>>Oracle continues to enhance my sequel Heatwave at a very rapid pace. The company is now in its fourth major release since the original announcement in December 2020. 1 of the main criticisms of my sequel, Heatwave, is that it only runs on O. C I. Oracle Cloud Infrastructure and as a lock in to Oracle's Cloud. Oracle recently announced that heat wave is now going to be available in AWS Cloud and it announced its intent to bring my sequel Heatwave to Azure. So my secret heatwave on AWS is a significant TAM expansion move for Oracle because of the momentum AWS Cloud continues to show. And evidently the Heatwave Engineering team has taken the development effort from O. C I. And is bringing that to A W S with a number of enhancements that we're gonna dig into today is senior vice president. My sequel Heatwave at Oracle is back with me on a cube conversation to discuss the latest heatwave news, and we're eager to hear any benchmarks relative to a W S or any others. Nippon has been leading the Heatwave engineering team for over 10 years and there's over 100 and 85 patents and database technology. Welcome back to the show and good to see you. >>Thank you. Very happy to be back. >>Now for those who might not have kept up with the news, uh, to kick things off, give us an overview of my sequel, Heatwave and its evolution. So far, >>so my sequel, Heat Wave, is a fully managed my secret database service offering from Oracle. Traditionally, my secret has been designed and optimised for transaction processing. So customers of my sequel then they had to run analytics or when they had to run machine learning, they would extract the data out of my sequel into some other database for doing. Unlike processing or machine learning processing my sequel, Heat provides all these capabilities built in to a single database service, which is my sequel. He'd fake So customers of my sequel don't need to move the data out with the same database. They can run transaction processing and predicts mixed workloads, machine learning, all with a very, very good performance in very good price performance. Furthermore, one of the design points of heat wave is is a scale out architecture, so the system continues to scale and performed very well, even when customers have very large late assignments. >>So we've seen some interesting moves by Oracle lately. The collaboration with Azure we've we've covered that pretty extensively. What was the impetus here for bringing my sequel Heatwave onto the AWS cloud? What were the drivers that you considered? >>So one of the observations is that a very large percentage of users of my sequel Heatwave, our AWS users who are migrating of Aurora or so already we see that a good percentage of my secret history of customers are migrating from GWS. However, there are some AWS customers who are still not able to migrate the O. C. I to my secret heat wave. And the reason is because of, um, exorbitant cost, which was charges. So in order to migrate the workload from AWS to go see, I digress. Charges are very high fees which becomes prohibitive for the customer or the second example we have seen is that the latency of practising a database which is outside of AWS is very high. So there's a class of customers who would like to get the benefits of my secret heatwave but were unable to do so and with this support of my secret trip inside of AWS, these customers can now get all the grease of the benefits of my secret he trip without having to pay the high fees or without having to suffer with the poorly agency, which is because of the ws architecture. >>Okay, so you're basically meeting the customer's where they are. So was this a straightforward lifted shift from from Oracle Cloud Infrastructure to AWS? >>No, it is not because one of the design girls we have with my sequel, Heatwave is that we want to provide our customers with the best price performance regardless of the cloud. So when we decided to offer my sequel, he headed west. Um, we have optimised my sequel Heatwave on it as well. So one of the things to point out is that this is a service with the data plane control plane and the console are natively running on AWS. And the benefits of doing so is that now we can optimise my sequel Heatwave for the E. W s architecture. In addition to that, we have also announced a bunch of new capabilities as a part of the service which will also be available to the my secret history of customers and our CI, But we just announced them and we're offering them as a part of my secret history of offering on AWS. >>So I just want to make sure I understand that it's not like you just wrapped your stack in a container and stuck it into a W s to be hosted. You're saying you're actually taking advantage of the capabilities of the AWS cloud natively? And I think you've made some other enhancements as well that you're alluding to. Can you maybe, uh, elucidate on those? Sure. >>So for status, um, we have taken the mind sequel Heatwave code and we have optimised for the It was infrastructure with its computer network. And as a result, customers get very good performance and price performance. Uh, with my secret he trade in AWS. That's one performance. Second thing is, we have designed new interactive counsel for the service, which means that customers can now provision there instances with the council. But in addition, they can also manage their schemas. They can. Then court is directly from the council. Autopilot is integrated. The council we have introduced performance monitoring, so a lot of capabilities which we have introduced as a part of the new counsel. The third thing is that we have added a bunch of new security features, uh, expose some of the security features which were part of the My Secret Enterprise edition as a part of the service, which gives customers now a choice of using these features to build more secure applications. And finally, we have extended my secret autopilot for a number of old gpus cases. In the past, my secret autopilot had a lot of capabilities for Benedict, and now we have augmented my secret autopilot to offer capabilities for elderly people. Includes as well. >>But there was something in your press release called Auto thread. Pooling says it provides higher and sustained throughput. High concerns concerns concurrency by determining Apple number of transactions, which should be executed. Uh, what is that all about? The auto thread pool? It seems pretty interesting. How does it affect performance? Can you help us understand that? >>Yes, and this is one of the capabilities of alluding to which we have added in my secret autopilot for transaction processing. So here is the basic idea. If you have a system where there's a large number of old EP transactions coming into it at a high degrees of concurrency in many of the existing systems of my sequel based systems, it can lead to a state where there are few transactions executing, but a bunch of them can get blocked with or a pilot tried pulling. What we basically do is we do workload aware admission control and what this does is it figures out, what's the right scheduling or all of these algorithms, so that either the transactions are executing or as soon as something frees up, they can start executing, so there's no transaction which is blocked. The advantage to the customer of this capability is twofold. A get significantly better throughput compared to service like Aurora at high levels of concurrency. So at high concurrency, for instance, uh, my secret because of this capability Uh oh, thread pulling offers up to 10 times higher compared to Aurora, that's one first benefit better throughput. The second advantage is that the true part of the system never drops, even at high levels of concurrency, whereas in the case of Aurora, the trooper goes up, but then, at high concurrency is, let's say, starting, uh, level of 500 or something. It depends upon the underlying shit they're using the troopers just dropping where it's with my secret heatwave. The truth will never drops. Now, the ramification for the customer is that if the truth is not gonna drop, the user can start off with a small shape, get the performance and be a show that even the workload increases. They will never get a performance, which is worse than what they're getting with lower levels of concurrency. So this let's leads to customers provisioning a shape which is just right for them. And if they need, they can, uh, go with the largest shape. But they don't like, you know, over pay. So those are the two benefits. Better performance and sustain, uh, regardless of the level of concurrency. >>So how do we quantify that? I know you've got some benchmarks. How can you share comparisons with other cloud databases especially interested in in Amazon's own databases are obviously very popular, and and are you publishing those again and get hub, as you have done in the past? Take us through the benchmarks. >>Sure, So benchmarks are important because that gives customers a sense of what performance to expect and what price performance to expect. So we have run a number of benchmarks. And yes, all these benchmarks are available on guitar for customers to take a look at. So we have performance results on all the three castle workloads, ol DB Analytics and Machine Learning. So let's start with the Rdp for Rdp and primarily because of the auto thread pulling feature. We show that for the IPCC for attended dataset at high levels of concurrency, heatwave offers up to 10 times better throughput and this performance is sustained, whereas in the case of Aurora, the performance really drops. So that's the first thing that, uh, tend to alibi. Sorry, 10 gigabytes. B B C c. I can come and see the performance are the throughput is 10 times better than Aurora for analytics. We have done a comparison of my secret heatwave in AWS and compared with Red Ship Snowflake Googled inquiry, we find that the price performance of my secret heatwave compared to read ship is seven times better. So my sequel, Heat Wave in AWS, provides seven times better price performance than red ship. That's a very, uh, interesting results to us. Which means that customers of Red Shift are really going to take the service seriously because they're gonna get seven times better price performance. And this is all running in a W s so compared. >>Okay, carry on. >>And then I was gonna say, compared to like, Snowflake, uh, in AWS offers 10 times better price performance. And compared to Google, ubiquity offers 12 times better price performance. And this is based on a four terabyte p PCH workload. Results are available on guitar, and then the third category is machine learning and for machine learning, uh, for training, the performance of my secret heatwave is 25 times faster compared to that shit. So all the three workloads we have benchmark's results, and all of these scripts are available on YouTube. >>Okay, so you're comparing, uh, my sequel Heatwave on AWS to Red Shift and snowflake on AWS. And you're comparing my sequel Heatwave on a W s too big query. Obviously running on on Google. Um, you know, one of the things Oracle is done in the past when you get the price performance and I've always tried to call fouls you're, like, double your price for running the oracle database. Uh, not Heatwave, but Oracle Database on a W s. And then you'll show how it's it's so much cheaper on on Oracle will be like Okay, come on. But they're not doing that here. You're basically taking my sequel Heatwave on a W s. I presume you're using the same pricing for whatever you see to whatever else you're using. Storage, um, reserved instances. That's apples to apples on A W s. And you have to obviously do some kind of mapping for for Google, for big query. Can you just verify that for me, >>we are being more than fair on two dimensions. The first thing is, when I'm talking about the price performance for analytics, right for, uh, with my secret heat rape, the cost I'm talking about from my secret heat rape is the cost of running transaction processing, analytics and machine learning. So it's a fully loaded cost for the case of my secret heatwave. There has been I'm talking about red ship when I'm talking about Snowflake. I'm just talking about the cost of these databases for running, and it's only it's not, including the source database, which may be more or some other database, right? So that's the first aspect that far, uh, trip. It's the cost for running all three kinds of workloads, whereas for the competition, it's only for running analytics. The second thing is that for these are those services whether it's like shit or snowflakes, That's right. We're talking about one year, fully paid up front cost, right? So that's what most of the customers would pay for. Many of the customers would pay that they will sign a one year contract and pay all the costs ahead of time because they get a discount. So we're using that price and the case of Snowflake. The costs were using is their standard edition of price, not the Enterprise edition price. So yes, uh, more than in this competitive. >>Yeah, I think that's an important point. I saw an analysis by Marx Tamer on Wiki Bond, where he was doing the TCO comparisons. And I mean, if you have to use two separate databases in two separate licences and you have to do et yelling and all the labour associated with that, that that's that's a big deal and you're not even including that aspect in in your comparison. So that's pretty impressive. To what do you attribute that? You know, given that unlike, oh, ci within the AWS cloud, you don't have as much control over the underlying hardware. >>So look hard, but is one aspect. Okay, so there are three things which give us this advantage. The first thing is, uh, we have designed hateful foreign scale out architecture. So we came up with new algorithms we have come up with, like, uh, one of the design points for heat wave is a massively partitioned architecture, which leads to a very high degree of parallelism. So that's a lot of hype. Each were built, So that's the first part. The second thing is that although we don't have control over the hardware, but the second design point for heat wave is that it is optimised for commodity cloud and the commodity infrastructure so we can have another guys, what to say? The computer we get, how much network bandwidth do we get? How much of, like objects to a brand that we get in here? W s. And we have tuned heat for that. That's the second point And the third thing is my secret autopilot, which provides machine learning based automation. So what it does is that has the users workload is running. It learns from it, it improves, uh, various premieres in the system. So the system keeps getting better as you learn more and more questions. And this is the third thing, uh, as a result of which we get a significant edge over the competition. >>Interesting. I mean, look, any I SV can go on any cloud and take advantage of it. And that's, uh I love it. We live in a new world. How about machine learning workloads? What? What did you see there in terms of performance and benchmarks? >>Right. So machine learning. We offer three capabilities training, which is fully automated, running in France and explanations. So one of the things which many of our customers told us coming from the enterprise is that explanations are very important to them because, uh, customers want to know that. Why did the the system, uh, choose a certain prediction? So we offer explanations for all models which have been derailed by. That's the first thing. Now, one of the interesting things about training is that training is usually the most expensive phase of machine learning. So we have spent a lot of time improving the performance of training. So we have a bunch of techniques which we have developed inside of Oracle to improve the training process. For instance, we have, uh, metal and proxy models, which really give us an advantage. We use adaptive sampling. We have, uh, invented in techniques for paralysing the hyper parameter search. So as a result of a lot of this work, our training is about 25 times faster than that ship them health and all the data is, uh, inside the database. All this processing is being done inside the database, so it's much faster. It is inside the database. And I want to point out that there is no additional charge for the history of customers because we're using the same cluster. You're not working in your service. So all of these machine learning capabilities are being offered at no additional charge inside the database and as a performance, which is significantly faster than that, >>are you taking advantage of or is there any, uh, need not need, but any advantage that you can get if two by exploiting things like gravity. John, we've talked about that a little bit in the past. Or trainee. Um, you just mentioned training so custom silicon that AWS is doing, you're taking advantage of that. Do you need to? Can you give us some insight >>there? So there are two things, right? We're always evaluating What are the choices we have from hybrid perspective? Obviously, for us to leverage is right and like all the things you mention about like we have considered them. But there are two things to consider. One is he is a memory system. So he favours a big is the dominant cost. The processor is a person of the cost, but memory is the dominant cost. So what we have evaluated and found is that the current shape which we are using is going to provide our customers with the best price performance. That's the first thing. The second thing is that there are opportunities at times when we can use a specialised processor for vaccinating the world for a bit. But then it becomes a matter of the cost of the customer. Advantage of our current architecture is on the same hardware. Customers are getting very good performance. Very good, energetic performance in a very good machine learning performance. If you will go with the specialised processor, it may. Actually, it's a machine learning, but then it's an additional cost with the customers we need to pay. So we are very sensitive to the customer's request, which is usually to provide very good performance at a very low cost. And we feel is that the current design we have as providing customers very good performance and very good price performance. >>So part of that is architectural. The memory intensive nature of of heat wave. The other is A W s pricing. If AWS pricing were to flip, it might make more sense for you to take advantage of something like like cranium. Okay, great. Thank you. And welcome back to the benchmarks benchmarks. Sometimes they're artificial right there. A car can go from 0 to 60 in two seconds. But I might not be able to experience that level of performance. Do you? Do you have any real world numbers from customers that have used my sequel Heatwave on A W s. And how they look at performance? >>Yes, absolutely so the my Secret service on the AWS. This has been in Vera for, like, since November, right? So we have a lot of customers who have tried the service. And what actually we have found is that many of these customers, um, planning to migrate from Aurora to my secret heat rape. And what they find is that the performance difference is actually much more pronounced than what I was talking about. Because with Aurora, the performance is actually much poorer compared to uh, like what I've talked about. So in some of these cases, the customers found improvement from 60 times, 240 times, right? So he travels 100 for 240 times faster. It was much less expensive. And the third thing, which is you know, a noteworthy is that customers don't need to change their applications. So if you ask the top three reasons why customers are migrating, it's because of this. No change to the application much faster, and it is cheaper. So in some cases, like Johnny Bites, what they found is that the performance of their applications for the complex storeys was about 60 to 90 times faster. Then we had 60 technologies. What they found is that the performance of heat we have compared to Aurora was 100 and 39 times faster. So, yes, we do have many such examples from real workloads from customers who have tried it. And all across what we find is if it offers better performance, lower cost and a single database such that it is compatible with all existing by sequel based applications and workloads. >>Really impressive. The analysts I talked to, they're all gaga over heatwave, and I can see why. Okay, last question. Maybe maybe two and one. Uh, what's next? In terms of new capabilities that customers are going to be able to leverage and any other clouds that you're thinking about? We talked about that upfront, but >>so in terms of the capabilities you have seen, like they have been, you know, non stop attending to the feedback from the customers in reacting to it. And also, we have been in a wedding like organically. So that's something which is gonna continue. So, yes, you can fully expect that people not dressed and continue to in a way and with respect to the other clouds. Yes, we are planning to support my sequel. He tripped on a show, and this is something that will be announced in the near future. Great. >>All right, Thank you. Really appreciate the the overview. Congratulations on the work. Really exciting news that you're moving my sequel Heatwave into other clouds. It's something that we've been expecting for some time. So it's great to see you guys, uh, making that move, and as always, great to have you on the Cube. >>Thank you for the opportunity. >>All right. And thank you for watching this special cube conversation. I'm Dave Volonte, and we'll see you next time.

>>Okay. Okay. We're back at Caesar's Forum. The Snowflake summit 2022. The cubes. Continuous coverage this day to wall to wall coverage. We're so excited to have the analyst panel here, some of my colleagues that we've done a number. You've probably seen some power panels that we've done. David McGregor is here. He's the senior vice president and research director at Ventana Research. To his left is Tony Blair, principal at DB Inside and my in the co host seat. Sanjeev Mohan Sanremo. Guys, thanks so much for coming on. I'm glad we can. Thank you. You're very welcome. I wasn't able to attend the analyst action because I've been doing this all all day, every day. But let me start with you, Dave. What have you seen? That's kind of interested you. Pluses, minuses. Concerns. >>Well, how about if I focus on what I think valuable to the customers of snowflakes and our research shows that the majority of organisations, the majority of people, do not have access to analytics. And so a couple of things they've announced I think address those are helped to address those issues very directly. So Snow Park and support for Python and other languages is a way for organisations to embed analytics into different business processes. And so I think that will be really beneficial to try and get analytics into more people's hands. And I also think that the native applications as part of the marketplace is another way to get applications into people's hands rather than just analytical tools. Because most most people in the organisation or not, analysts, they're doing some line of business function. Their HR managers, their marketing people, their salespeople, their finance people right there, not sitting there mucking around in the data. They're doing a job and they need analytics in that job. So, >>Tony, I thank you. I've heard a lot of data mesh talk this week. It's kind of funny. Can't >>seem to get away from it. You >>can't see. It seems to be gathering momentum, but But what have you seen? That's been interesting. >>What I have noticed. Unfortunately, you know, because the rooms are too small, you just can't get into the data mesh sessions, so there's a lot of interest in it. Um, it's still very I don't think there's very much understanding of it, but I think the idea that you can put all the data in one place which, you know, to me, stuff like it seems to be kind of sort of in a way, it sounds like almost like the Enterprise Data warehouse, you know, Clouded Cloud Native Edition, you know, bring it all in one place again. Um, I think it's providing, sort of, You know, it's I think, for these folks that think this might be kind of like a a linchpin for that. I think there are several other things that actually that really have made a bigger impression on me. Actually, at this event, one is is basically is, um we watch their move with Eunice store. Um, and it's kind of interesting coming, you know, coming from mongo db last week. And I see it's like these two companies seem to be going converging towards the same place at different speeds. I think it's not like it's going to get there faster than Mongo for a number of different reasons, but I see like a number of common threads here. I mean, one is that Mongo was was was a company. It's always been towards developers. They need you know, start cultivating data, people, >>these guys going the other way. >>Exactly. Bingo. And the thing is that but they I think where they're converging is the idea of operational analytics and trying to serve all constituencies. The other thing, which which also in terms of serving, you know, multiple constituencies is how snowflake is laid out Snow Park and what I'm finding like. There's an interesting I economy. On one hand, you have this very ingrained integration of Anaconda, which I think is pretty ingenious. On the other hand, you speak, let's say, like, let's say the data robot folks and say, You know something our folks wanna work data signs us. We want to work in our environment and use snowflake in the background. So I see those kind of some interesting sort of cross cutting trends. >>So, Sandy, I mean, Frank Sullivan, we'll talk about there's definitely benefits into going into the walled garden. Yeah, I don't think we dispute that, but we see them making moves and adding more and more open source capabilities like Apache iceberg. Is that a Is that a move to sort of counteract the narrative that the data breaks is put out there. Is that customer driven? What's your take on that? >>Uh, primarily I think it is to contract this whole notion that once you move data into snowflake, it's a proprietary format. So I think that's how it started. But it's hugely beneficial to the customers to the users, because now, if you have large amounts of data in parquet files, you can leave it on s three. But then you using the the Apache iceberg table format. In a snowflake, you get all the benefits of snowflakes. Optimizer. So, for example, you get the, you know, the micro partitioning. You get the meta data. So, uh, in a single query, you can join. You can do select from a snowflake table union and select from iceberg table, and you can do store procedures, user defined functions. So I think they what they've done is extremely interesting. Uh, iceberg by itself still does not have multi table transactional capabilities. So if I'm running a workload, I might be touching 10 different tables. So if I use Apache iceberg in a raw format, they don't have it. But snowflake does, >>right? There's hence the delta. And maybe that maybe that closes over time. I want to ask you as you look around this I mean the ecosystems pretty vibrant. I mean, it reminds me of, like reinvent in 2013, you know? But then I'm struck by the complexity of the last big data era and a dupe and all the different tools. And is this different, or is it the sort of same wine new new bottle? You guys have any thoughts on that? >>I think it's different and I'll tell you why. I think it's different because it's based around sequel. So if back to Tony's point, these vendors are coming at this from different angles, right? You've got data warehouse vendors and you've got data lake vendors and they're all going to meet in the middle. So in your case, you're taught operational analytical. But the same thing is true with Data Lake and Data Warehouse and Snowflake no longer wants to be known as the Data Warehouse. There a data cloud and our research again. I like to base everything off of that. >>I love what our >>research shows that organisation Two thirds of organisations have sequel skills and one third have big data skills, so >>you >>know they're going to meet in the middle. But it sure is a lot easier to bring along those people who know sequel already to that midpoint than it is to bring big data people to remember. >>Mrr Odula, one of the founders of Cloudera, said to me one time, John Kerry and the Cube, that, uh, sequel is the killer app for a Yeah, >>the difference at this, you know, with with snowflake, is that you don't have to worry about taming the zoo. Animals really have thought out the ease of use, you know? I mean, they thought about I mean, from the get go, they thought of too thin to polls. One is ease of use, and the other is scale. And they've had. And that's basically, you know, I think very much differentiates it. I mean, who do have the scale, but it didn't have the ease of use. But don't I >>still need? Like, if I have, you know, governance from this vendor or, you know, data prep from, you know, don't I still have to have expertise? That's sort of distributed in those those worlds, right? I mean, go ahead. Yeah. >>So the way I see it is snowflake is adding more and more capabilities right into the database. So, for example, they've they've gone ahead and added security and privacy so you can now create policies and do even set level masking, dynamic masking. But most organisations have more than snowflake. So what we are starting to see all around here is that there's a whole series of data catalogue companies, a bunch of companies that are doing dynamic data masking security and governance data observe ability, which is not a space snowflake has gone into. So there's a whole ecosystem of companies that that is mushrooming, although, you know so they're using the native capabilities of snowflake, but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other, like relational databases, you can run these cross platform capabilities in that layer. So so that way, you know, snowflakes done a great job of enabling that ecosystem about >>the stream lit acquisition. Did you see anything here that indicated there making strong progress there? Are you excited about that? You're sceptical. Go ahead. >>And I think it's like the last mile. Essentially. In other words, it's like, Okay, you have folks that are basically that are very, very comfortable with tableau. But you do have developers who don't want to have to shell out to a separate tool. And so this is where Snowflake is essentially working to address that constituency, um, to San James Point. I think part of it, this kind of plays into it is what makes this different from the ado Pere is the fact that this all these capabilities, you know, a lot of vendors are taking it very seriously to make put this native obviously snowflake acquired stream. Let's so we can expect that's extremely capabilities are going to be native. >>And the other thing, too, about the Hadoop ecosystem is Claudia had to help fund all those different projects and got really, really spread thin. I want to ask you guys about this super cloud we use. Super Cloud is this sort of metaphor for the next wave of cloud. You've got infrastructure aws, azure, Google. It's not multi cloud, but you've got that infrastructure you're building a layer on top of it that hides the underlying complexities of the primitives and the a p I s. And you're adding new value in this case, the data cloud or super data cloud. And now we're seeing now is that snowflake putting forth the notion that they're adding a super path layer. You can now build applications that you can monetise, which to me is kind of exciting. It makes makes this platform even less discretionary. We had a lot of talk on Wall Street about discretionary spending, and that's not discretionary. If you're monetising it, um, what do you guys think about that? Is this something that's that's real? Is it just a figment of my imagination, or do you see a different way of coming any thoughts on that? >>So, in effect, they're trying to become a data operating system, right? And I think that's wonderful. It's ambitious. I think they'll experience some success with that. As I said, applications are important. That's a great way to deliver information. You can monetise them, so you know there's there's a good economic model around it. I think they will still struggle, however, with bringing everything together onto one platform. That's always the challenge. Can you become the platform that's hard, hard to predict? You know, I think this is This is pretty exciting, right? A lot of energy, a lot of large ecosystem. There is a network effect already. Can they succeed in being the only place where data exists? You know, I think that's going to be a challenge. >>I mean, the fact is, I mean, this is a classic best of breed versus the umbrella play. The thing is, this is nothing new. I mean, this is like the you know, the old days with enterprise applications were basically oracle and ASAP vacuumed up all these. You know, all these applications in their in their ecosystem, whereas with snowflake is. And if you look at the cloud, folks, the hyper scale is still building out their own portfolios as well. Some are, You know, some hyper skills are more partner friendly than others. What? What Snowflake is saying is that we're going to give all of you folks who basically are competing against the hyper skills in various areas like data catalogue and pipelines and all that sort of wonderful stuff will make you basically, you know, all equal citizens. You know the burden is on you to basically we will leave. We will lay out the A P. I s Well, we'll allow you to basically, you know, integrate natively to us so you can provide as good experience. But the but the onus is on your back. >>Should the ecosystem be concerned, as they were back to reinvent 2014 that Amazon was going to nibble away at them or or is it different? >>I find what they're doing is different. Uh, for example, data sharing. They were the first ones out the door were data sharing at a large scale. And then everybody has jumped in and said, Oh, we also do data sharing. All the hyper scholars came in. But now what snowflake has done is they've taken it to the next level. Now they're saying it's not just data sharing. It's up sharing and not only up sharing. You can stream the thing you can build, test deploy, and then monetise it. Make it discoverable through, you know, through your marketplace >>you can monetise it. >>Yes. Yeah, so So I I think what they're doing is they are taking it a step further than what hyper scale as they are doing. And because it's like what they said is becoming like the data operating system You log in and you have all of these different functionalities you can do in machine learning. Now you can do data quality. You can do data preparation and you can do Monetisation. Who do you >>think is snowflakes? Biggest competitor? What do you guys think? It's a hard question, isn't it? Because you're like because we all get the we separate computer from storage. We have a cloud data and you go, Okay, that's nice, >>but there's, like, a crack. I think >>there's uniqueness. I >>mean, put it this way. In the old days, it would have been you know, how you know the prime household names. I think today is the hyper scholars and the idea what I mean again, this comes down to the best of breed versus by, you know, get it all from one source. So where is your comfort level? Um, so I think they're kind. They're their co op a Titian the hyper scale. >>Okay, so it's not data bricks, because why they're smaller. >>Well, there is some okay now within the best of breed area. Yes, there is competition. The obvious is data bricks coming in from the data engineering angle. You know, basically the snowflake coming from, you know, from the from the data analyst angle. I think what? Another potential competitor. And I think Snowflake, basically, you know, admitted as such potentially is mongo >>DB. Yeah, >>Exactly. So I mean, yes, there are two different levels of sort >>of a on a longer term collision course. >>Exactly. Exactly. >>Sort of service now and in salesforce >>thing that was that we actually get when I say that a lot of people just laughed. I was like, No, you're kidding. There's no way. I said Excuse me, >>But then you see Mongo last week. We're adding some analytics capabilities and always been developers, as you say, and >>they trashed sequel. But yet they finally have started to write their first real sequel. >>We have M c M Q. Well, now we have a sequel. So what >>were those numbers, >>Dave? Two thirds. One third. >>So the hyper scale is but the hyper scale urz are you going to trust your hyper scale is to do your cross cloud. I mean, maybe Google may be I mean, Microsoft, perhaps aws not there yet. Right? I mean, how important is cross cloud, multi cloud Super cloud Whatever you want to call it What is your data? >>Shows? Cloud is important if I remember correctly. Our research shows that three quarters of organisations are operating in the cloud and 52% are operating across more than one cloud. So, uh, two thirds of the organisations are in the cloud are doing multi cloud, so that's pretty significant. And now they may be operating across clouds for different reasons. Maybe one application runs in one cloud provider. Another application runs another cloud provider. But I do think organisations want that leverage over the hyper scholars right they want they want to be able to tell the hyper scale. I'm gonna move my workloads over here if you don't give us a better rate. Uh, >>I mean, I I think you know, from a database standpoint, I think you're right. I mean, they are competing against some really well funded and you look at big Query barely, you know, solid platform Red shift, for all its faults, has really done an amazing job of moving forward. But to David's point, you know those to me in any way. Those hyper skills aren't going to solve that cross cloud cloud problem, right? >>Right. No, I'm certainly >>not as quickly. No. >>Or with as much zeal, >>right? Yeah, right across cloud. But we're gonna operate better on our >>Exactly. Yes. >>Yes. Even when we talk about multi cloud, the many, many definitions, like, you know, you can mean anything. So the way snowflake does multi cloud and the way mongo db two are very different. So a snowflake says we run on all the hyper scalar, but you have to replicate your data. What Mongo DB is claiming is that one cluster can have notes in multiple different clouds. That is right, you know, quite something. >>Yeah, right. I mean, again, you hit that. We got to go. But, uh, last question, um, snowflake undervalued, overvalued or just about right >>in the stock market or in customers. Yeah. Yeah, well, but, you know, I'm not sure that's the right question. >>That's the question I'm asking. You know, >>I'll say the question is undervalued or overvalued for customers, right? That's really what matters. Um, there's a different audience. Who cares about the investor side? Some of those are watching, but But I believe I believe that the from the customer's perspective, it's probably valued about right, because >>the reason I I ask it, is because it has so hyped. You had $100 billion value. It's the past service now is value, which is crazy for this student Now. It's obviously come back quite a bit below its IPO price. So But you guys are at the financial analyst meeting. Scarpelli laid out 2029 projections signed up for $10 billion.25 percent free time for 20% operating profit. I mean, they better be worth more than they are today. If they do >>that. If I If I see the momentum here this week, I think they are undervalued. But before this week, I probably would have thought there at the right evaluation, >>I would say they're probably more at the right valuation employed because the IPO valuation is just such a false valuation. So hyped >>guys, I could go on for another 45 minutes. Thanks so much. David. Tony Sanjeev. Always great to have you on. We'll have you back for sure. Having us. All right. Thank you. Keep it right there. Were wrapping up Day two and the Cube. Snowflake. Summit 2022. Right back. Mm. Mhm.

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. >>Welcome back to the cubes, ongoing coverage, AWS, AWS reinvent virtual. The cube has gone virtual too, and continues to bring our digital coverage of events across the globe. It's been a big week, big couple of weeks at reinvent and a big week for machine intelligence in learning and AI and new services for customers. And with me to discuss the trends in this space is broadened Sahab, who is the vice president and general manager of machine learning services at AWS Rodan. Great to see you. Thanks for coming on the cube. >>Thank you, Dave. Thank you for having me. >>You're very welcome. Let's get right into it. I mean, I remember when SageMaker was announced it was 2017. Uh, it was really a seminal moment in the whole machine learning space, but take us through the journey over the last few years. Uh, what can you tell us? >>So, you know, what, when we came out with SageMaker customers were telling us that machine learning is hard and it was within, you know, it's only a few large organizations that could truly deploy machine learning at scale. And so we released SageMaker in 2017 and we have seen really broad adoption of SageMaker across the entire spectrum of industries. And today, most of the machine learning in the cloud, the vast majority of it happens on AWS. In fact, AWS has more than two weeks of the machine learning than any other provider. And, you know, we saw this morning that more than 90% of the TensorFlow in the cloud and more than 92% of the pipe out in the cloud happens on AWS. So what has happened in that is customers saw that it was much easier to do machine learning once they were using tools like SageMaker. >>And so many customers started applying a handful of models and they started to see that they were getting real business value. You know, machine learning was no longer a niche machine learning was no longer a fictional thing. It was something that they were getting real business value. And then they started to proliferate across that use cases. And so these customers went from deploying like tens of models to deploying hundreds and thousands of models inside. We have one customer that is deploying more than a million models. And so that is what we have seen is really making machine learning broadly accessible to our customers through the use of SageMaker. >>Yeah. So you probably very quickly went through the experimentation phase and people said, wow, you got the aha moments. And, and, and so adoption went through the roof. What kind of patterns have you seen in terms of the way in which people are using data and maybe some of the problems and challenges that has created for organizations that they've asked you to erect help them rectify? Yes. >>And in fact, in a SageMaker is today one of the fastest growing services in AWS history. And what we have seen happen is as customer scaled out the machine learning deployments, they asked us to help them solve the issues that used to come when you deploy machine learning at scale. So one of the things that happens is when you're doing machine learning, you spend a lot of time preparing the data, cleaning the data, making sure the data is done correctly, so it can train your models. And customers wanted to be able to do the data prep in the same service in which they were doing machine learning. And hence we launched Sage, make a data and learn where with a few clicks, you can connect a variety of data stores, AWS data stores, or third party data stores, and do all of your data preparation. >>Now, once you've done your data preparation, customers wanted to be able to store that data. And that's why we came out with SageMaker feature store and then customers want to be able to take this entire end to end pipeline and be able to automate the whole thing. And that is why we came up with SageMaker pipelines. And then one of the things that customers have asked us to help them address is this issue of statistical bias and explainability. And so we released SageMaker clarify that actually helps customers look at statistical bias to the entire machine learning workflow before you do, when you're doing a data processing before you train your model. And even after you have deployed your model and it gives us insights into why your model is behaving in a particular way. And then we had machine learning in the cloud and many customers have started deploying machine learning at the edge, and they want to be able to deploy these models at the edge and wanted a solution that says, Hey, can I take all of these machine learning capabilities that I have in the cloud, specifically, the model management and the MLR SKP abilities and deploy them to the edge devices. >>And that is why we launched SageMaker edge manager. And then customers said, you know, we still need our basic functionality of training and so on to be faster. And so we released a number of enhancements to SageMaker distributed training in terms of new data, parallel models and new model parallelism models that give the fastest training time on SageMaker across both the frameworks. And, you know, that is one of the key things that we have at AWS is we give customers choice. We don't force them onto a single framework. >>Okay, great. And we, I think we hit them all except, uh, I don't know if you talked about SageMaker debugger, but we will. So I want to come back to and ask you a couple of questions about these features. So it's funny. Sometimes people make fun of your names, but I like them because they said, it says what it does because, because people tell me that I spend all my time wrangling data. So you have data Wrangler, it's, you know, it's all about transformation cleaning. And, and because you don't want to spend 80% of your time wrangling data, you want to spend 80 of your time, you know, driving insights and, and monetization. So, so how, how does one engage with, with data Wrangler and how do you see the possibilities there? >>So data angler is part of SageMaker studio. SageMaker studio was the world's first, fully integrated development run for machine learning. So you come to SageMaker studio, you have a tab there, which you SageMaker data angler, and then you have a visual UI. So that visual UI with just a single click, you can connect to AWS data stores like, you know, red shift or a Tina or third party data stores like snowflake and Databricks and Mongo DB, which will be coming. And then you have a set of built-in data processes for machine learning. So you get that data and you do some interactive processing. Once you're happy with the results of your data, you can just send it off as an automated data pipeline job. And, you know, it's really today the easiest and fastest way to do machine learning and really take out that 80% that you were talking about. >>Has it been so hard to automate the Sage, the pipelines to bring CIC D uh, to, uh, data pipelines? Why has that been such a challenge? And how did you resolve that? >>You know, what has happened is when you look at machine learning, machine learning deals with both code and data, okay. Unlike software, which really has to deal with only code. And so we had the CIC D tools for software, but someone needed to extend it to operating on both data and code. And at the same time, you know, you want to provide reproducibility and lineage and trackability, and really getting that whole end to end system to work across code and data across multiple capabilities was what made it hard. And, you know, that is where we brought in SageMaker pipelines to make this easy for our customers. >>Got it. Thank you. And then let me ask you about, uh, clarify. And this is a huge issue in, in machine intelligence, uh, you know, humans by the very nature of bias that they build models, the models of bias in them. Uh, and so you bringing transplant the other problem with, with AI, and I'm not sure that you're solving this problem, but please clarify if you are no pun intended, but it's that black box AI is a black box. I don't know how the answer, how we got to the answer. It seems like you're attacking that, bringing more transparency and really trying to deal with the biases. I wonder if you could talk about how you do that and how people can expect this to affect their operations. >>I'm glad you asked this question because you know, customers have also asked us about the SageMaker clarify is really intended to address the questions that you brought up. One is it gives you the tools to provide a lot of statistical analysis on the data set that you started with. So let's say you were creating a model for loan approvals, and you want to make sure that, you know, you have equal number of male applicants and equal number of female applicants and so on. So SageMaker clarify, lets you run these kinds of analysis to make sure that your data set is balanced to start with. Now, once that happens, you have trained the model. Once you've trained the model, you want to make sure that the training process did not introduce any unintended statistical bias. So then you can use, SageMaker clarify to again, say, well, is the model behaving in the way I expected it to behave based on the training data I had. >>So let's say your training data set, you know, 50% of all the male applicants got the loans approved after training, you can use, clarify to say, does this model actually predict that 50% of the male applicants will get approved? And if it's more than less, you know, you have a problem. And then after that, we get to the problem you mentioned, which is how do we unravel the black box nature of this? And you know, we took the first steps of it last year with autopilot where we actually gave notebooks. But SageMaker clarify really makes it much better because it tells you why our model is predicting the way it's predicting. It gives you the reasons and it tells you, you know, here is why the model predicts that, you know, you had approved a loan and here's why the model said that you may or may not get a loan. So it really makes it easier, gives visibility and transparency and helps to convert insights that you get from model predictions into actionable insights because you now know why the model is predicting what it's predicting. >>That brings out the confidence level. Okay. Thank you for that. Let me, let me ask you about distributed training on SageMaker help us understand what problem you're solving. You're injecting auto parallelism. Is that about, about scale? Help us understand that. >>Yeah. So one of the things that's happening is, you know, our customers are starting to train really large models like, you know, three years back, they will train models with like 20 million parameters. You know, last year they would train models with like couple of hundred million parameters. Now customers are actually training models with billions of parameters. And when you have such large models, that training can take days and sometimes weeks. And so what we have done E are two concepts. One is we introduced a way of taking a model and training it in parallel and multiple GPU's. And that's, you know what we call a data parallel implementation. We have our own custom libraries for this, which give you the fastest performance on AWS. And then the other thing that happens is customer stakes. Some of these models that are fairly large, you know, like billions of parameters and we showed one of them today called T five and these models are so big that they cannot fit in the memory of a single GPU. And so what happens is today customers have to train such a model. They spend weeks of effort trying to paralyze that Marlon, what we introduced in SageMaker today is a mechanism that automatically takes these large models and distributes it across multiple GPU's the auto parallelization that you were talking about, making it much easier and much faster for customers to really work with these big models. >>Well, the GPU is a very expensive resource. And prior to this, you would have the GPU waiting, waiting, waiting, load me up and you don't want to do that with it. Expensive resources. Yeah. >>And you know, one of the things I mentioned before is Sage make a debugger. So one of the things that we also came out with today is the SageMaker profiler, which is only part of the debugger that lets you look at your GPU utilization at your CPU utilization at, in network utilization and so on. And so now, you know, when your training job has started at which point has the GPU utilization gone down and you can go in and fix it. So this really lets you meet, utilize your resources much better and ultimately reducing your cost of training and making it more efficient. Awesome. >>Let's talk about edge manager because I, you know, Andy Jassy, his keynote was interesting. He his, where he's talking about hybrid and his vision is basically an Amazon's vision is we want to bring AWS to the edge. We see the data center as just another edge node. And so, so this is, to me, another example of, uh, of AWS is, you know, edge strategy, talk about how that works and, and, and, and in practice, uh, how does, how does it work? Am I doing inference at the edge and then bringing back data into the cloud? Uh, am I, am I doing things locally? >>Yes. So, you know what? See each man got edge manager does, is it helps you manage, deploy and manage and manage models at the edge. The inference is happening on the edge device. Now considers his case. So Lenovo has been working with us. And what Lenovo wants to do is to take these models and do predictive maintenance on laptops. So you want to get an it shop and you have a couple of hundred thousand laptops. You would want to know when something may go down. And so the deployed is predictive maintenance models on the laptop. They're doing inference locally on the laptop, but you want to see are the models getting degraded and you want to be able to see is the quality up. So what H manager does is number one, it takes your models, optimizes them so they can run on an edge device and we get up to 25 X benefit and then once you've deployed it, it helps you monitor the quality of the models by letting you upload data samples to SageMaker so that you can see if there is drift in your models, that if there's any other degradation, >>All right. And jumpstart is where I go to. It's kind of the portal that I go to, to access all these cool tools. Is that right? Yep. >>And you know, we have a lot of getting started material, lots of false party models, lots of open source models and solutions. >>I probably we're out of time, but I could go on forever and we did thanks so much for, for bringing this knowledge to the cube audience. Really appreciate your time. >>Thank you. Thank you, Dave, for having me. >>And you're very welcome and good luck with the, the announcements. And thank you for watching everybody. This is Dave Volante for the cube and our coverage of AWS reinvent 2020 continues right after this short break.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, welcome back to the cubes. Ongoing coverage of AWS reinvent virtual Cuba's Gone Virtual along with most events these days are all events and continues to bring our digital coverage of reinvent With me is Rahul Pathak, who is the vice president of analytics at AWS A Ro. It's great to see you again. Welcome. And thanks for joining the program. >>They have Great co two and always a pleasure. Thanks for having me on. >>You're very welcome. Before we get into your leadership discussion, I want to talk about some of the things that AWS has announced. Uh, in the early parts of reinvent, I want to start with a glue elastic views. Very notable announcement allowing people to, you know, essentially share data across different data stores. Maybe tell us a little bit more about glue. Elastic view is kind of where the name came from and what the implication is, >>Uh, sure. So, yeah, we're really excited about blue elastic views and, you know, as you mentioned, the idea is to make it easy for customers to combine and use data from a variety of different sources and pull them together into one or many targets. And the reason for it is that you know we're really seeing customers adopt what we're calling a lake house architectural, which is, uh, at its core Data Lake for making sense of data and integrating it across different silos, uh, typically integrated with the data warehouse, and not just that, but also a range of other purpose. Both stores like Aurora, Relation of Workloads or dynamodb for non relational ones. And while customers typically get a lot of benefit from using purpose built stores because you get the best possible functionality, performance and scale forgiven use case, you often want to combine data across them to get a holistic view of what's happening in your business or with your customers. And before glue elastic views, customers would have to either use E. T. L or data integration software, or they have to write custom code that could be complex to manage, and I could be are prone and tough to change. And so, with elastic views, you can now use sequel to define a view across multiple data sources pick one or many targets. And then the system will actually monitor the sources for changes and propagate them into the targets in near real time. And it manages the anti pipeline and can notify operators if if anything, changes. And so the you know the components of the name are pretty straightforward. Blues are survivalists E T Elling data integration service on blue elastic views about our about data integration their views because you could define these virtual tables using sequel and then elastic because it's several lists and will scale up and down to deal with the propagation of changes. So we're really excited about it, and customers are as well. >>Okay, great. So my understanding is I'm gonna be able to take what's called what the parlance of materialized views, which in my laypersons terms assumes I'm gonna run a query on the database and take that subset. And then I'm gonna be ableto thio. Copy that and move it to another data store. And then you're gonna automatically keep track of the changes and keep everything up to date. Is that right? >>Yes. That's exactly right. So you can imagine. So you had a product catalog for example, that's being updated in dynamodb, and you can create a view that will move that to Amazon Elasticsearch service. You could search through a current version of your catalog, and we will monitor your dynamodb tables for any changes and make sure those air all propagated in the real time. And all of that is is taken care of for our customers as soon as they defined the view on. But they don't be just kept in sync a za long as the views in effect. >>Let's see, this is being really valuable for a person who's building Looks like I like to think in terms of data services or data products that are gonna help me, you know, monetize my business. Maybe, you know, maybe it's a simple as a dashboard, but maybe it's actually a product. You know, it might be some content that I want to develop, and I've got transaction systems. I've got unstructured data, may be in a no sequel database, and I wanna actually combine those build new products, and I want to do that quickly. So So take me through what I would have to do. You you sort of alluded to it with, you know, a lot of e t l and but take me through in a little bit more detail how I would do that, you know, before this innovation. And maybe you could give us a sense as to what the possibilities are with glue. Elastic views? >>Sure. So, you know, before we announced elastic views, a customer would typically have toe think about using a T l software, so they'd have to write a neat L pipeline that would extract data periodically from a range of sources. They then have to write transformation code that would do things like matchup types. Make sure you didn't have any invalid values, and then you would combine it on periodically, Write that into a target. And so once you've got that pipeline set up, you've got to monitor it. If you see an unusual spike in data volume, you might have to add more. Resource is to the pipeline to make a complete on time. And then, if anything changed in either the source of the destination that prevented that data from flowing in the way you would expect it, you'd have toe manually, figure that out and have data, quality checks and all of that in place to make sure everything kept working but with elastic views just gets much simpler. So instead of having to write custom transformation code, you right view using sequel and um, sequel is, uh, you know, widely popular with data analysts and folks that work with data, as you well know. And so you can define that view and sequel. The view will look across multiple sources, and then you pick your destination and then glue. Elastic views essentially monitors both the source for changes as well as the source and the destination for any any issues like, for example, did the schema changed. The shape of the data change is something briefly unavailable, and it can monitor. All of that can handle any errors, but it can recover from automatically. Or if it can't say someone dropped an important table in the source. That was part of your view. You can actually get alerted and notified to take some action to prevent bad data from getting through your system or to prevent your pipeline from breaking without your knowledge and then the final pieces, the elasticity of it. It will automatically deal with adding more resource is if, for example, say you had a spiky day, Um, in the markets, maybe you're building a financial services application and you needed to add more resource is to process those changes into your targets more quickly. The system would handle that for you. And then, if you're monetizing data services on the back end, you've got a range of options for folks subscribing to those targets. So we've got capabilities like our, uh, Amazon data exchange, where people can exchange and monetize data set. So it allows this and to end flow in a much more straightforward way. It was possible before >>awesome. So a lot of automation, especially if something goes wrong. So something goes wrong. You can automatically recover. And if for whatever reason, you can't what happens? You quite ask the system and and let the operator No. Hey, there's an issue. You gotta go fix it. How does that work? >>Yes, exactly. Right. So if we can recover, say, for example, you can you know that for a short period of time, you can't read the target database. The system will keep trying until it can get through. But say someone dropped a column from your source. That was a key part of your ultimate view and destination. You just can't proceed at that point. So the pipeline stops and then we notify using a PS or an SMS alert eso that programmatic action can be taken. So this effectively provides a really great way to enforce the integrity of data that's going between the sources and the targets. >>All right, make it kindergarten proof of it. So let's talk about another innovation. You guys announced quicksight que, uh, kind of speaking to the machine in my natural language, but but give us some more detail there. What is quicksight Q and and how doe I interact with it. What What kind of questions can I ask it >>so quick? Like you is essentially a deep, learning based semantic model of your data that allows you to ask natural language questions in your dashboard so you'll get a search bar in your quick side dashboard and quick site is our service B I service. That makes it really easy to provide rich dashboards. Whoever needs them in the organization on what Q does is it's automatically developing relationships between the entities in your data, and it's able to actually reason about the questions you ask. So unlike earlier natural language systems, where you have to pre define your models, you have to pre define all the calculations that you might ask the system to do on your behalf. Q can actually figure it out. So you can say Show me the top five categories for sales in California and it'll look in your data and figure out what that is and will prevent. It will present you with how it parse that question, and there will, in line in seconds, pop up a dashboard of what you asked and actually automatically try and take a chart or visualization for that data. That makes sense, and you could then start to refine it further and say, How does this compare to what happened in New York? And we'll be able to figure out that you're tryingto overlay those two data sets and it'll add them. And unlike other systems, it doesn't need to have all of those things pre defined. It's able to reason about it because it's building a model of what your data means on the flight and we pre trained it across a variety of different domains So you can ask a question about sales or HR or any of that on another great part accused that when it presents to you what it's parsed, you're actually able toe correct it if it needs it and provide feedback to the system. So, for example, if it got something slightly off you could actually select from a drop down and then it will remember your selection for the next time on it will get better as you use it. >>I saw a demo on in Swamis Keynote on December 8. That was basically you were able to ask Quick psych you the same question, but in different ways, you know, like compare California in New York or and then the data comes up or give me the top, you know, five. And then the California, New York, the same exact data. So so is that how I kind of can can check and see if the answer that I'm getting back is correct is ask different questions. I don't have to know. The schema is what you're saying. I have to have knowledge of that is the user I can. I can triangulate from different angles and then look and see if that's correct. Is that is that how you verify or there are other ways? >>Eso That's one way to verify. You could definitely ask the same question a couple of different ways and ensure you're seeing the same results. I think the third option would be toe, uh, you know, potentially click and drill and filter down into that data through the dash one on, then the you know, the other step would be at data ingestion Time. Typically, data pipelines will have some quality controls, but when you're interacting with Q, I think the ability to ask the question multiple ways and make sure that you're getting the same result is a perfectly reasonable way to validate. >>You know what I like about that answer that you just gave, and I wonder if I could get your opinion on this because you're you've been in this business for a while? You work with a lot of customers is if you think about our operational systems, you know things like sales or E r. P systems. We've contextualized them. In other words, the business lines have inject context into the system. I mean, they kind of own it, if you will. They own the data when I put in quotes, but they do. They feel like they're responsible for it. There's not this constant argument because it's their data. It seems to me that if you look back in the last 10 years, ah, lot of the the data architecture has been sort of generis ized. In other words, the experts. Whether it's the data engineer, the quality engineer, they don't really have the business context. But the example that you just gave it the drill down to verify that the answer is correct. It seems to me, just in listening again to Swamis Keynote the other day is that you're really trying to put data in the hands of business users who have the context on the domain knowledge. And that seems to me to be a change in mindset that we're gonna see evolve over the next decade. I wonder if you could give me your thoughts on that change in the data architecture data mindset. >>David, I think you're absolutely right. I mean, we see this across all the customers that we speak with there's there's an increasing desire to get data broadly distributed into the hands of the organization in a well governed and controlled way. But customers want to give data to the folks that know what it means and know how they can take action on it to do something for the business, whether that's finding a new opportunity or looking for efficiencies. And I think, you know, we're seeing that increasingly, especially given the unpredictability that we've all gone through in 2020 customers are realizing that they need to get a lot more agile, and they need to get a lot more data about their business, their customers, because you've got to find ways to adapt quickly. And you know, that's not gonna change anytime in the future. >>And I've said many times in the The Cube, you know, there are industry. The technology industry used to be all about the products, and in the last decade it was really platforms, whether it's SAS platforms or AWS cloud platforms, and it seems like innovation in the coming years, in many respects is coming is gonna come from the ecosystem and the ability toe share data we've We've had some examples today and then But you hit on. You know, one of the key challenges, of course, is security and governance. And can you automate that if you will and protect? You know the users from doing things that you know, whether it's data access of corporate edicts for governance and compliance. How are you handling that challenge? >>That's a great question, and it's something that really emphasized in my leadership session. But the you know, the notion of what customers are doing and what we're seeing is that there's, uh, the Lake House architectural concept. So you've got a day late. Purpose build stores and customers are looking for easy data movement across those. And so we have things like blue elastic views or some of the other blue features we announced. But they're also looking for unified governance, and that's why we built it ws late formation. And the idea here is that it can quickly discover and catalog customer data assets and then allows customers to define granular access policies centrally around that data. And once you have defined that, it then sets customers free to give broader access to the data because they put the guardrails in place. They put the protections in place. So you know you can tag columns as being private so nobody can see them on gun were announced. We announced a couple of new capabilities where you can provide row based control. So only a certain set of users can see certain rose in the data, whereas a different set of users might only be able to see, you know, a different step. And so, by creating this fine grained but unified governance model, this actually sets customers free to give broader access to the data because they know that they're policies and compliance requirements are being met on it gets them out of the way of the analyst. For someone who can actually use the data to drive some value for the business, >>right? They could really focus on driving value. And I always talk about monetization. However monetization could be, you know, a generic term, for it could be saving lives, admission of the business or the or the organization I meant to ask you about acute customers in bed. Uh, looks like you into their own APs. >>Yes, absolutely so one of quick sites key strengths is its embed ability. And on then it's also serverless, so you could embed it at a really massive scale. And so we see customers, for example, like blackboard that's embedding quick side dashboards into information. It's providing the thousands of educators to provide data on the effectiveness of online learning. For example, on you could embed Q into that capability. So it's a really cool way to give a broad set of people the ability to ask questions of data without requiring them to be fluent in things like Sequel. >>If I ask you a question, we've talked a little bit about data movement. I think last year reinvent you guys announced our A three. I think it made general availability this year. And remember Andy speaking about it, talking about you know, the importance of having big enough pipes when you're moving, you know, data around. Of course you do. Doing tearing. You also announced Aqua Advanced Query accelerator, which kind of reduces bringing the computer. The data, I guess, is how I would think about that reducing that movement. But then we're talking about, you know, glue, elastic views you're copying and moving data. How are you ensuring you know, maintaining that that maximum performance for your customers. I mean, I know it's an architectural question, but as an analytics professional, you have toe be comfortable that that infrastructure is there. So how does what's A. W s general philosophy in that regard? >>So there's a few ways that we think about this, and you're absolutely right. I think there's data volumes were going up, and we're seeing customers going from terabytes, two petabytes and even people heading into the exabyte range. Uh, there's really a need to deliver performance at scale. And you know, the reality of customer architectures is that customers will use purpose built systems for different best in class use cases. And, you know, if you're trying to do a one size fits all thing, you're inevitably going to end up compromising somewhere. And so the reality is, is that customers will have more data. We're gonna want to get it to more people on. They're gonna want their analytics to be fast and cost effective. And so we look at strategies to enable all of this. So, for example, glue elastic views. It's about moving data, but it's about moving data efficiently. So What we do is we allow customers to define a view that represents the subset of their data they care about, and then we only look to move changes as efficiently as possible. So you're reducing the amount of data that needs to get moved and making sure it's focused on the essential. Similarly, with Aqua, what we've done, as you mentioned, is we've taken the compute down to the storage layer, and we're using our nitro chips to help with things like compression and encryption. And then we have F. P. J s in line to allow filtering an aggregation operation. So again, you're tryingto quickly and effectively get through as much data as you can so that you're only sending back what's relevant to the query that's being processed. And that again leads to more performance. If you can avoid reading a bite, you're going to speed up your queries. And that Awkward is trying to do. It's trying to push those operations down so that you're really reducing data as close to its origin as possible on focusing on what's essential. And that's what we're applying across our analytics portfolio. I would say one other piece we're focused on with performance is really about innovating across the stack. So you mentioned network performance. You know, we've got 100 gigabits per second throughout now, with the next 10 instances and then with things like Grab it on to your able to drive better price performance for customers, for general purpose workloads. So it's really innovating at all layers. >>It's amazing to watch it. I mean, you guys, it's a It's an incredible engineering challenge as you built this hyper distributed system. That's now, of course, going to the edge. I wanna come back to something you mentioned on do wanna hit on your leadership session as well. But you mentioned the one size fits all, uh, system. And I've asked Andy Jassy about this. I've had a discussion with many folks that because you're full and and of course, you mentioned the challenges you're gonna have to make tradeoffs if it's one size fits all. The flip side of that is okay. It's simple is you know, 11 of the Swiss Army knife of database, for example. But your philosophy is Amazon is you wanna have fine grained access and to the primitives in case the market changes you, you wanna be able to move quickly. So that puts more pressure on you to then simplify. You're not gonna build this big hairball abstraction layer. That's not what he gonna dio. Uh, you know, I think about, you know, layers and layers of paint. I live in a very old house. Eso your That's not your approach. So it puts greater pressure on on you to constantly listen to your customers, and and they're always saying, Hey, I want to simplify, simplify, simplify. We certainly again heard that in swamis presentation the other day, all about, you know, minimizing complexity. So that really is your trade office. It puts pressure on Amazon Engineering to continue to raise the bar on simplification. Isn't Is that a fair statement? >>Yeah, I think so. I mean, you know, I think any time we can do work, so our customers don't have to. I think that's a win for both of us. Um, you know, because I think we're delivering more value, and it makes it easier for our customers to get value from their data way. Absolutely believe in using the right tool for the right job. And you know you talked about an old house. You're not gonna build or renovate a house of the Swiss Army knife. It's just the wrong tool. It might work for small projects, but you're going to need something more specialized. The handle things that matter. It's and that is, uh, that's really what we see with that, you know, with that set of capabilities. So we want to provide customers with the best of both worlds. We want to give them purpose built tools so they don't have to compromise on performance or scale of functionality. And then we want to make it easy to use these together. Whether it's about data movement or things like Federated Queries, you can reach into each of them and through a single query and through a unified governance model. So it's all about stitching those together. >>Yeah, so far you've been on the right side of history. I think it serves you well on your customers. Well, I wanna come back to your leadership discussion, your your leadership session. What else could you tell us about? You know, what you covered there? >>So we we've actually had a bunch of innovations on the analytics tax. So some of the highlights are in m r, which is our managed spark. And to do service, we've been able to achieve 1.7 x better performance and open source with our spark runtime. So we've invested heavily in performance on now. EMR is also available for customers who are running and containerized environment. So we announced you Marnie chaos on then eh an integrated development environment and studio for you Marco D M R studio. So making it easier both for people at the infrastructure layer to run em are on their eks environments and make it available within their organizations but also simplifying life for data analysts and folks working with data so they can operate in that studio and not have toe mess with the details of the clusters underneath and then a bunch of innovation in red shift. We talked about Aqua already, but then we also announced data sharing for red Shift. So this makes it easy for red shift clusters to share data with other clusters without putting any load on the central producer cluster. And this also speaks to the theme of simplifying getting data from point A to point B so you could have central producer environments publishing data, which represents the source of truth, say into other departments within the organization or departments. And they can query the data, use it. It's always up to date, but it doesn't put any load on the producers that enables these really powerful data sharing on downstream data monetization capabilities like you've mentioned. In addition, like Swami mentioned in his keynote Red Shift ML, so you can now essentially train and run models that were built in sage maker and optimized from within your red shift clusters. And then we've also automated all of the performance tuning that's possible in red ships. So we really invested heavily in price performance, and now we've automated all of the things that make Red Shift the best in class data warehouse service from a price performance perspective up to three X better than others. But customers can just set red shift auto, and it'll handle workload management, data compression and data distribution. Eso making it easier to access all about performance and then the other big one was in Lake Formacion. We announced three new capabilities. One is transactions, so enabling consistent acid transactions on data lakes so you can do things like inserts and updates and deletes. We announced row based filtering for fine grained access control and that unified governance model and then automated storage optimization for Data Lake. So customers are dealing with an optimized small files that air coming off streaming systems, for example, like Formacion can auto compact those under the covers, and you can get a 78 x performance boost. It's been a busy year for prime lyrics. >>I'll say that, z that it no great great job, bro. Thanks so much for coming back in the Cube and, you know, sharing the innovations and, uh, great to see you again. And good luck in the coming here. Well, >>thank you very much. Great to be here. Great to see you. And hope we get Thio see each other in person against >>I hope so. All right. And thank you for watching everybody says Dave Volonte for the Cube will be right back right after this short break

>>Yeah, yeah, hello again and welcome to our last session of the day before we head to the meat. The experts roundtables how T Mobile is building a data driven organization with thought spot and whip prone. Today we'll hear how T Mobile is leaving Excel hell by enabling all employees with self service analytics so they can get instant answers on curated data. We're lucky to be closing off the day with these two speakers. Evo Benzema, manager of business intelligence services at T Mobile Netherlands, and Sanjeev Chowed Hurry, lead architect AT T Mobile, Netherlands, from Whip Chrome. Thank you both very much for being with us today, for today's session will cover how mobile telco markets have specific dynamics and what it waas that T Mobile was facing. We'll also go over the Fox spot and whip pro solution and how they address T mobile challenges. Lastly, but not least, of course, we'll cover Team Mobil's experience and learnings and takeaways that you can use in your business without further ado Evo, take us away. >>Thank you very much. Well, let's first talk a little bit about T Mobile, Netherlands. We are part off the larger deutsche Telekom Group that ISS operating in Europe and the US We are the second largest mobile phone company in the Netherlands, and we offer the full suite awful services that you expect mobile landline in A in an interactive TV. And of course, Broadbent. Um so this is what the Mobile is appreciation at at the moment, a little bit about myself. I'm already 11 years at T Mobile, which is we part being part of the furniture. In the meantime, I started out at the front line service desk employee, and that's essentially first time I came into a touch with data, and what I found is that I did not have any possibility of myself to track my performance. Eso I build something myself and here I saw that this need was there because really quickly, roughly 2020 off my employer colleagues were using us as well. This was a little bit where my efficient came from that people need to have access to data across the organization. Um, currently, after 11 years running the BR Services Department on, I'm driving this transformation now to create a data driven organization with a heavy customer focus. Our big goal. Our vision is that within two years, 8% of all our employees use data on a day to day basis to make their decisions and to improve their decision. So over, tuition Chief. Now, thank >>you. Uh, something about the proof. So we prize a global I T and business process consulting and delivery company. Uh, we have a comprehensive portfolio of services with presents, but in 61 countries and maybe 1000 plus customers. As we're speaking with Donald, keep customers Region Point of view. We primary look to help our customers in reinventing the business models with digital first approach. That's how we look at our our customers toe move to digitalization as much as possible as early as possible. Talking about myself. Oh, I have little over two decades of experience in the intelligence and tell cope landscape. Calico Industries. I have worked with most of the telcos totally of in us in India and in Europe is well now I have well known cream feed on brownfield implementation off their house on big it up platforms. At present, I'm actively working with seminal data transform initiative mentioned by evil, and we are actively participating in defining the logical and physical footprint for future architectures for criminal. I understand we are also, in addition, taking care off and two and ownership off off projects, deliveries on operations, back to you >>so a little bit over about the general telco market dynamics. It's very saturated market. Everybody has mobile phones already. It's the growth is mostly gone, and what you see is that we have a lot of trouble around customer brand loyalty. People switch around from provider to provider quite easily, and new customers are quite expensive. So our focus is always to make customer loyal and to keep them in the company. And this is where the opportunities are as well. If we increase the retention of customers or reduce what we say turned. This is where the big potential is for around to use of data, and we should not do this by only offering this to the C suite or the directors or the mark managers data. But this needs to be happening toe all employees so that they can use this to really help these customers and and services customers is situated. This that we can create his loyalty and then This is where data comes in as a big opportunity going forward. Yeah. So what are these challenges, though? What we're facing two uses the data. And this is, uh, these air massive over our big. At least let's put it like that is we have a lot of data. We create around four billion new record today in our current platforms. The problem is not everybody can use or access this data. You need quite some technical expertise to add it, or they are pre calculated into mawr aggregated dashboard. So if you have a specific question, uh, somebody on the it side on the buy side should have already prepared something so that you can get this answer. So we have a huge back lock off questions and data answers that currently we cannot answer on. People are limited because they need technical expertise to use this data. These are the challenges we're trying to solve going forward. >>Uh, so the challenge we see in the current landscape is T mobile as a civil mentioned number two telco in Europe and then actually in Netherlands. And then we have a lot of acquisitions coming in tow of the landscape. So overall complexity off technical stack increases year by year and acquisition by acquisition it put this way. So we at this time we're talking about Claudia Irureta in for Matic Uh, aws and many other a complex silo systems. We actually are integrated where we see multiple. In some cases, the data silos are also duplicated. So the challenge here is how do we look into this data? How do we present this data to business and still ensure that Ah, mhm Kelsey of the data is reliable. So in this project, what we looked at is we curated that around 10% off the data of us and made it ready for business to look at too hot spot. And this also basically help us not looking at the A larger part of the data all together in one shot. What's is going to step by step with manageable set of data, obviously manages the time also and get control on cost has. >>So what did we actually do and how we did? Did we do it? And what are we going to do going forward? Why did we chose to spot and what are we measuring to see if we're successful is is very simply, Some stuff I already alluded to is usual adoption. This needs to be a tool that is useable by everybody. Eso This is adoption. The user experience is a major key to to focus on at the beginning. Uh, but lastly, and this is just also cold hard. Fact is, it needs to save time. It needs to be faster. It needs to be smarter than the way we used to do it. So we focused first on setting up the environment with our most used and known data set within the company. The data set that is used already on the daily basis by a large group. We know what it's how it works. We know how it acts on this is what we decided to make available fire talksport this cut down the time around, uh, data modeling a lot because we had this already done so we could go right away into training users to start using this data, and this is already going on very successfully. We have now 40 heavily engaged users. We go went life less than a month ago, and we see very successful feedback on user experience. We had either yesterday, even a beautiful example off loading a new data set and and giving access to user that did not have a training for talk sport or did not know what thoughts, what Waas. And we didn't in our he was actively using this data set by building its own pin boards and asking questions already. And this shows a little bit the speed off delivery we can have with this without, um, much investments on data modeling, because that's part was already done. So our second stage is a little bit more ambitious, and this is making sure that all this information, all our information, is available for frontline uh, employees. So a customer service but also chills employees that they can have data specifically for them that make them their life easier. So this is performance KP ice. But it could also be the beautiful word that everybody always uses customer Terry, 60 fuse. But this is giving the power off, asking questions and getting answers quickly to everybody in the company. That's the big stage two after that, and this is going forward a little bit further in the future and we are not completely there yet, is we also want Thio. Really? After we set up the government's properly give the power to add your own data to our curated data sets that that's when you've talked about. And then with that, we really hope that Oh, our ambition and our plan is to bring this really to more than 800 users on a daily basis to for uses on a daily basis across our company. So this is not for only marketing or only technology or only one segment. This is really an application that we want to set in our into system that works for everybody. And this is our ambition that we will work through in these three, uh, steps. So what did we learn so far? And and Sanjeev, please out here as well, But one I already said, this is no which, which data set you start. This is something. Start with something. You know, start with something that has a wide appeal to more than one use case and make sure that you make this decision. Don't ask somebody else. You know what your company needs? The best you should be in the driver seat off this decision. And this is I would be saying really the big one because this will enable you to kickstart this really quickly going forward. Um, second, wellness and this is why we introduce are also here together is don't do this alone. Do this together with, uh I t do this together with security. Do this together with business to tackle all these little things that you don't think about yourself. Maybe security, governance, network connections and stuff like that. Make sure that you do this as a company and don't try to do this on your own, because there's also again it's removes. Is so much obstacles going forward? Um, lastly, I want to mention is make sure that you measure your success and this is people in the data domain sometimes forget to measure themselves. Way can make sure everybody else, but we forget ourselves. But really try to figure out what makes its successful for you. And we use adoption percentages, usual experience, surveys and and really calculations about time saved. We have some rough calculations that we can calculate changes thio monetary value, and this will save us millions in years. by just automating time that is now used on, uh, now to taken by people on manual work. So, do you have any to adhere? A swell You, Susan, You? >>Yeah. So I'll just pick on what you want to mention about. Partner goes live with I t and other functions. But that is a very keating, because from my point of view, you see if you can see that the data very nice and data quality is also very clear. If we have data preparing at the right level, ready to be consumed, and data quality is taken, care off this feel 30 less challenges. Uh, when the user comes and questioned the gator, those are the things which has traded Quiz it we should be sure about before we expose the data to the Children. When you're confident about your data, you are confident that the user will also get the right numbers they're looking for and the number they have. Their mind matches with what they see on the screen. And that's where you see there. >>Yeah, and that that that again helps that adoption, and that makes it so powerful. So I fully agree. >>Thank you. Eva and Sanjeev. This is the picture perfect example of how a thought spot can get up and running, even in a large, complex organization like T Mobile and Sanjay. Thank you for sharing your experience on how whip rose system integration expertise paved the way for Evo and team to realize value quickly. Alright, everyone's favorite part. Let's get to some questions. Evil will start with you. How have your skill? Data experts reacted to thought spot Is it Onley non technical people that seem to be using the tool or is it broader than that? You may be on. >>Yes, of course, that happens in the digital environment. Now this. This is an interesting question because I was a little bit afraid off the direction off our data experts and are technically skilled people that know how to work in our fight and sequel on all these things. But here I saw a lot of enthusiasm for the tool itself and and from two sides, either to use it themselves because they see it's a very easy way Thio get to data themselves, but also especially that they see this as a benefit, that it frees them up from? Well, let's say mundane questions they get every day. And and this is especially I got pleasantly surprised with their reaction on that. And I think maybe you can also say something. How? That on the i t site that was experienced. >>Well, uh, yeah, from park department of you, As you mentioned, it is changing the way business is looking at. The data, if you ask me, have taken out talkto data rather than looking at it. Uh, it is making the interactivity that that's a keyword. But I see that the gap between the technical and function folks is also diminishing, if I may say so over a period of time, because the technical folks now would be able to work with functional teams on the depth and coverage of the data, rather than making it available and looking at the technical side off it. So now they can have a a fair discussion with the functional teams on. Okay, these are refute. Other things you can look at because I know this data is available can make it usable for you, especially the time it takes for the I t. G. When graduate dashboard, Uh, that time can we utilize toe improve the quality and reliability of the data? That's yeah. See the value coming. So if you ask me to me, I see the technical people moving towards more of a technical functional role. Tools such as >>That's great. I love that saying now we can talk to data instead of just looking at it. Um Alright, Evo, I think that will finish up with one last question for you that I think you probably could speak. Thio. Given your experience, we've seen that some organizations worry about providing access to data for everyone. How do you make sure that everyone gets the same answer? >>Yes. The big data Girlfriends question thesis What I like so much about that the platform is completely online. Everything it happens online and everything is terrible. Which means, uh, in the good old days, people will do something on their laptop. Beirut at a logic to it, they were aggregated and then they put it in a power point and they will share it. But nobody knew how this happened because it all happened offline. With this approach, everything is transparent. I'm a big I love the word transparency in this. Everything is available for everybody. So you will not have a discussion anymore. About how did you get to this number or how did you get to this? So the question off getting two different answers to the same question is removed because everything happens. Transparency, online, transparent, online. And this is what I think, actually, make that question moot. Asl Long as you don't start exporting this to an offline environment to do your own thing, you are completely controlling, complete transparent. And this is why I love to share options, for example and on this is something I would really keep focusing on. Keep it online, keep it visible, keep it traceable. And there, actually, this problem then stops existing. >>Thank you, Evelyn. Cindy, That was awesome. And thank you to >>all of our presenters. I appreciate your time so much. I hope all of you at home enjoyed that as much as I did. I know a lot of you did. I was watching the chat. You know who you are. I don't think that I'm just a little bit in awe and completely inspired by where we are from a technological perspective, even outside of thoughts about it feels like we're finally at a time where we can capitalize on the promise that cloud and big data made to us so long ago. I loved getting to see Anna and James describe how you can maximize the investment both in time and money that you've already made by moving your data into a performance cloud data warehouse. It was cool to see that doubled down on with the session, with AWS seeing a direct query on Red Shift. And even with something that's has so much scale like TV shows and genres combining all of that being able to search right there Evo in Sanjiv Wow. I mean being able to combine all of those different analytics tools being able to free up these analysts who could do much more important and impactful work than just making dashboards and giving self service analytics to so many different employees. That's incredible. And then, of course, from our experts on the panel, I just think it's so fascinating to see how experts that came from industries like finance or consulting, where they saw the imperative that you needed to move to thes third party data sets enriching and organizations data. So thank you to everyone. It was fascinating. I appreciate everybody at home joining us to We're not quite done yet. Though. I'm happy to say that we after this have the product roadmap session and that we are also then going to move into hearing and being able to ask directly our speakers today and meet the expert session. So please join us for that. We'll see you there. Thank you so much again. It was really a pleasure having you.

>>Yeah, yeah. Welcome back to the third session in our building, A vibrant data ecosystem track. This session is unleash the power of your cloud data warehouse. So what comes after you've moved your data to the cloud in this session will explore White Enterprise Analytics is finally ready for the cloud, and we'll discuss how you can consume Enterprise Analytics in the very same way he would cloud services. We'll also explore where analytics meets cloud and see firsthand how thought spot is open for everyone. Let's get going. I'm happy to say we'll be hearing from two folks from thought spot today, Michael said Cassie, VP of strategic partnerships, and Vika Valentina, senior product marketing manager. And I'm very excited to welcome from our partner at AWS Gal Bar MIA, product engineering manager with Red Shift. We'll also be sharing a live demo of thought spot for BTC Marketing Analytics directly on Red Shift data. Gal, please kick us off. >>Thank you, Military. And thanks. The talks about team and everyone attending today for joining us. When we talk about data driven organizations, we hear that 85% of businesses want to be data driven. However, on Lee. 37% have been successful in We ask ourselves, Why is that and believe it or not, Ah, lot of customers tell us that they struggled with live in defining what being data driven it even means, and in particular aligning that definition between the business and the technology stakeholders. Let's talk a little bit. Let's look at our own definition. A data driven organization is an organization that harnesses data is an asset. The drive sustained innovation and create actionable insights. The super charge, the experience of their customers so they demand more. Let's focus on a few things here. One is data is an asset. Data is very much like a product needs to evolve sustained innovation. It's not just innovation innovation, it's sustained. We need to continuously innovate when it comes to data actionable insights. It's not just interesting insights these air actionable that the business can take and act upon, and obviously the actual experience we. Whether whether the customers are internal or external, we want them to request Mawr insights and as such, drive mawr innovation, and we call this the for the flywheel. We use the flywheel metaphor here where we created that data set. Okay, Our first product. Any focused on a specific use case? We build an initial NDP around that we provided with that with our customers, internal or external. They provide feedback, the request, more features. They want mawr insights that enables us to learn bringing more data and reach that actual data. And again we create MAWR insights. And as the flywheel spins faster, we improve on operational efficiencies, supporting greater data richness, and we reduce the cost of experimentation and legacy environments were never built for this kind of agility. In many cases, customers have struggled to keep momentum in their fleet, flywheel in particular around operational efficiency and experimentation. This is where Richie fits in and helps customer make the transition to a true data driven organization. Red Shift is the most widely used data warehouse with tens of thousands of customers. It allows you to analyze all your data. It is the only cloud data warehouse that sits, allows you to analyze data that sits in your data lake on Amazon, a street with no loading duplication or CTL required. It is also allows you to scale with the business with its hybrid architectures it also accelerates performance. It's a shared storage that provides the ability to scale toe unlimited concurrency. While the UN instant storage provides low late and say access to data it also provides three. Key asks that customers consistently tell us that matter the most when it comes to cost. One is usage based pricing Instead of license based pricing. Great value as you scale your data warehouse using, for example, reserved instances they can save up to 75% compared to on the mind demand prices. And as your data grows, infrequently accessed data can be stored. Cost effectively in S three encouraged through Amazon spectrum, and the third aspect is predictable. Month to month spend with no hitting charges and surprises. Unlike and unlike other cloud data warehouses, where you need premium versions for additional enterprise capabilities. Wretched spicing include building security compression and data transfer. >>Great Thanks. Scout um, eso. As you can see, everybody wins with the cloud data warehouses. Um, there's this evolution of movement of users and data and organizations to get value with these cloud data warehouses. And the key is the data has to be accessible by the users, and this data and the ability to make business decisions on the data. It ranges from users on the front line all the way up to the boardroom. So while we've seen this evolution to the Cloud Data Warehouse, as you can see from the statistic from Forrester, we're still struggling with how much of that data actually gets used for analytics. And so what is holding us back? One of the main reasons is old technology really trying to work with today's modern cloud data warehouses? They weren't built for it. So you run into issues of trying to do data replication, getting the data out of the cloud data warehouse. You can do analysis and then maintaining these middle layers of data so that you can access it quickly and get the answers you need. Another issue that's holding us back is this idea that you have to have your data in perfect shape with the perfect pipeline based on the exact dashboard unique. Um, this isn't true. Now, with Cloud data warehouse and the speed of important business data getting into those cloud data warehouses, you need a solution that allows you to access it right away without having everything to be perfect from the start, and I think this is a great opportunity for GAL and I have a little further discussion on what we're seeing in the marketplace. Um, one of the primary ones is like, What are the limiting factors, your Siegel of legacy technologies in the market when it comes to this cloud transformation we're talking about >>here? It's a great question, Michael and the variety of aspect when it comes to legacy, the other warehouses that are slowing down innovation for companies and businesses. I'll focus on 21 is performance right? We want faster insights. Companies want the ability to analyze MAWR data faster. And when it comes to on prem or legacy data warehouses, that's hard to achieve because the second aspect comes into display, which is the lack of flexibility, right. If you want to increase your capacity of your warehouse, you need to ensure request someone needs to go and bring an actual machine and install it and expand your data warehouse. When it comes to the cloud, it's literally a click of a button, which allows you to increase the capacity of your data warehouse and enable your internal and external users to perform analytics at scale and much faster. >>It falls right into the explanation you provided there, right as the speed of the data warehouses and the data gets faster and faster as it scales, older solutions aren't built toe leverage that, um, you know, they're either they're having to make technical, you know, technical cuts there, either looking at smaller amounts of data so that they can get to the data quicker. Um, or it's taking longer to get to the data when the data warehouse is ready, when it could just be live career to get the answers you need. And that's definitely an issue that we're seeing in the marketplace. I think the other one that you're looking at is things like governance, lineage, regulatory requirements. How is the cloud you know, making it easier? >>That's That's again an area where I think the cloud shines. Because AWS AWS scale allows significantly more investment in securing security policies and compliance, it allows customers. So, for example, Amazon redshift comes by default with suck 1 to 3 p. C. I. Aiso fared rampant HIPPA compliance, all of them out of the box and at our scale. We have the capacity to implement those by default for all of our customers and allow them to focus. Their very expensive, valuable ICTY resource is on actual applications that differentiate their business and transform the customer experience. >>That's a great point, gal. So we've talked about the, you know, limiting factors. Technology wise, we've mentioned things like governance. But what about the cultural aspect? Right? So what do you see? What do you see in team struggling in meeting? You know, their cloud data warehouse strategy today. >>And and that's true. One of the biggest challenges for large large organizations when they moved to the cloud is not about the technology. It's about people, process and culture, and we see differences between organizations that talk about moving to the cloud and ones that actually do it. And first of all, you wanna have senior leadership, drive and be aligned and committed to making the move to the cloud. But it's not just that you want. We see organizations sometimes Carol get paralyzed. If they can't figure out how to move each and every last work clothes, there's no need to boil the ocean, so we often work with organizations to find that iterative motion that relative process off identifying the use cases are date identifying workloads in migrating them one at a time and and through that allowed organization to grow its knowledge from a cloud perspective as well as adopt its tooling and learn about the new capabilities. >>And from an analytics perspective, we see the same right. You don't need a pixel perfect dashboard every single time to get value from your data. You don't need to wait until the data warehouse is perfect or the pipeline to the data warehouse is perfect. With today's technology, you should be able to look at the data in your cloud data warehouse immediately and get value from it. And that's the you know, that's that change that we're pushing and starting to see today. Thanks. God, that was That was really interesting. Um, you know, as we look through that, you know, this transformation we're seeing in analytics, um, isn't really that old? 20 years ago, data warehouses were primarily on Prem and the applications the B I tools used for analytics around them were on premise well, and so you saw things like applications like Salesforce. That live in the cloud. You start having to pull data from the cloud on Prem in order to do analytics with it. Um, you know, then we saw the shift about 10 years ago in the explosion of Cloud Data Warehouse Because of their scale, cost reduced, reduce shin reduction and speed. You know, we're seeing cloud data. Warehouses like Amazon Red Shift really take place, take hold of the marketplace and are the predominant ways of storing data moving forward. What we haven't seen is the B I tools catch up. And so when you have this new cloud data warehouse technology, you really need tools that were custom built for it to take advantage of it, to be able to query the cloud data warehouse directly and get results very quickly without having to worry about creating, you know, a middle layer of data or pipelines in order to manage it. And, you know, one company captures that really Well, um, chick fil A. I'm sure everybody has heard of is one of the largest food chains in America. And, you know, they made a huge investment in red shift and one of the purposes of that investment is they wanted to get access to the data mawr quickly, and they really wanted to give their business users, um, the ability to do some ad hoc analysis on the data that they were capturing. They found that with their older tools, the problems that they were finding was that all the data when they're trying to do this analysis was staying at the analyst level. So somebody needed to create a dashboard in order to share that data with a user. And if the user's requirements changed, the analysts were starting to become burdened with requests for changes and the time it took to reflect those changes. So they wanted to move to fought spot with embrace to connect to Red Shift so they could start giving business users that capability. Query the database right away. And with this, um, they were able to find, you know, very common things in in the supply chain analysis around the ability to figure out what store should get, what product that was selling better. The other part was they didn't have to wait for the data to get settled into some sort of repository or second level database. They were able to query it quickly. And then with that, they're able to make changes right in the red shift database that were then reflected to customers and the business users right away. So what they found from this is by adopting thought spot, they were actually able to arm business users with the ability to make decisions very quickly. And they cleared up the backlog that they were having and the delay with their analysts. And they're also putting their analysts toe work on different projects where they could get better value from. So when you look at the way we work with a cloud data warehouse, um, you have to think of thoughts about embrace as the tool that access that layer. The perfect analytic partner for the Cloud Data Warehouse. We will do the live query for the business user. You don't need to know how to script and sequel, um Thio access, you know, red shift. You can type the question that you want the answer to and thought spot will take care of that query. We will do the indexing so that the results come back faster for you and we will also do the analysis on. This is one of the things I wanted to cover, which is our spot i. Q. This is new for our ability to use this with embrace and our partners at Red Shift is now. We can give you the ability to do auto analysis to look at things like leading indicators, trends and anomalies. So to put this in perspective amount imagine somebody was doing forecasting for you know Q three in the western region. And they looked at how their stores were doing. And they saw that, you know, one store was performing well, Spot like, you might be able to look at that analysis and see if there's a leading product that is underperforming based on perhaps the last few quarters of data. And bring that up to the business user for analysis right away. They don't need to have to figure that out. And, um, you know, slice and dice to find that issue on their own. And then finally, all the work you do in data management and governance in your cloud data warehouse gets reflected in the results in embrace right away. So I've done a lot of talking about embrace, and I could do more, but I think it would be far better toe. Have Vika actually show you how the product works, Vika. >>Thanks, Michael. We learned a lot today about the power of leveraging your red shift data and thought spot. But now let me show you how it works. The coronavirus pandemic has presented extraordinary challenges for many businesses, and some industries have fared better than others. One industry that seems to weather the storm pretty well actually is streaming media. So companies like Netflix and who Lou. And in this demo, we're going to be looking at data from B to C marketing efforts. First streaming media company in 2020 lately, we've been running campaigns for comedy, drama, kids and family and reality content. Each of our campaigns last four weeks, and they're staggered on a weekly basis. Therefore, we always have four campaigns running, and we can focus on one campaign launch per >>week, >>and today we'll be digging into how our campaigns are performing. We'll be looking at things like impressions, conversions and users demographic data. So let's go ahead and look at that data. We'll see what we can learn from what's happened this year so far, and how we can apply those learnings to future decision making. As you can already see on the thoughts about homepage, I've created a few pin boards that I use for reporting purposes. The homepage also includes what others on my team and I have been looking at most recently. Now, before we dive into a search, will first take a look at how to make a direct connection to the customer database and red shift to save time. I've already pre built the connection Red Shift, but I'll show you how easy it is to make that connection in just three steps. So first we give the connection name and we select our connection type and was on red Shift. Then we enter our red shift credentials, and finally, we select the tables that we want to use Great now ready to start searching. So let's start in this data to get a better idea of how our marketing efforts have been affected either positively or negatively by this really challenging situation. When we think of ad based online marketing campaigns, we think of impressions, clicks and conversions. Let's >>look at those >>on a daily basis for our purposes. So all this data is available to us in Thought spot, and we can easily you search to create a nice line chart like this that shows US trends over the last few months and based on experience. We understand that we're going to have more clicks than impressions and more impressions and conversions. If we started the chart for a minute, we could see that while impressions appear to be pretty steady over the course of the year, clicks and especially conversions both get a nice boost in mid to late March, right around the time that pandemic related policies were being implemented. So right off the bat, we found something interesting, and we can come back to this now. There are few metrics that we're gonna focus on as we analyze our marketing data. Our overall goal is obviously to drive conversions, meaning that we bring new users into our streaming service. And in order to get a visitor to sign up in the first place, we need them to get into our sign up page. A compelling campaign is going to generate clicks, so if someone is interested in our ad, they're more likely to click on it, so we'll search for Click through Rape 5% and we'll look this up by campaign name. Now even compare all the campaigns that we've launched this year to see which have been most effective and bring visitors star site. And I mentioned earlier that we have four different types of campaign content, each one aligned with one of our most popular genres. So by adding campaign content, yeah, >>and I >>just want to see the top 10. I could limit my church. Just these top 10 campaigns automatically sorted by click through rate and assigned a color for each category so we could see right away that comedy and drama each of three of the top 10 campaigns by click through rate reality is, too, including the top spot and kids and family makes one appearance as well. Without spot. We know that any non technical user can ask a question and get an answer. They can explore the answer and ask another question. When you get an answer that you want to share, keep an eye on moving forward, you pin the answer to pin board. So the BBC Marketing Campaign Statistics PIN board gives us a solid overview of our campaign related activities and metrics throughout 2020. The visuals here keep us up to date on click through rate and cost per click, but also another really important metrics that conversions or cost proposition. Now it's important to our business that we evaluate the effectiveness of our spending. Let's do another search. We're going to look at how many new customers were getting so conversions and the price cost per acquisition that we're spending to get each of these by the campaign contact category. So >>this is a >>really telling chart. We can basically see how much each new users costing us, based on the content that they see prior to signing up to the service. Drama and reality users are actually relatively expensive compared to those who joined based on comedy and kids and family content that they saw. And if all the genres kids and family is actually giving us the best bang for our marketing >>buck. >>And that's good news because the genres providing the best value are also providing the most customers. We mentioned earlier that we actually saw a sizable uptick in conversions as stay at home policies were implemented across much of the country. So we're gonna remove cost per acquisition, and we're gonna take a daily look how our campaign content has trended over the years so far. Eso By doing this now, we can see a comparison of the different genres daily. Some campaigns have been more successful than others. Obviously, for example, kids and family contact has always fared pretty well Azaz comedy. But as we moved into the stay at home area of the line chart, we really saw these two genres begin to separate from the rest. And even here in June, as some states started to reopen, we're seeing that they're still trending up, and we're also seeing reality start to catch up around that time. And while the first pin board that we looked at included all sorts of campaign metrics, this is another PIN board that we've created so solely to focus on conversions. So not only can we see which campaigns drug significant conversions, we could also dig into the demographics of new users, like which campaigns and what content brought users from different parts of the country or from different age groups. And all this is just a quick search away without spot search directly on a red shift. Data Mhm. All right, Thank you. And back to you, Michael. >>Great. Thanks, Vika. That was excellent. Um, so as you can see, you can very quickly go from zero to search with thought Spot, um, connected to any cloud data warehouse. And I think it's important to understand that we mentioned it before. Not everything has to be perfect. In your doubt, in your cloud data warehouse, um, you can use thought spot as your initial for your initial tool. It's for investigatory purposes, A Z you can see here with star, Gento, imax and anthem. And a lot of these cases we were looking at billions of rows of data within minutes. And as you as your data warehouse maturity grows, you can start to add more and more thoughts about users to leverage the data and get better analysis from it. So we hope that you've enjoyed what you see today and take the step to either do one of two things. We have a free trial of thoughts about cloud. If you go to the website that you see below and register, we can get you access the thought spots so you can start searching today. Another option, by contacting our team, is to do a zero to search workshop where 90 minutes will work with you to connect your data source and start to build some insights and exactly what you're trying to find for your business. Um thanks, everybody. I would especially like to thank golf from AWS for joining us on this today. We appreciate your participation, and I hope everybody enjoyed what they saw. I think we have a few questions now. >>Thank you, Vika, Gal and Michael. It's always exciting to see a live demo. I know that I'm one of those comedy numbers. We have just a few minutes left, but I would love to ask a couple of last questions Before we go. Michael will give you the first question. Do I need to have all of my data cleaned and ready in my cloud data warehouse before I begin with thought spot? >>That's a great question, Mallory. No, you don't. You can really start using thought spot for search right away and start getting analysis and start understanding the data through the automatic search analysis and the way that we query the data and we've seen customers do that. Chick fil a example that we talked about earlier is where they were able to use thoughts bought to notice an anomaly in the Cloud Data Warehouse linking between product and store. They were able to fix that very quickly. Then that gets reflected across all of the users because our product queries the Cloud Data Warehouse directly so you can get started right away without it having to be perfect. And >>that's awesome. And gal will leave a fun one for you. What can we look forward to from Amazon Red Shift next year? >>That's a great question. And you know, the team has been innovating extremely fast. We released more than 200 features in the last year and a half, and we continue innovating. Um, one thing that stands out is aqua, which is a innovative new technology. Um, in fact, lovely stands for Advanced Square Accelerator, and it allows customers to achieve performance that up to 10 times faster, uh, than what they've seen really outstanding and and the way we've achieved that is through a shift in paradigm in the actual technological implementation section. Uh, aqua is a new distributed and hardware accelerated processing layer, which effectively allows us to push down operations analytics operations like compression, encryption, filtering and aggregations to the storage there layer and allow the aqua nodes that are built with custom. AWS designed analytics processors to perform these operations faster than traditional soup use. And we no longer need to bring, you know, scan the data and bring it all the way to the computational notes were able to apply these these predicates filtering and encourage encryption and compression and aggregations at the storage level. And likewise is going to be available for every are a three, um, customer out of the box with no changes to come. So I apologize for being getting out a little bit, but this is really exciting. >>No, that's why we invited you. Call. Thank you on. Thank you. Also to Michael and Vika. That was excellent. We really appreciate it. For all of you tuning in at home. The final session of this track is coming up shortly. You aren't gonna want to miss it. We're gonna end strong, come back and hear directly from our customer a T mobile on how T Mobile is building a data driven organization with thought spot in which >>pro, It's >>up next, see you then.

>>Yeah, >>yeah. Hello and welcome to Day two at Beyond. I am so excited that you've chosen to join the building a vibrant data ecosystem track. I might be just a little bit biased, but I think it's going to be the best track of the day. My name is Mallory Lassen and I run partner Marketing here, a thought spot, and that might give you a little bit of a clue as to why I'm so excited about the four sessions we're about to hear from. We'll start off hearing from two thought spotters on how the power of embrace can allow you to directly query on the cloud data warehouse of your choice Next up. And I shouldn't choose favorites, but I'm very excited to watch Cindy housing moderate a panel off true industry experts. We'll hear from Deloitte Snowflake and Eagle Alfa as they describe how you can enrich your organization's data and better understand and benchmark by using third party data. They may even close off with a prediction or two about the future that could prove to be pretty thought provoking. So I'd stick around for that. Next we'll hear from the cloud juggernaut themselves AWS. We'll even get to see a live demo using TV show data, which I'm pretty sure is near and dear to our hearts. At this point in time and then last, I'm very excited to welcome our customer from T Mobile. They're going to describe how they partnered with whip pro and developed a full solution, really modernizing their analytics and giving self service to so many employees. We'll see what that's done for them. But first, let's go over to James Bell Z and Ana Son on the zero to search session. James, take us away. >>Thanks, Mallory. I'm James Bell C and I look after the solutions engineering and customer success teams have thought spot here in Asia Pacific and Japan today I'm joined by my colleague Anderson to give you a look at just how simple and quick it is to connect thought spot to your cloud data warehouse and extract value from the data within in the demonstration, and I will show you just how we can connect to data, make it simple for the business to search and then search the data itself or within this short session. And I want to point out that everything you're going to see in the demo is Run Live against the Cloud Data Warehouse. In this case, we're using snowflake, and there's no cashing of data or summary tables in terms of what you're going to see. But >>before we >>jump into the demo itself, I just like to provide a very brief overview of the value proposition for thought spot. If you're already familiar with thought spot, this will come as no surprise. But for those new to the platform, it's all about empowering the business to answer their own questions about data in the most simple way possible Through search, the personalized user experience provides a familiar search based way for anyone to get answers to their questions about data, not just the analysts. The search, indexing and ranking makes it easy to find the data you're looking for using business terms that you understand. While the smart ranking constantly adjust the index to ensure the most relevant information is provided to you. The query engine removes the complexity of SQL and complex joint paths while ensuring that users will always get thio the correct answers their questions. This is all backed up by an architecture that's designed to be consumed entirely through a browser with flexibility on deployment methods. You can run thought spot through our thoughts about cloud offering in your own cloud or on premise. The choice is yours, so I'm sure you're thinking that all sounds great. But how difficult is it to get this working? Well, I'm happy to tell you it's super easy. There's just forced steps to unlock the value of your data stored in snowflake, Red Shift, Google, Big Query or any of the other cloud data warehouses that we support. It's a simple is connecting to the Cloud Data Warehouse, choosing what data you want to make available in thought spot, making it user friendly. That column that's called cussed underscore name in the database is great for data management, but when users they're searching for it, they'll probably want to use customer or customer name or account or even client. Also, the business shouldn't need to know that they need to get data from multiple tables or the joint parts needed to get the correct results in thought spot. The worksheet allows you to make all of this simple for the users so they can simply concentrate on getting answers to their questions on Once the worksheet is ready, you can start asking those questions by now. I'm sure you're itching to see this in action. So without further ado, I'm gonna hand over to Anna to show you exactly how this works over to you. Anna, >>In this demo, I'm going to go to cover three areas. First, we'll start with how simple it is to get answers to your questions in class spot. Then we'll have a look at how to create a new connection to Cloud Data Warehouse. And lastly, how to create a use of friendly data layer. Let's get started to get started. I'm going to show you the ease off search with thoughts Spot. As you can see thought spot is or were based. I'm simply lobbying. Divide a browser. This means you don't need to install an application. Additionally, possible does not require you to move any data. So all your data stays in your cloud data warehouse and doesn't need to be moved around. Those sports called differentiator is used experience, and that is primarily search. As soon as we come into the search bar here, that's what suggestion is guiding uses through to the answers? Let's let's say that I would wanna have a look at spending across the different product categories, and we want Thio. Look at that for the last 12 months, and we also want to focus on a trending on monthly. And just like that, we get our answer straightaway without alive from Snowflake. Now let's say we want to focus on 11 product category here. We want to have a look at the performance for finished goods. As I started partially typing my search them here, Thoughts was already suggesting the data value that's available for me to use as a filter. The indexing behind the scene actually index everything about the data which allowed me to get to my data easily and quickly as an end user. Now I've got my next to my data answer here. I can also go to the next level of detail in here. In third spot to navigate on the next level of detail is simply one click away. There's no concept off drill path, pre defined drill path in here. That means we've ordered data that's available to me from Snowflake. I'm able to navigate to the level of detail. Allow me to answer those questions. As you can see as a business user, I don't need to do any coding. There's no dragon drop to get to the answer that I need right here. And she can see other calculations are done on the fly. There is no summary tables, no cubes building are simply able to ask the questions. Follow my train or thoughts, and this provides a better use experience for users as anybody can search in here, the more we interact with the spot, the more it learns about my search patterns and make those suggestions based on the ranking in here and that a returns on the fly from Snowflake. Now you've seen example of a search. Let's go ahead and have a look at How do we create a connection? Brand new one toe a cloud at a warehouse. Here we are here, let me add a new connection to the data were healthy by just clicking at new connection. Today we're going to connect Thio retail apparel data step. So let's start with the name. As you can see, we can easily connect to all the popular data warehouse easily. By just one single click here today, we're going to click to Snowflake. I'm gonna ask some detail he'd let me connect to my account here. Then we quickly enter those details here, and this would determine what data is available to me. I can go ahead and specify database to connect to as well, but I want to connect to all the tables and view. So let's go ahead and create a connection. Now the two systems are talking to each other. I can see all the data that's available available for me to connect to. Let's go ahead and connect to the starter apparel data source here and expanding that I can see all the data tables as available to me. I could go ahead and click on any table here, so there's affect herbal containing all the cells information. I also have the store and product information here I can make. I can choose any Data column that I want to include in my search. Available in soft spot, what can go ahead and select entire table, including all the data columns. I will. I would like to point out that this is important because if any given table that you have contains hundreds of columns it it may not be necessary for you to bring across all of those data columns, so thoughts would allow you to select what's relevant for your analysis. Now that's selected all the tables. Let's go ahead and create a connection. Now force what confirms the data columns that we have selected and start to read the medic metadata from Snowflake and automatically building that search index behind the scene. Now, if your daughter does contain information such as personal, identifiable information, then you can choose to turn those investing off. So none of that would be, um, on a hot spots platform. Now that my tables are ready here, I can actually go ahead and search straight away. Let's go ahead and have a look at the table here. I'm going to click on the fact table heat on the left hand side. It shows all the data column that we've brought across from Snowflake as well as the metadata that also brought over here as well. A preview off the data shows me off the data that's available on my snowflake platform. Let's take a look at the joints tap here. The joint step shows may relationship that has already been defined the foreign and primary care redefining snowflake, and we simply inherited he in fourth spot. However, you don't have toe define all of this relationship in snowflake to add a joint. He is also simple and easy. If I click on at a joint here, I simply select the table that I wanted to create a connection for. So select the fact table on the left, then select the product table onto the right here and then simply selected Data column would wish to join those two tables on Let's select Product ID and clicking next, and that's always required to create a joint between those two tables. But since we already have those strong relationship brought over from Snow Flag, I won't go ahead and do that Now. Now you have seen how the tables have brought over Let's go and have a look at how easy is to search coming to search here. Let's start with selecting the data table would brought over expanding the tables. You can see all the data column that we have previously seen from snowflake that. Let's say I wanna have a look at sales in last year. Let's start to type. And even before I start to type anything in the search bar passport already showing me all those suggestions, guiding me to the answers that's relevant to my need. Let's start with having a look at sales for 2019. And I want to see this across monthly for my trend and out off all of these product line he. I also want to focus on a product line called Jackets as I started partially typing the product line jacket for sport, already proactively recommending me all the matches that it has. So all the data values available for me to search as a filter here, let's go ahead and select jacket. And just like that, I get my answer straight away from Snowflake. Now that's relatively simple. Let's try something a little bit more complex. Let's say I wanna have a look at sales comparing across different regions, um, in us. So I want compare West compared to Southwest, and then I want to combat it against Midwest as well as against based on still and also want to see these trending monthly as well. Let's have look at monthly. If you can see that I can use terms such as monthly Key would like that to look at different times. Buckets. Now all of these is out of the box. As she can see, I didn't have to do any indexing. I didn't have to do any formulas in here. As long as there is a date column in the data set, crossbows able to dynamically calculate those time bucket so she can see. Just by doing that search, I was able to create dynamic groupings segment of different sales across the United States on the sales data here. Now that we've done doing search, you can see that across different tables here might not be the most user friendly layer we don't want uses having to individually select tables. And then, um, you know, selecting different columns with cryptic names in here. We want to make this easy for users, and that's when a work ship comes in. But those were were sheet encapsulate all of the data you want to make available for search as well as formulas, as well as business terminologies that the users are familiar with for a specific business area. Let's start with adding the daughter columns we need for this work shape. Want to slack all of the tables that we just brought across from Snowflake? Expanding each of those tables from the facts type of want sales from the fax table. We want sales as well as the date. Then on the store's table. We want store name as well as the stay eating, then expanding to the product we want name and finally product type. Now that we've got our work shit ready, let's go ahead and save it Now, in order to provide best experience for users to search, would want to optimize the work sheet here. So coming to the worksheet here, you can see the data column that we have selected. Let's start with changing this name to be more user friendly, so let's call it fails record. They will want to call it just simply date, store name, call it store, and then we also want state to be in lower case product name. Simply call it product and finally, product type can also further optimize this worksheet by adding, uh, other areas such as synonyms, so allow users to use terms of familiar with to do that search. So in sales, let's call this revenue and we all cannot also further configure the geo configuration. So want to identify state in here as state for us. And finally, we want Thio. Also add more friendly on a display on a currency. So let's change the currency type. I want to show it in U. S. Dollars. That's all we need. So let's try to change and let's get started on our search now coming back to the search here, Let's go ahead. Now select out worksheet that we have just created. If I don't select any specific tables or worksheets, force what Simply a search across everything that's available to you. Expanding the worksheet. We can see all of the data columns in heat that's we've made available and clicking on search bar for spot already. Reckon, making those recommendations in here to start off? Let's have a look at I wanna have a look at the revenue across different states for here today, so let's use the synonym that we have defined across the different states and we want to see this for here today. Um yesterday as well. I know that I also want to focus on the product line jacket that we have seen before, so let's go ahead and select jacket. Yeah, and just like that, I was able to get the answer straight away in third spot. Let's also share some data label here so we can see exactly the Mount as well to state that police performance across us in here. Now I've got information about the sales of jackets on the state. I want to ask next level question. I want to draw down to the store that has been selling these jackets right Click e. I want to drill down. As you can see out of the box. I didn't have to pre define any drill paths on a target. Reports simply allow me to navigate to the next level of detail to answer my own questions. One Click away. Now I see the same those for the jackets by store from year to date, and this is directly from snowflake data life Not gonna start relatively simple question. Let's go ahead and ask a question that's a little bit more complex. Imagine one. Have a look at Silas this year, and I want to see that by month, month over month or so. I want to see a month. Yeah, and I also want to see that our focus on a sale on the last week off the month. So that's where we see most. Sales comes in the last week off the month, so I want to focus on that as well. Let's focus on last week off each month. And on top of that, I also want to only focus on the top performing stores from last year. So I want to focus on the top five stores from last year, so only store in top five in sales store and for last year. And with that, we also want to focus just on the populist product types as well. So product type. Now, this could be very reasonable question that a business user would like to ask. But behind the scenes, this could be quite complex. But First part takes cares, or the complexity off the data allow the user to focus on the answer they want to get to. If we quickly have a look at the query here, this shows how forceful translate the search that were put in there into queries into that, we can pass on the snowflake. As you can see, the search uses all three tables as well shooting, utilizing the joints and the metadata layer that we have created. Switching over to the sequel here, this sequel actually generate on the fly pass on the snowflake in order for the snowflake to bring back to result and presented in the first spot. I also want to mention that in the latest release Off Hot Spot, we also bringing Embraced um, in the latest version, Off tosspot 6.3 story Q is also coming to embrace. That means one click or two analysis. Those who are in power users to monitor key metrics on kind of anomalies, identify leading indicators and isolate trends, as you can see in a matter of minutes. Using thought spot, we were able to connect to most popular on premise or on cloud data warehouses. We were able to get blazing fast answers to our searches, allow us to transform raw data to incite in the speed off thoughts. Ah, pass it back to you, James. >>Thanks, Anna. Wow, that was awesome. It's incredible to see how much committee achieved in such a short amount of time. I want to close this session by referring to a customer example of who, For those of you in the US, I'm sure you're familiar with who, Lou. But for our international audience, who Lou our immediate streaming service similar to a Netflix or Disney Plus, As you can imagine, the amount of data created by a service like this is massive, with over 32 million subscribers and who were asking questions of over 16 terabytes of data in snow folk. Using regular B I tools on top of this size of data would usually mean using summary or aggregate level data, but with thoughts. What? Who are able to get granular insights into the data, allowing them to understand what they're subscribes of, watching how their campaigns of performing and how their programming is being received, and take advantage of that data to reduce churn and increase revenue. So thank you for your time today. Through the session, you've seen just how simple it is to get thought spot up and running on your cloud data warehouse toe. Unlock the value of your data and minutes. If you're interested in trying this on your own data, you can sign up for a free 14 day trial of thoughts. What cloud? Right now? Thanks again, toe Anna for such awards and demo. And if you have any questions, please feel free to let us know. >>Awesome. Thank you, James and Anna. That was incredible. To see it in action and how it all came together on James. We do actually have a couple of questions in our last few minutes here, Anna. >>The first one will be >>for you. Please. This will be a two part question. One. What Cloud Data Warehouses does embrace support today. And to can we use embrace to connect to multiple data warehouses. Thank you, Mallory. Today embrace supports. Snowflake Google, Big query. Um, Red shift as you assign that Teradata advantage and essay Bahana with more sources to come in the future. And, yes, you can connect on live query from notable data warehouses. Most of our enterprise customers have gotta spread across several data warehouses like just transactional data and red Shift and South will start. It's not like, excellent on James will have the final question go to you, You please. Are there any size restrictions for how much data thought spot can handle? And does one need to optimize their database for performance, for example? Aggregations. >>Yeah, that's a great question. So, you know, as we've just heard from our customer, who there's, there's really no limits in terms of the amount of data that you can bring into thoughts Ponant connect to. We have many customers that have, in excess of 10 terabytes of data that they're connecting to in those cloud data warehouses. And, yeah, there's there's no need to pre aggregate or anything. Thought Spot works best with that transactional level data being able to get right down into the details behind it and surface those answers to the business uses. >>Excellent. Well, thank you both so much. And for everyone at home watching thank you for joining us for that session. You have a few minutes toe. Get up, get some water, get a bite of food. What? You won't want to miss this next panel in it. We have our chief data strategy off Officer Cindy, Housing speaking toe experts in the field from Deloitte Snowflake and Eagle Alfa. All on best practices for leveraging external data sources. See you there

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by AWS Global Partner Network Welcome to the Cubes. Live coverage of AWS reinvent 2020. I'm Lisa Martin. I've got two guests joining me. Next. Chris Gru's director of Business development, AWS Marketplace Service catalog and Control Tower at AWS. Chris, welcome. >>Thank you. Welcome. Good to see you. >>Likewise. And Matthew Polly is an alumni of the Cube. He is back VP of worldwide business development alliances and channels at Crowdstrike Matthew, Welcome toe. Welcome back. >>Great to be here. Lisa, Thanks for having me. >>And I see you're in your garage, your f one car in the background. Very jealous. So we're gonna be talking a little bit about not f one today, but about what's going on. Some of the the news that's coming from the partner Keynote. So, Chris, let's start with you. What's going on? The AWS marketplace news and also give our audience a real good understanding of what the marketplace is. >>Yeah, sure. So So AWS marketplace is actually an eight year old service within the AWS family, and and our charter is really providing a fine by deploy and manage experience for third party software. And so what our organization does. We work with my issues like Crowdstrike, and we really try to get them to package up their software in that same consumption format that other customers are buying AWS services. It's already the best service already. Those customers are used to buying services like Red Shift, and that's three and a consumption format, and they want to be able to buy third party software in that same manner. And so that's really been our charter since we were launched eight years ago. We've had a lot of great mo mentum since our launch. We now have over 8000 listings available in the catalog, and we have over 1.5 million subscriptions going through the catalog. One of things that we announced earlier today is that we are up to 300,000 active customers. That's actually up from 260,000, which is our previous numbers. So we continue to see really good momentum in terms of adoption, from both our eyes, community publishing listings and then from our customers that are actually buying out of the catalog. We work on all types of formats of software, so we provide machine images in an Amazon machine image format. But we also published and make available SAS products, container products and algorithms and models to run in things like our sage maker environment. And then, as of this morning in the Global Partner Summit, we announced the ability to sell professional services through eight of this marketplace as well. >>So lots of expansion, lots of growth. I'd love to get Chris your take on this expansion into offering professional services. What does that mean? And how have your 300,000 plus customers been influential in that? >>Yeah. And so what we've seen is marketplaces evolved is the transaction sizes have actually gone up dramatically. A couple years ago we launched a feature called Private Offers, which allows eyes views to do a negotiated subscription, submit that to an AWS customer and that they accept that goes right on their bill. We've seen very good adoption that we've got thousands of private offers now going through the system and what we found when the transaction sizes started to grow. Both our eyes V s that we're using the platform, as well as the consulting partners that are partners with US through Amazon Partner Network. They typically attached services to those transactions So pure and eyes V you might wanna package on something like an installation service training services. Or it could just be a bespoke statement of work that goes along with your technology and then on the consulting partner side. Resellers want to attach those same type of services to the software that they re sell, and up until this morning we weren't able to do that. And so it provided a lot of friction to our customers or buyers because what they had to do is they actually had to bottom line those transactions, or they had to do those transactions outside of marketplace. And And that wasn't a good experience for either RSV community or restore community or customers. So now, with this launch, we could actually allow customers to buy those services from those Eyes v partners and those resellers. By virtue of doing that to marketplace and basically how it works. It's similar to our private offer experience. They just submit a private offer to that customer. They could upload a statement of work. And if that customer accept, it goes directly on their AWS bill and they did. This marketplace takes care of all the collection, and the building that goes goes along with that transaction. And so we're really excited about this. We had over 100 launch partners that we're ready to go as of this morning, and we think this is gonna be a great feature, is gonna get a lot of adoption. Crowdstrike, which is a company that Matthews with is one of our launch partners for that feature. And so we just think this is gonna be a game changer for us on a number of levels. It's really gonna open up the type of transactions that we can now do to market place. >>Well, you mentioned Ah, good f word frictionless. That's something that every business really aims to do to make that experience just as seamless as possible. So Matthew talk to us about crowdstrike being part of its professional services, launched the opportunities that that opens up for the marketplace, customers and your customers? >>Sure. So just a quick background on crowdstrike were an endpoint protection cybersecurity company that has historically been protecting laptops desktops on premise, uh, devices from from breaches, basically identifying indications of attack or indications of compromise that that may surface on those end points. We do that by having agents run on those devices and point back to our massive body of data that runs in the cloud A W s. In fact, and so collecting tons and tons of data petabytes upon petabytes of data, literally trillions of events per week were able to easily identify and apply machine learning and artificial intelligence, Um, to that corpus of data to be able to identify when there is adversary activity on those devices. Now we've gone through a bit of a digital transformation ourselves, and we're looking at now. Not only, or we have launched products here recently, that not only protect those on premise devices like the desktops, laptops and on premise servers, but also protect workloads that are running in the cloud E C. Two instances, or RDS instances. What have you in in AWS? Or we've also launched what crowdstrike calls are Falcon Horizon product, which is a cloud security posture management product to be able to give people visibility into configurations that may create risk for their cloud environments. And we've been leveraging marketplace for about two years now. Um, it's been a fantastic opportunity for us to really leverage that frictionless sales motion that Chris talked about reducing sale cycles for us and for our channel partners. We have a number of our channel partners that leverage the CPP Oh capability within within the AWS marketplace toe actually transact business with their customers. It's been a It's been a fantastic, um you know, mechanism for for crowdstrike, for our partners and for our customers. Um, you know, we've been part of the enterprise contract scenarios where we don't have to go through that process of negotiating an end user license contract. We've signed up for the enterprise contract. Many of our customers have signed up for that enterprise contracts with reduces the legal iterations to get a transaction done. So that's been fantastic. And what we're doing now with the you know, the professional services offering is we're standing up a few of our professional services, Um, you know, offerings on the AWS marketplace so that our customers and our channel partners can actually transact business through the AWS marketplace toe, acquire those particular professional services offerings. And the one that I think is most interesting is a kind of cloud security assessment where our professional services team will go in and actually evaluate our their configurations. Are there unmanaged, um, you know, accounts running in AWS or what have you that could represent a security risk and make recommendations about how to improve the overall security posture of that cloud environment, leveraging something like crowd strikes Falcon Horizon, as I mentioned earlier, or our cloud workload protection offering. So it >>really >>is about streamlining the procurement, offering them. You know, the ability to thio, offering customers the ability to acquire through the AWS marketplace, whether that's the crowdstrike product or the Crowdstrike service offerings. >>So, Matthew, I imagine given this year that we're all not sitting together face to face in Las Vegas. The events of this year have also brought a lot of challenges from a security perspective. We've seen Ransomware going up dramatically, but also in this massive pitot to work working remotely. I can imagine your customers big opportunity for Crowdstrike to help them when endpoints just scattered. So in terms of that, as well as the impact with what you're doing with AWS marketplace seems like a great opportunity to provide your customers with faster access to ensuring that they can guarantee the security off their all of their data, which is business critical. >>Yeah, 100%. So the kind of global pandemic and work from anywhere has driven demand for crowd strikes capabilities in two ways. Number one people leaving the office and going home. There's a proliferation of physical devices, laptops for people to actually work from home, which obviously need to be protected. And a lot of times these were people that were working from home for the first time. You know, no longer within the protection of the, you know, the corporate network. Maybe they're using a VPN or what have you? But they needed the added protection of an endpoint protection capability like crowd strikes. And the second is a lot of this digital transformation has been accelerated. We've had a few customers tell us they had a three year plan for for their their digital transformation, and a lot of that is moving on. Premise service involves moving on premise servers to the cloud, and they've had to accelerate that two months or even even weeks in cases. And that's driving. You know, huge demand for understanding how to ensure there maintaining the proper security posture for those cloud environments. So speed is key right now, making sure that you're protected and transacting those those you know, those those sale cycles quickly leveraging native US marketplace all is accelerating. >>Yes, speaking of that acceleration and we've talked about that a lot. Matthew. This acceleration of digital transformation years now crammed into months. Chris, let's wrap with you in light of that acceleration, how has that affected positively? The AWS marketplace Bringing in professional services, allowing your customers to have much more available to them, to transact directly and and in a frictionless way, when speed is so critical? >>Yeah, I mean what it really leads to. It just gives us more selection, right? So if you take a step back and you think about the you know, the infamous Amazon fire, well, one of the key components of what makes a fine we'll go a selection. And there was a lot of solutions that we had. We just couldn't sell through marketplace without having some kind of services attach. While there's a lot of products that you could just point, click and go. There are a lot of technology. Do you need to? Some have some kind of hand holding And so, you know, by virtue launching services, this actually opens up the amateur in terms of selection that we could bring into the catalog. One of things that we've been focused on as a late is bringing in business applications as an example. And a lot of times a business application might need services to go on, actually wrap around that solution cell and, you know, be part of that implementation. And so that's the other great thing about this is it's going to give us more selection, and that's just gonna let our customers buy more and more products out of this market place. But do that in this very easy format, where it literally just lets them put these transactions directly on the AWS bill. So we think it's gonna be a great you know, not only for movie deals faster but also providing more solutions to our customers and just giving a better selection experience of AWS customer >>and being able to do that all remotely, which is these days is table stakes. Chris. Matthew, Thank you so much for joining me today. Talking about what's new with the Amazon marketplace. What you guys are doing with professional services and crowdstrike. We appreciate your time. >>Yep. Thank you. Thanks. Lisa. Yep. >>From my guests. I'm Lisa Martin. You're watching the cubes. Live coverage of aws reinvent 2020.

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all >>around the world. This is a cube conversation, Everyone. Welcome to this week's Cube insights, powered by ET are My name is Dave Volante, and we've been reporting every week really on the code. 19. Impact on Budgets Docker Korakia is back in with me soccer. It's great to see you really >>again for having >>your very welcome. Soccer is, of course, the director of research, that we are our data partner and man. I mean, you guys have just been digging into the data or a court reiterate We're down, you know, roughly around minus 5% for the year. The thing about what we're doing here and where they want to stress in the audience that that's going to change. The key point is we don't just do ah, placeholder and update you in December. Every time we get new information, we're going to convey it to you. So let's get right into it. What we want to do today is you kind of part two from the takeaways that we did last week. So let's start with the macro guys. If you bring up the first chart, take us through kind of the top three takeaways. And just to reiterate where we're at >>Yeah, no problem. And look, as you mentioned, uh, what we're doing right now is we're collecting the pulse of CIOs. And so things change on and we continue to expect them to change, you know, in the next few weeks, in the next few months, as things change with it. So just kind of give a recap of the survey and then kind of going through some of our top macro takeaways. So in March mid March, we launched our Technology Spending Intention Survey. We had 1250 CIOs approximately. Take that survey. They provided their updated 2020 verse 2019 spending intentions, right? So effectively, they first Davis, those 20 21st 19 spending intentions in January. And then they went ahead and up state of those based on what happened with move it and then in tandem with that, we did this kind of over 19 drill down survey where we asked CEOs to estimate the budget impact off overnight in versus what they originally forecast in the year. And so that leads us to our first take away here, where we essentially aggregated the data from all these CIOs in that Logan 19 drill down survey. And we saw a revision of 900 basis points so down to a decline of 5%. And so coming into the year, the consensus was about 4% growth. Ah, and now you can see we're down about 5% for the year. And again, that's subject to change. And we're going again re measure that a Z kind of get into June July and we have a couple of months under our belt with the folks at night. The second big take away here is, you know, the industries that are really indicating those declines and spend retail, consumer airlines, financials, telco I key services in consulting. Those are the verticals, as we mentioned last week, that we're really seeing some of the largest Pullbacks and spend from consumers and businesses. So it makes sense that they are revising their budgets downwards the most. And then finally, the last thing we captured that we spoke about last week as well as a few weeks before that, and I think that's really been playing out the last kind of week in 1/2 earnings is CIOs are continuing to press the pedal on digital transformation. Right? We saw that with Microsoft, with service now last night, right, those companies continued the post good numbers and you see good demand, what we're seeing and where those declines that we just mentioned earlier are coming from. It's it's the legacy that's the on premise that your place there's such a concentration of loss and deceleration within some of those companies. And we'll kind of get into that more a Z go through more slides. But that's really what kind of here, you know, that's really what we need to focus on is the declines are coming from very select vendors. >>Yeah, and of course you know where we were in earning season now, and we're paying close attention to that. A lot of people say I just ignore the earnings here, you know, you got the over 19 Mulligan, but But that's really not right. I mean, obviously you want to look at balance sheets, you want to look at cash flows, but also we're squinting through some of the data your point about I t services and insulting is interesting. I saw another research firm put out that you know, services and consulting was going to be OK. Our data does, you know, different. Uh, and we're watching. For instance, Jim Kavanaugh on IBM's earnings call was very specific about the metrics that they're watching. They're obviously very concerned about pricing and their ability. The book business. There we saw the cloud guys announced Google was up in the strong fifties. The estimate is DCP was even higher up in the 80% range. Azure, you know, we'll talk about this killing it. I mean, you guys have been all over of Microsoft and its presence, you know, high fifties aws solid at around 34% growth from a larger base. But as we've been reporting, you know, downturns. They've been they've been good to cloud. >>That's right. And I think, you know, based on the data that we've captured, um, you know, it's people are really pressing the pedal on cloud and SAS with this much remote work, you need to have you know, that structure in place to maintain productivity. >>Okay, let's bring up the next slide. Now. We've been reporting a lot on this sort of next generation work loads Bob one Dato all about storage and infrastructures of service. Compute. There's an obviously some database, but there's a new analytics workload emerging. Uh, and it's kind of replacing, or at least disinter mediating or disrupting the traditional e d ws. I've said for years. CDW is failed to live up to its expectations of 360 degree insights and real time data, and that's really what we're showing here is some of the traditional CDW guys are getting hit on Some of the emerging guys, um, are looking pretty good. So take us through what we're looking at here. Soccer. >>Yeah, no problem. So we're looking at the database data warehousing sector. What you're looking at here is replacement rates. Um And so, as example, if you see up in with roughly 20% replacement, what that means is one out of five people who took the survey for that particular sector for that vendor indicated that they were replacing, and so you can see here for their data. Cloudera, IBM, Oracle. They have very elevated and accelerating replacement rates. And so when we kind of think about this space. You can really see the bifurcation, right? Look how well positioned the Microsoft AWS is. Google Mongo, Snowflake, low replacements, right low, consistent replacements. And then, of course, on the left hand side of the screen, you're really seeing elevated, accelerating. And so this space is It kind of goes with that theme that we've been talking about that we covered last week by application, right when you think about the declines that you're seeing and spend again, it's very targeted for a lot of these kind of legacy legacy vendors. And we're again. We're seeing a lot of the next gen players that Microsoft AWS in your post very strong data. And so here, looking within database, it's very clear as to which vendors are well positioned for 2020 and which ones look like they're being ripped out and swapped out in the next few months. >>So this to me, is really interesting. So you know, you you've certainly reported on the impact that snowflake is having on Terra data. And in some of IBM's business, the old man, he's a business. You can see that here. You know, it's interesting. During the Hadoop days, Cloudera Horton works when they realize that it didn't really make money on Hadoop. They sort of getting the data management and data database and you're seeing that is under pressure. It's kind of interesting to me. Oracle, you know, is still not what we're seeing with terror data, right, Because they've got a stranglehold on the marketplace That's right, hanging in there. Right? But that snowflake would no replacements is very impressive. Mongo consistent performer. And in Google aws, Microsoft AWS supports with Red Shift. They did a one time license with Park Cell, which was an MPP database. They totally retooled a thing. And now they're sort of interestingly copycatting snowflake separating compute from storage and doing some other moves. And yet they're really strong partners. So interesting >>is going on and even, you know, red shift dynamodb all. They all look good. All these all these AWS products continue screen Very well. Ah, in the data warehousing space, So yeah, to your point, there's a clear divergence of which products CIOs want to use and which ones they no longer want in their stack. >>Yeah, the database market is very much now fragment that it used to be in an Oracle db two sequel server. As you mentioned, you got a lot of choices. The Amazon. I think I counted, you know, 10 data stores, maybe more. Dynamodb Aurora, Red shift on and on and on. So a really interesting space, a lot of activity in that new workload that I'm talking about taking, Ah, analytic databases, bringing data science, pooling into that space and really driving these real time insights that we've been reporting on. So that's that's quite an exciting space. Let's talk about this whole workflow. I t s m a service now. Just just announced, uh, we've been consistently crushing it. The Cube has been following them for many, many years, whether, you know, from the early days of Fred Luddy, Bruce Lukman, the short time John Donahoe. And now Bill McDermott is the CEO, but consistent performance since the AIPO. But what are we actually showing here? Saga? Yeah, You bring up that slot. Thank you. >>So our key take away on kind of the i t m m i t s m i t workflow spaces. Look, it's best in breed, which is service now, or some of the lower cost providers. Right There's really no room for middle of the pack, so >>this is an >>interesting charts. And so what you're looking at here, there's a few directives, so kind of walk you through it and then I'll walk through. The actual results is we're looking within service now accounts. And so we're seeing how these companies are doing within or among customers that are using service. Now, today, where you're looking at on the ex, access is essentially shared market share our shared customers, and then on the Y axis you're seeing essentially the spend velocity off those vendors within service. Now's outs, right? So if the vendor was doing well, you would see them moving up into the right, right? That means they're having more customer overlap with service now, and they're also accelerating Spend, but you can see if you will get zendesk. If you look at BMC, it's a managed right. You can see there either losing market share and spend within service now accounts or they're losing spend right and zendesk is another example Here, Um, and what's actually interesting is, and we've had a lot of anecdotal evidence from CIOs is that look they start with service. Now it's best in breed, but a few of them have said, Look, it's got expensive, Um, and so they would move over Rezendes. And then they would look at it versus a conference that last year, and we had a few CEO say, Look at last quarter of the price of zendesk. Andi moved away from Zendesk and subsequently well, with last year. And so it's just it's interesting that, you know, during these times where you know CIOs are reducing their budgets on that look, it's either best of breed or low cost. There's really no room in the middle, and so it's actually kind of interesting. In this space, it's It's an interesting dynamic and being usually it's best of breed or low cost. Rarely do you kind of see both win, and I think that's what kind of makes the space interesting. >>I've been following service now for a number of years. I just make a few comments there. First of all, you know, workday was the gold standard in enterprise software for the longest time and, you know, company and and and I I always considered service now to be kind of part of that you know Silicon Valley Mafia with Frank's Loop. But what's happened is, you know, Sluman did a masterful job of identifying the total available market and executing with demand, and now you know, his successors have picking it beyond there. You know, service now has a market cap that's not quite double, but I mean, I think workday last I checked was in the mid thirties. Service now is market valuation is up in the 60 billion range. I mean, they announced, um uh, just recently, very interestingly, they be expectations. They lowered their guidance relative to consensus guide, but I think the street hose, first of all, they beat their numbers and they've got that SAS model, that very predictable model. And I think people are saying, Look there, just leaving meat on the bone so they can continue to be because that's been their sort of m o these last several years. So you got to like their positioning and you get to talk to customers. They are pricey. You do hear complaints about that, and they've got a strong lock spec. But generally I got my experiences. If people can identify business value and clear productivity, they work through the lock in, you know, they'll just fight it out in the negotiations with procurement. >>That's right, and two things on that. So with service now and and even Salesforce, right, they are a platform like approach type of vendors right where you build on them. And that's what makes them such break companies, right? Even if they have, you know, little nicks and knacks here and there. When they report people see past that right, they understand their best of breed. You build your companies on the service now's and the sales forces of the world. And to the second point, you're exactly right. Businesses want to maintain consistent productivity on, and I think that, you know, is it kind of resonates with the theme, right, doubling down on Cloud and sas. Um, as as you have all this remote work, as you have kind of, you know, questionable are curating marquee a macro environment organizations want to make sure that their employees continue to execute that they're generating consistent productivity. And using these kind of best of breed tools is the way to go. >>It's interesting you mentioned, uh, salesforce and service now for years I've been saying they're on a collision course we haven't seen yet because they're both platforms. I still, uh I'm waiting for that to happen. Let's bring up the next card and let's get into networking way talk. Um Ah. Couple of weeks ago, about the whole shift from traditional Mpls moving to SD win. And this sort of really lays it out. Take us through the data here, please. >>Yeah, no problem. So we're just looking at a handful of vendors here. Really? We're looking at networking vendors that have the highest adoption rates within cloud accounts. And so what we did was we looked inside of aws azure GCC, right. We essentially isolated just those customers. And then we said which networking vendors are seeing the best spend data and the most adoptions within those cloud accounts. And so you get you can kind of see some, uh, some themes here, right? SD lan. Right. You can see Iraqi their VM. Where nsx. You see some next gen load balance saying are they're on the cdn side right then. And so you're seeing a theme here of more next gen players on You're not really seeing a lot of the mpls vendors here, right? They're the ones that have more flattening, decreasing and replacing data. And so the reason just kind of going on this slide is you know, when you kind of think about the networking space as a whole, this is where adoptions are going. This is this is where spends billing and expanded, arise it. And what we just talked about >>your networking such a fascinating space to me because you got you got the leader and Cisco That has helped 2/3 of the market for the longest time, despite competitors like Arista, Juniper and others trying to get in the Air Force and NSX. And the big Neisseria acquisition, you know, kind of potentially disrupted that. But you can see, you know, Cisco, they don't go down without a fight. And ah, there, let's take a look at the next card on Cdn. You know, this is interesting. Uh, you know, you think with all this activity around work from home and remote offices, there's a hot area, But what are we looking at here? >>Yeah, no problem. And that's right, right? You would think. And so we're looking at Cdn players here you would think with the uptake in traffic, you would see fantastic. That scores right for all the cdn vendor. So what you're looking at here and again there's a few lenses on here, so I kind of walk. You kind of walk the audience through here is first we isolated only those individuals that were accelerating their budgets due to work from home. Right. So we've had this conversation now for a few weeks where support employees working from home. You did see a decent number of organizations. I think it was 20 or 30% of organizations at the per server that indicated they're actually accelerate instead. So we're looking at those individuals. And then what we're doing is we're seeing how are how's Cloudflare and aka my performing within those accounts, right? And so we're looking at those specific customers and you could just see within Cloudflare and we practice and security and networking which by more the Cdn piece, How consistent elevated the date is right? This is spend in density, right? Not overall market share is obviously aka my you know, their brand father CD ends. They have the most market share and if you look at optimized to the right. Now you can see the spend velocity is not very good. It's actually negative across boats sector. So you know it's not. We're not saying that. Look, there's a changing of the guard that's occurring right now. We're still relatively small compared talk my But there's just such a start on trust here and again, it kind of goes to what we're talking about. Our macro themes, right? CIOs are continuing to invest in next gen Technologies, and better technologies on that is having an impact on some of these legacy. And, you know, grandfather providers. >>Well, I mean, I think as we enter this again, I've said a number of times. It's ironic overhead coming into a new decade. And you're seeing this throughout the I T. Stack, where you've got a lot of disruptors and you've got companies with large install bases, lot of on Prem or a lot of historical legacy. Yeah, and it's very hard for them to show growth. They often times squeeze R and D because they gotta serve Wall Street. And this is the kind of dilemma they're in, and the only good news with a comma here is there is less bad security go from negative 20% to a negative 8% net score. Um, but wow, what a what a contrast, but to your point, much, much smaller base, but still very relevant. We've seen this movie before. Let's let's wrap with another area that we've talked about. What is virtualization? Desktop virtualization? Beady eye again. A beneficiary of the work from home pivot. Um, And we're focused here, right on Fortune 500 net scores. But give us the low down on this start. >>Yeah, So this is something that look, I think it's it's pretty obvious to into the market you're seeing an uptake and spend across the board versus three months ago in a year ago and spending, etc. Among your desktop virtualization players, there's FBI, right? So that's gonna be your VPN right now. Obviously, they reported pretty good numbers there, so this is an obvious slide, but we wanted to kind of throw it in there. Just say, look, you know, these organizations are seeing nice upticks incent, you know, within the virtualization sectors, specifically within Fortune 500 again, that's kind of, you know, work from home spend that we're seeing here, >>right? So, I mean, this is really a 100% net score in the Fortune 500 for workspaces is pretty amazing. And I think the shared in on this that the end was actually quite large. It wasn't like single digits, Many dozens. I remember when Workspaces first came out, it maybe wasn't ready for prime time. But clearly there's momentum there, and we're seeing this across the board saga. Thanks so much for coming in this week. Really appreciate it. We're gonna be in touch with with you with the TR. We're gonna continue to report on this, but start Dr stay safe. And thanks again. >>Thanks again. Appreciate it. Looking for to do another one. >>All right. Thank you. Everybody for watching this Cube insights Powered by ET are this is Dave Volante for Dr Sadaaki. Remember, all these episodes are available as podcasts. I published weekly on wiki bond dot com Uh, and also on silicon angle dot com Don't forget tr dot Plus, Check out all the action there. Thanks for watching everybody. We'll see you next time. Yeah, yeah, yeah, yeah, yeah

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019, brought to you by Amazon web services and along with its ecosystem partners. >>Oh, welcome back. Inside the sands, we continue our coverage here. Live coverage on the cube of AWS. Reinvent 2019. We're in day three at has been wall to wall, a lot of fun here. Tuesday, Wednesday now Thursday. Dave Volante. I'm John Walls and we're joined by Christian Rahman who was the founder and CEO of for Christian. Good morning to you. Good morning. Thanks for having afternoon. If you're watching on the, uh, on the East coast right now. Um, let's talk about sleep a little bit. I know you're all about data, um, but let's go ahead and introduce the company to those at home who might not be familiar with what your, your poor focus was. The primary focus. Absolutely. So athlete is a managed ETL as a service company. ETL is extract, transform, and load basically about getting data from different data sources, like different applications and databases into a place where it can be analyzed. >>Typically a data warehouse or a data Lake. So let's talk about the big picture then. I mean, because this has been all about data, right? I mean, accessing data, coming from the edge, coming from multiple sources, IOT, all of this, right? You had this proliferation of data and applications that come with that. Um, what are you seeing that big picture wise in terms of what people are doing with their data, how they're trying to access their data, how to turn to drive more value from it and how you serve all those masters, if you will. So there are a few trends that we see these days. One is a, you know, an obvious one that data warehouses are moving to the cloud, right? So, you know, uh, companies used to have, uh, data warehouses on premises and now they're in the cloud. They're, uh, cheaper and um, um, and more scalable, right? With services like a Redshift and snowflake in particular on AWS. Um, and then, uh, another trend is that companies have a lot more applications than they used to. You know, in the, um, in the old days you would have maybe a few data ware, sorry, databases, uh, on premises that you would integrate into your data warehouses. Nowadays you have companies have hundreds or even thousands of applications, um, that effectively become data silos, right? Where, um, uh, analysts are seeing value in that data and they want to want to have access to it. >>So, I mean, ETL is obviously not going away. I mean, it's been here forever and it'll, it'll be here forever. The challenge with ETL has always been it's cumbersome and it's expensive. It's, and now we have this new cloud era. Um, how are you guys changing ETL? >>Yeah. ETL is something that everybody would like to see go away. Everybody would just like, not to do it, but I just want to get access to their data and it should be very unfortunate for you. Right. Well, so we started, uh, we started athlete because we saw that ETL is not going away. In fact, with all the, uh, all these applications and all these needs that analysts have, it's actually becoming a bigger problem than it used to be. Um, and so, uh, what we wanted to do is basically take, take some of that pain out, right? So that companies can get to analyzing their data faster and with less engineering effort. >>Yeah. I mean, you hear this, you know, the typical story is that data scientists spend 80% of their time wrangling data and it's, and it's true in any situation. So, um, are you trying to simplify, uh, or Cloudify ETL? And if so, how are you doing that? >>So with, uh, with the growth in the number of data analysts and the number of data analytics projects that companies wants to take on the, the traditional model of having a few engineers that know how to basically make the data available for analysts, that that model is essentially now broken. And so, uh, just like you want to democratize, uh, BI and democratize analytics, you essentially have to democratize ETL as well, right? Basically that process of making the data ready for analysis. And, uh, and that is really what we're doing at athlete. We're, we're opening up ETL to a much broader audience. >>So I'm interested in how I, so I'm in pain. It's expensive. It's time consuming. Help me Christian, how, how can you help me, sir? >>So, so first of all, we're, we're, um, uh, at least specifically we're a hundred percent AWS, so we're deeply focused on, uh, Redshift data warehouses and S3 and good data lakes. Uh, and you know, there's tremendous amount of innovation. Um, those two sort of sets of technologies now, um, Redshift made a bunch of very cool announcements era at AWS reinvent this year. Um, and so what we do is we take the, uh, the infrastructure piece out, you know, so you can deploy athlete as a hosted service, uh, where we manage all the infrastructure for you or you can deploy it within your VPC. Um, again, you know, in a much, much simplified way, uh, compared to a traditional ETL technologies. Um, and then, you know, beyond that taking, uh, building pipelines, you know, building data pipelines used to be something that would take engineers six months to 18 months, something like that. But, um, but now what we, what we see is companies using athlete, they're able to do it much faster often, um, often an hours or days. >>A couple of questions there. So it's exclusively red shift, is that right? Or other analytic databases and make is >>a hundred percent AWS we're deeply focused on, on integrating well with, with AWS technologies and services. So, um, so on the data warehousing side, we support Redshift and snowflake. >>Okay, great. So I was going to ask you if snowflake was part of that. So, well you saw red shift kind of, I sort of tongue in cheek joke. They took a page out of snowflake separating compute and storage that's going to make customers very happen so they get happy. So they can scale that independently. But there's a big trend going on. I wonder if you can address it in your, you were pointing out before that there's more data sources now because of the cloud. We were just having that conversation and you're seeing the data exchange, more data sources, things like Redshift and snowflake, uh, machine intelligence, other tools like Databricks coming in at the Sage maker, a Sage maker studios, making it simpler. So it's just going to keep going faster and faster and faster, which creates opportunities for you guys. So are you seeing that trend? It's almost like a new wave of compute and workload coming into the cloud? >>Yeah, it's, it's super interesting. Companies can now access, um, a lot more data, more varied data, bigger volumes of data that they could before and um, and they want faster access to it, both in terms of the time that it takes to, you know, to, to bite zero, right? Like the time, the time that it takes to get to the first, uh, first analysis. Um, and also, um, and also in terms of the, the, the data flow itself, right? They, they not want, um, up to the second or up to the millisecond, um, uh, essentially fresh data, uh, in their dashboards and for interactive analysis. And what about the analytics side of this then when we were talking about, you know, warehousing but, but also having access to it and doing something with it. Um, what's that evolution looking like now in this new world? So lots of, um, lots of new interesting technologies there to, um, um, you know, on the, on the BI side and, um, and our focus is on, on integrating really well with the warehouses and lakes so that those, those BI tools can plug in and, and, um, um, and, and, you know, um, get access to the data straight away. Okay. >>So architecturally, why are you, uh, how are you solving the problem? Why are you able to simplify? I'm presuming it's all built in the cloud. That's been, that's kind of an obvious one. Uh, but I wonder if you could talk about that a little bit because oftentimes when we talk to companies that have started born in the cloud, John furrier has been using this notion of, you know, cloud native. Well, the meme that we've started is you take out the T it cloud native and it's cloud naive. So you're cloud native. Now what happens oftentimes with cloud native guys is much simpler, faster, lower cost, agile, you know, cloud mentality. But maybe some, sometimes it's not as functional as a company that's been around for 40 years. So you have to build that up. What's the state of ETL, you know, in your situation. Can you maybe describe that a little bit? How is it that the architecture is different and how address functionality? >>Yeah, I mean, um, so a couple of things there. Uh, um, you, you mentioned Redshift earlier and how they now announce the separation of storage and compute. I think the same is true for e-tail, right? We can, we can build on, um, on these great services that AWS develops like S three and, and, uh, a database migration service and easy to, um, elastic MapReduce, right? We can, we can take advantage of all these, all these cloud primitives and um, um, and, and so the, the infrastructure becomes operationally, uh, easier that way. Um, and, and less expensive and all, all those good things. >>You know, I wonder, Christian, if I can ask you something, given you where you live in a complicated world, I mean, data's complicated and it's getting more complicated. We heard Andy Jassy on Tuesday really give a message to the, to the enterprise. It wasn't really so much about the startups as it previously been at, at AWS reinvent. I mean, certainly talking to developers, but he, he was messaging CEOs. He had two or three CEOs on stage. But what we're describing here with, with red shift, and I threw in Databricks age maker, uh, elastic MapReduce, uh, your tooling. Uh, we just had a company on that. Does governance and, and builders have to kind of cobble these things together? Do you see an opportunity to actually create solutions for the enterprise or is that antithetical to the AWS cloud model? What, what are your thoughts? >>Oh, absolutely know them. Um, uh, these cloud services are, are fantastic primitives, but um, but enterprises clearly have a lot of, and we, we're seeing a lot of that, right? We started out in venture Bactec and, and, and got, um, a lot of, a lot of venture backed tech companies up and running quickly. But now that we're sort of moving up market and, and uh, and into the enterprise, we're seeing that they have a requirements that go way beyond, uh, beyond what, what venture tech, uh, needs. Right. And in terms of security, governance, you know, in, in ETL specifically, right? That that manifests itself in terms of, uh, not allowing data to flow out of, of the, the company's virtual private cloud for example. That's something that's very important in enterprise, a much less important than in, uh, in, in venture-backed tech. Um, data lineage. Right? That's another one. Understanding how data, uh, makes it from, you know, all those sources into the warehouse. What happens along the way. Right. And, and regulated industries in particular, that's very important. >>Yeah. I mean, I, you know, AWS is mindset is we got engineers, we're going to throw engineers at the problem and solve it. Many enterprises look at it differently. We'll pay money to save time, you know, cause we don't have the time. We don't have the resource, I feel like I, I'd like to see sort of a increasing solutions focus. Maybe it's the big SIS that provide that. Now are you guys in the marketplace today? We are. Yup. That's awesome. So how's that? How's that going? >>Yeah. Um, you mean AWS market? Yes. Yes. Uh, yeah, it's, it's um, um, that's definitely one, one channel that, uh, where there's a lot of, a lot of promise I think both. Um, for, for for enterprise companies. Yeah. >>Cause I mean, you've got to work it obviously it doesn't, just the money just doesn't start rolling in you gotta you gotta market yourselves. >>But that's definitely simplifies that, um, that model. Right? So delivering, delivering solutions to the enterprise for sure. So what's down the road for you then, uh, from, from ETL leaps perspectives here or at leaps perspectives. Um, you've talked about the complexities and what's occurred and you're not going away. ETL is here to say problems are getting bigger. What do you see the next year, 12, 18, 24 months as far as where you want to focus on? What do you think your customers are going to need you to focus on? So the big challenge, right is that, um, um, bigger and bigger companies now are realizing that there is a ton of value in their data, in all these applications, right? But in order to, in order to get value out of it, um, you have to put, uh, engineering effort today into building and maintaining these data pipelines. >>And so, uh, so yeah, so our focus is on reducing that, reducing those engineering requirements. Um, right. So that both in terms of infrastructure, pipeline, operation, pipeline setup, uh, and, and those kinds of things. So where, uh, we believe that a lot of that that's traditionally been done with specialized engineering can be done with great software. So that's, that's what we're focused on building. I love the, you know, the company tagged the perfect data pipeline. I think of like the perfect summer, the guy catching a big wave out in Maui or someplace. Good luck on catching that perfect data pipeline you guys are doing. You're solving a real problem regulations. Yeah. Good to meet you. That cause more. We are alive at AWS reinvent 2019 and you are watching the cube.

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Welcome back to the queue from Las Vegas. We are live at AWS reinvent 19 Lisa Martin with John furrier. We've been having lots of great conversations. John, we're about to have another one cause we always love to talk about customer proof in the putting. Please welcome a couple of guests. We have Rafael, director of analytics and data management from triple a life. Welcome. Thanks for having me. Really appreciate it. Our pleasure. And from Burkle anchor Jane, the SVP of cloud platforms. Welcome. Thank you. Thank you so much. Pleasure to be here. So here we are in this, I can't see of people around us as, as growing exponential a by the hour here, but awkward. Let's start with you give her audience an understanding of Merkel, who you are and what you do. >>Yeah, absolutely. So Marco is a global performance marketing agency. We are part of a dental agent network and a, it's almost about 9,000 to 10,000 people worldwide. It's a global agency. What differentiates Merkel from rest of the other marketing agencies is our deep roots and data driven approach. We embrace technology. It's embedded in all our, all our solutions that we take to market. Um, and that's what we pride ourselves with. So, um, that's basically a high level pitch about Merkel. What differentiates us, my role, uh, I lead the cloud transformation for Merkel. Um, uh, basically think of my team as the think tanks who bring in the new technology, come up with a new way of rolling out solutions product I solutions, uh, disruptive solutions, which helps our clients and big fortune brands such as triple life insurance, uh, to transform their marketing ecosystem. >>So let's go ahead and dig. A lot of folks probably know AAA life, but, but Raphael, give us a little bit of an overview. This is a 50 year old organization. >>So we celebrate our 50th 50 year anniversary this year. Actually, we're founded in 1969. So everybody life insurance, we endeavor to be the provider of choice for a AAA member. Tell them to protect what matters most to them. And we offer a diverse set of insurance products across just about every channel. Um, and um, we engage with Merkel, uh, earlier, the, um, in 2018 actually to, to, uh, to build a nice solution that allows us to even better serve the needs of the members. Uh, my role, I am the, I lead our analytics and data management work. So helping us collect data and manage better and better leverage it to support the needs of members. >>So a trip, I can't even imagine the volumes of data that you're dealing with, but it's also, this is people's data, right? This is about insurance, life insurance, the volume of it. How have you, what were some of the things that you said? All right guys, we need to change how we're managing the data because we know there's probably a lot more business value, maybe new services that we can get our on it or eyes >>on it. >>So, so that was, that was it. So as an organization, uh, I want to underscore what you said. We make no compromises when it comes to the safety of our, of our members data. And we take every step possible to ensure that it is managed in a responsible and safe way. But we knew that on, on the platform that we had prior to this, we weren't, we weren't as italics. We wanted to be. We would find that threaten processes would take spans of weeks in order to operate or to run. And that just didn't allow us to provide the member experience that we wanted. So we built this new solution and this solution updates every day, right? There's no longer multi-week cycle times and tumbler processes happen in real time, which allows us to go to market with more accurate and more responsive programs to our members. >>Can you guys talk about the Amazon and AWS solution? How you guys using Amazon's at red shift? Can he says, you guys losing multiple databases, give us a peek into the Amazon services that you guys are taking advantage of that anchor. >>Yeah, please. Um, so basically when we were approached by AAA life to kind of come in and you know, present ourselves our credentials, one thing that differentiated there in that solution page was uh, bringing Amazon to the forefront because cloud, you know, one of the issue that Ravel and his team were facing were scalability aspect. You know, the performance was, was not up to the par, I believe you guys were um, on a two week cycle. That data was a definition every two weeks. And how can we turn that around and know can only be possible to, in our disruptive technologies that Amazon brings to the forefront. So what we built was basically it's a complete Amazon based cloud native architecture. Uh, we leveraged AWS with our chip as the data warehouse platform to integrate basically billions and billions of rows from a hundred plus sources that we are bringing in on a daily basis. >>In fact, actually some of the sources are the fresh on a real time basis. We are catching real time interactions of users on the website and then letting Kimberly the life make real time decisions on how we actually personalize their experience. So AWS, Redshift, you know, definitely the center's centerpiece. Then we are also leveraging a cloud native ELT technology extract load and transform technology called. It's a third party tool, but again, a very cloud native technology. So the whole solution leverage is Python to some extent. And then our veil can talk about AI and machine learning that how they are leveraging AWS ecosystem there. >>Yeah. So that was um, so, uh, I anchor said it right. One thing that differentiated Merkel was that cloud first approach, right? Uh, we looked at it what a, all of the analysts were saying. We went to all the key vendors in this space. We saw the, we saw the architecture is, and when Merkel walked in and presented that, um, that AWS architecture, it was great for me because if nausea immediately made sense, there was no wizardry around, I hope this database scales. I was confident that Redshift and Lambda and dynamo would this go to our use cases. So it became a lot more about are we solving the right business problem and less about do we have the right technologies. So in addition to what Ankur mentioned, we're leveraging our sort of living RNR studio, um, in AWS as well as top low frat for our machine learning models and for business intelligence. >>And more recently we've started transition from R to a Python as a practitioner on the keynote today. Slew a new thing, Sage maker studio, an IDE for machine learning framework. I mean this is like a common set. Like finally, I couldn't have been more excited right? That, that was my Superbowl moment. Um, I was, I was as I was, we were actually at dinner yesterday and I was mentioning Tonker, this is my wishlist, right? I want AWS to make a greater investment in that end user data scientists experience in auto ML and they knocked it out of the park. Everything they announced today, I was just, I was texting frat. Wow, this is amazing. I can't wait to go home. There's a lot of nuances to, and a lot of these announcements, auto ML for instance. Yeah. Really big deal the way they did it. >>And again, the ID who would've thought, I mean this is duh, why didn't we think about this sooner? Yeah. With auto ML that that focus on transparency. Right. And then I think about a year ago we went to market and we ended up not choosing any solutions because they hadn't solved for once you've got a model built, how do you effectively migrated from let's say an analyst who might not have the, the ML expertise to a data science team and the fact that AWS understood out of the gate that you need that transparent all for it. I'm really excited for that. What do you think the impacts are going to be more uptake on the data science side? What do you think the impact of this and the, so I think for, I think we're going to see, um, that a lot of our use cases are going to part a lot less effort to spin up. >>So we're going to see much more, much faster pilots. We're going to have a much clearer sense of is this worth it? Is this something we should continue to invest in and to me we should drive and I expect that a lot, much larger percentage of my team, the analysts are going to be involved in data and data science and machine learning. So I'm really excited about that. And also the ability to inquire, to integrate best practices into what we're doing out of the gate. Right? So software engineers figured out profiling, they figured out the bugging and these are things that machine learners are picking up. Now the fact that you're front and center is really excited. Superbowl moment. You can be like the new England Patriots, 17 straight AFC championship games. Boston. Gosh, I could resist. Uh, they're all Seattle. They're all Seattle here and Amazon. I don't even bring Seattle Patriots up here and Amazon, >>we are the ESPN of tech news that we have to get in as far as conversation. But I want to kind of talk a little bit, Raphael about the transformation because presumably in, in every industry, especially in insurance, there are so many born in the cloud companies that are a lot, they're a lot more agile and they are chasing what AAA life and your competitors and your peers are doing. What your S establishing with the help of anchor and Merkel, how does this allow you to actually take the data that you had, expand it, but also extract insights from maybe competitive advantages that you couldn't think about before? >>Yeah, so I think, uh, so as an organization, even though we're 50 years old, one of the things that drew me to the company and it's really exciting is it's unrelated to thrusting on its laurels, right? I think there's tremendous hunger and appetite within our executive group to better serve our members and to serve more members. And what this technology is allowed is the technology is not a limiting factor. It's an enabling factors. We're able to produce more models, more performant models, process more of IO data, build more features. Um, we've managed to do away with a lot of the, you know, if you take it and you look at it this way and squeeze it and maybe it'll work and systematize more aspects of our reporting and our campaign development and our model development and the observability, the visibility of just the ability to be agile and have our data be a partner to what we're trying to accomplish. That's been really great. >>You talked about the significant reduction in cycle times. If we go back up to the executive suite from a business differentiation perspective, is the senior leadership at AAA understanding what this cloud infrastructure is going to enable their business to achieve? >>Absolutely. So, so our successes here I think have been instrumental in encouraging our organization to continue to invest in cloud. And uh, we're an active, we're actively considering and discussing additional cloud initiatives, especially around the areas of machine learning and AI. >>And the auger question for you in terms of, of your expertise, in your experience as we look at how cloud is changing, John, you know, educate us on cloud cloud, Tuto, AI machine learning. What are, as, as these, as businesses, as industries have the opportunity to for next gen cloud, what are some of the next industries that you think are really prime to be completely transformed? >>Um, I'm in that are so many different business models. If you look around, one thing I would like to actually touch upon what we are seeing from Merkel standpoint is the digital transformation and how customers in today's world they are, you know, how brands are engaging with their customers and how customers are engaging with the brands. Especially that expectations customer is at the center stage here they are the ones who are driving the whole customer engagement journey, right? How all I am browsing a catalog of a particular brand on my cell phone and then I actually purchased right then and there and if I have an issue I can call them or I can go to social media and log a complaint. So that's whole multi channel, you know, aspect of this marketing ecosystem these days. I think cloud is the platform which is enabling that, right? >>This cannot happen without cloud. I'm going to look at, Raphael was just describing, you know, real time interaction, real time understanding the behavior of the customer in real time and engaging with them based on their need at that point of time. If you have technologies like Sage maker, if you have technologies like AWS Redship you have technologies like glue, Kinesis, which lets you bring in data from all these disparate sources and give you the ability to derive some insights from that data in that particular moment and then interact with the customer right then and there. That's exactly what we are talking about. And this can only happen through cloud so, so that's my 2 cents are where they are, what we from Merkel standpoint, we are looking into the market. That's what we are helping our brands through to >>client. I completely agree. I think that the change from capital and operation, right to no longer house to know these are all the sources and all the use cases and everything that needs to happen before you start the project and the ability to say, Hey, let's get going. Let's deliver value in the way that we've had and continue to have conversations and deliver new features, new stores, a new functionality, and at the same time, having AWS as a partner who's, who's building an incremental value. I think just last week I was really excited with the changes they've made to integrate Sage maker with their databases so you can score from the directly from the database. So it feels like all these things were coming together to allow us as a company to better off on push our aims and exciting time. >>It is exciting. Well guys, I wish we had more time, but we are out of time. Thank you Raphael and anchor for sharing with Merkel and AAA. Pleasure. All right. Take care. Or John furrier. I am Lisa Martin and you're watching the cube from Vegas re-invent 19 we'll be right back.

>>live from Miami, Florida. It's the Q covering. IBM is data in a I forum brought to you by IBM. >>Welcome back to the port of Miami, Everybody. You're watching the Cube, the leader in live tech coverage. We're here covering the IBM data and a I form. Rob Thomas is here. He's the general manager for data in A I and I'd be great to see again. >>Right. Great to see you here in Miami. Beautiful week here on the beach area. It's >>nice. Yeah. This is quite an event. I mean, I had thought it was gonna be, like, roughly 1000 people. It's over. Sold or 17. More than 1700 people here. This is a learning event, right? I mean, people here, they're here to absorb best practice, you know, learn technical hands on presentations. Tell us a little bit more about how this event has evolved. >>It started as a really small training event, like you said, which goes back five years. And what we saw those people, they weren't looking for the normal kind of conference. They wanted to be hands on. They want to build something. They want to come here and leave with something they didn't have when they arrived. So started as a little small builder conference and now somehow continues to grow every year, which were very thankful for. And we continue to kind of expand at sessions. We've had to add hotels this year, so it's really taken off >>you and your title has two of the three superpowers data. And of course, Cloud is the third superpower, which is part of IBMs portfolio. But people want to apply those superpowers, and you use that metaphor in your your keynote today to really transform their business. But you pointed out that only about a eyes only 4 to 10% penetrated within organizations, and you talked about some of the barriers that, but this is a real appetite toe. Learn isn't there. >>There is. Let's go talk about the superpower for a bit. A. I does give employees superpowers because they can do things now. They couldn't do before, but you think about superheroes. They all have an origin story. They always have somewhere where they started and applying a I an organization. It's actually not about doing something completely different. It's about extenuating. What you already d'oh doing something massively better. That's kind of in your DNA already. So we're encouraging all of our clients this week like use the time to understand what you're great at, what your value proposition is. And then how do you use a I to accentuate that? Because your superpower is only gonna last if it's starts with who you are as a company or as a >>person who was your favorite superhero is a kid. Let's see. I was >>kind of into the whole Hall of Justice. Super Superman, that kind of thing. That was probably my cartoon. >>I was a Batman guy. And the reason I love that movie because all the combination of tech, it's kind of reminds me, is what's happening here today. In the marketplace, people are taking data. They're taking a I. They're applying machine intelligence to that data to create new insights, which they couldn't have before. But to your point, there's a There's an issue with the quality of data and and there's a there's a skills gap as well. So let's let's start with the data quality problem described that problem and how are you guys attacking it? >>You're a I is only as good as your data. I'd say that's the fundamental problem and organization we worked with. 80% of the projects get slowed down or they get stopped because the company has a date. A problem. That's why we introduce this idea of the A i ladder, which is all of the steps that a company has to think about for how they get to a level of data maturity that supports a I. So how they collect their data, organize their data, analyze their data and ultimately begin to infuse a I into business processes soap. Every organization needs to climb that ladder, and they're all different spots. So for someone might be, we gotta focus on organization a data catalogue. For others, it might be we got do a better job of data collection data management. That's for every organization to figure out. But you need a methodical approach to how you attack the data problem. >>So I wanna ask you about the Aye aye ladder so you could have these verbs, the verbs overlay on building blocks. I went back to some of my notes in the original Ai ai ladder conversation that you introduced a while back. It was data and information architecture at the at the base and then building on that analytics machine learning. Aye, aye, aye. And then now you've added the verbs, collect, organized, analyze and infused. Should we think of this as a maturity model or building blocks and verbs that you can apply depending on where you are in that maturity model, >>I would think of it as building blocks and the methodology, which is you got to decide. Do wish we focus on our data collection and doing that right? Is that our weakness or is a data organization or is it the sexy stuff? The Aye. Aye. The data science stuff. We just This is just a tool to help organizations organize themselves on what's important. I asked every company I visit. Do you have a date? A strategy? You wouldn't believe the looks you get when you ask that question, you get either. Well, she's got one. He's got one. So we got seven or you get No, we've never had one. Or Hey, we just hired a CDO. So we hope to have one. But we use the eye ladder just as a tool to encourage companies to think about your data strategy >>should do you think in the context I want follow up on that data strategy because you see a lot of tactical data strategies? Well, we use Data Thio for this initiative of that initiative. Maybe in sales or marketing, or maybe in R and D. Increasingly, our organization's developing. And should they develop a holistic data strategy, or should they trying to just get kind of quick wins? What are you seeing in the marketplace? >>It depends on where you are in your maturity cycle. I do think it behooves every company to say We understand where we are and we understand where we want to go. That could be the high level data strategy. What are our focus and priorities gonna be? Once you understand focus and priorities, the best way to get things into production is through a bunch of small experiments to your point. So I don't think it's an either or, but I think it's really valuable tohave an overarching data strategy, and I recommended companies think about a hub and spokes model for this. Have a centralized chief date officer, but your business units also need a cheap date officer. So strategy and one place execution in another. There's a best practice to going about this >>the next you ask the question. What is a I? You get that question a lot, and you said it's about predicting, automating and optimizing. Can we unpack that a little bit? What's behind those three items? >>People? People overreact a hype on topics like II. And they think, Well, I'm not ready for robots or I'm not ready for self driving Vehicles like those Mayor may not happen. Don't know. But a eyes. Let's think more basic it's about can we make better predictions of the business? Every company wants to see a future. They want the proverbial crystal ball. A. I helped you make better predictions. If you have the data to do that, it helps you automate tasks, automate the things that you don't want to do. There's a lot of work that has to happen every day that nobody really wants to do you software to automate that there's about optimization. How do you optimize processes to drive greater productivity? So this is not black magic. This is not some far off thing. We're talking about basics better predictions, better automation, better optimization. >>Now interestingly, use the term black magic because because a lot of a I is black box and IBM is always made a point of we're trying to make a I transparent. You talk a lot about taking the bias out, or at least understanding when bias makes sense. When it doesn't make sense, Talk about the black box problem and how you're addressing. >>That starts with one simple idea. A eyes, not magic. I say that over and over again. This is just computer science. Then you have to look at what are the components inside the proverbial black box. With Watson, we have a few things. We've got tools for clients that want to build their own. Aye, aye, to think of it as a tool box you can choose. Do you want a hammer and you want a screwdriver? You wanna nail you go build your own, aye, aye. Using Watson. We also have applications, so it's basically an end user application that puts a I into practice things like Watson assistant to virtually no create a virtual agent for customer service or Watson Discovery or things like open pages with Watson for governance, risk and compliance. So, aye, aye, for Watson is about tools. You want to build your own applications if you want to consume an application, but we've also got in bed today. I capability so you can pick up Watson and put it inside of any software product in the >>world. He also mentioned that Watson was built with a lot of of of, of open source components, which a lot of people might not know. What's behind Watson. >>85% of the work that happens and Watson today is open source. Most people don't know that it's Python. It's our it's deploying into tensorflow. What we've done, where we focused our efforts, is how do you make a I easier to use? So we've introduced Auto Way. I had to watch the studio, So if you're building models and python, you can use auto. I tow automate things like feature engineering algorithm, selection, the kind of thing that's hard for a lot of data scientists. So we're not trying to create our own language. We're using open source, but then we make that better so that a data scientist could do their job better >>so again come back to a adoption. We talked about three things. Quality, trust and skills. We talked about the data quality piece we talked about the black box, you know, challenge. It's not about skills you mention. There's a 250,000 person Gap data science skills. How is IBM approaching how our customers and IBM approaching closing that gap? >>So think of that. But this in basic economic terms. So we have a supply demand mismatch. Massive demand for data scientists, not enough supply. The way that we address that is twofold. One is we've created a team called Data Science Elite. They've done a lot of work for the clients that were on stage with me, who helped a client get to their first big win with a I. It's that simple. We go in for 4 to 6 weeks. It's an elite team. It's not a long project we're gonna get you do for your success. Second piece is the other way to solve demand and supply mismatch is through automation. So I talked about auto. Aye, aye. But we also do things like using a eye for building data catalogs, metadata creation data matching so making that data prep process automated through A. I can also help that supply demand. Miss Max. The way that you solve this is we put skills on the field, help clients, and we do a lot of automation in software. That's how we can help clients navigate this. So the >>data science elite team. I love that concept because way first picked up on a couple of years ago. At least it's one of the best freebies in the business. But of course you're doing it with the customers that you want to have deeper relationships with, and I'm sure it leads toe follow on business. What are some of the things that you're most proud of from the data science elite team that you might be able to share with us? >>The clients stories are amazing. I talked in the keynote about origin stories, Roll Bank of Scotland, automating 40% of their customer service. Now customer SATs going up 20% because they put their customer service reps on those hardest problems. That's data science, a lead helping them get to a first success. Now they scale it out at Wonderman Thompson on stage, part of big W P p big advertising agency. They're using a I to comb through customer records they're using auto Way I. That's the data science elite team that went in for literally four weeks and gave them the confidence that they could then do this on their own. Once we left, we got countless examples where this team has gone in for very short periods of time. And clients don't talk about this because they have to talk about it cause they're like, we can't believe what this team did. So we're really excited by the >>interesting thing about the RVs example to me, Rob was that you basically applied a I to remove a lot of these mundane tasks that weren't really driving value for the organization. And an R B s was able to shift the skill sets. It's a more strategic areas. We always talk about that, but But I love the example C. Can you talk a little bit more about really, where, where that ship was, What what did they will go from and what did they apply to and how it impacted their businesses? A improvement? I think it was 20% improvement in NPS but >>realizes the inquiry's they had coming in were two categories. There were ones that were really easy. There were when they were really hard and they were spreading those equally among their employees. So what you get is a lot of unhappy customers. And then once they said, we can automate all the easy stuff, we can put all of our people in the hardest things customer sat shot through the roof. Now what is a virtual agent do? Let's decompose that a bit. We have a thing called intent classifications as part of Watson assistant, which is, it's a model that understands customer a tent, and it's trained based on the data from Royal Bank of Scotland. So this model, after 30 days is not very good. After 90 days, it's really good. After 180 days, it's excellent, because at the core of this is we understand the intent of customers engaging with them. We use natural language processing. It really becomes a virtual agent that's done all in software, and you can only do that with things like a I. >>And what is the role of the human element in that? How does it interact with that virtual agent. Is it a Is it sort of unattended agent or is it unattended? What is that like? >>So it's two pieces. So for the easiest stuff no humans needed, we just go do that in software for the harder stuff. We've now given the RVs, customer service agents, superpowers because they've got Watson assistant at their fingertips. The hardest thing for a customer service agent is only finding the right data to solve a problem. Watson Discovery is embedded and Watson assistant so they can basically comb through all the data in the bank to answer a question. So we're giving their employees superpowers. So on one hand, it's augmenting the humans. In another case, we're just automating the stuff the humans don't want to do in the first place. >>I'm gonna shift gears a little bit. Talk about, uh, red hat in open shift. Obviously huge acquisition last year. $34 billion Next chapter, kind of in IBM strategy. A couple of things you're doing with open shift. Watson is now available on open shifts. So that means you're bringing Watson to the data. I want to talk about that and then cloudpack for data also on open shifts. So what has that Red had acquisition done for? You obviously know a lot about M and A but now you're in the position of you've got to take advantage of that. And you are taking advantage of this. So give us an update on what you're doing there. >>So look at the cloud market for a moment. You've got around $600 million of opportunity of traditional I t. On premise, you got another 600 billion. That's public clouds, dedicated clouds. And you got about 400 billion. That's private cloud. So the cloud market is fragmented between public, private and traditional. I t. The opportunity we saw was, if we can help clients integrate across all of those clouds, that's a great opportunity for us. What red at open shift is It's a liberator. It says right. Your application once deployed them anywhere because you build them on red hot, open shift. Now we've brought cloudpack for data. Our data platform on the red hot open shift certified on that Watson now runs on red had open shift. What that means is you could have the best data platform. The best Aye, Aye. And you can run it on Google. Eight of us, Azure, Your own private cloud. You get the best, Aye. Aye. With Watson from IBM and run it in any of those places. So the >>reason why that's so powerful because you're able to bring those capabilities to the data without having to move the date around It was Jennifer showed an example or no, maybe was tail >>whenever he was showing Burt analyzing the data. >>And so the beauty of that is I don't have to move any any data, talk about the importance of not having Thio move that data. And I want I want to understand what the client prerequisite is. They really take advantage of that. This one >>of the greatest inventions out of IBM research in the last 10 years, that hasn't gotten a lot attention, which is data virtualization. Data federation. Traditional federation's been around forever. The issue is it doesn't perform our data virtualization performance 500% faster than anything else in the market. So what Jennifer showed that demo was I'm training a model, and I'm gonna virtualized a data set from Red shift on AWS and on premise repositories a my sequel database. We don't have to move the data. We just virtualized those data sets into cloudpack for data and then we can train the model in one place like this is actually breaking down data silos that exist in every organization. And it's really unique. >>It was a very cool demo because what she did is she was pulling data from different data stores doing joins. It was a health care application, really trying to understand where the bias was peeling the onion, right? You know, it is it is bias, sometimes biases. Okay, you just got to know whether or not it's actionable. And so that was that was very cool without having to move any of the data. What is the prerequisite for clients? What do they have to do to take advantage of this? >>Start using cloudpack for data. We've got something on the Web called cloudpack experiences. Anybody can go try this in less than two minutes. I just say go try it. Because cloudpack for data will just insert right onto any public cloud you're running or in your private cloud environment. You just point to the sources and it will instantly begin to start to create what we call scheme a folding. So a skiing version of the schema from your source writing compact for data. This is like instant access to your data. >>It sounds like magic. OK, last question. One of the big takeaways You want people to leave this event with? >>We are trying to inspire clients to give a I shot. Adoption is 4 to 10% for what is the largest economic opportunity we will ever see in our lives. That's not an acceptable rate of adoption. So we're encouraging everybody Go try things. Don't do one, eh? I experiment. Do Ah, 100. Aye, aye. Experiments in the next year. If you do, 150 of them probably won't work. This is where you have to change the cultural idea. Ask that comes into it, be prepared that half of them are gonna work. But then for the 52 that do work, then you double down. Then you triple down. Everybody will be successful. They I if they had this iterative mindset >>and with cloud it's very inexpensive to actually do those experiments. Rob Thomas. Thanks so much for coming on. The Cuban great to see you. Great to see you. All right, Keep right, everybody. We'll be back with our next guest. Right after this short break, we'll hear from Miami at the IBM A I A data form right back.

>> Narrator: Live from San Francisco celebrating 10 years of high tech coverage, It's the Cube! Covering VM World 2019 Brought to you by VMware and its ecosystem partners >> Hello everyone and welcome to the Cube's coverage here in San Francisco, California of the VMWorld 2019. I'm John Furrier with my cohost Dave Vellante Dave, 10 years covering VMWorld since 2010, it's been quite a ride, lot of changes. >> Dave: Sure has. >> John: We're going to do a Power Panel our format we normally do it remote guests in our Palo Alto and Boston studios in person because we're here. Why not do it? Of course, Keith Townsend, CTO Advisor friend of the Cube, Cube host sometimes and Sarbjeet Johal, cloud architect cloud expert, friends on Twitter. We're always jammin' on Twitter. So we'll have to take it to the video. Guys, thanks for joining us on the Power Panel. >> Good to see you, Gents. >> Good seein' ya. >> Good to be here. >> Yeah, I, I hope we don't come to blows, Sarbjeet. I mean we've had some passionate conversations over the past couple months. >> Yeah, Santoro, yes, yes. >> John: The activity has been at an all time high. I mean, snark aside, there's real things to talk about. >> Yes. >> I mean we are talking about VMware a software company, staying with their roots. We know what happened in 2016 The Amazon relationship cleared the air so to speak, pun intended. Vcloud air kind of goes it's way stock prices go up and to the right Yeah, fluctuations happening but still financially doing well. >> Keith: Yeah. >> Customers have clarity. They're an operate. They run, they target operators not developers. We're living in a DevOps world we talk about this all the time dev and ops this is the cloud world that they want Michael Dell was on the Cube Dell Technologies owns VMware they put Pivotal on VMware moves are being made. Keith, how do you make sense of it? What's your take? You've been on the inside. >> Well, you know, VMware has a tough time. Pat came in, 2013, we remember it. He said we are going to double down on virtualization. He is literally paying the cost for that hockey stick movement VMware has had this reputation of being an operator based company Infrastructure based, you go into accounts, you're stuck in this IT Infrastructure cells movement. VMware has done awesome over the past year. Few years, I had to eat a little crow and say that the move to eject Pivotal was the right thing for the Stock but for the reputation, VMware is stuck so Pat, what, tallied up 5 billion dollars in sales, in purchases last week to get out of this motion of being stuck in the IT Infrastructure realm Will it pay off? I think it's going to be a good conversation because they're going to need those Pivotal guys to push this PKS vision of theirs. This PKS and Kubernetes vision that they have >> Well they got to figure it out but certainly it's a software world and one of the things that's interesting we were talking before we started is, they are stuck in that operator world but it's part of DevOps, Dev and Ops. This is the world that they operate in Google's cloud shows how to do it. You got SRE's run things and developers this program infrastructure is code. This is the promise of this new generation. Sarbjeet, we talk about it all the time on Twitter developers coding away not dealing with the infrastructure, that's the goal >> Yeah, traditionally, developers never sort of mucked around with infrastructure. Gradually we are moving into where developers have to take care of infrastructure themselves the teams are like two person teams we hear that all the time. They are responsible for running the show from beginning to the end. Operations are under them, it's Dev and Ops are put together, right? But I'll speak from my own personal experience with working at VMware in the past that from all the companies which are operations focused, that's HP, IBM, and Oracle to a certain extent. So portfolio and all that. And BMC, and CA, those are pure companies in the operations space, right? I think VMware is one of those which values software a lot. So it's a purely, inside the VMware it's purely software driven. But to the outside, what they produce what they have produced in the past that's all operations, right? So I think they can move that switch because of the culture and then with Pivotal acquisition I think it will make it much easier because there's some following of the Pivotal stack, if you will the only caveat I think on that side is it is kind of a little bit of interlocking-ish, right? That is one of the fears I have. >> Who's not, even RedHat these days is, locking you in. >> Yeah, you know, I pulled some interesting stat metadata from a blog post from Paul Fazzone announcing the Pivotal acquisition. He mentioned Kubernetes 22 times. He mentioned Pivotal Cloud Foundry once. So VMware is all in on this open-shift type movement I think VMware is looking at the Red shift I mean Red OpenShift acquisition by IBM and thinking, "Man, I wish we didn't have this "Sense of relationship with Pivotal "So we could have went out and bought RedHat." >> Well that's a good point about Kubernetes, I think you're right on that. And remember, we've been covering Open Stack up until about a year ago, and they changed the name it's now something else, but I remember when Open Shift wasn't doing well. >> Keith: I do too! >> And what really was a tipping point for them was they had all the elements, but it was Kubernetes that really put them in a position to take advantage of what they were trying to do and I think you're right, I think VMware sees that, now that IBM owns RedHat and Open Shift, it's clear. But I think the vSphere deal with Project Pacific points out that they want to use Kubernetes as a distraction layer for developers, and have a developer interface to vSphere. So they get the operators with vSphere, they put Kubernetes in there and they say, "Hey developers, use us." Now I think that's a hedge also against Pivotal 'cause if that horse doesn't come across the track to the finish line, you know... >> It's definitely a hedge on Containers just a finer point of what you were saying there was a slight difference in the cash outlay for RedHat, 34 billion versus the cash outlay for Pivotal was 800 million. So they picked up an 800 million dollar asset or a 4 billion dollar asset for 2.7 billion. >> Hold on, explain that because 2.7 billion was the number we reported you're saying that VMware put out only 800 million in cash, which, what's that mean? >> That's correct. So they put out 800 million in cash to the existing shareholders of Pivotal, which is a minority of the shareholders. Michael Dell owns 70% of it, VMware owns 15% of it. So they take the public shareholders get the 800 million >> John: They get taken out, yep. >> Michael Dell gets more VMware stock, so now he owns more of VMware. VMware already owns 15% of Pivotal, so for 800 million, they get Pivotal. >> So, the VMware independent shareholders get... they get diluted. >> Right. >> Did they lose out in the deal is the question and I think the thing that most people are missing in this conversation is that Pivotal has a army of developers. Regardless of whether developers focus on PCF or Kubernetes is irrelevant. VMware has a army, a services army now that they can point towards the industry and say, "We have the chops to have "The conversation around why you should "Come to us for developing." >> So I want to come back to that but just, a good question is, Do the VMware shareholders get screwed? Near term, the stock drops, right? Which is what happens, right? Pivotal was up 77% on the day that the Dow dropped 800 points. Here's where I think it makes sense, and there are some external risks. Pivotal plus Carbon Black, the combination they shelled out 2.7 billion in cash. They're going to add a billion dollars to VMware's subscription business next year. VMware trades at 5x revenue multiple, so the shareholders will, in theory, get back 5 billion. In year two, it's going to be 3 billion that they're going to add to the subscription revenue so in theory, that's 15 billion of value added. I think that goes into the thinking, so, now, are people going to flock to VMware? Are Kubernetes developers going to flock to VMware? I mean to your point, that to me, that's the value of Pivotal is they can get VMware into the developer community. 'Cause where is VMware with developers? Nobody, no developers in this audience. >> That's true. >> What are your guys' thoughts on that? >> Yeah, I think that we have to dissect the workload of applications at the enterprise level, right? There are a variety of applications, right, from SAPs Oracles of the world those are two heavyweights in the application space. And then there's a long trail of ISVs, right. And then there's homegrown applications I think where Pivotal plays a big role is the homegrown applications. When you're shipping a lot as an ISV or within your enterprise, you're writing software you're shipping applications to the user base. It could be internal for partners, for customers, right, I think that's where Pivotal plays Pivotal is pivotal, if you will. >> I think that's a good bet too, one of the things we've been pulling the CESoEs data for when we got reinforced we started pulling CESoEs in our network, and it's interesting. They're under the gun to produce security solutions and manage the vendors and do all that stuff they're all telling us, the majority of them are telling us that they're building their own stacks internally to handle the crisis and the challenge of security, which I think's a leading indicator versus the kind of slow, slower CIO which LOVES multi-anything. Multi-vendor, control, a deal with contracts CESoEs, they don't have the DOGMA because they can't have the DOGMA. They got to deliver and they're saying, "We're going to build a stack "On one cloud. "Have a backup cloud, "I want all my developer resources "On this cloud, not fork my team "And I'm going to build a stack "And then I'm going to ship APIs "And say to my suppliers, in the RFP process, "If you support these APIs, "You could do business with us." >> Keith: So, if you don't -- >> That's kind of a cutting edge. If you don't, you can't, you can't. And that's the new normal. We're seeing it with the Jedi deal with Oracle not getting, playing 'cause they're not certified at the level that Amazon is, and you're going to start to see these new requirements emerging this is a huge point. I think that's where Pivotal could really shine not being the, quote, developer channel for VMware. I think it's more of really writing apps >> And John, I think people aren't even going to question that model. Capital One is probably the poster child for that model they actually went out and acquired a start-up, a security, a container security start up, integrated them into their operations and they still failed. Security in the cloud is hard. I think we'll get into a multi-cloud discussion this is one of the reasons why I'm not a big fan of multi-cloud from an architecture perspective, but from a practical challenge, security is one of the number one challenges. >> That's a great point on Capital One in fact, that's a great example. In fact, I love to argue this point. On Twitter, I was heavily arguing this point which is, yeah, they had a breach. But that was a very low-level it's like the equivalent of a S3 bucket not being configured, right? I mean it was so trivial of a problem but still, it takes one whole-- (hearty laughing) One, one entry point for malware to get in. One entry point to get into any network where it's IOT This is the huge challenge. So the question there is, automation. Do you do the, so, again, these are the, that's a solvable problem with Capital One. What we don't know is, what has Capital One done that we don't know that they've solved? So, again, I look at that breech as pretty, obviously, major, but it was a freakin' misconfigured firewall. >> So, come back to your comments on multi-cloud. I'm inferring from what you said, and I'd love to get your opinion, Sarbjeet. That multi-cloud is not an architectural strategy. I've said this. It's kind of a symptom of multiple vendors playing but so, can multi-cloud become, because certainly VMware IBM RedHat, Google with Anthos, maybe a little bit less Microsoft but those three-- >> Dell Technologies. >> Cisco, Cisco and certainly Dell all talking about multi-cloud is the clear strategy that's where CIOs are going, you're not buying it. Will it ever become a clear strategy from an architectural standpoint? >> Multi-cloud is the NSX and I don't mean NSX in VMware NSX it's the Acura NSX of enterprise IT. The idea of owning the NSX is great it brings me into the showroom, but I am going to buy, I'm going to go over to the Honda side or I'm going to go buy the MDX or something more reasonable. Multi-cloud, the idea, sure it's possible. It's possible for me to own a NSX sports car. But it's more practical for me to be able to shop around I can go to Google via cloud simple I mean I can go via cloud simple to Azure, GCP or I can go BMC, I have options to where I land, but to say that I am going to operate across all three? That's the NSX. >> If you had a NSX sports car, by the way, to use the analogy in my mind is great one, the roads aren't open yet. So, yeah, okay great. (hearty laughing) >> Or you go to Germany and you're in California. So, the transport, and again in the applications you could build tech for good applications all you want, and they're talking about tech for good here but if it's insecure, those apps are going to create more entry points. Again, for cyber threats, for malware, so again, the security equation, and you're right is super important, and they don't have it. >> Dave: What's your thought on all (mumble)? >> Sarbjeet: I think on multi-cloud you are, when you are going to use multi-cloud you going to expand the threat surface if you will 'cause you're putting stuff at different places. But I don't think it, like as you said Dave, the multi-cloud is not more of an architectural choice, it's more like a risk mitigation strategy from the vendor point of view. Like, Amazon, who they don't compete with or who they won't compete with in the future we don't know, right? So... >> You mean within the industry. >> Yeah, within the industry right-- >> Autos or healthcare or... >> Sarbjeet: Yeah, they will, they are talking about that, right? So if you put all, all sort of all your bets on that or Azure, let's say even Azure, right? They are not in that kind of category, but still if you go with one vendor, and that's mission critical and something happens like government breaks them up or they go under, sideways, whatever, right? And then your business is stuck with them and another thing is that the whole US business, if you think about it at a global scale, like where US stands and all that stuff and even global companies are using these hourglass providers based in US, these companies are becoming like they're becoming too big to fail, right? If you put everything on one company, right, and then something happens will we bail them out? Right, will the government bail them out? Like stuff like that. Like banks became too big to fail, I think. I think from that point of view, bigger companies will shift to multi-cloud for, to hedge, right, >> Risk Mitigation >> Risk mitigation. >> Yeah, that's, okay, that's fair. >> I mean, I believe in multi-cloud in one definition only. I think, for now, the nirvana of having different workload management across utility bases, that's fantasy. >> Keith: Yeah, that's fantasy. >> I think you could probably engineer it, but there might not be a workload for that or maybe data analytics I could see moving around as a use case, certainly, but I think-- >> D-R! >> The reality is, is that all companies will probably have multiple clouds, clearly like, if you're going to run Office 365, and it's going to be on Azure, you're an Azure customer, okay. You have Azure cloud. If you're building your security stack on Amazon, and got a development team, you're on Amazon. You got two clouds. You add Google in there, big tables, great for certain things you know, Big Query, you got Google. You might even have Alibaba if you're operating in China So, again, you going to have multiple clouds the question is, the workloads define cloud selection. So, I've been on this thing, if you got a workload, an app, that app should choose its best infrastructure possible that maximizes what the outcome is. >> And John, I think what people fail to realize, that users, when you give them a set of tools, they're going to do what users do, which is, be productive. Just like users went out and took credit cards swiped it and got Amazon. If you, if in your environment you have Amazon you have GCP, you have Azure, you have Salesforce, O-365, and a user has access to all five platforms, whether or not you built a multi-cloud application a user's going to find a way to get their work done with all five, and you're going to have multi-cloud fallout because users will build data sets and workloads across that, even if IT isn't the one that designed it. >> All right, guys, final question of the Power Panel Dave, I want to include this for you too, and I'll weigh in as well. Take a minute to share what you're thinking right now is on the industry. What's taking up your attention? What's dominating your Twittershpere right now? What's the bee in your bonnet? What's the hot-button issue that you're kicking the tires on, learning about, or promoting? Sarbjeet, we'll start with you. What's on top of the mind for you these days? >> I think with talk about multi-cloud all the time, that's in discussions all the time and then Blockchain is another like slow-moving train, if you will, I think it's arriving now, and we will see some solutions coming down the pike from different, like a platformization of the Blockchain, if you will, that's happening, I think those are two actually things I keep my eyes on and how developers going to move, which side to take and then how the AWSs dominance is challenged by Microsoft and Google there's one thing I usually talk about on Twittersphere, is that there's a data gravity and there's a scales gravity, right? So people who are getting trained on Amazon, they will tend to stay with them 'cause that's, at the end of the day, it's people using technology, right? So, moving from one to another is a challenge. Whoever throws in a lot of education at the developers and operators, they will win. >> Keith, what are you gettin' excited about? >> So, CTO advisor has this theory about the data framework, or data infrastructure. Multi-cloud is the conversation about workloads going here, there, irrelevant, it's all about the data. How do I have a consistent data policy? A data protection policy, data management policy across SAS, O-365, Sales Force Workday, my IAF providers, my PATH providers, and OMPRIM, how do I move that data and make sure another data management backup company won Best of VMWorld this year. This is like the third or fourth year and a reason it's not because of backup. It's because CIOs, CDOs are concerned about this data challenge, and as much as we want to talk about multi-cloud, I think well, the industry will discover the problem isn't in Kubernetes the solution isn't in Kubernetes it's going to be one of these cool start-ups or one of these legacy vendors such as NetAp, Dell, EMC that solves that data management layer. >> All right, great stuff. My hot button is cloud 2.0 as everyone knows, I think there's new requirements that are coming out, and what got my attention is this enterprise action of VMware, the CIA deal at Amazon, the Jedi deal show that there are new requirements that our customers are driving that the vendors don't have, and that's a function that cloud providers are going to provide, and I think that's that's the canary in the coal mine. >> I've got to chime in. I've got to chime in. Sorry, Lenard, but it's the combination what excites me is the combination of data plus machine intelligence and cloud scale. A new scenario of disruption moving beyond a remote set of cloud services to a ubiquitous set of digital services powered by data that are going to disrupt every industry. That's what I get excited about. >> Guys, great Power Panel. We'll pick this up online. We'll actually get the Power Panels working out of our Palo Alto studio. If you haven't seen the Power Panels, check them out. Search Power Panels the Cube on Google, you'll see the videos. We talk about an issue, we get experts it's an editorial product. You'll see more of that online. More coverage here at VMWorld 2019 after this short break. (lively techno music)