(bright upbeat music) >> In 2011, early Facebook employee and Cloudera co-founder Jeff Hammerbacher famously said, "The best minds of my generation are thinking about how to get people to click on ads, and that sucks!" Let's face it. More than a decade later, organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile and data-driven enterprise. What does that even mean, you ask? Well, it means that everyone in the organization has the data they need when they need it in a context that's relevant to advance the mission of an organization. Now, that could mean cutting costs, could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data warehouses, data marts, data hubs, and yes even data lakes were broken and left us wanting for more. Welcome to The Data Doesn't Lie... Or Does It? A series of conversations produced by theCUBE and made possible by Starburst Data. I'm your host, Dave Vellante, and joining me today are three industry experts. Justin Borgman is the co-founder and CEO of Starburst, Richard Jarvis is the CTO at EMIS Health, and Teresa Tung is cloud first technologist at Accenture. Today, we're going to have a candid discussion that will expose the unfulfilled, and yes, broken promises of a data past. We'll expose data lies: big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth inevitable? Will the data warehouse ever have feature parity with the data lake or vice versa? Is the so-called modern data stack simply centralization in the cloud, AKA the old guards model in new cloud close? How can organizations rethink their data architectures and regimes to realize the true promises of data? Can and will an open ecosystem deliver on these promises in our lifetimes? We're spanning much of the Western world today. Richard is in the UK, Teresa is on the West Coast, and Justin is in Massachusetts with me. I'm in theCUBE studios, about 30 miles outside of Boston. Folks, welcome to the program. Thanks for coming on. >> Thanks for having us. >> Okay, let's get right into it. You're very welcome. Now, here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >> Yeah, definitely a lie. My first startup was a company called Hadapt, which was an early SQL engine for IDU that was acquired by Teradata. And when I got to Teradata, of course, Teradata is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on prem, data in the cloud. Those companies were acquiring other companies and inheriting their data architecture. So despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >> So Richard, from a practitioner's point of view, what are your thoughts? I mean, there's a lot of pressure to cut cost, keep things centralized, serve the business as best as possible from that standpoint. What does your experience show? >> Yeah, I mean, I think I would echo Justin's experience really that we as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in a platform that's close to data experts people who really understand healthcare data from pharmacies or from doctors. And so, although if you were starting from a greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that businesses just don't grow up like that. And it's just really impossible to get that academic perfection of storing everything in one place. >> Teresa, I feel like Sarbanes-Oxley have kind of saved the data warehouse, right? (laughs) You actually did have to have a single version of the truth for certain financial data, but really for some of those other use cases I mentioned, I do feel like the industry has kind of let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralize? >> I think you got to have centralized governance, right? So from the central team, for things like Sarbanes-Oxley, for things like security, for certain very core data sets having a centralized set of roles, responsibilities to really QA, right? To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise, you're not going to be able to scale, right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately, you're going to collaborate with your partners. So partners that are not within the company, right? External partners. We're going to see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >> So Justin, you guys last, jeez, I think it was about a year ago, had a session on data mesh. It was a great program. You invited Zhamak Dehghani. Of course, she's the creator of the data mesh. One of our fundamental premises is that you've got this hyper specialized team that you've got to go through if you want anything. But at the same time, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess, a question for you Richard. How do you deal with that? Do you organize so that there are a few sort of rock stars that build cubes and the like or have you had any success in sort of decentralizing with your constituencies that data model? >> Yeah. So we absolutely have got rockstar data scientists and data guardians, if you like. People who understand what it means to use this data, particularly the data that we use at EMIS is very private, it's healthcare information. And some of the rules and regulations around using the data are very complex and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a consulting type experience from a set of rock stars to help a more decentralized business who needs to understand the data and to generate some valuable output. >> Justin, what do you say to a customer or prospect that says, "Look, Justin. I got a centralized team and that's the most cost effective way to serve the business. Otherwise, I got duplication." What do you say to that? >> Well, I would argue it's probably not the most cost effective, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you for many, many years to come. I think that's the story at Oracle or Teradata or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams, as much as they are experts in the technology, they don't necessarily understand the data itself. And this is one of the core tenets of data mesh that Zhamak writes about is this idea of the domain owners actually know the data the best. And so by not only acknowledging that data is generally decentralized, and to your earlier point about Sarbanes-Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for those laws to be compliant. But I think the reality is the data mesh model basically says data's decentralized and we're going to turn that into an asset rather than a liability. And we're going to turn that into an asset by empowering the people that know the data the best to participate in the process of curating and creating data products for consumption. So I think when you think about it that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two models comparing and contrasting. >> So do you think the demise of the data warehouse is inevitable? Teresa, you work with a lot of clients. They're not just going to rip and replace their existing infrastructure. Maybe they're going to build on top of it, but what does that mean? Does that mean the EDW just becomes less and less valuable over time or it's maybe just isolated to specific use cases? What's your take on that? >> Listen, I still would love all my data within a data warehouse. I would love it mastered, would love it owned by a central team, right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date, I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's going to be a new technology that's going to emerge that we're going to want to tap into. There's going to be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this new mesh layer that still takes advantage of the things I mentioned: the data products in the systems that are meaningful today, and the data products that actually might span a number of systems. Maybe either those that either source systems with the domains that know it best, or the consumer-based systems or products that need to be packaged in a way that'd be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to use all of them. >> So, Richard, let me ask you. Take Zhamak's principles back to those. You got the domain ownership and data as product. Okay, great. Sounds good. But it creates what I would argue are two challenges: self-serve infrastructure, let's park that for a second, and then in your industry, one of the most regulated, most sensitive, computational governance. How do you automate and ensure federated governance in that mesh model that Teresa was just talking about? >> Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to centralize the security and the governance of the data. And I think although a data warehouse makes that very simple 'cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at EMIS is we have a single security layer that sits on top of our data mesh, which means that no matter which user is accessing which data source, we go through a well audited, well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is audited in a very kind of standard way regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible, understanding where your source of truth is and securing that in a common way is still a valuable approach, and you can do it without having to bring all that data into a single bucket so that it's all in one place. And so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform, and ensuring that only data that's available under GDPR and other regulations is being used by the data users. >> Yeah. So Justin, we always talk about data democratization, and up until recently, they really haven't been line of sight as to how to get there, but do you have anything to add to this because you're essentially doing analytic queries with data that's all dispersed all over. How are you seeing your customers handle this challenge? >> Yeah, I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, the people who know the data the best, to create data as a product ultimately to be consumed. And we try to represent that in our product as effectively, almost eCommerce like experience where you go and discover and look for the data products that have been created in your organization, and then you can start to consume them as you'd like. And so really trying to build on that notion of data democratization and self-service, and making it very easy to discover and start to use with whatever BI tool you may like or even just running SQL queries yourself. >> Okay guys, grab a sip of water. After the short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence. Keep it right there. (bright upbeat music)

>> Announcer: From around the globe it's theCUBE. Presenting the Convergence of File and Object brought to you by Pure Storage. >> Okay now we're going to get the customer perspective on object and we'll talk about the convergence of file and object, but really focusing on the object pieces this is a content program that's being made possible by Pure Storage and it's co-created with theCUBE. Christopher CB Bohn is here. He's a lead architect for MicroFocus the enterprise data warehouse and principal data engineer at MicroFocus. CB welcome good to see you. >> Thanks Dave good to be here. >> So tell us more about your role at Microfocus it's a pan Microfocus role because we know the company is a multi-national software firm it acquired the software assets of HP of course including Vertica tell us where you fit. >> Yeah so Microfocus is you know, it's like I can says it's wide, worldwide company that it sells a lot of software products all over the place to governments and so forth. And it also grows often by acquiring other companies. So there is there the problem of integrating new companies and their data. And so what's happened over the years is that they've had a number of different discreet data systems so you've got this data spread all over the place and they've never been able to get a full complete introspection on the entire business because of that. So my role was come in, design a central data repository and an enterprise data warehouse, that all reporting could be generated against. And so that's what we're doing and we selected Vertica as the EDW system and Pure Storage FlashBlade as the communal repository. >> Okay so you obviously had experience with with Vertica in your previous role, so it's not like you were starting from scratch, but paint a picture of what life was like before you embarked on this sort of consolidated approach to your data warehouse. Was it just dispared data all over the place? A lot of M and A going on, where did the data live? >> CB: So >> Right so again the data is all over the place including under people's desks and just dedicated you know their own private SQL servers, It, a lot of data in a Microfocus is one on SQL server, which has pros and cons. Cause that's a great transactional database but it's not really good for analytics in my opinion. So but a lot of stuff was running on that, they had one Vertica instance that was doing some select reporting. Wasn't a very powerful system and it was what they call Vertica enterprise mode where it had dedicated nodes which had the compute and storage in the same locus on each server okay. So Vertica Eon mode is a whole new world because it separates compute from storage. Okay and at first was implemented in AWS so that you could spin up you know different numbers of compute nodes and they all share the same communal storage. But there has been a demand for that kind of capability, but in an on-prem situation. Okay so Pure storage was the first vendor to come along and have an S3 emulation that was actually workable. And so Vertica worked with Pure Storage to make that all happen and that's what we're using. >> Yeah I know back when back from where we used to do face-to-face, we would be at you know Pure Accelerate, Vertica was always there it stopped by the booth, see what they're doing so tight integration there. And you mentioned Eon mode and the ability to scale, storage and compute independently. And so and I think Vertica is the only one I know they were the first, I'm not sure anybody else does that both for cloud and on-prem, but so how are you using Eon mode, are you both in AWS and on-prem are you exclusively cloud? Maybe you could describe that a little bit. >> Right so there's a number of internal rules at Microfocus that you know there's, it's not AWS is not approved for their business processes. At least not all of them, they really wanted to be on-prem and all the transactional systems are on-prem. And so we wanted to have the analytics OLAP stuff close to the OLTP stuff right? So that's why they called there, co-located very close to each other. And so we could, what's nice about this situation is that these S3 objects, it's an S3 object store on the Pure Flash Blade. We could copy those over if we needed it to AWS and we could spin up a version of Vertica there, and keep going. It's like a tertiary GR strategy cause we actually have a, we're setting up a second, Flash Blade Vertica system geo located elsewhere for backup and we can get into it if you want to talk about how the latest version of the Pure software for the Flash Blade allows synchronization across network boundaries of those Flash Blade which is really nice because if, you know there's a giant sinkhole opens up under our Koll of facility and we lose that thing then we just have to switch to DNS. And we were back in business of the DR. And then the third one was to go, we could copy those objects over to AWS and be up and running there. So we're feeling pretty confident about being able to weather whatever comes along. >> Yeah I'm actually very interested in that conversation but before we go there. you mentioned you want, you're going to have the old lab close to the OLTP, was that for latency reasons, data movement reasons, security, all of the above. >> Yeah it's really all of the above because you know we are operating under the same sub-net. So to gain access to that data, you know you'd have to be within that VPN environment. We didn't want to going out over the public internet. Okay so and just for latency reasons also, you know we have a lot of data and we're continually doing ETL processes into Vertica from our production data, transactional databases. >> Right so they got to be approximate. So I'm interested in so you're using the Pure Flash Blade as an object store, most people think, oh object simple but slow. Not the case for you is that right? >> Not the case at all >> Why is that. >> This thing had hoop It's ripping, well you have to understand about Vertica and the way it stores data. It stores data in what they call storage containers. And those are immutable, okay on disc whether it's on AWS or if you had a enterprise mode Vertica, if you do an update or delete it actually has to go and retrieve that object container from disc and it destroys it and rebuilds it, okay which is why you don't, you want to avoid updates and deletes with vertica because the way it gets its speed is by sorting and ordering and encoding the data on disk. So it can read it really fast. But if you do an operation where you're deleting or updating a record in the middle of that, then you've got to rebuild that entire thing. So that actually matches up really well with S3 object storage because it's kind of the same way, it gets destroyed and rebuilt too okay. So that matches up very well with Vertica and we were able to design the system so that it's a panda only. Now we have some reports that we're running in SQL server. Okay which we're taking seven days. So we moved that to Vertica from SQL server and we rewrote the queries, which were had, which had been written in TC SQL with a bunch of loops and so forth and we were to get, this is amazing it went from seven days to two seconds, to generate this report. Which has tremendous value to the company because it would have to have this long cycle of seven days to get a new introspection in what they call the knowledge base. And now all of a sudden it's almost on demand two seconds to generate it. That's great and that's because of the way the data is stored. And the S3 you asked about, oh you know it, it's slow, well not in that context. Because what happens really with Vertica Eon mode is that it can, they have, when you set up your compute nodes, they have local storage also which is called the depot. It's kind of a cache okay. So the data will be drawn from the Flash Blade and cached locally. And that was, it was thought when they designed that, oh you know it's that'll cut down on the latency. Okay but it turns out that if you have your compute nodes close meaning minimal hops to the Flash Blade that you can actually tell Vertica, you know don't even bother caching that stuff just read it directly on the fly from the from the Flash Blade and the performance is still really good. It depends on your situation. But I know for example a major telecom company that uses the same topologies we're talking about here they did the same thing. They just dropped the cache cause the Flash Blade was able to deliver the data fast enough. >> So that's, you're talking about that's speed of light issues and just the overhead of switching infrastructure is that, it's eliminated and so as a result you can go directly to the storage array? >> That's correct yeah, it's like, it's fast enough that it's almost as if it's local to the compute node. But every situation is different depending on your needs. If you've got like a few tables that are heavily used, then yeah put them in the cache because that'll be probably a little bit faster. But if you're have a lot of ad hoc queries that are going on, you know you may exceed the storage of the local cache and then you're better off having it just read directly from the, from the Flash Blade. >> Got it so it's >> Okay. >> It's an append only approach. So you're not >> Right >> Overwriting on a record, so but then what you have automatically re index and that's the intelligence of the system. how does that work? >> Oh this is where we did a little bit of magic. There's not really anything like magic but I'll tell you what it is I mean. ( Dave laughing) Vertica does not have indexes. They don't exist. Instead I told you earlier that it gets a speed by sorting and encoding the data on disk and ordering it right. So when you've got an append-only situation, the natural question is well if I have a unique record, with let's say ID one, two, three, what happens if I append a new version of that, what happens? Well the way Vertica operates is that there's a thing called a projection which is actually like a materialized columnar data store. And you can have a, what they call a top-K projection, which says only put in this projection the records that meet a certain condition. So there's a field that we like to call a discriminator field which is like okay usually it's the latest update timestamp. So let's say we have record one, two, three and it had yesterday's date and that's the latest version. Now a new version comes in. When the data at load time vertical looks at that and then it looks in the projection and says does this exist already? If it doesn't then it adds it. If it does then that one now goes into that projection okay. And so what you end up having is a projection that is the latest snapshot of the data, which would be like, oh that's the reality of what the table is today okay. But inherent in that is that you now have a table that has all the change history of those records, which is awesome. >> Yeah. >> Because, you often want to go back and revisit, you know what it will happen to you. >> But that materialized view is the most current and the system knows that at least can (murmuring). >> Right so we then create views that draw off from that projection so that our users don't have to worry about any of that. They just get oh and say select from this view and they're getting the latest greatest snapshot of what the reality of the data is right now. But if they want to go back and say, well how did this data look two days ago? That's an easy query for them to do also. So they get the best of both worlds. >> So could you just plug any flash array into your system and achieve the same results or is there anything really unique about Pure? >> Yeah well they're the only ones that have got I think really dialed in the S3 object form because I don't think AWS actually publishes every last detail of that S3 spec. Okay so it had, there's a certain amount of reverse engineering they had to do I think. But they got it right. When we've, a couple maybe a year and a half ago or so there they were like at 99%, but now they worked with Vertica people to make sure that that object format was true to what it should be. So that it works just as if Vertica doesn't care, if it is on AWS or if it's on Pure Flash Blade because Pure did a really good job of dialing in that format and so Vertica doesn't care. It just knows S3, doesn't know what it doesn't care where it's going it just works. >> So the essentially vendor R and D abstracted that complexity so you didn't have to rewrite the application is that right? >> Right, so you know when Vertica ships it's software, you don't get a specific version for Pure or AWS, it's all in one package, and then when you configure it, it knows oh okay well, I'm just pointed at the, you know this port, on the Pure storage Flash Blade, and it just works. >> CB what's your data team look like? How is it evolving? You know a lot of customers I talked to they complain that they struggled to get value out of the data and they don't have the expertise, what does your team look like? How is it, is it changing or did the pandemic change things at all? I wonder if you could bring us up to date on that? >> Yeah but in some ways Microfocus has an advantage in that it's such a widely dispersed across the world company you know it's headquartered in the UK, but I deal with people I'm in the Bay Area, we have people in Mexico, Romania, India. >> Okay enough >> All over the place yeah all over the place. So when this started, it was actually a bigger project it got scaled back, it was almost to the point where it was going to be cut. Okay, but then we said, well let's try to do almost a skunkworks type of thing with reduced staff. And so we're just like a hand. You could count the number of key people on this on one hand. But we got it all together, and it's been a traumatic transformation for the company. Now there's, it's one approval and admiration from the highest echelons of this company that, hey this is really providing value. And the company is starting to get views into their business that they didn't have before. >> That's awesome, I mean, I've watched Microfocus for years. So to me they've always had a, their part of their DNA is private equity I mean they're sharp investors, they do great M and A >> CB: Yeah >> They know how to drive value and they're doing modern M and A, you know, we've seen what they what wait, what they did with SUSE, obviously driving value out of Vertica, they've got a really, some sharp financial people there. So that's they must have loved the the Skunkworks, fast ROI you know, small denominator, big numerator. (laughing) >> Well I think that in this case, smaller is better when you're doing development. You know it's a two-minute cooks type of thing and if you've got people who know what they're doing, you know I've got a lot of experience with Vertica, I've been on the advisory board for Vertica for a long time. >> Right And you know I was able to learn from people who had already, we're like the second or third company to do a Pure Flash Blade Vertica installation, but some of the best companies after they've already done it we are members of the advisory board also. So I learned from the best, and we were able to get this thing up and running quickly and we've got you know, a lot of other, you know handful of other key people who know how to write SQL and so forth to get this up and running quickly. >> Yeah so I mean, look it Pure is a fit I mean I sound like a fan boy, but Pure is all about simplicity, so is object. So that means you don't have to ra, you know worry about wrangling storage and worrying about LANs and all that other nonsense and file names but >> I have burned by hardware in the past you know, where oh okay they built into a price and so they cheap out on stuff like fans or other things in these components fail and the whole thing goes down, but this hardware is super good quality. And so I'm happy with the quality of that we're getting. >> So CB last question. What's next for you? Where do you want to take this initiative? >> Well we are in the process now of, we're when, so I designed a system to combine the best of the Kimball approach to data warehousing and the inland approach okay. And what we do is we bring over all the data we've got and we put it into a pristine staging layer. Okay like I said it's a, because it's append-only, it's essentially a log of all the transactions that are happening in this company, just as they appear okay. And then from the Kimball side of things we're designing the data marts now. So that's what the end users actually interact with. So we're taking the, we're examining the transactional systems to say, how are these business objects created? What's the logic there and we're recreating those logical models in Vertica. So we've done a handful of them so far, and it's working out really well. So going forward we've got a lot of work to do, to create just about every object that the company needs. >> CB you're an awesome guest really always a pleasure talking to you and >> Thank you. >> congratulations and good luck going forward stay safe. >> Thank you, you too Dave. >> All right thank you. And thank you for watching the Convergence of File and Object. This is Dave Vellante for theCUBE. (soft music)

(upbeat music) >> From the CUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hi everyone, this is Dave Vellante from the CUBE. And we're getting ready for the Snowflake Data cloud summit four geographies, eight tracks more than 40 sessions for this global event. Starts on November 17th, where we're tracking the rise of the Data cloud. You're going to hear a lot about that, now, by now, you know, the story of Snowflake or you know, what maybe you don't but a new type of cloud native database was introduced in the middle part of last decade. And a new set of analytics workloads has emerged that is powering a transformation within organizations. And it's doing this by putting data at the core of businesses and organizations. You know, for years we marched to the cadence of Moore's law. That was the innovation engine of our industry, but now that's changed it's data plus machine intelligence plus cloud. That's the new innovation cocktail for the technology industry and industries overall. And at the Data cloud summit we'll hear from Snowflake executives, founders, technologists, customers, and ecosystems partners. And of course, you going to hear from interviews on the CUBE. So, let's dig in a little bit more and help me are two Snowflake experts. Felipe Hoffa is a data cloud advocate and Kent Graziano is a chief technical evangelist post at Snowflake. Gents, great to see you. Thanks for coming on. >> Yeah, thanks for having us on, this is great. >> Thank you. >> So guys first, I got to congratulate you on getting to this point. You've achieved beyond escape velocity and obviously one of the most important IPOs of the year, but you got a lot of work to do. I know that what, what are the substantive aspects behind the Data cloud? >> I mean, it's a new concept right? We've been talking about infrastructure clouds and SaaS applications living in application clouds and Data cloud is the ability to really share all that data that we've been collected. You know, we've spent what how many a decade or more with big data now but have we been able to use it effectively? And that's really where the Data cloud is coming in and Snowflake and making that a more seamless, friendly, easy experience to get access to the data. I've been in data warehousing for nearly 30 years now. And our dream has always been to be able to augment an organization's analytics with data from outside their organization. And that's just been a massive pain in the neck with having to move files around and replicate the data and maybe losing track of where it came from or where it went. And the Data cloud is really giving our customers the ability to do that in a much more governed way, a much more seamless way and really make it push button to give anyone access to the data they need and have the performance to do the analytics in near real time. It's total game changer is as you already know and just it's crazy what we're able to do today compared it to what we could do when I started out in my career. >> Well, I'm going to come back to that 'cause I want to tap your historical perspective, but Felipe let me ask you, So, why did you join Snowflake? You're you're the newbie here? What attracted you? >> Exactly? I'm the newbie, I used to work at Google until August. I was there for 10 years. I was a developer advocate there also for data you might have heard about the BigQuery. I was doing a lot of that. And then as time went by Snowflake started showing up more and more in my feeds within my customers in my community. And it came the time, well, I felt that like, you know, when wherever you're working, once in a while you think I should leave this place I should try something new, I should move my career forward. While at Google, I thought that so many times, as anyone would do, and it was only when Snowflake showed up, like where Snowflake is going now, why Snowflake is being received by all the customers that I saw this opportunity. And I decided that moving to Snowflake would be a step forward for me. And so far I'm pretty happy, like the timing has been incredible, but more than the timing and everything, it's really, really a great place for data. What I love first is data, sharing data, analyzing data and how Snowflake is doing it's for me to mean phenomenal. >> So, Kent, I want to come back to you and I say tap maybe your historical perspective here. And you said it's always been a dream that you could do these other things bringing in external data. I would say this, that I don't want to push a little bit on this because I have often said that the EDW marketplace really never lived up to its promises of 360 degree views of the customer real time or near real time analytics. And, and it really has been as you kind of described are a real challenge for a lot of organizations. When Hadoop came in we got excited that it was going to actually finally live up to that vision and, and duped it a lot and don't get me wrong, I mean, the whole concept of bring that compute to data and lowering the cost and so forth. But it certainly didn't minimize complexity. And, and it seems like, feels like Snowflake is on the cusp of actually delivering on that promise that we've been talking about for 30 years. I wonder, if you could share your perspective is it, are we going to get there this time? >> Yeah. And as far as I can tell working with all of our customers some of them are there. I mean, they thought through those struggles that you were talking about that I saw throughout my career and now with getting on Snowflake they're delivering customer 360 they're integrating weblogs and IOT data with structured data from their ERP systems or CRM systems, their supply chain systems. And it really is coming to fruition. I mean, the industry leaders, you know, Bill Inman and Claudia Imhoff, they've had this vision the whole time but the technology just wasn't able to support it. And the cloud, as we said about the internet, changed everything. And then Ben wine teary, and they're in their vision and building the system, taking the best concepts from the Hadoop world and the data Lake world and the enterprise data warehouse world and putting it all together into this, this architecture that's now Snowflake and the Data cloud solve it. I mean, it's the classic benefit of hindsight is 2020 after years in the industry, they'd seen these problems and said like, how can we solve them? Does the Cloud let us solve these problems? And the answer was yes, but it did require writing everything from scratch and starting over with, because the architecture of the Cloud just allows you to do things that you just couldn't do before. >> Yeah. I'm glad you brought up you know, some of the originators of the data warehouse because it really wasn't their fault. They were trying to solve a problem. It was the marketers that took it and really kind of made promises that they couldn't keep. But, the reality is when you talk to customers in the so old EDW days and this is the other thing I want to tap you guys' brains on. It was very challenging. I mean, one customer one time referred to it as a snake, swallowing a basketball. And what he meant by that is every time there's a change Sarbanes Oxley comes and we have to ingest all this new data. It's like, Oh, it's to say everything slows down to a grinding halt. Every time Intel came out with a new microprocessor, they would go out and grab a new server as fast as they possibly could. He called it chasing the chips and it was this endless cycle of pain. And so, you know, the originators of the data whereas they didn't have the compute power they didn't have the Cloud. And so, and of course they didn't have the 30, 40 years of pain to draw upon. But I wonder if you could, could maybe talk a little bit about the kinds of things that can be done now that we haven't been able to do here to form. >> Well, yeah. I remember early on having a conversation with Bill about this idea of near real time data warehousing and saying, is this real, is this something really people need? And at the time he was a couple of decades ago, he said now to them they just want to load their data sooner than once a month. That was the goal. And that was going to be near real time for them. And, but now I'm seeing it with our customers. It's like, now we can do it, you know, with things like the Kafka technology and snow pipe in Snowflake that people are able to get that refresh way faster and have near real time analytics access to that data in a much more timely manner. And so it really is coming true. And the, the compute power that's there, as you said, we've now got this compute power in the Cloud that we never dreamed of. I mean, you would think of only certain, very large, massive global companies or governments could afford super computers. And that's what it would have taken. And now we've got nearly the power of a super computer in our mobile device that we all carry around with us. So being able to harness all that now in the Cloud is really opening up opportunities to do things with data and access data in a way that, again really, we just kind of dreamed of before as like we can democratize data when we get to this point. And I think that's where we are. We're at that inflection point where now it's possible to do it. So the challenge on organizations is going to be how do we do it effectively? How do we do it with agility? And how do we do it in a governed manner? You mentioned Sarbanes Oxley, GDPR, CCPA, all of those are out there. And so we have all of that as well. And so that's where we're going to get into it, right into the governance and being able to do that in a very quick, flexible, extensible manner and Snowflakes really letting people do it now. >> Well, yeah. And you know, again, we've been talking about Hadoop and I, again, for all my fond thoughts of that era, and it's not like Hadoop is gone but it was a lot of excitement around it, but governance was a huge problem. And it was kind of a bolt on. Now, Felipe I going to ask you, like, when you think about a company like Google, your former employer, you know, data is at the core of their business. And so many companies the data is not at the core of their business. Something else is, it's a process or a manufacturing facility or whatever it is. And the data is sort of on the outskirts. You know, we often talk about in, in stove pipes. And so we're now seeing organizations really put data at the core of their, it becomes central to their DNA. I'm curious as to your thoughts on that. And also, if you've got a lot of experience with developers, is there a developer angle here in this new data world? >> For sure, I mean, I love seeing everything like throughout my career at Google and my two months here and talking to so many companies, you never thought before like these are database companies but they are the ones that keep rowing. The ones that keep moving to the next stage of their development is because they are focusing on data. They are adapting the processes, they are learning from it. Me, I focus a lot on developers. So, I met when I started this career as an advocate of first, I was a software engineer and my work so far, has we worked, I really loved talking to the engineers on the other companies. Like, maybe I'm not the one solving the business problem, but at the end of the day, when these companies have a business problem that they want to grow, they want to have data. There are other engineers that are scientists like me that want to work for the company and bring the best technology to solve the problems. And Yeah, there's so much where data can help, yes, as we evolved the system for the company, and also for us, for understanding the systems things like of survivability, and recently there was a big company a big launch on survivability (indistinct) whether they are running all of their data warehousing needs. And all of that needs on snowflake, just because running these massive systems and being able to see how they're working generates a lot of data. And then how do you manage it? How do you analyze it? Or Snowflake is really there to help cover the two areas. >> It's interesting my business partner, John farrier cohost of the CUBE, he said, gosh I would say middle of the last decade, maybe even around the time 2013, when Snowflake was just coming out, he said, he predicted the data would be the new development kit. And it's really at the center of a lot of the data life cycle the what I call the data pipelines. I know people use that term differently but I'm very excited about the Data cloud summit and what we're going to learn there. And I get to interview a lot of really cool people. So, I appreciate you guys coming up, but, Kent who should attend the Data cloud summit, I mean, what should they expect to learn? >> Well, as you said earlier, Dave, there's so many tracks and there's really kind of something for everyone. So, we've got a track on unlocking the value of the Data cloud, which is really going to speak to the business leaders, you know, as to what that vision is, what can we do from an organizational perspective with the Data cloud to get that value from the data to move our businesses forward. But we've also done for the technicians migrating to snowflake. Sessions on how to do the migration, modernizing your data Lake, data science, how to do analytics with the, and data science in Snowflake and in the Data cloud, and even down to building apps. So the developers and building data products. So, you know, we've got stuff for developers, we've got stuff for data scientists. We've got stuff for the data architects like myself and the data engineers on how to build all of this out. And then there's going to be some industry solution spotlights as well. So we can talk about different verticals folks in FinTech and healthcare, there's going to be stuff for them. And then for our data superheroes we have a hallway track where we're going to get talks from the folks that are in our data superheroes which is really our community advocacy program. So these are folks who are out there in the trenches using Snowflake delivering value at their organizations. And they're going to talk down and dirty. How did they make this stuff happen? So it's going to be to some hope, really something for everyone, fireside chats with our executives. Of course something I'm really looking forward to myself. So was fun to hear from Frank and Christian and Benoit about what's the next big thing, what are we doing now? Where are we going with all of this? And then there is going to be a some awards we'll be giving out our data driver awards for our most innovative customers. So this is going to be a lot, a lot for everybody to consume and enjoy and learn about this, this new space of, of the Data cloud. >> Well, thank you for that Kent. And I'll second that, at least there's going to be a lot for everybody. If you're an existing Snowflake customer there's going to be plenty of two or one content, we can get in to the how to use and the best practice, if you're really not that familiar with Snowflake, or you're not a customer, there's a lot of one-on-one content going on. So, Felipe, I'd love to hear from you what people can expect at the Data cloud summit. >> Totally, so I would like to plus one to everyone that can say we have a phenomenal schedule that they, the executive will be there. I really wanted to especially highlight the session I'm preparing with Trevor Noah. I'm sure you might have heard of him. And we are having him at the Data cloud summit and we are going to have a session. We are going to talk about data. We are preparing a session. That's all about how people that love data that people that want to make that actionable. How can they bring storytelling and make it more, have more impact as he has well learn to do through his life? >> That's awesome, So, we have Trevor Noah, we're not just going to totally geek out here. we're going to have some great entertainment as well. So, I want you to go to snowflake.com and click on Data cloud summit 2020 there's four geos. It starts on November 17th and then runs through the week and in the following week in Japan. So, so check that out. We'll see you there. This is Dave Vellante for the CUBE. Thanks for watching. (upbeat music)

>> (Instructor)From the cube studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a cube conversation. >> Hi everyone. This is Dave Volante, the cube, and we're getting ready for the snowflake data cloud summit four geographies eight tracks, more than 40 sessions for this global event starts on November 17th, where we're tracking the rise of the data cloud. You're going to hear a lot about that now by now, you know the story of Snowflake or you know, what maybe you don't, but a new type of cloud native database was introduced in the middle part of last decade. And a new set of analytics workloads has emerged that is powering a transformation within organizations. And it's doing this by putting data at the core of businesses and organizations. You know for years, we marched to the cadence of Moore's law. That was the innovation engine of our industry, but now that's changed it's data plus machine intelligence plus cloud. That's the new innovation cocktail for the technology industry and industries overall. And at the data cloud summit, we'll hear from snowflake executives, founders, technologists, customers, and ecosystems partners. And of course, you're going to hear from interviews on the cube. So let's dig in a little bit more and to help me, are two snowflake experts, Filipe Hoffa is a data cloud advocate and Kent Graziano is a chief technical evangelists post at Snowflake. Gents great to see you. Thanks for coming on. >> Yeah thanks for having us on this is great. >> Thank you. >> So guys, first, I got to congratulate you on getting to this point. You've achieved beyond escape velocity, and obviously one of the most important IPOs of the year, but you got a lot of work to do I know that Filipe, let me start with you data cloud. What's a data cloud and what are we going to learn about it at the data cloud summit? >> Oh, that's an excellent question. And let me tell you a little bit about our story here. And I really, really, really admire what Kent has done. I joined the snowflake like less than two months ago, and for me it's been a huge learning experience. And I look up to Kent a lot on how we deliver the message and how do we deliver all of that. So I would love to hear his answer first. >> Okay, that's cool. Okay Kent later on. So talk of data cloud, that's a catchy phrase, right? But it vectors into at least two of the components of my innovation, innovation cocktail. What, what are the substantive substantive aspects behind the data cloud? >> I mean, it's a, it's a new concept, right? We've been talking about infrastructure clouds and SAS applications living in an application clouds so data cloud is the ability to really share all that data that we've been collecting. You know, we've, we've spent what, how many days a decade or more with big data now, but have we been able to use it effectively? And that's, that's really where the data cloud is coming in and snowflake in making that a more seamless, friendly, easy experience to get access to the data. I've been in data warehousing for nearly 30 years now. And our dream has always been to be able to augment an organization's analytics with data from outside their organization. And that's just been a massive pain in the neck with having to move files around and replicate the data and maybe losing track of where it came from or where it went. And the data cloud is really giving our customers the ability to do that in a much more governed way, a much more seamless way, and really make it push button to give anyone access to the data they need and have the performance to do the analytics in near real time. It's it's total game changer as, as you already know, and just it's crazy what we're able to do today, compared to what we could do when I started out in my career. >> Well, I'm going to come back to that cause I want to tap your historical perspective, but Filipe, let me ask you. So why did you join snowflake? You're you're the newbie here. What attracted you? >> Exactly, I'm the newbie. I used to work at Google until August. I was there for 10 years. I was a developer advocate there also for data, you might have heard about a big query. I was doing a lot of that and then as time went by, Snowflake started showing up more and more in my feeds, within my customers, in my community. And it came the time. When, I felt that like, you know, when wherever you're working, once in a while you think I should leave this place, I should try something new. I should move my career forward. While at Google, I thought that so many times as anyone would do, and it was only when snowflake showed up, like where snowflake is going now, how snowflake is, is being received by all the customers that I saw this opportunity. And I decided that moving to Snowflake would be a step forward for me. And so far I'm pretty happy. Like the timing has been incredible, but more than the timing and everything, it's really, really a great place for data. What I love first is data sharing data, analyzing data and how Snowflake is doing it it promotes me in phenomena. >> So Ken, I want to come back to you and I say, tap, maybe your historical perspective here. And you said, you know, it's always been a dream that you could do these other things bring in external data. I would say this, that I don't want to push a little bit on this because I have often said that the EDW marketplace really never lived up to its promises of 360 degree views of the customer in real time or near real time analytics. And, and it really has been, as you kind of described are a real challenge for a lot of organizations when Hadoop came in you know, we had, we we we got excited that it was kind of going to actually finally live up to that vision and and and we duped it a lot. And it don't get me wrong. I mean, the whole concept of, you know, bring the compute to data and the lowering the cost and so forth, but it certainly didn't minimize complexity. And, and it seems like, feels like Snowflake is on the cusp of actually delivering that promise that we've been talking about for 30 years. I wonder if you could share your perspective, is it, are we going to get there this time? >> Yeah. And as far as I can tell working with all of our customers, some of them are there. I mean, they're, they Fought through those struggles that you were talking about that I saw throughout my career and now with getting on Snowflake they're, they're delivering customer 360, they're integrating weblogs and IOT data with structured data from their ERP systems or CRM systems, their supply chain systems. And it really is coming to fruition. I mean, the, you know, the industry leaders, you know, Bill Inman and Claudia M Hoff, they've had this vision the whole time, but the technology just wasn't able to support it. And the cloud, as we said about the internet, changed everything and then Ben Y and Terry, in their vision and building the system, taking the best concepts from the Hadoop world and the data Lake world and the enterprise data warehouse world, and putting it all together into this, this architecture, that's now, you know Snowflake and the data cloud solved it. I mean, it's the, you know, the, the classic benefit of her insight is 2020 after years in the industry, they had seen these problems and said like, how can we solve them? Does the cloud let us solve these problems? And the answer was yes, but it did require writing everything from scratch and starting over with because the architecture the cloud just allows you to do things that you just couldn't do before. Yeah I'm glad you brought up, you know, some of the originators of the data warehouse, because it really wasn't their fault. They were trying to solve a problem. That was the marketers that took it and really kind of made promises that they couldn't keep. But the reality is when you talk to customers in the, in the, so the old EDW days, and this is the other thing I want to, I want to tap your guys' brains on. It was very challenging. I mean, one, one customer, one time referred to it as a snake, swallowing a basketball. And what he meant by that is you know, every time there's a change, you know, Sarbanes Oxley comes and we have to ingest all this new data. It's like, Oh, it's just everything slows down to a grinding halt. Every time Intel came out with a new microprocessor, they would go out and grab a new server as fast as they possibly could. He called it chasing the chips, and it was this endless cycle of pain. And so, you know, the originators of the data whereas they didn't, they didn't have you know the compute power, they didn't have the cloud. >> Yeah. >> And so, and of course they didn't have the 30- 40 years of pain to draw upon. But, but I wonder if you could, could maybe talk a little bit about the kinds of things that can be done now that we haven't been able to do here before. >> Well, yeah I remember early on having a conversation with, with Bill about this idea of near real time data warehousing and saying, is this real? Is this something really need people need? And at the time it was, was a couple of decades ago, he said no to them they just want to load their data sooner than once a month. >> Yeah. >> That was the goal. And that was going to be near real time for them. And, but now I'm seeing it with our customers. It's like, now we can do it, you know, with things like the Kafka technology and snow pipe in, in Snowflake, that people are able to get that refresh way faster and have near real time analytics access to that data in a much more timely manner. And so it really is coming true. And the, the compute power that's there, as you said, you know we, we've now got this compute power in the cloud that we never dreamed of. I mean, you would think of only certain very large, massive global companies or governments could afford supercomputers. And that's what it would have taken. And now we've got nearly the power of a supercomputer in our mobile device that we all carry around with us. So being able to harness all that now in the cloud is really opening up opportunities to do things with data and access data in a way that again really we just kind of dreamed of before. It's like, we can, we can democratize data when we get to this point. And I think that's the, that's where we are, we're at that inflection point where now it's, it's possible to do it. So the challenge on organizations is going to be, how do we do it effectively? How do we do it with agility? And how do we do it in a governed manner? You mentioned Sarbanes Oxley, GDPR, CCPA, all of those are out there. And so we have all of that as well. And so that's where, that's where we're going to get into it, right. Is into the governance and being able to do that in a very quick, flexible, extensible manner and you know, Snowflakes really letting people do it now. >> Well, yeah and you know, again, we've been talking about Hadoop and again, for all my, my fond thoughts of that era, and it's not like hadoop is gone, but, but it was a lot of excitement around it but but governance was a huge problem and it was kind of a ball tough enough. Felipe I got to ask you, like when you think about a company like Google your former employer, you know, data is at the core of their business. And so many companies, the data is not at the core of their business. Something else is it's a process or a manufacturing facility or you know whatever it is. And the data is sort of on the outskirts. You know, we often talk about in, in stove pipes. And so we're now seeing organizations really put data at the core of their it becomes, you know, central to their, to their DNA. I'm curious as to your thoughts on that. And also if you've got a lot of experience with developers, is there, is there a developer angle here in this new data world? >> Oh, for sure. I mean, I love seeing every, like throughout my career at Google and my two months here and talking to so many companies, you never thought before, like these are database companies, but the the ones that keep rowing. The ones that keep moving to the next stage of their development is because they are focusing on data. They are adapting the processes they learning from it. And me, I focus a lot on developers. So I mean when I started This career as an advocate. First I was a software engineer and my work so far, has been work, I really loved talking to the engineers on the other companies. Like maybe I'm not the one solving the business problem, but at the end of the day, when these companies have a business problem that they want to row, they want to have data. There are other engineers that are scientists likes me that are, that, that want to work for work for the company and bring the best technology to solve the problems. Yeah, there's so much where data can help as we evolve the system for the company. And also for us for understanding the systems, things like observability and recently, there was a big company, a big launch on observability the company name is observable, where they are running all of their data warehousing needs. And all of their data needs on Snowflake, just because running these massive systems and being able to see how they're working generates a lot of data. And then how do you manage it? How do you analyze it? Or snowflake is already there to help. >> Well you know >> I covered the two areas. >> It's interesting my, my business partner, John farrier, cohost of the cube, he said, gosh, I would say middle of the last decade, maybe even around the time, you know, 2013, when Snowflake was just coming out, he said, he predicted the data would be the new development kit. And you know, it's really at the center of a lot of, you know, the data life cycle, the, the, what I call the data pipelines. I know people use that term differently, but, but I'm, I'm very excited about the data cloud summit and what we're going to learn there. And I get to interview a lot of really cool people. And so I appreciate you guys coming on, but Kent, who, who should attend the data cloud summit, I mean, what, what are the, what should they expect to learn? >> Well, as you said earlier, Dave, there's, there's so many tracks and there's really kind of something for everyone. So we've got a track on unlocking the value of the data cloud, which is really going to speak to, you know, the business leaders, you know, as to what that vision is, what can we do from an organizational perspective, with the data cloud to get that value from the data to, to move our businesses forward. But we've also got, you know, for the technicians migrating to Snowflake training sessions on how to do the migration, modernizing your data like data science, you know how to do analytics with the, and data science in Snowflake and in the data cloud and even down to building apps. So the developers and building data products. So, you know, we've got stuff for developers, we've got stuff for data scientists. We've got stuff for the, the data architects like myself and the data engineers on how to, how to build all of this out. And then there's going to be some industry solutions spotlights as well. So we can talk about different verticals of folks in FinTech and, and in healthcare. There's going to be stuff for them. And then for our, our data superheroes, we have a hallway track where we're going to get talks from the folks that are in our data superheroes, which is really our community advocacy program. So these are folks who are out there in the trenches using Snowflake, delivering value at, at their organizations. And they're going to talk you know down and dirty. How did they make this stuff happen? So there's going to be just really something for everyone, fireside chats with our executives, of course, something I'm really looking forward to in myself. It's always fun to, to hear from Frank and Christian. And Benwah about, you know, what's the next big thing, you know, what are we doing now? Where are we going with all of this? And then there is going to be some awards. We'll be giving out our data driver awards for our most innovative customers. So this is going to be a lot, a lot for everybody to consume and enjoy and learn about this, this new space of, of the data cloud. >> Well, thank you for that Kent. And I'll second that, I mean, there's going to be a lot for everybody. If you're an existing Snowflake customer, there's going to be plenty of two on one content we can get in to the how to's and the best practice. If you're really not that familiar with Snowflake, or you're not a customer, there's a lot of one-on-one content going on. If you're an investor and you want to figure out, okay, what is this vision? And can, you know, will this company grow into its massive valuation and how are they going to do that? I think you're going to, you're going to hear about the data cloud and really try get a perspective. And you can make your own judgment as to, to, you know, whether or not you think that it's going to be as large a market as many people think. So Felipe, I'd love to hear from you what people can expect at the data cloud summit. >> Totally, so I would love to plus one to everyone that Kent said. We have a phenomenal schedule that the the executive will be there. And I really wanted to specially highlight the session I'm preparing with Trevor Noah. I'm sure you might have heard of him. And we are having him at the data cloud summit, and we are going to have a session. We're going to talk about data. We are preparing a session, That's all about how people that love data, that people that want to make data actionable. How can they bring storytelling and make it more, have more impact as he has well learned to do through his life. >> That's awesome, So yeah, Trevor Noah, we're not just going to totally geek out here. We're going to, we're going to have some great entertainment as well. So I want you to go to snowflake.com and click on data cloud summit, 2020 there's four geos. It starts on November 17th and then runs through the week and then the following week in Japan. So, so check that out. We'll see you there. This is Dave Volante for the cube. Thanks for watching. (soft music)

>> Narrator: From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE Conversation. >> Hi everyone, this is Dave Vellante at theCUBE, and we're getting ready for the Snowflake Data Cloud Summit. four geographies, eight tracks, more than 40 sessions for this global event. starts on November 17th, where we're tracking the rise of the data cloud. You're going to hear a lot about that. Now, by now, you know the story of Snowflake or, you know what? Maybe you don't. But a new type of cloud-native database was introduced in the middle part of the last decade. And a new set of analytics workloads has emerged, that is powering a transformation within the organizations. And it's doing this by putting data at the core of businesses and organizations. For years, we marched to the cadence of Moore's law. That was the innovation engine of our industry, but now that's changed. It's data, plus machine intelligence, plus cloud. That's the new innovation cocktail for the technology industry and industries overall. And in the Data Cloud Summit, we'll hear from Snowflake executives, founders, technologists, customers, and ecosystems partners. And of course, you're going to hear from interviews on theCUBE. So let's dig in a little bit more. And to help me are two Snowflake experts. Felipe Hoffa is a data cloud advocate and Kent Graziano is a chief technical evangelist, both at Snowflake. Gents, great to see you, thanks for coming on. >> Thanks for having us on, this is great. >> Thank you. >> So guys, first, I got to congratulate you on getting to this point. You've achieved beyond escape velocity and obviously one of the most important IPOs of the year, but you got a lot of work to do and I know that. Felipe, let me start with you. Data cloud, what's a data cloud and what are we going to learn about it at the Data Cloud Summit? >> Oh, that's an excellent question. And, let me tell you a little bit about our story here. And I really, really, really admire what Kent has done. I joined Snowflake like less than two months ago and for me, it's been a huge learning experience. And I look up to Kent a lot on how we deliver the method here, how do we deliver all of that? So, I would love to hear his answer first. >> Dave: Okay, that's cool. Okay Kent, leader on. (Kent laughing) So we took it. Data cloud, that's a catchy phrase, right? But it vectors into at least two of the components of my innovation cocktail. What are the substantive aspects behind the data cloud? >> I mean, it's a new concept, right? We've been talking about infrastructure clouds and SaaS applications living in the application cloud, so data cloud is the ability to really share all that data that we've been collecting. We've spent what? How many da-- A decade or more with big data now, but have we been able to use it effectively? And that's really where the data cloud is coming in and Snowflake, in making that a more seamless, friendly, easy experience to get access to the data. I've been in data warehousing for nearly 30 years now. And our dream has always been to be able to augment an organization's analytics with data from outside their organization. And that's just been a massive pain in the neck with having to move files around and replicate the data and maybe losing track of where it came from or where it went. And the data cloud is really giving our customers the ability to do that in a much more governed way, a much more seamless way, and really make it push button to give anyone access to the data they need and have the performance to do the analytics in near real-time. It's a total game changer as you already know. And just, it's crazy what we're able to do today compared to what we could do when I started out in my career. >> Well, I'm going to come back to that 'cause I want to tap your historical perspective. But Felipe, let me ask you, so why did you join Snowflake? You're the newbie here, what attracted you? >> And finally, I'm the newbie. I used to work at Google until August. I was there for 10 years, I was a developer advocate there also for data, you might have heard about the BigQuery, I was doing a lot of that. And though as time went by, Snowflake started showing up more and more in my feeds, within my customers, in my community. And it came the time when I felt like-- Wherever you're working, once in a while you think, "I should leave this place, "I should try something new, "I should move my career forward." While at Google, I thought that so many times as anyone will do. And it was only when Snowflake showed up, like where Snowflake is going now, how Snowflake is being received by all the customers, that I saw this opportunity. And I decided that moving to Snowflake would be a step forward for me. And so far I'm pretty happy, like the timing has been incredible, but more than the timing and everything, it's really, really a great place for data. What I love first is data, sharing data, analyzing data and how Snowflake is doing it, its promising phenomena. >> So, Kent, I want to come back to you and I said, tap maybe your historical perspective here. And you said, it's always been a dream that you could do these other things, bring in external data. I would say this, that I would want to push a little bit on this because I have often said that the EDW marketplace really never lived up to its promises of 360 degree views of the customer, in real-time or near real-time analytics. And it really has been, as you kind of described it, a real challenge for a lot of organizations. When Hadoop came in, we had-- We got excited that it was going to actually finally live up to that vision and Hadoop did a lot. And don't get me wrong, I mean, the whole concept of, bring the computer data and lowering the cost and so forth. But it certainly didn't minimize complexity. And it seems like, feels like Snowflake is on the cusp of actually delivering on that promise that we've been talking about for 30 years. I wonder if you could share your perspective as an o-- Are we going to get there this time? >> Yeah. And as far as I can tell working with all of our customers, some of them are there. I mean, they thought through those struggles that you were talking about, that I saw throughout my career. And now with getting on Snowflake they're delivering customer 360, they're integrating weblogs and IOT data with structured data from their ERP systems or CRM systems, their supply chain systems and it really is coming to fruition. I mean, the industry leaders, Bill Inmon and Claudia Imhoff, they've had this vision the whole time, but the technology just wasn't able to support it and the cloud, as we said about the internet, changed everything. And then Benoit and Thierry in their vision in building the system, taking the best concepts from the Hadoop world and the data lake world and the enterprise data warehouse world, and putting it all together into this architecture, that's now Snowflake and the data cloud, solved it. I mean, it's-- The classic benefit of hindsight is 20/20, after years in the industry, they had seen these problems and said like, "How can we solve them? "Does the cloud let us solve these problems?" And the answer was, yes, but it did require writing everything from scratch and starting over with, because the architecture of the cloud just allows you to do things that you just couldn't do before. >> Yeah, I'm glad you brought up some of the originators of the data warehouse, because it really wasn't their fault, they were trying to solve a problem. It was the marketers that took it and really kind of made promises that they couldn't keep. But, the reality is when you talk to customers in the sort of the old EDW days, and this is the other thing I want to tap you guys' brains on, it was very challenging. I mean, and one customer one time referred to it as a snake swallowing a basketball. And what he meant by that is, every time there's a change, or Sarbanes-Oxley comes and we have to ingest all this new data. It's like aargh! It's just everything slows down to a grinding halt. Every time Intel came out with a new microprocessor they would go out and grab a new server as fast as they possibly could, he called it chasing the chips. And it was this endless cycle of pain. And so, the originators of the data warehouse, they didn't have the compute power, they didn't have the cloud. And so-- And of course they didn't have like 30, 40 years of pain to draw upon. But I wonder if you could maybe talk a little bit about the kinds of things that can be done now that we haven't been able to do here tofore. >> Well, yeah. I remember early on having a conversation with Bill about this idea of near real-time data warehousing and saying, "Is this real? "Is this something really people need?" And at the time, it was a couple of decades ago, he said, "No, to them, they just want to load their data "sooner than once a month." That was the goal. And they-- That was going to be near real-time for them. And, but now I'm seeing it with our customers. It's like, now we can do it. With things like the Kafka technology and Snowpipe in Snowflake, that people are able to get that refresh way faster and have near real-time analytics access to that data in a much more timely manner. And so it really is coming true. And the compute power that's there, as you said, we've now got this compute power in the cloud that we never dreamed of. I mean, you would think of only certain, very large, massive global companies or governments could afford supercomputers. And that's what it would have taken. And now we've got nearly the power of a super computer in our mobile device that we all carry around with us. So being able to harness all of that now in the cloud, is really opening up opportunities to do things with data and access data in a way that, again, really, we just kind of dreamed of before. Its like, we can democratize data when we get to this point. And I think that's where we are, we're at that inflection point, where now it's possible to do it. So the challenge on organizations is going to be how do we do it effectively? How do we do it with agility? And how do we do it in a governed manner? You mentioned Sarbanes-Oxley, GDPR, CCPA, all of those are out there. And so we have all of that as well. And so that's where we're going to get into it, ride us into the governance and being able to do that in a very quick, flexible, extensible manner. And Snowflakes really letting people do it now. >> Well, yeah. And again, we've been talking about Hadoop, and again, for all my fond thoughts of that era, and it's not like Hadoop is gone, but there was a lot of excitement around it, but governance was a huge problem. And it was kind of a bolt on. And now, Felipe I got to ask you, when you think about a company like Google, your former employer, data is at the core of their business. And so many companies, the data is not at the core of their business, something else is, it's a process or a manufacturing facility or whatever it is. And the data is sort of on the outskirts. We often talk about in stovepipes. And so we're now seeing organizations really, put data at the core of their... And it becomes central to their DNA. I'm curious as to your thoughts on that. And also, if you've got a lot of experience with developers, is there a developer angle here in this new data world? >> Oh, for sure. I mean, I love seeing every-- Like throughout my career at Google and my two months here, I'm talking to so many companies, that you never thought before, like these are database companies. But the ones that keep growing, the ones that keep moving to the next stage of their development is because they are focusing on data, they are adopting the processes, They are learning from it. And, me per-- I focus a lot on developers, so I mean, when I started this career as an advocate, first, I was a software engineer. And my work so far, has been... (mumbles) I really love talking to the engineers on the other companies, like... Maybe I'm not the one solving the business problem, but at the end of the day, when these companies have a business problem through out the world, they want to have data. There are other engineers that are scientists like me that are... That want to work for the company and bring the best technology to solve the problems. Yeah, for example, there's so much where data can help. If, as we evolve the systems for the company and also for us for understanding these systems, things like observability. And recently, there was a big company, a big launch on observability, on the company names of Cyberroam, where they are running all of their data warehousing needs and all of their data needs on Snowflake. Just because running these massive systems and being able to see how they're working, generates a lot of data. And then how do you manage it? How do you analyze it? Snowflake is ready there to help and support the two areas. >> It's interesting, my business partner, John Furrier, co-host of theCUBE, he said, gosh, I would say the middle of the last decade, maybe even around the time, 2013, when Snowflake was just coming out. He said... He predicted that data would be the new development kit. And, it's really at the center of a lot of the data life cycle, the-- What I call the data pipelines, I know people use that term differently. But, I'm very excited about the Data Cloud Summit and what we're going to learn there. And I get to interview a lot of really cool people. And so I appreciate you guys coming on. But Kent, who should attend the Data Cloud Summit? I mean, what are the-- What should they expect to learn? >> Well, as you said earlier Dave, there's so many tracks and there's really kind of something for everyone. So we've got a track on unlocking the value of the data cloud, which is really going to speak to the business leaders, as to what that vision is, what can we do from an organizational perspective with the data cloud to get them value from the data to move our businesses forward? But we've also got for the technicians, migrating to Snowflake. Training sessions on how to do the migration and modernizing your data lake, data science. How to do analytics with, and data science in Snowflake and in the data cloud. And even down to building apps, for the developers and building data products. So, we've got stuff for developers, we've got stuff for data scientists, we've got stuff for the data architects like myself and the data engineers, on how to build all of this out. And then there's going to be some industry solutions spotlights as well. So we can talk about different verticals, folks in FinTech and in healthcare, there's going to be stuff for them. And then for our data superheroes, we have a hallway track where we're going to get talks from the folks that are in our data superheroes, which is really our community advocacy program. So these are folks that are out there in the trenches using Snowflake, delivering value at their organizations. And they're going to talk down and dirty of how did they make this stuff happen? So there's going to be just really, something for everyone. Fireside chats with our executives, of course, something I'm really looking forward to myself. It's always fun to hear from Frank and Christian and Benoit, about what's the next big thing, what are we doing now? Where are we going with all of this? And then there is going to be some awards. We'll be giving out our Data Driver Awards for our most innovative customers. So there's going to be a lot for everybody to consume and enjoy and learn about this new space of the data cloud. >> Well, thank you for that Kent and I'll second that, and there's going to be a lot for everybody. If you're an existing Snowflake customer, there's going to be plenty of two of one content, where we can get in to the how tos and the best practice. If you're really not that familiar with Snowflake or you're not a customer, there's a lot of one-on-one content going on. If you're an investor and you want to figure out, "Okay, what is this vision? "And can, will this company grow into its massive valuation? "And how are they going to do that?" I think you're going to hear about the data cloud and really try to get a perspective and you can make your own judgment as to whether or not you think that it's going to be as large a market as many people think. So Felipe, I'd love to hear from you what people can expect at the Data Cloud Summit. >> Totally. So I would love to plus one to every one that Kent said, we have a phenomenal schedule that day, the executives will be there. But I really wanted to especially highlight the session I'm preparing with Trevor Noah. I'm sure you must have heard of him. And we are having him at the Data Cloud Summit, and we are going to have a session. We are going to talk about data. We are preparing a session that's all about how people that love data, that people that want to make that actionable, how can they bring storytelling and make it have more impact as he has well learned to do through his life. >> That's awesome. So, yeah, Trevor Noah, we're not just going to totally geek out here. We're going to have some great entertainment as well. So I want you to go to snowflake.com and click on Data Cloud Summit 2020. There's four geos. It starts on November 17th and then runs through the week and then the following week in Japan. So, check that out, we'll see you there. This is Dave Vellante for theCUBE. Thanks for watching. (upbeat music)

>> Narrator: From "theCUBE" studios in Palo Alto, in Boston, connecting with thought leaders all around the world, this is "theCUBE" conversation. >> Hi everybody, this is Dave Vellante. and as you know, we've been tracking the next generation of cloud. Sometimes we call it cloud 2.0, Frank Slootman is here to really unpack this with me. Frank, great to see you. Thanks for coming on. >> Yeah, you as well Dave, good to see you. >> Yeah so, obviously hot off your IPO, a lot of buzz around that, that's fine. We could talk about that, but I really want to talk about the future. What, before we get off the IPO though, was something you told me when you were CEO of ServiceNow. You said, "Hey, we're priced to perfection." So it looks like Snowflake is going to be priced to perfection, it's a marathon though. You made that clear. I presume it's not any different here for you. >> Well, I think, you know, the ServiceNow journey was different in the sense that we were kind of underdogs and people sort of discovered over the years, the full potential of the company. And, I think with Snowflake, they pretty much just discovered it day one (laughs). It's a little bit more, sometimes it's nice to be an underdog or a bit of an overdog in this particular scenario, but yeah, it is what it is. And, it's all about execution, delivering the results, being great with our customers and, hopefully the (indistinct) where they may at that point. >> Yeah, you're a poorly kept secret at this point, Frank, after a while. I've got some excerpts of your book that I've been reading and of course I've been following your career since the 2000's. You're off sailing. You mentioned in your book that you were kind of retired, you were done, and then you got sucked back in. Now, why? Are you in this for the sport? What's the story here? >> Actually, that's not a bad way of characterizing it. I think I am in it, for the sport, the only way to become the best version of yourself is to be under the gun, every single day. And that's certainly what we are. It sort of has its own rewards, building great products, building great companies, regardless of what the spoils may be, it has its own reward. It's hard for people like us to get off the field and hang it up, so here we are. >> You're putting forth this vision now, the data cloud, which obviously it's good marketing, but I'm really happy because I don't like the term enterprise data warehouse. I don't think it reflects, what you're trying to accomplish. EDW, it's slow, only a few people really know how to use it, the time value of data is gone by the time, your business is moving faster than the data in EDW. And it really became, as the savior because of Sarbanes-Oxley, that's really, what became of a reporting mechanism. So I have never seen what you guys are doing as EDW. So I want you to talk about the data cloud, I want to get into the vision a little bit and maybe challenge you on a couple things so our audience can better understand it. >> Yeah, so the notion of a data cloud is actually a type of cloud that we haven't had. Data has been fragmented and locked up in a million different places, in different clouds, different cloud regions, obviously, on premise. And for data science teams, they're trying to drive analysis across datasets, which is incredibly hard, which is why a lot of this resorts to programming and things of that sort. It's hardly scalable because the data is not optimized, the economics are not optimized, there's no governance model and so on. But the data cloud is actually the ability to loosely couple and lightly federate data, regardless of where it is, so it doesn't have scale limitations or performance limitations the way traditional data warehouses have had it. So we really have a fighting chance of really killing the silos and unlocking the bunkers and allowing the full promise of data sciences and ML and AI to really happen. A` lot of the analysis that happens on data is on a single dataset because it's just too damn hard to drive analysis across multiple datasets. When we talk to our customers, they have very precise designs on what they're trying to do. They say, "Look, we are trying to discover through deep learning what the patterns are that lead to transactions, whether it's... If you're a streaming company, maybe it's that you're signing up for a channel or you're buying a movie or whatever it is. What is the pattern of datapoints that leads us to that desired outcome?" Once you have a very accurate description of the data relationships that result in that outcome, you can then search for it and scale it tens of million times over. That's what digital enterprises do, right? So in order to discover these patterns, enrich the data to the point where the patterns become incredibly predictive, that's what Snowflake is for, right? But it requires a completely federated data model because you're not going to find a data pattern in a single dataset, per se, right? So that's what it's all about. The outcomes of a data cloud are very, very closely related to the business outcomes that the user is seeking, right? It's not some infrastructure process. that has a very remote relationship with business outcome. This is very, very closely related. >> So it doesn't take a brain surgeon to look at the trillionaires' club. (chuckles) So I can see that. I can see the big trillion dollars, Apple, $2 trillion market cap companies, they get data at the core. Whereas most companies, most incumbents, it might be a bottling plant at the core or some manufacturing or some of the process, and they put data rounded in these silos. It seems like you're trying to really bring that innovation and put data at the core and you've got an architecture to do that. You're talking about your multi cluster shared storage architecture. You mentioned data sharing. Will this, in your opinion enable, for instance, incumbents to do what a lot of the startups were able to do with the cloud days. hey got access to data centers which they couldn't have before the cloud. Are you trying to do something similar with data? >> Yeah, so obviously there's no doubt that the cloud is a critical enabler. This wouldn't be happening without it. At the same time, the trails that have been blazed by Alexa, Facebook and Google. The reason that those enterprises are so extraordinarily valuable is because of what they know through data and how they can monetize what they know through data. But that power is now becoming available to every single enterprise out there, right. Because the data platforms and the underlying cloud capabilities, we are now delivering that to anybody who wants it. Now you still need to have strong data engineering, data science capabilities. It's not like falling off of a log, but fundamentally those capabilities are now broadly accessible in the marketplace. >> So we talking up front about some of the differences between what you've done early in your career, like I said, you're the worst kept secret, Data Domain I would say it was somewhat of a niche market. You blew it up until it was very disruptive, but it was somewhat limited in what could be done. And maybe some of that limitation, you know, wouldn't have occurred if you stayed an independent company. ServiceNow, you mopped the table up 'cause you really had no competition there. Not the case here. You've got some of the biggest competitors in the world. So talk about that and what gives you confidence that you can continue to dominate? >> It's actually interesting that you bring up these companies. Data Domain, it was a scenario where we were constrained on market and we were a data backup company as you recall, we needed to move into backup software, needed to move into primary storage. While we knew it, we couldn't execute on it because it took tremendous resources which, back in the day, it was much harder than what it is right now. So we ended up selling the company to EMC and now part of Dell, but we're left with some trauma from that experience in the sense that, why couldn't we execute on that transformation? So coming to ServiceNow, we were extremely, and certainly me personally, extremely attuned to the challenges that we had endured in our prior company, and one of the reasons why you saw ServiceNow break out at scale, at tremendous growth rates is because of what we learned from the prior journey. We were not going to ever get caught again in the situation where we could not sustain our markets and sustain our growth. So ServiceNow is very much, the execution model, very much a reaction to what we had encountered in the prior company. Now coming into Snowflake a totally different deal because not only is this a large market this is a developing market. I think you've pointed out in some of your broadcasting, that this market is very much influx. And the reason is that technology is now capable of doing things for people and enterprises that they could never do before. So people are spending way more resources than they ever thought possible on these new capabilities. So you can't think in terms of static markets and static data definitions, it means nothing. Okay, these things are so in transition right now. It's very difficult for people to scope the scale of this opportunity. >> Yeah, I want to understand your thinking around and, you know, I've written about the TAM and can Snowflake grow into it's valuation and the way I drew it, I said, okay, I've got data lakes and you've got an enterprise data warehouse, that's pretty well understood but I called it data as a service company the closest analogy to your data cloud. And then even beyond that when you start bringing in the Edge and real time data. Talk about how you're thinking about that TAM what you have to do to participate. Do you have to bring adjacent capabilities? Or is it this read data sharing that will get you there? In other words, you're not like a transaction system. You hear people talking about converged databases. You're going to talk about real time inference at the Edge that today anyway, isn't what Snowflake is about. Does that vision of data sharing in the data cloud, does that allow you to participate in that massive multi hundred billion dollar TAM that I laid out and probably others as well? >> Yeah, well, it's always difficult to define markets based on historical concept that probably not going to apply a whole lot or for much longer. I mean the way we think of it is that data is the beating heart of the digital enterprise and digital enterprises today, what are you looking at people like the car door dash or so on. They were built from the ground up to be digital enterprises. And data is the beating heart of their operation, data operations is their manufacturing if you will. Every other enterprise out there is working very hard to become digital or part digital and is going to learn to develop a data platform like what we're talking about here to data cloud as well as the expertise in terms of data engineering and data sciences to really fully become a digital enterprise, right? So we view data as driving operations, all the all the digital enterprise, that's really what it is, right? And it's completely data driven end-to-end. There's no people involved and the people are developing and supporting the process but in the execution, it is end-to-end data driven. Meaning that data is the signal that initiates the process he's taking, but as they're, as they're being detected, and then they fully execute the entire machinery, programmatic machinery if you will, all of the processes have been designed. Now for example, I may fit a certain pattern, that leads to some transactional law context, but that's not fully completed, that pattern until I click on some link and all of a sudden, poof, I have become a prime prospect. System detects that in the real time and then unleashes all its outreach and capabilities to get me to transact. You and I are experiencing this every day. When we're, when we're online, you just may not fully realize (laughs) that that's what's happening behind the scenes. That's really what this is all about. So to me, this is sort of the new online transaction processing is an end to end data digital process that is continually acquiring, analyzing and acting on data. >> Well, you've talked about the time, time value of, of data. It loses value over time. And to the extent that you can actually affect decisions, maybe prior, before you lose the customer, before you lose the patient, even even more importantly, or before you lose the battle. There's all kinds of mental models that you can apply this. So automation is a key part of that and then again, I think a lot of people, like you said, if you just try to look at historical markets, you can't really squint through those and apply them. You really have to open up your mind and think about the new possibilities. And so I could see >> Exactly. >> Your component of automation. I see what's happening in the RPA space, and I could see these just massive opportunities to really change society, change business. Your last thoughts. >> While there's just no scenario that I can envision where data is not completely core and central to a digital enterprise period. >> Yeah, I think I really do think Frank, your vision is misunderstood somewhat. I think people say, "Okay hey, we'll bet on Slootman, "Scott Pelley, the team." That's great to do that, but I think this is going to unfold in a way that people maybe haven't predicted and maybe you guys yourselves and your founders you know haven't, aren't able to predict as well, but you've got that good, strong architectural philosophy that you're pursuing and it just kind of feels right, doesn't it? >> One of the harder conversations and the this is one of the reasons why we also wrote our book "The Rise of the Data Cloud" is to convey to the marketplace that this is not an incremental evolution. It is just not sort of building on the past. There is a real step function here. And the way to think about it is that typically enterprises and institutions will look at a platform like Snowflake from a workload context. In other words, I have this business, I have this workload, which is very much historically defined by the way, and then they benchmark us against what they're already doing on some legacy platform and they decide, "Yeah, this is a good fit, we're going to put Snowflake here, maybe there." But, it's still very workload centric which means that we are, essentially, perpetuating the mentality of the past. We were doing it one workload at a time, we're creating the new silos and the new bunkers of data in the process. And we're really not approaching this with the level of vision that the data scientists really require to drive maximum benefit from data. So our argument, and this is not an easy argument, is to say to CIOs and any other C-level person that wants to listen and say, "Look, just thinking about operational context and operational excellence, it's like we have to have a platform that allows use unfettered access to the data that we may need to bring the analytical power to." If you have to bring analytical power to a diversity of datasets, how are we going to do that? The data lives in 500 different places, it's just not possible, other than with insane amounts of programming and complexity and then we don't have the performance and we don't have the economics and we don't have the governance and so on. So you really want to set yourself up with a data cloud so that you can unleash your data science capabilities, your machine learning, your deep learning capabilities and then really get the full throttle advantage of what the technology can do. If you going to perpetuate the silo-ing and bunkering of data by doing it one workload at a time, five, 10 years from now, we're having the same conversations we've been having over the last 40 years. >> Yeah, operationalizing your data is going to require busting down those silos and it's going to require something like the data cloud to really power that to the next decade and beyond. Frank Slootman, thanks so much for coming to "theCUBE" and helping us do a preview here of what's to come. >> You bet Dave, thanks. >> All right, thank you for watching everybody. This is Dave Vellante from the "theCUBE". We'll see you next time.

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> In June of this year, Snowflake filed a confidential document suggesting that it would do an IPO. Now of course, everybody knows about it, found out about it and it had a $20 billion valuation. So, many in the community and the investment community and so forth are excited about this IPO. It could be the hottest one of the year, and we're getting a number of questions from investors and practitioners and the entire Wiki bond, ETR and CUBE community. So, welcome everybody. This is Dave Vellante. This is "CUBE Insights" powered by ETR. In this breaking analysis, we're going to unpack five critical questions around Snowflake's IPO or pending IPO. And with me to discuss that is Erik Bradley. He's the Chief Engagement Strategists at ETR and he's also the Managing Director of VENN. Erik, thanks for coming on and great to see you as always. >> Great to see you too. Always enjoy being on the show. Thank you. >> Now for those of you don't know Erik, VENN is a roundtable that he hosts and he brings in CIOs, IT practitioners, CSOs, data experts and they have an open and frank conversation, but it's private to ETR clients. But they know who the individual is, what their role is, what their title is, et cetera and it's a kind of an ask me anything. And I participated in one of them this past week. Outstanding. And we're going to share with you some of that. But let's bring up the agenda slide if we can here. And these are really some of the questions that we're getting from investors and others in the community. There's really five areas that we want to address. The first is what's happening in this enterprise data warehouse marketplace? The second thing is kind of a one area. What about the legacy EDW players like Oracle and Teradata and Netezza? The third question we get a lot is can Snowflake compete with the big cloud players? Amazon, Google, Microsoft. I mean they're right there in the heart, in the thick of things there. And then what about that multi-cloud strategy? Is that viable? How much of a differentiator is that? And then we get a lot of questions on the TAM. Meaning the total available market. How big is that market? Does it justify the valuation for Snowflake? Now, Erik, you've been doing this now. You've run a couple VENNs, you've been following this, you've done some other work that you've done with Eagle Alpha. What's your, just your initial sort of takeaway from all this work that you've been doing. >> Yeah, sure. So my first take on Snowflake was about two and a half years ago. I actually hosted them for one of my VENN interviews and my initial thought was impressed. So impressed. They were talking at the time about their ability to kind of make ease of use of a multi-cloud strategy. At the time although I was impressed, I did not expect the growth and the hyper growth that we have seen now. But, looking at the company in its current iteration, I understand where the hype is coming from. I mean, it's 12 and a half billion private valuation in the last round. The least confidential IPO (laughs) anyone's ever seen (Dave laughs) with a 15 to $20 billion valuation coming out, which is more than Teradata, Margo and Cloudera combined. It's a great question. So obviously the success to this point is warranted, but we need to see what they're going to be able to do next. So I think the agenda you laid out is a great one and I'm looking forward to getting into some of those details. >> So let's start with what's happening in the marketplace and let's pull up a slide that I very much love to use. It's the classic X-Y. On the vertical axis here we show net score. And remember folks, net score is an indicator of spending momentum. ETR every quarter does like a clockwork survey where they're asking people, "Essentially are you spending more or less?" They subtract the less from the more and comes up with a net score. It's more complicated than, but like NPS, it's a very simple and reliable methodology. That's the vertical axis. And the horizontal axis is what's called market share. Market share is the pervasiveness within the data set. So it's calculated by the number of mentions of the vendor divided by the number of mentions within that sector. And what we're showing here is the EDW sector. And we've pulled out a few companies that I want to talk about. So the big three, obviously Microsoft, AWS and Google. And you can see Microsoft has a huge presence far to the right. AWS, very, very strong. A lot of Redshift in there. And then they're pretty high on the vertical axis. And then Google, not as much share, but very solid in that. Close to 60% net score. And then you can see above all of them from a vertical standpoint is Snowflake with a 77.5% net score. You can see them in the upper right there in the green. One of the highest Erik in the entire data set. So, let's start with some sort of initial comments on the big guys and Snowflakes. Your thoughts? >> Sure. Just first of all to comment on the data, what we're showing there is just the data warehousing sector, but Snowflake's actual net score is that high amongst the entire universe that we follow. Their data strength is unprecedented and we have forward-looking spending intention. So this bodes very well for them. Now, what you did say very accurately is there's a difference between their spending intentions on a net revenue level compared to AWS, Microsoft. There no one's saying that this is an apples-to-apples comparison when it comes to actual revenue. So we have to be very cognizant of that. There is domination (laughs) quite frankly from AWS and from Azure. And Snowflake is a necessary component for them not only to help facilitate a multi-cloud, but look what's happening right now in the US Congress, right? We have these tech leaders being grilled on their actual dominance. And one of the main concerns they have is the amount of data that they're collecting. So I think the environment is right to have another player like this. I think Snowflake really has a lot of longevity and our data is supporting that. And the commentary that we hear from our end users, the people that take the survey are supporting that as well. >> Okay, and then let's stay on this X-Y slide for a moment. I want to just pull out a couple of other comments here, because one of the questions we're asking is Whither, the legacy EDW players. So we've got in here, IBM, Oracle, you can see Teradata and then Hortonworks and MapR. We're going to talk a little bit about Hortonworks 'cause it's now Cloudera. We're going to talk a little bit about Hadoop and some of the data lakes. So you can see there they don't have nearly the net score momentum. Oracle obviously has a huge install base and is investing quite frankly in R&D and do an Exadata and it has its own cloud. So, it's got a lock on it's customers and if it keeps investing and adding value, it's not going away. IBM with Netezza, there's really been some questions around their commitment to that base. And I know that a lot of the folks in the VENNs that we've talked to Erik have said, "Well, we're replacing Netezza." Frank Slootman has been very vocal about going after Teradata. And then we're going to talk a little bit about the Hadoop space. But, can you summarize for us your thoughts in your research and the commentary from your community, what's going on with the legacy guys? Are these guys cooked? Can they hang on? What's your take? >> Sure. We focus on this quite a bit actually. So, I'm going to talk about it from the data perspective first, and then we'll go into some of the commentary and the panel. You even joined one yesterday. You know that it was touched upon. But, first on the data side, what we're noticing and capturing is a widening bifurcation between these cloud native and the legacy on-prem. It is undeniable. There is nothing that you can really refute. The data is concrete and it is getting worse. That gap is getting wider and wider and wider. Now, the one thing I will say is, nobody's going to rip out their legacy applications tomorrow. It takes years and years. So when you look at Teradata, right? Their market cap's only 2 billion, 2.3 billion. How much revenue growth do they need to stay where they are? Not much, right? No one's expecting them to grow 20%, which is what you're seeing on the left side of that screen. So when you look at the legacy versus the cloud native, there is very clear direction of what's happening. The one thing I would note from the data perspective is if you switched from net score or adoptions and you went to flat spending, you suddenly see Oracle and Teradata move over to that left a little bit, because again what I'm trying to say is I don't think they're going to catch up. No, but also don't think they're going away tomorrow. That these have large install bases, they have relationships. Now to kind of get into what you were saying about each particular one, IBM, they shut down Netezza. They shut it down and then they brought it back to life. How does that make you feel if you're the head of data architecture or you're DevOps and you're trying to build an application for a large company? I'm not going back to that. There's absolutely no way. Teradata on the other hand is known to be incredibly stable. They are known to just not fail. If you need to kind of re-architect or you do a migration, they work. Teradata also has a lot of compliance built in. So if you're a financials, if you have a regulated business or industry, there's still some data sets that you're not going to move up to the cloud. Whether it's a PII compliance or financial reasons, some of that stuff is still going to live on-prem. So Teradata is still has a very good niche. And from what we're hearing from our panels, then this is a direct quote if you don't mind me looking off screen for one second. But this is a great one. Basically said, "Teradata is the only one from the legacy camp who is putting up a fight and not giving up." Basically from a CIO perspective, the rest of them aren't an option anymore. But Teradata is still fighting and that's great to hear. They have their own data as a service offering and listen, they're a small market cap compared to these other companies we're talking about. But, to summarize, the data is very clear. There is a widening bifurcation between the two camps. I do not think legacy will catch up. I think all net new workloads are moving to data as a service, moving to cloud native, moving to hosted, but there are still going to be some existing legacy on-prem applications that will be supported with these older databases. And of those, Oracle and Teradata are still viable options. >> I totally agree with you and my colleague David Floyd is actually quite high on Teradata Vantage because he really does believe that a key component, we're going to talk about the TAM in a minute, but a key component of the TAM he believes must include the on-premises workloads. And Frank Slootman has been very clear, "We're not doing on-prem, we're not doing this halfway house." And so that's an opportunity for companies like Teradata, certainly Oracle I would put it in that camp is putting up a fight. Vertica is another one. They're very small, but another one that's sort of battling it out from the old NPP world. But that's great. Let's go into some of the specifics. Let's bring up here some of the specific commentary that we've curated here from the roundtables. I'm going to go through these and then ask you to comment. The first one is just, I mean, people are obviously very excited about Snowflake. It's easy to use, the whole thing zero to Snowflake in 90 minutes, but Snowflake is synonymous with cloud-native data warehousing. There are no equals. We heard that a lot from your VENN panelist. >> We certainly did. There was even more euphoria around Snowflake than I expected when we started hosting these series of data warehousing panels. And this particular gentleman that said that happens to be the global head of data architecture for a fortune 100 financials company. And you mentioned earlier that we did a report alongside Eagle Alpha. And we noticed that among fortune 100 companies that are also using the big three public cloud companies, Snowflake is growing market share faster than anyone else. They are positioned in a way where even if you're aligned with Azure, even if you're aligned with AWS, if you're a large company, they are gaining share right now. So that particular gentleman's comments was very interesting. He also made a comment that said, "Snowflake is the person who championed the idea that data warehousing is not dead yet. Use that old monthly Python line and you're not dead yet." And back in the day where the Hadoop came along and the data lakes turned into a data swamp and everyone said, "We don't need warehousing anymore." Well, that turned out to be a head fake, right? Hadoop was an interesting technology, but it's a complex technology. And it ended up not really working the way people want it. I think Snowflake came in at that point at an opportune time and said, "No, data warehousing isn't dead. We just have to separate the compute from the storage layer and look at what I can do. That increases flexibility, security. It gives you that ability to run across multi-cloud." So honestly the commentary has been nothing but positive. We can get into some of the commentary about people thinking that there's competition catching up to what they do, but there is no doubt that right now Snowflake is the name when it comes to data as a service. >> The other thing we heard a lot was ETL is going to get completely disrupted, you sort of embedded ETL. You heard one panelist say, "Well, it's interesting to see that guys like Informatica are talking about how fast they can run inside a Snowflake." But Snowflake is making that easy. That data prep is sort of part of the package. And so that does not bode well for ETL vendors. >> It does not, right? So ETL is a legacy of on-prem databases and even when Hadoop came along, it still needed that extra layer to kind of work with the data. But this is really, really disrupting them. Now the Snowflake's credit, they partner well. All the ETL players are partnered with Snowflake, they're trying to play nice with them, but the writings on the wall as more and more of this application and workloads move to the cloud, you don't need the ETL layer. Now, obviously that's going to affect their talent and Informatica the most. We had a recent comment that said, this was a CIO who basically said, "The most telling thing about the ETL players right now is every time you speak to them, all they talk about is how they work in a Snowflake architecture." That's their only metric that they talk about right now. And he said, "That's very telling." That he basically used it as it's their existential identity to be part of Snowflake. If they're not, they don't exist anymore. So it was interesting to have sort of a philosophical comment brought up in one of my roundtables. But that's how important playing nice and finding a niche within this new data as a service is for ETL, but to be quite honest, they might be going the same way of, "Okay, let's figure out our niche on these still the on-prem workloads that are still there." I think over time we might see them maybe as an M&A possibility, whether it's Snowflake or one of these new up and comers, kind of bring them in and sort of take some of the technology that's useful and layer it in. But as a large market cap, solo existing niche, I just don't know how long ETL is for this world. >> Now, yeah. I mean, you're right that if it wasn't for the marketing, they're not fighting fashion. But >> No. >> really there're some challenges there. Now, there were some contrarians in the panel and they signaled some potential icebergs ahead. And I guarantee you're going to see this in Snowflake's Red Herring when we actually get it. Like we're going to see all the risks. One of the comments, I'll mention the two and then we can talk about it. "Their engineering advantage will fade over time." Essentially we're saying that people are going to copycat and we've seen that. And the other point is, "Hey, we might see some similar things that happened to Hadoop." The public cloud players giving away these offerings at zero cost. Essentially marginal cost of adding another service is near zero. So the cloud players will use their heft to compete. Your thoughts? >> Yeah, first of all one of the reasons I love doing panels, right? Because we had three gentlemen on this panel that all had nothing but wonderful things to say. But you always get one. And this particular person is a CTO of a well known online public travel agency. We'll put it that way. And he said, "I'm going to be the contrarian here. I have seven different technologies from private companies that do the same thing that I'm evaluating." So that's the pressure from behind, right? The technology, they're going to catch up. Right now Snowflake has the best engineering which interestingly enough they took a lot of that engineering from IBM and Teradata if you actually go back and look at it, which was brought up in our panel as well. He said, "However, the engineering will catch up. They always do." Now from the other side they're getting squeezed because the big cloud players just say, "Hey, we can do this too. I can bundle it with all the other services I'm giving you and I can squeeze your pay. Pretty much give it a waive at the cost." So I do think that there is a very valid concern. When you come out with a $20 billion IPO evaluation, you need to warrant that. And when you see competitive pressures from both sides, from private emerging technologies and from the more dominant public cloud players, you're going to get squeezed there a little bit. And if pricing gets squeezed, it's going to be very, very important for Snowflake to continue to innovate. That comment you brought up about possibly being the next Cloudera was certainly the best sound bite that I got. And I'm going to use it as Clickbait in future articles, because I think everyone who starts looking to buy a Snowflake stock and they see that, they're going to need to take a look. But I would take that with a grain of salt. I don't think that's happening anytime soon, but what that particular CTO was referring to was if you don't innovate, the technology itself will become commoditized. And he believes that this technology will become commoditized. So therefore Snowflake has to continue to innovate. They have to find other layers to bring in. Whether that's through their massive war chest of cash they're about to have and M&A, whether that's them buying analytics company, whether that's them buying an ETL layer, finding a way to provide more value as they move forward is going to be very important for them to justify this valuation going forward. >> And I want to comment on that. The Cloudera, Hortonworks, MapRs, Hadoop, et cetera. I mean, there are dramatic differences obviously. I mean, that whole space was so hard, very difficult to stand up. You needed science project guys and lab coats to do it. It was very services intensive. As well companies like Cloudera had to fund all these open source projects and it really squeezed their R&D. I think Snowflake is much more focused and you mentioned some of the background of their engineers, of course Oracle guys as well. However, you will see Amazon's going to trot out a ton of customers using their RA3 managed storage and their flash. I think it's the DC two piece. They have a ton of action in the marketplace because it's just so easy. It's interesting one of the comments, you asked this yesterday, was with regard to separating compute from storage, which of course it's Snowflakes they basically invented it, it was one of their climbs to fame. The comment was what AWS has done to separate compute from storage for Redshift is largely a bolt on. Which I thought that was an interesting comment. I've had some other comments. My friend George Gilbert said, "Hey, despite claims to the contrary, AWS still hasn't separated storage from compute. What they have is really primitive." We got to dig into that some more, but you're seeing some data points that suggest there's copycatting going on. May not be as functional, but at the same time, Erik, like I was saying good enough is maybe good enough in this space. >> Yeah, and especially with the enterprise, right? You see what Microsoft has done. Their technology is not as good as all the niche players, but it's good enough and I already have a Microsoft license. So, (laughs) you know why am I going to move off of it. But I want to get back to the comment you mentioned too about that particular gentleman who made that comment about RedShift, their separation is really more of a bolt on than a true offering. It's interesting because I know who these people are behind the scenes and he has a very strong relationship with AWS. So it was interesting to me that in the panel yesterday he said he switched from Redshift to Snowflake because of that and some other functionality issues. So there is no doubt from the end users that are buying this. And he's again a fortune 100 financial organization. Not the same one we mentioned. That's a different one. But again, a fortune 100 well known financials organization. He switched from AWS to Snowflake. So there is no doubt that right now they have the technological lead. And when you look at our ETR data platform, we have that adoption reasoning slide that you show. When you look at the number one reason that people are adopting Snowflake is their feature set of technological lead. They have that lead now. They have to maintain it. Now, another thing to bring up on this to think about is when you have large data sets like this, and as we're moving forward, you need to have machine learning capabilities layered into it, right? So they need to make sure that they're playing nicely with that. And now you could go open source with the Apache suite, but Google is doing so well with BigQuery and so well with their machine learning aspects. And although they don't speak enterprise well, they don't sell to the enterprise well, that's changing. I think they're somebody to really keep an eye on because their machine learning capabilities that are layered into the BigQuery are impressive. Now, of course, Microsoft Azure has Databricks. They're layering that in, but this is an area where I think you're going to see maybe what's next. You have to have machine learning capabilities out of the box if you're going to do data as a service. Right now Snowflake doesn't really have that. Some of the other ones do. So I had one of my guest panelist basically say to me, because of that, they ended up going with Google BigQuery because he was able to run a machine learning algorithm within hours of getting set up. Within hours. And he said that that kind of capability out of the box is what people are going to have to use going forward. So that's another thing we should dive into a little bit more. >> Let's get into that right now. Let's bring up the next slide which shows net score. Remember this is spending momentum across the major cloud players and plus Snowflake. So you've got Snowflake on the left, Google, AWS and Microsoft. And it's showing three survey timeframes last October, April 20, which is right in the middle of the pandemic. And then the most recent survey which has just taken place this month in July. And you can see Snowflake very, very high scores. Actually improving from the last October survey. Google, lower net scores, but still very strong. Want to come back to that and pick up on your comments. AWS dipping a little bit. I think what's happening here, we saw this yesterday with AWS's results. 30% growth. Awesome. Slight miss on the revenue side for AWS, but look, I mean massive. And they're so exposed to so many industries. So some of their industries have been pretty hard hit. Microsoft pretty interesting. A little softness there. But one of the things I wanted to pick up on Erik, when you're talking about Google and BigQuery and it's ML out of the box was what we heard from a lot of the VENN participants. There's no question about it that Google technically I would say is one of Snowflake's biggest competitors because it's cloud native. Remember >> Yep. >> AWS did a license one time. License deal with PowerShell and had a sort of refactor the thing to be cloud native. And of course we know what's happening with Microsoft. They basically were on-prem and then they put stuff in the cloud and then all the updates happen in the cloud. And then they pushed to on-prem. But they have that what Frank Slootman calls that halfway house, but BigQuery no question technically is very, very solid. But again, you see Snowflake right now anyway outpacing these guys in terms of momentum. >> Snowflake is out outpacing everyone (laughs) across our entire survey universe. It really is impressive to see. And one of the things that they have going for them is they can connect all three. It's that multi-cloud ability, right? That portability that they bring to you is such an important piece for today's modern CIO as data architects. They don't want vendor lock-in. They are afraid of vendor lock-in. And this ability to make their data portable and to do that with ease and the flexibility that they offer is a huge advantage right now. However, I think you're a hundred percent right. Google has been so focused on the engineering side and never really focusing on the enterprise sales side. That is why they're playing catch up. I think they can catch up. They're bringing in some really important enterprise salespeople with experience. They're starting to learn how to talk to enterprise, how to sell, how to support. And nobody can really doubt their engineering. How many open sources have they given us, right? They invented Kubernetes and the entire container space. No one's really going to compete with them on that side if they learn how to sell it and support it. Yeah, right now they're behind. They're a distant third. Don't get me wrong. From a pure hosted ability, AWS is number one. Microsoft is yours. Sometimes it looks like it's number one, but you have to recognize that a lot of that is because of simply they're hosted 365. It's a SAS app. It's not a true cloud type of infrastructure as a service. But Google is a distant third, but their technology is really, really great. And their ability to catch up is there. And like you said, in the panels we were hearing a lot about their machine learning capability is right out of the box. And that's where this is going. What's the point of having this huge data if you're not going to be supporting it on new application architecture. And all of those applications require machine learning. >> Awesome. So we're. And I totally agree with what you're saying about Google. They just don't have it figured out how to sell the enterprise yet. And a hundred percent AWS has the best cloud. I mean, hands down. But a very, very competitive market as we heard yesterday in front of Congress. Now we're on the point about, can Snowflake compete with the big cloud players? I want to show one more data point. So let's bring up, this is the same chart as we showed before, but it's new adoptions. And this is really telling. >> Yeah. >> You can see Snowflake with 34% in the yellow, new adoptions, down yes from previous surveys, but still significantly higher than the other players. Interesting to see Google showing momentum on new adoptions, AWS down on new adoptions. And again, exposed to a lot of industries that have been hard hit. And Microsoft actually quite low on new adoption. So this is very impressive for Snowflake. And I want to talk about the multi-cloud strategy now Erik. This came up a lot. The VENN participants who are sort of fans of Snowflake said three things: It was really the flexibility, the security which is really interesting to me. And a lot of that had to do with the flexibility. The ability to easily set up roles and not have to waste a lot of time wrangling. And then the third was multi-cloud. And that was really something that came through heavily in the VENN. Didn't it? >> It really did. And again, I think it just comes down to, I don't think you can ever overstate how afraid these guys are of vendor lock-in. They can't have it. They don't want it. And it's best practice to make sure your sensitive information is being kind of spread out a little bit. We all know that people don't trust Bezos. So if you're in certain industries, you're not going to use AWS at all, right? So yeah, this ability to have your data portability through multi-cloud is the number one reason I think people start looking at Snowflake. And to go to your point about the adoptions, it's very telling and it bodes well for them going forward. Most of the things that we're seeing right now are net new workloads. So let's go again back to the legacy side that we were talking about, the Teradatas, IBMs, Oracles. They still have the monolithic applications and the data that needs to support that, right? Like an old ERP type of thing. But anyone who's now building a new application, bringing something new to market, it's all net new workloads. There is no net new workload that is going to go to SAP or IBM. It's not going to happen. The net new workloads are going to the cloud. And that's why when you switch from net score to adoption, you see Snowflake really stand out because this is about new adoption for net new workloads. And that's really where they're driving everything. So I would just say that as this continues, as data as a service continues, I think Snowflake's only going to gain more and more share for all the reasons you stated. Now get back to your comment about security. I was shocked by that. I really was. I did not expect these guys to say, "Oh, no. Snowflake enterprise security not a concern." So two panels ago, a gentleman from a fortune 100 financials said, "Listen, it's very difficult to get us to sign off on something for security. Snowflake is past it, it is enterprise ready, and we are going full steam ahead." Once they got that go ahead, there was no turning back. We gave it to our DevOps guys, we gave it to everyone and said, "Run with it." So, when a company that's big, I believe their fortune rank is 28. (laughs) So when a company that big says, "Yeah, you've got the green light. That we were okay with the internal compliance aspect, we're okay with the security aspect, this gives us multi-cloud portability, this gives us flexibility, ease of use." Honestly there's a really long runway ahead for Snowflake. >> Yeah, so the big question I have around the multi-cloud piece and I totally and I've been on record saying, "Look, if you're going looking for an agnostic multi-cloud, you're probably not going to go with the cloud vendor." (laughs) But I've also said that I think multi-cloud to date anyway has largely been a symptom as opposed to a strategy, but that's changing. But to your point about lock-in and also I think people are maybe looking at doing things across clouds, but I think that certainly it expands Snowflake's TAM and we're going to talk about that because they support multiple clouds and they're going to be the best at that. That's a mandate for them. The question I have is how much of complex joining are you going to be doing across clouds? And is that something that is just going to be too latency intensive? Is that really Snowflake's expertise? You're really trying to build that data layer. You're probably going to maybe use some kind of Postgres database for that. >> Right. >> I don't know. I need to dig into that, but that would be an opportunity from a TAM standpoint. I just don't know how real that is. >> Yeah, unfortunately I'm going to just be honest with this one. I don't think I have great expertise there and I wouldn't want to lead anyone a wrong direction. But from what I've heard from some of my VENN interview subjects, this is happening. So the data portability needs to be agnostic to the cloud. I do think that when you're saying, are there going to be real complex kind of workloads and applications? Yes, the answer is yes. And I think a lot of that has to do with some of the container architecture as well, right? If I can just pull data from one spot, spin it up for as long as I need and then just get rid of that container, that ethereal layer of compute. It doesn't matter where the cloud lies. It really doesn't. I do think that multi-cloud is the way of the future. I know that the container workloads right now in the enterprise are still very small. I've heard people say like, "Yeah, I'm kicking the tires. We got 5%." That's going to grow. And if Snowflake can make themselves an integral part of that, then yes. I think that's one of those things where, I remember the guy said, "Snowflake has to continue to innovate. They have to find a way to grow this TAM." This is an area where they can do so. I think you're right about that, but as far as my expertise, on this one I'm going to be honest with you and say, I don't want to answer incorrectly. So you and I need to dig in a little bit on this one. >> Yeah, as it relates to question four, what's the viability of Snowflake's multi-cloud strategy? I'll say unquestionably supporting multiple clouds, very viable. Whether or not portability across clouds, multi-cloud joins, et cetera, TBD. So we'll keep digging into that. The last thing I want to focus on here is the last question, does Snowflake's TAM justify its $20 billion valuation? And you think about the data pipeline. You go from data acquisition to data prep. I mean, that really is where Snowflake shines. And then of course there's analysis. You've got to bring in EMI or AI and ML tools. That's not Snowflake's strength. And then you're obviously preparing that, serving that up to the business, visualization. So there's potential adjacencies that they could get into that they may or may not decide to. But so we put together this next chart which is kind of the TAM expansion opportunity. And I just want to briefly go through it. We published this stuff so you can go and look at all the fine print, but it's kind of starts with the data lake disruption. You called it data swamp before. The Hadoop no schema on, right? Basically the ROI of Hadoop became reduction of investment as my friend Abby Meadow would say. But so they're kind of disrupting that data lake which really was a failure. And then really going after that enterprise data warehouse which is kind of I have it here as a 10 billion. It's actually bigger than that. It's probably more like a $20 billion market. I'll update this slide. And then really what Snowflake is trying to do is be data as a service. A data layer across data stores, across clouds, really make it easy to ingest and prepare data and then serve the business with insights. And then ultimately this huge TAM around automated decision making, real-time analytics, automated business processes. I mean, that is potentially an enormous market. We got a couple of hundred billion. I mean, just huge. Your thoughts on their TAM? >> I agree. I'm not worried about their TAM and one of the reasons why as I mentioned before, they are coming out with a whole lot of cash. (laughs) This is going to be a red hot IPO. They are going to have a lot of money to spend. And look at their management team. Who is leading the way? A very successful, wise, intelligent, acquisitive type of CEO. I think there is going to be M&A activity, and I believe that M&A activity is going to be 100% for the mindset of growing their TAM. The entire world is moving to data as a service. So let's take as a backdrop. I'm going to go back to the panel we did yesterday. The first question we asked was, there was an understanding or a theory that when the virus pandemic hit, people wouldn't be taking on any sort of net new architecture. They're like, "Okay, I have Teradata, I have IBM. Let's just make sure the lights are on. Let's stick with it." Every single person I've asked, they're just now eight different experts, said to us, "Oh, no. Oh, no, no." There is the virus pandemic, the shift from work from home. Everything we're seeing right now has only accelerated and advanced our data as a service strategy in the cloud. We are building for scale, adopting cloud for data initiatives. So, across the board they have a great backdrop. So that's going to only continue, right? This is very new. We're in the early innings of this. So for their TAM, that's great because that's the core of what they do. Now on top of it you mentioned the type of things about, yeah, right now they don't have great machine learning. That could easily be acquired and built in. Right now they don't have an analytics layer. I for one would love to see these guys talk to Alteryx. Alteryx is red hot. We're seeing great data and great feedback on them. If they could do that business intelligence, that analytics layer on top of it, the entire suite as a service, I mean, come on. (laughs) Their TAM is expanding in my opinion. >> Yeah, your point about their leadership is right on. And I interviewed Frank Slootman right in the heart of the pandemic >> So impressed. >> and he said, "I'm investing in engineering almost sight unseen. More circumspect around sales." But I will caution people. That a lot of people I think see what Slootman did with ServiceNow. And he came into ServiceNow. I have to tell you. It was they didn't have their unit economics right, they didn't have their sales model and marketing model. He cleaned that up. Took it from 120 million to 1.2 billion and really did an amazing job. People are looking for a repeat here. This is a totally different situation. ServiceNow drove a truck through BMCs install base and with IT help desk and then created this brilliant TAM expansion. Let's learn and expand model. This is much different here. And Slootman also told me that he's a situational CEO. He doesn't have a playbook. And so that's what is most impressive and interesting about this. He's now up against the biggest competitors in the world: AWS, Google and Microsoft and dozens of other smaller startups that have raised a lot of money. Look at the company like Yellowbrick. They've raised I don't know $180 million. They've got a great team. Google, IBM, et cetera. So it's going to be really, really fun to watch. I'm super excited, Erik, but I'll tell you the data right now suggest they've got a great tailwind and if they can continue to execute, this is going to be really fun to watch. >> Yeah, certainly. I mean, when you come out and you are as impressive as Snowflake is, you get a target on your back. There's no doubt about it, right? So we said that they basically created the data as a service. That's going to invite competition. There's no doubt about it. And Yellowbrick is one that came up in the panel yesterday about one of our CIOs were doing a proof of concept with them. We had about seven others mentioned as well that are startups that are in this space. However, none of them despite their great valuation and their great funding are going to have the kind of money and the market lead that Slootman is going to have which Snowflake has as this comes out. And what we're seeing in Congress right now with some antitrust scrutiny around the large data that's being collected by AWS as your Google, I'm not going to bet against this guy either. Right now I think he's got a lot of opportunity, there's a lot of additional layers and because he can basically develop this as a suite service, I think there's a lot of great opportunity ahead for this company. >> Yeah, and I guarantee that he understands well that customer acquisition cost and the lifetime value of the customer, the retention rates. Those are all things that he and Mike Scarpelli, his CFO learned at ServiceNow. Not learned, perfected. (Erik laughs) Well Erik, really great conversation, awesome data. It's always a pleasure having you on. Thank you so much, my friend. I really appreciate it. >> I appreciate talking to you too. We'll do it again soon. And stay safe everyone out there. >> All right, and thank you for watching everybody this episode of "CUBE Insights" powered by ETR. This is Dave Vellante, and we'll see you next time. (soft music)

>> Narrator: From around the Globe, it's theCube with digital coverage of Enterprise Data Automation, and event series brought to you by Io-Tahoe. >> Everybody, we're back. And this is Dave Vellante, and we're covering the whole notion of Automated Data in the Enterprise. And I'm really excited to have Paula D'Amico here. Senior Vice President of Enterprise Data Architecture at Webster Bank. Paula, good to see you. Thanks for coming on. >> Hi, nice to see you, too. >> Let's start with Webster bank. You guys are kind of a regional I think New York, New England, believe it's headquartered out of Connecticut. But tell us a little bit about the bank. >> Webster bank is regional Boston, Connecticut, and New York. Very focused on in Westchester and Fairfield County. They are a really highly rated regional bank for this area. They hold quite a few awards for the area for being supportive for the community, and are really moving forward technology wise, they really want to be a data driven bank, and they want to move into a more robust group. >> We got a lot to talk about. So data driven is an interesting topic and your role as Data Architecture, is really Senior Vice President Data Architecture. So you got a big responsibility as it relates to kind of transitioning to this digital data driven bank but tell us a little bit about your role in your Organization. >> Currently, today, we have a small group that is just working toward moving into a more futuristic, more data driven data warehousing. That's our first item. And then the other item is to drive new revenue by anticipating what customers do, when they go to the bank or when they log in to their account, to be able to give them the best offer. And the only way to do that is you have timely, accurate, complete data on the customer and what's really a great value on offer something to offer that, or a new product, or to help them continue to grow their savings, or do and grow their investments. >> Okay, and I really want to get into that. But before we do, and I know you're, sort of partway through your journey, you got a lot to do. But I want to ask you about Covid, how you guys handling that? You had the government coming down and small business loans and PPP, and huge volume of business and sort of data was at the heart of that. How did you manage through that? >> We were extremely successful, because we have a big, dedicated team that understands where their data is and was able to switch much faster than a larger bank, to be able to offer the PPP Long's out to our customers within lightning speed. And part of that was is we adapted to Salesforce very for we've had Salesforce in house for over 15 years. Pretty much that was the driving vehicle to get our PPP loans in, and then developing logic quickly, but it was a 24 seven development role and get the data moving on helping our customers fill out the forms. And a lot of that was manual, but it was a large community effort. >> Think about that too. The volume was probably much higher than the volume of loans to small businesses that you're used to granting and then also the initial guidelines were very opaque. You really didn't know what the rules were, but you were expected to enforce them. And then finally, you got more clarity. So you had to essentially code that logic into the system in real time. >> I wasn't directly involved, but part of my data movement team was, and we had to change the logic overnight. So it was on a Friday night it was released, we pushed our first set of loans through, and then the logic changed from coming from the government, it changed and we had to redevelop our data movement pieces again, and we design them and send them back through. So it was definitely kind of scary, but we were completely successful. We hit a very high peak. Again, I don't know the exact number but it was in the thousands of loans, from little loans to very large loans and not one customer who applied did not get what they needed for, that was the right process and filled out the right amount. >> Well, that is an amazing story and really great support for the region, your Connecticut, the Boston area. So that's fantastic. I want to get into the rest of your story now. Let's start with some of the business drivers in banking. I mean, obviously online. A lot of people have sort of joked that many of the older people, who kind of shunned online banking would love to go into the branch and see their friendly teller had no choice, during this pandemic, to go to online. So that's obviously a big trend you mentioned, the data driven data warehouse, I want to understand that, but what at the top level, what are some of the key business drivers that are catalyzing your desire for change? >> The ability to give a customer, what they need at the time when they need it. And what I mean by that is that we have customer interactions in multiple ways. And I want to be able for the customer to walk into a bank or online and see the same format, and being able to have the same feel the same love, and also to be able to offer them the next best offer for them. But they're if they want looking for a new mortgage or looking to refinance, or whatever it is that they have that data, we have the data and that they feel comfortable using it. And that's an untethered banker. Attitude is, whatever my banker is holding and whatever the person is holding in their phone, that is the same and it's comfortable. So they don't feel that they've walked into the bank and they have to do fill out different paperwork compared to filling out paperwork on just doing it on their phone. >> You actually do want the experience to be better. And it is in many cases. Now you weren't able to do this with your existing I guess mainframe based Enterprise Data Warehouses. Is that right? Maybe talk about that a little bit? >> Yeah, we were definitely able to do it with what we have today the technology we're using. But one of the issues is that it's not timely. And you need a timely process to be able to get the customers to understand what's happening. You need a timely process so we can enhance our risk management. We can apply for fraud issues and things like that. >> Yeah, so you're trying to get more real time. The traditional EDW. It's sort of a science project. There's a few experts that know how to get it. You can so line up, the demand is tremendous. And then oftentimes by the time you get the answer, it's outdated. So you're trying to address that problem. So part of it is really the cycle time the end to end cycle time that you're progressing. And then there's, if I understand it residual benefits that are pretty substantial from a revenue opportunity, other offers that you can make to the right customer, that you maybe know, through your data, is that right? >> Exactly. It's drive new customers to new opportunities. It's enhanced the risk, and it's to optimize the banking process, and then obviously, to create new business. And the only way we're going to be able to do that is if we have the ability to look at the data right when the customer walks in the door or right when they open up their app. And by doing creating more to New York times near real time data, or the data warehouse team that's giving the lines of business the ability to work on the next best offer for that customer as well. >> But Paula, we're inundated with data sources these days. Are there other data sources that maybe had access to before, but perhaps the backlog of ingesting and cleaning in cataloging and analyzing maybe the backlog was so great that you couldn't perhaps tap some of those data sources. Do you see the potential to increase the data sources and hence the quality of the data or is that sort of premature? >> Oh, no. Exactly. Right. So right now, we ingest a lot of flat files and from our mainframe type of front end system, that we've had for quite a few years. But now that we're moving to the cloud and off-prem and on-prem, moving off-prem, into like an S3 Bucket, where that data we can process that data and get that data faster by using real time tools to move that data into a place where, like snowflake could utilize that data, or we can give it out to our market. Right now we're about we do work in batch mode still. So we're doing 24 hours. >> Okay. So when I think about the data pipeline, and the people involved, maybe you could talk a little bit about the organization. You've got, I don't know, if you have data scientists or statisticians, I'm sure you do. You got data architects, data engineers, quality engineers, developers, etc. And oftentimes, practitioners like yourself, will stress about, hey, the data is in silos. The data quality is not where we want it to be. We have to manually categorize the data. These are all sort of common data pipeline problems, if you will. Sometimes we use the term data Ops, which is sort of a play on DevOps applied to the data pipeline. Can you just sort of describe your situation in that context? >> Yeah, so we have a very large data ops team. And everyone that who is working on the data part of Webster's Bank, has been there 13 to 14 years. So they get the data, they understand it, they understand the lines of business. So it's right now. We could the we have data quality issues, just like everybody else does. But we have places in them where that gets cleansed. And we're moving toward and there was very much siloed data. The data scientists are out in the lines of business right now, which is great, because I think that's where data science belongs, we should give them and that's what we're working towards now is giving them more self service, giving them the ability to access the data in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own, like Tableau dashboards, and then pushing the data back out. So they're going to more not, I don't want to say, a central repository, but a more of a robust repository, that's controlled across multiple avenues, where multiple lines of business can access that data. Is that help? >> Got it, Yes. And I think that one of the key things that I'm taking away from your last comment, is the cultural aspects of this by having the data scientists in the line of business, the lines of business will feel ownership of that data as opposed to pointing fingers criticizing the data quality. They really own that that problem, as opposed to saying, well, it's Paula's problem. >> Well, I have my problem is I have data engineers, data architects, database administrators, traditional data reporting people. And because some customers that I have that are business customers lines of business, they want to just subscribe to a report, they don't want to go out and do any data science work. And we still have to provide that. So we still want to provide them some kind of regiment that they wake up in the morning, and they open up their email, and there's the report that they subscribe to, which is great, and it works out really well. And one of the things is why we purchased Io-Tahoe was, I would have the ability to give the lines of business, the ability to do search within the data. And we'll read the data flows and data redundancy and things like that, and help me clean up the data. And also, to give it to the data analysts who say, all right, they just asked me they want this certain report. And it used to take okay, four weeks we're going to go and we're going to look at the data and then we'll come back and tell you what we can do. But now with Io-Tahoe, they're able to look at the data, and then in one or two days, they'll be able to go back and say, Yes, we have the data, this is where it is. This is where we found it. This is the data flows that we found also, which is what I call it, is the break of a column. It's where the column was created, and where it went to live as a teenager. (laughs) And then it went to die, where we archive it. And, yeah, it's this cycle of life for a column. And Io-Tahoe helps us do that. And we do data lineage is done all the time. And it's just takes a very long time and that's why we're using something that has AI in it and machine running. It's accurate, it does it the same way over and over again. If an analyst leaves, you're able to utilize something like Io-Tahoe to be able to do that work for you. Is that help? >> Yeah, so got it. So a couple things there, in researching Io-Tahoe, it seems like one of the strengths of their platform is the ability to visualize data, the data structure and actually dig into it, but also see it. And that speeds things up and gives everybody additional confidence. And then the other piece is essentially infusing AI or machine intelligence into the data pipeline, is really how you're attacking automation. And you're saying it repeatable, and then that helps the data quality and you have this virtual cycle. Maybe you could sort of affirm that and add some color, perhaps. >> Exactly. So you're able to let's say that I have seven cars, lines of business that are asking me questions, and one of the questions they'll ask me is, we want to know, if this customer is okay to contact, and there's different avenues so you can go online, do not contact me, you can go to the bank and you can say, I don't want email, but I'll take texts. And I want no phone calls. All that information. So, seven different lines of business asked me that question in different ways. One said, "No okay to contact" the other one says, "Customer 123." All these. In each project before I got there used to be siloed. So one customer would be 100 hours for them to do that analytical work, and then another analyst would do another 100 hours on the other project. Well, now I can do that all at once. And I can do those types of searches and say, Yes, we already have that documentation. Here it is, and this is where you can find where the customer has said, "No, I don't want to get access from you by email or I've subscribed to get emails from you." >> Got it. Okay. Yeah Okay. And then I want to go back to the cloud a little bit. So you mentioned S3 Buckets. So you're moving to the Amazon cloud, at least, I'm sure you're going to get a hybrid situation there. You mentioned snowflake. What was sort of the decision to move to the cloud? Obviously, snowflake is cloud only. There's not an on-prem, version there. So what precipitated that? >> Alright, so from I've been in the data IT information field for the last 35 years. I started in the US Air Force, and have moved on from since then. And my experience with Bob Graham, was with snowflake with working with GE Capital. And that's where I met up with the team from Io-Tahoe as well. And so it's a proven so there's a couple of things one is Informatica, is worldwide known to move data. They have two products, they have the on-prem and the off-prem. I've used the on-prem and off-prem, they're both great. And it's very stable, and I'm comfortable with it. Other people are very comfortable with it. So we picked that as our batch data movement. We're moving toward probably HVR. It's not a total decision yet. But we're moving to HVR for real time data, which is changed capture data, moves it into the cloud. And then, so you're envisioning this right now. In which is you're in the S3, and you have all the data that you could possibly want. And that's JSON, all that everything is sitting in the S3 to be able to move it through into snowflake. And snowflake has proven to have a stability. You only need to learn and train your team with one thing. AWS as is completely stable at this point too. So all these avenues if you think about it, is going through from, this is your data lake, which is I would consider your S3. And even though it's not a traditional data lake like, you can touch it like a Progressive or Hadoop. And then into snowflake and then from snowflake into sandbox and so your lines of business and your data scientists just dive right in. That makes a big win. And then using Io-Tahoe with the data automation, and also their search engine. I have the ability to give the data scientists and data analysts the way of they don't need to talk to IT to get accurate information or completely accurate information from the structure. And we'll be right back. >> Yeah, so talking about snowflake and getting up to speed quickly. I know from talking to customers you can get from zero to snowflake very fast and then it sounds like the Io-Tahoe is sort of the automation cloud for your data pipeline within the cloud. Is that the right way to think about it? >> I think so. Right now I have Io-Tahoe attached to my on-prem. And I want to attach it to my off-prem eventually. So I'm using Io-Tahoe data automation right now, to bring in the data, and to start analyzing the data flows to make sure that I'm not missing anything, and that I'm not bringing over redundant data. The data warehouse that I'm working of, it's an on-prem. It's an Oracle Database, and it's 15 years old. So it has extra data in it. It has things that we don't need anymore, and Io-Tahoe's helping me shake out that extra data that does not need to be moved into my S3. So it's saving me money, when I'm moving from off-prem to on-prem. >> And so that was a challenge prior, because you couldn't get the lines of business to agree what to delete, or what was the issue there? >> Oh, it was more than that. Each line of business had their own structure within the warehouse. And then they were copying data between each other, and duplicating the data and using that. So there could be possibly three tables that have the same data in it, but it's used for different lines of business. We have identified using Io-Tahoe identified over seven terabytes in the last two months on data that has just been repetitive. It's the same exact data just sitting in a different schema. And that's not easy to find, if you only understand one schema, that's reporting for that line of business. >> More bad news for the storage companies out there. (both laughs) So far. >> It's cheap. That's what we were telling people. >> And it's true, but you still would rather not waste it, you'd like to apply it to drive more revenue. And so, I guess, let's close on where you see this thing going. Again, I know you're sort of partway through the journey, maybe you could sort of describe, where you see the phase is going and really what you want to get out of this thing, down the road, mid-term, longer term, what's your vision or your data driven organization. >> I want for the bankers to be able to walk around with an iPad in their hand, and be able to access data for that customer, really fast and be able to give them the best deal that they can get. I want Webster to be right there on top with being able to add new customers, and to be able to serve our existing customers who had bank accounts since they were 12 years old there and now our multi whatever. I want them to be able to have the best experience with our bankers. >> That's awesome. That's really what I want as a banking customer. I want my bank to know who I am, anticipate my needs, and create a great experience for me. And then let me go on with my life. And so that follow. Great story. Love your experience, your background and your knowledge. I can't thank you enough for coming on theCube. >> Now, thank you very much. And you guys have a great day. >> All right, take care. And thank you for watching everybody. Keep right there. We'll take a short break and be right back. (gentle music)

(enlightening music) >> From theCUBE's studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> As we've been reporting, there's a new class of workloads emerging in the cloud. Early cloud was all about IaaS, spinning up storage, compute, and networking infrastructure to support startups, SaaS, easy experimentation, dev test, and increasingly moving business workloads into the cloud. Modern cloud workloads are combining data. They're infusing machine intelligence into application's AI. They're simplifying analytics and scaling with the cloud to deliver business insights in near real time. And at the center of this mega trend is a new class of data stores and analytic databases, what some called data warehouses, a term that I think is outdated really for today's speed of doing business. Welcome to this week's Wikibon CUBE Insights, powered by ETR. In this breaking analysis, we update our view of the emerging cloud native analytic database market. Today, we want to do three things. First, we'll update you on the basics of this market, what you really need to know in the space. The next thing we're going to do, take a look into the competitive environment, and as always, we'll dig into the ETR spending data to see which companies have the momentum in the market, and maybe, ahead of some of the others. Finally, we're going to close with some thoughts on how the competitive landscape is likely to evolve. And we want to answer the question will the cloud giants overwhelm the upstarts, or will the specialists continue to thrive? Let's take a look at some of the basics of this market. We're seeing the evolution of the enterprise data warehouse market space. It's an area that has been critical to supporting reporting and governance requirements for companies, especially post Sarbanes-Oxley, right? However, historically, as I've said many times, EDW has failed to deliver on its promises of a 360-degree view of the business and real-time customer insights. Classic enterprise data warehouses are too cumbersome, they're too complicated, they're too slow, and don't keep pace with the speed of the business. Now, EDW is about a $20 billion market, but the analytic database opportunity in the cloud, we think is much larger, why is that? It's because cloud computing unlocks the ability to rapidly combine multiple data sources, bring data science tooling into the mix, very quickly analyze data, and deliver insights to the business. More importantly, even more importantly, allow a line of business pros to access data in a self service mode. It's a new paradigm that uses the notion of DevOps as applied to the data pipeline, agile data or what we sometimes called DataOps. This is a highly competitive marketplace. In the early part of last decade, you saw Google bring BigQuery to market, Snowflake was founded, AWS did a one-time license deal to acquire the IP to ParAccel, an MPP database, on which it built Redshift. In the latter part of the decade, Microsoft threw his hat in the ring with SQL DW, which Microsoft has now evolved into Azure Synapse. They did so at the Build conference, a few weeks ago. There are other players as well like IBM. So you can see, there's a lot at stake here. The cloud vendors want your data, because they understand this is one of the key ingredients of the next decade of innovation. No longer is Moore's Law, the mainspring of growth. We've said this many times. Rather today, it's data driven, and AI to push insights and scale with the cloud. Here's the interesting dynamic that is emerging in the space. Snowflake is a cloud specialist in this field, having raised more than a billion dollars in venture, a billion four, a billion five. And it's up against the big cloud players, who are moving fast and often stealing moves from Snowflake and driving customers to their respective platforms. Here's an example that we reported on at last year's re:Invent. It's an article by Tony Baer. He wrote this on ZDNet talking about how AWS RA3 separates compute from storage, and of course, this was a founding architectural principle for Snowflake. Here's another example from the information. They were reporting on Microsoft here turning up the heat on Snowflake. And you can see the highlighted text, where the author talks about Microsoft trying to divert customers to its database. So you got this weird dynamic going on. Snowflake doesn't run on-prem, it only runs in the cloud. Runs on AWS, runs on Azure, runs on GCP. The cloud players again, they all want your data to go into their database. So they want you to put their data into their respective platforms. At the same time, they need SaaS ISVs to run in the cloud because it sells infrastructure services. So, is Snowflake, are they going to pivot to run on-prem to try to differentiate from the cloud giants? I asked Frank Slootman, Snowflake's CEO, about the on-prem opportunity, and his perspective earlier this year. Let's listen to what he said. >> Okay, we're not doing this endless hedging that people have done for 20 years, sort of keeping a leg in both worlds. Forget it, this will only work in the public cloud because this is how the utility model works, right? I think everybody is coming to this realization, right? I mean the excuses are running out at this point. We think that it'll, people will come to the public cloud a lot sooner than we will ever come to the private cloud. It's not that we can't run a private cloud, it just diminishes the potential and the value that we that we bring. >> Okay, so pretty definitive statements by Slootman. Now, the question I want to pose today is can Snowflake compete, given the conventional wisdom that we saw in the media articles that the cloud players are going to hurt Snowflake in this market. And if so, how will they compete? Well, let's see what the customers are saying and bring in some ETR survey data. This chart shows two of our favorite metrics from the ETR data set. That is Net Score, which is on the y-axis. Net Score, remember is a measure of spending momentum and market share, which is on the x-axis. Market share is a measure of pervasiveness in the data set. And what we show here are some of the key players in the EDW and cloud native analytic database market. I'll make a couple of points, and we'll dig into this a little bit further. First thing I want to share is you can see from this data, this is the April ETR survey, which was taken at the height of the US lockdown for the pandemic. The survey captured respondents from more than 1,200 CIOs and IT buyers, asking about their spending intentions for analytic databases for the companies that we show here on this kind of x-y chart. So the higher the company is on the vertical axis, the stronger the spending momentum relative to last year, and you could see Snowflake has a 77% Net Score. It leaves all players with AWS Redshift showing very strong, as well. Now in the box in the lower right, you see a chart. Those are the exact Net Scores for all the vendors in the Shared N. A Shared N is a number of citations for that vendor within the N of the 1,269. So you can see the N's are quite large, certainly large enough to feel comfortable with some of the conclusions that we're going to make today. Microsoft, they have a huge footprint. And they somewhat skew the data with its very high market share due to its volume. And you could see where Google sits, it's at good momentum, not as much presence in the marketplace. We've also added a couple of on-prem vendors, Teradata and Oracle primarily on-prem, just for context. They're two companies that compete, they obviously have some cloud offerings, but again, most of their base is on-prem. So what I want to do now is drill into this a little bit more by looking at Snowflake within the individual clouds. So let's look at Snowflake inside of AWS. That's what this next chart shows. So it's customer spending momentum Net Score inside of AWS accounts. And we cut the data to isolate those ETR survey respondents running AWS, so there's an N there of 672 that you can see. The bars show the Net Score granularity for Snowflake and Amazon Redshift. Now, note that we show 96 Shared N responses for Snowflake and 213 for Redshift within the overall N of 672 AWS accounts. The colors show 2020 spending intentions relative to 2019. So let's read left to right here. The replacements are red. And then, the bright red, then, you see spending less by 6% or more, that's the pinkish, and then, flat spending, the gray, increasing spending by more than 6%, that's the forest green, and then, adding to the platform new, that's the lime green. Now, remember Net Score is derived by subtracting the reds from the greens. And you can see that Snowflake has more spending momentum in the AWS cloud than Amazon Redshift, by a small margin, but look at, 80% of the AWS accounts plan to spend more on Snowflake with 35%, they're adding new. Very strong, 76% of AWS customers plan to spend more in 2020 relative to 2019 on Redshift with only 12% adding the platform new. But nonetheless, both are very, very strong, and you can see here, the key point is minimal red and pink, but not a lot of people leaving, not a lot of people spending less. It's going to be critical to see in the June ETR survey, which is in the field this month, if Snowflake is able to hold on to these new accounts that it's gained in the last couple of months. Now, let's look at how Snowflake is doing inside of Azure and compare it to Microsoft. So here's the data from the ETR survey, same view of the data here except we isolate on Azure accounts. The N there is 677 Azure accounts. And we show Snowflake and Microsoft cuts for analytic databases with 83 and 393 Shared N responses respectively. So again, enough I feel to draw some conclusions from this data. Now, note the Net Scores. Snowflake again, winning with 78% versus 51% from Microsoft. 51% is strong but 78% is there's a meaningful lead for Snowflake within the Microsoft base, very interesting. And once again, you see massive new ads, 41% for Snowflake, whereas Microsoft's Net Score is being powered really by growth from existing customers, that forest green. And again, very little red for both companies. So super positive there. Okay, let's take a look now at how Snowflake's doing inside of Google accounts, GCP, Google Cloud Platform. So here's the ETR data, same view of that data, but now, we isolate on GCP accounts. There are fewer, 298 running, then, you got those running Snowflake and Google Analytic databases, largely BigQuery, but could be some others in there but the Snowflake Shared N is 49, it's smaller than on the other clouds, because the company just announced support for GCP, just about a year ago. I think it was last June, but still large enough to draw conclusions from the data. I feel pretty comfortable with that. We're not slicing and dicing it too finely. And you could see Google Shared N at 147. Look at the story. I sound like a broken record. Snowflake is again winning by a meaningful margin if you measure this Net Score or spending momentum. So 77.6% Net Score versus Google at 54%, with Snowflake at 80% in the green. Both companies, very little red. So this is pretty impressive. Snowflake has greater spending momentum than the captive cloud providers in all three of the big US-based clouds. So the big question is can Snowflake hold serve, and continue to grow, and how are they going to to be able to do that? Look, as I said before, this is a very competitive market. We reported that how Snowflake is taking share from some of the legacy on-prem data warehouse players like Teradata and IBM, and from what our data suggests, Lumen and Oracle too. I've reported how IBM is stretched thin on its research and development budget, spends about $6 billion a year, but it's got to spend it across a lot of different lines. Oracle's got more targeted spending R&D. They can target more toward database and direct more of its free cash flow to database than IBM can. But Amazon, and Microsoft, and Google, they don't have that problem. They spend a ton of dough on R&D. And here's an example of the challenge that Snowflake faces. Take a look at this partial list that I drew together of recent innovations. And we show here a set of features that Snowflake has launched in 2020, and AWS since re:Invent last year. I don't have time to go into these, but we do know this that AWS is no slouch at adding features. Amazon, as a company, spends two x more on research and development than Snowflake is worth as a company. So why do I like Snowflake's chances. Well, there are several reasons. First, every dime that Snowflake spends on R&D, go-to market, and ecosystem, goes into making its databases better for its customers. Now, I asked Frank Slootman in the middle of the lockdown how he was allocating precious capital during the pandemic. Let's listen to his response. I've said, there's no layoffs on our radar, number one. Number two, we are hiring. And number three is, we have a higher level of scrutiny on the hires that we're making. And I am very transparent. In other words, I tell people, "Look, I prioritize the roles that are closest "to the drivetrain of the business." Right, it's kind of common sense. But I wanted to make sure that this is how we're thinking about this. There are some roles that are more postponable than others. I'm hiring in engineering, without any reservation because that is the long term, strategic interest of the company. >> But you know, that's only part of the story. And so I want to spend a moment here on some other differentiation, which is multi-cloud. Now, as many of you know, I've been sort of cynical of multi-cloud up until recently. I've said that multi-cloud is a symptom, more of a symptom of multi-vendor and largely, a bunch of vendor marketing hooey today. But that's beginning to change. I see multi-cloud as increasingly viable and important to organizations, not only because CIOs are being asked to clean up the crime scene, as I've often joked, but also because it's increasingly becoming a strategy, right cloud for the right workload. So first, let me reiterate what I said at the top. New workloads are emerging in the cloud, real-time AI, insights extraction, and real-time inferencing is going to be a competitive differentiator. It's all about the data. The new innovation cocktail stems from machine intelligence applied to that data with data science tooling and simplified interfaces that enable scaling with the cloud. You got to have simplicity if you're going to scale and cloud is the best way to scale. It's really the only way to scale globally. So as such, we see cross-cloud exploitation is a real differentiator for Snowflake and others that build high quality cloud native capabilities from multiple clouds, and I want to spend a minute on this topic generally and talk about what it means for Snowflake specifically. Now, we've been pounding the table lately saying that building capabilities natively for the cloud versus putting a wrapper around your stack and making it run in the cloud is key. It's a big difference, why is this? Because cloud native means taking advantage of the primitive capabilities within respective clouds to create the highest performance, the lowest latency, the most efficient services, for that cloud, and the most secure, really exploiting that cloud. And this is enabled only by natively building in the cloud, and that's why Slootman is so dogmatic on this issue. Multi-cloud can be a differentiator for Snowflake. We can think about data lives everywhere. And you want to keep data, where it lives ideally, you don't want to have to move it, whether it's on AWS, Azure, whatever cloud is holding that data. If the answer to your query requires tapping data that lives in multiple clouds across a data network, and the app needs fast answers, then, you need low latency access to that data. So here's what I think. I think Snowflake's game is to automate by extracting, abstracting, sorry, the complexity around the data location, of course, latency is a part of that, metadata, bandwidth concerns, the time to get to query and answers. All those factors that build complexity into the data pipeline and then optimizing that to get insights, irrespective of data location. So a differentiating formula is really to not only be the best analytic database but be cloud agnostic. AWS, for example, they got a cloud agenda, as do Azure and GCP. Their number one answer to multi-cloud is put everything on our cloud. Yeah, Microsoft and Google Anthos, they would argue against that but we know that behind the scenes, that's what they want. They got offerings across clouds but Snowflake is going to make this a top priority. They can lead with that, and they must be best at it. And if Snowflake can do this, it's going to have a very successful future, in our opinion. And by all accounts, and the data that we shared, Snowflake is executing well. All right, so that's a wrap for this week's CUBE Insights, powered by ETR. Don't forget, all these breaking analysis segments are available as podcasts, just Google breaking analysis with Dave Vellante. I publish every week on wikibon.com and siliconangle.com. Check out etr.plus. That's where all the survey data is and reach out to me, I'm @dvellante on Twitter, or you can hit me up on my LinkedIn posts, or email me at david.vellante@siliconangle.com. Thanks for watching, everyone. We'll see you next time. (enlightening music)

>> Narrator: From around the Globe, it's theCube with digital coverage of Enterprise Data Automation, and event series brought to you by Io-Tahoe. >> Everybody, we're back. And this is Dave Vellante, and we're covering the whole notion of Automated Data in the Enterprise. And I'm really excited to have Paula D'Amico here. Senior Vice President of Enterprise Data Architecture at Webster Bank. Paula, good to see you. Thanks for coming on. >> Hi, nice to see you, too. >> Let's start with Webster bank. You guys are kind of a regional I think New York, New England, believe it's headquartered out of Connecticut. But tell us a little bit about the bank. >> Webster bank is regional Boston, Connecticut, and New York. Very focused on in Westchester and Fairfield County. They are a really highly rated regional bank for this area. They hold quite a few awards for the area for being supportive for the community, and are really moving forward technology wise, they really want to be a data driven bank, and they want to move into a more robust group. >> We got a lot to talk about. So data driven is an interesting topic and your role as Data Architecture, is really Senior Vice President Data Architecture. So you got a big responsibility as it relates to kind of transitioning to this digital data driven bank but tell us a little bit about your role in your Organization. >> Currently, today, we have a small group that is just working toward moving into a more futuristic, more data driven data warehousing. That's our first item. And then the other item is to drive new revenue by anticipating what customers do, when they go to the bank or when they log in to their account, to be able to give them the best offer. And the only way to do that is you have timely, accurate, complete data on the customer and what's really a great value on offer something to offer that, or a new product, or to help them continue to grow their savings, or do and grow their investments. >> Okay, and I really want to get into that. But before we do, and I know you're, sort of partway through your journey, you got a lot to do. But I want to ask you about Covid, how you guys handling that? You had the government coming down and small business loans and PPP, and huge volume of business and sort of data was at the heart of that. How did you manage through that? >> We were extremely successful, because we have a big, dedicated team that understands where their data is and was able to switch much faster than a larger bank, to be able to offer the PPP Long's out to our customers within lightning speed. And part of that was is we adapted to Salesforce very for we've had Salesforce in house for over 15 years. Pretty much that was the driving vehicle to get our PPP loans in, and then developing logic quickly, but it was a 24 seven development role and get the data moving on helping our customers fill out the forms. And a lot of that was manual, but it was a large community effort. >> Think about that too. The volume was probably much higher than the volume of loans to small businesses that you're used to granting and then also the initial guidelines were very opaque. You really didn't know what the rules were, but you were expected to enforce them. And then finally, you got more clarity. So you had to essentially code that logic into the system in real time. >> I wasn't directly involved, but part of my data movement team was, and we had to change the logic overnight. So it was on a Friday night it was released, we pushed our first set of loans through, and then the logic changed from coming from the government, it changed and we had to redevelop our data movement pieces again, and we design them and send them back through. So it was definitely kind of scary, but we were completely successful. We hit a very high peak. Again, I don't know the exact number but it was in the thousands of loans, from little loans to very large loans and not one customer who applied did not get what they needed for, that was the right process and filled out the right amount. >> Well, that is an amazing story and really great support for the region, your Connecticut, the Boston area. So that's fantastic. I want to get into the rest of your story now. Let's start with some of the business drivers in banking. I mean, obviously online. A lot of people have sort of joked that many of the older people, who kind of shunned online banking would love to go into the branch and see their friendly teller had no choice, during this pandemic, to go to online. So that's obviously a big trend you mentioned, the data driven data warehouse, I want to understand that, but what at the top level, what are some of the key business drivers that are catalyzing your desire for change? >> The ability to give a customer, what they need at the time when they need it. And what I mean by that is that we have customer interactions in multiple ways. And I want to be able for the customer to walk into a bank or online and see the same format, and being able to have the same feel the same love, and also to be able to offer them the next best offer for them. But they're if they want looking for a new mortgage or looking to refinance, or whatever it is that they have that data, we have the data and that they feel comfortable using it. And that's an untethered banker. Attitude is, whatever my banker is holding and whatever the person is holding in their phone, that is the same and it's comfortable. So they don't feel that they've walked into the bank and they have to do fill out different paperwork compared to filling out paperwork on just doing it on their phone. >> You actually do want the experience to be better. And it is in many cases. Now you weren't able to do this with your existing I guess mainframe based Enterprise Data Warehouses. Is that right? Maybe talk about that a little bit? >> Yeah, we were definitely able to do it with what we have today the technology we're using. But one of the issues is that it's not timely. And you need a timely process to be able to get the customers to understand what's happening. You need a timely process so we can enhance our risk management. We can apply for fraud issues and things like that. >> Yeah, so you're trying to get more real time. The traditional EDW. It's sort of a science project. There's a few experts that know how to get it. You can so line up, the demand is tremendous. And then oftentimes by the time you get the answer, it's outdated. So you're trying to address that problem. So part of it is really the cycle time the end to end cycle time that you're progressing. And then there's, if I understand it residual benefits that are pretty substantial from a revenue opportunity, other offers that you can make to the right customer, that you maybe know, through your data, is that right? >> Exactly. It's drive new customers to new opportunities. It's enhanced the risk, and it's to optimize the banking process, and then obviously, to create new business. And the only way we're going to be able to do that is if we have the ability to look at the data right when the customer walks in the door or right when they open up their app. And by doing creating more to New York times near real time data, or the data warehouse team that's giving the lines of business the ability to work on the next best offer for that customer as well. >> But Paula, we're inundated with data sources these days. Are there other data sources that maybe had access to before, but perhaps the backlog of ingesting and cleaning in cataloging and analyzing maybe the backlog was so great that you couldn't perhaps tap some of those data sources. Do you see the potential to increase the data sources and hence the quality of the data or is that sort of premature? >> Oh, no. Exactly. Right. So right now, we ingest a lot of flat files and from our mainframe type of front end system, that we've had for quite a few years. But now that we're moving to the cloud and off-prem and on-prem, moving off-prem, into like an S3 Bucket, where that data we can process that data and get that data faster by using real time tools to move that data into a place where, like snowflake could utilize that data, or we can give it out to our market. Right now we're about we do work in batch mode still. So we're doing 24 hours. >> Okay. So when I think about the data pipeline, and the people involved, maybe you could talk a little bit about the organization. You've got, I don't know, if you have data scientists or statisticians, I'm sure you do. You got data architects, data engineers, quality engineers, developers, etc. And oftentimes, practitioners like yourself, will stress about, hey, the data is in silos. The data quality is not where we want it to be. We have to manually categorize the data. These are all sort of common data pipeline problems, if you will. Sometimes we use the term data Ops, which is sort of a play on DevOps applied to the data pipeline. Can you just sort of describe your situation in that context? >> Yeah, so we have a very large data ops team. And everyone that who is working on the data part of Webster's Bank, has been there 13 to 14 years. So they get the data, they understand it, they understand the lines of business. So it's right now. We could the we have data quality issues, just like everybody else does. But we have places in them where that gets cleansed. And we're moving toward and there was very much siloed data. The data scientists are out in the lines of business right now, which is great, because I think that's where data science belongs, we should give them and that's what we're working towards now is giving them more self service, giving them the ability to access the data in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own, like Tableau dashboards, and then pushing the data back out. So they're going to more not, I don't want to say, a central repository, but a more of a robust repository, that's controlled across multiple avenues, where multiple lines of business can access that data. Is that help? >> Got it, Yes. And I think that one of the key things that I'm taking away from your last comment, is the cultural aspects of this by having the data scientists in the line of business, the lines of business will feel ownership of that data as opposed to pointing fingers criticizing the data quality. They really own that that problem, as opposed to saying, well, it's Paula's problem. >> Well, I have my problem is I have data engineers, data architects, database administrators, traditional data reporting people. And because some customers that I have that are business customers lines of business, they want to just subscribe to a report, they don't want to go out and do any data science work. And we still have to provide that. So we still want to provide them some kind of regiment that they wake up in the morning, and they open up their email, and there's the report that they subscribe to, which is great, and it works out really well. And one of the things is why we purchased Io-Tahoe was, I would have the ability to give the lines of business, the ability to do search within the data. And we'll read the data flows and data redundancy and things like that, and help me clean up the data. And also, to give it to the data analysts who say, all right, they just asked me they want this certain report. And it used to take okay, four weeks we're going to go and we're going to look at the data and then we'll come back and tell you what we can do. But now with Io-Tahoe, they're able to look at the data, and then in one or two days, they'll be able to go back and say, Yes, we have the data, this is where it is. This is where we found it. This is the data flows that we found also, which is what I call it, is the break of a column. It's where the column was created, and where it went to live as a teenager. (laughs) And then it went to die, where we archive it. And, yeah, it's this cycle of life for a column. And Io-Tahoe helps us do that. And we do data lineage is done all the time. And it's just takes a very long time and that's why we're using something that has AI in it and machine running. It's accurate, it does it the same way over and over again. If an analyst leaves, you're able to utilize something like Io-Tahoe to be able to do that work for you. Is that help? >> Yeah, so got it. So a couple things there, in researching Io-Tahoe, it seems like one of the strengths of their platform is the ability to visualize data, the data structure and actually dig into it, but also see it. And that speeds things up and gives everybody additional confidence. And then the other piece is essentially infusing AI or machine intelligence into the data pipeline, is really how you're attacking automation. And you're saying it repeatable, and then that helps the data quality and you have this virtual cycle. Maybe you could sort of affirm that and add some color, perhaps. >> Exactly. So you're able to let's say that I have seven cars, lines of business that are asking me questions, and one of the questions they'll ask me is, we want to know, if this customer is okay to contact, and there's different avenues so you can go online, do not contact me, you can go to the bank and you can say, I don't want email, but I'll take texts. And I want no phone calls. All that information. So, seven different lines of business asked me that question in different ways. One said, "No okay to contact" the other one says, "Customer 123." All these. In each project before I got there used to be siloed. So one customer would be 100 hours for them to do that analytical work, and then another analyst would do another 100 hours on the other project. Well, now I can do that all at once. And I can do those types of searches and say, Yes, we already have that documentation. Here it is, and this is where you can find where the customer has said, "No, I don't want to get access from you by email or I've subscribed to get emails from you." >> Got it. Okay. Yeah Okay. And then I want to go back to the cloud a little bit. So you mentioned S3 Buckets. So you're moving to the Amazon cloud, at least, I'm sure you're going to get a hybrid situation there. You mentioned snowflake. What was sort of the decision to move to the cloud? Obviously, snowflake is cloud only. There's not an on-prem, version there. So what precipitated that? >> Alright, so from I've been in the data IT information field for the last 35 years. I started in the US Air Force, and have moved on from since then. And my experience with Bob Graham, was with snowflake with working with GE Capital. And that's where I met up with the team from Io-Tahoe as well. And so it's a proven so there's a couple of things one is Informatica, is worldwide known to move data. They have two products, they have the on-prem and the off-prem. I've used the on-prem and off-prem, they're both great. And it's very stable, and I'm comfortable with it. Other people are very comfortable with it. So we picked that as our batch data movement. We're moving toward probably HVR. It's not a total decision yet. But we're moving to HVR for real time data, which is changed capture data, moves it into the cloud. And then, so you're envisioning this right now. In which is you're in the S3, and you have all the data that you could possibly want. And that's JSON, all that everything is sitting in the S3 to be able to move it through into snowflake. And snowflake has proven to have a stability. You only need to learn and train your team with one thing. AWS as is completely stable at this point too. So all these avenues if you think about it, is going through from, this is your data lake, which is I would consider your S3. And even though it's not a traditional data lake like, you can touch it like a Progressive or Hadoop. And then into snowflake and then from snowflake into sandbox and so your lines of business and your data scientists just dive right in. That makes a big win. And then using Io-Tahoe with the data automation, and also their search engine. I have the ability to give the data scientists and data analysts the way of they don't need to talk to IT to get accurate information or completely accurate information from the structure. And we'll be right back. >> Yeah, so talking about snowflake and getting up to speed quickly. I know from talking to customers you can get from zero to snowflake very fast and then it sounds like the Io-Tahoe is sort of the automation cloud for your data pipeline within the cloud. Is that the right way to think about it? >> I think so. Right now I have Io-Tahoe attached to my on-prem. And I want to attach it to my off-prem eventually. So I'm using Io-Tahoe data automation right now, to bring in the data, and to start analyzing the data flows to make sure that I'm not missing anything, and that I'm not bringing over redundant data. The data warehouse that I'm working of, it's an on-prem. It's an Oracle Database, and it's 15 years old. So it has extra data in it. It has things that we don't need anymore, and Io-Tahoe's helping me shake out that extra data that does not need to be moved into my S3. So it's saving me money, when I'm moving from off-prem to on-prem. >> And so that was a challenge prior, because you couldn't get the lines of business to agree what to delete, or what was the issue there? >> Oh, it was more than that. Each line of business had their own structure within the warehouse. And then they were copying data between each other, and duplicating the data and using that. So there could be possibly three tables that have the same data in it, but it's used for different lines of business. We have identified using Io-Tahoe identified over seven terabytes in the last two months on data that has just been repetitive. It's the same exact data just sitting in a different schema. And that's not easy to find, if you only understand one schema, that's reporting for that line of business. >> More bad news for the storage companies out there. (both laughs) So far. >> It's cheap. That's what we were telling people. >> And it's true, but you still would rather not waste it, you'd like to apply it to drive more revenue. And so, I guess, let's close on where you see this thing going. Again, I know you're sort of partway through the journey, maybe you could sort of describe, where you see the phase is going and really what you want to get out of this thing, down the road, mid-term, longer term, what's your vision or your data driven organization. >> I want for the bankers to be able to walk around with an iPad in their hand, and be able to access data for that customer, really fast and be able to give them the best deal that they can get. I want Webster to be right there on top with being able to add new customers, and to be able to serve our existing customers who had bank accounts since they were 12 years old there and now our multi whatever. I want them to be able to have the best experience with our bankers. >> That's awesome. That's really what I want as a banking customer. I want my bank to know who I am, anticipate my needs, and create a great experience for me. And then let me go on with my life. And so that follow. Great story. Love your experience, your background and your knowledge. I can't thank you enough for coming on theCube. >> Now, thank you very much. And you guys have a great day. >> All right, take care. And thank you for watching everybody. Keep right there. We'll take a short break and be right back. (gentle music)

>> Narrator: From around the globe it's theCUBE! With digital coverage of UiPath Live, the release show. Brought to you by UiPath. >> Hi everybody this is Dave Valenti, welcome back to our RPA Drill Down. Ted Kummert is here he is Executive Vice President for Products and Engineering at UiPath. Ted, thanks for coming on, great to see you. >> Dave, it's great to be here, thanks so much. >> Dave your background is pretty interesting, you started as a Silicon Valley Engineer, they pulled you out, you did a huge stint at Microsoft. You got experience in SAS, you've got VC chops with Madrona. And at Microsoft you saw it all, the NT, the CE Space, Workflow, even MSN you did stuff with MSN, and then the all important data. So I'm interested in what attracted you to UiPath? >> Yeah Dave, I feel super fortunate to have worked in the industry in this span of time, it's been an amazing journey, and I had a great run at Microsoft it was fantastic. You mentioned one experience in the middle there, when I first went to the server business, the enterprise business, I owned our Integration and Workflow products, and I would say that's the first I encountered this idea. Often in the software industry there are ideas that have been around for a long time, and what we're doing is refining how we're delivering them. And we had ideas we talked about in terms of Business Process Management, Business Activity Monitoring, Workflow. The ways to efficiently able somebody to express the business process in a piece of software. Bring systems together, make everybody productive, bring humans into it. These were the ideas we talked about. Now in reality there were some real gaps. Because what happened in the technology was pretty different from what the actual business process was. And so lets fast forward then, I met Madrona Venture Group, Seattle based Venture Capital Firm. We actually made a decision to participate in one of UiPath's fundraising rounds. And that's the first I really came encountered with the company and had to have more than an intellectual understanding of RPA. 'Cause when I first saw it, I said "oh, I think that's desktop automation" I didn't look very close, maybe that's going to run out of runway, whatever. And then I got more acquainted with it and figured out "Oh, there's a much bigger idea here". And the power is that by really considering the process and the implementation from the humans work in, then you have an opportunity really to automate the real work. Not that what we were doing before wasn't significant, this is just that much more powerful. And that's when I got really excited. And then the companies statistics and growth and everything else just speaks for itself, in terms of an opportunity to work, I believe, in one of the most significant platforms going in the enterprise today, and work at one of the fastest growing companies around. It was like almost an automatic decision to decide to come to the company. >> Well you know, you bring up a good point you think about software historically through our industry, a lot of it was 'okay here's this software, now figure out how to map your processes to make it all work' and today the processes, especially you think about this pandemic, the processes are unknown. And so the software really has to be adaptable. So I'm wondering, and essentially we're talking about a fundamental shift in the way we work. And is there really a fundamental shift going on in how we write software and how would you describe that? >> Well there certainly are, and in a way that's the job of what we do when we build platforms for the enterprises, is try and give our customers a new way to get work done, that's more efficient and helps them build more powerful applications. And that's exactly what RPA does, the efficiency, it's not that this is the only way in software to express a lot of this, it just happens to be the quickest. You know in most ways. Especially as you start thinking about initiatives like our StudioX product to what we talk about as enabling citizen developers. It's an expression that allows customers to just do what they could have done otherwise much more quickly and efficient. And the value on that is always high, certainly in an unknown era like this, it's even more valuable, there are specific processes we've been helping automate in the healthcare, in financial services, with things like SBA Loan Processing, that we weren't thinking about six months ago, or they weren't thinking about six months ago. We're all thinking about how we're reinventing the way we work as individuals and corporations because of what's going on with the coronavirus crisis, having a platform like this that gives you agility and mapping the real work to what your computer state and applications all know how to do, is even more valuable in a climate like that. >> What attracted us originally to UiPath, we knew Bobby Patrick CMO, he said "Dave, go download a copy, go build some automations and go try it with some other companies". So that really struck us as wow, this is actually quite simple. Yet at the same time, and so you've of course been automating all these simple tasks, but now you've got real aspiration, you're glomming on to this term of Hyperautomation, you've made some acquisitions, you've got a vision, that really has taken you beyond 'paving the cow path' I sometimes say, of all these existing processes. It's really trying to discover new processes and opportunities for automation, which you would think after 50 or whatever years we've been in this industry, we'd have attacked a lot of it, but wow, seems like we have a long way to go. Again, especially what we're learning through this pandemic. Your thoughts on that? >> Yeah, I'd say Hyperautomation. It's actually a Gartner term, it's not our term. But there is a bigger idea here, built around the core automation platform. So let's talk for a second just what's not about the core platform and then what Hyperautomation really means around that. And I think of that as the bookends of how do I discover and plan, how do I improve my ability to do more automations, and find the real opportunities that I have. And how do I measure and optimize? And that's a lot of what we delivered in 20.4 as a new capability. So let's talk about discover and plan. One aspect of that is the wisdom of the crowd. We have a product we call Automation Hub that is all about that. Enabling people who have ideas, they're the ones doing the work, they have the observation into what efficiencies can be. Enabling them to either with our Ask Capture Utility capture that and document that, or just directly document that. And then, people across the company can then collaborate eventually moving on building the best ideas out of that. So there's capturing the crowd, and then there's a more scientific way of capturing actually what the opportunities are. So we've got two products we introduced. One is process mining, and process mining is about going outside in from the, let's call it the larger processes, more end to end processes in the enterprise. Things like order-to-cash and procure-to-pay, helping you understand by watching the events, and doing the analytics around that, where your bottle necks, where are you opportunities. And then task mining said "let's watch an individual, or group of individuals, what their tasks are, let's watch the log of events there, let's apply some machine learning processing to that, and say here's the repetitive things we've found." And really helping you then scientifically discover what your opportunities are. And these ideas have been along for a long time, process mining is not new. But the connection to an automation platform, we think is a new and powerful idea, and something we plan to invest a lot in going forward. So that's the first bookend. And then the second bookend is really about attaching rich analytics, so how do I measure it, so there's operationally how are my robots doing? And then there's everything down to return on investment. How do I understand how they are performing, verses what I would have spent if I was continuing to do them the old way. >> Yeah that's big 'cause (laughing) the hero reports for the executives to say "hey, this is actually working" but at the same time you've got to take a systems view. You don't want to just optimize one part of the system at the detriment to others. So you talk about process mining, which is kind of discovering the backend systems, ERP and the like, where the task mining it sounds like it's more the collaboration and front end. So that whole system thinking, really applies, doesn't it? >> Yeah. Very much so. Another part of what we talked about then, in the system is, how do we capture the ideas and how do we enable more people to build these automations? And that really gets down to, we talk about it in our company level vision, is a robot for every person. Every person should have a digital assistant. It can help you with things you do less frequently, it can help you with things you do all the time to do your job. And how do we help you create those? We've released a new tool we call StudioX. So for our RPA Developers we have Studio, and StudioX is really trying to enable a citizen developer. It's not unlike the art that we saw in Business Intelligence there was the era where analytics and reporting were the domain of experts, and they produced formalized reports that people could consume. But the people that had the questions would have to work with them and couldn't do the work themselves. And then along comes ClickView and Tableau and Power BI enabling the self services model, and all of a sudden people could do that work themselves, and that enabled powerful things. We think the same arch happens here, and StudioX is really our way of enabling that, citizen developer with the ideas to get some automation work done on their own. >> Got a lot in this announcement, things like document understanding, bring your own AI with AI fabric, how are you able to launch so many products, and have them fit together, you've made some acquisitions. Can you talk about the architecture that enables you to do that? >> Yeah, it's clearly in terms of ambition, and I've been there for 10 weeks, but in terms of ambition you don't have to have been there when they started the release after Forward III in October to know that this is the most ambitious thing that this company has ever done from a release perspective. Just in terms of the surface area we're delivering across now as an organization, is substantive. We talk about 1,000 feature improvements, 100's of discreet features, new products, as well as now our automation cloud has become generally available as well. So we've had muscle building over this past time to become world class at offering SAS, in addition to on-premises. And then we've got this big surface area, and architecture is a key component of how you can do this. How do you deliver efficiently the same software on-premises and in the cloud? Well you do that by having the right architecture and making the right bets. And certainly you look forward, how are companies doing this today? It's really all about Cloud-Native Platform. But it's about an architecture such that we can do that efficiently. So there is a lot about just your technical strategy. And then it's just about a ton of discipline and customer focus. It keeps you focused on the right things. StudioX was a great example of we were led by customers through a lot of what we actually delivered, a couple of the major features in it, certainly the out of box templates, the studio governance features, came out of customer suggestions. I think we had about 100 that we have sitting in the backlog, a lot of which we've already done, and really being disciplined and really focused on what customers are telling. So make sure you have the right technical strategy and architecture, really follow your customers, and really stay disciplined and focused on what matters most as you execute on the release. >> What can we learn from previous examples, I think about for instance SQL Server, you obviously have some knowledge in it, it started out pretty simple workloads and then at the time we all said "wow, it's a lot more powerful to come from below that it is, if a Db2, or an Oracle sort of goes down market", Microsoft proved that, obviously built in the robustness necessary, is there a similar metaphor here with regard to things like governance and security, just in terms of where UiPath started and where you see it going? >> Well I think the similarities have more to do with we have an idea of a bigger platform that we're now delivering against. In the database market, that was, we started, SQL Server started out as more of just a transactional database product, and ultimately grew to all of the workloads in the data platform, including transaction for transactional apps, data warehousing and as well as business intelligence. I see the same analogy here of thinking more broadly of the needs, and what the ability of an integrated platform, what it can do to enable great things for customers, I think that's a very consistent thing. And I think another consistent thing is know who you are. SQL Server knew exactly who it had to be when it entered the database market. That it was going to set a new benchmark on simplicity, TCO, and that was going to be the way it differentiated. In this case, we're out ahead of the market, we have a vision that's broader than a lot of the market is today. I think we see a lot of people coming in to this space, but we see them building to where we were, and we're out ahead. So we are operating from a leadership position, and I'm not going to tell you one's easier that the other, and both you have to execute with great urgency. But we're really executing out ahead, so we've got to keep thinking about, and there's no one's tail lights to follow, we have to be the ones really blazing the trail on what all of this means. >> I want to ask you about this incorporation of existing systems. Some markets they take off, it's kind of a one shot deal, and the market just embeds. I think you guys have bigger aspirations than that, I look at it like a service now, misunderstood early on, built the platform and now really is fundamental part of a lot of enterprises. I also look at things like EDW, which again, you have some experience in. In my view it failed to live up to a lot of it's promises even though it delivered a lot of value. You look at some of the big data initiatives, you know EDW still plugs in, it's the system of record, okay that's fine. How do you see RPA evolving? Are we going to incorporate, do we have to embrace existing business process systems? Or is this largely a do-over in your opinion? >> Well I think it's certainly about a new way of building automation, and it's starting to incorporate and include the other ways, for instance in the current release we added support for long running workflow, it was about human workflow based scenarios, now the human is collaborating with the robot, and we built those capabilities. So I do see us combining some of the old and new way. I think one of the most significant things here, is also that impact that AI and ML based technologies and skills can have on the power of the automations that we deliver. We've certainly got a surface area that, I think about our AI and ML strategy in two parts, that we are building first class first party skills, that we're including in the platform, and then we're building a platform for third parties and customers to bring their what their data science teams have delivered, so those can also be a part of our ecosystem, and part of automations. And so things like document understanding, how do I easily extract data from more structured, semi-structured and completely unstructured documents, accurately? And include those in my automations. Computer vision which gives us an ability to automate at a UI level across other types of systems than say a Windows and a browser base application. And task mining is built on a very robust, multi layer ML system, and the innovation opportunity that I think just consider there, you know continue there. You think it's a macro level if there's aspects of machine learning that are about captured human knowledge, well what exactly is an automation that captured where you're capturing a lot of human knowledge. The impact of ML and AI are going to be significant going out into the future. >> Yeah, I want to ask you about them, and I think a lot of people are just afraid of AI, as a separate thing and they have to figure out how to operationalize it. And I think companies like UiPath are really in a position to embed UI into applications AI into applications everywhere, so that maybe those folks that haven't climbed on the digital bandwagon, who are now with this pandemic are realizing "wow, we better accelerate this" they can actually tap machine intelligence through your products and others as well. Your thoughts on that sort of narrative? >> Yeah, I agree with that point of view, it's AI and ML is still maturing discipline across the industry. And you have to build new muscle, and you build new muscle and data science, and it forces you to think about data and how you manage your data in a different way. And that's a journey we've been on as a company to not only build our first party skills, but also to build the platform. It's what's given us the knowledge that to help us figure out, well what do we need to include here so our customers can bring their skills, actually to our platform, and I do think this is a place where we're going to see the real impact of AI and ML in a broader way. Based on the kind of apps it is and the kind of skills we can bring to bear. >> Okay last question, you're ten weeks in, when you're 50, 100, 200 weeks in, what should we be watching, what do you want to have accomplished? >> Well we're listening, we're obviously listening closely to our customers, right now we're still having a great week, 'cause there's nothing like shipping new software. So right now we're actually thinking deeply about where we're headed next. We see there's lots of opportunities and robot for every person, and that initiative, and so we're launched a bunch of important new capabilities there, and we're going to keep working with the market to understand how we can, how we can add additional capability there. We've just got the GA of our automation cloud, I think you should expect more and more services in our automation cloud going forward. I think this area we talked about, in terms of AI and ML and those technologies, I think you should expect more investment and innovation there from us and the community, helping our customers, and I think you will also see us then, as we talked about this convergence of the ways we bring together systems through integrate and build business process, I think we'll see a convergence into the platform of more of those methods. I look ahead to the next releases, and want to see us making some very significant releases that are advancing all of those things, and continuing our leadership in what we talk about now as the Hyperautomation platform. >> Well Ted, lot of innovation opportunities and of course everybody's hopping on the automation bandwagon. Everybody's going to want a piece of your RPA hide, and you're in the lead, we're really excited for you, we're excited to have you on theCUBE, so thanks very much for all your time and your insight. Really appreciate it. >> Yeah, thanks Dave, great to spend this time with you. >> All right thank you for watching everybody, this is Dave Velanti for theCUBE, and our RPA Drill Down Series, keep it right there we'll be right back, right after this short break. (calming instrumental music)

(upbeat music) >> Narrator: It's theCUBE, covering the Virtual Vertica Big Data Conference 2020. Brought to you by Vertica. >> Dave Vellante: Hello everyone, and welcome to theCUBE's exclusive coverage of the Vertica Virtual Big Data Conference. You're watching theCUBE, the leader in digital event tech coverage. And we're broadcasting remotely from our studios in Palo Alto and Boston. And, we're pleased to be covering wall-to-wall this digital event. Now, as you know, originally BDC was scheduled this week at the new Encore Hotel and Casino in Boston. Their theme was "Win big with big data". Oh sorry, "Win big with data". That's right, got it. And, I know the community was really looking forward to that, you know, meet up. But look, we're making the best of it, given these uncertain times. We wish you and your families good health and safety. And this is the way that we're going to broadcast for the next several months. Now, we want to unpack Colin Mahony's keynote, but, before we do that, I want to give a little context on the market. First, theCUBE has covered every BDC since its inception, since the BDC's inception that is. It's a very intimate event, with a heavy emphasis on user content. Now, historically, the data engineers and DBAs in the Vertica community, they comprised the majority of the content at this event. And, that's going to be the same for this virtual, or digital, production. Now, theCUBE is going to be broadcasting for two days. What we're doing, is we're going to be concurrent with the Virtual BDC. We got practitioners that are coming on the show, DBAs, data engineers, database gurus, we got a security experts coming on, and really a great line up. And, of course, we'll also be hearing from Vertica Execs, Colin Mahony himself right of the keynote, folks from product marketing, partners, and a number of experts, including some from Micro Focus, which is the, of course, owner of Vertica. But I want to take a moment to share a little bit about the history of Vertica. The company, as you know, was founded by Michael Stonebraker. And, Verica started, really they started out as a SQL platform for analytics. It was the first, or at least one of the first, to really nail the MPP column store trend. Not only did Vertica have an early mover advantage in MPP, but the efficiency and scale of its software, relative to traditional DBMS, and also other MPP players, is underscored by the fact that Vertica, and the Vertica brand, really thrives to this day. But, I have to tell you, it wasn't without some pain. And, I'll talk a little bit about that, and really talk about how we got here today. So first, you know, you think about traditional transaction databases, like Oracle or IMBDB tour, or even enterprise data warehouse platforms like Teradata. They were simply not purpose-built for big data. Vertica was. Along with a whole bunch of other players, like Netezza, which was bought by IBM, Aster Data, which is now Teradata, Actian, ParAccel, which was the basis for Redshift, Amazon's Redshift, Greenplum was bought, in the early days, by EMC. And, these companies were really designed to run as massively parallel systems that smoked traditional RDBMS and EDW for particular analytic applications. You know, back in the big data days, I often joked that, like an NFL draft, there was run on MPP players, like when you see a run on polling guards. You know, once one goes, they all start to fall. And that's what you saw with the MPP columnar stores, IBM, EMC, and then HP getting into the game. So, it was like 2011, and Leo Apotheker, he was the new CEO of HP. Frankly, he has no clue, in my opinion, with what to do with Vertica, and totally missed one the biggest trends of the last decade, the data trend, the big data trend. HP picked up Vertica for a song, it wasn't disclosed, but my guess is that it was around 200 million. So, rather than build a bunch of smart tokens around Vertica, which I always call the diamond in the rough, Apotheker basically permanently altered HP for years. He kind of ruined HP, in my view, with a 12 billion dollar purchase of Autonomy, which turned out to be one of the biggest disasters in recent M&A history. HP was forced to spin merge, and ended up selling most of its software to Microsoft, Micro Focus. (laughs) Luckily, during its time at HP, CEO Meg Whitman, largely was distracted with what to do with the mess that she inherited form Apotheker. So, Vertica was left alone. Now, the upshot is Colin Mahony, who was then the GM of Vertica, and still is. By the way, he's really the CEO, and he just doesn't have the title, I actually think they should give that to him. But anyway, he's been at the helm the whole time. And Colin, as you'll see in our interview, is a rockstar, he's got technical and business jobs, people love him in the community. Vertica's culture is really engineering driven and they're all about data. Despite the fact that Vertica is a 15-year-old company, they've really kept pace, and not been polluted by legacy baggage. Vertica, early on, embraced Hadoop and the whole open-source movement. And that helped give it tailwinds. It leaned heavily into cloud, as we're going to talk about further this week. And they got a good story around machine intelligence and AI. So, whereas many traditional database players are really getting hurt, and some are getting killed, by cloud database providers, Vertica's actually doing a pretty good job of servicing its install base, and is in a reasonable position to compete for new workloads. On its last earnings call, the Micro Focus CFO, Stephen Murdoch, he said they're investing 70 to 80 million dollars in two key growth areas, security and Vertica. Now, Micro Focus is running its Suse play on these two parts of its business. What I mean by that, is they're investing and allowing them to be semi-autonomous, spending on R&D and go to market. And, they have no hardware agenda, unlike when Vertica was part of HP, or HPE, I guess HP, before the spin out. Now, let me come back to the big trend in the market today. And there's something going on around analytic databases in the cloud. You've got companies like Snowflake and AWS with Redshift, as we've reported numerous times, and they're doing quite well, they're gaining share, especially of new workloads that are merging, particularly in the cloud native space. They combine scalable compute, storage, and machine learning, and, importantly, they're allowing customers to scale, compute, and storage independent of each other. Why is that important? Because you don't have to buy storage every time you buy compute, or vice versa, in chunks. So, if you can scale them independently, you've got granularity. Vertica is keeping pace. In talking to customers, Vertica is leaning heavily into the cloud, supporting all the major cloud platforms, as we heard from Colin earlier today, adding Google. And, why my research shows that Vertica has some work to do in cloud and cloud native, to simplify the experience, it's more robust in motor stack, which supports many different environments, you know deep SQL, acid properties, and DNA that allows Vertica to compete with these cloud-native database suppliers. Now, Vertica might lose out in some of those native workloads. But, I have to say, my experience in talking with customers, if you're looking for a great MMP column store that scales and runs in the cloud, or on-prem, Vertica is in a very strong position. Vertica claims to be the only MPP columnar store to allow customers to scale, compute, and storage independently, both in the cloud and in hybrid environments on-prem, et cetera, cross clouds, as well. So, while Vertica may be at a disadvantage in a pure cloud native bake-off, it's more robust in motor stack, combined with its multi-cloud strategy, gives Vertica a compelling set of advantages. So, we heard a lot of this from Colin Mahony, who announced Vertica 10.0 in his keynote. He really emphasized Vertica's multi-cloud affinity, it's Eon Mode, which really allows that separation, or scaling of compute, independent of storage, both in the cloud and on-prem. Vertica 10, according to Mahony, is making big bets on in-database machine learning, he talked about that, AI, and along with some advanced regression techniques. He talked about PMML models, Python integration, which was actually something that they talked about doing with Uber and some other customers. Now, Mahony also stressed the trend toward object stores. And, Vertica now supports, let's see S3, with Eon, S3 Eon in Google Cloud, in addition to AWS, and then Pure and HDFS, as well, they all support Eon Mode. Mahony also stressed, as I mentioned earlier, a big commitment to on-prem and the whole cloud optionality thing. So 10.0, according to Colin Mahony, is all about really doubling down on these industry waves. As they say, enabling native PMML models, running them in Vertica, and really doing all the work that's required around ML and AI, they also announced support for TensorFlow. So, object store optionality is important, is what he talked about in Eon Mode, with the news of support for Google Cloud and, as well as HTFS. And finally, a big focus on deployment flexibility. Migration tools, which are a critical focus really on improving ease of use, and you hear this from a lot of customers. So, these are the critical aspects of Vertica 10.0, and an announcement that we're going to be unpacking all week, with some of the experts that I talked about. So, I'm going to close with this. My long-time co-host, John Furrier, and I have talked some time about this new cocktail of innovation. No longer is Moore's law the, really, mainspring of innovation. It's now about taking all these data troves, bringing machine learning and AI into that data to extract insights, and then operationalizing those insights at scale, leveraging cloud. And, one of the things I always look for from cloud is, if you've got a cloud play, you can attract innovation in the form of startups. It's part of the success equation, certainly for AWS, and I think it's one of the challenges for a lot of the legacy on-prem players. Vertica, I think, has done a pretty good job in this regard. And, you know, we're going to look this week for evidence of that innovation. One of the interviews that I'm personally excited about this week, is a new-ish company, I would consider them a startup, called Zebrium. What they're doing, is they're applying AI to do autonomous log monitoring for IT ops. And, I'm interviewing Larry Lancaster, who's their CEO, this week, and I'm going to press him on why he chose to run on Vertica and not a cloud database. This guy is a hardcore tech guru and I want to hear his opinion. Okay, so keep it right there, stay with us. We're all over the Vertica Virtual Big Data Conference, covering in-depth interviews and following all the news. So, theCUBE is going to be interviewing these folks, two days, wall-to-wall coverage, so keep it right there. We're going to be right back with our next guest, right after this short break. This is Dave Vellante and you're watching theCUBE. (upbeat music)

(upbeat music) >> Hi everybody, welcome back. You're watching theCUBE, we go out to the events we extract the signal from the noise. This is a very special digital event and we're really covering the transformation not only the industry, but the transformation of Oracle Consulting and its rebirth. Mike Owens is here Group VP of Cloud Advisory and GM of Oracle Elevate, which is a partnership that Oracle announced last Open World with Deloitte, and Don Schmidt is here, who is a Managing Director at Deloitte. Gents, good to see you, welcome. >> Good to be here Dave. >> So, Don I want to start with you. Transformation, right? Everybody talks about that, there's a lot of trends going on in the industry. What do you guys see as the big gestalt transformation that's going on? >> Yeah I think there's an inflection point right now. Everybody have been saying they want to get out their data centers. The leaves haven't really been taking place, right? They've been kind of moving in small bits. We're now at the point where large transformation at scale, of getting out of your data centers, is now here. So, we are here to try to help our clients move faster. How can we do this more effectively, cost efficiently, and get them out of these data centers so they can move on with their day to day business? >> So data centers is just not an efficient use of capital for your customers. >> No, no there's lots of ways to do this a lot faster, cheaper, and get on to innovation. Spend your money there, not on hardware, floor space, power cooling, those fun things. >> Well you guys are spending money on data centers though right? So this is a good business for you all. >> Mike: We do it on behalf of other customers though. Right? >> Yeah and that's what's happening right? My customers, they essentially want to take all this IT labor cost and shift it into R&D get them on your backs and your backs right? Is this that what you see it? You know where are we in terms of that? I mean it started ten plus years ago but it really has started to uptake right? What's driving that? What's the catalyst there? >> You know so from my perspective, I've been doing this a while. A lot of it is either organizations are driving costs or what you're also seeing is IT organizations are no longer the utility in the organization and taking the orders, you're using them to try to top line value, but to do that, they actually have to take their business and change the model of it, so they can take that money and reinvest it in what Don had talked about, investment or continuous investment. So you're starting to hit those inflection points, you know years ago a CIO would be in his job for 15, 20 years, the average tenure for a CIO is you know three to five years on average, and it's because if they're not driving innovation or driving top line growth with an organization, organizations are now starting to flip that around so you're seeing a huge inflection point, with organizations really looking for IT not to be just a back office entity anymore, to truly drive them they have to transform that back to Don's point, because that inflection point, this large data center move over is a good sort of lever to kind of get them and really use it as opportunity to transform their organization. >> And the transformations are occurring, you know within industries, but at different pace. I mean some industries have transformed radically. You think about Ride shares, and digital music and the like others are taking more time, financial services, health care, they're riskier businesses, and you know there's more government in public policy so what do you see in terms of the catalyst for transformation and is there any kind of discernible, industry variance? >> Yeah there definitely is and he's mentioned some of the more start-up kind of organizations where Cloud was right for them at the early stages. These other organizations that have built these large application stacks and have been there for years, it's scary for them to say, "Let me take this big set of equipment and applications, and move it to the Cloud." It's a big effort. Starting from scratch with start-ups, that's a little different story right? So we are kind of at a different point, there are different stages within different industries, some are faster adopters than some of the others with government regulations and some of the technologies that have to kind of catch up to be able to provide those services. >> Do people generally want to take their sort of mission-critical apps which are largely running often on Oracle infrastructure database, they want to move that into the Cloud but do they want to bring that sort of Cloud-operating model to their on-prem and maybe reduce their overall data center footprint but preserve some of that? What are you guys seeing? >> So, two different probably viewpoints. So my viewpoint is, depends on the organization, depends on the regulatory they have, and there's a lot of factors in there. But I would say, as a standard organization would take their journey, mission-critical systems are historically not the first one in there. 'Cause back to the point of changing the operating model the way you want to do business and be effective, you don't go with the crown jewels first, historically, take some other work loads learn how to work in that operating model, how you're doing things change and then you evolve some of those pieces over time. There are organizations that basically, pull the band-aid off and just go right into it, right? But a lot of large enterprises sort of that's why we talk about Cloud as a journey, right? You take this journey you have to make the journey based on what's going on back what Don had talked about the regulatory requirements in history are the right controls in place for what they need at that point. If not, okay so what's an interim step to the journey? Could you bring Cloud in those capabilities on-prem and then have some of the other stuff off-prem? So it's really situational dependent, and we actually walk a customer through and now Don's organization does the same thing. You walk them through what makes best for their journey for where they're at in the industry and what they have today and what they're trying to achieve. >> So Don Deloitte doesn't just do IT it does business transformation, right? So it's like a chicken and egg, let's say that what comes first? The chicken or the egg? The IT transformation or the business transformation? >> I don't think it's an or it's an and. So have the total conversation of where's your Cloud journey for your entire enterprise, and then decide how that's going to be played out in both in IT and in the business. How the joint conversation from an enterprise perspective. >> So let's talk a little bit about the partnership, to your very well known brands, you guys get together, so what was the sort of impetus to get together? How's it going? Give us the update on that front. >> Yeah you know so from Oracle standpoint, Oracle has been really technology focused. It was really created by technologists, right? And back to the point of what we're trying to do with the Cloud and trying to do larger transformation, those aren't some of the skills that we have. We've been bringing in some of those skills in DNA, but if you look at it as why would you try to recreate this situation? Why would you not partner with an organization who does large business transformation like Deloitte? Right? And so the impetus of that is, how do we take the technology with the business transformation, pull that together and back of the one plus one equals three for my customer, right? That's what they really want, so how do we actually scale that into really big things and get big outcomes for our customers? Our partnership is not about trying to take a bunch of customers and move a couple application work loads. Our job, what we're really charted to do is make huge transformational leaps for our customers, using the combined capabilities of the two organizations. So this it's a hug paradigm for us to kind of do this. >> And in our collaboration with the two organizations just the opposite from what Mike just said right? So Deloitte wasn't really big in big IT, right? Business led transformations kind of what Deloitte's been known for, along with our cyber practice, and so we needed the deep skills of the technical experts. >> Right, so take me through what transformation engagement looks like. They don't call you up and say, "Hey want to buy me some transformation." Right? Where does it start? Who are the stake holders? How long does it take? I mean it could be multi year, I presume and never ends maybe but you want to get to business value first, so let's shorten up the time frame. Take me through typical engagement. Typical I know in quotes. And then, how long like take me through the point at which you start to get business value. What do I got to do to get there? >> Yeah so we see two different spectrums on a transformation. And it really aligns to what are your objectives. Do you just need to get out of the data center because you're on archaic dying hardware? Or do you want to take that, take your time and make a little bit more of a transformation journey? Or do you want to play somewhere in the middle of that spectrum? But on either one of those we'll come in and we'll do a discovery conversation. We'll understand what's in your data center, understand what the age or the health of your data center is, help the customers through, business case, TCO, how fast or how slow that journey needs to be for them, crave look our wave groups of how fast and we're going to sequence those over time to get out of their data center. In parallel we're going to be doing as Mike was saying running all the operational aspects. So while we're doing that discovery, we want to start standing up their Cloud center of excellence. Getting Cloud operations into the organization is a different skill set for IT to have, right? They're going to need to retrain themselves, retool themselves in the world of Cloud. So we kind of do that in parallel and then what we want to do is when we start a project, we want to start with a little POC or small little group of safe applications that we can prove how the model works. Move those into the Cloud, and then what we want to do is we want to scale at it, its large pace, right? Get the IT savings, get the cost cuts out of the organization. >> So I cleaned out my barn this weekend and the first thing I did is I got a dumpster. So I could throw some stuff out. So, is that part of the equation like getting rid of stuff? Is that part of the assessment? You know what's not delivering value that you can live without? >> Absolutely right, so there is kind of things that are just going to not go to Cloud, right? No longer need it, it's just laying around in the side, just get rid of that and move forward. >> And earlier one you'll see there's models depends you hear there's the 6 Rs, the 7 Rs and it's really the journey to Cloud it's almost you look at your status is it going to get re-platformed, is it going to get re-hosted, is it going to get retired back to your point. And if it's had something that's an appliance, right? That's something you're not going to put out to Cloud. Okay keep that in your data center. I have something that's so old, I hope it dies in the next two years. Don't spend the money move it to Cloud, let it die over the next two years. So back to the point, you kind of take this discovery and you go, where do things fall on the spectrum? And each one actually has a destination and a lever that you're going to pull, right? And if you're going to retire things okay so out of the business case, those are status quo for the next you're going to kill it over three years, right? Re-platform re-host means different things that they're going to take, right? Whether they do just to infrastructure or take advantage of PaaS or they'll go, "I'm going to blow up the entire application who directed to Cloud native services." Right? As you go through that journey you kind of map that out for them through the discovery process, and that tells you how much value you're going to get based on what you're going to do. >> But boy, this starts to get deep I mean as you used to peel the onions. So you just described what I would think of as wave 1. And then as you keep peeling you got the applications, you got the business process, you might have, reorganizations that's really where you guys have expertise, right? >> Well combined right? 'Cause yeah we're on the organizational side of things, but yeah there's a lot of things you have to sort through, right? And that's where the combined Elevate program really synergizes itself around the tools that we have. We both have tools that will help make sure we get this right, right? Deloitte has a product called Atadata, Oracle has a product called Soar, they married together properly into this transformational journey, to make sure we get the discovery done right and we get the migrations done right as well. >> Well you also have a lot of different stake holders, than you know, let's face it P&L Managers are going to try to hold on to their P&L. So you've got to bring in the senior executives. Clearly the CIOs involved is the CFO, CSWE. Who are the stake holders that you bring together in the room to kick this thing off? >> Depends on the message and depends on the outcome right? So if it's I need to get out of my data center, my data center strategy, historically the CIO. If it's there's an overall cost reduction and I want to re-implement my cost into innovating the business, sometimes that starts the CEO, CFO levels, right? >> Dave: Sure. >> So depends on that one but it is absolutely, back to your point of, the people want to hold their P&L huggers or kind of hold the cost or whatever. And one of the things, if you're not having the right conversations with people at the right level, the analogy that I've used for years is sometimes you're talking to a turkey about thanksgiving, right? So if you're trying to actually help transform and the entity is feeling that they're impacted by that negatively, even though there's a senior direction, so working through the right levels the organization to make sure you're showing how you're enabling them, it's key it's part of this journey. Helping them understand the future and how it's valuable, 'cause otherwise you'll get people that push back, even though it's the right thing for the company. We've seen that time and time again. >> Well it's potentially a huge engagement, so do you guys have specific plays or campaigns that you know I can do to get started maybe do a little test case, any particular offerings that-- >> Mike: I think-- >> Do you want to talk about the campaigns? >> So ]s under the program of Elevate, we've got a couple of campaigns. So the biggest one we've been talking about is around the data center transformation, so that's kind of the first campaign that we're working on together. The next one is around moving JD Edwards specific applications to Oracle's Cloud. And then the third one is around our analytics offering that Deloitte has and how we're going to market through to general put that in as well. Those are our three major campaigns. >> So data center transformation we hit it pretty hard. I'm sorry the third one was Cloud-- >> Analytics. >> Sorry analytics right okay which is kind of an instate that everybody wants to get to. The JDE migration, so you've got what, situations where people have just, the systems. >> And I would say it's actually more of a JDE modernization, alright? >> Okay. >> So you have an organization, right? They may have a JDE or JD Edwards instance that's really it's older, they're maybe on version nine or something like that, they don't want to go all the way to SaaS 'cause they can't simplify the business processes. They need to do that, but they also want to take advantage of the higher level capabilities of Cloud computing, right? IOT, Mobil, et cetera right? So as a modernization, one of the things we're doing is an approach together we work with customers depending where they're going and go hey great, you can actually modernize by taking up this version of JDE through an upgrade process, but that allows you then to move it over to Oracle Cloud infrastructure, which allows you to actually tap into all those platform services, the IOT and stuff like that to take to the next level. Then you can actually do the higher level analytics that sits on top of that. So it's really a journey where the customer wants to get. There's a various kind of four major phases that we can do or entry points with the customer on the JDE modernization, we kind of work them through. So that's a skill of some of the capabilities that Deloitte has as a deep JDE, and as well as Oracle Consulting, and we actually are going to market that together, matter of fact, we're even at conferences together, talking about our approaches here and our future. >> Okay. So that'll allow you to get to a Cloud PaaS layer that'll allow you to sort of modernize that and get out of the sort of technical debt that's built up. >> Where customers are not ready to maybe move their entire data center, right? This gets them on the journey, right? That's the important pieces. We want to get them on the Cloud journey. >> In the analytics campaign, so it seems to me that a lot of companies don't have their data driven, they want to be data driven, but they're not there yet. And so, their data's in silos and so I would imagine that that's all helping them understand where the data is, breaking down, busting down those silos and then actually putting in sort of an analytics approach that drives their, drives us from data to insights. Is that fair? >> Yeah fair. Yeah it's not just doing reporting and dashboards it's actually having KPI-driven insights into their information and their data within their organizations. And so Deloitte has some pre-configured applications for HR, finance, and supply chains. >> So the existing EDW for example would be fitter into that, but then you've got agile infrastructure and processes that you're putting in place, bringing in AI and machine intelligence. That's kind of the future state that you're in. >> And it also has, they look at the particular that's one of the things we like about the other stuff that Deloitte has done. They've actually done the investment of the processes back into those particular business units that they do and actually have KPI-driven ones it prebuilt configurations that actually adds value. These are the metrics that should be driving an HR organization. Here's the metrics that should be driving finance. So rather than doing better analytics, hey help me write my report better. No, we're going to help you transform the way you should be running your business from a business financial transformation, that's why the partnership with Deloitte. So it's really changing the game of true analytics, not better BI. >> Right okay, guys, two power houses. Thanks so much for explaining in The Cube and to our audience, appreciate it. (mumbling) >> Alright, thank you everybody for watching we'll be right back with our next guest you're watching The Cube, from Chicago. We'll be right back right after the short break. (upbeat music)

(upbeat music) >> Reporter: From Chicago, it's The Cube. Covering Oracle transformation date 2020. Brought to you by Oracle Consulting. >> Hi everybody, welcome back. You're watching theCUBE, we go out to the events we extract the signal from the noise. This is a very special digital event and we're really covering the transformation not only the industry, but the transformation of Oracle Consulting and its rebirth. Mike Owens is here Group VP of Cloud Advisory and GM of Oracle Elevate, which is a partnership that Oracle announced last Open World with Deloitte, and Don Schmidt is here, who is a Managing Director at Deloitte. Gents, good to see you, welcome. >> Good to be here Dave. >> So, Don I want to start with you. Transformation, right? Everybody talks about that, there's a lot of trends going on in the industry. What do you guys see as the big gestalt transformation that's going on? >> Yeah I think there's an inflection point right now. Everybody have been saying they want to get out their data centers. The leaves haven't really been taking place, right? They've been kind of moving in small bits. We're now at the point where large transformation at scale, of getting out of your data centers, is now here. So, we are here to try to help our clients move faster. How can we do this more effectively, cost efficiently, and get them out of these data centers so they can move on with their day to day business? >> So data centers is just not an efficient use of capital for your customers. >> No, no there's lots of ways to do this a lot faster, cheaper, and get on to innovation. Spend your money there, not on hardware, floor space, power cooling, those fun things. >> Well you guys are spending money on data centers though right? So this is a good business for you all. >> Mike: We do it on behalf of other customers though. Right? >> Yeah and that's what's happening right? My customers, they essentially want to take all this IT labor cost and shift it into R&D get them on your backs and your backs right? Is this that what you see it? You know where are we in terms of that? I mean it started ten plus years ago but it really has started to uptake right? What's driving that? What's the catalyst there? >> You know so from my perspective, I've been doing this a while. A lot of it is either organizations are driving costs or what you're also seeing is IT organizations are no longer the utility in the organization and taking the orders, you're using them to try to top line value, but to do that, they actually have to take their business and change the model of it, so they can take that money and reinvest it in what Don had talked about, investment or continuous investment. So you're starting to hit those inflection points, you know years ago a CIO would be in his job for 15, 20 years, the average tenure for a CIO is you know three to five years on average, and it's because if they're not driving innovation or driving top line growth with an organization, organizations are now starting to flip that around so you're seeing a huge inflection point, with organizations really looking for IT not to be just a back office entity anymore, to truly drive them they have to transform that back to Don's point, because that inflection point, this large data center move over is a good sort of lever to kind of get them and really use it as opportunity to transform their organization. >> And the transformations are occurring, you know within industries, but at different pace. I mean some industries have transformed radically. You think about Ride shares, and digital music and the like others are taking more time, financial services, health care, they're riskier businesses, and you know there's more government in public policy so what do you see in terms of the catalyst for transformation and is there any kind of discernible, industry variance? >> Yeah there definitely is and he's mentioned some of the more start-up kind of organizations where Cloud was right for them at the early stages. These other organizations that have built these large application stacks and have been there for years, it's scary for them to say, "Let me take this big set of equipment and applications, and move it to the Cloud." It's a big effort. Starting from scratch with start-ups, that's a little different story right? So we are kind of at a different point, there are different stages within different industries, some are faster adopters than some of the others with government regulations and some of the technologies that have to kind of catch up to be able to provide those services. >> Do people generally want to take their sort of mission-critical apps which are largely running often on Oracle infrastructure database, they want to move that into the Cloud but do they want to bring that sort of Cloud-operating model to their on-prem and maybe reduce their overall data center footprint but preserve some of that? What are you guys seeing? >> So, two different probably viewpoints. So my viewpoint is, depends on the organization, depends on the regulatory they have, and there's a lot of factors in there. But I would say, as a standard organization would take their journey, mission-critical systems are historically not the first one in there. 'Cause back to the point of changing the operating model the way you want to do business and be effective, you don't go with the crown jewels first, historically, take some other work loads learn how to work in that operating model, how you're doing things change and then you evolve some of those pieces over time. There are organizations that basically, pull the band-aid off and just go right into it, right? But a lot of large enterprises sort of that's why we talk about Cloud as a journey, right? You take this journey you have to make the journey based on what's going on back what Don had talked about the regulatory requirements in history are the right controls in place for what they need at that point. If not, okay so what's an interim step to the journey? Could you bring Cloud in those capabilities on-prem and then have some of the other stuff off-prem? So it's really situational dependent, and we actually walk a customer through and now Don's organization does the same thing. You walk them through what makes best for their journey for where they're at in the industry and what they have today and what they're trying to achieve. >> So Don Deloitte doesn't just do IT it does business transformation, right? So it's like a chicken and egg, let's say that what comes first? The chicken or the egg? The IT transformation or the business transformation? >> I don't think it's an or it's an and. So have the total conversation of where's your Cloud journey for your entire enterprise, and then decide how that's going to be played out in both in IT and in the business. How the joint conversation from an enterprise perspective. >> So let's talk a little bit about the partnership, to your very well known brands, you guys get together, so what was the sort of impetus to get together? How's it going? Give us the update on that front. >> Yeah you know so from Oracle standpoint, Oracle has been really technology focused. It was really created by technologists, right? And back to the point of what we're trying to do with the Cloud and trying to do larger transformation, those aren't some of the skills that we have. We've been bringing in some of those skills in DNA, but if you look at it as why would you try to recreate this situation? Why would you not partner with an organization who does large business transformation like Deloitte? Right? And so the impetus of that is, how do we take the technology with the business transformation, pull that together and back of the one plus one equals three for my customer, right? That's what they really want, so how do we actually scale that into really big things and get big outcomes for our customers? Our partnership is not about trying to take a bunch of customers and move a couple application work loads. Our job, what we're really charted to do is make huge transformational leaps for our customers, using the combined capabilities of the two organizations. So this it's a hug paradigm for us to kind of do this. >> And in our collaboration with the two organizations just the opposite from what Mike just said right? So Deloitte wasn't really big in big IT, right? Business led transformations kind of what Deloitte's been known for, along with our cyber practice, and so we needed the deep skills of the technical experts. >> Right, so take me through what transformation engagement looks like. They don't call you up and say, "Hey want to buy me some transformation." Right? Where does it start? Who are the stake holders? How long does it take? I mean it could be multi year, I presume and never ends maybe but you want to get to business value first, so let's shorten up the time frame. Take me through typical engagement. Typical I know in quotes. And then, how long like take me through the point at which you start to get business value. What do I got to do to get there? >> Yeah so we see two different spectrums on a transformation. And it really aligns to what are your objectives. Do you just need to get out of the data center because you're on archaic dying hardware? Or do you want to take that, take your time and make a little bit more of a transformation journey? Or do you want to play somewhere in the middle of that spectrum? But on either one of those we'll come in and we'll do a discovery conversation. We'll understand what's in your data center, understand what the age or the health of your data center is, help the customers through, business case, TCO, how fast or how slow that journey needs to be for them, crave look our wave groups of how fast and we're going to sequence those over time to get out of their data center. In parallel we're going to be doing as Mike was saying running all the operational aspects. So while we're doing that discovery, we want to start standing up their Cloud center of excellence. Getting Cloud operations into the organization is a different skill set for IT to have, right? They're going to need to retrain themselves, retool themselves in the world of Cloud. So we kind of do that in parallel and then what we want to do is when we start a project, we want to start with a little POC or small little group of safe applications that we can prove how the model works. Move those into the Cloud, and then what we want to do is we want to scale at it, its large pace, right? Get the IT savings, get the cost cuts out of the organization. >> So I cleaned out my barn this weekend and the first thing I did is I got a dumpster. So I could throw some stuff out. So, is that part of the equation like getting rid of stuff? Is that part of the assessment? You know what's not delivering value that you can live without? >> Absolutely right, so there is kind of things that are just going to not go to Cloud, right? No longer need it, it's just laying around in the side, just get rid of that and move forward. >> And earlier one you'll see there's models depends you hear there's the 6 Rs, the 7 Rs and it's really the journey to Cloud it's almost you look at your status is it going to get re-platformed, is it going to get re-hosted, is it going to get retired back to your point. And if it's had something that's an appliance, right? That's something you're not going to put out to Cloud. Okay keep that in your data center. I have something that's so old, I hope it dies in the next two years. Don't spend the money move it to Cloud, let it die over the next two years. So back to the point, you kind of take this discovery and you go, where do things fall on the spectrum? And each one actually has a destination and a lever that you're going to pull, right? And if you're going to retire things okay so out of the business case, those are status quo for the next you're going to kill it over three years, right? Re-platform re-host means different things that they're going to take, right? Whether they do just to infrastructure or take advantage of PaaS or they'll go, "I'm going to blow up the entire application who directed to Cloud native services." Right? As you go through that journey you kind of map that out for them through the discovery process, and that tells you how much value you're going to get based on what you're going to do. >> But boy, this starts to get deep I mean as you used to peel the onions. So you just described what I would think of as wave 1. And then as you keep peeling you got the applications, you got the business process, you might have, reorganizations that's really where you guys have expertise, right? >> Well combined right? 'Cause yeah we're on the organizational side of things, but yeah there's a lot of things you have to sort through, right? And that's where the combined Elevate program really synergizes itself around the tools that we have. We both have tools that will help make sure we get this right, right? Deloitte has a product called Atadata, Oracle has a product called Soar, they married together properly into this transformational journey, to make sure we get the discovery done right and we get the migrations done right as well. >> Well you also have a lot of different stake holders, than you know, let's face it P&L Managers are going to try to hold on to their P&L. So you've got to bring in the senior executives. Clearly the CIOs involved is the CFO, CSWE. Who are the stake holders that you bring together in the room to kick this thing off? >> Depends on the message and depends on the outcome right? So if it's I need to get out of my data center, my data center strategy, historically the CIO. If it's there's an overall cost reduction and I want to re-implement my cost into innovating the business, sometimes that starts the CEO, CFO levels, right? >> Dave: Sure. >> So depends on that one but it is absolutely, back to your point of, the people want to hold their P&L huggers or kind of hold the cost or whatever. And one of the things, if you're not having the right conversations with people at the right level, the analogy that I've used for years is sometimes you're talking to a turkey about thanksgiving, right? So if you're trying to actually help transform and the entity is feeling that they're impacted by that negatively, even though there's a senior direction, so working through the right levels the organization to make sure you're showing how you're enabling them, it's key it's part of this journey. Helping them understand the future and how it's valuable, 'cause otherwise you'll get people that push back, even though it's the right thing for the company. We've seen that time and time again. >> Well it's potentially a huge engagement, so do you guys have specific plays or campaigns that you know I can do to get started maybe do a little test case, any particular offerings that-- >> Mike: I think-- >> Do you want to talk about the campaigns? >> So ]s under the program of Elevate, we've got a couple of campaigns. So the biggest one we've been talking about is around the data center transformation, so that's kind of the first campaign that we're working on together. The next one is around moving JD Edwards specific applications to Oracle's Cloud. And then the third one is around our analytics offering that Deloitte has and how we're going to market through to general put that in as well. Those are our three major campaigns. >> So data center transformation we hit it pretty hard. I'm sorry the third one was Cloud-- >> Analytics. >> Sorry analytics right okay which is kind of an instate that everybody wants to get to. The JDE migration, so you've got what, situations where people have just, the systems. >> And I would say it's actually more of a JDE modernization, alright? >> Okay. >> So you have an organization, right? They may have a JDE or JD Edwards instance that's really it's older, they're maybe on version nine or something like that, they don't want to go all the way to SaaS 'cause they can't simplify the business processes. They need to do that, but they also want to take advantage of the higher level capabilities of Cloud computing, right? IOT, Mobil, et cetera right? So as a modernization, one of the things we're doing is an approach together we work with customers depending where they're going and go hey great, you can actually modernize by taking up this version of JDE through an upgrade process, but that allows you then to move it over to Oracle Cloud infrastructure, which allows you to actually tap into all those platform services, the IOT and stuff like that to take to the next level. Then you can actually do the higher level analytics that sits on top of that. So it's really a journey where the customer wants to get. There's a various kind of four major phases that we can do or entry points with the customer on the JDE modernization, we kind of work them through. So that's a skill of some of the capabilities that Deloitte has as a deep JDE, and as well as Oracle Consulting, and we actually are going to market that together, matter of fact, we're even at conferences together, talking about our approaches here and our future. >> Okay. So that'll allow you to get to a Cloud PaaS layer that'll allow you to sort of modernize that and get out of the sort of technical debt that's built up. >> Where customers are not ready to maybe move their entire data center, right? This gets them on the journey, right? That's the important pieces. We want to get them on the Cloud journey. >> In the analytics campaign, so it seems to me that a lot of companies don't have their data driven, they want to be data driven, but they're not there yet. And so, their data's in silos and so I would imagine that that's all helping them understand where the data is, breaking down, busting down those silos and then actually putting in sort of an analytics approach that drives their, drives us from data to insights. Is that fair? >> Yeah fair. Yeah it's not just doing reporting and dashboards it's actually having KPI-driven insights into their information and their data within their organizations. And so Deloitte has some pre-configured applications for HR, finance, and supply chains. >> So the existing EDW for example would be fitter into that, but then you've got agile infrastructure and processes that you're putting in place, bringing in AI and machine intelligence. That's kind of the future state that you're in. >> And it also has, they look at the particular that's one of the things we like about the other stuff that Deloitte has done. They've actually done the investment of the processes back into those particular business units that they do and actually have KPI-driven ones it prebuilt configurations that actually adds value. These are the metrics that should be driving an HR organization. Here's the metrics that should be driving finance. So rather than doing better analytics, hey help me write my report better. No, we're going to help you transform the way you should be running your business from a business financial transformation, that's why the partnership with Deloitte. So it's really changing the game of true analytics, not better BI. >> Right okay, guys, two power houses. Thanks so much for explaining in The Cube and to our audience, appreciate it. (mumbling) >> Alright, thank you everybody for watching we'll be right back with our next guest you're watching The Cube, from Chicago. We'll be right back right after the short break. (upbeat music)

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. (dramatic music) >> Hi, everyone, welcome to this week's Breaking Analysis. It's Friday, October 18th, and this is theCUBE Insights, powered by ETR. Today, ETR had its conference call, its webcast. It was in a quiet period, and it dropped this tome. I have spent the last several hours going through this dataset. It's just unbelievable. It's the fresh data from the October survey, and I'm going to share just some highlights with you. I wish I had a couple hours to go through all this stuff, but I'm going to just pull out some of the key points. Spending is flattening. We've talked about this in previous discussions with you. But, things are still healthy. We're just reverting back to pre 2018 levels and, obviously, keeping a very close eye on the spending data and the sectors. There is some uncertainty heading into Q four. It's not only tariffs, you know. 2020's an election year, so that causes some uncertainty and some concerns for people. But, the big theme from ETR is there's less experimentation going on. The last several years have been ones where we're pushing out digital initiatives, and there was a lot of experimentation, a lot of redundancy. So, I'm going to talk more about that. I'm going to focus on a couple of sectors. I'm going to share with you there's the overall sector analysis. Then, I'm going to focus in on Microsoft and AWS and talk a little bit about the cloud. Then, I'm going to give some other highlights and, particularly, around enterprise software. The other thing I'll say is that the folks from ETR are going to be in the Bay Area on October 28th through the 30th, and I would encourage you to spend some time with them. If you want to meet them, just, you know, contact me @dvellante on Twitter or David.Vellante@siliconangle.com. I have no dog in this fight. I get no money from these guys. We're just partners and friends, but I love their data. And, they've given me access to it, and it's great because I can share it with you, our community. So, let's get right into it. Alex, if you just bring up the first slide, what I want to show is the ETR pulse check survey demographics, so every quarter, ETR does these surveys. They've got a dataset comprising 4500 members, panelists if you will, that they survey each quarter. In this survey, 1336 responded, representing 457 billion in spending power, and you can see from this slide, you know, it's got a nice mix of large companies. Very heavily weighted toward North America, but you're talking about, you know, 12% AMIA out of 1300. Certainly substantial and statistically significant to get some trends overseas. You can see across all industries. And then, job titles, a lot of C level executives, VPs, architects, people who know what the spending climate looks like, so I really like the mix of data. Let me make some overall comments, and, Alex, the next slide sort of gives some snapshot here. The big theme is that there's a compression in tech spending, as they say. It's very tough to compare to compare to 2018, which was just a phenomenal year. I mentioned the tariffs. It was an election year. Election years bring uncertainty. Uncertainty brings conservatism, so that's something, obviously, that's weighing, I think, on buyers' minds. And, I'll give you some anecdotal comments in a moment that will underscore that. There's less redundancy in spending. This has been a theme of ETR's for quite some time now. The last few years have been a try everything type of mode. Digital initiatives were launched, let's say, starting in 2016. ETR called this, I love this, Tom DelVecchio, the CEO of ETR, called it a giant IT bake off where you were looking at, okay, cloud versus on prem or SaaS versus conventional models, new databases versus legacy databases, legacy storage versus sort of modern storage stacks. So, you had this big bake off going on. And, what's happening now is you're seeing less experimentation so less adoption of new technologies, and replacements are on the rise. So, people are making their bets. They're saying, "Okay, these technologies "are the ones we're going to bet on, "these emerging disruptive technologies." So, they're narrowing their scope of emerging technologies, and they're saying, "Okay, now, "we're going to replace the legacy stuff." So, you're seeing these new stacks emerging. I mentioned some others before, but things like cloud native versus legacy waterfall approaches. And, these new stacks are hitting both legacy and disruptive companies for the reasons that I mentioned before because we're replacing legacy, but at the same time, we're narrowing the scope of the new stuff. This is not necessarily good for the disruptors. Downturns, sometimes, are good for legacy because they're perceived as a safer bet. So, what I want to do, right now, is share with you some of the anecdotals from the survey, and I'll just, you know, call out some things. By the way, the first thing I would note is, you know, ETR did sort of an analysis of frequency of terms. Cloud, cost, replacing, change, moving, consolidation, migration, and contract were the big ones that stood out. But, let me just call a couple of the anecdotals. When they do these surveys, they'll ask open ended questions, and so these kind of give you a good idea as to how people are thinking. "We're projecting a hold based on impacts from tariffs. "Situation could change if tariff relief is reached. "We're really concerned about EU." Another one, "Shift to SaaS is accelerating "and driving TCO down. "Investing in 2019, we're implementing "and retiring old technologies in 2020. "There's an active effort to consolidate "the number of security vendor solutions. "We're doing more Microsoft." Let's see, "We have moved "to a completely outsourced infrastructure model, "so no longer purchasing storage," interesting. "In general, we're trying to reduce spending "based on current market conditions." So, people, again, are concerned. Storage, as a category, is way down. "We're moving from Teradata to AWS and a data lake." I'll make some comments, as well, later on about EDW and Snowflake in particular, who, you know, remains very healthy. "We're moving our data to G Suite and AWS. "We're migrating our SaaS offering to elastic. "We're sunsetting Cognos," which, of course, is owned by IBM. "Talend, we decided to drop after evaluating. "Tableau, we've decided to not integrate anymore," even though Tableau is, actually, looking very strong subsequent to the sales force acquisition. So, there's some comments there that people, again, are replacing and they're narrowing some of their focus on spending. All right, Alex, bring up the next slide. I want to share with you the sector momentum. So, we've talked about this methodology of net score. Every time ETR does one of these pulse surveys, they ask, "Are you spending more or are you spending less? "Or, are you spending the same?" And then, essentially, they subtract the spending less from the spending more, and the spending more included new adoptions. The spending less includes replacements. And, that comes out with a net score, and that net score is an indicator of momentum. And, what you can see here is, the momentum I've highlighted in red, is container orchestration, the container platforms, machine learning, AI, automation, big theme. We were just at the UiPath conference, huge theme on automation. And, of course, robotic process automation, RPA. Cloud computing remains very strong. This dotted red line that I put in there, that's at the, you know, 30%, 35% level. You kind of want to be above that line to really show momentum. Anything below that line is either holding serve, holding steady, but well below that line, when you start getting into the low 20s and the teens, is a red zone. That's a danger zone. You could see data warehouse software is kind of on that cusp. and I'm not, you know, a huge fan of the sector in general, but I love Snowflake and what they're doing and the share gains that are going on there. So, when you're below that red line, it's a game of share gain. Storage, same thing we've talked about. The overall storage sector is down. It's being pressured by cloud, as that anectdotal suggested. It's also being pressured by the fact that so much flash has been injected into the data center over the last couple of years. That given headroom for buyers. They don't need as much storage, so overall, the sector is soft. But then, you see companies, like Pure, continuing to gain share, so they're actually quite strong in this quarter survey. So, you could see some various sectors here. IT consulting and outsourced IT not looking strong, data center consolidation. By the way, you saw, in IBM's recent earnings, Jim Kavanaugh pointed to their outsourcing business as a real drag, you know. Some of these other sectors, you could see, actually, PC laptop, this is obviously a big impact for Dell and HP, you know, kind of holding steady. Actually, better than storage, so, you know, for that large of a segment, not necessarily such a bad thing. Okay, now, what I want to do, I want to shift focus and make some comments on Microsoft, specifically, and AWS. So, here's just some high level points on this slide on Microsoft. The N out of that total was 1200, so very large proportion of the survey is weighted toward Microsoft. So, a good observation space for Microsoft. Extremely positive spending outlook for this company. There's a lot of ways to get to Microsoft. You want cloud, there's Azure, you know. Visualization, you got Power BI. Collaboration, there's Teams. Of course, email and calendaring is Office 365. You need hiring data? Well, we just bought LinkedIn. CRM, ERP, there's Microsoft Dynamics. So, Microsoft is a lot of roads, to spend with Microsoft. Windows is not the future of Microsoft. Satya Nadella and company have done a great job of sort of getting out of that dogma and really expanding their TAM. You're seeing acceleration from Microsoft across all key sectors, cloud, apps, containers, MI, or machine intelligence, AI and ML, analytics, infrastructure software, data warehousing, servers, GitHub is strong, collaboration, as I mentioned. So, really, across the board, this portfolio of offerings powered by the scale of Azure is very strong. Microsoft has great velocity in the cloud, and it's a key bellwether. Now, the next slide, what it does is compares the cloud computing big three in the US, Azure, AWS, and GCP, Google Cloud Platform. This is, again, net score. This is infrastructure as a service, and so you can see here the yellow is Microsoft, that darker line is AWS, and GCP is that blue line down below. All three are actually showing great strength in the spending data. Azure has more momentum than AWS, so it's growing faster. We've seen this for a while, but I want to make a point here that didn't come up on the ETR call. But, AWS is probably two and a half to three times larger in infrastructure as a service than is Microsoft Azure, so remember, AWS has a $35 billion at least run rate business in infrastructure as a service. And, as I say, it's two and a half to three times, at least, larger than Microsoft, which is probably a run rate of, let's call it, 10 to 12 billion, okay. So, it's quite amazing that AWS is holding at that 66 to now dropping to 63% net score given that it's so large. And, of course, way behind is GCP, much smaller share. In fact, I think, probably, Alibaba has surpassed GCP in terms of overall market share. So, at any rate, you could see all three, strong momentum. The cloud continues its march. I'll make some comments on that a little bit later. But, Azure has really strong momentum. Let's talk, next slide if you will, Alex, about AWS. Smaller sample size, 731 out of the total, which is not surprising, right. Microsoft's been around a lot longer and plays in a lot more sectors. ETR has a positive to neutral outlook on AWS. Now, you have to be careful here because, remember, what ETR is doing is they're looking at the spending momentum and comparing that to consensus estimates, okay. So, ETR's business is helping, largely, Wall Street, you know, buy side analysts make bets, and so it's not only about how much money they make or what kind of momentum they have in aggregate. It's about how they're doing relative to expectation, something that I explained on the last Breaking Analysis. Spending on AWS continues to be very robust. They've got that flywheel effect. Make no mistake that this positive to neutral outlook is relative to expectations. Relative to overall market, AWS is, you know, kicking butt. Cloud, analytics, big data, data warehousing, containers, machine intelligence, even virtualization. AWS is growing and gaining share. My view, AWS will continue to outperform the marketplace for quite some time now, and it's gaining share from legacy players. Who's it hurting? You're seeing the companies within AWS's sort of sphere that are getting impacted by AWS. Oracle, IBM, SAP, you know, cloud Arrow, which we mentioned last time is at all time lows, Teradata. These accounts, inside of AWS respondents, are losing share. Now, who's gaining share? Snowflake is on a tear. Mongo is very strong. Microsoft, interestingly, remains strong in AWS. In fact, AWS runs a lot of Microsoft workloads. That's, you know, fairly well known. But, again, Snowflake, very strong inside of AWS accounts. There's no indication that, despite AWS's emphasis on database and, of course, data warehouse, that Snowflake's being impacted by that. The reverse, Snowflake is taking advantage of cloud momentum. The only real negative you can say about AWS is that Microsoft is accelerating faster than AWS, so that might upset Andy Jassy. But, he'll point out, I guess, what I pointed out before, that they're much larger. Take a look at AWS on this next slide. The net score across all AWS sectors, the ones I mentioned. And, this is the growth in Fortune 500, so you can see, very steady in the large accounts. That's that blue line, you know, dipped in the October 18 survey, but look at how strong it is, holding 67% in Fortune 500 accounts. And then, you can see, the yellow line is the market share. AWS continues to gain share in those large accounts when you weight that out in terms of spending. That's why I say AWS is going to continue to do very well in this overall market. So, just some, you know, comments on cloud. As I said, it continues to march, it continues to really be the watchword, the fundamental operating model. Microsoft, very strong, expanding its TAM everywhere, I mean, affecting, potentially, Slack, Box, Dropbox, New Relic, Splunk, IBM, and Security, Elastic. So, Microsoft, very strong here. AWS continues to grow, not as strong as '18, but much stronger than its peers, very well positioned in database and artificial intelligence. And so, not a lot of softness in AWS. I mentioned on one of the previous Breaking Analysis, Kubernetes', actually, container's a little soft, so we always keep an eye on that one. And, Google, again, struggling to make gains in cloud. One of the comments I made before is that the long term surveys for Google looked positive, but that's not showing up yet in the near term market shares. All right, Alex, if you want to bring up the next slide, I want to make some quick comments before I close, on enterprise software. There was a big workday scare this week. They kind of guided that their core HR business was not going to be as robust as it had been previously, so this pulled back all the SaaS vendors. And, you know, the stock got crushed, Salesforce got hit, ServiceNow got hit, Splunk got hit. But, I tell you, you look at the data in this massive dataset, ServiceNow remains strong, Salesforce looks, very slight deceleration, but very sound, especially in the Fortune 100 in that GPP, the giant public and private companies that I talked about on an earlier call. That's one of the best indicators of strength. Tableau, actually, very strong, especially in large accounts, so Salesforce seems to be doing a good job of integrating there. Splunk, (mumbles) coming up shortly, I think this month. Securities, the category is very strong, lifting all ships. Splunk looks really good. Despite some of the possible competition from Microsoft, there's no indication that Splunk is slowing. There's some anecdotal issues about pricing that I talked about before, but I think Splunk is really dealing with those. UiPath's another company. We were just out there this past week at the UiPath Forward conference. UiPath, in this dataset, when you take out some of the smaller respondents, smaller number of respondents, UiPath has one of the highest net scores in the entire sample. UiPath is on a tear. I talked to dozens of customers this week. Very strong momentum, and then moving into, got new areas, and I'll be focusing on the RPA sector a little later on. But, automation, in general, really has some tailwinds in the marketplace. And, you know, the other comment I'll make about RPA is a downturn actually could help RPA vendors, who, by the way, all the RPA vendors look strong. Automation Anywhere, UiPath, I mentioned, Blue Prism, you know, even some of the legacy companies like Pega look, actually, very strong. A downturn in the economy could help some of the RPA vendors because would be looking to do more with less, and automation, you know, could be something that they're looking toward. Snowflake I mentioned, again, they continue their tear. A very strong share in expansion. Slightly lower than previous quarters in terms of the spending momentum, but the previous quarters were off the charts. So, also very strong in large companies. All right, so let me wrap. So, buyers are planning for a slowdown. I mean, there's no doubt about that. It's something that we have to pay very close attention to, and I think the marker expects that. And, I think, you know, it's okay. There's less spaghetti against the wall, we're going to try everything, and that's having a moderating effect on spending, as is the less redundancy. People were running systems in parallel. As they say, they're placing bets, now, on both disruptive tech and on legacy tech, so they're replacing both in some cases. Or, they're not investing in some of the disruptive stuff because they're narrowing their investments in disruptive technologies, and they're also replacing some legacy. We're clearly seeing new adoptions down, according to ETR, and replacements up, and that's going to affect both legacy and disruptive vendors. So, caution is the watchword, but, overall, the market remains healthy. Okay, so thanks for watching. This is Dave Vellante for CUBE Insights, powered by ETR. Thanks for watching this Breaking Analysis. We'll see you next time. (dramatic music)