>>Hey everyone. Welcome back to the cube. Lisa Martin here with Dave ante, we're covering snowflake summit 22. This is Dave two of our wall to wall cube coverage of three days. We've been talking with a lot of customers partners, and we've got some more partners to talk with us. Next. Informatica two of our guests are back with us on the program. Rick TA Daniels joins us the G P global ecosystems and technology at Informatica and Peter COO vice president and chief strategist banking and financial services. Welcome guys. >>Thank you guys. Thanks for having us, Peter, >>Talk to us about what some of the trends are that you're seeing in the financial services space with respect to cloud and data and AI. >>Absolutely. You know, I'd say 10 years ago, the conversation around cloud was what is that? Right? How do we actually, or no way, because there was a lot of concerns about privacy and security and so forth. You know, now, as you see organizations modernizing their business capabilities, they're investing in cloud solutions for analytics applications, as well as data data being not only just a byproduct of transactions and interactions in financial services, it truly fuels business success. But we have a term here in Informatica where data really has no value unless it's fit for business. Use data has to be accessible in the systems and applications you use to run your business. It has to be clean. It has to be valid. It has to be transparent. People need to understand where it comes from, where it's going, how it's used and who's using it. It also has to be understood by the business. >>You can have all the data in the world and your business applications, but people don't know what they need it to use it for how they should use it. It has no value as well. And then lastly, it has to be protected when it matters most what we're seeing across financial services, that with the evolution of cloud now, really being the center of focus for many of the net new investments, data is scattered everywhere, not just in one cloud environment, but in multiple cloud environments, but they're still dealing with many of the on premise systems that have been running this industry for many, many years. So organizations need to have the ability to understand what they need to do with their data. More importantly, tie that to a measurable business outcome. So we're seeing the data conversation really at the board level, right? It's an asset of the business. It's no longer just owned by it. Data governance brings both business technology and data leaders together to really understand how do we use manage, govern and really leverage data for positive business outcomes. So we see that as an imperative that cuts across all sectors of financial services, both for large firms, as well as for the mid-market so >>Quick follow up. If I, may you say it's a board level. I totally agree. Is it also a line of business level? Are you seeing increasingly that line of businesses are leaning in owning the data, be building data products and the like >>Absolutely. Because at the end of the day business needs information in order to be successful. And data ownership now really belongs in the front office. Business executives understand that data again is not just a bunch of zeros and ones. These are critical elements for them make decisions and to run their business, whether it's to improve customer experience, whether it's to grow Wallace share, whether it's to comply with regulations, manage risks in today's environment. And of course being agile business knows that data's important. They have ownership of it and technology and data organizations help facilitate that solutions. And of course the investments to ensure that business can make the decisions and take the appropriate actions. >>A lot of asks and requirements on data. That's a big challenge for organizations. You mentioned. Well, one of the things that we've mentioned many times on this program recently is every company has to be a data company. There is no more, it's not an option anymore. If you wanna be successful, how does Informatica help customers navigate all of the requirements on data for them to be able to extract that business value and create new products and services in a timely fashion? >>So Informatica announced what we call the intelligent data management cloud platform. The platform has capabilities to help organizations access the data that they need, share it across to applications that run their business, be able to identify and deal with data, quality issues and requirements. Being able to provide that transparency, the lineage that people need across multiple environments. So we've been investing in this platform that really allows our customers to take advantage of these critical data management, data governance and data privacy requirements, all in one single solution. So we're no longer out there just selling piecemeal products. The platform is the offering that we provide across all industries. >>So how has that affected the way Informatica does business over the last several years? Snowflake is relatively new. You guys have been around a long time. How has your business evolved and specifically, how are you serving the snowflake yeah. Joint customers with >>Informatica? Yeah, I think then when I've been talking with folks here at the event, there are two big areas that keep coming up. So, so data governance, data governance, data governance, right? It's such a hot topic out there. And as Peter was mentioning, data governance is a critical enabler of access to data. In fact, there is an IDC study for last year that said that, you know, 80, 84% of executives, you know, no surprise, right? They wanna have data driven outcomes, data driven organizations, but only 30% of practitioners actually use data to make decisions. There's a huge gap there. And really that's where governance comes in and creating trust around data and not only creating trust, but delivering data to and users. So that's one big trend. The other one is departmental user adoption. We're seeing a, a huge push towards agility and rapid startup of new projects, new data driven transformations that are happening at the departmental level, you know, individual contributors, that sort of thing. So Informatica, we did a made announcement yesterday with snowflake of a whole host of innovations that are really targeting those two big trend areas. >>I wanna get into the announcements, but you know, the point about governance and, and users, business users being reluctant, it's kind of chicken and egg, isn't it. If, if I don't have the governance, I'm, I'm afraid to use it. But even if I do have it, there's the architecture of my, my, my company, my, my data organization, you know, may not facilitate that. And so I'm gonna change the architect, but then it's a wild west. So it has to be governed. Isn't that a challenge that company companies >>Absolutely, and, and governance is, is a lot more than just technology, right? It's of a people process problem. And there really is a community or an ecosystem inside every organization for governance. So it's really important that when you think about deploying governance and being successful, that every stakeholder have the ability to interact with this common framework, right. They get what they need out of it. It's tailored for how they wanna work. You've got your it folks, you got your chief data officer data stewards, you have your privacy folks and you have your business users. They're all different personas. So we really focus on creating a holistic, single pane of glass view with our cloud data governance and catalog offering that that really takes all the way from the raw technical data and actually delivers data in, in a shopping cart, like experience for actual enterprise users. Right? And, and so I think that's when data governance goes from historically data, governments was seen as an impediment. It was seen as a tax, I think, but now it's really an accelerator, an enabler and driving consumption of data, which in turn for our friends here at snowflake is exactly what they're looking for. >>Talk about the news. So data loader, what does that do? >>Well, it's all in the name. We say, no, the data loader it, it's a free utility that we announced here at, at snowflake summit that allows any user to sign up. It's completely free, no capacity limits. You just need an email address, three simple steps start rapidly loading data into snowflake. Right? So that first step is just get data in there. Start working with snowflake. Informatica is investing and making that easy for every single user out there. And especially those departmental users who wanna get started quickly. >>Yeah. So, I mean, that's a key part point of getting data into the snowflake data cloud, right? It's like any cloud, you gotta get data in. How does it work with, with customers? I mean, you guys are, are known, you have a long history of, you know, extract transform ETL. How does it work in the snowflake world? Is it, is it different? Is it, you remember the Hadoop days? It was, it was E LT, right? How are customers doing that today in this environment? >>Yeah, it's different. I mean, there, there are a lot of the, the same patterns are still in play. There's a lot more of a rapid data loading, right. Is a key theme. Just get it into snowflake and then work on the data, transform it inside of snowflake. So it's, it's a flavor of T right. But it's really pushing down to the snowflake data cloud as opposed to Hado with spark or something like that. Right. So that, that's definitely how customers are using it. And, you know, majority of our customers actually with snowflake are using our cloud technology, but we're also helping customers who are on premise customers, automate the migration from our on-premises technology to our cloud native platform as well. Yeah. >>And I'd say, you know, in addition to that, if you think about building a snowflake environment, Informatica helps with our data loader solution, but that's not enough. Then now you need to get value out of your data. So you can put raw data into the snowflake environment, but then you realize the data's not actually fit for business use, what do we need to do actually transform it to clean it, to govern it. And our customers that use Informatica with snowflake are managing the entire data management and data governance process so that they can allow the business to get value out of the snowflake investment. >>How quickly can you enable a business to get value from that data to be able to make business decisions that can transform right. Deliver competitive advantage? >>I think it really depends on an organization on a case by case basis. At the end of the day, you need to understand why are you doing this in the first place, right? What's the business outcome that you're trying to achieve next, identify what data elements do you actually need to capture, govern and manage in order to support the decisions and the actions that the business needs to take. If you don't have those things defined, that's where data governance comes into play. Then all you're doing is setting up a technical environment with a bunch of zeros in ones that no one knows what to do with. So we talk about data governance more holistically, say, you need to align it to your business outcomes, but ensure that you have people, processes, roles, and responsibilities, and the underlying technology to not just load data into snowflake, but to leverage it again for the business needs across the organization. >>Oh, good, please. >>I just wanted to add to that real quickly. Yeah. One of the things Informatica we're philosophically focused on is how do you accelerate the entire business of data management? So with our, our cloud platform, we have what's called our clear AI engine, right? So we use AI techniques, machine learning recommendations to accelerate with the, the knowledge of the metadata of what's gone on the organization. For example, that when we discover data assets figure out is this customer data, is it product data that dramatically shortens the time to find data assets deliver them? And so across our whole portfolio, we're taking things that were traditionally months to do. We're taking 'em down to weeks and days and even hours, right? So that's the whole goal is just accelerate that entire journey and life cycle through cloud native approaches and AI. Yeah, >>You kind of just answered my question. I think Rick, so you have this joint value statement together. We help customers. This is informatic and snowflake together. We help customers modernize their data. Architecture enable the most critical workloads, provide AI driven data governance and accelerate added value with advanced analytics. I mean, you definitely touched on some of those, but kind of unpack the rest of that. What do you mean by modernize? What is their data architecture? What is that? Let's start there. What does that look like? Modernizing a data. Yeah. >>So, so a lot with so many customers, right? They, they built data warehouses, core data and analytics systems on premises, right? They're using ETL technology using those, those either warehouse, appliances or databases. And what they're looking for is they wanna move to a cloud native model, right. And all the benefits of cloud in terms of TCO elasticity, instant scale up agility, all those benefits. So we're looking, we're looking to do with our, our modernization programs for our, for our current customer base that are on premises. We automate the process to get them to a fully cloud native, which means they can now do hybrid. They can do multi-cloud elastic processing. And it's all also in a consumption based model that we introduced about about a year and a half ago. So, so they're looking for all those elements of a cloud native platform and they're, but they're solving the same problems, right? We still have to connect data. We still have to transform data, prepare it, cleanse it, all those things exist, but in a, in a cloud native footprint, and that's what we're helping them get to. >>And the modern architecture these days, quite honestly, it's no longer about getting best breed tools and stitching them together and hoping that it will actually work. And Informatica is value proposition that our platform has all those capabilities as services. So our customers don't have to deal with the costs and the risks of trying to make everything work behind the scenes and what we've done with IDMC or intelligent data management cloud for financial services, retail, CPG, and healthcare and life sciences. In addition to our core capabilities and our clear AI machine learning engine, we also have industry accelerators, prebuilt data, quality rules for certain regulations in within banking. We've got master data management, customer models for healthcare insurance industry, all prebuilt. So these are accelerators that we've actually built over the years. And we're now making available to our customers who adopt informatic as intelligent data management cloud for their data management and governance needs. >>And then, and then the other part of this statement that that's interesting is provide AI driven data governance. You know, we are seeing a move toward, you know, decentralized data architectures and, and, and organizations. And we talk to snowflake about that. They go, yeah, we're globally distributed cloud. Okay, great. So that's decent place, but what we see a lot of customers doing to say, okay, we're gonna give lines of business responsibility for data. We're gonna argue about who owns what. And then once we settle that here's your own, here's your own data lake. Maybe they they'll try to cobble together a catalog or a super catalog. Right. And then they'll try to figure out, you know, some algorithms to, to determine data quality, you know, best, you know, okay. Don't use. Right, right. So that, so if I understand it, you automate all that. >>So what we're doing with AI machine learning is really helping the data professional, whether in the business, in technology or in between not only to get the job done faster, better, and cheaper, but actually do it intelligently. What do we mean by that? For example, our AI engine machine learning will look at data patterns and determine not only what's wrong with your data, but how should you fix it and recommend data quality rules to actually apply them and get those errors addressed. We also infer data relationships across a multi-cloud environment where those definitions were never there in the beginning. So we have the ability to scan the metadata and determine, Hey, this data set is actually related to that data set across multiple clouds. It makes the organization more productive, but more importantly, it increases the confidence level that these organizations have the right infrastructure in place in order to manage and govern their data for what they're trying to do from a business perspective. >>And I add that as well. I think you're talking a lot about data mesh architectures, right? That, that are really kind of popular right now. And I think those kind of, they live or die on, on data governance. Right? If you don't have data governance to share taxonomy, these things, it's very hard to, I think, scale those individual working groups. But if you have a platform where they, the data owners can publish out visibility to what their data means, how to use it, how to interpret it and get that insight, that context directly to the data consumers that's game changing. Right. And that's exactly what we're doing with our cloud data governance and catalog. >>Well, the data mesh, you talk about data mesh, there's four principles, right? It's like decentralized architecture data products. So if, once you figure out those two yep. You just created two more problems, which is the other two parts of the Princip four, two parts of the four principles, self service infrastructure, and computational governance. And that's like the hardest part of federated, federated, computational governance. That's the hardest part. That's the problem that you're solving. >>Yeah. Yeah, absolutely. I mean, think about the whole decentralization and self-service, well, I may be able to access my data in mesh architecture, but if I don't know what it means, how to use it for what purpose, when not to use it, you're creating more problems than what you originally expected to solve. So what we're doing is addressing the data management and the governance requirements, regardless of what the architecture is, whether it's a mesh architecture, a fabric architecture or a traditional data lake or a data store. >>Yeah. Mean, I say, I think data mesh is more of an organizational construct than it is. I, I'm not quite sure what data fabric is. I think Gartner confused the issue that data fabric was an old NetApp term. Yeah. You're probably working in NetApp at the time and it made sense in the NetApp context. And then I think Gartner didn't like the fact that Jamma Dani co-opted this cool term. So they created data fabric, but whatever. But my, my point being, I think when I talk to customers that are they're, they're trying to get more value outta data and they recognize that going through all these hyper specialized roles is time consuming and it's not working for them. And they're frustrated to your points and your joint statement. They want to accelerate that. And they're realizing, and the only way to do that is to distribute responsibility, get more people involved in the process. >>And, and that's, it kind of dovetails with some, the announcements we made on data governance for snowflake, right, is you're taking these, these operational controls of the snowflake layer that are typically managed by SQL and you, and that decentralized architecture data owner doesn't know how to set those patterns and things like that. Right. So we're saying, all right, we're, we're creating these deep integration so that again, we have a fit for persona type experience where they can publish data assets, they can set the rules and policies, and we're gonna push that down to snowflake. So when it actually comes to provisioning data and doing data sharing through snowflake, it's all a seamless experience for the end user and the data owner. Yeah. >>That's great. Beautiful, >>Seamless experience absolutely necessary these days for everybody above guys. Thanks so much for joining David me today, talking about Informatica what's new, what you're doing with snowflake and what you're enabling customers to do in terms of really extracting value from that data. We appreciate your insights. >>Thank you. Yep. >>Thank you for having us >>For our guests and Dave ante. I'm Lisa Martin. You're watching the cubes coverage of snowflake summit day two of the cubes coverage stick around Dave. And I will be right back with our next guest.

>>Hey everyone. Welcome back to the cube. Live in Las Vegas, Lisa Martin, with Dave Nicholson, we are covering AWS reinvent 2021. This was probably one of the most important and largest hybrid tech events this year with AWS and its enormous ecosystem of partners. We're going to be talking with a hundred guests in the next couple of days. We started a couple of days ago and about really the innovation that's going to be going on in the cloud and tech in the next decade. We're pleased to welcome Rick Tam Daniel's as our next guest VP of strategic ecosystems at Informatica. Rick. Welcome to >>The program. Thank you for having me. It's a, it's a pleasure to be back. >>Isn't it nice to be back in person? Oh, it's amazing. All these conversations you just can't replicate by video conferencing. Absolutely >>Great to reconnect with folks haven't seen in a few years as well. >>Absolutely. That's been the sentiment. I think one of the, one of the sentiments that we've heard the last three days, so one of the things thematically that we've also been hearing about in, in between all of the plethora of AWS announcements, typical reinvent is that every company has to become a data company, public sector, private sector, small business, large business. Talk to us about how Informatica and AWS are helping companies become data companies so that they don't get left behind. >>But one of the biggest things that we're hearing at reinvent is that customers are really concerned with data, fragmentation, data silos, access to trusted data, and how do they, how do they get that information to really affect data led transformation? In fact, we did a survey earlier in the year of chief, the chief data officers were found that up to 80, almost 80% of organizations had 50% or more of their data in hybrid or multi-cloud environments. And also a 79% are looking to leverage more than 100 data sources. And 30% are looking to leverage more than 1000 data sources. So Informatica we, with our intelligent data management cloud, we're really focused on enabling customers to bring together the data assets, no matter where they live, what format they're in, on-premise cloud, multi-cloud bringing that all together. >>Well, we sold this massive scatter 22 months ago now, right? Of everyone just, and the edge exploded and data exploded and volumes and data sources exploded hard for organizations to get their head around that, to go or that the data is going to be living in all these different places. You talked about a lot of customers and every industry being hybrid multi-cloud because based on strategy, based on acquisition, but to get their arms around that data and to be able to actually extract value from it fast is going to be the difference between those businesses that succeed and those that don't >>Absolutely. And our partnership with AWS, that's a long standing partnership and we're very much focused on addressing the challenges you're talking about. Uh, and in fact, earlier this year we announced our cloud first, our cloud native, uh, data governance and data catalog on AWS, which is really focused on creating that central point of trusted data access and visibility for the organization. And just today, we had an announcement about how we're bringing data democratization and really accelerating data democratization for AWS lake formation. >>What is, when you, when you, we talk about data democratization often, what does that mean to you? What does that mean to Informatica? How do you deliver that to customers so that they can really be able to extract as much value as they can? >>Yeah. So a great question. And really that whole data management journey is a big piece of this. So it starts with data discovery. How do I even begin to find my data assets? How do I get them from where they are to where they need to go in the cloud? How do I make sure they're clean, they're ready to use. I trust them. I understand where they came from. And so the solution that we announced today is really focused on how do we provide a business users with a self-service way of getting access to data lake data, sitting in Amazon S3 with lake formation governance, but doing it in a way that doesn't create an undue burden on those business users, around data compliance and data policies. And so what we've done is we brought our business user-friendly self-service experience an axon data marketplace together with AWS lake formation. >>So Informatica has had a long history in the data world. Um, I think of terms like MDM and ETL. Yes. Where does, where does Informatica is history dovetail with the present day in terms of cloud the con does the concept of extract translate load? I think that's what ETL stood for too many TLAs running as far as trying to transform, uh, w where does that play in today's world? Are you focused separately on cloud from on-premise data center or do you, or do you link the two? Yeah, >>So we focus on, uh, addressing data management, uh, when, no matter where the data lives. So on-premise cloud multi-cloud, uh, on our intelligent data management cloud platform is a, is the industry's first end-to-end cloud native as a service data management platform that delivers all those capabilities. I mentioned before, uh, to customers. So we can manage all those workloads that are distributed from a single cloud-based as a service data management platform. So >>The platform is, is as a service in the cloud, but you could be managing data assets that are in traditional on premises, data centers, the like, absolutely. >>Okay. >>So congratulations on the IPO. Of course we can't, we can't not talk to Informatica without that. I imagined the momentum is probably pretty great right about now when we think of AWS, I, when I think of AWS, I always think of momentum. We, I mean the, the volume of announcements, but also when I think about AWS, I think about their absolute focus on the customer, that working backwards approach from a partnership perspective. Is there alignment there? I imagine, like I said, with the IPO, a lot of momentum right now, probably a lot of excitement are, is infant medical also was focused and customer obsessed as AWS's. >>Yeah. So, um, first of all, thank you so much. Congratulations. Uh, so we had a very successful IPO last month. And in fact, just yesterday, our CEO I'm at Wailea presented our Q3 results, uh, which showcase the continued growth of our subscription revenue or cloud revenue. And in fact, our cloud revenue grew 44% year over year, which is really reflective of our big shift to being a cloud first company and also the success of our intelligent data management cloud platform. Right. And, and that platform, again, as I mentioned, it's spanning all those aspects of data management and we're delivering that for more than 5,000 customers globally. Uh, and just from an adoption perspective, we processed about 23 trillion transactions a month for customers in our cloud platform. And that's doubling every six to 12 months. So it's incredible amount of adoption. Some of the biggest enterprises in the world like Unilever, Sanofi folks like that are using the cloud is their preferred data management platform of choice in the cloud. >>Well, you know, of course, congratulations is in order for the IPO, but also really on what you just mentioned, the trajectory of where Informatica is going, because Informatica wasn't born yesterday. Right. And, uh, we shouldn't overlook the fact that there are challenges associated with moving from the world as it exists on premises for still 80% of it spend at least navigating that transition, going from private to public, getting the right kind of investment where people realize that cloud is a significant barrier to entry, uh, for, for a lot of companies. I think it's, it's, you know, you have a lot of folks cheering for you as you navigate this transition. >>Well, one thing I do I say is, yes, we have it in the business of data for a long time, but we also then the business of cloud quite a long time. So this is true. This is the 10th reinvent. This is also the ten-year anniversary of the Informatica AWS partnership, right? So we've been working in the cloud with AWS for, for that long innovating all of these different, different core services. So, um, and from that perspective, you know, I think we're doing a tremendous amount of innovation together, you know, solutions like when we talked about for lake formation, but we also announced today a couple of key programs that we partnered with AWS around, around modernization and migration, right? So that's a big area of focus as well is how do we help customers modernize and take advantage of all the great services that AWS offers? So that's how we announced our membership and what's called the workload migration program and also the data lead migrations program, which is part of the public sector focus at AWS as well. >>The station perspective that was talked a lot about by Adam yesterday. And we've talked about it a lot today, every organization needs to monitorize, even some of those younger ones that you think, oh, aren't, they already, you know, fairly modern, but where, where are your customer conversations happening from a modernization perspective is that elevated up the, the C stat that we've got to modernize our or our organization get better handle of our data, be able to use it more protected, secure it so that we can be competitive and deliver outstanding customer experiences. >>What happens is the pain points that the legacy infrastructure has from the business perspective really do elevate the conversation to the C-suite. They're looking at saying, Hey, especially with the pandemic, right? We have to transform our business. We have to have data. We have to have trust in data. How do we do that? And we're not going to get there >>On rigid on-premise infrastructure. We need to be in a cloud native footprint. And so we've been focused on helping customers get to those cloud native end points, but also to a truly cloud native data management, we talked about earlier can manage all those different workloads, right? From a single that SAS serverless type experience. Right? What have been some of the interesting conversations that you've had here? Again, we are in person yep. Fresh off the IPO, lots of announcements coming out. You guys made announcements today. What's been the sentiment from the, those customers and partners that you've talked about. >>Well, I'll give you guys actually a little sneak preview of another announcement we have coming tomorrow, uh, with our friends at Databricks. So we, uh, we are announcing a data, data democratization solution with Databricks accelerating some of the same, you know, addressing some of the same challenges we were talking about here, but in the data breaks in the Lakehouse environment. Um, so, so, but around that, and I had a great conversation with some partners here, some of the global system integrators, and they're just so happy to see that, right, because a lot of the infrastructure that's around data lakes are lake formation. It's pretty technical it's for a technical audience. And, and Informatica has really been focused on how do we expand the base of users that are able to tap into data and that's through no code experiences, right? It's through visual experiences. And we bring that tightly coupled together with the performance and the power and scale of platforms like Databricks and the AWS Redshift and S3, it's really transformative for customers. >>What are some of the things that here we are wrapping up the 10th, re-invent almost as tomorrow, but also wrapping up the end of 2021. What are some of the things that th th that there's obviously a lot of momentum with Informatica right now that from a partnership perspective, anything that you, you just gave us some breaking news. Thank you. We always love that. What are some of the things that you're looking forward to in 2022 that you think are really going to help Informatica customers just be incredibly competitive and utilizing data in the cloud on prem to their maximum? >>Well, I think as we go into the next year data complexity data fragmentation, it's gonna continue to grow. It's, it's, it's exploding out there. Uh, and one of the key components of our platform or the IDMC platform is we call it Clare, which is the industry first kind of metadata driven AI engine. And what we've done is we've taken the intelligence of machine learning and AI, and brought that to the business of data management. And we truly believe that the way customers are going to tame that data, they're going to address those problems and continue to scale and keep up is leveraging the power of AI in a cloud native cloud, first data management platform. >>Excellent. Rick, thank you so much for joining us today. Again, congratulations on last month, Informatica IPO, great solid, strong, deep partnership with AWS. We thank you for your insights and best of luck next year. >>Awesome. Thank you so much. Pleasure being here. Our >>Pleasure to have you for my co-host David Nicholson, I'm Martin. You're watching the cube, the global leader in live tech coverage.

>> Announcer: From around the globe, it's "theCUBE" with digital coverage of AWS "re:Invent" 2020. Sponsored by Intel, AWS and our community partners. >> Hi, everyone, welcome back to theCUBE's virtual coverage of AWS "re:Invent" virtual 2020. It's not an in-person event this year. It's remote, it's virtual, "theCUBE" is virtual and our guests and our interviewers will be remote as well. And so we're here covering the event for the next three weeks, throughout the next three cause we're weaving in commentary from "theCUBE", check out the cube.net and all of our coverage. And here at AWS we have special feature programming, we got a great segment here talking about big data in the cloud, governance, data lakes, all that good stuff. Rik Tamm-Daniels, vice-president strategic ecosystems and technology for Informatica, and Rick Turnock, head of enterprise data services, Invesco, customer of Informatica. Welcome to the cube. >> Hey John, thanks for having us. >> So Rik, with a K from Informatica, I want to ask you first, we've been covering the company journey for many, many years. Always been impressed with the focus on data and specifically cloud and all the things that you guys have been announcing over the years, have been spot on the mark. You know, AI with CLAIRE, you know, making things, cloud native, all that's kind of playing out now with the pandemic, "re:Invent", that's the story here. Building blocks with high level services, cloud native, but data is the critical piece again. More machine learning, more AI, more data management. What's your take on this year's "re:Invent". >> Absolutely John and again, we're always excited to be here at "re:Invent", we've been here since the very first one. You know, it's a deep talk to a couple of key trends there, especially the era of the global pandemic here. There's so many challenges that so many enterprises are experiencing. I think the big surprise has been, that has actually translated into a tremendous amount of demand for digital transformation, and cloud modernization in particular. So we've seen a huge uptake in our cloud relationships with AWS when it comes to transformational architecture solutions around data and analytics, and using data as a fundamental asset for digital transformation. And so some of those solution areas are things like data warehouse, modernization of the cloud, or end-to-end data governance. That's a huge topic as well for many enterprises today. >> Before coming into "re:Invent", I had a chance to sit down an exclusive interview with Andy Jassy. I just spoke with Matt Garman who's now heading up sales and marketing, who ran EC too. Rick, you're a customer of Informatica. Their big talking point to me and validation to the trends is, there's no excuse to go slow anymore because there's a reason to go fast cause there's consequences and the pandemic has highlighted that you got to move faster otherwise, you know, you're going to be on the wrong side of history and necessity is the mother of all invention. Okay, great. I buy that by the way. So I have no complaints on talking point there from Amazon Web Services. The problem is, you got to manage the data. (John chuckles) To go fast. The gas in the tank is data, and if it's screwed up, it's not going to go well, all right? So it's like putting gas in a car. So, this is where I see the data lake coming into the cloud and all the benefits and look at the successes of companies. The cloud is a real enabler. What's your take on this? The importance of data governance, because cloud scale is here, people want to go faster, data is like the key thing. >> Yeah. The data governance was a critical component when we started our enterprise data platform and looking at, you know, how can we build a modern-day architecture with scale, bringing our enterprise data, but doing it in a governed fashion. So, when we did it, we kind of looked at, you know, what are critical partners? How can we apply data governance and the full catalog capabilities of knowing what data's coming in, identifying it, and then really controlling the quality of it to meet the needs of the organization. It was a critical component for us because typically it's been difficult to get access to that right data. And as we look in the future and even current needs, we really need to understand our data and bring the right data in and make it easily accessible and governance, and quality of that is a critical component of it. >> I want to just follow up with that if you don't mind cause you know, I've done so many of these interviews, I've been on the block now 30 years in the industry, I've seen the waves come and go, and you see a lot of these mandates, you know, "Data governance, we're adding data governance." From the Ivory tower, or you hear, "Everything got to be a service." But when you peel back and look under the hood to make that happen, it's complicated. You've got to have put things in place and it's got to be set up properly, you got to do your work. How important it is to have... And well what's under the covers to this? Cause governance, yeah, it's a talking point, I get that. But to make it actually happen well, it's hard. >> We started really with the operating models from the start. So I kind of took over data governance seven years ago and had a governing global architecture that's been around for 40 years, and it was hard. So this was really our shot and time to get it right. So we did an operating model, a governance model, and it really ingrained it through the whole build and execution process. And so it was part with technology and it was foundational to the process to really deliver it. So it wasn't governance from a governance saying, it was really part of our operating model and process to build this out and really succeed at it. >> Rik, on the Informatica side, I got to get your take on the new solution you guys announced, "The Governed Data Lake", I think it was solution. Does this tie into that? Take a minute to explain the announcement, and how does this tie in? >> Yeah, absolutely John. So I think you take a step back, look at... We talked about some of the drivers of why companies are investing in cloud data lakes. And I think what comes down to is, when you think about that core foundation of data analytics, you know, they're really looking at, you know, how do we go ahead and create a tremendous leap forward in terms of their ability to leverage data as an asset. And again, as we talked about, one of the biggest challenges is trust around the data. And what the solution does though, is it really looks to say, "Okay, first and foremost, "let's create that foundation of trust "not just for the cloud data lake, "but for the entire enterprise. "To ensure that when we start to build this "new architecture, one, we understand the data assets "that are coming in at the very get-go." Right? It's much harder to add data governance after the fact, but you put it in upfront, you understand your existing data landscape. And once that data is there, you make sure you understand the quality of the information, you cleanse the data, you also make sure you put it under the right data management policies. So many policies that enterprises are subject to now like CCPA and GDPR. They have to be concerned about consumer privacy and being able as part of your governance foundational layer, to make sure that you're in compliance as data moves through your new architectures. It's fundamental having that end trust and confidence to be able to use that data downstream. So our solution looks to do that end-to-end across a cloud environment, and again, not just the cloud environment, but the full enterprise as well. One thing I do want to touch on if you don't mind is on the AI side of things and the tooling side of things. Because I think data governance has been around a while, as you said, it's not that it's a new concept. But how do you do it efficiently in today's world? >> John: Yeah. >> And this is where Informatica is focused on a concept of data 4.0. Which is the use of metadata and AI and machine learning and intelligence, to make this process much, much more efficient. >> Yeah that's a good point, Rik, from these two Rickes, I got to go, one's with a K, one with a C, and CK. So Rick, CK and from Invesco customer, I want to just check that with you because I was your customer of Informatica, by they brought up a good point about governance. And I saw this movie before, we've all seen this before, people just slap on solutions or tooling to a pre-existing architecture. You see that with security, you know, now it's, you can't have a conversation without saying, "Oh security's got to be baked in from the beginning." Okay cool, I get that. There's no debate there. Governance, same kind of thing, you know, you're hearing this over and over again, if you don't bake governance into the beginning of everything, you're going to be screwed. Okay? So how important is that foundation of trust for this peace. (Rick mumbling) >> It's critical and to do it at beginning, right? So you're profiling the data, you're defining entitlements and who has access to it, you're defining data quality rules that you can validate that, you define the policies, is there a PII data, all of that, as you do that from the start, then you have a well-governed and documented data catalog and taxonomy that has the policies and the controls in place to allow that to use. If you do it after the fact, then you're always going to be catching up. So a part of our process and policies and where the really Informatica tools delivered for us is to make it part of that process. And to use that as we continue to build out our data platform with the quality controls and all the governance processes built in. >> I got to ask on your journey, that's seven years ago, you took over the practice. You were probably right in the middle of the sea change when everyone kind of woke up and said, "Hey, you know, Amazon, you go back seven years, "look at Amazon where they were to where they are today." Okay? Significantly strong then, total bellwether now in terms of value opportunity. So, how did you look at the cloud migration? How do you think about the cloud architecture? Because I'm sure, and I'd love to get your story here about how you thought about cloud, in the midst of architecting the data foundational platform there. >> Yeah, we're a global company that had architecture, we grew it by acquisition. So a lot of our data architecture was on-prem, difficult really to pull that enterprise data together to meet the business needs. So when we started this, we really wanted to leverage cloud technology. We wanted a modern stack. We wanted scale, flexibility, agility, security, all the things that the cloud brought us too. So we did a search, and looking at that, and looked at competitors, but really landed on to Amazon just bought by core capabilities and scale they have innovation and just the services to bring a lot what we're looking at and really deliver on what we wanted from a platform. >> Why Informatica and AWS, why the combination? Can you share some of the reasons why you went with Informatica with AWS? >> Yeah, again, when we started this off, we looked at the competitors, right? And we were using IVQ. So we had an Informatica product on-prem, but we looked at a lot of the different governance competitors, and really the integrated platform that Informatica brings to us, what was the key deliverer, right? So we can really have the technical metadata with EDC and enterprise data client, catalog, scan our sources, our file, understanding the data and lineage of what it is. And we can tie that into axon and the governance tools to really define business costs returns. We were very critical of defining all our key data elements business glossary, and then we can see where that is by linking that to the technical metadata. So we know where our PII data, where are all our data and how it flows, both tactically and from a business process. And then the IDQ. So when we've defined and understand the data, we want to bring in the delight and how we want to conform it, to make it easily accessible, we can define data quality rules within the governance tool, and then execute that with IDQ, and really have a well-defined data quality process that really takes it from governance in theory to governance in really execution. >> That's awesome. Hey, you are using the data, you're using the cloud, you're getting everything you need out of it. That's the whole idea, isn't it? >> Yeah. >> That's good stuff, Rik at Informatica, tell us about what's going on, you mentioned data 4.0, I think people should pay attention to some of the interviews I've done with your team. They're online also, it's part of that next-gen, next level thinking. Here at "re:Invent", what should customers pay attention to, that you guys are doing? Great customer example here of cloud scale. What's the story for "re:Invent" this year for Informatica. >> But what John, it comes down to when customers think about their cloud journey, right? And the difference, especially with their data centric workloads and priorities and initiatives, all the different hurdles that they need to overcome. I think Informatica we're uniquely positioned to help customers address all those different challenges and you heard Rick speak about a whole bunch of those along the way. And I think particularly at "re:Invent", first of all, I just welcome folks to... They want to learn more about our data governance solution. Please come by our virtual booth. We also have a great interactive experience that encouraged folks to check out. One of the key components of our solution is our enterprise data catalog. And attendees at "re:Invent" can actually get hands on with our data catalog through the demo jam, the AWS demo jam as part of "re:invent". So I'd encourage folks to check that out as well, just to see what we're talking about yet actually. >> Awesome. Final question for you guys, as "re:Invent" is going on, a lot app stores are popping up, you seeing obviously the same trends, machine learning and you know, outpost is booming, so a cloud operations is clearly here, Rick from Invesco, what do you think the most important story is for your peers as they're here? It's a learning conference and you guys have seven years in the cloud working together with Informatica, in your opinion, what should people be paying attention to as they looked at this pandemic and what they got to get through? And then coming out of it with the growth strategy, it's all got to be more about the data, there's more data coming in, more sources, IoT data, certainly the work at home is causing these workloads, workplace, workflows, everything's changed, the future of work. What's your advice to peers out there on what to pay attention to and what to think about? >> We really started with a top-down strategy, right? To really the vision and the future. What do we want to get out of our data? Data is just data, right? But it's the information, it's the analytics, it's really delivering value for our clients, shareholders, and employees to really do their job, simplify our architecture. So really defining that vision of what you want and approach, and then executing on it, right? So how do you build it in a way to make it flexible and scalable, and how do you build an operating and governance model really to deliver on it because, you know, garbage in is garbage out, and you really got to have those processes, I think to really get the full value of what you're building. >> Get the data out there at the right place, at the right time and the right quality data. That's a lot more involved now and you need to be agile. And I think agile data is a way to go. Rick Turnock... >> And then with channels and capabilities that makes it easier, right? It makes it doable. And I think that's what cloud and the Informatica tools, right? Where in the past, you know, it was people hard coding and doing it right? The capability of that cloud and these tools give us makes it really achievable. >> You know, we have an old saying here in our CUBE team, you know, "If there's a problem, "you got to see if it's important, "and then look at the consequences "of not solving that problem, quantify the value of "solving or not solving that problem, "and then look and deploy solutions to do it." I think now with the data, you can actually do that and say, "This ain't quite the consequences of not doing this "or doing this, have a quantifiable value." I just loved that because it brings the whole ROI back to the table. And, you know, it's a dark art, it used to be, you mentioned the old days, you know, you got to do all this custom work, it was like a dark art. Oh yeah, the ROI calculation, payback. I mean, it was a moving train. That's the way it used to be. Not anymore. >> You got to do it to survive, really, if you're not doing it, you know, I don't know. >> Necessity is the mother of all inventions I think, now more than ever, data's going to be the key. Rik final word from Informatica. What should people pay attention to? >> Yeah, I mean, I think as you mentioned there, data is obviously a critical asset, right? And, and to your point with cloud, you can not only realize ROI quickly, but, you can actually iterate so much more quickly, where you can get that ROI immediately or you can validate that ROI, you can adjust your approach. But again, from an Informatica standpoint, we are seeing such a huge uptake in demand for customers who want to go to the cloud, who are modernizing. Every day we're investing heavily and how do we make sure that customers can get there quickly? They can maximize the ROI from their data assets, and we're doing it with all things, data management, from traditional data integration, all the way to the data governance, all the capabilities we talked about today. >> Yeah. Congratulations. That's the benefit of investing in a platform and having a set of out of the box tooling with SaaS, platform as a service, really it can enable success. And I think the pandemic is pretty obvious who's taking advantage of it, so congratulations and continued success. Thanks for coming on. Appreciate it. Rick Turnock, head of data service, enterprise data services at Invesco, customer of Informatica sharing his insight. Great insight there. Necessity is the mother of all inventions, baking it in from the beginning data governance foundational, it's not a bolt on, that's the message. I'm John Furrier with theCUBE. Thanks for watching. (soft music)

>> live from Las Vegas. It's the queue covering Inform Attica! World 2019. Brought to you by in from Attica. >> Welcome back, everyone to the cubes. Coverage of Infra Matic. A world here in Las Vegas. I'm your host, Rebecca Knight. I'm doing by two guests. For the segment we have Rick Tam Daniels. He is the VP. Strategic ecosystems and technology than from Attica. Welcome, Rick and Yoav. Enough! He is the VP product for Giga Space. Welcome >> to be here. >> So this is a fun segment. You are the winner of the infirm Attica World 2,019 Solution Expo Cloud and Innovation. I want to get to you in a second and hear all about Giga Space. But I want to start with you. Rick, talk a little bit about this award and about the genesis of it. Where did the idea come from? >> Yes, So one of the things we really wanted to do it in from Attica World this year is create address Some of the most important topics that the customers want to hear about. It's a cloud and I two of the hottest tops the industry every wants to know about it and We wanted to take a lot of our emerging partners there doing some very innovative things than from Attica technology and put them front center. So if you look at the Expo Hall floor right in the middle, we have this almost like an art gallery of all this cool innovation have going on around the inn from Attica. Technology on the idea was that we had attendees come in and actually review the solutions. They had to be really full demos for working demos. Andi could vote on the app. They could say what their favorites were, and the end result is happy announced. Giga Spaces are big winner. >> And so yeah, attendees would vote on the app and so get so big a space. Tell us about it. You're based in Israel. >> Yeah, so aren't is based in Israel or H Q is in New York. Basically, the biggest bass was we've been in the market for more than a decade, deployed like in the largest enterprise in the world. You like banks like Bank of America, like international. I ot like another electric largest airline, largest railway companies, and basically we provide the speed for the application and big data infer structures so they deploy, like real time use cases like fraud detection, economic pricing, predictive maintenance, all those different types of services that required the speed on the big data side. >> You're all about speed, >> all about spirit. If you need the speed, we're the provided for you. >> Well, that's that's very exciting. So talk a little bit about the conversations that you were having with some of the attendees. What kinds of questions were you getting? >> So I think a lot of customers, during customers of ours and informative are talking about the move from kind of historical analysis to more proactive, event driven analytics when you want to be able to instead of interact with the data you want today, one so and now you want to baby toe Dr Analytical on the moment as soon as it happened to provide it that burrito Theron your online processes and instead of kind of offline processes. So, for example, fraud detection, which is the most, is the example. You want to be able to 100 further analysis on on the payment of a soon as it happens and Emilie second level and not like a few seconds after the transaction was over. So it's again. We're talking about the speed. They're very to handle high or amount of data with related sub second response time. >> And how are you using in from Attica? >> Cool So well, We've been working lately with Informatica very tightly with both their product team, and there are in the team because Israel, India, the US, on integrating with some of their different products were basically we've built kind of what Gardner calls the digital integration hub. It's like the next Jan big data architecture, which provides you both. Informatica side that allows to ingest any type of data could be taxed logs, transaction payments, anything you have together with their medal, the meta data management and on top of it, using Giga spaces for the real Time analytics and the high performance in speed. >> So, Rick, I know that this was attendee chosen, so there's no rigging here, but I'd love to hear what your thoughts are in Giga Space in terms of the innovations that they're doing in these in these very important problems, like fraud detection and predictive maintenance, these air these air big problems. That company's heir really wrestling with. >> And I think what's exciting about the solution they had. It was a great business case, right? I think that really resonated. Attendees looking at Everyone can identify with Fraud Analytics. Everyone's unfortunately, probably on a victim of it, so they could see how it works. I think it also focuses on the aspect of a iva. How do operationalized a I? So is the whole model building piece of it, And Infra Matic has a strong player there as well. But now you say, Well, let's actually have the model we need to execute quickly. How do we do that? You know what the biggest spaces technology, but also combine it with the right historical context, right to make the right decisions. So they're really does hit on. How do you actually take a I and make it a real thing? >> And the other important part is the business case in what you were just saying in terms of if a if a customer is the victim of fraud here, she blames the institution, not the hacker on. And if there's a problem with with an airline maintenance problem, you blame the airline. Of course not the faulty problems that it was having. So so I think that that also really shows what what's in the future. What are you seeing? Kind of Mohr innovations that you want to add to the biggest space platform. So >> I think we're working to their lot about like Rick was mentioning about operationalize ing A. I so a lot of challenge today off moving from the research development training part of Day I or the machine anymore to move to production. Let's say you're a payment provider you have the more than you can detect fraud, but your ability for you to run it on millions of transactions a second in a sub lets a few millisecond level. That's the biggest challenge. And if you do it in there a few seconds after the transaction was over, then the you know the last of the fraud or the wire was already happened. So again, the operation was part of taking your more than formula that sound flat from putting in production with the scale of the ingestion rate low latest c you know, scaling on pick events like Black Friday or Cyber Monday. That's the biggest challenges on the production systems. >> Now the speed is of the essence. Rick, this has been a successful experiment trying this. What are you hearing from attendees? Did they like it where they sort of How do we Dad? Does this work? What is this about? >> I think they're really enjoyed it. Every time I look, I went over to the zone. It was full of people having deep conversations, really getting into the technology and understanding. Because as I mentioned these air topics that I think everyone came here to the show to really learn more about How are they going to get where they're going There, Cloud journey where they're going to go in there, eh? I journey. It's a great feedback from attendees. Lot of active participation. So I'm going >> to do it. We're going to see it in >> your batter. It's gonna be great. >> So now that you're the winner, you're going to be up there on the main stage, getting some recognition. That's exciting. What? What are you going to take back? Teo, I know you're based in both Israel and New York. What? What? What does this mean for your company? >> So I think the next step is taking it to the business side. Right? We want to make sure that the joint offering and the joy in partnership moves to the next stage taking it to the next customer. We have some joint customer. We have some new prospect. Were a lot of late from the show here, sitting next to me, sitting side by side with the other partners of Info Matic. I like data breaks and slow flaked and clothes are so we have a lot of joint offering and solving real time like business and off the largest, most challenging enterprise we have, like, you know, largest banks, largest airlines, largest like railways companies. So I think the next step is moving, taking it from the exhibition to the field. >> Great. Well, this is terrific. Congratulations. Once again. Really exciting. Really happy for you. Thanks so much for coming on the show. Thank you. You have been watching the cubes live coverage of in from Attica, World 2019. I'm Rebecca night. Stay tuned

>> Live from Las Vegas, it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Hey welcome back everyone, you're here live in Las Vegas for theCUBE, for Informatica World 2019. I'm John Furrier, co-host of theCUBE. We've got two great guests here from Snowflake. We've got Tarik Dwiek who's the Director of Technology Alliances at Snowflake, and Rik Tamm-Daniels, Vice President of Strategic Ecosystems and Technology at Informatica. Welcome back to theCUBE, good to see you guys. >> Good to see you as well. >> Thanks for coming on Snowflake. Congratulations, you guys are doing really well. >> Thank you. >> Big growth, new CEO, Frank Slootman, Informatica, The Data, Zar, Neutral Third Party, Switzerland, cloud, you've got Switzerland, what's the relationship, explain. >> Well, I think you know, it's funny that comment comes up a fair amount and yeah, I look at this way. It's not so much that you know, with Switzerland what we're focused on though is where customers are choosing to go in their journey, we want to provide them the best experience possible, right. So we end up going very deep in our strategic ecosystems, and Snowflakes is one of those partners that we've seen tremendous growth with, and customers are adopting, So, very excited about the partnership. >> How about your relationship with Informatica, Why are you here? What's the story? >> Yeah definitely, so at Snowflake, we put customers first, right? And as Rick mentioned, it's all about having a diverse ecosystem in the enterprise. Informatica is a leader. When you look at where customers are going with data, right? Obviously data integration is key. Data quality is key, data governance. All the areas that Informatica has been the best to breed in, it just makes sense for continued to make traction in these enterprise customers. >> Take a bit to explain the business model of Snowflake, what you guys do, quick one minute. >> Sure, so Snowflake's a data warehouse solution built from the ground up for the cloud. Why the distinction is important is because we're the only data warehouse born in the cloud. If you look at how the other solutions are doing it today, they're taking an architecture, an architecture created a decade ago for an on-premise world and they're just shifting into cloud. And the challenge that you have there is that you can't take full advantage of things like instant and infinite resources, both compute and storage, right? Independent scaling of computing storage. Elasticity right, the ability to scale up and down and out with a click of a button. And then even being able to support massive incurrence. Things like loading data at the same time that you're querying data. This is what Snowflake was built for. >> How about datasets from other people. That's one of the benefits of having data in the cloud. >> Correct, so our architecture is key. That's the key to our business and our product and what we've done is we separated compute from storage and we become a centralized database. And what we found by creating additional views, you can actually share your data with yourself and you can share with other customers. We've created this concept of data sharing. Data sharing has been around for decades, but it's been very painful. What we've done is created an online performant, secure way for customers to share the data. >> Rik this really highlights the value proposition for Informatica. I always say, you know, data is always, beauty of the data is in the eye of the beholder. Depending on where you're sitting in from. You could be on-premises, you have legacy, you could be born in the cloud and taking advantage of all that cloud stuff. Graham Thompson was on earlier he said, "Hey if you've got data in the cloud "why move it on premise?" So you know, there should be a choice of what's best. And that's what you guys come in. What specifically are you guys tying together with data warehouse in the cloud and and maybe a customer may want to choose to have for compliance reasons, or a viariety of other reasons on prem or another location. >> I think one of the big things about cloud data warehouses in particular, it's not all things being equal at the on-premise world, right? The level of agility you get with the Snowflake where it's infinite scale out, up in a few minutes. That empowers so much transformation in the organization. That's why it's so compelling, and so many folks are adopting it. And so what we're doing is we're helping customers on that journey though. Because they've got a very complex data environment and they got to first of all understand how's this all put together to be able to start modernizing moving to the cloud. >> I'm sorry if I asked the question where should a customer store their data; on the cloud or on-premise. I know where you'll come in on that. It's cloud all the way, because that's what you do. But this is something that architects in the enterprise have been dealing with because they do have legacy stuff. So and we've seen with the SAS business models, data has been really key for their success because it gives them risk-taking or, actually risk taking meaning they can do things, maybe testing to whatever. Test certain features on certain users. Basically use the data basically to create value. And then the upside of taking that risk is reward. You have more revenue, hockey stick growth and the numbers are pretty clear. Enterprises want that. >> They do. >> But they're not really set up for it. How do they get there? >> The best part with a SAS model is customers can de-risk by putting some of their data, for instance Snowflake, right? We work across AWS and Azure. So customers that maybe aren't all in yet on either cloud provider can start using Snowflake and put data in Snowflake and test it out. Test out the performance and the security of cloud. And if for whatever reason it doesn't work out they haven't risked very much if anything. And if it does work out then they've got a great proving ground for that. So the SAS opens up a lot of possibilities for enterprise customers. >> I brought this up with Graeme Connelly. You know, he's from Scotland so I understand his perspective. I'm from Silicon Valley so I took my perspective. I said you know, when I hear regulation I see you know, anti innovation, right? Like when I hear governments coming involved putting you know, regulation on things. We're seeing a very active regulatory environment on tech companies around data. GDPR one-year anniversary. This is a real issue. How do you turn that regulatory constraints around data, because what it means is more complexity around how to deal with the data. How do you turn that into an advantage. Obviously software abstraction certainly helps in tech, but customers are trying to move move faster with cloud. They can do that for all those reasons talked earlier. But now you got complexity around regulation. >> I think first off from a from a data warehouse perspective we were built with security and compliance in mind from day one, right? So you build in things like encryption, always-on encryption. You build things like role based access controls. Things like key management, right? And then when you think of Informatica within the data pipeline getting data from sources in and out of Snowflake, then you build additional data quality, data governance tools on top of that. Things like data catalog, right? Where you can, now just go discover what data you have out there, what data are you moving into the cloud, and what is the lineage of that data. >> Talk about this migration and movement because that becomes, people are generally skeptical when they hear migration like, oh my god migration. If they know it's going to cost some money or potentially technical risk. What's, how do you guys handle the migration in a way that's risk-free. >> I'll take that one. I'd say one of the things that we really put in front of all of our migration approaches for customers is the enterprise data catalog. And using the machine learning capabilities in the catalog to take what is a very complex landscape and make it very understandable accessible to the business. But then also understand how it's all put together. Where data's coming from, where it's going, who's consuming it. And once you have that view and that clarity of how things are put together it actually means you can take a use case based approach to adoption of the cloud and moving data. So you're actually realizing business value incrementally as you're moving. Which i think is really key right? if you do these massive multi-year projects and it takes a year to get any results it's not going to fly anymore, right? This is a much more agile world and so we're really empowering of that with the intelligence around data. >> Digital transformation has got three kind of categories we find when we poll people and do the research. You got the early adopters who have a full team they're cloud native, their jammin and their DevOps rockstars. They're kicking ass taking names. Then on the other end of spectrum you got you know, fear, oh my god, like I don't really have the talent. I'm going to do some, study it, spec it out, we got to figure it out. then you have people who are kind of like, you know, the fast followers, influenced kind of like focused. They tend to break down in the middle of projects. This seems to be the pattern. They get going and they get stuck in the mud. This is a real issue around culture and people. So I got to ask you, you know, a lot of these challenges around people and culture is huge skills gap. What is the biggest hiring skills gap that's needed to be filled so that people can be successful whether they're got a really rockstar team or smart team that just got to re-skill up. Or how do you take a project that's stuck in the mud and reboot it? These are challenges. >> I think when the nice things about Informatica is that you know, there's 100,000 folks out there who are familiar with Informatica's approach of implementations. So, by, you know, us bringing our technologies and embracing these journeys we're actually empowering customers to not have to get coders and data scientists. They're using some of those same data engineers but now they're bringing data to the cloud. >> And I think along the same lines we think of practitioners usually right? I need data scientists, I need more data engineers. I think a valuable asset that's that's becoming more clear now, is to have a new breed of data analyst, right? That understand how to put AI and machine learning together. How to start to grab all of the data that's out there for customers, right? Structured data, semi-structured data and make sure that they've got a single strategy along how to become data-driven. >> Give an example of some customers that you guys are working together with using Snowflake and Informatica. What are they, what are they doing? What's some of the use cases? What's some of the applications? >> Yeah so I think one of the biggest use cases is a data warehouse modernization, right? So you have the existing on-premise data warehouses. And I always like when I talk to customers think about, well realistically when you have a new use case on your on-premise warehouse. How long is it going to take you to actually see your first piece of data? I don't know a lot of people have extra capacity that's kind of hanging around in their warehouse right? We think about they have to make business cases, they have to get new Hardware, new licenses. It could take six months to see their first piece of data. So, you know I think it's a tremendous accelerator for them to go to the cloud. >> So the main thing there's agility. >> Yes, absolutely. >> Fast time to value. How's business with Snowflake? What's going on with you guys? What other use case you seeing besides the data warehouse. Modern data warehouse. >> Sure John, I can start with business in general. It's very exciting times at Snowflake right now. Late last year we got a funding round of $450 million for growth funding. Brings our total funding to just over $920 million. Our valuation doubled to 3.9 billion. That puts us in the top 25 highest valued private U.S. tech firms. Like I mentioned before we tripled the number of employees to over a thousand, across nine countries globally. We're going to expand to 20 or more in the next 12 months. And then in terms of my favorite part-- >> What's been the traction of that? Why this success? What's been the ah ha moment for customers with Snowflake? >> Yeah I think about what customers try and do in their data journey, there are probably three key things. Number one, they want to get access to all their data, right? And they want to do that in a very fast and economic way. They want to be able to get all the different variety of data that's out there. All the modern data types, right? Both the structured data, right? Their ERP is CRM systems, things about customers and product, and sales transactions, and then all this modern data, from web and social, from behavior data, from machine generate data in IOT. But they want to put all together. They don't want to have different, disparate systems to go and process this and try to bring back together today. That's been the challenge, is the complexity and the cost. And what we've done is start to remove those barriers. >> You know, I love the term now because I've hated it when it came out. Data Lake, during the Hadoop days we heard Data Lake. And then it turned into a data swamp. You start to see that get fixed a little bit. Because what people are afraid of is they're afraid of throwing all those data into a data swamp. They really want to get value out of it. This has been a hard thing the early days of Hadoop, but it was cool technically to be you know, putting Hadoop clusters together, and standing them up, but then it's like where's the value? >> I think the Data Lake concept in essence makes a lot of sense. Because you want to get all your data in one central place so you can ask these questions across all the different data types, and all different data sources. The challenge we had was you had the traditional data warehouse which couldn't support the new data types, and the diversity, just pure volume. And then you had newer no SQL like systems like Hadoop that could start to address just the sheer mass of data. But they were so complex that you needed an army, and you still do need an army, and then there's some limitations around performance, and other issues, and so no data projects we're making it into production. I think we still have a very small success rate when you think about data projects that actually make it to production. This is where with Snowflake, because we had the luxury to build it from the ground up, we saw the needs of both using a relational SQL database because SQL is still an amazing expressive language. People have invested skill sets and tools. And then be able to support the new semi-structured data types. All within the same system, right. All within SaaS model so you can start to remove complexity. it's self-managed. We have a self-managed SaaS offering, so customers don't have to worry about all the operational lifting. They can go and get inside to the data. And then because of the cloud they can take advantage of the elasticity in the scale and pay for what they use. >> What was the big bet on Snowflake that paid off. You had to kind of hone it down. >> But the biggest bet John was, we are architecting a database from scratch. Because if you look all the other solutions out there that get the fastest time to market is you can take an architecture that's been existing for a decade or so, and wrap it on a cloud. And that gets you some benefits of the cloud. For instance no need for upfront costs and implementing Hardware in the data center. You can offload some of the management and some of the maintenance to the cloud providers. But like I mentioned before you can't scale automatically. You can't take advantage of infinite scale, right? Because these systems were designed and on-premise role that had a thinking of finite resources. So I think our big bet was, do you create a new architecture. That's a big risk, but luckily it's paid off well. >> Big risk pay offs. Rik talk about the ecosystem. You guys have a big partner strategy. You have to. >> Yep. >> You guys are integrating integration points as comparing to you guys, not the sound like it's in a bad way but, Slack is going public so I'll use them as example. Slack is a software that's cloud-based but what made them really big besides, copying the message board kind of IRC chat, is that they have a huge integration points with all the key players that really fed that in. This is kind of something that in, as a metaphor is not directly directed to you guys but, you guys are very integration partner oriented. >> Yeah >> How is that playing out? Again, I'm sure this, I didn't see any strategy change still continuing. Give us the update, how's that going? It's a great example Snowflake here on theCUBE. This is core of Informatica. Take a minute to explain that strategy. >> Well I think the beginning of the journey of any of our ecosystem partners does start with the connectivity layer. But honestly you know, moving data from point A to point B. That's kind of, that's the tip of the iceberg, right? And so we've really focused on bringing really addressing all the challenges in the entire data journey. So it's one thing about first of all how do I even find the data to bring there. Now once I found it can I connect to it? Do I have the access to the data? Can I bring it to the right targets the customer wants consumed. But then once the data is there, is it usable, is it consumed, is it clean? If I'm doing customer 360, do I need to get my golden records? Or you mentioned GDPR, our whole data protection focus on, you know trying to create a perimeter between different parts of the enterprise, we're automatically applying masking encryption, those sorts of things. So we're really focused on integrating that as tightly as we can and making it seamless for customers to be able to tap into those capabilities when they need them. >> I mean feeding data to machine learning and then powering AI is a great example. If you don't have the right data at the right time for the machine learning, the AI doesn't work well. And then applications that are going to be using machine learning need to have access to data as fast as possible. Lag really hurts everything. This is a huge issue. >> Yeah I mean and we're looking at complete acceleration. You know that whole data discovery phase to build your models and train them. But to your point, garbage in garbage out, right? The old adage is still applicable today, and I think even but you've got security issues. What happens if your training data includes some sensitive code names that show up in your models all of a sudden, right? There's all these issues. But then you take it those models and operationalize them as well. Again, the inputs need to be clean, so. >> Cloud or on-premise, final word. Get your both take on it. Obviously your data warehouse in the cloud. For the customers that have an On-premise dynamic, whether it's legacy or whatever. I got to move to the cloud. I'm eventually going to have some cloud, and how it's going to look. What do they do? What's the State of the Union for dealing with data that's not just in the cloud. >> Yeah. >> Yeah >> You were first, go ahead. >> Yeah sure, I think again going back to having a SAS model, customers can pick specific project specific data sets to go and try out, right? Snowflake gives them a perfect example of, not even having to directly engage the cloud partner yet, right? They want to see if data can be ingested in the cloud in a very fast performant way. They want to see if security meets their needs, right? They want to test out all of the different things around management and ease of use. They can do that with Snowflake. Again, at a very low risk way. Because we are a SaaS platform. We've got a great model on elasticity. The customers can pay as they go just to try it out. So for me, when I think of these customers that are stuck there and trying to make a decision, I say look try Snowflake. It's a very risk-free way to start to analyze some data sets, and if it works for you then you've got a proof point of starting to move more and more workloads into the cloud. >> Rik, digital transformation. What are customers doing? What's the playbook? >> Yeah I think the recipe is, you know, one, the laser focus on value, right? Have you have your eyes on how am I going to get value as quickly as I can this transformation. Second thing is, understand what you have. Understand your existing landscape. That third piece is go. I get started, because I think the case for the cloud is so compelling for customers. I don't know a single customer that I talk with who is not already on the cloud journey. So it's really about making sure you get business value as you proceed down that journey. >> Get the proof points up front. >> Absolutely >> Think smaller steps >> Yep, incremental and casual >> Show the value. Sounds like agility DevOps. Guys thanks for coming on. Good to see you. It's Cube coverage here in Las Vegas, I'm John Furrier. Your host for theCube is Rebeca Night. Two days of wall-to-wall coverage. We'll back with more after this short break. (dramatic music)