>>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 ante. >>When Frank sluman took service, now public many people undervalued the company, positioning it as just a better help desk tool. You know, it turns out that the firm actually had a massive Tam expansion opportunity in it. SM customer service, HR, logistics, security marketing, and service management. Generally now stock price followed over the years, the stellar execution under Slootman and CFO, Mike scar Kelly's leadership. Now, when they took the reins at snowflake expectations were already set that they'd repeat the feet, but this time, if anything, the company was overvalued out of the gate, the thing is people didn't really better understand the market opportunity this time around, other than that, it was a bet on Salman's track record of execution and on data, pretty good bets, but folks really didn't appreciate that snowflake. Wasn't just a better data warehouse that it was building what they call a data cloud, and we've turned a data super cloud. >>Hello and welcome to this. Week's Wikibon cube insights powered by ETR in this breaking analysis, we'll do four things. First. We're gonna review the recent narrative and concerns about snowflake and its value. Second, we're gonna share survey data from ETR that will confirm precisely what the company's CFO has been telling anyone who will listen. And third, we're gonna share our view of what snowflake is building IE, trying to become the defacto standard data platform, and four convey our expectations for the upcoming snowflake summit. Next week at Caesar's palace in Las Vegas, Snowflake's most recent quarterly results they've been well covered and well documented. It basically hit its targets, which for snowflake investors was bad news wall street piled on expressing concerns about Snowflake's consumption, pricing model, slowing growth rates, lack of profitability and valuation. Given the, given the current macro market conditions, the stock dropped below its IPO offering price, which you couldn't touch on day one, by the way, as the stock opened well above that and, and certainly closed well above that price of one 20 and folks express concerns about some pretty massive insider selling throughout 2021 and early 2022, all this caused the stock price to drop quite substantially. >>And today it's down around 63% or more year to date, but the only real substantive change in the company's business is that some of its largest consumer facing companies, while still growing dialed back, their consumption this past quarter, the tone of the call was I wouldn't say contentious the earnings call, but Scarelli, I think was getting somewhat annoyed with the implication from some analyst questions that something is fundamentally wrong with Snowflake's business. So let's unpack this a bit first. I wanna talk about the consumption pricing on the earnings call. One of the analysts asked if snowflake would consider more of a subscription based model so that they could better weather such fluctuations and demand before the analyst could even finish the question, CFO Scarelli emphatically interrupted and said, no, <laugh> the analyst might as well have asked, Hey Mike, have you ever considered changing your pricing model and screwing your customers the same way most legacy SaaS companies lock their customers in? >>So you could squeeze more revenue out of them and make my forecasting life a little bit easier. <laugh> consumption pricing is one of the things that makes a company like snowflake so attractive because customers is especially large customers facing fluctuating demand can dial and their end demand can dial down usage for certain workloads that are maybe not yet revenue producing or critical. Now let's jump to insider trading. There were a lot of insider selling going on last year and into 2022 now, I mean a lot sloop and Scarelli Christine Kleinman. Mike SP several board members. They sold stock worth, you know, many, many hundreds of millions of dollars or, or more at prices in the two hundreds and three hundreds and even four hundreds. You remember the company at one point was valued at a hundred billion dollars, surpassing the value of service now, which is this stupid at this point in the company's tenure and the insider's cost basis was very often in the single digit. >>So on the one hand, I can't blame them. You know what a gift the market gave them last year. Now also famed investor, Peter Linsey famously said, insiders sell for many reasons, but they only buy for one. But I have to say there wasn't a lot of insider buying of the stock when it was in the three hundreds and above. And so yeah, this pattern is something to watch our insiders buying. Now, I'm not sure we'll keep watching snowflake. It's pretty generous with stock based compensation and insiders still own plenty of stock. So, you know, maybe not, but we'll see in future disclosures, but the bottom line is Snowflake's business. Hasn't dramatically changed with the exception of these large consumer facing companies. Now, another analyst pointed out that companies like snap, he pointed to company snap, Peloton, Netflix, and face Facebook have been cutting back. >>And Scarelli said, and what was a bit of a surprise to me? Well, I'm not gonna name the customers, but it's not the ones you mentioned. So I, I thought I would've, you know, if I were the analyst I would've follow up with, how about Walmart target visa, Amex, Expedia price line, or Uber? Any of those Mike? I, I doubt he would've answered me anything. Anyway, the one thing that Scarelli did do is update Snowflake's fiscal year 2029 outlook to emphasize the long term opportunity that the company sees. This chart shows a financial snapshot of Snowflake's current business using a combination of quarterly and full year numbers in a model of what the business will look like. According to Scarelli in Dave ante with a little bit of judgment in 2029. So this is essentially based on the company's framework. Snowflake this year will surpass 2 billion in revenues and targeting 10 billion by 2029. >>Its current growth rate is 84% and its target is 30% in the out years, which is pretty impressive. Gross margins are gonna tick up a bit, but remember Snowflake's cost a good sold they're dominated by its cloud cost. So it's got a governor. There has to pay AWS Azure and Google for its infrastructure. But high seventies is a, is a good target. It's not like the historical Microsoft, you know, 80, 90% gross margin. Not that Microsoft is there anymore, but, but snowflake, you know, was gonna be limited by how far it can, how much it can push gross margin because of that factor. It's got a tiny operating margin today and it's targeting 20% in 2029. So that would be 2 billion. And you would certainly expect it's operating leverage in the out years to enable much, much, much lower SGNA than the current 54%. I'm guessing R and D's gonna stay healthy, you know, coming in at 15% or so. >>But the real interesting number to watch is free cash flow, 16% this year for the full fiscal year growing to 25% by 2029. So 2.5 billion in free cash flow in the out years, which I believe is up from previous Scarelli forecast in that 10, you know, out year view 2029 view and expect the net revenue retention, the NRR, it's gonna moderate. It's gonna come down, but it's still gonna be well over a hundred percent. We pegged it at 130% based on some of Mike's guidance. Now today, snowflake and every other stock is well off this morning. The company had a 40 billion value would drop well below that midday, but let's stick with the 40 billion on this, this sad Friday on the stock market, we'll go to 40 billion and who knows what the stock is gonna be valued in 2029? No idea, but let's say between 40 and 200 billion and look, it could get even ugly in the market as interest rates rise. >>And if inflation stays high, you know, until we get a Paul Voker like action, which is gonna be painful from the fed share, you know, let's hope we don't have a repeat of the long drawn out 1970s stagflation, but that is a concern among investors. We're gonna try to keep it positive here and we'll do a little sensitivity analysis of snowflake based on Scarelli and Ante's 2029 projections. What we've done here is we've calculated in this chart. Today's current valuation at about 40 billion and run a CAGR through 2029 with our estimates of valuation at that time. So if it stays at 40 billion valuation, can you imagine snowflake grow into a 10 billion company with no increase in valuation by the end, by by 2029 fiscal 2029, that would be a major bummer and investors would get a, a 0% return at 50 billion, 4% Kager 60 billion, 7%. >>Kegar now 7% market return is historically not bad relative to say the S and P 500, but with that kind of revenue and profitability growth projected by snowflake combined with inflation, that would again be a, a kind of a buzzkill for investors. The picture at 75 billion valuation, isn't much brighter, but it picks up at, at a hundred billion, even with inflation that should outperform the market. And as you get to 200 billion, which would track by the way, revenue growth, you get a 30% plus return, which would be pretty good. Could snowflake beat these projections. Absolutely. Could the market perform at the optimistic end of the spectrum? Sure. It could. It could outperform these levels. Could it not perform at these levels? You bet, but hopefully this gives a little context and framework to what Scarelli was talking about and his framework, not with notwithstanding the market's unpredictability you're you're on your own. >>There. I can't help snowflake looks like it's going to continue either way in amazing run compared to other software companies historically, and whether that's reflected in the stock price. Again, I, I, I can't predict, okay. Let's look at some ETR survey data, which aligns really well with what snowflake is telling the street. This chart shows the breakdown of Snowflake's net score and net score. Remember is ETS proprietary methodology that measures the percent of customers in their survey that are adding the platform new. That's the lime green at 19% existing snowflake customers that are ex spending 6% or more on the platform relative to last year. That's the forest green that's 55%. That's a big number flat spend. That's the gray at 21% decreasing spending. That's the pinkish at 5% and churning that's the red only 1% or, or moving off the platform, tiny, tiny churn, subtract the red from the greens and you get a net score that, that, that nets out to 68%. >>That's an, a very impressive net score by ETR standards. But it's down from the highs of the seventies and mid eighties, where high seventies and mid eighties, where snowflake has been since January of 2019 note that this survey of 1500 or so organizations includes 155 snowflake customers. What was really interesting is when we cut the data by industry sector, two of Snowflake's most important verticals, our finance and healthcare, both of those sectors are holding a net score in the ETR survey at its historic range. 83%. Hasn't really moved off that, you know, 80% plus number really encouraging, but retail consumer showed a dramatic decline. This past survey from 73% in the previous quarter down to 54%, 54% in just three months time. So this data aligns almost perfectly with what CFO Scarelli has been telling the street. So I give a lot of credibility to that narrative. >>Now here's a time series chart for the net score and the provision in the data set, meaning how penetrated snowflake is in the survey. Again, net score measures, spending velocity and a specific platform and provision measures the presence in the data set. You can see the steep downward trend in net score this past quarter. Now for context note, the red dotted line on the vertical axis at 40%, that's a bit of a magic number. Anything above that is best in class in our view, snowflake still a well, well above that line, but the April survey as we reported on May 7th in quite a bit of detail shows a meaningful break in the snowflake trend as shown by ETRS call out on the bottom line. You can see a steady rise in the survey, which is a proxy for Snowflake's overall market penetration. So steadily moving up and up. >>Here's a bit of a different view on that data bringing in some of Snowflake's peers and other data platforms. This XY graph shows net score on the vertical axis and provision on the horizontal with the red dotted line. At 40%, you can see from the ETR callouts again, that snowflake while declining in net score still holds the highest net score in the survey. So of course the highest data platforms while the spending velocity on AWS and Microsoft, uh, data platforms, outperforms that have, uh, sorry, while they're spending velocity on snowflake outperforms, that of AWS and, and Microsoft data platforms, those two are still well above the 40% line with a stronger market presence in the category. That's impressive because of their size. And you can see Google cloud and Mongo DB right around the 40% line. Now we reported on Mongo last week and discussed the commentary on consumption models. >>And we referenced Ray Lenchos what we thought was, was quite thoughtful research, uh, that rewarded Mongo DB for its forecasting transparency and, and accuracy and, and less likelihood of facing consumption headwinds. And, and I'll reiterate what I said last week, that snowflake, while seeing demand fluctuations this past quarter from those large customers is, is not like a data lake where you're just gonna shove data in and figure it out later, no schema on, right. Just throw it into the pond. That's gonna be more discretionary and you can turn that stuff off. More likely. Now you, you bring data into the snowflake data cloud with the intent of driving insights, which leads to actions, which leads to value creation. And as snowflake adds capabilities and expands its platform features and innovations and its ecosystem more and more data products are gonna be developed in the snowflake data cloud and by data products. >>We mean products and services that are conceived by business users. And that can be directly monetized, not just via analytics, but through governed data sharing and direct monetization. Here's a picture of that opportunity as we see it, this is our spin on our snowflake total available market chart that we've published many, many times. The key point here goes back to our opening statements. The snowflake data cloud is evolving well beyond just being a simpler and easier to use and more elastic cloud database snowflake is building what we often refer to as a super cloud. That is an abstraction layer that companies that, that comprises rich features and leverages the underlying primitives and APIs of the cloud providers, but hides all that complexity and adds new value beyond that infrastructure that value is seen in the left example in terms of compressed cycle time, snowflake often uses the example of pharmaceutical companies compressing time to discover a drug by years. >>Great example, there are many others this, and, and then through organic development and ecosystem expansion, snowflake will accelerate feature delivery. Snowflake's data cloud vision is not about vertically integrating all the functionality into its platform. Rather it's about creating a platform and delivering secure governed and facile and powerful analytics and data sharing capabilities to its customers, partners in a broad ecosystem so they can create additional value. On top of that ecosystem is how snowflake fills the gaps in its platform by building the best cloud data platform in the world, in terms of collaboration, security, governance, developer, friendliness, machine intelligence, etcetera, snowflake believes and plans to create a defacto standard. In our view in data platforms, get your data into the data cloud and all these native capabilities will be available to you. Now, is that a walled garden? Some might say it is. It's an interesting question and <laugh>, it's a moving target. >>It's definitely proprietary in the sense that snowflake is building something that is highly differentiatable and is building a moat around it. But the more open snowflake can make its platform. The more open source it uses, the more developer friendly and the great greater likelihood people will gravitate toward snowflake. Now, my new friend Tani, she's the creator of the data mesh concept. She might bristle at this narrative in favor, a more open source version of what snowflake is trying to build, but practically speaking, I think she'd recognize that we're a long ways off from that. And I also think that the benefits of a platform that despite requiring data to be inside of the data cloud can distribute data globally, enable facile governed, and computational data sharing, and to a large degree be a self-service platform for data, product builders. So this is how we see snow, the snowflake data cloud vision evolving question is edge part of that vision on the right hand side. >>Well, again, we think that is going to be a future challenge where the ecosystem is gonna have to come to play to fill those gaps. If snowflake can tap the edge, it'll bring even more clarity as to how it can expand into what we believe is a massive 200 billion Tam. Okay, let's close on next. Week's snowflake summit in Las Vegas. The cube is very excited to be there. I'll be hosting with Lisa Martin and we'll have Frank son as well as Christian Kleinman and several other snowflake experts. Analysts are gonna be there, uh, customers. And we're gonna have a number of ecosystem partners on as well. Here's what we'll be looking for. At least some of the things, evidence that our view of Snowflake's data cloud is actually taking shape and evolving in the way that we showed on the previous chart, where we also wanna figure out where snowflake is with it. >>Streamlet acquisition. Remember streamlet is a data science play and an expansion into data, bricks, territory, data, bricks, and snowflake have been going at it for a while. Streamlet brings an open source Python library and machine learning and kind of developer friendly data science environment. We also expect to hear some discussion, hopefully a lot of discussion about developers. Snowflake has a dedicated developer conference in November. So we expect to hear more about that and how it's gonna be leveraging further leveraging snow park, which it has previously announced, including a public preview of programming for unstructured data and data monetization along the lines of what we suggested earlier that is building data products that have the bells and whistles of native snowflake and can be directly monetized by Snowflake's customers. Snowflake's already announced a new workload this past week in security, and we'll be watching for others. >>And finally, what's happening in the all important ecosystem. One of the things we noted when we covered service now, cause we use service now as, as an example because Frank Lupin and Mike Scarelli and others, you know, DNA were there and they're improving on that service. Now in his post IPO, early adult years had a very slow pace. In our view was often one of our criticism of ecosystem development, you know, ServiceNow. They had some niche SI uh, like cloud Sherpa, and eventually the big guys came in and, and, and began to really lean in. And you had some other innovators kind of circling the mothership, some smaller companies, but generally we see sluman emphasizing the ecosystem growth much, much more than with this previous company. And that is a fundamental requirement in our view of any cloud or modern cloud company now to paraphrase the crazy man, Steve bomber developers, developers, developers, cause he screamed it and ranted and ran around the stage and was sweating <laugh> ecosystem ecosystem ecosystem equals optionality for developers and that's what they want. >>And that's how we see the current and future state of snowflake. Thanks today. If you're in Vegas next week, please stop by and say hello with the cube. Thanks to my colleagues, Stephanie Chan, who sometimes helps research breaking analysis topics. Alex, my is, and OS Myerson is on production. And today Andrew Frick, Sarah hiney, Steven Conti Anderson hill Chuck all and the entire team in Palo Alto, including Christian. Sorry, didn't mean to forget you Christian writer, of course, Kristin Martin and Cheryl Knight, they helped get the word out. And Rob ho is our E IIC over at Silicon angle. Remember, all these episodes are available as podcast, wherever you listen to search breaking analysis podcast, I publish each week on wikibon.com and Silicon angle.com. You can email me directly anytime David dot Valante Silicon angle.com. If you got something interesting, I'll respond. If not, I won't or DM me@deteorcommentonmylinkedinpostsandpleasedocheckoutetr.ai for the best survey data in the enterprise tech business. This is Dave Valante for the insights powered by ETR. Thanks for watching. And we'll see you next week. I hope if not, we'll see you next time on breaking analysis.

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE in ETR. This is Breaking Analysis with Dave Vellante. >> Snowflake is not going to grow into its valuation by stealing the croissant from the breakfast table of the on-prem data warehouse vendors. Look, even if snowflake got 100% of the data warehouse business, it wouldn't come close to justifying its market cap. Rather Snowflake has to create an entirely new market based on completely changing the way organizations think about monetizing data. Every organization I talk to says it wants to be, or many say they already are data-driven. why wouldn't you aspire to that goal? There's probably nothing more strategic than leveraging data to power your digital business and creating competitive advantage. But many businesses are failing, or I predict, will fail to create a true data-driven culture because they're relying on a flawed architectural model formed by decades of building centralized data platforms. Welcome everyone to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, I want to share some new thoughts and fresh ETR data on how organizations can transform their businesses through data by reinventing their data architectures. And I want to share our thoughts on why we think Snowflake is currently in a very strong position to lead this effort. Now, on November 17th, theCUBE is hosting the Snowflake Data Cloud Summit. Snowflake's ascendancy and its blockbuster IPO has been widely covered by us and many others. Now, since Snowflake went public, we've been inundated with outreach from investors, customers, and competitors that wanted to either better understand the opportunities or explain why their approach is better or different. And in this segment, ahead of Snowflake's big event, we want to share some of what we learned and how we see it. Now, theCUBE is getting paid to host this event, so I need you to know that, and you draw your own conclusions from my remarks. But neither Snowflake nor any other sponsor of theCUBE or client of SiliconANGLE Media has editorial influence over Breaking Analysis. The opinions here are mine, and I would encourage you to read my ethics statement in this regard. I want to talk about the failed data model. The problem is complex, I'm not debating that. Organizations have to integrate data and platforms with existing operational systems, many of which were developed decades ago. And as a culture and a set of processes that have been built around these systems, and they've been hardened over the years. This chart here tries to depict the progression of the monolithic data source, which, for me, began in the 1980s when Decision Support Systems or DSS promised to solve our data problems. The data warehouse became very popular and data marts sprung up all over the place. This created more proprietary stovepipes with data locked inside. The Enron collapse led to Sarbanes-Oxley. Now, this tightened up reporting. The requirements associated with that, it breathed new life into the data warehouse model. But it remained expensive and cumbersome, I've talked about that a lot, like a snake swallowing a basketball. The 2010s ushered in the big data movement, and Data Lakes emerged. With a dupe, we saw the idea of no schema online, where you put structured and unstructured data into a repository, and figure it all out on the read. What emerged was a fairly complex data pipeline that involved ingesting, cleaning, processing, analyzing, preparing, and ultimately serving data to the lines of business. And this is where we are today with very hyper specialized roles around data engineering, data quality, data science. There's lots of batch of processing going on, and Spark has emerged to improve the complexity associated with MapReduce, and it definitely helped improve the situation. We're also seeing attempts to blend in real time stream processing with the emergence of tools like Kafka and others. But I'll argue that in a strange way, these innovations actually compound the problem. And I want to discuss that because what they do is they heighten the need for more specialization, more fragmentation, and more stovepipes within the data life cycle. Now, in reality, and it pains me to say this, it's the outcome of the big data movement, as we sit here in 2020, that we've created thousands of complicated science projects that have once again failed to live up to the promise of rapid cost-effective time to insights. So, what will the 2020s bring? What's the next silver bullet? You hear terms like the lakehouse, which Databricks is trying to popularize. And I'm going to talk today about data mesh. These are other efforts they look to modernize datalakes and sometimes merge the best of data warehouse and second-generation systems into a new paradigm, that might unify batch and stream frameworks. And this definitely addresses some of the gaps, but in our view, still suffers from some of the underlying problems of previous generation data architectures. In other words, if the next gen data architecture is incremental, centralized, rigid, and primarily focuses on making the technology to get data in and out of the pipeline work, we predict it's going to fail to live up to expectations again. Rather, what we're envisioning is an architecture based on the principles of distributed data, where domain knowledge is the primary target citizen, and data is not seen as a by-product, i.e, the exhaust of an operational system, but rather as a service that can be delivered in multiple forms and use cases across an ecosystem. This is why we often say the data is not the new oil. We don't like that phrase. A specific gallon of oil can either fuel my home or can lubricate my car engine, but it can't do both. Data does not follow the same laws of scarcity like natural resources. Again, what we're envisioning is a rethinking of the data pipeline and the associated cultures to put data needs of the domain owner at the core and provide automated, governed, and secure access to data as a service at scale. Now, how is this different? Let's take a look and unpack the data pipeline today and look deeper into the situation. You all know this picture that I'm showing. There's nothing really new here. The data comes from inside and outside the enterprise. It gets processed, cleanse or augmented so that it can be trusted and made useful. Nobody wants to use data that they can't trust. And then we can add machine intelligence and do more analysis, and finally deliver the data so that domain specific consumers can essentially build data products and services or reports and dashboards or content services, for instance, an insurance policy, a financial product, a loan, that these are packaged and made available for someone to make decisions on or to make a purchase. And all the metadata associated with this data is packaged along with the dataset. Now, we've broken down these steps into atomic components over time so we can optimize on each and make them as efficient as possible. And down below, you have these happy stick figures. Sometimes they're happy. But they're highly specialized individuals and they each do their job and they do it well to make sure that the data gets in, it gets processed and delivered in a timely manner. Now, while these individual pieces seemingly are autonomous and can be optimized and scaled, they're all encompassed within the centralized big data platform. And it's generally accepted that this platform is domain agnostic. Meaning the platform is the data owner, not the domain specific experts. Now there are a number of problems with this model. The first, while it's fine for organizations with smaller number of domains, organizations with a large number of data sources and complex domain structures, they struggle to create a common data parlance, for example, in a data culture. Another problem is that, as the number of data sources grows, organizing and harmonizing them in a centralized platform becomes increasingly difficult, because the context of the domain and the line of business gets lost. Moreover, as ecosystems grow and you add more data, the processes associated with the centralized platform tend to get further genericized. They again lose that domain specific context. Wait (chuckling), there are more problems. Now, while in theory organizations are optimizing on the piece parts of the pipeline, the reality is, as the domain requires a change, for example, a new data source or an ecosystem partnership requires a change in access or processes that can benefit a domain consumer, the reality is the change is subservient to the dependencies and the need to synchronize across these discrete parts of the pipeline or actually, orthogonal to each of those parts. In other words, in actuality, the monolithic data platform itself remains the most granular part of the system. Now, when I complain about this faulty structure, some folks tell me this problem has been solved. That there are services that allow new data sources to really easily be added. A good example of this is Databricks Ingest, which is, it's an auto loader. And what it does is it simplifies the ingestion into the company's Delta Lake offering. And rather than centralizing in a data warehouse, which struggles to efficiently allow things like Machine Learning frameworks to be incorporated, this feature allows you to put all the data into a centralized datalake. More so the argument goes, that the problem that I see with this, is while the approach does definitely minimizes the complexities of adding new data sources, it still relies on this linear end-to-end process that slows down the introduction of data sources from the domain consumer beside of the pipeline. In other words, the domain experts still has to elbow her way into the front of the line or the pipeline, in this case, to get stuff done. And finally, the way we are organizing teams is a point of contention, and I believe is going to continue to cause problems down the road. Specifically, we've again, we've optimized on technology expertise, where for example, data engineers, well, really good at what they do, they're often removed from the operations of the business. Essentially, we created more silos and organized around technical expertise versus domain knowledge. As an example, a data team has to work with data that is delivered with very little domain specificity, and serves a variety of highly specialized consumption use cases. All right. I want to step back for a minute and talk about some of the problems that people bring up with Snowflake and then I'll relate it back to the basic premise here. As I said earlier, we've been hammered by dozens and dozens of data points, opinions, criticisms of Snowflake. And I'll share a few here. But I'll post a deeper technical analysis from a software engineer that I found to be fairly balanced. There's five Snowflake criticisms that I'll highlight. And there are many more, but here are some that I want to call out. Price transparency. I've had more than a few customers telling me they chose an alternative database because of the unpredictable nature of Snowflake's pricing model. Snowflake, as you probably know, prices based on consumption, just like AWS and other cloud providers. So just like AWS, for example, the bill at the end of the month is sometimes unpredictable. Is this a problem? Yes. But like AWS, I would say, "Kill me with that problem." Look, if users are creating value by using Snowflake, then that's good for the business. But clearly this is a sore point for some users, especially for procurement and finance, which don't like unpredictability. And Snowflake needs to do a better job communicating and managing this issue with tooling that can predict and help better manage costs. Next, workload manage or lack thereof. Look, if you want to isolate higher performance workloads with Snowflake, you just spin up a separate virtual warehouse. It's kind of a brute force approach. It works generally, but it will add expense. I'm kind of reminded of Pure Storage and its approach to storage management. The engineers at Pure, they always design for simplicity, and this is the approach that Snowflake is taking. Usually, Pure and Snowflake, as I have discussed in a moment, is Pure's ascendancy was really based largely on stealing share from Legacy EMC systems. Snowflake, in my view, has a much, much larger incremental market opportunity. Next is caching architecture. You hear this a lot. At the end of the day, Snowflake is based on a caching architecture. And a caching architecture has to be working for some time to optimize performance. Caches work well when the size of the working set is small. Caches generally don't work well when the working set is very, very large. In general, transactional databases have pretty small datasets. And in general, analytics datasets are potentially much larger. Is it Snowflake in the analytics business? Yes. But the good thing that Snowflake has done is they've enabled data sharing, and it's caching architecture serves its customers well because it allows domain experts, you're going to hear this a lot from me today, to isolate and analyze problems or go after opportunities based on tactical needs. That said, very big queries across whole datasets or badly written queries that scan the entire database are not the sweet spot for Snowflake. Another good example would be if you're doing a large audit and you need to analyze a huge, huge dataset. Snowflake's probably not the best solution. Complex joins, you hear this a lot. The working set of complex joins, by definition, are larger. So, see my previous explanation. Read only. Snowflake is pretty much optimized for read only data. Maybe stateless data is a better way of thinking about this. Heavily right intensive workloads are not the wheelhouse of Snowflake. So where this is maybe an issue is real-time decision-making and AI influencing. A number of times, Snowflake, I've talked about this, they might be able to develop products or acquire technology to address this opportunity. Now, I want to explain. These issues would be problematic if Snowflake were just a data warehouse vendor. If that were the case, this company, in my opinion, would hit a wall just like the NPP vendors that proceeded them by building a better mouse trap for certain use cases hit a wall. Rather, my promise in this episode is that the future of data architectures will be really to move away from large centralized warehouses or datalake models to a highly distributed data sharing system that puts power in the hands of domain experts at the line of business. Snowflake is less computationally efficient and less optimized for classic data warehouse work. But it's designed to serve the domain user much more effectively in our view. We believe that Snowflake is optimizing for business effectiveness, essentially. And as I said before, the company can probably do a better job at keeping passionate end users from breaking the bank. But as long as these end users are making money for their companies, I don't think this is going to be a problem. Let's look at the attributes of what we're proposing around this new architecture. We believe we'll see the emergence of a total flip of the centralized and monolithic big data systems that we've known for decades. In this architecture, data is owned by domain-specific business leaders, not technologists. Today, it's not much different in most organizations than it was 20 years ago. If I want to create something of value that requires data, I need to cajole, beg or bribe the technology and the data team to accommodate. The data consumers are subservient to the data pipeline. Whereas in the future, we see the pipeline as a second class citizen, with a domain expert is elevated. In other words, getting the technology and the components of the pipeline to be more efficient is not the key outcome. Rather, the time it takes to envision, create, and monetize a data service is the primary measure. The data teams are cross-functional and live inside the domain versus today's structure where the data team is largely disconnected from the domain consumer. Data in this model, as I said, is not the exhaust coming out of an operational system or an external source that is treated as generic and stuffed into a big data platform. Rather, it's a key ingredient of a service that is domain-driven and monetizable. And the target system is not a warehouse or a lake. It's a collection of connected domain-specific datasets that live in a global mesh. What is a distributed global data mesh? A data mesh is a decentralized architecture that is domain aware. The datasets in the system are purposely designed to support a data service or data product, if you prefer. The ownership of the data resides with the domain experts because they have the most detailed knowledge of the data requirement and its end use. Data in this global mesh is governed and secured, and every user in the mesh can have access to any dataset as long as it's governed according to the edicts of the organization. Now, in this model, the domain expert has access to a self-service and obstructed infrastructure layer that is supported by a cross-functional technology team. Again, the primary measure of success is the time it takes to conceive and deliver a data service that could be monetized. Now, by monetize, we mean a data product or data service that it either cuts cost, it drives revenue, it saves lives, whatever the mission is of the organization. The power of this model is it accelerates the creation of value by putting authority in the hands of those individuals who are closest to the customer and have the most intimate knowledge of how to monetize data. It reduces the diseconomies at scale of having a centralized or a monolithic data architecture. And it scales much better than legacy approaches because the atomic unit is a data domain, not a monolithic warehouse or a lake. Zhamak Dehghani is a software engineer who is attempting to popularize the concept of a global mesh. Her work is outstanding, and it's strengthened our belief that practitioners see this the same way that we do. And to paraphrase her view, "A domain centric system must be secure and governed with standard policies across domains." It has to be trusted. As I said, nobody's going to use data they don't trust. It's got to be discoverable via a data catalog with rich metadata. The data sets have to be self-describing and designed for self-service. Accessibility for all users is crucial as is interoperability, without which distributed systems, as we know, fail. So what does this all have to do with Snowflake? As I said, Snowflake is not just a data warehouse. In our view, it's always had the potential to be more. Our assessment is that attacking the data warehouse use cases, it gave Snowflake a straightforward easy-to-understand narrative that allowed it to get a foothold in the market. Data warehouses are notoriously expensive, cumbersome, and resource intensive, but they're a critical aspect to reporting and analytics. So it was logical for Snowflake to target on-premise legacy data warehouses and their smaller cousins, the datalakes, as early use cases. By putting forth and demonstrating a simple data warehouse alternative that can be spun up quickly, Snowflake was able to gain traction, demonstrate repeatability, and attract the capital necessary to scale to its vision. This chart shows the three layers of Snowflake's architecture that have been well-documented. The separation of compute and storage, and the outer layer of cloud services. But I want to call your attention to the bottom part of the chart, the so-called Cloud Agnostic Layer that Snowflake introduced in 2018. This layer is somewhat misunderstood. Not only did Snowflake make its Cloud-native database compatible to run on AWS than Azure in the 2020 GCP, what Snowflake has done is to obstruct cloud infrastructure complexity and create what it calls the data cloud. What's the data cloud? We don't believe the data cloud is just a marketing term that doesn't have any substance. Just as SAS is Simplified Application Software and iOS made it possible to eliminate the value drain associated with provisioning infrastructure, a data cloud, in concept, can simplify data access, and break down fragmentation and enable shared data across the globe. Snowflake, they have a first mover advantage in this space, and we see a number of fundamental aspects that comprise a data cloud. First, massive scale with virtually unlimited compute and storage resource that are enabled by the public cloud. We talk about this a lot. Second is a data or database architecture that's built to take advantage of native public cloud services. This is why Frank Slootman says, "We've burned the boats. We're not ever doing on-prem. We're all in on cloud and cloud native." Third is an obstruction layer that hides the complexity of infrastructure. and fourth is a governed and secured shared access system where any user in the system, if allowed, can get access to any data in the cloud. So a key enabler of the data cloud is this thing called the global data mesh. Now, earlier this year, Snowflake introduced its global data mesh. Over the course of its recent history, Snowflake has been building out its data cloud by creating data regions, strategically tapping key locations of AWS regions and then adding Azure and GCP. The complexity of the underlying cloud infrastructure has been stripped away to enable self-service, and any Snowflake user becomes part of this global mesh, independent of the cloud that they're on. Okay. So now, let's go back to what we were talking about earlier. Users in this mesh will be our domain owners. They're building monetizable services and products around data. They're most likely dealing with relatively small read only datasets. They can adjust data from any source very easily and quickly set up security and governance to enable data sharing across different parts of an organization, or, very importantly, an ecosystem. Access control and governance is automated. The data sets are addressable. The data owners have clearly defined missions and they own the data through the life cycle. Data that is specific and purposely shaped for their missions. Now, you're probably asking, "What happens to the technical team and the underlying infrastructure and the cluster it's in? How do I get the compute close to the data? And what about data sovereignty and the physical storage later, and the costs?" All these are good questions, and I'm not saying these are trivial. But the answer is these are implementation details that are pushed to a self-service layer managed by a group of engineers that serves the data owners. And as long as the domain expert/data owner is driving monetization, this piece of the puzzle becomes self-funding. As I said before, Snowflake has to help these users to optimize their spend with predictive tooling that aligns spend with value and shows ROI. While there may not be a strong motivation for Snowflake to do this, my belief is that they'd better get good at it or someone else will do it for them and steal their ideas. All right. Let me end with some ETR data to show you just how Snowflake is getting a foothold on the market. Followers of this program know that ETR uses a consistent methodology to go to its practitioner base, its buyer base each quarter and ask them a series of questions. They focus on the areas that the technology buyer is most familiar with, and they ask a series of questions to determine the spending momentum around a company within a specific domain. This chart shows one of my favorite examples. It shows data from the October ETR survey of 1,438 respondents. And it isolates on the data warehouse and database sector. I know I just got through telling you that the world is going to change and Snowflake's not a data warehouse vendor, but there's no construct today in the ETR dataset to cut a data cloud or globally distributed data mesh. So you're going to have to deal with this. What this chart shows is net score in the y-axis. That's a measure of spending velocity, and it's calculated by asking customers, "Are you spending more or less on a particular platform?" And then subtracting the lesses from the mores. It's more granular than that, but that's the basic concept. Now, on the x-axis is market share, which is ETR's measure of pervasiveness in the survey. You can see superimposed in the upper right-hand corner, a table that shows the net score and the shared N for each company. Now, shared N is the number of mentions in the dataset within, in this case, the data warehousing sector. Snowflake, once again, leads all players with a 75% net score. This is a very elevated number and is higher than that of all other players, including the big cloud companies. Now, we've been tracking this for a while, and Snowflake is holding firm on both dimensions. When Snowflake first hit the dataset, it was in the single digits along the horizontal axis and continues to creep to the right as it adds more customers. Now, here's another chart. I call it the wheel chart that breaks down the components of Snowflake's net score or spending momentum. The lime green is new adoption, the forest green is customers spending more than 5%, the gray is flat spend, the pink is declining by more than 5%, and the bright red is retiring the platform. So you can see the trend. It's all momentum for this company. Now, what Snowflake has done is they grabbed a hold of the market by simplifying data warehouse. But the strategic aspect of that is that it enables the data cloud leveraging the global mesh concept. And the company has introduced a data marketplace to facilitate data sharing across ecosystems. This is all about network effects. In the mid to late 1990s, as the internet was being built out, I worked at IDG with Bob Metcalfe, who was the publisher of InfoWorld. During that time, we'd go on speaking tours all over the world, and I would listen very carefully as he applied Metcalfe's law to the internet. Metcalfe's law states that the value of the network is proportional to the square of the number of connected nodes or users on that system. Said another way, while the cost of adding new nodes to a network scales linearly, the consequent value scores scales exponentially. Now, apply that to the data cloud. The marginal cost of adding a user is negligible, practically zero, but the value of being able to access any dataset in the cloud... Well, let me just say this. There's no limitation to the magnitude of the market. My prediction is that this idea of a global mesh will completely change the way leading companies structure their businesses and, particularly, their data architectures. It will be the technologists that serve domain specialists as it should be. Okay. Well, what do you think? DM me @dvellante or email me at david.vellante@siliconangle.com or comment on my LinkedIn? Remember, these episodes are all available as podcasts, so please subscribe wherever you listen. I publish weekly on wikibon.com and siliconangle.com, and don't forget to check out etr.plus for all the survey analysis. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching. Be well, and we'll see you next time. (upbeat music)

>> Okay, listen, we're gearing up for the start of the Snowflake Data Cloud Summit and we want to go back to the early roots of Snowflake. We got some of the founding engineers here, Abdul Muneer, Ashish Modivala, and Alison Lee. They're three individuals that were at Snowflake in the early years and participated in many of the technical decisions that led to the platform that is making Snowflake famous today. Folks, great to see you. Thanks so much for taking some time out of your busy schedules. >> Than you for having us. >> Same. >> Hey, it's got to be really gratifying to see this platform that you've built, you know, taking off and changing businesses. So, I'm sure it was always smooth sailing, right? There were no debates, were there ever? >> Never. >> Now, I've never seen an engineer get into a debate. (laughter) >> All right, so seriously though, so take us back to the early days, you guys, you know, choose whoever wants to start but, what was it like early on? We're talking 2013 here, right? >> That's right. >> When I think back to the early days of Snowflake, I just think of all of us sitting in one room at the time you know, we just had an office that was one room with you know, 12 or 13 engineers sitting there, clacking away at our keyboards, working really hard, churning out code, punctuated by, you know, somebody asking a question about, "Hey, what should we do about this? Or what should we do about that?" And then everyone kind of looking up from their keyboards and getting into discussions and debates about, about the work that we were doing. >> So Abdul, it was just kind of heads down, headphones on, just coding, or >> I think there was a lot of talking and followed by a lot of typing. And, and I think there were periods of time where, you know, anyone could just walk in into the office and probably out of the office and all they'd hear is probably people typing away at their keyboards. And one of my vivid, most vivid memories is is actually I used to sit right across from Alison and there's these huge two, two huge monitors monitors between us. And I would just hear her typing away at our keyboard. And sometimes I was thinking and and all that typing got me nervous because it seemed like Alison knew exactly what, what she needed to do, and I was just still thinking about it. >> So Ashish was this like bliss for you as a developer, an engineer, or was it, was it a stressful time? What was the mood? >> When you don't have a whole lot of customers there's a lot of bliss, but at the same time, there's a lot of pressure on us to make sure that we build the product. There was a timeline ahead of us, we knew we had to build this in a certain timeframe. So one thing I'll add to what Alison and Abdul said is we did a lot of white boarding as well. There were a lot of discussions and those discussions were a lot of fun. They actually cemented what we wanted to build. They made sure that everyone was in tune and there we have it. >> (Dave) Yeah, so, I mean, it is a really exciting time doing any startup. When you have to make decisions in development and variably you come to a fork in the road. So I'm curious as to what some of those forks might've been, how you guys decided, you know, which fork to take. Was there a Yoda in the room that served as the Jedi master? I mean, how are those decisions made? Maybe you could talk about that a little bit. >> Yeah. That's an interesting question. And I think one of, as I think back, one of the memories that, that sticks out in my mind is this epic meeting in one of our conference rooms called North star. And many of our conference rooms are named after ski resorts because the founders are really into skiing and that's why, that's where the Snowflake names comes from. So there was this epic meeting and and I'm not even sure exactly what topic we were discussing. I think it was, it was the signup flow and there were a few different options on the table. and one of the options that, that people were gravitating to one of the founders didn't like it. And they said a few times that there's this makes no sense, there's no other system in the world that does it this way. And I think one of the other founders said that's exactly why we should do it this way. And, or at least seriously considered this option. So I think there was always this this tendency and this impulse that that we needed to think big and think differently and not see the world the way it is but the, the way we wanted it to be and then work our way backwards and try to make it happen. >> Alison, any fork in the road moments that you remember? >> Well, I'm just thinking back to a really early meeting with Ashish and a few of our founders where we were debating something, probably not super exciting to a lot of people outside of hardcore database people which was how to represent our column metadata. And I think it's funny that you, that you mentioned Yoda because we often make jokes about one of our founders Terry and referred to him as Yoda, because he has this tendency to say very concise things that kind of make you scratch your head and say, "Wow why didn't I think of that?" Or, you know, what exactly does that mean? I never thought about it that way. So I think when I think of the Yoda in the room, it was definitely Terry. >> Ashish, anything you can add to this conversation? >> I'll agree with Alison on the Yoda comment, for sure. Another big fork in the road I recall was when we changed one of our meta store where we store our on internal metadata. We used to use a tool called MySQL and we changed it to another database called FoundationDB, I think that was a big game changer for us. And, you know, it was a tough decision, it took us a long time. For the longest time we even had our own little branch it was called FoundationDB and everybody who was developing on that branch. It's a little embarrassing, but, you know, those are the kinds of decisions that alter the shape of Snowflake. >> Yeah, I mean, these are really, you know, down in the weeds hardcore stuff that a lot of people might not be exposed to. What would you say was the least obvious technical decision that you had to make at the time? And I want to ask you about the most obvious too, but what was the one that was so out of the box? I mean, you kind of maybe mentioned it a little bit before but I wonder if we could double click on that? >> Well, I think one of the core decisions in our architecture is the separation of compute and storage. And, you know, that is really core to our architecture, and there are so many features that we have today for instance, data sharing, zero copy cloning, that we couldn't have without that architecture. And I think it was both not obvious, and when we told people about it in the early days there was definitely skepticism about being able to make that work and being able to have that architecture and still get great performance. >> Exactly. >> Yeah. Anything that was like clearly obvious that maybe that, maybe that was the least and the most that, that separation from compute and store, because it allowed you to actually take advantage of Cloud native. But was there an obvious one that you know, is it sort of dogma that you, you know philosophically live by, you know, to this day? >> I think one really obvious thing is the sort of no tuning, no knobs, ease of use story behind Snowflake. And I say, it's really obvious because everybody wants their system to be easy to use. But then I would say there were tons of decisions behind that, that it's not always obvious, the implications, of such a choice, right? And really sticking to that. And I think that that's really like a core principle behind Snowflake, that led to a lot of non-obvious decisions as a result of sticking to that principle. >> So >> I think, to add to that, now you've grabbed us thinking. I think another really interesting one was really, should we start from scratch or should we use something that already exists and build on top of that? And I think that was one of these almost philosophical kind of stances that we took, that a lot of the systems that were out there were the way they were, because, because they weren't built for the, for the platforms that they were running on. And the big thing that we were targeting was the Cloud. And so one of the big stances we took was that we were going to build from scratch. And we weren't going to borrow a single line of code from many other database out there. And this was something that really shocked a lot of people and many times that this was pretty crazy, and it was, but this is how you build great products. >> That's awesome. All right Ashish, I should give you the last word. We got like just like 30 seconds left, bring us home. >> Till date, actually one of those said shocks people when you talk to them and they say, "Wow, you are naturally using any other database, and you build this entirely yourself." The number of people who actually can build a database from scratch are fairly limited, the group is fairly small. And so it was really a humongous task, and as you've mentioned, you know, it really changed the direction of how we designed a database. What we, what does the database really mean to us, right? The way Snowflake has built a database, it's really a number of organs that come together and form the body. And that's also a concept that's novel to the database industry. >> Guys, congratulations, you must be so proud and it's going to be awesome watching the next decade. So thank you so much for sharing your stories. >> Thanks too. >> Thank you. >> Thank you.

>> Hello everyone, we're here at the Data Cloud Summit, and we have a real treat for you. I call it the CMO Power Panel. And we're going to explore how data is transforming marketing, branding and promotion. And with me are three phenomenal marketing pros and chief marketing officers. Denise Persson is the CMO of Snowflake, Scott Holden of ThoughtSpot and Laura Langdon of Wipro. Folks, great to see you. Thanks so much for coming on "theCUBE." >> Great to be here with you David. >> Awesome, Denise, let's start with you. I want to talk about the role and the changing role of the CMOs, has changed a lot, you know, I suppose of course with all this data, but I wonder what you're experiencing and can you share with us why marketing especially is being impacted by data. >> Well data's really what has helped turn us marketers into revenue drivers, into call centers. And it's clearly a much better place to be. What I'm personally most excited about is the real time access we have to data today. In the past, I used to get a stale report a few weeks after a marketing program was over and at that time we couldn't make any changes to the investments we'd already made. Today, we get data in the midst of running a program. So it can reallocate investments at the time a program is up and running and that's really profound. Today as well, I would say that adaptability has truly become the true superpowers of marketing today and data is really what enables us to adapt to scale. We can adapt to customer's behavior and preferences at scale and that's truly a profound new way of working as well. >> That's interesting what you say cause you know, in tough times used to be okay, sales and engineering, put a brick wall around those and you know, you name it marketing, say, "Okay, cut." But now it's like, you go to marketing and say, "Okay, what's the data say, "how do we have to pivot?" And Scott, I wonder what have data and cloud really brought to the modern marketer that you might not have had before through to this modern era? >> Well, this era, I don't think there's ever been a better time to be a marketer than there is right now. And the primary reason is that we have access to data and insights like we've never had before and I'm not exaggerating when I say that I have a hundred times more access to data than I had a decade ago. It's just phenomenal. When you look at the power of cloud, search, AI, these new consumer experiences for analytics, we can do things in seconds that used to take days. And so it's become in us, as Denise said a super power for us to have access to so much data. And it's, you know, COVID has been hard. A lot of our marketing teams who never worked harder making this pivot from the physical world to the virtual world but they're, you know, at least we're working. And the other part of it is that digital has just created this phenomenal opportunity for us because the beauty of digital and digital transformation is that everything now is trackable, which makes it measurable and means that we can actually get insights that we can act on in a smarter way. And you know, it's worth giving an example. If you just look at this show, right? Like this event that we're viewing. In a physical world, all of you watching at home you'd be in front of us in a room and we'd be able to know if you're in the room, right? We'd track to the scanners when you walked in but that's basically it. At that point, we don't really get a good sense for how much you like, what we're saying. You know, maybe you filled out a survey, but only five to 10% of people ever do that. In a digital world, we know how long you stick around. And as a result, like it's easy, people can just with a click, you know, change the channel. And so the bar for content has gone way up as we do these events but we know how long people are sticking around. And that's, what's so special about it. You know, Denise and her team, as the host of this show they're going to know how long people watch this segment. And that knowing is powerful. I mean, it's simple as you know, using a product like ThoughtSpot, you could just ask a question, you know, how many, you know, what's the average view time by session and Bloomer chart pops up. You're going to know what's working and what's not. And that's something that you can take and act on in the future. And that's what our customers are doing. So, you know, Snowflake and ThoughtSpot, we share our customer with Hulu and they're tracking programs. So, what people are watching at home, how long they're watching, what they're watching next. And they're able to do that in a super granular way and improve their content as a result. And that's the power of this new world we live in that's made the cloud and data so accessible to folks like us. >> Well, thank you for that. And I want to come back to that notion and understand how you're bringing data into your marketing ops, but I want to bring Laura in. Laura, Wipro, you guys partner with a lot of brands, a lot of companies around the world. I mean, thousands of partners, obviously Snowflake in ThoughtSpot or two. How are you using data to optimize these co-marketing relationships? You know, specifically, what what are the trends that you're seeing around things like customer experience? >> So, you know, we use data for all of our marketing decisions, our own, as well as with our partners. And I think what's really been interesting about partner marketing data is we can feed that back to our sales team, right? So, it's very directional for them as well and their efforts moving forward. So, I think that's a place where specifically to partners, it's really powerful. We can also use our collected data to go out to customers to better effect. And then you know, regarding these trends, we just did a survey on the state of the intelligent enterprise. We interviewed 300 companies, US and UK, and there were three interesting I thought statistics relevant to this. Only 22% of the companies that we interviewed felt that their marketing was where it needed to be from an automation standpoint. So lots of room for us to grow, right? Lots of space for us to play. And 61% of them believe that it was critical that they implement this technology to become a more intelligent enterprise. But when they ranked on readiness by function, marketing came in six, right? So HR, RND, finance were all ahead of marketing followed by sales. You know, and then the final data point that I think was interesting was 40% of those agreed that the technology was the most important thing, that thought leadership was critical. You know, and I think that's where marketers really can bring our tried and true experience to bear and merge it with this technology. >> Great, thank you. So, Denise, I've been getting the Kool-Aid injection this week around Data Cloud. I've been pushing people but now that I have the CMO in front of me, I want to ask about the Data Cloud and what it means specifically for the customers and what are some of the learnings maybe that you've experienced that can support some of the things that that Laura and Scott were just discussing. >> Yeah, as Scott said before, idea of a hundred times more data than he ever has before. And that's again, if you look at all the companies we talked to around the world it's not about the amount of data that they have that is the problem, it's the ability to access that data. That data for most companies is trapped across silos, across the organization. It sits in data applications, systems or records. Some of that data sits with your partners that you want to access. And that's really what the data cloud comes in. Data cloud is really mobilizing that data for you. It brings all that data together for you in one place. So you can finally access that data and really provide ubiquitous access to that data to everyone in your organization that needs it and can truly unlock the value of that data. And from a marketing perspective, I mean, we are responsible for the customer experience you know, we provide to our customers and if you have access to all the data on your customers, that's when you have that to customer 360, that we've all been talking about for so many years. And if you have all that data, you can truly, you know, look at their, you know, buying behaviors, put all those dots together and create those exceptional customer experiences. You can do things such as the retailers do in terms of personal decision, for instance, right? And those are the types of experiences, you know, our customers are expecting today. They are expecting a 100% personalized experience for them you know, all the time. And if you don't have all the data, you can't really put those experiences together at scale. And that is really where the data cloud comes in. Again, the data cloud is not only about mobilizing your own data within your enterprise. It's also about having access to data from your partners or extending access to your own data in a secure way to your partners within your ecosystems. >> Yeah, so I'm glad you mentioned a couple of things. I've been writing about this a lot and in particularly the 360 that we were dying for, but haven't really been able to tap. I didn't call it the data cloud, I don't have a marketing gene. I had another sort of boring name for it, but I think there's similar vectors there. So I appreciate that. Scott, I want to come back to this notion of building data DNA in your marketing, you know, fluency and how you put data at the core of your marketing ops. I've been working with a lot of folks in banking and manufacturing and other industries that are that are struggling to do this. How are you doing it? What are some of the challenges that you can share and maybe some advice for your peers out there? >> Yeah, sure, you brought up this concept of data fluency and it's an important one. And there's been a lot of talk in the industry about data literacy and being able to read data. But I think it's more important to be able to speak data, to be fluent and as marketers, we're all storytellers. And when you combine data with storytelling, magic happens. And so, getting a data fluency is a great goal for us to have for all of the people in our companies. And to get to that end, I think one of the things that's happening is that people are hiring wrong and they're thinking about it, they're making some mistakes. And so a couple of things come to mind especially when I look at marketing teams that I'm familiar with. They're hiring a lot of data analysts and data scientists and those folks are amazing and every team needs them. But if you go too big on that, you do yourself a disservice. The second key thing is that you're basically giving your frontline folks, your marketing managers or people on the front lines, an excuse not to get involved with data. And then that's a big mistake because it used to be really hard. But with the technologies available to us now, these new consumer like experiences for data analytics, anybody can do it. And so we as leaders have to encourage them to do it. And I'll give you just a you know, an example, you know, I've got about 32 people on my marketing team and I don't have any data analysts on my team. Across our entire company, we have a couple of analysts and a couple of data engineers. And what's happening is the world is changing where those folks, they're enablers, they architect the system. They bring in the different data sources. They use technologies like Snowflake as being so great at making it easier for people to pull spectrum technology together and to get access to data out of it quickly, but they're pulling it together and then simple things like, "Hey I just want to see this "weekly instead of monthly." You don't need to waste your expensive data science talent. You know, Gardener puts a stat out there that 50% of data scientists are doing basic visualization work. That's not a good use of their time. The products are easy enough now that everyday marketing managers can do that. And when you have a marketing manager come to you and say, you know, "I just figured out "this campaign which looks great on the surface "is doing poorly from an ROI perspective. That's a magic moment. And so we all need to coach our teams to get there. And I would say, you know, lead by example, give them an opportunity to access data and turn it into a story, that's really powerful. And then lastly, praise people who do it, like, use it as something to celebrate inside our companies is a great way to kind of get this initiative. >> I love it. And talking about democratizing data and making it self service, people feel ownership. You know, Laura, Denise was talking about the ecosystem and you're kind of the ecosystem pro here. How does the ecosystem help marketers succeed? Maybe you can talk about the power of many versus the resource of one. >> Sure, you know, I think it's a game changer and it will continue to be. And I think it's really the next level for marketers to harness this power that's out there and use it, you know, and it's something that's important to us, but it's also something we're starting to see our customers demand. You know, we went from a one size fits all solution to they want to bring the best in class to their organization. We all need to be really agile and flexible right now. And I think this ecosystem allows that, you know, you think about the power of Snowflake, Snowflake mining data for you and then a ThoughtSpot really giving you the dashboard to have what you want. And then an implementation partner like a Wipro coming in, and really being able to plug in whatever else you need to deliver. And I think it's really super powerful and I think it gives us you know, it just gives us so much to play with and so much room to grow as marketers. >> Thank you, Denise, why don't you bring us home. We're almost out of time here, but marketing, art, science, both? What are your thoughts? >> Definitely both, I think that's the exciting part about marketing. It is a balancing act between art and science. Clearly, it's probably more science today than it used to be but the art part is really about inspiring change. It's about changing people's behavior and challenging the status quo, right? That's the art part. The science part, that's about making the right decisions all the time, right? It's making sure we are truly investing in what's going to drive revenue for us. >> Guys, thanks so much for coming on "theCUBE." Great discussion, I really appreciate it. Okay, and thank you for watching. Keep it right there. Wall-to-wall coverage of the Snowflake Data Cloud Summit on "theCUBE."

>> Hello everyone, and welcome back to our wall-to-wall coverage of the Datacloud summit, this is Dave Vellante, and we're seeing the emergence of a next generation workload in the cloud, more facile access, and governed sharing of data is accelerating time to insights and action. Alright, allow me to introduce our next guest. Aimee Irwin is here, she's the vice president of strategy for Experian, and Matt Glickman is VP of customer product strategy at Snowflake, with an emphasis on financial services, folks, welcome to theCUBE, thanks so much for coming on. >> Thanks Dave, nice to be here. >> Hey so Aimee, obviously 2020's been pretty unique and crazy and challenging time for a lot of people, I don't know why, I've been checking my credit score a lot more for some reason on the app, I love the app, I had to lock it the other day, I locked my credit, somebody tried to do, and it worked, I was so happy, so thank you for that. So, we know Experian, but there's a ton of data behind what you do, I wonder if you could share kind of where you sit in the data space, and how you've seen organizations leverage data up to this point, and really if you could address some of the changes you're seeing as a result of the pandemic, that would be great. >> Sure, sure. Well, as you mentioned, Experian is best known as a credit bureau. I work in our marketing services business unit, and what we do is we really help brands leverage the power of data and technology to make the right marketing decisions, and better understand and connect with consumers. So we offer marketers products around data, identity, activation, measurement, we have a consumer-view data file that's based on offline PII and contains demographic interest, transaction data, and other attributes on about 300 million people in the US. And on the identity side we've always been known for our safe haven, or privacy-friendly matching, that allows marketers to connect their first party data to Experian or other third parties, but in today's world, with the growth in importance of digital advertising, and consumer behavior shifting to digital, Experian also is working to connect that offline data to the digital world, for a complete view of the customer. You mentioned COVID, we actually, we serve many different verticals, and what we're seeing from our clients during COVID is that there's a varying impact of the pandemic. The common theme is that those who have successfully pivoted their businesses to digital are doing much better, as we all know, COVID accelerated very strong trends to digital, both in e-commerce and in media-viewing habits. We work with a lot of retailers, retail is a tale of two cities, with big box and grocery growing, and apparel retail really struggling. We've helped our clients, leveraging our data to better understand the shifts in these consumer behaviors, and better psych-map their customers during this really challenging time. So think about, there's a group of customers that is still staying home, that is sheltered in place, there's a group of customers starting to significantly vary their consumer behavior, but is starting to venture out a little, and then there's a group of customers that's doing largely what they did before, in a somewhat modified fashion, so we're helping our clients segment those customers into groups to try and understand the right messaging and right offers for each of those groups, and we're also helping them with at-risk audiences. So that's more on the financial side, which of your customers are really struggling due to the pandemic, and how do you respond. >> That's awesome, thank you. You know, it's funny, I saw a twitter poll today asking if we measure our screen time, and I said, "oh my, no." So, Matt, let me ask you, you spent a ton of time in financial services, you really kind of cut your teeth there, and it's always been very data-oriented, you're seeing a lot of changes, tell us about how your customers are bringing it together, data, the skills, the people, obviously a big part of the equation, and applications to really put data at the center of the universe, what's new and different that these companies are getting out of the investments in data and skills? >> That's a great question, the acceleration that Aimee mentioned is real. We're seeing, particularly this year, but I think even in the past few years, the reluctance of customers to embrace the cloud is behind us, and now there's this massive acceleration to be able to go faster, and in some ways, the new entrants into this category have an advantage versus the companies that have been in this space, whether it's financial services or beyond, and in a lot of ways, they all are seeing the cloud and services like Snowflake as a way to not only catch up, but leapfrog your competitors, and really deliver a differentiated experience to your customers, to your business, internally or externally. And this past, however long this crisis has been going on, has really only accelerated that, because now there's a new demand to understand your customer better, your business better, with your traditional data sources, and also new, alternative data sources, and also being able to take a pulse. One of the things that we learned, which was an eye-opening experience, was as the crisis unfolded, one of our data partners decided to take the datasets about where the cases were happening from the Johns Hopkins, and World Health Organization, and put that on our platform, and it became a runaway hit. Thousands of our customers overnight were using this data to understand how their business was doing, versus how the crisis was unfolding in real time. And this has been a game-changer, and it's only scratching the surface of what now the world will be able to do when data is really at their fingertips, and you're not hindered by your legacy platforms. >> I wrote about that back in the early days of the pandemic when you guys did that, and talked about some of the changes that you guys enabled, and you know, you're right about cloud, in financial services cloud used to be an evil word, and now it's almost, it's become a mandate. Aimee, I wonder if you could tell us a little bit more about what your customers are having to work through in order to achieve some of these outcomes. I mean, you know, I'm interested in the starting point, I've been talking a lot, and writing a lot, and talking to practitioners about what I call the data life cycle, sometimes people call it the data pipeline, it's a complicated matter, but those customers and companies that can put data at the center and really treat that pipeline as the heart of their organization, if you will, are really succeeding. What are you seeing, and what really is the starting point, there? >> Yes, yeah, that's a good question, and as you mentioned, first party, I mean we start with first party data, right? First party data is critical to understanding consumers. And different verticals, different companies, different brands have varying levels of first party data. So a retailers going to have a lot more first party data, a financial services company, than say, an auto manufacturer. And while many marketers have that first party data, to really have a 360 view of the customer, they need third party data as well, and that's where Experian comes in, we help brands connect those disparate datasets, both first and third party data to better understand consumers, and create a single customer view, which has a number of applications. I think the last stat I heard was that there's about eight devices, on average, per person. I always joke that we're going to have these enormous, and that number's growing, we're going to have these enormous charging stations in our house, and I think we already do, because of all the different devices. And we seamlessly move from device to device, along our customer journey, and, if the brand doesn't understand who we are, it's much harder for the brand to connect with consumers and create a positive customer experience. And we cite that about 95 percent of companies, they are looking to achieve that single customer view, they recognize that they need that, and they've aligned various teams from e-commerce, to marketing, to sales, to at a minimum adjust their first party data, and then connect that data to better understand consumers. So, consumers can interact with a brand through a website, a mobile app, in-store visits, you know, by the phone, TV ads, et cetera, and a brand needs to use all of those touchpoints, often collected by different parts of the organization, and then add in that third party data to really understand the consumers. In terms of specific use cases, there's about three that come to mind. So first there's relevant advertising, and reaching the right customer, there's measurement, so being able to evaluate your advertising efforts, if you see an ad on, if I see an ad on my mobile, and then I buy by visiting a desktop website, understanding, or I get a direct mail piece, understanding that those interactions are all connected to the same person is critical for measurement. And then there's personalization, which includes improved customer experience amongst your own touchpoints with that consumer, personalized marketing communication, and then of course analytics, so those are the use cases we're seeing. >> Great, thank you Aimee. Now Matt, you can't really talk about data without talking about governance and compliance, and I remember back in 2006, when the federal rules of civil procedure went in, it was easy, the lawyers just said, "no, nobody can have access," but that's changed, and one of the things I like about what Snowflake's doing with the data cloud is it's really about democratizing access, but doing so in a way that gives people confidence that they only have access to the right data. So maybe you could talk a little bit about how you're thinking about this topic, what you're doing to help customers navigate, which has traditionally been such a really challenging problem. >> Another great question, this is where I think the major disruption is happening. And what Aimee described, being able to join together first and third party datasets, being able to do this was always a challenge, because data had to be moved around, I had to ship my first party data to the other side, and the third party data had to be shipped to me, and being able to join those datasets together was problematic at best, and now with the focus on privacy and protecting PII, this is something that has to change, and the good news is, with the data cloud, data does not have to move. Data can stay where it belongs, Experian can keep its data, Experian's customers can hold onto their data, yet the data can be joined together on this universal, global platform that we call the data cloud. On top of that, and particularly with the regulations that are coming out that are going to prevent data from being collected on either a mobile device or as cookies on web browsers, new approaches, and we're seeing this a lot in our space, both in financials and media, is to set up these data clean rooms, where both sides can give access to one another, but not have to reveal any PII to do that join. This is going to be huge, now you actually can protect your customers' and your consumers' private identities, but still accomplish that join that Aimee mentioned, to be able to relate the cause and effect of these campaigns, and really understand the signals that these datasets are trying to say about one another, again without having to move data, without having to reveal PII, we're seeing this happening now, this is the next big thing, that we're going to see explode over the months and years to come. >> I totally agree, massive changes coming in public policy in this area, and we only have a few minutes left, and I wonder if for our audience members that are looking for some advice, what's the, Aimee, what's the one thing you'd recommend they start doing differently, or consider putting in place that's going to set them up for success over the next decade? >> Yeah, that's a good question. You know, I think, I always say, first, harness all of your first party data across all touchpoints, get that first party data in one place and working together, second, connect that data with trusted third parties, and Matt suggested some ways to do that, and then third, always put the customer first, speak their language, where and when they want to be reached out to, and use the information you have to really create a better customer experience for your customers. >> Matt, what would you add to that? Bring us home, if you would. >> Applications. The idea that data, your data can now be pulled into your own business applications the same way that Netflix and Spotify are pulled into your consumer and lifestyle applications, again, without data moving, these personalized application experiences is what I encourage everyone to be thinking about from first principles. What would you do in your next app that you're going to build, if you had all your consumers, if the consumers had access to their data in the app, and not having to think about things from scratch, leverage the data cloud, leverage these service providers like Experian, and build the applications of tomorrow. >> I'm super excited when I talk to practitioners like yourselves, about the future of data, guys, thanks so much for coming on theCUBE, it was a really a pleasure having you, and I hope we can continue this conversation in the future. >> Thank you. >> Thanks. >> Alright, thank you for watching, keep it right there, we got great content, and tons of content coming at the Snowflake data cloud summit, this is Dave Vellante for theCUBE, keep it right there.

>> My name is Dave Vellante. And with me are two world-class technologists, visionaries and entrepreneurs. Benoit Dageville, he co-founded Snowflake and he's now the President of the Product Division, and Florian Douetteau is the Co-founder and CEO of Dataiku. Gentlemen, welcome to the cube to first timers, love it. >> Yup, great to be here. >> Now Florian you and Benoit, you have a number of customers in common, and I've said many times on theCUBE, that the first era of cloud was really about infrastructure, making it more agile, taking out costs. And the next generation of innovation, is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake, and Dataiku make a good match for customers? >> I think that because it's our values that aligned, when it gets all about actually today, and knowing complexity of our customers, so you close the gap. Where we need to commoditize the access to data, the access to technology, it's not only about data. Data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible, within an organization. And another value is about just the openness of the platform, building a future together. Having a platform that is not just about the platform, but also for the ecosystem of partners around it, bringing the level of accessibility, and flexibility you need for the 10 years of that. >> Yeah, so that's key, that it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we know we all know that you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so the challenge before snowflake, I would say, was really to put all the data in one place, and run all the computes, all the workloads that you wanted to run against that data. And of course existing legacy platforms were not able to support that level of concurrency, many workload, we talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place didn't make sense at all. And therefore be what customers did this to create silos, silos of data everywhere, with different system, having a subset of the data. And of course now, you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data into cloud. So it's a really cloud native. We really thought about how solve that problem, how to create, leverage cloud, and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake, at its dedicated compute resources to run. And that makes it agile, right? Florian talked about data scientist having to run analysis, so they need a lot of compute resources, but only for a few hours. And with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system, it will automatically shut down. Therefore they would not pay for the resources that they don't use. So it's a very agile system, where you can do this analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. I mean to me, I mean of course everybody's trying to copy it now, it was like, I remember that bringing the notion of bringing compute to the data, in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say the first data scientist I ever interviewed on theCUBE, it was the amazing Hillary Mason, right after she started at Bitly, and she made data sciences sounds so compelling, but data science is a hard. So same question for you, what do you see as the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective, is that once you solve the issue of the data silo, with Snowflake, you don't want to bring another silo, which will be a silo of skills. And essentially, thanks to the talent gap, between the talent available to the markets, or are released to actually find recruits, train data scientists, and what needs to be done. And so you need actually to simplify the access to technologies such as, every organization can make it, whatever the talent, by bridging that gap. And to get there, there's a need of actually backing up the silos. Having a collaborative approach, where technologies and business work together, and actually all puts up their ends into those data projects together. >> It makes sense, Florain let's stay with you for a minute, if I can. Your observation space, it's pretty, pretty global. And so you have a unique perspective on how can companies around the world might be using data, and data science. Are you seeing any trends, maybe differences between regions, or maybe within different industries? What are you seeing? >> Yeah, definitely I do see trends that are not geographic, that much, but much more in terms of maturity of certain industries and certain sectors. Which are, that certain industries invested a lot, in terms of data, data access, ability to store data. As well as experience, and know region level of maturity, where they can invest more, and get to the next steps. And it's really relying on the ability of certain leaders, certain organizations, actually, to have built these long-term data strategy, a few years ago when no stats reaping of the benefits. >> A decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientist. And then everybody, all the statisticians became data scientists, and they got a raise. But data science requires more than just statistics acumen. What skills do you see as critical for the next generation of data science? >> Yeah, it's a great question because I think the first generation of data scientists, became data scientists because they could have done some Python quickly, and be flexible. And I think that the skills of the next generation of data scientists will definitely be different. It will be, first of all, being able to speak the language of the business, meaning how you translates data insight, predictive modeling, all of this into actionable insights of business impact. And it would be about how you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python, or do predictive models of some sorts. It's about how you actually build this bridge with the business, and obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools, new technologies, and they will still need to keep this level of flexibility to understand quickly what are the next tools they need to use a new languages, or whatever to get there. >> As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right? This crises has told us that the world really can change from one day to the next. And this has dramatic and perform the aspects. For example companies all of a sudden, show their revenue line dropping, and they had to do less with data. And some other companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely changed from one day to the other. So this agility of adjusting the resources that you have to do the task, and need that can change, using solution like Snowflake really helps that. Then we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID, and their business benefited. But others had to drop. And what is nice with cloud, it allows you to adjust compute resources to your business needs, and really address it in house. The other aspect is understanding what happening, right? You need to analyze. We saw all our customers basically, wanted to understand what is the going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data which are not necessarily data about their business, but also they are from the outside. For example, COVID data, where is the States, what is the impact, geographic impact on COVID, the time. And access to this data is critical. So this is the premise of the data cloud, right? Having one single place, where you can put all the data of the world. So our customer obviously then, started to consume the COVID data from that our data marketplace. And we had delete already thousand customers looking at this data, analyzing these data, and to make good decisions. So this agility and this, adapting from one hour to the next is really critical. And that goes with data, with cloud, with interesting resources, and that doesn't exist on premise. So indeed I think the lesson learned is we are living in a world, which is changing all the time, and we have to understand it. We have to adjust, and that's why cloud some ways is great. >> Excellent thank you. In theCUBE we like to talk about disruption, of course, who doesn't? And also, I mean, you look at AI, and the impact that it's beginning to have, and kind of pre-COVID. You look at some of the industries that were getting disrupted by, everyone talks about digital transformation. And you had on the one end of the spectrum, industries like publishing, which are highly disrupted, or taxis. And you can say, okay, well that's Bits versus Adam, the old Negroponte thing. But then the flip side of, you say look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is ripe for disruption, defense. So there a number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science, and what I call machine intelligence, or AI, in the coming years and decade? >> Honestly, I think it's all of them, or at least most of them, because for some industries, the impact is very visible, because we have talking about brand new products, drones, flying cars, or whatever that are very visible for us. But for others, we are talking about a part from changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted, when you look at it from the consumer side, or the outside insights in Germany, it's probably impacted just because the way you use data (mumbles) for flexibility you need. Is there kind of the cost gain you can get by leveraging the latest technologies, is just the numbers. And so it's will actually comes from the industry that also. And overall, I think that 2020, is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience, maturity meaning that when you've got to crisis you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions, and look for one and a backlog. And I think that's a very important learning from 2020, that will tell things about 2021. And the resilience, it's like, data analytics today is a function transforming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient, so probably not on prem or not fully on prem, at some point. And the kind of resilience where you need to be able to blend for literally anything, like no hypothesis in terms of BLOs, can be taken for granted. And that's something that is new, and which is just signaling that we are just getting to a next step for data analytics. >> I wonder Benoir if you have anything to add to that. I mean, I often wonder, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? What's going to happen to big retail stores? I mean, maybe bring us home with maybe some of your finals thoughts. >> Yeah, I would say I don't see that as a negative, right? The human being will always be involved very closely, but then the machine, and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So I think it's going to be a compliment not a replacement. And everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do, that I will not be worried about the effect of being more efficient, and bare at our work. And indeed, I fundamentally think that data, processing of images, and doing AI on these images, and discovering patterns, and potentially flagging disease way earlier than it was possible. It is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, guys, I wish we had more time. I've got to leave it there, but so thanks so much for coming on theCUBE. It was really a pleasure having you.

>> Hi and welcome to Data Cloud catalyst Women in Tech Round Table Panel discussion. I am so excited to have three fantastic female executives with me today, who have been driving transformations through data throughout their entire career. With me today is Lisa Davis, SVP and CIO OF Blue shield of California. We also have Nishita Henry who is the Chief Innovation Officer at Deloitte and Teresa Briggs who is on a variety of board of directors including our very own Snowflake. Welcome ladies. >> Thank you. >> So I am just going to dive right in, you all have really amazing careers and resumes behind you, am really curious throughout your career, how have you seen the use of data evolve throughout your career and Lisa am going to start with you. >> Thank you, having been in technology my entire career, technology and data has really evolved from being the province of a few in an organization to frankly being critical to everyone's business outcomes. Now every business leader really needs to embrace data analytics and technology. We've been talking about digital transformation, probably the last five, seven years, we've all talked about, disrupt or be disrupted, At the core of that digital transformation is the use of data. Data and analytics that we derive insights from and actually improve our decision making by driving a differentiated experience and capability into market. So data has involved as being I would say almost tactical, in some sense over my technology career to really being a strategic asset of what we leverage personally in our own careers, but also what we must leverage as companies to drive a differentiated capability to experience and remain relative in the market today. >> Nishita curious your take on, how you have seen data evolve? >> Yeah, I agree with Lisa, it has definitely become a the lifeblood of every business, right? It used to be that there were a few companies in the business of technology, every business is now a technology business. Every business is a data business, it is the way that they go to market, shape the market and serve their clients. Whether you're in construction, whether you're in retail, whether you're in healthcare doesn't matter, right? Data is necessary for every business to survive and thrive. And I remember at the beginning of my career, data was always important, but it was about storing data, it was about giving people individual reports, it was about supplying that data to one person or one business unit in silos. And it then evolved right over the course of time into integrating data into saying, alright, how does one piece of data correlate to the other and how can I get insights out of that data? Now, its gone to the point of how do I use that data to predict the future? How do I use that data to automate the future? How do I use that data not just for humans to make decisions, but for other machines to make decisions, right? Which is a big leap and a big change in how we use data, how we analyze data and how we use it for insights and involving our businesses. >> Yeah its really changed so tremendously just in the past five years, its amazing. So Teresa we've talked a lot about the Data Cloud, where do you think we are heading with that and also how can future leaders really guide their careers in data especially in those jobs where we don't traditionally think of them in the data science space? Teresa your thoughts on that. >> Yeah, well since I'm on the Snowflake Board, I'll talk a little bit about the Snowflake Data Cloud, we're getting your company's data out of the silos that exist all over your organization. We're bringing third party data in to combine with your own data and we're wrapping a governance structure around it and feeding it out to your employees so they can get their jobs done, as simple as that. I think we've all seen the pandemic accelerate the digitization of our work. And if you ever doubted that the future of work is here, it is here and companies are scrambling to catch up by providing the right amount of data, collaboration tools, workflow tools for their workers to get their jobs done. Now, it used to be as prior people have mentioned that in order to work with data you had to be a data scientist, but I was an auditor back in the day we used to work on 16 column spreadsheets. And now if you're an accounting major coming out of college joining an auditing firm, you have to be tech and data savvy because you're going to be extracting, manipulating, analyzing and auditing data, that massive amounts of data that sit in your clients IT systems. I'm on the board of Warby Parker, and you might think that their most valuable asset is their amazing frame collection, but it's actually their data, their 360 degree view of the customer. And so if you're a merchant, or you're in strategy, or marketing or talent or the Co-CEO, you're using data every day in your work. And so I think it's going to become a ubiquitous skill that any anyone who's a knowledge worker has to be able to work with data. >> Yeah I think its just going to be organic to every role going forward in the industry. So, Lisa curious about your thoughts about Data Cloud, the future of it and how people can really leverage it in their jobs for future leaders. >> Yeah, absolutely most enterprises today are, I would say, hybrid multicloud enterprises. What does that mean? That means that we have data sitting on-prem, we have data sitting in public clouds through software as a service applications. We have a data everywhere. Most enterprises have data everywhere, certainly those that have owned infrastructure or weren't born on the web. One of the areas that I love that Data Cloud is addressing is area around data portability and mobility. Because I have data sitting in various locations through my enterprise, how do I aggregate that data to really drive meaningful insights out of that data to drive better business outcomes? And at Blue Shield of California, one of our key initiatives is what we call an Experienced Cube. What does that mean? That means how do I drive transparency of data between providers, members and payers? So that not only do I reduce overhead on providers and provide them a better experience, our hospital systems are doctors, but ultimately, how do we have the member have it their power of their fingertips the value of their data holistically, so that we're making better decisions about their health care. One of the things Teresa was talking about, was the use of this data and I would drive to data democratization. We got to put the power of data into the hands of everyone, not just data scientists, yes we need those data scientists to help us build AI models to really drive and tackle these tough old, tougher challenges and business problems that we may have in our environments. But everybody in the company both on the IT side, both on the business side, really need to understand of how do we become a data insights driven enterprise, put the power of the data into everyone's hands so that we can accelerate capabilities, right? And leverage that data to ultimately drive better business results. So as a leader, as a technology leader, part of our responsibility, our leadership is to help our companies do that. And that's really one of the exciting things that I'm doing in my role now at Blue Shield of California. >> Yeah its really, really exciting time. I want to shift gears a little bit and focus on women in Tech. So I think in the past five to ten years there has been a lot of headway in this space but the truth is women are still under represented in the tech space. So what can we do to attract more women into technology quite honestly. So Nishita curious what your thoughts are on that? >> Great question and I am so passionate about this for a lot of reasons, not the least of which is I have two daughters of my own and I know how important it is for women and young girls to actually start early in their love for technology and data and all things digital, right? So I think it's one very important to start early started early education, building confidence of young girls that they can do this, showing them role models. We at Deloitte just partnered with LV Engineer to actually make comic books centered around young girls and boys in the early elementary age to talk about how heroes in tech solve everyday problems. And so really helping to get people's minds around tech is not just in the back office coding on a computer, tech is about solving problems together that help us as citizens, as customers, right? And as humanity, so I think that's important. I also think we have to expand that definition of tech, as we just said it's not just about right, database design, It's not just about Java and Python coding, it's about design, it's about the human machine interfaces, it's about how do you use it to solve real problems and getting people to think in that kind of mindset makes it more attractive and exciting. And lastly, I'd say look we have a absolute imperative to get a diverse population of people, not just women, but minorities, those with other types of backgrounds, disabilities, et cetera involved because this data is being used to drive decision making in all involved, right, and how that data makes decisions, it can lead to unnatural biases that no one intended but can happen just 'cause we haven't involved a diverse enough group of people around it. >> Absolutely, lisa curious about your thoughts on this. >> I agree with everything Nishita said, I've been passionate about this area, I think it starts with first we need more role models, we need more role models as women in these leadership roles throughout various sectors. And it really is it starts with us and helping to pull other women forward. So I think certainly it's part of my responsibility, I think all of us as female executives that if you have a seat at the table to leverage that seat at the table to drive change, to bring more women forward more diversity forward into the boardroom and into our executive suites. I also want to touch on a point Nishita made about women we're the largest consumer group in the company yet we're consumers but we're not builders. This is why it's so important that we start changing that perception of what tech is and I agree that it starts with our young girls, we know the data shows that we lose our like young girls by middle school, very heavy peer pressure, it's not so cool to be smart, or do robotics, or be good at math and science, we start losing our girls in middle school. So they're not prepared when they go to high school, and they're not taking those classes in order to major in these STEM fields in college. So we have to start the pipeline early with our girls. And then I also think it's a measure of what your boards are doing, what is the executive leadership in your goals around diversity and inclusion? How do we invite more diverse population to the decision making table? So it's really a combination of efforts. One of the things that certainly is concerning to me is during this pandemic, I think we're losing one in four women in the workforce now because of all the demands that our families are having to navigate through this pandemic. The last statistic I saw in the last four months is we've lost 850,000 women in the workforce. This pipeline is critical to making that change in these leadership positions. >> Yeah its really a critical time and now we are coming to the end of this conversation I want to ask you Teresa what would be a call to action to everyone listening both men and women since its to be solved by everyone to address the gender gap in the industry? >> I'd encourage each of you to become an active sponsor. Research shows that women and minorities are less likely to be sponsored than white men. Sponsorship is a much more active form than mentorship. Sponsorship involves helping someone identify career opportunities and actively advocating for them and those roles opening your network, giving very candid feedback. And we need men to participate too, there are not enough women in tech to pull forward and sponsor the high potential women that are in our pipelines. And so we need you to be part of the solution. >> Nishita real quickly what would be your call to action to everyone? >> I'd say look around your teams, see who's on them and make deliberate decisions about diversifying those teams, as positions open up, make sure that you have a diverse set of candidates, make sure that there are women that are part to that team and make sure that you are actually hiring and putting people into positions based on potential not just experience. >> And real quickly Lisa, we'll close it out with you what would your call to action be? >> Wow, it's hard to what Nishita and what Tricia shared I think we're very powerful actions. I think it starts with us. Taking action at our own table, making sure you're driving diverse panels and hiring setting goals for the company, having your board engaged and holding us accountable and driving to those goals will help us all see a better outcome with more women at the executive table and diverse populations. >> Great advice and great action for all of us to take. Thank you all so much for spending time with me today and talking about this really important issue, I really appreciate it. Stay with us.

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

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

>> Narrator: Live from San Jose, it's theCUBE. Presenting Big Data, Silicon Valley. Brought to you by SiliconANGLE Media and its ecosystem partners. >> Hi, I'm George Gilbert, and we are broadcasting from the Strata Data Conference, we're right around the corner at the Forager Tasting Room & Eatery. We have this wonderful location here, and we are very lucky to have with us Michael Nixon, from Snowflake, which is a leading cloud data warehouse. And David Abercrombie from Sharethrough which is a leading ad tech company. And between the two of them, they're going to tell us some of the most advance these cases we have now for cloud-native data warehousing. Michael, why don't you start with giving us some context for how on a cloud platform one might rethink a data warehouse? >> Yeah, thank you. That's a great question because let me first answer it from the end-user, business value perspective, when you run a workload on a cloud, there's a certain level of expectation you want out of the cloud. You want scalability, you want unlimited scalability, you want to be able to support all your users, you want to be able to support the data types, whatever they may be that comes in into your organization. So, there's a level of expectation that one should expect from a service point of view once you're in a cloud. So, a lot of the technology that were built up to this point have been optimized for on-premises types of data warehousing where perhaps that level of service and currency and unlimited scalability was not really expected but, guess what? Once it comes to the cloud, it's expected. So those on-premises technologies aren't suitable in the cloud, so for enterprises and, I mean, companies, organizations of all types from finance, banking, manufacturing, ad tech as we'll have today, they want that level of service in the cloud. And so, those technologies will not work, and so it requires a rethinking of how those architectures are built. And it requires being built for the cloud. >> And just to, alright, to break this down and be really concrete, some of the rethinking. We separate compute from storage, which is a familiar pattern that we've learned in the cloud but we also then have to have this sort of independent elasticity between-- >> Yes. Storage and the compute, and then Snowflake's taken it even a step further where you can spin out multiple compute clusters. >> Right. >> Tell us how that works and why that's so difficult and unique. >> Yeah, you know, that's taking us under the covers a little bit, but what makes our infrastructure unique is that we have a three-layer architecture. We separate, just as you said, storage from the compute layer, from the services layer. And that's really important because as I mentioned before, you want unlimited capacity, unlimited resources. So, if you scale, compute, and today's world on on-premises MPP, what that really means is that you have to bring the storage along with the compute because compute is tied to the storage so when you scale the storage along with the compute, usually that involves a lot of burden on the data warehouse manager because now they have to redistribute the data and that means redistributing keys, managing keys if you will. And that's a burden, and by the reverse, if all you wanted to do was increase storage but not the compute, because compute was tied to storage. Why you have to buy these additional compute notes, and that might add to the cost when, in fact, all you really wanted to pay for was for additional storage? So, by separating those, you keep them independent, and so you can scale storage apart from compute and then, once you have your compute resources in place, the virtual warehouses that you're talking about that have completed the job, you spun them up, it's done its job, and you take it down, guess what? You can release those resources, and of course, in releasing those resources, basically you can cut your cost as well because, for us, it's pure usage-based pricing. You only pay for what you use, and that's really fantastic. >> Very different from the on-prem model where, as you were saying, tied compute and storage together, so. >> Yeah, let's think about what that means architecturally, right? So if you have an on-premises data warehouse, and you want to scale your capacity, chances are you'll have to have that hardware in place already. And having that hardware in place already means you're paying that expense and, so you may pay for that expense six months prior to need it. Let's take a retailer example. >> Yeah. >> You're gearing up for a peak season, which might be Christmas, and so you put that hardware in place sometime in June, you'll always put it in advanced because why? You have to bring up the environment, so you have to allow time for implementation or, if you will, deployment to make sure everything is operational. >> Okay. >> And then what happens is when that peak period comes, you can't expand in that capacity. But what happens once that peak period is over? You paid for that hardware, but you don't really need it. So, our vision is, or the vision we believe you should have when you move workloads to the cloud is, you pay for those when you need them. >> Okay, so now, David, help us understand, first, what was the business problem you were trying to solve? And why was Snowflake, you know, sort of uniquely suited for that? >> Well, let me talk a little bit about Sharethrough. We're ad tech, at the core of our business we run an ad exchange, where we're doing programmatic training with the bids, with the real-time bidding spec. The data is very high in volume, with 12 billion impressions a month, that's a lot of bids that we have to process, a lot of bid requests. The way it operates, the bids and the bid responses and programmatic training are encoded in JSONs, so our ad exchange is basically exchanging messages in JSON with our business partners. And the JSONs are very complicated, there's a lot of richness and detail, such that the advertisers can decide whether or not they want to bid. Well, this data is very complicated, very high-volume. And advertising, like any business, we really need to have good analytics to understand how our business is operating, how our publishers are doing, how our advertisers are doing. And it all depends upon this very high-volume, very complex JSON event data stream. So, Snowflake was able to ingest our high-volume data very gracefully. The JSON parsing techniques of Snowflake allow me to expose the complicated data structure in a way that's very transparent and usable to our analysts. Our use of Snowflake has replaced clunkier tools where the analysts basically had to be programmers, writing programs in Scala or something to do in analysis. And now, because we've transparently and easily exposed the complicated structures within Snowflake in a relational database, they can use good old-fashioned SQL to run their queries, literally, afternoon analysis is now a five-minute query. >> So, let me, as I'm listening to you describe this. We've had various vendors telling us about these workflows in the sort of data prep and data science tool change. It almost sounds to me like Snowflake is taking semi-structured or complex data and it's sort of unraveling it and normalizing is kind of an overloaded term but it's making it business-ready, so you don't need as much of that manual data prep. >> Yeah, exactly, you don't need as much manual data prep, or you don't need as much expertise. For instance, Snowflake's JSON capabilities, in terms of drilling down the JSON tree with dot path notation, or expanding nested objects is very expressive, very powerful, but still your typical analyst or your BI tool certainly wouldn't know how to do that. So, in Snowflake, we sort of have our cake and eat it too. We can have our JSONs with their full richness in our database, but yet we can simplify and expose the data elements that are needed for analysis, so that an analyst, their first day on the job, they can get right to work and start writing queries. >> So let me ask you about, a little more about the programmatic ad use case. So if you have billions of impressions per month, I'm guessing that means you have quite a few times more, in terms of bids, and then there's the, you know once you have, I guess a successful one, you want to track what happens. >> Correct. >> So tell us a little more about that, what that workload looks like, in terms of, what analytics you're trying to perform, what's your tracking? >> Yeah, well, you're right. There's different steps in our funnel. The impression request expands out by a factor of a dozen as we send it to all the different potential bidders. We track all that data, the responses come back, we track that, we track our decisions and why we selected the bidder. And then, once the ad is shown, of course there's various beacons and tracking things that fire. We'd have to track all of that data, and the only way we could make sense out of our business is by bringing all that data together. And in a way that is reliable, transparent, and visible, and also has data integrity, that's another thing I like about the Snowflake database is that it's a good old-fashioned SQL database that I can declare my primary keys, I can run QC checks, I can ensure high data integrity that is demanded by BI and other sorts of analytics. >> What would be, as you continue to push the boundaries of the ad tech service, what's some functionality that you're looking to add, and Snowflake as your partner, either that's in there now that you still need to take advantage of or things that you're looking to in the future? >> Well, moving forward, of course, we, it's very important for us to be able to quickly gauge the effectiveness of new products. The ad tech market is fast-changing, there's always new ways of bidding, new products that are being developed, new ways for the ad ecosystem to work. And so, as we roll those out, we need to be able to quickly analyze, you know, "Is this thing working or not?" You know, kind of an agile environment, pivot or prove it. Does this feature work or not? So, having all the data in one place makes that possible for that very quick assessment of the viability of a new feature, new product. >> And, dropping down a little under the covers for how that works, does that mean, like you still have the base JSON data that you've absorbed, but you're going to expose it with different schemas or access patterns? >> Yeah, indeed. For instance, we make use of the SQL schemas, roles, and permissions internally where we can have the different teams have their own domain of data that they can expose internally, and looking forward, there's the share house feature of Snowflake that we're looking to implement with our partners, where, rather than sending them data, like a daily dump of data, we can give them access to their data in our database through this top layer that Michael mentioned, the service layer, essentially allows me to create a view grant select onto another customer. So I no longer have to send daily data dumps to partners or have some sort of API for getting data. They can simply query the data themselves so we'll be implementing that feature with our major partners. >> I would be remiss in not asking at a data conference like this, now that there's the tie-in with CuBOL and Spark Integration and Machine Learning, is there anything along that front that you're planning to exploit in the near future? >> Well, yeah, Sharethrough, we're very experimental, playful, we're always examining new data technologies and new ways of doing things but now with Snowflake as sort of our data warehouse of curated data. I've got two petabytes of referential integrity data, and that is reliable. We can move forward into our other analyses and other uses of data knowing that we have captured every event exactly once, and we know exactly where it fits in a business context, in a relational manner. It's clean, good data integrity, reliable, accessible, visible, and it's just plain old SQL. (chuckles) >> That's actually a nice way to sum it up. We've got the integrity that we've come to expect and love from relational databases. We've got the flexibility of machine-oriented data, or JSON. But we don't have to give up the query engine, and then now you have more advanced features, analytic features that you can take advantage of coming down the pipe. >> Yeah, again we're a modern platform for the modern age, that's basically cloud-based computing. With a platform like Snowflake in the backend, you can now move those workloads that you're accustomed to to the cloud and have in the environment that you're familiar with, and it saves you a lot of time and effort. You can focus on more strategic projects. >> Okay, well, with that, we're going to take a short break. This has been George Gilbert, we're with Michael Nixon of Snowflake, and David Abercrombie of Sharethrough listening to how the most modern ad tech companies are taking advantage of the most modern cloud data warehouses. And we'll be back after a short break here at the Strata Data Conference, thanks. (quirky music)

(calm music) >> Welcome to theCUBE's live coverage of Snowflake Summit '22 from Las Vegas, Caesars Forum. Lisa Martin here with Dave Vellante, we're going to unpack some really cool stuff next, in the next 10 minutes with you, Carl Perry joins us, the Director of Product Management at Snowflake, he's here to talk about Snowflake's new Unistore workloads, how it's driving the next phase of innovation, welcome to the program. >> Oh, thank you so much for having me, this is awesome. >> There's a ton of momentum here, I saw the the numbers from fiscal 23Q1, product revenue 394 million, 85% growth, a lot of customers here, the customer growth is incredible as well, talk to us about Unistore, what is it? Unpack it and how have the customers been influential in it's development? >> Yeah, so Unistore is a way for customers to take their transactional workloads, for their enterprise applications and now have them run on or be built on top of Snowflake and now, you have your transactional data, along with all of your historical data, so now you have a single unified platform for doing anything you need to do with your data, whether it's transactional, single row look-ups, we can do that, whether it's the analytical data across again, transactional and historical data in a single query, our customers are super excited about this. >> So, what are Hybrid Tables? Is that just an extension of external tables? >> Yeah, that's a great question. So, Hybrid Tables are a new table-type that we've added to Snowflake and Hybrid Tables are really kind of just like another table with a couple of key differences, so number one is that Hybrid Tables provide fast, fine-grain read and write operations, so when you do something like a select star from customers where customer ID=832, that's going to return extremely fast, but on top of that same data, your transactional data, you can actually perform amazing analytical queries that return extremely fast and that's what Hybrid Tables at their core are. >> So, what does this mean for, so you're bringing that world of transaction and analytics together, what does it mean for customers? Walk us through Carl, an example of- >> Yeah, so it's great, so Adobe is a customer that is looking at using and leveraging Hybrid Tables today, and then more broadly Unistore, and frankly, Adobe has been an amazing customer since they started their journey, just really quickly, they're in phase three, the first phase was customers had data in Snowflake that they wanted to take advantage of with the Adobe Campaign Platform and so what they did is they built a connector basically into and being able to access customer data, and then they started to look at, "Well, this thing's working really well, let's try to leverage Snowflake for all our analytical needs." And so that was kind of phase two, and now phase three is like, look let's go and reimagine what we can do with the Adobe Campaign Platform by having both the transactional and analytical data in the same platform, so that they can really enable their customers to do personalization, ad campaign management, understanding the ethicacy of those things at a scale that they haven't been able to do before. >> Prior to this capability, they would what? Have to go outside of the Snowflake Data Cloud? And do something else? And then come back in? >> Exactly, right? So, they'd have a transactional system where all of the transactional state for what the customer was doing inside Adobe Campaign, setting up all their campaigns and everything, and that would be stored inside a database, right? And then they would need to ensure that, that data was moved over to Snowflake for further analytical purposes, right? You know you imagine the complexity that our customers have to manage every single day, a separate transactional system, an ETL pipeline to keep that data flowing and then Snowflake, right? And with Unistore, we really believe that customers will be able to remove that complexity from their lives and have that single platform that really makes their lives easier. >> I mean, they'll still have a transactional system, will they not? Or do you see a day where they sort of sunset that? >> I mean, there's a set of workloads that are not going to be the best choice today for Unistore and Hybrid Tables, right? And so we know that customers will continue to have their own transactional systems, right? And there's lots of transactional systems that customers rely and have entire applications, and systems built around, right? Right now with Hybrid Tables and Unistore, customers can take those enterprise applications, not consumer-facing applications and move them over to leverage Snowflake, and then really think about re-imagining how they can use their data that's both realtime transactional, as well as all the historical data without the need to move things between systems or use a ton of different services. >> The Adobe example that you just gave seems like, I loved how you described the phases they're in, they're discovering, it's like peeling the onion and just discovering more, and more, but what it sounds like is that Snowflake has enabled Adobe to transform part of it's business, how is Unistore positioned to be so transformational for your customers? >> Well, I mean I think there's a couple of things, so one, they have this like level of complexity today for a set of applications that they can completely stop worrying about, right? No need to maintain that separate transactional system for that again, enterprise application, no need to maintain that ETL pipeline, that's kind of like one step, the next step is, I mean all your data's in Snowflake, so you can start leveraging that data for insight and action immediately, there's no delay in being able to take advantage of that data, right? And then number three, which I think is the most compelling part is because it's part of Snowflake, you getting the benefit of Snowflake's entire ecosystem, whether it's first party capabilities like easy to manage and enforce really powerful governance, and security policies, right? Being able to take data from the market place and actually join it with my realtime transactional data, this is game-changing and then most importantly is the third-party ecosystem of partners who are building all these incredible solutions on top of Snowflake, I can't even begin to imagine what they're going to do with Hybrid Tables in Unistore. >> So, Carl I have to ask you, so I talked to a lot of customers and I talked to a lot of technology companies, explain, so Snowflake obviously was the first to separate compute from storage and you know the cloud, cloud database and then tons of investment came into that space, kind of follow you on, so that's cool, you reached escape velocity, awesome, but a lot of the companies that I talked to are saying, "We're converging transaction and analytics," I think (speaking softly) calls it HTAP or something, they came up with a name, explain the difference between what you're doing and what everybody else is doing, and why, what customer benefits you're delivering? >> Yeah, so I mean I think that's a really great question and to use the term you used HTAP, right? It's a industry understood term, really when people think about HTAP, what that is about is taking your transactional data that you have and enabling you to do fast analytical capabilities on that, and that's great, but there are a couple of problems that historical HTAP solutions have suffered from, so number one, that acceleration, that colander format of data is all in memory, so you're bound by the total amount of memory that you can use to accelerate the queries that you want to, so that's kind of problem one, this is not the approach that Snowflake is taking, most importantly, it's not just about accelerating queries on transactional data, whether it's a single-row lookup or a complex aggregate, it's about being able to leverage that data within the data cloud, right? I don't want to have a separate dataset on a transactional system or an HTAP system that can give me great analytics on transactional data and then I can't use it with all the other data that I have, it's truly about enabling the transformation with the data cloud and completely taking away silos, so that your data, whether it's realtime, whether it's historical, can be treated as a single dataset, this is the key thing that is different about Unistore, you can take the power of the data cloud, all of it, all of the partners, all the solutions and all the capabilities we continue to add, and leverage your data in ways that nobody's thought of possible before. >> Governance is a huge, huge component of that, right? So, in the press release, you have this statement, "As part of the Unistore Snowflake is introducing Hybrid Tables," you explained that, "Which offer fast, single-row operations and allow customers to build transactional business applications directly on Snowflake"- >> Yep. >> That's a little interesting tidbit, so you expect customers are going to build transactional applications inside the data cloud? And somewhat minimize the work that is going to be required by their existing transactional databases, correct? >> Exactly and I think, so let me say a couple things on this, right? So, first of all, there's a class of applications that will be able to just build on top of Hybrid Tables and run on Snowflake directly, for their transactional needs, I think what's super interesting here though is when you again start to talk about all your data, one example that we're going to walk through tomorrow in our talk is being able to do a transaction that updates data in a Hybrid Table and then updates data in a Standard Snowflake Table, and then either being able to atomically commit, or rollback that transaction, this is a transaction that's spanning multiple different table types inside Snowflake and you'll have consistency of either the rollback or the commit, this type of functionality doesn't exist elsewhere and being able to take, and build transactional applications with these capabilities, we think is transformative- >> And that's all going to happen inside the Snowflake Data Cloud, with all the capabilities and it's not like you know what you're doing with Dell and Pure, it's nice, but it's read-only, you can't you know add and delete, and do all that stuff, this is Native? First class citizen inside the database? >> Yep, just like other table types, you'll be able to take on and leverage the power of the data cloud as a normal table that you'd be able to use elsewhere. >> Got to ask you, your energy in the way that you're talking about this is fantastic, the transformation that it's going to be, how central it is to the product innovations that Snowflake is coming out with, what's been the feedback from customers? As there's so many thousands of folks here today, the keynote was standing in your room only, there was an overflow, what are you hearing on the floor here? >> Well, I mean, I think it was funny in the talk when I announced that primary keys are going to be required and enforced, and we got a standing ovation, I was like, "Wow, I didn't expect people to be so excited about primary key enforcement." I mean, what's been amazing both about the private preview and the feedback we're getting there, and then some of the early feedback we're getting from customers is that they want to understand and they're really thinking about like, "Wait, I can use Snowflake for all of this now?" And honestly I think that people are kind of like, "But wait, what would I do if I could have those applications running on Snowflake and not have to worry about multiple systems? Wait, I can combine it with all my historical data and anything that's in the data cloud, like what can I do?" Is the question they're asking and I think that this is the most fascinating thing, customers are going to build things they haven't been able to build before and I'm super excited to see what they do. >> But more specifically, my takeaway is that customers, actually application builders are going to be able to build applications that have data inherent to those apps, I mean John Furrier years ago said, "You know data is the new development kit." And it never happened the data, the data stack if you will separate from the application development stack, you're bringing those two worlds together, so what do you think the implications are of that? >> Well, I mean I think that we're going to dramatically simplify our customers lives, right? A thing that we focus on at Snowflake is relentless customer innovation, so we can make their lives better, so I mean frankly we talk to customers like, "Wait, I can do all this? Wait, are you sure that I'll be able to do this?" And we walk through what we can do, and what we can't do, and they really are like, "Wow, this could just dramatically simplify our lives and wait, what could we do with our data here?" And so, I think with the announcement of Unistore, and also all the Native app stuff that we're announcing today, I think we're really trying to enable customers and app developers there to think about, and being able to leverage Snowflake as their transactional system, the system of source, so I mean, I'm super excited about this, I came to Snowflake to work on this and I'm like, "Can't believe we get to talk about it." >> How do you, how, how? How does this work? What's the secret sauce behind it? Is it architecture or is it? >> Yeah, so I mean I think a big part of it is the architecture that we chose, so you know number one, a key product philosophy that we have at Snowflake is we have one product, we don't have many, we don't put the onus of complexity onto our customers and so building that into Snowflake is actually really hard, so underlying Hybrid Tables, which is the feature that powers Unistore is a row storage engine, a row-based storage engine, right? And then data is asynchronously copied over into a colander format and what this provides, because it's just another table that's deeply integrated with Snowflake is the compiler's completely aware of this, so you can write a query that spans multiple tables and take advantage of it, and we'll take over all the complexity, whether it needs to be a fast response to a single-row lookup, or it needs to aggregate and scan a ton of data, we'll make sure that we choose the right thing and provide you with the best performance that we have- >> You built that intelligence inside of that? >> Completely built in and amazing, but provided in a very simple fashion. >> You said you came to Snowflake to do this? How long ago was that? >> I came here a little over a year and a half. >> Okay, and had they started working on this obviously beforehand, or at least envisioning it, right? >> Yeah, this I mean, this is absolutely incredible, I have been working on this now for a year and a half, some of the team members have been working on it for more and it's incredible to finally be able to talk to customers and everybody about it, and for them to tell us what they're trying to do. I've already talked to a bunch of customers like, "Well wait, I could do this, or this, what about this scenario?" And it's awesome to hear their requirements, right? The thing that's been most amazing and you'll hear it in the talk tomorrow with Adobe who's been a great customer is like, "Customers give us insanely hard requirements." And what I love about this company is not, "Well, you know it's easier to do it this way." It's like, "No, how can we actually make their life easier?" And so, we really focus on doing that with Snowflake. >> And that's one of the things Frank talked about this morning with that mission alignment being critical there. So, it's in private preview now, when can folks expect to get their hands on it? >> Well, we don't have a date right now we're talking about, but you can go signup to be notified of the public preview when we get there, I think it's like snowflake.com/try-unistore, but we'll publish that later and you know if you're interested in the private preview, talk to your account team and we'll see if we can get you in. >> Carl, thank you so much for joining Dave and me in an action-packed 15 minutes, talking about the power of Unistore, what it's going to enable organizations to do and it sounds like you're tapping the surface, there's just so much more innovation that's to come, you're going to have to come back. >> Yes, that sounds awesome, thank you so much. >> Our pleasure. For Carl and Dave Vellante, I'm Lisa Martin, you're watching theCUBE's live coverage of Snowflake Summit '22 from the show floor in Las Vegas, we're going to be right back with our next guest. (calm music)

>>mhm. >>Good morning. Live from Las Vegas. That snowflake Summit 22. Lisa Martin With Day Volonte David's Great. We have three wall to wall days of coverage at Snowflake Summit 22 this year. >>Yeah, it's all about data and bringing data to applications. And we've got some big announcements coming this week. Super exciting >>collaboration around data. We are excited to welcome our first two guests before the keynote. We have seen Nielsen in S V. P of data and Analytics Service offering head at emphasis. And Chris Dignan alumni is back with us to chief revenue officer at stuff like guys. Great to have you on the programme. Thanks for having us. Thank you very much. So he'll tell us what's going on with emphasis and snowflake and the partnership. Give us all that good stuff. >>Yeah, No, I think with the convergence of, uh, data digital and computing economy, um, you know that convergence is creating so much possibilities for for customers, uh, snowflake and emphases working together to help our customers realise the vision and these possibilities that are getting driven. We share a very strategic partnership where we are thinking ahead for our customers in terms of what, uh, we can do together in order to build solutions in order to bring out the expertise that is needed for such transformations and also influencing the thinking, Um, and the and the point of view in the market together so that, you know there is there is cohesive approach to doing this transformation and getting to those business outcomes. So it's a It's a partnership that's very successful and its strategic for for our customers, and we continue to invest for the market. >>Got some great customer. Some of my favourite CVS, Nike, William Sanoma. Gotta love that one. Chris talked to us about the snowflake data cloud. What makes it so unique and compelling in the market? >>Well, I think our customers, really they are going through digital transformation today, and they're moving from on premise to the cloud and historically speaking, there just hasn't been the right tool set to help them do that. I think snowflake brings to the table an opportunity for them to take all of their data and take it and and allow it to go from one cloud to the other so they can sit on a W s it can sit on Azure can sit on G, C, P and I can move around from cloud to cloud, and they can do analytics on top of that. >>So data has been traditionally really hard. And we saw that in the big data movement. But we learned a lot. Uh, and AI has been, you know, challenging. So what are you seeing with with customers? What are they struggling with? And how are you guys helping them? >>Yeah. So if you look at the customer journey, they have invested in a number of technologies in the past and are now at a juncture where they need to transform that landscape. They have the challenges of legacy debt that they need to, you know, get rid of or transform. They have the challenges of really bringing, you know, a cohesive understanding within the enterprise as to what these possibilities are for their business. Given the strategy that they are pursuing, um, business and I t cycles are not necessarily aligned. Um, you have the challenge of very fragmented data landscape that they have created over a period of time. How do you, you know, put all these together and work with a specific outcome in mind so that you're not doing transformation for the purpose of transformation. But to be able to actually drive new business models, new data driven products and services ability for you to collaborate with your partners and create unique competitive advantage in the market. And how do you bring those purposes together with the transformation that that's really happening? And and that's where you know our our customers, um, you know, grapple with the challenges of bringing it together. So, >>Chris, how do you see? Because it was talking about, uh, legacy that I think technical debt. Um, you kind of started out making the data warehouse easier. Then this data cloud thing comes out. You're like, Oh, that's an interesting vision and all of a sudden it's way more than vision. You get this huge ecosystem you're extending, we're gonna hear the announcements this morning. We won't. We won't spill the beans, but but really expanding the data cloud. So it's hard to keep up with with where you're at. So I think modernisation, right? So how do you think about modernisation? How are your customers thinking about it? And what's the scope of Snowflake. >>Well, you know, I think historically, you asked about AI and Ml and, you know, in the A I world historically, they've lacked data, and I think because we're the data cloud, we're bringing data, you know, and making it available and democratising it for everybody. And then, you know, partners like emphasis are actually helping us bring, you know, applications and new business models to to the table to our customers and their innovating on top of the data that we already have in the Snowflake Data Club. >>Chris, can you talk about some of the verticals where you guys are successful with emphasis that the three that I mentioned are retailers, But I know that finance, healthcare and life sciences are are huge for smooth, like talk to me, give us a perspective of the verticals that are coming to you. Guys saying help us out with transport. >>You know, I'll give you just an example. So So in the in the retail space, for example, Kraft Heinz is a is a joint customer of ours. And, you know, they've been all in on on snowflakes, Data Cloud and one of our big customers as well it is is Albertsons, and Albertans realises, Oh my gosh, I have all this information around the consumer in in the grocery stores and Kraft Heinz. They want access to that, and they actually can make supply chain decisions a lot faster if they have access to it. So with snowflakes data sharing, we can actually allow them to share data. Albertans share data directly with Kraft, Heinz and Kraft. Heinz can actually make supply chain decisions in real time so that these are some of the stuff that emphasis and stuff like help our customers self. >>So traditionally, the data pipeline goes through some very highly specialised individuals, whether the data engineer, the data scientists and data analyst. So that example that you just gave our organisation you mentioned before democratisation. So democratisation needs to be as a businessperson, I actually can get access to the data. So in that example that you gave between Kraft, Heinz and and and Albertson, is it the the highly hyper specialised teams sharing that data? Or is it actually extending into the line of business focus? >>That's so that's the interesting part for us is I think, snowflake, we just recently reorganise my sales team this year into verticals, and the reason we did that is customers no longer want to talk to us about speeds and feeds of how fast my database goes. They want to actually talk about business outcomes. How do I solve for demand forecasting? How do I supply fix my supply chain issues? Those are things. Those are the. That's how we're aligning with emphasis. So well is they've been doing this for a long time, Can only we haven't. And so we need their help on getting us to the next level of of the sales motion and talking to our customers on solving these business challenges in >>terms of that next level. So no question for you. Where are the customer conversations happening? At what level? I mean, we've seen such dramatic changes in the market in the last couple of years. Now we're dealing with inflation rising interest rates. Ukraine. Are you seeing the conversations in terms of building data platforms rising up the C suite? As every company recognises, we're going to be a data company. We're not gonna be a business. >>Absolutely. And I think all the macroeconomic forces that you talked about that's working on the enterprises globally is actually leading them to think about how to future proof their business models. Right? And there are tonnes of learning that they've hired in the last two or three years and digitising in embracing more digital models. The conversation with the customers have really pivoted towards business outcome. It is a C suite conversation. It is no longer just an incremental change for the for the companies they recognise. That data has been touted as a strategic asset for a long time, but I think it's taking a purpose and a meaning as to what it does for for the customers, the conversations are around industry verticals. You know, what are the specific challenges and opportunities that the the enterprises have, uh, and how you realise those and these cuts across multiple different layers. You know, we're talking about how your democratised data, which in our point of view, is absolute, must in terms of putting a foundation that doesn't take super specialised people to be able to run every operation and every bit of data that you process we have invested in building autonomous data and a state that can process data as it comes in without any manual intervention and take it all the way to consumption but also investing in those industry solutions. Along with snowflake, we launched the healthcare and life Sciences solution. We launched the only channel for retail and CPG. And these are great examples of how Snowflake Foundation enables democratisation on one side but also help solve business problems. In fact, with Snowflake, we have a very, uh, special partnership because our point of view on data economy is about how you connect with the network partners externally, and snowflake brings native capabilities. On this, we leverage that to Dr Exchanges for our customers and one of the services company in the recycling business. Uh, we're actually building and in exchange, which will allow the data points from multiple different sources and partners to come together. So they have a better understanding of their customers, their operations, the field operations and things >>like building a data ecosystem. Yes. Alright, They they Is it a two sided market place where you guys are observers and providing the the technology and the process, you know, guidance. What's your role in that? >>Yeah. So, um, we were seeing their revolution coming? Uh, two stages. Maybe even more. Um, customers are comfortable building an ecosystem. That's kind of private for them. Which means that they know who they are sharing data with. They know what the data is getting used for. And how do you really put governance on this? So that on one side you can trust it on the other side. There is a good use of that data, Uh, and not, uh, you know, compromise on their quality or privacy and some of the other regulations. But we do see this opening up to the two sided market places as well. Uh, some of the industry's lend themselves extremely well for that kind of play. We have seen that happening in trading area. We've seen that happen. And, uh, you know, the credit checks and things like that which are usually open for, you know, those kind of ecosystem. But the conversations and the and the programmes are really leading towards towards that in the market. >>You know, Lisa, one of things I wrote about this weekend is I was decided to come to stuff like summit and and see one of the, you know, thesis I have is that we're going to move not just beyond analytics, including analytics, but also building data products that can be monetised and and I'm hoping we're going to see some of that here. Are you seeing that Christian in the customer? It's It's >>a great question, David. So So we have You know, I just thought of it as as he was talking about. We have a customer who's a very large customer of ours who's in the financial services space, and they handle roughly 40% of the credit card transactions that happen in the US and they're coming to us and saying they want to go from zero in data business today to a $2 billion business over the next five years, and they're leaning on us to help them do that. And one of the things that's exciting for me is they're coming to us not saying Hey, how do you do it? You know, they're saying, Hey, we want to build a consumption model on top of snowflake and we want to use you as the delivery mechanism and the billing mechanism to help us actually monetise that data. So yes, the answer is. You know, I I used to sell to, you know, chief Data Officers and and see IOS. Now I'm talking to VPs of sales and I'm talking to chief operating officers and I'm talking to CEOs about how do we actually create a new revenue stream? And that's just I mean, it's exhilarating to have those conversations. That's >>data products. They don't have to worry about the infrastructure that comes from the cloud. They don't have to worry about the governance, as Senior was saying, Just put >>it in stuff like Just >>put stuff like that. So I call it The super cloud is kind of a, you know, a funny little tongue in cheek. But it's happening. It's this layer. It's not just multiple clouds. You see a lot of your critical competitors adjacent competitors saying, Hey, we're now running in in Google or we're running in Azure. We've been running on AWS. This is different. This is different, isn't it? It's a cloud that floats above the The infrastructure of the hyper scale is, and that's that's a new era. I think >>it's a new error. I think they're you know, I think the hyper scholars want to, you know, keep us as a as a data warehouse and and we're not. The customers are not letting them so So I think that's you know where emphasis kind of saw the light early on. And they were our innovation partner of the year, uh, this past year and they're helping us in our customers innovate, >>but you're uniquely qualified to do that where? I don't think it's the hyper scholars agenda. At least I never say never with the hyper scale is, but yeah, they have focused on providing infrastructure. And, yeah, they have databases and other tools. But that that cross cloud that continuum to your point, talking to VPs of sales and how do you generate revenue? That maybe, is a conversation that they have, but not explicitly as to how to actually do it in a data >>cloud. That's right. I mean, those and those are the Those are the fun conversations because you're you're saying, Hey, we can actually create a new revenue stream. And how can we actually help you solve our joint customers problems? So, yes, it is. Well, >>that's competitive differentiation for businesses. I mean, this is, as I mentioned Every company has to be a data company. If they're not, they're probably not going to be around much longer. They've got to be able to to leverage a data platform like snowflake, to find insights, be able to act on them and create value new services, new products to stay competitive, to stay ahead of the competition. That's no longer nice to have >>100%. I mean, I think they're they're all scared. I mean, you know, like if you look in the financial services space, they look at some of the fintech, as you know, the giant £800 gorillas look at the small fintech has huge threats to the business, and they're coming to us and say, How can we innovate our business now? And they're looking at us as the the innovator, and they're looking at emphasis to help them do that. So I think these are These are incredible times. >>So the narrative on Wall Street, of course, this past earnings season was consumption and who has best visibility and and they they were able to snowflake had a couple of large customers dial down consumption, some consumer facing. Here's the thing. If you're selling a data product for more than it costs you to make. If you dial down consumption in the future, you're gonna dial down revenue. So that's it's going to become less and less discretionary over time. And that, to me, is the next error. That's really exciting. >>The key, The key there is understanding the unit of measure. I think that's the number. One question that we get from customers is what is the unit of measure that we care about, that we want to monetise because to your point, it costs you more to make the product. You're not going to sell it right? And so I think that those are the things that the energy that we're spending with customers today is advising them, jointly advising them on how to actually monetise the specific, you know, unit of measure that they care >>about because when they get the Amazon bill or the snowflake bill, the CFO starts knocking the door. The answer has to be well, look at all the revenue that we generated and all the operating profit and the free cash flow that we drove, and then it's like, Oh, I get it. Keep doing it well, if I'm >>if I'm going on sales calls with the VP of sales and his their sales team, fantastic, right generated helping them generate revenue, right? That's a great conversation >>dynamic. And I think the adoption is really driven through the value, uh, that they can drive in their ecosystem. Their products are similar to products and services that these companies sell. And if you're embedding data inside Syria into your products services, that makes you that much more competitive in the market and drive value for your stakeholders. And that's essentially the future business model that we're talking about. On one side, the other one is the agility. Things aren't remaining constant, they are constantly changing, and we talked about some of those forces earlier. All of this is changing. The landscape is changing the the needs in the economy and things like that, and how you adapt to those kind of models in the future and pivoted on data capabilities that lets you identify new opportunities and and create new value. >>Speaking of creating new value last question guys, before we wrap, what's the go to market approach here between the two companies working customers go to get engaged. I imagine both sides. >>Yeah. I mean, the way that partnership looks good to me is is sell with co selling. So So I think, you know, we look at developing joint solutions with emphasis. They've done a wonderful job of leading into our partnership. So, you know, Sue Neill and I have a regular cadence where we talked every quarter, and our sales teams and our partner teams are are all leaning in and co selling. I don't know if you >>have Absolutely, um, you know, we we proactively identify, you know, the opportunities for our customers. And we work together at all levels within, you know, between the two companies to be able to bring a cohesive solution and a proposition for the customers. Really help them understand how to, you know, what is it that they can, um, get to and how you get that journey actually executed. And it's a partnership that works very seamlessly through that entire process, not just upstream when we're selling, but also downstream and we're executing. And we've had tremendous success together and look forward to more. >>Congratulations on that success, guys. Thank you so much for coming on talking about new possibilities with data and AI and sharing some of the impact that the technologies are making. We appreciate your insights. >>Thank you. Thank >>you. Thank you So much >>for our guests and a Volonte. I'm Lisa Martin. You're watching the Cube live in Las Vegas from Snowflake Summit 22 back after the keynote with more breaking news. Mhm, mhm.

>> From theCUBE Studios in Palo Alto, in Boston bringing you Data Driven Insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> In the pre-cloud era hardware companies would run benchmarks, showing how database and or application performance ran better on their systems relative to competitors or previous generation boxes. And they would make a big deal out of it. And the independent software vendors, you know they'd do a little golf clap if you will, in the form of a joint press release it became a game of leaprog amongst hardware competitors. That was pretty commonplace over the years. The Dell Snowflake Deal underscores that the value proposition between hardware companies and ISVs is changing and has much more to do with distribution channels, volumes and the amount of data that lives On-Prem in various storage platforms. For cloud native ISVs like Snowflake they're realizing that despite their Cloud only dogma they have to grit their teeth and deal with On-premises data or risk getting shut out of evolving architectures. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we unpack what little is known about the Snowflake announcement from Dell Technologies World and discuss the implications of a changing Cloud landscape. We'll also share some new data for Cloud and Database platforms from ETR that shows Snowflake has actually entered the Earth's orbit when it comes to spending momentum on its platform. Now, before we get into the news I want you to listen to Frank's Slootman's answer to my question as to whether or not Snowflake would ever architect the platform to run On-Prem because it's doable technically, here's what he said, play the clip >> Forget it, this will only work in the Public Cloud. Because it's, this is how the utility model works, right. I think everybody is coming through this realization, right? I mean, excuses are running out at this point. You know, we think that it'll, people will come to the Public Cloud a lot sooner than we will ever come to the Private Cloud. It's not that we can't run a private Cloud. It's just diminishes the potential and the value that we bring. >> So you may be asking yourselves how do you square that circle? Because basically the Dell Snowflake announcement is about bringing Snowflake to the private cloud, right? Or is it let's get into the news and we'll find out. Here's what we know at Dell Technologies World. One of the more buzzy announcements was the, by the way this was a very well attended vet event. I should say about I would say 8,000 people by my estimates. But anyway, one of the more buzzy announcements was Snowflake can now run analytics on Non-native Snowflake data that lives On-prem in a Dell object store Dell's ECS to start with. And eventually it's software defined object store. Here's Snowflake's clark, Snowflake's Clark Patterson describing how it works this past week on theCUBE. Play the clip. The way it works is I can now access Non-native Snowflake data using what materialized views, external tables How does that work? >> Some combination of the, all the above. So we've had in Snowflake, a capability called External Tables, which you refer to, it goes hand in hand with this notion of external stages. Basically there's a through the combination of those two capabilities, it's a metadata layer on data, wherever it resides. So customers have actually used this in Snowflake for data lake data outside of Snowflake in the Cloud, up until this point. So it's effectively an extension of that functionality into the Dell On-Premises world, so that we can tap into those things. So we use the external stages to expose all the metadata about what's in the Dell environment. And then we build external tables in Snowflake. So that data looks like it is in Snowflake. And then the experience for the analyst or whomever it is, is exactly as though that data lives in the Snowflake world. >> So as Clark explained, this capability of External tables has been around in the Cloud for a while, mainly to suck data out of Cloud data lakes. Snowflake External Tables use file level metadata, for instance, the name of the file and the versioning so that it can be queried in a stage. A stage is just an external location outside of Snowflake. It could be an S3 bucket or an Azure Blob and it's soon will be a Dell object store. And in using this feature, the Dell looks like it lives inside of Snowflake and Clark essentially, he's correct to say to an analyst that looks exactly like the data is in Snowflake, but uh, not exactly the data's read only which means you can't do what are called DML operations. DML stands for Data Manipulation Language and allows for things like inserting data into tables or deleting and modifying existing data. But the data can be queried. However, the performance of those queries to External Tables will almost certainly be slower. Now users can build things like materialized views which are going to speed things up a bit, but at the end of the day, it's going to run faster than the Cloud. And you can be almost certain that's where Snowflake wants it to run, but some organizations can't or won't move data into the Cloud for a variety of reasons, data sovereignty, compliance security policies, culture, you know, whatever. So data can remain in place On-prem, or it can be moved into the Public Cloud with this new announcement. Now, the compute today presumably is going to be done in the Public Cloud. I don't know where else it's going to be done. They really didn't talk about the compute side of things. Remember, one of Snowflake's early innovations was to separate compute from storage. And what that gave them is you could more efficiently scale with unlimited resources when you needed them. And you could shut off the compute when you don't need us. You didn't have to buy, and if you need more storage you didn't have to buy more compute and vice versa. So everybody in the industry has copied that including AWS with Redshift, although as we've reported not as elegantly as Snowflake did. RedShift's more of a storage tiering solution which minimizes the compute required but you can't really shut it off. And there are companies like Vertica with Eon Mode that have enabled this capability to be done On-prem, you know, but of course in that instance you don't have unlimited elastic compute scale on-Prem but with solutions like Dell Apex and HPE GreenLake, you can certainly, you can start to simulate that Cloud elasticity On-prem. I mean, it's not unlimited but it's sort of gets you there. According to a Dell Snowflake joint statement, the companies the quote, the companies will pursue product integrations and joint go to market efforts in the second half of 2022. So that's a little vague and kind of benign. It's not really clear when this is going to be available based on that statement from the two first, but, you know, we're left wondering will Dell develop an On-Prem compute capability and enable queries to run locally maybe as part of an extended apex offering? I mean, we don't know really not sure there's even a market for that but it's probably a good bet that again, Snowflake wants that data to land in the Snowflake data Cloud kind of makes you wonder how this deal came about. You heard Sloop on earlier Snowflake has always been pretty dogmatic about getting data into its native snowflake format to enable the best performance as we talked about but also data sharing and governance. But you could imagine that data architects they're building out their data mesh we've reported on this quite extensively and their data fabric and those visions around that. And they're probably telling Snowflake, Hey if you want to be a strategic partner of ours you're going to have to be more inclusive of our data. That for whatever reason we're not putting in your Cloud. So Snowflake had to kind of hold its nose and capitulate. Now the good news is it further opens up Snowflakes Tam the total available market. It's obviously good marketing posture. And ultimately it provides an on ramp to the Cloud. And we're going to come back to that shortly but let's look a little deeper into what's happening with data platforms and to do that we'll bring in some ETR data. Now, let me just say as companies like Dell, IBM, Cisco, HPE, Lenovo, Pure and others build out their hybrid Clouds. The cold hard fact is not only do they have to replicate the Cloud Operating Model. You will hear them talk about that a lot, but they got to do that. So it, and that's critical from a user experience but in order to gain that flywheel momentum they need to build a robust ecosystem that goes beyond their proprietary portfolios. And, you know, honestly they're really not even in the first inning most companies and for the likes of Snowflake to sort of flip this, they've had to recognize that not everything is moving into the Cloud. Now, let's bring up the next slide. One of the big areas of discussion at Dell Tech World was Apex. That's essentially Dell's nascent as a service offering. Apex is infrastructure as a Service Cloud On-prem and obviously has the vision of connecting to the Cloud and across Clouds and out to the Edge. And it's no secret that database is one of the most important ingredients of infrastructure as a service generally in Cloud Infrastructure specifically. So this chart here shows the ETR data for data platforms inside of Dell accounts. So the beauty of ETR platform is you can cut data a million different ways. So we cut it. We said, okay, give us the Cloud platforms inside Dell accounts, how are they performing? Now, this is a two dimensional graphic. You got net score or spending momentum on the vertical axis and what ETR now calls Overlap formally called Market Share which is a measure of pervasiveness in the survey. That's on the horizontal axis that red dotted line at 40% represents highly elevated spending on the Y. The table insert shows the raw data for how the dots are positioned. Now, the first call out here is Snowflake. According to ETR quote, after 13 straight surveys of astounding net scores, Snowflake has finally broken the trend with its net score dropping below the 70% mark among all respondents. Now, as you know, net score is measured by asking customers are you adding the platform new? That's the lime green in the bar that's pointing from Snowflake in the graph and or are you increasing spend by 6% or more? That's the forest green is spending flat that's the gray is you're spend decreasing by 6% or worse. That's the pinkish or are you decommissioning the platform bright red which is essentially zero for Snowflake subtract the reds from the greens and you get a net score. Now, what's somewhat interesting is that snowflakes net score overall in the survey is 68 which is still huge, just under 70%, but it's net score inside the Dell account base drops to the low sixties. Nonetheless, this chart tells you why Snowflake it's highly elevated spending momentum combined with an increasing presence in the market over the past two years makes it a perfect initial data platform partner for Dell. Now and in the Ford versus Ferrari dynamic. That's going on between the likes of Dell's apex and HPE GreenLake database deals are going to become increasingly important beyond what we're seeing with this recent Snowflake deal. Now noticed by the way HPE is positioned on this graph with its acquisition of map R which is now part of HPE Ezmeral. But if these companies want to be taken seriously as Cloud players, they need to further expand their database affinity to compete ideally spinning up databases as part of their super Clouds. We'll come back to that that span multiple Clouds and include Edge data platforms. We're a long ways off from that. But look, there's Mongo, there's Couchbase, MariaDB, Cloudera or Redis. All of those should be on the short list in my view and why not Microsoft? And what about Oracle? Look, that's to be continued on maybe as a future topic in a, in a Breaking Analysis but I'll leave you with this. There are a lot of people like John Furrier who believe that Dell is playing with fire in the Snowflake deal because he sees it as a one way ticket to the Cloud. He calls it a one way door sometimes listen to what he said this past week. >> I would say that that's a dangerous game because we've seen that movie before, VMware and AWS. >> Yeah, but that we've talked about this don't you think that was the right move for VMware? >> At the time, but if you don't nurture the relationship AWS will take all those customers ultimately from VMware. >> Okay, so what does the data say about what John just said? How is VMware actually doing in Cloud after its early missteps and then its subsequent embracing of AWS and other Clouds. Here's that same XY graphic spending momentum on the Y and pervasiveness on the X and the same table insert that plots the dots and the, in the breakdown of Dell's net score granularity. You see that at the bottom of the chart in those colors. So as usual, you see Azure and AWS up and to the right with Google well behind in a distant third, but still in the mix. So very impressive for Microsoft and AWS to have both that market presence in such elevated spending momentum. But the story here in context is that the VMware Cloud on AWS and VMware's On-Prem Cloud like VMware Cloud Foundation VCF they're doing pretty well in the market. Look, at HPE, gaining some traction in Cloud. And remember, you may not think HPE and Dell and VCF are true Cloud but these are customers answering the survey. So their perspective matters more than the purest view. And the bad news is the Dell Cloud is not setting the world on fire from a momentum standpoint on the vertical axis but it's above the line of zero and compared to Dell's overall net score of 20 you could see it's got some work to do. Okay, so overall Dell's got a pretty solid net score to you know, positive 20, as I say their Cloud perception needs to improve. Look, Apex has to be the Dell Cloud brand not Dell reselling VMware. And that requires more maturity of Apex it's feature sets, its selling partners, its compensation models and it's ecosystem. And I think Dell clearly understands that. I think they're pretty open about that. Now this includes partners that go beyond being just sellers has to include more tech offerings in the marketplace. And actually they got to build out a marketplace like Cloud Platform. So they got a lot of work to do there. And look, you've got Oracle coming up. I mean they're actually kind of just below the magic 40% in the line which is pro it's pretty impressive. And we've been telling you for years, you can hate Oracle all you want. You can hate its price, it's closed system all of that it's red stack shore. You can say it's legacy. You can say it's old and outdated, blah, blah, blah. You can say Oracle is irrelevant in trouble. You are dead wrong. When it comes to mission critical workloads. Oracle is the king of the hill. They're a founder led company that knows exactly what it's doing and they're showing Cloud momentum. Okay, the last point is that while Microsoft AWS and Google have major presence as shown on the X axis. VMware and Oracle now have more than a hundred citations in the survey. You can see that on the insert in the right hand, right most column. And IBM had better keep the momentum from last quarter going, or it won't be long before they get passed by Dell and HP in Cloud. So look, John might be right. And I would think Snowflake quietly agrees that this Dell deal is all about access to Dell's customers and their data. So they can Hoover it into the Snowflake Data Cloud but the data right now, anyway doesn't suggest that's happening with VMware. Oh, by the way, we're keeping an eye close eye on NetApp who last September ink, a similar deal to VMware Cloud on AWS to see how that fares. Okay, let's wrap with some closing thoughts on what this deal means. We learned a lot from the Cloud generally in AWS, specifically in two pizza teams, working backwards, customer obsession. We talk about flywheel all the time and we've been talking today about marketplaces. These have all become common parlance and often fundamental narratives within strategic plans investor decks and customer presentations. Cloud ecosystems are different. They take both competition and partnerships to new heights. You know, when I look at Azure service offerings like Apex, GreenLake and similar services and I see the vendor noise or hear the vendor noise that's being made around them. I kind of shake my head and ask, you know which movie were these companies watching last decade? I really wish we would've seen these initiatives start to roll out in 2015, three years before AWS announced Outposts not three years after but Hey, the good news is that not only was Outposts a wake up call for the On-Prem crowd but it's showing how difficult it is to build a platform like Outposts and bring it to On-Premises. I mean, Outpost isn't currently even a rounding era in the marketplace. It really doesn't do much in terms of database support and support of other services. And, you know, it's unclear where that that is going. And I don't think it has much momentum. And so the Hybrid Cloud Vendors they've had time to figure it out. But now it's game on, companies like Dell they're promising a consistent experience between On-Prem into the Cloud, across Clouds and out to the Edge. They call it MultCloud which by the way my view has really been multi-vendor Chuck, Chuck Whitten. Who's the new co-COO of Dell called it Multi-Cloud by default. (laughing) That's really, I think an accurate description of that. I call this new world Super Cloud. To me, it's different than MultiCloud. It's a layer that runs on top of hyperscale infrastructure kind of hides the underlying complexity of the Cloud. It's APIs, it's primitives. And it stretches not only across Clouds but out to the Edge. That's a big vision and that's going to require some seriously intense engineering to build out. It's also going to require partnerships that go beyond the portfolios of companies like Dell like their own proprietary stacks if you will. It's going to have to replicate the Cloud Operating Model and to do that, you're going to need more and more deals like Snowflake and even deeper than Snowflake, not just in database. Sure, you'll need to have a catalog of databases that run in your On-Prem and Hybrid and Super Cloud but also other services that customers can tap. I mean, can you imagine a day when Dell offers and embraces a directly competitive service inside of apex. I have trouble envisioning that, you know not with their historical posture, you think about companies like, you know, Nutanix, you know, or Cisco where they really, you know those relationships cooled quite quickly but you know, look, think about it. That's what AWS does. It offers for instance, Redshift and Snowflake side by side happily and the Redshift guys they probably hate Snowflake. I wouldn't blame them, but the EC Two Folks, they love them. And Adam SloopesKy understands that ISVs like Snowflake are a key part of the Cloud ecosystem. Again, I have a hard time envisioning that occurring with Dell or even HPE, you know maybe less so with HPE, but what does this imply that the Edge will allow companies like Dell to a reach around on the Cloud and somehow create a new type of model that begrudgingly accommodates the Public Cloud but drafts of the new momentum of the Edge, which right now to these companies is kind of mostly telco and retail. It's hard to see that happening. I think it's got to evolve in a more comprehensive and inclusive fashion. What's much more likely is companies like Dell are going to substantially replicate that Cloud Operating Model for the pieces that they own pieces that they control which admittedly are big pieces of the market. But unless they're able to really tap that ecosystem magic they're not going to be able to grow much beyond their existing install bases. You take that lime green we showed you earlier that new adoption metric from ETR as an example, by my estimates, AWS and Azure are capturing new accounts at a rate between three to five times faster than Dell and HPE. And in the more mature US and mere markets it's probably more like 10 X and a major reason is because of the Cloud's robust ecosystem and the optionality and simplicity of transaction that that is bringing to customers. Now, Dell for its part is a hundred billion dollar revenue company. And it has the capability to drive that kind of dynamic. If it can pivot its partner ecosystem mindset from kind of resellers to Cloud services and technology optionality. Okay, that's it for now? Thanks to my colleagues, Stephanie Chan who helped research topics for Breaking Analysis. Alex Myerson is on the production team. Kristen Martin and Cheryl Knight and Rob Hof, on editorial they helped get the word out and thanks to Jordan Anderson for the new Breaking Analysis branding and graphics package. Remember these episodes are all available as podcasts wherever you listen. All you do is search Breaking Analysis podcasts. You could check out ETR website @etr.ai. We publish a full report every week on wikibon.com and siliconangle.com. You want to get in touch. @dave.vellente @siliconangle.com. You can DM me @dvellante. You can make a comment on our LinkedIn posts. This is Dave Vellante for the Cube Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (upbeat music)

>> theCube presents Dell Technologies World, brought to you by Dell. >> Hi everyone, welcome back to Dell Technologies World 2022. You're watching theCube's coverage of this, three-day coverage wall to wall. My name is David Vellante John Furrier's here, Lisa Martin, David Nicholson. Talk of the town here is data. And one of the big announcements at the show is Snowflake and Dell partnering up, building ecosystems. Snowflake reaching into on-prem, allowing customers to actually access the Snowflake Data Cloud without moving the data or if they want to move the data they can. This is really one of the hotter announcements of the show. Martin Glynn is here, he's the Senior Director of Storage Product Management at Dell Technologies. And Clark Patterson, he's the Head of Product Marketing for Snowflake. Guys, welcome. >> Thanks for having us. >> So a lot of buzz around this and, you know, Clark, you and I have talked about the need to really extend your data vision. And this really is the first step ever you've taken on-prem. Explain the motivation for this from your customer's perspective. >> Yeah. I mean, if you step back and think about Snowflake's vision and our mission of mobilizing the world's data, it's all around trying to break down silos for however customers define what a silo is, right? So we've had a lot of success breaking down silos from a workload perspective where we've expanded the platform to be data warehousing, and data engineering, and machine learning, and data science, and all the kind of compute intensive ways that people work with us. We've also had a lot of success in our sharing capabilities and how we're breaking down silos of organizations, right? So I can share data more seamlessly within my team, I can do it across totally disparate organizations, and break down silos that way. So this partnership is really like the next leg of the stool, so to speak, where we're breaking down the silos of the the data and where the data lives ultimately, right? So up until this point, Cloud, all focus there, and now we have this opportunity with Dell to expand that and into on-premises world and people can bring all those data sets together. >> And the data target for this Martin, is Dell ECS, right? Your object store, and it's got S3 compatibility. Explain that. >> Yeah, we've actually got sort of two flavors. We'll start with ECS, which is our turnkey object storage solution. Object storage offers sort of the ultimate in flexibility, you know, potential performance, ease of use, right? Which is why it fits so well with Snowflake's mission for sort of unlocking, you know, the data within the data center. So we'll offer it to begin with ECS, and then we also recently announced our software defined object scale solution. So add even more flexibility there. >> Okay. And the clock, the way it works is I can now access non-native Snowflake data using what? Materialized views, external tables, how does that work? >> Some combination of all the above. So we've had in Snowflake a capability called external tables which we refer to, it goes hand in hand with this notion of external stages. Basically through the combination of those two capabilities, it's a metadata layer on data wherever it resides. So customers have actually used this in Snowflake for data lake data outside of Snowflake in the Cloud up until this point. So it's effectively an extension of that functionality into the Dell on-premises world, so that we can tap into those things. So we use the external stages to expose all the metadata about what's in the Dell environment. And then we build external tables in Snowflake so that data looks like it is in Snowflake. And then the experience for the analyst or whomever it is, is exactly as though that data lives in the Snowflake world. >> Okay. So for a while you've allowed non-native Snowflake data but it had to be in the Cloud. >> Correct. >> It was the first time it's on-prem, >> that's correct >> that's the innovation here. Okay. And if I want to bring it into the Cloud, can I? >> Yeah, the connection here will help in a migration sense as well, right? So that's the good thing is, it's really giving the user the choice. So we are integrating together as partners to make connection as seamless as possible. And then the end user will say like, look I've got data that needs to live on-premises, for whatever reasons, data sovereignty whatever they decide. And they can keep it there and still do the analytics in another place. But if there's a need and a desire to use this as an opportunity to migrate some of that data to Cloud, that connection between our two platforms will make that easier. >> Well, Michael always says, "Hey, it's customer choice, we're flexible." So you're cool with that? That's been the mission since we kind of came together, right? Is if our customers needed to stay in their data center, if that makes more sense from a cost perspective or, you know, a data gravity perspective, then they can do that. But we also want to help them unlock the value of that data. So if they need to copy it up to the public Cloud and take advantage of it, we're going to integrate directly with Snowflake to make that really easy to do. >> So there are engineering integrations here, obviously that's required. Can you describe what that looks like? Give us the details on when it's available. >> Sure. So it's going to be sort of second half this year that you'll see, we're demoing it this week, but the availability we second half this year. And fundamentally, it's the way Clark described it, that Snowflake will reach into our S3 interface using the standard S3 interface. We're qualifying between the way they expect that S3 interface to present the data and the way our platform works, just to ensure that there's smooth interaction between the two. So that's sort of the first simplest use case. And then the second example we gave where the customer can copy some of that data up to the public Cloud. We're basically copying between two S3 buckets and making sure that Snowflake's Snowpipe is aware that data's being made available and can easily ingest it. >> And then that just goes into a virtual warehouse- >> Exactly. >> and customer does to know or care. >> Yep Exactly. >> Yeah. >> The compute happens in Snowflake the way it does in any other manner. >> And I know you got to crawl, walk, run second half of this year, but I would imagine, okay, you're going to start with AWS, correct? And then eventually you go to other Clouds. I mean, that's going to take other technical integrations, I mean, obviously. So should we assume there's a roadmap here or is this a one and done? >> I would assume that, I mean, based on our multi-Cloud approach, that's kind of our approach at least, yeah. >> Kind of makes sense, right? I mean, that would seem to be a natural progression. My other thought was, okay, I've got operational systems. They might be transaction systems running on a on a PowerMax. >> Yeah. >> Is there a way to get the data into an object store and make that available, now that opens up even more workloads. I know you're not committing to doing that, but it just, conceptually, it seems like something a customer might want to do. >> Yeah. I, a hundred percent, agree. I mean, I think when we brought our team together we started with a blank slate. It was what's the best solution we can build. We landed on this sort of first step, but we got lots of feedback from a lot of our big joint customers about you know, this system over there, this potential integration over here, and whether it's, you know, PowerMax type systems or other file workloads with native Snowflake data types. You know, I think this is just the beginning, right? We have lots of potential here. >> And I don't think you've announced pricing, right? It's premature for that. But have you thought about, and how are you thinking about the pricing model? I mean, you're a consumption based pricing, is that kind of how this is going to work? Or is it a sort of a new pricing model or haven't you figured that out yet? >> I don't know if you've got any details on that, but from a Snowflake perspective, I would assume it's consistent with how our customers engage with us today. >> Yeah. >> And we'll offer both possibilities, right? So you can either continue with the standard, you know, sort of CapEx motion, maybe that's the most optimal for you from a cost perspective, or you can take advantage through our OpEx option, right? So you can do consumption on-prem also. >> Okay. So it could be a dual model, right? Depending on what the customer wants. If they're a Snowflake customer, obviously it's going to be consumption based, however, you guys price. What's happening, Clark, in in the market? Explain why Snowflake has so much momentum and, you know, traction in the marketplace. >> So like I spent a lot of time doing analysis on why we win and lose, core part of my role. And, you know, there's a couple of, there's really three things that come up consistently as to why people people are really excited about Snowflake platform. One is the most simplest thing of all. It feels like is just ease of use and it just works, right? And I think the way that this platform was built for the Cloud from the ground up all the way back 10 years ago, really a lot allows us to deliver that seamless experience of just like instant compute when you want it, it goes away, you know, only pay for what you use. Very few knobs to turn and things like that. And so people absolutely love that factor. The other is multi-Cloud. So, you know, there's definitely a lot of organizations out there that have a multi-Cloud strategy, and, you know, what that means to them can be highly variable, but regardless, they want to be able to interact across Clouds in some capacity. And of course we are a single platform, like literally one single interface, consistent across all the three Cloud providers that we work upon. And it gives them that flexibility to mix and match Cloud infrastructure under any Snowflake however they see fit. The last piece of it is sharing. And, you know, I think it's that ability as I kind of alluded to around like breaking down organizational silos, and allow people to be able to actually connect with each other in ways that you couldn't do before. Like, if you think about how you and I would've shared data before, I'd be like, "Hey, Dave, I'm going to unload this table into a spreadsheet and I'm going to send it over in email." And there's the whole host of issues that get introduced in that and world, now it's like instantly available. I have a lot of control over it, it's governed it's all these other things. And I can create kind of walled gardens, so to speak, of how far out I want that to go. It could be in a controlled environment of organizations that I want to collaborate with, or I can put it on our marketplace and expose it to the whole world, because I think there's a value in that. And if I choose I can monetize it, right? So those, you know, the ease of use aspect of it, absolutely, it's just a fantastic platform. The multi-Cloud aspect of it and our unique differentiation around sharing in our marketplace and monetization. >> Yeah, on the sharing front. I mean, it's now discoverable. Like if you send me an email, like what'd you call that? When did you send that email? And then the same time I can forward that to somebody else's not governed. >> Yeah. >> All right. So that just be creates a nightmare for the compliance. >> Right. Yeah. You think about how you revoke access in that situation. You just don't, right? Now I can just turn it off and you go in to run your query. >> Don't get access on that data anymore. Yeah. Okay. And then the other thing I wanted to ask you, Clark is Snowflake started really as analytics platform, simplifying data warehousing, you're moving into that world of data science, you know, the whole data lake movement, bringing those two worlds together. You know, I was talking to Ben Ward about this, maybe there's a semantic layer that helps us kind of talk between those two worlds, but you don't care, right? If it's in an object store, it can play in both of those worlds, right? >> That's right. >> Yeah, it's up to you to figure it out and the customer- >> Yeah. >> from a storage standpoint. Here it is, serve it up. >> And that's the thrust of this announcement, right? Is bringing together two great companies, the Dell platform, the Snowflake platform, and allowing organizations to bring that together. And they decide like it, as we all know, customers decide how they're going to build their architecture. And so this is just another way that we're helping them leverage the capabilities of our two great platforms. >> Does this push or pull or little bit of both? I mean, where'd this come from? Or customers saying, "Hey, it would be kind of cool if we could have this." Or is it more, "Hey, what do you guys think?" You know, where are you at with that? >> It was definitely both, right? I mean, so we certainly started with, you know, a high level idea that, you know, the technologies are complimentary, right? I mean, as Clark just described, and at the same time we had customers coming to us saying, "Hey, wait a minute, I'm doing this over here, and this over here, how can I make this easier?" So that was like I said, we started with a blank sheet and lots of long customer conversations and this is what resulted. So >> So what are the sequence of events to kind of roll this out? You said it's second half, you know, when do you start getting customers involved? Do you have your already, you know, to poke at this and what's that look like? >> Yeah, sure. I can weigh in there. So, absolutely. We've had a few of our big customers that have been involved sort of in the design already who understand how they want to use it. So I think our expectation is that now that the sort of demonstrations have been in place, we have some pre functionality, we're going to see some initial testing and usage, some beta type situations with our customers. And then second half, we'll ramp from there. >> It's got to be a huge overlap between Dell customers and Snowflake customers. I mean, it's hundred billion. You can't not bump into Dell somewhere. >> Exactly. Yeah, you know. >> So where do you guys want to see this relationship go, kind of how should we measure success? Maybe you could each give your perspectives of that. >> I mean, for us, I think it's really showing the value of the Snowflake platform in this new world where there's a whole new ecosystem of data that is accessible to us, right? So seeing those organizations that are saying like, "Look, I'm doing new things with on-premises data that I didn't think that I could do before", or, "I'm driving efficiency in how I do analytics, and data engineering, and data science, in ways that I couldn't do before," 'cause they were locked out of using a Snowflake-like technology, right? So I think for me, that's going to be that real excitement. I'm really curious to see how the collaboration and the sharing component comes into this, you know, where you can think of having an on-premises data strategy and a need, right? But you can really connect to Cloud native customers and partners and suppliers that live in the Snowflake ecosystem, and that wasn't possible before. And so that is very conceivable and very possible through this relationship. So seeing how those edges get created in in our world and how people start to collaborate across data, both in the Cloud and on-prem is going to be really exciting. >> I remember I asked Frank, it was kind of early in the pandemic. I asked him, come on, tell me about how you're managing things. And he was awesome. And I asked him to at the time, you know, "You're ever going to do, you know, bring this platform on-prem?" He's like unequivocal, "No way, that's never going to happen. We're not going to do it halfway house ware Cloud only." And I kept thinking, but there's got to be a way to expand that team. There's so much data out there, and so boom, now we see the answer . Martin, from your standpoint, what does success look like? >> I think it starts with our partnership, right? So I've been doing this a long time. Probably the first time I've worked so closely with a partner like Snowflake. Joint customer conversations, joint solutioning, making sure what we're building is going to be really, truly as useful as possible to them. And I think we're going to let them guide us as we go forward here, right? You mentioned, you know, systems or record or other potential platforms. We're going to let them tell us where exactly the most value will come from the integration between the two companies. >> Yeah. Follow data. I mean, remember in the old days a hardware company like Dell would go to an ISP like Snowflake and say, "Hey, we ran some benchmarks. Your software runs really fast on our hardware, can we work together?" And you go, "Yeah, of course. Yeah, no problem." But wow! What a different dynamic it is today. >> Yeah. Yeah, absolutely. >> All right guys. Hey, thanks so much for coming to theCube. It's great to see you. We'll see you at the Snowflake Summit in June. >> Snowflake Summit in a month and a half. >> Looking forward to that. All right. Thank you again. >> Thank you Dave. >> All right. Keep it right there everybody. This is Dave Vellante, wall to wall coverage of Dell Tech World 2022. We'll be right back. (gentle music)

>> theCube presents Dell Technologies World, brought to you by Dell. >> Hi everyone, welcome back to Dell Technologies World 2022. You're watching theCube's coverage of this, three-day coverage wall to wall. My name is David Vellante John Furrier's here, Lisa Martin, David Nicholson. Talk of the town here is data. And one of the big announcements at the show is Snowflake and Dell partnering up, building ecosystems. Snowflake reaching into on-prem, allowing customers to actually access the Snowflake Data Cloud without moving the data or if they want to move the data they can. This is really one of the hotter announcements of the show. Martin Glynn is here, he's the Senior Director of Storage Product Management at Dell Technologies. And Clark Patterson, he's the Head of Product Marketing for Snowflake. Guys, welcome. >> Thanks for having us. >> So a lot of buzz around this and, you know, Clark, you and I have talked about the need to really extend your data vision. And this really is the first step ever you've taken on-prem. Explain the motivation for this from your customer's perspective. >> Yeah. I mean, if you step back and think about Snowflake's vision and our mission of mobilizing the world's data, it's all around trying to break down silos for however customers define what a silo is, right? So we've had a lot of success breaking down silos from a workload perspective where we've expanded the platform to be data warehousing, and data engineering, and machine learning, and data science, and all the kind of compute intensive ways that people work with us. We've also had a lot of success in our sharing capabilities and how we're breaking down silos of organizations, right? So I can share data more seamlessly within my team, I can do it across totally disparate organizations, and break down silos that way. So this partnership is really like the next leg of the stool, so to speak, where we're breaking down the silos of the the data and where the data lives ultimately, right? So up until this point, Cloud, all focus there, and now we have this opportunity with Dell to expand that and into on-premises world and people can bring all those data sets together. >> And the data target for this Martin, is Dell ECS, right? Your object store, and it's got S3 compatibility. Explain that. >> Yeah, we've actually got sort of two flavors. We'll start with ECS, which is our turnkey object storage solution. Object storage offers sort of the ultimate in flexibility, you know, potential performance, ease of use, right? Which is why it fits so well with Snowflake's mission for sort of unlocking, you know, the data within the data center. So we'll offer it to begin with ECS, and then we also recently announced our software defined object scale solution. So add even more flexibility there. >> Okay. And the clock, the way it works is I can now access non-native Snowflake data using what? Materialized views, external tables, how does that work? >> Some combination of all the above. So we've had in Snowflake a capability called external tables which we refer to, it goes hand in hand with this notion of external stages. Basically through the combination of those two capabilities, it's a metadata layer on data wherever it resides. So customers have actually used this in Snowflake for data lake data outside of Snowflake in the Cloud up until this point. So it's effectively an extension of that functionality into the Dell on-premises world, so that we can tap into those things. So we use the external stages to expose all the metadata about what's in the Dell environment. And then we build external tables in Snowflake so that data looks like it is in Snowflake. And then the experience for the analyst or whomever it is, is exactly as though that data lives in the Snowflake world. >> Okay. So for a while you've allowed non-native Snowflake data but it had to be in the Cloud. >> Correct. >> It was the first time it's on-prem, >> that's correct >> that's the innovation here. Okay. And if I want to bring it into the Cloud, can I? >> Yeah, the connection here will help in a migration sense as well, right? So that's the good thing is, it's really giving the user the choice. So we are integrating together as partners to make connection as seamless as possible. And then the end user will say like, look I've got data that needs to live on-premises, for whatever reasons, data sovereignty whatever they decide. And they can keep it there and still do the analytics in another place. But if there's a need and a desire to use this as an opportunity to migrate some of that data to Cloud, that connection between our two platforms will make that easier. >> Well, Michael always says, "Hey, it's customer choice, we're flexible." So you're cool with that? That's been the mission since we kind of came together, right? Is if our customers needed to stay in their data center, if that makes more sense from a cost perspective or, you know, a data gravity perspective, then they can do that. But we also want to help them unlock the value of that data. So if they need to copy it up to the public Cloud and take advantage of it, we're going to integrate directly with Snowflake to make that really easy to do. >> So there are engineering integrations here, obviously that's required. Can you describe what that looks like? Give us the details on when it's available. >> Sure. So it's going to be sort of second half this year that you'll see, we're demoing it this week, but the availability we second half this year. And fundamentally, it's the way Clark described it, that Snowflake will reach into our S3 interface using the standard S3 interface. We're qualifying between the way they expect that S3 interface to present the data and the way our platform works, just to ensure that there's smooth interaction between the two. So that's sort of the first simplest use case. And then the second example we gave where the customer can copy some of that data up to the public Cloud. We're basically copying between two S3 buckets and making sure that Snowflake's Snowpipe is aware that data's being made available and can easily ingest it. >> And then that just goes into a virtual warehouse- >> Exactly. >> and customer does to know or care. >> Yep Exactly. >> Yeah. >> The compute happens in Snowflake the way it does in any other manner. >> And I know you got to crawl, walk, run second half of this year, but I would imagine, okay, you're going to start with AWS, correct? And then eventually you go to other Clouds. I mean, that's going to take other technical integrations, I mean, obviously. So should we assume there's a roadmap here or is this a one and done? >> I would assume that, I mean, based on our multi-Cloud approach, that's kind of our approach at least, yeah. >> Kind of makes sense, right? I mean, that would seem to be a natural progression. My other thought was, okay, I've got operational systems. They might be transaction systems running on a on a PowerMax. >> Yeah. >> Is there a way to get the data into an object store and make that available, now that opens up even more workloads. I know you're not committing to doing that, but it just, conceptually, it seems like something a customer might want to do. >> Yeah. I, a hundred percent, agree. I mean, I think when we brought our team together we started with a blank slate. It was what's the best solution we can build. We landed on this sort of first step, but we got lots of feedback from a lot of our big joint customers about you know, this system over there, this potential integration over here, and whether it's, you know, PowerMax type systems or other file workloads with native Snowflake data types. You know, I think this is just the beginning, right? We have lots of potential here. >> And I don't think you've announced pricing, right? It's premature for that. But have you thought about, and how are you thinking about the pricing model? I mean, you're a consumption based pricing, is that kind of how this is going to work? Or is it a sort of a new pricing model or haven't you figured that out yet? >> I don't know if you've got any details on that, but from a Snowflake perspective, I would assume it's consistent with how our customers engage with us today. >> Yeah. >> And we'll offer both possibilities, right? So you can either continue with the standard, you know, sort of CapEx motion, maybe that's the most optimal for you from a cost perspective, or you can take advantage through our OpEx option, right? So you can do consumption on-prem also. >> Okay. So it could be a dual model, right? Depending on what the customer wants. If they're a Snowflake customer, obviously it's going to be consumption based, however, you guys price. What's happening, Clark, in in the market? Explain why Snowflake has so much momentum and, you know, traction in the marketplace. >> So like I spent a lot of time doing analysis on why we win and lose, core part of my role. And, you know, there's a couple of, there's really three things that come up consistently as to why people people are really excited about Snowflake platform. One is the most simplest thing of all. It feels like is just ease of use and it just works, right? And I think the way that this platform was built for the Cloud from the ground up all the way back 10 years ago, really a lot allows us to deliver that seamless experience of just like instant compute when you want it, it goes away, you know, only pay for what you use. Very few knobs to turn and things like that. And so people absolutely love that factor. The other is multi-Cloud. So, you know, there's definitely a lot of organizations out there that have a multi-Cloud strategy, and, you know, what that means to them can be highly variable, but regardless, they want to be able to interact across Clouds in some capacity. And of course we are a single platform, like literally one single interface, consistent across all the three Cloud providers that we work upon. And it gives them that flexibility to mix and match Cloud infrastructure under any Snowflake however they see fit. The last piece of it is sharing. And, you know, I think it's that ability as I kind of alluded to around like breaking down organizational silos, and allow people to be able to actually connect with each other in ways that you couldn't do before. Like, if you think about how you and I would've shared data before, I'd be like, "Hey, Dave, I'm going to unload this table into a spreadsheet and I'm going to send it over in email." And there's the whole host of issues that get introduced in that and world, now it's like instantly available. I have a lot of control over it, it's governed it's all these other things. And I can create kind of walled gardens, so to speak, of how far out I want that to go. It could be in a controlled environment of organizations that I want to collaborate with, or I can put it on our marketplace and expose it to the whole world, because I think there's a value in that. And if I choose I can monetize it, right? So those, you know, the ease of use aspect of it, absolutely, it's just a fantastic platform. The multi-Cloud aspect of it and our unique differentiation around sharing in our marketplace and monetization. >> Yeah, on the sharing front. I mean, it's now discoverable. Like if you send me an email, like what'd you call that? When did you send that email? And then the same time I can forward that to somebody else's not governed. >> Yeah. >> All right. So that just be creates a nightmare for the compliance. >> Right. Yeah. You think about how you revoke access in that situation. You just don't, right? Now I can just turn it off and you go in to run your query. >> Don't get access on that data anymore. Yeah. Okay. And then the other thing I wanted to ask you, Clark is Snowflake started really as analytics platform, simplifying data warehousing, you're moving into that world of data science, you know, the whole data lake movement, bringing those two worlds together. You know, I was talking to Ben Ward about this, maybe there's a semantic layer that helps us kind of talk between those two worlds, but you don't care, right? If it's in an object store, it can play in both of those worlds, right? >> That's right. >> Yeah, it's up to you to figure it out and the customer- >> Yeah. >> from a storage standpoint. Here it is, serve it up. >> And that's the thrust of this announcement, right? Is bringing together two great companies, the Dell platform, the Snowflake platform, and allowing organizations to bring that together. And they decide like it, as we all know, customers decide how they're going to build their architecture. And so this is just another way that we're helping them leverage the capabilities of our two great platforms. >> Does this push or pull or little bit of both? I mean, where'd this come from? Or customers saying, "Hey, it would be kind of cool if we could have this." Or is it more, "Hey, what do you guys think?" You know, where are you at with that? >> It was definitely both, right? I mean, so we certainly started with, you know, a high level idea that, you know, the technologies are complimentary, right? I mean, as Clark just described, and at the same time we had customers coming to us saying, "Hey, wait a minute, I'm doing this over here, and this over here, how can I make this easier?" So that was like I said, we started with a blank sheet and lots of long customer conversations and this is what resulted. So >> So what are the sequence of events to kind of roll this out? You said it's second half, you know, when do you start getting customers involved? Do you have your already, you know, to poke at this and what's that look like? >> Yeah, sure. I can weigh in there. So, absolutely. We've had a few of our big customers that have been involved sort of in the design already who understand how they want to use it. So I think our expectation is that now that the sort of demonstrations have been in place, we have some pre functionality, we're going to see some initial testing and usage, some beta type situations with our customers. And then second half, we'll ramp from there. >> It's got to be a huge overlap between Dell customers and Snowflake customers. I mean, it's hundred billion. You can't not bump into Dell somewhere. >> Exactly. Yeah, you know. >> So where do you guys want to see this relationship go, kind of how should we measure success? Maybe you could each give your perspectives of that. >> I mean, for us, I think it's really showing the value of the Snowflake platform in this new world where there's a whole new ecosystem of data that is accessible to us, right? So seeing those organizations that are saying like, "Look, I'm doing new things with on-premises data that I didn't think that I could do before", or, "I'm driving efficiency in how I do analytics, and data engineering, and data science, in ways that I couldn't do before," 'cause they were locked out of using a Snowflake-like technology, right? So I think for me, that's going to be that real excitement. I'm really curious to see how the collaboration and the sharing component comes into this, you know, where you can think of having an on-premises data strategy and a need, right? But you can really connect to Cloud native customers and partners and suppliers that live in the Snowflake ecosystem, and that wasn't possible before. And so that is very conceivable and very possible through this relationship. So seeing how those edges get created in in our world and how people start to collaborate across data, both in the Cloud and on-prem is going to be really exciting. >> I remember I asked Frank, it was kind of early in the pandemic. I asked him, come on, tell me about how you're managing things. And he was awesome. And I asked him to at the time, you know, "You're ever going to do, you know, bring this platform on-prem?" He's like unequivocal, "No way, that's never going to happen. We're not going to do it halfway house ware Cloud only." And I kept thinking, but there's got to be a way to expand that team. There's so much data out there, and so boom, now we see the answer . Martin, from your standpoint, what does success look like? >> I think it starts with our partnership, right? So I've been doing this a long time. Probably the first time I've worked so closely with a partner like Snowflake. Joint customer conversations, joint solutioning, making sure what we're building is going to be really, truly as useful as possible to them. And I think we're going to let them guide us as we go forward here, right? You mentioned, you know, systems or record or other potential platforms. We're going to let them tell us where exactly the most value will come from the integration between the two companies. >> Yeah. Follow data. I mean, remember in the old days a hardware company like Dell would go to an ISP like Snowflake and say, "Hey, we ran some benchmarks. Your software runs really fast on our hardware, can we work together?" And you go, "Yeah, of course. Yeah, no problem." But wow! What a different dynamic it is today. >> Yeah. Yeah, absolutely. >> All right guys. Hey, thanks so much for coming to theCube. It's great to see you. We'll see you at the Snowflake Summit in June. >> Snowflake Summit in a month and a half. >> Looking forward to that. All right. Thank you again. >> Thank you Dave. >> All right. Keep it right there everybody. This is Dave Vellante, wall to wall coverage of Dell Tech World 2022. We'll be right back. (gentle music)