(upbeat music) (air whooshing) >> Good afternoon, cloud nerds, and welcome back to beautiful Las Vegas, Nevada. We are at AWS re:invent day four. Afternoon of day four here on theCUBE. I'm Savannah Peterson, joined by my fabulous cohost, Paul Gillin. Paul, you look sharp today. How you doing? >> Oh, you're just as fabulous, Savannah. You always look sharp. >> I appreciate that. They pay you enough to keep me buttered up over here at- (Paul laughing) It's wonderful. >> You're holding up well. >> Yeah, thank you. I am excited about our next conversation. Two fabulous gentlemen. Please welcome Sam and Monty, welcome to the show. >> Thank you. >> And it was great. Of the PR 2%, the most interesting man alive. (Paul and Savannah laughing) >> In person. Yeah, yeah. >> In the flesh. Our favorite guests so far. So how's the show been for you guys? >> Sam: It's been phenomenal. >> Just spending a lot of time with customers and partners and AWS. It's been great. It's been great. >> It is great. It's really about the community. It feels good to be back. >> Monty: Eating good food, getting my steps in above goals. >> I feel like the balance is good. We walk enough of these convention centers that you can enjoy the libations and the delicious food that's in Las Vegas and still not go home feeling like a cow. It is awesome. It's a win-win. >> To Sam's point though, meeting with customers, meeting with other technology providers that we may be able to partner with. And most importantly, in my role especially, meeting with all of our AWS key stakeholders in the partnership. So yeah, it's been great. >> Everyone's here. It's just different having a conversation in person. Even like us right now. So just in case folks aren't familiar, tell me about Talend. >> Yeah. Well, Talend is a data integration company. We've been around for a while. We have tons of different ways to get data from point A to point B, lots of different sources, lots of different connectors, and it's all about creating accessibility to that data. And then on top of that, we also have a number of solutions around governance, data health, data quality, data observability, which I think is really taking off. And so that's kind of how we're changing the business here. >> Casual change, data and governance. I don't know if anyone's talking about that at all on the snow floor. >> Been on big topic here. We've had a lot of conversations with the customers about that. >> So governance, what new dynamics has the cloud introduced into data governance? >> Well, I think historically, customers have been able to have their data on-prem. They put it into things like data lakes. And now having the flexibility to be able to bring that data to the clouds, it opens up a lot of doors, but it also opens up a lot of risks. So if you think about the chief data officer role, where you have, okay, I want to be able to bring my data to the users. I want to be able to do that at scale, operationally. But at the same time you have a tension then between the governance and the rules that really restrict the way that you can do that. Very strong tension between those two things. >> It really is a delicate balance. And especially as people are trying to accelerate and streamline their cloud projects, a lot to consider. How do you all help them do that? Monty, let's go to you. >> Yeah, we keep saying data, data, what is it really? It's ones and zeros. In this day and age, everything we see, we touch, we do, we either use data, or we create data, and then that... >> Savannah: We are data quite literally. >> We literally are data. And so then what you end up with is all these disparate data silos and different applications with different data, and how do you bring all that together? And that's where customers really struggle. And what we do is we bring it all together, and we make it actionable for the customer. We make it very simple for them to take the data, use it for the outcomes that they're looking for in their business initiatives. >> Expand on that. What do you mean make it actionable? Do you tag it? Do you organize it in some way? What's different about your approach? >> I mean, it's a really flexible platform. And I think we're part of a broader ecosystem. Even internally, we are a data driven company. Coming into the company in April, I was able to come in and get this realtime view of like, "Hey, here's where our teams are." And it's all in front of me in a Tableau dashboard that's populated from Talend integration, bringing data out of our different systems, different systems like Workday where we're giving offers out to people. And so everything from managing headcount to where our AWS spend is, all of that stuff. >> Now, we've heard a lot of talk about data and in fact the keynote yesterday that was focused mainly on data and getting data out of silos. How do you play with AWS in that role? Because AWS has other data integration partners. >> Sam: For sure. >> What's different about your relationship? Yeah. >> Go ahead. >> Yeah, we've had a strong relationship with AWS for many years now. We've got more than 80 connectors into the different AWS services. So we're not new to the AWS game. We align with the sales teams, we align with the partner teams, and then of course, we align with all the different business units and verticals so that we can enact that co-sell motion together with AWS. >> Sam: Yeah. And I think from our product standpoint, again, just being a hyper flexible platform, being able to put, again, any different type of source of data, to any type of different destination, so things like Redshift, being able to bring data into those cloud data warehouses is really how we do that. And then I think we have between bringing data from A to B, we're also able to do that along a number of different dimensions. Whether that's just like, "Hey, we just need to do this once a day to batch, all the way down to event driven things, streaming and the like. >> That customization must be really valuable for your customers as well. So one of the big themes of the show has been cost reduction. Obviously with the economic times as we're potentially dipping our toes into as well, is just in general, always wanting to increase margins. How do you help customers cut cost? >> Well, it's cost cutting, but it's also speed to market. The faster you can get a product to market, the faster you can help your customers. Let's say healthcare life sciences, pharmaceutical companies, patient outcomes. >> Great and timely example there. >> Patient outcomes, how do they get drugs to market quicker? Well, AstraZeneca leveraged our platform along with AWS. And they even said >> Cool. >> for every dollar that they spend on data initiatives, they get $40 back. That's a billion dollars >> Wow. >> savings by getting a drug to market one month faster. >> Everybody wins. >> How do you accelerate that process? >> Well, by giving them the right data, taking all the massive data that I mentioned, siloed in everywhere, and making it so that the data scientists can take all of this data and make use of it, makes sense of it, and move their drug production along much quicker. >> Yeah. And I think there's other things too like being very flexible in the way that it's deployed. Again, I think like you have this historical story of like, it takes forever for data to get updated, to get put together. >> Savannah: I need it now. And in context. >> And I think where we're coming from is almost more of a developer focus where your jobs are able to be deployed in any way you want. If you want to containerize those, you want to scale them, you need to schedule them that way. We plug into a lot of different ecosystems. I think that's a differentiation as well. >> I want to hang out on this one just for a second 'cause it's such a great customer success story and so powerful. I mean, in VC land, if you can take a dollar and make two, they'll give you a 10x valuation, 40. That is so compelling. I mean, do you think other customers could expect that kind of savings? A billion dollars is nothing to laugh at especially when we're talking about developing a vaccine. Yeah, go for it, Sam. >> It really depends on the use case. I think what we're trying to do is being able to say, "Hey, it's not just about cost cutting, but it's about tailoring the offerings." We have other customers like major fast food vendors. They have mobile apps and when you pull up that mobile app and you're going to do a delivery, they want to be able to have a customized offering. And it's not like mass market, 20% off. It's like, they want to have a very tailored offer to that customer or to that person that's pulling open that app. And so we're able to help them architect and bring that data together so that it's immediately available and reliable to be able to give those promotions. >> We had ARP on the show yesterday. We're talking about 50 million subscribers and how they customize each one of their experiences. We all want it to be about us. We don't want that generic at... Yeah, go for it, Paul. >> Oh, okay. >> Yeah. >> Well, I don't want to break break the rhythm here, but one area where you have differentiated, about two years ago you introduced something called the trust score. >> Sam: Yeah. >> Can you explain what that is and how that has resonated with your customers? >> Yeah, let's talk about this. >> Yeah, the thing about the trust score is, how many times have you gotten a set of data? And you look at it and you say, "Where did you get this data? Something doesn't look right here." And with the trust score, what we're able to do is quantify and value the different attributes of the data. Whether it's how much this is being used. We can profile the data, and we have a trust score that runs over time where you can actually then look at each of these data sets. You can look at aggregates of data sets to then say... If you're the data engineer, you can say, "Oh my, something has gone wrong with this particular dataset." Go in, quickly pull up the data. You can see if some third party integration has polluted your data source. I mean, this happens all the time. And I think if you sort of compare this to the engineering world, you're always looking to solve those problems sooner, earlier in the chain. You don't want your consumer calling you saying, "Hey, I've got a problem with the data, or I've got a problem- >> You don't want them to know there was ever a problem in theory. >> Yeah, the trust score helps those data engineers and those people that are taking care of the data address those problems sooner. >> How much data does somebody need to be able to get to the point where they can have a trust score? If you know what I'm trying to say. How do we train that? >> I mean, it can be all the way from just like a single data source that's getting updated, all the way to very large complex ones. That's where we've introduced this hierarchy of data sets. So it's not just like, "Hey, you've got a billion data sources here and here are the trust scores." But it's like, you can actually architect this to say like, "Okay, well, I have these data sets that belong to finance." And then finance will actually get, "Here's the trust score for these data sets that they rely on." >> What causes datasets to become untrustworthy? >> Yeah. Yeah. I mean, it happens all the time. >> A of different things, right? >> In my history, in the different companies that I've been at, on the product side, we have seen different integrations that maybe somebody changes something. In upstream, some of those integrations can actually be quite brittle. And as a consumer of that data, it's not necessarily your fault, but that data ends up getting put into your production database. All of a sudden your data engineering team is spending two days unwinding those transactions, fixing the data that's in there. And all the while, that bad data that's in your production system, is causing a problem for somebody that is ultimately relying on that. >> Is that usually a governance problem? >> I think governance is probably a separate set of constraints. This is sort of the tension between wanting to get all of the data available to your consumers versus wanting to have the quality around it as well. >> It's tough balance. And I think that it's really interesting. Everybody wants great data, and you could be making decisions that affect people's wellness, quite frankly. >> For sure. >> Very dramatically if you're ill-informed. So that's very exciting. >> To your point, we are all data. So if the data is bad, we're not going to get the outcomes that we want ultimately, >> I know. We certainly want the best outcomes for ourselves. >> We track that data health for its entire life cycle throughout the process. >> That's cool. And that probably increases your confidence in the trust score as well 'cause you're looking at so much data all the time. You got a smart thing going on over here. I like it. I like it a lot. >> We believe in it and so does AWS because they are a strong partner of ours, and so do customers. I think we mentioned we've had some phenomenal customer conversations along with- >> What a success story and case study. I want to dust your shoulders off right now if I wasn't tethered in. That's super impressive. So what's next for you all? >> Yeah, so I think we're going to continue down this path of data health and data governance. Again, I kind of talked about the... you're talking about data health being this differentiator on top of just moving the data around and being really good at that. I think you're also going to have different things around country level or state level governance, literal laws that you need to comply with. And so like- >> Savannah: CCPA- >> I mean, a long list- >> Oodles. Yeah. Yeah, yeah, yeah. >> I think we're going to be doing some interesting things there. We are continuing to proliferate the sources of data that we connect to. We're always looking for the latest and greatest things to put the data into. I think you're going to see some interesting things come out of that too. >> And we continue to grow our relationship with AWS, our already strong relationship. So you can procure Talend products to the AWS marketplace. We just announced Redshift serverless support for Talend. >> All their age. >> Which sounds amazing, but because we've been doing this for so long with AWS, dirty little secret, that was easy for us to do because we're already doing all this stuff. So we made the announcement and everyone was like, "Congratulations." Like, "Thanks." >> Look at you all. Full of the humble brags. I love it. >> Talend has gone through some twists and turns over the last couple of years. Company went private, was purchased by Thoma Bravo about a year and a half ago. At that time, your CEO said that it was a chance to really refocus the company on some core strategic initiatives and move forward. Both of you joined obviously after that happened. But what did you see about sort of the new Talend that attracted you, made you want to come over here? >> For sure. Yeah. I think, when I got a chance to talk to the board and talk to Chris, our chair, we talked about there being the growth thesis behind it. So I think Thoma been a great partner to Talend. I think we're able to do some things internally that would be I think, fairly challenging for companies that are in the public markets right now. I think especially, just a lot of pressure on different prices and the cost capital and all of that. >> Right now. >> That was a really casual way of stating that. But yeah, just a little pressure. >> Little bit of pressure. And who knows? Who knows how long that's going to last, right? But I think we've got a great board in place. They've been very strong strategic partner for us talking about all the different ways that we can grow. I think it's been a good partner for us- >> One of the strengths of Thoma's strategy is synergy between the companies they've acquired. >> Oh, for sure. >> They've acquired about 40 software companies. Are you seeing synergy? You talk to those other companies a lot? >> Yeah, so I have an operating partner. I talk with him on a weekly, sometimes daily basis. If we have questions or like, "Hey, what are you seeing in this space?" We can get plugged in to advisors very quickly. I think it's been a very helpful thing where... otherwise, you're relying on your personal network or things like that. >> This is why Monty was saying it was easy for you guys to go serverless. >> And we keep talking about trust, but in this case, Thoma Bravo really trusts our senior leadership team to make the right decisions that Sam and I are here making as we move forward. It's a great relationship. >> Sam: A good team. >> It sounds like it. All the love. I can feel the love even from you guys talking about it, it's genuine. You're not just getting paid to show this. That's fantastic. >> Are we getting paid for this or... >> Yeah. (Savannah giggling) (Paul laughing) I mean, some folks in the audience are probably going to want your autograph after this, although you get that a lot- >> Pictures are available after- >> Yeah, selfies are 10 bucks. That's how I get my boos budget. So last question for you. We have a challenge here on the theCUBE re:invent. We're looking for your 32nd hot take. Think of it as your thought leadership sizzle reel. Biggest takeaway, key themes from the show or looking forward into 2023? Sam, you're ready to rock, go. >> Yeah, totally. >> I think you're going to continue to hear the tension between being able to bring the data to the masses versus the simplicity and being able to do that in a way that is compliant with all the different laws, and then clean data. It's like a lot of different challenges that arise when you do this at scale. And so I think if you look at the things that AWS is announcing, I think you look at any sort of vendor in the data space are announcing, you see them sort of coming around to that set of ideas. Gives me a lot of confidence in the direction that we're going that we're doing the right stuff and we're meeting customers and prospects and partners, and everybody is like... We kind of get into this conversation and I'll say, "Yeah, that's it. We want to get involved in that." >> You can really feel the momentum. Yeah, it's true. It's great. What about you, Monty? >> I mean, I don't need 30 seconds. I mentioned it. >> Great. >> Between Talend and AWS, we're aligned from the sales teams to the product teams, the partner teams and the alliances. We're just moving forward and growing this relationship. >> I love it. That was perfect. And on that note, Sam, Monty, thank you so much for joining us. >> Yeah, thanks for having us. >> I'm sure your careers are going to continue to be rad at Talend and I can't wait to continue the conversation. >> Sam: Yeah, it's a great team. >> Yeah, clearly. I mean, look at you two. If you're any representation of the culture over there, they're doing something great. (Monty laughing) I thank all of you for tuning in to our nearly... Well, shoot. I think now over 100 interviews at AWS Reinvent in Sin City. We are hanging out here. Paul and I've got a couple more for you. So we hope to see you tuning in with Paul Gillin. I'm Savannah Peterson. You're watching theCUBE, the leader in high tech coverage. (upbeat music)

>>with me are and Crystal Graham, a k a a C. She's the C R O of talent, and Chris Degnan is the C R. O of Snowflake. We have to go to market heavies on this section, folks. Welcome to the Cube. >>Thank you. >>Thanks for having us. >>That's our pleasure. And so let's let's talk about digital transformation, right? Everybody loves to talk about it. It zone overused term. I know, but what does it mean? Let's talk about the vision of the data cloud for snowflake and digital transformation. A. C. We've been hearing a lot about digital transformation over the past few years. It means a lot of things to a lot of people. What are you hearing from customers? How are they thinking about when I come, sometimes called DX and what's important to them? Maybe address some of the challenges even that they're facing >>Dave. That's a great question to our customers. Digital transformation literally means staying in business or not. Um, it's that simple. Um, the reality is most agree on the opportunity to modernize data management infrastructure that they need to do that to create the speed and efficiency and cost savings that digital transformation promises. But but now it's beyond that. What's become front and center for our customers is the need for trusted data, supported by an agile infrastructure that and allow a company to pivot operations as they need. Um, let me give you an example of that. One of our customers, a medical device company, was on their digital journey when Cove it hit. They started last year in 2019, and as the pandemic hit at the earlier part of this year, they really needed to take a closer look at their supply chain. On went through an entire supply chain optimization, having been completely disrupted in the you think about the logistics, the transportation, the location of where they needed to get parts, all those things when they were actually facing a need to increase production by about 20 times. In order to meet the demand on DSO, you can imagine what that required them to do and how reliant they were on clean, compliant, accurate data that they could use to make extremely critical decisions for their business. And in that situation, not just for their business but decisions. That would be the about saving lives, so the stakes have gotten a lot higher, and that's that's just one industry. It's it's really across all industries. So when you think about that, really, when you talk to any of our customers, digital transformation is really mean. It really means now having the confidence in data to support the business at critical times with accurate, trusted information. >>Chris, I've always said a key part of digital transformation is really putting data at the core of everything you know, Not not the manufacturing plant, that the core in the data around it, but putting data at the center. It seems like that's what Snowflake is bringing to the table. Can you comment? >>Yeah. I mean, I think if if I look across what's happening and especially a Z A. C said, you know, through co vid is customers are bringing more and more data sets. They wanna make smarter business decisions based on data making, data driven decisions. And we're seeing acceleration of of data moving to the cloud because they're just in abundance of data. And it's challenging to actually manage that data on premise and and as we see those those customers move those large data sets. Think what A C said is spot on is that customers don't just want to have their data in the cloud. But they actually want to understand what the data is, understand, who has access to that data, making sure that they're actually making smart business decisions based on that data. And I think that's where the partnership between both talent and stuff like are really tremendous, where you know we're helping our customers bring their data assets to to the cloud, really landing it and allowing them to do multiple, different types of workloads on top of this data cloud platform and snowflake. And then I think again what talent is bringing to the table is really helping the customer make sure that they trust the data that they're actually seeing. And I think that's a really important aspect of digital transformation today. >>Awesome and I want to get into the partnership. But I don't wanna leave the pandemic just yet. A c. I want to ask you how it's affected customer priorities and timelines with regard to modernizing their data operations and what I mean to that they think about the end and life cycle of going from raw data insights and how they're approaching those life cycles. Data quality is a key part of, you know, a good data quality. You're gonna I mean, obviously you want to reiterate, and you wanna move fast. But if if it's garbage out, then you got to start all over again. So what are you seeing in terms of the effect of the pandemic and the urgency of modernizing those data operations? >>Yeah, but like Chris just said it accelerated things for those companies that hadn't quite started their digital journey. Maybe it was something that they had budgeted for but hadn't quite resourced completely many of them. This is what it took to to really get them off the dying from that perspective, because there was no longer the the opportunity to wait. They needed to go and take care of this really critical component within their business. So, um, you know what? What Covic, I think, has taught companies have taught all of us is how vulnerable even the largest. Um, you know, companies on most robust enterprises could be those companies that had already begun Their digital transformation, maybe even years ago, had already started that process and we're in a better. We're in a great position in their journey. They fared a lot better and we're able to be agile. Were able Thio in a shift. Priorities were able to go after what they needed to do toe to run their businesses better and be able to do so with riel clarity and confidence. And I think that's really the second piece of it is, um or the last six months people's lives have really depended on the data people's lives that have really dependent on uncertainty. The pandemic has highlighted the importance of reliable and trustworthy information, not just the proliferation of data. And as Chris mentioned this data being available, it's really about making sure that you can use that data as an asset Ondas and that the greatest weapon we all have, really there is the information and good information to make a great business decisions. >>Of course, Chris, the other thing we've seen is the acceleration toe to the cloud, which is obviously you're born in the cloud. It's been a real tailwind. What are you seeing in that regard from your I was gonna say in the field, but from your zoom >>advantage. Yeah, well, I think you know, a C talked about supply chain, um, analytics in in her previous example. And I think one of the things that that we did is we hosted a data set. The covert data set over 19 data set within snowflakes, data marketplace. And we saw customers that were, you know, initially hesitant to move to the cloud really accelerate there. They're used to just snowflake in the cloud with this cove Cove. A data set on Ben. We had other customers that are, you know, in the retail space, for example, and use the cova data set to do supply chain analytics and and and accelerated. You know, it helped them make smarter business decisions on that. So So I'd say that you know, Cove, it has, you know, made customers that maybe we're may be hesitant to to start their journey in the cloud, move faster. And I've seen that, you know, really go at a blistering pace right now. >>You know, you just talked about, you know, value because it's all about value. But the old days of data quality in the early days of Chief Data, Officer all the focus was on risk avoidance. How do I get rid of data? How long do I have to keep it? And that has flipped dramatically. You know, sometime during the last decade, >>you can't get away too much from the need for quality data and and govern data. I think that's the first step. You can't really get to, um, you know, to trust the data without those components. And but to your point, the chief Data officers role, I would say, has changed pretty significantly. And in the round tables that I've participated in over the last, you know, several months. It's certainly a topic that they bring to the table that they'd like Thio chat with their peers about in terms of how they're navigating through the balance, that they still need toe to manage to the quality they still need to manage to the governance they still need. Thio ensure that that they're delivering that trusted information to the business. But now, on the flip side as well, they're being relied upon to bring new insights. And that's on bit's, um, really requiring them to work more cross functionally than they may have needed to in the past where that's been become a big part of their job is being that evangelist for data the evangelist. For that, those insights and being able to bring in new ideas for how the business can operate and identified, you know, not just not just operational efficiencies, but revenue opportunities, ways that they can shift. All you need to do is take a look at, for example, retail. You know, retail was heavily impacted by the pandemic this year on git shows how easily an industry could be could be just kind of thrown off its course simply by by a just a significant change like that. Andi need to be able to to adjust. And this is where, um when I've talked to some of the CEOs of the retail customers that we work with, they've had to really take a deep look at how they can leverage their the data at their fingertips to identify new in different ways in which they can respond to customer demands. So it's a it's a whole different dynamic. For sure, I it doesn't mean that that you walk away from the other and the original part of the role of the or the areas in which they were maybe more defined a few years ago when the role of the chief data officer became very popular. I do believe it's more of a balance at this point and really being able to deliver great value to the organization with the insights that they could bring >>well, is he stayed on that for a second. So you have this concept of data health, and I guess what kind of getting tad is that In the early days of Big Data Hadoop, it was just a lot of rogue efforts going on. People realize, Wow, there's no governance And what what seems like what snowflake and talent are trying to do is to make that the business doesn't have to worry about it. Build, build that in, don't bolt it on. But what's what's this notion of data health that you talk about? >>Companies can measure and do measure just about everything, every aspect of their business health. Um, except what's interesting is they don't have a great way to measure the health of their data, and this is an asset that they truly rely on. Their future depends on is that health of their data. And so if we take a little bit of a step back, maybe let's take a look at an example of a customer experiences to kind of make a little bit of a delineation between the differences of data, data, quality, data trust in what data health truly is. We work with a lot of health, a lot of hotel chains. And like all companies today, hotels collect a ton of information. There's mountains of information, private information about their customers through the loyalty clubs and all the information that they collect from there, the front desk, the systems that store their data. You can start to imagine the amount of information that a hotel chain has about an individual, and frequently that information has, you know, errors in it, such as duplicate entries, you know. Is it a Seagram, or is it in Chris Telegram? Same person, Slightly different, depending on how I might have looked or how I might have checked in at the time. And sometimes the data is also mismanaged, where because it's in so many different locations, it could be accessed by the wrong person of someone that wasn't necessarily intended to have that kind of visibility. And so these are examples of when you look at something like that. Now you're starting to get into, you know, privacy regulations and other kinds of things that could be really impactful to a business if data is in the wrong hands or the wrong data is in the wrong hands. So, you know, in a world of misinformation and mistrust, which is around us every single day, um, talent has really invented a way for businesses to verify the veracity, the accuracy of their data. And that's where data health really comes in Is being able to use a trust score to measure the data health on. That's what we have recently introduced is this concept of the trust score, something that can actually provide and measure, um, at the accuracy and the health of the data all the way down to an individual report. We believe that that that truly, you know, provides the explainable trust issue resolution, the kinds of things that companies are looking for in that next stage of overall data management. >>Thank you, Chris. Bring us home. So, one of the key aspects of what snowflake is doing is building out the ecosystem is very, very important. Really talk about how how you guys we're partnering and adding value in particular things that you're seeing customers do today within the ecosystem or with the help of the ecosystem and stuff like that they weren't able to do previously. >>Yeah. I mean, I think you know a C mentioned it. You mentioned it. You know, we spent I spent a lot of my zoom days talking Thio, chief data officers and as I'm talking to the chief data officers that they are so concerned their responsibility on making sure that the business users air getting accurate data so that they view that as data governance is one aspect of it. But the other aspect is the circumference of the data of where it sits and who has access to that data and making sure it's super secure. And I think you know, snowflake is a tremendous landing spot being a data warehouse or data cloud data platform as a service, you know, we take care of all the, you know, securing that data. And I think where talent really helps our customer base is helps them exactly What what is he talked about is making sure that you know myself as a business users someone like myself who's looking at data all the time, trying to make decisions on how many sales people I wanna hire house my forecast coming. You know, how's the how's the product working all that stuff? I need to make sure that I'm actually looking at at good data. And I think the combination of all sitting in a single repository like snowflake and then layering it on top or laying a tool like talent on top of it, where I can actually say, Yeah, that is good data. It helps me make smarter decisions faster. And ultimately, I think that's really where the ecosystem plays. An incredibly important, important role for snowflake in our customers, >>guys to great cast. I wish we had more time, but we gotta go on dso Thank you so much for sharing your perspectives. A great conversation

>> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Everyone, welcome to this CUBE conversation here in theCUBE studios in Palo Alto. We're here for remote interview. We're continuing with the COVID coverage, the quarantine crew. I'm John Furrier, host of theCUBE. Got a great guest, Christal Bemont. The CEO of Talend, just joined the club in the middle of the pandemic. Christal, thanks for joining us and nice seeing you. >> It's a pleasure to be here. Thank you for having me. Well, I think it's a really great conversation to have a couple of threads that are interesting to me. One is, Talend's... We've been covering for a long time, obviously. Their position in the marketplace, we've been following their trajectory. You're new to the company, but you joined right in the middle of, as COVID was going down. And we're still in this mode and it looks like it's going to be for some time. I'd love to get your thoughts as we're in this mode. First, what attracted you to Talend, your new? And, what's it been like there since you've been there, you can't meet people face to face. So you must be doing a lot of remote interviews, then remote conversations. >> Well, you're right about that, I had a very short window that I could get out on the road. And I'm so grateful that I did because visiting our offices, our customers and our partners is critical to, really surrounding ourselves with amazing people that we have Talend. But you know, I'll just go back to why I joined Talend and it really goes to the customers, our customer stories just captured my attention right away. The way that Talend shows up to drive outcomes for customers that are tangible, that are quantifiable, and that are game changing was something that interested me. And it really is that at the heart of every conversation is data. So it was a simple decision for me to say, those are the types of things I want to be involved in. And so Talend was definitely something that became very attractive. >> It's interesting, we've watched the progression of the big data market and now 10 years in, and the explosion of cloud, obviously, everyone's talking about data as a key ingredient for application development. And you're still seeing kind of the challenges of how do you manage the data. And then how do you put that into action for insights, because now you have these connected experiences. And even more highlighted with the COVID pandemic, you still got to run the business, you still need the data. The workforce is remote. The future of work, work force, workplace, workloads and workflows all have data. This is a real. >> That's right >> Challenge with now the connected experience being the number one problem and making that good, and making that valuable. What's your take on? >> That's right. I couldn't agree more. You know, we talked a lot about digital transformation for years, quite frankly. And I would say, you know, we've been in a digital transformation evolution. And I think what has happened now is COVID is an accelerant and it's a, now it's a digital revolution and at the heart or maybe the cornerstone, if you will, of the any digital transformation is data transformation. You think about digital transformation is about mindset. It's about changing your entire way that you operate as a company. It's not just about systems and technology, that's a really critical part. But everything that fuels the ability to get outcomes out of a digital transformation is data. And so the ability to leverage. Like you said, there's connected data, there's more data than we've ever had. And that's a massive opportunity. But having a lot of data is not always the answer. Sometimes that becomes a big responsibility with regulations, and also something that if not carefully governed, not really something you can leverage properly to run your business. So data is at the heart of all the things going on at this moment. >> It's interesting to, you know, a lot of the main trends outside of kind of the inside the industry discussions around data and the role of data. The consumer side of it, is seeing it with fake news. You're seeing it with the data around COVID. Anyone can make data tell a story. There's always you know, >> Right. causation versus correlation, that discussion. But when you start thinking people being exposed to the data problems, there's an opportunity in there and one of the big things is trust. What data can I trust? What's authentic? And then, how do I make sure that it's not just supporting a story? There's all kinds of things going on around it. It makes it seem like a broader challenge. Trust seems to be at the heart of it. What do you trust? Who's the source? It's just all life now as data infiltrated all of our lives. It's certainly now exposed. >> You couldn't be more right on that one. And you can see it play out, in the media, you can see it play out again. This accelerating set of circumstances that are playing out every single day, as people are staying so closely, watchful of data informing decisions that everyone's making around the world in a lot of different ways. And you've seen a lot of times where there's a question about the quality of the data, the accuracy of the data, who's providing the data. And, that's the environment that Talend, really supports and lives in, even prior to COVID. But it just underscores the importance of not just having a complete set of data. And I would say, even taking it further than just having what we would traditionally call quality of data. And really taking it down to something, you used really important word is, trust. How can you make sure that the data that you're making decisions on is something you can trust, and when it comes to health and well being that's certainly something that you can't afford not to have? And it's an area that is underserved right now that we've spent a lot of time thinking about and how we're starting to show up to provide those solutions to our customers. >> I want to get into the customer conversation. I think there's a lot of use cases I want to unpack with you. But I want to first get your vision on how you guys see the future. What is the vision of Talend? And how do you see it? What's the plan? What's the big story there? >> You know, there's a couple of things. I look at this and say, right now in the industry and in our customers, which we cover all different segments, all different sizes of customers all around the globe. They have a variety of use cases, if you will. A variety of needs, everything from the most simple ingestion to some of the more complex transformation and governance projects that they're running. And first and foremost, we show up uniquely as a platform, a platform that allows people to activate and utilize different parts of our services that we can provide to an entire organization. And that's something that is really important to us. And we also look at how do we make the process in which they're using Talend and the skills that are required, you know, really push the envelope on making those as simple as possible. The ability to get to time to value as quickly as possible is our ultimate goal. And then looking, you know, finally, the third lane is to make sure that we can provide not just, as I said, the completeness of data, but that it's really data that they can boil down to something that has intrinsic and quantifiable trust. Because all the time we spend, all the money that's spent on collecting the data is really only as good as the, ability to say I can emphatically trust it, and I can tell you why. And I can show you the footprint of that data. And that's something really important right now more than ever. >> I was talking to my family, I've four kids, and they're all kind of growing up now. And, we're having these conversations on COVID and the question of AI comes up all the time and AI is very, cool for kids, but they don't really know how to talk about machine learning. So I got to ask you around how you see the machine learning piece come in because data feeds AI, I mean you got, it's a real... And that's how I described my kids, data is the fuel for AI and you got to feed that in there. But it's not that easy. What's your reaction to that? Because I think a lot of companies are saying, I have to automate things, the DevOps world and agility come into the mainstream operations of businesses. And there's a agility piece, there's a value of the data is being recognized. But now I got to put it to practice. What's the playbook? What's your reaction to all that? >> Yeah, I think you're right. I mean, first of all, AI and machine learning have a really important role in the simplification, the ability to move at speed and to, perform functions that quite frankly are going to move us into an entirely new realm of possibility. I still will contend, whether you're feeding that with, anything that you feed data into with data has to be really good quality data. AI machine learning is only as good as the information that you're feeding it with. And so, it is really, really critical that we leverage these technologies to their fullest extent, but that we make sure that we feed it in the right way. So I think it's a really big part of our future. I think it's something that's going to be important. But we have to have the certainty that we're using them in a way that's coming to, a place of the right outcome. And that starts with what you feed it to use to go use to improve the processes. >> Christal, one of the patterns we're seeing is that decision makers and CXOs are looking at the COVID pandemic and saying, okay, I did my thing with triage. Now, I got to reset and get the foundation set again and look at the projects that are going to be important. And I got to figure out the holistic architecture 'cause I need a growth strategy, and I got a reset maybe some of the team members projects and whatnot. What's your view on this? Because now new decisions have to be made, roles that might change as well. So this is going to change, how come he's going to make decisions? What's your reaction to that with the customers? They are trying to figure this out, what's your advice? >> Yeah, that's absolutely right. And this is about re-instrumenting a business, reinventing it in many cases, a great example is Domino's, who is maybe surprisingly, for some a pioneer in, digital transformation that's been a number of years in the making, that really has shown that with being in a state of being able to adapt quickly to circumstances and to be forward looking, how critical it is. And so I think this has been a wake up call for organizations across the globe to say we have have to be on the ready, we have to be able to be instrumented in a way that we can make quick decisions and Domino's case it became, originally the ability to you know, they were the first pizza delivery to try out drones for pizza delivery and, you know, to... And have gaming devices where you can order pizza because that's where their customers read and when COVID hit contact list became a criteria and so you can really see how they are able to separate themselves. You see people being leaders that have been further along in their transformation. So I think what this has done is expose some vulnerabilities, quite frankly. And this is a wake up call for companies around the globe that can no longer afford to be in a state where they can't pivot quickly. And looking backwards is no longer the thing that informs people in a state of something like COVID, because there really aren't examples or patterns to look at. So re-instrumenting the business is really critical, data has to be transformed to perform better for companies. >> It's interesting you bring that, a point about the pivot and the companies resetting and reinventing for that growth strategy is that, you're seeing brand impacts and also financial results are directly related to it. So if you're not ready, this has, it could have a real detrimental impact on the brand value, and ultimately financial results. And this is kind of forcing people to say, it's not just an IT problem. It's a business model change and data is shown now to be the key ingredient, because that's where the agility is going to come from, that's where the value is there. And this is all been talked about in the industry before. But now it's kind of our mainstream. This is now the new reality that my brand opportunity and the financial results, my company are at stake. Can you comment on your thinking around that? Because this is a top line, high order bit, if you will conversation among the top boardrooms. >> Yeah, it is. And I agree with you, many of these conversations have been going on for a while now, right. And I think this just exposes the criticality of what happens when you're not in a state of being able to really reinvent yourself or like I said, re-instrument, and if you're already in that state, how much better off you are. Brands are taking a hit in terms of their ability to show up and it goes beyond just their ability to perform, as a business, but to really show up differently for their customers, support people in a different way. And really make sure that they can respond also from a social perspective, how are they going to help and contribute to what the world is facing. And so, it really is asking companies to really fire on all cylinders, quite frankly. >> I want to give you a thoughts on two thought tracks and they're kind of connected, so bear with me. One is, we've heard a lot from the marketplace that with the pandemic, the reality of the IT teams that collect the data and the business teams that have to make the decisions are changing, obviously with the work at home and all the different dynamics around the re-architecting. And then you have the competitive advantage now which people are pointing to as speed and scale. So you've got your internal kind of organizations that are managing wrangling data, ingesting data, the business teams with the customers, and that's kind of was the slow rolling way it was before. Now you got that changing. And now you got pressure to be faster and more scalable. So scale is a competitive advantage, speeds that competitive advantage. These are important kind of flywheel elements of the new models that people are being successful, what is your reaction to that? >> I couldn't agree more. It is a competitive weapon, quite frankly. It is an operational accelerant. And it is an innovation catalyst. And, you know, time is no one's friend, quite frankly, it's one of those odd things right now where for all of us that are working from home and time has this odd sense of reality to it. But it's... You know, really quite frankly you cannot act fast enough. But what's interesting about enabling companies to act fast, that has to come down to the ability for them to be able to, spend the time in the right places. So for example, when I think about the number one thing that we can do is it takes a lot for organization sometimes to put the information in the hands of the right people at the right time. So that the time that's being spent by an overall company, not just an individual within a company but the entire company. You have to be able to decrease that, so that the time that they're spending is actually on helping drive outcomes. And so some of this and you just struck a chord on in everything I think about is, how quickly we can get the right data in the hands of the right people because, in AstraZeneca's case for example, the difference of being able to do that, their highest cost in their business is clinical trials. Being able to get information you can use and reduce a month of, how fast they can bring those clinical trials to bear is saving them hundreds of millions of dollars. But that right now AstraZeneca is an important player in helping us solve for this. So you think about how important it is to get information to the right people, and time is of critical essence right now. >> Yeah, it's interesting (indistinct) that business model advantage, but also you got a lot of... That's an opportunity not for many, but there's also a lot of, I won't say heavy lifting, but maybe a drag, some might call it compliance. You know, GDPR, whatnot. Balancing that kind of, I won't say drag. I mean, I think it's a drag personally, but I think we have to have those things in place. You want to maintain the compliance, rigidity that's out there, but also have room to innovate. That balance is very difficult. And it's really mostly highlighted in the data bases because that's where the action is around data privacy and those compliance things. But if you got an innovation formula there that you're talking about, and you got compliance, if you get one wrong and right, you got to balance it. What's your take on that? Because that's a huge challenge. It's one of those things that's kind of not talked about much, but pretty much there. >> You're right, indeed it is a complete balance but you can't have one without the other. In highly regulated industries, especially with companies like AstraZeneca. But really, if you think about any company the ironic thing right now is that when you're looking at, even a single report, but certainly across an entire company or line of business, right now you can see that there's quality measures and governance that, we put into play. But the ability to actually, quantifiably say on a single piece of data that you can track, where that data has been, who's touched it? How complete is it? And really kind of put a measurable trust score against it, there's work to be done there. But, with GDPR, with HIPAA, and interestingly enough, we're looking to, kind of challenge some of the norms with COVID that says, we now want to collect data that is formally considered privacy, and maybe something that would be regulated. And now we want to share it for the greater good of, making sure that we can track and trace where people are at that maybe are infected and so forth. And so you're starting to see this interesting conversion of challenging the fact that we've got at least be able to support people in their governance of data, but take that a step further, really. >> Awesome, final question. You had Talend Connect, which is your big kind of confab. What best practices are emerging out of Talend these days for customers? If you had to kind of highlight the top use cases or best practices that customers and your potential customers could leverage right now with data, what are you guys putting out there? What are the key best practices? 'Cause everyone has a new reality sets of knowledge, we talk deeply about it, but what's the best practices? What are you guys offering? >> Well, I think, one of the things that I alluded to before is really making sure that we show up as a strategic business partner. And this is really important to us, you know, there all this these things that we've been talking about, they're heavy lifting for organizations to really look at how they bring the digital revolution to the forefront. There's a lot to consider. And so our part in that is to say, we believe that when you power your business on Talend, and you're able to solve for a number for different problems across platform, then that's really important that we show up in the way that we can meet our customers where they're at, so that's one. Making it simple, you know, really pushing the boundaries on the level of expertise, the specialization, the time to value of making sure that they can leverage. Again, spending their time on the things that are important, which are making sure that they're spending it in quality data and data they trust. And then really making sure that final lane is covered up saying, we want to make sure that data is accessible when you need it, where you need it. Things like IoT and edge devices, this proliferation of data is just becoming immense. And so, taking the data, giving it to people, but in a way that they can have confidence. It's the same thing you just said before, there's a lot to consider. And there's in a way a burden of people not knowing maybe all the data they have and how it's being used. We feel responsibility to make sure that we're part of helping that become easy and identifiable and really taking it to the next step beyond quality, so it's really across all of it just simply putting people in a position to be able to make good decisions and not have to do so much of the heavy lifting. And making sure that they know for a fact that it's something that they've made a good decision around because of the data has been trusted, and they can have the confidence in that. >> Awesome, we think data is added advantage. It's just getting more important then ever as the days go on. So great, great insight. Christal, thank you for that insight. Before we end, take a minute to put the plug in for Talend. What do you up to? You guys are hiring, you looking for folks? What's the business plan? Why you guys winning? What's the hot product? Take a minute to give up a quick update on Talend. >> Sure, we're in a great situation where, this is a point in time at Talend where (indistinct) a great trajectory in front of us, we see speed and scale of our organization that has an opportunity in front of it to really help solve problems for every part of the market, whether it's the, smaller businesses who are certainly in it at a point where they're, having a big impact to the largest organizations. And we feel that there's a set of solutions that we can really work to drive as a partner, to each of those customers to solve for the problems that put them in a position to really be able to re-instrument and to reinvent their business. And when we partner like we have with the companies that I mentioned, Domino's and AstraZeneca, and many others, it comes back to why I join Talend, we have the ability to change the outcome of really separating organizations from the pack and data is the competitive advantage. It is the thing that will put people on a different trajectory. And I'm excited about what we bring to the table and I'm really excited about what's to come and how we'll continue to push the envelope for how we help our customers. >> That's awesome, congratulations. Congrats on the new role of Talend to CEO, Christal Bemont. >> Thank you. >> Big up Talend, data is at the heart of the value proposition. We've been saying that for 10 years now more than ever, it's exposed that the value is there, speed and scales the new table stakes for competitiveness and business models for the applications. Again, great CUBE captures, great insight. Christal thank you for joining me today. I'm John Furrier, host of theCUBE. It's been a CUBE conversation. Thanks for watching. (upbeat music)

(energetic music) >> Hey, welcome back, get ready. Jeff Frick here with theCUBE. We're at our Palo Alto Studios for a CUBEConversation, get a little bit of a break from the conference madness which is in full force right now. And we're excited to have our next guest, he's Mike Tuchen, the CEO of Talend coming off a really good quarter. Mike, great to see you. >> Thank you, Jeff. >> You guys are on fire! >> You know, it's a great time to be in the data business right now. (Jeff laughs) >> So give us a little update, what's going on recently? You've got a big show coming up, I imagine there's lots of announcements that are going to come out that you probably can't tell us about at the show, but go ahead and give a plug. It's coming up really soon and let's get into it. >> Yeah, exactly. So just in a couple weeks Talend Connect in London on the 15th and 16th and Talend Connect in Paris on the 17th and 18th. And Talend Connect is our user conference, so we'll have hundreds of people there, a lot of partners there, we'll roll out a whole bunch of new product announcements and talk about a lot of the great stuff that our customers are doing with Talend. >> So you've got an interesting way to kind of package up what you guys do in a really simple way and that's, you said before we turned on the cameras, the first mile, you know there's always so much conversation about the last miles, not necessarily in data but you know, in getting cable to your home and broadband and this, that, and the other but you talked about the first mile. Arguably, that's a lot more important than the last mile. >> Well you can't even get started on anything else until you solve the first mile problem and that's what we do. And the problem is, right now, every single customer in the world is waking up to the power of data and they need to be data-driven. They know it can make a huge difference in their business, and competitively the market leaders are all incredibly data-driven and if companies aren't equally data-driven then they fall behind. And so there's an incredible surge of interest in data-driven, becoming data-driven right now. The challenge that everyone faces is in order to get started down that path, your data is locked up in a lot of different places, it's dirty, it's inconsistent. And until you bring it together, clean it up, and make it consistent you can't do anything with it. That's the first mile. That's what we do. >> So how does it change now? I mean there's obviously been EDL and data cleansing issues for a very, very long time. So when you look at some of the trends, the growth of public cloud, obviously the explosion of data. Now you guys are taking a little bit different approach than kind of the historical method so how do you do it differently and why is it so important? >> From our perspective, we made a bet about five years ago when I joined, that the entire landscape, the IT landscape was being reinvented from the ground up. Not just the data world, the data world for sure, but the entire IT landscape was being reinvented. And that meant you had to solve the problem differently. And so from our perspective, there's four or five big trends that are completely reshaping the IT landscape. Number one of course is the move to the cloud. You've talked about it just a second ago but we're probably 10 years into a 20 or 30 year shift to the cloud and it's actually accelerating right now. We're now seeing not just early adopters but mainstream companies are now making a big bet on the cloud and deciding that's where they're going to be for the foreseeable future. We're seeing the move to more and more self service, where rather than having an IT team solve all your data problems, they're seeing data analysts and data scientists are solving the problems themselves. So creating a world where all of those different roles can play together in a team sport kind of way is an important way. It's moving to more and more real time, right? Everything back 10, 20 years ago used to be done in batch. So at the end of the day, or end of the week, or end of the month you collect a whole bunch of stuff and package it together and crank it through. But think about today's applications, right? The expectation is it's done in real time. If you make a deposit in a bank, you expect to look up the bank balance and see it right there. You don't expect to see it there the next day. >> Right, right. >> You expect your apps to be immediately responsive, that's real time, right? It's now this ubiquitous expectation. And that means that data integration needs to follow that. Tightly connected with that is the move to machine learning. Companies now don't want to do all of the analytics and insight generation with a whole bunch of people looking at data. 'Cause machines can do that a whole lot better, right? Machines are really, really good at finding patterns. And so those are some of the big trends that we see that are completely reshaping the landscape. So clearly, data integration today is just very different than where it was five or 10 years ago. >> It's so funny, we go to a lot of shows and there's always a lot of conversations about innovation and how do you innovate? And to me, one of the really simple answers, not necessarily simple to implement, is you give more people in the organization more access to more data and the tools to manipulate it and then ultimately, hopefully, to make decisions you know, based on that output. So it is kind of unlocking it, it is giving more people that access you talked about. Self service and cloud and really pushing that out and the other funny thing, you talked about real time, is you used to make decisions based on a sample of things that happened in the past. Now with the capacity of the machines, the complete, basically infinite capacity from an individual company point of view of a cloud application, now hopefully, I'm making decisions on all the data while it's happening. Completely different way. >> Yes, yes. And as a matter of fact, the outliers sometimes are really an important part of the data. And so looking at not just where does most the data fall, but why are the outliers there? What do they mean, right? In a fraud detection case the outliers are the frauds, usually, right? So it's an important part of the data and looking at the entire data set allows you to find that. If you're looking at a sample, you'll miss it. >> So as we look forward to machine learning, kind of the last part of your four key drivers, that's a big impact on the way these things work. My favorite little example on machine learning in AI is the new Google Gmail, on that little tiny response that it suggests that on your reply. Which seems relatively straightforward, right? Thanks, I'll get right back to you, you know they're relatively short usually. But the amount of machine learning and artificial intelligence and data analysis that goes into the generation of my three responses versus your three response options back to me is pretty phenomenal. And you guys are now going to be able to bake that into all types of general type processes. >> That's right, and that's right. You know, you described a really cool consumer scenario around e-mail, but there's a bunch of commercial scenarios around things like predictive maintenance. GE, with its big gas turbines. If that thing goes offline at the wrong time it can be real expensive. Because then you have customers who are out of service and it turns out it takes hours to spin up a new gas turbine that might be sitting idle. But if you can do it in a maintenance window it's just not a big deal at all. And so if they can predict when parts are about to fail, that's a savings of literally billions of dollars across their install base. We have one of the major car companies did a really cool analysis around predicting potential recalls based on, in manufacturing, as tools were starting to go out of alignment. And what they could do was start to track and say, if it gets more than this far out of alignment the odds of a recall go up dramatically, and so now's the time to intervene and readjust that tool because a recall is a very, very expensive thing. If you can fix it upfront in the tool you're saving millions of dollars. >> Right. >> Fascinating examples of real world industrial scenarios using machinery. >> Right, and disconnected kind of data sets that actually are tied together in hindsight but probably the person who's responsible for keeping that machine up and running isn't really thinking about the impact to the company if there's a recall on that particular model of car. >> Yeah, exactly. Who would have known that the tolerance, you know, acceptable tolerance was exactly this, right? How would you set that in advance? But it turns out when you actually start running the correlations and throw some learning algorithms at it, you can really start pinpointing it and say, for this tool, it's this, for this other tool it might be something else. >> So the other kind of big trend that you did mention in this explosion of data is using so many more data sets. Going beyond the data that you own, that you generate, that you create, and pulling in a lot of this external data whether it's weather data, whether it's social sentiment data. There's so many data repositories now that you can integrate in with that proprietary data to then drive kind of a secret sauce algorithm that gives you that competitive advantage. You see more and more of that and I think you mentioned kind of the sloppy, crazy variability in all these data sets as you're trying to pull them into these systems. >> That's right, that's right. And we're seeing a bunch of customers doing that. There was an interesting scenario of, we have a customer that does soil testing for farmers with a neat, little device, kind of an IoT scenario, they plug it in, it does the soil test, sends it up to the cloud. Now it correlates that soil with the weather patterns in that area to say, here is the seeding and fertilizing regimen that we should be using for this plot of land. Right? Really cool scenario. >> Well, I'll tell you even a crazier version. I talked to a guy that ran a drone company with the sensors that did a similar type of thing. They run the drone and they analyze the field. And I had to ask him, I'm like, "Come on, I mean people have been sampling fields forever, "this can't be new, right?" And then it feeds back to their little Monsanto engine or whatever that tells you what to do. He goes, "Yeah, but here's what's different, Jeff. "Again, we used to take a sample. "We would take sample points on that field "and we would make a decision based on that sample." He goes, "Now I can track literally every single plant." >> That's cool. >> "Every single plant with the consistency of this "drone coverage and now I can micro, micro, micro "the application of water, the application of hydrogen," or whatever they give, the herbicides, et cetera. Pretty amazing. >> Yeah, and what we're seeing now is that the tractor companies are doing that on a, as you say, on a per seed basis as they're driving through the field based on samples that have been taken, based on drone surveys of what's there, and based on the weather patterns. I mean it's really cool what we're doing in terms of precision farming right now. >> Right. So I'll just take that kind of one step further. The other trend that's coming down the pike which is big and not going to have less data but a lot more is IoT. So from where you're sitting you've been in this business a while, as you look at kind of this next generation of explosion of all this additional machine-generated data, what type of future do you see? How is that going to play? What kind of opportunities is that going to open up? There's a whole nother, multiple orders of magnitude of data coming soon. >> Yeah, no, so IoT is clearly a... It multiplies the amount of data by literally an order of magnitude of, and many of the streams are real time in nature and the absolute requirement then is that you're doing some sort of machine learning to take advantage of it. To me, you can take almost any industry and talk about a potential machine learning scenario in the industry. My favorite one right now is cars, right? This was, you know, it's now, it's in real life. It's not a future thing. If you're driving a Tesla right now, your car is actually starting to fix itself sometimes. Literally, I got a call one time as I was driving down the road, they say, "Hey, we've detected this fault in your car "and if it's okay with you we're going to reset it "right now and it'll be fine." And I was like, "What was the problem?" They're like, "Don't worry about it." Well, that's pretty cool, right? When was the last time-- >> Did they at least ask you to pull over first? (Jeff and Mike laugh) >> But no, the whole idea of having a car that's self-diagnosing and fixing itself is really cool. That's a game changer, I think. >> On so many ways, I mean not only that but you generalize that to a much broader audience. I mean it used to be you made your product, you sent it to your distributor, and you maybe had some assumptions of how it's used, how it's not used. Are people using the features that you created? Are they not using them? Are they using them they way you thought? And now with this connected feedback loop, the ability for manufacturers to know how people are using their tools even beyond just the prescriptive maintenance is a phenomenal impact. >> Yes, and in that particular scenario for those kind of smart devices, not just the one-way feedback loop, but closing the loop and the in field update ability is you know, you combine those two, and wow! It's a whole new world. >> Right, I guess software really is eating the world. I guess you had it right way back when. All right, Mike, well thanks for stopping by. Good luck on your event across the pond here in a couple weeks and great to catch up. >> All right, thank you, Jeff. >> All right, he's Mike, I'm Jeff. You're watching theCUBE. It's a CUBEConversation in our Palo Alto Studios. Thanks for watching and we'll see you next time. (energetic music)

>> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is braking analysis with Dave Vellante. >> A new era of data is upon us, and we're in a state of transition. You know, even our language reflects that. We rarely use the phrase big data anymore, rather we talk about digital transformation or digital business, or data-driven companies. Many have come to the realization that data is a not the new oil, because unlike oil, the same data can be used over and over for different purposes. We still use terms like data as an asset. However, that same narrative, when it's put forth by the vendor and practitioner communities, includes further discussions about democratizing and sharing data. Let me ask you this, when was the last time you wanted to share your financial assets with your coworkers or your partners or your customers? Hello everyone, and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we want to share our assessment of the state of the data business. We'll do so by looking at the data mesh concept and how a leading financial institution, JP Morgan Chase is practically applying these relatively new ideas to transform its data architecture. Let's start by looking at what is the data mesh. As we've previously reported many times, data mesh is a concept and set of principles that was introduced in 2018 by Zhamak Deghani who's director of technology at ThoughtWorks, it's a global consultancy and software development company. And she created this movement because her clients, who were some of the leading firms in the world had invested heavily in predominantly monolithic data architectures that had failed to deliver desired outcomes in ROI. So her work went deep into trying to understand that problem. And her main conclusion that came out of this effort was the world of data is distributed and shoving all the data into a single monolithic architecture is an approach that fundamentally limits agility and scale. Now a profound concept of data mesh is the idea that data architectures should be organized around business lines with domain context. That the highly technical and hyper specialized roles of a centralized cross functional team are a key blocker to achieving our data aspirations. This is the first of four high level principles of data mesh. So first again, that the business domain should own the data end-to-end, rather than have it go through a centralized big data technical team. Second, a self-service platform is fundamental to a successful architectural approach where data is discoverable and shareable across an organization and an ecosystem. Third, product thinking is central to the idea of data mesh. In other words, data products will power the next era of data success. And fourth data products must be built with governance and compliance that is automated and federated. Now there's lot more to this concept and there are tons of resources on the web to learn more, including an entire community that is formed around data mesh. But this should give you a basic idea. Now, the other point is that, in observing Zhamak Deghani's work, she is deliberately avoided discussions around specific tooling, which I think has frustrated some folks because we all like to have references that tie to products and tools and companies. So this has been a two-edged sword in that, on the one hand it's good, because data mesh is designed to be tool agnostic and technology agnostic. On the other hand, it's led some folks to take liberties with the term data mesh and claim mission accomplished when their solution, you know, maybe more marketing than reality. So let's look at JP Morgan Chase in their data mesh journey. Is why I got really excited when I saw this past week, a team from JPMC held a meet up to discuss what they called, data lake strategy via data mesh architecture. I saw that title, I thought, well, that's a weird title. And I wondered, are they just taking their legacy data lakes and claiming they're now transformed into a data mesh? But in listening to the presentation, which was over an hour long, the answer is a definitive no, not at all in my opinion. A gentleman named Scott Hollerman organized the session that comprised these three speakers here, James Reid, who's a divisional CIO at JPMC, Arup Nanda who is a technologist and architect and Serita Bakst who is an information architect, again, all from JPMC. This was the most detailed and practical discussion that I've seen to date about implementing a data mesh. And this is JP Morgan's their approach, and we know they're extremely savvy and technically sound. And they've invested, it has to be billions in the past decade on data architecture across their massive company. And rather than dwell on the downsides of their big data past, I was really pleased to see how they're evolving their approach and embracing new thinking around data mesh. So today, we're going to share some of the slides that they use and comment on how it dovetails into the concept of data mesh that Zhamak Deghani has been promoting, and at least as we understand it. And dig a bit into some of the tooling that is being used by JP Morgan, particularly around it's AWS cloud. So the first point is it's all about business value, JPMC, they're in the money business, and in that world, business value is everything. So Jr Reid, the CIO showed this slide and talked about their overall goals, which centered on a cloud first strategy to modernize the JPMC platform. I think it's simple and sensible, but there's three factors on which he focused, cut costs always short, you got to do that. Number two was about unlocking new opportunities, or accelerating time to value. But I was really happy to see number three, data reuse. That's a fundamental value ingredient in the slide that he's presenting here. And his commentary was all about aligning with the domains and maximizing data reuse, i.e. data is not like oil and making sure there's appropriate governance around that. Now don't get caught up in the term data lake, I think it's just how JP Morgan communicates internally. It's invested in the data lake concept, so they use water analogies. They use things like data puddles, for example, which are single project data marts or data ponds, which comprise multiple data puddles. And these can feed in to data lakes. And as we'll see, JPMC doesn't strive to have a single version of the truth from a data standpoint that resides in a monolithic data lake, rather it enables the business lines to create and own their own data lakes that comprise fit for purpose data products. And they do have a single truth of metadata. Okay, we'll get to that. But generally speaking, each of the domains will own end-to-end their own data and be responsible for those data products, we'll talk about that more. Now the genesis of this was sort of a cloud first platform, JPMC is leaning into public cloud, which is ironic since the early days, in the early days of cloud, all the financial institutions were like never. Anyway, JPMC is going hard after it, they're adopting agile methods and microservices architectures, and it sees cloud as a fundamental enabler, but it recognizes that on-prem data must be part of the data mesh equation. Here's a slide that starts to get into some of that generic tooling, and then we'll go deeper. And I want to make a couple of points here that tie back to Zhamak Deghani's original concept. The first is that unlike many data architectures, this puts data as products right in the fat middle of the chart. The data products live in the business domains and are at the heart of the architecture. The databases, the Hadoop clusters, the files and APIs on the left-hand side, they serve the data product builders. The specialized roles on the right hand side, the DBA's, the data engineers, the data scientists, the data analysts, we could have put in quality engineers, et cetera, they serve the data products. Because the data products are owned by the business, they inherently have the context that is the middle of this diagram. And you can see at the bottom of the slide, the key principles include domain thinking, an end-to-end ownership of the data products. They build it, they own it, they run it, they manage it. At the same time, the goal is to democratize data with a self-service as a platform. One of the biggest points of contention of data mesh is governance. And as Serita Bakst said on the Meetup, metadata is your friend, and she kind of made a joke, she said, "This sounds kind of geeky, but it's important to have a metadata catalog to understand where data resides and the data lineage in overall change management. So to me, this really past the data mesh stink test pretty well. Let's look at data as products. CIO Reid said the most difficult thing for JPMC was getting their heads around data product, and they spent a lot of time getting this concept to work. Here's the slide they use to describe their data products as it related to their specific industry. They set a common language and taxonomy is very important, and you can imagine how difficult that was. He said, for example, it took a lot of discussion and debate to define what a transaction was. But you can see at a high level, these three product groups around wholesale, credit risk, party, and trade and position data as products, and each of these can have sub products, like, party, we'll have to know your customer, KYC for example. So a key for JPMC was to start at a high level and iterate to get more granular over time. So lots of decisions had to be made around who owns the products and the sub-products. The product owners interestingly had to defend why that product should even exist, what boundaries should be in place and what data sets do and don't belong in the various products. And this was a collaborative discussion, I'm sure there was contention around that between the lines of business. And which sub products should be part of these circles? They didn't say this, but tying it back to data mesh, each of these products, whether in a data lake or a data hub or a data pond or data warehouse, data puddle, each of these is a node in the global data mesh that is discoverable and governed. And supporting this notion, Serita said that, "This should not be infrastructure-bound, logically, any of these data products, whether on-prem or in the cloud can connect via the data mesh." So again, I felt like this really stayed true to the data mesh concept. Well, let's look at some of the key technical considerations that JPM discussed in quite some detail. This chart here shows a diagram of how JP Morgan thinks about the problem, and some of the challenges they had to consider were how to write to various data stores, can you and how can you move data from one data store to another? How can data be transformed? Where's the data located? Can the data be trusted? How can it be easily accessed? Who has the right to access that data? These are all problems that technology can help solve. And to address these issues, Arup Nanda explained that the heart of this slide is the data in ingestor instead of ETL. All data producers and contributors, they send their data to the ingestor and the ingestor then registers the data so it's in the data catalog. It does a data quality check and it tracks the lineage. Then, data is sent to the router, which persists the data in the data store based on the best destination as informed by the registration. This is designed to be a flexible system. In other words, the data store for a data product is not fixed, it's determined at the point of inventory, and that allows changes to be easily made in one place. The router simply reads that optimal location and sends it to the appropriate data store. Nowadays you see the schema infer there is used when there is no clear schema on right. In this case, the data product is not allowed to be consumed until the schema is inferred, and then the data goes into a raw area, and the inferer determines the schema and then updates the inventory system so that the data can be routed to the proper location and properly tracked. So that's some of the detail of how the sausage factory works in this particular use case, it was very interesting and informative. Now let's take a look at the specific implementation on AWS and dig into some of the tooling. As described in some detail by Arup Nanda, this diagram shows the reference architecture used by this group within JP Morgan, and it shows all the various AWS services and components that support their data mesh approach. So start with the authorization block right there underneath Kinesis. The lake formation is the single point of entitlement and has a number of buckets including, you can see there the raw area that we just talked about, a trusted bucket, a refined bucket, et cetera. Depending on the data characteristics at the data catalog registration block where you see the glue catalog, that determines in which bucket the router puts the data. And you can see the many AWS services in use here, identity, the EMR, the elastic MapReduce cluster from the legacy Hadoop work done over the years, the Redshift Spectrum and Athena, JPMC uses Athena for single threaded workloads and Redshift Spectrum for nested types so they can be queried independent of each other. Now remember very importantly, in this use case, there is not a single lake formation, rather than multiple lines of business will be authorized to create their own lakes, and that creates a challenge. So how can that be done in a flexible and automated manner? And that's where the data mesh comes into play. So JPMC came up with this federated lake formation accounts idea, and each line of business can create as many data producer or consumer accounts as they desire and roll them up into their master line of business lake formation account. And they cross-connect these data products in a federated model. And these all roll up into a master glue catalog so that any authorized user can find out where a specific data element is located. So this is like a super set catalog that comprises multiple sources and syncs up across the data mesh. So again to me, this was a very well thought out and practical application of database. Yes, it includes some notion of centralized management, but much of that responsibility has been passed down to the lines of business. It does roll up to a master catalog, but that's a metadata management effort that seems compulsory to ensure federated and automated governance. As well at JPMC, the office of the chief data officer is responsible for ensuring governance and compliance throughout the federation. All right, so let's take a look at some of the suspects in this world of data mesh and bring in the ETR data. Now, of course, ETR doesn't have a data mesh category, there's no such thing as that data mesh vendor, you build a data mesh, you don't buy it. So, what we did is we use the ETR dataset to select and filter on some of the culprits that we thought might contribute to the data mesh to see how they're performing. This chart depicts a popular view that we often like to share. It's a two dimensional graphic with net score or spending momentum on the vertical axis and market share or pervasiveness in the data set on the horizontal axis. And we filtered the data on sectors such as analytics, data warehouse, and the adjacencies to things that might fit into data mesh. And we think that these pretty well reflect participation that data mesh is certainly not all compassing. And it's a subset obviously, of all the vendors who could play in the space. Let's make a few observations. Now as is often the case, Azure and AWS, they're almost literally off the charts with very high spending velocity and large presence in the market. Oracle you can see also stands out because much of the world's data lives inside of Oracle databases. It doesn't have the spending momentum or growth, but the company remains prominent. And you can see Google Cloud doesn't have nearly the presence in the dataset, but it's momentum is highly elevated. Remember that red dotted line there, that 40% line, anything over that indicates elevated spending momentum. Let's go to Snowflake. Snowflake is consistently shown to be the gold standard in net score in the ETR dataset. It continues to maintain highly elevated spending velocity in the data. And in many ways, Snowflake with its data marketplace and its data cloud vision and data sharing approach, fit nicely into the data mesh concept. Now, a caution, Snowflake has used the term data mesh in it's marketing, but in our view, it lacks clarity, and we feel like they're still trying to figure out how to communicate what that really is. But is really, we think a lot of potential there to that vision. Databricks is also interesting because the firm has momentum and we expect further elevated levels in the vertical axis in upcoming surveys, especially as it readies for its IPO. The firm has a strong product and managed service, and is really one to watch. Now we included a number of other database companies for obvious reasons like Redis and Mongo, MariaDB, Couchbase and Terradata. SAP as well is in there, but that's not all database, but SAP is prominent so we included them. As is IBM more of a database, traditional database player also with the big presence. Cloudera includes Hortonworks and HPE Ezmeral comprises the MapR business that HPE acquired. So these guys got the big data movement started, between Cloudera, Hortonworks which is born out of Yahoo, which was the early big data, sorry early Hadoop innovator, kind of MapR when it's kind of owned course, and now that's all kind of come together in various forms. And of course, we've got Talend and Informatica are there, they are two data integration companies that are worth noting. We also included some of the AI and ML specialists and data science players in the mix like DataRobot who just did a monster $250 million round. Dataiku, H2O.ai and ThoughtSpot, which is all about democratizing data and injecting AI, and I think fits well into the data mesh concept. And you know we put VMware Cloud in there for reference because it really is the predominant on-prem infrastructure platform. All right, let's wrap with some final thoughts here, first, thanks a lot to the JP Morgan team for sharing this data. I really want to encourage practitioners and technologists, go to watch the YouTube of that meetup, we'll include it in the link of this session. And thank you to Zhamak Deghani and the entire data mesh community for the outstanding work that you're doing, challenging the established conventions of monolithic data architectures. The JPM presentation, it gives you real credibility, it takes Data Mesh well beyond concept, it demonstrates how it can be and is being done. And you know, this is not a perfect world, you're going to start somewhere and there's going to be some failures, the key is to recognize that shoving everything into a monolithic data architecture won't support massive scale and agility that you're after. It's maybe fine for smaller use cases in smaller firms, but if you're building a global platform in a data business, it's time to rethink data architecture. Now much of this is enabled by the cloud, but cloud first doesn't mean cloud only, doesn't mean you'll leave your on-prem data behind, on the contrary, you have to include non-public cloud data in your Data Mesh vision just as JPMC has done. You've got to get some quick wins, that's crucial so you can gain credibility within the organization and grow. And one of the key takeaways from the JP Morgan team is, there is a place for dogma, like organizing around data products and domains and getting that right. On the other hand, you have to remain flexible because technologies is going to come, technology is going to go, so you got to be flexible in that regard. And look, if you're going to embrace the metaphor of water like puddles and ponds and lakes, we suggest maybe a little tongue in cheek, but still we believe in this, that you expand your scope to include data ocean, something John Furry and I have talked about and laughed about extensively in theCUBE. Data oceans, it's huge. It's the new data lake, go transcend data lake, think oceans. And think about this, just as we're evolving our language, we should be evolving our metrics. Much the last the decade of big data was around just getting the stuff to work, getting it up and running, standing up infrastructure and managing massive, how much data you got? Massive amounts of data. And there were many KPIs built around, again, standing up that infrastructure, ingesting data, a lot of technical KPIs. This decade is not just about enabling better insights, it's a more than that. Data mesh points us to a new era of data value, and that requires the new metrics around monetizing data products, like how long does it take to go from data product conception to monetization? And how does that compare to what it is today? And what is the time to quality if the business owns the data, and the business has the context? the quality that comes out of them, out of the shoot should be at a basic level, pretty good, and at a higher mark than out of a big data team with no business context. Automation, AI, and very importantly, organizational restructuring of our data teams will heavily contribute to success in the coming years. So we encourage you, learn, lean in and create your data future. Okay, that's it for now, remember these episodes, they're all available as podcasts wherever you listen, all you got to do is search, breaking analysis podcast, and please subscribe. Check out ETR's website at etr.plus for all the data and all the survey information. We publish a full report every week on wikibon.com and siliconangle.com. And you can get in touch with us, email me david.vellante@siliconangle.com, you can DM me @dvellante, or you can comment on my LinkedIn posts. This is Dave Vellante for theCUBE insights powered by ETR. Have a great week everybody, stay safe, be well, and we'll see you next time. (upbeat music)

(upbeat music) >> On February 19th of this year, Barron's dropped an article declaring Oracle, a cloud giant and the article explained why the stock was a buy. Investors took notice and the stock ran up 18% over the next nine trading days and it peaked on March 9th, the day before Oracle announced its latest earnings. The company beat consensus earnings on both top-line and EPS last quarter, but investors, they did not like Oracle's tepid guidance and the stock pulled back. But it's still, as you can see, well above its pre-Barron's article price. What does all this mean? Is Oracle a cloud giant? What are its growth prospects? Now many parts of Oracle's business are growing including Fusion ERP, Fusion HCM, NetSuite, we're talking deep into the double digits, 20 plus percent growth. It's OnPrem legacy licensed business however, continues to decline and that moderates, the overall company growth because that OnPrem business is so large. So the overall Oracle's growing in the low single digits. Now what stands out about Oracle is it's recurring revenue model. That figure, the company says now it represents 73% of its revenue and that's going to continue to grow. Now two other things stood out on the earnings call to us. First, Oracle plans on increasing its CapEX by 50% in the coming quarter, that's a lot. Now it's still far less than AWS Google or Microsoft Spend on capital but it's a meaningful data point. Second Oracle's consumption revenue for Autonomous Database and Cloud Infrastructure, OCI or Oracle Cloud Infrastructure grew at 64% and 139% respectively and these two factors combined with the CapEX Spend suggest that the company has real momentum. I mean look, it's possible that the CapEx announcements maybe just optics in they're front loading, some spend to show the street that it's a player in cloud but I don't think so. Oracle's Safra Catz's usually pretty disciplined when it comes to it's spending. Now today on March 17th, Oracle announced updates towards Autonomous Data Warehouse and with me is David Floyer who has extensively researched Oracle over the years and today we're going to unpack the Oracle Autonomous Data Warehouse, ADW announcement. What it means to customers but we also want to dig into Oracle's strategy. We want to compare it to some other prominent database vendors specifically, AWS and Snowflake. David Floyer, Welcome back to The Cube, thanks for making some time for me. >> Thank you Vellante, great pleasure to be here. >> All right, I want to get into the news but I want to start with this idea of the autonomous database which Oracle's announcement today is building on. Oracle uses the analogy of a self-driving car. It's obviously powerful metaphor as they call it the self-driving database and my takeaway is that, this means that the system automatically provisions, it upgrades, it does all the patching for you, it tunes itself. Oracle claims that all reduces labor costs or admin costs by 90%. So I ask you, is this the right interpretation of what Oracle means by autonomous database? And is it real? >> Is that the right interpretation? It's a nice analogy. It's a test to that analogy, isn't it? I would put it as the first stage of the Autonomous Data Warehouse was to do the things that you talked about, which was the tuning, the provisioning, all of that sort of thing. The second stage is actually, I think more interesting in that what they're focusing on is making it easy to use for the end user. Eliminating the requirement for IT, staff to be there to help in the actual using of it and that is a very big step for them but an absolutely vital step because all of the competition focusing on ease of use, ease of use, ease of use and cheapness of being able to manage and deploy. But, so I think that is the really important area that Oracle has focused on and it seemed to have done so very well. >> So in your view, is this, I mean you don't really hear a lot of other companies talking about this analogy of the self-driving database, is this unique? Is it differentiable for Oracle? If so, why, or maybe you could help us understand that a little bit better. >> Well, the whole strategy is unique in its breadth. It has really brought together a whole number of things together and made it of its type the best. So it has a single, whole number of data sources and database types. So it's got a very broad range of different ways that you can look at the data and the second thing that is also excellent is it's a platform. It is fully self provisioned and its functionality is very, very broad indeed. The quality of the original SQL and the query languages, etc, is very, very good indeed and it's a better agent to do joints for example, is excellent. So all of the building blocks are there and together with it's sharing of the same data with OLTP and inference and in memory data paces as well. All together the breadth of what they have is unique and very, very powerful. >> I want to come back to this but let's get into the news a little bit and the announcement. I mean, it seems like what's new in the autonomous data warehouse piece for Oracle's new tooling around four areas that so Andy Mendelsohn, the head of this group instead of the guy who releases his baby, he talked about four things. My takeaway, faster simpler loads, simplified transforms, autonomous machine learning models which are facilitating, What do you call it? Citizen data science and then faster time to insights. So tooling to make those four things happen. What's your take and takeaways on the news? >> I think those are all correct. I would add the ease of use in terms of being able to drag and drop, the user interface has been dramatically improved. Again, I think those, strategically are actually more important that the others are all useful and good components of it but strategically, I think is more important. There's ease of use, the use of apex for example, are more important. And, >> Why are they more important strategically? >> Because they focus on the end users capability. For example, one of other things that they've started to introduce is Python together with their spatial databases, for example. That is really important that you reach out to the developer as they are and what tools they want to use. So those type of ease of use things, those types of things are respecting what the end users use. For example, they haven't come out with anything like click or Tableau. They've left that there for that marketplace for the end user to use what they like best. >> Do you mean, they're not trying to compete with those two tools. They indeed had a laundry list of stuff that they supported, Talend, Tableau, Looker, click, Informatica, IBM, I had IBM there. So their claim was, hey, we're open. But so that's smart. That's just, hey, they realized that people use these tools. >> I'm trying to exclude other people, be a platform and be an ecosystem for the end users. >> Okay, so Mendelsohn who made the announcement said that Oracle's the smartphone of databases and I think, I actually think Alison kind of used that or maybe that was us planing to have, I thought he did like the iPhone of when he announced the exit data way back when the integrated hardware and software but is that how you see it, is Oracle, the smartphone of databases? >> It is, I mean, they are trying to own the complete stack, the hardware with the exit data all the way up to the databases at the data warehouses and the OLTP databases, the inference databases. They're trying to own the complete stack from top to bottom and that's what makes autonomy process possible. You can make it autonomous when you control all of that. Take away all of the requirements for IT in the business itself. So it's democratizing the use of data warehouses. It is pushing it out to the lines of business and it's simplifying it and making it possible to push out so that they can own their own data. They can manage their own data and they do not need an IT person from headquarters to help them. >> Let's stay in this a little bit more and then I want to go into some of the competitive stuff because Mendelsohn mentioned AWS several times. One of the things that struck me, he said, hey, we're basically one API 'cause we're doing analytics in the cloud, we're doing data in the cloud, we're doing integration in the cloud and that's sort of a big part of the value proposition. He made some comparisons to Redshift. Of course, I would say, if you can't find a workload where you beat your big competitor then you shouldn't be in this business. So I take those things with a grain of salt but one of the other things that caught me is that migrating from OnPrem to Oracle, Oracle Cloud was very simple and I think he might've made some comparisons to other platforms. And this to me is important because he also brought in that Gartner data. We looked at that Gardner data when they came out with it in the operational database class, Oracle smoked everybody. They were like way ahead and the reason why I think that's important is because let's face it, the Mission Critical Workloads, when you look at what's moving into AWS, the Mission Critical Workloads, the high performance, high criticality OLTP stuff. That's not moving in droves and you've made the point often that companies with their own cloud particularly, Oracle you've mentioned this about IBM for certain, DB2 for instance, customers are going to, there should be a lower risk environment moving from OnPrem to their cloud, because you could do, I don't think you could get Oracle RAC on AWS. For example, I don't think EXIF data is running in AWS data centers and so that like component is going to facilitate migration. What's your take on all that spiel? >> I think that's absolutely right. You all crown Jewels, the most expensive and the most valuable applications, the mission-critical applications. The ones that have got to take a beating, keep on taking. So those types of applications are where Oracle really shines. They own a very large high percentage of those Mission Critical Workloads and you have the choice if you're going to AWS, for example of either migrating to Oracle on AWS and that is frankly not a good fit at all. There're a lot of constraints to running large systems on AWS, large mission critical systems. So that's not an option and then the option, of course, that AWS will push is move to a Roller, change your way of writing applications, make them tiny little pieces and stitch them all together with microservices and that's okay if you're a small organization but that has got a lot of problems in its own, right? Because then you, the user have to stitch all those pieces together and you're responsible for testing it and you're responsible for looking after it. And that as you grow becomes a bigger and bigger overhead. So AWS, in my opinion needs to have a move towards a tier-one database of it's own and it's not in that position at the moment. >> Interesting, okay. So, let's talk about the competitive landscape and the choices that customers have. As I said, Mendelssohn mentioned AWS many times, Larry on the calls often take shy, it's a compliment to me. When Larry Ellison calls you out, that means you've made it, you're doing well. We've seen it over the years, whether it's IBM or Workday or Salesforce, even though Salesforce's big Oracle customer 'cause AWS, as we know are Oracle customer as well, even though AWS tells us they've off called when you peel the onion >> Five years should be great, some of the workers >> Well, as I said, I believe they're still using Oracle in certain workloads. Way, way, we digress. So AWS though, they take a different approach and I want to push on this a little bit with database. It's got more than a dozen, I think purpose-built databases. They take this kind of right tool for the right job approach was Oracle there converging all this function into a single database. SQL JSON graph databases, machine learning, blockchain. I'd love to talk about more about blockchain if we have time but seems to me that the right tool for the right job purpose-built, very granular down to the primitives and APIs. That seems to me to be a pretty viable approach versus kind of a Swiss Army approach. How do you compare the two? >> Yes, and it is to many initial programmers who are very interested for example, in graph databases or in time series databases. They are looking for a cheap database that will do the job for a particular project and that makes, for the program or for that individual piece of work is making a very sensible way of doing it and they pay for ads on it's clear cloud dynamics. The challenge as you have more and more data and as you're building up your data warehouse in your data lakes is that you do not want to have to move data from one place to another place. So for example, if you've got a Roller,, you have to move the database and it's a pretty complicated thing to do it, to move it to Redshift. It's a five or six steps to do that and each of those costs money and each of those take time. More importantly, they take time. The Oracle approach is a single database in terms of all the pieces that obviously you have multiple databases you have different OLTP databases and data warehouse databases but as a single architecture and a single design which means that all of the work in terms of moving stuff from one place to another place is within Oracle itself. It's Oracle that's doing that work for you and as you grow, that becomes very, very important. To me, very, very important, cost saving. The overhead of all those different ones and the databases themselves originate with all as open source and they've done very well with it and then there's a large revenue stream behind the, >> The AWS, you mean? >> Yes, the original database is in AWS and they've done a lot of work in terms of making it set with the panels, etc. But if a larger organization, especially very large ones and certainly if they want to combine, for example data warehouse with the OLTP and the inference which is in my opinion, a very good thing that they should be trying to do then that is incredibly difficult to do with AWS and in my opinion, AWS has to invest enormously in to make the whole ecosystem much better. >> Okay, so innovation required there maybe is part of the TAM expansion strategy but just to sort of digress for a second. So it seems like, and by the way, there are others that are doing, they're taking this converged approach. It seems like that is a trend. I mean, you certainly see it with single store. I mean, the name sort of implies that formerly MemSQL I think Monte Zweben of splice machine is probably headed in a similar direction, embedding AI in Microsoft's, kind of interesting. It seems like Microsoft is willing to build this abstraction layer that hides that complexity of the different tooling. AWS thus far has not taken that approach and then sort of looking at Snowflake, Snowflake's got a completely different, I think Snowflake's trying to do something completely different. I don't think they're necessarily trying to take Oracle head-on. I mean, they're certainly trying to just, I guess, let's talk about this. Snowflake simplified EDW, that's clear. Zero to snowflake in 90 minutes. It's got this data cloud vision. So you sign on to this Snowflake, speaking of layers they're abstracting the complexity of the underlying cloud. That's what the data cloud vision is all about. They, talk about this Global Mesh but they've not done a good job of explaining what the heck it is. We've been pushing them on that, but we got, >> Aspiration of moment >> Well, I guess, yeah, it seems that way. And so, but conceptually, it's I think very powerful but in reality, what snowflake is doing with data sharing, a lot of reading it's probably mostly read-only and I say, mostly read-only, oh, there you go. You'll get better but it's mostly read and so you're able to share the data, it's governed. I mean, it's exactly, quite genius how they've implemented this with its simplicity. It is a caching architecture. We've talked about that, we can geek out about that. There's good, there's bad, there's ugly but generally speaking, I guess my premise here I would love your thoughts. Is snowflakes trying to do something different? It's trying to be not just another data warehouse. It's not just trying to compete with data lakes. It's trying to create this data cloud to facilitate data sharing, put data in the hands of business owners in terms of a product build, data product builders. That's a different vision than anything I've seen thus far, your thoughts. >> I agree and even more going further, being a place where people can sell data. Put it up and make it available to whoever needs it and making it so simple that it can be shared across the country and across the world. I think it's a very powerful vision indeed. The challenge they have is that the pieces at the moment are very, very easy to use but the quality in terms of the, for example, joints, I mentioned, the joints were very powerful in Oracle. They don't try and do joints. They, they say >> They being Snowflake, snowflake. Yeah, they don't even write it. They would say use another Postgres >> Yeah. >> Database to do that. >> Yeah, so then they have a long way to go. >> Complex joints anyway, maybe simple joints, yeah. >> Complex joints, so they have a long way to go in terms of the functionality of their product and also in my opinion, they sure be going to have more types of databases inside it, including OLTP and they can do that. They have obviously got a great market gap and they can do that by acquisition as well as they can >> They've started. I think, I think they support JSON, right. >> Do they support JSON? And graph, I think there's a graph database that's either coming or it's there, I can't keep all that stuff in my head but there's no reason they can't go in that direction. I mean, in speaking to the founders in Snowflake they were like, look, we're kind of new. We would focus on simple. A lot of them came from Oracle so they know all database and they know how hard it is to do things like facilitate complex joints and do complex workload management and so they said, let's just simplify, we'll put it in the cloud and it will spin up a separate data warehouse. It's a virtual data warehouse every time you want one to. So that's how they handle those things. So different philosophy but again, coming back to some of the mission critical work and some of the larger Oracle customers, they said they have a thousand autonomous database customers. I think it was autonomous database, not ADW but anyway, a few stood out AON, lift, I think Deloitte stood out and as obviously, hundreds more. So we have people who misunderstand Oracle, I think. They got a big install base. They invest in R and D and they talk about lock-in sure but the CIO that I talked to and you talked to David, they're looking for business value. I would say that 75 to 80% of them will gravitate toward business value over the fear of lock-in and I think at the end of the day, they feel like, you know what? If our business is performing, it's a better business decision, it's a better business case. >> I fully agree, they've been very difficult to do business with in the past. Everybody's in dread of the >> The audit. >> The knock on the door from the auditor. >> Right. >> And that from a purchasing point of view has been really bad experience for many, many customers. The users of the database itself are very happy indeed. I mean, you talk to them and they understand why, what they're paying for. They understand the value and in terms of availability and all of the tools for complex multi-dimensional types of applications. It's pretty well, the only game in town. It's only DB2 and SQL that had any hope of doing >> Doing Microsoft, Microsoft SQL, right. >> Okay, SQL >> Which, okay, yeah, definitely competitive for sure. DB2, no IBM look, IBM lost its dominant position in database. They kind of seeded that. Oracle had to fight hard to win it. It wasn't obvious in the 80s who was going to be the database King and all had to fight. And to me, I always tell people the difference is that the chairman of Oracle is also the CTO. They spend money on R and D and they throw off a ton of cash. I want to say something about, >> I was just going to make one extra point. The simplicity and the capability of their cloud versions of all of this is incredibly good. They are better in terms of spending what you need or what you use much better than AWS, for example or anybody else. So they have really come full circle in terms of attractiveness in a cloud environment. >> You mean charging you for what you consume. Yeah, Mendelsohn talked about that. He made a big point about the granularity, you pay for only what you need. If you need 33 CPUs or the other databases you've got to shape, if you need 33, you've got to go to 64. I know that's true for everyone. I'm not sure if that's true too for snowflake. It may be, I got to dig into that a little bit, but maybe >> Yes, Snowflake has got a front end to hiding behind. >> Right, but I didn't want to push it that a little bit because I want to go look at their pricing strategies because I still think they make you buy, I may be wrong. I thought they make you still do a one-year or two-year or three-year term. I don't know if you can just turn it off at any time. They might allow, I should hold off. I'll do some more research on that but I wanted to make a point about the audits, you mentioned audits before. A big mistake that a lot of Oracle customers have made many times and we've written about this, negotiating with Oracle, you've got to bring your best and your brightest when you negotiate with Oracle. Some of the things that people didn't pay attention to and I think they've sort of caught onto this is that Oracle's SOW is adjudicate over the MSA, a lot of legal departments and procurement department. Oh, do we have an MSA? With all, Yes, you do, okay, great and because they think the MSA, they then can run. If they have an MSA, they can rubber stamp it but the SOW really dictateS and Oracle's gotcha there and they're really smart about that. So you got to bring your best and the brightest and you've got to really negotiate hard with Oracle, you get trouble. >> Sure. >> So it is what it is but coming back to Oracle, let's sort of wrap on this. Dominant position in mission critical, we saw that from the Gartner research, especially for operational, giant customer base, there's cloud-first notion, there's investing in R and D, open, we'll put a question Mark around that but hey, they're doing some cool stuff with Michael stuff. >> Ecosystem, I put that, ecosystem they're promoting their ecosystem. >> Yeah, and look, I mean, for a lot of their customers, we've talked to many, they say, look, there's actually, a tail at the tail way, this saves us money and we don't have to migrate. >> Yeah. So interesting, so I'll give you the last word. We started sort of focusing on the announcement. So what do you want to leave us with? >> My last word is that there are platforms with a certain key application or key parts of the infrastructure, which I think can differentiate themselves from the Azures or the AWS. and Oracle owns one of those, SAP might be another one but there are certain platforms which are big enough and important enough that they will, in my opinion will succeed in that cloud strategy for this. >> Great, David, thanks so much, appreciate your insights. >> Good to be here. Thank you for watching everybody, this is Dave Vellante for The Cube. We'll see you next time. (upbeat music)

>> Hello everyone, and welcome back to The Data Cloud Summit 2020. We're going to dig into the all-important ecosystem, and focus in little bit on the intersection of the data cloud and trust. And with me are Ann-Christel Graham, AKA A.C., she's the CRO of Talend, and Chris Degnan is the CRO of Snowflake. We have the go-to-market heavies on this section, folks. Welcome to theCUBE. >> Thank you. >> Thanks for having us. >> Yeah, it's our pleasure. And so let's talk about digital transformation, right? Everybody loves to talk about it. It's an overused term, I know, but what does it mean? Let's talk about the vision of the data cloud for Snowflake and digital transformation. A.C., we've been hearing a lot about digital transformation over the past few years. It means a lot of things to a lot of people. What are you hearing from customers? How are they thinking about what I sometimes call DX? And what's important to them, maybe address some of the challenges even that they're facing? >> Dave, that's a great question. To our customers, digital transformation literally means staying in business or not. It's that simple. The reality is most agree on the opportunity to modernize data management infrastructure, that they need to do that to create the speed, and efficiency, and cost savings that digital transformation promises. But now it's beyond that. What's become front and center for our customers is the need for trusted data supported by an agile infrastructure that can allow a company to pivot operations as they need. Let me give you an example of that. One of our customers, a medical device company, was on their digital journey when COVID hit. They started last year in 2019. And as the pandemic hit, at the earlier part of this year, they really needed to take a closer look at their supply chain, and went through an entire supply chain optimization, having been completely disrupted in the, you think about the logistics, the transportation, the location of where they needed to get parts, all those things, when they were actually facing a need to increase production by about 20 times in order to meet the demand. And so you can imagine what that required them to do, and how reliant they were on clean, compliant, accurate data that they could use to make extremely critical decisions for their business. And in that situation, not just for their business, but decisions that would be about saving lives. So the stakes have gotten a lot higher and that's just one industry, it's really across all industries. So when you think about that, really, when you talk to any of our customers, digital transformation really means now having the confidence in data to support the business at critical times with accurate, trusted information. >> I mean, if you're not a digital business today, you're kind of out of business. Chris, I've always said a key part of digital transformation is really putting data at the core of everything. You know, not the manufacturing plant at the core and the data around it, but putting data at the center. And it seems like that's what Snowflake is bringing to the table. Can you comment? >> Yeah, I mean, I think if I look across what's happening, especially as A.C. said, you know, through COVID, is customers are bringing more and more data sets. They want to make smarter business decisions based on making data-driven decisions. And we are seeing acceleration of data moving to the cloud because there's just an abundance of data, and it's challenging to actually manage that data on-premise. And as we see those customers move those large data sets, I think what A.C. said is spot on, is that customers don't just want to have their data in the cloud, but they actually want to understand what the data is, understand who's has access to that data, making sure that they're actually making smart business decisions based on that data set. And I think that's where the partnership between both Talend and Snowflake are really tremendous, where, you know, we're helping our customers bring their data assets to to the cloud, really landing it, and allowing them to do multiple different types of workloads on top of this data cloud platform in Snowflake. And then I think, again, what Talend is bringing to the table is really helping the customer make sure that they trust the data that they're actually seeing. And I think that's a really important aspect of digital transformation today. >> Awesome, and I want to get into the partnership, but I don't want to leave the pandemic just yet. A.C., I want to ask you how it's affected customer priorities and timelines with regard to modernizing their data operations. And what I mean to that, I think about the end-to-end life cycle of going from kind of raw data to insights and how they're approaching those life cycles. Data quality is a key part of it. If you don't have good data quality, I mean, obviously you want to iterate, and you want to move fast, but if it's garbage out, then you got to to start all over again. So what are you seeing in terms of the effect of the pandemic and the urgency of modernizing those data operations? >> Yeah, well, like Chris just said, it accelerated things. For those companies that hadn't quite started their digital journey, maybe it was something that they had budgeted for, but hadn't quite resourced completely, many of them, this is what it took to really get them off the dime from that perspective, because there was no longer the opportunity to wait. They needed to go and take care of this really critical component within their business. So, you know, what COVID I think has taught companies, taught all of us, is how vulnerable even the largest companies and most robust enterprises can be. Those companies that had already begun their digital transformation, maybe even years ago, had already started that process and were in a great position in their journey, they fared a lot better, and we're able to be agile, were able to, you know, shift priorities, were able to go after what they needed to do to run their businesses better and be able to do so with real clarity and confidence. And I think that's really the second piece of it is for the last six months, people's lives have really depended on the data. People's lives have really depended on certainty. The pandemic has highlighted the importance of reliable and trustworthy information, not just the proliferation of data. And as Chris mentioned, just data being available. It's really about making sure that you can use that data as an asset. And that the greatest weapon we all have really there is the information and good information to make great business decisions. >> And, of course, Chris, the other thing we've seen is the acceleration to the cloud, which is obviously you (indistinct) born in the cloud. It's been a real tailwind. What are you seeing in that regard from your, I was going to say in the field, but from your Zoom vantage point. >> (laughs) Yeah, well, I think, you know, A.C. talked about supply chain analytics in her previous example. And I think one of the things that we did is we hosted a dataset, the COVID data set, COVID-19 dataset within Snowflake's data marketplace. And we saw customers that were, you know, initially hesitant to move to the cloud really accelerate their usage of Snowflake in the cloud with this COVID data set. And then we had other customers that are, you know, in the retail space, for example, and use the COVID data set to do supply chain analytics and accelerated, you know, it helped them make smarter business decisions on that. So, I'd say that, you know, COVID has made customers that were maybe hesitant to start their journey in the cloud move faster. And I've seen that, you know, really go at a blistering pace right now. >> You know, A.C., you just talked about value, 'cause it's all about value, but you know, the old days of data quality and the early days of chief data officer, all the focus was on risk avoidance, how do I get rid of data, how long do I have to keep it? And that has flipped dramatically, you know, sometime during the last decade. I wonder if you could talk about that a little bit. 'Cause I know you talk to a lot of CDOs out there, and have you seen that flip, where the value piece is really dwarfing that risk piece? And not that you can ignore the risk, but that's almost table stakes. What are your thoughts? >> You know, that's interesting, saying it's almost table stakes. I think you can't get away too much from the need for quality data and governed data. I think that's the first step, you can't really get to trust the data without those components. And, but to your point, the chief data officer's role, I would say, has changed pretty significantly. And in the round tables that I've participated in over the last, you know, several months, it's certainly a topic that they bring to the table that they'd like to, you know, chat with their peers about in terms of how they're navigating through the balance, that they still need to manage to the quality, they still need to manage to the governance, they still need to ensure that they're delivering that trusted information to the business. But now on the flip side as well, they're being relied upon to bring new insights and it's really requiring them to work more cross-functionally than they may have needed to in the past, where that's become a big part of their job is being that evangelist for data, the evangelist for those insights, and being able to bring in new ideas for how the business can operate. And identify, you know, not just operational efficiencies, but revenue opportunities, ways that they can shift. All you need to do is take a look at, for example, retail. You know, retail was heavily impacted by the pandemic this year, and it shows how easily an industry can be just kind of thrown off its course simply by just a significant change like that. And they need to be able to adjust. And this is where, when I've talked to some of the CDOs of the retail customers that we work with, they've had to really take a deep look at how they can leverage the data at their fingertips to identify new and different ways in which they can respond to customer demands. So it's a whole different dynamic, for sure. It doesn't mean that you walk away from the other end, the original part of the role or the areas in which they were maybe more defined a few years ago when the role of the chief data officer became very popular. I do believe it's more of a balance at this point, and really being able to deliver great value to the organization with the insights that they can bring. >> Well A.C., stay on that for a second. So you have this concept of data health, and I guess what I'm kind of getting at is that the early days of big data, Hadoop, it was just a lot of rogue efforts going on. People realized, wow, there there's no governance. And what's what seems like with Snowflake and Talend are trying to do is to make that so the business doesn't have to worry about it, build that in, don't bolt it on. But what's this notion of data health that you talk about? >> Well, it's interesting. Companies can measure and do measure just about everything, every aspect of their business health. Except what's interesting is they don't have a great way to measure the health of their data. And this is an asset that they truly rely on. Their future depends on is that health of their data. And so if we take a little bit of a step back, maybe let's take a look at an example of a customer experience just to kind of make a little bit of a delineation between the differences of data quality, data trust, and what data health truly is. We work with a lot of hotel chains, and like all companies today, hotels collect a ton of information. There's mountains of information, private information about their customers, through the loyalty clubs, and all the information that they collect from their the front desk, the systems that store their data. You can start to imagine the amount of information that a hotel chain has about an individual. And frequently, that information has errors in it, such as duplicate entries, you know, is it A.C. Graham, or is it Ann-Christel Graham? Same person, slightly different, depending on how I might've looked, or how I might've checked in at the time. And sometimes the data's also mismanaged, where because it's in so many different locations, it could be accessed by the wrong person, if someone that wasn't necessarily intended to have that kind of visibility. And so these are examples of when you look at something like that, now you're starting to get into, you know, privacy regulations, and other kinds of things that can be really impactful to a business if data's in the wrong hands or if the wrong data is in the wrong hands. So, you know, in a world of misinformation and mistrust, which is around us every single day, Talend has really invented a way for businesses to verify the veracity, the accuracy of their data. And that's where data health really comes in is being able to use a trust score to measure the data health. And that's what we've recently introduced is this concept of the trust score, something that can actually provide and measure the accuracy and the health of the data, all the way down to an individual report. And we believe that that truly provides the explainable trust, issue resolution, the kinds of things that companies are looking for in that next stage of overall data management. >> Thank you. Chris, bring us home. So one of the key aspects of what Snowflake is doing is building out the ecosystem. It's very, very important. Maybe talk about how you guys are partnering and adding value, in particular things that you're seeing customers do today within the ecosystem or with the help of the ecosystem and Snowflake, that they weren't able to do previously? >> Yeah, I mean, I think, you know, A.C. mentioned it, you mentioned it. I spend a lot of my Zoom days talking to chief data officers. And as I'm talking to these chief data officers, they are so concerned, their responsibility on making sure that the business users are getting accurate data, so that they view that as data governance, as one aspect of it. But the other aspect is the circumference of the data, of where it sits, and who has access to that data, and making sure it's super secure. And I think, you know, Snowflake is a tremendous landing spot, being a data warehouse or a cloud data platform as a service. You know, we take care of all the securing that data. And I think where Talend really helps our customer base is helps them exactly what A.C. talked about, is making sure that myself as a business user, someone like myself, who's looking at data all the time, trying to make decisions on how many salespeople I want to hire, how's my forecast coming, you know, how's the product working, all that stuff. I need to make sure that I'm actually looking at good data. And I think the combination of it all sitting in a single repository like Snowflake, and then layering a tool like Talend on top of it where I can actually say, yeah, that is good data, it helps me make smarter decisions faster. And ultimately, I think that's really where the ecosystem plays an incredibly important role for Snowflake and our customers >> Guys, two great guests. I wish we had more time, but we got to go. And so thank you so much for sharing your perspectives, a great conversation. >> Thank you for having us, Dave. >> Thanks Dave. >> All right, and thank you for watching. Keep it right there. We'll be back with more from The Data Cloud Summit 2020.

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

>> Narrator: Live from Orlando, Florida, its theCUBE. Covering Pentaho World 2017. Brought to you by Hitachi Vantara. >> We are kicking off day one of Pentaho World. Brought to you, of course, by Hitachi Vantara. I'm your host, Rebecca Knight, along with my co-hosts. We have Dave Vellante and James Kobielus. Guys I'm thrilled to be here in Orlando, Florida. Kicking off Pentaho World with theCUBE. >> Hey Rebecca, twice in one week. >> I know, this is very exciting, very exciting. So we were just listening to the key notes. We heard a lot about the big three, the power of the big three. Which is internet of things, predictive analytics, big data. So the question for you both is where is Hitachi Vantara in this marketplace? And are they doing what they need to do to win? >> Well so the first big question everyone is asking is what the heck is Hitachi-Vantara? (laughing) What is that? >> Maybe we should have started there. >> We joke, some people say it sounds like a SUV, Japanese company, blah blah blah. When we talked to Brian-- >> Jim: A well engineered SUV. >> So Brian Householder told us, well you know it really is about vantage and vantage points. And when you listen to their angles on insights and data, anywhere and however you want it. So they're trying to give their customers an advantage and a vantage point on data and insights. So that's kind of interesting and cool branding. The second big, I think, point is Hitachi has undergone a massive transformation itself. Certainly Hitachi America, which is really not a brand they use anymore, but Hitachi Data Systems. Brian Householder talked in his keynote, when he came in 14 years ago, Hitachi was 80 percent hardware, and infrastructure, and storage. And they've transformed that. They're about 50/50 last year. In terms of infrastructure versus software and services. But what they've done, in my view, is taken now the next step. I think Hitachi has said, alright listen, storage is going to the cloud, Dell and EMC are knocking each others head off. China is coming in to play. Do we really want to try and dominate that business? Rather, why don't we play from our strengths? Which is devices, internet of things, the industrial internet. So they buy Pentaho two years ago, and we're going to talk more about that, bring in an analytics platform. And this sort of marrying IT and OT, information technology and operation technology, together to go attack what is a trillion dollar marketplace. >> That's it so Pentaho was a very strategic acquisition. For Hitachi, of course, Hitachi data system plus Hitachi insides, plus Pentaho equals Hitachi Vantara. Pentaho was one of the pioneering vendors more than a decade ago. In the whole open source analytics arena. If you cast your mind back to the middle millennium decade, open source was starting to come into its own. Of course, we already had Linux an so forth, but in terms of the data world, we're talking about the pre-Hadoop era, the pre-Spark era. We're talking about the pre-TensorFlow era. Pentaho, I should say at that time. Which is, by the way, now a product group within Hitachi Vantara. It's not a stand alone company. Pentaho established itself as the spearhead for open-source, predictive analytics, and data mining. They made something called Weka, which is an open-source data mining toolkit that was actually developed initially in New Zealand. The core of their offering, to market, in many ways became very much a core player in terms of analytics as a service a so forth, but very much established themselves, Pentaho, as an up and coming solution provider taking a more or less, by the book, open source approach for delivering solutions to market. But they were entering a market that was already fairly mature in terms of data mining. Because you are talking about the mid-2000's. You already had SaaS, and SPSS, and some of the others that had been in that space. And done quite well for a long time. And so cut ahead to the present day. Pentaho had evolved to incorporate some fairly robust data integration, data transformation, all ETL capabilities into their portfolio. They had become a big data player in their own right, With a strong focus on embedded analytics, as the keynoters indicated this morning. There's a certain point where in this decade it became clear that they couldn't go it any further, in terms of differentiating themselves in this space. In a space that dominated by Hadoop and Spark, and AI things like TensorFlow. Unless they are part of a more diversified solution provider that offered, especially I think the critical thing was the edge orientation of the industrial internet of things. Which is really where many of the opportunities are now for a variety of new markets that are opening up, including autonomous vehicles, which was the focus of here all-- >> Let's clarify some things a little bit. So Pentaho actually started before the whole Hadoop movement. >> Yeah, yeah. >> That's kind of interesting. You know they were young company when Hadoop just started to take off. And they said alright we can adopt these techniques and processes as well. So they weren't true legacy, right? >> Jim: No. >> So they were able to ride that sort of modern wave. But essentially they're in the business of data, I call it data management. And maybe that's not the right term. They do ingest, they're doing ETL, transformation anyway. They're embedding, they've got analytics, they're embedding analytics. Like you said, they're building on top of Weka. >> James: In the first flesh and BI as a hot topic in the market in the mid-200's, they became a fairly substantial BI player. That actually helped them to grow in terms of revenue and customers. >> So they're one of those companies that touches on a lot of different areas. >> Yes. >> So who do we sort of compare them to? Obviously, what you think of guys like Informatica. >> Yeah, yeah. >> Who do heavy ETL. >> Yes. You mentioned BI, you mentioned before. Like, guys like Saas. What about Tableau? >> Well, BBI would be like, there's Tableau, and ClickView and so forth. But there's also very much-- >> Talend. >> Cognos under IBM. And, of course, there's the business objects Portfolio under SAP. >> David: Right. And Talend would be? >> In fact I think Talend is in many ways is the closest analog >> Right. >> to Pentaho in terms of predominatly open-source, go to market approach, that involves both the robust data integration and cleansing and so forth from the back end. And also, a deep dive of open source analytics on the front end. >> So they're differentiation they sort of claim is they're sort of end to end integration. >> Jim: Yeah. >> Which is something we've been talking about at Wikibon for a while. And George is doing some work there, you probably are too. It's an age old thing in software. Do you do best-of-breed or do you do sort of an integrated suite? Now the interesting thing about Pentaho is, they don't own their own cloud. Hitachi Vantara doesn't own their own cloud. So they do a lot of, it's an integrated pipeline, but it doesn't include its own database and other tooling. >> Jim: Yeah. >> Right, and so there is an interesting dynamic occurring that we want to talk to Donna Perlik about obviously, is how they position relative to roll your own. And then how they position, sort of, in the cloud world. >> And we should ask also how are they positioning now in the world of deep learning frameworks? I mean they don't provide, near as I know, their own deep learning frameworks to compete with the likes of TensorFlow, or MXNet, or CNT or so forth. So where are they going in that regard? I'd like to know. I mean there are some others that are big players in this space, like IBM, who don't offer their own deep learning framework, but support more than one of the existing frameworks in a portfolio that includes much of the other componentry. So in other words, what I'm saying is you don't need to have your own deep learning framework, or even open-source deep learning code-based, to compete in this new marketplace. And perhaps Pentaho, or Hitachi Vantara, roadmapping, maybe they'll take an IBM like approach. Where they'll bundle support, or incorporate support, for two or more of these third party tools, or open source code bases into their solution. Weka is not theirs either. It's open source. I mean Weka is an open source tool that they've supported from the get go. And they've done very well by it. >> It's just kind of like early day machine leraning. >> David: Yeah. >> Okay, so we've heard about Hitachi's transformation internally. And then their messaging today was, of course-- >> Exactly, that's where I really wanted to go next was we're talking about it from the product and the technology standpoint. But one of the things we kept hearing about today was this idea of the double bottom line. And this is how Hitachi Vantara is really approaching the marketplace, by really focusing on better business, better outcomes, for their customers. And obviously for Hitachi Vantara, too, but also for bettering society. And that's what we're going to see on theCUBE today. We're going to have a lot of guests who will come on and talk about how they're using Pentaho to solve problems in healthcare data, in keeping kids from dropping out of college, from getting computing and other kinds of internet power to underserved areas. I think that's another really important approach that Hitachi Vantara is taking in its model. >> The fact that Hitachi Vantara, I know, received Pentaho Solution, has been on the market for so long and they have such a wide range of reference customers all over the world, in many vertical. >> Rebecca: That's a great point. >> The most vertical. Willing to go on camera and speak at some length of how they're using it inside their business and so forth. Speaks volumes about a solution provider. Meaning, they do good work. They provide good offerings. They're companies have invested a lot of money in, and are willing to vouch for them. That says a lot. >> Rebecca: Right. >> And so the acquisition was in 2015. I don't believe it was a public number. It's Hitachi Limited. I don't think they had to report it, but the number I heard was about a half a billion. >> Jim: Uh-hm >> Which for a company with the potential of Pentaho, is actually pretty cheap, believe it or not. You see a lot of unicorns, billion dollar plus companies. But the more important thing is it allows Hitachi to further is transformation and really go after this trillion dollar business. Which is really going to be interesting to see how that unfolds. Because while Hitachi has a long-term view, it always takes a long-term view, you still got to make money. It's fuzzy, how you make money in IOT these days. Obviously, you can make money selling devices. >> How do you think money, open source anything? You know, so yeah. >> But they're sort of open source, with a hybrid model, right? >> Yeah. >> And we talked to Brian about this. There's a proprietary component in there so they can make their margin. Wikibon, we see this three tier model emerging. A data model, where you've got the edge in some analytics, real time analytics at the edge, and maybe persists some of that data, but they're low cost devices. And then there's a sort of aggregation point, or a hub. I think Pentaho today called it a gateway. Maybe it was Brian from Forester. A gateway where you're sort of aggregating data, and then ultimately the third tier is the cloud. And that cloud, I think, vectors into two areas. One is Onprem and one was public cloud. What's interesting with Brian from Forester was saying that basically said that puts the nail in the coffin of Onprem analytics and Onprem big data. >> Uh-hm >> I don't buy that. >> I don't buy that either. >> No, I think the cloud is going to go to your data. Wherever the data lives. The cloud model of self-service and agile and elastic is going to go to your data. >> Couple of weeks ago, of course we Wikibon, we did a webinar for our customers all around the notion of a true private cloud. And Dave, of course, Peter Burse were on it. Explaining that hybrid clouds, of course, public and private play together. But where the cloud experience migrates to where the data is. In other words, that data will be both in public and in private clouds. But you will have the same reliability, high availability, scaleability, ease of programming, so forth, wherever you happen to put your data assets. In other words, many companies we talk to do this. They combine zonal architecture. They'll put some of their resources, like some of their analytics, will be in the private cloud for good reason. The data needs to stay there for security and so forth. But much in the public cloud where its way cheaper quite often. Also, they can improve service levels for important things. What I'm getting at is that the whole notion of a true private cloud is critically important to understand that its all datacentric. Its all gravitating to where the data is. And really analytics are gravitating to where the data is. And increasingly the data is on the edge itself. Its on those devices where its being persistent, much of it. Because there's no need to bring much of the raw data to the gateway or to the cloud. If you can do the predominate bulk of the inferrencing on that data at edge devices. And more and more the inferrencing, to drive things like face recognition from you Apple phone, is happening on the edge. Most of the data will live there, and most of the analytics will be developed centrally. And then trained centrally, and pushed to those edge devices. That's the way it's working. >> Well, it is going to be an exciting conference. I can't wait to hear more from all of our guests, and both of you, Dave Vellante and Jim Kobielus. I'm Rebecca Knight, we'll have more from theCUBE's live coverage of Pentaho World, brought to you by Hitachi Vantara just after this.

>> Narrator: Live from Boston, Massachusetts, this is theCUBE, covering Spark Summit East 2017. Brought to you by Databricks. Now, here are your hosts Dave Vellante and George Gilbert. >> Welcome back to Boston, everybody, this is theCUBE, the leader in live tech coverage. We're here in Boston, in snowy Boston. This is Spark Summit. Spark Summit does a East Coast version, they do a West Coast version, they've got one in Europe this year. theCUBE has been a partner with Databricks as the live broadcast partner. Our friend Bill Peterson is here. He's the head of partner marketing at MapR. Bill, good to see you again. >> Thank you, thanks for having me. >> So how's the show going for you? >> It's great. >> Give us the vibe. We're kind of windin' down day two. >> It is. The show's been great, we've got a lot of traffic coming by, a lot of deep technical questions which is-- >> Dave: Hardcore at the show-- >> It is, it is. I spend a lot of time there smiling and going, "Yeah, talk to him." (laughs) But it's great. We're getting those deep technical questions and it's great. We actually just got one on Lustre, which I had to think for a minute, oh, HPC. It was like way back in there. >> Dave: You know, Cray's on the floor. >> Oh, yeah that's true. But a lot of our customers as well. UnitedHealth Group, Wells Fargo, AMEX coming by. Which is great to see them and talk to them, but also they've got some deep technical questions for us. So it's moving the needle with existing customers but also new business, which is great. >> So I got to ask a basic question. What is MapR? MapR started in the early days of Hadoop distro, vendor, one of the big three. When somebody says to you what is MapR, what do you say? My answer today is MapR is an enterprise software company that delivers a converged data platform. That converged data platform consists of a file system, a NoSQL database, a Hadoop distribution, a Spark distribution, and a set of data management tools. And as a customer of MapR, you get all of those. You can turn 'em all on if you'd like. You can just turn on the file system, for example, if you wanted to just use the file system for storage. But the enterprise software piece of that is all the hardening we do behind the scenes on things like snapshots, mirroring, data governance, multi-tenancy, ease of use performance, all of that baked in to the solution, or the platform as we're calling it now. So as you're kind of alluding to, a year ago now we kind of got out of that business of saying okay, lead 100% with Hadoop and then while we have your attention, or if we don't, hey wait, we got all this other stuff in the basket we want to show you, we went the platform play and said we're going to include everything and it's all there and then the baseline underneath is the hardening of it, the file system, the database, and the streaming product, actually, which I didn't mention, which is kind of the core, and everything plays off of there. And that honestly has been really well-received. And it just, I feel, makes it so much easier because-- It happened here, we get the question, okay, how are you different from Cloudera or Hortonworks? And some of it here, given the nature of the attendees, is very technical, but there's been a couple of business users that I've talked to. And when I talk about us as an enterprise software company delivering a plethora of solutions versus just Hadoop, you can see the light going on sometimes in people's eyes. And I got it today, earlier, "I had no idea you had a file system," which, to me, just drives me insane because the file system is pretty cool, right? >> Well you guys are early on in investing in that file system and recovery capabilities and all the-- >> Two years in stealth writing it. >> Nasty, gnarly, hard stuff that was kind of poo-pooed early on. >> Yeah, yeah. MapR was never patient about waiting for the open source community to just figure it out and catch up. You always just said all right, we're going to solve this problem and go sell. >> And I'm glad you said that. I want to be clear. We're not giving up on open source or anything, right? Open source is still a big piece. 50% of our engineers' time is working on open source projects. That's still super important to us. And then back in November-ish last year we announced the MapR Ecosystem Packs, which is our effort to help our customers that are using open source components to stay current. 'Cause that's a pain in the butt. So this is a set of packages that have a whole bunch of components. We lead with Spark and Drill, and that was by customer request, that they were having a hard time keeping current with Spark and Drill. So the packs allow them to come up to current level within the converged data platform for all of their open source components. And that's something we're going to do at dot Level, so I think we're at 2.1 or 2 now. The dot levels will bring you up on everything and then the big ones, like the 3.0s, the 4.0s, will bring Spark and Drill current. And so we're going to kind of leapfrog those. So that's still a really important part of our business and we don't want to forget that part, but what we're trying here to do is, via the platform, is deliver all of that in one entity, right? >> So the converged data platform is relevant presumably because you've got the history of Hadoop, 'cause you got all these different components and you got to cobble 'em together and they're different interfaces and different environments, you're trying to unify that and you have unified that, right? >> Yeah, yeah. >> So what is your customer feedback with regard to the converged data platform? >> Yeah so it's a great question because for existing customers, it was like, ah, thank you. It was one of those, right, because we're listening. Actually, again, glad you said that. This week, in addition to Spark Summit we're doing our yearly customer advisory board so we've got, like a lot of vendors, we've got a 30 plus company customer advisory board that we bring in and we sit down with them for a couple of days and they give us feedback on what we should and shouldn't be doing and where, directional and all that, which is super important. And that's where a lot of this converged data platform came out of is the need for... There was just too much, it's kind of confusing. I'll give the example of streams, right? We came out with our streaming product last year and okay, I'm using Hadoop, I'm using your file system, I'm using NoSQL, now you're adding streams, this is great, but now, like MEP, the Ecosystem Packages, I have to keep everything current. You got to make it easier for me, you got to make my life easier for me. So for existing customers it's a stay current, I like this, the model, I can turn on and off what I want when I want. Great model for them, existing business. For new business it gets us out of that Hadoop-only mode, right? I kind of jokingly call us Hadoop plus plus plus plus. We keep adding solutions and add it to a single, cohesive data platform that we keep updated. And as I mentioned here, talking to new customers or new prospects, our potential new business, when I describe the model you can just see the light going on and they realize wow, there's a lot more to this than I had imagined. I got it earlier today, I thought you guys only did Hadoop. Which is a little infuriating as a marketer, but I think from a mechanism and a delivery and a message and a story point of view, it's really helped. >> More Cube time will help get this out there. (laughs) >> Well played, well played. >> It's good to have you back on. Okay, so Spark comes along a couple years ago and it was like ah, what's going to happen to Hadoop? So you guys embraced Spark. Talk more specifically about Spark, where it fits in your platform and the ecosystem generally. >> Spark, Hadoop, others as a entity to bring data into the converged data platform, that's one way to think about it. Way oversimplified, obviously, but that's a really great way, I think, to think about it is if we're going to provide this platform that anybody can query on, you can run analytics against. We talk a lot about now converged applications. So taking historical data, taking operational data, so streaming data, great example. Putting those together and you could use the Data Lake example if you want, that's fine. But putting them into a converged application in the middle where they overlap, kind of typical Venn diagram where they overlap, and that middle part is the converged application. What's feeding that? Well, Spark could be feeding that, Hadoop could be feeding that. Just yesterday we announced a Docker for containers, that could be feeding into the converged data platform as well. So we look at all of these things as an opportunity for us to manage data and to make data accessible at the enterprise level. And then that enterprise level goes back to what I was talkin' before, it's got to have all of those things, like multi-tenancy and snapshots and mirroring and data governance, security, et cetera. But Spark is a big component of that. All of the customers who came by here that I mentioned earlier, which are some really good names for us, are all using Spark to drive data into the converged data platform. So we look at it as we can help them build new applications within converged data platform with that data. So whether it's Spark data, Hadoop data, container data, we don't really care. >> So along those lines, if the focus of intense interest right now is on Spark, and Spark says oh, and we work with all these databases, data storers, file systems, if you approach a customer who's Spark first, what's the message relative to all the other data storers that they can get to through, without getting too techy, their API? >> Sure, sure. I think as you know, George, we support a whole bunch of APIs. So I guess for us it's the breadth. >> But I'm thinking of Spark in particular. If someone says specifically, I want to run Databricks, but I need something underneath it to capture the data and to manage it. >> Well I think that's the beauty of our file system there. As I mentioned, if you think about it from an architectural point of view, our file system along the bottom, or it could be our database or our streaming product, but in this instance-- >> George: That's what I'm getting at too, all three. >> Picture that as the bottom layer as your storage-- I shouldn't say storage layer but as the bottom layer. 'Cause it's not just storage, it's more than storage. Middle layer is maybe some of your open source tools and the like, and then above that is what I called your data delivery mechanisms. Which would be Spark, for example, one bucket. Another bucket could be Hadoop, and another bucket could be these microservices we're talking about. Let my draw the picture another way using a partner, SAP. One of the things we've had some success with SAP is SAP HANA sitting up here. SAP would love to have you put all your data in HANA. It's probably not going to happen. >> George: Yeah, good luck. >> Yeah, good luck, right? But what if you, hey customer, what if you put zero to two years worth of data, historical data, in HANA. Okay, maybe the customer starts nodding their head like you just did. Hey customer, what if you put two to five years worth of data in Business Warehouse. Guess what, you already own that. You've been an SAP customer for awhile, you already have it. Okay, the customer's now really nodding their head. You got their attention. To your original question, whether it's Spark or whatever, five plus years, put it in MapR. >> Oh, and then like HANA Vora could do the query. >> Drill can query across all of them. >> Oh, right including the Business Warehouse, okay. >> So we're running in the file system. That, to me, and we do this obviously with our joint SAP MapR customers, that to me is kind of a really cool vision. And to your original question, if that was Spark at the top feeding it rather than SAP, sure, right? Why not? >> What can you share with us, Bill, about business metrics around MapR? However you choose to share it, head count, want to give us gross margins by product, that's great, but-- (laughs) >> Would you like revenues too, Dave? >> We know they're very high because you're a software company, so that's actually a bad question. I've already profit-- (laughs) >> You don't have to give us top line revenues-- >> So what are you guys saying publicly about the company, its growth. >> That's fair. >> Give us the latest. >> Fantastic, number one. Hiring like crazy, we're well north of 500 people now. I actually, you want to hear a funny story? I yesterday was texting in the booth, with a candidate from my team, back and forth on salary. Did the salary negotiation on text right there in the booth and closed her, she starts on the 27th, so. >> Dave: Congratulations. >> I'm very excited about that. So moving along on that. Seven, 800 plus customers as we talk about... We just finished our fiscal year on January 31st, so we're on Feb one fiscal year. And we always do a momentum press release, which will be coming out soon. Hiring, again, like crazy, as I mentioned, executive staff is all filled in and built to scale which we're really excited about. We talk a lot about the kind of uptake of-- it used to be of the file system, Hadoop, et cetera on its own, but now in this one the momentum release we'll be doing, we'll talk about the converged data platform and the uplift we've seen from that. So we obviously can't talk revenue numbers and the like, but everything... David, I got to tell you, we've been doin' this a long time, all of that is just all moving in the right direction. And then the other example I'll give you from my world, in the partner world. Last year I rebranded our partner to the converged partner program. We're going with this whole converged thing, right? And we established three levels, elite, preferred, and affiliate with different levels there. But also, there's revenue requirements at each level, so elite, preferred, and affiliate, and there's resell and influence revenues, we have MDF funds, not only from the big guys coming to us, but we're paying out MDF funds now to select partners as well. So all of this stuff I always talk about as the maturity of the company, right? We're maturing in our messaging, we're maturing in the level of people who are joining, and we're maturing in the customers and the deals, the deal sizes and volumes that we're seeing. It's all movin' in the right direction. >> Dave: Great, awesome, congratulations. >> Bill: Thank you, yeah, I'm excited. >> Can you talk about number of customers or number of employees relative to last year? >> Oh boy. Honestly, George, I don't know off the top of my head. I apologize, I don't know the metric, but I know it's north of 500 today, of employees, and it's like seven, 800 customers. >> Okay, okay. >> Yeah, yeah. >> And a little bit more on this partner, elite, preferred, and affiliate. >> Affiliate, yeah. >> What did you call it, the converged partners program? >> Converged-- Yeah, yeah. >> What are some of the details of that? >> Sure. So the elites are invite only, and those are some of the bigger ones. So for us, we're-- >> Dave: Like, some examples. >> Cisco, SAP, AWS, others, but those are some of the big ones. And they were looking at things like resell and influence revenue. That's what I track in my... I always jokingly say at MapR, even though we're kind of a big startup now, I always jokingly say at MapR you have three jobs. You have the job you were hired for, you have your Thursday night job, and you have your Sunday night job. (Dave and George laugh) In the job that I was hired for, partner marketing, I track influence and resell revenue. So at the elite level, we're doing both. Like Cisco resells us, so this S-Series, we're in their SKU, their sales reps can go sell an S-Series for big data workloads or analytical workloads, MapR, on it, off you go. Our job then is cashing checks, which I like. That's a good job to have in this business. At the preferred level it's kind of that next tier of big players, but revenue thresholds haven't moved into the elite yet. Partners in there, like the MicroStrategies of the world, we're doing a lot with them, Tableau, Talend, a lot of the BI vendors in there. And then the affiliates are the smaller guys who maybe we'll do one piece of a campaign during the year with them. So I'll give you an example, Attunity, you guys know those guys right here? >> Sure >> Yeah, yeah. >> Last year we were doing a campaign on DWO, data warehouse offload. We wanted to bring them in but this was a MapR campaign running for a quarter, and we're typical, like a lot of companies, we run four campaigns a year and then my partner in field stuff kind of opts into that and we run stuff to support it. And then corporate marketing does something. Pretty traditional. But what I try and do is pull these partners into those campaigns. So we did a webinar with Attunity as part of that campaign. So at the affiliate level, the lower level, we're not doing a full go-to-market like we would with the elites at the top, but they're being brought into our campaigns and then obviously hopefully, we hope on the other side they're going to pull us in as well. >> Great, last question. What should we pay attention to, what's comin' up? >> Yeah, so-- >> Let's see, we got some events, we got Strata coming up you'll be out your way, or out MapR way. >> As my Twitter handle says, seat 11A. That's where I am. (laughs) Yeah, I mean the Docker announcement we're really excited about, and microservices. You'll see more from us on the whole microservices thing. Streaming is still a big one, we think, for this year. You guys probably agree. That's why we announced the MapR streaming product last year. So again, from a go-to-market point of view and kind of putting some meat behind streaming not only MapR but with partners, so streaming as a component and a delivery model for managing data in CDP. I think that's a big one. Machine learning is something that we're seeing more and more touching us from a number of customers but also from the partner perspective. I see all the partner requests that come in to join the partner program, and there's been an uptick in the machine learning customers that want to come in and-- Excuse me, partners, that want to be talking to us. Which I think is really interesting. >> Where you would be the sort of prediction serving layer? >> Exactly, exactly. Or a data store. A lot of them are looking for just an easy data store that the MapR file system can do. >> Infrastructure to support that, yeah. >> Commodity, right? The whole old promise of Hadoop or just a generic file system is give me easy access to storage on commodity hardware. The machine learning-- >> That works. >> Right. The existing machine learning vendors need an answer for that. When the customer asks them, they want just an easy answer, say oh, we just use MapR FS for that and we're done. Okay, that's fine with me, I'll take that one. >> So that's the operational end of that machine learning pipeline that we call DevOps for data scientists? >> Correct, right. I guess the nice synergy there is the whole, going back to the Docker microservices one, there's a DevOps component there as well. So, might be interesting marrying those together. >> All right, we got to go, Bill, thanks very much, good to see you again. >> All right, thank you. >> All right, George and I will be back to wrap. We're going to part two of our big data forecast right now, so stay with us, right back. (digital music) (synth music)