>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> This past week we saw two of the Big 3 cloud providers present the latest update on their respective cloud visions, their business progress, their announcements and innovations. The content at these events had many overlapping themes, including modern cloud infrastructure at global scale, applying advanced machine intelligence, AKA AI, end-to-end data platforms, collaboration software. They talked a lot about the future of work automation. And they gave us a little taste, each company of the Metaverse Web 3.0 and much more. Despite these striking similarities, the differences between these two cloud platforms and that of AWS remains significant. With Microsoft leveraging its massive application software footprint to dominate virtually all markets and Google doing everything in its power to keep up with the frenetic pace of today's cloud innovation, which was set into motion a decade and a half ago by AWS. Hello and welcome to this week's Wikibon CUBE Insights, powered by ETR. In this Breaking Analysis, we unpack the immense amount of content presented by the CEOs of Microsoft and Google Cloud at Microsoft Ignite and Google Cloud Next. We'll also quantify with ETR survey data the relative position of these two cloud giants in four key sectors: cloud IaaS, BI analytics, data platforms and collaboration software. Now one thing was clear this past week, hybrid events are the thing. Google Cloud Next took place live over a 24-hour period in six cities around the world, with the main gathering in New York City. Microsoft Ignite, which normally is attended by 30,000 people, had a smaller event in Seattle, in person with a virtual audience around the world. AWS re:Invent, of course, is much different. Yes, there's a virtual component at re:Invent, but it's all about a big live audience gathering the week after Thanksgiving, in the first week of December in Las Vegas. Regardless, Satya Nadella keynote address was prerecorded. It was highly produced and substantive. It was visionary, energetic with a strong message that Azure was a platform to allow customers to build their digital businesses. Doing more with less, which was a key theme of his. Nadella covered a lot of ground, starting with infrastructure from the compute, highlighting a collaboration with Arm-based, Ampere processors. New block storage, 60 regions, 175,000 miles of fiber cables around the world. He presented a meaningful multi-cloud message with Azure Arc to support on-prem and edge workloads, as well as of course the public cloud. And talked about confidential computing at the infrastructure level, a theme we hear from all cloud vendors. He then went deeper into the end-to-end data platform that Microsoft is building from the core data stores to analytics, to governance and the myriad tooling Microsoft offers. AI was next with a big focus on automation, AI, training models. He showed demos of machines coding and fixing code and machines automatically creating designs for creative workers and how Power Automate, Microsoft's RPA tooling, would combine with Microsoft Syntex to understand documents and provide standard ways for organizations to communicate with those documents. There was of course a big focus on Azure as developer cloud platform with GitHub Copilot as a linchpin using AI to assist coders in low-code and no-code innovations that are coming down the pipe. And another giant theme was a workforce transformation and how Microsoft is using its heritage and collaboration and productivity software to move beyond what Nadella called productivity paranoia, i.e., are remote workers doing their jobs? In a world where collaboration is built into intelligent workflows, and he even showed a glimpse of the future with AI-powered avatars and partnerships with Meta and Cisco with Teams of all firms. And finally, security with a bevy of tools from identity, endpoint, governance, et cetera, stressing a suite of tools from a single provider, i.e., Microsoft. So a couple points here. One, Microsoft is following in the footsteps of AWS with silicon advancements and didn't really emphasize that trend much except for the Ampere announcement. But it's building out cloud infrastructure at a massive scale, there is no debate about that. Its plan on data is to try and provide a somewhat more abstracted and simplified solutions, which differs a little bit from AWS's approach of the right database tool, for example, for the right job. Microsoft's automation play appears to provide simple individual productivity tools, kind of a ground up approach and make it really easy for users to drive these bottoms up initiatives. We heard from UiPath that forward five last month, a little bit of a different approach of horizontal automation, end-to-end across platforms. So quite a different play there. Microsoft's angle on workforce transformation is visionary and will continue to solidify in our view its dominant position with Teams and Microsoft 365, and it will drive cloud infrastructure consumption by default. On security as well as a cloud player, it has to have world-class security, and Azure does. There's not a lot of debate about that, but the knock on Microsoft is Patch Tuesday becomes Hack Wednesday because Microsoft releases so many patches, it's got so much Swiss cheese in its legacy estate and patching frequently, it becomes a roadmap and a trigger for hackers. Hey, patch Tuesday, these are all the exploits that you can go after so you can act before the patches are implemented. And so it's really become a problem for users. As well Microsoft is competing with many of the best-of-breed platforms like CrowdStrike and Okta, which have market momentum and appear to be more attractive horizontal plays for customers outside of just the Microsoft cloud. But again, it's Microsoft. They make it easy and very inexpensive to adopt. Now, despite the outstanding presentation by Satya Nadella, there are a couple of statements that should raise eyebrows. Here are two of them. First, as he said, Azure is the only cloud that supports all organizations and all workloads from enterprises to startups, to highly regulated industries. I had a conversation with Sarbjeet Johal about this, to make sure I wasn't just missing something and we were both surprised, somewhat, by this claim. I mean most certainly AWS supports more certifications for example, and we would think it has a reasonable case to dispute that claim. And the other statement, Nadella made, Azure is the only cloud provider enabling highly regulated industries to bring their most sensitive applications to the cloud. Now, reasonable people can debate whether AWS is there yet, but very clearly Oracle and IBM would have something to say about that statement. Now maybe it's not just, would say, "Oh, they're not real clouds, you know, they're just going to hosting in the cloud if you will." But still, when it comes to mission-critical applications, you would think Oracle is really the the leader there. Oh, and Satya also mentioned the claim that the Edge browser, the Microsoft Edge browser, no questions asked, he said, is the best browser for business. And we could see some people having some questions about that. Like isn't Edge based on Chrome? Anyway, so we just had to question these statements and challenge Microsoft to defend them because to us it's a little bit of BS and makes one wonder what else in such as awesome keynote and it was awesome, it was hyperbole. Okay, moving on to Google Cloud Next. The keynote started with Sundar Pichai doing a virtual session, he was remote, stressing the importance of Google Cloud. He mentioned that Google Cloud from its Q2 earnings was on a $25-billion annual run rate. What he didn't mention is that it's also on a 3.6 billion annual operating loss run rate based on its first half performance. Just saying. And we'll dig into that issue a little bit more later in this episode. He also stressed that the investments that Google has made to support its core business and search, like its global network of 22 subsea cables to support things like, YouTube video, great performance obviously that we all rely on, those innovations there. Innovations in BigQuery to support its search business and its threat analysis that it's always had and its AI, it's always been an AI-first company, he's stressed, that they're all leveraged by the Google Cloud Platform, GCP. This is all true by the way. Google has absolutely awesome tech and the talk, as well as his talk, Pichai, but also Kurian's was forward thinking and laid out a vision of the future. But it didn't address in our view, and I talked to Sarbjeet Johal about this as well, today's challenges to the degree that Microsoft did and we expect AWS will at re:Invent this year, it was more out there, more forward thinking, what's possible in the future, somewhat less about today's problem, so I think it's resonates less with today's enterprise players. Thomas Kurian then took over from Sundar Pichai and did a really good job of highlighting customers, and I think he has to, right? He has to say, "Look, we are in this game. We have customers, 9 out of the top 10 media firms use Google Cloud. 8 out of the top 10 manufacturers. 9 out of the top 10 retailers. Same for telecom, same for healthcare. 8 out of the top 10 retail banks." He and Sundar specifically referenced a number of companies, customers, including Avery Dennison, Groupe Renault, H&M, John Hopkins, Prudential, Minna Bank out of Japan, ANZ bank and many, many others during the session. So you know, they had some proof points and you got to give 'em props for that. Now like Microsoft, Google talked about infrastructure, they referenced training processors and regions and compute optionality and storage and how new workloads were emerging, particularly data-driven workloads in AI that required new infrastructure. He explicitly highlighted partnerships within Nvidia and Intel. I didn't see anything on Arm, which somewhat surprised me 'cause I believe Google's working on that or at least has come following in AWS's suit if you will, but maybe that's why they're not mentioning it or maybe I got to do more research there, but let's park that for a minute. But again, as we've extensively discussed in Breaking Analysis in our view when it comes to compute, AWS via its Annapurna acquisition is well ahead of the pack in this area. Arm is making its way into the enterprise, but all three companies are heavily investing in infrastructure, which is great news for customers and the ecosystem. We'll come back to that. Data and AI go hand in hand, and there was no shortage of data talk. Google didn't mention Snowflake or Databricks specifically, but it did mention, by the way, it mentioned Mongo a couple of times, but it did mention Google's, quote, Open Data cloud. Now maybe Google has used that term before, but Snowflake has been marketing the data cloud concept for a couple of years now. So that struck as a shot across the bow to one of its partners and obviously competitor, Snowflake. At BigQuery is a main centerpiece of Google's data strategy. Kurian talked about how they can take any data from any source in any format from any cloud provider with BigQuery Omni and aggregate and understand it. And with the support of Apache Iceberg and Delta and Hudi coming in the future and its open Data Cloud Alliance, they talked a lot about that. So without specifically mentioning Snowflake or Databricks, Kurian co-opted a lot of messaging from these two players, such as life and tech. Kurian also talked about Google Workspace and how it's now at 8 million users up from 6 million just two years ago. There's a lot of discussion on developer optionality and several details on tools supported and the open mantra of Google. And finally on security, Google brought out Kevin Mandian, he's a CUBE alum, extremely impressive individual who's CEO of Mandiant, a leading security service provider and consultancy that Google recently acquired for around 5.3 billion. They talked about moving from a shared responsibility model to a shared fate model, which is again, it's kind of a shot across AWS's bow, kind of shared responsibility model. It's unclear that Google will pay the same penalty if a customer doesn't live up to its portion of the shared responsibility, but we can probably assume that the customer is still going to bear the brunt of the pain, nonetheless. Mandiant is really interesting because it's a services play and Google has stated that it is not a services company, it's going to give partners in the channel plenty of room to play. So we'll see what it does with Mandiant. But Mandiant is a very strong enterprise capability and in the single most important area security. So interesting acquisition by Google. Now as well, unlike Microsoft, Google is not competing with security leaders like Okta and CrowdStrike. Rather, it's partnering aggressively with those firms and prominently putting them forth. All right. Let's get into the ETR survey data and see how Microsoft and Google are positioned in four key markets that we've mentioned before, IaaS, BI analytics, database data platforms and collaboration software. First, let's look at the IaaS cloud. ETR is just about to release its October survey, so I cannot share the that data yet. I can only show July data, but we're going to give you some directional hints throughout this conversation. This chart shows net score or spending momentum on the vertical axis and overlap or presence in the data, i.e., how pervasive the platform is. That's on the horizontal axis. And we've inserted the Wikibon estimates of IaaS revenue for the companies, the Big 3. Actually the Big 4, we included Alibaba. So a couple of points in this somewhat busy data chart. First, Microsoft and AWS as always are dominant on both axes. The red dotted line there at 40% on the vertical axis. That represents a highly elevated spending velocity and all of the Big 3 are above the line. Now at the same time, GCP is well behind the two leaders on the horizontal axis and you can see that in the table insert as well in our revenue estimates. Now why is Azure bigger in the ETR survey when AWS is larger according to the Wikibon revenue estimates? And the answer is because Microsoft with products like 365 and Teams will often be considered by respondents in the survey as cloud by customers, so they fit into that ETR category. But in the insert data we're stripping out applications and SaaS from Microsoft and Google and we're only isolating on IaaS. The other point is when you take a look at the early October returns, you see downward pressure as signified by those dotted arrows on every name. The only exception was Dell, or Dell and IBM, which showing slightly improved momentum. So the survey data generally confirms what we know that AWS and Azure have a massive lead and strong momentum in the marketplace. But the real story is below the line. Unlike Google Cloud, which is on pace to lose well over 3 billion on an operating basis this year, AWS's operating profit is around $20 billion annually. Microsoft's Intelligent Cloud generated more than $30 billion in operating income last fiscal year. Let that sink in for a moment. Now again, that's not to say Google doesn't have traction, it does and Kurian gave some nice proof points and customer examples in his keynote presentation, but the data underscores the lead that Microsoft and AWS have on Google in cloud. And here's a breakdown of ETR's proprietary net score methodology, that vertical axis that we showed you in the previous chart. It asks customers, are you adopting the platform new? That's that lime green. Are you spending 6% or more? That's the forest green. Is you're spending flat? That's the gray. Is you're spending down 6% or worse? That's the pinkest color. Or are you replacing the platform, defecting? That's the bright red. You subtract the reds from the greens and you get a net score. Now one caveat here, which actually is really favorable from Microsoft, the Microsoft data that we're showing here is across the entire Microsoft portfolio. The other point is, this is July data, we'll have an update for you once ETR releases its October results. But we're talking about meaningful samples here, the ends. 620 for AWS over a thousand from Microsoft in more than 450 respondents in the survey for Google. So the real tell is replacements, that bright red. There is virtually no churn for AWS and Microsoft, but Google's churn is 5x, those two in the survey. Now 5% churn is not high, but you'd like to see three things for Google given it's smaller size. One is less churn, two is much, much higher adoption rates in the lime green. Three is a higher percentage of those spending more, the forest green. And four is a lower percentage of those spending less. And none of these conditions really applies here for Google. GCP is still not growing fast enough in our opinion, and doesn't have nearly the traction of the two leaders and that shows up in the survey data. All right, let's look at the next sector, BI analytics. Here we have that same XY dimension. Again, Microsoft dominating the picture. AWS very strong also in both axes. Tableau, very popular and respectable of course acquired by Salesforce on the vertical axis, still looking pretty good there. And again on the horizontal axis, big presence there for Tableau. And Google with Looker and its other platforms is also respectable, but it again, has some work to do. Now notice Streamlit, that's a recent Snowflake acquisition. It's strong in the vertical axis and because of Snowflake's go-to-market (indistinct), it's likely going to move to the right overtime. Grafana is also prominent in the Y axis, but a glimpse at the most recent survey data shows them slightly declining while Looker actually improves a bit. As does Cloudera, which we'll move up slightly. Again, Microsoft just blows you away, doesn't it? All right, now let's get into database and data platform. Same X Y dimensions, but now database and data warehouse. Snowflake as usual takes the top spot on the vertical axis and it is actually keeps moving to the right as well with again, Microsoft and AWS is dominant in the market, as is Oracle on the X axis, albeit it's got less spending velocity, but of course it's the database king. Google is well behind on the X axis but solidly above the 40% line on the vertical axis. Note that virtually all platforms will see pressure in the next survey due to the macro environment. Microsoft might even dip below the 40% line for the first time in a while. Lastly, let's look at the collaboration and productivity software market. This is such an important area for both Microsoft and Google. And just look at Microsoft with 365 and Teams up into the right. I mean just so impressive in ubiquitous. And we've highlighted Google. It's in the pack. It certainly is a nice base with 174 N, which I can tell you that N will rise in the next survey, which is an indication that more people are adopting. But given the investment and the tech behind it and all the AI and Google's resources, you'd really like to see Google in this space above the 40% line, given the importance of this market, of this collaboration area to Google's success and the degree to which they emphasize it in their pitch. And look, this brings up something that we've talked about before on Breaking Analysis. Google doesn't have a tech problem. This is a go-to-market and marketing challenge that Google faces and it's up against two go-to-market champs and Microsoft and AWS. And Google doesn't have the enterprise sales culture. It's trying, it's making progress, but it's like that racehorse that has all the potential in the world, but it's just missing some kind of key ingredient to put it over at the top. It's always coming in third, (chuckles) but we're watching and Google's obviously, making some investments as we shared with earlier. All right. Some final thoughts on what we learned this week and in this research: customers and partners should be thrilled that both Microsoft and Google along with AWS are spending so much money on innovation and building out global platforms. This is a gift to the industry and we should be thankful frankly because it's good for business, it's good for competitiveness and future innovation as a platform that can be built upon. Now we didn't talk much about multi-cloud, we haven't even mentioned supercloud, but both Microsoft and Google have a story that resonates with customers in cross cloud capabilities, unlike AWS at this time. But we never say never when it comes to AWS. They sometimes and oftentimes surprise you. One of the other things that Sarbjeet Johal and John Furrier and I have discussed is that each of the Big 3 is positioning to their respective strengths. AWS is the best IaaS. Microsoft is building out the kind of, quote, we-make-it-easy-for-you cloud, and Google is trying to be the open data cloud with its open-source chops and excellent tech. And that puts added pressure on Snowflake, doesn't it? You know, Thomas Kurian made some comments according to CRN, something to the effect that, we are the only company that can do the data cloud thing across clouds, which again, if I'm being honest is not really accurate. Now I haven't clarified these statements with Google and often things get misquoted, but there's little question that, as AWS has done in the past with Redshift, Google is taking a page out of Snowflake, Databricks as well. A big difference in the Big 3 is that AWS doesn't have this big emphasis on the up-the-stack collaboration software that both Microsoft and Google have, and that for Microsoft and Google will drive captive IaaS consumption. AWS obviously does some of that in database, a lot of that in database, but ISVs that compete with Microsoft and Google should have a greater affinity, one would think, to AWS for competitive reasons. and the same thing could be said in security, we would think because, as I mentioned before, Microsoft competes very directly with CrowdStrike and Okta and others. One of the big thing that Sarbjeet mentioned that I want to call out here, I'd love to have your opinion. AWS specifically, but also Microsoft with Azure have successfully created what Sarbjeet calls brand distance. AWS from the Amazon Retail, and even though AWS all the time talks about Amazon X and Amazon Y is in their product portfolio, but you don't really consider it part of the retail organization 'cause it's not. Azure, same thing, has created its own identity. And it seems that Google still struggles to do that. It's still very highly linked to the sort of core of Google. Now, maybe that's by design, but for enterprise customers, there's still some potential confusion with Google, what's its intentions? How long will they continue to lose money and invest? Are they going to pull the plug like they do on so many other tools? So you know, maybe some rethinking of the marketing there and the positioning. Now we didn't talk much about ecosystem, but it's vital for any cloud player, and Google again has some work to do relative to the leaders. Which brings us to supercloud. The ecosystem and end customers are now in a position this decade to digitally transform. And we're talking here about building out their own clouds, not by putting in and building data centers and installing racks of servers and storage devices, no. Rather to build value on top of the hyperscaler gift that has been presented. And that is a mega trend that we're watching closely in theCUBE community. While there's debate about the supercloud name and so forth, there little question in our minds that the next decade of cloud will not be like the last. All right, we're going to leave it there today. Many thanks to Sarbjeet Johal, and my business partner, John Furrier, for their input to today's episode. Thanks to Alex Myerson who's on production and manages the podcast and Ken Schiffman as well. Kristen Martin and Cheryl Knight helped get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE, who does some wonderful editing. And check out SiliconANGLE, a lot of coverage on Google Cloud Next and Microsoft Ignite. Remember, all these episodes are available as podcast wherever you listen. Just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. And you can always get in touch with me via email, david.vellante@siliconangle.com or you can DM me at dvellante or comment on my LinkedIn posts. And please do check out etr.ai, the best survey data in the enterprise tech business. This is Dave Vellante for the CUBE Insights, powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (gentle music)

(upbeat music) >> Okay, we're back at AWS re:Invent 2021. You're watching theCUBE. My name is Dave Vellante, and we're here with Jay Theodore who's the CTO of Enterprise and AI at Esri and he's joined by Dave Cardella, who's the Principal Product Manager for Developer Technologies also at Esri. Guys, thanks for coming on. Welcome to theCUBE. >> Thanks, Dave. >> Thanks, Dave. >> Jay, maybe you could give us a little background on Esri. What do you guys do? What are you all about? >> Sure. Esri is an old timer, we are a 50-year old software company. We are the pioneers in GIS and the world leader in GIS - geographic information system. We build geospatial infrastructure that's built for the cloud, built for the edge, built for the field also, you can say. So, we do mapping and analytics. We help our customers solve very complex challenges by bringing location intelligence into the mix. Our customers sort of like run the world, transform the world and we sort of like empower them with the technology we have. So, that's what we do. >> The original edge, and now of course, AWS is coming to you. >> Yeah. (both interviewees chuckling) >> Who are your customers, your main customers? Maybe share that. >> Yeah. We've got over 350,000 customers in... (Dave Cardella chuckling) Yeah. We're all- >> Dave Vellante: Scale. >> Yeah. (Dave Vellante laughing) In the public sector, especially, commercial businesses, non-profit organizations, and that really represents tens of millions of users globally. >> So, let's talk a little bit more about how things are changing. As they say, the edge is coming to you. Maybe AI, you know, 50 years ago... Actually, 50 years ago is probably a lot of talk about AI. When I came into the business, you know, it was a lot of chatter about it. But now, it's real. All this data that we have and the compute power, the cost is coming down. So, AI is in your title? >> Jay: Yes. >> Tell us more about that. >> I think that AI's come to age. When I went to grad school, AI was still in theory because we didn't have the compute and of course we didn't have all the data that was collected, right? Now, there's a lot of observation data coming in through IOT and many senses and so on. So, what do you do with that? Like, human interpretation is pretty challenged, I would say. So, that's where AI comes in, to augment the intelligence that we have in terms of extracting information. So, geospatial AI, specifically which we focus on, is to try to take location that's embedded with this kind of information and sort of like extract knowledge and information out of them, right? Intelligence out of them. So, that's what we focus on: to compliment location intelligence with AI, which we call geospatial AI. >> So, you can observe how things are changing, maybe report on that and that's got to be a huge thing that we can talk about. So, maybe talk about some of the big trends that are driving your business. What are those? >> Yeah, that's a great question. So, I was listening to Sandy Carter's 'Keynote' yesterday and she really emphasized the importance of data. And, data is crucial to what we do as a technology company, and we curate data globally and we get our data from best of breed sources, and that includes commercial data providers, it includes natural mapping agencies, and also a community maps program where we get data from our customers, from our global network of distributors and partners, and we take that data, we curate it, we host it and we deliver it back. And so, just recently for example, we're really excited 'cause we released the 2020 Global Land Cover. And so, Esri is the first company to release this data at 10 meter resolution for the entire planet, and it's made up of well over 400,000 earth observations from various satellites. So, you know data is a... It's not only a nice-to-have anymore, it's actually a must-have. And so, so is location when we talk about data. They go hand in hand. >> 10 meters so I can look at the hole in the roof of my barn... >> Well... (Dave Cardella chuckling) >> Dave Vellante: Pretty much. >> It depends on what you're trying to do, right? So, I think you know, to talk about it, it's within context. GIS is all about context, right? It's bringing location into context in your decision-making process. It's sort of like the where along with the when, what, how and why. That's what GIS brings in. So, a lot of problems are challenging because we need to bring these things together. It's sort of like you're tearing various layers of data that you have and then bringing them within context. Very often, the context that human minds understand and reflected in the physical world is geographic location, right? So, that's what you bring in. And I would say that there's various kinds of data, also. Various types of data, formats of data: structured data, unstructured data, data captured from extraterrestrial, you know, like, you can say, satellite imagery from drones, from IOT. So, it's like on the ground, above the ground, under the ground. All these sensors are bringing in data, right? So, what GIS does is try of map that data to a place on the earth at very high precision, if you're looking at it locally, or at a certain position if it's regionally, trying to find patterns, trying to understand what's emerging, and then, as you take this and infuse geospatial layer into this, you can even predict what is going to happen based on the past. So, that's sort of like... You could say GIS being used for real world problems, like if you take some examples, COVID... The pandemic is one example. Being able to first discover where it happened, where it's spreading, you know, that's the tracking aspect and then how you respond to that and then how you recover, you know, recovering as humans, as businesses and so on. So, we have widespread use of that. The most popular would be the John Hopkins' Dashboard, >> Dave Vellante: Board, yeah. >> that everyone's seen. >> Vellante: We all use it... >> It's gotten trillions of hits and so on, right? That's one example. Another example is addressing racial equity by using location information. Similarly, social justice. Now, these are all problems that we face today, right? So, GIS is extensively used by our customers to solve such problems. And then of course, you have the climate change challenge itself, right? Where you're hovering all kinds of complex data that we can't comprehend because you have to go back decades and try to bring all that together to compute. So, all of this together comes in the form of a geospatial cloud that we have as an offering. >> So, okay. That's amazing. I mean, you're building a super cloud, we call it. You know, and... So, how do you deal with... How do you work with AWS? What's the relationship there? Where do developers fit in? Maybe you can talk about a little bit. >> Yeah. Yeah, that's a great question. So, we've got two main integration points with AWS. A lot of our location services that we expose data and capabilities through are built on AWS. So, we use storage, we use cloud caching and AWS's various data sets across the world quite heavily. So, that's one integration point. The other is a relatively new product that Amazon has released called Amazon Location Service. And so, what it does is it brings location and spatial intelligence directly into a developer's AWS dashboard. So, the experience that they're already used to, they now get the power of Esri services and location intelligence right at their fingertips. >> So, you're .. We started talking about the edge, your data architecture is very distributed, right? But, of course, you're bringing it back. So, how does that all work? You process it locally and then sending some data back? Are you sending all data back? What does that flow look like? >> I think the key thing is that our customers work with data of all kinds, all formats, all sizes and some are in real-time, some are big data and archive, right? So, most recently, just to illustrate that point, this year, we released RGS Enterprise on Kubernetes. It's the entire geospatial cloud made available for enterprise customers, and that's made available on AWS, on EKS. Now, when it's available on EKS, that means all these capabilities are microservices, so, they can be massively scaled. They're DevOps friendly and you've got the full mapping and analytics system that's made available for this. >> Dave Vellante: Oh. >> And we sort of like built it, you know, cloud native from the ground up and the more important thing that we have now is connectivity with Redshift. Why is that important? Because a lot of our customers have geospatial data in these cloud data warehouses. Redshift is very important for them. And so, you can connect to that, you can discover these massive petabytes of data sets and then you can set up what we call the query layer. It's basically pushing analytics into Redshift and being able to bring out that data for mapping, visualization, for AI workflows and so on. It's pretty amazing and it's pretty exciting at this time. >> And, I mean... So much data. And then... What, do you tear it down into glacier of just to save some cost, or is it going to all stay in S3 or is it... >> So, we already work with S3, we've worked with RDS, we support Amazon Aurora, our customers are very happy with that. So, Redshift is a new offering for us to connect to Redshift. >> Dave Vellante: AOK. >> So, the way the query layer works is all of your observation data is in Redshift, your other kinds of data... Your authoritative data sets could come from various other sources including in Amazon Aurora, for example, okay? And then, you overlay them and use them. Now, the data in Redshift is usually massive, so, when you run the analytic query, we let you cache that as a materialized view or as a snapshot that you can refresh and you can work against that. This is really good because it compliments our ability to actually take that data, to put it on a map image which we render service side, it's got very complex cartographic ready symbology and rendering and everything in there. And you get these beautiful rendering of maps that comes out of Redshift data. >> And you're pushing AI throughout your stack, is that, you know? >> Yeah. AI is just like infused, right? I mean, it's... I would say, human intelligence augmented for data scientists, for everyone, you know. Whether you're using it through notebooks or whether you're using it through applications that we have or the developer APIs themselves. >> So, what are some of the big initiatives you're working on near-term, mid-term? >> Yeah. So, you mentioned what's really driving innovation and it's related to the question that you just asked right now and I really believe developers drive innovation. They're force multipliers in the solutions that they build. And so, that's really the integration point that Esri has with AWS, it's developers. And earlier this year, we released the RGS platform which is our platform as a service offering that exposes these powerful location services that Jay just explained. There's a set of on-demand services that developers can bring in their applications as they want and they can bring in one, they can bring in two or three, whatever they need, but they're there when they need them. And also, developers have their client API of choice. So, we have our own client APIs that we offer but you're not pigeonholed into that when you're working with RGS platform. A developer can bring their own API. >> Okay, so he called the platform as a service. Are you making your data available as well? Your data, your tooling and then selling that as a service? >> Our data has always been available as a service, I would say. >> Okay, yeah. >> Everything that we do, our GIS tools, are accessible as a web service. >> Vellante: Is that new, or... that's always been the way? >> No, that's always been there. That's always been that way. The difference now is everything is built from the ground up to be cloud native. >> Dave Vellante: Okay. >> From the ground up to be connected to every data set that's available on AWS, every compute that can be exploited from small to massive in terms of compute, and also reaching out to bring all the apps and the developer experiences, pushing out to customers. >> So, 50 years ago, you weren't obviously using the cloud, but so, you were running everything on-prem now you're all in the cloud, or you're kind of got a mix? What is the clearer picture of that? >> So, we have two major offerings. There's RGS Online, where obviously it's offered as a service and it's GIS as a service provider for everyone. And that's available everywhere. The other offering we have is actually RGS Enterprise where some customers run them on premises, some run it in the cloud, especially AWS. Many run it on the edge, some in the field and there's connectivity between this. A lot of our customers are hybrid. So, they make the best of both. Depending on the kinds of data- >> Dave Vellante: You give them a choice. >> the kinds of workflows... Giving them the choice, exactly. And I would say, you know, taking Werner's 'Keynote' this morning, he talked about what's the next frontier, right? The next frontier could very well be when AWS gets to space and makes compute available there. It's sitting alongside the data that's captured and we've always, like I said, for 50 years, worked with satellite imagery, >> Dave Vellante: Yeah. >> or worked with IOT, or worked with drone data. It's just getting GIS closer to where the data is. >> So, the ultimate edge space. >> Yes. >> All right, I'll give you guys... Give us a quick wrap if you would. Final thoughts. >> I think its... Go ahead. >> Go, ahead Dave. >> Yeah. I really resonate with data and content. We're a technology company- there's no doubt about that- but without good data, not only supplied by ourselves, but our customers, Jay mentioned it earlier, our customers bring their own data to our platform and that's really what drives the analytics and the accuracy in the answers to the problems that people are trying to solve. >> Bring their first-party data with your data and then one plus one is... >> Yes. Yeah, and the key thing about that (Cardella chuckling) is not some of the data, it's all of the data that you have. You don't more need to be constrained. >> Yeah, you're not sampling. >> Yes, exactly. >> Yeah. >> All right, guys. Thanks so much. Really interesting story. Congratulations. >> Thank you, Dave. >> Dave, thank you. >> Nice meeting you. >> Thank you for watching. This is Dave Vellante for theCUBE, the leader in global tech coverage. We'll be right back. (upbeat music)

>> Announcer: From around the globe, it's theCUBE, with digital coverage of AWS re:Invent 2020. Special coverage sponsored by AWS Worldwide Public Sector. >> Welcome to theCUBE Virtual and our coverage of AWS re:Invent 2020, the specialized programming for Worldwide Public Sector. I'm Lisa Martin. I'm joined by Nick Speece the chief federal technologist for Snowflake. Nick, welcome to theCUBE. >> Thank you, Lisa. It's great to be here. >> Likewise, chief federal technologist, that's the first time I've ever heard of that title. Tell me a little bit about that. >> It's probably the last time you'll hear it. So chief federal technologist is really somebody in the company who is focused on bringing the needs of the federal government back to our corporate headquarters, making sure that the product as it's developed and evolves has the federal requirements in mind. >> Excellent. So the last couple of months for Snowflake big, biggest software IPO and software history your market cap right now is at 66 billion 515 data workloads running on Snowflake's platform every day 250 petabytes of data under management, a lot is going on. Let's talk about Snowflake. You guys operate only in the cloud, why was that decision made and how does that impact businesses analysis of data? >> Yeah, so great question and the answer is actually in the opening that you gave for us and thank you for that reinforcement. Snowflake can't exist anywhere, but the cloud. Technology over the last five to 10 years has really seen a move from what the cloud originally was, which was I have a virtual machine in my data center, I'm going to run it on your stuff not mine, into more comprehensive service offerings like Snowflake. We can't reach the kind of scale that Snowflake operates at every day and that our customers demand without the technology of clouds like AWS. The technology has to be there, the underlying and underpinning architecture has to be there, otherwise our customers get left in the dark and we can't can't have that. >> And especially today as data volumes are massively increasing and we know that that's only going to go up. We know that IT is only going to be more complex but when we talk to businesses in any industry the value of the data is in the insights the ability to extract that data in real time glean insights from it so that businesses can make data-based decisions that pivot their business, especially critical during the year that we have now known as 2020. Talk to me a little bit about though digging into your marketing material, everyone, there's all these terms, right, that everyone uses and you guys use single source of truth. What does it actually mean for single source, for stuff like? >> Yeah, so we talk about cloud, we talk about single source of truth and when you're looking at data problems, the problem and the solution are the same thing. A massive amount of data is a raw resource, that's all it is. And trying to refine that raw resource into something that is insightful or something that is useful to a business process is a challenge that every customer in every market, in every region undergoes. And how you overcome that is critical. And one of the primary focuses of Snowflake is to evolve the data cloud. Snowflake platform is the underlying technology for the data cloud but the data cloud is where we're going. And what I mean by data cloud. If you have a data set, your internal data, that is your truth, but it might not be the truth. So in Snowflake we encourage our customers to collaborate on data sets. For example, if you want to know how many people are living in a certain borough in New York City you could go around with a clicker and count everyone, or you could just ask the Census Bureau. That's the nature of the data cloud and what we're talking about here. Going to the subject matter experts who have the data that you need, using our marketplace, using our private exchanges, using our data sharing to build your own data cloud and become part of the next gen architecture for data sharing and collaboration, to get to the source of the truth, to make better decisions, to gain better insights. It's great to combine your data with enrich data from other sources, especially when it comes to making federal decisions and governance decisions. >> Absolutely that's critical. That the biggest challenge customers have is being able to sort through all that and find. I like how you put this as their single source of truth. Can you give us some examples of some federal agencies maybe even just anonymously that are using the power of Snowflake to do just that? >> Absolutely. We've got customers in the healthcare space and in some of the law enforcement spaces and especially in public education that are trying to increase the awareness of the folks that are subscribing to their services, for example, folks that are looking for healthcare help. If you're filing claims for a certain healthcare providers or certain care facilities, we want to make sure that those claims that are forwarded to those entities are legitimate, first of all, for example, if you're filing a claim for knee surgery in Florida, you probably didn't have one in California, three hours later. So those kinds of enforcement activities, and not just trying to do audits but also to benefit everybody who's receiving care. There's a lot of push now about genetic sequencing, DNA and RNA vaccination is huge with COVID-19, getting access to massive amounts of data to do analysis against and figure out the best approach, that's critical for where we go in the next 10 to 15 years in healthcare. Snowflake is very, very honored and happy to be propelling that move in the healthcare space. >> It is that's going to be absolutely critical but we're also seeing it, you know, everywhere else, such as for universities and education, suddenly this need, the last few months for real-time learning. Talk to me about data analysis. Can Snowflake help companies, you talked about enriching data sets so not just companies sources of data but additional data sets that they can add in and evaluate and analyze to make great decisions, but from a historical real-time perspective, talk to me how Snowflake helps with that data analysis. >> Yeah, sure. Right. So Snowflake in and of itself can do some analysis work. We've got some great visualization tools in our new UI that was released recently on public preview. So there's some analysis tools built into Snowflake but really where the value comes from is in taking your tools that you already use today and connecting it to a data source or platform that can wrangle that data, that can move that data through automated pipelines to give you a model view of that data that's beneficial. For example, data scientists and data engineers spend 80% of their time, and I know a lot of statistics are made up on the spot, that was not a promise, but trying to move this data through and refine it and build features to get to the point where you can ask a question is 80% of these very valuable professionals time. Shortening those timelines is what Snowflake really aims to do in the analysis space. We're not trying to replace the analysis tools that you use today, we work fine with all of them. The big difference is presenting them with enough data volume to give you real insights and eliminate bias as much as we can in data sets. >> What are some of the things that differentiates Snowflake from data warehouses and other folks in the market? >> Yeah. Great question. The big difference is Snowflake was built natively for the cloud. We weren't adapted to the cloud, we didn't adopt the cloud at some point in the future, Snowflake was built from scratch to be in the cloud. And since this is the appropriate show to mention it the primary difference between us is we were built to use object storage foundationally underneath our technology. And I know that sounds really nerdy and it is, but it adds a tremendous amount of value. If you think about how we used to collaborate 10 years ago we'd have a spreadsheet that if I open that spreadsheet for my share drive and you tried to open it at the same time, you'd get locked out. You're told you couldn't have it. And if tradition stays true I would probably be on vacation for two weeks. Contrast that now with the massive Google Doc platform and Office 365, object storage has changed the way that we collaborate on the same kinds of documents. Multiple people interacting with one thing at one time without contention, that's the reason why Snowflake has to operate in the cloud. We bring that same paradigm, multiple actors on a single object and give you that source of truth the truth that you absolutely need to make decisions. >> And that's critical these days as we know. We're in living in uncertain times and one of the things I think we can expect is the uncertainty to continue, but also for many industries people to stay remote or some big percentage for quite a while. So the ability to have those collaboration tools and be able to collaborate in real time is table stakes for so many companies. But when we're talking about some of the things going on this year, security, we can't not talk about security. You know, all these folks from home accessing corporate networks, you know, maybe not through VPNs or behind firewalls, the cloud is paramount to that. How does Snowflake address the security issue? >> Absolutely. So I'll start by saying our security is inherited from the wonderful security platform that AWS has underneath it. So we inherit all the security around data storage the EC Compute, all of the different entities and end points that AWS already secures Snowflake takes the same precautions. More than that, we've also built and rolled this access control to ensure that people are getting access only to the data that they should be getting access to, we recently implemented data masking as well, so certain roles are not able to see unmasked data, but they can still do queries that use the underlying data to filter. So there's a lot of different capabilities built in, encryption at rest, encryption in flight, AES-256 encryption keys used in a hierarchial model. These are phenomenal security architectures that are paramount to the security of the folks that are using our platform. Because we know at the end of the day the first day we have a leak in Snowflake is probably our last day in business. We got to be good at that which is why it's our top priority. >> I didn't, to ever talk about security as an inherited, I must be a dominant trait if we're going to be talking about, you know, genetics and chromosomes and mRNA and things like that. So walk me through last question, a government organization, or say they're an AWS customer or they want to start using Snowflake, what's that process? How do they go about doing that to leverage those inherited security capabilities that you talked about? >> Well, thankfully AWS has helped us put a FedRAMP moderate certified Snowflake region together in AWS, East commercial, so we're very happy to have a FedRAMP moderate region. They can access Snowflake through the AWS Marketplace or from Snowflake.com, you can start a trial in just a couple of minutes. Our security is built into all of our regions although the FedRAMP regions are specialized in some of the encryption technology we use, but we always, always always protect our users' data, regardless of where it is. >> You make it sound easy, I got to say. (laughing) >> That's because it is. (laughing) Thank you cloud. >> That's good. And well, that's good and it should be, especially because there's so much complexity and uncertainty everywhere else in the world right now. Last question for you. As I mentioned in the beginning, the biggest IPO in software history, just a couple of months ago during probably one of the most strangest time of any of us have ever, and our relatives ever witnessed, what can we expect from Snowflake in 2021? Are you going to bring all the good vibes that we all need? (laughing) >> Well, good vibes is our business model. You know, Snowflake is a phenomenal platform. We've had a ton of success driven by the success of our cloud provider partners, driven by the success of our wonderful customers. We have over 4,000 people using Snowflake now to great effect. You can look for more features, you can look for more functions, but really the evolution of the data cloud, our big push is to help our customers get into the data cloud, get the truth out of their data and make better decisions every day. And you'll see more of that from us as time continues. >> One more question I wanted to sneak in, how did you work with those customers to evolve the data cloud? What's that feedback loop like? >> It's, a lot of it comes down to silos that the customers have built up over years and years and years of operation. That's the first step. In Snowflake there isn't such thing really as a data silo there's data put into Snowflake, everything is unified, you can do queries across databases, that's the first thing. The second thing is browsing our data marketplace. It's just like an App Store for your phone but instead it's data sets and the data sets are published by the experts who know that material better than anyone. I mentioned earlier bringing in everything from housing evaluation data to COVID-19 data from California and Boston, bringing World Health Organization data, John Hopkins University data, joining that with the data that you already use today along with weather and population counts, the main thing here, the strategy is almost endless. More and more data sets are being published over every day. We have over a hundred contributors in the marketplace now. >> That's exciting that we have the technology and the power like this to help the world re, you know, recover from such a crazy time. It's nice to know that, that there was the power of that behind that, and the smart folks like you chief federal technologists, helping to fine tune that and really ensure that organizations across the government can maximize the value of data and find their single source of truth. Nick, it's been a blast having you on theCUBE. Thank you for joining me. >> Thank you for having me. >> For next piece, I'm Lisa Martin. You're watching theCUBE Virtual. (upbeat music)

>>From the cube studios in Palo Alto in Boston. It's the cube covering the IBM thing brought to you by IBM. Everybody we're back. This is Dave Vellante the cube, and this is our wall-to-wall coverage, IBM's digital thing experienced for 2020. We're really excited to have Jerry Cuomo on. He's the, uh, vice president of blockchain technologies and an IBM fellow and longtime cube alum. Jerry, good to see you again. Thanks for coming on and wish we were face to face, but yeah, this'll do. Good to see you too. Yes, thanks for having me. So we've been talking a lot of and talking to, I've been running a CEO series a, of course, a lot of the interviews around, uh, IBM think are focused on, on COBIT 19. But I wonder if you could start off by just talking a little bit about, you know, blockchain, why blockchain, why now, especially in the context of this pandemic. >>David's, it's as if we've been working out in the gym, but not knowing why we needed to be fixed. And I know now why we need to be fit. You know, blockchain is coming just in time. Mmm. You know, with the trust factor and the preserving privacy factor. Okay. The way we move forward the world is now becoming more digital than ever people working from home. Um, the reliance and online services is, that's critical. our ability to work as a community accompanies companies. The shared data is critical. you know, blockchain brings a magical ingredient and that's the ingredient of trust, you know, in sharing data. Okay. When, if that data and the sources that are providing that data arc okay. From verified and trusted, we're more likely to use that data and you the, any friction that's caused for fear of trepidation that the data is going to be misused. >>Mmm. It goes start to go away. And when that happens, you speed up an exchange and we need speed. Time is of the essence. So blockchain brings a platform for trusted data exchange while preserving privacy. And that provides a foundation. I can do some amazing things in this time of crisis, right? Yeah. And it's, it's not only trust, it's also expediency and you know, cutting out a lot of the red tape. And I want to talk about some of the applications. You're heavily involved in that in the distributed ledger, a project, you know, one of the early leads on that. Um, talk about some of the ways in which you're flying that distributed a ledger. And let's go into some of the examples. So we're, we're really fortunate to be an early adopter blockchain and, and provider of blockchain technology and kind of the fruit of that. >>Um, as I said, it couldn't happen any sooner where we have, Mmm, I would say over a thousand, alright. Users using IBM blockchain, which is powered by the opensource Hyperledger fabric, I'd say over a hundred of those users, um, have reached a level of production networks. you know, it's been great to see some of the proprietors of those networks now repurpose the networks towards hastening the relief of, uh, and one, a couple of examples that stand out, Dave. Mmm. You've seen what's happening to our supply chain. And then I think we got some rebound happening as we speak, but companies all of a sudden woke up one morning and their supply chains were, I'm exhausted. So suppliers, we're out of key goods and the buyers needed very rapidly to expand. They're, the supplier is in their, in their supply chain. there are laws and regulations about what it takes to onboard a new supplier. >>You want to make sure you're not onboarding bad actors. So in IBM for example, we have over 20,000 suppliers to our business and it takes 30 to 40 days who, uh, validate and verify one of those suppliers. We don't have 30 to 45 days, you know, think about you're a healthcare company or a food company. So working with a partner called Jane yard, uh, co-created a network called trust yourself buyer. And we've been able to repurpose, trust your supplier now or companies that are looking, you know, around Kobe 19 to rapidly okay, expand, you know, their, their supply chain. So if you imagine that taking us 45 days or 40 days to onboard a new supplier, okay. Pick, pick a company in our supply chain, Lenovo, that supplier may very well want to go to Lenovo to and provide services to them. Well guess what, it's going to take 40 days, the onboard to Lenovo. >>But if they're part of the trust or supplier network and they've already onboarded to IBM, they're well on their way. You're being visible to all of these other buyers that are part of the IBM network, like Lenovo and many others. And instead of taking 40 days, maybe it only takes five days. All right. So radically, radically, you know, improving the time it takes them. You know, with companies like Ford making ventilators and masks, it will kind of be able to onboard Ford into, you know, health care, uh, companies. But you know, we want to be able to do it with speed. So trust your supplier is a great use of blockchain. Two, expand a buyer and suppliers. Mmm. Exposure. Mmm. And they expand their network to quickly onboard. And you know, with the trust that you get an exchanging data from blockchain with the Mmm provenance, that Hey, this company information was truly vetted by one of the trusted members of the network. >>There's no fee or trepidation that somehow these records were tampered with or, or misused. So that's one example they have of using blockchain. That's a huge, uh, example that you gave because you're right, there are thousands and thousands of companies that are pivoting to making, like you said, ventilators and masks and yeah, they're moving so fast and there's gotta be a trust involved. On the one hand, they're moving fast to try to save their businesses or you know, in the case of Ford, you help save the, the country or the world. On the other hand, you know, there's risks there. So that, that helps. I want to understand me. Pasa basically is, if I understand it, you can privately share, uh, information on folks that are asymptomatic but might be carriers of covert 19. Am I getting that right on? Okay. So me Pasa starts as a project, uh, from a company called has Sarah and their CEO Jonathan Levy. >>And among other things, Jonathan Levy is an amazing, uh, software developer and he's helped us and the community at large, bill, the Hyperledger fabric, uh, blockchain technology, that's part of IBM. Mmm. The power is IBM blockchain. So Jonathan, I have this idea because w what was happening is there were many, many data sources, you know, from the very popular and well known, uh, Johns Hopkins source. And we have information coming from the weather company. There are other governments, um, putting out data. Jonathan had this, this idea of a verified Mmm. Data hub, right? So how do we kind of bring that information together in a hub where a developer can now to get access to not just one feed, but many feeds knowing that both the data is an a normalized format. So that's easy to consume. And like if you're consuming 10 different data sources, you don't have to think about 10 different ways to interact it. >>No kind of normalizing it through a fewer, like maybe one, but also that we really authentically know that this is the world health organization. This is indeed John Hopkins. So we have that trust. So, okay. Yeah. With me, Pasa being I'm a data hub four, uh, information verified information related to the Kronos virus, really laying a foundation now for a new class of applications that can mash up information to create new insights, perhaps applying Mmm. Artificial intelligence machine learning to really look not just at any one of those, uh, data sources, but now look across data sources, um, and start to make some informed decisions. No, I have to say operate with the lights on, uh, and with certainty that the information is correct. So me Pasa is that foundation and we have a call for code happening that IBM is hosting for developers to come out and okay. Bring their best ideas forward and X for exposing me Pasa as a service to the, in this hackathon so that developers can bring some of their best ideas and kind of help those best ideas come alive with me. Me has a resource. >>That's great. So we've got two, we got the supply chain, we just need to share the Pasa. There's the other one then I think we can all relate to is the secure key authentication, >>which I love. >>Uh, maybe you can explain that and talk about the role that blockchain >>we're launching fits, right. So you know, there is people working from home and digital identity verification. It is key. You know, think about it. You're working remotely, you're using tools like zoom. Um, there's a huge spike in calls and online requests from tele-health or government benefits programs. Yeah. So this is all happening. Everything behind the scenes is, yeah. Around that is, is this user who they say they are, is this doctor who they say they are, et cetera. And there are scams and frauds out there. So working with speed, it means working with certainty. and with the verified me networks set out to do a couple of years ago and the beautiful part is, you know, it's ready to go now for this, for this particular usage it's been using. Mmm. Basically think about it as my identity is my identity and I get to lease out information too different institutions to use it for my benefit, not necessarily just for their benefit. >>So it's almost like digital rights management. Like if you put out a digital piece of art or music, you can control the rights. Who gets to use it? What's the terms and conditions, um, on, on your terms? So verified me, um, allows through a mobile app users to invite institutions who represent them, verify them. No. And so I'll allow my department of motor vehicle and my employer, Mmm. Two to verify me, right? Because I want to go back to work sooner. I want to make sure my work environment, um, I'm making this up. I want to make sure my work environment, the people have been tested and vaccinated, but I don't want to necessarily, you know, kind of abuse people's privacy. Right? So I'll opt in, I'll share that information. I'll get my, my doctor and my, uh, department of motor vehicle to say, yes, this is Gary. >>He's from this address. Yes, he has been vaccinated and now I can kind of onboard to services as much quicker whether that service is going through TSA. Do you get on an airplane badging back into my office or you know, signing on to a, you know, telemedicine, a service or government, a benefits program, et cetera. So verify me is using the self, uh, at the station through a mobile application to help speed up the process of knowing that that is truly you and you truly want this service. Uh, and you are also calling the shots as to that. What happens with your information that, you know, it's not spread all over the interweb it's under your control at all time. Right. So I think it's the best of all worlds. The national Institute for standards and technology looked at, verified me. They're like, Oh my gosh, this is like the perfect storm of goodness for identity. >>They actually appointed, yeah, it has a term, it's called triple blind data exchange. It sounds like a magical act. A triple blind data exchange means the requester. Mmm. Doesn't know who the provider is and less know the requester. Um, allows the provider to know, Mmm, the provider doesn't know who the requester requested, doesn't know who the prior provider is that is double-blind. And then the network provider doesn't know either. Right. But somehow across disformed and that's the magic of blockchain. I'm allowing that to happen and with that we can move forward knowing we're sharing information where it matters without the risk of it leaking out to places we don't want to do. So great application of secure key and verified me. Yeah, I love that. Then the whole concept of being able to control your own data. You hear so much today about, you know, testing and in contact tracing using mobile technology to do that. >>But big privacy concerns. I've always felt like, you know, blockchain for so many applications in healthcare or just being able to, as you say, control your own data. I want to better understand the technology behind this. When I think about blockchain, Mmm. I obviously you don't think about it. Cryptography, you've mentioned developers a number of times. There's software engineering. Yeah. Distributed ledger. Um, I mean there's, there's game theory in the, in the, in the cryptocurrency world, we're not talking about that, but there's the confluence of these technologies coming to them. What's the technology underneath these, these applications? Talking about it there, there is an open source, an organization called Hyperledger. It's part of the Linux foundation. They're the gold standard and open source, openly governed, Mmm. Technology you know, early on in 2018 yep. 18, 26. I mean, we got involved, started contributing code and developers. >>Two Hyperledger fabric, which is the industry's first permissioned blockchain technology. Permission meaning members are accountable. So the network versus Bitcoin where members are anonymous and to pass industry Reggie regulations, you can't be anonymous. You have to be accountable. Um, that's not to say that you can't, okay. Work privately, you know, so you're accountable. But transactions in the network, Mmm. Only gets shared with those that have a need, need to know. So that the foundation is Hyperledger fabric. And IBM has a commercial offering called the IBM blockchain platform that embodies that. That kind of is a commercial distribution of Hyperledger fabric plus a set of advanced tools to make it really easy to work with. The open source. All the networks that I talked about are operating their network across the worldwide IBM public cloud. And so cloud technology lays a really big part of blockchain because blockchains are networks. >>Mmm. You know, our technology, IBM blockchain platform runs really well in the IBM wow. But it also allows you to run anywhere, right? Or like to say where it matters most. So you may have companies, I'm running blockchain nodes in the IBM cloud. You may have others running it on their own premises behind their firewall. You might have others running an Amazon and Microsoft Azure. Right. So we use, um, you may have heard of red hat open shift, the container technology so that we can run Mmm. Parts of a blockchain network, I guess they said where they matter most and you get strengthened a blockchain network based on the diversity of the operators. Because if it was all operated by one operator, there would be a chance maybe that there can be some collusion happening. But now if you could run it know across different geographies across the IBM cloud. >>So almost three networks all run on use this technology or run on the IBM cloud. And Dave, one more thing. If you look at these applications, they're just modern application, you know, their mobile front ends, their web portals and all of that kind of, okay. Okay. The blockchain part of these applications, usually it's only 20% of the overall endeavor that companies are going through. The other 80% it's business as usual. I'm building a modern cloud application. So what we're doing in IBM with, but you know, red hat with OpenShift with our cloud packs, which brings various enterprise software across different disciplines, blends and domains like integration, application, data, security. All of those things come together to fill the other 80% the above and beyond blockchain. So these three companies, okay. You know, 99 plus others are building applications as modern cloud applications that leverage this blockchain technology. So you don't have to be a cryptographer or you know, a distributed database expert. It's all, it's all embodied in this code. Mmm. Available on the IBM cloud, 29 cents a CPU hour. It was approximately the price. So it's quite affordable. And you know, that's what we've delivered. >>Well, the thing about that, that last point about the cloud is it law, it allows organizations, enterprises to experiment very cheaply, uh, and so they can get, uh, an MVP out or a proof of concept out very quickly, very cheaply, and then iterate, uh, extremely quickly. That to me is the real benefit, the cloud era and the pricing model. >>I just mentioned, David, as I said it when I started, you know, it's like we were working out in a gym, but we weren't quite sure. We knew why we were, we were so keen on getting fit. And what I see now is this, you know, blossoming of users who are looking at, you know, a new agreement. We thought we understood digital transformation. Mmm. But there's a whole new nice to be digitized right now. You know, we're probably not going to be jumping on planes and trains, uh, working as, as, as more intimately as we were face to face. So the need for new digital applications that link people together. Uh, w we're seeing so many use cases from, um, trade finance to food safety, to proxy voting for stock, know all of these applications that we're kind of moving along at a normal speed. I've been hyper accelerated, uh, because of the crisis we're in. So blockchain no. Couldn't come any sooner. >>Yeah. You know, I want to ask you, as a technologist, uh, you know, I've learned over the years, there's a lot of ways to skin a cat. Um, could you do the types of things that you're talking about without blockchain? Um, I'm, I'm sure there are ways, but, but why is blockchain sort of the right path, >>Dave? Mmm. You can, you can certainly do things with databases. Mmm. But if you want the trust, it's as simple as this. A database traditionally has a single administrator that sets the rules up for when a transaction comes in. Mmm. What it takes to commit that transaction. And if the rules are met, the transactions committed, um, the database administrator has access who commands like delete and update. So at some level you can never be a hundred percent sure that that data was the data that was intended in there. With a blockchain, there's multiple administrators to the ledger. So the ledger is distributed and shared across multiple administrators. When a transaction is submitted, it is first proposed for those administrators, a process of consent happens. And then, and only then when the majority of the group agrees that it's a valid transaction, is it committed? And when it's committed, it's committed in a way that's cryptographically linked two other transactions in the ledger, I'm making it. >>Mmm tamper-proof right. Or very difficult to tamper with. And unlike databases, blockchains are append only so they don't have update and delete. Okay. All right. So if you really want that center of trusted data that is a tested, you know, that has checks and balances across different organizations, um, blockchain is the key to do it, you know? So could you do it in data with a database? Yes. But you have to trust that central organization. And for many applications, that's just fine. All right. But if we want to move quickly, we really want to share systems of record. Mmm. I hear you. Sharing a system of record, you have regulatory obligations, you can say, Oh, sorry, the record was wrong, but it was put in there by, by this other company. Well, they'll say, well, >>okay, >>nice for the other company, but sorry, you're the one in trouble. So with a blockchain, we have to bring assurances that we can't get into that kind of situation, right? So that shared Mmm. Distributed database that is kind of provides this tamper resistant audit log becomes the Colonel cross. And then with the privacy preservation that you get from encryption and privacy techniques, um, like we have like these things, both channels, um, you can transact, um Hm. And be accountable, but also, Mmm. Only share of transactions with those that have a need to know, right? So you get that level of privacy in there. And that combination of trust and privacy is the secret sauce that makes blockchain unique and quite timely for this. So yeah, check it out. I mean, on the IBM cloud, it's effortless. So to get up and running, you know, building a cloud native application with blockchain and you know, if you're used to doing things, um, on other clouds or back at the home base, we have the IBM blockchain software, which you can deploy. Yeah. Open shift anywhere. So we have what you need in a time of need. >>And as a technologist, again, you're being really, I think, honest and careful about the word tamper. You call it tamper resistant. And if I understand it right, that, I mean, obviously you can fish for somebody's credentials. Yeah. That's, you know, that's one thing. But if I understand that, that more than 50% of the peers in the community, it must agree to tamper in order for the system. You tampered with it. And, and that is the beauty of, of blockchain and the brilliance. Okay. >>Okay. Yeah. And, and, and for, um, performance reasons we've created optimizations. Like you can set a consensus policy up because maybe one transaction it's okay just to have a couple people agree and say, Oh, well, you know, out of the a hundred nodes, Mmm. Three agree, it's good enough. Okay. Other, other policies may be more stringent depending on the nature of the data and the transaction, right? So you can tone, you can kind of tune that in based on the class of transaction. And so it's kind of good and that's how we can get performance levels in the, you know, thousand plus. In fact, IBM and RBC, um, recently did, um, a series of performance analysis because RBC said, Hey, can I use this for some of my bank to bank exchanges and we need to support over a thousand transactions per second. They were able, in their use case, there's support over 3000. Transact for a second. Okay. Mmm. You know, that we were very encouraged by that. I'm glad you clarified that because, so essentially you're saying you can risk adjust the policies if you will. >>That's great to know. Mmm. I could go on forever on this topic. Well, we're unfortunately, Jerry, we're well over our time, but I want to thank you for coming back, planning this important topic. Thrilled. IBM has taken a leadership position here, and I think, you know, to your point, this pandemic is just going to, can accelerate a lot of things and blockchain is, but in my view anyway, one of them. Thank you, Dave. Oh, great questions and I really appreciate it. So everyone out there, um, stay safe. Stay healthy. All right. Thank you Jerry, and thank you for watching everybody. This is Dave Volante for the cube. Our coverage of the IBM think digital 2020 event. We'll be right back. Perfect. The short break.