(upbeat music) >> Hey everyone. Welcome to theCUBE's special program series "Women of the Cloud", brought to you by AWS. I'm your host for the program, Lisa Martin. Very pleased to welcome back one of our alumni to this special series, Dr. Tendu Yogurtcu joins us, the CTO of Precisely. >> Lisa: Tendu, it's great to see you, it's been a while, but I'm glad that you're doing so well. >> Geez, it's so great seeing you too, and thank you for having me. >> My pleasure. I want the audience to understand a little bit about you. Talk to me a little bit about you, about your role and what are some of the great things that you're doing at Precisely. >> Of course. As CTO, my current role is driving technology vision and innovation, and also coming up with expansion strategies for Precisely's future growth. Precisely is the leader in data integrity. We deliver data with trust, with maximum accuracy, consistency, and also with context. And as a CTO, keeping an eye on what's coming in the business space, what's coming up with the emerging challenges is really key for me. Prior to becoming CTO, I was General Manager for the Syncsort big data business. And previously I had several engineering and R&D leadership roles. I also have a bit of academia experience. I served as a part-time faculty in computer science department in a university. And I am a person who is very tuned to giving back to my community. So I'm currently serving as a advisory board member in the same university. And I'm also serving as a advisory board member for a venture capital firm. And I take pride in being a dedicated advocate for STEM education and STEM education for women in particular, and girls in the underserved areas. >> You have such a great background. The breadth of your background, the experience that you have in the industry as well in academia is so impressive. I've known you a long time. I'd love the audience to get some recommendations from you. For those of the audience looking to grow and expand their careers in technology, what are some of the things that you that you've experienced that you would recommend people do? >> First, stay current. What is emerging today is going to be current very quickly. Especially now we are seeing more change and change at the increased speed than ever. So keeping an eye on on what's happening in the market if you want to be marketable. Now, some of the things that I will say, we have shortage of skills with data science, data engineering with security cyber security with cloud, right? We are here talking about cloud in particular. So there is a shortage of skills in the emerging technologies, AI, ML, there's a shortage of skills also in the retiring technologies. So we are in this like spectrum of skills shortage. So stay tuned to what's coming up. That's one. And on the second piece is that the quicker you tie what you are doing to the goals of the business, whether that's revenue growth whether that's customer retention or cost optimization you are more likely to grow in your career. You have to be able to articulate what you are doing and how that brings value to business to your boss, to your customers. So that becomes an important one. And then third one is giving back. Do something for the women in technology while being a woman in technology. Give back to your community whether that's community is gender based or whether it's your alumni, whether it's your community social community in your neighborhood or in your country or ethnicity. Give back to your community. I think that's becoming really important. >> I think so too. I think that paying it forward is so critical. I'm sure that you have a a long list of mentors and sponsors that have guided you along the way. Giving back to the community paying it forward I think is so important. For others who might be a few years behind us or even maybe have been in tech for the same amount of time that are looking to grow and expand their career having those mentors and sponsors of women who've been through the trenches is inspiring. It's so helpful. And it really is something that we need to do from a diversity perspective alone, right? >> Correct. Correct. And we have seen that, we have seen, for example Covid impact in women in particular. Diverse studies done by girls who quote on Accenture that showed that actually 50% of the women above age 35 were actually dropping out of the technology. And those numbers are scary. However, on the other side we have also seen incredible amount of technology innovation during that time with cloud adoption increasing with the ability to actually work remotely if you are even living in not so secure areas, for example that created more opportunities for women to come back to workforce as well. So we can turn the challenges to opportunities and watch out for those. I would say tipping points. >> I love that you bring up such a great point. There are so, so the, the data doesn't lie, right? The data shows that there's a significant amount of churn for women in technology. But to your point, there are so many opportunities. You mentioned a minute ago the skills gap. One of the things we talk about often on theCUBE and we're talking about cybersecurity which is obviously it's a global risk for companies in every industry, is that there's massive opportunity for people of, of any type to be able to grow their skills. So knowing that there's trend, but there's also so much opportunity for women in technology to climb the ladder is kind of exciting. I think. >> It is. It is exciting. >> Talk to me a little bit about, I would love for the audience to understand some of your hands-on examples where you've really been successful helping organizations navigate digital transformation and their entry and success with cloud computing. What are some of those success stories that you're really proud of? >> Let me think about, first of all what we are seeing is with the digital transformation in general, every single business every single vertical is becoming a technology company. Telecom companies are becoming a technology company. Financial services are becoming a technology company and manufacturing is becoming a technology company. So every business is becoming technology driven. And data is the key. Data is the enabler for every single business. So when we think about the challenges, one of the examples that I give a big challenge for our customers is I can't find the critical data, I can't access it. What are my critical data elements? Because I have so high volumes growing exponentially. What are the critical data elements that I should care and how do I access that? And we work at Precisely with 99 of Fortune 100. So we have two 12,000 customers in over a hundred countries which means we have customers whose businesses are purely built on cloud, clean slate. We also have businesses who have very complex set of data platforms. They have financial services, insurance, for example. They have critical transactional workloads still running on mainframes, IBM i servers, SAP systems. So one of the challenges that we have, and I work with key customers, is on how do we make data accessible for advanced analytics in the cloud? Cloud opens up a ton of open source tools, AI, ML stack lots of tools that actually the companies can leverage for that analytics in addition to elasticity in addition to easy to set up infrastructure. So how do we make sure the data can be actually available from these transactional systems, from mainframes at the speed that the business requires. So it's not just accessing data at the speed the business requires. One of our insurance customers they actually created this data marketplace on Amazon Cloud. And the, their challenge was to make sure they can bring the fresh data on a nightly basis initially and which became actually half an hour, every half an hour. So the speed of the business requirements have changed over time. We work with them very closely and also with the Amazon teams on enabling bringing data and workloads from the mainframes and executing in the cloud. So that's one example. Another big challenge that we see is, can I trust my data? And data integrity is more critical than ever. The quality of data, actually, according to HBR Harvard Business Review survey, 47% of every new record of data has at least one critical data error, 47%. So imagine, I was talking with the manufacturing organization couple of weeks ago and they were giving me an example. They have these three letter quotes for parts and different chemicals they use in the manufacturing. And the single letter error calls a shutdown of the whole manufacturing line. >> Wow. >> So that kind of challenge, how do I ensure that I can actually have completeness of data cleanness of data and consistency in that data? Moreover, govern that on a continuous basis becomes one of the use cases that we help customers. And in that particular case actually we help them put a data governance framework and data quality in their manufacturing line. It's becoming also a critical for, for example ESG, environment, social and governance, supply chain, monitoring the supply chain, and assessing ESG metrics. We see that again. And then the third one, last one. I will give an example because I think it's important. Hybrid cloud becoming critical. Because there's a purest view for new companies. However, facilitating flexible deployment models and facilitating cloud and hybrid cloud is also where we really we can help our customers. >> You brought up some amazingly critical points where it comes to data. You talked about, you know, a minute ago, every company in every industry has to become a technology company. You could also say every company across every industry has to become a data company. They have to become a software company. But to your point, and what it sounds like precisely is really helping organizations to do is access the data access data that has high integrity data that is free of errors. Obviously that's business critical. You talked about the high percentage of errors that caused manufacturing shutdown. Businesses can't, can't have that. That could potentially be life-ending for an organization. So it sounds like what you're talking about data accessibility, data integrity data governance and having that all in real time is table stakes for businesses. Whether it's your grocery store, your local coffee shop a manufacturing company, and e-commerce company. It's table stakes globally these days. >> It is, and you made a very good point actually, Lisa when you talked about the local coffee shop or the retail. One other interesting statistic is that almost 80% of every data has a location attribute. So when we talk about data integrity we no longer talk about just, and consistency of data. We also talk about context, right? When you are going, for example, to a new town you are probably getting some reminders about where your favorite coffee shop is or what telecom company has an office in that particular town. Or if you're an insurance company and a hurricane is hitting southern Florida. Then you want to know how the path of that hurricane is going to impact your customers and predict the claims before they happen. Also understand the propensity of the potential customers that you don't yet have. So location and context, those additional attributes of demographics, visitations are creating actually more confident business insights. >> Absolutely. And and as the consumer we're becoming more and more demanding. We want to be able to transact things so easily whether it's in our personal life at the grocery store, at that cafe, or in our business life. So those demands from the customer are also really influencing the direction that companies need to go. And it's actually, I think it's quite exciting that the amount of personalization the location data that you talk about that comes in there and really helps companies in every industry deliver these the cloud can, these amazing, unique personalized experiences that really drive business forward. We could talk about that all day long. I have no problem. But I want to get in our final minutes here, Tendu. What do you see as in your crystal ball as next for the cloud? How do you see your role as CTO evolving? >> Sure. For what we are seeing in the cloud I think we will start seeing more and more focus on sustainability. Sustainable technologies and governance. Obviously cloud migrations cloud modernizations are helping with that. And we, we are seeing many of our customers they started actually assessing the ESG supply chain and reporting on metrics whether it's the percentage of face or energy consumption. Also on the social metrics on diversity age distribution and as well as compliance piece. So sustainability governance I think that will become one area. Second, security, we talked about IT security and data privacy. I think we will see more and more investments around those. Cybersecurity in particular. And ethical data access and ethics is becoming center to everything we are doing as we have those personalized experiences and have more opportunities in the cloud. And the third one is continued automation with AI, ML and more focus on automation because cloud enables that at scale. And the work that we need to do is too time-intensive and too manual with the amount of data. Data is powering every business. So automation is going to be an increased focus how my role evolves with that. So I have this unique combination. I have been open to non-linear career paths throughout my growth. So I have an understanding of how to innovate and build products that solve real business problems. I also have an understanding of how to sell them build partnerships that combined with the the scale of growth, the hyper growth that we have absorbed in precisely 10 times growth within the last 10 years through a combination of organic innovation and acquisitions really requires the speed of change. So change, implementing change at scale as well as at speed. So taking those and bringing them to the next challenge is the evolution of my role. How do I bring those and tackle keep an eye on what's coming as a challenge in the industry and how they apply those skills that I have developed throughout my career to that next challenge and evolve with it, bring the innovation to data to cloud and the next challenge that we are going to see. >> There's so much on the horizon. It's, there are certainly challenges, you know within technology, but there's so much opportunity. You've done such a great job highlighting your career path the, the big impact that you're helping organizations make leveraging cloud and the opportunity that's there for the rest of us to really get in there get our hands dirty and solve problems. Tendu, I always love our conversations. It's been such a pleasure having you back, back on theCUBE. Thank you for joining us on this special program series today. >> Thank you Lisa. And also thanks to AWS for the opportunity. >> Absolutely. This is brought, brought to us by AWS. For Dr.Tendu, you are good to go. I'm Lisa Martin. You're watching theCUBE special program series Women of the Cloud. We thank you so much for watching and we'll see you soon. (upbeat music)

>>Hey everyone, welcome to the Cube's coverage of AWS Reinvent 2022. Lisa Martin here with you with Subaru ier, one of our alumni who's now the CEO of Aerospike. Sabu. Great to have you on the program. Thank you for joining us. >>Great as always, to be on the cube. Luisa, good to meet you. >>So, you know, every company these days has got to be a data company, whether it's a retailer, a manufacturer, a grocer, a automotive company. But for a lot of companies, data is underutilized, yet a huge asset that is value added. Why do you think companies are struggling so much to make data a value added asset? >>Well, you know, we, we see this across the board when I talk to customers and prospects. There's a desire from the business and from it actually to leverage data to really fuel newer applications, newer services, newer business lines, if you will, for companies. I think the struggle is one, I think one the, you know, the plethora of data that is created, you know, surveys say that over the next three years data is gonna be, you know, by 2025, around 175 zetabytes, right? A hundred and zetabytes of data is gonna be created. And that's really a, a, a growth of north of 30% year over year. But the more important, and the interesting thing is the real time component of that data is actually growing at, you know, 35% cagr. And what enterprises desire is decisions that are made in real time or near real time. >>And a lot of the challenges that do exist today is that either the infrastructure that enterprises have in place was never built to actually manipulate data in real time. The second is really the ability to actually put something in place which can handle spikes yet be cost efficient if you'll, so you can build for really peak loads, but then it's very expensive to operate that particular service at normal loads. So how do you build something which actually works for you, for both you, both users, so to speak? And the last point that we see out there is even if you're able to, you know, bring all that data, you don't have the processing capability to run through that data. So as a result, most enterprises struggle with one, capturing the data, you know, making decisions from it in real time and really operating it at the cost point that they need to operate it at. >>You know, you bring up a great point with respect to real time data access. And I think one of the things that we've learned the last couple of years is that access to real time data, it's not a nice to have anymore. It's business critical for organizations in any industry. Talk about that as one of the challenges that organizations are facing. >>Yeah. When, when, when we started Aerospike, right when the company started, it started with the premise that data is gonna grow, number one, exponentially. Two, when applications open up to the internet, there's gonna be a flood of users and demands on those applications. And that was true primarily when we started the company in the ad tech vertical. So ad tech was the first vertical where there was a lot of data both on the supply side and the demand side from an inventory of ads that were available. And on the other hand, they had like microseconds or milliseconds in which they could make a decision on which ad to put in front of you and I so that we would click or engage with that particular ad. But over the last three to five years, what we've seen is as digitization has actually permeated every industry out there, the need to harness data in real time is pretty much present in every industry. >>Whether that's retail, whether that's financial services, telecommunications, e-commerce, gaming and entertainment. Every industry has a desire. One, the innovative companies, the small companies rather, are innovating at a pace and standing up new businesses to compete with the larger companies in each of these verticals. And the larger companies don't wanna be left behind. So they're standing up their own competing services or getting into new lines of business that really harness and are driven by real time data. So this compelling pressures, one, the customer exp you know, customer experience is paramount and we as customers expect answers in, you know, an instant in real time. And on the other hand, the way they make decisions is based on a large data set because you know, larger data sets actually propel better decisions. So there's competing pressures here, which essentially drive the need. One from a business perspective, two from a customer perspective to harness all of this data in real time. So that's what's driving an inces need to actually make decisions in real or near real time. >>You know, I think one of the things that's been in short supply over the last couple of years is patients we do expect as consumers, whether we're in our business lives, our personal lives that we're going to be getting, be given information and data that's relevant, it's personal to help us make those real time decisions. So having access to real time data is really business critical for organizations across any industries. Talk about some of the main capabilities that modern data applications and data platforms need to have. What are some of the key capabilities of a modern data platform that need to be delivered to meet demanding customer expectations? >>So, you know, going back to your initial question Lisa, around why is data really a high value but underutilized or underleveraged asset? One of the reasons we see is a lot of the data platforms that, you know, some of these applications were built on have been then around for a decade plus and they were never built for the needs of today, which is really driving a lot of data and driving insight in real time from a lot of data. So there are four major capabilities that we see that are essential ingredients of any modern data platform. One is really the ability to, you know, operate at unlimited scale. So what we mean by that is really the ability to scale from gigabytes to even petabytes without any degradation in performance or latency or throughput. The second is really, you know, predictable performance. So can you actually deliver predictable performance as your data size grows or your throughput grows or your concurrent user on that application of service grows? >>It's really easy to build an application that operates at low scale or low throughput or low concurrency, but performance usually starts degrading as you start scaling one of these attributes. The third thing is the ability to operate and always on globally resilient application. And that requires a, a really robust data platform that can be up on a five, nine basis globally, can support global distribution because a lot of these applications have global users. And the last point is, goes back to my first answer, which is, can you operate all of this at a cost point? Which is not prohibitive, but it makes sense from a TCO perspective. Cuz a lot of times what we see is people make choices of data platforms and as ironically their service or applications become more successful and more users join their journey, the revenue starts going up, the user base starts going up, but the cost basis starts crossing over the revenue and they're losing money on the service, ironically, as the service becomes more popular. So really unlimited scale, predictable performance always on, on a globally resilient basis and low tco. These are the four essential capabilities of any modern data platform. >>So then talk to me with those as the four main core functionalities of a modern data platform. How does aerospace deliver that? >>So we were built, as I said, from the from day one to operate at unlimited scale and deliver predictable performance. And then over the years as we work with customers, we build this incredible high availability capability which helps us deliver the always on, you know, operations. So we have customers who are, who have been on the platform 10 years with no downtime for example, right? So we are talking about an amazing continuum of high availability that we provide for customers who operate these, you know, globally resilient services. The key to our innovation here is what we call the hybrid memory architecture. So, you know, going a little bit technically deep here, essentially what we built out in our architecture is the ability on each node or each server to treat a bank of SSDs or solid state devices as essentially extended memory. So you're getting memory performance, but you're accessing these SSDs, you're not paying memory prices, but you're getting memory performance as a result of that. >>You can attach a lot more data to each node or each server in your distributed cluster. And when you kind of scale that across basically a distributed cluster you can do with aerospike, the same things at 60 to 80% lower server count and as a result 60 to 80% lower TCO compared to some of the other options that are available in the market. Then basically, as I said, that's the key kind of starting point to the innovation. We layer around capabilities like, you know, replication change, data notification, you know, synchronous and asynchronous replication. The ability to actually stretch a single cluster across multiple regions. So for example, if you're operating a global service, you can have a single aerospace cluster with one node in San Francisco, one northern New York, another one in London. And this would be basically seamlessly operating. So that, you know, this is strongly consistent. >>Very few no SQL data platforms are strongly consistent or if they are strongly consistent, they will actually suffer performance degradation. And what strongly consistent means is, you know, all your data is always available, it's guaranteed to be available, there is no data lost anytime. So in this configuration that I talked about, if the node in London goes down, your application still continues to operate, right? Your users see no kind of downtime and you know, when London comes up, it rejoins the cluster and everything is back to kind of the way it was before, you know, London left the cluster so to speak. So the op, the ability to do this globally resilient, highly available kind of model is really, really powerful. A lot of our customers actually use that kind of a scenario and we offer other deployment scenarios from a higher availability perspective. So everything starts with HMA or hybrid memory architecture and then we start building out a lot of these other capabilities around the platform. >>And then over the years, what our customers have guided us to do is as they're putting together a modern kind of data infrastructure, we don't live in a silo. So aerospace gets deployed with other technologies like streaming technologies or analytics technologies. So we built connectors into Kafka, pulsar, so that as you're ingesting data from a variety of data sources, you can ingest them at very high ingest speeds and store them persistently into Aerospike. Once the data is in Aerospike, you can actually run spark jobs across that data in a, in a multithreaded parallel fashion to get really insight from that data at really high, high throughput and high speed, >>High throughput, high speed, incredibly important, especially as today's landscape is increasingly distributed. Data centers, multiple public clouds, edge IOT devices, the workforce embracing more and more hybrid these days. How are you ex helping customers to extract more value from data while also lowering costs? Go into some customer examples cause I know you have some great ones. >>Yeah, you know, I think we have, we have built an amazing set of customers and customers actually use us for some really mission critical applications. So, you know, before I get into specific customer examples, let me talk to you about some of kind of the use cases which we see out there. We see a lot of aerospace being used in fraud detection. We see us being used in recommendations and since we use get used in customer data profiles or customer profiles, customer 360 stores, you know, multiplayer gaming and entertainment, these are kind of the repeated use case digital payments. We power most of the digital payment systems across the globe. Specific example from a, from a specific example perspective, the first one I would love to talk about is PayPal. So if you use PayPal today, then you know when you actually paying somebody your transaction is, you know, being sent through aero spike to really decide whether this is a fraudulent transaction or not. >>And when you do that, you know, you and I as a customer not gonna wait around for 10 seconds for PayPal to say yay or me, we expect, you know, the decision to be made in an instant. So we are powering that fraud detection engine at PayPal for every transaction that goes through PayPal before us, you know, PayPal was missing out on about 2% of their SLAs, which was essentially millions of dollars, which they were losing because, you know, they were letting transactions go through and taking the risk that it, it's not a fraudulent transaction with the aerospace. They can now actually get a much better sla and the data set on which they compute the fraud score has gone up by, you know, several factors. So by 30 x if you will. So not only has the data size that is powering the fraud engine actually grown up 30 x with Aerospike. Yeah. But they're actually making decisions in an instant for, you know, 99.95% of their transactions. So that's, >>And that's what we expect as consumers, right? We want to know that there's fraud detection on the swipe regardless of who we're interacting with. >>Yes. And so that's a, that's a really powerful use case and you know, it's, it's a great customer, great customer success story. The other one I would talk about is really Wayfair, right? From retail and you know, from e-commerce. So everybody knows Wayfair global leader in really, you know, online home furnishings and they use us to power their recommendations engine and you know, it's basically if you're purchasing this, people who bought this but also bought these five other things, so on and so forth, they have actually seen the card size at checkout go by up to 30% as a result of actually powering their recommendations in G by through Aerospike. And they, they were able to do this by reducing the server count by nine x. So on one ninth of the servers that were there before aerospace, they're now powering their recommendation engine and seeing card size checkout go up by 30%. Really, really powerful in terms of the business outcome and what we are able to, you know, drive at Wayfair >>Hugely powerful as a business outcome. And that's also what the consumer wants. The consumer is expecting these days to have a very personalized, relevant experience that's gonna show me if I bought this, show me something else that's related to that. We have this expectation that needs to be really fueled by technology. >>Exactly. And you know, another great example you asked about, you know, customer stories, Adobe, who doesn't know Adobe, you know, they, they're on a, they're on a mission to deliver the best customer experience that they can and they're talking about, you know, great customer 360 experience at scale and they're modernizing their entire edge compute infrastructure to support this. With Aerospike going to Aerospike, basically what they have seen is their throughput go up by 70%, their cost has been reduced by three x. So essentially doing it at one third of the cost while their annual data growth continues at, you know, about north of 30%. So not only is their data growing, they're able to actually reduce their cost to actually deliver this great customer experience by one third to one third and continue to deliver great customer 360 experience at scale. Really, really powerful example of how you deliver Customer 360 in a world which is dynamic and you know, on a dataset which is constantly growing at north, north of 30% in this case. >>Those are three great examples, PayPal, Wayfair, Adobe talking about, especially with Wayfair when you talk about increasing their cart checkout sizes, but also with Adobe increasing throughput by over 70%. I'm looking at my notes here. While data is growing at 32%, that's something that every organization has to contend with data growth is continuing to scale and scale and scale. >>Yep. I, I'll give you a fun one here. So, you know, you may not have heard about this company, it's called Dream 11 and it's a company based out of India, but it's a very, you know, it's a fun story because it's the world's largest fantasy sports platform and you know, India is a nation which is cricket crazy. So you know, when, when they have their premier league going on, you know, there's millions of users logged onto the dream alone platform building their fantasy lead teams and you know, playing on that particular platform, it has a hundred million users, a hundred million plus users on the platform, 5.5 million concurrent users and they have been growing at 30%. So they are considered a, an amazing success story in, in terms of what they have accomplished and the way they have architected their platform to operate at scale. And all of that is really powered by aerospace where think about that they are able to deliver all of this and support a hundred million users, 5.5 million concurrent users all with you know, 99 plus percent of their transactions completing in less than one millisecond. Just incredible success story. Not a brand that is you know, world renowned but at least you know from a what we see out there, it's an amazing success story of operating at scale. >>Amazing success story, huge business outcomes. Last question for you as we're almost out of time is talk a little bit about Aerospike aws, the partnership GRAVITON two better together. What are you guys doing together there? >>Great partnership. AWS has multiple layers in terms of partnerships. So you know, we engage with AWS at the executive level. They plan out, really roll out of new instances in partnership with us, making sure that, you know, those instance types work well for us. And then we just released support for Aerospike on the graviton platform and we just announced a benchmark of Aerospike running on graviton on aws. And what we see out there is with the benchmark, a 1.6 x improvement in price performance and you know, about 18% increase in throughput while maintaining a 27% reduction in cost, you know, on graviton. So this is an amazing story from a price performance perspective, performance per wat for greater energy efficiencies, which basically a lot of our customers are starting to kind of talk to us about leveraging this to further meet their sustainability target. So great story from Aero Aerospike and aws, not just from a partnership perspective on a technology and an executive level, but also in terms of what joint outcomes we are able to deliver for our customers. >>And it sounds like a great sustainability story. I wish we had more time so we would talk about this, but thank you so much for talking about the main capabilities of a modern data platform, what's needed, why, and how you guys are delivering that. We appreciate your insights and appreciate your time. >>Thank you very much. I mean, if, if folks are at reinvent next week or this week, come on and see us at our booth. We are in the data analytics pavilion. You can find us pretty easily. Would love to talk to you. >>Perfect. We'll send them there. So Ira, thank you so much for joining me on the program today. We appreciate your insights. >>Thank you Lisa. >>I'm Lisa Martin. You're watching The Cubes coverage of AWS Reinvent 2022. Thanks for watching.

>> And it'll be the fastest 15 minutes of your day from there. >> In three- >> We go Lisa. >> Wait. >> Yes >> Wait, wait, wait. I'm sorry I didn't pin the right speed. >> Yap, no, no rush. >> There we go. >> The beauty of not being live. >> I think, in the background. >> Fantastic, you all ready to go there, Lisa? >> Yeah. >> We are speeding around the horn and we are coming to you in five, four, three, two. >> Hey everyone, welcome to theCUBE's coverage of AWS re:Invent 2022. Lisa Martin here with you with Subbu Iyer one of our alumni who's now the CEO of Aerospike. Subbu, great to have you on the program. Thank you for joining us. >> Great as always to be on theCUBE Lisa, good to meet you. >> So, you know, every company these days has got to be a data company, whether it's a retailer, a manufacturer, a grocer, a automotive company. But for a lot of companies, data is underutilized yet a huge asset that is value added. Why do you think companies are struggling so much to make data a value added asset? >> Well, you know, we see this across the board. When I talk to customers and prospects there is a desire from the business and from IT actually to leverage data to really fuel newer applications, newer services newer business lines if you will, for companies. I think the struggle is one, I think one the, the plethora of data that is created. Surveys say that over the next three years data is going to be you know by 2025 around 175 zettabytes, right? A hundred and zettabytes of data is going to be created. And that's really a growth of north of 30% year over year. But the more important and the interesting thing is the real time component of that data is actually growing at, you know 35% CAGR. And what enterprises desire is decisions that are made in real time or near real time. And a lot of the challenges that do exist today is that either the infrastructure that enterprises have in place was never built to actually manipulate data in real time. The second is really the ability to actually put something in place which can handle spikes yet be cost efficient to fuel. So you can build for really peak loads, but then it's very expensive to operate that particular service at normal loads. So how do you build something which actually works for you for both users, so to speak. And the last point that we see out there is even if you're able to, you know bring all that data you don't have the processing capability to run through that data. So as a result, most enterprises struggle with one capturing the data, making decisions from it in real time and really operating it at the cost point that they need to operate it at. >> You know, you bring up a great point with respect to real time data access. And I think one of the things that we've learned the last couple of years is that access to real time data it's not a nice to have anymore. It's business critical for organizations in any industry. Talk about that as one of the challenges that organizations are facing. >> Yeah, when we started Aerospike, right? When the company started, it started with the premise that data is going to grow, number one exponentially. Two, when applications open up to the internet there's going to be a flood of users and demands on those applications. And that was true primarily when we started the company in the ad tech vertical. So ad tech was the first vertical where there was a lot of data both on the supply set and the demand side from an inventory of ads that were available. And on the other hand, they had like microseconds or milliseconds in which they could make a decision on which ad to put in front of you and I so that we would click or engage with that particular ad. But over the last three to five years what we've seen is as digitization has actually permeated every industry out there the need to harness data in real time is pretty much present in every industry. Whether that's retail, whether that's financial services telecommunications, e-commerce, gaming and entertainment. Every industry has a desire. One, the innovative companies, the small companies rather are innovating at a pace and standing up new businesses to compete with the larger companies in each of these verticals. And the larger companies don't want to be left behind. So they're standing up their own competing services or getting into new lines of business that really harness and are driven by real time data. So this compelling pressures, one, you know customer experience is paramount and we as customers expect answers in you know an instant, in real time. And on the other hand, the way they make decisions is based on a large data set because you know larger data sets actually propel better decisions. So there's competing pressures here which essentially drive the need one from a business perspective, two from a customer perspective to harness all of this data in real time. So that's what's driving an incessant need to actually make decisions in real or near real time. >> You know, I think one of the things that's been in short supply over the last couple of years is patience. We do expect as consumers whether we're in our business lives our personal lives that we're going to be getting be given information and data that's relevant it's personal to help us make those real time decisions. So having access to real time data is really business critical for organizations across any industries. Talk about some of the main capabilities that modern data applications and data platforms need to have. What are some of the key capabilities of a modern data platform that need to be delivered to meet demanding customer expectations? >> So, you know, going back to your initial question Lisa around why is data really a high value but underutilized or under-leveraged asset? One of the reasons we see is a lot of the data platforms that, you know, some of these applications were built on have been then around for a decade plus. And they were never built for the needs of today, which is really driving a lot of data and driving insight in real time from a lot of data. So there are four major capabilities that we see that are essential ingredients of any modern data platform. One is really the ability to, you know, operate at unlimited scale. So what we mean by that is really the ability to scale from gigabytes to even petabytes without any degradation in performance or latency or throughput. The second is really, you know, predictable performance. So can you actually deliver predictable performance as your data size grows or your throughput grows or your concurrent user on that application of service grows? It's really easy to build an application that operates at low scale or low throughput or low concurrency but performance usually starts degrading as you start scaling one of these attributes. The third thing is the ability to operate and always on globally resilient application. And that requires a really robust data platform that can be up on a five nine basis globally, can support global distribution because a lot of these applications have global users. And the last point is, goes back to my first answer which is, can you operate all of this at a cost point which is not prohibitive but it makes sense from a TCO perspective. 'Cause a lot of times what we see is people make choices of data platforms and as ironically their service or applications become more successful and more users join their journey the revenue starts going up, the user base starts going up but the cost basis starts crossing over the revenue and they're losing money on the service, ironically as the service becomes more popular. So really unlimited scale predictable performance always on a globally resilient basis and low TCO. These are the four essential capabilities of any modern data platform. >> So then talk to me with those as the four main core functionalities of a modern data platform, how does Aerospike deliver that? >> So we were built, as I said from day one to operate at unlimited scale and deliver predictable performance. And then over the years as we work with customers we build this incredible high availability capability which helps us deliver the always on, you know, operations. So we have customers who are who have been on the platform 10 years with no downtime for example, right? So we are talking about an amazing continuum of high availability that we provide for customers who operate these, you know globally resilient services. The key to our innovation here is what we call the hybrid memory architecture. So, you know, going a little bit technically deep here essentially what we built out in our architecture is the ability on each node or each server to treat a bank of SSDs or solid-state devices as essentially extended memory. So you're getting memory performance but you're accessing these SSDs. You're not paying memory prices but you're getting memory performance. As a result of that you can attach a lot more data to each node or each server in a distributed cluster. And when you kind of scale that across basically a distributed cluster you can do with Aerospike the same things at 60 to 80% lower server count. And as a result 60 to 80% lower TCO compared to some of the other options that are available in the market. Then basically, as I said that's the key kind of starting point to the innovation. We lay around capabilities like, you know replication, change data notification, you know synchronous and asynchronous replication. The ability to actually stretch a single cluster across multiple regions. So for example, if you're operating a global service you can have a single Aerospike cluster with one node in San Francisco one node in New York, another one in London and this would be basically seamlessly operating. So that, you know, this is strongly consistent, very few no SQL data platforms are strongly consistent or if they are strongly consistent they will actually suffer performance degradation. And what strongly consistent means is, you know all your data is always available it's guaranteed to be available there is no data lost any time. So in this configuration that I talked about if the node in London goes down your application still continues to operate, right? Your users see no kind of downtime and you know, when London comes up it rejoins the cluster and everything is back to kind of the way it was before, you know London left the cluster so to speak. So the ability to do this globally resilient highly available kind of model is really, really powerful. A lot of our customers actually use that kind of a scenario and we offer other deployment scenarios from a higher availability perspective. So everything starts with HMA or Hybrid Memory Architecture and then we start building a lot of these other capabilities around the platform. And then over the years what our customers have guided us to do is as they're putting together a modern kind of data infrastructure, we don't live in the silo. So Aerospike gets deployed with other technologies like streaming technologies or analytics technologies. So we built connectors into Kafka, Pulsar, so that as you're ingesting data from a variety of data sources you can ingest them at very high ingest speeds and store them persistently into Aerospike. Once the data is in Aerospike you can actually run Spark jobs across that data in a multi-threaded parallel fashion to get really insight from that data at really high throughput and high speed. >> High throughput, high speed, incredibly important especially as today's landscape is increasingly distributed. Data centers, multiple public clouds, Edge, IoT devices, the workforce embracing more and more hybrid these days. How are you helping customers to extract more value from data while also lowering costs? Go into some customer examples 'cause I know you have some great ones. >> Yeah, you know, I think, we have built an amazing set of customers and customers actually use us for some really mission critical applications. So, you know, before I get into specific customer examples let me talk to you about some of kind of the use cases which we see out there. We see a lot of Aerospike being used in fraud detection. We see us being used in recommendations engines we get used in customer data profiles, or customer profiles, Customer 360 stores, you know multiplayer gaming and entertainment. These are kind of the repeated use case, digital payments. We power most of the digital payment systems across the globe. Specific example from a specific example perspective the first one I would love to talk about is PayPal. So if you use PayPal today, then you know when you're actually paying somebody your transaction is, you know being sent through Aerospike to really decide whether this is a fraudulent transaction or not. And when you do that, you know, you and I as a customer are not going to wait around for 10 seconds for PayPal to say yay or nay. We expect, you know, the decision to be made in an instant. So we are powering that fraud detection engine at PayPal. For every transaction that goes through PayPal. Before us, you know, PayPal was missing out on about 2% of their SLAs which was essentially millions of dollars which they were losing because, you know, they were letting transactions go through and taking the risk that it's not a fraudulent transaction. With Aerospike they can now actually get a much better SLA and the data set on which they compute the fraud score has gone up by you know, several factors. So by 30X if you will. So not only has the data size that is powering the fraud engine actually gone up 30X with Aerospike but they're actually making decisions in an instant for, you know, 99.95% of their transactions. So that's- >> And that's what we expect as consumers, right? We want to know that there's fraud detection on the swipe regardless of who we're interacting with. >> Yes, and so that's a really powerful use case and you know, it's a great customer success story. The other one I would talk about is really Wayfair, right, from retail and you know from e-commerce. So everybody knows Wayfair global leader in really in online home furnishings and they use us to power their recommendations engine. And you know it's basically if you're purchasing this, people who bought this also bought these five other things, so on and so forth. They have actually seen their cart size at checkout go up by up to 30%, as a result of actually powering their recommendations engine through Aerospike. And they were able to do this by reducing the server count by 9X. So on one ninth of the servers that were there before Aerospike, they're now powering their recommendations engine and seeing cart size checkout go up by 30%. Really, really powerful in terms of the business outcome and what we are able to, you know, drive at Wayfair. >> Hugely powerful as a business outcome. And that's also what the consumer wants. The consumer is expecting these days to have a very personalized relevant experience that's going to show me if I bought this show me something else that's related to that. We have this expectation that needs to be really fueled by technology. >> Exactly, and you know, another great example you asked about you know, customer stories, Adobe. Who doesn't know Adobe, you know. They're on a mission to deliver the best customer experience that they can. And they're talking about, you know great Customer 360 experience at scale and they're modernizing their entire edge compute infrastructure to support this with Aerospike. Going to Aerospike basically what they have seen is their throughput go up by 70%, their cost has been reduced by 3X. So essentially doing it at one third of the cost while their annual data growth continues at, you know about north of 30%. So not only is their data growing they're able to actually reduce their cost to actually deliver this great customer experience by one third to one third and continue to deliver great Customer 360 experience at scale. Really, really powerful example of how you deliver Customer 360 in a world which is dynamic and you know on a data set which is constantly growing at north of 30% in this case. >> Those are three great examples, PayPal, Wayfair, Adobe, talking about, especially with Wayfair when you talk about increasing their cart checkout sizes but also with Adobe increasing throughput by over 70%. I'm looking at my notes here. While data is growing at 32%, that's something that every organization has to contend with data growth is continuing to scale and scale and scale. >> Yap, I'll give you a fun one here. So, you know, you may not have heard about this company it's called Dream11 and it's a company based out of India but it's a very, you know, it's a fun story because it's the world's largest fantasy sports platform. And you know, India is a nation which is cricket crazy. So you know, when they have their premier league going on and there's millions of users logged onto the Dream11 platform building their fantasy league teams and you know, playing on that particular platform, it has a hundred million users a hundred million plus users on the platform, 5.5 million concurrent users and they have been growing at 30%. So they are considered an amazing success story in terms of what they have accomplished and the way they have architected their platform to operate at scale. And all of that is really powered by Aerospike. Think about that they're able to deliver all of this and support a hundred million users 5.5 million concurrent users all with, you know 99 plus percent of their transactions completing in less than one millisecond. Just incredible success story. Not a brand that is, you know, world renowned but at least you know from what we see out there it's an amazing success story of operating at scale. >> Amazing success story, huge business outcomes. Last question for you as we're almost out of time is talk a little bit about Aerospike AWS the partnership Graviton2 better together. What are you guys doing together there? >> Great partnership. AWS has multiple layers in terms of partnerships. So, you know, we engage with AWS at the executive level. They plan out, really roll out of new instances in partnership with us, making sure that, you know those instance types work well for us. And then we just released support for Aerospike on the Graviton platform and we just announced a benchmark of Aerospike running on Graviton on AWS. And what we see out there is with the benchmark a 1.6X improvement in price performance. And you know about 18% increase in throughput while maintaining a 27% reduction in cost, you know, on Graviton. So this is an amazing story from a price performance perspective, performance per watt for greater energy efficiencies, which basically a lot of our customers are starting to kind of talk to us about leveraging this to further meet their sustainability target. So great story from Aerospike and AWS not just from a partnership perspective on a technology and an executive level, but also in terms of what joint outcomes we are able to deliver for our customers. >> And it sounds like a great sustainability story. I wish we had more time so we would talk about this but thank you so much for talking about the main capabilities of a modern data platform, what's needed, why, and how you guys are delivering that. We appreciate your insights and appreciate your time. >> Thank you very much. I mean, if folks are at re:Invent next week or this week come on and see us at our booth and we are in the data analytics pavilion and you can find us pretty easily. Would love to talk to you. >> Perfect, we'll send them there. Subbu Iyer, thank you so much for joining me on the program today. We appreciate your insights. >> Thank you Lisa. >> I'm Lisa Martin, you're watching theCUBE's coverage of AWS re:Invent 2022. Thanks for watching. >> Clear- >> Clear cutting. >> Nice job, very nice job.

(upbeat techno music) >> Collibra is a company that was founded in 2008 right before the so-called modern big data era kicked into high gear. The company was one of the first to focus its business on data governance. Now, historically, data governance and data quality initiatives, they were back office functions, and they were largely confined to regulated industries that had to comply with public policy mandates. But as the cloud went mainstream the tech giants showed us how valuable data could become, and the value proposition for data quality and trust, it evolved from primarily a compliance driven issue, to becoming a linchpin of competitive advantage. But, data in the decade of the 2010s was largely about getting the technology to work. You had these highly centralized technical teams that were formed and they had hyper-specialized skills, to develop data architectures and processes, to serve the myriad data needs of organizations. And it resulted in a lot of frustration, with data initiatives for most organizations, that didn't have the resources of the cloud guys and the social media giants, to really attack their data problems and turn data into gold. This is why today, for example, there's quite a bit of momentum to re-thinking monolithic data architectures. You see, you hear about initiatives like Data Mesh and the idea of data as a product. They're gaining traction as a way to better serve the the data needs of decentralized business users. You hear a lot about data democratization. So these decentralization efforts around data, they're great, but they create a new set of problems. Specifically, how do you deliver, like a self-service infrastructure to business users and domain experts? Now the cloud is definitely helping with that but also, how do you automate governance? This becomes especially tricky as protecting data privacy has become more and more important. In other words, while it's enticing to experiment, and run fast and loose with data initiatives, kind of like the Wild West, to find new veins of gold, it has to be done responsibly. As such, the idea of data governance has had to evolve to become more automated and intelligent. Governance and data lineage is still fundamental to ensuring trust as data. It moves like water through an organization. No one is going to use data that is entrusted. Metadata has become increasingly important for data discovery and data classification. As data flows through an organization, the continuously ability to check for data flaws and automating that data quality, they become a functional requirement of any modern data management platform. And finally, data privacy has become a critical adjacency to cyber security. So you can see how data governance has evolved into a much richer set of capabilities than it was 10 or 15 years ago. Hello and welcome to theCUBE's coverage of Data Citizens made possible by Collibra, a leader in so-called Data intelligence and the host of Data Citizens 2022, which is taking place in San Diego. My name is Dave Vellante and I'm one of the hosts of our program which is running in parallel to Data Citizens. Now at theCUBE we like to say we extract the signal from the noise, and over the next couple of days we're going to feature some of the themes from the keynote speakers at Data Citizens, and we'll hear from several of the executives. Felix Van de Maele, who is the co-founder and CEO of Collibra, will join us. Along with one of the other founders of Collibra, Stan Christiaens, who's going to join my colleague Lisa Martin. I'm going to also sit down with Laura Sellers, she's the Chief Product Officer at Collibra. We'll talk about some of the the announcements and innovations they're making at the event, and then we'll dig in further to data quality with Kirk Haslbeck. He's the Vice President of Data Quality at Collibra. He's an amazingly smart dude who founded Owl DQ, a company that he sold to Collibra last year. Now, many companies they didn't make it through the Hadoop era, you know they missed the industry waves and they became driftwood. Collibra, on the other hand, has evolved its business, they've leveraged the cloud, expanded its product portfolio and leaned in heavily to some major partnerships with cloud providers as well as receiving a strategic investment from Snowflake, earlier this year. So, it's a really interesting story that we're thrilled to be sharing with you. Thanks for watching and I hope you enjoy the program. (upbeat rock music) Last year theCUBE covered Data Citizens, Collibra's customer event, and the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know starting with the Hadoop movement, we had Data lakes, we had Spark, the ascendancy of programming languages like Python, the introduction of frameworks like Tensorflow, the rise of AI, Low Code, No Code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives, and we said at the time, you know maybe it's time to rethink data innovation. While a lot of the effort has been focused on, you more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation. Meaning, making it easier for domain experts to both gain insights from data, trust the data, and begin to use that data in new ways, fueling data products, monetization, and insights. Data Citizens 2022 is back and we're pleased to have Felix Van de Maele who is the founder and CEO of Collibra. He's on theCUBE. We're excited to have you Felix. Good to see you again. >> Likewise Dave. Thanks for having me again. >> You bet. All right, we're going to get the update from Felix on the current data landscape, how he sees it why data intelligence is more important now than ever, and get current on what Collibra has been up to over the past year, and what's changed since Data citizens 2021, and we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends and we're not just snapping back to the 2010s, that's clear, and that's really true as well in the world of data. So what's different in your mind, in the data landscape of the 2020s, from the previous decade, and what challenges does that bring for your customers? >> Yeah, absolutely, and and I think you said it well, Dave and the intro that, that rising complexity and fragmentation, in the broader data landscape, that hasn't gotten any better over the last couple of years. When when we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use, has only gotten more more difficult. So that trend that's continuing, I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under, respect to data, as data becomes more mission critical, as data becomes more impactful than important, the level of scrutiny with respect to privacy, security, regulatory compliance, as only increasing as well. Which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity, and fragmentation. So, it's become much more acute. And to your earlier point, we do live in a different world and and the past couple of years we could probably just kind of brute force it, right? We could focus on, on the top line, there was enough kind of investments to be, to be had. I think nowadays organizations are focused or are, are, are are, are, are in a very different environment where there's much more focus on cost control, productivity, efficiency, how do we truly get the value from that data? So again, I think it just another incentive for organization to now truly look at data and to scale with data, not just from a a technology and infrastructure perspective, but how do we actually scale data from an organizational perspective, right? You said at the, the people and process, how do we do that at scale? And that's only, only, only becoming much more important, and we do believe that the, the economic environment that we find ourselves in today is going to be catalyst for organizations to really take that more seriously if, if, if you will, than they maybe have in the have in the past. >> You know, I don't know when you guys founded Collibra, if you had a sense as to how complicated it was going to get, but you've been on a mission to really address these problems from the beginning. How would you describe your, your, your mission and what are you doing to address these challenges? >> Yeah, absolutely. We, we started Collibra in 2008. So, in some sense and the, the last kind of financial crisis and that was really the, the start of Collibra, where we found product market fit, working with large financial institutions to help them cope with the increasing compliance requirements that they were faced with because of the, of the financial crisis. And kind of here we are again, in a very different environment of course 15 years, almost 15 years later, but data only becoming more important. But our mission to deliver trusted data for every user, every use case and across every source, frankly, has only become more important. So, what has been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to again, be able to provide everyone, and that's why we call it Data Citizens, we truly believe that everyone in the organization should be able to use trusted data in an easy, easy matter. That mission is is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we still relatively early in that, in that journey. >> Well that's interesting, because you know, in my observation it takes 7 to 10 years to actually build a company, and then the fact that you're still in the early days is kind of interesting. I mean, you, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your current momentum? >> Yeah, absolutely. Again, there's a lot of tailwind organizations that are only maturing their data practices and we've seen that kind of transform or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world with its Adobe, Heineken, Bank of America and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's, it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the org in the, in the market with some of the cloud partners like Google, Amazon, Snowflake, Data Breaks, and and others, right? As those kind of new modern data infrastructures, modern data architectures, are definitely all moving to the cloud. A great opportunity for us, our partners, and of course our customers, to help them kind of transition to the cloud even faster. And so we see a lot of excitement and momentum there. We did an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course data quality isn't new but I think there's a lot of reasons why we're so excited about quality and observability now. One, is around leveraging AI machine learning again to drive more automation. And a second is that those data pipelines, that are now being created in the cloud, in these modern data architecture, architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously, has become absolutely critical so that they're really excited about, about that as well. And on the organizational side, I'm sure you've heard the term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believed in. Federated, focused on domains, giving a lot of ownership to different teams. I think that's the way to scale data organizations, and so that aligns really well with our vision and from a product perspective, we've seen a lot of momentum with our customers there as well. >> Yeah, you know, a couple things there. I mean, the acquisition of OwlDQ, you know Kirk Haslbeck and, and their team. It's interesting, you know the whole data quality used to be this back office function and and really confined to highly regulated industries. It's come to the front office, it's top of mind for Chief Data Officers. Data mesh, you mentioned you guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're, you're a critical part of many ecosystems and you're developing your own ecosystem. So, let's chat a little bit about the, the products. We're going to go deeper into products later on, at Data Citizens 22, but we know you're debuting some, some new innovations, you know, whether it's, you know, the the under the covers in security, sort of making data more accessible for people, just dealing with workflows and processes, as you talked about earlier. Tell us a little bit about what you're introducing. >> Yeah, absolutely. We we're super excited, a ton of innovation. And if we think about the big theme and like, like I said, we're still relatively early in this, in this journey towards kind of that mission of data intelligence that really bolts and compelling mission. Either customers are still start, are just starting on that, on that journey. We want to make it as easy as possible for the, for organization to actually get started, because we know that's important that they do. And for our organization and customers, that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again to make it easier for, really to, to accomplish that mission and vision around that Data Citizen, that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving, a lot of kind of ease of adoption, ease of use, but also then, how do we make sure that, as clear becomes this kind of mission critical enterprise platform, from a security performance, architecture scale supportability, that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme. From an innovation perspective, from a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One, is around data marketplace. Again, a lot of our customers have plans in that direction, How to make it easy? How do we make How do we make available to true kind of shopping experience? So that anybody in the organization can, in a very easy search first way, find the right data product, find the right dataset, that they can then consume. Usage analytics, how do you, how do we help organizations drive adoption? Tell them where they're working really well and where they have opportunities. Homepages again to, to make things easy for, for people, for anyone in your organization, to kind of get started with Collibra. You mentioned Workflow Designer, again, we have a very powerful enterprise platform, one of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a, a new Low-Code, No-Code kind of workflow designer experience. So, so really customers can take it to the next level. There's a lot more new product around Collibra protect, which in partnership with Snowflake, which has been a strategic investor in Collibra, focused on how do we make access governance easier? How do we, how do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PIA data, is managed as a much more effective, effective rate. Really excited about that product. There's more around data quality. Again, how do we, how do we get that deployed as easily, and quickly, and widely as we can? Moving that to the cloud has been a big part of our strategy. So, we launch our data quality cloud product, as well as making use of those, those native compute capabilities and platforms, like Snowflake, Databricks, Google, Amazon, and others. And so we are bettering a capability, a capability that we call push down, so we're actually pushing down the computer and data quality, to monitoring into the underlying platform, which again from a scale performance and ease of use perspective, is going to make a massive difference. And then more broadly, we talked a little bit about the ecosystem. Again, integrations, we talk about being able to connect to every source. Integrations are absolutely critical, and we're really excited to deliver new integrations with Snowflake, Azure and Google Cloud storage as well. So that's a lot coming out, the team has been work, at work really hard, and we are really really excited about what we are coming, what we're bringing to market. >> Yeah, a lot going on there. I wonder if you could give us your, your closing thoughts. I mean, you you talked about, you know, the marketplace, you know you think about Data Mesh, you think of data as product, one of the key principles, you think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been, been so hard. So, how do you see sort of the future and, you know give us the, your closing thoughts please? >> Yeah, absolutely. And, and I think we we're really at a pivotal moment and I think you said it well. We, we all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not going to fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to, deliver this data intelligence vision, this data intelligence platform. We are still early, making it as easy as we can, as kind of our, as our mission. And so I'm really, really excited to see what we, what we are going to, how the marks are going to evolve over the next, next few quarters and years. I think the trend is clearly there. We talked about Data Mesh, this kind of federated approach focus on data products, is just another signal that we believe, that a lot of our organization are now at the time, they're understanding need to go beyond just the technology. I really, really think about how to actually scale data as a business function, just like we've done with IT, with HR, with sales and marketing, with finance. That's how we need to think about data. I think now is the time, given the economic environment that we are in, much more focus on control, much more focus on productivity, efficiency, and now is the time we need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >> Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much. Good luck in, in San Diego. I know you're going to crush it out there. >> Thank you Dave. >> Yeah, it's a great spot for an in-person event and and of course the content post-event is going to be available at collibra.com and you can of course catch theCUBE coverage at theCUBE.net and all the news at siliconangle.com. This is Dave Vellante for theCUBE, your leader in enterprise and emerging tech coverage. (upbeat techno music)

>>Collibra is a company that was founded in 2008 right before the so-called modern big data era kicked into high gear. The company was one of the first to focus its business on data governance. Now, historically, data governance and data quality initiatives, they were back office functions and they were largely confined to regulatory regulated industries that had to comply with public policy mandates. But as the cloud went mainstream, the tech giants showed us how valuable data could become and the value proposition for data quality and trust. It evolved from primarily a compliance driven issue to becoming a lynchpin of competitive advantage. But data in the decade of the 2010s was largely about getting the technology to work. You had these highly centralized technical teams that were formed and they had hyper specialized skills to develop data architectures and processes to serve the myriad data needs of organizations. >>And it resulted in a lot of frustration with data initiatives for most organizations that didn't have the resources of the cloud guys and the social media giants to really attack their data problems and turn data into gold. This is why today for example, this quite a bit of momentum to rethinking monolithic data architectures. You see, you hear about initiatives like data mesh and the idea of data as a product. They're gaining traction as a way to better serve the the data needs of decentralized business Uni users, you hear a lot about data democratization. So these decentralization efforts around data, they're great, but they create a new set of problems. Specifically, how do you deliver like a self-service infrastructure to business users and domain experts? Now the cloud is definitely helping with that, but also how do you automate governance? This becomes especially tricky as protecting data privacy has become more and more important. >>In other words, while it's enticing to experiment and run fast and loose with data initiatives kinda like the Wild West, to find new veins of gold, it has to be done responsibly. As such, the idea of data governance has had to evolve to become more automated. And intelligence governance and data lineage is still fundamental to ensuring trust as data. It moves like water through an organization. No one is gonna use data that isn't trusted. Metadata has become increasingly important for data discovery and data classification. As data flows through an organization, the continuously ability to check for data flaws and automating that data quality, they become a functional requirement of any modern data management platform. And finally, data privacy has become a critical adjacency to cyber security. So you can see how data governance has evolved into a much richer set of capabilities than it was 10 or 15 years ago. >>Hello and welcome to the Cube's coverage of Data Citizens made possible by Calibra, a leader in so-called Data intelligence and the host of Data Citizens 2022, which is taking place in San Diego. My name is Dave Ante and I'm one of the hosts of our program, which is running in parallel to data citizens. Now at the Cube we like to say we extract the signal from the noise, and over the, the next couple of days, we're gonna feature some of the themes from the keynote speakers at Data Citizens and we'll hear from several of the executives. Felix Von Dala, who is the co-founder and CEO of Collibra, will join us along with one of the other founders of Collibra, Stan Christians, who's gonna join my colleague Lisa Martin. I'm gonna also sit down with Laura Sellers, she's the Chief Product Officer at Collibra. We'll talk about some of the, the announcements and innovations they're making at the event, and then we'll dig in further to data quality with Kirk Hasselbeck. >>He's the vice president of Data quality at Collibra. He's an amazingly smart dude who founded Owl dq, a company that he sold to Col to Collibra last year. Now many companies, they didn't make it through the Hado era, you know, they missed the industry waves and they became Driftwood. Collibra, on the other hand, has evolved its business. They've leveraged the cloud, expanded its product portfolio, and leaned in heavily to some major partnerships with cloud providers, as well as receiving a strategic investment from Snowflake earlier this year. So it's a really interesting story that we're thrilled to be sharing with you. Thanks for watching and I hope you enjoy the program. >>Last year, the Cube Covered Data Citizens Collibra's customer event. And the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know, starting with the Hado movement, we had data lakes, we'd spark the ascendancy of programming languages like Python, the introduction of frameworks like TensorFlow, the rise of ai, low code, no code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives. And we said at the time, you know, maybe it's time to rethink data innovation. While a lot of the effort has been focused on, you know, more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation, meaning making it easier for domain experts to both gain insights for data, trust the data, and begin to use that data in new ways, fueling data, products, monetization and insights data citizens 2022 is back and we're pleased to have Felix Van Dema, who is the founder and CEO of Collibra. He's on the cube or excited to have you, Felix. Good to see you again. >>Likewise Dave. Thanks for having me again. >>You bet. All right, we're gonna get the update from Felix on the current data landscape, how he sees it, why data intelligence is more important now than ever and get current on what Collibra has been up to over the past year and what's changed since Data Citizens 2021. And we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends, and we're not just snapping back to the 2010s. That's clear, and that's really true as well in the world of data. So what's different in your mind, in the data landscape of the 2020s from the previous decade, and what challenges does that bring for your customers? >>Yeah, absolutely. And, and I think you said it well, Dave, and and the intro that that rising complexity and fragmentation in the broader data landscape, that hasn't gotten any better over the last couple of years. When when we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use has only gotten kinda more, more difficult. So that trend that's continuing, I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under respect to data, as data becomes more mission critical, as data becomes more impactful than important, the level of scrutiny with respect to privacy, security, regulatory compliance, as only increasing as well, which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity and fragmentation. >>So it's become much more acute. And, and to your earlier point, we do live in a different world and and the the past couple of years we could probably just kind of brute for it, right? We could focus on, on the top line. There was enough kind of investments to be, to be had. I think nowadays organizations are focused or are, are, are, are, are, are in a very different environment where there's much more focus on cost control, productivity, efficiency, How do we truly get value from that data? So again, I think it just another incentive for organization to now truly look at data and to scale it data, not just from a a technology and infrastructure perspective, but how do you actually scale data from an organizational perspective, right? You said at the the people and process, how do we do that at scale? And that's only, only only becoming much more important. And we do believe that the, the economic environment that we find ourselves in today is gonna be catalyst for organizations to really dig out more seriously if, if, if, if you will, than they maybe have in the have in the best. >>You know, I don't know when you guys founded Collibra, if, if you had a sense as to how complicated it was gonna get, but you've been on a mission to really address these problems from the beginning. How would you describe your, your, your mission and what are you doing to address these challenges? >>Yeah, absolutely. We, we started Colli in 2008. So in some sense and the, the last kind of financial crisis, and that was really the, the start of Colli where we found product market fit, working with large finance institutions to help them cope with the increasing compliance requirements that they were faced with because of the, of the financial crisis and kind of here we are again in a very different environment, of course 15 years, almost 15 years later. But data only becoming more important. But our mission to deliver trusted data for every user, every use case and across every source, frankly, has only become more important. So what has been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to again, be able to provide everyone, and that's why we call it data citizens. We truly believe that everyone in the organization should be able to use trusted data in an easy, easy matter. That mission is is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we are still relatively early in that, in that journey. >>Well, that's interesting because, you know, in my observation it takes seven to 10 years to actually build a company and then the fact that you're still in the early days is kind of interesting. I mean, you, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your, your current momentum? >>Yeah, absolutely. Again, there's, there's a lot of tail organizations that are only maturing the data practices and we've seen it kind of transform or, or, or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world where it's Adobe, Heineken, Bank of America, and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's, it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the org in, in the, in the markets with some of the cloud partners like Google, Amazon, Snowflake, data bricks and, and others, right? As those kind of new modern data infrastructures, modern data architectures that are definitely all moving to the cloud, a great opportunity for us, our partners and of course our customers to help them kind of transition to the cloud even faster. >>And so we see a lot of excitement and momentum there within an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course, data quality isn't new, but I think there's a lot of reasons why we're so excited about quality and observability now. One is around leveraging ai, machine learning, again to drive more automation. And the second is that those data pipelines that are now being created in the cloud, in these modern data architecture arch architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously has become absolutely critical so that they're really excited about about that as well. And on the organizational side, I'm sure you've heard a term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believe. Then federated focused on domains, giving a lot of ownership to different teams. I think that's the way to scale data organizations. And so that aligns really well with our vision and, and from a product perspective, we've seen a lot of momentum with our customers there as well. >>Yeah, you know, a couple things there. I mean, the acquisition of i l dq, you know, Kirk Hasselbeck and, and their team, it's interesting, you know, the whole data quality used to be this back office function and, and really confined to highly regulated industries. It's come to the front office, it's top of mind for chief data officers, data mesh. You mentioned you guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're, you're a critical part of many ecosystems and you're developing your own ecosystem. So let's chat a little bit about the, the products. We're gonna go deeper in into products later on at, at Data Citizens 22, but we know you're debuting some, some new innovations, you know, whether it's, you know, the, the the under the covers in security, sort of making data more accessible for people just dealing with workflows and processes as you talked about earlier. Tell us a little bit about what you're introducing. >>Yeah, absolutely. We're super excited, a ton of innovation. And if we think about the big theme and like, like I said, we're still relatively early in this, in this journey towards kind of that mission of data intelligence that really bolts and compelling mission, either customers are still start, are just starting on that, on that journey. We wanna make it as easy as possible for the, for our organization to actually get started because we know that's important that they do. And for our organization and customers that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again, to make it easier for really to, to accomplish that mission and vision around that data citizen that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving. >>A lot of kind of ease of adoption, ease of use, but also then how do we make sure that lio becomes this kind of mission critical enterprise platform from a security performance architecture scale supportability that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme from an innovation perspective, From a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One is around data marketplace. Again, a lot of our customers have plans in that direction, how to make it easy. How do we make, how do we make available to true kind of shopping experience that anybody in your organization can, in a very easy search first way, find the right data product, find the right dataset, that data can then consume usage analytics. How do you, how do we help organizations drive adoption, tell them where they're working really well and where they have opportunities homepages again to, to make things easy for, for people, for anyone in your organization to kind of get started with ppia, you mentioned workflow designer, again, we have a very powerful enterprise platform. >>One of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a new low code, no code kind of workflow designer experience. So, so really customers can take it to the next level. There's a lot more new product around K Bear Protect, which in partnership with Snowflake, which has been a strategic investor in kib, focused on how do we make access governance easier? How do we, how do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PII data is managed as much more effective, effective rate, really excited about that product. There's more around data quality. Again, how do we, how do we get that deployed as easily and quickly and widely as we can? Moving that to the cloud has been a big part of our strategy. >>So we launch more data quality cloud product as well as making use of those, those native compute capabilities in platforms like Snowflake, Data, Bricks, Google, Amazon, and others. And so we are bettering a capability, a capability that we call push down. So actually pushing down the computer and data quality, the monitoring into the underlying platform, which again, from a scale performance and ease of use perspective is gonna make a massive difference. And then more broadly, we, we talked a little bit about the ecosystem. Again, integrations, we talk about being able to connect to every source. Integrations are absolutely critical and we're really excited to deliver new integrations with Snowflake, Azure and Google Cloud storage as well. So there's a lot coming out. The, the team has been work at work really hard and we are really, really excited about what we are coming, what we're bringing to markets. >>Yeah, a lot going on there. I wonder if you could give us your, your closing thoughts. I mean, you, you talked about, you know, the marketplace, you know, you think about data mesh, you think of data as product, one of the key principles you think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been been so hard. So how do you see sort of the future and, you know, give us the, your closing thoughts please? >>Yeah, absolutely. And I, and I think we we're really at this pivotal moment, and I think you said it well. We, we all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not gonna fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to deliver this data intelligence vision, this data intelligence platform. We are still early, making it as easy as we can. It's kind of, of our, it's our mission. And so I'm really, really excited to see what we, what we are gonna, how the marks gonna evolve over the next, next few quarters and years. I think the trend is clearly there when we talk about data mesh, this kind of federated approach folks on data products is just another signal that we believe that a lot of our organization are now at the time. >>The understanding need to go beyond just the technology. I really, really think about how do we actually scale data as a business function, just like we've done with it, with, with hr, with, with sales and marketing, with finance. That's how we need to think about data. I think now is the time given the economic environment that we are in much more focus on control, much more focused on productivity efficiency and now's the time. We need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >>Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much and good luck in, in San Diego. I know you're gonna crush it out there. >>Thank you Dave. >>Yeah, it's a great spot for an in-person event and, and of course the content post event is gonna be available@collibra.com and you can of course catch the cube coverage@thecube.net and all the news@siliconangle.com. This is Dave Valante for the cube, your leader in enterprise and emerging tech coverage. >>Hi, I'm Jay from Collibra's Data Office. Today I want to talk to you about Collibra's data intelligence cloud. We often say Collibra is a single system of engagement for all of your data. Now, when I say data, I mean data in the broadest sense of the word, including reference and metadata. Think of metrics, reports, APIs, systems, policies, and even business processes that produce or consume data. Now, the beauty of this platform is that it ensures all of your users have an easy way to find, understand, trust, and access data. But how do you get started? Well, here are seven steps to help you get going. One, start with the data. What's data intelligence? Without data leverage the Collibra data catalog to automatically profile and classify your enterprise data wherever that data lives, databases, data lakes or data warehouses, whether on the cloud or on premise. >>Two, you'll then wanna organize the data and you'll do that with data communities. This can be by department, find a business or functional team, however your organization organizes work and accountability. And for that you'll establish community owners, communities, make it easy for people to navigate through the platform, find the data and will help create a sense of belonging for users. An important and related side note here, we find it's typical in many organizations that data is thought of is just an asset and IT and data offices are viewed as the owners of it and who are really the central teams performing analytics as a service provider to the enterprise. We believe data is more than an asset, it's a true product that can be converted to value. And that also means establishing business ownership of data where that strategy and ROI come together with subject matter expertise. >>Okay, three. Next, back to those communities there, the data owners should explain and define their data, not just the tables and columns, but also the related business terms, metrics and KPIs. These objects we call these assets are typically organized into business glossaries and data dictionaries. I definitely recommend starting with the topics that are most important to the business. Four, those steps that enable you and your users to have some fun with it. Linking everything together builds your knowledge graph and also known as a metadata graph by linking or relating these assets together. For example, a data set to a KPI to a report now enables your users to see what we call the lineage diagram that visualizes where the data in your dashboards actually came from and what the data means and who's responsible for it. Speaking of which, here's five. Leverage the calibra trusted business reporting solution on the marketplace, which comes with workflows for those owners to certify their reports, KPIs, and data sets. >>This helps them force their trust in their data. Six, easy to navigate dashboards or landing pages right in your platform for your company's business processes are the most effective way for everyone to better understand and take action on data. Here's a pro tip, use the dashboard design kit on the marketplace to help you build compelling dashboards. Finally, seven, promote the value of this to your users and be sure to schedule enablement office hours and new employee onboarding sessions to get folks excited about what you've built and implemented. Better yet, invite all of those community and data owners to these sessions so that they can show off the value that they've created. Those are my seven tips to get going with Collibra. I hope these have been useful. For more information, be sure to visit collibra.com. >>Welcome to the Cube's coverage of Data Citizens 2022 Collibra's customer event. My name is Dave Valante. With us is Kirk Hasselbeck, who's the vice president of Data Quality of Collibra Kirk, good to see you. Welcome. >>Thanks for having me, Dave. Excited to be here. >>You bet. Okay, we're gonna discuss data quality observability. It's a hot trend right now. You founded a data quality company, OWL dq, and it was acquired by Collibra last year. Congratulations. And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >>Yeah, absolutely. It's, it's definitely exciting times for data quality, which you're right, has been around for a long time. So why now and why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before and the variety has changed and the volume has grown. And, and while I think that remains true, there are a couple other hidden factors at play that everyone's so interested in as, as to why this is becoming so important now. And, and I guess you could kind of break this down simply and think about if Dave, you and I were gonna build, you know, a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, you know, what the ramifications could be, what, what those incidents would look like, or maybe better yet, we try to build a, a new trading algorithm with a crossover strategy where the 50 day crosses the, the 10 day average. >>And imagine if the data underlying the inputs to that is incorrect. We will probably have major financial ramifications in that sense. So, you know, it kind of starts there where everybody's realizing that we're all data companies and if we are using bad data, we're likely making incorrect business decisions. But I think there's kind of two other things at play. You know, I, I bought a car not too long ago and my dad called and said, How many cylinders does it have? And I realized in that moment, you know, I might have failed him because, cause I didn't know. And, and I used to ask those types of questions about any lock brakes and cylinders and, and you know, if it's manual or, or automatic and, and I realized I now just buy a car that I hope works. And it's so complicated with all the computer chips, I, I really don't know that much about it. >>And, and that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the, the individuals loading and consuming all of this data for the company actually may not know that much about the data itself, and that's not even their job anymore. So we'll talk more about that in a minute, but that's really what's setting the foreground for this observability play and why everybody's so interested. It, it's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >>You know, the other thing too about data quality, and for years we did the MIT CDO IQ event, we didn't do it last year, Covid messed everything up. But the observation I would make there thoughts is, is it data quality? Used to be information quality used to be this back office function, and then it became sort of front office with financial services and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well, they sort of flipped the bit from sort of a data as a, a risk to data as a, as an asset. And now as we say, we're gonna talk about observability. And so it's really become front and center just the whole quality issue because data's so fundamental, hasn't it? >>Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my, my favorite stock ticker app and I check out the NASDAQ market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And, and that's kind of what's going on. There's, there's so many numbers and they're coming from all of these different sources and data providers and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor, but with the scale that we've achieved in early days, even before calibra. And what's been so exciting is we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting and why I think the CDO is, is listening right intently nowadays to this topic is, so maybe we could surface all of these problems with the right solution of data observability and with the right scale and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks, that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that, you know, with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's, it's not ever going to be based on one or two domain experts anymore. >>So, So how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they, are they cousins? What's your perspective on that? >>Yeah, it's, it's super interesting. It's an emerging market. So the language is changing a lot of the topic and areas changing the way that I like to say it or break it down because the, the lingo is constantly moving is, you know, as a target on this space is really breaking records versus breaking trends. And I could write a condition when this thing happens, it's wrong and when it doesn't it's correct. Or I could look for a trend and I'll give you a good example. You know, everybody's talking about fresh data and stale data and, and why would that matter? Well, if your data never arrived or only part of it arrived or didn't arrive on time, it's likely stale and there will not be a condition that you could write that would show you all the good in the bads. That was kind of your, your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data, but it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there there, there's more than a couple of these happening every day. >>So what's the Collibra angle on all this stuff made the acquisition, you got data quality observability coming together, you guys have a lot of expertise in, in this area, but you hear providence of data, you just talked about, you know, stale data, you know, the, the whole trend toward real time. How is Calibra approaching the problem and what's unique about your approach? >>Well, I think where we're fortunate is with our background, myself and team, we sort of lived this problem for a long time, you know, in, in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with before it was called data observability or reliability was basically the, the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution, it's more advanced than some of the observation techniques that that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights, and they want to see break records and breaking trends together so they can correlate the root cause. And we hear that all the time. I have so many things going wrong, just show me the big picture, help me find the thing that if I were to fix it today would make the most impact. So we're really focused on root cause analysis, business impact, connecting it with lineage and catalog metadata. And as that grows, you can actually achieve total data governance at this point with the acquisition of what was a Lineage company years ago, and then my company Ldq now Collibra, Data quality Collibra may be the best positioned for total data governance and intelligence in the space. >>Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was, you know, they just said, Oh, it's a glitch, you know, so they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens 22 that you're announcing, you gotta announce new products, right? You're yearly event what's, what's new. Give us a sense as to what products are coming out, but specifically around data quality and observability. >>Absolutely. There's this, you know, there's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and Big Query and Data Bricks is Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a SaaS like model. And we've started to hook in to these databases. And while we've always worked with the the same databases in the past, they're supported today we're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now is everyone's concerned with something called Egress. Did your, my data that I've spent all this time and money with my security team securing ever leave my hands, did it ever leave my secure VPC as they call it? >>And with these native integrations that we're building and about to unveil, here's kind of a sneak peek for, for next week at Data Citizens. We're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration, you could log into the Collibra data quality app and have all of your data quality running inside the database that you've probably already picked as your your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress, cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >>So this is interesting because what you just described, you know, you mentioned Snowflake, you mentioned Google, Oh actually you mentioned yeah, data bricks. You know, Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool, but then Google's got the open data cloud. If you heard, you know, Google next and now data bricks doesn't call it the data cloud, but they have like the open source data cloud. So you have all these different approaches and there's really no way up until now I'm, I'm hearing to, to really understand the relationships between all those and have confidence across, you know, it's like Jak Dani, you should just be a note on the mesh. And I don't care if it's a data warehouse or a data lake or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And, and, and that's what you're bringing to the table, Is that right? Did I get that right? >>Yeah, that's right. And it's, for us, it's, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now, we can send them the, the operating ability to crunch all of the calculations, the governance, the quality, and get the answers. And what that's doing, it's basically zero network costs, zero egress cost, zero latency of time. And so when you were to log into Big Query tomorrow using our tool or like, or say Snowflake for example, you have instant data quality metrics, instant profiling, instant lineage and access privacy controls, things of that nature that just become less onerous. What we're seeing is there's so much technology out there, just like all of the major brands that you mentioned, but how do we make it easier? The future is about less clicks, faster time to value, faster scale, and eventually lower cost. And, and we think that this positions us to be the leader there. >>I love this example because, you know, Barry talks about, wow, the cloud guys are gonna own the world and, and of course now we're seeing that the ecosystem is finding so much white space to add value, connect across cloud. Sometimes we call it super cloud and so, or inter clouding. All right, Kirk, give us your, your final thoughts and on on the trends that we've talked about and Data Citizens 22. >>Absolutely. Well, I think, you know, one big trend is discovery and classification. Seeing that across the board, people used to know it was a zip code and nowadays with the amount of data that's out there, they wanna know where everything is, where their sensitive data is. If it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases how fast they can get controls and insights out of their tools. So I think we're gonna see more one click solutions, more SAS based solutions and solutions that hopefully prove faster time to value on, on all of these modern cloud platforms. >>Excellent. All right, Kurt Hasselbeck, thanks so much for coming on the Cube and previewing Data Citizens 22. Appreciate it. >>Thanks for having me, Dave. >>You're welcome. Right, and thank you for watching. Keep it right there for more coverage from the Cube. Welcome to the Cube's virtual Coverage of Data Citizens 2022. My name is Dave Valante and I'm here with Laura Sellers, who's the Chief Product Officer at Collibra, the host of Data Citizens. Laura, welcome. Good to see you. >>Thank you. Nice to be here. >>Yeah, your keynote at Data Citizens this year focused on, you know, your mission to drive ease of use and scale. Now when I think about historically fast access to the right data at the right time in a form that's really easily consumable, it's been kind of challenging, especially for business users. Can can you explain to our audience why this matters so much and what's actually different today in the data ecosystem to make this a reality? >>Yeah, definitely. So I think what we really need and what I hear from customers every single day is that we need a new approach to data management and our product teams. What inspired me to come to Calibra a little bit a over a year ago was really the fact that they're very focused on bringing trusted data to more users across more sources for more use cases. And so as we look at what we're announcing with these innovations of ease of use and scale, it's really about making teams more productive in getting started with and the ability to manage data across the entire organization. So we've been very focused on richer experiences, a broader ecosystem of partners, as well as a platform that delivers performance, scale and security that our users and teams need and demand. So as we look at, Oh, go ahead. >>I was gonna say, you know, when I look back at like the last 10 years, it was all about getting the technology to work and it was just so complicated. But, but please carry on. I'd love to hear more about this. >>Yeah, I, I really, you know, Collibra is a system of engagement for data and we really are working on bringing that entire system of engagement to life for everyone to leverage here and now. So what we're announcing from our ease of use side of the world is first our data marketplace. This is the ability for all users to discover and access data quickly and easily shop for it, if you will. The next thing that we're also introducing is the new homepage. It's really about the ability to drive adoption and have users find data more quickly. And then the two more areas of the ease of use side of the world is our world of usage analytics. And one of the big pushes and passions we have at Collibra is to help with this data driven culture that all companies are trying to create. And also helping with data literacy, with something like usage analytics, it's really about driving adoption of the CLE platform, understanding what's working, who's accessing it, what's not. And then finally we're also introducing what's called workflow designer. And we love our workflows at Libra, it's a big differentiator to be able to automate business processes. The designer is really about a way for more people to be able to create those workflows, collaborate on those workflow flows, as well as people to be able to easily interact with them. So a lot of exciting things when it comes to ease of use to make it easier for all users to find data. >>Y yes, there's definitely a lot to unpack there. I I, you know, you mentioned this idea of, of of, of shopping for the data. That's interesting to me. Why this analogy, metaphor or analogy, I always get those confused. I let's go with analogy. Why is it so important to data consumers? >>I think when you look at the world of data, and I talked about this system of engagement, it's really about making it more accessible to the masses. And what users are used to is a shopping experience like your Amazon, if you will. And so having a consumer grade experience where users can quickly go in and find the data, trust that data, understand where the data's coming from, and then be able to quickly access it, is the idea of being able to shop for it, just making it as simple as possible and really speeding the time to value for any of the business analysts, data analysts out there. >>Yeah, I think when you, you, you see a lot of discussion about rethinking data architectures, putting data in the hands of the users and business people, decentralized data and of course that's awesome. I love that. But of course then you have to have self-service infrastructure and you have to have governance. And those are really challenging. And I think so many organizations, they're facing adoption challenges, you know, when it comes to enabling teams generally, especially domain experts to adopt new data technologies, you know, like the, the tech comes fast and furious. You got all these open source projects and get really confusing. Of course it risks security, governance and all that good stuff. You got all this jargon. So where do you see, you know, the friction in adopting new data technologies? What's your point of view and how can organizations overcome these challenges? >>You're, you're dead on. There's so much technology and there's so much to stay on top of, which is part of the friction, right? It's just being able to stay ahead of, of and understand all the technologies that are coming. You also look at as there's so many more sources of data and people are migrating data to the cloud and they're migrating to new sources. Where the friction comes is really that ability to understand where the data came from, where it's moving to, and then also to be able to put the access controls on top of it. So people are only getting access to the data that they should be getting access to. So one of the other things we're announcing with, with all of the innovations that are coming is what we're doing around performance and scale. So with all of the data movement, with all of the data that's out there, the first thing we're launching in the world of performance and scale is our world of data quality. >>It's something that Collibra has been working on for the past year and a half, but we're launching the ability to have data quality in the cloud. So it's currently an on-premise offering, but we'll now be able to carry that over into the cloud for us to manage that way. We're also introducing the ability to push down data quality into Snowflake. So this is, again, one of those challenges is making sure that that data that you have is d is is high quality as you move forward. And so really another, we're just reducing friction. You already have Snowflake stood up. It's not another machine for you to manage, it's just push down capabilities into Snowflake to be able to track that quality. Another thing that we're launching with that is what we call Collibra Protect. And this is that ability for users to be able to ingest metadata, understand where the PII data is, and then set policies up on top of it. So very quickly be able to set policies and have them enforced at the data level. So anybody in the organization is only getting access to the data they should have access to. >>Here's Topica data quality is interesting. It's something that I've followed for a number of years. It used to be a back office function, you know, and really confined only to highly regulated industries like financial services and healthcare and government. You know, you look back over a decade ago, you didn't have this worry about personal information, g gdpr, and, you know, California Consumer Privacy Act all becomes, becomes so much important. The cloud is really changed things in terms of performance and scale and of course partnering for, for, with Snowflake it's all about sharing data and monetization, anything but a back office function. So it was kind of smart that you guys were early on and of course attracting them and as a, as an investor as well was very strong validation. What can you tell us about the nature of the relationship with Snowflake and specifically inter interested in sort of joint engineering or, and product innovation efforts, you know, beyond the standard go to market stuff? >>Definitely. So you mentioned there were a strategic investor in Calibra about a year ago. A little less than that I guess. We've been working with them though for over a year really tightly with their product and engineering teams to make sure that Collibra is adding real value. Our unified platform is touching pieces of our unified platform or touching all pieces of Snowflake. And when I say that, what I mean is we're first, you know, able to ingest data with Snowflake, which, which has always existed. We're able to profile and classify that data we're announcing with Calibra Protect this week that you're now able to create those policies on top of Snowflake and have them enforce. So again, people can get more value out of their snowflake more quickly as far as time to value with, with our policies for all business users to be able to create. >>We're also announcing Snowflake Lineage 2.0. So this is the ability to take stored procedures in Snowflake and understand the lineage of where did the data come from, how was it transformed with within Snowflake as well as the data quality. Pushdown, as I mentioned, data quality, you brought it up. It is a new, it is a, a big industry push and you know, one of the things I think Gartner mentioned is people are losing up to $15 million without having great data quality. So this push down capability for Snowflake really is again, a big ease of use push for us at Collibra of that ability to, to push it into snowflake, take advantage of the data, the data source, and the engine that already lives there and get the right and make sure you have the right quality. >>I mean, the nice thing about Snowflake, if you play in the Snowflake sandbox, you, you, you, you can get sort of a, you know, high degree of confidence that the data sharing can be done in a safe way. Bringing, you know, Collibra into the, into the story allows me to have that data quality and, and that governance that I, that I need. You know, we've said many times on the cube that one of the notable differences in cloud this decade versus last decade, I mean ob there are obvious differences just in terms of scale and scope, but it's shaping up to be about the strength of the ecosystems. That's really a hallmark of these big cloud players. I mean they're, it's a key factor for innovating, accelerating product delivery, filling gaps in, in the hyperscale offerings cuz you got more stack, you know, mature stack capabilities and you know, it creates this flywheel momentum as we often say. But, so my question is, how do you work with the hyperscalers? Like whether it's AWS or Google, whomever, and what do you see as your role and what's the Collibra sweet spot? >>Yeah, definitely. So, you know, one of the things I mentioned early on is the broader ecosystem of partners is what it's all about. And so we have that strong partnership with Snowflake. We also are doing more with Google around, you know, GCP and kbra protect there, but also tighter data plex integration. So similar to what you've seen with our strategic moves around Snowflake and, and really covering the broad ecosystem of what Collibra can do on top of that data source. We're extending that to the world of Google as well and the world of data plex. We also have great partners in SI's Infosys is somebody we spoke with at the conference who's done a lot of great work with Levi's as they're really important to help people with their whole data strategy and driving that data driven culture and, and Collibra being the core of it. >>Hi Laura, we're gonna, we're gonna end it there, but I wonder if you could kind of put a bow on, you know, this year, the event your, your perspectives. So just give us your closing thoughts. >>Yeah, definitely. So I, I wanna say this is one of the biggest releases Collibra's ever had. Definitely the biggest one since I've been with the company a little over a year. We have all these great new product innovations coming to really drive the ease of use to make data more valuable for users everywhere and, and companies everywhere. And so it's all about everybody being able to easily find, understand, and trust and get access to that data going forward. >>Well congratulations on all the pro progress. It was great to have you on the cube first time I believe, and really appreciate you, you taking the time with us. >>Yes, thank you for your time. >>You're very welcome. Okay, you're watching the coverage of Data Citizens 2022 on the cube, your leader in enterprise and emerging tech coverage. >>So data modernization oftentimes means moving some of your storage and computer to the cloud where you get the benefit of scale and security and so on. But ultimately it doesn't take away the silos that you have. We have more locations, more tools and more processes with which we try to get value from this data. To do that at scale in an organization, people involved in this process, they have to understand each other. So you need to unite those people across those tools, processes, and systems with a shared language. When I say customer, do you understand the same thing as you hearing customer? Are we counting them in the same way so that shared language unites us and that gives the opportunity for the organization as a whole to get the maximum value out of their data assets and then they can democratize data so everyone can properly use that shared language to find, understand, and trust the data asset that's available. >>And that's where Collibra comes in. We provide a centralized system of engagement that works across all of those locations and combines all of those different user types across the whole business. At Collibra, we say United by data and that also means that we're united by data with our customers. So here is some data about some of our customers. There was the case of an online do it yourself platform who grew their revenue almost three times from a marketing campaign that provided the right product in the right hands of the right people. In other case that comes to mind is from a financial services organization who saved over 800 K every year because they were able to reuse the same data in different kinds of reports and before there was spread out over different tools and processes and silos, and now the platform brought them together so they realized, oh, we're actually using the same data, let's find a way to make this more efficient. And the last example that comes to mind is that of a large home loan, home mortgage, mortgage loan provider where they have a very complex landscape, a very complex architecture legacy in the cloud, et cetera. And they're using our software, they're using our platform to unite all the people and those processes and tools to get a common view of data to manage their compliance at scale. >>Hey everyone, I'm Lisa Martin covering Data Citizens 22, brought to you by Collibra. This next conversation is gonna focus on the importance of data culture. One of our Cube alumni is back, Stan Christians is Collibra's co-founder and it's Chief Data citizens. Stan, it's great to have you back on the cube. >>Hey Lisa, nice to be. >>So we're gonna be talking about the importance of data culture, data intelligence, maturity, all those great things. When we think about the data revolution that every business is going through, you know, it's so much more than technology innovation. It also really re requires cultural transformation, community transformation. Those are challenging for customers to undertake. Talk to us about what you mean by data citizenship and the role that creating a data culture plays in that journey. >>Right. So as you know, our event is called Data Citizens because we believe that in the end, a data citizen is anyone who uses data to do their job. And we believe that today's organizations, you have a lot of people, most of the employees in an organization are somehow gonna to be a data citizen, right? So you need to make sure that these people are aware of it. You need that. People have skills and competencies to do with data what necessary and that's on, all right? So what does it mean to have a good data culture? It means that if you're building a beautiful dashboard to try and convince your boss, we need to make this decision that your boss is also open to and able to interpret, you know, the data presented in dashboard to actually make that decision and take that action. Right? >>And once you have that why to the organization, that's when you have a good data culture. Now that's continuous effort for most organizations because they're always moving, somehow they're hiring new people and it has to be continuous effort because we've seen that on the hand. Organizations continue challenged their data sources and where all the data is flowing, right? Which in itself creates a lot of risk. But also on the other set hand of the equation, you have the benefit. You know, you might look at regulatory drivers like, we have to do this, right? But it's, it's much better right now to consider the competitive drivers, for example, and we did an IDC study earlier this year, quite interesting. I can recommend anyone to it. And one of the conclusions they found as they surveyed over a thousand people across organizations worldwide is that the ones who are higher in maturity. >>So the, the organizations that really look at data as an asset, look at data as a product and actively try to be better at it, don't have three times as good a business outcome as the ones who are lower on the maturity scale, right? So you can say, ok, I'm doing this, you know, data culture for everyone, awakening them up as data citizens. I'm doing this for competitive reasons, I'm doing this re reasons you're trying to bring both of those together and the ones that get data intelligence right, are successful and competitive. That's, and that's what we're seeing out there in the market. >>Absolutely. We know that just generally stand right, the organizations that are, are really creating a, a data culture and enabling everybody within the organization to become data citizens are, We know that in theory they're more competitive, they're more successful. But the IDC study that you just mentioned demonstrates they're three times more successful and competitive than their peers. Talk about how Collibra advises customers to create that community, that culture of data when it might be challenging for an organization to adapt culturally. >>Of course, of course it's difficult for an organization to adapt but it's also necessary, as you just said, imagine that, you know, you're a modern day organization, laptops, what have you, you're not using those, right? Or you know, you're delivering them throughout organization, but not enabling your colleagues to actually do something with that asset. Same thing as through with data today, right? If you're not properly using the data asset and competitors are, they're gonna to get more advantage. So as to how you get this done, establish this. There's angles to look at, Lisa. So one angle is obviously the leadership whereby whoever is the boss of data in the organization, you typically have multiple bosses there, like achieve data officers. Sometimes there's, there's multiple, but they may have a different title, right? So I'm just gonna summarize it as a data leader for a second. >>So whoever that is, they need to make sure that there's a clear vision, a clear strategy for data. And that strategy needs to include the monetization aspect. How are you going to get value from data? Yes. Now that's one part because then you can leadership in the organization and also the business value. And that's important. Cause those people, their job in essence really is to make everyone in the organization think about data as an asset. And I think that's the second part of the equation of getting that right, is it's not enough to just have that leadership out there, but you also have to get the hearts and minds of the data champions across the organization. You, I really have to win them over. And if you have those two combined and obviously a good technology to, you know, connect those people and have them execute on their responsibilities such as a data intelligence platform like s then the in place to really start upgrading that culture inch by inch if you'll, >>Yes, I like that. The recipe for success. So you are the co-founder of Collibra. You've worn many different hats along this journey. Now you're building Collibra's own data office. I like how before we went live, we were talking about Calibra is drinking its own champagne. I always loved to hear stories about that. You're speaking at Data Citizens 2022. Talk to us about how you are building a data culture within Collibra and what maybe some of the specific projects are that Collibra's data office is working on. >>Yes, and it is indeed data citizens. There are a ton of speaks here, are very excited. You know, we have Barb from m MIT speaking about data monetization. We have Dilla at the last minute. So really exciting agen agenda. Can't wait to get back out there essentially. So over the years at, we've doing this since two and eight, so a good years and I think we have another decade of work ahead in the market, just to be very clear. Data is here to stick around as are we. And myself, you know, when you start a company, we were for people in a, if you, so everybody's wearing all sorts of hat at time. But over the years I've run, you know, presales that sales partnerships, product cetera. And as our company got a little bit biggish, we're now thousand two. Something like people in the company. >>I believe systems and processes become a lot important. So we said you CBRA isn't the size our customers we're getting there in of organization structure, process systems, et cetera. So we said it's really time for us to put our money where is and to our own data office, which is what we were seeing customers', organizations worldwide. And they organizations have HR units, they have a finance unit and over time they'll all have a department if you'll, that is responsible somehow for the data. So we said, ok, let's try to set an examples that other people can take away with it, right? Can take away from it. So we set up a data strategy, we started building data products, took care of the data infrastructure. That's sort of good stuff. And in doing all of that, ISA exactly as you said, we said, okay, we need to also use our product and our own practices and from that use, learn how we can make the product better, learn how we make, can make the practice better and share that learning with all the, and on, on the Monday mornings, we sometimes refer to eating our dog foods on Friday evenings. >>We referred to that drinking our own champagne. I like it. So we, we had a, we had the driver to do this. You know, there's a clear business reason. So we involved, we included that in the data strategy and that's a little bit of our origin. Now how, how do we organize this? We have three pillars, and by no means is this a template that everyone should, this is just the organization that works at our company, but it can serve as an inspiration. So we have a pillar, which is data science. The data product builders, if you'll or the people who help the business build data products. We have the data engineers who help keep the lights on for that data platform to make sure that the products, the data products can run, the data can flow and you know, the quality can be checked. >>And then we have a data intelligence or data governance builders where we have those data governance, data intelligence stakeholders who help the business as a sort of data partner to the business stakeholders. So that's how we've organized it. And then we started following the CBRA approach, which is, well, what are the challenges that our business stakeholders have in hr, finance, sales, marketing all over? And how can data help overcome those challenges? And from those use cases, we then just started to build a map and started execution use of the use case. And a important ones are very simple. We them with our, our customers as well, people talking about the cata, right? The catalog for the data scientists to know what's in their data lake, for example, and for the people in and privacy. So they have their process registry and they can see how the data flows. >>So that's a starting place and that turns into a marketplace so that if new analysts and data citizens join kbra, they immediately have a place to go to, to look at, see, ok, what data is out there for me as an analyst or a data scientist or whatever to do my job, right? So they can immediately get access data. And another one that we is around trusted business. We're seeing that since, you know, self-service BI allowed everyone to make beautiful dashboards, you know, pie, pie charts. I always, my pet pee is the pie chart because I love buy and you shouldn't always be using pie charts. But essentially there's become proliferation of those reports. And now executives don't really know, okay, should I trust this report or that report the reporting on the same thing. But the numbers seem different, right? So that's why we have trusted this reporting. So we know if a, the dashboard, a data product essentially is built, we not that all the right steps are being followed and that whoever is consuming that can be quite confident in the result either, Right. And that silver browser, right? Absolutely >>Decay. >>Exactly. Yes, >>Absolutely. Talk a little bit about some of the, the key performance indicators that you're using to measure the success of the data office. What are some of those KPIs? >>KPIs and measuring is a big topic in the, in the data chief data officer profession, I would say, and again, it always varies with to your organization, but there's a few that we use that might be of interest. Use those pillars, right? And we have metrics across those pillars. So for example, a pillar on the data engineering side is gonna be more related to that uptime, right? Are the, is the data platform up and running? Are the data products up and running? Is the quality in them good enough? Is it going up? Is it going down? What's the usage? But also, and especially if you're in the cloud and if consumption's a big thing, you have metrics around cost, for example, right? So that's one set of examples. Another one is around the data sciences and products. Are people using them? Are they getting value from it? >>Can we calculate that value in ay perspective, right? Yeah. So that we can to the rest of the business continue to say we're tracking all those numbers and those numbers indicate that value is generated and how much value estimated in that region. And then you have some data intelligence, data governance metrics, which is, for example, you have a number of domains in a data mesh. People talk about being the owner of a data domain, for example, like product or, or customer. So how many of those domains do you have covered? How many of them are already part of the program? How many of them have owners assigned? How well are these owners organized, executing on their responsibilities? How many tickets are open closed? How many data products are built according to process? And so and so forth. So these are an set of examples of, of KPIs. There's a, there's a lot more, but hopefully those can already inspire the audience. >>Absolutely. So we've, we've talked about the rise cheap data offices, it's only accelerating. You mentioned this is like a 10 year journey. So if you were to look into a crystal ball, what do you see in terms of the maturation of data offices over the next decade? >>So we, we've seen indeed the, the role sort of grow up, I think in, in thousand 10 there may have been like 10 achieve data officers or something. Gartner has exact numbers on them, but then they grew, you know, industries and the number is estimated to be about 20,000 right now. Wow. And they evolved in a sort of stack of competencies, defensive data strategy, because the first chief data officers were more regulatory driven, offensive data strategy support for the digital program. And now all about data products, right? So as a data leader, you now need all of those competences and need to include them in, in your strategy. >>How is that going to evolve for the next couple of years? I wish I had one of those balls, right? But essentially I think for the next couple of years there's gonna be a lot of people, you know, still moving along with those four levels of the stack. A lot of people I see are still in version one and version two of the chief data. So you'll see over the years that's gonna evolve more digital and more data products. So for next years, my, my prediction is it's all products because it's an immediate link between data and, and the essentially, right? Right. So that's gonna be important and quite likely a new, some new things will be added on, which nobody can predict yet. But we'll see those pop up in a few years. I think there's gonna be a continued challenge for the chief officer role to become a real executive role as opposed to, you know, somebody who claims that they're executive, but then they're not, right? >>So the real reporting level into the board, into the CEO for example, will continue to be a challenging point. But the ones who do get that done will be the ones that are successful and the ones who get that will the ones that do it on the basis of data monetization, right? Connecting value to the data and making that value clear to all the data citizens in the organization, right? And in that sense, they'll need to have both, you know, technical audiences and non-technical audiences aligned of course. And they'll need to focus on adoption. Again, it's not enough to just have your data office be involved in this. It's really important that you're waking up data citizens across the organization and you make everyone in the organization think about data as an asset. >>Absolutely. Because there's so much value that can be extracted. Organizations really strategically build that data office and democratize access across all those data citizens. Stan, this is an exciting arena. We're definitely gonna keep our eyes on this. Sounds like a lot of evolution and maturation coming from the data office perspective. From the data citizen perspective. And as the data show that you mentioned in that IDC study, you mentioned Gartner as well, organizations have so much more likelihood of being successful and being competitive. So we're gonna watch this space. Stan, thank you so much for joining me on the cube at Data Citizens 22. We appreciate it. >>Thanks for having me over >>From Data Citizens 22, I'm Lisa Martin, you're watching The Cube, the leader in live tech coverage. >>Okay, this concludes our coverage of Data Citizens 2022, brought to you by Collibra. Remember, all these videos are available on demand@thecube.net. And don't forget to check out silicon angle.com for all the news and wiki bod.com for our weekly breaking analysis series where we cover many data topics and share survey research from our partner ETR Enterprise Technology Research. If you want more information on the products announced at Data Citizens, go to collibra.com. There are tons of resources there. You'll find analyst reports, product demos. It's really worthwhile to check those out. Thanks for watching our program and digging into Data Citizens 2022 on the Cube, your leader in enterprise and emerging tech coverage. We'll see you soon.

(upbeat music) >> Last year, the Cube covered Data Citizens, Collibra's customer event. And the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know, starting with the Hadoop movement. We had data lakes, we had Spark, the ascendancy of programming languages like Python, the introduction of frameworks like TensorFlow, the rise of AI, low code, no code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives. And we said at the time, you know, maybe it's time to rethink data innovation. While a lot of the effort has been focused on more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation, meaning making it easier for domain experts to both gain insights from data, trust the data, and begin to use that data in new ways, fueling data products, monetization, and insights. Data Citizens 2022 is back, and we're pleased to have Felix Van de Maele, who is the founder and CEO of Collibra. He's on the Cube. We're excited to have you, Felix. Good to see you again. >> Likewise Dave. Thanks for having me again. >> You bet. All right, we're going to get the update from Felix on the current data landscape, how he sees it, why data intelligence is more important now than ever, and get current on what Collibra has been up to over the past year, and what's changed since Data Citizens 2021. And we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends, and we're not just snapping back to the 2010s. That's clear. And that's really true, as well, in the world of data. So what's different in your mind in the data landscape of the 2020s from the previous decade, and what challenges does that bring for your customers? >> Yeah, absolutely. And I think you said it well, Dave, in the intro that rising complexity and fragmentation in the broader data landscape that hasn't gotten any better over the last couple of years. When we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use, has only gotten kind of more difficult. So that trend is continuing. I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under with respect to data, as data becomes more mission critical, as data becomes more impactful and important, the level of scrutiny with respect to privacy, security, regulatory compliance, is only increasing as well. Which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity and fragmentation. So it's become much more acute. And to your earlier point, we do live in a different world, and the past couple of years, we could probably just kind of brute force it, right? We could focus on the top line. There was enough kind of investments to be had. I think nowadays organizations are focused, or are in a very different environment where there's much more focus on cost control, productivity, efficiency. How do we truly get value from that data? So again, I think it's just another incentive for organizations to now truly look at that data and to scale that data, not just from a technology and infrastructure perspective, but how do we actually scale data from an organizational perspective, right? Like you said, the people and process, how do we do that at scale? And that's only becoming much more important. And we do believe that the economic environment that we find ourselves in today is going to be a catalyst for organizations to really take that more seriously if you will than they maybe have in the past. >> You know, I don't know when you guys founded Collibra, if you had a sense as to how complicated it was going to get, but you've been on a mission to really address these problems from the beginning. How would you describe your mission, and what are you doing to address these challenges? >> Yeah, absolutely. We started Collibra in 2008. So in some sense in the last kind of financial crisis. And that was really the start of Collibra, where we found product market fit working with large financial institutions to help them cope with the increasing compliance requirements that they were faced with because of the financial crisis, and kind of here we are again in a very different environment of course, 15 years, almost 15 years later. But data only becoming more important. But our mission to deliver trusted data for every user, every use case, and across every source, frankly has only become more important. So while it's been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to, again, be able to provide everyone, and that's why we call it Data Citizens. We truly believe that everyone in the organization should be able to use trusted data in an easy, easy manner. That mission is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we're still relatively early in that journey. >> Well, that's interesting because, you know, in my observation, it takes seven to 10 years to actually build a company, and then the fact that you're still in the early days is kind of interesting. I mean, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your your current momentum? >> Yeah, absolutely. Again, there's a lot of tailwinds, organizations are only maturing their data practices, and we've seen it kind of transform, or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world, whether it's Adobe, Heineken, Bank of America, and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the market with some of the cloud partners like Google, Amazon, Snowflake, Databricks, and others, right? As those kind of new modern data infrastructures, modern data architectures, are definitely all moving to the cloud. A great opportunity for us, our partners, and of course our customers, to help them kind of transition to the cloud even faster. And so we see a lot of excitement and momentum there. We did an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course data quality isn't new, but I think there's a lot of reasons why we're so excited about quality and observability now. One is around leveraging AI, machine learning, again to drive more automation. And the second is that those data pipelines that are now being created in the cloud, in these modern data architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously has become absolutely critical. So we're really excited about that as well. And on the organizational side, I'm sure you've heard a term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believed in. Federated, focused on domains, giving a lot of ownership to different teams. I think that's the way to scale the data organizations, and so that aligns really well with our vision, and from a product perspective, we've seen a lot of momentum with our customers there as well. >> Yeah, you know, a couple things there. I mean, the acquisition of OwlDQ, you know, Kirk Haslbeck and their team, it's interesting, you know, the whole data quality used to be this back office function and really confined to highly regulated industries. It's come to the front office, it's top of mind for chief data officers, data mesh, you mentioned. You guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're a critical part of many ecosystems, and you're developing your own ecosystem. So let's chat a little bit about the products. We're going to go deeper into products later on at Data Citizens '22, but we know you're debuting some new innovations, you know, whether it's, you know, the under the covers in security, sort of making data more accessible for people, just dealing with workflows and processes as you talked about earlier. Tell us a little bit about what you're introducing. >> Yeah, absolutely. We're super excited, a ton of innovation. And if we think about the big theme, and like I said, we're still relatively early in this journey towards kind of that mission of data intelligence, that really bold and compelling mission. Either customers are just starting on that journey, and we want to make it as easy as possible for the organization to actually get started, because we know that's important that they do. And for our organization and customers that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again, to make it easier for, really to accomplish that mission and vision around that data citizen that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving, a lot of kind of ease of adoption, ease of use, but also then, how do we make sure that as Collibra becomes this kind of mission critical enterprise platform from a security performance architecture scale, supportability that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme. From an innovation perspective, from a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One is around data marketplace. Again, a lot of our customers have plans in that direction. How do we make it easy? How do we make available a true kind of shopping experience so that anybody in your organization can, in a very easy search first way, find the right data product, find the right data set that data can then consume, use its analytics. How do we help organizations drive adoption, tell them where they're working really well, and where they have opportunities. Home pages, again, to make things easy for people, for anyone in your organization, to kind of get started with Collibra. You mentioned workflow designer, again, we have a very powerful enterprise platform. One of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a new low code, no code, kind of workflow designer experience. So really customers can take it to the next level. There's a lot more new product around Collibra Protect, which in partnership with Snowflake, which has been a strategic investor in Collibra, focused on how do we make access governance easier? How do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PII data, is managed in a much more effective way. Really excited about that product. There's more around data quality. Again, how do we get that deployed as easily and quickly and widely as we can? Moving that to the cloud has been a big part of our strategy. So we launched our data quality cloud product as well as making use of those native compute capabilities in platforms like Snowflake, Databricks, Google, Amazon, and others. And so we are bettering a capability that we call push down. So we're actually pushing down the computer and data quality, the monitoring, into the underlying platform, which again, from a scale performance and ease of use perspective is going to make a massive difference. And then more broadly, we talked a little bit about the ecosystem. Again, integrations that we talk about, being able to connect to every source. Integrations are absolutely critical, and we're really excited to deliver new integrations with Snowflake, Azure, and Google Cloud Storage as well. So there's a lot coming out. The team has been at work really hard, and we are really, really excited about what we are coming, what we're bringing to markets. >> Yeah, a lot going on there. I wonder if you could give us your closing thoughts. I mean, you talked about the marketplace, you know, you think about data mesh, you think of data as product, one of the key principles. You think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been been so hard, so how do you see sort of the future? And, you know, give us your closing thoughts please. >> Yeah, absolutely. And I think we're really at this pivotal moment, and I think you said it well. We all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not going to fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to deliver this data intelligence vision, the data intelligence platform. We are still early, making it as easy as we can. It's kind of our, as our mission. And so I'm really, really excited to see what we are going to, how the markets are going to evolve over the next few quarters and years. I think the trend is clearly there, when we talk about data mesh, this kind of federated approach, focus on data products is just another signal that we believe that a lot of our organizations are now at the time, they understand the need to go beyond just the technology, how to really, really think about how to actually scale data as a business function, just like we've done with IT, with HR, with sales and marketing, with finance. That's how we need to think about data. I think now's the time given the economic environment that we are in, much more focus on control, much more focus on productivity, efficiency, and now's the time we need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >> Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much, and good luck in San Diego. I know you're going to crush it out there. >> Thank you Dave. >> Yeah, it's a great spot for an in person event, and of course, the content post event is going to be available at collibra.com, and you can of course catch the Cube coverage at thecube.net, and all the news at siliconangle.com. This is Dave Vellante for the Cube, your leader in enterprise and emerging tech coverage. (light music)

(upbeat music) >> The stampede to cloud and massive VC investments has led to the emergence of a new generation of object store based data lakes. And with them two important trends, actually three important trends. First, a new category that combines data lakes and data warehouses aka the lakehouse is emerged as a leading contender to be the data platform of the future. And this novelty touts the ability to address data engineering, data science, and data warehouse workloads on a single shared data platform. The other major trend we've seen is query engines and broader data fabric virtualization platforms have embraced NextGen data lakes as platforms for SQL centric business intelligence workloads, reducing, or somebody even claim eliminating the need for separate data warehouses. Pretty bold. However, cloud data warehouses have added complimentary technologies to bridge the gaps with lakehouses. And the third is many, if not most customers that are embracing the so-called data fabric or data mesh architectures. They're looking at data lakes as a fundamental component of their strategies, and they're trying to evolve them to be more capable, hence the interest in lakehouse, but at the same time, they don't want to, or can't abandon their data warehouse estate. As such we see a battle royale is brewing between cloud data warehouses and cloud lakehouses. Is it possible to do it all with one cloud center analytical data platform? Well, we're going to find out. My name is Dave Vellante and welcome to the data platform's power panel on theCUBE. Our next episode in a series where we gather some of the industry's top analysts to talk about one of our favorite topics, data. In today's session, we'll discuss trends, emerging options, and the trade offs of various approaches and we'll name names. Joining us today are Sanjeev Mohan, who's the principal at SanjMo, Tony Baers, principal at dbInsight. And Doug Henschen is the vice president and principal analyst at Constellation Research. Guys, welcome back to theCUBE. Great to see you again. >> Thank guys. Thank you. >> Thank you. >> So it's early June and we're gearing up with two major conferences, there's several database conferences, but two in particular that were very interested in, Snowflake Summit and Databricks Data and AI Summit. Doug let's start off with you and then Tony and Sanjeev, if you could kindly weigh in. Where did this all start, Doug? The notion of lakehouse. And let's talk about what exactly we mean by lakehouse. Go ahead. >> Yeah, well you nailed it in your intro. One platform to address BI data science, data engineering, fewer platforms, less cost, less complexity, very compelling. You can credit Databricks for coining the term lakehouse back in 2020, but it's really a much older idea. You can go back to Cloudera introducing their Impala database in 2012. That was a database on top of Hadoop. And indeed in that last decade, by the middle of that last decade, there were several SQL on Hadoop products, open standards like Apache Drill. And at the same time, the database vendors were trying to respond to this interest in machine learning and the data science. So they were adding SQL extensions, the likes Hudi and Vertical we're adding SQL extensions to support the data science. But then later in that decade with the shift to cloud and object storage, you saw the vendor shift to this whole cloud, and object storage idea. So you have in the database camp Snowflake introduce Snowpark to try to address the data science needs. They introduced that in 2020 and last year they announced support for Python. You also had Oracle, SAP jumped on this lakehouse idea last year, supporting both the lake and warehouse single vendor, not necessarily quite single platform. Google very recently also jumped on the bandwagon. And then you also mentioned, the SQL engine camp, the Dremios, the Ahanas, the Starbursts, really doing two things, a fabric for distributed access to many data sources, but also very firmly planning that idea that you can just have the lake and we'll help you do the BI workloads on that. And then of course, the data lake camp with the Databricks and Clouderas providing a warehouse style deployments on top of their lake platforms. >> Okay, thanks, Doug. I'd be remiss those of you who me know that I typically write my own intros. This time my colleagues fed me a lot of that material. So thank you. You guys make it easy. But Tony, give us your thoughts on this intro. >> Right. Well, I very much agree with both of you, which may not make for the most exciting television in terms of that it has been an evolution just like Doug said. I mean, for instance, just to give an example when Teradata bought AfterData was initially seen as a hardware platform play. In the end, it was basically, it was all those after functions that made a lot of sort of big data analytics accessible to SQL. (clears throat) And so what I really see just in a more simpler definition or functional definition, the data lakehouse is really an attempt by the data lake folks to make the data lake friendlier territory to the SQL folks, and also to get into friendly territory, to all the data stewards, who are basically concerned about the sprawl and the lack of control in governance in the data lake. So it's really kind of a continuing of an ongoing trend that being said, there's no action without counter action. And of course, at the other end of the spectrum, we also see a lot of the data warehouses starting to edit things like in database machine learning. So they're certainly not surrendering without a fight. Again, as Doug was mentioning, this has been part of a continual blending of platforms that we've seen over the years that we first saw in the Hadoop years with SQL on Hadoop and data warehouses starting to reach out to cloud storage or should say the HDFS and then with the cloud then going cloud native and therefore trying to break the silos down even further. >> Now, thank you. And Sanjeev, data lakes, when we first heard about them, there were such a compelling name, and then we realized all the problems associated with them. So pick it up from there. What would you add to Doug and Tony? >> I would say, these are excellent points that Doug and Tony have brought to light. The concept of lakehouse was going on to your point, Dave, a long time ago, long before the tone was invented. For example, in Uber, Uber was trying to do a mix of Hadoop and Vertical because what they really needed were transactional capabilities that Hadoop did not have. So they weren't calling it the lakehouse, they were using multiple technologies, but now they're able to collapse it into a single data store that we call lakehouse. Data lakes, excellent at batch processing large volumes of data, but they don't have the real time capabilities such as change data capture, doing inserts and updates. So this is why lakehouse has become so important because they give us these transactional capabilities. >> Great. So I'm interested, the name is great, lakehouse. The concept is powerful, but I get concerned that it's a lot of marketing hype behind it. So I want to examine that a bit deeper. How mature is the concept of lakehouse? Are there practical examples that really exist in the real world that are driving business results for practitioners? Tony, maybe you could kick that off. >> Well, put it this way. I think what's interesting is that both data lakes and data warehouse that each had to extend themselves. To believe the Databricks hype it's that this was just a natural extension of the data lake. In point of fact, Databricks had to go outside its core technology of Spark to make the lakehouse possible. And it's a very similar type of thing on the part with data warehouse folks, in terms of that they've had to go beyond SQL, In the case of Databricks. There have been a number of incremental improvements to Delta lake, to basically make the table format more performative, for instance. But the other thing, I think the most dramatic change in all that is in their SQL engine and they had to essentially pretty much abandon Spark SQL because it really, in off itself Spark SQL is essentially stop gap solution. And if they wanted to really address that crowd, they had to totally reinvent SQL or at least their SQL engine. And so Databricks SQL is not Spark SQL, it is not Spark, it's basically SQL that it's adapted to run in a Spark environment, but the underlying engine is C++, it's not scale or anything like that. So Databricks had to take a major detour outside of its core platform to do this. So to answer your question, this is not mature because these are all basically kind of, even though the idea of blending platforms has been going on for well over a decade, I would say that the current iteration is still fairly immature. And in the cloud, I could see a further evolution of this because if you think through cloud native architecture where you're essentially abstracting compute from data, there is no reason why, if let's say you are dealing with say, the same basically data targets say cloud storage, cloud object storage that you might not apportion the task to different compute engines. And so therefore you could have, for instance, let's say you're Google, you could have BigQuery, perform basically the types of the analytics, the SQL analytics that would be associated with the data warehouse and you could have BigQuery ML that does some in database machine learning, but at the same time for another part of the query, which might involve, let's say some deep learning, just for example, you might go out to let's say the serverless spark service or the data proc. And there's no reason why Google could not blend all those into a coherent offering that's basically all triggered through microservices. And I just gave Google as an example, if you could generalize that with all the other cloud or all the other third party vendors. So I think we're still very early in the game in terms of maturity of data lakehouses. >> Thanks, Tony. So Sanjeev, is this all hype? What are your thoughts? >> It's not hype, but completely agree. It's not mature yet. Lakehouses have still a lot of work to do, so what I'm now starting to see is that the world is dividing into two camps. On one hand, there are people who don't want to deal with the operational aspects of vast amounts of data. They are the ones who are going for BigQuery, Redshift, Snowflake, Synapse, and so on because they want the platform to handle all the data modeling, access control, performance enhancements, but these are trade off. If you go with these platforms, then you are giving up on vendor neutrality. On the other side are those who have engineering skills. They want the independence. In other words, they don't want vendor lock in. They want to transform their data into any number of use cases, especially data science, machine learning use case. What they want is agility via open file formats using any compute engine. So why do I say lakehouses are not mature? Well, cloud data warehouses they provide you an excellent user experience. That is the main reason why Snowflake took off. If you have thousands of cables, it takes minutes to get them started, uploaded into your warehouse and start experimentation. Table formats are far more resonating with the community than file formats. But once the cost goes up of cloud data warehouse, then the organization start exploring lakehouses. But the problem is lakehouses still need to do a lot of work on metadata. Apache Hive was a fantastic first attempt at it. Even today Apache Hive is still very strong, but it's all technical metadata and it has so many different restrictions. That's why we see Databricks is investing into something called Unity Catalog. Hopefully we'll hear more about Unity Catalog at the end of the month. But there's a second problem. I just want to mention, and that is lack of standards. All these open source vendors, they're running, what I call ego projects. You see on LinkedIn, they're constantly battling with each other, but end user doesn't care. End user wants a problem to be solved. They want to use Trino, Dremio, Spark from EMR, Databricks, Ahana, DaaS, Frink, Athena. But the problem is that we don't have common standards. >> Right. Thanks. So Doug, I worry sometimes. I mean, I look at the space, we've debated for years, best of breed versus the full suite. You see AWS with whatever, 12 different plus data stores and different APIs and primitives. You got Oracle putting everything into its database. It's actually done some interesting things with MySQL HeatWave, so maybe there's proof points there, but Snowflake really good at data warehouse, simplifying data warehouse. Databricks, really good at making lakehouses actually more functional. Can one platform do it all? >> Well in a word, I can't be best at breed at all things. I think the upshot of and cogen analysis from Sanjeev there, the database, the vendors coming out of the database tradition, they excel at the SQL. They're extending it into data science, but when it comes to unstructured data, data science, ML AI often a compromise, the data lake crowd, the Databricks and such. They've struggled to completely displace the data warehouse when it really gets to the tough SLAs, they acknowledge that there's still a role for the warehouse. Maybe you can size down the warehouse and offload some of the BI workloads and maybe and some of these SQL engines, good for ad hoc, minimize data movement. But really when you get to the deep service level, a requirement, the high concurrency, the high query workloads, you end up creating something that's warehouse like. >> Where do you guys think this market is headed? What's going to take hold? Which projects are going to fade away? You got some things in Apache projects like Hudi and Iceberg, where do they fit Sanjeev? Do you have any thoughts on that? >> So thank you, Dave. So I feel that table formats are starting to mature. There is a lot of work that's being done. We will not have a single product or single platform. We'll have a mixture. So I see a lot of Apache Iceberg in the news. Apache Iceberg is really innovating. Their focus is on a table format, but then Delta and Apache Hudi are doing a lot of deep engineering work. For example, how do you handle high concurrency when there are multiple rights going on? Do you version your Parquet files or how do you do your upcerts basically? So different focus, at the end of the day, the end user will decide what is the right platform, but we are going to have multiple formats living with us for a long time. >> Doug is Iceberg in your view, something that's going to address some of those gaps in standards that Sanjeev was talking about earlier? >> Yeah, Delta lake, Hudi, Iceberg, they all address this need for consistency and scalability, Delta lake open technically, but open for access. I don't hear about Delta lakes in any worlds, but Databricks, hearing a lot of buzz about Apache Iceberg. End users want an open performance standard. And most recently Google embraced Iceberg for its recent a big lake, their stab at having supporting both lakes and warehouses on one conjoined platform. >> And Tony, of course, you remember the early days of the sort of big data movement you had MapR was the most closed. You had Horton works the most open. You had Cloudera in between. There was always this kind of contest as to who's the most open. Does that matter? Are we going to see a repeat of that here? >> I think it's spheres of influence, I think, and Doug very much was kind of referring to this. I would call it kind of like the MongoDB syndrome, which is that you have... and I'm talking about MongoDB before they changed their license, open source project, but very much associated with MongoDB, which basically, pretty much controlled most of the contributions made decisions. And I think Databricks has the same iron cloud hold on Delta lake, but still the market is pretty much associated Delta lake as the Databricks, open source project. I mean, Iceberg is probably further advanced than Hudi in terms of mind share. And so what I see that's breaking down to is essentially, basically the Databricks open source versus the everything else open source, the community open source. So I see it's a very similar type of breakdown that I see repeating itself here. >> So by the way, Mongo has a conference next week, another data platform is kind of not really relevant to this discussion totally. But in the sense it is because there's a lot of discussion on earnings calls these last couple of weeks about consumption and who's exposed, obviously people are concerned about Snowflake's consumption model. Mongo is maybe less exposed because Atlas is prominent in the portfolio, blah, blah, blah. But I wanted to bring up the little bit of controversy that we saw come out of the Snowflake earnings call, where the ever core analyst asked Frank Klutman about discretionary spend. And Frank basically said, look, we're not discretionary. We are deeply operationalized. Whereas he kind of poo-pooed the lakehouse or the data lake, et cetera, saying, oh yeah, data scientists will pull files out and play with them. That's really not our business. Do any of you have comments on that? Help us swing through that controversy. Who wants to take that one? >> Let's put it this way. The SQL folks are from Venus and the data scientists are from Mars. So it means it really comes down to it, sort that type of perception. The fact is, is that, traditionally with analytics, it was very SQL oriented and that basically the quants were kind of off in their corner, where they're using SaaS or where they're using Teradata. It's really a great leveler today, which is that, I mean basic Python it's become arguably one of the most popular programming languages, depending on what month you're looking at, at the title index. And of course, obviously SQL is, as I tell the MongoDB folks, SQL is not going away. You have a large skills base out there. And so basically I see this breaking down to essentially, you're going to have each group that's going to have its own natural preferences for its home turf. And the fact that basically, let's say the Python and scale of folks are using Databricks does not make them any less operational or machine critical than the SQL folks. >> Anybody else want to chime in on that one? >> Yeah, I totally agree with that. Python support in Snowflake is very nascent with all of Snowpark, all of the things outside of SQL, they're very much relying on partners too and make things possible and make data science possible. And it's very early days. I think the bottom line, what we're going to see is each of these camps is going to keep working on doing better at the thing that they don't do today, or they're new to, but they're not going to nail it. They're not going to be best of breed on both sides. So the SQL centric companies and shops are going to do more data science on their database centric platform. That data science driven companies might be doing more BI on their leagues with those vendors and the companies that have highly distributed data, they're going to add fabrics, and maybe offload more of their BI onto those engines, like Dremio and Starburst. >> So I've asked you this before, but I'll ask you Sanjeev. 'Cause Snowflake and Databricks are such great examples 'cause you have the data engineering crowd trying to go into data warehousing and you have the data warehousing guys trying to go into the lake territory. Snowflake has $5 billion in the balance sheet and I've asked you before, I ask you again, doesn't there has to be a semantic layer between these two worlds? Does Snowflake go out and do M&A and maybe buy ad scale or a data mirror? Or is that just sort of a bandaid? What are your thoughts on that Sanjeev? >> I think semantic layer is the metadata. The business metadata is extremely important. At the end of the day, the business folks, they'd rather go to the business metadata than have to figure out, for example, like let's say, I want to update somebody's email address and we have a lot of overhead with data residency laws and all that. I want my platform to give me the business metadata so I can write my business logic without having to worry about which database, which location. So having that semantic layer is extremely important. In fact, now we are taking it to the next level. Now we are saying that it's not just a semantic layer, it's all my KPIs, all my calculations. So how can I make those calculations independent of the compute engine, independent of the BI tool and make them fungible. So more disaggregation of the stack, but it gives us more best of breed products that the customers have to worry about. >> So I want to ask you about the stack, the modern data stack, if you will. And we always talk about injecting machine intelligence, AI into applications, making them more data driven. But when you look at the application development stack, it's separate, the database is tends to be separate from the data and analytics stack. Do those two worlds have to come together in the modern data world? And what does that look like organizationally? >> So organizationally even technically I think it is starting to happen. Microservices architecture was a first attempt to bring the application and the data world together, but they are fundamentally different things. For example, if an application crashes, that's horrible, but Kubernetes will self heal and it'll bring the application back up. But if a database crashes and corrupts your data, we have a huge problem. So that's why they have traditionally been two different stacks. They are starting to come together, especially with data ops, for instance, versioning of the way we write business logic. It used to be, a business logic was highly embedded into our database of choice, but now we are disaggregating that using GitHub, CICD the whole DevOps tool chain. So data is catching up to the way applications are. >> We also have databases, that trans analytical databases that's a little bit of what the story is with MongoDB next week with adding more analytical capabilities. But I think companies that talk about that are always careful to couch it as operational analytics, not the warehouse level workloads. So we're making progress, but I think there's always going to be, or there will long be a separate analytical data platform. >> Until data mesh takes over. (all laughing) Not opening a can of worms. >> Well, but wait, I know it's out of scope here, but wouldn't data mesh say, hey, do take your best of breed to Doug's earlier point. You can't be best of breed at everything, wouldn't data mesh advocate, data lakes do your data lake thing, data warehouse, do your data lake, then you're just a node on the mesh. (Tony laughs) Now you need separate data stores and you need separate teams. >> To my point. >> I think, I mean, put it this way. (laughs) Data mesh itself is a logical view of the world. The data mesh is not necessarily on the lake or on the warehouse. I think for me, the fear there is more in terms of, the silos of governance that could happen and the silo views of the world, how we redefine. And that's why and I want to go back to something what Sanjeev said, which is that it's going to be raising the importance of the semantic layer. Now does Snowflake that opens a couple of Pandora's boxes here, which is one, does Snowflake dare go into that space or do they risk basically alienating basically their partner ecosystem, which is a key part of their whole appeal, which is best of breed. They're kind of the same situation that Informatica was where in the early 2000s, when Informatica briefly flirted with analytic applications and realized that was not a good idea, need to redouble down on their core, which was data integration. The other thing though, that raises the importance of and this is where the best of breed comes in, is the data fabric. My contention is that and whether you use employee data mesh practice or not, if you do employee data mesh, you need data fabric. If you deploy data fabric, you don't necessarily need to practice data mesh. But data fabric at its core and admittedly it's a category that's still very poorly defined and evolving, but at its core, we're talking about a common meta data back plane, something that we used to talk about with master data management, this would be something that would be more what I would say basically, mutable, that would be more evolving, basically using, let's say, machine learning to kind of, so that we don't have to predefine rules or predefine what the world looks like. But so I think in the long run, what this really means is that whichever way we implement on whichever physical platform we implement, we need to all be speaking the same metadata language. And I think at the end of the day, regardless of whether it's a lake, warehouse or a lakehouse, we need common metadata. >> Doug, can I come back to something you pointed out? That those talking about bringing analytic and transaction databases together, you had talked about operationalizing those and the caution there. Educate me on MySQL HeatWave. I was surprised when Oracle put so much effort in that, and you may or may not be familiar with it, but a lot of folks have talked about that. Now it's got nowhere in the market, that no market share, but a lot of we've seen these benchmarks from Oracle. How real is that bringing together those two worlds and eliminating ETL? >> Yeah, I have to defer on that one. That's my colleague, Holger Mueller. He wrote the report on that. He's way deep on it and I'm not going to mock him. >> I wonder if that is something, how real that is or if it's just Oracle marketing, anybody have any thoughts on that? >> I'm pretty familiar with HeatWave. It's essentially Oracle doing what, I mean, there's kind of a parallel with what Google's doing with AlloyDB. It's an operational database that will have some embedded analytics. And it's also something which I expect to start seeing with MongoDB. And I think basically, Doug and Sanjeev were kind of referring to this before about basically kind of like the operational analytics, that are basically embedded within an operational database. The idea here is that the last thing you want to do with an operational database is slow it down. So you're not going to be doing very complex deep learning or anything like that, but you might be doing things like classification, you might be doing some predictives. In other words, we've just concluded a transaction with this customer, but was it less than what we were expecting? What does that mean in terms of, is this customer likely to turn? I think we're going to be seeing a lot of that. And I think that's what a lot of what MySQL HeatWave is all about. Whether Oracle has any presence in the market now it's still a pretty new announcement, but the other thing that kind of goes against Oracle, (laughs) that they had to battle against is that even though they own MySQL and run the open source project, everybody else, in terms of the actual commercial implementation it's associated with everybody else. And the popular perception has been that MySQL has been basically kind of like a sidelight for Oracle. And so it's on Oracles shoulders to prove that they're damn serious about it. >> There's no coincidence that MariaDB was launched the day that Oracle acquired Sun. Sanjeev, I wonder if we could come back to a topic that we discussed earlier, which is this notion of consumption, obviously Wall Street's very concerned about it. Snowflake dropped prices last week. I've always felt like, hey, the consumption model is the right model. I can dial it down in when I need to, of course, the street freaks out. What are your thoughts on just pricing, the consumption model? What's the right model for companies, for customers? >> Consumption model is here to stay. What I would like to see, and I think is an ideal situation and actually plays into the lakehouse concept is that, I have my data in some open format, maybe it's Parquet or CSV or JSON, Avro, and I can bring whatever engine is the best engine for my workloads, bring it on, pay for consumption, and then shut it down. And by the way, that could be Cloudera. We don't talk about Cloudera very much, but it could be one business unit wants to use Athena. Another business unit wants to use some other Trino let's say or Dremio. So every business unit is working on the same data set, see that's critical, but that data set is maybe in their VPC and they bring any compute engine, you pay for the use, shut it down. That then you're getting value and you're only paying for consumption. It's not like, I left a cluster running by mistake, so there have to be guardrails. The reason FinOps is so big is because it's very easy for me to run a Cartesian joint in the cloud and get a $10,000 bill. >> This looks like it's been a sort of a victim of its own success in some ways, they made it so easy to spin up single note instances, multi note instances. And back in the day when compute was scarce and costly, those database engines optimized every last bit so they could get as much workload as possible out of every instance. Today, it's really easy to spin up a new node, a new multi node cluster. So that freedom has meant many more nodes that aren't necessarily getting that utilization. So Snowflake has been doing a lot to add reporting, monitoring, dashboards around the utilization of all the nodes and multi node instances that have spun up. And meanwhile, we're seeing some of the traditional on-prem databases that are moving into the cloud, trying to offer that freedom. And I think they're going to have that same discovery that the cost surprises are going to follow as they make it easy to spin up new instances. >> Yeah, a lot of money went into this market over the last decade, separating compute from storage, moving to the cloud. I'm glad you mentioned Cloudera Sanjeev, 'cause they got it all started, the kind of big data movement. We don't talk about them that much. Sometimes I wonder if it's because when they merged Hortonworks and Cloudera, they dead ended both platforms, but then they did invest in a more modern platform. But what's the future of Cloudera? What are you seeing out there? >> Cloudera has a good product. I have to say the problem in our space is that there're way too many companies, there's way too much noise. We are expecting the end users to parse it out or we expecting analyst firms to boil it down. So I think marketing becomes a big problem. As far as technology is concerned, I think Cloudera did turn their selves around and Tony, I know you, you talked to them quite frequently. I think they have quite a comprehensive offering for a long time actually. They've created Kudu, so they got operational, they have Hadoop, they have an operational data warehouse, they're migrated to the cloud. They are in hybrid multi-cloud environment. Lot of cloud data warehouses are not hybrid. They're only in the cloud. >> Right. I think what Cloudera has done the most successful has been in the transition to the cloud and the fact that they're giving their customers more OnRamps to it, more hybrid OnRamps. So I give them a lot of credit there. They're also have been trying to position themselves as being the most price friendly in terms of that we will put more guardrails and governors on it. I mean, part of that could be spin. But on the other hand, they don't have the same vested interest in compute cycles as say, AWS would have with EMR. That being said, yes, Cloudera does it, I think its most powerful appeal so of that, it almost sounds in a way, I don't want to cast them as a legacy system. But the fact is they do have a huge landed legacy on-prem and still significant potential to land and expand that to the cloud. That being said, even though Cloudera is multifunction, I think it certainly has its strengths and weaknesses. And the fact this is that yes, Cloudera has an operational database or an operational data store with a kind of like the outgrowth of age base, but Cloudera is still based, primarily known for the deep analytics, the operational database nobody's going to buy Cloudera or Cloudera data platform strictly for the operational database. They may use it as an add-on, just in the same way that a lot of customers have used let's say Teradata basically to do some machine learning or let's say, Snowflake to parse through JSON. Again, it's not an indictment or anything like that, but the fact is obviously they do have their strengths and their weaknesses. I think their greatest opportunity is with their existing base because that base has a lot invested and vested. And the fact is they do have a hybrid path that a lot of the others lack. >> And of course being on the quarterly shock clock was not a good place to be under the microscope for Cloudera and now they at least can refactor the business accordingly. I'm glad you mentioned hybrid too. We saw Snowflake last month, did a deal with Dell whereby non-native Snowflake data could access on-prem object store from Dell. They announced a similar thing with pure storage. What do you guys make of that? Is that just... How significant will that be? Will customers actually do that? I think they're using either materialized views or extended tables. >> There are data rated and residency requirements. There are desires to have these platforms in your own data center. And finally they capitulated, I mean, Frank Klutman is famous for saying to be very focused and earlier, not many months ago, they called the going on-prem as a distraction, but clearly there's enough demand and certainly government contracts any company that has data residency requirements, it's a real need. So they finally addressed it. >> Yeah, I'll bet dollars to donuts, there was an EBC session and some big customer said, if you don't do this, we ain't doing business with you. And that was like, okay, we'll do it. >> So Dave, I have to say, earlier on you had brought this point, how Frank Klutman was poo-pooing data science workloads. On your show, about a year or so ago, he said, we are never going to on-prem. He burnt that bridge. (Tony laughs) That was on your show. >> I remember exactly the statement because it was interesting. He said, we're never going to do the halfway house. And I think what he meant is we're not going to bring the Snowflake architecture to run on-prem because it defeats the elasticity of the cloud. So this was kind of a capitulation in a way. But I think it still preserves his original intent sort of, I don't know. >> The point here is that every vendor will poo-poo whatever they don't have until they do have it. >> Yes. >> And then it'd be like, oh, we are all in, we've always been doing this. We have always supported this and now we are doing it better than others. >> Look, it was the same type of shock wave that we felt basically when AWS at the last moment at one of their reinvents, oh, by the way, we're going to introduce outposts. And the analyst group is typically pre briefed about a week or two ahead under NDA and that was not part of it. And when they dropped, they just casually dropped that in the analyst session. It's like, you could have heard the sound of lots of analysts changing their diapers at that point. >> (laughs) I remember that. And a props to Andy Jassy who once, many times actually told us, never say never when it comes to AWS. So guys, I know we got to run. We got some hard stops. Maybe you could each give us your final thoughts, Doug start us off and then-- >> Sure. Well, we've got the Snowflake Summit coming up. I'll be looking for customers that are really doing data science, that are really employing Python through Snowflake, through Snowpark. And then a couple weeks later, we've got Databricks with their Data and AI Summit in San Francisco. I'll be looking for customers that are really doing considerable BI workloads. Last year I did a market overview of this analytical data platform space, 14 vendors, eight of them claim to support lakehouse, both sides of the camp, Databricks customer had 32, their top customer that they could site was unnamed. It had 32 concurrent users doing 15,000 queries per hour. That's good but it's not up to the most demanding BI SQL workloads. And they acknowledged that and said, they need to keep working that. Snowflake asked for their biggest data science customer, they cited Kabura, 400 terabytes, 8,500 users, 400,000 data engineering jobs per day. I took the data engineering job to be probably SQL centric, ETL style transformation work. So I want to see the real use of the Python, how much Snowpark has grown as a way to support data science. >> Great. Tony. >> Actually of all things. And certainly, I'll also be looking for similar things in what Doug is saying, but I think sort of like, kind of out of left field, I'm interested to see what MongoDB is going to start to say about operational analytics, 'cause I mean, they're into this conquer the world strategy. We can be all things to all people. Okay, if that's the case, what's going to be a case with basically, putting in some inline analytics, what are you going to be doing with your query engine? So that's actually kind of an interesting thing we're looking for next week. >> Great. Sanjeev. >> So I'll be at MongoDB world, Snowflake and Databricks and very interested in seeing, but since Tony brought up MongoDB, I see that even the databases are shifting tremendously. They are addressing both the hashtag use case online, transactional and analytical. I'm also seeing that these databases started in, let's say in case of MySQL HeatWave, as relational or in MongoDB as document, but now they've added graph, they've added time series, they've added geospatial and they just keep adding more and more data structures and really making these databases multifunctional. So very interesting. >> It gets back to our discussion of best of breed, versus all in one. And it's likely Mongo's path or part of their strategy of course, is through developers. They're very developer focused. So we'll be looking for that. And guys, I'll be there as well. I'm hoping that we maybe have some extra time on theCUBE, so please stop by and we can maybe chat a little bit. Guys as always, fantastic. Thank you so much, Doug, Tony, Sanjeev, and let's do this again. >> It's been a pleasure. >> All right and thank you for watching. This is Dave Vellante for theCUBE and the excellent analyst. We'll see you next time. (upbeat music)

>>Welcome back to the cubes coverage of Veeam on 2022 at the RS. Nice to be at the aria. My co-host Dave Nicholson here. We spend a lot of time at the Venetian convention center, formerly the sand. So it's nice to have a more intimate venue. I really like it here. George Burg is joining us. He's the vice president of data protection at vast data, a company that some of you may not know about. George. >>Welcome a pleasure. Thank you so much for having me. >>So VAs is smoking hot, raised a ton of dough. You've got great founders, hard charging, interesting tech. We've covered a little bit on the Wikibon research side, but give us the overview of the company. Yeah, >>If I could please. So we're here at the, you know, the Veeam show and, you know, the theme is modern data protection, and I don't think there's any company that epitomizes modern data protection more than vast data. The fact that we're able to do an all flash system at exabyte scale, but the economics of cloud object based deep, cheap, and deep archive type solutions and an extremely resilient platform is really game changing for the marketplace. So, and quite frankly, a marketplace from a data protection target space that I think is, is ripe for change and in need of change based on the things that are going on in the marketplace today. >>Yeah. So a lot of what you said is gonna be surprising to people, wait a minute, you're talking about data protection and all flash sure. I thought you'd use cheap and deep disc or, you know, even tape for that or, you know, spin it up in the cloud in a, in a deep archive or a glacier. Explain your approach in, in architecture. Yeah. At a >>High level. Yeah. So great question. We get that question every day and got it in the booth yesterday, probably about 40 or 50 times. How could it be all flash that at an economic point that is the fitting that of, you know, data protection. Yeah. >>What is this Ferrari minivan of which you speak? >>Yeah, yeah, yeah. The minivan that goes 180 miles an hour, right. That, you know, it's, it's really all about the architecture, right? The component tree is, is somewhat similar to what you'll see in other devices. However, it's how we're leveraging them in the architecture and design, you know, from our founders years ago and building a solution that just not, was not available in the marketplace. So yeah, sure. We're using, you know, all flash QLC drives, but the technology, you know, the advanced next generation algorithms or erasure coding or rage striping allows us to be extremely efficient. We also have some technologies around what we call similarity, some advanced data reduction. So you need less, less capacity if you will, with a vast system. So that obviously help obviously helps us out tremendously with their economics. But the other thing is I could sell a customer exactly what they need. If you think about the legacy data protection market purpose built back of appliances, for example, you know, ALA, Adele, Aita, and HP, you know, they're selling systems that are somewhat rigid. There's always a controller in a capacity. It's tied to a model number right. Soon as you need more performance, you buy another, as soon as you need more capacity, you buy another, it's really not modular in any way. It's great >>Model. If you want to just keep, keep billing the >>Customer. Yeah. If, if that, if yeah. And, and I, I think, I think at this point, the purpose, you know, Dave, the purpose built backup appliance market is, is hungry for a change. Right. You know, there's, there's not anyone that has one. It doesn't exist. I'm not just talking about having two because of replication. I'm it's because of organic growth. Ransomware needs to have a second unit, a second copy. And just, and just scalability. Well, you >>Guys saw that fatigue with that model of, oh, you need more buy more, >>Right? Oh, without a doubt, you said we're gonna attack that. Yeah. Yeah. Sorry. No, no, no. That's great. Without a doubt. So, so we can configure a solution exactly. To the need. Cause let's face it. Every single data center, every single vertical market, it's a work of art. You know, everyone's retention policies are different. Everyone's compliance needs are different. There might be some things that are self mandated or government mandated and they're all gonna be somewhat different. Right? The fact of the matter is the way that our, our architecture works, disaggregated shared everything. Architecture is different because when we go back to those model numbers and there's more rigid purpose built back of appliances, or, or maybe a raise designed specifically for data protection, they don't offer that flexibility. And, you know, I, I, I think our, our, our, our entry point is sized to exactly what the need is. Our ease of scalability. You need more performance. We just add another compute, another compute box, what we call our C box. If you need more capacity, we just add another data box, a D box, you know, where the data resides. And, you know, I, you know, especially here at Veeam, I think customers are really clamoring for that next generation solution. They love the idea that there's a low point of entry, but they also love the idea that, that it's easy to scale on demand, you know, as, as needed and as needed basis. >>So just, I wanna be just, I want to go down another layer on that architecturally. Cause I think it's important for people to understand. Sure, exactly what you're saying. When you're talking about scaling, there's this concept of the, of the sort of devil's triangle, the tyranny of this combination of memory, CPU and storage. Sure. And if you're too rigid, like in an appliance, you end up paying for things you don't need. Correct. When all I need is a little more capacity. Correct. All I need is a little more horsepower. Well, you wanna horsepower? No, you gotta buy a bunch of capacity. Exactly. Oh, need capacity. No, no. You need to buy expensive CPUs and suck a bunch of power. All I need is capacity. So what, so go through that, just a little more detail in terms of sure. How you cobble these systems together. Sure. My, the way my brain works, it's always about Legos. So feel free to use Legos. >>Yeah. We, so, so with our disaggregated solution, right. We've separated basically hardware from software. Right. So, so, so that's a good thing, right? From an economic standpoint, but also a design and architecture standpoint, but also an underlining underpinning of that solution is we've also separated the capacity from the performance. And as you just mentioned, those are typically relatively speaking for every other solution on the planet. Those are tied together. Right? Right. So we've disaggregated that as well within our architecture. So we, we again have basically three tier, tier's not the right word, three components that build out a vast cluster. And again, we don't sell like a solution designed by a model number. And that's typically our C boxes connected via NVMe over fabric to a D box C is all the performance D is all the capacity because they're modular. You can end up like our, our baseline product would start out as a one by one, one C box one D box, right? >>Connected again, via different, different size and Vme fabrics. And that could scale to hundreds. When we do have customers with dozens of C boxes, meeting high performance requirements, keep in mind when, when vast data came to market, our founders brought it to the market for high performance computing machine learning, AI data protection was an afterthought, but those found, you know, foundational things that we're able to build in that modularity with performance at scale, it behooves itself, it's perfect fit for data protection. So we see in clients today, just yesterday, two clients standing next to each other in the same market in the same vertical. I have a 30 day retention. I have a 90 day retention. I have to keep one year worth of full backups. I have to keep seven years worth of full backups. We can accommodate both and size it to exactly what the need is. >>Now, the moment that they need one more terabyte, we license into 100 terabyte increments so they can actually buy it in a sense, almost in arrears, we don't turn it off. We don't, there's not a hard cat. They have access to that capacity within the solution that they provide and they can have access immediate access. And without going through, let's face it. A lot of the other companies that we're both thinking of that have those traditional again, purpose-built solutions or arrays. They want you to buy everything up front in advance, signing license agreements. We're the exact opposite. We want you to buy for the need as, and as needed basis. And also because the fact that we're, multi-protocol multi-use case, you see people doing many things within even a single vast cluster. >>I, I wanna come back to the architecture if I, I can and just understand it better. And I said, David, Flo's written a lot about this on our site, but I've had three key meetings in my life with Mosia and I, and I you've obviously know the first week you showed up in my offices at IDC in the late 1980s said, tell me everything, you know about the IBM mainframe IO subsystem. I'm like, oh, this is gonna be a short meeting. And then they came back a year later and showed us symmetric. I was like, wow, that's pretty impressive. The second one was, I gave a speech at 43 south of 42 south. He came up and gave me a big hug. I'm like, wow. He knows me. And the third one, he was in my offices at, in Mabo several years ago. And we were arguing about the flash versus spinning disc. And he's like, I can outperform an all flash array because we've tuned our algorithms for spinning disc. Everybody else is missing that. You're basically saying the opposite. Correct. We've turned tuned our algorithms to, for QC David Flos says Dave, there's a lot of ways to skin a cat in this technology industry. So I wanted to make sure I got that right. Basically you're skinning the cat with different >>Approach. Yeah. We've also changed really the approach of backup. I mean, the, the term backup is really legacy. I mean, that's 10, 12 years of our recovery. The, the story today is really about, about restore resiliency and recovery. So when you think about those legacy solutions, right, they were built to ingest fast, right? We wanna move the data off our primary systems, our, our primary applications and we needed to fit within a backup window. Restore was an afterthought. Restore was, I might occasionally need to restore something. Something got lost, something got re corrupted. I have to restore something today with the, you know, let's face it, the digital pandemic of, of, of cyber threats and, and ransomware it's about sometimes restoring everything. So if you look at a legacy system, they ingest, I'm sorry. They, they, they write very fast. They, they, they can bring the data in very quickly, but their restore time is typically about 20 to 25%. >>So their reading at only 20, 25% of their right speed, you know, is their rate speed. We flip the script on that. We actually read eight times faster than we write. So I could size again to the performance that you need. If you need 40 terabytes, an hour 50 terabytes an hour, we can do that. But those systems that write at 40 terabytes an hour are restoring at only eight. We're writing at a similarly size system, which actually comes out about 51 terabytes an hour 54 terabytes. We're restoring at 432 terabytes an hour. So we've broken the mold of data protection targets. We're no longer the bottleneck. We're no longer part of your recovery plan going to be the issue right now, you gotta start thinking about network connectivity. Do I have, you know, you know, with the, with our Veeam partners, do we have the right data movers, whether virtual or physical, where am I gonna put the data? >>We've really helped customer aided customers to rethinking their whole Dr. Plan, cuz let's face it. When, when ransomware occurs, you might not be able to get in the building, your phones don't work. Who do you call right? By the time you get that all figured out and you get to the point where you're start, you want to start recovering data. If I could recover 50 times faster than a purpose built backup appliance. Right? Think about it. Is it one day or is it 50 days? Am I gonna be back online? Is it one hour? Is it 50 hours? How many millions of dollars, tens of thousands of dollars were like, will that cost us? And that's why our architecture though our thought process and how the system was designed lends itself. So well for the requirements of today, data protection, not backup it's about data protection. >>Can you give us a sense as to how much of your business momentum is from data protection? >>Yeah, sure. So I joined VAs as we were talking chatting before I come on about six months ago. And it's funny, we had a lot of vast customers on their own because they wanted to leverage the platform and they saw the power of VAs. They started doing that. And then as our founders, you know, decided to lean in heavily into this marketplace with investments, not just in people, but also in technology and research and development, and also partnering with the likes of, of Veeam. We, we don't have a data mover, right. We, we require a data mover to bring us the data we've leaned in tremendously. Last quarter was really our, probably our first quarter where we had a lot of marketing and momentum around data protection. We sold five X last quarter than we did all of last year. So right now the momentum's great pipeline looks phenomenal and you know, we're gonna continue to lean in here. >>Describe the relationship with Veeam, like kind of, sort of started recently. It sounds like as customer demand. Yeah. But what's that like, what are you guys doing in terms of engineering integration go to market? >>Yeah. So, so we've gone through all the traditional, you know, verifications and certifications and, and, and I'm proud to say that we kind of blew the, the, the roof off the requirements of a Veeam environ. Remember Veeam was very innovative. 10, 12 years ago, they were putting flash in servers because they, they, they want a high performing environment, a feature such as instant recovery. We've now enabled. When I talked about all those things about re about restore. We had customers yesterday come to us that have tens of thousands of VMs. Imagine that I can spin them up instantaneously and run Veeam's instant recovery solution. While then in the background, restoring those items that is powerful and you need a very fast high performance system to enable that instant. Recovery's not new. It's been in the market for very long, but you can ask nine outta 10 customers walk in the floor. >>They're not able to leverage that today in the systems that they have, or it's over architected and very expensive and somewhat cost prohibitive. So our relationship with Veeam is really skyrocketing actually, as part of that, that success and our, our last quarter, we did seven figure deals here in the United States. We've done deals in Australia. We were chatting. I, I, I happened to be in Dubai and we did a deal there with the government there. So, you know, there's no, there's no specific vertical market. They're all different. You know, it's, it's really driven by, you know, they have a great, you know, cyber resilient message. I mean, you get seen by the last couple of days today and they just want that power that vast. Now there are other systems in the marketplace today that leverage all flash, but they don't have the economic solution that we have. >>No, your, your design anticipated the era that we're we're in right now from it, it anticipated the ability to scale in, to scale, you know, in >>A variety. Well, listen, anticipation of course, co coincidental architecture. It's a fantastic fit either way, either way. I mean, it's a fantastic fit for today. And that's the conversations that we're having with, with all the customers here, it's really all about resiliency. And they know, I mean, one of the sessions, I think it was mentioned 82 or 84% of, of all clients interviewed don't believe that they can do a restore after a cyber attack or it'll cost them millions of dollars. So that there's a tremendous amount of risk there. So time is, is, is ultimately equals dollars. So we see a, a big uptick there, but we're, we're actually continuing our validation work and testing with Veeam. They've been very receptive, very receptive globally. Veeam's channel has also been very receptive globally because you know, their customers are, you know, hungry for innovation as well. And I really strongly believe ASBO brings that >>George, we gotta go, but thank you. Congratulations. Pleasure on the momentum. Say hi to Jeff for us. >>We'll we'll do so, you know, and we'll, can I leave you with one last thought? Yeah, >>Please do give us your final thought. >>If I could, in closing, I think it's pretty important when, when customers are, are evaluating vast, if I could give them three data points, 100% of customers that Triva test vast POC, vast BVAs 100% Gartner peer insights recently did a survey. You know, they, they do it with our, you know, blind survey, dozens of vast customers and never happened before where 100% of the respondents said, yes, I would recommend VA and I will buy VAs again. It was more >>Than two respondents. >>It was more, it was dozens. They won't do it. If it's not dozens, it's dozens. It's not dozen this >>Check >>In and last but not. And, and last but not least our customers are, are speaking with their wallet. And the fact of the matter is for every customer that spends a dollar with vast within a year, they spend three more. So, I mean, if there's no better endorsement, if you have a customer base, a client base that are coming back and looking for more use cases, not just data protection, but again, high performance computing machine learning AI for a company like VA data. >>Awesome. And a lot of investment in engineering, more investment in engineering than marketing. How do I know? Because your capacity nodes, aren't the C nodes. They're the D nodes somehow. So the engineers obviously won that naming. >>They'll always win that one and we, and we, and we let them, we need them. Thank you. So that awesome product >>Sales, it's the golden rule. All right. Thank you, George. Keep it right there. VEON 20, 22, you're watching the cube, Uber, Uber right back.

(cheery music) >> Thanks, Adam. Thanks for everyone in the studio. Dave, we've got some great main stage CUBE interviews. Normally we'll sit at the desk, and do a remote, but since it's a virtual event, and a physical event, it's a hybrid event. We've got two amazing Google leaders to talk with us. I had a chance to sit down with Amol who was gone yesterday during our breaking news segment. They had the big news. We had two great guests, Amol Phadke. He's our first interview. He's the head of Google's telecom industry. Again, he came in, broke into our segment yesterday with breaking news. Obviously released with Ericsson, and the O-RAN Alliance. I had a great chance to chat with him. A wide ranging conversation for 13 minutes. Enjoy my interview with Amol, right now. (cheery music) Well welcome to the CUBE's coverage for Mobile World Congress, 2021. I'm John Furrier, your host of the CUBE. We're here in person as well as remote. It's a hybrid event. We're on the ground at Mobile World Congress, bringing all the action here. We're remote with Amol Phadke, who's the Managing Director of the Telecom Industry Solutions team at Google Cloud, a big leader, and driving a lot of the change. Amol, thank you for coming on theCUBE here in the hybrid event from Mobile World Congress. >> Thank you, John. Thank you, John. Thank you for having me, So, hybrid event, which means it's in person, we're on the floor, as well as doing remote interviews and people are virtual. This is the new normal. Kind of highlights where we are in this telecom world, because the last time, Mobile World Congress actually had a physical event was winter of 2019. A ton has changed in the industry. Look at the momentum at the Edge. Hybrid cloud is now standard. Multi-cloud is being set up as we speak. This is all now the new normal, what is your take? And so it's pretty active in your industry. Tell us your opinion. >> Yes, John I mean the last two years have been seismic to say the least, right? I mean, in terms of the change that the CSP industries had had to do. You know, John, in the last two years, the importance of a CSP infrastructure has never become so important, right? The infrastructure is paramount. I'm talking to you remotely over the CSP infrastructure right now, and everything that we are doing in the last two years, whether it's working, or studying, or entertaining ourselves, all on that CSP infrastructure. So from that perspective, they are really becoming a critical national global information fabric on which the society is actually depending on. And that we see at Google as well, in the sense that we have seen up to 60% increase in demand, John, in the last two years, for that infrastructure. And then when we look at the industry itself, unfortunately all of that huge demand is not translating into revenue, because as an industry, the revenue is still flat-lining. In fact, the forecasted revenue for globally, for all the industry over the next 12 months is three to five per cent negative on revenue, right? So one starts to think, how come there is so much demand over the last two years, post-pandemic, and that's not translating to revenue? Having said that, the other thing that's happening is this demand is driving significant CapEx and OPEX investments in the infrastructure, as much as eight to $900 billion over the next decade is going to get spent in this infrastructure, from our perspective, Which means it's really a perfect storm. John, We have massive demand, massive need to invest to meet that demand, yet not translating to revenue, and the crux of all this is customer experience, because ultimately all of that translates into not having that kind of radically disruptive or transformational customer experience, right? So that's a backdrop that we find ourselves in the industry, and that really sets the stage for us to look at these challenges in terms of how does the CSP industry as a whole, grow top line, radically transform CSPCO, at the same time, reinventing the customer experience and finding those capital efficiencies. It's almost an impossible problem to find solution. >> It's a perfect storm. The waves are kind of coming together to form one big wave. You mentioned CapEx and OPEX. That's obviously changing the investments of their post-pandemic growth, and change in user behavior and expectations. The modern applications are being built on top of the infrastructure, that's changing. All of this is being driven by Cloud Native, and that's clear. You're seeing a lot more open kind of approaches, IT and OT coming together, whatever you want to do, this is just, it's a collision, right? It's a collision of many things. And this positive innovation coming out of it. So I have to ask you, what are you seeing as a solution that are showing the most promise for these telco industry leaders, because they're digitally transforming, so they got to re-factor their platforms while enabling innovation, which is a key growth for the revenue. >> Yes. So John, from a solution standpoint, what we actually did first and foremost as Google Cloud, was look at ourselves. So just like the transformation we just talked about in the CSP industry, we are seeing Google being transformed over the last two decades or so, right. And it's important to understand that there's a lot Google data over the last two decades that we can actually not externalize all of that innovation, all of that open source, all of that multicloud, was originally built for all the Google applications that all of us use daily, whether it's YouTube, or email or maps, you know. Same infrastructure, same open source, same multicloud. And we decided to sort of use the same paradigm to build the telecom solutions that I'm going to talk about next, right. So that's important to bear in mind, that those assets were there, and we wanted to externalize those assets, right. There are really four big solutions that are resonating really well with our CSP partners, John. You know, number one to your point, is how can they monetize the Edge? All of this happens at the Edge. All of this gets converged at the Edge. We believe with 5G acting as the brilliant catalyst to really drive this Edge deployment. CSPs would be in a very strong position, partnering with Cloud players like ourselves to drive growth, not just for their top line, but also to add value to the actual end enterprises that are seeking to use that Edge. Let me give you a couple of examples. We've been working with industries like retail and manufacturing, to create end solutions in a post-pandemic world. Solutions like contact-less shopping, or visual inspection of an assembly line in a manufacturing plant, without the need for having a human there, because of the digitalization of workforce. Which meant these kinds of solutions, can actually work well at the Edge driven by 5G. But of course they can't be done in isolation. So what we do is we partner with CSPs. We bring our set of solutions, and we actually launch in December 30 partners that are already on our Google Cloud Solutions. And then we partner with the CSPs based on our infrastructure, and their infrastructure to ultimately bring this all to life at the end customer, which often tends to be an enterprise, whether it's a manufacturing, plant, or a retail chain. >> Yeah, you guys got some great examples there. I love that Edge story. I think it's huge. I think it's only going to get bigger. I got to ask you while I got you here, because again, you're in the industry, you're the managing director, so you have to oversee this whole telecom industry. But it's bigger, it's beyond Telecom, where it's now Telecom's just one other Edge network, piece of the pie of the surety computing, as we say. So I got to ask you, one of the big things that Google brings to the table is the developer mojo, and opensource, and scale obviously. Scale's unprecedented, everyone knows that. But ecosystems are super important, and Telco's kind of really aren't good at that, right? So, you know, the Telco ecosystem was, I mean, okay, I'd say, okay, but mostly driven by carriers and moving bits from point A to point B. But now you've got a developer mindset, public cloud, developer ecosystem. How is this changing the landscape of the CSPs and how is it changing this cloud service provider's ability to execute, because that's the key in this new world? What's your opinion? >> Absolutely, John. So, there are two things, there are two dimensions to look at. One is when we came to market a couple of years ago with AnToks, we recognized exactly what you said, John, which is the world is moving to multi-cloud, hybrid cloud. We needed to provide a common platform that the developer community can utilize through microservices and API. And that platform had to by definition, work not just from Google Cloud, but any cloud. It could work on any public cloud, can work on CSP's private cloud. And of course, supports on some Google Cloud, right? The reason was, once you deploy and cause, once as a seamless application development platform, you could put all kinds of developer apps on top. So I just talked about 5G Edge John, a minute ago, those apps can sit on Antoks, but at the same time, IT to your point, John, IT apps could also sit on the same AnToks paradigm, and network apps. So as networks start becoming Cloud Native, whether it's SRAN, whether it's O-Ran, whether it's 5G core, same principle. And that's why we believe when we partner with CSPs, we are saying, "Hey, you give this AnToks to an ecosystem of community, whether that community is network, whether that community is IT, whether the communities Edge apps, all of those can reside seamlessly on this sort of AnToks fabric, John. >> Yeah, and that's going to set the table for multicloud, which is basically cloud words for multi-vendor, multi app. Amol, I've got to ask you while I have you here, first of all, thank you for coming on and sharing your insights. It's really great industry perspective. And obviously Google Cloud's got huge scale, and great leadership. And again, you know, the big, cloud players are moving in and helping out, and enabling a lot of value. I got to ask you, if you don't mind sharing, if someone asked you, "Amol, tell me about the impact that public cloud is having on the Telco industry." What would you say? What's the answer to that? Because a lot of people are like, okay, public cloud, I get it. I know what it looks like, but now everyone's knows it's going hybrid. So everyone will ask you the question, "What is public cloud doing for the telecom sector?" >> Yeah, I think it's doing three things, John, and great question by the way. Number one, we are actually providing unprecedented amount of insights on data that the CSPs traditionally already had, but have never looked at it from the angle we have looked at it. Whether that insights are at the network layer, whether those insights are to personalize customer experiences on the front-end systems. Or whether those insights are to drive care solutions in contact centers, and so on, and so forth. So it's a massive uplift of customer experience that we can help with, right. So that's a very important point, because we do have a significant amount of leadership, John at Google Cloud on analytics and data and insights, right? So, and we offer those roads to these people. Number two, is really what I talked about, which is helping them build an ecosystem, because let's take retail as an example. As a minimum, there are five constituents in that ecosystem, John. There is a CSP, there is Google Cloud, there's an actual retail store. There is a hardware supplier, there's a software developer. All of them as a minimum, have to work together to build that ecosystem, which is where we give those solutions, right? So that's the second part. And then the third part is, as they move towards Cloud Native, we are really helping them change their business model to become a DevOps, a Cloud Native mindset, not just a Cloud Native network or IP. But a Cloud Native mindset that creates unparalleled agility and flexibility in how they work as a business. So those are the three things I would say, as a response to that question. >> And also the retail's a great vertical for Google to go in there, given the Amazon fear out there. People want this for certainly low hanging fruit. I think the DevOps piece is going to be a big, winning opportunity to see how the developers get driven into the landscape. I think that's a huge point. Amol, that's really great insight. A final question for you, while I got you here. If someone says, "Hey, what's happened in the industry since 2019?" Last time we had Mobile World Congress, they were talking speeds and feeds. Now the world has changed. We're coming out of the pandemic. California is opening up. There's going to be a physical event. The world's going hybrid, certainly on the event, and certainly cloud. What's different in the telecom industry, from, you know, many, many months ago, over a year and a half ago, from 2019? >> I would say primarily, it's the adoption of digital everywhere, which previously, you know, there were all these inhibitions and oh, would this work? Would my customer systems become fully digital? Would I be able to offer AR VR experiences? Ah, that's a futuristic thing, you know. And suddenly the pandemic has created this acceleration that says, "Oh, even post-pandemic, half my customers are always going to talk to me, via our digital channel only." Which means the way they experience us, has to be through these new experiences whether it's AR VR, whether it's some other thing or applications. So that has been accelerated John, and the CSPs have therefore really started to go to the application, and to the services. Which is why you are seeing less on, you know, speeds and feeds because 5G is here, 5G's been deployed. Now, how do we monetize 5G? How can we leverage that biggest number? So that's the biggest- >> There's down stack, and then there's a top of the stack for applications. And certainly there's a lot of assets in the telecom landscape, a lot of value, a lot of refactoring going on, and new opportunities that are out there. Great, great conversation. Well, thank you, Amol Phadka, Managing Director, Telecom Industry Solutions. Thanks for comin' on the CUBE, appreciate it. >> Thank you, John. Thank you having me. >> Okay, Mobile World Congress here, in person, and hybrid, and remote. I'm John Furrier, host of theCUBE. Thank you for watching. We are here in person at the Cloud City Expo Community Area. Thanks for watching. Okay, that was us. That was me, online. Now, I'm here in person, as you can see Dave. That's a lot of fun. I love doing those interviews. So we had a chance to grab Google's top people when we could. They're not here, obviously. Amazon Web Services, Microsoft, and Google, the three hyperscalers, Dave, didn't make it out here. They didn't have a booth, but we had a chance to grab them. And that was head of the industry marketing, and I mean the industry group. So he's like the managing door. He runs the business side. >> It's an important sector for Google. You know, Amazon was really first, with that push into telco. Thomas Curran last March, laid out Google strategy for Telco. It's a huge sector. They know it. They understand how the cloud can disrupt it, and play a massive role there. >> Yeah. >> And Google, of course. >> They're not going to object to the public cloud narrative that Danielle Royston- >> No. >> I think they like it open source, Android coming to telco. Who knows what it's going to look like? >> That's what we call digital- >> So the next interview I did was with Shailesh Shukla. He is the Senior Vice-president. He's the Senior Leader at Google Cloud for Networking. And if you know, Google, Dave, Google's networking is really well known in the industry for being really awesome, because they power obviously Google Search, and a variety of other things. They pioneered the concept of SRE, Site Reliability Engineer, which is now a de facto position for DevOps, which is a cloud now persona inside almost every company, and certainly a very important position. And so- >> Probably the biggest global network, right? Undersea cables, and- >> I mean, Microsoft's got a big hyper-scale, because they've had MSN, and bunch of other stuff, infrastructure globally. But Amazon, Google and Microsoft all have massive scale, and Google again, very well engineered. They're total, and they're as we know, I live in Palo Alto, so I can attest that they're very strong. So this next interview is really from a networking perspective, because as infrastructure, as code gets more prolific and more penetrated, it's going to be programmable. And that's really going to be a key new enabler. So let's hear from Shailesh, Head of Networking at Google Cloud, and my interview with him. (cheery music) Welcome to theCUBE's coverage of Mobile World Congress, 2021. We are here in person in Barcelona, as well as remote. It's a hybrid event. You're going to have the physical space, in Barcelona for the first time, since 2019, and virtual worlds connecting. I've got a great guest here from Google, Shailesh Shukla, Vice-president and General Manager of the Networking Team, Google Cloud. Shailesh, it's great to see you. Thank you for coming on theCUBE for the special presentation from Mobile World Congress. Obviously, the Edge networking core, Edge human devices, all coming together. Thanks for coming on. >> Thank you so much, John. It's great to see you again. And it's always a pleasure talking to theCUBE. And I want to say hello to everybody, from, you know, in Mobile World Congress. >> Yeah, and people don't know your background. You have a great history in networking. You've been there, many ways of innovation. You've been part of directly, big companies that were now known. Big names are all there. But now we haven't had a Mobile World Congress, since 2019. Think about that. That's, you know, many months, 20 something months gone by, since the world has changed in telco. I got to ask you, what is the disruption happening? Because think about that. Since 2019, a lot's changed in telco. Cloud-scale has happened. You've got the Edge developing. It's IT like now. What's your take? Shailesh, tell us. >> Yeah, John, as you correctly pointed out the last 18 months have been very difficult. And you know, I'll acknowledge that right up front, for a number of people around the world. I empathize with that. Now in the telecom, and kind of the broader Edge world, I would say that the last 18, 24 months have actually been transformative. O-RAN, it turns out was a very interesting sort of, you know, driver of completely new ways of both living, as well as working, right, as we all have experienced. I don't think that I've had a chance to see you live in 24 months. So, what we are seeing is the following. Number one, a number of telecom carriers around the world have started the investment process for 5G, right, and deployment process. And that actually changes the game, as you know, due to latency, due to all of the capabilities around kind of incalculable bandwidth, right. Much lower latency, as well as, much higher kind of enterprise oriented capabilities, right? So network's licensing, as an example, quality of service, you know, by a traffic type, and for a given enterprise. So that's number one. Number two, I would say that the cloud is becoming a lot more kind of mainstream in the world, broader world of telecom. What we are seeing is an incredible amount of partnerships between telecom carriers and cloud providers, right? So instead of thinking of those two as separate universes, those are starting to come together. So I believe that over a period of time, you will see the notion of kind of Cloud Native capability for both the IT side of the house, as well as the network side of the house is becoming, you know, kind of mainstream, right. And then the third thing is that increasingly it's a lot more about enabling new markets, new applications, in the enterprise world, right. So certainly it opens up a new kind of revenue stream for service providers and carriers around the world. But it also does something unique, which is brings together the cloud capabilities right, around elasticity, flexibility, intelligence, and so on, with the enterprise customer base that most of the cloud providers already have. And with the combination of 5G, brings it to the telecom world. And those, you know, I started to call it, as a kind of the triad, right? The triad of an enterprise, the telecom service provider, and the cloud provider, all working together to solve real business problems. >> Yeah, and it's totally a great call out there on the pandemic. I think the pandemic has shown us, coming out of it now, that cloud-scale matters. And you look at all the successes between work, play, and how we've all kind of adjusted, the cloud technologies were a big part of that, those solutions that got us through it. Now you've got the Edge developing with 5G. And I got to ask you this question, because when we have CUBE interviews with all the leaders of engineering teams, whether it's in the industry, or customers in the enterprise, and even in the telcos, the modern application teams have end-to-end visibility into the workload. You're starting to see more and more of that. You starting to see more open source in everything, right. So okay, I buy that. You got an SRE on the team, you got some modern developers, you're shifting left, you've got Devs set up. All good, all cloud. However, you're a networking guy. You know this. Routing packets across multiple networks is difficult. So if you're going to have end-to-end visibility, you got to have end-to-end intelligence on the networking. How is that being solved? Because this is a critical discussion here at Mobile World Congress. Okay, I buy Cloud Native, I buy observability, I buy open source, but I got to have end-to-end visibility for security, and workload management and managing all the data. What's the answer on the network side? >> Yeah, so that's a great question. And the simple way to think about this, is first and foremost, you need kind of global infrastructure, right? So that's a given, and of course, you know, Google with its kind of global infrastructure, and some of the largest networks in the world, we have that present, right. So that's important. Second is, to be able to abstract a way that underlying infrastructure, and make it available to applications, to a set of APIs. Right, so I'll give an analogy here. Just as you know, say 10 years ago, around 10 years ago, Android came into the market from Google, in the following way. What it did, was that it abstracted away the underlying devices with a simple kind of layer on top of operating system, which exposed APIs northbound. So then application developers can write new applications. And that actually unleashed, you know, a ton of kind of creativity right, around the world. And that's precisely what we believe is kind of the next step, as you said, on an end-to-end observability basis, right? If you can do an abstraction away from all of the underlying kind of core infrastructure, provide the right APIs, the right kind of information around observability, around telemetric, instead of making, you know, cloud and the infrastructure, the black box. Make it open, make it kind of visible to the applications. Bring that to the applications, and let the thousand flowers bloom, right? The creativity in each vertical area is so significant, because there are independent software vendors. There are systems integrators. There are individual developers. So one of the things that we are doing right now, is utilizing open source technologies, such as Kubernetes, right? Which is something that Google actually brought into the market. And it has become kind of the de facto standard for all of the container and modernization of applications. So by leveraging those open technologies, creating this common control plane, exposing APIs, right, for everything from application development, to observability, you certainly have the ability to solve business problems through a large number of entities in the systems integrator and the ISC and the developer community. So that's the approach that we are taking, John. >> I love the Android analogy of the abstraction layer, because at that time, the iPhone was closed. It still is. And they got their own little strategy there. Android went the other way. They went open, went open abstraction. Now abstraction layers are good. And now I want to get your thoughts on this, because anyone in operating systems knows abstractions are great for innovation. How does that apply to the real world on telco? Because I get how it could add some programmability in there. I get the control plane piece. Putting it into the operator's hands, how do you guys see, and how do you guys talk about the Edge service offering? What does it mean for the telco? Because if they get this right, this is going to be in telco cloud developer play. It's going to be a telco cloud ecosystem play. It's an opportunity for a new kind of telco system. How do you see that rolling out in real world? >> Great question, John. So the way I look at it, actually even we should take a step back, right? So the confluence of 5G, the kind of cloud capabilities and the Edge is, you know, very clear to me that it's going to unleash a significant amount of innovation. We are in early stages, no question, but it's going to drive innovation. So one almost has to start by saying what exactly is Edge, right? So the way I look at it, is that the Edge can be a continuum all the way from kind of an IOT device in automobiles, right? Or an enterprise Edge, like a factory location, or a retail store, or kind of a bank branch. To the telecom Edge, which is where the service providers have, not only their points of presence, and central offices, but increasingly a very large amount of intelligent RAN sites as well, right. And then the, kind of public cloud Edge, right. Where, for example, Google has, you know, 25 plus kind of regions around the world. 144, you know, PoPS, lots of CDN locations. We have, you know, few thousand nodes deployed deep inside service provider networks for caching of content, and so on. So if you think about these as different places in the network that you can deploy, compute, storage and intelligence act, right. And do that in a smart way, right? For example, if you were to run the learning algorithms in the cloud with its flexibility and elasticity, and run the inferencing at the Edge, very Edge, at the point of sort of a sale, or a point, a very consumer standing. Now you suddenly have the ability to create a variety of Edge applications. So going back to the new question, what have we seen, right? So what we are seeing, is depending on the vertical, there are different types of Edge applications, okay. So let's take a few examples. And I'll give you some, a favorite example of mine, which is in the sports arena, right? So in baseball, when you are in a stadium, and soon there are people sort of starting to be in stadiums, right? And a pitcher is throwing the pitch, right, the trajectory of the ball, the speed of the pitch, where the batter is, you know, what the strike zone is, and all of these things, if they can be in a stadium in real time, analyzed, and presented to the consumer as additional intelligence, and additional insight, suddenly it actually creates kind of a immersive experience. Even though you may be in the stadium, looking at the real thing, you are also seeing an immersive experience. And of course at home, you get a completely different experience, right? So the idea is that in sports, in media and entertainment, the power of Edge compute, and the power of AI ML, right, can be utilized to create completely new immersive experiences. Similarly, in a factory or an automotive environment, you have the ability to use AI ML, and the power of the Edge and 5G coming together, to find where the defects are, in a manufacturing environment, right? So every vertical, what we're finding is, there are very specific applications, which you can call as kind of killer apps, right in the Edge world, that over time will become prevalent and mainstream. And they will drive the innovation. They will drive deployment, and they also will drive ultimately, kind of the economics of all of this. >> You're laying out, essentially the role of the public cloud in the telco market. I'd love to get your thoughts, because a lot of people are saying, "Oh, the cloud, it's all Edge now. It's going back to on-premises." This is not the case. I mean, I've been really vocal on this. The public cloud and cloud operations is now the new normal. So developers are there. So I want you to explain real quick, the role of the public cloud in the telecom market and the Telecom Edge, because now they're working together. You've got abstraction, you mentioned that Android-like environment coming, there's going to be an Android-like effect, that abstraction. You got O-RAN out there, creating these connection points, for interoperability, for radio signals, and the End Transceivers or the Edge of the radios. All of this is happening. How is Google powering this? What is the role of public cloud in this? >> Yeah, so let me first talk about genetically the role of public cloud. Then I'll talk about Google, okay, in particular. So, if at the end of the day, the goal here is to create applications in a very simple and efficient manner, right? So what do you like, if you look for that as the goal, then the public cloud brings, you know, three fundamental things. Number one, is what I would call as elasticity and flexibility, right? So why is this important? Because as we discussed earlier, Edge is not one place, it's a variety of kind of different locations. If there is a mechanism to create this common control plane, and have the ability to kind of have elastic compute, elastic networking, elastic storage, and have this deployed in a flexible manner. Literally if you think, think about it like an effortless Edge is what we are starting to call it. You can move workload and capability, and run it precisely where it makes sense, right? Like I said, earlier, training and learning algorithms in the deep cloud. Inferencing, at the very edge, right? So if you can make that decision, then it becomes very powerful. So that's the first point, you know, elasticity and flexibility that cloud can bring. Second is, intelligence. The whole notion of leveraging the power of data, and the power of AI and ML is extremely crucial for creation of new services. So that's something that the public cloud brings. And the third is this notion of, write once, deploy anywhere, right? This notion of kind of a full stack capability that when open, kind of developer ecosystem can be brought in, right? Like we talked about Kubernetes earlier. So if there's a way in which you can bring in those developer and ISV ecosystem, which is already present in the world of public cloud, that's something that is the third thing that public cloud brings. And Google strategy very simply, is to play on all of these, right? Because we, you know, Google has incredibly rich deployment experience around the world for some of the largest services on the planet, right? With some of the biggest infrastructure in the networking world. Second, is we have a very open and flexible approach, right? So open as you know, we not only leverage kind of the Kubernetes environment, but also there are many other areas, Key Native, and so on where Google has brought a lot of open kind of capabilities to the broader market. And the third, is the enablement of the ecosystem. So last year we actually announced 200 applications, you know, from 30 ISVs in multiple verticals that we're now going to be deployed on Google Cloud, in order to solve specific business pain points, right. And building out that ecosystem, working with telecom service providers, with systems integrators, with equipment players, is the way that we believe Google Cloud can make a difference in this world of developing Edge applications. We are seeing great traction, John, you know, whether it is in the carrier world. Carrier such as Orange, Telecom Italia, TELUS, SK Telecom, Vodafone. These have all publicly announced their work with Google Cloud, leveraging the power of data, analytics, AI ML, and our very flexible infrastructure. And then a variety of kind of partners and OEM players, in the industry. As an example, Nokia, right, Amdocs, and Netcracker, and many others. So we are really excited in the traction that we are getting. And we believe that public cloud is going to be a key part of the evolution of the telecom industry. >> Shailesh, it's great to have you on. Shailesh Shukla, VP and GM of Networking at Google Cloud. And I would just add to that final point there, that open and this Android-like open environment is going to create a thousand flowers to bloom. Those are new applications, new modern applications, new companies, a new ecosystem in the Telco Cloud. So congratulations. Thanks for coming on and sharing your insights. Google Cloud, you guys are about the data, and being open. Thanks for comin' on. >> Thank you, John. Good to talk to you. >> Okay, so keeps coverage of Mobile World Congress. Google Cloud, featured interview here on theCUBE. Really a big part of the public cloud is going to be a big driver. Call it public cloud, hybrid cloud, whatever you want to call it. It's the cloud, cloud and Edge with 5G, making a big difference and changing the landscape, and trying innovation for the telco space. I'm John Furrier, your CUBE host. Thanks for watching. Okay, Dave, that's the Google support. They are obviously singing the same song as Danielle Royston, every vertical. >> Two great interviews, John. Really nice job. We can see the tech. The strategy is becoming more clear. You know, one of the big four. >> Yeah, I just love, these guys are so smart. Every vertical is going to be impacted by elastic infrastructure, AI, machine learning, and this new code deployment, write once, deploy anywhere. That's theCUBE. We love being here it's a cloud show now. Mobile World Congress, back to the studio for more awesome Cloud City content.

(upbeat music) >> Welcome to the Cubes coverage of Mobile World Congress, 2021. We are here in person in Barcelona, as well as remote. It's a hybrid event. You're going to have the physical space in Barcelona for the first time, since 2019 and virtual worlds connecting. I've got a great guest here from Google, Shailesh Shukla, Vice President General Manager of the networking team, Google Cloud. Shailesh, great to see you. Thank you for coming on the Cube for the special presentation for Mobile World Congress. As the edge networking core edge human devices, all coming together, thanks for coming on. >> Thank you so much, John. It's great to see you again. And it's always a pleasure talking to "theCUBE" and I wanted to say hello to everybody from, you know, in mobile world Congress. >> Yeah, and people don't know your background. You've got a great history in networking. You've been there, many ways of innovation. You've been part of directly a big companies that were now known big names are all there, but now we haven't had a Mobile World Congress 2019. Think about that, that's, you know, many months, 27 months gone by, since the world has changed in TelcoOR I got to ask you, what is the disruption happening? Because think about that since 2019, a lot's changed in TelcoOR cloud is scale has happened. You've got the edge developing. It's IT like now, what's your take Shailesh tell us? >> Yeah John, as you correctly pointed out, you know, last 18 months have been very difficult and you know, I'll acknowledge that right upfront for a number of people around the world. Empathize with that now in the TelcoOR and kind of the broader edge world. I would say that the last 18, 24 months have actually been transformative COVID it turns out was a very interesting sort of, driver of completely new ways of both living as well as working right, as we all have experienced. I don't think that I've had a chance to see you live in 24 months. So what we are seeing is the following, number one, number of TelcoORs carriers around the world have started the investment process for 5g right? And deployment process. And that actually changes the game as you know, due to latency, due to all of the capabilities around kind of incredible bandwidth, right? Much lower latency, as well as much higher kind of enterprise oriented capabilities, right? So network slicing as an example, quality of service, you know, by a traffic type and for a given enterprise. So that's number one. Number two, I would say that the cloud is becoming a lot more kind of mainstream in the world, broader world of telecom. What we are seeing is a incredible amount of partnerships between telecom carriers and cloud providers, right? So instead of thinking of those two as separate universes, those are starting to come together. So I believe that over a period of time, you will see the notion of kind of cloud native capability for both the IT side of the house, as well as the network side of the house is becoming, you know, kind of mainstream, right. And then the third thing is that increasingly it's, a lot more about enabling new markets, new applications in the enterprise world, right? So certainly it opens up a new kind of revenue stream for service providers and carriers around the world. But it also does something unique, which is brings together the cloud capabilities, right around elasticity, flexibility, intelligence, and so on with the enterprise customer base that most of the cloud providers already have. And with the combination of 5g brings it to the telecom world. And those, you know, I started to call it as a, kind of the triad, right? The triad of an enterprise, the telecom service provider and the cloud provider, all working together to solve real business problems. >> And it's totally a great call out there on the pandemic. I think the pandemic has shown us coming out of it now that cloud scale matters. And you look at all its successes between work play and how we've all kind of adjusted the cloud technologies. We're a big part of that, those solutions that, that got us through it. Now you've got the edge developing with 5g. And I got to ask this question because when we have CUBE interviews with all the leaders of engineering teams, whether it's in the industry or at customers in the enterprise, and even in the telcos, the modern application teams have end to end visibility into the workload. You start to see more and more of that. You starting to see more open source in everything, right? And so, okay. I buy that. You've got an SRE on the team. You've got some modern developers you're shifting left, you've got Develops, all good, all cloud. However, you're a networking guy. You know this, routing packets across multiple networks is difficult. So if you're going to have end to end visibility, you got to have an end to end intelligence on the networking. How is that being solved? Because this is a critical discussion at here at mobile world Congress. Okay, I buy cloud native, I buy observability, I buy open source, but I got to have end-to-end visibility for security and workload management and managing all the data. What's the answer on the network side? >> Yeah, so that's a great question. And the simple way to think about this is first and foremost you need kind of global infrastructure, right? So that's a given and of course, you know, Google with its kind of global infrastructure and some of the largest networks in the world, we have that presence, right. So that's important. Second is to be able to abstract away that underlying infrastructure and make it available to applications through an set of APIs, right? So I'll give an analogy here just as you know, say 10 years ago, around 10 years ago, Android came into the market from Google in the following way. What it did was that it abstracted away the underlying devices with a simple kind of layer on top of operating system, which exposed APIs not bound. So that application developers can write new applications. And that actually unleashed, you know, it ton of kind of creativity right around the world. And that's precisely what we believe is kind of the next step, as you said, on an end to end observability basis, right? What if you can do an abstraction away from all of the underlying kind of core infrastructure provide the right API the right kind of information around observability around telemetric instead of making, cloud and infrastructure, the black box, make it open, make it kind of visible to the applications, bring that to the applications and let the let a thousand flowers bloom, right? The creativity in each vertical area is so significant because there are independent software vendors. There are systems integrators, they're individual developers. So one of the things that we are doing right now is utilizing open source technologies, such as Kubernetes, right? Which is something that Google actually brought into the market. And it has become kind of the de facto standard for all of the container and modernization of applications. So by leveraging those open technologies, creating this common control plane, exposing APIs, right? For everything from application development to observability, you certainly have the ability to solve business problems through a large number of entities in the systems integrator and the ISV and the developer community. So that's the approach that we're taking John >> I love the Android analogy of this obstruction layer, because at that time the iPhone was closed. It still is. And they got their own little strategy there. Android went the other way. They went open when open abstraction now obstruction layers are good. And now I want to get your thoughts on this because anyone in operating systems knows abstractions are great for innovation. How does that apply to the real world on telco? Because I get how it could add some programmability in there. I get the control plane piece, putting it into the operator's hands. How do you guys see and how do you guys talk about the edge service offering? What does it mean for the telco? Because well, they get this, right. This is going to be in telco cloud developer play. It's going to be a cloud ecosystem play. It's an opportunity for a new kind of telco system. How do you see that rolling out in the real world? >> Great question, John. So the way I look at it, actually even we should take a step back, right? So the confluence of 5g, the kind of cloud capabilities and the edge is, you know, very clear to me that it's going to unleash and significant amount of innovation. We are in early stages. No question, but it's going to drive innovation. So one almost has to start by saying what exactly is edge, right? So the way I look at it is that the edge can be a continuum all the way from kind of an IOT device, an automobile, or an enterprise edge, like a factory location or a retail store, or kind of a bank branch to the telecom edge, which is where the service providers have. Not only their points of presence and central offices, but increasingly a very large amount of intelligent land sites as well, right? And then the kind of the public cloud edge, right? Where, for example, Google has, you know, twenty-five plus kind of regions around the world, 144, you know, pops, lots of CDN locations. We have, you know, a few thousand nodes deployed deep inside service provider networks for caching of content and so on. So if you think about these as different places in the network that you can deploy compute storage and intelligence act, right. And do that in a smart way, right? For example, if you were to run the learning algorithms in the cloud with its flexibility and elasticity and run the inferencing at the edge, very edge at the point of sort of a sale or a point a very consumer standing. Now you suddenly have the ability to create a variety of edge applications. So going back to the new question, what have we seen? So what we are seeing is depending on the vertical, there are different types of edge applications, okay. So let's take a few examples and I'll give you some, a favorite example of mine, which is in the sports arena. So in baseball, right, when you are in a stadium and soon there are people sort of starting to be in stadiums, right. And if pitcher is throwing the pitch, right, the trajectory of the ball, the speed of the pitch, where the batter is, you know, what the strike zone is and all of these things, if they can be in a stadium in real time, analyzed and presented to the consumer as additional intelligence and additional insight, suddenly it actually creates kind of a immersive experience even though you may be in the stadium, looking at the real thing, you are also seeing an immersive experience. And of course at home, you get a completely different experience, right? So the idea is that in sports, in media and entertainment, the power of edge compute and the power of AI ML, right, can be utilized to create completely new immersive experiences. Similarly, in a factory or an automotive environment, you have the ability to use AI ML and the power of the edge and 5g coming together to find where the defects are in a manufacturing environment, right? So every vertical, what we're finding is there are very specific applications, which you can call us kind of killer apps, right in the edge world, that over time will become prevalent and mainstream and they will drive the innovation. They will drive deployment, and they also will drive ultimately kind of the economics of all of this. >> You're laying out, essentially the role of the public cloud and the telco market. I'd love to get your thoughts because a lot of people are saying, "Oh, the cloud, it's all edge now it's still going back to a on premises." This is not the case. I mean, I've been really vocal on this. The public cloud and cloud operations is now the new normal. So developers are there. So I want you to explain real quick, the role of the public cloud in the telecom market and the telecom edge, because now they're working together, you got a distraction, you mentioned that Android leg environment coming, it's going to be an Android, like effect that eked abstraction, you got old ran out there creating these connection points for interoperability, for radio signals and the in transceivers or the edge of the radios. All of this is happening. How is Google powering this? What is the role of public cloud in this? >> Yeah, so let me first talk about generically, the role of public cloud. Then I'll talk about Google, okay. In this, in particular. So if at the end of the day, the goal here is to create applications in a very simple and efficient manner, right? So what do you like if you look put that as the goal, then the public cloud brings, you know, three fundamental things. Number one is what I would call as elasticity and flexibility, right? So why is this important? Because as we discussed earlier, edge is not one place. It's a variety of kind of different locations. If there is a mechanism to create this common control plane and have the ability to kind of have elastic compute, elastic networking, elastic storage, and have this deployed in a flexible manner. Literally if you think about it like an effortless edge is what we are starting to call it. You can move workload and capability and run it precisely where it makes sense, right? Like I said, earlier, training and learning algorithms in the deep cloud, inferencing at the very edge, right? So if you can make that decision, then it becomes very powerful. So that's the first point, elasticity and flexibility that the cloud can bring. Second is intelligence, the whole notion of leveraging the power of data and the power of AI and ML is extremely crucial for creation of new services. So that's something that the public cloud brings. And the third is this notion of right once deploy anywhere, right? This notion of kind of a full stack capability that open kind of developer ecosystem can be brought in. Like we talked about Kubernetes earlier. So if there's a way in which you can bring in those developer and ISV ecosystem, which is already present in the world of public cloud, that's something that is the third thing that public law brings. And Google strategy very simply is to play on all of these. Because Google has incredibly rich deployment experience around the world for some of the largest services on the planet, right? With some of the biggest infrastructure in the networking world. Second is we have a very open and flexible approach, right? So open, as you know, we not only leverage kind of the Kubernetes environment, but also there are many other areas, Guinea native, and so on where Google has brought a lot of open kind of capabilities to the broader market. And the third is the enablement of the ecosystem. So last year, we actually announced 200 applications, from 30 ISV in multiple verticals that we're now going to be deployed on Google cloud in order to solve specific business pain points, right. And building out that ecosystem, working with telecom service providers with systems integrators with equipment players is the way that we believe Google cloud can make a difference in this world of developing edge applications. We are seeing great traction, John, you know, whether it is in the carrier world, you know, carrier such as orange telecom Italia, telus SK Telekom, Vodafone. These have all publicly announced their work with Google cloud, leveraging the power of data analytics, AIML, and our very flexible infrastructure. And then a variety of kind of partners and OEM players in the industry, as an example, Nokia, right. Am docs and Net cracker and many others. So we are really excited in the traction that we are getting, and we believe that public cloud is going to be a key part of the evolution of the telecom industry. >> Shukla it's great to have you on Shailesh Shukla VP and GM of networking at Google cloud. And I would just add to that final point there that open and this Android like open environment is going to create a thousand flowers to bloom those, a new applications, new modern applications, new companies, a new ecosystem in the telco cloud. So, congratulations, thanks for coming on and sharing your insights on Google cloud, you guys are about the data and being open. Thanks for coming on. >> Thank you, John. Great to talk to you, okay. >> It keeps coverage of mobile Congress, Google cloud featured interview here on theCUBE. Really a big part of the public cloud is going to be a big driver. Call it public cloud, hybrid cloud. Whatever you want to call it, it's the cloud cloud and edge with 5g making a big difference and changing the landscape in front of innovation for the telco space. I'm John Farrow, your CUBE host, thanks for watching.

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante cloud repatriation is a term often used by technology companies the ones that don't operate a public cloud the marketing narrative most typically implies that customers have moved work to the public cloud and for a variety of reasons expense performance security etc are disillusioned with the cloud and as a result are repatriating workloads back to their safe comfy and cost-effective on-premises data center while we have no doubt this does sometimes happen the data suggests that this is a single digit de minimis phenomenon hello and welcome to this week's wikibon cube insights powered by etr some have written about the repatriation myth but in this breaking analysis we'll share hard data that we feel debunks the narrative and is currently being promoted by some we'll also take this opportunity to do our quarterly cloud revenue update and share with you our latest figures for the big four cloud vendors let's start by acknowledging that the definition of cloud is absolutely evolving and in this sense much of the vendor marketing is valid no longer is cloud just a distant set of remote services that lives up there in the cloud the cloud is increasingly becoming a ubiquitous sensing thinking acting set of resources that touches nearly every aspect of our lives the cloud is coming on prem and work is being done to connect clouds to each other and the cloud is extending to the near and far edge there's little question about that today's cloud is not just compute storage connectivity and spare capacity but increasingly it's a variety of services to analyze data and predict slash anticipate changes monitor and interpret streams of information apply machine intelligence to data to optimize business outcomes it's tooling to share data protect data visualize data and bring data to life supporting a whole new set of innovative applications notice there's a theme there data increasingly the cloud is where the high value data lives from a variety of sources and it's where organizations go to mine it because the cloud vendors have the best platforms for data and this is part of why the repatriation narrative is somewhat dubious actually a lot dubious because the volume of data in the cloud is growing at rates much faster than data on prem at least by a couple thousand basis points by our estimates annually so cloud data is where the action is and we'll talk about the edge in a moment but a new era of application development is emerging with containers at the center the concept of write wants run anywhere allows developers to take advantage of systems that run on-prem say a transaction system and tap data from multiple sources in various locations there might be multiple clouds or at the edge or wherever and combine that with immense cheap processing power that we've discussed extensively in previous breaking analysis episodes and you see this new breed of apps emerging that's powered by ai those are hitting the market so this is not a zero-sum game the cloud vendors have given the world an infrastructure gift by spending like crazy on capex more than a hundred billion last year on capex for example for the big four and in our view the players that don't own a cloud should stop being so defensive about it they should thank the hyperscalers and lay out a vision as to how they'll create a new abstraction layer on top of the public cloud and you know that's what they're doing and they'll certainly claim to be actively working on this vision but consider the pace of play between the hyperscalers and their traditional on-prem providers we believe the innovation gap is actually widening meaning the public cloud players are accelerating their innovation lead and will 100 compete for hybrid applications they have the resources the developer affinity they're doing custom silicon and have the expertise there and the tam expansion goals that loom large so while it's not a zero-sum game and hybrid is definitely real we think the cloud vendors continue to gain share most rapidly unless the hybrid crowd can move faster now of course there's the edge and that is a wild card but it seems that again the cloud players are very well positioned to innovate with custom silicon programmable infrastructure capex build-outs at the edge and new thinking around system architectures but let's get back to the core story here and take a look at cloud adoptions you hear many marketing messages that call into question the public cloud at its recent think conference ibm ceo arvind krishna said that only about 25 of workloads had moved into the public cloud and he made the statement that you know this might surprise you implying you might think it should be much higher than that well we're not surprised by that figure especially especially if you narrow it to mission critical work which ibm does in its annual report actually we think that's probably high for mission critical work moving to the cloud we think it's a lot lower than that but regardless we think there are other ways to measure cloud adoption and this chart here from david michelle's book c seeing digital shows the adoption rates for major technological innovations over the past century and the number of years how many years it took to get to 50 percent household adoption electricity took a long time as did telephones had that infrastructure that last mile build out radios and tvs were much faster given the lower infrastructure requirements pcs actually took a long time and the web around nine years from when the mosaic browser was introduced we took a stab at estimating the pace of adoption of public cloud and and within a decade it reached 50 percent adoption in top enterprises and today that figures easily north of 90 so as we said at the top cloud adoption is actually quite strong and that adoption is driving massive growth for the public cloud now we've updated our quarterly cloud figures and want to share them with you here are our latest estimates for the big four cloud players with only alibaba left to report now remember only aws and alibaba report clean or relatively clean i ass figures so we use survey data and financial analysis to estimate the actual numbers for microsoft in google it's a subset of what they report in q121 we estimate that the big 4is and pas revenue approached 27 billion that's q121 that figure represents about 40 growth relative to q1 2020. so our trailing 12-month calculation puts us at 94 billion so we're now on roughly 108 billion dollar run rate as you may recall we've predicted that figure will surpass 115 billion by year end when it's all said and done aws it remains the leader amongst the big four with just over half of the market that's down from around 63 percent for the full year of 2018. unquestionably as we've reported microsoft they're everywhere they're ubiquitous in the market and they continue to perform very well but anecdotally customers and partners in our community continue to report to us that the quality of the aws cloud is noticeably better in terms of reliability and overall security etc but it doesn't seem to change the trajectory of the share movements as microsoft's software dominance makes doing business with azure really easy now as of this recording alibaba has yet to report but we'll update these figures once their earnings are released let's dig into the growth rates associated with these revenue figures and make some specific comments there this chart here shows the growth trajectory for each of the big four google trails the pack in revenue but it's growing faster than the others from of course a smaller base google is being very aggressive on pricing and customer acquisition to that we say good google needs to grow faster in our view and they most certainly can afford to be aggressive as we said combined the big four are growing revenue at 40 on a trailing 12-month basis and that compares with low single-digit growth for on-prem infrastructure and we just don't see this picture changing in the near to midterm like storage growth revenue from the big public cloud players is expected to outpace spending on traditional on on-prem platforms by at least 2 000 basis points for the foreseeable future now interestingly while aws is growing more slowly than the others from a much larger 54 billion run rate we actually saw sequential quarterly growth from aws and q1 which breaks a two-year trend from where aws's q1 growth rate dropped sequentially from q4 interesting now of course at aws we're watching the changing of the guards andy jassy becoming ceo of amazon adam silipsky boomeranging back to aws from a very successful stint at tableau and max peterson taking over for for aws public sector replacing teresa carlson who is now president and heading up go to market at splunk so lots of changes and we think this is actually a real positive for aws as it promotes from within we like that it taps previous amazon dna from tableau salesforce and it promotes the head of aws to run all of amazon a signal to us that amazon will dig its heels in and further resist calls to split aws from the mothership so let's dig in a little bit more to this repatriation mythbuster theme the revenue numbers don't tell the entire story so it's worth drilling down a bit more let's look at the demand side of the equation and pull in some etr survey data now to set this up we want to explain the fundamental method used by etr around its net score metric net score measures spending momentum and measures five factors as shown in this wheel chart that shows the breakdown of spending for the aws cloud it shows the percentage of customers within the platform that are either one adopting the platform new that's the lime green in this wheel chart two increasing spending by more than five percent that's the forest green three flat spending between plus or minus five percent that's the gray and four decreasing spend by six percent or more that's the pink and finally five replacing the platform that's the bright red now dare i say that the bright red is a proxy for or at least an indicator of repatriation sure why not let's say that now net score is derived by subtracting the reds from the greens anything above 40 percent we consider to be elevated aws is at 57 so very high not much sign of leaving the cloud nest there but we know it's nuanced and you can make an argument for corner cases of repatriation but come on the numbers just don't bear out that narrative let's compare aws with some of the other vendors to test this theory theory a bit more this chart lines up net score granularity for aws microsoft and google it compares that to ibm and oracle now other than aws and google these figures include the entire portfolio for each company but humor me and let's make an assumption that cloud defections are lower than the overall portfolio average because cloud has more momentum it's getting more spend spending so just stare at the red bars for a moment the three cloud players show one two and three percent replacement rates respectively but ibm and oracle while still in the single digits which is good show noticeably higher replacement rates and meaningfully lower new adoptions in the lime green as well the spend more category in the forest green is much higher within the cloud companies and the spend less in the pink is notably lower and you can see the sample sizes on the right-hand side of the chart we're talking about many hundreds over 1300 in the case of microsoft and if we look if we put hpe or dell in the charts it would say several hundred responses many hundreds it would look similar to ibm and oracle where you have higher reds a bigger fat middle of gray and lower greens it's just the way it is it shouldn't surprise anyone and it's you know these are respectable but it's just what happens with mature companies so if customers are repatriating there's little evidence here we believe what's really happening is that vendor marketing people are talking to customers who are purposefully spinning up test and dev work in the cloud with the intent of running a workload or portions of that workload on prem and when they move into production they're counting that as repatriation and they're taking liberties with the data to flood the market okay well that's fair game and all's fair in tech marketing but that's not repatriation that's experimentation or sandboxing or testing and deving it's not i'm leaving the cloud because it's too expensive or less secure or doesn't perform for me we're not saying that those things don't happen but it's certainly not visible in the numbers as a meaningful trend that should factor into buying decisions now we perfectly recognize that organizations can't just refactor their entire applications application portfolios into the cloud and migrate and we also recognize that lift and shift without a change in operating model is not the best strategy in real migrations they take a long time six months to two years i used to have these conversations all the time with my colleague stu miniman and i spoke to him recently about these trends and i wanted to see if six months at red hat and ibm had changed his thinking on all this and the answer was a clear no but he did throw a little red hat kool-aid at me saying saying that the way they think about the cloud blueprint is from a developer perspective start by containerizing apps and then the devs don't need to think about where the apps live whether they're in the cloud whether they're on prem where they're at the edge and red hat the story is brings a consistency of operations for developers and operators and admins and the security team etc or any plat on any platform but i don't have to lock in to a platform and bring that everywhere with me i can work with anyone's platform so that's a very strong story there and it's how arvin krishna plans to win what he calls the architectural battle for hybrid cloud okay so let's take a take a look at how the big cloud vendors stack up with the not so big cloud platforms and all those in between this chart shows one of our favorite views plotting net score or spending velocity on the vertical axis and market share or pervasiveness in the data set on the horizontal axis the red shaded area is what we call the hybrid zone and the dotted red lines that's where the elite live anything above 40 percent net score on the on on the vertical axis we consider elevated anything to the right of 20 on the horizontal axis implies a strong market presence and by those kpis it's really a two horse race between aws and microsoft now as we suggested google still has a lot of work to do and if they're out buying market share that's a start now you see alibaba shown in the upper left hand corner high spending momentum but from a small sample size as etr's china respondent level is obviously much lower than it is in the u.s and europe and the rest of apac now that shaded res red zone is interesting and gives credence to the other big non-cloud owning vendor narrative that is out there that is the world is hybrid and it's true over the past several quarters we've seen this hybrid zone performing well prominent examples include vmware cloud on aws vmware cloud which would include vcf vmware cloud foundation dell's cloud which is heavily based on vmware and red hat open shift which perhaps is the most interesting given its ubiquity as we were talking about before and you can see it's very highly elevated on the net score axis right there with all the public cloud guys red hat is essentially the switzerland of cloud which in our view puts it in a very strong position and then there's a pack of companies hovering around the 20 vertical axis level that are hybrid that by the way you see openstack there that's from a large telco presence in the data set but any rate you see hpe oracle and ibm ibm's position in the cloud just tells you how important red hat is to ibm and without that acquisition you know ibm would be far less interesting in this picture oracle is oracle and actually has one of the strongest hybrid stories in the industry within its own little or not so little world of the red stack hpe is also interesting and we'll see how the big green lake ii as a service pricing push will impact its momentum in the cloud category remember the definition of cloud here is whatever the customer says it is so if a cio says we're buying cloud from hpe or ibm or cisco or dell or whomever we take her or his word for it and that's how it works cloud is in the eye of the buyer so you have the cloud expanding into the domain of on-premises and the on-prem guys finally getting their proverbial acts together with hybrid that they've been talking about since 2009 but it looks like it's finally becoming real and look it's true you're not going to migrate everything into the cloud but the cloud folks are in a very strong position they are on the growth flywheel as we've shown they each have adjacent businesses that are data based disruptive and dominant whether it's in retail or search or a huge software estate they are winning the data wars as well that seems to be pretty clear to us and they have a leg up in ai and i want to look at that can we all agree that ai is important i think we can machine intelligence is being infused into every application and today much of the ai work is being done in the cloud as modeling but in the future we see ai moving to the edge in real time and real-time inferencing is a dominant workload but today again 90 of it is building models and analyzing data a lot of that work happens in the cloud so who has the momentum in ai let's take a look here's that same xy graph with the net score against market share and look who has the dominant mind share and position and spending momentum microsoft aws and google you can see in the table insert in the lower right hand side they're the only three in the data set of 1 500 responses that have more than 100 n aws and microsoft have around 200 or even more in the case of microsoft and their net scores are all elevated above the 60 percent level remember that 40 percent that red line indicates the elevation mark the high elevation mark so the hyperscalers have both the market presence and the spend momentum so we think the rich get richer now they're not alone there are several companies above the 40 line databricks is bringing ai and data science to the world of data lakes with its managed services and it's executing very well salesforce is infusing infusing ai into its platform via einstein you got sap on there anaconda is kind of the gold standard that platform for data science and you can see c3 dot ai is tom siebel's company going after enterprise ai and data robot which like c3 ai is a small sample in the data set but they're highly elevated and they're simplifying machine learning now there's ibm watson it's actually doing okay i mean sure we'd like to see it higher given that ginny rometty essentially bet ibm's future on watson but it has a decent presence in the market and a respectable net score and ibm owns a cloud so okay at least it's a player not the dominance that many had hoped for when watson beat ken jennings in jeopardy back 10 years ago but it's okay and then is oracle they're now getting into the act like it always does they want they watched they waited they invested they spent money on r d and then boom they dove into the market and made a lot of noise and acted like they invented the concept oracle is infusing ai into its database with autonomous database and autonomous data warehouse and look that's what oracle does it takes best of breed industry concepts and technologies to make its products better you got to give oracle credit it invests in real tech and it runs the most mission critical apps in the world you can hate them if you want but they smoke everybody in that game all right let's take a look at another view of the cloud players and see how they stack up and where the big spenders live in the all-important fortune 500 this chart shows net score over time within the fortune 500 aws is particularly interesting because its net score overall is in the high 50s but in this large big spender category aws net score jumps noticeably to nearly 70 percent so there's a strong indication that aws the largest player also has momentum not just with small companies and startups but where it really counts from a revenue perspective in the largest companies so we think that's a very positive sign for aws all right let's wrap the realities of cloud repatriation are clear corner cases exist but it's not a trend to take to the bank although many public cloud users may think about repatriation most will not act on it those that do are the exception not the rule and the etr data shows that test and dev in the clouds is part of the cloud operating model even if the app will ultimately live on prem that's not repatriation that's just smart development practice and not every workload is will or should live in the cloud hybrid is real we agree and the big cloud players know it and they're positioning to bring their stacks on prem and to the edge and despite the risk of a lock-in and higher potential monthly bills and concerns over control the hyperscalers are well com positioned to compete in hybrid to win hybrid the legacy vendors must embrace the cloud and build on top of those giants and add value where the clouds aren't going to or can't or won't they got to find places where they can move faster than the hyperscalers and so far they haven't shown a clear propensity to do that hey that's how we see it what do you think okay well remember these episodes are all available as podcasts wherever you listen you do a search breaking analysis podcast and please subscribe to the series check out etr's website at dot plus we also publish a full report every week on wikibon.com and siliconangle.com a lot of ways to get in touch you can email me at david.velante at siliconangle.com or dm me at dvalante on twitter comment on our linkedin post i always appreciate that this is dave vellante for the cube insights powered by etr have a great week everybody stay safe be well and we'll see you next time you

>>Welcome back everyone to Dell Technologies World 2021 the virtual version. You're watching the cubes continuing coverage of the event and we're gonna talk about the Edge, the transformation of telco in the future of our expanding tech universe. With me is Aaron Jason, who's the vice president? Edge and Telkom marketing at Dell Technologies erin great to see you. I love this topic. >>Absolutely. It's it's pretty popular these days. I'm glad to be here with you. Thanks. >>It is popular, you know, cloud was kind of the shiny new toy last decade and it's still growing at double digits but it's kind of mainstream and now the Edge is all the rage. What's the best way to think about? What is the Edge? How do you define that? >>Yeah, you know, that's probably one of the most common questions I get is we start really doubling down on what we're doing it in the Edge world today. Um you know, I tried to basically not overcomplicated too much, you know, last year we really tried to to talk about it as being where you're the physical world, in the virtual world, connect. Um but you know, really it's more about what customers are looking to do with that technology. And so what we're really thinking about it today is the edges really where customers data is being used near point of generation to really define and build the essential value for that customer and that essential value is gonna be different in each vertical in each industry. Right? So in manufacturing, that essential value is created in the factory and retail, it's going to be, you know, at point of sale, whether that's in a store or on your device, in a virtual interaction, um in health care, it's going to be the point of care, Right? So it's gonna be the ambulance or the emergency room or the radiology lab. and of course in farming that essential values created in the field itself. So um, you know, for for many customers, it's really trying to figure out, you know, how do they take technology closer to the point of that value creation to be able to drive new new capabilities for the business, whether it's for what they're trying to accomplish or what they're trying to do in helping their customers. So really that's how we're thinking about the edge today. It's where that value generation occurs for a company. And how do we take technology to that point of generation to deliver value for them? >>Yeah, I like that. I mean to me the edge, I know what it's not, I know the edges, not a mega data center, but but everything else could be the edge. I mean, it's it's to me it's the place that's the most logical, the most logical place to process the data. So as you say, it could be a factory, it could be a hospital, it could be a retail store, it could be, could be a race track, it could be a farm, I mean virtually anything. So the edges, it's always been here, but it's changing. I mean most of the edge data has historically been analog. Everything now is getting instrumented. What are the factors that you think will make this, this industry's vision of the edge real in your opinion? >>Yeah. You know, it's it's really bringing together a handful of technologies that have really started to mature after over the last decade or so. Um the ones that have been around for a little bit, things like IOT have been emerging in the last several years. Um even Ai and machine learning many of those algorithms have been around for decades, but we've only recently been able to bring the compute power required to do that in edge environments in the last decade or so. Um it's so really the two key sort of killer technologies that have matured in the last couple of years is really the mic realization of computing. So being able to put compute almost anywhere on the planet and then the emergence of five G networking, giving us the ability to provide very high performance, low latency and high bandwidth environments to connect all those things together and get the data to those analytics environments. From that computer perspective. I mean, I still like to talk about moore's law as an example of that that ever marched that's been going on for, you know, half a century or more now is continuing to push forward um at a rate that is that that that that just really hasn't slowed down for the most part, you know, the example that I use with people, as, you know, you know, I still remember when I got my first calculator watch as a kid, you know, that Casio calculator watch that so many of us had, And my dad told me the story when he gave it to me, he's like, Hey, look, this has the same amount of compute power as the landing module on the moon, and I didn't know it at the time, but that was my first sort of entry and education around what Moore's law provided. And it's not so much speed. I mean, people think about that as it doubles in speed every 18 months, but it's really more about the density of compute that happens that moore's law drought, pushes along, so I can now squish more and more compute power into a smudge smaller location and I can now take that performance out to the edge in a way that I haven't been able to do before. I mean I think about my history, I joined E M C, that was acquired by Dell Technologies a couple years back. I joined that back in the late nineties when the biggest baddest storage array on the planet was one whole terabyte in size. And now I can fit that in the palm of my hand. In fact, when I walk around, you know, when I used to walk around with my, with my back, my laptop and go into offices, um you know, if I had my laptop and my tablet and my my my smartwatch, I had 12 to 16 cores on me and a couple of terabytes of capacity all connected with the equivalent of tens of T ones. Right? So what was once a small or or a mid sized data center just in the last decade or so? We now all walk around a small data centers and the power that that compute now brings to the edge allows us to take analytics that was really once done in data centers. I may have captured it at the edge, but I had to move it into a data lake. I had to stage it and analyze it. It was more of a historical way of looking at data. Now I can put compute right next to the point of data generation and give insight instantaneously as data is being generated. And that's opening up whole new ways that industries can drive new value for them and for their customers. And that's really what's exciting about it is this combination of these technologies that are all sort of maturing and coming together at the same time. Um, and there's just so much doing, it happened that space and devils really, really excited to be part of bringing that into these environments for our customers. >>I'm gonna give you a stat that a lot of people, I don't, I don't think realize, uh, you talked about moore's law and you're absolutely right. It's really, you know, technically moore's law is about the density, right? But the outcome of being able to do that is performance. And if you do the math, you know, moore's law doubling performance every two years, roughly, The math on that is that means 44 improvement per year in performance. Everybody talks about how moore's laws is dead. It's not, it's just changing. Here's the, here's the stat. If you take a system on a chip, take like for instance apples a 14 and go back five years from 2015 to 2021. If you add up the performance of the CPU the combinatorial factors of the CPU gpu and in the N. P. U. The neural processing unit, just those three, The growth rate has been 118 a year vs 44%. So it's actually accelerating and that doesn't include the accelerators and the DSPS and all the other alternative processors. So, and to your point and by the way that a 14 shipping cost Apple 50 bucks. So and and that fits in the palm of your hand to the point that you were just making So imagine that processing power at the edge most of of of of of ai today is modeling, let's say in the cloud, the vast majority is going to be a i influencing at the edge. So you are right on on that point. >>Yeah, there's no question about it. So, to your point, I mean, moore's law is just of course CPU itself. All right. And it comes out to roughly, on average, it's about 10 x every five years. 100 X every 10 years, 1000 X every 15 years. I mean, it's incredible how much power you can put in a small footprint today. And then if you factor in the accelerators and everything else um, it's actually if anything that innovation is going faster and faster and to your point, um you know, the while the modeling is still going to typically happen in data centers as you pull together lots of different data sets to be able to analyze and create new models. But those models are getting pushed right out to the edge on these compute devices literally feet away at times from the point of data generation to be able to give us really real time analytics and influencing. The other cool thing about this too is you know we're going from sort of more looking backwards and making business analytics based on what has already happened in the past to being able to do that in the very near past. And of course now with modern analytics and models that are being created for ai we're able to do more predictive analytics so we can actually identify errors, identify challenges before they even occur based on pattern matching that they're saying. Um So it's really opening up new doors and new areas that we've never been able to see before that's really all powered by by these capabilities. >>It's insane the amount of data that is coming. We think data is overwhelming today. You ain't seen nothing yet. Um Now erin you cover the edge and the telecom business up. I was beside it when I when I when I found that out because the telecom businesses is ripe for transformation. Um So what do you how is Dell thinking about that? Why are you sort of putting those together? What are the synergies that you see in in the commonalities in those 22 sectors? >>Yeah. I mean at the end of the day it's really all about serving the enterprise customers in the in the organizations of all kinds um that the industry is trying to bring these edge technologies too and that's no different with the telecommunications industry. Right? So you know when when the when the four G world changed about 10 years ago um you know the telecom industry was able to bring the plumbing the network piping out to all the endpoints but they really didn't capture the over the top revenue opportunities that Four G technologies opened up right. That really went to the hyper scholars. It went to you know, a lot of the companies that we all know and love like uh you know, Uber and Airbnb and netflix and others um and that really when the four Gr that was really more about opening up consumer opportunities as we move to five G. And as we move these ultra low latency and high bandwidth capabilities out to the enterprise edge, it's really the B two B opportunities that are opening up and so on the telecom side we're partnering with the telecommunication companies to modernize their network, enroll five G. L. Quickly. But one of the more important things is that we're partnering with them to be able to build services over the top of that that they can then sell into their customer base and their business customer base. So whether that's mech, whether that's private mobility, um delivering data services over the top of those networks, there's a tremendous opportunity for the telecoms to be able to go and capture um Ed revenue opportunities and we're here to help them to partner with them to be able to do that. Now if you put yourself in the shoes of the customer, the enterprise business, a manufacturer or retail, who's looking to be able to leverage these technologies, there's a variety of ways in which they're going to be able to to to consume these technologies. In some cases they'll be getting it direct from vendors direct from Dell Technologies and others. They might be using solutions integrators to be able to combine these technologies together for a particular solution. They may get some of those technologies from their telecom provider and even others, they might get it from the cloud provider. So um Dell wants to make sure that we're being able to help our customers across a variety of ways in which they want to consume those technologies and we have to businesses focused on that. We've got one business focused on edge solutions where we partner with oT vendors closely as well as cloud providers to be able to provide a technology and infrastructure based on which we can consolidate edge workloads To be able to allow customers that want to be able to run those um those services on prem and by those from a direct vendor. Um there's other customers that want to get those through the telecoms. And so we work closely with the telecommunication providers to provide them that modern cloud native disaggregated network that they're looking to build to support 5G. And then help them build those services on the top that they can sell either way whether the customer wants to get that from a vendor like Dell or from a service provider like like uh like an A T and T and Verizon or others. Um Dell looks to partner with them and be a way to provide that underlying infrastructure that connects all of that together for them. >>Well, I mean the beauty of the telco networks is their hardened. But the problem for the telco networks is they're they're hardened and so you've got the over over the top vendors bow guarding their network. The cost per bit is coming down, data is going through the roof and the telcos can't, they can't participate in that over the top and get to those subscribers. But with Five G. And the technologies that you're talking about bringing to the telecoms world, they're they're gonna transform and many are going to start competing directly and this is just a whole new world out there. I wonder Aaron if you could talk about um what you're specifically talking about at Del Tech World this year as it relates to Edge. >>Sure. So the both of the businesses hedge in telecom have a couple announcements this year. This this year, Deltek World, um starting with Edge um as you may recall back in uh in in the fall of last year when we had our last technologies world, we announced our intent to launch an edge business. Um so that that was formulated and stood up over the last couple of months and and we're really focusing on a couple of different areas. How do we look at our overall Dell technologies portfolio and be able to bring particular products and solutions that exist already and be able to apply those uh to edge use cases. We're looking at building a platform which would allow us to be able to consolidate a variety of workloads. And of course we're working on partnerships specifically in the ot space to be able to vertical eyes these offers to help particular uh particular industries. Right now we're focusing on manufacturing and retail but we'll expand that over time. So at Del Tech World this year we're launching our first set of of solutions family which is going to be the Dell Technologies manufacturing edge solutions, the first one that's gonna be launching as a reference architecture with PTC um thing works on top of what we're also proud to be announcing this week, which is our apex private cloud offering. So this is the first example of of of a partnership with an O. T. Provider on top of apex private cloud so that we can bring in as a service platform offering to the Enterprise edge uh for manufacturers. And combined with one of the industry's leading oT software vendors of thing works. So that's one of the solutions were doing um we're also looking to launch a product which is we're taking our existing um streaming data platform from our unified storage team and taking that, which was once running in the data center out to edge these cases as well. And that allows us to be able to capture click stream data in manufacturing and other environments, buffer and cash that in a in an appliance and then be able to move that off to a data like for longer term analytics. While it's in that buffered state though we open provide a P. I. S. So that you can actually do real time influencing against those click stream data as it's flowing through the appliance on its way to the data lake for longer term analytics. So those are two key areas that we're gonna be focusing on from an edge perspective on the telecom side. Um we're really this is going to be a big year from us as we move towards creating a common end end five G platform from quarter Iran and then also start focusing on partnerships and ecosystems on top of that platform. Uh last week at Red hat summit we actually announced a reference architecture for red hat. Open shift on top of Dell technologies infrastructure servers and networking. And here at Dell technologies world. This week we're announcing a reference architecture with VM ware. So running VM ware telecom cloud platform. Also on top of Dell technologies. Power edge servers and power such as um so this allows us to create that foundation that open cloud native. These are container and virtual layers on top of our hard work to give that that cloud native disaggregated uh, network claim to be able to now run and build core edge and ran solutions on top of and you'll be hearing more about what we're doing in this space in the coming months. >>Nice. That's great. The open ran stuff is really exciting now, last question. So mobile world Congress, the biggest telco show is coming up in late june Yeah, still on. According to the G S M, a lot of people have tapped out um, and but the cube is planning to be there with a hybrid presence, both virtual and physical. We'll see um I wonder if there's anything you want to talk about just in terms of what's happening in telco telco transformation, you guys got any get any events coming up, what can you tell us? >>Yeah, so we took a close look at mobile world congress and and uh this has been a challenging year for everybody. Um you know, Dell as well as many other vendors made the decision this year that we would actually not participate, but we look forward to participating uh with full gusto next year when it's back in a physical environment. Um So what we've decided to do is we are going to be having our own virtual launch event on june 9th. Um And in that event, the theme of that is going to be the modern ecosystem in the neighboring leveraging the power of open. Um So we'll be talking a little bit more about what we're doing from that open cloud, native network infrastructure and then also talk a little bit more about what Dell technologies looking to do to bring a broad ecosystem of technology vendors together and deliver that ecosystem platform for the telecom industry. So registration actually opens this week at Dell Technologies World. So if you go to Dell technologies dot com can register for the event. Um we're really excited to be talking to the telecom providers and also other hardware and software vendors that are in that space to see how we can work together to really drive this next generation of five G. >>That's awesome. I'll be looking for that and and look forward to collaborating with you on that, bringing your thought leadership and the cube community we would really love to to partner on that. Aaron, thanks so much for coming to the cube. Really exciting area and best of luck to you. >>Right. Thank you. I appreciate the time. >>All right. And thank you for watching everybody says Dave Volonte for the Cubes, continuous coverage of Del Tech World 2021. The virtual version will be right back right after this short break.

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante recent survey data from etr shows that enterprise tech spending is tracking with projected u.s gdp growth at six to seven percent this year many markers continue to point the way to a strong recovery including hiring trends and the loosening of frozen it project budgets however skills shortages are blocking progress at some companies which bodes well for an increased reliance on external i.t services moreover while there's much to talk about well there's much talk about the rotation out of work from home plays and stocks such as video conferencing vdi and other remote worker tech we see organizations still trying to figure out the ideal balance between funding headquarter investments that have been neglected and getting hybrid work right in particular the talent gap combined with a digital mandate means companies face some tough decisions as to how to fund the future while serving existing customers and transforming culturally hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we welcome back eric porter bradley of etr who will share fresh data perspectives and insights from the latest survey data eric great to see you welcome thank you very much dave always good to see you and happy to be on the show again okay we're going to share some macro data and then we're going to dig into some highlights from etr's most recent march covid survey and also the latest april data so eric the first chart that we want to show it shows cio and it buyer responses to expected i.t spend for each quarter of 2021 versus 2020. and you can see here a steady quarterly improvement eric what are the key takeaways from your perspective sure well first of all for everyone out there this particular survey had a record-setting number of uh participation we had uh 1 500 i.t decision makers participate and we had over half of the fortune 500 and over a fifth of the global 1000. so it was a really good survey this is the seventh iteration of the covet impact survey specifically and this is going to transition to an over large macro survey going forward so we could continue it and you're 100 right what we've been tracking here since uh march of last year was how is spending being impacted because of covid where is it shifting and what we're seeing now finally is that there is a real re-acceleration in spend i know we've been a little bit more cautious than some of the other peers out there that just early on slapped an eight or a nine percent number but what we're seeing is right now it's at a midpoint of over six uh about six point seven percent and that is accelerating so uh we are still hopeful that that will continue uh really that spending is going to be in the second half of the year as you can see on the left part of this chart that we're looking at uh it was about 1.7 versus 3 for q1 spending year over year so that is starting to accelerate through the back half you know i think it's prudent to be be cautious relative because normally you'd say okay tech is going to grow a couple of points higher than gdp but it's it's really so hard to predict this year okay the next chart is here that we want to show you is we ask respondents to indicate what strategies they're employing in the short term as a result of coronavirus and you can see a few things that i'll call out and then i'll ask eric to chime in first there's been no meaningful change of course no surprise in tactics like remote work and halting travel however we're seeing very positive trends in other areas trending downward like hiring freezes and freezing i.t deployments downward trend in layoffs and we also see an increase in the acceleration of new i.t deployments and in hiring eric what are your key takeaways well first of all i think it's important to point out here that uh we're also capturing that people believe remote work productivity is still increasing now the trajectory might be coming down a little bit but that is really key i think to the backdrop of what's happening here so people have a perception that productivity of remote work is better than hybrid work and that's from the i.t decision makers themselves um but what we're seeing here is that uh most importantly these organizations are citing plans to increase hiring and that's something that i think is really important to point out it's showing a real thawing and to your point in right in the beginning of the intro uh we are seeing deployments stabilize versus prior survey levels which means early on they had no plans to launch new tech deployments then they said nope we're going to start and now that's stalling and i think it's exactly right what you said is there's an i.t skills shortage so people want to continue to do i.t deployments because they have to support work from home and a hybrid back return to the office but they just don't have the skills to do so and i think that's really probably the most important takeaway from this chart um is that stalling and to really ask why it's stalling yeah so we're going to get into that for sure and and i think that's a really key point is that that that accelerating it deployments is some it looks like it's hit a wall in the survey and so but before before we get deep into the skills let's let's take a look at this next chart and we're asking people here how a return to the new normal if you will and back to offices is going to change spending with on-prem architectures and applications and so the first two bars they're cloud-friendly if you add them up at 63 percent of the respondents say that either they'll stay in the cloud for the most part or they're going to lower the on-prem spend when they go back to the office the next three bars are on-prem friendly if you add those up as 29 percent of the respondents say their on-prem spend is going to bounce back to pre-covert levels or actually increase and of course 12 percent of that number by the way say they they've never altered their on-prem spend so eric no surprise but this bodes well for cloud but but it it isn't it also a positive for on-prem this we've had this dual funding premise meaning cloud continues to grow but neglected data center spend also gets a boost what's your thoughts you know really it's interesting it's people are spending on all fronts you and i were talking in a prep it's like you know we're we're in battle and i've got naval i've got you know air i've got land uh i've got to spend on cloud and digital transformation but i also have to spend for on-prem uh the hybrid work is here and it needs to be supported so this spending is going to increase you know when you look at this chart you're going to see though that roughly 36 percent of all respondents say that their spending is going to remain mostly on cloud so this you know that is still the clear direction uh digital transformation is still happening covid accelerated it greatly um you know you and i as journalists and researchers already know this is where the puck is going uh but spend has always lagged a little bit behind because it just takes some time to get there you know inversely 27 said that their on-prem spending will decrease so when you look at those two i still think that the trend is the friend for cloud spending uh even though yes they do have to continue spending on hybrid some of it's been neglected there are refresh cycles coming up so overall it just points to more and more spending right now it really does seem to be a very strong backdrop for it growth so i want to talk a little bit about the etr taxonomy before we bring up the next chart we get a lot of questions about this and of course when you do a massive survey like you're doing you have to have consistency for time series so you have to really think through what that what the buckets look like if you will so this next chart takes a look at the etr taxonomy and it breaks it down into simple to understand terms so the green is the portion of spending on a vendor's tech within a category that is accelerating and the red is the portion that is decelerating so eric what are the key messages in this data well first of all dave thank you so much for pointing that out we used to do uh just what we call a next a net score it's a proprietary formula that we use to determine the overall velocity of spending some people found it confusing um our data scientists decided to break this sector breakdown into what you said which is really more of a mode analysis in that sector how many of the vendors are increasing versus decreasing so again i just appreciate you bringing that up and allowing us to explain the the the reasoning behind our analysis there but what we're seeing here uh goes back to something you and i did last year when we did our predictions and that was that it services and consulting was going to have a true rebound in 2021 and that's what this is showing right here so in this chart you're going to see that consulting and services are really continuing their recovery uh 2020 had a lot of declines and they have the biggest sector over year-over-year acceleration sector-wise the other thing to point out in this which we'll get to again later is that the inverse analysis is true for video conferencing uh we will get to that so i'm going to leave a little bit of ammunition behind for that one but what we're seeing here is it consulting services being the real favorable and video conferencing uh having a little bit more trouble great okay and then let's let's take a look at that services piece and this next chart really is a drill down into that space and emphasizes eric what you were just talking about and we saw this in ibm's earnings where still more than 60 percent of ibm's business comes from services and the company beat earnings you know in part due to services outperforming expectations i think it had a somewhat easier compare and some of this pen-up demand that we've been talking about bodes well for ibm and in other services companies it's not just ibm right eric no it's not but again i'm going to point out that you and i did point out ibm in our in our predictions one we did in late december so it is nice to see one of the reasons we don't have a more favorable rating on ibm at the moment is because they are in the the process of spinning out uh this large unit and so there's a little bit of you know corporate action there that keeps us off on the sideline but i would also want to point out here uh tata infosys and cognizant because they're seeing year-over-year acceleration in both it consulting and outsourced i t services so we break those down separately and those are the three names that are seeing acceleration in both of those so again a tata emphasis and cognizant are all looking pretty well positioned as well so we've been talking a little bit about this skill shortage and this is what's i think so hard for for forecasters um is that you know on the one hand there's a lot of pent up demand you know it's like scott gottlieb said it's like woodstock coming out of the covid uh but on the other hand if you have a talent gap you've got to rely on external services so there's a learning curve there's a ramp up it's an external company and so it takes time to put those together so so this data that we're going to show you next uh is is really important in my view and ties what we're saying we're saying at the top it asks respondents to comment on their staffing plans the light blue is we're increasing staff the gray is no change in the magenta or whatever whatever color that is that sort of purplish color anyway that color is is decreasing and the picture is very positive across the board full-time staff offshoring contract employees outsourced professional services all up trending upwards and this eric is more evidence of the services bounce back yeah it certainly is david and what happened is when we caught this trend we decided to go one level deeper and say all right we're seeing this but we need to know why and that's what we always try to do here data will tell you what's happening it doesn't always tell you why and that's one of the things that etr really tries to dig in with through the insights interviews panels and also going direct with these more custom survey questions uh so in this instance i think the real takeaway is that 30 of the respondents said that their outsourced and managed services are going to increase over the next three months that's really powerful that's a large portion of organizations in a very short time period so we're capturing that this acceleration is happening right now and it will be happening in real time and i don't see it slowing down you and i are speaking about we have to you know increase cloud spend we have to increase hybrid spend there are refresh cycles coming up and there's just a real skill shortage so this is a long-term setup that bodes very well for it services and consulting you know eric when i came out of college i somebody told me read read read read as much as you can and and so i would and they said read the wall street journal every day and i so i did it and i would read the tech magazines and back then it was all paper and what happens is you begin to connect the dots and so the reason i bring that up is because i've now been had taken a bath in the etr data for the better part of two years and i'm beginning to be able to connect the dots you know the data is not always predictive but many many times it is and so this next data gets into the fun stuff where we name names a lot of times people don't like it because the marketing people and organizations say well the data's wrong of course that's the first thing they do is attack the data but you and i know we've made some really great calls work from home for sure you're talking about the services bounce back uh we certainly saw the rise of crowdstrike octa zscaler well before people were talking about that same thing with video conferencing and so so anyway this is the fun stuff and it looks at positive versus negative sentiment on on companies so first how does etr derive this data and how should we interpret it and what are some of your takeaways [Music] sure first of all how we derive the data or systematic um survey responses that we do on a quarterly basis and we standardize those responses to allow for time series analysis so we can do trend analysis as well we do find that our data because it's talking about forward-looking spending intentions is really more predictive because we're talking about things that might be happening six months three months in the future not things that a lot of other competitors and research peers are looking at things that already happened uh they're looking in the past etr really likes to look into the future and our surveys are set up to do so so thank you for that question it's an enjoyable lead-in but to get to the fun stuff like you said uh what we do here is we put ratings on the data sets i do want to put the caveat out there that our spending intentions really only captures top-line revenue it is not indicative of profit margin or any other line items so this is only going to be viewed as what we are rating the data set itself not the company um you know that's not what we're in the game of doing so i think that's very important for the marketing and the vendors out there themselves when they when they take a look at this we're just talking about what we can control which is our data we're going to talk about a few of the names here on this highlighted vendors list one we're going to go back to that you and i spoke about i guess about six months ago or maybe even earlier which was the observability space um you and i were noticing that it was getting very crowded a lot of new entrants um there was a lot of acquisition from more of the legacy or standard entrance players in the space and that is continuing so i think in a minute we're going to move into that observability space but what we're seeing there is that it's becoming incredibly crowded and we're possibly seeing signs of them cannibalizing each other uh we're also going to move on a little bit into video conferencing where we're capturing some spend deceleration and then ultimately we're going to get into a little bit of a storage refresh cycle and talk about that but yeah these are the highlighted vendors for april um we usually do this once a quarter and they do change based on the data but they're not usually whipsawed around the data doesn't move that quickly yeah so you can see the some of the big names on the left-hand side some of the sas companies that have momentum obviously servicenow has been doing very very well we've talked a lot about snowflake octa crowdstrike z scalar in all very positive as well as you know several others i i guess i'd add some some things i mean i think if thinking about the next decade it's it's cloud which is not going to be like the same cloud as last decade a lot of machine learning and deep learning and ai and the cloud is extending to the edge in the data center data obviously very important data is decentralized and distributed so data architectures are changing a lot of opportunities to connect across clouds and actually create abstraction layers and then something that we've been covering a lot is processor performance is actually accelerating relative to moore's law it's probably instead of doubling every two years it's quadrupling every two years and so that is a huge factor especially as it relates to powering ai and ai inferencing at the edge this is a whole new territory custom silicon is is really becoming in vogue uh and so we're something that we're watching very very closely yeah i completely completely agree on that and i do think that the the next version of cloud will be very different another thing to point out on that too is you can't do anything that you're talking about without collecting the data and and organizations are extremely serious about that now it seems it doesn't matter what industry they're in every company is a data company and that also bodes well for the storage call we do believe that there is going to just be a huge increase in the need for storage um and yes hopefully that'll become portable across multi-cloud and hybrid as well now as eric said the the etr data's it's it's really focused on that top line spend so if you look at the uh on on the right side of that chart you saw you know netapp was kind of negative was very negative right but there's a company that's in in transformation now they've lowered expectations and they've recently beat expectations that's why the stock has been doing better but but at the macro from a spending standpoint it's still challenged so you have big footprint companies like netapp and oracle is another one oracle's stock is at an all-time high but the spending relative to sort of previous cycles or relative to you know like for instance snowflake much much smaller not as high growth but they're managing expectations they're managing their transition they're managing profitability zoom is another one zoom looking looking negative but you know zoom's got to use its market cap now to to transform and increase its tam uh and then splunk is another one we're going to talk about splunk is in transition it acquired signal fx it just brought on this week teresa carlson who was the head of aws public sector she's the president and head of sales so they've got a go to market challenge and they brought in teresa carlson to really solve that but but splunk has been trending downward we called that you know several quarters ago eric and so i want to bring up the data on splunk and this is splunk eric in analytics and it's not trending in the right direction the green is accelerating span the red is and the bars is decelerating spend the top blue line is spending velocity or net score and the yellow line is market share or pervasiveness in the data set your thoughts yeah first i want to go back is a great point dave about our data versus a disconnect from an equity analysis perspective i used to be an equity analyst that is not what we do here and you you may the main word you said is expectations right stocks will trade on how they do compared to the expectations that are set uh whether that's buy side expectations sell side expectations or management's guidance themselves we have no business in tracking any of that what we are talking about is top line acceleration or deceleration so uh that was a great point to make and i do think it's an important one for all of our listeners out there now uh to move to splunk yes i've been capturing a lot of negative commentary on splunk even before the data turned so this has been about a year-long uh you know our analysis and review on this name and i'm dating myself here but i know you and i are both rock and roll fans so i'm gonna point out a led zeppelin song and movie and say that the song remains the same for splunk we are just seeing uh you know recent spending intentions are taking yet another step down both from prior survey levels from year ago levels uh this we're looking at in the analytics sector and spending intentions are decelerating across every single customer group if we went to one of our other slide analysis um on the etr plus platform and you do by customer sub sample in analytics it's dropping in every single vertical it doesn't matter which one uh it's really not looking good unfortunately and you had mentioned this as an analytics and i do believe the next slide is an information security yeah let's bring that up and it's unfortunately it's not doing much better so this is specifically fortune 500 accounts and information security uh you know there's deep pockets in the fortune 500 but from what we're hearing in all the insights and interviews and panels that i personally moderate for etr people are upset they didn't like the the strong tactics that splunk has used on them in the past they didn't like the ingestion model pricing the inflexibility and when alternatives came along people are willing to look at the alternatives and that's what we're seeing in both analytics and big data and also for their sim in security yeah so i think again i i point to teresa carlson she's got a big job but she's very capable she's gonna she's gonna meet with a lot of customers she's a go to market pro she's gonna have to listen hard and i think you're gonna you're gonna see some changes there um okay so there's more sorry there's more bad news on splunk so bring this up is is is net score for splunk in elastic accounts uh this is for analytics so there's 106 elastic accounts that uh in the data set that also have splunk and it's trending downward for splunk that's why it's green for elastic and eric the important call out from etr here is how splunk's performance in elastic accounts compares with its performance overall the elk stack which obviously elastic is a big part of that is causing pain for splunk as is data dog and you mentioned the pricing issue uh is it is it just well is it pricing in your assessment or is it more fundamental you know it's multi-level based on the commentary we get from our itdms that take the survey so yes you did a great job with this analysis what we're looking at is uh the spending within shared accounts so if i have splunk already how am i spending i'm sorry if i have elastic already how is my spending on splunk and what you're seeing here is it's down to about a 12 net score whereas splunk overall has a 32 net score among all of its customers so what you're seeing there is there is definitely a drain that's happening where elastic is draining spend from splunk and usage from them uh the reason we used elastic here is because all observabilities the whole sector seems to be decelerating splunk is decelerating the most but elastic is the only one that's actually showing resiliency so that's why we decided to choose these two but you pointed out yes it's also datadog datadog is cloud native uh they're more devops oriented they tend to be viewed as having technological lead as compared to splunk so that's a really good point a dynatrace also is expanding their abilities and splunk has been making a lot of acquisitions to push their cloud services they are also changing their pricing model right they're they're trying to make things a little bit more flexible moving off ingestion um and moving towards uh you know consumption so they are trying and the new hires you know i'm not gonna bet against them because the one thing that splunk has going for them is their market share in our survey they're still very well entrenched so they do have a lot of accounts they have their foothold so if they can find a way to make these changes then they you know will be able to change themselves but the one thing i got to say across the whole sector is competition is increasing and it does appear based on commentary and data that they're starting to cannibalize themselves it really seems pretty hard to get away from that and you know there are startups in the observability space too that are going to be you know even more disruptive i think i think i want to key on the pricing for a moment and i've been pretty vocal about this i think the the old sas pricing model where essentially you essentially lock in for a year or two years or three years pay up front or maybe pay quarterly if you're lucky that's a one-way street and i think it's it's a flawed model i like what snowflake's doing i like what datadog's doing look at what stripe is doing look what twilio is doing these are cons you mentioned it because it's consumption based pricing and if you've got a great product put it out there and you know damn the torpedoes and i think that is a game changer i i look at for instance hpe with green lake i look at dell with apex they're trying to mimic that model you know they're there and apply it to to infrastructure it's much harder with infrastructure because you got to deploy physical infrastructure but but that is a model that i think is going to change and i think all of the traditional sas pricing is going to is going to come under disruption over the next you know better part of the decades but anyway uh let's move on we've we've been covering the the apm space uh pretty extensively application performance management and this chart lines up some of the big players here comparing net score or spending momentum from the april 20th survey the gray is is um is sorry the the the gray is the april 20th survey the blue is jan 21 and the yellow is april 21. and not only are elastic and data dog doing well relative to splunk eric but everything is down from last year so this space as you point out is undergoing a transformation yeah the pressures are real and it's you know it's sort of that perfect storm where it's not only the data that's telling us that but also the direct feedback we get from the community uh pretty much all the interviews i do i've done a few panels specifically on this topic for anyone who wants to you know dive a little bit deeper we've had some experts talk about this space and there really is no denying that there is a deceleration in spend and it's happening because that spend is getting spread out among different vendors people are using you know a data dog for certain aspects they're using elastic where they can because it's cheaper they're using splunk because they have to but because it's so expensive they're cutting some of the things that they're putting into splunk which is dangerous particularly on the security side if i have to decide what to put in and whatnot that's not really the right way to have security hygiene um so you know this space is just getting crowded there's disruptive vendors coming from the emerging space as well and what you're seeing here is the only bit of positivity is elastic on a survey over survey basis with a slight slight uptick everywhere else year over year and survey over survey it's showing declines it's just hard to ignore and then you've got dynatrace who based on the the interviews you do in the venn you're you know one on one or one on five you know the private interviews that i've been invited to dynatrace gets very high scores uh for their road map you've got new relic which has been struggling you know financially but they've got a purpose built they've got a really good product and a purpose-built database just for this apm space and then of course you've got cisco with appd which is a strong business for them and then as you mentioned you've got startups coming in you've got chaos search which ed walsh is now running you know leave the data in place in aws and really interesting model honeycomb it's going to be really disruptive jeremy burton's company observed so this space is it's becoming jump ball yeah there's a great line that came out of one of them and that was that the lines are blurring it used to be that you knew exactly that app dynamics what they were doing it was apm only or it was logging and monitoring only and a lot of what i'm hearing from the itdm experts is that the lines are blurring amongst all of these names they all have functionality that kind of crosses over each other and the other interesting thing is it used to be application versus infrastructure monitoring but as you know infrastructure is becoming code more and more and more and as infrastructure becomes code there's really no difference between application and infrastructure monitoring so we're seeing a convergence and a blurring of the lines in this space which really doesn't bode well and a great point about new relic their tech gets good remarks uh i just don't know if their enterprise level service and sales is up to snuff right now um as one of my experts said a cto of a very large public online hospitality company essentially said that he would be shocked that within 18 months if all of these players are still uh standalone that there needs to be some m a or convergence in this space okay now we're going to call out some of the data that that really has jumped out to etr in the latest survey and some of the names that are getting the most queries from etr clients which are many of which are investor clients so let's start by having a look at one of the most important and prominent work from home names zoom uh let's let's look at this eric is the ride over for zoom oh i've been saying it for a little bit of a time now actually i do believe it is um i will get into it but again pointing out great dave uh the reason we're presenting today splunk elastic and zoom are they are the most viewed on the etr plus platform uh trailing behind that only slightly is f5 i decided not to bring f5 to the table today because we don't have a rating on the data set um so then i went one deep one below that and it's pure so the reason we're presenting these to you today is that these are the ones that our clients and our community are most interested in which is hopefully going to gain interest to your viewers as well so to get to zoom um yeah i call zoom the pandec pandemic bull market baby uh this was really just one that had a meteoric ride you look back january in 2020 the stock was at 60 and 10 months later it was like like 580. that's in 10 months um that's cooled down a little bit uh into the mid 300s and i believe that cooling down should continue and the reason why is because we are seeing a huge deceleration in our spending intentions uh they're hitting all-time lows it's really just a very ugly data set um more importantly than the spending intentions for the first time we're seeing customer growth in our survey flattened in the past we could we knew that the the deceleration and spend was happening but meanwhile their new customer growth was accelerating so it was kind of hard to really make any call based on that this is the first time we're seeing flattening customer growth trajectory and that uh in tandem with just dominance from microsoft in every sector they're involved in i don't care if it's ip telephony productivity apps or the core video conferencing microsoft is just dominating so there's really just no way to ignore this anymore the data and the commentary state that zoom is facing some headwinds well plus you've pointed out to me that a lot of your private conversations with buyers says that hey we're we're using the freebie version of zoom you know we're not paying them and so in that combined with teams i mean it's it's uh it's i think you know look zoom has to figure it out they they've got to they've got to figure out how to use their elevated market cap to transform and expand their tan um but let's let's move on here's the data on pure storage and we've highlighted a number of times this company is showing elevated spending intentions um pure announces earnings in in may ibm uh just announced storage what uh it was way down actually so sort of still pure more positive but i'll comment on a moment but what does this data tell you eric yeah you know right now we started seeing this data last survey in january and that was the first time we really went positive on the data set itself and it's just really uh continuing so we're seeing the strongest year-over-year acceleration in the entire survey um which is a really good spot to be pure is also a leading position in among its sector peers and the other thing that was pretty interesting from the data set is among all storage players pure has the highest positive public cloud correlation so what we can do is we can see which respondents are accelerating their public cloud spend and then cross-reference that with their storage spend and pure is best positioned so as you and i both know uh you know digital transformation cloud spending is increasing you need to be aligned with that and among all storage uh sector peers uh pure is best positioned in all of those in spending intentions and uh adoptions and also public cloud correlation so yet again just another really strong data set and i have an anecdote about why this might be happening because when i saw the date i started asking in my interviews what's going on here and there was one particular person he was a director of cloud operations for a very large public tech company now they have hybrid um but their data center is in colo so they don't own and build their own physical building he pointed out that doran kovid his company wanted to increase storage but he couldn't get into his colo center due to covert restrictions they weren't allowed you had so 250 000 square feet right but you're only allowed to have six people in there so it's pretty hard to get to your rack and get work done he said he would buy storage but then the cola would say hey you got to get it out of here it's not even allowed to sit here we don't want it in our facility so he has all this pent up demand in tandem with pent up demand we have a refresh cycle the ssd you know depreciation uh you know cycle is ending uh you know ssds are moving on and we're starting to see uh new technology in that space nvme sorry for technology increasing in that space so we have pent up demand and we have new technology and that's really leading to a refresh cycle and this particular itdm that i spoke to and many of his peers think this has a long tailwind that uh storage could be a good sector for some time to come that's really interesting thank you for that that extra metadata and i want to do a little deeper dive on on storage so here's a look at storage in the the industry in context and some of the competitive i mean it's been a tough market for the reasons that we've highlighted cloud has been eating away that flash headroom it used to be you'd buy storage to get you know more spindles and more performance and you were sort of forced to buy more flash gave more headroom but it's interesting what you're saying about the depreciation cycle so that's good news so etr combines just for people's benefit here combines primary and secondary storage into a single category so you have companies like pure and netapp which are really pure play you know primary storage companies largely in the sector along with veeam cohesity and rubric which are kind of secondary data or data protection so my my quick thoughts here are that pure is elevated and remains what i call the one-eyed man in the land of the blind but that's positive tailwinds there so that's good news rubric is very elevated but down it's a big it's big competitor cohesity is way off its highs and i have to say to me veeam is like the steady eddy consistent player here they just really continue to do well in the data protection business and and the highs are steady the lows are steady dell is also notable they've been struggling in storage their isg business which comprises service and storage it's been soft during covid and and during even you know this new product rollout so it's notable with this new mid-range they have in particular the uptick in dell this survey because dell so large a small uptick can be very good for dell hpe has a big announcement next month in storage so that might improve based on a product cycle of course the nimble brand continues to do well ibm as i said just announced a very soft quarter you know down double digits again uh and there in a product cycle shift and netapp is that looks bad in the etr data from a spending momentum standpoint but their management team is transforming the company into a cloud play which eric is why it was interesting that pure has the greatest momentum in in cloud accounts so that is sort of striking to me i would have thought it would be netapp so that's something that we want to pay attention to but i do like a lot of what netapp is doing uh and other than pure they're the only big kind of pure play in primary storage so long winded uh uh intro there eric but anything you'd add no actually i appreciate it was long winded i i'm going to be honest with you storage is not my uh my best sector as far as a researcher and analyst goes uh but i actually think a lot of what you said is spot on um you know we do capture a lot of large organizations spend uh we don't capture much mid and small so i think when you're talking about these large large players like netapp and um you know not looking so good all i would state is that we are capturing really big organizations spending attention so these are names that should be doing better to be quite honest uh in those accounts and you know at least according to our data we're not seeing it and it's long-term depression as you can see uh you know netapp now has a negative spending velocity in this analysis so you know i can go dig around a little bit more but right now the names that i'm hearing are pure cohesity uh um i'm hearing a little bit about hitachi trying to reinvent themselves in the space but you know i'll take a wait-and-see approach on that one but uh pure and cohesity are the ones i'm hearing a lot from our community so storage is transforming to cloud as a service you're seeing things like apex and in green lake from dell and hpe and container storage little so not really a lot of people paying attention to it but pure about a company called portworx which really specializes in container storage and there's many startups there they're trying to really change the way david flynn has a startup in that space he's the guy who started fusion i o so a lot a lot of transformations happening here okay i know it's been a long segment we have to summarize and then let me go through a summary and then i'll give you the last word eric so tech spending appears to be tracking us gdp at six to seven percent this talent shortage could be a blocker to accelerating i.t deployments and that's kind of good news actually for for services companies digital transformation you know it's it remains a priority and that bodes well not only for services but automation uipath went public this week we we profiled that you know extensively that went public last wednesday um organizations they've i said at the top face some tough decisions on how to allocate resources you know running the business growing the business transforming the business and we're seeing a bifurcation of spending and some residual effects on vendors and that remains a theme that we're watching eric your final thoughts yeah i'm going to go back quickly to just the overall macro spending because there's one thing i think is interesting to point out and we're seeing a real acceleration among mid and small so it seems like early on in the covid recovery or kovitz spending it was the deep pockets that moved first right fortune 500 knew they had to support remote work they started spending first round that in the fortune 500 we're only seeing about five percent spent but when you get into mid and small organizations that's creeping up to eight nine so i just think it's important to point out that they're playing catch-up right now uh also would point out that this is heavily skewed to north america spending we're seeing laggards in emea they just don't seem to be spending as much they're in a very different place in their recovery and uh you know i do think that it's important to point that out um lastly i also want to mention i know you do such a great job on following a lot of the disruptive vendors that you just pointed out pure doing container storage we also have another bi-annual survey that we do called emerging technology and that's for the private names that's going to be launching in may for everyone out there who's interested in not only the disruptive vendors but also private equity players uh keep an eye out for that we do that twice a year and that's growing in its respondents as well and then lastly one comment because you mentioned the uipath ipo it was really hard for us to sit on the sidelines and not put some sort of rating on their data set but ultimately um the data was muted unfortunately and when you're seeing this kind of hype into an ipo like we saw with snowflake the data was resoundingly strong we had no choice but to listen to what the data said for snowflake despite the hype um we didn't see that for uipath and we wanted to and i'm not making a large call there but i do think it's interesting to juxtapose the two that when snowflake was heading to its ipo the data was resoundingly positive and for uipath we just didn't see that thank you for that and eric thanks for coming on today it's really a pleasure to have you and uh so really appreciate the the uh collaboration and look forward to doing more of these we enjoy the partnership greatly dave we're very very happy to have you in the etr family and looking forward to doing a lot lot more with you in the future ditto okay that's it for today remember these episodes are all available as podcasts wherever you listen all you got to do is search breaking analysis podcast and please subscribe to the series check out etr's website it's etr dot plus we also publish a full report every week on wikibon.com at siliconangle.com you can email me david.velante at siliconangle.com you can dm me on twitter at dvalante or comment on our linkedin post i could see you in clubhouse this is dave vellante for eric porter bradley for the cube insights powered by etr have a great week stay safe be well and we'll see you next time

>> From theCUBE studios in Palo Alto and Boston, Bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Recent survey data from ETR shows that enterprise tech spending is tracking with projected US GDP growth at six to 7% this year. Many markers continue to point the way to a strong recovery, including hiring trends and the loosening of frozen IT Project budgets. However skills shortages are blocking progress at some companies which bodes well for an increased reliance on external IT services. Moreover, while there's much talk about the rotation out of work from home plays and stocks such as video conferencing, VDI, and other remote worker tech, we see organizations still trying to figure out the ideal balance between funding headquarter investments that have been neglected and getting hybrid work right. In particular, the talent gap combined with a digital mandate, means companies face some tough decisions as to how to fund the future while serving existing customers and transforming culturally. Hello everyone, and welcome to this week's Wikibon CUBE's Insights powered by ETR. In this "Breaking Analysis", we welcome back Erik Porter Bradley of ETR who will share fresh data, perspectives and insights from the latest survey data. Erik, great to see you. Welcome. >> Thank you very much, Dave. Always good to see you and happy to be on the show again. >> Okay, we're going to share some macro data and then we're going to dig into some highlights from ETR's most recent March COVID survey and also the latest April data. So Erik, the first chart that we want to show, it shows CIO and IT buyer responses to expected IT spend for each quarter of 2021 versus 2020, and you can see here a steady quarterly improvement. Erik, what are the key takeaways, from your perspective? >> Sure, well, first of all, for everyone out there, this particular survey had a record-setting number of participation. We had a 1,500 IT decision makers participate and we had over half of the Fortune 500 and over a fifth of the Global 1000. So it was a really good survey. This is seventh iteration of the COVID Impact Survey specifically, and this is going to transition to an overlarge macro survey going forward so we can continue it. And you're 100% right, what we've been tracking here since March of last year was, how is spending being impacted because of COVID? Where is it shifting? And what we're seeing now finally is that there is a real re-acceleration in spend. I know we've been a little bit more cautious than some of the other peers out there that just early on slapped an eight or a 9% number, but what we're seeing is right now, it's at a midpoint of over six, about 6.7% and that is accelerating. So, we are still hopeful that that will continue, and really, that spending is going to be in the second half of the year. As you can see on the left part of this chart that we're looking at, it was about 1.7% versus 3% for Q1 spending year-over-year. So that is starting to accelerate through the back half. >> I think it's prudent to be cautious (indistinct) 'cause normally you'd say, okay, tech is going to grow a couple of points higher than GDP, but it's really so hard to predict this year. Okay, the next chart here that we want to show you is we asked respondents to indicate what strategies they're employing in the short term as a result of coronavirus and you can see a few things that I'll call out and then I'll ask Erik to chime in. First, there's been no meaningful change of course, no surprise in tactics like remote work and holding travel, however, we're seeing very positive trends in other areas trending downward, like hiring freezes and freezing IT deployments, a downward trend in layoffs, and we also see an increase in the acceleration of new IT deployments and in hiring. Erik, what are your key takeaways? >> Well, first of all, I think it's important to point out here that we're also capturing that people believe remote work productivity is still increasing. Now, the trajectory might be coming down a little bit, but that is really key, I think, to the backdrop of what's happening here. So people have a perception that productivity of remote work is better than hybrid work and that's from the IT decision makers themselves, but what we're seeing here is that, most importantly, these organizations are citing plans to increase hiring, and that's something that I think is really important to point out. It's showing a real following, and to your point right in the beginning of the intro, we are seeing deployments stabilize versus prior survey levels, which means early on, they had no plans to launch new tech deployments, then they said, "Nope, we're going to start." and now that stalling, and I think it's exactly right, what you said, is there's an IT skills shortage. So people want to continue to do IT deployments 'cause they have to support work from home and a hybrid back return to the office, but they just don't have the skills to do so, and I think that's really probably the most important takeaway from this chart, is that stalling and to really ask why it's stalling. >> Yeah, so we're going to get into that for sure, and I think that's a really key point, is that accelerating IT deployments, it looks like it's hit a wall in the survey, but before we get deep into the skills, let's take a look at this next chart, and we're asking people here how our return to the new normal, if you will, and back to offices is going to change spending with on-prem architectures and applications. And so the first two bars, they're Cloud-friendly, if you add them up, it's 63% of the respondents, say that either they'll stay in the Cloud for the most part, or they're going to lower their on-prem spend when they go back to the office. The next three bars are on-prem friendly. If you add those up it's 29% of the respondents say their on-prem spend is going to bounce back to pre-COVID levels or actually increase, and of course, 12% of that number, by the way, say they've never altered their on-prem spend. So Erik, no surprise, but this bodes well for Cloud, but isn't it also a positive for on-prem? We've had this dual funding premise, meaning Cloud continues to grow, but neglected data center spend also gets a boost. What's your thoughts? >> Really, it's interesting. It's people are spending on all fronts. You and I were talking in the prep, it's like we're in battle and I've got naval, I've got air, I've got land, I've got to spend on Cloud and digital transformation, but I also have to spend for on-prem. The hybrid work is here and it needs to be supported. So this is spending is going to increase. When you look at this chart, you're going to see though, that roughly 36% of all respondents say that their spending is going to remain mostly on Cloud. So that is still the clear direction, digital transformation is still happening, COVID accelerated it greatly, you and I, as journalists and researchers already know this is where the puck is going, but spend has always lagged a little bit behind 'cause it just takes some time to get there. Inversely, 27% said that their on-prem spending will decrease. So when you look at those two, I still think that the trend is the friend for Cloud spending, even though, yes, they do have to continue spending on hybrid, some of it's been neglected, there are refresh cycles coming up, so, overall it just points to more and more spending right now. It really does seem to be a very strong backdrop for IT growth. >> So I want to talk a little bit about the ETR taxonomy before we bring up the next chart. We get a lot of questions about this, and of course, when you do a massive survey like you're doing, you have to have consistency for time series, so you have to really think through what the buckets look like, if you will. So this next chart takes a look at the ETR taxonomy and it breaks it down into simple-to-understand terms. So the green is the portion of spending on a vendor's tech within a category that is accelerating, and the red is the portion that is decelerating. So Erik, what are the key messages in this data? >> Well, first of all, Dave, thank you so much for pointing that out. We used to do, just what we call a Net score. It's a proprietary formula that we use to determine the overall velocity of spending. Some people found it confusing. Our data scientists decided to break this sector, break down into what you said, which is really more of a mode analysis. In that sector, how many of the vendors are increasing versus decreasing? So again, I just appreciate you bringing that up and allowing us to explain the reasoning behind our analysis there. But what we're seeing here goes back to something you and I did last year when we did our predictions, and that was that IT services and consulting was going to have a true rebound in 2021, and that's what this is showing right here. So in this chart, you're going to see that consulting and services are really continuing their recovery, 2020 had a lot of the clients and they have the biggest sector year-over-year acceleration sector wise. The other thing to point out on this, which we'll get to again later, is that the inverse analysis is true for video conferencing. We will get to that, so I'm going to leave a little bit of ammunition behind for that one, but what we're seeing here is IT consulting services being the real favorable and video conferencing having a little bit more trouble. >> Great, okay, and then let's take a look at that services piece, and this next chart really is a drill down into that space and emphasizes, Erik, what you were just talking about. And we saw this in IBM's earnings, where still more than 60% of IBM's business comes from services and the company beat earnings, in part, due to services outperforming expectations, I think it had a somewhat easier compare and some of this pent-up demand that we've been talking about bodes well for IBM and other services companies, it's not just IBM, right, Erik? >> No, it's not, but again, I'm going to point out that you and I did point out IBM in our predictions when we did in late December, so, it is nice to see. One of the reasons we don't have a more favorable rating on IBM at the moment is because they are in the process of spinning out this large unit, and so there's a little bit of a corporate action there that keeps us off on the sideline. But I would also want to point out here, Tata, Infosys and Cognizant 'cause they're seeing year-over-year acceleration in both IT consulting and outsourced IT services. So we break those down separately and those are the three names that are seeing acceleration in both of those. So again, at the Tata, Infosys and Cognizant are all looking pretty well positioned as well. >> So we've been talking a little bit about this skills shortage, and this is what's, I think, so hard for forecasters, is that in the one hand, There's a lot of pent up demand, Scott Gottlieb said it's like Woodstock coming out of the COVID, but on the other hand, if you have a talent gap, you've got to rely on external services. So there's a learning curve, there's a ramp up, it's an external company, and so it takes time to put those together. So this data that we're going to show you next, is really important in my view and ties what we were saying at the top. It asks respondents to comment on their staffing plans. The light blue is "We're increasing staff", the gray is "No change" and the magenta or whatever, whatever color that is that sort of purplish color, anyway, that color is decreasing, and the picture is very positive across the board. Full-time staff, offshoring, contract employees, outsourced professional services, all up trending upwards, and this Erik is more evidence of the services bounce back. >> Yeah, it's certainly, yes, David, and what happened is when we caught this trend, we decided to go one level deeper and say, all right, we're seeing this, but we need to know why, and that's what we always try to do here. Data will tell you what's happening, it doesn't always tell you why, and that's one of the things that ETR really tries to dig in with through the insights, interviews panels, and also going direct with these more custom survey questions. So in this instance, I think the real takeaway is that 30% of the respondents said that their outsourced and managed services are going to increase over the next three months. That's really powerful, that's a large portion of organizations in a very short time period. So we're capturing that this acceleration is happening right now and it will be happening in real time, and I don't see it slowing down. You and I are speaking about we have to increase Cloud spend, we have to increase hybrid spend, there are refresh cycles coming up, and there's just a real skills shortage. So this is a long-term setup that bodes very well for IT services and consulting. >> You know, Erik, when I came out of college, somebody told me, "Read, read, read, read as much as you can." And then they said, "Read the Wall Street Journal every day." and so I did it, and I would read the tech magazines and back then it was all paper, and what happens is you begin to connect the dots. And so the reason I bring that up is because I've now taken a bath in the ETR data for the better part of two years and I'm beginning to be able to connect the dots. The data is not always predictive, but many, many times it is. And so this next data gets into the fun stuff where we name names. A lot of times people don't like it because they're either marketing people at organizations, say, "Well, data's wrong." because that's the first thing they do, is attack the data. But you and I know, we've made some really great calls, work from home, for sure, you're talking about the services bounce back. We certainly saw the rise of CrowdStrike, Okta, Zscaler, well before people were talking about that, same thing with video conferencing. And so, anyway, this is the fun stuff and it looks at positive versus negative sentiment on companies. So first, how does ETR derive this data and how should we interpret it, and what are some of your takeaways? >> Sure, first of all, how we derive the data, are systematic survey responses that we do on a quarterly basis, and we standardize those responses to allow for time series analysis so we can do trend analysis as well. We do find that our data, because it's talking about forward-looking spending intentions, is really more predictive because we're talking about things that might be happening six months, three months in the future, not things that a lot of other competitors and research peers are looking at things that already happened, they're looking in the past, ETR really likes to look into the future and our surveys are set up to do so. So thank you for that question, It's a enjoyable lead in, but to get to the fun stuff, like you said, what we do here is we put ratings on the datasets. I do want to put the caveat out there that our spending intentions really only captures top-line revenue. It is not indicative of profit margin or any other line items, so this is only to be viewed as what we are rating the data set itself, not the company, that's not what we're in the game of doing. So I think that's very important for the marketing and the vendors out there themselves when they take a look at this. We're just talking about what we can control, which is our data. We're going to talk about a few of the names here on this highlighted vendors list. One, we're going to go back to that you and I spoke about, I guess, about six months ago, or maybe even earlier, which was the observability space. You and I were noticing that it was getting very crowded, a lot of new entrants, there was a lot of acquisition from more of the legacy or standard players in the space, and that is continuing. So I think in a minute, we're going to move into that observability space, but what we're seeing there is that it's becoming incredibly crowded and we're possibly seeing signs of them cannibalizing each other. We're also going to move on a little bit into video conferencing, where we're capturing some spend deceleration, and then ultimately, we're going to get into a little bit of a storage refresh cycle and talk about that. But yeah, these are the highlighted vendors for April, we usually do this once a quarter and they do change based on the data, but they're not usually whipsawed around, the data doesn't move that quickly. >> Yeah, so you can see some of the big names in the left-hand side, some of the SAS companies that have momentum. Obviously, ServiceNow has been doing very, very well. We've talked a lot about Snowflake, Okta, CrowdStrike, Zscaler, all very positive, as well as several others. I guess I'd add some things. I mean, I think if thinking about the next decade, it's Cloud, which is not going to be like the same Cloud as the last decade, a lot of machine learning and deep learning and AI and the Cloud is extending to the edge and the data center. Data, obviously, very important, data is decentralized and distributed, so data architectures are changing. A lot of opportunities to connect across Clouds and actually create abstraction layers, and then something that we've been covering a lot is processor performance is actually accelerating relative to Moore's law. It's probably instead of doubling every two years, it's quadrupling every two years, and so that is a huge factor, especially as it relates to powering AI and AI inferencing at the edge. This is a whole new territory, custom Silicon is really becoming in vogue and so something that we're watching very, very closely. >> Yeah, I completely, agree on that and I do think that the next version of Cloud will be very different. Another thing to point out on that too, is you can't do anything that you're talking about without collecting the data and organizations are extremely serious about that now. It seems it doesn't matter what industry they're in, every company is a data company, and that also bodes well for the storage goal. We do believe that there is going to just be a huge increase in the need for storage, and yes, hopefully that'll become portable across multi-Cloud and hybrid as well. >> Now, as Erik said, the ETR data, it's really focused on that top-line spend. So if you look on the right side of that chart, you saw NetApp was kind of negative, was very negative, right? But it is a company that's in transformation now, they've lowered expectations and they've recently beat expectations, that's why the stock has been doing better, but at the macro, from a spending standpoint, it's still stout challenged. So you have big footprint companies like NetApp and Oracle is another one. Oracle's stock is at an all time high, but the spending relative to sort of previous cycles are relative to, like for instance, Snowflake, much, much smaller, not as high growth, but they're managing expectations, they're managing their transition, they're managing profitability. Zoom is another one, Zoom looking negative, but Zoom's got to use its market cap now to transform and increase its TAM. And then Splunk is another one we're going to talk about. Splunk is in transition, it acquired SignalFX, It just brought on this week, Teresa Carlson, who was the head of AWS Public Sector. She's the president and head of sales, so they've got a go-to-market challenge and they brought in Teresa Carlson to really solve that, but Splunk has been trending downward, we called that several quarters ago, Erik, and so I want to bring up the data on Splunk, and this is Splunk, Erik, in analytics, and it's not trending in the right direction. The green is accelerating spend, the red is in the bars is decelerating spend, the top blue line is spending velocity or Net score, and the yellow line is market share or pervasiveness in the dataset. Your thoughts. >> Yeah, first I want to go back. There's a great point, Dave, about our data versus a disconnect from an equity analysis perspective. I used to be an equity analyst, that is not what we do here. And the main word you said is expectations, right? Stocks will trade on how they do compare to the expectations that are set, whether that's buy-side expectations, sell-side expectations or management's guidance themselves. We have no business in tracking any of that, what we are talking about is the top-line acceleration or deceleration. So, that was a great point to make, and I do think it's an important one for all of our listeners out there. Now, to move to Splunk, yes, I've been capturing a lot of negative commentary on Splunk even before the data turns. So this has been a about a year-long, our analysis and review on this name and I'm dating myself here, but I know you and I are both rock and roll fans, so I'm going to point out a Led Zeppelin song and movie, and say that the song remains the same for Splunk. We are just seeing recent spending attentions are taking yet another step down, both from prior survey levels, from year ago levels. This, we're looking at in the analytics sector and spending intentions are decelerating across every single group, and we went to one of our other slide analysis on the ETR+ platform, and you do by customer sub-sample, in analytics, it's dropping in every single vertical. It doesn't matter which one. it's really not looking good, unfortunately, and you had mentioned this is an analytics and I do believe the next slide is an information security. >> Yeah, let's bring that up. >> And unfortunately it's not doing much better. So this is specifically Fortune 500 accounts and information security. There's deep pockets in the Fortune 500, but from what we're hearing in all the insights and interviews and panels that I personally moderate for ETR, people are upset, that they didn't like the strong tactics that Splunk has used on them in the past, they didn't like the ingestion model pricing, the inflexibility, and when alternatives came along, people are willing to look at the alternatives, and that's what we're seeing in both analytics and big data and also for their SIM and security. >> Yeah, so I think again, I pointed Teresa Carlson. She's got a big job, but she's very capable. She's going to meet with a lot of customers, she's a go-to-market pro, she's going to to have to listen hard, and I think you're going to see some changes there. Okay, so sorry, there's more bad news on Splunk. So (indistinct) bring this up is Net score for Splunk and Elastic accounts. This is for analytics, so there's 106 Elastic accounts in the dataset that also have Splunk and it's trending downward for Splunk, that's why it's green for Elastic. And Erik, the important call out from ETR here is how Splunk's performance in Elastic accounts compares with its performance overall. The ELK stack, which obviously Elastic is a big part of that, is causing pain for Splunk, as is Datadog, and you mentioned the pricing issue, well, is it pricing in your assessment or is it more fundamental? >> It's multi-level based on the commentary we get from our ITDMs teams that take the survey. So yes, you did a great job with this analysis. What we're looking at is the spending within shared accounts. So if I have Splunk already, how am I spending? I'm sorry if I have Elastic already, how am I spending on Splunk? And what you're seeing here is it's down to about a 12% Net score, whereas Splunk overall, has a 32% Net score among all of its customers. So what you're seeing there is there is definitely a drain that's happening where Elastic is draining spend from Splunk and usage from them. The reason we used Elastic here is because all observabilities, the whole sector seems to be decelerating. Splunk is decelerating the most, but Elastic is the only one that's actually showing resiliency, so that's why we decided to choose these two, but you pointed out, yes, it's also Datadog. Datadog is Cloud native. They're more dev ops-oriented. They tend to be viewed as having technological lead as compared to Splunk. So a really good point. Dynatrace also is expanding their abilities and Splunk has been making a lot of acquisitions to push their Cloud services, they are also changing their pricing model, right? They're trying to make things a little bit more flexible, moving off ingestion and moving towards consumption. So they are trying, and the new hires, I'm not going to bet against them because the one thing that Splunk has going for them is their market share in our survey, they're still very well entrenched. So they do have a lot of accounts, they have their foothold. So if they can find a way to make these changes, then they will be able to change themselves, but the one thing I got to say across the whole sector is competition is increasing, and it does appear based on commentary and data that they're starting to cannibalize themselves. It really seems pretty hard to get away from that, and you know there are startups in the observability space too that are going to be even more disruptive. >> I think I want to key on the pricing for a moment, and I've been pretty vocal about this. I think the old SAS pricing model where you essentially lock in for a year or two years or three years, pay up front, or maybe pay quarterly if you're lucky, that's a one-way street and I think it's a flawed model. I like what Snowflake's doing, I like what Datadog's doing, look at what Stripe is doing, look at what Twilio is doing, you mentioned it, it's consumption-based pricing, and if you've got a great product, put it out there and damn, the torpedoes, and I think that is a game changer. I look at, for instance, HPE with GreenLake, I look at Dell with Apex, they're trying to mimic that model and apply it to infrastructure, it's much harder with infrastructure 'cause you've got to deploy physical infrastructure, but that is a model that I think is going to change, and I think all of the traditional SAS pricing is going to come under disruption over the next better part of the decades, but anyway, let's move on. We've been covering the APM space pretty extensively, application performance management, and this chart lines up some of the big players here. Comparing Net score or spending momentum from the April 20th survey, the gray is, sorry, the gray is the April 20th survey, the blue is Jan 21 and the yellow is April 21, and not only are Elastic and Datadog doing well relative to Splunk, Erik, but everything is down from last year. So this space, as you point out, is undergoing a transformation. >> Yeah, the pressures are real and it's sort of that perfect storm where it's not only the data that's telling us that, but also the direct feedback we get from the community. Pretty much all the interviews I do, I've done a few panels specifically on this topic, for anyone who wants to dive a little bit deeper. We've had some experts talk about this space and there really is no denying that there is a deceleration in spend and it's happening because that spend is getting spread out among different vendors. People are using a Datadog for certain aspects, they are using Elastic where they can 'cause it's cheaper. They're using Splunk because they have to, but because it's so expensive, they're cutting some of the things that they're putting into Splunk, which is dangerous, particularly on the security side. If I have to decide what to put in and whatnot, that's not really the right way to have security hygiene. So this space is just getting crowded, there's disruptive vendors coming from the emerging space as well, and what you're seeing here is the only bit of positivity is Elastic on a survey-over-survey basis with a slight, slight uptick. Everywhere else, year-over-year and survey-over-survey, it's showing declines, it's just hard to ignore. >> And then you've got Dynatrace who, based on the interviews you do in the (indistinct), one-on-one, or one-on-five, the private interviews that I've been invited to, Dynatrace gets very high scores for their roadmap. You've got New Relic, which has been struggling financially, but they've got a really good product and a purpose-built database just for this APM space, and then of course, you've got Cisco with AppD, which is a strong business for them, and then as you mentioned, you've got startups coming in, you got ChaosSearch, which Ed Walsh is now running, leave the data in place in AWS and really interesting model, Honeycomb is getting really disruptive, Jeremy Burton's company, Observed. So this space is it's becoming jumped ball. >> Yeah, there's a great line that came out of one of them, and that was that the lines are blurring. It used to be that you knew exactly that AppDynamics, what they were doing, it was APM only, or it was logging and monitoring only, and a lot of what I'm hearing from the ITDM experts is that the lines are blurring amongst all of these names. They all have functionality that kind of crosses over each other. And the other interesting thing is it used to be application versus infrastructure monitoring, but as you know, infrastructure is becoming code more and more and more, and as infrastructure becomes code, there's really no difference between application and infrastructure monitoring. So we're seeing a convergence and a blurring of the lines in this space, which really doesn't bode well, and a great point about New Relic, their tech gets good remarks. I just don't know if their enterprise level service and sales is up to snuff right now. As one of my experts said, a CTO of a very large public online hospitality company essentially said that he would be shocked that within 18 months if all of these players are still standalone, that there needs to be some M and A or convergence in this space. >> Okay, now we're going to call out some of the data that really has jumped out to ETR in the latest survey, and some of the names that are getting the most queries from ETR clients, many of which are investor clients. So let's start by having a look at one of the most important and prominent work from home names, Zoom. Let's look at this. Erik is the ride over for Zoom? >> Ah, I've been saying it for a little bit of a time now actually. I do believe it is, and we'll get into it, but again, pointing out, great, Dave, the reason we're presenting today Splunk, Elastic and Zoom, they are the most viewed on the ETR+ platform. Trailing behind that only slightly is F5, I decided not to bring F5 to the table today 'cause we don't have a rating on the data set. So then I went one deep, one below that and it's pure. So the reason we're presenting these to you today is that these are the ones that our clients and our community are most interested in, which is hopefully going to gain interest to your viewers as well. So to get to Zoom, yeah, I call Zoom the pandemic bull market baby. This was really just one that had a meteoric ride. You look back, January in 2020, the stock was at $60 and 10 months later, it was like 580, that's in 10 months. That's cooled down a little bit into the mid-300s, and I believe that cooling down should continue, and the reason why is because we are seeing huge deceleration in our spending intentions. They're hitting all-time lows, it's really just a very ugly dataset. More importantly than the spending intentions, for the first time, we're seeing customer growth in our survey flatten. In the past, we knew that the deceleration of spend was happening, but meanwhile, their new customer growth was accelerating, so it was kind of hard to really make any call based on that. This is the first time we're seeing flattening customer growth trajectory, and that in tandem with just dominance from Microsoft in every sector they're involved in, I don't care if it's IP telephony, productivity apps or the core video conferencing, Microsoft is just dominating. So there's really just no way to ignore this anymore. The data and the commentary state that Zoom is facing some headwinds. >> Well, plus you've pointed out to me that a lot of your private conversations with buyers says that, "Hey, we're, we're using the freebie version of Zoom, and we're not paying them." And that combined with Teams, I mean, it's... I think, look, Zoom, they've got to figure out how to use their elevated market cap to transform and expand their TAM, but let's move on. Here's the data on Pure Storage and we've highlighted a number of times this company is showing elevated spending intentions. Pure announced it's earnings in May, IBM just announced storage, it was way down actually. So still, Pure, more positive, but I'll on that comment in a moment, but what does this data tell you, Erik? >> Yeah, right now we started seeing this data last survey in January, and that was the first time we really went positive on the data set itself, and it's just really continuing. So we're seeing the strongest year-over-year acceleration in the entire survey, which is a really good spot to be. Pure is also a leading position among its sector peers, and the other thing that was pretty interesting from the data set is among all storage players, Pure has the highest positive public Cloud correlation. So what we can do is we can see which respondents are accelerating their public Cloud spend and then cross-reference that with their storage spend and Pure is best positioned. So as you and I both know, digital transformation Cloud spending is increasing, you need to be aligned with that. And among all storage sector peers, Pure is best positioned in all of those, in spending intentions and adoptions and also public Cloud correlation. So yet again, to start another really strong dataset, and I have an anecdote about why this might be happening, because when I saw the data, I started asking in my interviews, what's going on here? And there was one particular person, he was a director of Cloud operations for a very large public tech company. Now, they have hybrid, but their data center is in colo, So they don't own and build their own physical building. He pointed out that during COVID, his company wanted to increase storage, but he couldn't get into his colo center due to COVID restrictions. They weren't allowed. You had 250,000 square feet, right, but you're only allowed to have six people in there. So it's pretty hard to get to your rack and get work done. He said he would buy storage, but then the colo would say, "Hey, you got to get it out of here. It's not even allowed to sit here. We don't want it in our facility." So he has all this pent up demand. In tandem with pent up demand, we have a refresh cycle. The SSD depreciation cycle is ending. SSDs are moving on and we're starting to see a new technology in that space, NVMe sorry, technology increasing in that space. So we have pent up demand and we have new technology and that's really leading to a refresh cycle, and this particular ITDM that I spoke to and many of his peers think this has a long tailwind that storage could be a good sector for some time to come. >> That's really interesting, thank you for that extra metadata. And I want to do a little deeper dive on storage. So here's a look at storage in the industry in context and some of the competitive. I mean, it's been a tough market for the reasons that we've highlighted, Cloud has been eating away that flash headroom. It used to be you'd buy storage to get more spindles and more performance and we're sort of forced to buy more, flash, gave more headroom, but it's interesting what you're saying about the depreciation cycle. So that's good news. So ETR combines, just for people's benefit here, combines primary and secondary storage into a single category. So you have companies like Pure and NetApp, which are really pure play primary storage companies, largely in the sector, along with Veeam, Cohesity and Rubrik, which are kind of secondary data or data protection. So my quick thoughts here that Pure is elevated and remains what I call the one-eyed man in the land of the blind, but that's positive tailwinds there, so that's good news. Rubrik is very elevated but down, it's big competitor, Cohesity is way off its highs, and I have to say to me, Veeam is like the Steady Eddy consistent player here. They just really continue to do well in the data protection business, and the highs are steady, the lows are steady. Dell is also notable, they've been struggling in storage. Their ISG business, which comprises servers and storage, it's been softer in COVID, and during even this new product rollout, so it's notable with this new mid range they have in particular, the uptick in Dell, this survey, because Dell is so large, a small uptick can be very good for Dell. HPE has a big announcement next month in storage, so that might improve based on a product cycle. Of course, the Nimble brand continues to do well, IBM, as I said, just announced a very soft quarter, down double digits again, and they're in a product cycle shift. And NetApp, it looks bad in the ETR data from a spending momentum standpoint, but their management team is transforming the company into a Cloud play, which Erik is why it was interesting that Pure has the greatest momentum in Cloud accounts, so that is sort of striking to me. I would have thought it would be NetApp, so that's something that we want to pay attention to, but I do like a lot of what NetApp is doing, and other than Pure, they're the only big kind of pure play in primary storage. So long-winded, intro there, Erik, but anything you'd add? >> No, actually I appreciate it as long-winded. I'm going to be honest with you, storage is not my best sector as far as a researcher and analyst goes, but I actually think that a lot of what you said is spot on. We do capture a lot of large organizations spend, we don't capture much mid and small, so I think when you're talking about these large, large players like NetApp not looking so good, all I would state is that we are capturing really big organization spending attention, so these are names that should be doing better to be quite honest, in those accounts, and at least according to our data, we're not seeing it in. It's longterm depression, as you can see, NetApp now has a negative spending velocity in this analysis. So, I can go dig around a little bit more, but right now the names that I'm hearing are Pure, Cohesity. I'm hearing a little bit about Hitachi trying to reinvent themselves in the space, but I'll take a wait-and-see approach on that one, but pure Cohesity are the ones I'm hearing a lot from our community. >> So storage is transforming to Cloud as a service. You've seen things like Apex in GreenLake from Dell and HPE and container storage. A little, so not really a lot of people paying attention to it, but Pure bought a company called Portworx which really specializes in container storage, and there's many startups there, they're trying to really change the way. David Flynn, has a startup in that space, he's the guy who started Fusion-io. So a lot of transformations happening here. Okay, I know it's been a long segment, we have to summarize, and let me go through a summary and then I'll give you the last word, Erik. So tech spending appears to be tracking US GDP at 6 to 7%. This talent shortage could be a blocker to accelerating IT deployments, so that's kind of good news actually for services companies. Digital transformation, it remains a priority, and that bodes, well, not only for services, but automation. UiPath went public this week, we profiled that extensively, that went public last Wednesday. Organizations that sit at the top face some tough decisions on how to allocate resources. They're running the business, growing the business, transforming the business, and we're seeing a bifurcation of spending and some residual effects on vendors, and that remains a theme that we're watching. Erik, your final thoughts. >> Yeah, I'm going to go back quickly to just the overall macro spending, 'cause there's one thing I think is interesting to point out and we're seeing a real acceleration among mid and small. So it seems like early on in the COVID recovery or COVID spending, it was the deep pockets that moved first, right? Fortune 500 knew they had to support remote work, they started spending first. Around that in the Fortune 500, we're only seeing about 5% spend, but when you get into mid and small organizations, that's creeping up to eight, nine. So I just think it's important to point out that they're playing catch up right now. I also would point out that this is heavily skewed to North America spending. We're seeing laggards in EMEA, they just don't seem to be spending as much. They're in a very different place in their recovery, and I do think that it's important to point that out. Lastly, I also want to mention, I know you do such a great job on following a lot of the disruptive vendors that you just pointed out, with Pure doing container storage, we also have another bi-annual survey that we do called Emerging Technology, and that's for the private names. That's going to be launching in May, for everyone out there who's interested in not only the disruptive vendors, but also private equity players. Keep an eye out for that. We do that twice a year and that's growing in its respondents as well. And then lastly, one comment, because you mentioned the UiPath IPO, it was really hard for us to sit on the sidelines and not put some sort of rating on their dataset, but ultimately, the data was muted, unfortunately, and when you're seeing this kind of hype into an IPO like we saw with Snowflake, the data was resoundingly strong. We had no choice, but to listen to what the data said for Snowflake, despite the hype. We didn't see that for UiPath and we wanted to, and I'm not making a large call there, but I do think it's interesting to juxtapose the two, that when snowflake was heading to its IPO, the data was resoundingly positive, and for UiPath, we just didn't see that. >> Thank you for that, and Erik, thanks for coming on today. It's really a pleasure to have you, and so really appreciate the collaboration and look forward to doing more of these. >> Yeah, we enjoy the partnership greatly, Dave. We're very happy to have you on the ETR family and looking forward to doing a lot, lot more with you in the future. >> Ditto. Okay, that's it for today. Remember, these episodes are all available as podcasts wherever you listen. All you have to do is search "Breaking Analysis" podcast, and please subscribe to the series. Check out ETR website it's etr.plus. We also publish a full report every week on wikibon.com and siliconangle.com. You can email me, david.vellante@siliconangle.com, you can DM me on Twitter @dvellante or comment on our LinkedIn posts. I could see you in Clubhouse. This is Dave Vellante for Erik Porter Bradley for the CUBE Insights powered by ETR. Have a great week, stay safe, be well and we'll see you next time. (bright music)

>> Announcer: From around the globe, it's theCUBE, with digital coverage of Mirantis Launchpad 2020, brought to you by Mirantis. >> Welcome back I'm Stu Miniman, and this is theCUBE's coverage of Mirantis Launchpad 2020. Big event, multiple tracks powered by theCUBE365. Happy to welcome you to the program. We have a first time guest, Willem du Plessis. He's the Director of Customer Success and Operations with Mirantis. Willem, thanks so much for joining us. >> Hi Stu, thanks for having me. >> So customer success, of course, a big topic in the industry last few years. CX a is so important. Employee success and enabling that, but what, give us a little bit, your background and the purview that you and your team cover. >> Exactly, yeah, so everything under my umbrella would be basically post-sales. The whole customer experience after the point of a sale's been made so the whole account management, thereafter, the success of the accounts, as well as the health of the account, thereafter, that will be anything basically post-sales would be under my umbrella. >> Wonderful, well, the big piece is the shift. As we know, software went from shrink wrapped, and hardware talking about CapX to the cloud really ushered in OpX we're touching more subscription managed services and the like, so Mirantis has a subscription offering. Why don't you lay out for us the new pieces of this and how Mirantis puts together its offerings? >> Yeah, absolutely. So with the launch of our new product, Docker Enterprise Container Cloud, we're making two subscriptions available as well, named ProdCare, which is a 24/7 mission critical support offering and OpsCare, being a fully managed platform as a service subscription. Now, these offerings have been available on the Mirantis Cloud platform side of our business for quite some time, we've been very successful with them, so it's really excited making them available to our Docker Enterprise customers. So what we're trying to achieve with these accounts or with these subscriptions, rather, you know, 30% of the Fortune 100 companies are Mirantis customers, so we work on a day to day basis with their container and Kubernetes initiatives. So when we speak to these customers, there are really two trends that are becoming very clear, the first being the requirements of service providers or vendors being able to provide a true 24/7 experience. What I mean by that is not the ability to just react to an incident on a 24/7 basis. That's what I mean, what I mean is all of these companies would have operation centers spread across the globe. So it is at every hour of the day, it would be business as usual. And what these companies require is a, a partner or a service provider that can match that level, that way of operating. That is the first trend that we're noting. The second piece is really the, the evolution of the dev environment. The dev environment is no longer really seen as a secondary or a lower class citizen, if you want to call it, it's really become part of the whole DevOps pipeline, so it is really part of a mission critical process so that what customers, what we hear from our customers is that they require a real enterprise-grade subscription that they can cover this whole pipeline under and, you know, have the same quality of service from whether that is a dev or a production environment. So if you have a failure on your dev environment and your developer cannot push code, that is, is the same level of criticality than there would then they would be on if the failure was on the production environment. So this whole pipeline is decidedly seen as a mission critical component. And that's a great, that's really where ProdCare comes in. It is really this 24/7 mission critical follow the sun, enterprise-grade subscription that provides our customers with enhanced SLAs that, like I said, we've been running on the Mirantis Cloud platform side for quite some time, we've had some significant success with some really large companies. The second offering that we're making available with, like I said, is OpsCare. Now OpsCare is an ITIL-based managed service subscription, where we provide a platform as a service experience to a customer on their infrastructure of choice. So it is really irrelevant for us what your infrastructure is, whether that is on-prem or in the public cloud, as long as the product can support the infrastructure, you know, the subscription would be available for you and the experience would be very much the same. So what OpsCare, like I said, entails is, is this whole ITIL framework that would include, you know, the monitoring and managing of your alerts, the incident management process, the problem management process, as well as change management that would include the lifecycle management of the whole environment. And that would just enable our customers to run on the latest and greatest offer of our product at all times. And same as with ProdCare that's been available for our brass cloud platform customers for quite a while, and have seen some significant success with that, as well. >> Well, we definitely have seen that growth of the managed care offerings like you're talking about with OpsCare, you know, shift left is so important for companies to be able to focus on what's critically important. As you said, developers need to be enabled, it can't just be waiting for things or be, you know, relegated to, you know, have to wait in line or use something that's not optimal. What are some of those outcomes? What can companies do that they weren't able before? What are some of those successes that you're seeing with the managed care OpsCare solution? >> Yeah, so the real way we OpsCare really comes to its own is allowing the customer ability to focus on what is important to their business and spend less time on what we call, keep the lights on. What I mean by that is they're solely focused on developing the application, developing the workload and spend basically no time on managing the infrastructure and, you know, maintaining it, or, you know, providing, do whatever to, to keep the platform stable, because that is done by Mirantis, already. So for example, if we take 2020 year to date, all the platforms running under OpsCare has an availability number of above four nines, and that is a significant number. So that really just sets such a strong foundation for a customer to just have that sole focus on, on what is important to them and, you know, just sets that foundation for them to develop their workload, to develop their business, and achieve their goals. >> Well, what about when it comes to the managing and monitoring of the environment? What kind of metrics are your customers having? Help us understand what the customer still does themselves or the reporting they're getting and what Mirantis, I'm assuming there's probably a Tam involved for at least some of the larger accounts there. Help us understand that shared responsibility, if you would for these type of environments. >> Yeah, exactly. So the whole ITIL framework, as I explained earlier, incident management, problem management, change, all of that, this is wrapped around why a customer success manager that is, you know, brings a single level of ownership on an accountability, and just have a customer direct for a single point of contact as a business partner. So all this is all our customers, their primary KPI or metric that we look at is just the availability of the platform. That is the primary SLA and thereafter, all of the other things happening, you know, the success of the workload and so on, because there's a lot of things that makes the result of the workload, not just the platform or the infrastructure, it's the quality of the workload, and so on, and so forth. But the main metric our customer would be looking at is that availability number, you know, how available and how stable and accessible is the environment, and, you know, like I said, just removing that requirement for them to spend, basically, no time on the platform or the infrastructure, and just focusing on the workload. >> Yeah, when it comes to in the field, your field, your partners, that line between ProdCare and OpsCare, obviously, the trend is going towards, you know, the fully managed option, but what guidance do you have out there, or what trends do you seeing? Is it a certain size company, that tends to be trending that way? Are there certain verticals that may be are further ahead? What's the reality, today? What do you expect to see over the next kind of six, 12 months? >> Yeah, so most of the companies that we see that as, that is engaging with us on an OpsCare, or managed service engagement, you know, they have the ambitions to go down the block model and build, operate, transfer, you know, to take the operations over themselves, at some point, and we have that option available to them, if they wish to choose it further along the line. What we do find is, is that they, that they don't really, you know, exercise that later on. It is, we do find it is such a smooth integration with our customers, that they tend to stay on OpsCare and see the value. This is actually a money saver for them, if they could, just focus their efforts on building, you know, focusing their time on the workload on top of the platform. From a vertical perspective, it's really anything and everything. We have customers in the science and research, we have TELCOs, large manufacturing, manufacturing, a lot of large organizations. There's really the breadth of the verticals that we see that are utilizing OpsCare and not even to mention ProdCare, that's really everything in there, as well. So it is not a really a subscription that is, that is custom for one vertical. It is basically something that we, that any vertical can actually utilize and find a significant amount of value in. >> All right, well, what final words do you have that you want to leave everyone with today? >> Yeah, so over the last six to nine months, you know, we've invested a significant amount of resources in the Docker Enterprise support business and we just with one focus, and that is just to take the support business to the next level and improve or give the customers an optimal customer experience. So with the availability of all these new subscriptions, I'm really excited to engage with our Docker Enterprise customers with these new, enhanced SLAs and just be able to work with them on these, like I said, enhanced subscriptions and just see, just give them a better customer experience. So, I'm really looking forward to working with them on the subscriptions. >> Willem, thank you so much for all the updates and want to welcome everyone to be sure to check out all the rest of the tracks on the Launchpad 2020 event. I'm Stu Miniman and thank you for watching theCUBE. (soft electronic music)