admit, yeah, you know, they're jamming us on price and the license cost they give, but we do get value out of it. And so my question to you, Richard, is, is do the, let's call it data warehouse systems or the proprietary systems. Are they gonna deliver a greater ROI sooner? And is that in allure of, of that customers, you know, are attracted to, or can open platforms deliver as fast in ROI? >>I think the answer to that is it can depend a bit. It depends on your businesses skillset. So we are lucky that we have a number of proprietary teams that work in databases that provide our operational data capability. And we have teams of analytics and big data experts who can work with open data sets and open data formats. And so for those different teams, they can get to an ROI more quickly with different technologies for the business though, we can't do better for our operational data stores than proprietary databases. Today we can back off very tight SLAs to them. We can demonstrate reliability from millions of hours of those databases being run at enterprise scale, but for an analytics workload where increasing our business is growing in that direction, we can't do better than open data formats with cloud-based data mesh type technologies. And so it's not a simple answer. That one will always be the right answer for our business. We definitely have times when proprietary databases provide a capability that we couldn't easily represent or replicate with open technologies. >>Yeah. Richard, stay with you. You mentioned, you know, you know, some things before that, that strike me, you know, the data brick snowflake, you know, thing is, oh, is a lot of fun for analysts like me. You've got data bricks coming at it. Richard, you mentioned you have a lot of rockstar, data engineers, data bricks coming at it from a data engineering heritage. You get snowflake coming at it from an analytics heritage. Those two worlds are, are colliding people like PJI Mohan said, you know what? I think it's actually harder to play in the data engineering. So I E it's easier to for data engineering world to go into the analytics world versus the reverse, but thinking about up and coming engineers and developers preparing for this future of data engineering and data analytics, how, how should they be thinking about the future? What, what's your advice to those young people? >>So I think I'd probably fall back on general programming skill sets. So the advice that I saw years ago was if you have open source technologies, the pythons and Javas on your CV, you commander 20% pay, hike over people who can only do proprietary programming languages. And I think that's true of data technologies as well. And from a business point of view, that makes sense. I'd rather spend the money that I save on proprietary licenses on better engineers, because they can provide more value to the business that can innovate us beyond our competitors. So I think I would my advice to people who are starting here or trying to build teams to capitalize on data assets is begin with open license, free capabilities, because they're very cheap to experiment with. And they generate a lot of interest from people who want to join you as a business. And you can make them very successful early, early doors with, with your analytics journey. >>It's interesting. Again, analysts like myself, we do a lot of TCO work and have over the last 20 plus years. And in world of Oracle, you know, normally it's the staff, that's the biggest nut in total cost of ownership, not an Oracle. It's the it's the license cost is by far the biggest component in the, in the blame pie. All right, Justin, help us close out this segment. We've been talking about this sort of data mesh open, closed snowflake data bricks. Where does Starburst sort of as this engine for the data lake data lake house, the data warehouse fit in this, in this world? >>Yeah. So our view on how the future ultimately unfolds is we think that data lakes will be a natural center of gravity for a lot of the reasons that we described open data formats, lowest total cost of ownership, because you get to choose the cheapest storage available to you. Maybe that's S3 or Azure data lake storage, or Google cloud storage, or maybe it's on-prem object storage that you bought at a, at a really good price. So ultimately storing a lot of data in a deal lake makes a lot of sense, but I think what makes our perspective unique is we still don't think you're gonna get everything there either. We think that basically centralization of all your data assets is just an impossible endeavor. And so you wanna be able to access data that lives outside of the lake as well. So we kind of think of the lake as maybe the biggest place by volume in terms of how much data you have, but to, to have comprehensive analytics and to truly understand your business and understand it holistically, you need to be able to go access other data sources as well. And so that's the role that we wanna play is to be a single point of access for our customers, provide the right level of fine grained access controls so that the right people have access to the right data and ultimately make it easy to discover and consume via, you know, the creation of data products as well. >>Great. Okay. Thanks guys. Right after this quick break, we're gonna be back to debate whether the cloud data model that we see emerging and the so-called modern data stack is really modern, or is it the same wine new bottle? When it comes to data architectures, you're watching the cube, the leader in enterprise and emerging tech coverage. >>Your data is capable of producing incredible results, but data consumers are often left in the dark without fast access to the data they need. Starers makes your data visible from wherever it lives. Your company is acquiring more data in more places, more rapidly than ever to rely solely on a data centralization strategy. Whether it's in a lake or a warehouse is unrealistic. A single source of truth approach is no longer viable, but disconnected data silos are often left untapped. We need a new approach. One that embraces distributed data. One that enables fast and secure access to any of your data from anywhere with Starburst, you'll have the fastest query engine for the data lake that allows you to connect and analyze your disparate data sources no matter where they live Starburst provides the foundational technology required for you to build towards the vision of a decentralized data mesh Starburst enterprise and Starburst galaxy offer enterprise ready, connectivity, interoperability, and security features for multiple regions, multiple clouds and everchanging global regulatory requirements. The data is yours. And with Starburst, you can perform analytics anywhere in light of your world. >>Okay. We're back with Justin Boardman. CEO of Starbust Richard Jarvis is the CTO of EMI health and Theresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie it's it is it's is that it's not modern. Justin, what do you say? >>Yeah. I mean, I think new isn't modern, right? I think it's the, it's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exist just yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess, the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, a microservices layer on top of leg legacy apps. How do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more, more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Well thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. De-risked experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that. I'm a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and, and principles there >>Of, of how it should look like or, or how >>It's yeah. What it should be. >>Yeah. Yeah. Well, I think, you know, in, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of it was starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go buy a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, my takeaway there is it's inclusive, whether it's a data Mar data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include there on Preem data? O obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on Preem? I mean, most implementations I've seen in data mesh, frankly really aren't, you know, adhering to the philosophy. They're maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data. Me, the fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both worlds. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds. That, again, going back to Richard's point, that is meaningful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, talking about data as product, wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight. And in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured, managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to researchers. >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned you, you appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have, and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use case? >>Yeah, it makes sense. It's got the context. If the, if the domains own the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's send Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Theresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave Valante for the cube, and we'll see you next time. >>The explosion of data sources has forced organizations to modernize their systems and architecture and come to terms with one size does not fit all for data management today. Your teams are constantly moving and copying data, which requires time management. And in some cases, double paying for compute resources. Instead, what if you could access all your data anywhere using the BI tools and SQL skills your users already have. 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>>Okay. We're back with Justin Boorman CEO of Starburst. Richard Jarvis is the CTO of EMI health and Teresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie or it's it is it's is that it's not modern, Justin, what do you say? >>Yeah, I mean, I think new isn't modern, right? I think it's, the's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just this, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data's still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exists just, yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, of microservices layer on top of leg legacy apps. Ho how do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Oh thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. Drisk experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that I'm, you know, a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, the paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and principles there >>Of, of how it should look like or, or how >>Yeah. What it should be? >>Yeah. Yeah. Well, I think, you know, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go by a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, I might take away there is it's inclusive, whether it's a data Mart, data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include Theresa on, on Preem data? Obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on pre I mean most implementations I've seen and data mesh, frankly really aren't, you know, adhering to the philosophy there. Maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data mesh. The fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both world. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds that, again, going back to Richard's point, that is needful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, you're talking about data as product. Wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight and in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured and managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to research is >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the right, correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use >>Case? Yeah, it makes sense. It's got the context. If the, if the domains on the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's end, Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Teresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave ante for the, and we'll see you next time.

>>Hey everyone. Welcome back to the cube. Lisa Martin here with Dave ante, we're covering snowflake summit 22. This is Dave two of our wall to wall cube coverage of three days. We've been talking with a lot of customers partners, and we've got some more partners to talk with us. Next. Informatica two of our guests are back with us on the program. Rick TA Daniels joins us the G P global ecosystems and technology at Informatica and Peter COO vice president and chief strategist banking and financial services. Welcome guys. >>Thank you guys. Thanks for having us, Peter, >>Talk to us about what some of the trends are that you're seeing in the financial services space with respect to cloud and data and AI. >>Absolutely. You know, I'd say 10 years ago, the conversation around cloud was what is that? Right? How do we actually, or no way, because there was a lot of concerns about privacy and security and so forth. You know, now, as you see organizations modernizing their business capabilities, they're investing in cloud solutions for analytics applications, as well as data data being not only just a byproduct of transactions and interactions in financial services, it truly fuels business success. But we have a term here in Informatica where data really has no value unless it's fit for business. Use data has to be accessible in the systems and applications you use to run your business. It has to be clean. It has to be valid. It has to be transparent. People need to understand where it comes from, where it's going, how it's used and who's using it. It also has to be understood by the business. >>You can have all the data in the world and your business applications, but people don't know what they need it to use it for how they should use it. It has no value as well. And then lastly, it has to be protected when it matters most what we're seeing across financial services, that with the evolution of cloud now, really being the center of focus for many of the net new investments, data is scattered everywhere, not just in one cloud environment, but in multiple cloud environments, but they're still dealing with many of the on premise systems that have been running this industry for many, many years. So organizations need to have the ability to understand what they need to do with their data. More importantly, tie that to a measurable business outcome. So we're seeing the data conversation really at the board level, right? It's an asset of the business. It's no longer just owned by it. Data governance brings both business technology and data leaders together to really understand how do we use manage, govern and really leverage data for positive business outcomes. So we see that as an imperative that cuts across all sectors of financial services, both for large firms, as well as for the mid-market so >>Quick follow up. If I, may you say it's a board level. I totally agree. Is it also a line of business level? Are you seeing increasingly that line of businesses are leaning in owning the data, be building data products and the like >>Absolutely. Because at the end of the day business needs information in order to be successful. And data ownership now really belongs in the front office. Business executives understand that data again is not just a bunch of zeros and ones. These are critical elements for them make decisions and to run their business, whether it's to improve customer experience, whether it's to grow Wallace share, whether it's to comply with regulations, manage risks in today's environment. And of course being agile business knows that data's important. They have ownership of it and technology and data organizations help facilitate that solutions. And of course the investments to ensure that business can make the decisions and take the appropriate actions. >>A lot of asks and requirements on data. That's a big challenge for organizations. You mentioned. Well, one of the things that we've mentioned many times on this program recently is every company has to be a data company. There is no more, it's not an option anymore. If you wanna be successful, how does Informatica help customers navigate all of the requirements on data for them to be able to extract that business value and create new products and services in a timely fashion? >>So Informatica announced what we call the intelligent data management cloud platform. The platform has capabilities to help organizations access the data that they need, share it across to applications that run their business, be able to identify and deal with data, quality issues and requirements. Being able to provide that transparency, the lineage that people need across multiple environments. So we've been investing in this platform that really allows our customers to take advantage of these critical data management, data governance and data privacy requirements, all in one single solution. So we're no longer out there just selling piecemeal products. The platform is the offering that we provide across all industries. >>So how has that affected the way Informatica does business over the last several years? Snowflake is relatively new. You guys have been around a long time. How has your business evolved and specifically, how are you serving the snowflake yeah. Joint customers with >>Informatica? Yeah, I think then when I've been talking with folks here at the event, there are two big areas that keep coming up. So, so data governance, data governance, data governance, right? It's such a hot topic out there. And as Peter was mentioning, data governance is a critical enabler of access to data. In fact, there is an IDC study for last year that said that, you know, 80, 84% of executives, you know, no surprise, right? They wanna have data driven outcomes, data driven organizations, but only 30% of practitioners actually use data to make decisions. There's a huge gap there. And really that's where governance comes in and creating trust around data and not only creating trust, but delivering data to and users. So that's one big trend. The other one is departmental user adoption. We're seeing a, a huge push towards agility and rapid startup of new projects, new data driven transformations that are happening at the departmental level, you know, individual contributors, that sort of thing. So Informatica, we did a made announcement yesterday with snowflake of a whole host of innovations that are really targeting those two big trend areas. >>I wanna get into the announcements, but you know, the point about governance and, and users, business users being reluctant, it's kind of chicken and egg, isn't it. If, if I don't have the governance, I'm, I'm afraid to use it. But even if I do have it, there's the architecture of my, my, my company, my, my data organization, you know, may not facilitate that. And so I'm gonna change the architect, but then it's a wild west. So it has to be governed. Isn't that a challenge that company companies >>Absolutely, and, and governance is, is a lot more than just technology, right? It's of a people process problem. And there really is a community or an ecosystem inside every organization for governance. So it's really important that when you think about deploying governance and being successful, that every stakeholder have the ability to interact with this common framework, right. They get what they need out of it. It's tailored for how they wanna work. You've got your it folks, you got your chief data officer data stewards, you have your privacy folks and you have your business users. They're all different personas. So we really focus on creating a holistic, single pane of glass view with our cloud data governance and catalog offering that that really takes all the way from the raw technical data and actually delivers data in, in a shopping cart, like experience for actual enterprise users. Right? And, and so I think that's when data governance goes from historically data, governments was seen as an impediment. It was seen as a tax, I think, but now it's really an accelerator, an enabler and driving consumption of data, which in turn for our friends here at snowflake is exactly what they're looking for. >>Talk about the news. So data loader, what does that do? >>Well, it's all in the name. We say, no, the data loader it, it's a free utility that we announced here at, at snowflake summit that allows any user to sign up. It's completely free, no capacity limits. You just need an email address, three simple steps start rapidly loading data into snowflake. Right? So that first step is just get data in there. Start working with snowflake. Informatica is investing and making that easy for every single user out there. And especially those departmental users who wanna get started quickly. >>Yeah. So, I mean, that's a key part point of getting data into the snowflake data cloud, right? It's like any cloud, you gotta get data in. How does it work with, with customers? I mean, you guys are, are known, you have a long history of, you know, extract transform ETL. How does it work in the snowflake world? Is it, is it different? Is it, you remember the Hadoop days? It was, it was E LT, right? How are customers doing that today in this environment? >>Yeah, it's different. I mean, there, there are a lot of the, the same patterns are still in play. There's a lot more of a rapid data loading, right. Is a key theme. Just get it into snowflake and then work on the data, transform it inside of snowflake. So it's, it's a flavor of T right. But it's really pushing down to the snowflake data cloud as opposed to Hado with spark or something like that. Right. So that, that's definitely how customers are using it. And, you know, majority of our customers actually with snowflake are using our cloud technology, but we're also helping customers who are on premise customers, automate the migration from our on-premises technology to our cloud native platform as well. Yeah. >>And I'd say, you know, in addition to that, if you think about building a snowflake environment, Informatica helps with our data loader solution, but that's not enough. Then now you need to get value out of your data. So you can put raw data into the snowflake environment, but then you realize the data's not actually fit for business use, what do we need to do actually transform it to clean it, to govern it. And our customers that use Informatica with snowflake are managing the entire data management and data governance process so that they can allow the business to get value out of the snowflake investment. >>How quickly can you enable a business to get value from that data to be able to make business decisions that can transform right. Deliver competitive advantage? >>I think it really depends on an organization on a case by case basis. At the end of the day, you need to understand why are you doing this in the first place, right? What's the business outcome that you're trying to achieve next, identify what data elements do you actually need to capture, govern and manage in order to support the decisions and the actions that the business needs to take. If you don't have those things defined, that's where data governance comes into play. Then all you're doing is setting up a technical environment with a bunch of zeros in ones that no one knows what to do with. So we talk about data governance more holistically, say, you need to align it to your business outcomes, but ensure that you have people, processes, roles, and responsibilities, and the underlying technology to not just load data into snowflake, but to leverage it again for the business needs across the organization. >>Oh, good, please. >>I just wanted to add to that real quickly. Yeah. One of the things Informatica we're philosophically focused on is how do you accelerate the entire business of data management? So with our, our cloud platform, we have what's called our clear AI engine, right? So we use AI techniques, machine learning recommendations to accelerate with the, the knowledge of the metadata of what's gone on the organization. For example, that when we discover data assets figure out is this customer data, is it product data that dramatically shortens the time to find data assets deliver them? And so across our whole portfolio, we're taking things that were traditionally months to do. We're taking 'em down to weeks and days and even hours, right? So that's the whole goal is just accelerate that entire journey and life cycle through cloud native approaches and AI. Yeah, >>You kind of just answered my question. I think Rick, so you have this joint value statement together. We help customers. This is informatic and snowflake together. We help customers modernize their data. Architecture enable the most critical workloads, provide AI driven data governance and accelerate added value with advanced analytics. I mean, you definitely touched on some of those, but kind of unpack the rest of that. What do you mean by modernize? What is their data architecture? What is that? Let's start there. What does that look like? Modernizing a data. Yeah. >>So, so a lot with so many customers, right? They, they built data warehouses, core data and analytics systems on premises, right? They're using ETL technology using those, those either warehouse, appliances or databases. And what they're looking for is they wanna move to a cloud native model, right. And all the benefits of cloud in terms of TCO elasticity, instant scale up agility, all those benefits. So we're looking, we're looking to do with our, our modernization programs for our, for our current customer base that are on premises. We automate the process to get them to a fully cloud native, which means they can now do hybrid. They can do multi-cloud elastic processing. And it's all also in a consumption based model that we introduced about about a year and a half ago. So, so they're looking for all those elements of a cloud native platform and they're, but they're solving the same problems, right? We still have to connect data. We still have to transform data, prepare it, cleanse it, all those things exist, but in a, in a cloud native footprint, and that's what we're helping them get to. >>And the modern architecture these days, quite honestly, it's no longer about getting best breed tools and stitching them together and hoping that it will actually work. And Informatica is value proposition that our platform has all those capabilities as services. So our customers don't have to deal with the costs and the risks of trying to make everything work behind the scenes and what we've done with IDMC or intelligent data management cloud for financial services, retail, CPG, and healthcare and life sciences. In addition to our core capabilities and our clear AI machine learning engine, we also have industry accelerators, prebuilt data, quality rules for certain regulations in within banking. We've got master data management, customer models for healthcare insurance industry, all prebuilt. So these are accelerators that we've actually built over the years. And we're now making available to our customers who adopt informatic as intelligent data management cloud for their data management and governance needs. >>And then, and then the other part of this statement that that's interesting is provide AI driven data governance. You know, we are seeing a move toward, you know, decentralized data architectures and, and, and organizations. And we talk to snowflake about that. They go, yeah, we're globally distributed cloud. Okay, great. So that's decent place, but what we see a lot of customers doing to say, okay, we're gonna give lines of business responsibility for data. We're gonna argue about who owns what. And then once we settle that here's your own, here's your own data lake. Maybe they they'll try to cobble together a catalog or a super catalog. Right. And then they'll try to figure out, you know, some algorithms to, to determine data quality, you know, best, you know, okay. Don't use. Right, right. So that, so if I understand it, you automate all that. >>So what we're doing with AI machine learning is really helping the data professional, whether in the business, in technology or in between not only to get the job done faster, better, and cheaper, but actually do it intelligently. What do we mean by that? For example, our AI engine machine learning will look at data patterns and determine not only what's wrong with your data, but how should you fix it and recommend data quality rules to actually apply them and get those errors addressed. We also infer data relationships across a multi-cloud environment where those definitions were never there in the beginning. So we have the ability to scan the metadata and determine, Hey, this data set is actually related to that data set across multiple clouds. It makes the organization more productive, but more importantly, it increases the confidence level that these organizations have the right infrastructure in place in order to manage and govern their data for what they're trying to do from a business perspective. >>And I add that as well. I think you're talking a lot about data mesh architectures, right? That, that are really kind of popular right now. And I think those kind of, they live or die on, on data governance. Right? If you don't have data governance to share taxonomy, these things, it's very hard to, I think, scale those individual working groups. But if you have a platform where they, the data owners can publish out visibility to what their data means, how to use it, how to interpret it and get that insight, that context directly to the data consumers that's game changing. Right. And that's exactly what we're doing with our cloud data governance and catalog. >>Well, the data mesh, you talk about data mesh, there's four principles, right? It's like decentralized architecture data products. So if, once you figure out those two yep. You just created two more problems, which is the other two parts of the Princip four, two parts of the four principles, self service infrastructure, and computational governance. And that's like the hardest part of federated, federated, computational governance. That's the hardest part. That's the problem that you're solving. >>Yeah. Yeah, absolutely. I mean, think about the whole decentralization and self-service, well, I may be able to access my data in mesh architecture, but if I don't know what it means, how to use it for what purpose, when not to use it, you're creating more problems than what you originally expected to solve. So what we're doing is addressing the data management and the governance requirements, regardless of what the architecture is, whether it's a mesh architecture, a fabric architecture or a traditional data lake or a data store. >>Yeah. Mean, I say, I think data mesh is more of an organizational construct than it is. I, I'm not quite sure what data fabric is. I think Gartner confused the issue that data fabric was an old NetApp term. Yeah. You're probably working in NetApp at the time and it made sense in the NetApp context. And then I think Gartner didn't like the fact that Jamma Dani co-opted this cool term. So they created data fabric, but whatever. But my, my point being, I think when I talk to customers that are they're, they're trying to get more value outta data and they recognize that going through all these hyper specialized roles is time consuming and it's not working for them. And they're frustrated to your points and your joint statement. They want to accelerate that. And they're realizing, and the only way to do that is to distribute responsibility, get more people involved in the process. >>And, and that's, it kind of dovetails with some, the announcements we made on data governance for snowflake, right, is you're taking these, these operational controls of the snowflake layer that are typically managed by SQL and you, and that decentralized architecture data owner doesn't know how to set those patterns and things like that. Right. So we're saying, all right, we're, we're creating these deep integration so that again, we have a fit for persona type experience where they can publish data assets, they can set the rules and policies, and we're gonna push that down to snowflake. So when it actually comes to provisioning data and doing data sharing through snowflake, it's all a seamless experience for the end user and the data owner. Yeah. >>That's great. Beautiful, >>Seamless experience absolutely necessary these days for everybody above guys. Thanks so much for joining David me today, talking about Informatica what's new, what you're doing with snowflake and what you're enabling customers to do in terms of really extracting value from that data. We appreciate your insights. >>Thank you. Yep. >>Thank you for having us >>For our guests and Dave ante. I'm Lisa Martin. You're watching the cubes coverage of snowflake summit day two of the cubes coverage stick around Dave. And I will be right back with our next guest.

(Upbeat music) >> Hey everyone. Welcome back to theCUBE's continuing coverage day two Snowflake Summit '22 live from Caesar's forum in Las Vegas. Lisa Martin here with Dave Vellante, bringing you wall to wall coverage yesterday, today, and tomorrow. We're excited to welcome Matthew Carroll to the program. The CEO of Immuta, we're going to be talking about removing barriers to secure data access security. Matthew, welcome. >> Thank you for having me, appreciate it. >> Talk to the audience a little bit about Immuta you're a Snowflake premier technology partner, but give him an overview of Immuta what you guys do, your vision, all that good stuff. >> Yeah, absolutely, thanks. Yeah, if you think about what Immunta at it's core is, we're a data security platform for the modern data stack, right? So what does that mean? It means that we embed natively into a Snowflake and we enforce policies on data, right? So, the rules to be able to use it, to accelerate data access, right? So, that means connecting to the data very easily controlling it with any regulatory or security policy on it as well as contractual policies, and then being able to audit it. So, that way, any corporation of any size can leverage their data and share that data without risking leaking it or potentially violating a regulation. >> What are some of the key as we look at industry by industry challenges that Immuta is helping those customers address and obviously quickly since everything is accelerating. >> Yeah. And it's, you're seeing it 'cause the big guys like Snowflake are verticalizing, right? You're seeing a lot of industry specific, you know, concepts. With us, if you think of, like, where we live obviously policies on data regulated, right? So healthcare, how do we automate HIPAA compliance? How do we redesign clinical trial management post COVID, right? If you're going to have billions of users and you're collecting that data, pharmaceutical companies can't wait to collect that data. They need to remove those barriers. So, they need to be able to collect it, secure it, and be able to share it. Right? So, double and triple blinded studies being redesigned in the cloud. Government organizations, how do we share security information globally with different countries instantaneously? Right? So these are some of the examples where we're helping organizations transform and be able to kind of accelerate their adoption of data. >> Matt, I don't know if you remember, I mean, I know you remember coming to our office. But we had an interesting conversation and I was telling Lisa. Years ago I wrote a piece of you know, how to build on top of, AWS. You know, there's so much opportunity. And we had a conversation, at our office, theCUBE studios in Marlborough, Massachusetts. And we both, sort of, agreed that there was this new workload emerging. We said, okay, there's AWS, there's Snowflake at the time, we were thinking, and you bring machine learning, at time where we were using data bricks, >> Yeah. >> As the example, of course now it's been a little bit- >> Yeah. Careful. >> More of a battle, right, with those guys. But, and so, you see them going in their different directions, but the premise stands is that there's an ecosystem developing, new workloads developing, on top of the hyper scale infrastructure. And you guys play a part in that. So, describe what you're seeing there 'cause you were right on in that conversation. >> Yeah. Yeah. >> It's nice to be, right. >> Yeah. So when you think of this design pattern, right, is you have a data lake, you have a warehouse, and you have an exchange, right? And this architecture is what you're seeing around you now, is this is every single organization in the world is adopting this design pattern. The challenge that where we fit into kind of a sliver of this is, the way we used to do before is application design, right? And we would build lots of applications, and we would build all of our business logic to enforce security controls and policies inside each app. And you'd go through security and get it approved. In this paradigm, any user could potentially access any data. There's just too many data sources, too many users, and too many things that can go wrong. And to scale that is really hard. So, like, with Immuta, what we've done, versus what everyone else has done is we natively embedded into every single one of those compute partners. So ,Snowflake, data breaks, big query, Redshift, synapse on and on. Natively underneath the covers, so that was BI tools, those data science tools hit Snowflake. They don't have to rewrite any of their code, but we automatically enforce policy without them having to do anything. And then we consistently audit that. I call that the separation of policy from platform. So, just like in the world in big data, when we had to separate compute from storage, in this world, because we're global, right? So we're, we have a distributed workforce and our data needs to abide by all these new security rules and regulations. We provide a flexible framework for them to be able to operate at that scale. And we're the only ones in the world doing it. >> Dave Vellante: See the key there is, I mean, Snowflake is obviously building out its data cloud and the functions that it's building in are quite impressive. >> Yeah. >> Dave Vellante: But you know at some point a customer's going to say, look I have other stuff, whether it's in an Oracle database, or data lake or wherever, and that should just be a node on this global, whatever you want to call it, mesh or fabric. And then if I'm hearing you right, you participate in all of that. >> Correct? Yeah We kind of, we were able to just natively inject into each, and then be able to enforce that policy consistently, right? So, hey, can you access HIPAA data? Who are you? Are you authorized to use this? What's the purpose you want to query this data? Is it for fraud? Is it for marketing? So, what we're trying to do as part of this new design paradigm is ensure that we can automate nearly the entire data access process, but with the confidence and de-risk it, that's kind of the key thing. But the one thing I will mention is I think we talk a lot about the core compute, but I think, especially at this summit, data sharing is everything. Right? And this concept of no copy data sharing, because the data is too big and there's too many sets to share, that's the keys to the kingdom. You got to get your lake and your warehouse set with good policy, so you can effectively share it. >> Yeah, so, I wanted to just to follow up, if I may. So, you'd mentioned separating compute from storage and a lot of VC money poured into that. A lot of VC money poured into cloud database. How do you see, do you see Snowflake differentiating substantially from all the other cloud databases? And how so? >> I think it's the ease of use, right? Apple produces a phone that isn't much different than other competitors. Right? But what they do is, end to end, they provide an experience that's very simple. Right? And so yes. Are there other warehouses? Are there other ways to, you know you heard about their analytic workloads now, you know through unistore, where they're going to be able to process analytical workloads as well as their ad hoc queries. I think other vendors are obviously going to have the same capabilities, but I think the user experience of Snowflake right now is top tier. Right? Is I can, whether I'm a small business, I can load my debt in there and build an app really quickly. Or if I'm a JP Morgan or, you know, a West Farmer's I can move legacy, you know monolithic architectures in there in months. I mean, these are six months transitions. When think about 20 years of work is now being transitioned to the cloud in six months. That's the difference. >> So measuring ease of views and time to value, time to market. >> Yeah. That's it's everything is time to value. No one wants to manage the infrastructure. In the Hudup world, no one wants to have expensive customized engineers that are, you know, keeping up your Hudup infrastructure any longer. Those days are completely over. >> Can you share an example of a joint customer, where really the joint value proposition that Immuta and Snowflake bring, are delivering some pretty substantial outcomes? >> Yeah. I, what we're seeing is and we're obviously highly incentivized to get them in there because it's easier on us, right? Because we can leverage their row and com level security. We can leverage their features that they've built in to provide a better experience to our customers. And so when we talk about large banks, they're trying to move Terra data workloads into Snowflake. When we talk about clinical trial management, they're trying to get away from physical copies of data, and leverage the exchanges of mechanism, so you can manage data contracts, right? So like, you know, when we think of even like a company like Latch, right? Like Latch uses us to be able to oversee all of the consumer data they have. Without like a Snowflake, what ends up happening is they end up having to double down and invest on their own people building out all their own infrastructure. And they don't have the capital to invest in third party tools like us that keep them safe, prevent data leaks, allow them to do more and get more value out of their data, which is what they're good at. >> So TCO reduction I'm hearing. >> Matthew Carroll: Yes, exactly. >> Matt, where are you as a company, you've obviously made a lot of progress since we last talked. Maybe give us the update on you know, the headcount, and fundraising, and- >> Yeah, we're just at about 250 people, which scares me every day, but it's awesome. But yeah, we've just raised 100 million dollars- >> Lisa Martin: Saw that, congratulations. >> Series E, thank you, with night dragon leading it. And night dragon was very tactical as well. We are moving, we found that data governance, I think what you're seeing in the market now is the catalog players are really maturing, and they're starting to add a suite of features around governance, right? So quality control, observability, and just traditional asset management around their data. What we are finding is is that there's a new gap in this space, right? So if you think about legacy it's we had infrastructure security we had the four walls and we protect our four walls. Then we moved to network security. We said, oh, the adversary is inside zero trust. So, let's protect all of our endpoints, right? But now we're seeing is data is the security flaw data could be, anyone could potentially access it in this organization. So how do we protect data? And so what we have matured into is a data security company. What we have found is, there's this next generation of data security products that are missing. And it's this blend between authentication like an, an Okta or an AuthO and auth- I'm sorry, authorization. Like Immuta, where we're authorizing certain access. And we have to pair together, with the modern observability, like a data dog, to provide an a layer above this modern data stack, to protect the data to analyze the users, to look for threats. And so Immuta has transformed with this capital. And we brought Dave DeWalt onto our board because he's a cybersecurity expert, he gives us that understanding of what is it like to sell into this modern cyber environment. So now, we have this platform where we can discover data, analyze it, tag it, understand its risk, secure it to author and enforce policies. And then monitor, the key thing is monitoring. Who is using the data? Why are they using the data? What are the risks to that? In order to enforce the security. So, we are a data security platform now with this raise. >> Okay. That, well, that's a new, you know, vector for you guys. I always saw you as an adjacency, but you're saying smack dab in the heart >> Matthew Carroll: Yes. Yeah. We're jumping right in. What we've seen is there is a massive global gap. Data is no longer just in one country. So it is, how do we automate policy enforcement of regulatory oversight, like GDPR or CCPA, which I think got this whole category going. But then we quickly realized is, well we have data jurisdiction. So, where does that data have to live? Where can I send it to? Because from Europe to us, what's the export treaty? We don't have defined laws anymore. So we needed a flexible framework to handle that. And now what we're seeing is data leaks, upon data leaks, and you know, the Snowflakes and the other cloud compute vendors, the last thing they ever want is a data leak out of their ecosystem. So, the security aspects are now becoming more and more important. It's going to be an insider threat. It's someone that already has access to that and has the rights to it. That's going to be the risk. And there is no pattern for a data scientist. There's no zero trust model for data. So we have to create that. >> How are you, last question, how are you going to be using a 100 million raised in series E funding, which you mentioned, how are you going to be leveraging that investment to turn the volume up on data security? >> Well, and we still have also another 80 million still in the bank from our last raise, so 180 million now, and potentially more soon, we'll kind of throw that out there. But, the first thing is M and A I believe in a recessing market, we're going to see these platforms consolidate. Larger customer of ours are driving us to say, Hey, we need less tools. We need to make this easier. So we can go faster. They're, even in a recessing market, these customers are not going to go slower. They're moving in the cloud as fast as possible, but it needs to be easier, right? It's going back to the mid nineties kind of Lego blocks, right? Like the IBM, the SAP, the Informatica, right? So that's number one. Number two is investing globally. Customer success, engineering, support, 24 by seven support globally. Global infrastructure on cloud, moving to true SaaS everywhere in the world. That's where we're going. So sales, engineering, and customer success globally. And the third is, is doubling down on R and D. That monitor capability, we're going to be building software around. How do we monitor and understand risk of users, third parties. So how do you handle data contracts? How do you handle data use agreements? So those are three areas we're focused on. >> Dave Vellante: How are you scaling go to market at this point? I mean, I presume you are. >> Yeah, well, I think as we're leveraging these types of engagements, so like our partners are the big cloud compute vendors, right? Those data clouds. We're injecting as much as we can into them and helping them get more workloads onto their infrastructure because it benefits us. And then obviously we're working with GSIs and then RSIs to kind of help with this transformation, but we're all in, we're actually deprecating support of legacy connectors. And we're all in on cloud compute. >> How did the pivot to all in on security, how did it affect your product portfolio? I mean, is that more positioning or was there other product extensions that where you had to test product market fit? >> Yeah. This comes out of customer drive. So we've been holding customer advisory boards across Europe, Asia and U.S. And what we just saw was a pattern of some of these largest banks and pharmaceutical companies and insurance companies in the world was, hey we need to understand who is actually on our data. We have a better understanding of our data now, but we don't actually understand why they're using our data. Why are they running these types of queries? Is this machine, you know logic, that we're running on this now, we invested all this money in AI. What's the risk? They just don't know. And so, yeah, it's going to change our product portfolio. We modularized our platform to the street components over the past year, specifically now, so we can start building custom applications on top of it, for specific users like the CSO, like, you know, the legal department, and like third party regulators to come in, as well as as going back to data sharing, to build data use agreements between one or many entities, right? So an SMP global can expose their data to third parties and have one consistent digital contract, no more long memo that you have to read the contract, like, Immuta can automate those data contracts between one or many entities. >> Dave Vellante: And make it a checkbox item. >> It's just a checkbox, but then you can audit it all, right? >> The key thing is this, I always tell people, there's negligence and gross negligence. Negligence, you can go back and fix something, gross negligence you don't have anything to put into controls. Regulators want you to be at least negligent, grossly negligent. They get upset. (laughs) >> Matthew, it sounds like great stuff is going on at Immuta, lots of money in the bank. And it sounds like a very clear and strategic vision and direction. We thank you so much for joining us on theCUBE this morning. >> Thank you so much >> For our guest and Dave Vellante, I'm Lisa Martin, you're watching theCUBE's coverage of day two, Snowflake Summit '22, coming at ya live, from the show floor in Las Vegas. Be right back with our next guest. (Soft music)

>>Welcome back to the cubes. Continuing coverage of day, one of the snowflake summit 22 live from seizures forum in Las Vegas. I'm Lisa Martin. My co-host for the week is Dave ante, Dave and I are pleased to welcome Naga Raj Sastry to the program, the vice president of data and analytics at HCL technologies. Welcome. Great to have you. >>Same here. Thank you for inviting me here. >>Isn't it great to be back in person? >>Oh, love it. >>This the keynote this morning. I don't know if you had a chance to see it standing room only there was overflow rooms. People are ready for this, and it was a jam packed morning of announcements. >>Absolutely. >>Talk to us a little bit about the HCL snowflake partnership, but anybody in the audience who may not be familiar with HCL, give us a little bit of a background, vision, mission differentiation, and then that snowflake duo. >>Sure, sure. So let me first start off with, um, uh, talking about H at seal, we are 11.5 billion organization. Uh, we have three modes of working mode. One is everything to do with our infrastructure business and application services and maintenance mode. Two is anything that we do in the cutting edge, uh, ecosystem, whether it is cloud, whether it is application modernization, ERPs, uh, SA all of those put together is more to data. Analytics is part of our more to culture. Um, the whole ecosystem is called digital services business and, uh, within digital, uh, services, the one of the arms is data and analytics. We are about a billion dollars in terms of revenues from a data and analytics perspective, uh, of the 11 billion that I was talking to you about. And mode three is everything to do with our software services. So we have got our own software products, and that's a third of our business. So that's about HCL. So at C and, uh, snowflake relationship, we are a elite partner with snowflake. We are one of the fastest growing partners. We achieved the elite level within 18 months of us signing up as a snowflake partner. We're close to about 50 plus implementations worldwide, and, uh, about 800 people who are snowflake professionals within, within that CLE ecosystem, large customers that we serve. >>And how long have you been partners? >>Uh, about 18 to 20 months now. >>Okay. So, so the, during the last couple of tumultuous years, why snowflake, what was it about their vision, their strategy, their leadership that really to spoke to HCL as this is a partner for us? >>So, so one of the, uh, biggest things that we realized, uh, probably about four years ago was in terms of, you know, you had all the application databases or RDBMSs PPS, the huddle P ecosystems, which are getting expense systems, which were getting expensive, not in terms of the cost, but in terms of the pro processing times, the way the queries were getting created. And we knew that there was, there is something that is going to come and the people and the people. Yeah. >>And, uh, and we knew that, you know, there will be a hyperscaler that will come. And, uh, of course there was Azure was already there. AWS was there, Google was just picking it up. And at that point in time, we realized that, you know, there will be a cloud data warehouse because we had started reading about snowflake at that point in time. So fast forward a couple of years after that, and we realized that if we are to be in this business, you know, the, the right way of doing it is by getting partnering a partnering with the right tooling company. And snowflake brings that to table. We all know that now. And, uh, with, with what, what the keynote speakers were also saying, right, from 150 member team about five years ago in, uh, conference to about 12,000 people now. So you know that this is the right thing to do, and this is the right place to be at. So we, we devised a methodology in terms of saying that let's get into the partnership, let's get our resources trained and certified on the snowflake ecosystem. And let's take a point of view to our customers in terms of how data migrations and transformations have to be done in the snowflake arena. When >>You, when you think about your modes, you talked about modes one, two, and three. If I feel like snowflake touches on each of those, maybe not so much of the infrastructure and the apps, but although maybe going forward, it does increasingly. So, yeah, that's my question is where do you see snowflake vectoring into your modes? >>So it doesn in both in the first two modes, uh, and mode three also, uh, because, and I'll give you the reasons why mode one is predominantly because you can do application development on cloud yep. On the data cloud now, um, which basically means that I can have a qu application run on snowflake. Eventually that's the goal. Second is, uh, in, in more two, because it is a cloud data warehouse, it fits in exactly because the application data is in snowflake. I've got my, uh, regular data sets within snowflake. Both are talking to each other. There is zero, um, lapse time from a user perspective, >>It's a direct >>Tip. And then more three, the reason why I said more three was because software as a service or software services and products is because I can power by snowflake. I can implement that. So that's why it cuts across our entire ecosystem. >>The, the dig, the whole thing is called your dig business, correct? Yes. Is that right? So that's, this is the, the next wave of digital business that we're seeing here, cuz it's digital is data <laugh> right. That's really what it's about. It's about putting that data to work. >>So the president of our digital business, a BJA who was, who had done the, who had done a session in the, in the afternoon today, he says the D in the digital is data. >>There is right. >>And, uh, that's what we are seeing with our customers, large implementations that we do in this ecosystem. There is one other thing that we are focusing, uh, very heavily on is industrial solutions or industry led solutions. Like whether it is for healthcare, whether it's for retail or financial services, name, a vertical. And we have got our own capabilities around industrialized solutions that's fit that fit certain use cases. >>So in thinking about the D in, in digital is really data. If you think about the operating model for data, it's obviously evolved, you mentioned, had do, went to the cloud and all the data went to the cloud, but today it's, you've got an application development model, you got database, which is sort of hardened. And then you've got your data pipeline and your, your data stack and, and that's kind of the operating model. There's sort of siloed to a great degree. Mm-hmm <affirmative> how is that operating model changing as a result of, of data? So >>I answered it in two parts. Part is if you, if you realize over the years, what used to happen is you had a CIO in an organization or C more CIO, but, and then you had enterprise architecture teams, application development teams, support teams, and so on and so forth in the last 36 months. If you see there is an emergence of a new role, which is called the da chief data and analytics officer. So the data and analytics officer is a role that has been created. And the purpose of creating that role is to ensure that organizations will pull out our call out resources within the CIO organizations who are enterprise architects, who are data architects, who are application architects or security architects, and bring them under into the ecosystem of the data office from an operating model perspective. So that innovations can be driven. >>Data driven enterprises could be created and innovations can come through there. The other part of that is the use cases get prioritized when you start innovating. And then it is a factory model in terms of how those use cases get built, which is, which is, which is a no brainer in my mind, at least. But that is how the operating model is coming up from a people perspective, from a technology perspective. Also there is an operating model that is emerging. If you see all the hyperscalers that are there today, snowflake with its LA most latest and greatest announcements. If you see the way the industry is going, is everything will be housed into one ecosystem and the beauty of this entire thing. And if you, you are to, you'll be able to fathom it effectively, right? Because if you are, if I'm, multi-cloud kind of an environment and if I'm on snowflake, I don't care why, because I'm snowflake, which is, which can work around across the multi clouds. So my data is in one place >>Effectively. Yeah. It's interesting what you were saying about the chief data officer, the chief data officer, that role emerged out of the, the ashes, like a Phoenix of, of, you know, compliance data quality and, and healthcare and financial services and government, the highly regulated industries. And then it took a while, but it, it increasingly became, wow, this is a really front front of the board level role, if you will, you know, data, and now you're seeing it. It's it's, it is integrated with digital. >>Absolutely. And there is one other point, if you think about it, the emergence of the chief data officer came in because there were issues associated to data quality. Yeah. There were issues associated to data cataloging as to how data is cataloged. And there were issues in terms of trustability of the data. Now, the trustability of the data can be in two places. One is a data quality, Hey, bad data, garbage and garbage out. But then the other aspect of the trustability is in terms of, can I do the seven CS of data quality and say that, okay, I can hallmark this data platinum or gold or silver or bronze or UN hallmark data. And with snowflake, the advantage is if I, if you have a hallmark data set, that is a, say a platinum or a gold, and thanks to the virtual warehouse, the same data set gets penetrated across the enterprise. That's the beauty with which it comes. And then of course the metadata aspect of it, bringing in the technical metadata and the business metadata together for the purpose of creating the data catalogs is another key cool thing and enabled again by snowflake. >>What are some of it when you're in customer conversations, some of the myths or misconceptions that customers historically have typically been making when it comes to creating a data strategy, some of the misconceptions, and then what is your recommendation for those folks since every company, these days to be competitive has to be a data company. >>Yeah. So around data structures, the, the whole thought process has to be, uh, either do in the past, we used to go with, from source applications, we would gather requirements. Then we would figure out what sources are there, do a profiling of the data and then say, okay, the target data, data model should be this >>Too slow, >>Too slow right now, fast forward to the digital transformation. There is producers of data, which is basically that applications that are being modernized today are producers of data. They're actually telling you that I'm producing this kind of data. This is the kind of events that I'm producing. And this is my structure. Now the whole deal is I don't need to figure out what the requirements are. I know what the use case the application is going to be helping me with. So therefore the entire data model is supported. So, but at the same point in time, the newer generation applications that are getting created are not only created getting created in terms of the customer experience. Of course, that is very critical, but they're also taking into account aspects around metadata, the technical metadata associated within an application, the data quality rules or business rules that are implemented within an application, all of that is getting documented as a result, the whole timeline from source to profile to model, which used to be X number of days in the past is X minus, at least 20% now or 30% actually. So that is how the structures, uh, the data structures are coming into a play future futuristic thought process would be, there will be producers of data and there'll be consumers of data. Where is ETL then or ELT. Then there is not going to be any ETL or ELT because a producer is going to say that I'm producing the data for this. A consumer says that, okay, I wanna consume the data for this purpose. There, they meet through an API layer. So where is ETL eventually going to go away? >>Well, and those consumers of, if you think about the, the way it works today, the, the data operating model, if you will, the transaction systems and other systems draw off a bunch of exhaust, they gets thrown over the fence to the analytics system. They're not operation the data, the data pipeline, the data systems are not operationalized in a way that they need to be. And obviously Snowflake's trying to change that. >>So data >>That's a big change, please. >>Yeah. Sorry. Didn't mean to cut you off. My >>Apologies. No, no. I'm >>So data operations is a very, very critical aspect. And if you think about it holistically, we used to have ETL pipelines T pipelines. And then we used to have queries being written on top of metadata or PPS and HaLoop and all of that and reporting tools that would have number of reports that were created and certain self-service BI reports into the ecosystem. Now, when you think in terms of a cloud data warehouse, what is happening? Is this the way you are architecting your solution today in terms of data pipelines, those data pipelines are self manageable or self-healing do not need the number of people where there was no documentation in terms of what ETL pipelines were written in the past on certain ETL tools or why something is failing. Nobody knew why something was failing because these are age old code, but take it forward today. >>What happens is our organizations are migrating from on-prem to cloud and to the cloud data warehouse. And the overall cost of ownership is decreasing. The reason is the way we are implementing the data pipelines, the way the data operations are being done in terms of, you know, even before a pipeline is kicked, uh, or kicked in, then, you know, there is a check process to say whether the source application is ready or not ready. So such things, small, small things, which are part and parcel of the entire data operations lifecycle are taking the center stage as a result, self fueling mechanisms are coming in. And because of those self fueling mechanisms, metrics are being captured as a result, you know exactly where to focus on and where not to focus on as, as a result, the number of resources needed to support gets reduced. Cost of one service >>Is low, much higher trust self-service infrastructure, uh, data context in the hands of, of business users. Data is now more discoverable it's governed. So you can now create data products more quickly. So speed and scale become extremely important. >>Absolutely. And in fact, one of the things that, that, uh, that is changing is the way search is getting implemented here to in the past, you created an index and then, you know, the data is searchable, but now it is contextual search. Can I contextualize the entire search? Can I create a machine learning algorithm that will actually say that, okay, Nara as a persona was looking for this kind of data and then Nara as a person, or comes back again and looks for some different kind of data. Can the machine learning algorithm go and figure out, okay, what is, what is going on in a garage's mind? What is he trying to look at? And then, you know, improve the, the whole learnability of the, of the entire algorithm. That's how search is going to also take, get into a change kind of a scenario. >>Excellent NAAU garage. Thank you so much for joining us, talking about data modernization at speed, end scale HCL, what you're doing, what you're doing with snowflake, and the sounds like incredible power that you're enabling. And we're only just scratching the surface. I have a feeling there's a lot more under there that you guys are gonna uncover. >>Sure. So we have, we have a tool or an accelerator. We call it an accelerator in the HCL parlance, but just actually a tool. So when you think about data modernization onto snowflake, it is predominantly migrating the data set from your existing ecosystem onto snowflake. That is one aspect of it. The second aspect of it is the modernization of the ETL or E LT pipelines. The third aspect associated to the data that is there within this, these ecosystems is the reconciliation older application, uh, sorry, older legacy, uh, platform snowflake legacy platform gives me result. X does snowflake give me result X that kind of a reconciliation has to be done. Data reconciliation and testing. And then the third fourth layer associated is the reporting and visualization. So these four layers are part and parcel of something that we call as advantage. Migrate advantage migrate will convert your ter data, data, uh, model into a snowflake understandable data model automatically whether it's ter data, whether it is Oracle, extra data, green plum, <inaudible> you name a ecosystem. >>We have the mechanism to convert a data model from whatever it is into snowflake readable, understandable data model. The second aspect is the et L E L T pipeline. Whether you want to go from Informatica to DBT or Informatica to something else or data stage to something else doesn't matter. There is a, there is an algorithm, or there is a tool which is called the ETL pipeline. We call it gateway suit, gateway suit actually converts the code. It reads the code that is there on the left hand side, which is the legacy code, understands the logic, it reverse engineers and understands the logic. And then what it does is we use that understanding or that logic that has been called out into spark code or DBT or any other tool of your choice from a customer standpoint. That's the second layer. Third layer I talked about, which is basically data testing, automated data testing and data reconciliation and the last, but not the least is the reporting because older ways of reporting and visualization with, with current day reporting and visualization, which is more persona based, the art of visualization is something difficult or different in this, in this aspect, come over to our booth at 2 1, 1 4, and you'll see, uh, advantage migrate in the works >>Advantage. Migrate. There you go. Nero, thank you so much for joining us on the program and unpacking HCL, giving us really that technical dissection of what you guys are doing and together with snowflake. We appreciate your time. >>Thank you. My pleasure. Thank you >>For our guest and Dave ante. This is Lisa Martin live from the show floor of snowflake summit 22, Dave and I will be right back with our final guest of day one in just a minute.

(upbeat music) >> The stampede to cloud and massive VC investments has led to the emergence of a new generation of object store based data lakes. And with them two important trends, actually three important trends. First, a new category that combines data lakes and data warehouses aka the lakehouse is emerged as a leading contender to be the data platform of the future. And this novelty touts the ability to address data engineering, data science, and data warehouse workloads on a single shared data platform. The other major trend we've seen is query engines and broader data fabric virtualization platforms have embraced NextGen data lakes as platforms for SQL centric business intelligence workloads, reducing, or somebody even claim eliminating the need for separate data warehouses. Pretty bold. However, cloud data warehouses have added complimentary technologies to bridge the gaps with lakehouses. And the third is many, if not most customers that are embracing the so-called data fabric or data mesh architectures. They're looking at data lakes as a fundamental component of their strategies, and they're trying to evolve them to be more capable, hence the interest in lakehouse, but at the same time, they don't want to, or can't abandon their data warehouse estate. As such we see a battle royale is brewing between cloud data warehouses and cloud lakehouses. Is it possible to do it all with one cloud center analytical data platform? Well, we're going to find out. My name is Dave Vellante and welcome to the data platform's power panel on theCUBE. Our next episode in a series where we gather some of the industry's top analysts to talk about one of our favorite topics, data. In today's session, we'll discuss trends, emerging options, and the trade offs of various approaches and we'll name names. Joining us today are Sanjeev Mohan, who's the principal at SanjMo, Tony Baers, principal at dbInsight. And Doug Henschen is the vice president and principal analyst at Constellation Research. Guys, welcome back to theCUBE. Great to see you again. >> Thank guys. Thank you. >> Thank you. >> So it's early June and we're gearing up with two major conferences, there's several database conferences, but two in particular that were very interested in, Snowflake Summit and Databricks Data and AI Summit. Doug let's start off with you and then Tony and Sanjeev, if you could kindly weigh in. Where did this all start, Doug? The notion of lakehouse. And let's talk about what exactly we mean by lakehouse. Go ahead. >> Yeah, well you nailed it in your intro. One platform to address BI data science, data engineering, fewer platforms, less cost, less complexity, very compelling. You can credit Databricks for coining the term lakehouse back in 2020, but it's really a much older idea. You can go back to Cloudera introducing their Impala database in 2012. That was a database on top of Hadoop. And indeed in that last decade, by the middle of that last decade, there were several SQL on Hadoop products, open standards like Apache Drill. And at the same time, the database vendors were trying to respond to this interest in machine learning and the data science. So they were adding SQL extensions, the likes Hudi and Vertical we're adding SQL extensions to support the data science. But then later in that decade with the shift to cloud and object storage, you saw the vendor shift to this whole cloud, and object storage idea. So you have in the database camp Snowflake introduce Snowpark to try to address the data science needs. They introduced that in 2020 and last year they announced support for Python. You also had Oracle, SAP jumped on this lakehouse idea last year, supporting both the lake and warehouse single vendor, not necessarily quite single platform. Google very recently also jumped on the bandwagon. And then you also mentioned, the SQL engine camp, the Dremios, the Ahanas, the Starbursts, really doing two things, a fabric for distributed access to many data sources, but also very firmly planning that idea that you can just have the lake and we'll help you do the BI workloads on that. And then of course, the data lake camp with the Databricks and Clouderas providing a warehouse style deployments on top of their lake platforms. >> Okay, thanks, Doug. I'd be remiss those of you who me know that I typically write my own intros. This time my colleagues fed me a lot of that material. So thank you. You guys make it easy. But Tony, give us your thoughts on this intro. >> Right. Well, I very much agree with both of you, which may not make for the most exciting television in terms of that it has been an evolution just like Doug said. I mean, for instance, just to give an example when Teradata bought AfterData was initially seen as a hardware platform play. In the end, it was basically, it was all those after functions that made a lot of sort of big data analytics accessible to SQL. (clears throat) And so what I really see just in a more simpler definition or functional definition, the data lakehouse is really an attempt by the data lake folks to make the data lake friendlier territory to the SQL folks, and also to get into friendly territory, to all the data stewards, who are basically concerned about the sprawl and the lack of control in governance in the data lake. So it's really kind of a continuing of an ongoing trend that being said, there's no action without counter action. And of course, at the other end of the spectrum, we also see a lot of the data warehouses starting to edit things like in database machine learning. So they're certainly not surrendering without a fight. Again, as Doug was mentioning, this has been part of a continual blending of platforms that we've seen over the years that we first saw in the Hadoop years with SQL on Hadoop and data warehouses starting to reach out to cloud storage or should say the HDFS and then with the cloud then going cloud native and therefore trying to break the silos down even further. >> Now, thank you. And Sanjeev, data lakes, when we first heard about them, there were such a compelling name, and then we realized all the problems associated with them. So pick it up from there. What would you add to Doug and Tony? >> I would say, these are excellent points that Doug and Tony have brought to light. The concept of lakehouse was going on to your point, Dave, a long time ago, long before the tone was invented. For example, in Uber, Uber was trying to do a mix of Hadoop and Vertical because what they really needed were transactional capabilities that Hadoop did not have. So they weren't calling it the lakehouse, they were using multiple technologies, but now they're able to collapse it into a single data store that we call lakehouse. Data lakes, excellent at batch processing large volumes of data, but they don't have the real time capabilities such as change data capture, doing inserts and updates. So this is why lakehouse has become so important because they give us these transactional capabilities. >> Great. So I'm interested, the name is great, lakehouse. The concept is powerful, but I get concerned that it's a lot of marketing hype behind it. So I want to examine that a bit deeper. How mature is the concept of lakehouse? Are there practical examples that really exist in the real world that are driving business results for practitioners? Tony, maybe you could kick that off. >> Well, put it this way. I think what's interesting is that both data lakes and data warehouse that each had to extend themselves. To believe the Databricks hype it's that this was just a natural extension of the data lake. In point of fact, Databricks had to go outside its core technology of Spark to make the lakehouse possible. And it's a very similar type of thing on the part with data warehouse folks, in terms of that they've had to go beyond SQL, In the case of Databricks. There have been a number of incremental improvements to Delta lake, to basically make the table format more performative, for instance. But the other thing, I think the most dramatic change in all that is in their SQL engine and they had to essentially pretty much abandon Spark SQL because it really, in off itself Spark SQL is essentially stop gap solution. And if they wanted to really address that crowd, they had to totally reinvent SQL or at least their SQL engine. And so Databricks SQL is not Spark SQL, it is not Spark, it's basically SQL that it's adapted to run in a Spark environment, but the underlying engine is C++, it's not scale or anything like that. So Databricks had to take a major detour outside of its core platform to do this. So to answer your question, this is not mature because these are all basically kind of, even though the idea of blending platforms has been going on for well over a decade, I would say that the current iteration is still fairly immature. And in the cloud, I could see a further evolution of this because if you think through cloud native architecture where you're essentially abstracting compute from data, there is no reason why, if let's say you are dealing with say, the same basically data targets say cloud storage, cloud object storage that you might not apportion the task to different compute engines. And so therefore you could have, for instance, let's say you're Google, you could have BigQuery, perform basically the types of the analytics, the SQL analytics that would be associated with the data warehouse and you could have BigQuery ML that does some in database machine learning, but at the same time for another part of the query, which might involve, let's say some deep learning, just for example, you might go out to let's say the serverless spark service or the data proc. And there's no reason why Google could not blend all those into a coherent offering that's basically all triggered through microservices. And I just gave Google as an example, if you could generalize that with all the other cloud or all the other third party vendors. So I think we're still very early in the game in terms of maturity of data lakehouses. >> Thanks, Tony. So Sanjeev, is this all hype? What are your thoughts? >> It's not hype, but completely agree. It's not mature yet. Lakehouses have still a lot of work to do, so what I'm now starting to see is that the world is dividing into two camps. On one hand, there are people who don't want to deal with the operational aspects of vast amounts of data. They are the ones who are going for BigQuery, Redshift, Snowflake, Synapse, and so on because they want the platform to handle all the data modeling, access control, performance enhancements, but these are trade off. If you go with these platforms, then you are giving up on vendor neutrality. On the other side are those who have engineering skills. They want the independence. In other words, they don't want vendor lock in. They want to transform their data into any number of use cases, especially data science, machine learning use case. What they want is agility via open file formats using any compute engine. So why do I say lakehouses are not mature? Well, cloud data warehouses they provide you an excellent user experience. That is the main reason why Snowflake took off. If you have thousands of cables, it takes minutes to get them started, uploaded into your warehouse and start experimentation. Table formats are far more resonating with the community than file formats. But once the cost goes up of cloud data warehouse, then the organization start exploring lakehouses. But the problem is lakehouses still need to do a lot of work on metadata. Apache Hive was a fantastic first attempt at it. Even today Apache Hive is still very strong, but it's all technical metadata and it has so many different restrictions. That's why we see Databricks is investing into something called Unity Catalog. Hopefully we'll hear more about Unity Catalog at the end of the month. But there's a second problem. I just want to mention, and that is lack of standards. All these open source vendors, they're running, what I call ego projects. You see on LinkedIn, they're constantly battling with each other, but end user doesn't care. End user wants a problem to be solved. They want to use Trino, Dremio, Spark from EMR, Databricks, Ahana, DaaS, Frink, Athena. But the problem is that we don't have common standards. >> Right. Thanks. So Doug, I worry sometimes. I mean, I look at the space, we've debated for years, best of breed versus the full suite. You see AWS with whatever, 12 different plus data stores and different APIs and primitives. You got Oracle putting everything into its database. It's actually done some interesting things with MySQL HeatWave, so maybe there's proof points there, but Snowflake really good at data warehouse, simplifying data warehouse. Databricks, really good at making lakehouses actually more functional. Can one platform do it all? >> Well in a word, I can't be best at breed at all things. I think the upshot of and cogen analysis from Sanjeev there, the database, the vendors coming out of the database tradition, they excel at the SQL. They're extending it into data science, but when it comes to unstructured data, data science, ML AI often a compromise, the data lake crowd, the Databricks and such. They've struggled to completely displace the data warehouse when it really gets to the tough SLAs, they acknowledge that there's still a role for the warehouse. Maybe you can size down the warehouse and offload some of the BI workloads and maybe and some of these SQL engines, good for ad hoc, minimize data movement. But really when you get to the deep service level, a requirement, the high concurrency, the high query workloads, you end up creating something that's warehouse like. >> Where do you guys think this market is headed? What's going to take hold? Which projects are going to fade away? You got some things in Apache projects like Hudi and Iceberg, where do they fit Sanjeev? Do you have any thoughts on that? >> So thank you, Dave. So I feel that table formats are starting to mature. There is a lot of work that's being done. We will not have a single product or single platform. We'll have a mixture. So I see a lot of Apache Iceberg in the news. Apache Iceberg is really innovating. Their focus is on a table format, but then Delta and Apache Hudi are doing a lot of deep engineering work. For example, how do you handle high concurrency when there are multiple rights going on? Do you version your Parquet files or how do you do your upcerts basically? So different focus, at the end of the day, the end user will decide what is the right platform, but we are going to have multiple formats living with us for a long time. >> Doug is Iceberg in your view, something that's going to address some of those gaps in standards that Sanjeev was talking about earlier? >> Yeah, Delta lake, Hudi, Iceberg, they all address this need for consistency and scalability, Delta lake open technically, but open for access. I don't hear about Delta lakes in any worlds, but Databricks, hearing a lot of buzz about Apache Iceberg. End users want an open performance standard. And most recently Google embraced Iceberg for its recent a big lake, their stab at having supporting both lakes and warehouses on one conjoined platform. >> And Tony, of course, you remember the early days of the sort of big data movement you had MapR was the most closed. You had Horton works the most open. You had Cloudera in between. There was always this kind of contest as to who's the most open. Does that matter? Are we going to see a repeat of that here? >> I think it's spheres of influence, I think, and Doug very much was kind of referring to this. I would call it kind of like the MongoDB syndrome, which is that you have... and I'm talking about MongoDB before they changed their license, open source project, but very much associated with MongoDB, which basically, pretty much controlled most of the contributions made decisions. And I think Databricks has the same iron cloud hold on Delta lake, but still the market is pretty much associated Delta lake as the Databricks, open source project. I mean, Iceberg is probably further advanced than Hudi in terms of mind share. And so what I see that's breaking down to is essentially, basically the Databricks open source versus the everything else open source, the community open source. So I see it's a very similar type of breakdown that I see repeating itself here. >> So by the way, Mongo has a conference next week, another data platform is kind of not really relevant to this discussion totally. But in the sense it is because there's a lot of discussion on earnings calls these last couple of weeks about consumption and who's exposed, obviously people are concerned about Snowflake's consumption model. Mongo is maybe less exposed because Atlas is prominent in the portfolio, blah, blah, blah. But I wanted to bring up the little bit of controversy that we saw come out of the Snowflake earnings call, where the ever core analyst asked Frank Klutman about discretionary spend. And Frank basically said, look, we're not discretionary. We are deeply operationalized. Whereas he kind of poo-pooed the lakehouse or the data lake, et cetera, saying, oh yeah, data scientists will pull files out and play with them. That's really not our business. Do any of you have comments on that? Help us swing through that controversy. Who wants to take that one? >> Let's put it this way. The SQL folks are from Venus and the data scientists are from Mars. So it means it really comes down to it, sort that type of perception. The fact is, is that, traditionally with analytics, it was very SQL oriented and that basically the quants were kind of off in their corner, where they're using SaaS or where they're using Teradata. It's really a great leveler today, which is that, I mean basic Python it's become arguably one of the most popular programming languages, depending on what month you're looking at, at the title index. And of course, obviously SQL is, as I tell the MongoDB folks, SQL is not going away. You have a large skills base out there. And so basically I see this breaking down to essentially, you're going to have each group that's going to have its own natural preferences for its home turf. And the fact that basically, let's say the Python and scale of folks are using Databricks does not make them any less operational or machine critical than the SQL folks. >> Anybody else want to chime in on that one? >> Yeah, I totally agree with that. Python support in Snowflake is very nascent with all of Snowpark, all of the things outside of SQL, they're very much relying on partners too and make things possible and make data science possible. And it's very early days. I think the bottom line, what we're going to see is each of these camps is going to keep working on doing better at the thing that they don't do today, or they're new to, but they're not going to nail it. They're not going to be best of breed on both sides. So the SQL centric companies and shops are going to do more data science on their database centric platform. That data science driven companies might be doing more BI on their leagues with those vendors and the companies that have highly distributed data, they're going to add fabrics, and maybe offload more of their BI onto those engines, like Dremio and Starburst. >> So I've asked you this before, but I'll ask you Sanjeev. 'Cause Snowflake and Databricks are such great examples 'cause you have the data engineering crowd trying to go into data warehousing and you have the data warehousing guys trying to go into the lake territory. Snowflake has $5 billion in the balance sheet and I've asked you before, I ask you again, doesn't there has to be a semantic layer between these two worlds? Does Snowflake go out and do M&A and maybe buy ad scale or a data mirror? Or is that just sort of a bandaid? What are your thoughts on that Sanjeev? >> I think semantic layer is the metadata. The business metadata is extremely important. At the end of the day, the business folks, they'd rather go to the business metadata than have to figure out, for example, like let's say, I want to update somebody's email address and we have a lot of overhead with data residency laws and all that. I want my platform to give me the business metadata so I can write my business logic without having to worry about which database, which location. So having that semantic layer is extremely important. In fact, now we are taking it to the next level. Now we are saying that it's not just a semantic layer, it's all my KPIs, all my calculations. So how can I make those calculations independent of the compute engine, independent of the BI tool and make them fungible. So more disaggregation of the stack, but it gives us more best of breed products that the customers have to worry about. >> So I want to ask you about the stack, the modern data stack, if you will. And we always talk about injecting machine intelligence, AI into applications, making them more data driven. But when you look at the application development stack, it's separate, the database is tends to be separate from the data and analytics stack. Do those two worlds have to come together in the modern data world? And what does that look like organizationally? >> So organizationally even technically I think it is starting to happen. Microservices architecture was a first attempt to bring the application and the data world together, but they are fundamentally different things. For example, if an application crashes, that's horrible, but Kubernetes will self heal and it'll bring the application back up. But if a database crashes and corrupts your data, we have a huge problem. So that's why they have traditionally been two different stacks. They are starting to come together, especially with data ops, for instance, versioning of the way we write business logic. It used to be, a business logic was highly embedded into our database of choice, but now we are disaggregating that using GitHub, CICD the whole DevOps tool chain. So data is catching up to the way applications are. >> We also have databases, that trans analytical databases that's a little bit of what the story is with MongoDB next week with adding more analytical capabilities. But I think companies that talk about that are always careful to couch it as operational analytics, not the warehouse level workloads. So we're making progress, but I think there's always going to be, or there will long be a separate analytical data platform. >> Until data mesh takes over. (all laughing) Not opening a can of worms. >> Well, but wait, I know it's out of scope here, but wouldn't data mesh say, hey, do take your best of breed to Doug's earlier point. You can't be best of breed at everything, wouldn't data mesh advocate, data lakes do your data lake thing, data warehouse, do your data lake, then you're just a node on the mesh. (Tony laughs) Now you need separate data stores and you need separate teams. >> To my point. >> I think, I mean, put it this way. (laughs) Data mesh itself is a logical view of the world. The data mesh is not necessarily on the lake or on the warehouse. I think for me, the fear there is more in terms of, the silos of governance that could happen and the silo views of the world, how we redefine. And that's why and I want to go back to something what Sanjeev said, which is that it's going to be raising the importance of the semantic layer. Now does Snowflake that opens a couple of Pandora's boxes here, which is one, does Snowflake dare go into that space or do they risk basically alienating basically their partner ecosystem, which is a key part of their whole appeal, which is best of breed. They're kind of the same situation that Informatica was where in the early 2000s, when Informatica briefly flirted with analytic applications and realized that was not a good idea, need to redouble down on their core, which was data integration. The other thing though, that raises the importance of and this is where the best of breed comes in, is the data fabric. My contention is that and whether you use employee data mesh practice or not, if you do employee data mesh, you need data fabric. If you deploy data fabric, you don't necessarily need to practice data mesh. But data fabric at its core and admittedly it's a category that's still very poorly defined and evolving, but at its core, we're talking about a common meta data back plane, something that we used to talk about with master data management, this would be something that would be more what I would say basically, mutable, that would be more evolving, basically using, let's say, machine learning to kind of, so that we don't have to predefine rules or predefine what the world looks like. But so I think in the long run, what this really means is that whichever way we implement on whichever physical platform we implement, we need to all be speaking the same metadata language. And I think at the end of the day, regardless of whether it's a lake, warehouse or a lakehouse, we need common metadata. >> Doug, can I come back to something you pointed out? That those talking about bringing analytic and transaction databases together, you had talked about operationalizing those and the caution there. Educate me on MySQL HeatWave. I was surprised when Oracle put so much effort in that, and you may or may not be familiar with it, but a lot of folks have talked about that. Now it's got nowhere in the market, that no market share, but a lot of we've seen these benchmarks from Oracle. How real is that bringing together those two worlds and eliminating ETL? >> Yeah, I have to defer on that one. That's my colleague, Holger Mueller. He wrote the report on that. He's way deep on it and I'm not going to mock him. >> I wonder if that is something, how real that is or if it's just Oracle marketing, anybody have any thoughts on that? >> I'm pretty familiar with HeatWave. It's essentially Oracle doing what, I mean, there's kind of a parallel with what Google's doing with AlloyDB. It's an operational database that will have some embedded analytics. And it's also something which I expect to start seeing with MongoDB. And I think basically, Doug and Sanjeev were kind of referring to this before about basically kind of like the operational analytics, that are basically embedded within an operational database. The idea here is that the last thing you want to do with an operational database is slow it down. So you're not going to be doing very complex deep learning or anything like that, but you might be doing things like classification, you might be doing some predictives. In other words, we've just concluded a transaction with this customer, but was it less than what we were expecting? What does that mean in terms of, is this customer likely to turn? I think we're going to be seeing a lot of that. And I think that's what a lot of what MySQL HeatWave is all about. Whether Oracle has any presence in the market now it's still a pretty new announcement, but the other thing that kind of goes against Oracle, (laughs) that they had to battle against is that even though they own MySQL and run the open source project, everybody else, in terms of the actual commercial implementation it's associated with everybody else. And the popular perception has been that MySQL has been basically kind of like a sidelight for Oracle. And so it's on Oracles shoulders to prove that they're damn serious about it. >> There's no coincidence that MariaDB was launched the day that Oracle acquired Sun. Sanjeev, I wonder if we could come back to a topic that we discussed earlier, which is this notion of consumption, obviously Wall Street's very concerned about it. Snowflake dropped prices last week. I've always felt like, hey, the consumption model is the right model. I can dial it down in when I need to, of course, the street freaks out. What are your thoughts on just pricing, the consumption model? What's the right model for companies, for customers? >> Consumption model is here to stay. What I would like to see, and I think is an ideal situation and actually plays into the lakehouse concept is that, I have my data in some open format, maybe it's Parquet or CSV or JSON, Avro, and I can bring whatever engine is the best engine for my workloads, bring it on, pay for consumption, and then shut it down. And by the way, that could be Cloudera. We don't talk about Cloudera very much, but it could be one business unit wants to use Athena. Another business unit wants to use some other Trino let's say or Dremio. So every business unit is working on the same data set, see that's critical, but that data set is maybe in their VPC and they bring any compute engine, you pay for the use, shut it down. That then you're getting value and you're only paying for consumption. It's not like, I left a cluster running by mistake, so there have to be guardrails. The reason FinOps is so big is because it's very easy for me to run a Cartesian joint in the cloud and get a $10,000 bill. >> This looks like it's been a sort of a victim of its own success in some ways, they made it so easy to spin up single note instances, multi note instances. And back in the day when compute was scarce and costly, those database engines optimized every last bit so they could get as much workload as possible out of every instance. Today, it's really easy to spin up a new node, a new multi node cluster. So that freedom has meant many more nodes that aren't necessarily getting that utilization. So Snowflake has been doing a lot to add reporting, monitoring, dashboards around the utilization of all the nodes and multi node instances that have spun up. And meanwhile, we're seeing some of the traditional on-prem databases that are moving into the cloud, trying to offer that freedom. And I think they're going to have that same discovery that the cost surprises are going to follow as they make it easy to spin up new instances. >> Yeah, a lot of money went into this market over the last decade, separating compute from storage, moving to the cloud. I'm glad you mentioned Cloudera Sanjeev, 'cause they got it all started, the kind of big data movement. We don't talk about them that much. Sometimes I wonder if it's because when they merged Hortonworks and Cloudera, they dead ended both platforms, but then they did invest in a more modern platform. But what's the future of Cloudera? What are you seeing out there? >> Cloudera has a good product. I have to say the problem in our space is that there're way too many companies, there's way too much noise. We are expecting the end users to parse it out or we expecting analyst firms to boil it down. So I think marketing becomes a big problem. As far as technology is concerned, I think Cloudera did turn their selves around and Tony, I know you, you talked to them quite frequently. I think they have quite a comprehensive offering for a long time actually. They've created Kudu, so they got operational, they have Hadoop, they have an operational data warehouse, they're migrated to the cloud. They are in hybrid multi-cloud environment. Lot of cloud data warehouses are not hybrid. They're only in the cloud. >> Right. I think what Cloudera has done the most successful has been in the transition to the cloud and the fact that they're giving their customers more OnRamps to it, more hybrid OnRamps. So I give them a lot of credit there. They're also have been trying to position themselves as being the most price friendly in terms of that we will put more guardrails and governors on it. I mean, part of that could be spin. But on the other hand, they don't have the same vested interest in compute cycles as say, AWS would have with EMR. That being said, yes, Cloudera does it, I think its most powerful appeal so of that, it almost sounds in a way, I don't want to cast them as a legacy system. But the fact is they do have a huge landed legacy on-prem and still significant potential to land and expand that to the cloud. That being said, even though Cloudera is multifunction, I think it certainly has its strengths and weaknesses. And the fact this is that yes, Cloudera has an operational database or an operational data store with a kind of like the outgrowth of age base, but Cloudera is still based, primarily known for the deep analytics, the operational database nobody's going to buy Cloudera or Cloudera data platform strictly for the operational database. They may use it as an add-on, just in the same way that a lot of customers have used let's say Teradata basically to do some machine learning or let's say, Snowflake to parse through JSON. Again, it's not an indictment or anything like that, but the fact is obviously they do have their strengths and their weaknesses. I think their greatest opportunity is with their existing base because that base has a lot invested and vested. And the fact is they do have a hybrid path that a lot of the others lack. >> And of course being on the quarterly shock clock was not a good place to be under the microscope for Cloudera and now they at least can refactor the business accordingly. I'm glad you mentioned hybrid too. We saw Snowflake last month, did a deal with Dell whereby non-native Snowflake data could access on-prem object store from Dell. They announced a similar thing with pure storage. What do you guys make of that? Is that just... How significant will that be? Will customers actually do that? I think they're using either materialized views or extended tables. >> There are data rated and residency requirements. There are desires to have these platforms in your own data center. And finally they capitulated, I mean, Frank Klutman is famous for saying to be very focused and earlier, not many months ago, they called the going on-prem as a distraction, but clearly there's enough demand and certainly government contracts any company that has data residency requirements, it's a real need. So they finally addressed it. >> Yeah, I'll bet dollars to donuts, there was an EBC session and some big customer said, if you don't do this, we ain't doing business with you. And that was like, okay, we'll do it. >> So Dave, I have to say, earlier on you had brought this point, how Frank Klutman was poo-pooing data science workloads. On your show, about a year or so ago, he said, we are never going to on-prem. He burnt that bridge. (Tony laughs) That was on your show. >> I remember exactly the statement because it was interesting. He said, we're never going to do the halfway house. And I think what he meant is we're not going to bring the Snowflake architecture to run on-prem because it defeats the elasticity of the cloud. So this was kind of a capitulation in a way. But I think it still preserves his original intent sort of, I don't know. >> The point here is that every vendor will poo-poo whatever they don't have until they do have it. >> Yes. >> And then it'd be like, oh, we are all in, we've always been doing this. We have always supported this and now we are doing it better than others. >> Look, it was the same type of shock wave that we felt basically when AWS at the last moment at one of their reinvents, oh, by the way, we're going to introduce outposts. And the analyst group is typically pre briefed about a week or two ahead under NDA and that was not part of it. And when they dropped, they just casually dropped that in the analyst session. It's like, you could have heard the sound of lots of analysts changing their diapers at that point. >> (laughs) I remember that. And a props to Andy Jassy who once, many times actually told us, never say never when it comes to AWS. So guys, I know we got to run. We got some hard stops. Maybe you could each give us your final thoughts, Doug start us off and then-- >> Sure. Well, we've got the Snowflake Summit coming up. I'll be looking for customers that are really doing data science, that are really employing Python through Snowflake, through Snowpark. And then a couple weeks later, we've got Databricks with their Data and AI Summit in San Francisco. I'll be looking for customers that are really doing considerable BI workloads. Last year I did a market overview of this analytical data platform space, 14 vendors, eight of them claim to support lakehouse, both sides of the camp, Databricks customer had 32, their top customer that they could site was unnamed. It had 32 concurrent users doing 15,000 queries per hour. That's good but it's not up to the most demanding BI SQL workloads. And they acknowledged that and said, they need to keep working that. Snowflake asked for their biggest data science customer, they cited Kabura, 400 terabytes, 8,500 users, 400,000 data engineering jobs per day. I took the data engineering job to be probably SQL centric, ETL style transformation work. So I want to see the real use of the Python, how much Snowpark has grown as a way to support data science. >> Great. Tony. >> Actually of all things. And certainly, I'll also be looking for similar things in what Doug is saying, but I think sort of like, kind of out of left field, I'm interested to see what MongoDB is going to start to say about operational analytics, 'cause I mean, they're into this conquer the world strategy. We can be all things to all people. Okay, if that's the case, what's going to be a case with basically, putting in some inline analytics, what are you going to be doing with your query engine? So that's actually kind of an interesting thing we're looking for next week. >> Great. Sanjeev. >> So I'll be at MongoDB world, Snowflake and Databricks and very interested in seeing, but since Tony brought up MongoDB, I see that even the databases are shifting tremendously. They are addressing both the hashtag use case online, transactional and analytical. I'm also seeing that these databases started in, let's say in case of MySQL HeatWave, as relational or in MongoDB as document, but now they've added graph, they've added time series, they've added geospatial and they just keep adding more and more data structures and really making these databases multifunctional. So very interesting. >> It gets back to our discussion of best of breed, versus all in one. And it's likely Mongo's path or part of their strategy of course, is through developers. They're very developer focused. So we'll be looking for that. And guys, I'll be there as well. I'm hoping that we maybe have some extra time on theCUBE, so please stop by and we can maybe chat a little bit. Guys as always, fantastic. Thank you so much, Doug, Tony, Sanjeev, and let's do this again. >> It's been a pleasure. >> All right and thank you for watching. This is Dave Vellante for theCUBE and the excellent analyst. We'll see you next time. (upbeat music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> The pandemic has changed the way we think about and predict the future. As we enter the third year of a global pandemic, we see the significant impact that it's had on technology strategy, spending patterns, and company fortunes Much has changed. And while many of these changes were forced reactions to a new abnormal, the trends that we've seen over the past 24 months have become more entrenched, and point to the way that's coming ahead in the technology business. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis, we welcome our partner and colleague and business friend, Erik Porter Bradley, as we deliver what's becoming an annual tradition for Erik and me, our predictions for Enterprise Technology in 2022 and beyond Erik, welcome. Thanks for taking some time out. >> Thank you, Dave. Luckily we did pretty well last year, so we were able to do this again. So hopefully we can keep that momentum going. >> Yeah, you know, I want to mention that, you know, we get a lot of inbound predictions from companies and PR firms that help shape our thinking. But one of the main objectives that we have is we try to make predictions that can be measured. That's why we use a lot of data. Now not all will necessarily fit that parameter, but if you've seen the grading of our 2021 predictions that Erik and I did, you'll see we do a pretty good job of trying to put forth prognostications that can be declared correct or not, you know, as black and white as possible. Now let's get right into it. Our first prediction, we're going to go run into spending, something that ETR surveys for quarterly. And we've reported extensively on this. We're calling for tech spending to increase somewhere around 8% in 2022, we can see there on the slide, Erik, we predicted spending last year would increase by 4% IDC. Last check was came in at five and a half percent. Gardner was somewhat higher, but in general, you know, not too bad, but looking ahead, we're seeing an acceleration from the ETR September surveys, as you can see in the yellow versus the blue bar in this chart, many of the SMBs that were hard hit by the pandemic are picking up spending again. And the ETR data is showing acceleration above the mean for industries like energy, utilities, retail, and services, and also, notably, in the Forbes largest 225 private companies. These are companies like Mars or Koch industries. They're predicting well above average spending for 2022. So Erik, please weigh in here. >> Yeah, a lot to bring up on this one, I'm going to be quick. So 1200 respondents on this, over a third of which were at the C-suite level. So really good data that we brought in, the usual bucket of, you know, fortune 500, global 2000 make up the meat of that median, but it's 8.3% and rising with momentum as we see. What's really interesting right now is that energy and utilities. This is usually like, you know, an orphan stock dividend type of play. You don't see them at the highest point of tech spending. And the reason why right now is really because this state of tech infrastructure in our energy infrastructure needs help. And it's obvious, remember the Florida municipality break reach last year? When they took over the water systems or they had the ability to? And this is a real issue, you know, there's bad nation state actors out there, and I'm no alarmist, but the energy and utility has to spend this money to keep up. It's really important. And then you also hit on the retail consumer. Obviously what's happened, the work from home shift created a shop from home shift, and the trends that are happening right now in retail. If you don't spend and keep up, you're not going to be around much longer. So I think the really two interesting things here to call out are energy utilities, usually a laggard in IT spend and it's leading, and also retail consumer, a lot of changes happening. >> Yeah. Great stuff. I mean, I recall when we entered the pandemic, really ETR was the first to emphasize the impact that work from home was going to have, so I really put a lot of weight on this data. Okay. Our next prediction is we're going to get into security, it's one of our favorite topics. And that is that the number one priority that needs to be addressed by organizations in 2022 is security and you can see, in this slide, the degree to which security is top of mind, relative to some other pretty important areas like cloud, productivity, data, and automation, and some others. Now people may say, "Oh, this is obvious." But I'm going to add some context here, Erik, and then bring you in. First, organizations, they don't have unlimited budgets. And there are a lot of competing priorities for dollars, especially with the digital transformation mandate. And depending on the size of the company, this data will vary. For example, while security is still number one at the largest public companies, and those are of course of the biggest spenders, it's not nearly as pronounced as it is on average, or in, for example, mid-sized companies and government agencies. And this is because midsized companies or smaller companies, they don't have the resources that larger companies do. Larger companies have done a better job of securing their infrastructure. So these mid-size firms are playing catch up and the data suggests cyber is even a bigger priority there, gaps that they have to fill, you know, going forward. And that's why we think there's going to be more demand for MSSPs, managed security service providers. And we may even see some IPO action there. And then of course, Erik, you and I have talked about events like the SolarWinds Hack, there's more ransomware attacks, other vulnerabilities. Just recently, like Log4j in December. All of this has heightened concerns. Now I want to talk a little bit more about how we measure this, you know, relatively, okay, it's an obvious prediction, but let's stick our necks out a little bit. And so in addition to the rise of managed security services, we're calling for M&A and/or IPOs, we've specified some names here on this chart, and we're also pointing to the digital supply chain as an area of emphasis. Again, Log4j really shone that under a light. And this is going to help the likes of Auth0, which is now Okta, SailPoint, which is called out on this chart, and some others. We're calling some winners in end point security. Erik, you're going to talk about sort of that lifecycle, that transformation that we're seeing, that migration to new endpoint technologies that are going to benefit from this reset refresh cycle. So Erik, weigh in here, let's talk about some of the elements of this prediction and some of the names on that chart. >> Yeah, certainly. I'm going to start right with Log4j top of mind. And the reason why is because we're seeing a real paradigm shift here where things are no longer being attacked at the network layer, they're being attacked at the application layer, and in the application stack itself. And that is a huge shift left. And that's taking in DevSecOps now as a real priority in 2022. That's a real paradigm shift over the last 20 years. That's not where attacks used to come from. And this is going to have a lot of changes. You called out a bunch of names in there that are, they're either going to work. I would add to that list Wiz. I would add Orca Security. Two names in our emerging technology study, in addition to the ones you added that are involved in cloud security and container security. These names are either going to get gobbled up. So the traditional legacy names are going to have to start writing checks and, you know, legacy is not fair, but they're in the data center, right? They're, on-prem, they're not cloud native. So these are the names that money is going to be flowing to. So they're either going to get gobbled up, or we're going to see some IPO's. And on the other thing I want to talk about too, is what you mentioned. We have CrowdStrike on that list, We have SentinalOne on the list. Everyone knows them. Our data was so strong on Tanium that we actually went positive for the first time just today, just this morning, where that was released. The trifecta of these are so important because of what you mentioned, under resourcing. We can't have security just tell us when something happens, it has to automate, and it has to respond. So in this next generation of EDR and XDR, an automated response has to happen because people are under-resourced, salaries are really high, there's a skill shortage out there. Security has to become responsive. It can't just monitor anymore. >> Yeah. Great. And we should call out too. So we named some names, Snyk, Aqua, Arctic Wolf, Lacework, Netskope, Illumio. These are all sort of IPO, or possibly even M&A candidates. All right. Our next prediction goes right to the way we work. Again, something that ETR has been on for awhile. We're calling for a major rethink in remote work for 2022. We had predicted last year that by the end of 2021, there'd be a larger return to the office with the norm being around a third of workers permanently remote. And of course the variants changed that equation and, you know, gave more time for people to think about this idea of hybrid work and that's really come in to focus. So we're predicting that is going to overtake fully remote as the dominant work model with only about a third of the workers back in the office full-time. And Erik, we expect a somewhat lower percentage to be fully remote. It's now sort of dipped under 30%, at around 29%, but it's still significantly higher than the historical average of around 15 to 16%. So still a major change, but this idea of hybrid and getting hybrid right, has really come into focus. Hasn't it? >> Yeah. It's here to stay. There's no doubt about it. We started this in March of 2020, as soon as the virus hit. This is the 10th iteration of the survey. No one, no one ever thought we'd see a number where only 34% of people were going to be in office permanently. That's a permanent number. They're expecting only a third of the workers to ever come back fully in office. And against that, there's 63% that are saying their permanent workforce is going to be either fully remote or hybrid. And this, I can't really explain how big of a paradigm shift this is. Since the start of the industrial revolution, people leave their house and go to work. Now they're saying that's not going to happen. The economic impact here is so broad, on so many different areas And, you know, the reason is like, why not? Right? The productivity increase is real. We're seeing the productivity increase. Enterprises are spending on collaboration tools, productivity tools, We're seeing an increased perception in productivity of their workforce. And the CFOs can cut down an expense item. I just don't see a reason why this would end, you know, I think it's going to continue. And I also want to point out these results, as high as they are, were before the Omicron wave hit us. I can only imagine what these results would have been if we had sent the survey out just two or three weeks later. >> Yeah. That's a great point. Okay. Next prediction, we're going to look at the supply chain, specifically in how it's affecting some of the hardware spending and cloud strategies in the future. So in this chart, ETRS buyers, have you experienced problems procuring hardware as a result of supply chain issues? And, you know, despite the fact that some companies are, you know, I would call out Dell, for example, doing really well in terms of delivering, you can see that in the numbers, it's pretty clear, there's been an impact. And that's not not an across the board, you know, thing where vendors are able to deliver, especially acute in PCs, but also pronounced in networking, also in firewall servers and storage. And what's interesting is how companies are responding and reacting. So first, you know, I'm going to call the laptop and PC demand staying well above pre-COVID norms. It had peaked in 2012. Pre-pandemic it kept dropping and dropping and dropping, in terms of, you know, unit volume, where the market was contracting. And we think can continue to grow this year in double digits in 2022. But what's interesting, Erik, is when you survey customers, is despite the difficulty they're having in procuring network hardware, there's as much of a migration away from existing networks to the cloud. You could probably comment on that. Their networks are more fossilized, but when it comes to firewalls and servers and storage, there's a much higher propensity to move to the cloud. 30% of customers that ETR surveyed will replace security appliances with cloud services and 41% and 34% respectively will move to cloud compute and storage in 2022. So cloud's relentless march on traditional on-prem models continues. Erik, what do you make of this data? Please weigh in on this prediction. >> As if we needed another reason to go to the cloud. Right here, here it is yet again. So this was added to the survey by client demand. They were asking about the procurement difficulties, the supply chain issues, and how it was impacting our community. So this is the first time we ran it. And it really was interesting to see, you know, the move there. And storage particularly I found interesting because it correlated with a huge jump that we saw on one of our vendor names, which was Rubrik, had the highest net score that it's ever had. So clearly we're seeing some correlation with some of these names that are there, you know, really well positioned to take storage, to take data into the cloud. So again, you didn't need another reason to, you know, hasten this digital transformation, but here we are, we have it yet again, and I don't see it slowing down anytime soon. >> You know, that's a really good point. I mean, it's not necessarily bad news for the... I mean, obviously you wish that it had no change, would be great, but things, you know, always going to change. So we'll talk about this a little bit later when we get into the Supercloud conversation, but this is an opportunity for people who embrace the cloud. So we'll come back to that. And I want to hang on cloud a bit and share some recent projections that we've made. The next prediction is the big four cloud players are going to surpass 167 billion, an IaaS and PaaS revenue in 2022. We track this. Observers of this program know that we try to create an apples to apples comparison between AWS, Azure, GCP and Alibaba in IaaS and PaaS. So we're calling for 38% revenue growth in 2022, which is astounding for such a massive market. You know, AWS is probably not going to hit a hundred billion dollar run rate, but they're going to be close this year. And we're going to get there by 2023, you know they're going to surpass that. Azure continues to close the gap. Now they're about two thirds of the size of AWS and Google, we think is going to surpass Alibaba and take the number three spot. Erik, anything you'd like to add here? >> Yeah, first of all, just on a sector level, we saw our sector, new survey net score on cloud jumped another 10%. It was already really high at 48. Went up to 53. This train is not slowing down anytime soon. And we even added an edge compute type of player, like CloudFlare into our cloud bucket this year. And it debuted with a net score of almost 60. So this is really an area that's expanding, not just the big three, but everywhere. We even saw Oracle and IBM jump up. So even they're having success, taking some of their on-prem customers and then selling them to their cloud services. This is a massive opportunity and it's not changing anytime soon, it's going to continue. >> And I think the operative word there is opportunity. So, you know, the next prediction is something that we've been having fun with and that's this Supercloud becomes a thing. Now, the reason I say we've been having fun is we put this concept of Supercloud out and it's become a bit of a controversy. First, you know, what the heck's the Supercloud right? It's sort of a buzz-wordy term, but there really is, we believe, a thing here. We think there needs to be a rethinking or at least an evolution of the term multi-cloud. And what we mean is that in our view, you know, multicloud from a vendor perspective was really cloud compatibility. It wasn't marketed that way, but that's what it was. Either a vendor would containerize its legacy stack, shove it into the cloud, or a company, you know, they'd do the work, they'd build a cloud native service on one of the big clouds and they did do it for AWS, and then Azure, and then Google. But there really wasn't much, if any, leverage across clouds. Now from a buyer perspective, we've always said multicloud was a symptom of multi-vendor, meaning I got different workloads, running in different clouds, or I bought a company and they run on Azure, and I do a lot of work on AWS, but generally it wasn't necessarily a prescribed strategy to build value on top of hyperscale infrastructure. There certainly was somewhat of a, you know, reducing lock-in and hedging the risk. But we're talking about something more here. We're talking about building value on top of the hyperscale gift of hundreds of billions of dollars in CapEx. So in addition, we're not just talking about transforming IT, which is what the last 10 years of cloud have been like. And, you know, doing work in the cloud because it's cheaper or simpler or more agile, all of those things. So that's beginning to change. And this chart shows some of the technology vendors that are leaning toward this Supercloud vision, in our view, building on top of the hyperscalers that are highlighted in red. Now, Jerry Chan at Greylock, they wrote a piece called Castles in the Cloud. It got our thinking going, and he and the team at Greylock, they're building out a database of all the cloud services and all the sub-markets in cloud. And that got us thinking that there's a higher level of abstraction coalescing in the market, where there's tight integration of services across clouds, but the underlying complexity is hidden, and there's an identical experience across clouds, and even, in my dreams, on-prem for some platforms, so what's new or new-ish and evolving are things like location independence, you've got to include the edge on that, metadata services to optimize locality of reference and data source awareness, governance, privacy, you know, application independent and dependent, actually, recovery across clouds. So we're seeing this evolve. And in our view, the two biggest things that are new are the technology is evolving, where you're seeing services truly integrate cross-cloud. And the other big change is digital transformation, where there's this new innovation curve developing, and it's not just about making your IT better. It's about SaaS-ifying and automating your entire company workflows. So Supercloud, it's not just a vendor thing to us. It's the evolution of, you know, the, the Marc Andreessen quote, "Every company will be a SaaS company." Every company will deliver capabilities that can be consumed as cloud services. So Erik, the chart shows spending momentum on the y-axis and net score, or presence in the ETR data center, or market share on the x-axis. We've talked about snowflake as the poster child for this concept where the vision is you're in their cloud and sharing data in that safe place. Maybe you could make some comments, you know, what do you think of this Supercloud concept and this change that we're sensing in the market? >> Well, I think you did a great job describing the concept. So maybe I'll support it a little bit on the vendor level and then kind of give examples of the ones that are doing it. You stole the lead there with Snowflake, right? There is no better example than what we've seen with what Snowflake can do. Cross-portability in the cloud, the ability to be able to be, you know, completely agnostic, but then build those services on top. They're better than anything they could offer. And it's not just there. I mean, you mentioned edge compute, that's a whole nother layer where this is coming in. And CloudFlare, the momentum there is out of control. I mean, this is a company that started off just doing CDN and trying to compete with Okta Mite. And now they're giving you a full soup to nuts with security and actual edge compute layer, but it's a fantastic company. What they're doing, it's another great example of what you're seeing here. I'm going to call out HashiCorp as well. They're more of an infrastructure services, a little bit more of an open-source freemium model, but what they're doing as well is completely cloud agnostic. It's dynamic. It doesn't care if you're in a container, it doesn't matter where you are. They recently IPO'd and they're down 25%, but their data looks so good across both of our emerging technology and TISA survey. It's certainly another name that's playing on this. And another one that we mentioned as well is Rubrik. If you need storage, compute, and in the cloud layer and you need to be agnostic to it, they're another one that's really playing in this space. So I think it's a great concept you're bringing up. I think it's one that's here to stay and there's certainly a lot of vendors that fit into what you're describing. >> Excellent. Thank you. All right, let's shift to data. The next prediction, it might be a little tough to measure. Before I said we're trying to be a little black and white here, but it relates to Data Mesh, which is, the ideas behind that term were created by Zhamak Dehghani of ThoughtWorks. And we see Data Mesh is really gaining momentum in 2022, but it's largely going to be, we think, confined to a more narrow scope. Now, the impetus for change in data architecture in many companies really stems from the fact that their Hadoop infrastructure really didn't solve their data problems and they struggle to get more value out of their data investments. Data Mesh prescribes a shift to a decentralized architecture in domain ownership of data and a shift to data product thinking, beyond data for analytics, but data products and services that can be monetized. Now this a very powerful in our view, but they're difficult for organizations to get their heads around and further decentralization creates the need for a self-service platform and federated data governance that can be automated. And not a lot of standards around this. So it's going to take some time. At our power panel a couple of weeks ago on data management, Tony Baer predicted a backlash on Data Mesh. And I don't think it's going to be so much of a backlash, but rather the adoption will be more limited. Most implementations we think are going to use a starting point of AWS and they'll enable domains to access and control their own data lakes. And while that is a very small slice of the Data Mesh vision, I think it's going to be a starting point. And the last thing I'll say is, this is going to take a decade to evolve, but I think it's the right direction. And whether it's a data lake or a data warehouse or a data hub or an S3 bucket, these are really, the concept is, they'll eventually just become nodes on the data mesh that are discoverable and access is governed. And so the idea is that the stranglehold that the data pipeline and process and hyper-specialized roles that they have on data agility is going to evolve. And decentralized architectures and the democratization of data will eventually become a norm for a lot of different use cases. And Erik, I wonder if you'd add anything to this. >> Yeah. There's a lot to add there. The first thing that jumped out to me was that that mention of the word backlash you said, and you said it's not really a backlash, but what it could be is these are new words trying to solve an old problem. And I do think sometimes the industry will notice that right away and maybe that'll be a little pushback. And the problems are what you already mentioned, right? We're trying to get to an area where we can have more assets in our data site, more deliverable, and more usable and relevant to the business. And you mentioned that as self-service with governance laid on top. And that's really what we're trying to get to. Now, there's a lot of ways you can get there. Data fabric is really the technical aspect and data mesh is really more about the people, the process, and the governance, but the two of those need to meet, in order to make that happen. And as far as tools, you know, there's even cataloging names like Informatica that play in this, right? Istio plays in this, Snowflake plays in this. So there's a lot of different tools that will support it. But I think you're right in calling out AWS, right? They have AWS Lake, they have AWS Glue. They have so much that's trying to drive this. But I think the really important thing to keep here is what you said. It's going to be a decade long journey. And by the way, we're on the shoulders of giants a decade ago that have even gotten us to this point to talk about these new words because this has been an ongoing type of issue, but ultimately, no matter which vendors you use, this is going to come down to your data governance plan and the data literacy in your business. This is really about workflows and people as much as it is tools. So, you know, the new term of data mesh is wonderful, but you still have to have the people and the governance and the processes in place to get there. >> Great, thank you for that, Erik. Some great points. All right, for the next prediction, we're going to shine the spotlight on two of our favorite topics, Snowflake and Databricks, and the prediction here is that, of course, Databricks is going to IPO this year, as expected. Everybody sort of expects that. And while, but the prediction really is, well, while these two companies are facing off already in the market, they're also going to compete with each other for M&A, especially as Databricks, you know, after the IPO, you're going to have, you know, more prominence and a war chest. So first, these companies, they're both looking pretty good, the same XY graph with spending velocity and presence and market share on the horizontal axis. And both Snowflake and Databricks are well above that magic 40% red dotted line, the elevated line, to us. And for context, we've included a few other firms. So you can see kind of what a good position these two companies are really in, especially, I mean, Snowflake, wow, it just keeps moving to the right on this horizontal picture, but maintaining the next net score in the Y axis. Amazing. So, but here's the thing, Databricks is using the term Lakehouse implying that it has the best of data lakes and data warehouses. And Snowflake has the vision of the data cloud and data sharing. And Snowflake, they've nailed analytics, and now they're moving into data science in the domain of Databricks. Databricks, on the other hand, has nailed data science and is moving into the domain of Snowflake, in the data warehouse and analytics space. But to really make this seamless, there has to be a semantic layer between these two worlds and they're either going to build it or buy it or both. And there are other areas like data clean rooms and privacy and data prep and governance and machine learning tooling and AI, all that stuff. So the prediction is they'll not only compete in the market, but they'll step up and in their competition for M&A, especially after the Databricks IPO. We've listed some target names here, like Atscale, you know, Iguazio, Infosum, Habu, Immuta, and I'm sure there are many, many others. Erik, you care to comment? >> Yeah. I remember a year ago when we were talking Snowflake when they first came out and you, and I said, "I'm shocked if they don't use this war chest of money" "and start going after more" "because we know Slootman, we have so much respect for him." "We've seen his playbook." And I'm actually a little bit surprised that here we are, at 12 months later, and he hasn't spent that money yet. So I think this prediction's just spot on. To talk a little bit about the data side, Snowflake is in rarefied air. It's all by itself. It is the number one net score in our entire TISA universe. It is absolutely incredible. There's almost no negative intentions. Global 2000 organizations are increasing their spend on it. We maintain our positive outlook. It's really just, you know, stands alone. Databricks, however, also has one of the highest overall net sentiments in the entire universe, not just its area. And this is the first time we're coming up positive on this name as well. It looks like it's not slowing down. Really interesting comment you made though that we normally hear from our end-user commentary in our panels and our interviews. Databricks is really more used for the data science side. The MLAI is where it's best positioned in our survey. So it might still have some catching up to do to really have that caliber of usability that you know Snowflake is seeing right now. That's snowflake having its own marketplace. There's just a lot more to Snowflake right now than there is Databricks. But I do think you're right. These two massive vendors are sort of heading towards a collision course, and it'll be very interesting to see how they deploy their cash. I think Snowflake, with their incredible management and leadership, probably will make the first move. >> Well, I think you're right on that. And by the way, I'll just add, you know, Databricks has basically said, hey, it's going to be easier for us to come from data lakes into data warehouse. I'm not sure I buy that. I think, again, that semantic layer is a missing ingredient. So it's going to be really interesting to see how this plays out. And to your point, you know, Snowflake's got the war chest, they got the momentum, they've got the public presence now since November, 2020. And so, you know, they're probably going to start making some aggressive moves. Anyway, next prediction is something, Erik, that you and I have talked about many, many times, and that is observability. I know it's one of your favorite topics. And we see this world screaming for more consolidation it's going all in on cloud native. These legacy stacks, they're fighting to stay relevant, but the direction is pretty clear. And the same XY graph lays out the players in the field, with some of the new entrants that we've also highlighted, like Observe and Honeycomb and ChaosSearch that we've talked about. Erik, we put a big red target around Splunk because everyone wants their gold. So please give us your thoughts. >> Oh man, I feel like I've been saying negative things about Splunk for too long. I've got a bad rap on this name. The Splunk shareholders come after me all the time. Listen, it really comes down to this. They're a fantastic company that was designed to do logging and monitoring and had some great tool sets around what you could do with it. But they were designed for the data center. They were designed for prem. The world we're in now is so dynamic. Everything I hear from our end user community is that all net new workloads will be going to cloud native players. It's that simple. So Splunk has entrenched. It's going to continue doing what it's doing and it does it really, really well. But if you're doing something new, the new workloads are going to be in a dynamic environment and that's going to go to the cloud native players. And in our data, it is extremely clear that that means Datadog and Elastic. They are by far number one and two in net score, increase rates, adoption rates. It's not even close. Even New Relic actually is starting to, you know, entrench itself really well. We saw New Relic's adoption's going up, which is super important because they went to that freemium model, you know, to try to get their little bit of an entrenched customer base and that's working as well. And then you made a great list here, of all the new entrants, but it goes beyond this. There's so many more. In our emerging technology survey, we're seeing Century, Catchpoint, Securonix, Lucid Works. There are so many options in this space. And let's not forget, the biggest data that we're seeing is with Grafana. And Grafana labs as yet to turn on their enterprise. Elastic did it, why can't Grafana labs do it? They have an enterprise stack. So when you look at how crowded this space is, there has to be consolidation. I recently hosted a panel and every single guy on that panel said, "Please give me a consolidation." Because they're the end users trying to actually deploy these and it's getting a little bit confusing. >> Great. Thank you for that. Okay. Last prediction. Erik, might be a little out of your wheelhouse, but you know, you might have some thoughts on it. And that's a hybrid events become the new digital model and a new category in 2022. You got these pure play digital or virtual events. They're going to take a back seat to in-person hybrids. The virtual experience will eventually give way to metaverse experiences and that's going to take some time, but the physical hybrid is going to drive it. And metaverse is ultimately going to define the virtual experience because the virtual experience today is not great. Nobody likes virtual. And hybrid is going to become the business model. Today's pure virtual experience has to evolve, you know, theCUBE first delivered hybrid mid last decade, but nobody really wanted it. We did Mobile World Congress last summer in Barcelona in an amazing hybrid model, which we're showing in some of the pictures here. Alex, if you don't mind bringing that back up. And every physical event that we're we're doing now has a hybrid and virtual component, including the pre-records. You can see in our studios, you see that the green screen. I don't know. Erik, what do you think about, you know, the Zoom fatigue and all this. I know you host regular events with your round tables, but what are your thoughts? >> Well, first of all, I think you and your company here have just done an amazing job on this. So that's really your expertise. I spent 20 years of my career hosting intimate wall street idea dinners. So I'm better at navigating a wine list than I am navigating a conference floor. But I will say that, you know, the trend just goes along with what we saw. If 35% are going to be fully remote. If 70% are going to be hybrid, then our events are going to be as well. I used to host round table dinners on, you know, one or two nights a week. Now those have gone virtual. They're now panels. They're now one-on-one interviews. You know, we do chats. We do submitted questions. We do what we can, but there's no reason that this is going to change anytime soon. I think you're spot on here. >> Yeah. Great. All right. So there you have it, Erik and I, Listen, we always love the feedback. Love to know what you think. Thank you, Erik, for your partnership, your collaboration, and love doing these predictions with you. >> Yeah. I always enjoy them too. And I'm actually happy. Last year you made us do a baker's dozen, so thanks for keeping it to 10 this year. >> (laughs) We've got a lot to say. I know, you know, we cut out. We didn't do much on crypto. We didn't really talk about SaaS. I mean, I got some thoughts there. We didn't really do much on containers and AI. >> You want to keep going? I've got another 10 for you. >> RPA...All right, we'll have you back and then let's do that. All right. All right. Don't forget, these episodes are all available as podcasts, wherever you listen, all you can do is search Breaking Analysis podcast. Check out ETR's website at etr.plus, they've got a new website out. It's the best data in the industry, and we publish a full report every week on wikibon.com and siliconangle.com. You can always reach out on email, David.Vellante@siliconangle.com I'm @DVellante on Twitter. Comment on our LinkedIn posts. This is Dave Vellante for the Cube Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (mellow music)

(bright music) >> In the 2010s, organizations became keenly aware that data would become the key ingredient to driving competitive advantage, differentiation, and growth. But to this day, putting data to work remains a difficult challenge for many, if not most organizations. Now, as the cloud matures, it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible. We've also seen better tooling in the form of data workflows, streaming, machine intelligence, AI, developer tools, security, observability, automation, new databases and the like. These innovations they accelerate data proficiency, but at the same time, they add complexity for practitioners. Data lakes, data hubs, data warehouses, data marts, data fabrics, data meshes, data catalogs, data oceans are forming, they're evolving and exploding onto the scene. So in an effort to bring perspective to the sea of optionality, we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond. Hello everyone, my name is Dave Velannte with theCUBE, and I'd like to welcome you to a special Cube presentation, analysts predictions 2022: the future of data management. We've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade. Let me introduce our six power panelists. Sanjeev Mohan is former Gartner Analyst and Principal at SanjMo. Tony Baer, principal at dbInsight, Carl Olofson is well-known Research Vice President with IDC, Dave Menninger is Senior Vice President and Research Director at Ventana Research, Brad Shimmin, Chief Analyst, AI Platforms, Analytics and Data Management at Omdia and Doug Henschen, Vice President and Principal Analyst at Constellation Research. Gentlemen, welcome to the program and thanks for coming on theCUBE today. >> Great to be here. >> Thank you. >> All right, here's the format we're going to use. I as moderator, I'm going to call on each analyst separately who then will deliver their prediction or mega trend, and then in the interest of time management and pace, two analysts will have the opportunity to comment. If we have more time, we'll elongate it, but let's get started right away. Sanjeev Mohan, please kick it off. You want to talk about governance, go ahead sir. >> Thank you Dave. I believe that data governance which we've been talking about for many years is now not only going to be mainstream, it's going to be table stakes. And all the things that you mentioned, you know, the data, ocean data lake, lake houses, data fabric, meshes, the common glue is metadata. If we don't understand what data we have and we are governing it, there is no way we can manage it. So we saw Informatica went public last year after a hiatus of six. I'm predicting that this year we see some more companies go public. My bet is on Culebra, most likely and maybe Alation we'll see go public this year. I'm also predicting that the scope of data governance is going to expand beyond just data. It's not just data and reports. We are going to see more transformations like spark jawsxxxxx, Python even Air Flow. We're going to see more of a streaming data. So from Kafka Schema Registry, for example. We will see AI models become part of this whole governance suite. So the governance suite is going to be very comprehensive, very detailed lineage, impact analysis, and then even expand into data quality. We already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management, data catalogs, also data access governance. So what we are going to see is that once the data governance platforms become the key entry point into these modern architectures, I'm predicting that the usage, the number of users of a data catalog is going to exceed that of a BI tool. That will take time and we already seen that trajectory. Right now if you look at BI tools, I would say there a hundred users to BI tool to one data catalog. And I see that evening out over a period of time and at some point data catalogs will really become the main way for us to access data. Data catalog will help us visualize data, but if we want to do more in-depth analysis, it'll be the jumping off point into the BI tool, the data science tool and that is the journey I see for the data governance products. >> Excellent, thank you. Some comments. Maybe Doug, a lot of things to weigh in on there, maybe you can comment. >> Yeah, Sanjeev I think you're spot on, a lot of the trends the one disagreement, I think it's really still far from mainstream. As you say, we've been talking about this for years, it's like God, motherhood, apple pie, everyone agrees it's important, but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking. I think one thing that deserves mention in this context is ESG mandates and guidelines, these are environmental, social and governance, regs and guidelines. We've seen the environmental regs and guidelines and posts in industries, particularly the carbon-intensive industries. We've seen the social mandates, particularly diversity imposed on suppliers by companies that are leading on this topic. We've seen governance guidelines now being imposed by banks on investors. So these ESGs are presenting new carrots and sticks, and it's going to demand more solid data. It's going to demand more detailed reporting and solid reporting, tighter governance. But we're still far from mainstream adoption. We have a lot of, you know, best of breed niche players in the space. I think the signs that it's going to be more mainstream are starting with things like Azure Purview, Google Dataplex, the big cloud platform players seem to be upping the ante and starting to address governance. >> Excellent, thank you Doug. Brad, I wonder if you could chime in as well. >> Yeah, I would love to be a believer in data catalogs. But to Doug's point, I think that it's going to take some more pressure for that to happen. I recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the nineties and that didn't happen quite the way we anticipated. And so to Sanjeev's point it's because it is really complex and really difficult to do. My hope is that, you know, we won't sort of, how do I put this? Fade out into this nebula of domain catalogs that are specific to individual use cases like Purview for getting data quality right or like data governance and cybersecurity. And instead we have some tooling that can actually be adaptive to gather metadata to create something. And I know its important to you, Sanjeev and that is this idea of observability. If you can get enough metadata without moving your data around, but understanding the entirety of a system that's running on this data, you can do a lot. So to help with the governance that Doug is talking about. >> So I just want to add that, data governance, like any other initiatives did not succeed even AI went into an AI window, but that's a different topic. But a lot of these things did not succeed because to your point, the incentives were not there. I remember when Sarbanes Oxley had come into the scene, if a bank did not do Sarbanes Oxley, they were very happy to a million dollar fine. That was like, you know, pocket change for them instead of doing the right thing. But I think the stakes are much higher now. With GDPR, the flood gates opened. Now, you know, California, you know, has CCPA but even CCPA is being outdated with CPRA, which is much more GDPR like. So we are very rapidly entering a space where pretty much every major country in the world is coming up with its own compliance regulatory requirements, data residents is becoming really important. And I think we are going to reach a stage where it won't be optional anymore. So whether we like it or not, and I think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption. We were focused on features and these features were disconnected, very hard for business to adopt. These are built by IT people for IT departments to take a look at technical metadata, not business metadata. Today the tables have turned. CDOs are driving this initiative, regulatory compliances are beating down hard, so I think the time might be right. >> Yeah so guys, we have to move on here. But there's some real meat on the bone here, Sanjeev. I like the fact that you called out Culebra and Alation, so we can look back a year from now and say, okay, he made the call, he stuck it. And then the ratio of BI tools to data catalogs that's another sort of measurement that we can take even though with some skepticism there, that's something that we can watch. And I wonder if someday, if we'll have more metadata than data. But I want to move to Tony Baer, you want to talk about data mesh and speaking, you know, coming off of governance. I mean, wow, you know the whole concept of data mesh is, decentralized data, and then governance becomes, you know, a nightmare there, but take it away, Tony. >> We'll put this way, data mesh, you know, the idea at least as proposed by ThoughtWorks. You know, basically it was at least a couple of years ago and the press has been almost uniformly almost uncritical. A good reason for that is for all the problems that basically Sanjeev and Doug and Brad we're just speaking about, which is that we have all this data out there and we don't know what to do about it. Now, that's not a new problem. That was a problem we had in enterprise data warehouses, it was a problem when we had over DoOP data clusters, it's even more of a problem now that data is out in the cloud where the data is not only your data lake, is not only us three, it's all over the place. And it's also including streaming, which I know we'll be talking about later. So the data mesh was a response to that, the idea of that we need to bait, you know, who are the folks that really know best about governance? It's the domain experts. So it was basically data mesh was an architectural pattern and a process. My prediction for this year is that data mesh is going to hit cold heart reality. Because if you do a Google search, basically the published work, the articles on data mesh have been largely, you know, pretty uncritical so far. Basically loading and is basically being a very revolutionary new idea. I don't think it's that revolutionary because we've talked about ideas like this. Brad now you and I met years ago when we were talking about so and decentralizing all of us, but it was at the application level. Now we're talking about it at the data level. And now we have microservices. So there's this thought of have we managed if we're deconstructing apps in cloud native to microservices, why don't we think of data in the same way? My sense this year is that, you know, this has been a very active search if you look at Google search trends, is that now companies, like enterprise are going to look at this seriously. And as they look at it seriously, it's going to attract its first real hard scrutiny, it's going to attract its first backlash. That's not necessarily a bad thing. It means that it's being taken seriously. The reason why I think that you'll start to see basically the cold hearted light of day shine on data mesh is that it's still a work in progress. You know, this idea is basically a couple of years old and there's still some pretty major gaps. The biggest gap is in the area of federated governance. Now federated governance itself is not a new issue. Federated governance decision, we started figuring out like, how can we basically strike the balance between getting let's say between basically consistent enterprise policy, consistent enterprise governance, but yet the groups that understand the data and know how to basically, you know, that, you know, how do we basically sort of balance the two? There's a huge gap there in practice and knowledge. Also to a lesser extent, there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data. You know, basically through the full life cycle, from develop, from selecting the data from, you know, building the pipelines from, you know, determining your access control, looking at quality, looking at basically whether the data is fresh or whether it's trending off course. So my prediction is that it will receive the first harsh scrutiny this year. You are going to see some organization and enterprises declare premature victory when they build some federated query implementations. You going to see vendors start with data mesh wash their products anybody in the data management space that they are going to say that where this basically a pipelining tool, whether it's basically ELT, whether it's a catalog or federated query tool, they will all going to get like, you know, basically promoting the fact of how they support this. Hopefully nobody's going to call themselves a data mesh tool because data mesh is not a technology. We're going to see one other thing come out of this. And this harks back to the metadata that Sanjeev was talking about and of the catalog just as he was talking about. Which is that there's going to be a new focus, every renewed focus on metadata. And I think that's going to spur interest in data fabrics. Now data fabrics are pretty vaguely defined, but if we just take the most elemental definition, which is a common metadata back plane, I think that if anybody is going to get serious about data mesh, they need to look at the data fabric because we all at the end of the day, need to speak, you know, need to read from the same sheet of music. >> So thank you Tony. Dave Menninger, I mean, one of the things that people like about data mesh is it pretty crisply articulate some of the flaws in today's organizational approaches to data. What are your thoughts on this? >> Well, I think we have to start by defining data mesh, right? The term is already getting corrupted, right? Tony said it's going to see the cold hard light of day. And there's a problem right now that there are a number of overlapping terms that are similar but not identical. So we've got data virtualization, data fabric, excuse me for a second. (clears throat) Sorry about that. Data virtualization, data fabric, data federation, right? So I think that it's not really clear what each vendor means by these terms. I see data mesh and data fabric becoming quite popular. I've interpreted data mesh as referring primarily to the governance aspects as originally intended and specified. But that's not the way I see vendors using it. I see vendors using it much more to mean data fabric and data virtualization. So I'm going to comment on the group of those things. I think the group of those things is going to happen. They're going to happen, they're going to become more robust. Our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half, so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access. Again, whether you define it as mesh or fabric or virtualization isn't really the point here. But this notion that there are different elements of data, metadata and governance within an organization that all need to be managed collectively. The interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not, it's almost double, 68% of organizations, I'm sorry, 79% of organizations that were using virtualized access express satisfaction with their access to the data lake. Only 39% express satisfaction if they weren't using virtualized access. >> Oh thank you Dave. Sanjeev we just got about a couple of minutes on this topic, but I know you're speaking or maybe you've always spoken already on a panel with (indistinct) who sort of invented the concept. Governance obviously is a big sticking point, but what are your thoughts on this? You're on mute. (panelist chuckling) >> So my message to (indistinct) and to the community is as opposed to what they said, let's not define it. We spent a whole year defining it, there are four principles, domain, product, data infrastructure, and governance. Let's take it to the next level. I get a lot of questions on what is the difference between data fabric and data mesh? And I'm like I can't compare the two because data mesh is a business concept, data fabric is a data integration pattern. How do you compare the two? You have to bring data mesh a level down. So to Tony's point, I'm on a warpath in 2022 to take it down to what does a data product look like? How do we handle shared data across domains and governance? And I think we are going to see more of that in 2022, or is "operationalization" of data mesh. >> I think we could have a whole hour on this topic, couldn't we? Maybe we should do that. But let's corner. Let's move to Carl. So Carl, you're a database guy, you've been around that block for a while now, you want to talk about graph databases, bring it on. >> Oh yeah. Okay thanks. So I regard graph database as basically the next truly revolutionary database management technology. I'm looking forward for the graph database market, which of course we haven't defined yet. So obviously I have a little wiggle room in what I'm about to say. But this market will grow by about 600% over the next 10 years. Now, 10 years is a long time. But over the next five years, we expect to see gradual growth as people start to learn how to use it. The problem is not that it's not useful, its that people don't know how to use it. So let me explain before I go any further what a graph database is because some of the folks on the call may not know what it is. A graph database organizes data according to a mathematical structure called a graph. The graph has elements called nodes and edges. So a data element drops into a node, the nodes are connected by edges, the edges connect one node to another node. Combinations of edges create structures that you can analyze to determine how things are related. In some cases, the nodes and edges can have properties attached to them which add additional informative material that makes it richer, that's called a property graph. There are two principle use cases for graph databases. There's semantic property graphs, which are use to break down human language texts into the semantic structures. Then you can search it, organize it and answer complicated questions. A lot of AI is aimed at semantic graphs. Another kind is the property graph that I just mentioned, which has a dazzling number of use cases. I want to just point out as I talk about this, people are probably wondering, well, we have relation databases, isn't that good enough? So a relational database defines... It supports what I call definitional relationships. That means you define the relationships in a fixed structure. The database drops into that structure, there's a value, foreign key value, that relates one table to another and that value is fixed. You don't change it. If you change it, the database becomes unstable, it's not clear what you're looking at. In a graph database, the system is designed to handle change so that it can reflect the true state of the things that it's being used to track. So let me just give you some examples of use cases for this. They include entity resolution, data lineage, social media analysis, Customer 360, fraud prevention. There's cybersecurity, there's strong supply chain is a big one actually. There is explainable AI and this is going to become important too because a lot of people are adopting AI. But they want a system after the fact to say, how do the AI system come to that conclusion? How did it make that recommendation? Right now we don't have really good ways of tracking that. Machine learning in general, social network, I already mentioned that. And then we've got, oh gosh, we've got data governance, data compliance, risk management. We've got recommendation, we've got personalization, anti money laundering, that's another big one, identity and access management, network and IT operations is already becoming a key one where you actually have mapped out your operation, you know, whatever it is, your data center and you can track what's going on as things happen there, root cause analysis, fraud detection is a huge one. A number of major credit card companies use graph databases for fraud detection, risk analysis, tracking and tracing turn analysis, next best action, what if analysis, impact analysis, entity resolution and I would add one other thing or just a few other things to this list, metadata management. So Sanjeev, here you go, this is your engine. Because I was in metadata management for quite a while in my past life. And one of the things I found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it, but graphs can, okay? Graphs can do things like say, this term in this context means this, but in that context, it means that, okay? Things like that. And in fact, logistics management, supply chain. And also because it handles recursive relationships, by recursive relationships I mean objects that own other objects that are of the same type. You can do things like build materials, you know, so like parts explosion. Or you can do an HR analysis, who reports to whom, how many levels up the chain and that kind of thing. You can do that with relational databases, but yet it takes a lot of programming. In fact, you can do almost any of these things with relational databases, but the problem is, you have to program it. It's not supported in the database. And whenever you have to program something, that means you can't trace it, you can't define it. You can't publish it in terms of its functionality and it's really, really hard to maintain over time. >> Carl, thank you. I wonder if we could bring Brad in, I mean. Brad, I'm sitting here wondering, okay, is this incremental to the market? Is it disruptive and replacement? What are your thoughts on this phase? >> It's already disrupted the market. I mean, like Carl said, go to any bank and ask them are you using graph databases to get fraud detection under control? And they'll say, absolutely, that's the only way to solve this problem. And it is frankly. And it's the only way to solve a lot of the problems that Carl mentioned. And that is, I think it's Achilles heel in some ways. Because, you know, it's like finding the best way to cross the seven bridges of Koenigsberg. You know, it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique, it's still unfortunately kind of stands apart from the rest of the community that's building, let's say AI outcomes, as a great example here. Graph databases and AI, as Carl mentioned, are like chocolate and peanut butter. But technologically, you think don't know how to talk to one another, they're completely different. And you know, you can't just stand up SQL and query them. You've got to learn, know what is the Carl? Specter special. Yeah, thank you to, to actually get to the data in there. And if you're going to scale that data, that graph database, especially a property graph, if you're going to do something really complex, like try to understand you know, all of the metadata in your organization, you might just end up with, you know, a graph database winter like we had the AI winter simply because you run out of performance to make the thing happen. So, I think it's already disrupted, but we need to like treat it like a first-class citizen in the data analytics and AI community. We need to bring it into the fold. We need to equip it with the tools it needs to do the magic it does and to do it not just for specialized use cases, but for everything. 'Cause I'm with Carl. I think it's absolutely revolutionary. >> Brad identified the principal, Achilles' heel of the technology which is scaling. When these things get large and complex enough that they spill over what a single server can handle, you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down. So that's still a problem to be solved. >> Sanjeev, any quick thoughts on this? I mean, I think metadata on the word cloud is going to be the largest font, but what are your thoughts here? >> I want to (indistinct) So people don't associate me with only metadata, so I want to talk about something slightly different. dbengines.com has done an amazing job. I think almost everyone knows that they chronicle all the major databases that are in use today. In January of 2022, there are 381 databases on a ranked list of databases. The largest category is RDBMS. The second largest category is actually divided into two property graphs and IDF graphs. These two together make up the second largest number databases. So talking about Achilles heel, this is a problem. The problem is that there's so many graph databases to choose from. They come in different shapes and forms. To Brad's point, there's so many query languages in RDBMS, in SQL. I know the story, but here We've got cipher, we've got gremlin, we've got GQL and then we're proprietary languages. So I think there's a lot of disparity in this space. >> Well, excellent. All excellent points, Sanjeev, if I must say. And that is a problem that the languages need to be sorted and standardized. People need to have a roadmap as to what they can do with it. Because as you say, you can do so many things. And so many of those things are unrelated that you sort of say, well, what do we use this for? And I'm reminded of the saying I learned a bunch of years ago. And somebody said that the digital computer is the only tool man has ever device that has no particular purpose. (panelists chuckle) >> All right guys, we got to move on to Dave Menninger. We've heard about streaming. Your prediction is in that realm, so please take it away. >> Sure. So I like to say that historical databases are going to become a thing of the past. By that I don't mean that they're going to go away, that's not my point. I mean, we need historical databases, but streaming data is going to become the default way in which we operate with data. So in the next say three to five years, I would expect that data platforms and we're using the term data platforms to represent the evolution of databases and data lakes, that the data platforms will incorporate these streaming capabilities. We're going to process data as it streams into an organization and then it's going to roll off into historical database. So historical databases don't go away, but they become a thing of the past. They store the data that occurred previously. And as data is occurring, we're going to be processing it, we're going to be analyzing it, we're going to be acting on it. I mean we only ever ended up with historical databases because we were limited by the technology that was available to us. Data doesn't occur in patches. But we processed it in patches because that was the best we could do. And it wasn't bad and we've continued to improve and we've improved and we've improved. But streaming data today is still the exception. It's not the rule, right? There are projects within organizations that deal with streaming data. But it's not the default way in which we deal with data yet. And so that's my prediction is that this is going to change, we're going to have streaming data be the default way in which we deal with data and how you label it and what you call it. You know, maybe these databases and data platforms just evolved to be able to handle it. But we're going to deal with data in a different way. And our research shows that already, about half of the participants in our analytics and data benchmark research, are using streaming data. You know, another third are planning to use streaming technologies. So that gets us to about eight out of 10 organizations need to use this technology. And that doesn't mean they have to use it throughout the whole organization, but it's pretty widespread in its use today and has continued to grow. If you think about the consumerization of IT, we've all been conditioned to expect immediate access to information, immediate responsiveness. You know, we want to know if an item is on the shelf at our local retail store and we can go in and pick it up right now. You know, that's the world we live in and that's spilling over into the enterprise IT world We have to provide those same types of capabilities. So that's my prediction, historical databases become a thing of the past, streaming data becomes the default way in which we operate with data. >> All right thank you David. Well, so what say you, Carl, the guy who has followed historical databases for a long time? >> Well, one thing actually, every database is historical because as soon as you put data in it, it's now history. They'll no longer reflect the present state of things. But even if that history is only a millisecond old, it's still history. But I would say, I mean, I know you're trying to be a little bit provocative in saying this Dave 'cause you know, as well as I do that people still need to do their taxes, they still need to do accounting, they still need to run general ledger programs and things like that. That all involves historical data. That's not going to go away unless you want to go to jail. So you're going to have to deal with that. But as far as the leading edge functionality, I'm totally with you on that. And I'm just, you know, I'm just kind of wondering if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way applications work. Saying that an application should respond instantly, as soon as the state of things changes. What do you say about that? >> I think that's true. I think we do have to think about things differently. It's not the way we designed systems in the past. We're seeing more and more systems designed that way. But again, it's not the default. And I agree 100% with you that we do need historical databases you know, that's clear. And even some of those historical databases will be used in conjunction with the streaming data, right? >> Absolutely. I mean, you know, let's take the data warehouse example where you're using the data warehouse as its context and the streaming data as the present and you're saying, here's the sequence of things that's happening right now. Have we seen that sequence before? And where? What does that pattern look like in past situations? And can we learn from that? >> So Tony Baer, I wonder if you could comment? I mean, when you think about, you know, real time inferencing at the edge, for instance, which is something that a lot of people talk about, a lot of what we're discussing here in this segment, it looks like it's got a great potential. What are your thoughts? >> Yeah, I mean, I think you nailed it right. You know, you hit it right on the head there. Which is that, what I'm seeing is that essentially. Then based on I'm going to split this one down the middle is that I don't see that basically streaming is the default. What I see is streaming and basically and transaction databases and analytics data, you know, data warehouses, data lakes whatever are converging. And what allows us technically to converge is cloud native architecture, where you can basically distribute things. So you can have a node here that's doing the real-time processing, that's also doing... And this is where it leads in or maybe doing some of that real time predictive analytics to take a look at, well look, we're looking at this customer journey what's happening with what the customer is doing right now and this is correlated with what other customers are doing. So the thing is that in the cloud, you can basically partition this and because of basically the speed of the infrastructure then you can basically bring these together and kind of orchestrate them sort of a loosely coupled manner. The other parts that the use cases are demanding, and this is part of it goes back to what Dave is saying. Is that, you know, when you look at Customer 360, when you look at let's say Smart Utility products, when you look at any type of operational problem, it has a real time component and it has an historical component. And having predictive and so like, you know, my sense here is that technically we can bring this together through the cloud. And I think the use case is that we can apply some real time sort of predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction, we have this real-time input. >> Sanjeev, did you have a comment? >> Yeah, I was just going to say that to Dave's point, you know, we have to think of streaming very different because in the historical databases, we used to bring the data and store the data and then we used to run rules on top, aggregations and all. But in case of streaming, the mindset changes because the rules are normally the inference, all of that is fixed, but the data is constantly changing. So it's a completely reversed way of thinking and building applications on top of that. >> So Dave Menninger, there seem to be some disagreement about the default. What kind of timeframe are you thinking about? Is this end of decade it becomes the default? What would you pin? >> I think around, you know, between five to 10 years, I think this becomes the reality. >> I think its... >> It'll be more and more common between now and then, but it becomes the default. And I also want Sanjeev at some point, maybe in one of our subsequent conversations, we need to talk about governing streaming data. 'Cause that's a whole nother set of challenges. >> We've also talked about it rather in two dimensions, historical and streaming, and there's lots of low latency, micro batch, sub-second, that's not quite streaming, but in many cases its fast enough and we're seeing a lot of adoption of near real time, not quite real-time as good enough for many applications. (indistinct cross talk from panelists) >> Because nobody's really taking the hardware dimension (mumbles). >> That'll just happened, Carl. (panelists laughing) >> So near real time. But maybe before you lose the customer, however we define that, right? Okay, let's move on to Brad. Brad, you want to talk about automation, AI, the pipeline people feel like, hey, we can just automate everything. What's your prediction? >> Yeah I'm an AI aficionados so apologies in advance for that. But, you know, I think that we've been seeing automation play within AI for some time now. And it's helped us do a lot of things especially for practitioners that are building AI outcomes in the enterprise. It's helped them to fill skills gaps, it's helped them to speed development and it's helped them to actually make AI better. 'Cause it, you know, in some ways provide some swim lanes and for example, with technologies like AutoML can auto document and create that sort of transparency that we talked about a little bit earlier. But I think there's an interesting kind of conversion happening with this idea of automation. And that is that we've had the automation that started happening for practitioners, it's trying to move out side of the traditional bounds of things like I'm just trying to get my features, I'm just trying to pick the right algorithm, I'm just trying to build the right model and it's expanding across that full life cycle, building an AI outcome, to start at the very beginning of data and to then continue on to the end, which is this continuous delivery and continuous automation of that outcome to make sure it's right and it hasn't drifted and stuff like that. And because of that, because it's become kind of powerful, we're starting to actually see this weird thing happen where the practitioners are starting to converge with the users. And that is to say that, okay, if I'm in Tableau right now, I can stand up Salesforce Einstein Discovery, and it will automatically create a nice predictive algorithm for me given the data that I pull in. But what's starting to happen and we're seeing this from the companies that create business software, so Salesforce, Oracle, SAP, and others is that they're starting to actually use these same ideals and a lot of deep learning (chuckles) to basically stand up these out of the box flip-a-switch, and you've got an AI outcome at the ready for business users. And I am very much, you know, I think that's the way that it's going to go and what it means is that AI is slowly disappearing. And I don't think that's a bad thing. I think if anything, what we're going to see in 2022 and maybe into 2023 is this sort of rush to put this idea of disappearing AI into practice and have as many of these solutions in the enterprise as possible. You can see, like for example, SAP is going to roll out this quarter, this thing called adaptive recommendation services, which basically is a cold start AI outcome that can work across a whole bunch of different vertical markets and use cases. It's just a recommendation engine for whatever you needed to do in the line of business. So basically, you're an SAP user, you look up to turn on your software one day, you're a sales professional let's say, and suddenly you have a recommendation for customer churn. Boom! It's going, that's great. Well, I don't know, I think that's terrifying. In some ways I think it is the future that AI is going to disappear like that, but I'm absolutely terrified of it because I think that what it really does is it calls attention to a lot of the issues that we already see around AI, specific to this idea of what we like to call at Omdia, responsible AI. Which is, you know, how do you build an AI outcome that is free of bias, that is inclusive, that is fair, that is safe, that is secure, that its audible, et cetera, et cetera, et cetera, et cetera. I'd take a lot of work to do. And so if you imagine a customer that's just a Salesforce customer let's say, and they're turning on Einstein Discovery within their sales software, you need some guidance to make sure that when you flip that switch, that the outcome you're going to get is correct. And that's going to take some work. And so, I think we're going to see this move, let's roll this out and suddenly there's going to be a lot of problems, a lot of pushback that we're going to see. And some of that's going to come from GDPR and others that Sanjeev was mentioning earlier. A lot of it is going to come from internal CSR requirements within companies that are saying, "Hey, hey, whoa, hold up, we can't do this all at once. "Let's take the slow route, "let's make AI automated in a smart way." And that's going to take time. >> Yeah, so a couple of predictions there that I heard. AI simply disappear, it becomes invisible. Maybe if I can restate that. And then if I understand it correctly, Brad you're saying there's a backlash in the near term. You'd be able to say, oh, slow down. Let's automate what we can. Those attributes that you talked about are non trivial to achieve, is that why you're a bit of a skeptic? >> Yeah. I think that we don't have any sort of standards that companies can look to and understand. And we certainly, within these companies, especially those that haven't already stood up an internal data science team, they don't have the knowledge to understand when they flip that switch for an automated AI outcome that it's going to do what they think it's going to do. And so we need some sort of standard methodology and practice, best practices that every company that's going to consume this invisible AI can make use of them. And one of the things that you know, is sort of started that Google kicked off a few years back that's picking up some momentum and the companies I just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing. You know, so like for the SAP example, we know, for example, if it's convolutional neural network with a long, short term memory model that it's using, we know that it only works on Roman English and therefore me as a consumer can say, "Oh, well I know that I need to do this internationally. "So I should not just turn this on today." >> Thank you. Carl could you add anything, any context here? >> Yeah, we've talked about some of the things Brad mentioned here at IDC and our future of intelligence group regarding in particular, the moral and legal implications of having a fully automated, you know, AI driven system. Because we already know, and we've seen that AI systems are biased by the data that they get, right? So if they get data that pushes them in a certain direction, I think there was a story last week about an HR system that was recommending promotions for White people over Black people, because in the past, you know, White people were promoted and more productive than Black people, but it had no context as to why which is, you know, because they were being historically discriminated, Black people were being historically discriminated against, but the system doesn't know that. So, you know, you have to be aware of that. And I think that at the very least, there should be controls when a decision has either a moral or legal implication. When you really need a human judgment, it could lay out the options for you. But a person actually needs to authorize that action. And I also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases. In some extent, they always will. So we'll always be chasing after them. But that's (indistinct). >> Absolutely Carl, yeah. I think that what you have to bear in mind as a consumer of AI is that it is a reflection of us and we are a very flawed species. And so if you look at all of the really fantastic, magical looking supermodels we see like GPT-3 and four, that's coming out, they're xenophobic and hateful because the people that the data that's built upon them and the algorithms and the people that build them are us. So AI is a reflection of us. We need to keep that in mind. >> Yeah, where the AI is biased 'cause humans are biased. All right, great. All right let's move on. Doug you mentioned mentioned, you know, lot of people that said that data lake, that term is not going to live on but here's to be, have some lakes here. You want to talk about lake house, bring it on. >> Yes, I do. My prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering. I say offering that doesn't mean it's going to be the dominant thing that organizations have out there, but it's going to be the pro dominant vendor offering in 2022. Now heading into 2021, we already had Cloudera, Databricks, Microsoft, Snowflake as proponents, in 2021, SAP, Oracle, and several of all of these fabric virtualization/mesh vendors joined the bandwagon. The promise is that you have one platform that manages your structured, unstructured and semi-structured information. And it addresses both the BI analytics needs and the data science needs. The real promise there is simplicity and lower cost. But I think end users have to answer a few questions. The first is, does your organization really have a center of data gravity or is the data highly distributed? Multiple data warehouses, multiple data lakes, on premises, cloud. If it's very distributed and you'd have difficulty consolidating and that's not really a goal for you, then maybe that single platform is unrealistic and not likely to add value to you. You know, also the fabric and virtualization vendors, the mesh idea, that's where if you have this highly distributed situation, that might be a better path forward. The second question, if you are looking at one of these lake house offerings, you are looking at consolidating, simplifying, bringing together to a single platform. You have to make sure that it meets both the warehouse need and the data lake need. So you have vendors like Databricks, Microsoft with Azure Synapse. New really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements, can meet the user and query concurrency requirements. Meet those tight SLS. And then on the other hand, you have the Oracle, SAP, Snowflake, the data warehouse folks coming into the data science world, and they have to prove that they can manage the unstructured information and meet the needs of the data scientists. I'm seeing a lot of the lake house offerings from the warehouse crowd, managing that unstructured information in columns and rows. And some of these vendors, Snowflake a particular is really relying on partners for the data science needs. So you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement. >> Thank you Doug. Well Tony, if those two worlds are going to come together, as Doug was saying, the analytics and the data science world, does it need to be some kind of semantic layer in between? I don't know. Where are you in on this topic? >> (chuckles) Oh, didn't we talk about data fabrics before? Common metadata layer (chuckles). Actually, I'm almost tempted to say let's declare victory and go home. And that this has actually been going on for a while. I actually agree with, you know, much of what Doug is saying there. Which is that, I mean I remember as far back as I think it was like 2014, I was doing a study. I was still at Ovum, (indistinct) Omdia, looking at all these specialized databases that were coming up and seeing that, you know, there's overlap at the edges. But yet, there was still going to be a reason at the time that you would have, let's say a document database for JSON, you'd have a relational database for transactions and for data warehouse and you had basically something at that time that resembles a dupe for what we consider your data life. Fast forward and the thing is what I was seeing at the time is that you were saying they sort of blending at the edges. That was saying like about five to six years ago. And the lake house is essentially on the current manifestation of that idea. There is a dichotomy in terms of, you know, it's the old argument, do we centralize this all you know in a single place or do we virtualize? And I think it's always going to be a union yeah and there's never going to be a single silver bullet. I do see that there are also going to be questions and these are points that Doug raised. That you know, what do you need for your performance there, or for your free performance characteristics? Do you need for instance high concurrency? You need the ability to do some very sophisticated joins, or is your requirement more to be able to distribute and distribute our processing is, you know, as far as possible to get, you know, to essentially do a kind of a brute force approach. All these approaches are valid based on the use case. I just see that essentially that the lake house is the culmination of it's nothing. It's a relatively new term introduced by Databricks a couple of years ago. This is the culmination of basically what's been a long time trend. And what we see in the cloud is that as we start seeing data warehouses as a check box items say, "Hey, we can basically source data in cloud storage, in S3, "Azure Blob Store, you know, whatever, "as long as it's in certain formats, "like, you know parquet or CSP or something like that." I see that as becoming kind of a checkbox item. So to that extent, I think that the lake house, depending on how you define is already reality. And in some cases, maybe new terminology, but not a whole heck of a lot new under the sun. >> Yeah. And Dave Menninger, I mean a lot of these, thank you Tony, but a lot of this is going to come down to, you know, vendor marketing, right? Some people just kind of co-op the term, we talked about you know, data mesh washing, what are your thoughts on this? (laughing) >> Yeah, so I used the term data platform earlier. And part of the reason I use that term is that it's more vendor neutral. We've tried to sort of stay out of the vendor terminology patenting world, right? Whether the term lake houses, what sticks or not, the concept is certainly going to stick. And we have some data to back it up. About a quarter of organizations that are using data lakes today, already incorporate data warehouse functionality into it. So they consider their data lake house and data warehouse one in the same, about a quarter of organizations, a little less, but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake. So it's pretty obvious that three quarters of organizations need to bring this stuff together, right? The need is there, the need is apparent. The technology is going to continue to converge. I like to talk about it, you know, you've got data lakes over here at one end, and I'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a server and you ignore it, right? That's not what a data lake is. So you've got data lake people over here and you've got database people over here, data warehouse people over here, database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities. So it's obvious that they're going to meet in the middle. I mean, I think it's like Tony says, I think we should declare victory and go home. >> As hell. So just a follow-up on that, so are you saying the specialized lake and the specialized warehouse, do they go away? I mean, Tony data mesh practitioners would say or advocates would say, well, they could all live. It's just a node on the mesh. But based on what Dave just said, are we gona see those all morphed together? >> Well, number one, as I was saying before, there's always going to be this sort of, you know, centrifugal force or this tug of war between do we centralize the data, do we virtualize? And the fact is I don't think that there's ever going to be any single answer. I think in terms of data mesh, data mesh has nothing to do with how you're physically implement the data. You could have a data mesh basically on a data warehouse. It's just that, you know, the difference being is that if we use the same physical data store, but everybody's logically you know, basically governing it differently, you know? Data mesh in space, it's not a technology, it's processes, it's governance process. So essentially, you know, I basically see that, you know, as I was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring, but there are going to be cases where, for instance, if I need, let's say like, Upserve, I need like high concurrency or something like that. There are certain things that I'm not going to be able to get efficiently get out of a data lake. And, you know, I'm doing a system where I'm just doing really brute forcing very fast file scanning and that type of thing. So I think there always will be some delineations, but I would agree with Dave and with Doug, that we are seeing basically a confluence of requirements that we need to essentially have basically either the element, you know, the ability of a data lake and the data warehouse, these need to come together, so I think. >> I think what we're likely to see is organizations look for a converge platform that can handle both sides for their center of data gravity, the mesh and the fabric virtualization vendors, they're all on board with the idea of this converged platform and they're saying, "Hey, we'll handle all the edge cases "of the stuff that isn't in that center of data gravity "but that is off distributed in a cloud "or at a remote location." So you can have that single platform for the center of your data and then bring in virtualization, mesh, what have you, for reaching out to the distributed data. >> As Dave basically said, people are happy when they virtualized data. >> I think we have at this point, but to Dave Menninger's point, they are converging, Snowflake has introduced support for unstructured data. So obviously literally splitting here. Now what Databricks is saying is that "aha, but it's easy to go from data lake to data warehouse "than it is from databases to data lake." So I think we're getting into semantics, but we're already seeing these two converge. >> So take somebody like AWS has got what? 15 data stores. Are they're going to 15 converge data stores? This is going to be interesting to watch. All right, guys, I'm going to go down and list do like a one, I'm going to one word each and you guys, each of the analyst, if you would just add a very brief sort of course correction for me. So Sanjeev, I mean, governance is going to to be... Maybe it's the dog that wags the tail now. I mean, it's coming to the fore, all this ransomware stuff, which you really didn't talk much about security, but what's the one word in your prediction that you would leave us with on governance? >> It's going to be mainstream. >> Mainstream. Okay. Tony Baer, mesh washing is what I wrote down. That's what we're going to see in 2022, a little reality check, you want to add to that? >> Reality check, 'cause I hope that no vendor jumps the shark and close they're offering a data niche product. >> Yeah, let's hope that doesn't happen. If they do, we're going to call them out. Carl, I mean, graph databases, thank you for sharing some high growth metrics. I know it's early days, but magic is what I took away from that, so magic database. >> Yeah, I would actually, I've said this to people too. I kind of look at it as a Swiss Army knife of data because you can pretty much do anything you want with it. That doesn't mean you should. I mean, there's definitely the case that if you're managing things that are in fixed schematic relationship, probably a relation database is a better choice. There are times when the document database is a better choice. It can handle those things, but maybe not. It may not be the best choice for that use case. But for a great many, especially with the new emerging use cases I listed, it's the best choice. >> Thank you. And Dave Menninger, thank you by the way, for bringing the data in, I like how you supported all your comments with some data points. But streaming data becomes the sort of default paradigm, if you will, what would you add? >> Yeah, I would say think fast, right? That's the world we live in, you got to think fast. >> Think fast, love it. And Brad Shimmin, love it. I mean, on the one hand I was saying, okay, great. I'm afraid I might get disrupted by one of these internet giants who are AI experts. I'm going to be able to buy instead of build AI. But then again, you know, I've got some real issues. There's a potential backlash there. So give us your bumper sticker. >> I'm would say, going with Dave, think fast and also think slow to talk about the book that everyone talks about. I would say really that this is all about trust, trust in the idea of automation and a transparent and visible AI across the enterprise. And verify, verify before you do anything. >> And then Doug Henschen, I mean, I think the trend is your friend here on this prediction with lake house is really becoming dominant. I liked the way you set up that notion of, you know, the data warehouse folks coming at it from the analytics perspective and then you get the data science worlds coming together. I still feel as though there's this piece in the middle that we're missing, but your, your final thoughts will give you the (indistinct). >> I think the idea of consolidation and simplification always prevails. That's why the appeal of a single platform is going to be there. We've already seen that with, you know, DoOP platforms and moving toward cloud, moving toward object storage and object storage, becoming really the common storage point for whether it's a lake or a warehouse. And that second point, I think ESG mandates are going to come in alongside GDPR and things like that to up the ante for good governance. >> Yeah, thank you for calling that out. Okay folks, hey that's all the time that we have here, your experience and depth of understanding on these key issues on data and data management really on point and they were on display today. I want to thank you for your contributions. Really appreciate your time. >> Enjoyed it. >> Thank you. >> Thanks for having me. >> In addition to this video, we're going to be making available transcripts of the discussion. We're going to do clips of this as well we're going to put them out on social media. I'll write this up and publish the discussion on wikibon.com and siliconangle.com. No doubt, several of the analysts on the panel will take the opportunity to publish written content, social commentary or both. I want to thank the power panelists and thanks for watching this special CUBE presentation. This is Dave Vellante, be well and we'll see you next time. (bright music)