(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)

>>Hello, everyone. Welcome to the cubes presentation of the 80 minutes startup showcase. Season two, episode two, the theme is data as code, the future of analytics. I'm John furry, your host. We had a great day lineup for you. Fast growing startups, great lineup of companies, founders, and stories around data as code. And we're going to kick it off here with our opening keynote with Rahul Pathak VP of analytics at AWS cube alumni. Right? We'll thank you for coming on and being the opening keynote for this awesome event. >>Yeah. And it's great to see you, and it's great to be part of this event, uh, excited to, um, to help showcase some of the great innovation that startups are doing on top of AWS. >>Yeah. We last spoke at AWS reinvent and, uh, a lot's happened there, service loss of serverless as the center of the, of the action, but all these start-ups rock set Dremio Cribble monks next Liccardo, a HANA imply all doing great stuff. Data as code has a lot of traction. So a lot of still momentum going on in the marketplace. Uh, pretty exciting. >>No, it's, uh, it's awesome. I mean, I think there's so much innovation happening and you know, the, the wonderful part of working with data is that the demand for services and products that help customers drive insight from data is just skyrocketing and has no sign of no sign of slowing down. And so it's a great time to be in the data business. >>It's interesting to see the theme of the show getting traction, because you start to see data being treated almost like how developers write software, taking things out of branches, working on them, putting them back in, uh, machine learnings, uh, getting iterated on you, seeing more models, being trained differently with better insights, action ones that all kind of like working like code. And this is a whole nother way. People are reinventing their businesses. This has been a big, huge wave. What's your reaction to that? >>Uh, I think it's spot on, I mean, I think the idea of data's code and bringing some of the repeatability of processes from software development into how people built it, applications is absolutely fundamental and especially so in machine learning where you need to think about the explainability of a model, what version of the world was it trained on? When you build a better model, you need to be able to explain and reproduce it. So I think your insights are spot on and these ideas are showing up in all stages of the data work flow from ingestion to analytics to I'm out >>This next way is about modernization and going to the next level with cloud-scale. Uh, thank you so much for coming on and being the keynote presenter here for this great event. Um, I'll let you take it away. Reinventing businesses, uh, with ads analytics, right? We'll take it away. >>Okay, perfect. Well, folks, we're going to talk about, uh, um, reinventing your business with, uh, data. And if you think about it, the first wave of reinvention was really driven by the cloud. As customers were able to really transform how they thought about technology and that's well on her way. Although if you stop and think about it, I think we're only about five to 10% of the way done in terms of it span being on the cloud. So lots of work to do there, but we're seeing another wave of reinvention, which is companies reinventing their businesses with data and really using data to transform what they're doing to look for new opportunities and look for ways to operate more efficiently. And I think the past couple of years of the pandemic, it really only accelerated that trend. And so what we're seeing is, uh, you know, it's really about the survival of the most informed folks for the best data are able to react more quickly to what's happening. >>Uh, we've seen customers being able to scale up if they're in, say the delivery business or scale down, if they were in the travel business at the beginning of all of this, and then using data to be able to find new opportunities and new ways to serve customers. And so it's really foundational and we're seeing this across the board. And so, um, you know, it's great to see the innovation that's happening to help customers make sense of all of this. And our customers are really looking at ways to put data to work. It's about making better decisions, finding new efficiencies and really finding new opportunities to succeed and scale. And, um, you know, when it comes to, uh, good examples of this FINRA is a great one. You may not have heard of them, but that the U S equities regulators, all trading that happens in equities, they keep track of they're look at about 250 billion records per day. >>Uh, the examiner, I was only EMR, which is our spark and Hadoop service, and they're processing 20 terabytes of data running across tens of thousands of nodes. And they're looking for fraud and bad actors in the market. So, um, you know, huge, uh, transformation journey for FINRA over the years of customer I've gotten to work with personally since really 2013 onward. So it's been amazing to see their journey, uh, Pinterest, not a great customer. I'm sure everyone's familiar with, but, um, you know, they're about visual search and discovery and commerce, and, um, they're able to scale their daily lot searches, um, really a factor of three X or more, uh, drive down their costs. And they're using the Amazon Opus search service. And really what we're trying to do at AWS is give our customers the most comprehensive set of services for the end-to-end journey around, uh, data from ingestion to analytics and machine learning. And we will want to provide a comprehensive set of capabilities for ingestion, cataloging analytics, and then machine learning. And all of these are things that our partners and the startups that are run on us have available to them to build on as they build and deliver value for their customers. >>And, you know, the way we think about this is we want customers to be able to modernize what they're doing and their infrastructure. And we provide services for that. It's about unifying data, wherever it lives, connecting it. So the customers can build a complete picture of their customers and business. And then it's about innovation and really using machine learning to bring all of this unified data, to bear on driving new innovation and new opportunities for customers. And what we're trying to do AWS is really provide a scalable and secure cloud platform that customers and partners can build on a unifying is about connecting data. And it's also about providing well-governed access to data. So one of the big trends that we see is customers looking for the ability to make self-service data available to that customer there and use. And the key to that is good foundational governance. >>Once you can define good access controls, you then are more comfortable setting data free. And, um, uh, the other part of it is, uh, data lakes play a huge role because you need to be able to think about structured and unstructured data. In fact, about 80% of the data being generated today, uh, is unstructured. And you want to be able to connect data that's in data lakes with data that's in purpose-built data stores, whether that's databases on AWS databases, outside SAS products, uh, as well as things like data warehouses and machine learning systems, but really connecting data as key. Uh, and then, uh, innovation, uh, how can we bring to bear? And we imagine all processes with new technologies like AI and machine learning, and AI is also key to unlocking a lot of the value that's in unstructured data. If you can figure out what's in an imagine the sentiment of audio and do that in real-time that lets you then personalize and dynamically tailor experiences, all of which are super important to getting an edge, um, in, uh, in the modern marketplace. And so at AWS, we, when we think about connecting the dots across sources of data, allowing customers to use data, lakes, databases, analytics, and machine learning, we want to provide a common catalog and governance and then use these to help drive new experiences for customers and their apps and their devices. And then this, you know, in an ideal world, we'll create a closed loop. So you create a new experience. You observe our customers interact with it, that generates more data, which is a data source that feeds into the system. >>And, uh, you know, on AWS, uh, thinking about a modern data strategy, uh, really at the core is a data lakes built on us three. And I'll talk more about that in a second. Then you've got services like Athena included, lake formation for managing that data, cataloging it and querying it in place. And then you have the ability to use the right tool for the right job. And so we're big believers in purpose-built services for data because that's where you can avoid compromising on performance functionality or scale. Uh, and then as I mentioned, unification and inter interconnecting, all of that data. So if you need to move data between these systems, uh, there's well-trodden pathways that allow you to do that, and then features built into services that enable that. >>And, um, you know, some of the core ideas that guide the work that we do, um, scalable data lakes at key, um, and you know, this is really about providing arbitrarily scalable high throughput systems. It's about open format data for future-proofing. Uh, then we talk about purpose-built systems at the best possible functionality, performance, and cost. Uh, and then from a serverless perspective, this has been another big trend for us. We announced a bunch of serverless services and reinvented the goal here is to really take away the need to manage infrastructure from customers. They can really focus about driving differentiated business value, integrated governance, and then machine learning pervasively, um, not just as an end product for data scientists, but also machine learning built into data, warehouses, visualization and a database. >>And so it's scalable data lakes. Uh, data three is really the foundation for this. One of our, um, original services that AWS really the backbone of so much of what we do, uh, really unmatched your ability, availability, and scale, a huge portfolio of analytics services, uh, both that we offer, but also that our partners and customers offer and really arbitrary skin. We've got individual customers and estimator in the expert range, many in the hundreds of petabytes. And that's just growing. You know, as I mentioned, we see roughly a 10 X increase in data volume every five years. So that's a exponential increase in data volumes, Uh, from a purpose-built perspective, it's the right tool for the right job, the red shift and data warehousing Athena for querying all your data. Uh, EMR is our managed sparking to do, uh, open search for log analytics and search, and then Kinesis and Amex care for CAFCA and streaming. And that's been another big trend is, uh, real time. Data has been exploding and customers wanting to make sense of that data in real time, uh, is another big deal. >>Uh, some examples of how we're able to achieve differentiated performance and purpose-built systems. So with Redshift, um, using managed storage and it's led us and since types, uh, the three X better price performance, and what's out there available to all our customers and partners in EMR, uh, with things like spark, we're able to deliver two X performance of open source with a hundred percent compatibility, uh, almost three X and Presto, uh, with on two, which is our, um, uh, new Silicon chips on AWS, better price performance, about 10 to 12% better price performance, and 20% lower costs. And then, uh, all compatible source. So drop your jobs, then have them run faster and cheaper. And that translates to customer benefits for better margins for partners, uh, from a serverless perspective, this is about simplifying operations, reducing total cost of ownership and freeing customers from the need to think about capacity management. If we invent, we, uh, announced serverless redshifts EMR, uh, serverless, uh, Kinesis and Kafka, um, and these are all game changes for customers in terms of freeing our customers and partners from having to think about infrastructure and allowing them to focus on data. >>And, um, you know, when it comes to several assumptions in analytics, we've really got a very full and complete set. So, uh, whether that's around data warehousing, big data processing streaming, or cataloging or governance or visualization, we want all of our customers to have an option to run something struggles as well as if they have specialized needs, uh, uh, instances are available as well. And so, uh, really providing a comprehensive deployment model, uh, based on the customer's use cases, uh, from a governance perspective, uh, you know, like information is about easy build and management of data lakes. Uh, and this is what enables data sharing and self service. And, um, you know, with you get very granular access controls. So rule level security, uh, simple data sharing, and you can tag data. So you can tag a group of analysts in the year when you can say those only have access to the new data that's been tagged with the new tags, and it allows you to very, scaleably provide different secure views onto the same data without having to make multiple copies, another big win for customers and partners, uh, support transactions on data lakes. >>So updates and deletes. And time-travel, uh, you know, John talked about data as code and with time travel, you can look at, um, querying on different versions of data. So that's, uh, a big enabler for those types of strategies. And with blue, you're able to connect data in multiple places. So, uh, whether that's accessing data on premises in other SAS providers or, uh, clouds, uh, as well as data that's on AWS and all of this is, uh, serverless and interconnected. And, um, and really it's about plugging all of your data into the AWS ecosystem and into our partner ecosystem. So this API is all available for integration as well, but then from an AML perspective, what we're really trying to do is bring machine learning closer to data. And so with our databases and warehouses and lakes and BI tools, um, you know, we've infused machine learning throughout our, by, um, the state of the art machine running that we offer through SageMaker. >>And so you've got a ML in Aurora and Neptune for broths. Uh, you can train machine learning models from SQL, directly from Redshift and a female. You can use free inference, and then QuickSight has built in forecasting built in natural language, querying all powered by machine learning, same with anomaly detection. And here are the ideas, you know, how can we up our systems get smarter at the surface, the right insights for our customers so that they don't have to always rely on smart people asking the right questions, um, and you know, uh, really it's about bringing data back together and making it available for innovation. And, uh, thank you very much. I appreciate your attention. >>Okay. Well done reinventing the business with AWS analytics rural. That was great. Thanks for walking through that. That was awesome. I have to ask you some questions on the end-to-end view of the data. That seems to be a theme serverless, uh, in there, uh, Mel integration. Um, but then you also mentioned picking the right tool for the job. So then you've got like all these things moving on, simplify it for me right now. So from a business standpoint, how do they modernize? What's the steps that the clients are taking with analytics, what's the best practice? How do they, what's the what's the high order bit here? >>Uh, so the basic hierarchy is, you know, historically legacy systems are rigid and inflexible, and they weren't really designed for the scale of modern data or the variety of it. And so what customers are finding is they're moving to the cloud. They're moving from legacy systems with punitive licensing into more flexible, more systems. And that allows them to really think about building a decoupled, scalable future proof architecture. And so you've got the ability to combine data lakes and databases and data warehouses and connect them using common KPIs and common data protection. And that sets you up to deal with arbitrary scale and arbitrary types. And it allows you to evolve as the future changes since it makes it easy to add in a new type of engine, as we invent a better one a few years from now. Uh, and then, uh, once you've kind of got your data in a cloud and interconnected in this way, you can now build complete pictures of what's going on. You can understand all your touch points with customers. You can understand your complete supply chain, and once you can build that complete picture of your business, you can start to use analytics and machine learning to find new opportunities. So, uh, think about modernizing, moving to the cloud, setting up for the future, connecting data end to end, and then figuring out how to use that to your advantage. >>I know as you mentioned, modern data strategy gives you the best of both worlds. And you've mentioned, um, briefly, I want to get a little bit more, uh, insight from you on this. You mentioned open, open formats. One of the themes that's come out of some of the interviews, these companies we're going to be hearing from today is open source. The role opens playing. Um, how do you see that integrating in? Because again, this is just like software, right? Open, uh, open source software, open source data. It seems to be a trend. What does open look like to you? How do you see that progressing? >>Uh, it's a great question. Uh, open operates on multiple dimensions, John, as you point out, there's open data formats. These are things like JSI and our care for analytics. This allows multiple engines tend to operate on data and it'll, it, it creates option value for customers. If you're going to data in an open format, you can use it with multiple technologies and that'll be future-proofed. You don't have to migrate your data. Now, if you're thinking about using a different technology. So that's one piece now that sort of software, um, also, um, really a big enabler for innovation and for customers. And you've got things like squat arc and Presto, which are popular. And I know some of the startups, um, you know, that we're talking about as part of the showcase and use these technologies, and this allows for really the world to contribute, to innovating and these engines and moving them forward together. And we're big believers in that we've got open source services. We contribute to open-source, we support open source projects, and that's another big part of what we do. And then there's open API is things like SQL or Python. Uh, again, uh, common ways of interacting with data that are broadly adopted. And this one, again, create standardization. It makes it easier for customers to inter-operate and be flexible. And so open is really present all the way through. And it's a big part, I think, of, uh, the present and the future. >>Yeah. It's going to be fun to watch and see how that grows. It seems to be a lot of traction there. I want to ask you about, um, the other comment I thought was cool. You had the architectural slides out there. One was data lakes built on S3, and you had a theme, the glue in lake formation kind of around S3. And then you had the constellation of, you know, Kinesis SageMaker and other things around it. And you said, you know, pick the tool for the right job. And then you had the other slide on the analytics at the center and you had Redshift and all the other, other, other services around it around serverless. So one was more about the data lake with Athena glue and lake formation. The other one's about serverless. Explain that a little bit more for me, because I'm trying to understand where that fits. I get the data lake piece. Okay. Athena glue and lake formation enables it, and then you can pick and choose what you need on the serverless side. What does analytics in the center mean? >>So the idea there is that really, we wanted to talk about the fact that if you zoom into the analytics use case within analytics, everything that we offer, uh, has a serverless option for our customers. So, um, you could look at the bucket of analytics across things like Redshift or EMR or Athena, or, um, glue and league permission. You have the option to use instances or containers, but also to just not worry about infrastructure and just think declaratively about the data that you want to. >>Oh, so basically you're saying the analytics is going serverless everywhere. Talking about volumes, you mentioned 10 X volumes. Um, what are other stats? Can you share in terms of volumes? What are people seeing velocity I've seen data warehouses can't move as fast as what we're seeing in the cloud with some of your customers and how they're using data. How does the volume and velocity community have any kind of other kind of insights into those numbers? >>Yeah, I mean, I think from a stats perspective, um, you know, take Redshift, for example, customers are processing. So reading and writing, um, multiple exabytes of data there across from each shift. And, uh, you know, one of the things that we've seen in, uh, as time has progressed as, as data volumes have gone up and did a tapes have exploded, uh, you've seen data warehouses get more flexible. So we've added things like the ability to put semi-structured data and arbitrary, nested data into Redshift. Uh, we've also seen the seamless integration of data warehouses and data lakes. So, um, actually Redshift was one of the first to enable a straightforward acquiring of data. That's sitting in locally and drives as well as feed and that's managed on a stream and, uh, you know, those trends will continue. I think you'll kind of continue to see this, um, need to query data wherever it lives and, um, and, uh, allow, uh, leaks and warehouses and purpose-built stores to interconnect. >>You know, one of the things I liked about your presentation was, you know, kind of had the theme of, you know, modernize, unify, innovate, um, and we've been covering a lot of companies that have been, I won't say stumbling, but like getting to the future, some go faster than others, but they all kind of get stuck in an area that seems to be the same spot. It's the silos, breaking down the silos and get in the data lakes and kind of blending that purpose built data store. And they get stuck there because they're so used to silos and their teams, and that's kind of holding back the machine learning side of it because the machine learning can't do its job if they don't have access to all the data. And that's where we're seeing machine learning kind of being this new iterative model where the models are coming in faster. And so the silo brake busting is an issue. So what's your take on this part of the equation? >>Uh, so there's a few things I plan it. So you're absolutely right. I think that transition from some old data to interconnected data is always straightforward and it operates on a number of levels. You want to have the right technology. So, um, you know, we enable things like queries that can span multiple stores. You want to have good governance, you can connect across multiple ones. Uh, then you need to be able to get data in and out of these things and blue plays that role. So there's that interconnection on the technical side, but the other piece is also, um, you know, you want to think through, um, organizationally, how do you organize, how do you define it once data when they share it? And one of the asylees for enabling that sharing and, um, think about, um, some of the processes that need to get put in place and create the right incentives in your company to enable that data sharing. And then the foundational piece is good guardrails. You know, it's, uh, it can be scary to open data up. And, uh, the key to that is to put good governance in place where you can ensure that data can be shared and distributed while remaining protected and adhering to the privacy and compliance and security regulations that you have for that. And once you can assert that level of protection, then you can set that data free. And that's when, uh, customers really start to see the benefits of connecting all of it together, >>Right? And then we have a batch of startups here on this episode that are doing a lot of different things. Uh, some have, you know, new lake new lakes are forming observability lakes. You have CQL innovation on the front end data, tiering innovation at the data tier side, just a ton of innovation around this new data as code. How do you see as executive at AWS? You're enabling all this, um, where's the action going? Where are the white spaces? Where are the opportunities as this architecture continues to grow, um, and get traction because of the relevance of machine learning and AI and the apps are embedding data in there now as code where's the opportunities for these startups and how can they continue to grow? >>Yeah, the, I mean, the opportunity is it's amazing, John, you know, we talked a little bit about this at the beginning, but the, there is no slow down insight for the volume of data that we're generating pretty much everything that we have, whether it's a watch or a phone or the systems that we interact with are generating data and, uh, you know, customers, uh, you know, we talk a lot about the things that'll stay the same over time. And so, you know, the data volumes will continue to go up. Customers are gonna want to keep analyzing that data to make sense of it. They're going to want to be able to do it faster and more cheaply than they were yesterday. And then we're going to want to be able to make decisions and innovate, uh, in a shorter cycle and run more experiments than they were able to do. >>And so I think as long as, and they're always going to want this data to be secure and well-protected, and so I think as long as we, and the startups that we work with can continue to push on making these things better. Can I deal with more data? Can I deal with it more cheaply? Can I make it easier to get insight? And can I maintain a super high bar in security investments in these areas will just be off. Um, because, uh, the demand side of this equation is just in a great place, given what we're seeing in terms of theater and the architect for forum. >>I also love your comment about, uh, ML integration being the last leg of the equation here or less likely the journey, but you've got that enablement of the AIP solves a lot of problems. People can see benefits from good machine learning and AI is creating opportunities. Um, and also you also have mentioned the end to end with security piece. So data and security are kind of going hand in hand these days, not just the governments and the compliance stuff we're talking about security. So machine learning integration kind of connects all of this. Um, what's it all mean for the customers, >>For customers. That means that with machine learning and really enabling themselves to use machine learning, to make sense of data, they're able to find patterns that can represent new opportunities, um, quicker than ever before. And they're able to do it, uh, dynamically. So, you know, in a prior version of the world, we'd have little bit of systems and they would be relatively rigid and then we'd have to improve them. Um, with machine learning, this can be dynamic and near real time and you can customize them. So, uh, that just represents an opportunity to deepen relationships with customers and create more value and to find more efficiency in how businesses are run. So that piece is there. Um, and you know, your ideas around, uh, data's code really come into play because machine learning needs to be repeatable and explainable. And that means versioning, uh, keeping track of everything that you've done from a code and data and learning and training perspective >>And data sets are updating the machine learning. You got data sets growing, they become code modules that can be reused and, uh, interrogated, um, security okay. Is a big as a big theme data, really important security is seen as one of our top use cases. Certainly now in this day and age, we're getting a lot of, a lot of breaches and hacks coming in, being defended. It brings up the open, brings up the data as code security is a good proxy for kind of where this is going. What's your what's take on that and your reaction to that. >>So I'm, I'm security. You can, we can never invest enough. And I think one of the things that we, um, you know, guide us in AWS is security, availability, durability sort of jobs, you know, 1, 2, 3, and, um, and it operates at multiple levels. You need to protect data and rest with encryption, good key management and good practices though. You need to protect data on the wire. You need to have a good sense of what data is allowed to be seen by whom. And then you need to keep track of who did what and be able to verify and come back and prove that, uh, you know, uh, only the things that were allowed to happen actually happened. And you can actually then use machine learning on top of all of this apparatus to say, uh, you know, can I detect things that are happening that shouldn't be happening in near real time so they could put a stop to them. So I don't think any of us can ever invest enough in securing and protecting my data and our systems, and it is really fundamental or adding customer trust and it's just good business. So I think it is absolutely crucial. And we think about it all the time and are always looking for ways to raise >>Well, I really appreciate you taking the time to give the keynote final word here for the folks watching a lot of these startups that are presenting, they're doing well. Business wise, they're being used by large enterprises and people buying their products and using their services for customers are implementing more and more of the hot startups products they're relevant. What's your advice to the customer out there as they go on this journey, this new data as code this new future of analytics, what's your recommendation. >>So for customers who are out there, uh, recommend you take a look at, um, what, uh, the startups on AWS are building. I think there's tremendous innovation and energy, uh, and, um, there's really great technology being built on top of a rock solid platform. And so I encourage customers thinking about it to lean forward, to think about new technology and to embrace, uh, move to the cloud suite, modernized, you know, build a single picture of our data and, and figure out how to innovate and when >>Well, thanks for coming on. Appreciate your keynote. Thanks for the insight. And thanks for the conversation. Let's hand it off to the show. Let the show begin. >>Thank you, John pleasure, as always.

>>And welcome to this special cube conversation. I'm John for a, your host of the cube. We're here in Palo Alto, California, and I'm here with a very special guest coming down from Seattle remotely into the cube studios is the leader at AWS Amazon web services, the vice president of database analytics and machine learning Swami. Great to see you cube alumni recently taking over the database business at AWS as a leader. Congratulations. And thanks for coming on the cube. >>Hey, my pleasure to be here, John, very excited to talk to you. >>Yeah. We've had many conversations on the cube and also in person and also online around all the major mega trends. You've had your hand in all the action, going back to your days when you were in school learning and, and writing papers. And 10 years ago, Amazon web services launched AWS dynamo, DB, fast, flexible, no SQL database that everyone loves today, which has inspired a generation of what I would call database distributing cloud scale, single digit millisecond performance at scale. And again, the key scale. And again, this is 10 years ago, so it seems like yesterday, but you guys are celebrating and your name was on the original paper with CTO Verner. Vogel's your celebrity. Congratulations. >>Thank you. Not sure about the celebrating part, but I'm very excited. At least I played a hand in building such an amazing technology that has enabled so many amazing customers along the way as well. So >>Trivia on the, on the paper as you were an intern at AWS, so you're getting your PhD. And then since, since rising through the ranks and involved in a lot of products over the years, and then leading the machine learning and AI, which is now changing the game at the industry level, but I got to ask you getting back to the story here. A lot of customers have built amazing things on top of dynamo DB, not to mention lots of other AWS and Amazon tech riding on it. Can you share some of the highlights that came out of the original paper? And so with some examples, because I think this is a point in time, 10 years ago, where you start to, so the KickUp of cloud scale, not just, just for developers and building startups, you're really starting to see the scale rise. >>Yeah, I actually, I mean, as you probably know, based on what he read to explain the Genesis of dynamo DB itself had to explain the Genesis of how Amazon got into building the original dynamo, right? And this was during the time when miner, I joined Ron esteem as an intern and, and Amazon was one of the pioneers in pushing the boundary of scale. And a year over year, our Q4 holiday season tends to be really, really bad for all the right reasons. We all want our holiday shopping done during that time. And you want to be able to scale your website, arters fulfillment centers, all of them at that time. And those are the times around 2005. And the answer is when people think our database, they think of a single database server that actually runs on a box and has a certain characteristics and does a scale and availability and whatnot. >>And it's usually relational. And then when we had a major disruption during Q4 that's when yeah, ask ourselves the question, why are we actually using a relational database for some of these things when they really didn't need the data model complexity of relational database. And normally I would say most companies where to actually ask an intern or a few engineers who are early in the career saying like, what the hell are you suggesting? Just go away. But Amazon being enabling Buddhists to build what they want. And they actually let us start reimagining what a database or our scale could look like. And that led to dynamo. And since she unstained mine, then we migrated from an traditional relational database stair this one for some of the amazon.com services. And then I moved on to actually start building some butts off our storage service and then our managed relational database service, I explicitly remember. >>And one of our customer advisory board, we're just the set off some of our leading customers who actually give us feedback on roadmap. Another son, Don, who's the CEO and chief geek of spunk bargain faker. And him actually looking at the Trinity me, I was starting in the corner and saying like you all, both tomorrow and why do I need to keep shotting my, my sequel database and reshooting assigned scaling. And this is the time when the state of the art in most databases were around. Like, you start sharding your relational database and constantly reshaping. And this is when most websites are starting to experience the kind of scale which we consider a normal month. During those times it was mostly, most companies used to have a single relational database backend and start scaling that way. And that conversation led entirely under duress, unaided read, lot of AWS leaders and myself saying like, Hey, what is a cloud database reimagined without the hampering SQL look like? And that led us to start building dynamo DB, but just a key value database at that time. Now we support document might've too, but that single digit millisecond latency at any scale imagine. So >>I think about that time at that time, 10 years ago, when you were having this conversation and I know the smug mug and I, he said, he's in totally geek and he's, he's good to point that out. You also have Netflix as customers too. I'd like to hear how that's evolved, but, but I think back at the time, if you look back then I got to ask you most people we've talked about this before. No one database rules, a world that's now standard people now don't see one database back then it was a one database kind of mindset back then. Yeah. And then you had that big data movement happening with Hadoop. You had the object store developing. So you're in you're you're circling around that area. What was it like then? I mean, take, take us through that because there was obvious visibility that, Hey, let's just store this. Now you see data lakes and that's all happening. But back then object store was kind of new. Yeah. >>Ah, it's a great question. Now, one of the things I realized early on, especially when I was working with binary, when you're saying amazon.com itself as an example, that the access patterns for various applications and Amazon, but let alone AWS customers tend to be very, very, very, some of them really just needed an object store. Some of them needed a relational database. Some of them really wanted a key value store within a fast latency. Some of them really needed a durable cash. And, but it so happens when you have a giant hammer. You use that for everything looks like a map, which is essentially the story at that time. And so everyone kept using the same database, irrespective of what the problem was because nobody else, I mean, thought about like, what else can we build that is better? So this let us do, literally I remember writing a paper with Bernard internally that is widely used in Amazon explaining what are all the menu of booklets that access. >>And then how do we go about actually solving for each of these things so that they can actually grow and innovate faster. And, and this was led to actually the Genesis of not only building IDs and so forth, but also dynamo and various other non-relational data. There's a still let alone not so storage access patterns and what not. So, and this was one of the big revelations he had just that there is not a single database that is going to meet the customer, needs us. The diversity of workloads in the internet is growing. And this was a key pivotal moment because with cloud now applications can scale very more instantly than before now. Building an application for Superbowl is very easier than before. That means that on, I mean, everybody is pushing the boundaries of what scale means, and they are expecting more from their obligations. That's when you need technologies like dynamo, DB, and that's exactly what dynamo already be set out to do. And since then, we are continuing to innovate on behalf of our customers and the purpose of the database story as well. And this concept has resonated well across the board. If you see that the database industry has also embraced this method, >>It's natural that you obviously evolved into the machine learning side of it because that's data is big part of that. And you see back then you, you bringing up kind of like flashes for me where it's like those, the data conversations back then and the data movement was just beginning. So the idea that you can have diversity in access methods of the kind of databases was a use case driven by the application, not so much database saying, this is how you have to work, that the script was flipped. It it's changed from infrastructure dictating to the applications, what to do. Now, the applications are going to the infrastructure and saying, give me what I want. I want to access something here in an office store, something here in no SQL that became the Genesis of infrastructure as code at a, at a global level. And so your paper kind of set the, the, the wave, the influence for this, no SQL did big data movement. It's created tons of value, maybe a third Mongo might've been influenced by this other people have been influenced. Can you share some stories of how people adopted the concept of dynamo DB and how that's changed in the industry and how has that helped the industry evolve? >>I mean, plus file data. Most share our experience of building and dynamo style data store. Very, it is a non-relational API and showing what are some of the experiences that the Venter in building such an paper and these set out early on itself, that it is should not be just a design paper, but it should be something that we shared our experiences. So even now, when I talked to my friends and colleagues and various other companies, one thing they always tell me is they appreciated the openness with which we were sharing. Some of the examples and learnings that we learned to not optimizing for percentile latencies, and what are some of the scalability challenges, how we solved and some of the techniques around things like sloppy Cora or various other stuff. We invented a lot of towns along the way too, but people really appreciated several of some of our findings and as talking about it. >>And since then I met so many other innovations are happening in the industry and the AWS, but also across the entire academia and industry in this space, the databases I've been going through what I call as a period of Renaissance, where one of the things, if you see our own arc, when Roger and I started on the database, front Disney started over the promo saying like, if you were to build a database where cloud is the new normal, this is again in 2008, we asked ourselves that question and what the belt that led us to start building things like dynamo, DB, RDS star. I know that alone, we reimagined data viruses with Redshift and several, and then several other databases like time stream for time series workloads started running Neptune for graph and whatnot. But at the moment we started actually asking that question and working backwards from customers. Then you will start being able to innovate accordingly. And this has worked really well. Then more than a hundred thousand AWS customers have chosen dynamo DB for mobile gaming tech IOT. Many of these are fast growing businesses, such as ledge, Darryl BNB, red fan, as soon as enterprises like Samsung Toyota, capital one and so far. So these are like really some meaningful clouds, let alone amazon.com. I run this. >>We have an internal customer is always good to have that entire inside customer. You know, I really find this a really profound use case because you're just talking, you know, in Amazonian terms, I'll just translate for the audience working backwards from the customer, which is the customer obsession you guys have. So here's, what's going on off the way I see it. You got dynamo, DB, paper, you and Verner, and the team Paul was a great as a great video on your blog posts that goes into the, to the talk he gave at around that time, which is fun to watch if you look back, but you have a radical enabler here, that's disrupting and changing S3 RDS, Aurora. These are game-changing concepts inside the, the landscape of AWS at the same time, you're working backwards from the customer. So the question I have for you as a leader and as a builder, how did you balance the working backwards from the customer while bringing something brand new and radical at that time to the market? >>Yeah, this is one of the S I mean hardest things to be, as leaders need to balance on. If you see many times, then we actually worked backwards from customers. The literal later translated this, literally do what customers are asking for, which is true nine out of 10 times, but there is one or a 10 times, you got to read between the lines on what they are asking. Because many times customers when are articulate that they need to go fast. If in the right way, they might say, Hey, I wish my heart storage goes faster, but they're not going to tell you they need a car, but you need to know and be able to translate and read between the lines we call it under the bucket of innovate on behalf of customers. And that is exactly the kind of a mantra we had when we were thinking about concepts like dynamo DB, because essentially at that time, almost everybody would, if I asked, they would just say, I wish a relational database could actually be able to scale from not just like a hundred gigabyte to one terabyte are, it can take up to like 2 million transactions, a second and so forth and still be cheap and made in reality as relational databases, the way they were engineered at that time, those are not going to meet the scale needs. >>So this is fair. We hunted read between the lines on what are some of the key Mustang needs from customers and then work backwards and then innovate on behalf of these workloads, be enabled by the sun oh four, which are some of the reasons that led to us launching some of the initial sets on dynamo on a single digit millisecond latency and seamless scale. At that time, databases didn't have the elasticity to go from like 10 requests, a second to like a hundred thousand or 1 million requests a second, and then scaled right back in an hour. So that was not possible. And we kind of enabled that. And that was an, a pretty big game changer that showed the elasticity of the cloud to a database. Well, >>Yeah, I think also just to, not to nerd out on this, but it enables a lot of other kind of cool scaled concepts, like queuing storage. It's all kind of together. This database piece of that you guys are solving. And again, props to you guys on the team. Congratulations. I have to ask, you know, more generally, how has your thinking changed since the paper? I'll see, you've got more experience under your belt. You don't yet have the gray hairs yet, but we'll see those soon come in, but you know, you're, you got a lot more experience. You're running teams, you're launching a lot of products. How has your thinking changed in the industry since the paper what's happening now? What's the big evolution. What are those new things now that are in the innovate on behalf of the customer? What's between the lines now, how do you see this happening? >>I mean, now since wanting dynamo via a victim, I had the opportunity to work on various problems in the big data space. There we've worked on some are fire things that you might be aware of in the analytics all the way from Redshift to quick side, too. Then I moved on to start some of our efforts, having built systems that enabled customer to store process and credit, and then analyze them. One of the realizations, I had this, the in around 2015 or 2016, I kinda had that machine learning was hitting a critical point where now it is ready for being scaled at option. Their cloud has basically enabled limitless compute and limitless storage, which are the factors that are holding back machine learning technology. Then I realized that now we have a unique opportunity to bring machine learning BI to everybody, not just folks with PhD in machine learning. >>And that's when I moved on from database and analytics areas, they started machine learning. We're just a descent area because machine learning is powered by data and then started building capabilities like SageMaker, which is our end to end ML platform to build, train and deploy them on models. And this, what does the leading enterprise platform by several gaggled users and then also a bunch of our AI services since then, I view the reason I'm giving all this historical context is one of the biggest realization I had early on itself. And 2016 as first machine learning is one of the most disruptive technologies. She will then country in our generation. This is right after cloud. I think these still are the most amazing combination that is going to revolutionize how we build applications and how we actually reason about that. Now, the second thing is that at the end of the day, when you look at the ANC and journey, it is not just about one database or one data Varroa. >>So one data lake product, or even 1:00 AM out platform. It is about the end to end journey where a customer is storing their order database. And then they are actually building a data lake that test customer history and order history. And they want to be able to personalize. And for their viewer experience are actually forecast what products to staff in their fulfillment center, but then all these things need to work and to handle. And that view is one of the big things that struck me for the past five years. And I've been on this journey in addition to building this Emma building blocks to connect the dots so that customers can go on this modern end to end data strategy as I call it, right. It goes beyond a single database technology or data technology, but putting now all of these end to end together so that customers don't end up spending six months connecting the dots, which has been the state of the down for the last couple of years. And we are bringing it down to matter of the Sundays. Now >>He's incredible Swami. Thank you so much for spending the time with us here in the, >>Yeah, my pleasure. Thanks again, Sean. Thanks for having me.

>> Narrator: From theCUBE Studios in Palo Alto, in Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> Tech spending is poised to rebound as the economy reopens in 2021. CIOs and IT buyers, they expect a 4% increase in 2021 spending based on ETR's latest surveys. And we believe that number will actually be higher, in the six to 7% range even. The big drivers are continued fine tuning of, and investment in digital strategies, for example, cloud security, AI data and automation. Application modernization initiatives continue to attract attention, and we also expect more support with work from home demand, for instance laptops, et cetera. And we're even seeing pent-up demand for data center infrastructure and other major risks to this scenario, they remain the pace of the reopening, of course, no surprise there, however, even if there are speed bumps to the vaccine rollout and achieving herd immunity, we believe tech spending will grow at least two points faster than GDP, which is currently forecast at 4.1%. Hello and welcome to this week's (indistinct) on Cube Insights powered by ETR. In this breaking analysis, we want to update you on our latest macro view of the market, and then highlight a few key sectors that we've been watching, namely cloud with a particular drill down on Microsoft and AWS, security, database, and then we'll look at Dell and VMware as a proxy for the data center. Now here's a look at what IT buyers and CIOs think. This chart shows the latest survey data from ETR and it compares the December results with the year earlier survey. Consistent with our earlier reporting, we see a kind of a swoosh-like recovery with a slower first half and accelerating in the second half. And we think that CIOs are being prudently conservative, 'cause if GDP grows at 4% plus, we fully expect tech spending to outperform. Now let's look at the factors that really drive some of our thinking on that. This is data that we've shown before it asks buyers if they're initiating any of the following strategies in the coming quarter, in the face of the pandemic and you can see there's no change in work from home, really no change in business travel, but hiring freezes, freezing new deployments, these continue to trend down. New deployments continue to be up, layoffs are trending down and hiring is also up. So these are all good signs. Now having said that, one part of our scenario assumes workers return and the current 75% of employees that work from home will moderate by the second half to around 35%. Now that's double the historical average, and that large percentage, that will necessitate continued work from home infrastructure spend, we think and drive HQ spending as well in the data center. Now the caveat of course is that lots of companies are downsizing corporate headquarters, so that could weigh on this dual investment premise that we have, but generally with the easy compare in these tailwinds, we expect solid growth in this coming year. Now, what sectors are showing growth? Well, the same big four that we've been talking about for 10 months, machine intelligence or AI/ML, RPA and broader automation agendas, these lead the pack along with containers and cloud. These four, you can see here above that red dotted line at 40%, that's a 40% net score which is a measure of spending momentum. Now cloud, it's the most impressive because what you see in this chart is spending momentum or net score in the vertical axis and market share or pervasiveness in the data center on the horizontal axis. Now cloud it stands out, as it's has a large market share and it's got spending velocity tied to it. So, I mean that is really impressive for that sector. Now, what we want to do here is do a quick update on the big three cloud revenue for 2020. And so we're looking back at 2020, and this really updates the chart that we showed last week at our CUBE on Cloud event, the only differences Azure, Microsoft reported and this chart shows IaaS estimates for the big three, we had had Microsoft Azure in Q4 at 6.8 billion, it came in at 6.9 billion based on our cloud model. Now the points we previously made on this chart, they stand out. AWS is the biggest, and it's growing more slowly but it throws off more absolute dollars, Azure grew 48% sent last quarter, we had it slightly lower and so we've adjusted that and that's incredible. And Azure continues to close that gap on AWS and we'll see how AWS and Google do when they report next week. We definitely think based on Microsoft result that AWS has upside to these numbers, especially given the Q4 push, year end, and the continued transition to cloud and even Google we think can benefit. Now what we want to do is take a closer look at Microsoft and AWS and drill down into those two cloud leaders. So take a look at this graphic, it shows ETR's survey data for net score across Microsoft's portfolio, and we've selected a couple of key areas. Virtually every sector is in the green and has forward momentum relative to the October survey. Power Automate, which is RPA, Teams is off the chart, Azure itself we've reported on that, is the linchpin of Microsoft's innovation strategy, serverless, AI analytics, containers, they all have over 60% net scores. Skype is the only dog and Microsoft is doing a fabulous job of transitioning its customers to Teams away from Skype. I think there are still people using Skype. Yes, I know it's crazy. Now let's take a look at the AWS portfolio drill down, there's a similar story here for Amazon and virtually all sectors are well into the 50% net scores or above. Yeah, it's lower than Microsoft, but still AWS, very, very large, so across the board strength for the company and it's impressive for a $45 billion cloud company. Only Chime is lagging behind AWS and maybe, maybe AWS needs a Teams-like version to migrate folks off of Chime. Although you do see it's an uptick there relative to the last survey, but still not burning the house down. Now let's take a look at security. It's a sector that we've highlighted for several quarters, and it's really undergoing massive change. This of course was accelerated by the work from home trend, and this chart ranks the CIO and CSO priorities for security, and here you see identity access management stands out. So this bodes well for the likes of Okta and SailPoint, of course endpoint security also ranks highly, and that's good news for a company like CrowdStrike or Forescout, Carbon Black, which was acquired by VMware. And you can see network security is right there as well, I mean, it's all kind of network security but Cisco, Palo Alto, Fortinet are some of the names that we follow closely there, and cloud security, Microsoft, Amazon and Zscaler also stands out. Now, what we want to do now is drill in a little bit and take a look at the vendor map for security. So this chart shows one of our favorite views, it's getting net score or spending momentum on the vertical axis and market share on the horizontal. Okta, note in the upper right of that little chart there that table, Okta remains the highest net score of all the players that we're showing here, SailPoint and CrowdStrike definitely looming large, Microsoft continues to be impressive because of its both presence, you can see that dot in the upper right there and it's momentum, and you know, for context, we've included some of the legacy names like RSA and McAfee and Symantec, you could see them in the red as is IBM, and then the rest of the pack, they're solidly in the green, we've said this before security remains a priority, it's a very strong market, CIOs and CSOs have to spend on it, they're accelerating that spending, and it's a fragmented space with lots of legitimate players, and it's undergoing a major change, and with the SolarWinds hack, it's on everyone's radar even more than we've seen with earlier high profile breaches, we have some other data that we'll share in the future, on that front, but in the interest of time, we'll press on here. Now, one of the other sectors that's undergoing significant changes, database. And so if you take a look at the latest survey data, so we're showing that same xy-view, the first thing that we call your attention to is Snowflake, and we've been reporting on this company for years now, and sharing ETR data for well over a year. The company continues to impress us with spending momentum, this last survey it increased from 75% last quarter to 83% in the latest survey. This is unbelievable because having now done this for quite some time, many, many quarters, these numbers are historically not sustainable and very rarely do you see that kind of increase from the mid-70s up into the '80s. So now AWS is the other big call out here. This is a company that has become a database powerhouse, and they've done that from a standing start and they've become a leader in the market. Google's momentum is also impressive, especially with it's technical chops, it gets very, very high marks for things like BigQuery, and so you can see it's got momentum, it does not have the presence in the market to the right, that for instance AWS and Microsoft have, and that brings me to Microsoft is also notable, because it's so large and look at the momentum, it's got very, very strong spending momentum as well, so look, this database market it's seeing dramatically different strategies. Take Amazon for example, it's all about the right tool for the right job, they get a lot of different data stores with specialized databases, for different use cases, Aurora for transaction processing, Redshift for analytics, I want a key value store, hey, some DynamoDB, graph database? You got little Neptune, document database? They've got that, they got time series database, so very, very granular portfolio. You got Oracle on the other end of the spectrum. It along with several others are converging capabilities and that's a big trend that we're seeing across the board, into, sometimes we call it a mono database instead of one database fits all. Now Microsoft's world kind of largely revolves around SQL and Azure SQL but it does offer other options. But the big difference between Microsoft and AWS is AWS' approach is really to maximize the granularity in the technical flexibility with fine-grained access to primitives and APIs, that's their philosophy, whereas Microsoft with synapse for example, they're willing to build that abstraction layer as a means of simplifying the experiences. AWS, they've been reluctant to do this, their approach favors optionality and their philosophy is as the market changes, that will give them the ability to move faster. Microsoft's philosophy favors really abstracting that complexity, now that adds overhead, but it does simplify, so these are two very interesting counter poised strategies that we're watching and we think there's room for both, they're just not necessarily one better than the other, it's just different philosophies and different approaches. Now Snowflake for its part is building a data cloud on top of AWS, Google and Azure, so it's another example of adding value by abstracting away the underlying infrastructure complexity and it obviously seems to be working well, albeit at a much smaller scale at this point. Now let's talk a little bit about some of the on-prem players, the legacy players, and we'll use Dell and VMware as proxies for these markets. So what we're showing here in this chart is Dell's net scores across select parts of its portfolio and it's a pretty nice picture for Dell, I mean everything, but Desktop is showing forward momentum relative to previous surveys, laptops continue to benefit from the remote worker trend, in fact, PCs actually grew this year if you saw our spot on Intel last week, PCs had peaked, PC volume at peaked in 2011 and it actually bumped up this year but it's not really, we don't think sustainable, but nonetheless it's been a godsend during the pandemic as data center infrastructure has been softer. Dell's cloud is up and that really comprises a bunch of infrastructure along with some services, so that's showing some strength that both, look at storage and server momentum, they seem to be picking up and this is really important because these two sectors have been lagging for Dell. But this data supports our pent-up demand premise for on-prem infrastructure, and we'll see if the ETR survey which is forward-looking translates into revenue growth for Dell and others like HPE. Now, what about Dell's favorite new toy over at VMware? Let's take a look at that picture for VMware, it's pretty solid. VMware cloud on AWS, we've been reporting on that for several quarters now, it's showing up in the ETR survey and it is well, it's somewhat moderating, it's coming down from very high spending momentum, so it's still, we think very positive. NSX momentum is coming back in the survey, I'm not sure what happened there, but it's been strong, VMware's on-prem cloud with VCF VMware Cloud Foundation, that's strong, Tanzu was a bit surprising because containers are very hot overall, so that's something we're watching, seems to be moderating, maybe the market says okay, you did great VMware, you're embracing containers, but Tanzu is maybe not the, we'll see, we'll see how that all plays out. I think it's the right strategy for VMware to embrace that container strategy, but we said remember, everybody said containers are going to kill VMware, well, VMware rightly, they've embraced cloud with VMware cloud on AWS, they're embracing containers. So we're seeing much more forward-thinking strategies and management philosophies. Carbon Black, that benefits from the security tailwind, and then the core infrastructure looks good, vSAN, vSphere and VDI. So the big thing that we're watching for VMware, is of course, who's going to be the next CEO. Is it going to be Zane Rowe, who's now the acting CEO? And of course he's been the CFO for years. Who's going to get that job? Will it be Sanjay Poonen? The choice I think is going to say much about the direction of VMware going forward in our view. Succeeding Pat Gelsinger is like, it's going to be like following Peyton Manning at QB, but this summer we expect Dell to spin out VMware or do some other kind of restructuring, and restructure both VMware and Dell's balance sheet, it wants to get both companies back to investment grade and it wants to set a new era in motion or it's going to set a new era in motion. Now that financial transaction, maybe it does call for a CFO in favor of such a move and can orchestrate such a move, but certainly Sanjay Poonen has been a loyal soldier and he's performed very well in his executive roles, not just at VMware, but previous roles, SAP and others. So my opinion there's no doubt he's ready and he's earned it, and with, of course with was no offense to Zane Rowe by the way, he's an outstanding executive too, but the big questions for Dell and VMware's what will the future of these two companies look like? They've dominated, VMware especially has dominated the data center for a decade plus, they're responding to cloud, and some of these new trends, they've made tons of acquisitions and Gelsinger has orchestrated TAM expansion. They still got to get through paying down the debt so they can really double down on an innovation agenda from an R&D perspective, that's been somewhat hamstrung and to their credit, they've done a great job of navigating through Dell's tendency to take VMware cash and restructure its business to go public, and now to restructure both companies to do the pivotal acquisition, et cetera, et cetera, et cetera and clean up it's corporate structure. So it's been a drag on VMware's ability to use its free cash flow for R&D, and again it's been very impressive what it's been able to accomplish there. On the Dell side of the house, it's R&D largely has gone to kind of new products, follow-on products and evolutionary kind of approach, and it would be nice to see Dell be able to really double down on the innovation agenda especially with the looming edge opportunity. Look R&D is the lifeblood of a tech company, and there's so many opportunities across the clouds and at The Edge we've talked this a lot, I haven't talked much about or any about IBM, we wrote a piece last year on IBM's innovation agenda, really hinges on its R&D. It seems to be continuing to favor dividends and stock buybacks, that makes it difficult for the company to really invest in its future and grow, its promised growth, Ginni Rometty promised growth, that never really happened, Arvind Krishna is now promising growth, hopefully it doesn't fall into the same pattern of missed promises, and my concern there is that R&D, you can't just flick a switch and pour money and get a fast return, it takes years to get that. (Dave chuckles) We talked about Intel last week, so similar things going on, but I digress. Look, these guys are going to require in my view, VMware, Dell, I'll put HPE in there, they're going to require organic investment to get back to growth, so we're watching these factors very, very closely. Okay, got to wrap up here, so we're seeing IT spending growth coming in as high as potentially 7% this year, and it's going to be powered by the same old culprits, cloud, AI, automation, we'll be doing an RPA update soon here, application modernization, and the new work paradigm that we think will force increased investments in digital initiatives. The doubling of the expectation of work from home is significant, and so we see this hybrid world, not just hybrid cloud but hybrid work from home and on-prem, this new digital world, and it's going to require investment in both cloud and on-prem, and we think that's going to lift both boats but cloud, clearly the big winner. And we're not by any means suggesting that their growth rates are going to somehow converge, they're not, cloud will continue to outpace on-prem by several hundred basis points, throughout the decade we think. And AWS and Microsoft are in the top division of that cloud bracket. Security markets are really shifting and we continue to like the momentum of companies in identity and endpoint and cloud security, especially the pure plays like CrowdStrike and Okta and SailPoint, and Zscaler and others that we've mentioned over the past several quarters, but CSOs tell us they want to work with the big guys too, because they trust them, especially Palo Alto networks, Cisco obviously in the mix, their security business continues to outperform the balance of Cisco's portfolio, and these companies, they have resources to withstand market shifts and we'll do a deeper drill down at the security soon and update you on other trends, on other companies in that space. Now the database world, it continues to heat up, I used to say on theCUBE all the time that decade and a half ago database was boring and now database is anything but, and thank you to cloud databases and especially Snowflake, it's data cloud vision, it's simplicity, we're seeing lots of different ways though, to skin the cat, and while there's disruption, we believe Oracle's position is solid because it owns Mission-Critical, that's its stronghold, and we really haven't seen those workloads migrate into the cloud, and frankly, I think it's going to be hard to rest those away from Oracle. Now, AWS and Microsoft, they continue to be the easy choice for a lot of their customers. Microsoft migrating its software state, AWS continues to innovate, we've got a lot of database choices, the right tool for the right job, so there's lots of innovation going on in databases beyond these names as well, and we'll continue to update you on these markets shortly. Now, lastly, it's quite notable how well some of the legacy names have navigated through COVID. Sure, they're not rocketing like many of the work-from-home stocks, but they've been able to thus far survive, and in the example of Dell and VMware, the portfolio diversity has been a blessing. The bottom line is the first half of 2021 seems to be shaping up as we expected, momentum for the strongest digital plays, low interest rates helping large established companies hang in there with strong balance sheets, and large customer bases. And what will be really interesting to see is what happens coming out of the pandemic. Will the rich get richer? Yeah, well we think so. But we see the legacy players adjusting their business models, embracing change in the market and steadily moving forward. And we see at least a dozen new players hitting the radar that could become leaders in the coming decade, and as always, we'll be highlighting many of those in our future episodes. Okay, that's it for now, listen, these episodes remember, they're all available as podcasts, all you got to do is search for Breaking Analysis Podcasts and you'll you'll get them so please listen, like them, if you like them, share them, really, I always appreciate that, I publish weekly on wikibon.com and siliconangle.com, and really would appreciate your comments and always do in my LinkedIn posts, or you can always DM me @dvellante or email me at david.vellante@siliconangle.com, and tell me what you think is happening out there. Don't forget to check out ETR+ for all the survey action, this is David Vellante, thanks for watching theCUBE Insights powered by ETR. Stay safe, we'll see you next time. (downbeat music)

>>from around the globe. It's the Cube with digital coverage of aws reinvent 2024 sponsored by Intel and AWS. Yeah. >>Welcome back here to our coverage here on the Cube of AWS reinvent 2020. It's now pleasure. Welcome. Sean. Vice to the program was the vice president of databases at AWS and Sean. Good day to you. How you doing, sir? >>I'm doing great. Thank you for having me. >>You bet. You bet. Thanks for carving out time. I know it was a very a busy couple of weeks for the A. W s team on DSO certainly was kicked off key notes today. We heard right away that there's some fairly significant announcements that I know certainly affect your world at AWS. Tell us a little bit about those announcements, and then we'll do a little deeper divers. You you go through >>sure, you know. And he made three big announcements this morning as it relates to databases, one of whom was around Aurora serverless V two on. Do you could just think of that as, uh um, no infrastructure whatsoever to manage and Aurora server list that can scale for, you know, from zero to hundreds of thousands of transactions in a fraction of a second, literally with no infrastructure to manage. So it's a really easy way to build applications in the cloud. Eso excited about that? Another big announcement WAAS related to a lot of our customers today are really they're using the right tool for the right job. In other words, they're not trying toe GM all of their data into one database management systems. They're breaking app down into smaller parts. They pick the right tool for the right job. And with that context, we announce glue elastic views, which just allows you to very easily write a sequel. Query most. There's a lot of developers that understand sequel. So if I could easily write a sequel query to reach out to the source databases and then materialize, um, that data into a different target, Um, that's a really simple way toe. Build new customer experiences and make the most of the databases you have. Aan den. The third big announcement remained today was called Babble Eso Babel. Babel Fish is really a a compatibility or a sequel server compatibility layer on Aurora post grass. So if you have ah sequel server application. You've been trying to migrate it to post grass, and you've been wishing for an easier way to get that done. Babel Fish allows you to take your T sequel or your Microsoft sequel server application connected to post grass. Using your same client drivers with little to no code change eso That's a big deal for those that are trying to migrate from commercial systems to open source. And then finally, we didn't stop there as we thought about Babel, Um, and talked to a lot of customers about it. We actually are open sourcing the technology, so it will be available later in 21. All the development will be done open transparently hosted on get hub and licensed under Apache 20 so those that's kind of one lap around the track, if you will, of the big announcements from today How big >>the open source announcement to me. I mean, that's fairly significant that that you're opening up this new opportunity thio the entire community, um, that you're willing to open it up, and I'm sure you're gonna have you know, I mean, this is this is gonna be I would imagine Ah, very popular destination for a lot of folks. >>Yeah, I think so, too. You know, I'm I'm personally, I'm a believer that every customer can use data to build a foundation for future innovation. And to me, a lot of things start and end with data. As we know, data really is a foundational component of at a swell A systems and, you know, and you know, what we found is not every customer can plan for every contingency that happens. But what they can do is build a strong foundation. So, you know, and with a strong foundation, you really stand the best chance to overcome whatever that next unexpected thing is or innovate new ways. And with that is a backdrop. We think this open source piece is a big deal. Why? I'll tell you, you know, it's just us right now. But if I told you the story behind the story, I have met so many customers over the last few years that you know, John, if you and I were sitting down with them, it kind of sounds like this. You sit down, you talk to somebody and they'll say things like, Hey, I've built, you know, we've built years and years and years of application development against sequel server. We really don't like the punitive commercial licensing and, you know, we're trying to get over Thio open source, but we need an easier way and, you know, and we thought about that long and hard and, you know, we came up with the team, came up with a wonderful solution for this, But to tell you the truth, as we were building Babel fish and talking to customers, what became really clear with the community enterprises in I S V s and s eyes is they all basically said, Hey, if there was a way where we could go and extend this, um for, you know, like it could be Boy, if this thing supported to more features, that would be awesome. But if it was open source, that would be even better, because then we could we could take things under our own control so that that's what truly motivated this decision to go open source and based on conversations we've had in the decisions we made, we actually think it's it's really big. It's really big for everybody who has been trying to move off of commercial systems and over toe open source. You. >>Let's talk about transforming your kind of your database mindset in general right now from a client's perspective, especially for somebody who was considering, you know, substantial moves, you know, a major reconfigurations off their processes. What's the process that you go through with them to evaluate their needs, to evaluate their capabilities, to evaluate their storage? All that, you know, that comes into play here and help them to get thio kind of the end of the rainbow >>because it z absolutely, you know, so it really depends on who you're talking Thio and no, at this stage of the game, the clouds been around now for 10, 14 years. I think it is something in that range, you know? So a lot of the early cloud adopters, you know, they've been here and they've been building in a certain way. Um and you know, you and I know early cloud adopters by way of watching streaming media, ordering rideshare, taking a selfie, you know, and you know, we have these great application experiences and we expect them to work all the time at Super Low. Leighton See, they should always be available. So you know, the single biggest thing we learned from Early Cloud builders was there's no such thing as one size football. There's one thing doesn't fit anything at all. Um, that's kind of the way data was, you know, 20 years ago. But today, if you take the learning from these early cloud builders, the journey that we go on with, let's say a mid to late stage cloud a doctor. We're all excited on, you know, sort of. If they can start now today, where Early Cloud Wilders have done a bunch of pioneering, they get excited. So So what happens is, um, there's usually to kind of conversations. One is how do we you know, we've got all these databases that we self managed on premise. How do we bring those into the cloud? And then how do we stop doing undifferentiated heavy lifting? In other words, what they're saying is, we don't want to do patching and back up and monitoring that Z instead, our precious resources should be working on innovations for the business. So in that context, you and I would end up talking to somebody about moving to fully managed services like an already s, for example, um and then the other conversation we have with customers is is the one about breaking free, which is hey, a burn on commercial. I wanna move for open source. And in that context, there are a lot of customers today that they'll move to the cloud. And then and then when they get there as a first step, their second step is to is to migrate over toe open source. And then that third piece is folks that are trying to build for the cloud, these modern APS. And in that context, they follow the playbook of these early cloud builders, which is what you take this big app. You break it into smaller parts and then they pick the right tool for the right job. So that's that's kind of the conversation that we go through there. And finally, what I would say is, most customers say that they'll say to me, What do you mean by picking the right tool for the right job? And the mindset is very different than the one that we all grew up in from 20 years ago. 20 years ago, you just bought a database platform. And then whatever the business was trying to do, you you you would try to support that access pattern on on that database choice. But today, the new world that we live in, it really is. Let's start with the business use case first, understand the access pattern and then pick the best optimized database storage for that. So that's that's kind of how those conversations go. >>You've got what, 15, 14, 15 different data based instruments, you know, like in your tool chest? Um, how how is that evolution occurred? Um because I'm sure, you know one, but got another big at another big at another, looking at different capabilities, different needs. So I mean, >>kind of walked me >>through that a little bit and how you've gotten to the point that you've got 15 >>Tonto eso. So one of the things that you know I'd start off with here, like the question is, Well, if there's 15 today, is there gonna be 100 tomorrow? The real answer is, I don't know, you know, And but what I do know is there's really a handful of categories around data models and access patterns that if you will kind of fill out the portfolio if you will. Um, the first one is around relation. Also, relational databases have been around for a long time. It has a certain set of characteristics that people have come to appreciate and understand and, you know, and we provide a set of services that provide fully managed relational services. Let it be for things like Oracle or sequel, server or open source, like Maria DB or my sequel or Post Press and even Aurora, which provides commercial grade performance availability and scale it about 1/10 the cost of commercial. So you know, there's a handful of different services in that context. But there's new services in this key value. And think of a key value access pattern along the lines of you. Imagine. We order you order a ride share and you're trying to track a vehicle every second. So on your phone you can see it moving across your phone. And now imagine if you were building that at our a million people going to do that all at the same time or 10. So in that kind of access pattern, a product like dynamodb is excellent because It's designed for basically unlimited scale, really high throughput. So developer doesn't have toe really worry about a million people. 10 million people are one. This thing can just scale inevitably. Yeah, it's just not an issue. And, you know, I'll give you one other example like, um, in Neptune, which is a graph database. So you and I would know graph databases by way of seeing a product recommendation, for example, Um, and you know, grab the beauty of a graph databases. It's optimized for highly connected data. In other words, as a developer, I can what I can do with a few lines of code and a graph database because it's optimized for all these different relationships. I might try to do that in a different system that I might write 1500 lines of codes and because it was never designed for something like highly connect the data like graph. So that's kind of the evolution of how things there's just these different categories that have to do with access patterns and data models. And our strategy is simple. In each category, we wanna have the very best AP is available for our customers. Let's >>talk about security here for a moment because you have, you know, these just these tremendous reservoirs now, right that you've built up in capabilities got, you know, new data centers going up every day. It seems like around around the country and around the world, security or securing data nevermore important on dnep ver mawr, I guess on the radar of the bad actors to at the same time because of the value of that data. So just if you would paint the picture in terms of security awareness three encryption devices that you're now deploying the stuff that's keeping you up at night, I would think probably falls into this category a little bit. Eso Let's just take it on security and the level of concern. And then what you at a w s are doing about that? >>Yeah. So, you know, when I talked to customers, I always remind people security is a shared responsibility on De So Amazon's piece of that is the infrastructure that we build the processes that we have, you know, from how people you know can enter a building toe, what they can do in an environment. The auditing to the encryption systems that rebuild. Um, there's there's three infrastructure responsibility, which, you know, we think about every second of every day. Um, Andi, it's, you know, yes, it's one of those things that keeps you up at night. But you have to kind of have this level of paranoia, if you will. There's bad actors everywhere. And, you know, that mindset is kind of, you know, kind of helps you stay focused on Ben. There's the customers responsibility to in in terms of how they think about security. So, you know, um and what that means is, uh, you know, best practices around how they how they integrate identity and access management into their solution. Um, you know how they use how they rotate encryption keys, how they apply encryption and all the safeguards that you would expect the customer do so together, you know, we work with our customers to ensure that our systems are are secure. Um, and the only other thing that I would add to this is that, you know, kind of in the old world. And I keep bringing up the old world because security in the old world was sort of one of those things. Like if you go back 20 years ago. You know, security sometimes is one of those things that you think about a little bit later in the cycle. And I've met a lot of customers that tryto bolt on security and it never works. It's just hard to just bolt it into an app. But the really nice thing about thes fully managed services in the cloud they have security built right in. So security, performance and availability is built right into these fully managed A p I s eso customer doesn't have to think about Well, how do I add this capability onto it? You know, in some sense, it could be a simple is turning a feature on or something like encryption being turned on by default, and they don't have to do anything. So, you know, there it's just a completely different world that we live in today, and we try to improve it every second of every day. >>Well, Sean, it's nice to know that you're experiencing the paranoia for all your customers. That Zaveri very gracious yesterday There. Hey, thanks for the time. I appreciate it. I know you're very busy the next couple of weeks with the number of leadership sessions and intermediate sessions as well with AWS reinvent. So thanks again for carving a little bit of time for us here today on the Cube. >>You bet, John. Thank you. I really appreciate it. >>Take care.

(futuristic electronic music) >> Live from Las Vegas, its theCUBE covering AWS re:Invent 2018. Brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> Hey welcome back everyone. Live here in Las Vegas with AWS, Amazon Web Services, re:Invent 2018's CUBE coverage. Two sets, wall-to-wall coverage here on the ground floor. I'm here with Dave Vellante. Dave, six years we've been coming to re:Invent. Every year except for the first year. What a progression. We got great news. Always raising the bar, as they say at Amazon. This year, big announcements. One of them is blockchain. Really kind of laying out early formation of how they're going to roll out, thinking about blockchain. We're here to talk about here, with Rahul Pathak, who's the GM of analytics, and data lakes, and blockchain. Managing that. And Shawn Bice who's the vice president of non-relational databases. Guys, welcome to theCUBE. >> Thank you. >> Thank you, it's great to be here. >> I wish my voice was a little bit stronger. I love this segment. You know, we've been doing blockchain. We've been following one of the big events in the industry. If you separate out the whole token ICO scam situation, token economics is actually a great business model opportunity. Blockchain is an infrastructure, a decentralized infrastructure, that's great. But it's early. Day one really for you guys in a literal sense. How are you guys doing blockchain? Take a minute to explain the announcement because there are use cases, low-hanging use cases, that look a lot like IoT and supply chain that people are interested in. So take a minute to explain the announcements and what it means. >> Absolutely, so when we began looking at blockchain and blockchain use cases, we really realized there are two things that customers are trying to do. One case is really keep an immutable record of transactions and in a scenario where centralized trust is okay. And for that we have Amazon QLDB, which is an immutable cryptographically verifiable ledger. And then in scenarios where customers really wanted the decentralized trust and the smart contracts, that's where blockchain frameworks like Hyperledger Fabric and Ethereum play a role. But they're just super complicated to use and that's why we built Managed Blockchain, to make it easy to stand up, scale, and monitor these networks, so customers can focus on building applications. And in terms of use cases on the decentralized side, it's really quite diverse. I mean, we've got a customer, Guardian Life Insurance, so they're looking at Managed Blockchain 'cause they have this distributed network of partners, providers, patients, and customers, and they want to provide decentralized verifiable records of what's taking place. And it's just a broad set of use cases. >> And then we saw in the video this morning, I think it was Indonesian farmers, right? Wasn't that before the keynote? Did you see that? It was good. >> I missed that one. >> Yeah, so they don't have bank accounts. >> Oh, got it. >> And they got a reward system, so they're using the blockchain to reward farmers to participate. >> So a lot of people ask the question is, why do I need blockchain? Why don't you just put in database? So there are unique, which is true by the way, 'cause latency's an issue. (chuckles) Certainly, you might want to avoid blockchain in the short term, until that gets fixed. Assume that every one will get fixed over time, but what are some of the use cases where blockchain actually is relevant? Can you be specific because that's really people starting to make their selection criteria on. Look, I still use a database. I'm going to have all kinds of token and models around, but in a database. Where is the blockchain specifically resonating right now? >> I'll take a shot at this or we can do it together, but when you think of QLDB, it's not that customers are asking us for a ledger database. What they were really saying is, hey, we'd like to have this complete immutable, cryptographically verifiable trail of data. And it wasn't necessarily a blockchain conversation, wasn't necessarily a database conversation, it was like, I really would like to have this complete cyrptographic verifiable trail of data. And it turns out, as you sort of look at the use cases, in particular, the centralized trust scenario, QLDB does exactly that. It's not about decentralized trust. It's really about simply being able to have a database that when you write to that database, you write a transaction to the database, you can't change it. You know, a typical database people are like, well, hey, wait a second, what does immutable really mean? And once you get people to understand that once that transaction is written to a journal, it cannot be changed at all and attached, then all of a sudden there's that breakthrough moment of it being immutable and having that cryptographic trail. >> And the advantage relative to a distributive blockchain is performance, scale, and all the challenges that people always say. >> Yeah, exactly. Like with QLDB, you can find it's going to be two to three times faster cause you're not doing that distributing consensus. >> How about data lakes? Let's talk about data lakes. What problem were you guys trying to solve with the data lakes? There's a lot of them, but. (chuckles) >> That's a great question. So, essentially it's been hard for customers to set up data lakes 'cause you have to figure out where to get data from, you have to land it in S3, you've got to secure it, you've then got to secure every analytic service that you've got, you might have to clean your data. So with lake formation, what we're trying to do is make it super easy to set up data lakes. So we have blueprints for common databases and data sources. We bring that data into an S3 data lake and we've created a central catalog for that data where customers can define granular access policies with the table, and the column, and the row level. We've also got ML-based data cleansing and data deduplication. And so now customers can just use lake formation, set up data lakes, curate their data, protect it in a single place, and have those policies that enforce across all of the analytic services that they might use. >> So does it help solve the data swamp problem, get more value out of the data lake? And if so, how? >> Absolutely, so the way it does that is by automatically cataloging all datas that comes in. So we can recognize what the data is and then we allow customers to add business metadata to that so they can tag this as customer data, or PII data, or this is my table of sales history. And that then becomes searchable. So we automatically generate a catalog as data comes in and that addresses the, what do I have in my data lake problem. >> Okay, so-- >> Go ahead. >> So, Rahul, you're the general manager. Shawn, what's your job, what do you do? >> So our team builds all the non-relational databases at Amazon. So DynamoDB, Neptune, ElastiCache, Timestream, which you'll hear about today, QLDB, et cetera. So all those things-- >> Beanstalk too, Elastic Beanstalk? >> No we do not build Beanstalk. >> Okay, we're a customer of DynamoDB, by the way. >> Great! >> We're happy customers. >> That's great! >> And we use ElastiCache, right? >> Yup, the elastic >> There you go! >> surge still has it. >> So-- >> Haven't used Neptune yet. >> What's the biggest problem stigmas that you guys are trying to raise the bar on? What's the key focus as you get this new worlds and use cases coming together? These are new use cases. How are you guys evaluating it? How are you guys raising the bar? >> You know, that's a really good question you ask. What I've found in my experience is developers that have been building apps for a long time, most people are familiar with relational databases. For years we've been building apps in that context, but when you kind of look at how people are building apps today, it's very different than how they did in the past. Today developer do what they do best. They take an application, a big application, break it down into smaller parts, and they pick the right tool for the right job. >> I think the game developer mark is going to be a canary in the coal mine for developers, and it's a good spot for data formation in these kind of unstructured, non-relational scenarios. Okay, now all this engagement data, could be first person shooter, whatever it is, just throw it, I need to throw it somewhere, and I'll get to it and let it be ready to be worked on by analytics. >> Well, yeah, if you think about that gamer scenario, think about if you and I are building a game, who knows if there's going to be one user, ten players, or 10 million, or 100 million. And if we had 100 million, it's all about the performance being steady. At 100 million or ten. >> You need a fleet of servers. (John laughing) >> And a fleet of servers! >> Have you guys played Fortnite? Or do you have kids that play? >> I look over my kid's shoulder. I might play it. >> I've played, but-- >> They run all their analytics on us. They've got about 14 petabytes in S3 using S3 as their data lake, with EMR and Athena for analytics. >> We got a season-- >> I mean, think about that F1 example on keynotes today. Great example of insights. We apply that kind of concept to Fortnite, by the way, Fortnite has theCUBE in there. It's always a popular term. We noticed that, the hastag, #wherestheCUBEtoday. (Rahul chuckling) I couldn't resist. But the analytics you could get out of all that data, every interaction, all that gesture data. I mean, what are some of the things they're doing? Can you share how they're using the new tech to scale up and get these insights? >> Yeah, absolutely. So they're doing a bunch of things. I mean, one is just the health of the systems when you've got hundreds of millions of players. You need to know if you're up and it's working. The second is around engagement. What games, what collection of people work well together. And then it's what incentives they create in the game, what power ups people buy that lead to continued engagement, 'cause that defines success over the long term. What gets people coming back? And then they have an offline analytics process where they're looking at reporting, and history, and telemetry, so it's very comprehensive. So you're exactly right about gaming and analytics being a huge consumer of databases. >> Now, Shawn, didn't you guys have hard news today on DynamoDB, or? >> Yeah today we announce DynamoDB On-Demand, so customers that basically have workloads that could spike up and then all of a sudden drop off, a lot of these customers basically don't even want to think about capacity planning. They don't want to guess. They just want to basically pay only for what they're using. So we announced DynamoDB On-Demand. The developer experience is simple. You create a table and you putyour read/write capacity in the on-demand mode, and you literally only pay for the request that your workload puts through system. >> It's a great service actually. Again, making life easier for customers. Lower the bill, manage capacity, make things go better, faster, enables value. >> It's all about improving the customer experience. >> Alright, guys, I really appreciate you coming in. I'm really interested in following what you guys do in the future. I'm sure a lot of people watching will be as well, as analytics and AI become a real part of, as you guys move the stack and create that API model for, what you did for infrastructure, for apps. A total game changer, we believe. We're interested in following you guys, I'm sure others are. Where are you going to be this year? What's your focus? Where can people find out more besides going to Amazon site? Is there certain events you're going to be at? How do people get more information and what's the plans? >> There's actually some sessions on lake formation, blockchain that we're doing here. We'll have a continuous stream of summits, so as the AWS Summit calendar for 2019 gets published that's a great place to go for more information. And then just engage with us either on social media or through the web and we'll be happy to follow up. >> Alright, well, we'll do a good job on amplifying. A lot of people are interested, certainly blockchain, super hot. But people want better, stronger, more stable, but they want the decentralized immutable database model. >> Cryptographically verifiable! >> And see as everyone knows. >> Scalable! >> Anyone who wants to keep those, they talk about CUBE coins but I haven't said CUBE coin once on this episode. Wait for those tokens to be released soon. More coverage after this short break, stay with us. I'm John Furrier, and Dave Vellante, we'll be right back. 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>> Live from New York City it's theCube. Covering Lenovo Transform 2.0. Brought to you by Lenovo. >> Welcome back to theCube's live coverage of Lenovo Transform, I'm your host Rebecca Knight along with my co-host Stu Miniman. We're joined by Madhu Matta; He is the VP and GM High Performance Computing and Artificial Intelligence at Lenovo and Dr. Daniel Gruner the CTO of SciNet at University of Toronto. Thanks so much for coming on the show gentlemen. >> Thank you for having us. >> Our pleasure. >> So, before the cameras were rolling, you were talking about the Lenovo mission in this area to use the power of supercomputing to help solve some of society's most pressing challenges; and that is climate change, and curing cancer. Can you talk a little bit, tell our viewers a little bit about what you do and how you see your mission. >> Yeah so, our tagline is basically, Solving humanity's greatest challenges. We're also now the number one supercomputer provider in the world as measured by the rankings of the top 500 and that comes with a lot of responsibility. One, we take that responsibility very seriously, but more importantly, we work with some of the largest research institutions, universities all over the world as they do research, and it's amazing research. Whether it's particle physics, like you saw this morning, whether it's cancer research, whether it's climate modeling. I mean, we are sitting here in New York City and our headquarters is in Raleigh, right in the path of Hurricane Florence, so the ability to predict the next anomaly, the ability to predict the next hurricane is absolutely critical to get early warning signs and a lot of survival depends on that. So we work with these institutions jointly to develop custom solutions to ensure that all this research one it's powered and second to works seamlessly, and all their researchers have access to this infrastructure twenty-four seven. >> So Danny, tell us a little bit about SciNet, too. Tell us what you do, and then I want to hear how you work together. >> And, no relation with Skynet, I've been assured? Right? >> No. Not at all. It is also no relationship with another network that's called the same, but, it doesn't matter. SciNet is an organization that's basically the University of Toronto and the associated research hospitals, and we happen to run Canada's largest supercomputer. We're one of a number of computer sites around Canada that are tasked with providing resources and support, support is the most important, to academia in Canada. So, all academics, from all the different universities, in the country, they come and use our systems. From the University of Toronto, they can also go and use the other systems, it doesn't matter. Our mission is, as I said, we provide a system or a number of systems, we run them, but we really are about helping the researchers do their research. We're all scientists. All the guys that work with me, we're all scientists initially. We turned to computers because that was the way we do the research. You can not do astrophysics other than computationally, observationally and computationally, but nothing else. Climate science is the same story, you have so much data and so much modeling to do that you need a very large computer and, of course, very good algorithms and very careful physics modeling for an extremely complex system, but ultimately it needs a lot of horsepower to be able to even do a single simulation. So, what I was showing with Madhu at that booth earlier was results of a simulation that was done just prior us going into production with our Lenovo system where people were doing ocean circulation calculations. The ocean is obviously part of the big Earth system, which is part of the climate system as well. But, they took a small patch of the ocean, a few kilometers in size in each direction, but did it at very, very high resolution, even vertically going down to the bottom of the ocean so that the topography of the ocean floor can be taken into account. That allows you to see at a much smaller scale the onset of tides, the onset of micro-tides that allow water to mix, the cold water from the bottom and the hot water from the top; The mixing of nutrients, how life goes on, the whole cycle. It's super important. Now that, of course, gets coupled with the atmosphere and with the ice and with the radiation from the sun and all that stuff. That calculation was run by a group from, the main guy was from JPL in California, and he was running on 48,000 cores. Single runs at 48,000 cores for about two- to three-weeks and produced a petabyte of data, which is still being analyzed. That's the kind of resolution that's been enabled... >> Scale. >> It gives it a sense of just exactly... >> That's the scale. >> By a system the size of the one we have. It was not possible to do that in Canada before this system. >> I tell you both, when I lived on the vendor side and as an analyst, talking to labs and universities, you love geeking out. Because first of all, you always have a need for newer, faster things because the example you just gave is like, "Oh wait." "If I can get the next generation chipset." "If the networking can be improved." You know you can take that petabyte of data and process it so much faster. >> If I could only get more money to buy a bigger one. >> We've talked to the people at CERN and JPL and things like that. - Yeah. >> And it's like this is where most companies are it's like, yeah it's a little bit better, and it might make things a little better and make things nice, but no, this is critical to move along the research. So talk a little bit more about the infrastructure and what you look for and how that connects to the research and how you help close that gap over time. >> Before you go, I just want to also highlight a point that Danny made on solving humanity's greatest challenges which is our motto. He talked about the data analysis that he just did where they are looking at the surface of the ocean, as well as, going down, what is it, 264 nautical layers underneath the ocean? To analyze that much data, to start looking at marine life and protecting marine life. As you start to understand that level of nautical depth, they can start to figure out the nutrients value and other contents that are in that water to be able to start protecting the marine life. There again, another of humanity's greatest challenge right there that he's giving you... >> Nothing happens in isolation; It's all interconnected. >> Yeah. >> When you finally got a grant, you're able to buy a computer, how do you buy the computer that's going to give you the most bang for your buck? The best computer to do the science that we're all tasked with doing? It's tough, right? We don't fancy ourselves as computer architects; we engage the computer companies who really know about architecture to help us do it. The way we did our procurement was, 'Ok vendors, we have a set pot of money, we're willing to spend every last penny of this money, you give us the biggest and the baddest for our money." Now, it has to have a certain set of criteria. You have to be able to solve a number of benchmarks, some sample calculations that we provided. The ones that give you the best performance that's a bonus. It also has to be able to do it with the least amount of power, so we don't have to heat up the world and pay through the nose with power. Those are objective criteria that anybody can understand. But then, there's also the other criteria, so, how well will it run? How is it architected? How balanced is it? Did we get the iOS sub-system for all the storage that was the one that actually meets the criteria? What other extras do we have that will help us make the system run in a much smoother way and for a wide variety of disciplines because we run the biologists together with the physicists and the engineers and the humanitarians, the humanities people. Everybody uses the system. To make a long story short, the proposal that we got from Lenovo won the bid both in terms of what we got for in terms of hardware and also the way it was put together, which was quite innovative. >> Yeah. >> I want to hear about, you said give us the biggest, the baddest, we're willing to empty our coffers for this, so then where do you go from there? How closely do you work with SciNet, how does the relationship evolve and do you work together to innovate and kind of keep going? >> Yeah. I see it as not a segment or a division. I see High Performance Computing as a practice, and with any practice, it's many pieces that come together; you have a conductor, you have the orchestra, but the end of the day the delivery of that many systems is the concert. That's the way to look at it. To deliver this, our practice starts with multiple teams; one's a benchmarking team that understands the application that Dr. Gruner and SciNet will be running because they need to tune to the application the performance of the cluster. The second team is a set of solution architects that are deep engineers and understand our portfolio. Those two work together to say against this application, "Let's build," like he said, "the biggest, baddest, best-performing solution for that particular application." So, those two teams work together. Then we have the third team that kicks in once we win the business, which is coming on site to deploy, manage, and install. When Dr. Gruner talks about the infrastructure, it's a combination of hardware and software that all comes together and the software is open-source based that we built ourselves because we just felt there weren't the right tools in the industry to manage this level of infrastructure at that scale. All this comes together to essentially rack and roll onto their site. >> Let me just add to that. It's not like we went for it in a vacuum. We had already talked to the vendors, we always do. You always go, and they come to you and 'when's your next money coming,' and it's a dog and pony show. They tell you what they have. With Lenovo, at least the team, as we know it now, used to be the IBM team, iXsystems team, who built our previous system. A lot of these guys were already known to us, and we've always interacted very well with them. They were already aware of our thinking, where we were going, and that we're also open to suggestions for things that are non-conventional. Now, this can backfire, some data centers are very square they will only prescribe what they want. We're not prescriptive at all, we said, "Give us ideas about what can make this work better." These are the intangibles in a procurement process. You also have to believe in the team. If you don't know the team or if you don't know their track record then that's a no-no, right? Or, it takes points away. >> We brought innovations like DragonFly, which Dr. Dan will talk about that, as well as, we brought in for the first time, Excelero, which is a software-defined storage vendor and it was a smart part of the bid. We were able to flex muscles and be more creative versus just the standard. >> My understanding, you've been using water cooling for about a decade now, maybe? - Yes. >> Maybe you could give us a little bit about your experiences, how it's matured over time, and then Madhu will talk and bring us up to speed on project Neptune. >> Okay. Our first procurement about 10 years ago, again, that was the model we came up with. After years of wracking our brains, we could not decide how to build a data center and what computers to buy, it was like a chicken and egg process. We ended up saying, 'Okay, this is what we're going to do. Here's the money, here's is our total cost of operation that we can support." That included the power bill, the water, the maintenance, the whole works. So much can be used for infrastructure, and the rest is for the operational part. We said to the vendors, "You guys do the work. We want, again, the biggest and the baddest that we can operate within this budget." So, obviously, it has to be energy efficient, among other things. We couldn't design a data center and then put in the systems that we didn't know existed or vice-versa. That's how it started. The initial design was built by IBM, and they designed the data center for us to use water cooling for everything. They put rear door heat exchanges on the racks as a means of avoiding the use of blowing air and trying to contain the air which is less efficient, the air, and is also much more difficult. You can flow water very efficiently. You open the door of one of these racks. >> It's amazing. >> And it's hot air coming out, but you take the heat, right there in-situ, you remove it through a radiator. It's just like your car radiator. >> Car radiator. >> It works very well. Now, it would be nice if we could do even better by doing the hot water cooling and all that, but we're not in a university environment, we're in a strip mall out in the boonies, so we couldn't reuse the heat. Places like LRZ they're reusing the heat produced by the computers to heat their buildings. >> Wow. >> Or, if we're by a hospital, that always needs hot water, then we could have done it. But, it's really interesting how the option of that design that we ended up with the most efficient data center, certainly in Canada, and one of the most efficient in North America 10 years ago. Our PUE was 1.16, that was the design point, and this is not with direct water cooling through the chip. >> Right. Right. >> All right, bring us up to speed. Project Neptune, in general? >> Yes, so Neptune, as the name suggests, is the name of the God of the Sea and we chose that to brand our entire suite of liquid cooling products. Liquid cooling products is end to end in the sense that it's not just hardware, but, it's also software. The other key part of Neptune is a lot of these, in fact, most of these, products were built, not in a vacuum, but designed and built in conjunction with key partners like Barcelona Supercomputer, LRZ in Germany, in Munich. These were real-life customers working with us jointly to design these products. Neptune essentially allows you, very simplistically put, it's an entire suite of hardware and software that allows you to run very high-performance processes at a level of power and cooling utilization that's like using a much lower processor, it dissipates that much heat. The other key part is, you know, the normal way of cooling anything is run chilled water, we don't use chilled water. You save the money of chillers. We use ambient temperature, up to 50 degrees, 90% efficiency, 50 degree goes in, 60 degree comes out. It's really amazing, the entire suite. >> It's 50 Celsius, not Fahrenheit. >> It's Celsius, correct. >> Oh. >> Dr. Bruner talked about SciNet with the rado-heat exchanger. You actually got to stand in front of it to feel the magic of this, right? As geeky as that is. You open the door and it's this hot 60-, 65-degree C air. You close the door it's this cool 20-degree air that's coming out. So, the costs of running a data center drop dramatically with either the rado-heat exchanger, our direct to node product, which we just got released the SE650, or we have something call the thermal-transfer module, which replaces a normal heat sink. Where for an air cool we bring water cool goodness to an air cool product. >> Danny, I wonder if you can give us the final word, just the climate science in general, how's the community doing? Any technological things that are holding us back right now or anything that excites you about the research right now? >> Technology holds you back by the virtual size of the calculations that you need to do, but, it's also physics that hold you back. >> Yes. Because doing the actual modeling is very difficult and you have to be able to believe that the physics models actually work. This is one of the interesting things that Dick Peltier, who happens to be our scientific director and he's also one of the top climate scientists in the world, he's proven through some of his calculations that the models are actually pretty good. The models were designed for current conditions, with current data, so that they would reproduce the evolution of the climate that we can measure today. Now, what about climate that started happening 10,000 years ago, right? The climate was going on; it's been going on forever and ever. There's been glaciations; there's been all these events. It turns out that it has been recorded in history that there are some oscillations in temperature and other quantities that happen about every 1,000 years and nobody had been able to prove why they would happen. It turns out that the same models that we use for climate calculations today, if you take them back and do what's called paleoclimate, you start with approximating the conditions that happened 10,000 years ago, and then you move it forward, these things reproduce, those oscillations, exactly. It's very encouraging that the climate models actually make sense. We're not talking in a vacuum. We're not predicting the end of the world, just because. These calculations are right. They're correct. They're predicting the temperature of the earth is climbing and it's true, we're seeing it, but it will continue unless we do something. Right? It's extremely interesting. Now he's he's beginning to apply those results of the paleoclimate to studies with anthropologists and archeologists. We're trying to understand the events that happened in the Levant in the Middle East thousands of years ago and correlate them with climate events. Now, is that cool or what? >> That's very cool. >> So, I think humanity's greatest challenge is again to... >> I know! >> He just added global warming to it. >> You have a fun job. You have a fun job. >> It's all the interdisciplinarity that now has been made possible. Before we couldn't do this. Ten years ago we couldn't run those calculations, now we can. So it's really cool. - Amazing. Great. Well, Madhu, Danny, thank you so much for coming on the show. >> Thank you for having us. >> It was really fun talking to you. >> Thanks. >> I'm Rebecca Knight for Stu Miniman. We will have more from the Lenovo Transform just after this. (tech music)

(electronic dance music) (Intel Jingle) (ethereal electronic dance music) ♪ Okay ♪ (upbeat techno dance music) ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Yeah everybody get loose yeah ♪ ♪ Yeah ♪ ♪ Ye-yeah yeah ♪ ♪ Yeah yeah ♪ ♪ Everybody everybody yeah ♪ ♪ Whoo whoo ♪ ♪ Whoo whoo ♪ ♪ Whoo yeah ♪ ♪ Everybody get loose whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ >> As a courtesy to the presenters and those around you, please silence all mobile devices, thank you. (electronic dance music) ♪ Everybody get loose ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ (upbeat salsa music) ♪ Ha ha ha ♪ ♪ Ah ♪ ♪ Ha ha ha ♪ ♪ So happy ♪ ♪ Whoo whoo ♪ (female singer scatting) >> Ladies and gentlemen, please take your seats. Our program will begin momentarily. ♪ Hey ♪ (female singer scatting) (male singer scatting) ♪ Hey ♪ ♪ Whoo ♪ (female singer scatting) (electronic dance music) ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ Red don't go ♪ ♪ All hands are in don't go ♪ ♪ In don't go ♪ ♪ Oh red go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are red don't go ♪ ♪ All hands are in red red red red ♪ ♪ All hands are in don't go ♪ ♪ All hands are in red go ♪ >> Ladies and gentlemen, there are available seats. Towards house left, house left there are available seats. If you are please standing, we ask that you please take an available seat. We will begin momentarily, thank you. ♪ Let go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ (upbeat electronic dance music) ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ I live ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Hey ♪ ♪ Yeah ♪ ♪ Oh ♪ ♪ Ah ♪ ♪ Ah ah ah ah ah ah ♪ ♪ Just make me ♪ ♪ Just make me ♪ (bouncy techno music) >> Ladies and gentlemen, once again we ask that you please take the available seats to your left, house left, there are many available seats. If you are standing, please make your way there. The program will begin momentarily, thank you. Good morning! This is Lenovo Transform 2.0! (keyboard clicks) >> Progress. Why do we always talk about it in the future? When will it finally get here? We don't progress when it's ready for us. We need it when we're ready, and we're ready now. Our hospitals and their patients need it now, our businesses and their customers need it now, our cities and their citizens need it now. To deliver intelligent transformation, we need to build it into the products and solutions we make every day. At Lenovo, we're designing the systems to fight disease, power businesses, and help you reach more customers, end-to-end security solutions to protect your data and your companies reputation. We're making IT departments more agile and cost efficient. We're revolutionizing how kids learn with VR. We're designing smart devices and software that transform the way you collaborate, because technology shouldn't just power industries, it should power people. While everybody else is talking about tomorrow, we'll keep building today, because the progress we need can't wait for the future. >> Please welcome to the stage Lenovo's Rod Lappen! (electronic dance music) (audience applauding) >> Alright. Good morning everyone! >> Good morning. >> Ooh, that was pretty good actually, I'll give it one more shot. Good morning everyone! >> Good morning! >> Oh, that's much better! Hope everyone's had a great morning. Welcome very much to the second Lenovo Transform event here in New York. I think when I got up just now on the steps I realized there's probably one thing in common all of us have in this room including myself which is, absolutely no one has a clue what I'm going to say today. So, I'm hoping very much that we get through this thing very quickly and crisply. I love this town, love New York, and you're going to hear us talk a little bit about New York as we get through here, but just before we get started I'm going to ask anyone who's standing up the back, there are plenty of seats down here, and down here on the right hand side, I think he called it house left is the professional way of calling it, but these steps to my right, your left, get up here, let's get you all seated down so that you can actually sit down during the keynote session for us. Last year we had our very first Lenovo Transform. We had about 400 people. It was here in New York, fantastic event, today, over 1,000 people. We have over 62 different technology demonstrations and about 15 breakout sessions, which I'll talk you through a little bit later on as well, so it's a much bigger event. Next year we're definitely going to be shooting for over 2,000 people as Lenovo really transforms and starts to address a lot of the technology that our commercial customers are really looking for. We were however hampered last year by a storm, I don't know if those of you who were with us last year will remember, we had a storm on the evening before Transform last year in New York, and obviously the day that it actually occurred, and we had lots of logistics. Our media people from AMIA were coming in. They took the, the plane was circling around New York for a long time, and Kamran Amini, our General Manager of our Data Center Infrastructure Group, probably one of our largest groups in the Lenovo DCG business, took 17 hours to get from Raleigh, North Carolina to New York, 17 hours, I think it takes seven or eight hours to drive. Took him 17 hours by plane to get here. And then of course this year, we have Florence. And so, obviously the hurricane Florence down there in the Carolinas right now, we tried to help, but still Kamran has made it today. Unfortunately, very tragically, we were hoping he wouldn't, but he's here today to do a big presentation a little bit later on as well. However, I do want to say, obviously, Florence is a very serious tragedy and we have to take it very serious. We got, our headquarters is in Raleigh, North Carolina. While it looks like the hurricane is just missing it's heading a little bit southeast, all of our thoughts and prayers and well wishes are obviously with everyone in the Carolinas on behalf of Lenovo, everyone at our headquarters, everyone throughout the Carolinas, we want to make sure everyone stays safe and out of harm's way. We have a great mixture today in the crowd of all customers, partners, industry analysts, media, as well as our financial analysts from all around the world. There's over 30 countries represented here and people who are here to listen to both YY, Kirk, and Christian Teismann speak today. And so, it's going to be a really really exciting day, and I really appreciate everyone coming in from all around the world. So, a big round of applause for everyone whose come in. (audience applauding) We have a great agenda for you today, and it starts obviously a very consistent format which worked very successful for us last year, and that's obviously our keynote. You'll hear from YY, our CEO, talk a little bit about the vision he has in the industry and how he sees Lenovo's turned the corner and really driving some great strategy to address our customer's needs. Kirk Skaugen, our Executive Vice President of DCG, will be up talking about how we've transformed the DCG business and once again are hitting record growth ratios for our DCG business. And then you'll hear from Christian Teismann, our SVP and General Manager for our commercial business, get up and talk about everything that's going on in our IDG business. There's really exciting stuff going on there and obviously ThinkPad being the cornerstone of that I'm sure he's going to talk to us about a couple surprises in that space as well. Then we've got some great breakout sessions, I mentioned before, 15 breakout sessions, so while this keynote section goes until about 11:30, once we get through that, please go over and explore, and have a look at all of the breakout sessions. We have all of our subject matter experts from both our PC, NBG, and our DCG businesses out to showcase what we're doing as an organization to better address your needs. And then obviously we have the technology pieces that I've also spoken about, 62 different technology displays there arranged from everything IoT, 5G, NFV, everything that's really cool and hot in the industry right now is going to be on display up there, and I really encourage all of you to get up there. So, I'm going to have a quick video to show you from some of the setup yesterday on a couple of the 62 technology displays we've got on up on stage. Okay let's go, so we've got a demonstrations to show you today, one of the greats one here is the one we've done with NC State, a high-performance computing artificial intelligence demonstration of fresh produce. It's about modeling the population growth of the planet, and how we're going to supply water and food as we go forward. Whoo. Oh, that is not an apple. Okay. (woman laughs) Second one over here is really, hey Jonas, how are you? Is really around virtual reality, and how we look at one of the most amazing sites we've got, as an install on our high-performance computing practice here globally. And you can see, obviously, that this is the Barcelona supercomputer, and, where else in New York can you get access to being able to see something like that so easily? Only here at Lenovo Transform. Whoo, okay. (audience applauding) So there's two examples of some of the technology. We're really encouraging everyone in the room after the keynote to flow into that space and really get engaged, and interact with a lot of the technology we've got up there. It seems I need to also do something about my fashion, I've just realized I've worn a vest two days in a row, so I've got to work on that as well. Alright so listen, the last thing on the agenda, we've gone through the breakout sessions and the demo, tonight at four o'clock, there's about 400 of you registered to be on the cruise boat with us, the doors will open behind me. the boat is literally at the pier right behind us. You need to make sure you're on the boat for 4:00 p.m. this evening. Outside of that, I want everyone to have a great time today, really enjoy the experience, make it as experiential as you possibly can, get out there and really get in and touch the technology. There's some really cool AI displays up there for us all to get involved in as well. So ladies and gentlemen, without further adieu, it gives me great pleasure to introduce to you a lover of tennis, as some of you would've heard last year at Lenovo Transform, as well as a lover of technology, Lenovo, and of course, New York City. I am obviously very pleasured to introduce to you Yang Yuanqing, our CEO, as we like to call him, YY. (audience applauding) (upbeat funky music) >> Good morning, everyone. >> Good morning. >> Thank you Rod for that introduction. Welcome to New York City. So, this is the second year in a row we host our Transform event here, because New York is indeed one of the most transformative cities in the world. Last year on this stage, I spoke about the Fourth Industrial Revolution, and our vision around the intelligent transformation, how it would fundamentally change the nature of business and the customer relationships. And why preparing for this transformation is the key for the future of our company. And in the last year I can assure you, we were being very busy doing just that, from searching and bringing global talents around the world to the way we think about every product and every investment we make. I was here in New York just a month ago to announce our fiscal year Q1 earnings, which was a good day for us. I think now the world believes it when we say Lenovo has truly turned the corner to a new phase of growth and a new phase of acceleration in executing the transformation strategy. That's clear to me is that the last few years of a purposeful disruption at Lenovo have led us to a point where we can now claim leadership of the coming intelligent transformation. People often asked me, what is the intelligent transformation? I was saying this way. This is the unlimited potential of the Fourth Industrial Revolution driven by artificial intelligence being realized, ordering a pizza through our speaker, and locking the door with a look, letting your car drive itself back to your home. This indeed reflect the power of AI, but it just the surface of it. The true impact of AI will not only make our homes smarter and offices more efficient, but we are also completely transformed every value chip in every industry. However, to realize these amazing possibilities, we will need a structure built around the key components, and one that touches every part of all our lives. First of all, explosions in new technology always lead to new structures. This has happened many times before. In the early 20th century, thousands of companies provided a telephone service. City streets across the US looked like this, and now bundles of a microscopic fiber running from city to city bring the world closer together. Here's what a driving was like in the US, up until 1950s. Good luck finding your way. (audience laughs) And today, millions of vehicles are organized and routed daily, making the world more efficient. Structure is vital, from fiber cables and the interstate highways, to our cells bounded together to create humans. Thankfully the structure for intelligent transformation has emerged, and it is just as revolutionary. What does this new structure look like? We believe there are three key building blocks, data, computing power, and algorithms. Ever wondered what is it behind intelligent transformation? What is fueling this miracle of human possibility? Data. As the Internet becomes ubiquitous, not only PCs, mobile phones, have come online and been generating data. Today it is the cameras in this room, the climate controls in our offices, or the smart displays in our kitchens at home. The number of smart devices worldwide will reach over 20 billion in 2020, more than double the number in 2017. These devices and the sensors are connected and generating massive amount of data. By 2020, the amount of data generated will be 57 times more than all the grains of sand on Earth. This data will not only make devices smarter, but will also fuel the intelligence of our homes, offices, and entire industries. Then we need engines to turn the fuel into power, and the engine is actually the computing power. Last but not least the advanced algorithms combined with Big Data technology and industry know how will form vertical industrial intelligence and produce valuable insights for every value chain in every industry. When these three building blocks all come together, it will change the world. At Lenovo, we have each of these elements of intelligent transformations in a single place. We have built our business around the new structure of intelligent transformation, especially with mobile and the data center now firmly part of our business. I'm often asked why did you acquire these businesses? Why has a Lenovo gone into so many fields? People ask the same questions of the companies that become the leaders of the information technology revolution, or the third industrial transformation. They were the companies that saw the future and what the future required, and I believe Lenovo is the company today. From largest portfolio of devices in the world, leadership in the data center field, to the algorithm-powered intelligent vertical solutions, and not to mention the strong partnership Lenovo has built over decades. We are the only company that can unify all these essential assets and deliver end to end solutions. Let's look at each part. We now understand the important importance data plays as fuel in intelligent transformation. Hundreds of billions of devices and smart IoTs in the world are generating better and powering the intelligence. Who makes these devices in large volume and variety? Who puts these devices into people's home, offices, manufacturing lines, and in their hands? Lenovo definitely has the front row seats here. We are number one in PCs and tablets. We also produces smart phones, smart speakers, smart displays. AR/VR headsets, as well as commercial IoTs. All of these smart devices, or smart IoTs are linked to each other and to the cloud. In fact, we have more than 20 manufacturing facilities in China, US, Brazil, Japan, India, Mexico, Germany, and more, producing various devices around the clock. We actually make four devices every second, and 37 motherboards every minute. So, this factory located in my hometown, Hu-fi, China, is actually the largest laptop factory in the world, with more than three million square feet. So, this is as big as 42 soccer fields. Our scale and the larger portfolio of devices gives us access to massive amount of data, which very few companies can say. So, why is the ability to scale so critical? Let's look again at our example from before. The early days of telephone, dozens of service providers but only a few companies could survive consolidation and become the leader. The same was true for the third Industrial Revolution. Only a few companies could scale, only a few could survive to lead. Now the building blocks of the next revolution are locking into place. The (mumbles) will go to those who can operate at the scale. So, who could foresee the total integration of cloud, network, and the device, need to deliver intelligent transformation. Lenovo is that company. We are ready to scale. Next, our computing power. Computing power is provided in two ways. On one hand, the modern supercomputers are providing the brute force to quickly analyze the massive data like never before. On the other hand the cloud computing data centers with the server storage networking capabilities, and any computing IoT's, gateways, and miniservers are making computing available everywhere. Did you know, Lenovo is number one provider of super computers worldwide? 170 of the top 500 supercomputers, run on Lenovo. We hold 89 World Records in key workloads. We are number one in x86 server reliability for five years running, according to ITIC. a respected provider of industry research. We are also the fastest growing provider of hyperscale public cloud, hyper-converged and aggressively growing in edge computing. cur-ges target, we are expand on this point soon. And finally to run these individual nodes into our symphony, we must transform the data and utilize the computing power with advanced algorithms. Manufactured, industry maintenance, healthcare, education, retail, and more, so many industries are on the edge of intelligent transformation to improve efficiency and provide the better products and services. We are creating advanced algorithms and the big data tools combined with industry know-how to provide intelligent vertical solutions for several industries. In fact, we studied at Lenovo first. Our IT and research teams partnered with our global supply chain to develop an AI that improved our demand forecasting accuracy. Beyond managing our own supply chain we have offered our deep learning supply focused solution to other manufacturing companies to improve their efficiency. In the best case, we have improved the demand, focused the accuracy by 30 points to nearly 90 percent, for Baosteel, the largest of steel manufacturer in China, covering the world as well. Led by Lenovo research, we launched the industry-leading commercial ready AR headset, DaystAR, partnering with companies like the ones in this room. This technology is being used to revolutionize the way companies service utility, and even our jet engines. Using our workstations, servers, and award-winning imaging processing algorithms, we have partnered with hospitals to process complex CT scan data in minutes. So, this enable the doctors to more successfully detect the tumors, and it increases the success rate of cancer diagnosis all around the world. We are also piloting our smart IoT driven warehouse solution with one of the world's largest retail companies to greatly improve the efficiency. So, the opportunities are endless. This is where Lenovo will truly shine. When we combine the industry know-how of our customers with our end-to-end technology offerings, our intelligent vertical solutions like this are growing, which Kirk and Christian will share more. Now, what will drive this transformation even faster? The speed at which our networks operate, specifically 5G. You may know that Lenovo just launched the first-ever 5G smartphone, our Moto Z3, with the new 5G Moto model. We are partnering with multiple major network providers like Verizon, China Mobile. With the 5G model scheduled to ship early next year, we will be the first company to provide a 5G mobile experience to any users, customers. This is amazing innovation. You don't have to buy a new phone, just the 5G clip on. What can I say, except wow. (audience laughs) 5G is 10 times the fast faster than 4G. Its download speed will transform how people engage with the world, driverless car, new types of smart wearables, gaming, home security, industrial intelligence, all will be transformed. Finally, accelerating with partners, as ready as we are at Lenovo, we need partners to unlock our full potential, partners here to create with us the edge of the intelligent transformation. The opportunities of intelligent transformation are too profound, the scale is too vast. No company can drive it alone fully. We are eager to collaborate with all partners that can help bring our vision to life. We are dedicated to open partnerships, dedicated to cross-border collaboration, unify the standards, share the advantage, and market the synergies. We partner with the biggest names in the industry, Intel, Microsoft, AMD, Qualcomm, Google, Amazon, and Disney. We also find and partner with the smaller innovators as well. We're building the ultimate partner experience, open, shared, collaborative, diverse. So, everything is in place for intelligent transformation on a global scale. Smart devices are everywhere, the infrastructure is in place, networks are accelerating, and the industries demand to be more intelligent, and Lenovo is at the center of it all. We are helping to drive change with the hundreds of companies, companies just like yours, every day. We are your partner for intelligent transformation. Transformation never stops. This is what you will hear from Kirk, including details about Lenovo NetApp global partnership we just announced this morning. We've made the investments in every single aspect of the technology. We have the end-to-end resources to meet your end-to-end needs. As you attend the breakout session this afternoon, I hope you see for yourself how much Lenovo has transformed as a company this past year, and how we truly are delivering a future of intelligent transformation. Now, let me invite to the stage Kirk Skaugen, our president of Data Center growth to tell you about the exciting transformation happening in the global Data C enter market. Thank you. (audience applauding) (upbeat music) >> Well, good morning. >> Good morning. >> Good morning! >> Good morning! >> Excellent, well, I'm pleased to be here this morning to talk about how we're transforming the Data Center and taking you as our customers through your own intelligent transformation journey. Last year I stood up here at Transform 1.0, and we were proud to announce the largest Data Center portfolio in Lenovo's history, so I thought I'd start today and talk about the portfolio and the progress that we've made over the last year, and the strategies that we have going forward in phase 2.0 of Lenovo's transformation to be one of the largest data center companies in the world. We had an audacious vision that we talked about last year, and that is to be the most trusted data center provider in the world, empowering customers through the new IT, intelligent transformation. And now as the world's largest supercomputer provider, giving something back to humanity, is very important this week with the hurricanes now hitting North Carolina's coast, but we take this most trusted aspect very seriously, whether it's delivering the highest quality products on time to you as customers with the highest levels of security, or whether it's how we partner with our channel partners and our suppliers each and every day. You know we're in a unique world where we're going from hundreds of millions of PCs, and then over the next 25 years to hundred billions of connected devices, so each and every one of you is going through this intelligent transformation journey, and in many aspects were very early in that cycle. And we're going to talk today about our role as the largest supercomputer provider, and how we're solving humanity's greatest challenges. Last year we talked about two special milestones, the 25th anniversary of ThinkPad, but also the 25th anniversary of Lenovo with our IBM heritage in x86 computing. I joined the workforce in 1992 out of college, and the IBM first personal server was launching at the same time with an OS2 operating system and a free mouse when you bought the server as a marketing campaign. (audience laughing) But what I want to be very clear today, is that the innovation engine is alive and well at Lenovo, and it's really built on the culture that we're building as a company. All of these awards at the bottom are things that we earned over the last year at Lenovo. As a Fortune now 240 company, larger than companies like Nike, or AMEX, or Coca-Cola. The one I'm probably most proud of is Forbes first list of the top 2,000 globally regarded companies. This was something where 15,000 respondents in 60 countries voted based on ethics, trustworthiness, social conduct, company as an employer, and the overall company performance, and Lenovo was ranked number 27 of 2000 companies by our peer group, but we also now one of-- (audience applauding) But we also got a perfect score in the LGBTQ Equality Index, exemplifying the diversity internally. We're number 82 in the top working companies for mothers, top working companies for fathers, top 100 companies for sustainability. If you saw that factory, it's filled with solar panels on the top of that. And now again, one of the top global brands in the world. So, innovation is built on a customer foundation of trust. We also said last year that we'd be crossing an amazing milestone. So we did, over the last 12 months ship our 20 millionth x86 server. So, thank you very much to our customers for this milestone. (audience applauding) So, let me recap some of the transformation elements that have happened over the last year. Last year I talked about a lot of brand confusion, because we had the ThinkServer brand from the legacy Lenovo, the System x, from IBM, we had acquired a number of networking companies, like BLADE Network Technologies, et cetera, et cetera. Over the last year we've been ramping based on two brand structures, ThinkAgile for next generation IT, and all of our software-defined infrastructure products and ThinkSystem as the world's highest performance, highest reliable x86 server brand, but for servers, for storage, and for networking. We have transformed every single aspect of the customer experience. A year and a half ago, we had four different global channel programs around the world. Typically we're about twice the mix to our channel partners of any of our competitors, so this was really important to fix. We now have a single global Channel program, and have technically certified over 11,000 partners to be technical experts on our product line to deliver better solutions to our customer base. Gardner recently recognized Lenovo as the 26th ranked supply chain in the world. And, that's a pretty big honor, when you're up there with Amazon and Walmart and others, but in tech, we now are in the top five supply chains. You saw the factory network from YY, and today we'll be talking about product shipping in more than 160 countries, and I know there's people here that I've met already this morning, from India, from South Africa, from Brazil and China. We announced new Premier Support services, enabling you to go directly to local language support in nine languages in 49 countries in the world, going directly to a native speaker level three support engineer. And today we have more than 10,000 support specialists supporting our products in over 160 countries. We've delivered three times the number of engineered solutions to deliver a solutions orientation, whether it's on HANA, or SQL Server, or Oracle, et cetera, and we've completely reengaged our system integrator channel. Last year we had the CIO of DXE on stage, and here we're talking about more than 175 percent growth through our system integrator channel in the last year alone as we've brought that back and really built strong relationships there. So, thank you very much for amazing work here on the customer experience. (audience applauding) We also transformed our leadership. We thought it was extremely important with a focus on diversity, to have diverse talent from the legacy IBM, the legacy Lenovo, but also outside the industry. We made about 19 executive changes in the DCG group. This is the most senior leadership team within DCG, all which are newly on board, either from our outside competitors mainly over the last year. About 50 percent of our executives were now hired internally, 50 percent externally, and 31 percent of those new executives are diverse, representing the diversity of our global customer base and gender. So welcome, and most of them you're going to be able to meet over here in the breakout sessions later today. (audience applauding) But some things haven't changed, they're just keeping getting better within Lenovo. So, last year I got up and said we were committed with the new ThinkSystem brand to be a world performance leader. You're going to see that we're sponsoring Ducati for MotoGP. You saw the Ferrari out there with Formula One. That's not a surprise. We want the Lenovo ThinkSystem and ThinkAgile brands to be synonymous with world record performance. So in the last year we've gone from 39 to 89 world records, and partners like Intel would tell you, we now have four times the number of world record workloads on Lenovo hardware than any other server company on the planet today, with more than 89 world records across HPC, Java, database, transaction processing, et cetera. And we're proud to have just brought on Doug Fisher from Intel Corporation who had about 10-17,000 people on any given year working for him in workload optimizations across all of our software. It's just another testament to the leadership team we're bringing in to keep focusing on world-class performance software and solutions. We also per ITIC, are the number one now in x86 server reliability five years running. So, this is a survey where CIOs are in a blind survey asked to submit their reliability of their uptime on their x86 server equipment over the last 365 days. And you can see from 2016 to 2017 the downtime, there was over four hours as noted by the 750 CXOs in more than 20 countries is about one percent for the Lenovo products, and is getting worse generation from generation as we went from Broadwell to Pearlie. So we're taking our reliability, which was really paramount in the IBM System X heritage, and ensuring that we don't just recognize high performance but we recognize the highest level of reliability for mission-critical workloads. And what that translates into is that we at once again have been ranked number one in customer satisfaction from you our customers in 19 of 22 attributes, in North America in 18 of 22. This is a survey by TVR across hundreds of customers of us and our top competitors. This is the ninth consecutive study that we've been ranked number one in customer satisfaction, so we're taking this extremely seriously, and in fact YY now has increased the compensation of every single Lenovo employee. Up to 40 percent of their compensation bonus this year is going to be based on customer metrics like quality, order to ship, and things of this nature. So, we're really putting every employee focused on customer centricity this year. So, the summary on Transform 1.0 is that every aspect of what you knew about Lenovo's data center group has transformed, from the culture to the branding to dedicated sales and marketing, supply chain and quality groups, to a worldwide channel program and certifications, to new system integrator relationships, and to the new leadership team. So, rather than me just talk about it, I thought I'd share a quick video about what we've done over the last year, if you could run the video please. Turn around for a second. (epic music) (audience applauds) Okay. So, thank you to all our customers that allowed us to publicly display their logos in that video. So, what that means for you as investors, and for the investor community out there is, that our customers have responded, that this year Gardner just published that we are the fastest growing server company in the top 10, with 39 percent growth quarter-on-quarter, and 49 percent growth year-on-year. If you look at the progress we've made since the transformation the last three quarters publicly, we've grown 17 percent, then 44 percent, then 68 percent year on year in revenue, and I can tell you this quarter I'm as confident as ever in the financials around the DCG group, and it hasn't been in one area. You're going to see breakout sessions from hyperscale, software-defined, and flash, which are all growing more than a 100 percent year-on-year, supercomputing which we'll talk about shortly, now number one, and then ultimately from profitability, delivering five consecutive quarters of pre-tax profit increase, so I think, thank you very much to the customer base who's been working with us through this transformation journey. So, you're here to really hear what's next on 2.0, and that's what I'm excited to talk about today. Last year I came up with an audacious goal that we would become the largest supercomputer company on the planet by 2020, and this graph represents since the acquisition of the IBM System x business how far we were behind being the number one supercomputer. When we started we were 182 positions behind, even with the acquisition for example of SGI from HP, we've now accomplished our goal actually two years ahead of time. We're now the largest supercomputer company in the world. About one in every four supercomputers, 117 on the list, are now Lenovo computers, and you saw in the video where the universities are said, but I think what I'm most proud of is when your customers rank you as the best. So the awards at the bottom here, are actually Readers Choice from the last International Supercomputing Show where the scientific researchers on these computers ranked their vendors, and we were actually rated the number one server technology in supercomputing with our ThinkSystem SD530, and the number one storage technology with our ThinkSystem DSS-G, but more importantly what we're doing with the technology. You're going to see we won best in life sciences, best in data analytics, and best in collaboration as well, so you're going to see all of that in our breakout sessions. As you saw in the video now, 17 of the top 25 research institutions in the world are now running Lenovo supercomputers. And again coming from Raleigh and watching that hurricane come across the Atlantic, there are eight supercomputers crunching all of those models you see from Germany to Malaysia to Canada, and we're happy to have a SciNet from University of Toronto here with us in our breakout session to talk about what they're doing on climate modeling as well. But we're not stopping there. We just announced our new Neptune warm water cooling technology, which won the International Supercomputing Vendor Showdown, the first time we've won that best of show in 25 years, and we've now installed this. We're building out LRZ in Germany, the first ever warm water cooling in Peking University, at the India Space Propulsion Laboratory, at the Malaysian Weather and Meteorological Society, at Uninett, at the largest supercomputer in Norway, T-Systems, University of Birmingham. This is truly amazing technology where we're actually using water to cool the machine to deliver a significantly more energy-efficient computer. Super important, when we're looking at global warming and some of the electric bills can be millions of dollars just for one computer, and could actually power a small city just with the technology from the computer. We've built AI centers now in Morrisville, Stuttgart, Taipei, and Beijing, where customers can bring their AI workloads in with experts from Intel, from Nvidia, from our FPGA partners, to work on their workloads, and how they can best implement artificial intelligence. And we also this year launched LICO which is Lenovo Intelligent Compute Orchestrator software, and it's a software solution that simplifies the management and use of distributed clusters in both HPC and AI model development. So, what it enables you to do is take a single cluster, and run both HPC and AI workloads on it simultaneously, delivering better TCO for your environment, so check out LICO as well. A lot of the customers here and Wall Street are very excited and using it already. And we talked about solving humanity's greatest challenges. In the breakout session, you're going to have a virtual reality experience where you're going to be able to walk through what as was just ranked the world's most beautiful data center, the Barcelona Supercomputer. So, you can actually walk through one of the largest supercomputers in the world from Barcelona. You can see the work we're doing with NC State where we're going to have to grow the food supply of the world by 50 percent, and there's not enough fresh water in the world in the right places to actually make all those crops grow between now and 2055, so you're going to see the progression of how they're mapping the entire globe and the water around the world, how to build out the crop population over time using AI. You're going to see our work with Vestas is this largest supercomputer provider in the wind turbine areas, how they're working on wind energy, and then with University College London, how they're working on some of the toughest particle physics calculations in the world. So again, lots of opportunity here. Take advantage of it in the breakout sessions. Okay, let me transition to hyperscale. So in hyperscale now, we have completely transformed our business model. We are now powering six of the top 10 hyperscalers in the world, which is a significant difference from where we were two years ago. And the reason we're doing that, is we've coined a term called ODM+. We believe that hyperscalers want more procurement power than an ODM, and Lenovo is doing about $18 billion of procurement a year. They want a broader global supply chain that they can get from a local system integrator. We're more than 160 countries around the world, but they want the same world-class quality and reliability like they get from an MNC. So, what we're doing now is instead of just taking off the shelf motherboards from somewhere, we're starting with a blank sheet of paper, we're working with the customer base on customized SKUs and you can see we already are developing 33 custom solutions for the largest hyperscalers in the world. And then we're not just running notebooks through this factory where YY said, we're running 37 notebook boards a minute, we're now putting in tens and tens and tens of thousands of server board capacity per month into this same factory, so absolutely we can compete with the most aggressive ODM's in the world, but it's not just putting these things in in the motherboard side, we're also building out these systems all around the world, India, Brazil, Hungary, Mexico, China. This is an example of a new hyperscale customer we've had this last year, 34,000 servers we delivered in the first six months. The next 34,000 servers we delivered in 68 days. The next 34,000 servers we delivered in 35 days, with more than 99 percent on-time delivery to 35 data centers in 14 countries as diverse as South Africa, India, China, Brazil, et cetera. And I'm really ashamed to say it was 99.3, because we did have a forklift driver who rammed their forklift right through the middle of the one of the server racks. (audience laughing) At JFK Airport that we had to respond to, but I think this gives you a perspective of what it is to be a top five global supply chain and technology. So last year, I said we would invest significantly in IP, in joint ventures, and M and A to compete in software defined, in networking, and in storage, so I wanted to give you an update on that as well. Our newest software-defined partnership is with Cloudistics, enabling a fully composable cloud infrastructure. It's an exclusive agreement, you can see them here. I think Nag, our founder, is going to be here today, with a significant Lenovo investment in the company. So, this new ThinkAgile CP series delivers the simplicity of the public cloud, on-premise with exceptional support and a marketplace of essential enterprise applications all with a single click deployment. So simply put, we're delivering a private cloud with a premium experience. It's simple in that you need no specialists to deploy it. An IT generalist can set it up and manage it. It's agile in that you can provision dozens of workloads in minutes, and it's transformative in that you get all of the goodness of public cloud on-prem in a private cloud to unlock opportunity for use. So, we're extremely excited about the ThinkAgile CP series that's now shipping into the marketplace. Beyond that we're aggressively ramping, and we're either doubling, tripling, or quadrupling our market share as customers move from traditional server technology to software-defined technology. With Nutanix we've been public, growing about more than 150 percent year-on-year, with Nutanix as their fastest growing Nutanix partner, but today I want to set another audacious goal. I believe we cannot just be Nutanix's fastest growing partner but we can become their largest partner within two years. On Microsoft, we are already four times our market share on Azure stack of our traditional business. We were the first to launch our ThinkAgile on Broadwell and on Skylake with the Azure Stack Infrastructure. And on VMware we're about twice our market segment share. We were the first to deliver an Intel-optimized Optane-certified VSAN node. And with Optane technology, we're delivering 50 percent more VM density than any competitive SSD system in the marketplace, about 10 times lower latency, four times the performance of any SSD system out there, and Lenovo's first to market on that. And at VMworld you saw CEO Pat Gelsinger of VMware talked about project dimension, which is Edge as a service, and we're the only OEM beyond the Dell family that is participating today in project dimension. Beyond that you're going to see a number of other partnerships we have. I'm excited that we have the city of Bogota Columbia here, an eight million person city, where we announced a 3,000 camera video surveillance solution last month. With pivot three you're going to see city of Bogota in our breakout sessions. You're going to see a new partnership with Veeam around backup that's launching today. You're going to see partnerships with scale computing in IoT and hyper-converged infrastructure working on some of the largest retailers in the world. So again, everything out in the breakout session. Transitioning to storage and data management, it's been a great year for Lenovo, more than a 100 percent growth year-on-year, 2X market growth in flash arrays. IDC just reported 30 percent growth in storage, number one in price performance in the world and the best HPC storage product in the top 500 with our ThinkSystem DSS G, so strong coverage, but I'm excited today to announce for Transform 2.0 that Lenovo is launching the largest data management and storage portfolio in our 25-year data center history. (audience applauding) So a year ago, the largest server portfolio, becoming the largest fastest growing server OEM, today the largest storage portfolio, but as you saw this morning we're not doing it alone. Today Lenovo and NetApp, two global powerhouses are joining forces to deliver a multi-billion dollar global alliance in data management and storage to help customers through their intelligent transformation. As the fastest growing worldwide server leader and one of the fastest growing flash array and data management companies in the world, we're going to deliver more choice to customers than ever before, global scale that's never been seen, supply chain efficiencies, and rapidly accelerating innovation and solutions. So, let me unwrap this a little bit for you and talk about what we're announcing today. First, it's the largest portfolio in our history. You're going to see not just storage solutions launching today but a set of solution recipes from NetApp that are going to make Lenovo server and NetApp or Lenovo storage work better together. The announcement enables Lenovo to go from covering 15 percent of the global storage market to more than 90 percent of the global storage market and distribute these products in more than 160 countries around the world. So we're launching today, 10 new storage platforms, the ThinkSystem DE and ThinkSystem DM platforms. They're going to be centrally managed, so the same XClarity management that you've been using for server, you can now use across all of your storage platforms as well, and it'll be supported by the same 10,000 plus service personnel that are giving outstanding customer support to you today on the server side. And we didn't come up with this in the last month or the last quarter. We're announcing availability in ordering today and shipments tomorrow of the first products in this portfolio, so we're excited today that it's not just a future announcement but something you as customers can take advantage of immediately. (audience applauding) The second part of the announcement is we are announcing a joint venture in China. Not only will this be a multi-billion dollar global partnership, but Lenovo will be a 51 percent owner, NetApp a 49 percent owner of a new joint venture in China with the goal of becoming in the top three storage companies in the largest data and storage market in the world. We will deliver our R and D in China for China, pooling our IP and resources together, and delivering a single route to market through a complementary channel, not just in China but worldwide. And in the future I just want to tell everyone this is phase one. There is so much exciting stuff. We're going to be on the stage over the next year talking to you about around integrated solutions, next-generation technologies, and further synergies and collaborations. So, rather than just have me talk about it, I'd like to welcome to the stage our new partner NetApp and Brad Anderson who's the senior vice president and general manager of NetApp Cloud Infrastructure. (upbeat music) (audience applauding) >> Thank You Kirk. >> So Brad, we've known each other a long time. It's an exciting day. I'm going to give you the stage and allow you to say NetApp's perspective on this announcement. >> Very good, thank you very much, Kirk. Kirk and I go back to I think 1994, so hey good morning and welcome. My name is Brad Anderson. I manage the Cloud Infrastructure Group at NetApp, and I am honored and privileged to be here at Lenovo Transform, particularly today on today's announcement. Now, you've heard a lot about digital transformation about how companies have to transform their IT to compete in today's global environment. And today's announcement with the partnership between NetApp and Lenovo is what that's all about. This is the joining of two global leaders bringing innovative technology in a simplified solution to help customers modernize their IT and accelerate their global digital transformations. Drawing on the strengths of both companies, Lenovo's high performance compute world-class supply chain, and NetApp's hybrid cloud data management, hybrid flash and all flash storage solutions and products. And both companies providing our customers with the global scale for them to be able to meet their transformation goals. At NetApp, we're very excited. This is a quote from George Kurian our CEO. George spent all day yesterday with YY and Kirk, and would have been here today if it hadn't been also our shareholders meeting in California, but I want to just convey how excited we are for all across NetApp with this partnership. This is a partnership between two companies with tremendous market momentum. Kirk took you through all the amazing results that Lenovo has accomplished, number one in supercomputing, number one in performance, number one in x86 reliability, number one in x86 customers sat, number five in supply chain, really impressive and congratulations. Like Lenovo, NetApp is also on a transformation journey, from a storage company to the data authority in hybrid cloud, and we've seen some pretty impressive momentum as well. Just last week we became number one in all flash arrays worldwide, catching EMC and Dell, and we plan to keep on going by them, as we help customers modernize their their data centers with cloud connected flash. We have strategic partnerships with the largest hyperscalers to provide cloud native data services around the globe and we are having success helping our customers build their own private clouds with just, with a new disruptive hyper-converged technology that allows them to operate just like hyperscalers. These three initiatives has fueled NetApp's transformation, and has enabled our customers to change the world with data. And oh by the way, it has also fueled us to have meet or have beaten Wall Street's expectations for nine quarters in a row. These are two companies with tremendous market momentum. We are also building this partnership for long term success. We think about this as phase one and there are two important components to phase one. Kirk took you through them but let me just review them. Part one, the establishment of a multi-year commitment and a collaboration agreement to offer Lenovo branded flash products globally, and as Kurt said in 160 countries. Part two, the formation of a joint venture in PRC, People's Republic of China, that will provide long term commitment, joint product development, and increase go-to-market investment to meet the unique needs to China. Both companies will put in storage technologies and storage expertise to form an independent JV that establishes a data management company in China for China. And while we can dream about what phase two looks like, our entire focus is on making phase one incredibly successful and I'm pleased to repeat what Kirk, is that the first products are orderable and shippable this week in 160 different countries, and you will see our two companies focusing on the here and now. On our joint go to market strategy, you'll see us working together to drive strategic alignment, focused execution, strong governance, and realistic expectations and milestones. And it starts with the success of our customers and our channel partners is job one. Enabling customers to modernize their legacy IT with complete data center solutions, ensuring that our customers get the best from both companies, new offerings the fuel business success, efficiencies to reinvest in game-changing initiatives, and new solutions for new mission-critical applications like data analytics, IoT, artificial intelligence, and machine learning. Channel partners are also top of mind for both our two companies. We are committed to the success of our existing and our future channel partners. For NetApp channel partners, it is new pathways to new segments and to new customers. For Lenovo's channel partners, it is the competitive weapons that now allows you to compete and more importantly win against Dell, EMC, and HP. And the good news for both companies is that our channel partner ecosystem is highly complementary with minimal overlap. Today is the first day of a very exciting partnership, of a partnership that will better serve our customers today and will provide new opportunities to both our companies and to our partners, new products to our customers globally and in China. I am personally very excited. I will be on the board of the JV. And so, I look forward to working with you, partnering with you and serving you as we go forward, and with that, I'd like to invite Kirk back up. (audience applauding) >> Thank you. >> Thank you. >> Well, thank you, Brad. I think it's an exciting overview, and these products will be manufactured in China, in Mexico, in Hungary, and around the world, enabling this amazing supply chain we talked about to deliver in over 160 countries. So thank you Brad, thank you George, for the amazing partnership. So again, that's not all. In Transform 2.0, last year, we talked about the joint ventures that were coming. I want to give you a sneak peek at what you should expect at future Lenovo events around the world. We have this Transform in Beijing in a couple weeks. We'll then be repeating this in 20 different locations roughly around the world over the next year, and I'm excited probably more than ever about what else is coming. Let's talk about Telco 5G and network function virtualization. Today, Motorola phones are certified on 46 global networks. We launched the world's first 5G upgradable phone here in the United States with Verizon. Lenovo DCG sells to 58 telecommunication providers around the world. At Mobile World Congress in Barcelona and Shanghai, you saw China Telecom and China Mobile in the Lenovo booth, China Telecom showing a video broadband remote access server, a VBRAS, with video streaming demonstrations with 2x less jitter than they had seen before. You saw China Mobile with a virtual remote access network, a VRAN, with greater than 10 times the throughput and 10x lower latency running on Lenovo. And this year, we'll be launching a new NFV company, a software company in China for China to drive the entire NFV stack, delivering not just hardware solutions, but software solutions, and we've recently hired a new CEO. You're going to hear more about that over the next several quarters. Very exciting as we try to drive new economics into the networks to deliver these 20 billion devices. We're going to need new economics that I think Lenovo can uniquely deliver. The second on IoT and edge, we've integrated on the device side into our intelligent devices group. With everything that's going to consume electricity computes and communicates, Lenovo is in a unique position on the device side to take advantage of the communications from Motorola and being one of the largest device companies in the world. But this year, we're also going to roll out a comprehensive set of edge gateways and ruggedized industrial servers and edge servers and ISP appliances for the edge and for IoT. So look for that as well. And then lastly, as a service, you're going to see Lenovo delivering hardware as a service, device as a service, infrastructure as a service, software as a service, and hardware as a service, not just as a glorified leasing contract, but with IP, we've developed true flexible metering capability that enables you to scale up and scale down freely and paying strictly based on usage, and we'll be having those announcements within this fiscal year. So Transform 2.0, lots to talk about, NetApp the big news of the day, but a lot more to come over the next year from the Data Center group. So in summary, I'm excited that we have a lot of customers that are going to be on stage with us that you saw in the video. Lots of testimonials so that you can talk to colleagues of yourself. Alamos Gold from Canada, a Canadian gold producer, Caligo for data optimization and privacy, SciNet, the largest supercomputer we've ever put into North America, and the largest in Canada at the University of Toronto will be here talking about climate change. City of Bogota again with our hyper-converged solutions around smart city putting in 3,000 cameras for criminal detection, license plate detection, et cetera, and then more from a channel mid market perspective, Jerry's Foods, which is from my home state of Wisconsin, and Minnesota which has about 57 stores in the specialty foods market, and how they're leveraging our IoT solutions as well. So again, about five times the number of demos that we had last year. So in summary, first and foremost to the customers, thank you for your business. It's been a great journey and I think we're on a tremendous role. You saw from last year, we're trying to build credibility with you. After the largest server portfolio, we're now the fastest-growing server OEM per Gardner, number one in performance, number one in reliability, number one in customer satisfaction, number one in supercomputing. Today, the largest storage portfolio in our history, with the goal of becoming the fastest growing storage company in the world, top three in China, multibillion-dollar collaboration with NetApp. And the transformation is going to continue with new edge gateways, edge servers, NFV solutions, telecommunications infrastructure, and hardware as a service with dynamic metering. So thank you for your time. I've looked forward to meeting many of you over the next day. We appreciate your business, and with that, I'd like to bring up Rod Lappen to introduce our next speaker. Rod? (audience applauding) >> Thanks, boss, well done. Alright ladies and gentlemen. No real secret there. I think we've heard why I might talk about the fourth Industrial Revolution in data and exactly what's going on with that. You've heard Kirk with some amazing announcements, obviously now with our NetApp partnership, talk about 5G, NFV, cloud, artificial intelligence, I think we've hit just about all the key hot topics. It's with great pleasure that I now bring up on stage Mr. Christian Teismann, our senior vice president and general manager of commercial business for both our PCs and our IoT business, so Christian Teismann. (techno music) Here, take that. >> Thank you. I think I'll need that. >> Okay, Christian, so obviously just before we get down, you and I last year, we had a bit of a chat about being in New York. >> Exports. >> You were an expat in New York for a long time. >> That's true. >> And now, you've moved from New York. You're in Munich? >> Yep. >> How does that feel? >> Well Munich is a wonderful city, and it's a great place to live and raise kids, but you know there's no place in the world like New York. >> Right. >> And I miss it a lot, quite frankly. >> So what exactly do you miss in New York? >> Well there's a lot of things in New York that are unique, but I know you spent some time in Japan, but I still believe the best sushi in the world is still in New York City. (all laughing) >> I will beg to differ. I will beg to differ. I think Mr. Guchi-san from Softbank is here somewhere. He will get up an argue very quickly that Japan definitely has better sushi than New York. But obviously you know, it's a very very special place, and I have had sushi here, it's been fantastic. What about Munich? Anything else that you like in Munich? >> Well I mean in Munich, we have pork knuckles. >> Pork knuckles. (Christian laughing) Very similar sushi. >> What is also very fantastic, but we have the real, the real Oktoberfest in Munich, and it starts next week, mid-September, and I think it's unique in the world. So it's very special as well. >> Oktoberfest. >> Yes. >> Unfortunately, I'm not going this year, 'cause you didn't invite me, but-- (audience chuckling) How about, I think you've got a bit of a secret in relation to Oktoberfest, probably not in Munich, however. >> It's a secret, yes, but-- >> Are you going to share? >> Well I mean-- >> See how I'm putting you on the spot? >> In the 10 years, while living here in New York, I was a regular visitor of the Oktoberfest at the Lower East Side in Avenue C at Zum Schneider, where I actually met my wife, and she's German. >> Very good. So, how about a big round of applause? (audience applauding) Not so much for Christian, but more I think, obviously for his wife, who obviously had been drinking and consequently ended up with you. (all laughing) See you later, mate. >> That's the beauty about Oktoberfest, but yes. So first of all, good morning to everybody, and great to be back here in New York for a second Transform event. New York clearly is the melting pot of the world in terms of culture, nations, but also business professionals from all kind of different industries, and having this event here in New York City I believe is manifesting what we are trying to do here at Lenovo, is transform every aspect of our business and helping our customers on the journey of intelligent transformation. Last year, in our transformation on the device business, I talked about how the PC is transforming to personalized computing, and we've made a lot of progress in that journey over the last 12 months. One major change that we have made is we combined all our device business under one roof. So basically PCs, smart devices, and smart phones are now under the roof and under the intelligent device group. But from my perspective makes a lot of sense, because at the end of the day, all devices connect in the modern world into the cloud and are operating in a seamless way. But we are also moving from a device business what is mainly a hardware focus historically, more and more also into a solutions business, and I will give you during my speech a little bit of a sense of what we are trying to do, as we are trying to bring all these components closer together, and specifically also with our strengths on the data center side really build end-to-end customer solution. Ultimately, what we want to do is make our business, our customer's businesses faster, safer, and ultimately smarter as well. So I want to look a little bit back, because I really believe it's important to understand what's going on today on the device side. Many of us have still grown up with phones with terminals, ultimately getting their first desktop, their first laptop, their first mobile phone, and ultimately smartphone. Emails and internet improved our speed, how we could operate together, but still we were defined by linear technology advances. Today, the world has changed completely. Technology itself is not a limiting factor anymore. It is how we use technology going forward. The Internet is pervasive, and we are not yet there that we are always connected, but we are nearly always connected, and we are moving to the stage, that everything is getting connected all the time. Sharing experiences is the most driving force in our behavior. In our private life, sharing pictures, videos constantly, real-time around the world, with our friends and with our family, and you see the same behavior actually happening in the business life as well. Collaboration is the number-one topic if it comes down to workplace, and video and instant messaging, things that are coming from the consumer side are dominating the way we are operating in the commercial business as well. Most important beside technology, that a new generation of workforce has completely changed the way we are working. As the famous workforce the first generation of Millennials that have now fully entered in the global workforce, and the next generation, it's called Generation Z, is already starting to enter the global workforce. By 2025, 75 percent of the world's workforce will be composed out of two of these generations. Why is this so important? These two generations have been growing up using state-of-the-art IT technology during their private life, during their education, school and study, and are taking these learnings and taking these behaviors in the commercial workspace. And this is the number one force of change that we are seeing in the moment. Diverse workforces are driving this change in the IT spectrum, and for years in many of our customers' focus was their customer focus. Customer experience also in Lenovo is the most important thing, but we've realized that our own human capital is equally valuable in our customer relationships, and employee experience is becoming a very important thing for many of our customers, and equally for Lenovo as well. As you have heard YY, as we heard from YY, Lenovo is focused on intelligent transformation. What that means for us in the intelligent device business is ultimately starting with putting intelligence in all of our devices, smartify every single one of our devices, adding value to our customers, traditionally IT departments, but also focusing on their end users and building products that make their end users more productive. And as a world leader in commercial devices with more than 33 percent market share, we can solve problems been even better than any other company in the world. So, let's talk about transformation of productivity first. We are in a device-led world. Everything we do is connected. There's more interaction with devices than ever, but also with spaces who are increasingly becoming smart and intelligent. YY said it, by 2020 we have more than 20 billion connected devices in the world, and it will grow exponentially from there on. And users have unique personal choices for technology, and that's very important to recognize, and we call this concept a digital wardrobe. And it means that every single end-user in the commercial business is composing his personal wardrobe on an ongoing basis and is reconfiguring it based on the work he's doing and based where he's going and based what task he is doing. I would ask all of you to put out all the devices you're carrying in your pockets and in your bags. You will see a lot of you are using phones, tablets, laptops, but also cameras and even smartwatches. They're all different, but they have one underlying technology that is bringing it all together. Recognizing digital wardrobe dynamics is a core factor for us to put all the devices under one roof in IDG, one business group that is dedicated to end-user solutions across mobile, PC, but also software services and imaging, to emerging technologies like AR, VR, IoT, and ultimately a AI as well. A couple of years back there was a big debate around bring-your-own-device, what was called consumerization. Today consumerization does not exist anymore, because consumerization has happened into every single device we build in our commercial business. End users and commercial customers today do expect superior display performance, superior audio, microphone, voice, and touch quality, and have it all connected and working seamlessly together in an ease of use space. We are already deep in the journey of personalized computing today. But the center point of it has been for the last 25 years, the mobile PC, that we have perfected over the last 25 years, and has been the undisputed leader in mobility computing. We believe in the commercial business, the ThinkPad is still the core device of a digital wardrobe, and we continue to drive the success of the ThinkPad in the marketplace. We've sold more than 140 million over the last 26 years, and even last year we exceeded nearly 11 million units. That is about 21 ThinkPads per minute, or one Thinkpad every three seconds that we are shipping out in the market. It's the number one commercial PC in the world. It has gotten countless awards but we felt last year after Transform we need to build a step further, in really tailoring the ThinkPad towards the need of the future. So, we announced a new line of X1 Carbon and Yoga at CES the Consumer Electronics Show. And the reason is not we want to sell to consumer, but that we do recognize that a lot of CIOs and IT decision makers need to understand what consumers are really doing in terms of technology to make them successful. So, let's take a look at the video. (suspenseful music) >> When you're the number one business laptop of all time, your only competition is yourself. (wall shattering) And, that's different. Different, like resisting heat, ice, dust, and spills. Different, like sharper, brighter OLA display. The trackpoint that reinvented controls, and a carbon fiber roll cage to protect what's inside, built by an engineering and design team, doing the impossible for the last 25 years. This is the number one business laptop of all time, but it's not a laptop. It's a ThinkPad. (audience applauding) >> Thank you very much. And we are very proud that Lenovo ThinkPad has been selected as the best laptop in the world in the second year in a row. I think it's a wonderful tribute to what our engineers have been done on this one. And users do want awesome displays. They want the best possible audio, voice, and touch control, but some users they want more. What they want is super power, and I'm really proud to announce our newest member of the X1 family, and that's the X1 extreme. It's exceptionally featured. It has six core I9 intel chipset, the highest performance you get in the commercial space. It has Nvidia XTX graphic, it is a 4K UHD display with HDR with Dolby vision and Dolby Atmos Audio, two terabyte in SSD, so it is really the absolute Ferrari in terms of building high performance commercial computer. Of course it has touch and voice, but it is one thing. It has so much performance that it serves also a purpose that is not typical for commercial, and I know there's a lot of secret gamers also here in this room. So you see, by really bringing technology together in the commercial space, you're creating productivity solutions of one of a kind. But there's another category of products from a productivity perspective that is incredibly important in our commercial business, and that is the workstation business . Clearly workstations are very specifically designed computers for very advanced high-performance workloads, serving designers, architects, researchers, developers, or data analysts. And power and performance is not just about the performance itself. It has to be tailored towards the specific use case, and traditionally these products have a similar size, like a server. They are running on Intel Xeon technology, and they are equally complex to manufacture. We have now created a new category as the ultra mobile workstation, and I'm very proud that we can announce here the lightest mobile workstation in the industry. It is so powerful that it really can run AI and big data analysis. And with this performance you can go really close where you need this power, to the sensors, into the cars, or into the manufacturing places where you not only wannna read the sensors but get real-time analytics out of these sensors. To build a machine like this one you need customers who are really challenging you to the limit. and we're very happy that we had a customer who went on this journey with us, and ultimately jointly with us created this product. So, let's take a look at the video. (suspenseful music) >> My world involves pathfinding both the hardware needs to the various work sites throughout the company, and then finding an appropriate model of desktop, laptop, or workstation to match those needs. My first impressions when I first seen the ThinkPad P1 was I didn't actually believe that we could get everything that I was asked for inside something as small and light in comparison to other mobile workstations. That was one of the I can't believe this is real sort of moments for me. (engine roars) >> Well, it's better than general when you're going around in the wind tunnel, which isn't alway easy, and going on a track is not necessarily the best bet, so having a lightweight very powerful laptop is extremely useful. It can take a Xeon processor, which can support ECC from when we try to load a full car, and when we're analyzing live simulation results. through and RCFT post processor or example. It needs a pretty powerful machine. >> It's come a long way to be able to deliver this. I hate to use the word game changer, but it is that for us. >> Aston Martin has got a lot of different projects going. There's some pretty exciting projects and a pretty versatile range coming out. Having Lenovo as a partner is certainly going to ensure that future. (engine roars) (audience applauds) >> So, don't you think the Aston Martin design and the ThinkPad design fit very well together? (audience laughs) So if Q, would get a new laptop, I think you would get a ThinkPad X P1. So, I want to switch gears a little bit, and go into something in terms of productivity that is not necessarily on top of the mind or every end user but I believe it's on top of the mind of every C-level executive and of every CEO. Security is the number one threat in terms of potential risk in your business and the cost of cybersecurity is estimated by 2020 around six trillion dollars. That's more than the GDP of Japan and we've seen a significant amount of data breach incidents already this years. Now, they're threatening to take companies out of business and that are threatening companies to lose a huge amount of sensitive customer data or internal data. At Lenovo, we are taking security very, very seriously, and we run a very deep analysis, around our own security capabilities in the products that we are building. And we are announcing today a new brand under the Think umbrella that is called ThinkShield. Our goal is to build the world's most secure PC, and ultimately the most secure devices in the industry. And when we looked at this end-to-end, there is no silver bullet around security. You have to go through every aspect where security breaches can potentially happen. That is why we have changed the whole organization, how we look at security in our device business, and really have it grouped under one complete ecosystem of solutions, Security is always something where you constantly are getting challenged with the next potential breach the next potential technology flaw. As we keep innovating and as we keep integrating, a lot of our partners' software and hardware components into our products. So for us, it's really very important that we partner with companies like Intel, Microsoft, Coronet, Absolute, and many others to really as an example to drive full encryption on all the data seamlessly, to have multi-factor authentication to protect your users' identity, to protect you in unsecured Wi-Fi locations, or even simple things like innovation on the device itself, to and an example protect the camera, against usage with a little thing like a thinkShutter that you can shut off the camera. SO what I want to show you here, is this is the full portfolio of ThinkShield that we are announcing today. This is clearly not something I can even read to you today, but I believe it shows you the breadth of security management that we are announcing today. There are four key pillars in managing security end-to-end. The first one is your data, and this has a lot of aspects around the hardware and the software itself. The second is identity. The third is the security around online, and ultimately the device itself. So, there is a breakout on security and ThinkShield today, available in the afternoon, and encourage you to really take a deeper look at this one. The first pillar around productivity was the device, and around the device. The second major pillar that we are seeing in terms of intelligent transformation is the workspace itself. Employees of a new generation have a very different habit how they work. They split their time between travel, working remotely but if they do come in the office, they expect a very different office environment than what they've seen in the past in cubicles or small offices. They come into the office to collaborate, and they want to create ideas, and they really work in cross-functional teams, and they want to do it instantly. And what we've seen is there is a huge amount of investment that companies are doing today in reconfiguring real estate reconfiguring offices. And most of these kind of things are moving to a digital platform. And what we are doing, is we want to build an entire set of solutions that are just focused on making the workspace more productive for remote workforce, and to create technology that allow people to work anywhere and connect instantly. And the core of this is that we need to be, the productivity of the employee as high as possible, and make it for him as easy as possible to use these kind of technologies. Last year in Transform, I announced that we will enter the smart office space. By the end of last year, we brought the first product into the market. It's called the Hub 500. It's already deployed in thousands of our customers, and it's uniquely focused on Microsoft Skype for Business, and making meeting instantly happen. And the product is very successful in the market. What we are announcing today is the next generation of this product, what is the Hub 700, what has a fantastic audio quality. It has far few microphones, and it is usable in small office environment, as well as in major conference rooms, but the most important part of this new announcement is that we are also announcing a software platform, and this software platform allows you to run multiple video conferencing software solutions on the same platform. Many of you may have standardized for one software solution or for another one, but as you are moving in a world of collaborating instantly with partners, customers, suppliers, you always will face multiple software standards in your company, and Lenovo is uniquely positioned but providing a middleware platform for the device to really enable multiple of these UX interfaces. And there's more to come and we will add additional UX interfaces on an ongoing base, based on our customer requirements. But this software does not only help to create a better experience and a higher productivity in the conference room or the huddle room itself. It really will allow you ultimately to manage all your conference rooms in the company in one instance. And you can run AI technologies around how to increase productivity utilization of your entire conference room ecosystem in your company. You will see a lot more devices coming from the node in this space, around intelligent screens, cameras, and so on, and so on. The idea is really that Lenovo will become a core provider in the whole movement into the smart office space. But it's great if you have hardware and software that is really supporting the approach of modern IT, but one component that Kirk also mentioned is absolutely critical, that we are providing this to you in an as a service approach. Get it what you want, when you need it, and pay it in the amount that you're really using it. And within UIT there is also I think a new philosophy around IT management, where you're much more focused on the value that you are consuming instead of investing into technology. We are launched as a service two years back and we already have a significant number of customers running PC as a service, but we believe as a service will stretch far more than just the PC device. It will go into categories like smart office. It might go even into categories like phone, and it will definitely go also in categories like storage and server in terms of capacity management. I want to highlight three offerings that we are also displaying today that are sort of building blocks in terms of how we really run as a service. The first one is that we collaborated intensively over the last year with Microsoft to be the launch pilot for their Autopilot offering, basically deploying images easily in the same approach like you would deploy a new phone on the network. The purpose really is to make new imaging and enabling new PC as seamless as it's used to be in the phone industry, and we have a complete set of offerings, and already a significant number customers have deployed Autopilot with Lenovo. The second major offering is Premier Support, like in the in the server business, where Premier Support is absolutely critical to run critical infrastructure, we see a lot of our customers do want to have Premier Support for their end users, so they can be back into work basically instantly, and that you have the highest possible instant repair on every single device. And then finally we have a significant amount of time invested into understanding how the software as a service really can get into one philosophy. And many of you already are consuming software as a service in many different contracts from many different vendors, but what we've created is one platform that really can manage this all together. All these things are the foundation for a device as a service offering that really can manage this end-to-end. So, implementing an intelligent workplace can be really a daunting prospect depending on where you're starting from, and how big your company ultimately is. But how do you manage the transformation of technology workspace if you're present in 50 or more countries and you run an infrastructure for more than 100,000 people? Michelin, famous for their tires, infamous for their Michelin star restaurant rating, especially in New York, and instantly recognizable by the Michelin Man, has just doing that. Please welcome with me Damon McIntyre from Michelin to talk to us about the challenges and transforming collaboration and productivity. (audience applauding) (electronic dance music) Thank you, David. >> Thank you, thank you very much. >> We on? >> So, how do you feel here? >> Well good, I want to thank you first of all for your partnership and the devices you create that helped us design, manufacture, and distribute the best tire in the world, okay? I just had to say it and put out there, alright. And I was wondering, were those Michelin tires on that Aston Martin? >> I'm pretty sure there is no other tire that would fit to that. >> Yeah, no, thank you, thank you again, and thank you for the introduction. >> So, when we talk about the transformation happening really in the workplace, the most tangible transformation that you actually see is the drastic change that companies are doing physically. They're breaking down walls. They're removing cubes, and they're moving to flexible layouts, new desks, new huddle rooms, open spaces, but the underlying technology for that is clearly not so visible very often. So, tell us about Michelin's strategy, and the technology you are deploying to really enable this corporation. >> So we, so let me give a little bit a history about the company to understand the daunting tasks that we had before us. So we have over 114,000 people in the company under 170 nationalities, okay? If you go to the corporate office in France, it's Clermont. It's about 3,000 executives and directors, and what have you in the marketing, sales, all the way up to the chain of the global CIO, right? Inside of the Americas, we merged in Americas about three years ago. Now we have the Americas zone. There's about 28,000 employees across the Americas, so it's really, it's really hard in a lot of cases. You start looking at the different areas that you lose time, and you lose you know, your productivity and what have you, so there, it's when we looked at different aspects of how we were going to manage the meeting rooms, right? because we have opened up our areas of workspace, our CIO, CEOs in our zones will no longer have an office. They'll sit out in front of everybody else and mingle with the crowd. So, how do you take those spaces that were originally used by an individual but now turn them into like meeting rooms? So, we went through a large process, and looked at the Hub 500, and that really met our needs, because at the end of the day what we noticed was, it was it was just it just worked, okay? We've just added it to the catalog, so we're going to be deploying it very soon, and I just want to again point that I know everybody struggles with this, and if you look at all the minutes that you lose in starting up a meeting, and we know you know what I'm talking about when I say this, it equates to many many many dollars, okay? And so at the end the day, this product helps us to be more efficient in starting up the meeting, and more productive during the meeting. >> Okay, it's very good to hear. Another major trend we are seeing in IT departments is taking a more hands-off approach to hardware. We're seeing new technologies enable IT to create a more efficient model, how IT gets hardware in the hands of end-users, and how they are ultimately supporting themselves. So what's your strategy around the lifecycle management of the devices? >> So yeah you mentioned, again, we'll go back to the 114,000 employees in the company, right? You imagine looking at all the devices we use. I'm not going to get into the number of devices we have, but we have a set number that we use, and we have to go through a process of deploying these devices, which we right now service our own image. We build our images, we service them through our help desk and all that process, and we go through it. If you imagine deploying 25,000 PCs in a year, okay? The time and the daunting task that's behind all that, you can probably add up to 20 or 30 people just full-time doing that, okay? So, with partnering with Lenovo and their excellent technology, their technical teams, and putting together the whole process of how we do imaging, it now lifts that burden off of our folks, and it shifts it into a more automated process through the cloud, okay? And, it's with the Autopilot on the end of the project, we'll have Autopilot fully engaged, but what I really appreciate is how Lenovo really, really kind of got with us, and partnered with us for the whole process. I mean it wasn't just a partner between Michelin and Lenovo. Microsoft was also partnered during that whole process, and it really was a good project that we put together, and we hope to have something in a full production mode next year for sure. >> So, David thank you very, very much to be here with us on stage. What I really want to say, customers like you, who are always challenging us on every single aspect of our capabilities really do make the big difference for us to get better every single day and we really appreciate the partnership. >> Yeah, and I would like to say this is that I am, I'm doing what he's exactly said he just said. I am challenging Lenovo to show us how we can innovate in our work space with your devices, right? That's a challenge, and it's going to be starting up next year for sure. We've done some in the past, but I'm really going to challenge you, and my whole aspect about how to do that is bring you into our workspace. Show you how we make how we go through the process of making tires and all that process, and how we distribute those tires, so you can brainstorm, come back to the table and say, here's a device that can do exactly what you're doing right now, better, more efficient, and save money, so thank you. >> Thank you very much, David. (audience applauding) Well it's sometimes really refreshing to get a very challenging customers feedback. And you know, we will continue to grow this business together, and I'm very confident that your challenge will ultimately help to make our products even more seamless together. So, as we now covered productivity and how we are really improving our devices itself, and the transformation around the workplace, there is one pillar left I want to talk about, and that's really, how do we make businesses smarter than ever? What that really means is, that we are on a journey on trying to understand our customer's business, deeper than ever, understanding our customer's processes even better than ever, and trying to understand how we can help our customers to become more competitive by injecting state-of-the-art technology in this intelligent transformation process, into core processes. But this cannot be done without talking about a fundamental and that is the journey towards 5G. I really believe that 5G is changing everything the way we are operating devices today, because they will be connected in a way like it has never done before. YY talked about you know, 20 times 10 times the amount of performance. There are other studies that talk about even 200 times the performance, how you can use these devices. What it will lead to ultimately is that we will build devices that will be always connected to the cloud. And, we are preparing for this, and Kirk already talked about, and how many operators in the world we already present with our Moto phones, with how many Telcos we are working already on the backend, and we are working on the device side on integrating 5G basically into every single one of our product in the future. One of the areas that will benefit hugely from always connected is the world of virtual reality and augmented reality. And I'm going to pick here one example, and that is that we have created a commercial VR solution for classrooms and education, and basically using consumer type of product like our Mirage Solo with Daydream and put a solution around this one that enables teachers and schools to use these products in the classroom experience. So, students now can have immersive learning. They can studying sciences. They can look at environmental issues. They can exploring their careers, or they can even taking a tour in the next college they're going to go after this one. And no matter what grade level, this is how people will continue to learn in the future. It's quite a departure from the old world of textbooks. In our area that we are looking is IoT, And as YY already elaborated, we are clearly learning from our own processes around how we improve our supply chain and manufacturing and how we improve also retail experience and warehousing, and we are working with some of the largest companies in the world on pilots, on deploying IoT solutions to make their businesses, their processes, and their businesses, you know, more competitive, and some of them you can see in the demo environment. Lenovo itself already is managing 55 million devices in an IoT fashion connecting to our own cloud, and constantly improving the experience by learning from the behavior of these devices in an IoT way, and we are collecting significant amount of data to really improve the performance of these systems and our future generations of products on a ongoing base. We have a very strong partnership with a company called ADLINK from Taiwan that is one of the leading manufacturers of manufacturing PC and hardened devices to create solutions on the IoT platform. The next area that we are very actively investing in is commercial augmented reality. I believe augmented reality has by far more opportunity in commercial than virtual reality, because it has the potential to ultimately improve every single business process of commercial customers. Imagine in the future how complex surgeries can be simplified by basically having real-time augmented reality information about the surgery, by having people connecting into a virtual surgery, and supporting the surgery around the world. Visit a furniture store in the future and see how this furniture looks in your home instantly. Doing some maintenance on some devices yourself by just calling the company and getting an online manual into an augmented reality device. Lenovo is exploring all kinds of possibilities, and you will see a solution very soon from Lenovo. Early when we talked about smart office, I talked about the importance of creating a software platform that really run all these use cases for a smart office. We are creating a similar platform for augmented reality where companies can develop and run all their argumented reality use cases. So you will see that early in 2019 we will announce an augmented reality device, as well as an augmented reality platform. So, I know you're very interested on what exactly we are rolling out, so we will have a first prototype view available there. It's still a codename project on the horizon, and we will announce it ultimately in 2019, but I think it's good for you to take a look what we are doing here. So, I just wanted to give you a peek on what we are working beyond smart office and the device productivity in terms of really how we make businesses smarter. It's really about increasing productivity, providing you the most secure solutions, increase workplace collaboration, increase IT efficiency, using new computing devices and software and services to make business smarter in the future. There's no other company that will enable to offer what we do in commercial. No company has the breadth of commercial devices, software solutions, and the same data center capabilities, and no other company can do more for your intelligent transformation than Lenovo. Thank you very much. (audience applauding) >> Thanks mate, give me that. I need that. Alright, ladies and gentlemen, we are done. So firstly, I've got a couple of little housekeeping pieces at the end of this and then we can go straight into going and experiencing some of the technology we've got on the left-hand side of the room here. So, I want to thank Christian obviously. Christian, awesome as always, some great announcements there. I love the P1. I actually like the Aston Martin a little bit better, but I'll take either if you want to give me one for free. I'll take it. We heard from YY obviously about the industry and how the the fourth Industrial Revolution is impacting us all from a digital transformation perspective, and obviously Kirk on DCG, the great NetApp announcement, which is going to be really exciting, actually that Twitter and some of the social media panels are absolutely going crazy, so it's good to see that the industry is really taking some impact. Some of the publications are really great, so thank you for the media who are obviously in the room publishing right no. But now, I really want to say it's all of your turn. So, all of you up the back there who are having coffee, it's your turn now. I want everyone who's sitting down here after this event move into there, and really take advantage of the 15 breakouts that we've got set there. There are four breakout sessions from a time perspective. I want to try and get you all out there at least to use up three of them and use your fourth one to get out and actually experience some of the technology. So, you've got four breakout sessions. A lot of the breakout sessions are actually done twice. If you have not downloaded the app, please download the app so you can actually see what time things are going on and make sure you're registering correctly. There's a lot of great experience of stuff out there for you to go do. I've got one quick video to show you on some of the technology we've got and then we're about to close. Alright, here we are acting crazy. Now, you can see obviously, artificial intelligence machine learning in the browser. God, I hate that dance, I'm not a Millenial at all. It's effectively going to be implemented by healthcare. I want you to come around and test that out. Look at these two guys. This looks like a Lenovo management meeting to be honest with you. These two guys are actually concentrating, using their brain power to race each others in cars. You got to come past and give that a try. Give that a try obviously. Fantastic event here, lots of technology for you to experience, and great partners that have been involved as well. And so, from a Lenovo perspective, we've had some great alliance partners contribute, including obviously our number one partner, Intel, who's been a really big loyal contributor to us, and been a real part of our success here at Transform. Excellent, so please, you've just seen a little bit of tech out there that you can go and play with. I really want you, I mean go put on those black things, like Scott Hawkins our chief marketing officer from Lenovo's DCG business was doing and racing around this little car with his concentration not using his hands. He said it's really good actually, but as soon as someone comes up to speak to him, his car stops, so you got to try and do better. You got to try and prove if you can multitask or not. Get up there and concentrate and talk at the same time. 62 different breakouts up there. I'm not going to go into too much detai, but you can see we've got a very, very unusual numbering system, 18 to 18.8. I think over here we've got a 4849. There's a 4114. And then up here we've got a 46.1 and a 46.2. So, you need the decoder ring to be able to understand it. Get over there have a lot of fun. Remember the boat leaves today at 4:00 o'clock, right behind us at the pier right behind us here. There's 400 of us registered. Go onto the app and let us know if there's more people coming. It's going to be a great event out there on the Hudson River. Ladies and gentlemen that is the end of your keynote. I want to thank you all for being patient and thank all of our speakers today. Have a great have a great day, thank you very much. 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>> Hi, I'm Peter Burris. Welcome to Wikibon's Action Item. (electronic music) Once again, we're broadcasting from our beautiful theCUBE Studios in Palo Alto, California. Here in the studio with me, George Gilbert, and remote, we have Neil Raden, Jim Kobielus, and David Floyer. Welcome, guys! >> Hey. >> Hi, there. >> We've got a really interesting topic today. We're going to be talking about graph databases, which probably just immediately turned off everybody. But we're actually not going to talk so much about it from a technology standpoint. We're really going to spend most of our time talking about it from the standpoint of the business problems that IT and technology are being asked to address, and the degree to which graph databases, in fact, can help us address those problems, and what do we need to do to actually address them. Human beings tend to think in terms of relationships of things to each other. So what the graph community talks about is graphed-shaped problems. And by graph-shaped problem we might mean that someone owns something and someone owns something else, or someone shares an asset, or it could be any number of different things. But we tend to think in terms of things and the relationship that those things have to other things. Now, the relational model has been an extremely successful way of representing data for a lot of different applications over the course of the last 30 years, and it's not likely to go away. But the question is, do these graph-shaped problems actually lend themselves to a new technology that can work with relational technology to accelerate the rate at which we can address new problems, accelerate the performance of those new problems, and ensure the flexibility and plasticity that we need within the application set, so that we can consistently use this as a basis for going out and extending the quality of our applications as we take on even more complex problems in the future. So let's start here. Jim Kobielus, when we think about graph databases, give us a little hint on the technology and where we are today. >> Yeah, well, graph databases have been around for quite a while in various forms, addressing various core-use cases such as social network analysis, recommendation engines, fraud detection, semantic search, and so on. The graph database technology is essentially very closely related to relational, but it's specialized to, when you think about it, Peter, the very heart of a graph-shaped business problem, the entity relationship polygram. And anybody who's studied databases has mastered, at least at a high level, entity relationship diagrams. The more complex these relationships grow among a growing range of entities, the more complex sort of the network structure becomes, in terms of linking them together at a logical level. So graph database technology was developed a while back to be able to support very complex graphs of entities, and relationships, in order to do, a lot of it's analytic. A lot of it's very focused on fast query, they call query traversal, among very large graphs, to find quick answers to questions that might involve who owns which products that they bought at which stores in which cities and are serviced by which support contractors and have which connections or interrelationships with other products they may have bought from us and our partners, so forth and so on. When you have very complex questions of this sort, they lend themselves to graph modeling. And to some degree, to the extent that you need to perform very complex queries of this sort very rapidly, graph databases, and there's a wide range of those on the market, have been optimized for that. But we also have graph abstraction layers over RDBMSes and multi-model databases. You'll find them running in IBM's databases, or Microsoft Cosmos DB, and so forth. You don't need graph-specialized databases in order to do graph queries, in order to manipulate graphs. That's the issue here. When does a specialized graph database serve your needs better than a non-graph-optimized but nonetheless graph-enabling database? That's the core question. >> So, Neil Raden, let's talk a little bit about the classes of business problems that could in fact be served by representing data utilizing a graph model. So these graph-shaped problems, independent of the underlying technology. Let's start there. What kinds of problems can business people start thinking about solving by thinking in terms of graphs of things and relationships amongst things? >> It all comes down to connectedness. That's the basis of a graph database, is how things are connected, either weakly or strongly. And these connected relationships can be very complicated. They can be based on very complex properties. A relational database is not based on, not only is it not based on connectedness, it's not based on connectedness at all. I'd like to say it's based on un-connectedness. And the whole idea in a relational database is that the intelligence about connectedness is buried in the predicate of a query. It's not in the database itself. So I don't know how overlaying graph abstractions on top of a relational database are a good idea. On the other hand, I don't know how stitching a relational database into your existing operation is going to work, either. We're going to have to see. But I can tell you that a major part of data science, machine learning, and AI is going to need to address the issue of causality, not just what's related to each other. And there's a lot of science behind using graphs to get at the causality problem. >> And we've seen, well, let's come back to that. I want to come back to that. But George Gilbert, we've kind of experienced a similar type of thing back in the '90s with the whole concept of object-orientated databases. They were represented as a way of re-conceiving data. The problem was that they had to go from the concept all the way down to the physical thing, and they didn't seem to work. What happened? >> Well it turns out, the big argument was, with object-oriented databases, we can model anything that's so much richer, especially since we're programming with objects. And it turns out, though, that theoretically, especially at that time, you could model anything down at the physical level or even the logical level in a relational database, and so those code bases were able to handle sort of similar, both ends of the use cases, both ends of the spectrum. But now that we have such extreme demands on our data management, rather than look at a whole application or multiple applications even sharing a single relational database, like some of the big enterprise apps, we have workloads within apps like recommendation engines, or a knowledge graph, which explains the relationship between people, places, and things. Or digital twins, or mapping your IT infrastructure and applications, and how they all hold together. You could do that in a relational database, but in a graph database, you can organize it so that you can have really fast analysis of these structures. But, the trade-off is, you're going to be much more restricted in how you can update the stuff. >> Alright, so we think about what happened, then, with some of the object-orientated technology, the original world database, the database was bound to the application, and the developer used the database to tell the application where to go find the data. >> George: Right. >> Relational data allowed us not to tell the applications where to find things, but rather how to find things, and that was persisted, and was very successful for a long time. Object-orientated technologies, in many respects, went back to the idea that the developer had to be very concrete about telling the application where the data was, but we didn't want to do things that way. Now, something's happened, David Floyer. One of the reasons why we had this challenge of representing data in a more abstract way across a lot of different forms without having it also being represented physically, and therefore a lot of different copies and a lot of different representations of the data which broke systems of record and everything else, was that the underlying technology was focused on just persisting data and not necessarily delivering it into these new types of datas, databases, data models, et cetera. But Flash changes that, doesn't it? Can't we imagine a world in which we can have our data in Flash and then, which is a technology that's more focused on delivering data, and then having that data be delivered to a lot of different representations, including things like graph databases, graph models. Is that accurate? >> Absolutely. In a moment I'll take it even further. I think the first point is that when we were designing real-time applications, transactional applications, we were very constrained, indeed, by the amount of data that we could get to. So, as a database administrator, I used to have a rule which you could, database developers could not issue more than 100 database calls. And the reason was that, they could always do more than that, but the applications became very unstable and they became very difficult to maintain. The cost of maintenance went up a lot. The whole area of Flash allows us to do a number of things, and the area of UniGrid enables us to do a number of things very differently. So that we can, for example, share data and have many different views of it. We can use UniGrid to be able to bring far greater amounts of power, compute power, GPUs, et cetera, to bear on specific workloads. I think the most useful thing to think about this is this type of architecture can be used to create systems of intelligence, where you have the traditional relational databases dealing with systems of record, and then you can have the AI systems, graph systems, all the other components there looking at the best way of providing data and making decisions in real time that can be fed back into the systems of record. >> Alright, alright. So let's-- >> George: I want to add to something on this. >> So, Neil, let me come back to you very quickly, sorry, George. Let me come back to Neil. I want to spend, go back to this question of what does a graph-shaped problem look like? Let's kind of run down it. We talked about AI, what about IoT, guys? Is IoT going to help us, is IoT going to drive this notion of looking at the world in terms of graphs more or less? What do you think, Neil? >> I don't know. I hadn't really thought about it, Peter, to tell you the truth. I think that one thing we leave out when we talk about graphs is we talk about, you know, nodes and edges and relationships and so forth, but you can also build a graph with very rich properties. And one thing you can get from a graph query that you can't get from a relational query, unless you write careful predicate, is it can actually do some thinking for you. It can tell you something you don't know. And I think that's important. So, without being too specific about IoT, I have to say that, you know, streaming data and trying to relate it to other data, getting down to, very quickly, what's going on, root-cause analysis, I think graph would be very helpful. >> Great, and, Jim Kobielus, how about you? >> I think, yeah I think that IoT is tailor-made for, or I should say, graph modeling and graph databases are tailor-made for the IoT. Let me explain. I think the IoT, the graph is very much a metadata technology, it's expressing context in a connected universe. Where the IoT is concerned it's all about connectivity, and so graphs, increasingly complex graphs of, say, individuals and the devices and the apps they use and locations and various contexts and so forth, these are increasingly graph-based. They're hierarchical and shifting and changing, and so in order to contextualize and personalize experience in a graph, in an IoT world, I think graph databases will be embedded in the very fabric of these environments. Microsoft has a strategy they announced about a year ago to build more of an intelligent edge around, a distributed graph across all their offerings. So I think graphs will become more important in this era, undoubtedly. >> George, what do you think? Business problems? >> Business problems on IoT. The knowledge graph that holds together digital twin, both of these lend themselves to graph modeling, but to use the object-oriented databases as an example, where object modeling took off was in the applications server, where you had the ability to program, in object-oriented language, and that mapped to a relational database. And that is an option, not the only one, but it's an option for handling graph-model data like a digital twin or IT operations. >> Well that suggests that what we're thinking about here, if we talk about graph as a metadata, and I think, Neil, this partly answers the question that you had about why would anybody want to do this, that we're representing the output of a relational data as a node in a network of data types or data forms so that the data itself may still be relationally structured, but from an application standpoint, the output of that query is, itself, a thing that is then used within the application. >> But to expand on that, if you store it underneath, as fully normalized, in relational language, laid out so that there's no duplicates and things like that, it gives you much faster update performance, but the really complex queries, typical of graph data models, would be very, very slow. So, once we have, say, more in memory technology, or we can manage under the covers the sort of multiple representations of the data-- >> Well that's what Flash is going to allow us to do. >> Okay. >> What David Floyer just talked about. >> George: Okay. >> So we can have a single, persistent, physical storage >> Yeah. >> but it can be represented in a lot of different ways so that we avoid some of the problems that you're starting to raise. If we had to copy the data and have physical, physical copies of the data on disc in a lot of different places then we would run into all kinds of consistency and update. It would probably break the model. We'd probably come back to the notion of a single data store. >> George: (mumbles) >> I want to move on here, guys. One really quick thing, David Floyer, I want to ask you. If there's, you mentioned when you were database administrator and you put restrictions on how many database actions an application or transaction was allowed to generate. When we think about what a business is going to have to do to take advantage of this, are there any particular, like one thing that we need to think about? What's going to change within an IT organization to take advantage of graph database? And we'll do the action items. >> Right. So the key here is the number of database calls can grow by a factor of probably a thousand times what it is now with what we can see is coming as technologies over the next couple of years. >> So let me put that in context, David. That's a single transaction now generating a hundred thousand, >> Correct. >> a hundred thousand database calls. >> Well, access calls to data. >> Right. >> Whatever type of database. And the important thing here is that a lot of that is going to move out, with the discussion of IoT, to where the data is coming in. Because the quicker you can do that, the earlier you can analyze that data, and you talked about IoT with possible different sources coming in, a simple one like traffic lights, for example. The traffic lights are being affected by the traffic lights around them within the city. Those sort of problems are ideal for this sort of graph database. And having all of that data locally and being processed locally in memory very, very close to where those sensors are, is going to be the key to developing solutions in this area. >> So, Neil, I've got one question from you, or one question for you. I'm going to put you on the spot. I just had a thought. And here's the thought. We talk a lot about, in some of the new technologies that could in fact be employed here, whether it be blockchain or even going back to SOA, but when we talk about what a system is going to have the authority to do about the idea of writing contracts that describe very, very discretely, what a system is or is not going to do. I have a feeling those contracts are not going to be written in relational terms. I have a feeling that, like most legal documents, they will be written in what looks more like graph terms. I'm extending that a little bit, but this has rights to do this at this point in time. Is that also, this notion of incorporating more contracts directly to how systems work, to assure that we have the appropriate authorities laid out. What do you think? Is that going to be easier or harder as a consequence of thinking in terms of these graph-shaped models? >> Boy, I don't know. Again, another thing I hadn't really thought about. But I do see some real gaps in thinking. Let me give you an analogy. OLAP databases came on the scene back in the '90s whatever. People in finance departments and whatever they loved OLAP. What they hated was the lack of scalability. And now what we see now is scalability isn't a problem and OLAP solutions are suddenly bursting out all over the place. So I think there's a role for a mental model of how you model your data and how you use it that's different from the relational model. I think the relational model has prominence and has that advantage of, what's it called? Occupancy or something. But I think that the graph is going to show some real capabilities that people are lacking right now. I think some of them are at the very high end, things, like I said, getting to causality. But I think that graph theory itself is so much richer than the simple concept of graphs that's implemented in graph databases today. >> Yeah, I agree with that totally. Okay, let's do the action item round. Jim Kobielus, I want to start with you. Jim, action item. >> Yeah, for data professionals and analytic professionals, focus on what graphs can't do, cannot do, because you hear a lot of hyperbolic, they're not useful for unstructured data or for machine learning in database. They're not as useful for schema on read. What they are useful for is the same core thing that relational is useful for which is schema on write applied to structured data. Number one. Number two, and I'll be quick on this, focus on the core use cases that are already proven out for graph databases. We've already ticked them off here, social network analysis, recommendation engines, influencer analysis, semantic web. There's a rich range of mature use cases for which semantic techniques are suited. And then finally, and I'll be very quick here, bear in mind that relational databases have been supporting graph modeling, graph traversal and so forth, for quite some time, including pretty much all the core mature enterprise databases. If you're using those databases already, and they can perform graph traversals and so forth reasonably well for your intended application, stick with that. No need to investigate the pure play, graph-optimized databases on the market. However, that said, there's plenty of good ones, including AWS is coming out with Neptune. Please explore the other alternatives, but don't feel like you have to go to a graph database first and foremost. >> Alright. David Floyer, action item. >> Action item. You are going to need to move your data center and your applications from the traditional way of thinking about it, of handling things, which is sequential copies going around, usually taking it two or three weeks. You're going to have to move towards a shared data model where the same set of data can have multiple views of it and multiple uses for multiple different types of databases. >> George Gilbert, action item. >> Okay, so when you're looking at, you have a graph-oriented problem, in other words the data is shaped like a graph, question is what type of database do you use? If you have really complex query and analysis use cases, probably best to use a graph database. If you have really complex update requirements, best to use a combination, perhaps of relational and graph or something like multi-model. We can learn from Facebook where, for years, they've built their source of truth for the social graph on a bunch of sharded MySQL databases with some layers on top. That's for analyzing the graph and doing graph searches. I'm sorry, for updating the graph and maintaining it and its integrity. But for reading the graph, they have an entirely different layer for comprehensive queries and manipulating and traversing all those relationships. So, you don't get a free lunch either way. You have to choose your sweet spots and the trade-offs associated with them. >> Alright, Neil Raden, action item. >> Well, first of all, I don't think the graph databases are subject to a lot of hype. I think it's just the opposite. I think they haven't gotten much hype at all. And maybe we're going to see that. But another thing is, a fundamental difference when you're looking at a graph and a graph query, it uses something called open world reasoning. A relational database uses closed world reasoning. I'll give you an example. Country has capital city. Now you have in your graph that China has capital city Beijing, China has capital city Beijing. That doesn't violate the graph. The graph simply understands and intuits that they're different names for the same thing. Now, if you love to write correlated sub-queries for many, many different relationships, I'd say stick to your relational database. I see unique capabilities in a graph that would be difficult to implement in a relational database. >> Alright. Thank you very much, guys. Let's talk about what the action item is for all of us. This week we talked about graph databases. We do believe that they have enormous potential, but we first off have to draw a distinction between graph theory, which is a way of looking at the world and envisioning and conceptualizing solutions to problems, and graph database technology, which has the advantages of being able, for certain classes of data models, to be able to very quickly both write and read data that is based on relationships and hierarchies and network structures that are difficult to represent in a normalized relational database manager. Ultimately, our expectation is that over the next few years, we're going to see an explosion in the class of business problems that lend themselves to a graph-modeling orientation. IoT is an example, very complex analytics systems will be an example, but it is not the only approach or the only way of doing things. But what is interesting, what is especially interesting, is over the last few years, a change in the underlying hardware technology is allowing us to utilize and expand the range of tools that we might use to support these new classes of applications. Specifically, the move to Flash allows us to sustain a single physical copy of data and then have that be represented in a lot of different ways to support a lot of different model forms and a lot of different application types, without undermining the fundamental consistency and integrity of the data itself. So that is going to allow us to utilize new types of technologies in ways that we haven't utilized before, because before, whether it was object-oriented technology or OLAP technology, there was always this problem of having to create new physical copies of data which led to enormous data administrative nightmares. So looking forward, the ability to use Flash as a basis for physically storing the data and delivering it out to a lot of different model and tool forms creates an opportunity for us to use technologies that, in fact, may more naturally map to the way that human beings think about things. Now, where is this likely to really play? We talked about IoT, we talked about other types of technologies. Where it's really likely to play is when the domain expertise of a business person is really pushing the envelope on the nature of the business problem. Historically, applications like accounting or whatnot, were very focused on highly stylized data models, things that didn't necessarily exist in the real world. You don't have double-entry bookkeeping running in the wild. You do have it in the legal code, but for some of the things that we want to build in the future, people, the devices they own, where they are, how they're doing things, that lends itself to a real-world experience and human beings tend to look at those using a graph orientation. And the expectations over the next few years, because of the changes in the physical technology, how we can store data, we will be able to utilize a new set of tools that are going to allow us to more quickly bring up applications, more naturally manage data associated with those applications, and, very important, utilize targeted technology in a broader set of complex application portfolios that are appropriate to solve that particular part of the problem, whether it's a recommendation engine or something else. Alright, so, once again, I want to thank the remote guys, Jim Kobielus, Neil Raden, and David Floyer. Thank you very much for being here. George Gilbert, you're in the studio with me. And, once again, I'm Peter Burris and you've been listening to Action Item. Thank you for joining us and we'll talk to you again soon. (electronic music)