(upbeat music) >> Hello, welcome back to theCUBE's AWS RE:Invent 2022 Coverage. I'm John Furrier, host of theCUBE. Got a great lineup here, Itamar Ankorion SVP Technology Alliance at Qlik and Peter McDonald, vice President, cloud partnerships and business development Snowflake. We're going to talk about bringing SAP data to life, for joint Snowflake, Qlik and AWS Solution. Gentlemen, thanks for coming on theCUBE Really appreciate it. >> Thank you. >> Thank you, great meeting you John. >> Just to get started, introduce yourselves to the audience, then going to jump into what you guys are doing together, unique relationship here, really compelling solution in cloud. Big story about applications and scale this year. Let's introduce yourselves. Peter, we'll start with you. >> Great. I'm Peter MacDonald. I am vice president of Cloud Partners and business development here at Snowflake. On the Cloud Partner side, that means I manage AWS relationship along with Microsoft and Google Cloud. What we do together in terms of complimentary products, GTM, co-selling, things like that. Importantly, working with other third parties like Qlik for joint solutions. On business development, it's negotiating custom commercial partnerships, large companies like Salesforce and Dell, smaller companies at most for our venture portfolio. >> Thanks Peter and hi John. It's great to be back here. So I'm Itamar Ankorion and I'm the senior vice president responsible for technology alliances here at Qlik. With that, own strategic alliances, including our key partners in the cloud, including Snowflake and AWS. I've been in the data and analytics enterprise software market for 20 plus years, and my main focus is product management, marketing, alliances, and business development. I joined Qlik about three and a half years ago through the acquisition of Attunity, which is now the foundation for Qlik data integration. So again, we focus in my team on creating joint solution alignment with our key partners to provide more value to our customers. >> Great to have both you guys, senior executives in the industry on theCUBE here, talking about data, obviously bringing SAP data to life is the theme of this segment, but this reinvent, it's all about the data, big data end-to-end story, a lot about data being intrinsic as the CEO says on stage around in the organizations in all aspects. Take a minute to explain what you guys are doing as from a company standpoint. Snowflake and Qlik and the solutions, why here at AWS? Peter, we'll start with you at Snowflake, what you guys do as a company, your mission, your focus. >> That was great, John. Yeah, so here at Snowflake, we focus on the data platform and until recently, data platforms required expensive on-prem hardware appliances. And despite all that expense, customers had capacity constraints, inexpensive maintenance, and had limited functionality that all impeded these organizations from reaching their goals. Snowflake is a cloud native SaaS platform, and we've become so successful because we've addressed these pain points and have other new special features. For example, securely sharing data across both the organization and the value chain without copying the data, support for new data types such as JSON and structured data, and also advance in database data governance. Snowflake integrates with complimentary AWS services and other partner products. So we can enable holistic solutions that include, for example, here, both Qlik and AWS SageMaker, and comprehend and bring those to joint customers. Our customers want to convert data into insights along with advanced analytics platforms in AI. That is how they make holistic data-driven solutions that will give them competitive advantage. With Snowflake, our approach is to focus on customer solutions that leverage data from existing systems such as SAP, wherever they are in the cloud or on-premise. And to do this, we leverage partners like Qlik native US to help customers transform their businesses. We provide customers with a premier data analytics platform as a result. Itamar, why don't you talk about Qlik a little bit and then we can dive into the specific SAP solution here and some trends >> Sounds great, Peter. So Qlik provides modern data integration and analytics software used by over 38,000 customers worldwide. Our focus is to help our customers turn data into value and help them close the gap between data all the way through insight and action. We offer click data integration and click data analytics. Click data integration helps to automate the data pipelines to deliver data to where they want to use them in real-time and make the data ready for analytics and then Qlik data analytics is a robust platform for analytics and business intelligence has been a leader in the Gartner Magic Quadrant for over 11 years now in the market. And both of these come together into what we call Qlik Cloud, which is our SaaS based platform. So providing a more seamless way to consume all these services and accelerate time to value with customer solutions. In terms of partnerships, both Snowflake and AWS are very strategic to us here at Qlik, so we have very comprehensive investment to ensure strong joint value proposition to we can bring to our mutual customers, everything from aligning our roadmaps through optimizing and validating integrations, collaborating on best practices, packaging joint solutions like the one we'll talk about today. And with that investment, we are an elite level, top level partner with Snowflake. We fly that our technology is Snowflake-ready across the entire product set and we have hundreds of joint customers together and with AWS we've also partnered for a long time. We're here to reinvent. We've been here with the first reinvent since the inaugural one, so it kind of gives you an idea for how long we've been working with AWS. We provide very comprehensive integration with AWS data analytics services, and we have several competencies ranging from data analytics to migration and modernization. So that's our focus and again, we're excited about working with Snowflake and AWS to bring solutions together to market. >> Well, I'm looking forward to unpacking the solutions specifically, and congratulations on the continued success of both your companies. We've been following them obviously for a very long time and seeing the platform evolve beyond just SaaS and a lot more going on in cloud these days, kind of next generation emerging. You know, we're seeing a lot of macro trends that are going to be powering some of the things we're going to get into real quickly. But before we get into the solution, what are some of those power dynamics in the industry that you're seeing in trends specifically that are impacting your customers that are taking us down this road of getting more out of the data and specifically the SAP, but in general trends and dynamics. What are you hearing from your customers? Why do they care? Why are they going down this road? Peter, we'll start with you. >> Yeah, I'll go ahead and start. Thanks. Yeah, I'd say we continue to see customers being, being very eager to transform their businesses and they know they need to leverage technology and data to do so. They're also increasingly depending upon the cloud to bring that agility, that elasticity, new functionality necessary to react in real-time to every evolving customer needs. You look at what's happened over the last three years, and boy, the macro environment customers, it's all changing so fast. With our partnerships with AWS and Qlik, we've been able to bring to market innovative solutions like the one we're announcing today that spans all three companies. It provides a holistic solution and an integrated solution for our customer. >> Itamar let's get into it, you've been with theCUBE, you've seen the journey, you have your own journey, many, many years, you've seen the waves. What's going on now? I mean, what's the big wave? What's the dynamic powering this trend? >> Yeah, in a nutshell I'll call it, it's all about time. You know, it's time to value and it's about real-time data. I'll kind of talk about that a bit. So, I mean, you hear a lot about the data being the new oil, but it's definitely, we see more and more customers seeing data as their critical enabler for innovation and digital transformation. They look for ways to monetize data. They look as the data as the way in which they can innovate and bring different value to the customers. So we see customers want to use more data so to get more value from data. We definitely see them wanting to do it faster, right, than before. And we definitely see them looking for agility and automation as ways to accelerate time to value, and also reduce overall costs. I did mention real-time data, so we definitely see more and more customers, they want to be able to act and make decisions based on fresh data. So yesterday's data is just not good enough. >> John: Yeah. >> It's got to be down to the hour, down to the minutes and sometimes even lower than that. And then I think we're also seeing customers look to their core business systems where they have a lot of value, like the SAP, like mainframe and thinking, okay, our core data is there, how can we get more value from this data? So that's key things we see all the time with customers. >> Yeah, we did a big editorial segment this year on, we called data as code. Data as code is kind of a riff on infrastructure as code and you start to see data becoming proliferating into all aspects, fresh data. It's not just where you store it, it's how you share it, it's how you turn it into an application intrinsically involved in all aspects. This is the big theme this year and that's driving all the conversations here at RE:Invent. And I'm guaranteeing you, it's going to happen for another five and 10 years. It's not stopping. So I got to get into the solution, you guys mentioned SAP and you've announced the solution by Qlik, Snowflake and AWS for your customers using SAP. Can you share more about this solution? What's unique about it? Why is it important and why now? Peter, Itamar, we'll start with you first. >> Let me jump in, this is really, I'll jump because I'm excited. We're very excited about this solution and it's also a solution by the way and again, we've seen proven customer success with it. So to your point, it's ready to scale, it's starting, I think we're going to see a lot of companies doing this over the next few years. But before we jump to the solution, let me maybe take a few minutes just to clarify the need, why we're seeing, why we're seeing customers jump to do this. So customers that use SAP, they use it to manage the core of their business. So think order processing, management, finance, inventory, supply chain, and so much more. So if you're running SAP in your company, that data creates a great opportunity for you to drive innovation and modernization. So what we see customers want to do, they want to do more with their data and more means they want to take SAP with non-SAP data and use it together to drive new insights. They want to use real-time data to drive real-time analytics, which they couldn't do to date. They want to bring together descriptive with predictive analytics. So adding machine learning in AI to drive more value from the data. And naturally they want to do it faster. So find ways to iterate faster on their solutions, have freedom with the data and agility. And I think this is really where cloud data platforms like Snowflake and AWS, you know, bring that value to be able to drive that. Now to do that you need to unlock the SAP data, which is a lot of also where Qlik comes in because typical challenges these customers run into is the complexity, inherent in SAP data. Tens of thousands of tables, proprietary formats, complex data models, licensing restrictions, and more than, you have performance issues, they usually run into how do we handle the throughput, the volumes while maintaining lower latency and impact. Where do we find knowledge to really understand how to get all this done? So these are the things we've looked at when we came together to create a solution and make it unique. So when you think about its uniqueness, because we put together a lot, and I'll go through three, four key things that come together to make this unique. First is about data delivery. How do you have the SAP data delivery? So how do you get it from ECC, from HANA from S/4HANA, how do you deliver the data and the metadata and how that integration well into Snowflake. And what we've done is we've focused a lot on optimizing that process and the continuous ingestion, so the real-time ingestion of the data in a way that works really well with the Snowflake system, data cloud. Second thing is we looked at SAP data transformation, so once the data arrives at Snowflake, how do we turn it into being analytics ready? So that's where data transformation and data worth automation come in. And these are all elements of this solution. So creating derivative datasets, creating data marts, and all of that is done by again, creating an optimized integration that pushes down SQL based transformations, so they can be processed inside Snowflake, leveraging its powerful engine. And then the third element is bringing together data visualization analytics that can also take all the data now that in organizing inside Snowflake, bring other data in, bring machine learning from SageMaker, and then you go to create a seamless integration to bring analytic applications to life. So these are all things we put together in the solution. And maybe the last point is we actually took the next step with this and we created something we refer to as solution accelerators, which we're really, really keen about. Think about this as prepackaged templates for common business analytic needs like order to cash, finance, inventory. And we can either dig into that a little more later, but this gets the next level of value to the customers all built into this joint solution. >> Yeah, I want to get to the accelerators, but real quick, Peter, your reaction to the solution, what's unique about it? And obviously Snowflake, we've been seeing the progression data applications, more developers developing on top of Snowflake, data as code kind of implies developer ecosystem. This is kind of interesting. I mean, you got partnering with Qlik and AWS, it's kind of a developer-like thinking real solution. What's unique about this SAP solution that's, that's different than what customers can get anywhere else or not? >> Yeah, well listen, I think first of all, you have to start with the idea of the solution. This are three companies coming together to build a holistic solution that is all about, you know, creating a great opportunity to turn SAP data into value this is Itamar was talking about, that's really what we're talking about here and there's a lot of technology underneath it. I'll talk more about the Snowflake technology, what's involved here, and then cover some of the AWS pieces as well. But you know, we're focusing on getting that value out and accelerating time to value for our joint customers. As Itamar was saying, you know, there's a lot of complexity with the SAP data and a lot of value there. How can we manage that in a prepackaged way, bringing together best of breed solutions with proven capabilities and bringing this to market quickly for our joint customers. You know, Snowflake and AWS have been strong partners for a number of years now, and that's not only on how Snowflake runs on top of AWS, but also how we integrate with their complementary analytics and then all products. And so, you know, we want to be able to leverage those in addition to what Qlik is bringing in terms of the data transformations, bringing data out of SAP in the visualization as well. All very critical. And then we want to bring in the predictive analytics, AWS brings and what Sage brings. We'll talk about that a little bit later on. Some of the technologies that we're leveraging are some of our latest cutting edge technologies that really make things easier for both our partners and our customers. For example, Qlik leverages Snowflakes recently released Snowpark for Python functionality to push down those data transformations from clicking the Snowflake that Itamar's mentioning. And while we also leverage Snowpark for integrations with Amazon SageMaker, but there's a lot of great new technology that just makes this easy and compelling for customers. >> I think that's the big word, easy button here for what may look like a complex kind of integration, kind of turnkey, really, really compelling example of the modern era we're living in, as we always say in theCUBE. You mentioned accelerators, SAP accelerators. Can you give an example of how that works with the technology from the third party providers to deliver this business value Itamar, 'cause that was an interesting comment. What's the example? Give an example of this acceleration. >> Yes, certainly. I think this is something that really makes this truly, truly unique in the industry and again, a great opportunity for customers. So we kind talked earlier about there's a lot of things that need to be done with SP data to turn it to value. And these accelerator, as the name suggests, are designed to do just that, to kind of jumpstart the process and reduce the time and the risk involved in such project. So again, these are pre-packaged templates. We basically took a lot of knowledge, and a lot of configurations, best practices about to get things done and we put 'em together. So think about all the steps, it includes things like data extraction, so already knowing which tables, all the relevant tables that you need to get data from in the contexts of the solution you're looking for, say like order to cash, we'll get back to that one. How do you continuously deliver that data into Snowflake in an in efficient manner, handling things like data type mappings, metadata naming conventions and transformations. The data models you build all the way to data mart definitions and all the transformations that the data needs to go through moving through steps until it's fully analytics ready. And then on top of that, even adding a library of comprehensive analytic dashboards and integrations through machine learning and AI and put all of that in a way that's in pre-integrated and tested to work with Snowflake and AWS. So this is where again, you get this entire recipe that's ready. So take for example, I think I mentioned order to cash. So again, all these things I just talked about, I mean, for those who are not familiar, I mean order to cash is a critical business process for every organization. So especially if you're in retail, manufacturing, enterprise, it's a big... This is where, you know, starting with booking a sales order, following by fulfilling the order, billing the customer, then managing the accounts receivable when the customer actually pays, right? So this all process, you got sales order fulfillment and the billing impacts customer satisfaction, you got receivable payments, you know, the impact's working capital, cash liquidity. So again, as a result this order to cash process is a lifeblood for many businesses and it's critical to optimize and understand. So the solution accelerator we created specifically for order to cash takes care of understanding all these aspects and the data that needs to come with it. So everything we outline before to make the data available in Snowflake in a way that's really useful for downstream analytics, along with dashboards that are already common for that, for that use case. So again, this enables customers to gain real-time visibility into their sales orders, fulfillment, accounts receivable performance. That's what the Excel's are all about. And very similarly, we have another one for example, for finance analytics, right? So this will optimize financial data reporting, helps customers get insights into P&L, financial risk of stability or inventory analytics that helps with, you know, improve planning and inventory management, utilization, increased efficiencies, you know, so in supply chain. So again, these accelerators really help customers get a jumpstart and move faster with their solutions. >> Peter, this is the easy button we just talked about, getting things going, you know, get the ball rolling, get some acceleration. Big part of this are the three companies coming together doing this. >> Yeah, and to build on what Itamar just said that the SAP data obviously has tremendous value. Those sales orders, distribution data, financial data, bringing that into Snowflake makes it easily accessible, but also it enables it to be combined with other data too, is one of the things that Snowflake does so well. So you can get a full view of the end-to-end process and the business overall. You know, for example, I'll just take one, you know, one example that, that may not come to mind right away, but you know, looking at the impact of weather conditions on supply chain logistics is relevant and material and have interest to our customers. How do you bring those different data sets together in an easy way, bringing the data out of SAP, bringing maybe other data out of other systems through Qlik or through Snowflake, directly bringing data in from our data marketplace and bring that all together to make it work. You know, fundamentally organizational silos and the data fragmentation exist otherwise make it really difficult to drive modern analytics projects. And that in turn limits the value that our customers are getting from SAP data and these other data sets. We want to enable that and unleash. >> Yeah, time for value. This is great stuff. Itamar final question, you know, what are customers using this? What do you have? I'm sure you have customers examples already using the solution. Can you share kind of what these examples look like in the use cases and the value? >> Oh yeah, absolutely. Thank you. Happy to. We have customers across different, different sectors. You see manufacturing, retail, energy, oil and gas, CPG. So again, customers in those segments, typically sectors typically have SAP. So we have customers in all of them. A great example is like Siemens Energy. Siemens Energy is a global provider of gas par services. You know, over what, 28 billion, 30 billion in revenue. 90,000 employees. They operate globally in over 90 countries. So they've used SAP HANA as a core system, so it's running on premises, multiple locations around the world. And what they were looking for is a way to bring all these data together so they can innovate with it. And the thing is, Peter mentioned earlier, not just the SAP data, but also bring other data from other systems to bring it together for more value. That includes finance data, these logistics data, these customer CRM data. So they bring data from over 20 different SAP systems. Okay, with Qlik data integration, feeding that into Snowflake in under 20 minutes, 24/7, 365, you know, days a year. Okay, they get data from over 20,000 tables, you know, over million, hundreds of millions of records daily going in. So it is a great example of the type of scale, scalability, agility and speed that they can get to drive these kind of innovation. So that's a great example with Siemens. You know, another one comes to mind is a global manufacturer. Very similar scenario, but you know, they're using it for real-time executive reporting. So it's more like feasibility to the production data as well as for financial analytics. So think, think, think about everything from audit to texts to innovate financial intelligence because all the data's coming from SAP. >> It's a great time to be in the data business again. It keeps getting better and better. There's more data coming. It's not stopping, you know, it's growing so fast, it keeps coming. Every year, it's the same story, Peter. It's like, doesn't stop coming. As we wrap up here, let's just get customers some information on how to get started. I mean, obviously you're starting to see the accelerators, it's a great program there. What a great partnership between the two companies and AWS. How can customers get started to learn about the solution and take advantage of it, getting more out of their SAP data, Peter? >> Yeah, I think the first place to go to is talk to Snowflake, talk to AWS, talk to our account executives that are assigned to your account. Reach out to them and they will be able to educate you on the solution. We have packages up very nicely and can be deployed very, very quickly. >> Well gentlemen, thank you so much for coming on. Appreciate the conversation. Great overview of the partnership between, you know, Snowflake and Qlik and AWS on a joint solution. You know, getting more out of the SAP data. It's really kind of a key, key solution, bringing SAP data to life. Thanks for coming on theCUBE. Appreciate it. >> Thank you. >> Thank you John. >> Okay, this is theCUBE coverage here at RE:Invent 2022. I'm John Furrier, your host of theCUBE. Thanks for watching. (upbeat music)

(upbeat music) >> Hello, welcome back to theCUBE's AWS RE:Invent 2022 Coverage. I'm John Furrier, host of theCUBE. Got a great lineup here, Itamar Ankorion SVP Technology Alliance at Qlik and Peter McDonald, vice President, cloud partnerships and business development Snowflake. We're going to talk about bringing SAP data to life, for joint Snowflake, Qlik and AWS Solution. Gentlemen, thanks for coming on theCUBE Really appreciate it. >> Thank you. >> Thank you, great meeting you John. >> Just to get started, introduce yourselves to the audience, then going to jump into what you guys are doing together, unique relationship here, really compelling solution in cloud. Big story about applications and scale this year. Let's introduce yourselves. Peter, we'll start with you. >> Great. I'm Peter MacDonald. I am vice president of Cloud Partners and business development here at Snowflake. On the Cloud Partner side, that means I manage AWS relationship along with Microsoft and Google Cloud. What we do together in terms of complimentary products, GTM, co-selling, things like that. Importantly, working with other third parties like Qlik for joint solutions. On business development, it's negotiating custom commercial partnerships, large companies like Salesforce and Dell, smaller companies at most for our venture portfolio. >> Thanks Peter and hi John. It's great to be back here. So I'm Itamar Ankorion and I'm the senior vice president responsible for technology alliances here at Qlik. With that, own strategic alliances, including our key partners in the cloud, including Snowflake and AWS. I've been in the data and analytics enterprise software market for 20 plus years, and my main focus is product management, marketing, alliances, and business development. I joined Qlik about three and a half years ago through the acquisition of Attunity, which is now the foundation for Qlik data integration. So again, we focus in my team on creating joint solution alignment with our key partners to provide more value to our customers. >> Great to have both you guys, senior executives in the industry on theCUBE here, talking about data, obviously bringing SAP data to life is the theme of this segment, but this reinvent, it's all about the data, big data end-to-end story, a lot about data being intrinsic as the CEO says on stage around in the organizations in all aspects. Take a minute to explain what you guys are doing as from a company standpoint. Snowflake and Qlik and the solutions, why here at AWS? Peter, we'll start with you at Snowflake, what you guys do as a company, your mission, your focus. >> That was great, John. Yeah, so here at Snowflake, we focus on the data platform and until recently, data platforms required expensive on-prem hardware appliances. And despite all that expense, customers had capacity constraints, inexpensive maintenance, and had limited functionality that all impeded these organizations from reaching their goals. Snowflake is a cloud native SaaS platform, and we've become so successful because we've addressed these pain points and have other new special features. For example, securely sharing data across both the organization and the value chain without copying the data, support for new data types such as JSON and structured data, and also advance in database data governance. Snowflake integrates with complimentary AWS services and other partner products. So we can enable holistic solutions that include, for example, here, both Qlik and AWS SageMaker, and comprehend and bring those to joint customers. Our customers want to convert data into insights along with advanced analytics platforms in AI. That is how they make holistic data-driven solutions that will give them competitive advantage. With Snowflake, our approach is to focus on customer solutions that leverage data from existing systems such as SAP, wherever they are in the cloud or on-premise. And to do this, we leverage partners like Qlik native US to help customers transform their businesses. We provide customers with a premier data analytics platform as a result. Itamar, why don't you talk about Qlik a little bit and then we can dive into the specific SAP solution here and some trends >> Sounds great, Peter. So Qlik provides modern data integration and analytics software used by over 38,000 customers worldwide. Our focus is to help our customers turn data into value and help them close the gap between data all the way through insight and action. We offer click data integration and click data analytics. Click data integration helps to automate the data pipelines to deliver data to where they want to use them in real-time and make the data ready for analytics and then Qlik data analytics is a robust platform for analytics and business intelligence has been a leader in the Gartner Magic Quadrant for over 11 years now in the market. And both of these come together into what we call Qlik Cloud, which is our SaaS based platform. So providing a more seamless way to consume all these services and accelerate time to value with customer solutions. In terms of partnerships, both Snowflake and AWS are very strategic to us here at Qlik, so we have very comprehensive investment to ensure strong joint value proposition to we can bring to our mutual customers, everything from aligning our roadmaps through optimizing and validating integrations, collaborating on best practices, packaging joint solutions like the one we'll talk about today. And with that investment, we are an elite level, top level partner with Snowflake. We fly that our technology is Snowflake-ready across the entire product set and we have hundreds of joint customers together and with AWS we've also partnered for a long time. We're here to reinvent. We've been here with the first reinvent since the inaugural one, so it kind of gives you an idea for how long we've been working with AWS. We provide very comprehensive integration with AWS data analytics services, and we have several competencies ranging from data analytics to migration and modernization. So that's our focus and again, we're excited about working with Snowflake and AWS to bring solutions together to market. >> Well, I'm looking forward to unpacking the solutions specifically, and congratulations on the continued success of both your companies. We've been following them obviously for a very long time and seeing the platform evolve beyond just SaaS and a lot more going on in cloud these days, kind of next generation emerging. You know, we're seeing a lot of macro trends that are going to be powering some of the things we're going to get into real quickly. But before we get into the solution, what are some of those power dynamics in the industry that you're seeing in trends specifically that are impacting your customers that are taking us down this road of getting more out of the data and specifically the SAP, but in general trends and dynamics. What are you hearing from your customers? Why do they care? Why are they going down this road? Peter, we'll start with you. >> Yeah, I'll go ahead and start. Thanks. Yeah, I'd say we continue to see customers being, being very eager to transform their businesses and they know they need to leverage technology and data to do so. They're also increasingly depending upon the cloud to bring that agility, that elasticity, new functionality necessary to react in real-time to every evolving customer needs. You look at what's happened over the last three years, and boy, the macro environment customers, it's all changing so fast. With our partnerships with AWS and Qlik, we've been able to bring to market innovative solutions like the one we're announcing today that spans all three companies. It provides a holistic solution and an integrated solution for our customer. >> Itamar let's get into it, you've been with theCUBE, you've seen the journey, you have your own journey, many, many years, you've seen the waves. What's going on now? I mean, what's the big wave? What's the dynamic powering this trend? >> Yeah, in a nutshell I'll call it, it's all about time. You know, it's time to value and it's about real-time data. I'll kind of talk about that a bit. So, I mean, you hear a lot about the data being the new oil, but it's definitely, we see more and more customers seeing data as their critical enabler for innovation and digital transformation. They look for ways to monetize data. They look as the data as the way in which they can innovate and bring different value to the customers. So we see customers want to use more data so to get more value from data. We definitely see them wanting to do it faster, right, than before. And we definitely see them looking for agility and automation as ways to accelerate time to value, and also reduce overall costs. I did mention real-time data, so we definitely see more and more customers, they want to be able to act and make decisions based on fresh data. So yesterday's data is just not good enough. >> John: Yeah. >> It's got to be down to the hour, down to the minutes and sometimes even lower than that. And then I think we're also seeing customers look to their core business systems where they have a lot of value, like the SAP, like mainframe and thinking, okay, our core data is there, how can we get more value from this data? So that's key things we see all the time with customers. >> Yeah, we did a big editorial segment this year on, we called data as code. Data as code is kind of a riff on infrastructure as code and you start to see data becoming proliferating into all aspects, fresh data. It's not just where you store it, it's how you share it, it's how you turn it into an application intrinsically involved in all aspects. This is the big theme this year and that's driving all the conversations here at RE:Invent. And I'm guaranteeing you, it's going to happen for another five and 10 years. It's not stopping. So I got to get into the solution, you guys mentioned SAP and you've announced the solution by Qlik, Snowflake and AWS for your customers using SAP. Can you share more about this solution? What's unique about it? Why is it important and why now? Peter, Itamar, we'll start with you first. >> Let me jump in, this is really, I'll jump because I'm excited. We're very excited about this solution and it's also a solution by the way and again, we've seen proven customer success with it. So to your point, it's ready to scale, it's starting, I think we're going to see a lot of companies doing this over the next few years. But before we jump to the solution, let me maybe take a few minutes just to clarify the need, why we're seeing, why we're seeing customers jump to do this. So customers that use SAP, they use it to manage the core of their business. So think order processing, management, finance, inventory, supply chain, and so much more. So if you're running SAP in your company, that data creates a great opportunity for you to drive innovation and modernization. So what we see customers want to do, they want to do more with their data and more means they want to take SAP with non-SAP data and use it together to drive new insights. They want to use real-time data to drive real-time analytics, which they couldn't do to date. They want to bring together descriptive with predictive analytics. So adding machine learning in AI to drive more value from the data. And naturally they want to do it faster. So find ways to iterate faster on their solutions, have freedom with the data and agility. And I think this is really where cloud data platforms like Snowflake and AWS, you know, bring that value to be able to drive that. Now to do that you need to unlock the SAP data, which is a lot of also where Qlik comes in because typical challenges these customers run into is the complexity, inherent in SAP data. Tens of thousands of tables, proprietary formats, complex data models, licensing restrictions, and more than, you have performance issues, they usually run into how do we handle the throughput, the volumes while maintaining lower latency and impact. Where do we find knowledge to really understand how to get all this done? So these are the things we've looked at when we came together to create a solution and make it unique. So when you think about its uniqueness, because we put together a lot, and I'll go through three, four key things that come together to make this unique. First is about data delivery. How do you have the SAP data delivery? So how do you get it from ECC, from HANA from S/4HANA, how do you deliver the data and the metadata and how that integration well into Snowflake. And what we've done is we've focused a lot on optimizing that process and the continuous ingestion, so the real-time ingestion of the data in a way that works really well with the Snowflake system, data cloud. Second thing is we looked at SAP data transformation, so once the data arrives at Snowflake, how do we turn it into being analytics ready? So that's where data transformation and data worth automation come in. And these are all elements of this solution. So creating derivative datasets, creating data marts, and all of that is done by again, creating an optimized integration that pushes down SQL based transformations, so they can be processed inside Snowflake, leveraging its powerful engine. And then the third element is bringing together data visualization analytics that can also take all the data now that in organizing inside Snowflake, bring other data in, bring machine learning from SageMaker, and then you go to create a seamless integration to bring analytic applications to life. So these are all things we put together in the solution. And maybe the last point is we actually took the next step with this and we created something we refer to as solution accelerators, which we're really, really keen about. Think about this as prepackaged templates for common business analytic needs like order to cash, finance, inventory. And we can either dig into that a little more later, but this gets the next level of value to the customers all built into this joint solution. >> Yeah, I want to get to the accelerators, but real quick, Peter, your reaction to the solution, what's unique about it? And obviously Snowflake, we've been seeing the progression data applications, more developers developing on top of Snowflake, data as code kind of implies developer ecosystem. This is kind of interesting. I mean, you got partnering with Qlik and AWS, it's kind of a developer-like thinking real solution. What's unique about this SAP solution that's, that's different than what customers can get anywhere else or not? >> Yeah, well listen, I think first of all, you have to start with the idea of the solution. This are three companies coming together to build a holistic solution that is all about, you know, creating a great opportunity to turn SAP data into value this is Itamar was talking about, that's really what we're talking about here and there's a lot of technology underneath it. I'll talk more about the Snowflake technology, what's involved here, and then cover some of the AWS pieces as well. But you know, we're focusing on getting that value out and accelerating time to value for our joint customers. As Itamar was saying, you know, there's a lot of complexity with the SAP data and a lot of value there. How can we manage that in a prepackaged way, bringing together best of breed solutions with proven capabilities and bringing this to market quickly for our joint customers. You know, Snowflake and AWS have been strong partners for a number of years now, and that's not only on how Snowflake runs on top of AWS, but also how we integrate with their complementary analytics and then all products. And so, you know, we want to be able to leverage those in addition to what Qlik is bringing in terms of the data transformations, bringing data out of SAP in the visualization as well. All very critical. And then we want to bring in the predictive analytics, AWS brings and what Sage brings. We'll talk about that a little bit later on. Some of the technologies that we're leveraging are some of our latest cutting edge technologies that really make things easier for both our partners and our customers. For example, Qlik leverages Snowflakes recently released Snowpark for Python functionality to push down those data transformations from clicking the Snowflake that Itamar's mentioning. And while we also leverage Snowpark for integrations with Amazon SageMaker, but there's a lot of great new technology that just makes this easy and compelling for customers. >> I think that's the big word, easy button here for what may look like a complex kind of integration, kind of turnkey, really, really compelling example of the modern era we're living in, as we always say in theCUBE. You mentioned accelerators, SAP accelerators. Can you give an example of how that works with the technology from the third party providers to deliver this business value Itamar, 'cause that was an interesting comment. What's the example? Give an example of this acceleration. >> Yes, certainly. I think this is something that really makes this truly, truly unique in the industry and again, a great opportunity for customers. So we kind talked earlier about there's a lot of things that need to be done with SP data to turn it to value. And these accelerator, as the name suggests, are designed to do just that, to kind of jumpstart the process and reduce the time and the risk involved in such project. So again, these are pre-packaged templates. We basically took a lot of knowledge, and a lot of configurations, best practices about to get things done and we put 'em together. So think about all the steps, it includes things like data extraction, so already knowing which tables, all the relevant tables that you need to get data from in the contexts of the solution you're looking for, say like order to cash, we'll get back to that one. How do you continuously deliver that data into Snowflake in an in efficient manner, handling things like data type mappings, metadata naming conventions and transformations. The data models you build all the way to data mart definitions and all the transformations that the data needs to go through moving through steps until it's fully analytics ready. And then on top of that, even adding a library of comprehensive analytic dashboards and integrations through machine learning and AI and put all of that in a way that's in pre-integrated and tested to work with Snowflake and AWS. So this is where again, you get this entire recipe that's ready. So take for example, I think I mentioned order to cash. So again, all these things I just talked about, I mean, for those who are not familiar, I mean order to cash is a critical business process for every organization. So especially if you're in retail, manufacturing, enterprise, it's a big... This is where, you know, starting with booking a sales order, following by fulfilling the order, billing the customer, then managing the accounts receivable when the customer actually pays, right? So this all process, you got sales order fulfillment and the billing impacts customer satisfaction, you got receivable payments, you know, the impact's working capital, cash liquidity. So again, as a result this order to cash process is a lifeblood for many businesses and it's critical to optimize and understand. So the solution accelerator we created specifically for order to cash takes care of understanding all these aspects and the data that needs to come with it. So everything we outline before to make the data available in Snowflake in a way that's really useful for downstream analytics, along with dashboards that are already common for that, for that use case. So again, this enables customers to gain real-time visibility into their sales orders, fulfillment, accounts receivable performance. That's what the Excel's are all about. And very similarly, we have another one for example, for finance analytics, right? So this will optimize financial data reporting, helps customers get insights into P&L, financial risk of stability or inventory analytics that helps with, you know, improve planning and inventory management, utilization, increased efficiencies, you know, so in supply chain. So again, these accelerators really help customers get a jumpstart and move faster with their solutions. >> Peter, this is the easy button we just talked about, getting things going, you know, get the ball rolling, get some acceleration. Big part of this are the three companies coming together doing this. >> Yeah, and to build on what Itamar just said that the SAP data obviously has tremendous value. Those sales orders, distribution data, financial data, bringing that into Snowflake makes it easily accessible, but also it enables it to be combined with other data too, is one of the things that Snowflake does so well. So you can get a full view of the end-to-end process and the business overall. You know, for example, I'll just take one, you know, one example that, that may not come to mind right away, but you know, looking at the impact of weather conditions on supply chain logistics is relevant and material and have interest to our customers. How do you bring those different data sets together in an easy way, bringing the data out of SAP, bringing maybe other data out of other systems through Qlik or through Snowflake, directly bringing data in from our data marketplace and bring that all together to make it work. You know, fundamentally organizational silos and the data fragmentation exist otherwise make it really difficult to drive modern analytics projects. And that in turn limits the value that our customers are getting from SAP data and these other data sets. We want to enable that and unleash. >> Yeah, time for value. This is great stuff. Itamar final question, you know, what are customers using this? What do you have? I'm sure you have customers examples already using the solution. Can you share kind of what these examples look like in the use cases and the value? >> Oh yeah, absolutely. Thank you. Happy to. We have customers across different, different sectors. You see manufacturing, retail, energy, oil and gas, CPG. So again, customers in those segments, typically sectors typically have SAP. So we have customers in all of them. A great example is like Siemens Energy. Siemens Energy is a global provider of gas par services. You know, over what, 28 billion, 30 billion in revenue. 90,000 employees. They operate globally in over 90 countries. So they've used SAP HANA as a core system, so it's running on premises, multiple locations around the world. And what they were looking for is a way to bring all these data together so they can innovate with it. And the thing is, Peter mentioned earlier, not just the SAP data, but also bring other data from other systems to bring it together for more value. That includes finance data, these logistics data, these customer CRM data. So they bring data from over 20 different SAP systems. Okay, with Qlik data integration, feeding that into Snowflake in under 20 minutes, 24/7, 365, you know, days a year. Okay, they get data from over 20,000 tables, you know, over million, hundreds of millions of records daily going in. So it is a great example of the type of scale, scalability, agility and speed that they can get to drive these kind of innovation. So that's a great example with Siemens. You know, another one comes to mind is a global manufacturer. Very similar scenario, but you know, they're using it for real-time executive reporting. So it's more like feasibility to the production data as well as for financial analytics. So think, think, think about everything from audit to texts to innovate financial intelligence because all the data's coming from SAP. >> It's a great time to be in the data business again. It keeps getting better and better. There's more data coming. It's not stopping, you know, it's growing so fast, it keeps coming. Every year, it's the same story, Peter. It's like, doesn't stop coming. As we wrap up here, let's just get customers some information on how to get started. I mean, obviously you're starting to see the accelerators, it's a great program there. What a great partnership between the two companies and AWS. How can customers get started to learn about the solution and take advantage of it, getting more out of their SAP data, Peter? >> Yeah, I think the first place to go to is talk to Snowflake, talk to AWS, talk to our account executives that are assigned to your account. Reach out to them and they will be able to educate you on the solution. We have packages up very nicely and can be deployed very, very quickly. >> Well gentlemen, thank you so much for coming on. Appreciate the conversation. Great overview of the partnership between, you know, Snowflake and Qlik and AWS on a joint solution. You know, getting more out of the SAP data. It's really kind of a key, key solution, bringing SAP data to life. Thanks for coming on theCUBE. Appreciate it. >> Thank you. >> Thank you John. >> Okay, this is theCUBE coverage here at RE:Invent 2022. I'm John Furrier, your host of theCUBE. Thanks for watching. (upbeat music)

(upbeat music) (upbeat music) (upbeat music) >> Welcome back to Las Vegas. Lisa Martin here, with Dave Vellante. We are covering day two of our coverage of Snowflake Summit '22. of Snowflake Summit '22. It's been a cannon of content coming your way, the last couple of days. We love talking with customers, with partners. We've got a partner on the program from ThoughtSpot. We're going to be diving into digital transformation with self-service analytics for the modern data stack. Please welcome Kuntal Vahalia, SVP of Channel and Alliances at ThoughtSpot. Welcome Kuntal. >> Thank you, Lisa. Dave, thank you for having us. >> Dave: Good to see you. >> Talk to the audience a little bit about ThoughtSpot. Give 'em an overview, and then de dive into the partnership with Snowflake. >> Yeah, absolutely. So ThoughtSpot is the, what we call live analytics, for the modern data stack, right? We want to be the experience layer for all the data that's getting modernized and moving into the cloud, right? And then specifically to Snowflake, we, of course, we have seen over the last two days here Snowflake has made tremendous innovations where they've accelerated a customer's journey into the cloud, especially the data cloud. Our job is to go really unlock that data, right? Generate that value, make it consumable at the at the experience level layer, right? So what we want to do here with Snowflake is here with Snowflake is make analytics self service for the end users, for the end users, on top of the Snowflake data cloud, right? And we want to empower everyone to create, consume, and operationalize data driven insights. We think if the end users can gender their own insights through live analytics, we could do have a completely different operating model for a business, right? And I think we can do that in accelerated fashion on, sitting on top of Snowflake data cloud. >> End users? Lines of business? >> It's line of business users, so we directly go to end users. That's one of our differentiation, not just IT, not just IT, but as end users as well, so we could be all things to all enterprise, to all enterprise, across our line of businesses. >> So what kind of impact are you seeing with your customers? You know, ones that are leaning into ThoughtSpot and Snowflake and sort of rethinking their data approach? >> Yeah. I mean the impact could be immense, right? As I said, this is not just about analytics. If we are successful in empowering end users, it completely changes the velocity of the business. We are now driving innovation at every node, at every layer in the organization. Not just IT, not just smaller segments in the organization, we are doing this anywhere, in any pocket, right? So I think the impact could be massive, if we do this right. And I think we are starting to see that, we have a lot of customers here actually, joint customers, Capital One, Canadian Tires, Walmart, they're all joint customers, where we have seen starting to see some of those impacts, where we have data getting modernized, the stack being ready, and then we're coming in at the top as the experience layer, which is driving that new digital operating model. >> Describe the maturity curve when you go, you mentioned some of the the the leaders, I mean, take a Walmart. I mean, they kind of invented the whole, you know, beer and diapers thing, right? So obviously a company with tremendous resources and and and advanced technology. Compare. Compare. So some of those leaders with sort of the other end of the spectrum, when you come into a company and you see, okay, here's, okay, here's, what does that spectrum look like? And and what's the upside for the, I don't want to call 'em laggards, but I'll call 'em laggards. >> Yeah, yeah, absolutely. I mean, this, this, I think we are still early on. I mean, as this is not just a exercise in getting the data ready, this is also an exercise in in change management, because now, as I said, we are going beyond IT. We are going to line of business users as well, so a lot of change management required, and we have seen companies that are actually putting this in front of the frontline workers, empowering frontline workers to consume analytics and to drive self-service via search and AI, and AI, they're on a different curve. They are actually being competitive in the market. That's an advantage for them, right? >> Right. >> So we are seeing a lot of companies, like Walmart, already ahead in that journey with us still early days, right? We got to go, land in one line of business, go from there to other line of business till we go enterprise wide. >> Can you, it sounds like you might be a facilitator of connecting heads of business with the IT and the tech folks at ThoughtSpot. >> Absolutely. I mean, that is the Holy Grail. How do we get IT And line of business work frictionless, where everyone has their roles defined, right? And still get to the outcome where innovation is happening now with IT on the data cloud and then go beyond IT into the broader business? So yeah, I think that's definitely one of the our goals and outcomes of what we do. >> So what are the roles there? So the business obviously wants to do more business. Okay. They put analytics in their hands and it helps them get there. What role does IT play? Making sure that those services are available? Are they a service provider? Is it more of a governance and compliance thing? >> Yeah, I mean, step number one is still to get the data ready and I think IT still owns the key to that kingdom, especially around governance, security, so I think IT still has to get the data stack ready, right? Step number two is for IT to really build a framework for how to consume analytics for how to consume analytics for the end users. Step number three then is, is the rule is, Hey, we don't need IT to now deliver dashboards or KPIs to the business every day that that's how traditional dashboards work. In our world, once IT does step number one and step number two the business can take over and they can now go operate the business on their own using live analytics. >> Creating self-serve >> Absolutely. Self-service analytics using service in AI. >> What have you seen, in terms of from the IT folks perspective, we talked about change management a minute ago, It's very challenging to do, but these days every company has to be a data company. >> Kuntal: Yeah. >> They don't have a choice. >> Yeah. >> What are you seeing from a change management perspective within the IT function across your customers and then be willing to let go in some cases? and then be willing to let go in some cases? >> Actually, >> Actually, what we have seen is, you know, think about the the technical debt that IT is owning over the last few years, it's just increasing, right? IT is looking for ways to A. cut cost, to A. cut cost, B. deliver more B. deliver more with probably the same amount of resources they have, so in some ways they welcome this new operating model, as long as they can keep the governance, they can keep the security, they can keep the framework around how business is run, as long as IT has a say in that, they're more than welcome to invite business, to really drive innovation at the edges through self-service analytics, so what we found is IT is a is a welcome partner, in this journey, especially when they have to get the data ready and modernize the data set for us. >> You guys announcing a partnership with Matillion this week, what? Tell us what that's all about. The one earlier. >> We did. So we did announce a partnership, so I think, as I said, step number one is getting the data ready, and I think we have heard from Frank and the rest of this team this week, even Snowflake is taking a best of breed approach on the data stack, right? So we want the computer So we want the computer and the storage to be ready, but for that, the data pipeline has to be ready, which is where Matillion comes in with the low code, no code approach, so we think between Matillion, Snowflake, and ThoughtSpot, we could be the accelerated best of breed approach for customers to realize value and and be live on the, on the modern data stack. >> Is that your, is that your stack? >> As we said, we, we meet the customers where they are, but we think this is accelerated path. >> What are the advantages of, you know, what are you optimizing on in that stack? in that stack? >> First with Matillion, we have, what we concept, we have this concept of Spot Apps, so this is ThoughtSpot's way to really capture the IP and the templates for customers to move fast, right? That's where we bake in a lot of the industry IP, a lot of functional IP around end sources, and and endpoints, so we have some of those spot apps built with Matillion, built with Matillion, so now customers able to ingest data into the so now customers able to ingest data into the into the cloud faster using Matillion, right? So that's, that's something we worked with, same thing with Snowflake, you know, we are now starting to go verticalize with Snowflake, So we are starting to build a lot of IP around financial services, healthcare and whatnot, which is where I think we are, again, accelerating customer's path on the modern data stack, all the way to the experience layer. >> A as a partner of Snowflake's, what does all the narrative around the data cloud, we've been talking about that for a while, a lot of conversation around the data cloud the last couple of days, where do partners fit into that overall narrative? >> Yeah, I think multiple places, right? First thing, First thing, First thing, every layer of the data cloud still needs innovation, still needs partners, and every partner adds a different set of value. Just like we add value at the, at the top layer, which is the experience layer, But I think, you know, we have channel partners we have a lot of SIs and GSIs here, and GSIs here, especially once we take a best of breed approach, to delivering customer outcomes, SIs are the neutral ground. They're the ones who are going to have the Matillion expertise, and the Snowflake expertise, and thoughts for expertise, all baked into one DNA practice, data analytics practice, so I think at every layer, partners have a role to play and every layer partners have role, have value to add. have value to add. >> What's the engagement process like for customers when you you're talking about the the the the three way partnership Matillion, Matillion, ThoughtSpot, and stuff like, how do customers get involved, what's your go to market look like? >> Right. I mean, obviously, I mean, we, we, we are humble, we know where we are. I mean, we, a little bit smaller than, than Snowflake Snowflake has a head start, so they've been about five years ahead of us, so we are largely targeting customers that are that are Snowflake ready, where there is some semblance of data cloud, where data seems to be organized and ready to go, right? so once we think the customer is at that point in the journey, we have very strong partnership across both, across entire organization, at a product level, at a field engagement level, and our field teams really understand the value the joint value between the two organizations, so we, we start to see Snowflake feel, and ThoughtSpot feel, starting to work together on key accounts, once we think the data is ready, and wherever we need to accelerate the data, that's where we bring in Matillion as well, to ingest more data into, into the data cloud, but that's largely been the engagement model between the three companies. >> How do you see the announcements that they made around applications affecting what you guys are doing and your ecosystem? >> Yeah, I mean, I think that's a validation. I think to us, I think to us, we always said step number one is to modernize the data, move into the cloud. That's step number one, but we still have to unlock the data. Like the data still needs to be consumed, And we always said, Hey, we are that app that could drive the consumption of data, but now with some of the announcement we have seen, I think the validation is there saying, "Hey, yes." There, even Snowflake is ready to move in a more accelerated fashion into the application world where they want to drive consumption, not just with the analytics layer, but with lot of other applications that's out there. >> Yeah. >> What are some of the things that you've heard this week, in the last couple of days, that really validate that really validate the the partnership with Snowflake, from your perspective? >> Yeah. I mean, I think the first thing is, is this concept of modern data stack, which is best of breed. I think we have been thinking about that for a long time, for the last year or so. We have seen this come through at this event here, right? We see Matillion, Snowflake, and then the SIs around it, all coming together, so I think to us, that's the biggest validation that the modern data stack is the right approach, especially best of breed, to drive the right customer outcomes, so to me, that's big. Second is this concept of really accelerating applications on top of the data cloud. I think that's, again a validation of what we've been trying to do over the last few years, which is, the data has modernized, let's now drive consumption and adoption of that data, so I think those are the two big take areas. >> So, so the modern data stack, to get to the modern data stack, you got to do some work. >> Yep. >> But so the, the play is to hold out the carrot, which you just kind of just did, 'cause once you get there, then you can really start to hit the steep part of the S-curve, right? >> That's right. >> What, what are the, what would you say are are the sort of prerequisites that customers need to think about to really jump on that modern data stack curve? >> Um, I think they they got to first have a vision around the outcomes, what outcomes we are driving. I think it's one thing to say, "Hey, we just going to move the data over from from legacy into the cloud." I mean, that's just, that's just migration, that doesn't drive the outcomes. To us, what makes sense is, let's start with the right outcomes around supply chain, around retail, around e-commerce, let's name it, right? I think, it starts there. From there on, let's figure out, what do we need? What's what, what technologies do we need in the stack to enable those outcomes, right? It could be ThoughtSpot at the top, it could be something else at the top, and same thing, it's Matillion, and Snowflake, right? But it really starts with what outcomes we going to drive in what industry and what KPIs are important for our customers. >> What's next for ThoughtSpot and Snowflake? I was just looking at the notes here. Over 250 plus joint customers, you mentioned some Disney+, Capital One, I've seen them around here. What's next for these two powerhouses? >> Well, I think we're just getting started, to be honest. I mean those 250 customers, first, we got to go drive success for them. I mean, we are a 10 year old company with a two year runway because we transferred our business transformed our business to cloud, less than two years ago, so this 250 joint logos are actually all happened in the last two years and that's driven us to be in the, probably in the top five adoption drivers for Snowflake, all in the last two years, So goal number one is to really, let's go drive customer success for these joint logos. Second, let's go expand them, right? Consumption is the key criteria, both for Snowflake, as well as ThoughtSpot. We are very well aligned, our pricing models aligned there, our incentives aligned there, We really want customers to go adopt and consume the stack, and then of course, really, we want to go verticalize ourselves, start speaking the language of the customers, and really just get bigger. I mean, we still got to build a machine around this. >> Lisa: Yep. >> Lisa, this is, this is all still early days for us. >> Early innings. A lot of, but a ton of potential. The, the field is ripe. >> The field is right open. I think in, and we will, I think we are, bottom of the third or bottom of the second, I think you still have a long game to play, right? >> Well good. Most people always use bottom the first. I'm glad to hear it's really bottom of the second or third. That's pretty good. >> Yeah, well, 250 logos are there. >> Lisa: Yeah. >> And it's further along 'cause of the, the I don't want to say it like this, but I'm going to say it anyway. The failure of the big data movement, it pushed us along quite, quite a ways, in terms of thinking, putting data at the core, the technology kind of failed us, you know and the, and the, you know and the, and the, the centralization of the architectures, the centralization of the architectures, it failed us, But then the cloud came along. >> That's right. >> We learned a lot and now, you know, technology's advanced I think people's thinking is advanced and they realize increasingly the importance of data >> And ecosystem is coming. I mean, I think you look around here, this is a secret sauce for the future. >> Dave: Yep. This is what's going to really get us moving faster over the next few innings because now the rest of the ecosystem is coming along. >> Yep. The momentum is here. That flywheel is moving. >> That's right. >> Definitely. Kuntal, thank you very much for joining David and me on the program talking about >> Kuntal: Lisa, Dave, thank you so much for your time. >> what ThoughtSpot's all about, what you're up to, a lot of momentum. We wish you the best of luck as you progress into those later innings. >> Thank you >> For Dave Vellante. I'm Lisa Martin. You're watching theCube. We are live in Las Vegas at Snowflake Summit '22. Dave and I are going to be right back with our next guest, so stick around. (mellow techno music) (mellow techno music) (mellow techno music) (mellow techno music)

(upbeat music) >> Good morning. Welcome back to theCUBE's continuing coverage of Snowflake Summit 22, live from Las Vegas. Lisa Martin, here with Dave Villante. We are at Caesar's Forum, having lots of great conversations. As I mentioned, this is just the start of day two, a tremendous amount of content yesterday. I'm coming at you today. Two guests join us from Slalom, now, we've got Chris Samuels, Principal Machine Learning, and Bethany Mudd, Senior Director, Experience Design. Welcome to theCube, guys. >> Hi, thanks for having us. >> Thank you. >> So, Slalom and Snowflake, over 200 joint customers, over 1,800 plus engagements, lots of synergies there, partnership. We're here today to talk about intelligent products. Talk to us about what- how do you define intelligent products, and then kind of break that down? >> Yeah, I can, I can start with the simple version, right? So, when we think about intelligent products, what they're doing, is they're doing more than they were explicitly programmed to do. So, instead of having a developer write all of these rules and have, "If this, then that," right, we're using data, and real time insights to make products that are more performing and improving over time. >> Chris: Yeah, it's really bringing together an ecosystem of a series of things to have integrated capabilities working together that themselves offer constant improvement, better understanding, better flexibility, and better usability, for everyone involved. >> Lisa: And there are four pillars of intelligent products that let's walk through those: technology, intelligence, experiences, and operations. >> Sure. So for technology, like most modern data architectures, it has sort of a data component and it has a modern cloud platform, but here, the key is is sort of things being disconnected, things being self contained, and decoupled, such that there's better integration time, better iteration time, more cross use, and more extensibility and scalability with the cloud native portion of that. >> And the intelligence piece? >> The intelligence piece is the data that's been processed by machine learning algorithms, or by predictive analytics that provides sort of the most valuable, or more- most insightful inferences, or conclusions. So, by bringing together again, the tech and the intelligence, that's, you know, sort of the, two of the pillars that begin to move forward that enable sort of the other two pillars, which are- >> Experiences and operations. >> Yeah. >> Perfect. >> And if we think about those, all of the technology, all of the intelligence in the world, doesn't mean anything if it doesn't actually work for people. Without use, there is no value. So, as we're designing these products, we want to make sure that they're supporting people. As we're automating, there are still people accountable for those tasks. There are still impacts to people in the real world. So, we want to make sure that we're doing that intentionally. So, we're building the greater good. >> Yeah. And from the operations perspective, it's you can think of traditional DevOps becoming MLOps, where there's an overall platform and a framework in place to manage not only the software components of it, but the overall workflow, and the data flow, and the model life cycle such that we have tools and people from different backgrounds and different teams developing and maintaining this than you would previously see with something like product engineering. >> Dave: Can you guys walk us through an example of how you work with a customer? I'm envisioning, you know, meeting with a lot of yellow stickies, and prioritization, and I don't know if that's how it works, but take us through like the start and the sequence. >> You have my heart, I am a workshop lover. Anytime you have the scratch off, like, lottery stickers on something, you know it's a good one. But, as we think about our approach, we typically start with either a discovery or mobilized phase. We're really, we're starting by gathering context, and really understanding the business, the client, the users, and that full path the value. Who are all the teams that are going to have to come together and start working together to deliver this intelligent product? And once we've got that context, we can start solutioning and ideating on that. But, really it comes down to making sure that we've earned the right, and we've got the smarts to move into the space intelligently. >> Yeah, and, truly, it's the intelligent product itself is sort of tied to the use case. The business knows what the most- what is potentially the most valuable here. And so, so by communicating and working and co-creating with the business, we can define then, okay, here are the use cases and here are where machine learning and the overall intelligent product can maybe add more disruptive value than others. By saying, let's pretend that, you know, maybe your ML model or your predictive analytics is like a dial that we could turn up to 11. Which one of those dials turning turned up to 11 could add the most value or disruption to your business? And therefore, you know, how can we prioritize and then work toward that pie-in-the-sky goal. >> Okay. So the client comes and says, "This is the outcome we want." Okay, and then you help them. You gather the right people, sort of extract all the little, you know, pieces of knowledge, and then help them prioritize so they can focus. And then what? >> Yeah. So, from there we're going to take the approach that seeing is solving. We want to make sure that we get the right voices in the room, and we've got the right alignment. So, we're going to map out everything. We're going to diagram what that experience is going to look like, how technology's going to play into it, all of the roles and actors involved. We're going to draw a map of the ecosystem that everyone can understand, whether you're in marketing, or the IT sort of area, once again, so we can get crisp on that outcome and how we're going to deliver it. And, from there, we start building out that roadmap and backlog, and we deliver iteratively. So, by not thinking of things as getting to the final product after a three year push, we really want to shrink those build, measure, and learn loops. So, we're getting all of that feedback and we're listening and evolving and growing the same way that our products are. >> Yeah. Something like an intelligent product is is pretty heady. So it's a pretty heavy concept to talk about. And so, the question becomes, "What is the outcome that ultimately needs to be achieved?" And then, who, from where in the business across the different potentially business product lines or business departments needs to be brought together? What data needs to be brought together? Such that the people can understand how they themselves can shape. The stakeholders can, how the product itself can be shaped. And therefore, what is the ultimate outcome, collectively, for everybody involved? 'Cause while your data might be fueling, you know, finances or someone else's intelligence and that kind of thing, bringing it all together allows for a more seamless product that might benefit more of the overall structure of the organization. >> Can you talk a little bit about how Slalom and Snowflake are enabling, like a customer example? A customer to take that data, flex that muscle, and create intelligent products that delight and surprise their customers? >> Chris: Yeah, so here's a great story. We worked to co-create with Kawasaki Heavy Industries. So, we created an intelligent product with them to enable safer rail travel, more preventative, more efficient, preventative maintenance, and a more efficient and real time track status feedback to the rail operators. So, in this case, we brought, yeah, the intelligent product itself was, "Okay, how do you create a better rail monitoring service?" And while that itself was the primary driver of the data, multiple other parts of the organization are using sort of the intelligent product as part of their now daily routine, whether it's from the preventative maintenance perspective, or it's from route usage, route prediction. Or, indeed, helping KHI move forward into making trains a more software centered set of products in the future. >> So, taking that example, I would imagine when you running- like I'm going to call that a project. I hope that's okay. So, when I'm running a project, that I would imagine that sometimes you run into, "Oh, wow. Okay." To really be successful at this, the company- project versus whole house. The company doesn't have the right data architecture, the right skills or the right, you know, data team. Now, is it as simple as, oh yeah, just put it all into Snowflake? I doubt it. So how do you, do you encounter that often? How do you deal with that? >> Bethany: It's a journey. So, I think it's really about making sure we're meeting clients where they are. And I think that's something that we actually do pretty well. So, as we think about delivery co-creation, and co-delivering is a huge part of our model. So, we want to make sure that we have the client teams, with us. So, as we start thinking about intelligent products, it can be incorporating a small feature, with subscription based services. It doesn't have to be creating your own model and sort of going deep. It really does come down to like what value do you want to get out of this? Right? >> Yeah. It is important that it is a journey, right? So, it doesn't have to be okay, there's a big bang applied to you and your company's tech industry or tech ecosystem. You can just start by saying, "Okay, how will I bring my data together at a data lake? How do I see across my different pillars of excellence in my own business?" And then, "How do I manage, potentially, this in an overall MLOps platform such that it can be sustainable and gather more insights and improve itself with time, and therefore be more impactful to the ultimate users of the tool?" 'Cause again, as Bethany said that without use, these things are just tools on the shelf somewhere that have little value. >> So, it's a journey, as you both said, completely agree with that. It's a journey that's getting faster and faster. Because, I mean, we've seen so much acceleration in the last couple of the years, the consumer demands have massively changed. >> Bethany: Absolutely. >> In every industry, how do Slalom and Snowflake come together to help businesses define the journey, but also accelerate it, so that they can stay ahead or get ahead of the competition? >> Yeah. So, one thing I think is interesting about the technology field right now is I feel like we're at the point where it's not the technology or the tools that's limiting us or, you know, constraining what we can build, it's our imaginations. Right? And, when I think about intelligent products and all of the things that are capable, that you can achieve with AI and ML, that's not widely known. There's so much tech jargon. And, we put all of those statistical words on it, and you know the things you don't know. And, instead, really, what we're doing is we're providing different ways to learn and grow. So, I think if we can demystify and humanize some of that language, I really would love to see all of these companies better understand the crayons and the tools in their toolbox. >> Speaking from a creative perspective, I love it. >> No, And I'll do the tech nerd bit. So, there is- you're right. There is a portion where you need to bring data together, and tech together, and that kind of thing. So, something like Snowflake is a great enabler for how to actually bring the data of multiple parts of an organization together into, you know, a data warehouse, or a data lake, and then be able to manage that sort of in an MLOps platform, particularly with some of the press that Snowflake has put out this week. Things becoming more Python-native, allowing for more ML experimentation, and some more native insights on the platform, rather than going off Snowflake platform to do some of that kind of thing. Makes Snowflake an incredibly valuable portion of the data management and of the tech and of the engineering of the overall product. >> So, I agree, Bethany, lack of imagination sometimes is the barrier we get so down into the weeds, but there's also lack of skills, as mentioned the organizational, you know, structural issues, politics, you know, whatever it is, you know, specific agendas, how do you guys help with that? Can, will you bring in, you know, resources to help and fill gaps? >> Yeah, so we will bring in a cross-disciplinary team of experts. So, you will see an experienced designer, as well as your ML architects, as well as other technical architects, and what we call solution owners, because we want to make sure that we've got a lot of perspectives, so we can see that problem from a lot of different angles. The other thing that we're bringing in is a repeatable process, a repeatable engineering methodology, which, when you zoom out, and you look at it, it doesn't seem like that big of a deal. But, what we're doing, is we're training against it. We're building tools, we're building templates, we're re-imagining what our deliverables look like for intelligent products, just so, we're not only speeding up the development and getting to those outcomes faster, but we're also continuing to grow and we can gift those things to our clients, and help support them as well. >> And not only that, what we do at Slalom is we want to think about transition from the beginning. And so, by having all the stakeholders in the room from the earliest point, both the business stakeholders, the technical stakeholders, if they have data scientists, if they have engineers, who's going to be taking this and maintaining this intelligent product long after we're gone, because again, we will transition, and someone else will be taking over the maintenance of this team. One, they will understand, you know, early from beginning the path that it is on, and be more capable of maintaining this, and two, understand sort of the ethical concerns behind, okay, here's how parts of your system affect this other parts of the system. And, you know, sometimes ML gets some bad press because it's misapplied, or there are concerns, or models or data are used outside of context. And there's some, you know, there are potentially some ill effects to be had. By bringing those people together much earlier, it allows for the business to truly understand and the stakeholders to ask the questions that they- that need to be continually asked to evaluate, is this the right thing to do? How do I, how does my part affect the whole? And, how do I have an overall impact that is in a positive way and is something, you know, truly being done most effectively. >> So, that's that knowledge transfer. I hesitate to even say that because it makes it sound so black and white, because you're co-creating here. But, essentially, you're, you know, to use the the cliche, you're teaching them how to fish. Not, you know, going to ongoing, you know, do the fishing for them, so. >> Lisa: That thought diversity is so critical, as is the internal alignment. Last question for you guys, before we wrap here, where can customers go to get started? Do they engage Slalom, Snowflake? Can they do both? >> Chris: You definitely can. We can come through. I mean, we're fortunate that snowflake has blessed us with the title of partner of the year again for the fifth time. >> Lisa: Congratulations. >> Thank you, thank you. We are incredibly humbled in that. So, we would do a lot of work with Snowflake. You could certainly come to Slalom, any one of our local markets, or build or emerge. We'll definitely work together. We'll figure out what the right team is. We'll have lots and lots of conversations, because it is most important for you as a set of business stakeholders to define what is right for you and what you need. >> Yeah. Good stuff, you guys, thank you so much for joining Dave and me, talking about intelligent products, what they are, how you co-design them, and the impact that data can make with customers if they really bring the right minds together and get creative. We appreciate your insights and your thoughts. >> Thank you. >> Thanks for having us guys. Yeah. >> All right. For Dave Villante, I am Lisa Martin. You're watching theCUBE's coverage, day two, Snowflake Summit 22, from Las Vegas. We'll be right back with our next guest. (upbeat music)

>>Hey everyone. Welcome back to Las Vegas. This is the Cube's live coverage of day. One of snowflake summit 22 fourth annual. We're very happy to be here. A lot of people here, Lisa Martin with Dave Valante, David's always great to be at these events with you, but me. This one is shot out of the cannon from day one, data, data, data, data. That's what you heard of here. First, we have two guests joining us next, please. Welcome Matthew Scalian. Who's an alumni of the cube CEO and founder of Matillion and Jer staying chief data architect and global head of data engineering from Western union. Welcome gentlemen. Thank >>You. Great to be here. >>We're gonna unpack the Western union story in a second. I love that, but Matthew, I wanted to start with you, give the audience who might not be familiar with Matillion an overview, your vision, your differentiators, your joint value statement with snowflake, >>Of course. Well, first of all, thank you for having me on the cube. Again, Matillion S mission is to make the world's data useful, and we do that by providing a technology platform that allows our customers to load transform, synchronize, and orchestrate data on the snowflake data cloud. And on, on the cloud in general, we've been doing that for a number of years. We're co headquartered in the UK and the us, hence my dat accents. And we work with all sorts of companies, commercial scale, large end enterprises, particularly including of course, I'm delighted to say our friends at Western union. So that's why we're here today. >>And we're gonna talk about that in a second, but I wanna understand what's new with the data integration platform from Matillion perspective, lots of stuff coming out, give us an overview. >>Yeah, of course, it's been a really busy year and it's great to be here at snowflake summit to be able to share some of what we've been working on. You know, the Matillion platform is all about making our customers as productive as possible in terms of time to value insight on that analytics, data science, AI projects, like get you to value faster. And so the more technology we can put in the platform and the easier we can make it to use, the better we can achieve that goal. So this year we've, we've shipped a product that we call MDL 2.0, that's enterprise focused, exquisitely, easy to use batch data pipelines. So customers can load data even more simply into the snowflake data cloud, very excitingly we've also launched Matillion CDC. And so this is an industry first cloud native writer, head log based change data capture. >>I haven't come up with a shorter way of saying that, but, and surprise customers need this technology and it's been around for years, but mostly pre-cloud technology. That's been repurposed for the cloud. And so Matillion has rebuilt that concept for the cloud. And we launched that earlier this year. And of course we've continued to build out the core Matillion ETL platform that today over a thousand joint snowflake Matillion customers use, including Western union, of course we've been adding features there such as universal connectivity. And so a challenge that all data integration vendors have is having the right connectors for their source systems. Universal connectivity allows you to connect to any source system without writing code point and click. We shape that as well. So it's been a busy year, >>Was really simple. Sorry. I love that. He said that and it also sounded great with your accent. I didn't wanna >>Thank you. Excellent. Javier, talk about your role at Western union in, in what you've seen in terms of the evolution of the, the data stack. >>So in the last few years, well, a little bit of Western union, a 70 or 170 year old company, pretty much everybody knows what Western union is, right? Driving an interesting synergy from what Matthew says, when data moves money moves, that's what we do when he moves the da, he moves the data. We move the money. That's the synergy between, you know, us and the organization that support us from data move perspective. So what I've seen in the last few years is obviously a shift towards the cloud, but, you know, within the cloud itself, obviously there's a lot of players as well. And we as customers have always been wishing to have a short, smaller footprint of data so that the movement becomes a little lesser. You know, interestingly enough, in this conference, I've heard some very interesting stuff, which kind of helping me to bring that footprint down to a manageable number, to be more governed, to be more, you know, effective in terms of delivering more end results for my customers as well. >>So Matillion has been a great partner for us from our cloud adoption perspective. During the COVID times, we were a re we are a, you know, multi-channel organization. We have retail stores as well, our digital presence, but people just couldn't go to the retail stores. So we had to find ways to accelerate our adoption, make sure our systems are scaling and making sure that we are delivering the same experience to our customers. And that's where, you know, tools like Matillion came in and really, really partnered up with us to kind of bring it up to the level. >>So talk specifically about the stack evolution. Cause I have this sort of theory that everybody talks about injecting data and, and machine intelligence and AI and machine learning into apps. But the application development stack is like totally separate from the, the data analytics and the data pipeline stack. And the database is somewhere over here as well. How is that evolving? Are those worlds coming together? >>Some part of those words are coming together, but where I still see the difference is your heavy lifting will still happen on the data stack. You cannot have that heavy lifting on the app because if once the apps becomes heavy, you'll have trouble communicating with, with, with the organizations. You know, you need to be as lean as possible in the front end and make sure things are curated. Things are available on demand as soon as possible. And that's why you see all these API driven applications are doing really, really well because they're delivering those results back to the, the leaner applications much faster. So I'm a big proponent of, yes, it can be hybrid, but the majority of the heavy lifting still needs to happen down at the data layer, which is where I think snowflake plays a really good role >>In APIs are the connective tissue >>APIs connections. Yes. >>Also I think, you know, in terms of the, the data stack, there's another parallel that you can draw from applications, right? So technology is when they're new, we tend to do things in a granular way. We write a lot of code. We do a lot of sticking of things together with plasters and sticky tape. And it's the purview of high end engineers and people enthusiastic about that to get started. Then the business starts to see the value in this stuff, and we need to move a lot faster. And technology solutions come in and this is what the, the data cloud is all about, right? The technology getting out of the way and allowing people to focus on higher order problems of innovating around analytics, data applications, AI, machine learning, you know, that's also where Matillion sit as well as other companies in this modern enterprise data stack is technology vendors are coming in allowing organizations to move faster and have high levels of productivity. So I think that's a good parallel to application development. >>And's just follow up on that. When you think about data prep and you know, all the focus on data quality, you've got a data team, you know, in the data pipeline, a very specialized, maybe even hyper specialized data engineers, quality engineers, data, quality engineers, data analysts, data scientist, but they, and they serve a lot of different business lines. They don't necessarily have the business, they don't have the business context typically. So it's kind of this back and forth. Do you see that changing in your organization or, or the are the lines of business taking more responsibility for the data and, and addressing that problem? It's, >>It's like you die by thousand paper cuts or you just die. Right? That's the kind >>Of, right, >>Because if I say it's, it's good to be federated, it comes with its own flaws. But if I say, if it's good to be decentralized, then I'm the, the guy to choke, right? And in my role, I'm the guy to choke. So I've selectively tried to be a pseudo federated organization, where do I do have folks reporting into our organization, but they sit close to the line of business because the business understands data better. We are working with them hand in glove. We have dedicated teams that support them. And our problem is we are also regional. We are 200 countries. So the regional needs are very different than our us needs. Majority of the organizations that you probably end up talking to have like very us focused, 50 per more than 50% of our revenue is international. So we do, we are dealing with people who are international, their needs for data, their needs for quality and their needs for the, the delivery of those analytics and the data is completely different. And so we have to be a little bit more closer to the business than traditionally. Some, some organizations feel that they need >>To, is there need for the underlying infrastructure and the operational details that as diverse, or is that something that you bring standardization to the, >>So the best part about this, the cloud that happened to us is exactly that, because at one point of time, I had infrastructure in one country. I had another infrastructure sitting in another country, regional teams, making different different decisions of bringing in different tools. Now I can standardize. I will say, Matillion is our standard for doing ETL work. If this is the use case, but then it gets deployed across the geographies because the cloud helps us or the cloud platform helps us to manage it. Sitting down here. I have three centers around the world, you know, Costa Rica, India, and the us. I can manage 24 7 sitting here. No >>Problem. So the underlying our infrastructure is, is global, but the data needs are dealt with locally. Yep. >>One of the pav question, I was just thinking JVE is super well positioned funds for you, which is around that business domain knowledge versus technical expertise. Cause again, early in technology journeys tend, things tend to be very technical and therefore only high end engineers can do it, but high end engineers are scar. Right? Right. And, and also, I mean, we survey our hundreds of large enterprise customers and they tell us they spend two thirds of their time doing stuff they don't really want to do like reinventing the wheel, basic data movement and the low order staff. And so if you can make those people more productive and allow them to focus on higher value problems, but also bring pseudo technical people into it. Overall, the business can go a lot faster. And the way you do that is by making it easier. That's why Matillion is a low code NOCO platform, but Jer and Western union are doing this right. I >>Mean, I can't compete with AWS and Google to hire people. So I need to find people who are smart to figure the products that we have to make them work. I don't want them to spend time on infrastructure, Adam, I don't want them to spend time on trying to manage platforms. I want them to deliver the data, deliver the results to the business so that they can build and serve their customers better. So it's a little bit of a different approach, different mindset. I used to be in consulting for 17 years. I thought I knew it all, but it changed overnight when I own all of these systems. And I'm like, I need to be a little bit more smarter than this. I need to be more proactive and figure out what my business needs rather than what just from a technology needs. It's more what the business needs and how I can deliver that needs to them. So simple analogy, you know, I can build the best architecture in the world. It's gonna cost me an arm and leg, but I can't drive it because the pipeline is not there. So I can have a Ferrari, but I can't drive it. It's still capped at 80, 80 miles an hour. So rather than spend, rather than building one Ferrari, let me have 10 Toyotas or 10 Fs, which will go further along and do better for my cus my, for my customers. >>So how do you see this whole, we hearing about the data cloud. We hear about the marketplace, data products now, application development inside the data cloud. How do you see that affecting not so much the productivity of the data teams. I don't wanna necessarily say, but the product, the value that, that customers like you can get out >>Data. So data is moving closer to the business. That's the value I see, because you are injecting the business and you're injecting the application much more closer to the data because it, in the past, it was days and days of, you know, churn the data to actually clear results. Now the data has moved much closer. So I have a much faster turnaround time. The business can adapt and actually react much, much faster. It took us like 16 to 30 days to deliver, you know, data for marketing. Now I can turn it down in four hours. If I see something happening, I'll give you an example. The war in Ukraine happened. Let is shut down operations in Russia. Ukraine is cash swamp. There's no cash in Ukraine. We have cash. We roll out campaign, $0 money, transferred to Ukraine within four hours of the world going on. That's the impact that we have >>Massive impact. That's huge, especially with such a macro challenge going on, on the, in, in the world. Thank you so much for sharing the Matillion snowflake partnership story, how it's helping Western union really transform into a data company. We love hearing stories of organizations that are 170 years old that have always really been technology focused, but to see it come to life so quickly is pretty powerful. Guys. Thank you so much for your time. Thanks >>Guys. Thank you, having it. Thank >>You >>For Dave Velante and our guests. I'm Lisa Martin. You're watching the cubes live coverage of snowflake summit 22 live from Las Vegas. Stick around. We'll be back after a short break.

(gentle music) >> Hey everybody. Welcome back to theCUBE. We are live in Las Vegas at Caesar's Forum, Lisa Martin with Dave Vellante, covering Snowflake Summit 22, this is day one action packed, it kind of feels like we were shot out of a cannon, which is great. We love that at theCUBE. Our next guest from Slalom joins us, Hilary Feier the GM of Data and Analytics. Hilary, it's great to have you on the program. >> It's great to be here, so excited to be here. >> Isn't it great to be back in person? >> It is, it's amazing, it's like filling my cup just to be back with people again. >> I felt the same. And you could tell that on stage during the keynote which was not only standing room only, but there was an overflow room. >> I was in the overflow room. >> Lisa: Were you? >> I have to admit it. We had a breakfast meeting and we got there right on time and we ended up in overflow, but it was great. We, there was just great energy and it was exciting to see all the progress that's coming down the pipe. >> Tremendous progress, tremendous innovation, a lot of evolution since we last saw Snowflake in person, which was 2019. Talk to us from Slalom's partnership perspective how is data evolving, the use of data evolving, what are you hearing from the front lines of the customer? >> From the front lines of the customer, we're seeing a lot of customers go to the cloud, and Snowflake's at the forefront of that evolution. We're seeing them take advantage of this separation of compute and storage to be able to scale to different levels and concurrency at different levels and collaborate. And we always say, what we're actually seeing them unlock is this modern culture of data, where people and organizations can fully take advantage at all different levels of this accessible but governed data. And I think Snowflake makes that a reality. >> So we go to a lot of events of course, and you hear both sides of the story when you talk to a company like Snowflake, or one of the hyperscalers, like yeah, cloud makes ton of sense. When you talk to some of the more established companies, call 'em legacy companies, everybody's like oh no, people are repatriating, they're moving back on-prem, or they can't move data, or they won't move data in the cloud. The truth is probably someplace in the middle. But when you look at the numbers cloud is growing, substantially faster. What are you seeing with customers when, with regard to modernization, the role of cloud and the role of Snowflake? >> I think they're flocking to the cloud, I think COVID had people flock there right. You realize the agility it provides for you, it is unparalleled. And to some extent, I'd had conversations with customers years ago that they were like, hey I know security, I do it better than anybody. And I go, honestly AWS, Google, like the the hyper cloud providers, they know security, and Snowflake doing that data layer across all of 'em. They do security at a whole different level than any data center or any IT group that I've seen out there. >> Have you, we've seen the secure, the threat landscape changed dramatically in the last couple of years where it's now no longer, are we going to get hit, it's when. >> Right. >> How have you seen the security conversation elevate when you're talking with customers in terms of up the executive stack? Is that now something that it, since we? >> It's a top priority, it's a board priority. I can tell you last year I actually spent time internally helping implement Snowflake for us at Slalom, and it's our president's top priority was security. And that was one of the reasons honestly that we went that way, we were a little out of date, we needed to modernize, we needed to migrate, and we wanted to practice what we preach with our customers. So we did a little bit of both, and we did more than technology. We did a lot of process change, a lot of people up leveling, 'cause we really feel like technology's only a piece of the puzzle. You have to bring the people along for the journey in order to make that a reality. >> So what was the business driver to make that change? >> I think it was honestly to empower more people, and then we also had the threat of systems that were falling over and just not meeting the needs of the business. We were pretty data driven and the systems weren't keeping up. >> And they were on-prem systems, they were hosted in the cloud? >> They were kind of on-prem, kind of hosted in the cloud. They were SQL1, EC2 instances, but we just, we didn't, we weren't able to scale, literally was falling over. Like we have a day a week where all of the reporting comes out because we're time driven, and it would fall over, literally. >> Dave: So you had a halfway house, sort of? >> Yeah. >> Okay, and then you moved much of it, most of it, all of it, into Snowflake? >> All of it. >> All of it into Snowflake? >> All of it. >> Dave: And. >> And then some. >> Dave: Okay. >> Because we had certain systems that we were afraid, like Workday, right. All the PII, all the privacy data. We were afraid to bring that into our SQL server before, but we were able to bring that into Snowflake now and it unlocks in a governed, we have security, in very compliant ways, we have a lot of interesting things that we've done in this past year. To both empower more people, but do it in a governed and secured way. >> And how long did that migration take? >> I'd say it took about a year, and it was. >> Dave: Pretty fast. >> And it was a tough year, honestly. >> Yeah they're ugly, migrations. >> We do it with internal consultants and some of them in the beginning of COVID, we were looked at as an opportunity. Let's get them, let's do it internally. And then we got super busy, the market just took off and then we were begging for resources. We were like, okay where can we find somebody to help us with this? >> Cobblers kids. >> Yeah, we were the cobblers kids. But we got it done. >> And as a partner drinking the Snowflake champagne. Talk to me about the ability to influence the technology, the direction, the roadmap. We've heard so much innovation announced this morning alone. Do you have that capability as a Snowflake partner? >> Yeah, for sure. So I feel like we're always on the forefront. We're doing these strategy projects with our clients, and so we want to keep our ears to what's going on in the innovation. We look at a lot of the other partners that are here. There's a whole ecosystem that's grown up around Snowflake and it's amazing to see the advancements that are happening and the cloud allows you to leapfrog just so quickly the advancements. And, you know we talked about this before we started that you know, I've been in this data space for 30 years and it's changed a lot, the progression, the real time data, what you can do, the separation of compute and storage. It's amazing what you can do. And yet some of the same problems are pervasive. I have too much data, not enough information. And so we're seeing the advent of more governance and catalogs, and you know that whole semantic layer is coming into play. >> Yeah, the problem is data is plentiful, insights aren't, and then monetizing data is really, really hard. I, what's your take on Snowflake's ability to change that dynamic? >> I think they're making it a lot easier. I mean, some of the advancements they're coming out with, and more and more companies are looking to monetize and we're doing that in partnership with some companies like Meredith Corporation. They're a, I don't know if you know who they are? But they're like allrecipes.com. If you go there, they collect a lot of that data. We have a partnership together where we're looking, and they're on Snowflake and we're doing a joint data monetization offering out to customers. >> Snowflake and Slalom have over 200 joint customers. Slalom has won Partner of the Year now, five times. Congratulations by that. >> Hilary: Thank you. >> What is the secret, what's the secret sauce? What does the future of the partnership look like given the flywheel that is Snowflake, that is incredibly fast. >> Yeah, I think the secret sauce to me is we started early, and we liked the product, but we had a lot of core values in common. If you look you know, the customer obsession, do the right thing always, just get it done, right. Like, you know really very, very similar. And so that translates out in the field and that's why we team so well together. But at the end of the day our secret sauce is we know the product. We invested really early in getting skilled up on Snowflake, and we did, we were the first partner to do Train the Trainer, and so we've literally certified hundreds of folks on the product, and we stay on the leading and bleeding edge. And we're now working with their professional services arm to really take a joint offering to the market around, helping organizations, not just migrate but really modernize because that's when you truly take advantage of the cloud. And some people were quick to migrate and they're not seeing those advantages and we want to make sure we're unlocking all the advantages of actually modernizing. >> What do you think last question is we are almost out of time here. What do you think in the 30 years you said you've been in this business, you talked about the modern culture of data. What does it take for a legacy organization to pivot, to be able to pivot, to be able to adopt a modern culture of data, if they're so used to old school processes? >> I think it's having someone with a bold vision at the top. That's willing to say, hey, we want to go to the new frontier, and then sticking to the guns and taking a holistic approach. Don't just put in technology, don't just change a process, But think about it holistically, we have a whole framework where we look at five different dimensions, and we help our customers go through and maybe you don't want to get to, the most mature stage across all five, but figure out where you want to get to and then start actually slogging it out and going step by step to get it done. >> And it's all about people, process and technology. Those three together are absolutely critical. >> It sure is. >> Excellent, Hilary, thank you for joining Dave and me on theCUBE talking about the Slalom partnership. What you're doing with Snowflake and on top of Snowflake we appreciate your time and your insights. >> Thank you so much, really appreciate it. >> Dave: Thanks Hilary. >> For our guest and Dave Vellante, I am Lisa Martin. You're watching theCUBE's live coverage from Snowflake Summit 22, live from Las Vegas. (gentle music)

>>Alteryx is a company with a long history that goes all the way back to the late 1990s. Now the one consistent theme over 20 plus years has been that Ultrix has always been a data company early in the big data and Hadoop cycle. It saw the need to combine and prep different data types so that organizations could analyze data and take action Altrix and similar companies played a critical role in helping companies become data-driven. The problem was the decade of big data, brought a lot of complexities and required immense skills just to get the technology to work as advertised this in turn limited, the pace of adoption and the number of companies that could really lean in and take advantage of the cloud began to change all that and set the foundation for today's theme to Zuora of digital transformation. We hear that phrase a ton digital transformation. >>People used to think it was a buzzword, but of course we learned from the pandemic that if you're not a digital business, you're out of business and a key tenant of digital transformation is democratizing data, meaning enabling, not just hypo hyper specialized experts, but anyone business users to put data to work. Now back to Ultrix, the company has embarked on a major transformation of its own. Over the past couple of years, brought in new management, they've changed the way in which it engaged with customers with the new subscription model and it's topgraded its talent pool. 2021 was even more significant because of two acquisitions that Altrix made hyper Ana and trifecta. Why are these acquisitions important? Well, traditionally Altryx sold to business analysts that were part of the data pipeline. These were fairly technical people who had certain skills and were trained in things like writing Python code with hyper Ana Altryx has added a new persona, the business user, anyone in the business who wanted to gain insights from data and, or let's say use AI without having to be a deep technical expert. >>And then Trifacta a company started in the early days of big data by cube alum, Joe Hellerstein and his colleagues at Berkeley. They knocked down the data engineering persona, and this gives Altryx a complimentary extension into it where things like governance and security are paramount. So as we enter 2022, the post isolation economy is here and we do so with a digital foundation built on the confluence of cloud native technologies, data democratization and machine intelligence or AI, if you prefer. And Altryx is entering that new era with an expanded portfolio, new go-to market vectors, a recurring revenue business model, and a brand new outlook on how to solve customer problems and scale a company. My name is Dave Vellante with the cube and I'll be your host today. And the next hour, we're going to explore the opportunities in this new data market. And we have three segments where we dig into these trends and themes. First we'll talk to Jay Henderson, vice president of product management at Ultrix about cloud acceleration and simplifying complex data operations. Then we'll bring in Suresh Vetol who's the chief product officer at Altrix and Adam Wilson, the CEO of Trifacta, which of course is now part of Altrix. And finally, we'll hear about how Altryx is partnering with snowflake and the ecosystem and how they're integrating with data platforms like snowflake and what this means for customers. And we may have a few surprises sprinkled in as well into the conversation let's get started. >>We're kicking off the program with our first segment. Jay Henderson is the vice president of product management Altryx and we're going to talk about the trends and data, where we came from, how we got here, where we're going. We get some launch news. Well, Jay, welcome to the cube. >>Great to be here, really excited to share some of the things we're working on. >>Yeah. Thank you. So look, you have a deep product background, product management, product marketing, you've done strategy work. You've been around software and data, your entire career, and we're seeing the collision of software data cloud machine intelligence. Let's start with the customer and maybe we can work back from there. So if you're an analytics or data executive in an organization, w J what's your north star, where are you trying to take your company from a data and analytics point of view? >>Yeah, I mean, you know, look, I think all organizations are really struggling to get insights out of their data. I think one of the things that we see is you've got digital exhaust, creating large volumes of data storage is really cheap, so it doesn't cost them much to keep it. And that results in a situation where the organization's, you know, drowning in data, but somehow still starving for insights. And so I think, uh, you know, when I talk to customers, they're really excited to figure out how they can put analytics in the hands of every single person in their organization, and really start to democratize the analytics, um, and, you know, let the, the business users and the whole organization get value out of all that data they have. >>And we're going to dig into that throughout this program data, I like to say is plentiful insights, not always so much. Tell us about your launch today, Jay, and thinking about the trends that you just highlighted, the direction that your customers want to go and the problems that you're solving, what role does the cloud play in? What is what you're launching? How does that fit in? >>Yeah, we're, we're really excited today. We're launching the Altryx analytics cloud. That's really a portfolio of cloud-based solutions that have all been built from the ground up to be cloud native, um, and to take advantage of things like based access. So that it's really easy to give anyone access, including folks on a Mac. Um, it, you know, it also lets you take advantage of elastic compute so that you can do, you know, in database processing and cloud native, um, solutions that are gonna scale to solve the most complex problems. So we've got a portfolio of solutions, things like designer cloud, which is our flagship designer product in a browser and on the cloud, but we've got ultra to machine learning, which helps up-skill regular old analysts with advanced machine learning capabilities. We've got auto insights, which brings a business users into the fold and automatically unearths insights using AI and machine learning. And we've got our latest edition, which is Trifacta that helps data engineers do data pipelining and really, um, you know, create a lot of the underlying data sets that are used in some of this, uh, downstream analytics. >>Let's dig into some of those roles if we could a little bit, I mean, you've traditionally Altryx has served the business analysts and that's what designer cloud is fit for, I believe. And you've explained, you know, kind of the scope, sorry, you've expanded that scope into the, to the business user with hyper Anna. And we're in a moment we're going to talk to Adam Wilson and Suresh, uh, about Trifacta and that recent acquisition takes you, as you said, into the data engineering space in it. But in thinking about the business analyst role, what's unique about designer cloud cloud, and how does it help these individuals? >>Yeah, I mean, you know, really, I go back to some of the feedback we've had from our customers, which is, um, you know, they oftentimes have dozens or hundreds of seats of our designer desktop product, you know, really, as they look to take the next step, they're trying to figure out how do I give access to that? Those types of analytics to thousands of people within the organization and designer cloud is, is really great for that. You've got the browser-based interface. So if folks are on a Mac, they can really easily just pop, open the browser and get access to all of those, uh, prep and blend capabilities to a lot of the analysis we're doing. Um, it's a great way to scale up access to the analytics and then start to put it in the hands of really anyone in the organization, not just those highly skilled power users. >>Okay, great. So now then you add in the hyper Anna acquisition. So now you're targeting the business user Trifacta comes into the mix that deeper it angle that we talked about, how does this all fit together? How should we be thinking about the new Altryx portfolio? >>Yeah, I mean, I think it's pretty exciting. Um, you know, when you think about democratizing analytics and providing access to all these different groups of people, um, you've not been able to do it through one platform before. Um, you know, it's not going to be one interface that meets the, of all these different groups within the organization. You really do need purpose built specialized capabilities for each group. And finally, today with the announcement of the alternates analytics cloud, we brought together all of those different capabilities, all of those different interfaces into a single in the end application. So really finally delivering on the promise of providing analytics to all, >>How much of this you've been able to share with your customers and maybe your partners. I mean, I know OD is fairly new, but if you've been able to get any feedback from them, what are they saying about it? >>Uh, I mean, it's, it's pretty amazing. Um, we ran a early access, limited availability program that led us put a lot of this technology in the hands of over 600 customers, um, over the last few months. So we have gotten a lot of feedback. I tell you, um, it's been overwhelmingly positive. I think organizations are really excited to unlock the insights that have been hidden in all this data. They've got, they're excited to be able to use analytics in every decision that they're making so that the decisions they have or more informed and produce better business outcomes. Um, and, and this idea that they're going to move from, you know, dozens to hundreds or thousands of people who have access to these kinds of capabilities, I think has been a really exciting thing that is going to accelerate the transformation that these customers are on. >>Yeah, those are good. Good, good numbers for, for preview mode. Let's, let's talk a little bit about vision. So it's democratizing data is the ultimate goal, which frankly has been elusive for most organizations over time. How's your cloud going to address the challenges of putting data to work across the entire enterprise? >>Yeah, I mean, I tend to think about the future and some of the investments we're making in our products and our roadmap across four big themes, you know, in the, and these are really kind of enduring themes that you're going to see us making investments in over the next few years, the first is having cloud centricity. You know, the data gravity has been moving to the cloud. We need to be able to provide access, to be able to ingest and manipulate that data, to be able to write back to it, to provide cloud solution. So the first one is really around cloud centricity. The second is around big data fluency. Once you have all of the data, you need to be able to manipulate it in a performant manner. So having the elastic cloud infrastructure and in database processing is so important, the third is around making AI a strategic advantage. >>So, uh, you know, getting everyone involved and accessing AI and machine learning to unlock those insights, getting it out of the hands of the small group of data scientists, putting it in the hands of analysts and business users. Um, and then the fourth thing is really providing access across the entire organization. You know, it and data engineers, uh, as well as business owners and analysts. So, um, cloud centricity, big data fluency, um, AI is a strategic advantage and, uh, personas across the organization are really the four big themes you're going to see us, uh, working on over the next few months and, uh, coming coming year. >>That's good. Thank you for that. So, so on a related question, how do you see the data organizations evolving? I mean, traditionally you've had, you know, monolithic organizations, uh, very specialized or I might even say hyper specialized roles and, and your, your mission of course is the customer. You, you, you, you and your customers, they want to democratize the data. And so it seems logical that domain leaders are going to take more responsibility for data, life cycles, data ownerships, low code becomes more important. And perhaps this kind of challenges, the historically highly centralized and really specialized roles that I just talked about. How do you see that evolving and, and, and what role will Altryx play? >>Yeah. Um, you know, I think we'll see sort of a more federated systems start to emerge. Those centralized groups are going to continue to exist. Um, but they're going to start to empower, you know, in a much more de-centralized way, the people who are closer to the business problems and have better business understanding. I think that's going to let the centralized highly skilled teams work on, uh, problems that are of higher value to the organization. The kinds of problems where one or 2% lift in the model results in millions of dollars a day for the business. And then by pushing some of the analytics out to, uh, closer to the edge and closer to the business, you'll be able to apply those analytics in every single decision. So I think you're going to see, you know, both the decentralized and centralized models start to work in harmony and a little bit more about almost a federated sort of a way. And I think, you know, the exciting thing for us at Altryx is, you know, we want to facilitate that. We want to give analytic capabilities and solutions to both groups and types of people. We want to help them collaborate better, um, and drive business outcomes with the analytics they're using. >>Yeah. I mean, I think my take on another one, if you could comment is to me, the technology should be an operational detail and it has been the, the, the dog that wags the tail, or maybe the other way around, you mentioned digital exhaust before. I mean, essentially it's digital exhaust coming out of operationals systems that then somehow, eventually end up in the hand of the domain users. And I wonder if increasingly we're going to see those domain users, users, those, those line of business experts get more access. That's your goal. And then even go beyond analytics, start to build data products that could be monetized, and that maybe it's going to take a decade to play out, but that is sort of a new era of data. Do you see it that way? >>Absolutely. We're actually making big investments in our products and capabilities to be able to create analytic applications and to enable somebody who's an analyst or business user to create an application on top of the data and analytics layers that they have, um, really to help democratize the analytics, to help prepackage some of the analytics that can drive more insights. So I think that's definitely a trend we're going to see more. >>Yeah. And to your point, if you can federate the governance and automate that, then that can happen. I mean, that's a key part of it, obviously. So, all right, Jay, we have to leave it there up next. We take a deep dive into the Altryx recent acquisition of Trifacta with Adam Wilson who led Trifacta for more than seven years. It's the recipe. Tyler is the chief product officer at Altryx to explain the rationale behind the acquisition and how it's going to impact customers. Keep it right there. You're watching the cube. You're a leader in enterprise tech coverage. >>It's go time, get ready to accelerate your data analytics journey with a unified cloud native platform. That's accessible for everyone on the go from home to office and everywhere in between effortless analytics to help you go from ideas to outcomes and no time. It's your time to shine. It's Altryx analytics cloud time. >>Okay. We're here with. Who's the chief product officer at Altryx and Adam Wilson, the CEO of Trifacta. Now of course, part of Altryx just closed this quarter. Gentlemen. Welcome. >>Great to be here. >>Okay. So let me start with you. In my opening remarks, I talked about Altrix is traditional position serving business analysts and how the hyper Anna acquisition brought you deeper into the business user space. What does Trifacta bring to your portfolio? Why'd you buy the company? >>Yeah. Thank you. Thank you for the question. Um, you know, we see, uh, we see a massive opportunity of helping, um, brands, um, democratize the use of analytics across their business. Um, every knowledge worker, every individual in the company should have access to analytics. It's no longer optional, um, as they navigate their businesses with that in mind, you know, we know designer and are the products that Altrix has been selling the past decade or so do a really great job, um, addressing the business analysts, uh, with, um, hyper Rana now kind of renamed, um, Altrix auto. We even speak with the business owner and the line of business owner. Who's looking for insights that aren't real in traditional dashboards and so on. Um, but we see this opportunity of really helping the data engineering teams and it organizations, um, to also make better use of analytics. Um, and that's where the drive factor comes in for us. Um, drive factor has the best data engineering cloud in the planet. Um, they have an established track record of working across multiple cloud platforms and helping data engineers, um, do better data pipelining and work better with, uh, this massive kind of cloud transformation that's happening in every business. Um, and so fact made so much sense for us. >>Yeah. Thank you for that. I mean, you, look, you could have built it yourself would have taken, you know, who knows how long, you know, but, uh, so definitely a great time to market move, Adam. I wonder if we could dig into Trifacta some more, I mean, I remember interviewing Joe Hellerstein in the early days. You've talked about this as well, uh, on the cube coming at the problem of taking data from raw refined to an experience point of view. And Joe in the early days, talked about flipping the model and starting with data visualization, something Jeff, her was expert at. So maybe explain how we got here. We used to have this cumbersome process of ETL and you may be in some others changed that model with ELL and then T explain how Trifacta really changed the data engineering game. >>Yeah, that's exactly right. Uh, David, it's been a really interesting journey for us because I think the original hypothesis coming out of the campus research, uh, at Berkeley and Stanford that really birth Trifacta was, you know, why is it that the people who know the data best can't do the work? You know, why is this become the exclusive purview of the highly technical? And, you know, can we rethink this and make this a user experience, problem powered by machine learning that will take some of the more complicated things that people want to do with data and really help to automate those. So, so a broader set of, of users can, um, can really see for themselves and help themselves. And, and I think that, um, there was a lot of pent up frustration out there because people have been told for, you know, for a decade now to be more data-driven and then the whole time they're saying, well, then give me the data, you know, in the shape that I could use it with the right level of quality and I'm happy to be, but don't tell me to be more data-driven and then, and, and not empower me, um, to, to get in there and to actually start to work with the data in meaningful ways. >>And so, um, that was really, you know, what, you know, the origin story of the company and I think is, as we, um, saw over the course of the last 5, 6, 7 years that, um, you know, uh, real, uh, excitement to embrace this idea of, of trying to think about data engineering differently, trying to democratize the, the ETL process and to also leverage all these exciting new, uh, engines and platforms that are out there that allow for processing, you know, ever more diverse data sets, ever larger data sets and new and interesting ways. And that's where a lot of the push-down or the ELT approaches that, you know, I think it could really won the day. Um, and that, and that for us was a hallmark of the solution from the very beginning. >>Yeah, this is a huge point that you're making is, is first of all, there's a large business, it's probably about a hundred billion dollar Tam. Uh, and the, the point you're making, because we've looked, we've contextualized most of our operational systems, but the big data pipeline is hasn't gotten there. But, and maybe we could talk about that a little bit because democratizing data is Nirvana, but it's been historically very difficult. You've got a number of companies it's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome, but it's been hard. And so what's going to be different about Altryx as you bring these puzzle pieces together, how is this going to impact your customers who would like to take that one? >>Yeah, maybe, maybe I'll take a crack at it. And Adam will, um, add on, um, you know, there hasn't been a single platform for analytics, automation in the enterprise, right? People have relied on, uh, different products, um, to solve kind of, uh, smaller problems, um, across this analytics, automation, data transformation domain. Um, and, um, I think uniquely Alcon's has that opportunity. Uh, we've got 7,000 plus customers who rely on analytics for, um, data management, for analytics, for AI and ML, uh, for transformations, uh, for reporting and visualization for automated insights and so on. Um, and so by bringing drive factor, we have the opportunity to scale this even further and solve for more use cases, expand the scenarios where it's applied and so multiple personas. Um, and we just talked about the data engineers. They are really a growing stakeholder in this transformation of data and analytics. >>Yeah, good. Maybe we can stay on this for a minute cause you, you you're right. You bring it together. Now at least three personas the business analyst, the end user slash business user. And now the data engineer, which is really out of an it role in a lot of companies, and you've used this term, the data engineering cloud, what is that? How is it going to integrate in with, or support these other personas? And, and how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores? >>Yeah, no, that's great. Uh, yeah, I think for us, we really looked at this and said, you know, we want to build an open and interactive cloud platform for data engineers, you know, to collaboratively profile pipeline, um, and prepare data for analysis. And that really meant collaborating with the analysts that were in the line of business. And so this is why a big reason why this combination is so magic because ultimately if we can get the data engineers that are creating the data products together with the analysts that are in the line of business that are driving a lot of the decision making and allow for that, what I would describe as collaborative curation of the data together, so that you're starting to see, um, uh, you know, increasing returns to scale as this, uh, as this rolls out. I just think that is an incredibly powerful combination and, and frankly, something that the market is not crack the code on yet. And so, um, I think when we, when I sat down with Suresh and with mark and the team at Ultrix, that was really part of the, the, the big idea, the big vision that was painted and got us really energized about the acquisition and about the potential of the combination. >>And you're really, you're obviously writing the cloud and the cloud native wave. Um, and, but specifically we're seeing, you know, I almost don't even want to call it a data warehouse anyway, because when you look at what's, for instance, Snowflake's doing, of course their marketing is around the data cloud, but I actually think there's real justification for that because it's not like the traditional data warehouse, right. It's, it's simplified get there fast, don't necessarily have to go through the central organization to share data. Uh, and, and, and, but it's really all about simplification, right? Isn't that really what the democratization comes down to. >>Yeah. It's simplification and collaboration. Right. I don't want to, I want to kind of just what Adam said resonates with me deeply. Um, analytics is one of those, um, massive disciplines inside an enterprise that's really had the weakest of tools. Um, and we just have interfaces to collaborate with, and I think truly this was all drinks and a superpower was helping the analysts get more out of their data, get more out of the analytics, like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources, um, understanding data models better, I think, um, uh, curating those insights. I boring Adam's phrase again. Um, I think that creates a real value inside the organization because frankly in scaling analytics and democratizing analytics and data, we're still in such early phases of this journey. >>So how should we think about designer cloud, which is from Altrix it's really been the on-prem and the server desktop offering. And of course Trifacta is with cloud cloud data warehouses. Right. Uh, how, how should we think about those two products? Yeah, >>I think, I think you should think about them. And, uh, um, as, as very complimentary right designer cloud really shares a lot of DNA and heritage with, uh, designer desktop, um, the low code tooling and that interface, uh, the really appeals to the business analysts, um, and gets a lot of the things that they do well, we've also built it with interoperability in mind, right. So if you started building your workflows in designer desktop, you want to share that with design and cloud, we want to make it super easy for you to do that. Um, and I think over time now we're only a week into, um, this Alliance with, um, with, um, Trifacta, um, I think we have to get deeper inside to think about what does the data engineer really need? What's the business analysts really need and how to design a cloud, and Trifacta really support both of those requirements, uh, while kind of continue to build on the trifecta on the amazing Trifacta cloud platform. >>You know, >>I think we're just going to say, I think that's one of the things that, um, you know, creates a lot of, uh, opportunity as we go forward, because ultimately, you know, Trifacta took a platform, uh, first mentality to everything that we built. So thinking about openness and extensibility and, um, and how over time people could build things on top of factor that are a variety of analytic tool chain, or analytic applications. And so, uh, when you think about, um, Ultrix now starting to, uh, to move some of its capabilities or to provide additional capabilities, uh, in the cloud, um, you know, Trifacta becomes a platform that can accelerate, you know, all of that work and create, uh, uh, a cohesive set of, of cloud-based services that, um, share a common platform. And that maintains independence because both companies, um, have been, uh, you know, fiercely independent, uh, and, and really giving people choice. >>Um, so making sure that whether you're, uh, you know, picking one cloud platform and other, whether you're running things on the desktop, uh, whether you're running in hybrid environments, that, um, no matter what your decision, um, you're always in a position to be able to get out your data. You're always in a position to be able to cleanse transform shape structure, that data, and ultimately to deliver, uh, the analytics that you need. And so I think in that sense, um, uh, you know, this, this again is another reason why the combination, you know, fits so well together, giving people, um, the choice. Um, and as they, as they think about their analytics strategy and their platform strategy going forward, >>Yeah. I make a chuckle, but one of the reasons I always liked Altrix is cause you kinda did the little end run on it. It can be a blocker sometimes, but that created problems, right? Because the organization said, wow, this big data stuff has taken off, but we need security. We need governance. And it's interesting because you've got, you know, ETL has been complex, whereas the visualization tools, they really, you know, really weren't great at governance and security. It took some time there. So that's not, not their heritage. You're bringing those worlds together. And I'm interested, you guys just had your sales kickoff, you know, what was their reaction like? Uh, maybe Suresh, you could start off and maybe Adam, you could bring us home. >>Um, thanks for asking about our sales kickoff. So we met for the first time and you've got a two years, right. For, as, as it is for many of us, um, in person, uh, um, which I think was a, was a real breakthrough as Qualtrics has been on its transformation journey. Uh, we added a Trifacta to, um, the, the potty such as the tour, um, and getting all of our sales teams and product organizations, um, to meet in person in one location. I thought that was very powerful for other the company. Uh, but then I tell you, um, um, the reception for Trifacta was beyond anything I could have imagined. Uh, we were working out him and I will, when he's so hot on, on the deal and the core hypotheses and so on. And then you step back and you're going to share the vision with the field organization, and it blows you away, the energy that it creates among our sellers out of partners. >>And I'm sure Madam will and his team were mocked, um, every single day, uh, with questions and opportunities to bring them in. But Adam, maybe you should share. Yeah, no, it was, uh, it was through the roof. I mean, uh, uh, the, uh, the amount of energy, the, uh, certainly how welcoming everybody was, uh, uh, you know, just, I think the story makes so much sense together. I think culturally, the company is, are very aligned. Um, and, uh, it was a real, uh, real capstone moment, uh, to be able to complete the acquisition and to, and to close and announced, you know, at the kickoff event. And, um, I think, you know, for us, when we really thought about it, you know, when we ended, the story that we told was just, you have this opportunity to really cater to what the end users care about, which is a lot about interactivity and self-service, and at the same time. >>And that's, and that's a lot of the goodness that, um, that Altryx is, has brought, you know, through, you know, you know, years and years of, of building a very vibrant community of, you know, thousands, hundreds of thousands of users. And on the other side, you know, Trifacta bringing in this data engineering focus, that's really about, uh, the governance things that you mentioned and the openness, um, that, that it cares deeply about. And all of a sudden, now you have a chance to put that together into a complete story where the data engineering cloud and analytics, automation, you know, coming together. And, um, and I just think, you know, the lights went on, um, you know, for people instantaneously and, you know, this is a story that, um, that I think the market is really hungry for. And certainly the reception we got from, uh, from the broader team at kickoff was, uh, was a great indication. >>Well, I think the story hangs together really well, you know, one of the better ones I've seen in, in this space, um, and, and you guys coming off a really, really strong quarter. So congratulations on that jets. We have to leave it there. I really appreciate your time today. Yeah. Take a look at this short video. And when we come back, we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses. You're watching the cube you're leader in enterprise tech coverage. >>This is your data housed neatly insecurely in the snowflake data cloud. And all of it has potential the potential to solve complex business problems, deliver personalized financial offerings, protect supply chains from disruption, cut costs, forecast, grow and innovate. All you need to do is put your data in the hands of the right people and give it an opportunity. Luckily for you. That's the easy part because snowflake works with Alteryx and Alteryx turns data into breakthroughs with just a click. Your organization can automate analytics with drag and drop building blocks, easily access snowflake data with both sequel and no SQL options, share insights, powered by Alteryx data science and push processing to snowflake for lightning, fast performance, you get answers you can put to work in your teams, get repeatable processes they can share in that's exciting because not only is your data no longer sitting around in silos, it's also mobilized for the next opportunity. Turn your data into a breakthrough Alteryx and snowflake >>Okay. We're back here in the queue, focusing on the business promise of the cloud democratizing data, making it accessible and enabling everyone to get value from analytics, insights, and data. We're now moving into the eco systems segment the power of many versus the resources of one. And we're pleased to welcome. Barb Hills camp was the senior vice president partners and alliances at Ultrix and a special guest Terek do week head of technology alliances at snowflake folks. Welcome. Good to see you. >>Thank you. Thanks for having me. Good to see >>Dave. Great to see you guys. So cloud migration, it's one of the hottest topics. It's the top one of the top initiatives of senior technology leaders. We have survey data with our partner ETR it's number two behind security, and just ahead of analytics. So we're hovering around all the hot topics here. Barb, what are you seeing with respect to customer, you know, cloud migration momentum, and how does the Ultrix partner strategy fit? >>Yeah, sure. Partners are central company's strategy. They always have been. We recognize that our partners have deep customer relationships. And when you connect that with their domain expertise, they're really helping customers on their cloud and business transformation journey. We've been helping customers achieve their desired outcomes with our partner community for quite some time. And our partner base has been growing an average of 30% year over year, that partner community and strategy now addresses several kinds of partners, spanning solution providers to global SIS and technology partners, such as snowflake and together, we help our customers realize the business promise of their journey to the cloud. Snowflake provides a scalable storage system altereds provides the business user friendly front end. So for example, it departments depend on snowflake to consolidate data across systems into one data cloud with Altryx business users can easily unlock that data in snowflake solving real business outcomes. Our GSI and solution provider partners are instrumental in providing that end to end benefit of a modern analytic stack in the cloud providing platform, guidance, deployment, support, and other professional services. >>Great. Let's get a little bit more into the relationship between Altrix and S in snowflake, the partnership, maybe a little bit about the history, you know, what are the critical aspects that we should really focus on? Barb? Maybe you could start an Interra kindly way in as well. >>Yeah, so the relationship started in 2020 and all shirts made a big bag deep with snowflake co-innovating and optimizing cloud use cases together. We are supporting customers who are looking for that modern analytic stack to replace an old one or to implement their first analytic strategy. And our joint customers want to self-serve with data-driven analytics, leveraging all the benefits of the cloud, scalability, accessibility, governance, and optimizing their costs. Um, Altrix proudly achieved. Snowflake's highest elite tier in their partner program last year. And to do that, we completed a rigorous third party testing process, which also helped us make some recommended improvements to our joint stack. We wanted customers to have confidence. They would benefit from high quality and performance in their investment with us then to help customers get the most value out of the destroyed solution. We developed two great assets. One is the officer starter kit for snowflake, and we coauthored a joint best practices guide. >>The starter kit contains documentation, business workflows, and videos, helping customers to get going more easily with an altered since snowflake solution. And the best practices guide is more of a technical document, bringing together experiences and guidance on how Altryx and snowflake can be deployed together. Internally. We also built a full enablement catalog resources, right? We wanted to provide our account executives more about the value of the snowflake relationship. How do we engage and some best practices. And now we have hundreds of joint customers such as Juniper and Sainsbury who are actively using our joint solution, solving big business problems much faster. >>Cool. Kara, can you give us your perspective on the partnership? >>Yeah, definitely. Dave, so as Barb mentioned, we've got this standing very successful partnership going back years with hundreds of happy joint customers. And when I look at the beginning, Altrix has helped pioneer the concept of self-service analytics, especially with use cases that we worked on with for, for data prep for BI users like Tableau and as Altryx has evolved to now becoming from data prep to now becoming a full end to end data science platform. It's really opened up a lot more opportunities for our partnership. Altryx has invested heavily over the last two years in areas of deep integration for customers to fully be able to expand their investment, both technologies. And those investments include things like in database pushed down, right? So customers can, can leverage that elastic platform, that being the snowflake data cloud, uh, with Alteryx orchestrating the end to end machine learning workflows Alteryx also invested heavily in snow park, a feature we released last year around this concept of data programmability. So all users were regardless of their business analysts, regardless of their data, scientists can use their tools of choice in order to consume and get at data. And now with Altryx cloud, we think it's going to open up even more opportunities. It's going to be a big year for the partnership. >>Yeah. So, you know, Terike, we we've covered snowflake pretty extensively and you initially solve what I used to call the, I still call the snake swallowing the basketball problem and cloud data warehouse changed all that because you had virtually infinite resources, but so that's obviously one of the problems that you guys solved early on, but what are some of the common challenges or patterns or trends that you see with snowflake customers and where does Altryx come in? >>Sure. Dave there's there's handful, um, that I can come up with today, the big challenges or trends for us, and Altrix really helps us across all of them. Um, there are three particular ones I'm going to talk about the first one being self-service analytics. If we think about it, every organization is trying to democratize data. Every organization wants to empower all their users, business users, um, you know, the, the technology users, but the business users, right? I think every organization has realized that if everyone has access to data and everyone can do something with data, it's going to make them competitively, give them a competitive advantage with Altrix is something we share that vision of putting that power in the hands of everyday users, regardless of the skillsets. So, um, with self-service analytics, with Ultrix designer they've they started out with self-service analytics as the forefront, and we're just scratching the surface. >>I think there was an analyst, um, report that shows that less than 20% of organizations are truly getting self-service analytics to their end users. Now, with Altryx going to Ultrix cloud, we think that's going to be a huge opportunity for us. Um, and then that opens up the second challenge, which is machine learning and AI, every organization is trying to get predictive analytics into every application that they have in order to be competitive in order to be competitive. Um, and with Altryx creating this platform so they can cater to both the everyday business user, the quote unquote, citizen data scientists, and making a code friendly for data scientists to be able to get at their notebooks and all the different tools that they want to use. Um, they fully integrated in our snow park platform, which I talked about before, so that now we get an end to end solution caring to all, all lines of business. >>And then finally this concept of data marketplaces, right? We, we created snowflake from the ground up to be able to solve the data sharing problem, the big data problem, the data sharing problem. And Altryx um, if we look at mobilizing your data, getting access to third-party datasets, to enrich with your own data sets, to enrich with, um, with your suppliers and with your partners, data sets, that's what all customers are trying to do in order to get a more comprehensive 360 view, um, within their, their data applications. And so with Altryx alterations, we're working on third-party data sets and marketplaces for quite some time. Now we're working on how do we integrate what Altrix is providing with the snowflake data marketplace so that we can enrich these workflows, these great, great workflows that Altrix writing provides. Now we can add third party data into that workflow. So that opens up a ton of opportunities, Dave. So those are three I see, uh, easily that we're going to be able to solve a lot of customer challenges with. >>So thank you for that. Terrick so let's stay on cloud a little bit. I mean, Altrix is undergoing a major transformation, big focus on the cloud. How does this cloud launch impact the partnership Terike from snowflakes perspective and then Barb, maybe, please add some color. >>Yeah, sure. Dave snowflake started as a cloud data platform. We saw our founders really saw the challenges that customers are having with becoming data-driven. And the biggest challenge was the complexity of having imagine infrastructure to even be able to do it, to get applications off the ground. And so we created something to be cloud-native. We created to be a SAS managed service. So now that that Altrix is moving to the same model, right? A cloud platform, a SAS managed service, we're just, we're just removing more of the friction. So we're going to be able to start to package these end to end solutions that are SAS based that are fully managed. So customers can, can go faster and they don't have to worry about all of the underlying complexities of, of, of stitching things together. Right? So, um, so that's, what's exciting from my viewpoint >>And I'll follow up. So as you said, we're investing heavily in the cloud a year ago, we had two pre desktop products, and today we have four cloud products with cloud. We can provide our users with more flexibility. We want to make it easier for the users to leverage their snowflake data in the Alteryx platform, whether they're using our beloved on-premise solution or the new cloud products were committed to that continued investment in the cloud, enabling our joint partner solutions to meet customer requirements, wherever they store their data. And we're working with snowflake, we're doing just that. So as customers look for a modern analytic stack, they expect that data to be easily accessible, right within a fast, secure and scalable platform. And the launch of our cloud strategy is a huge leap forward in making Altrix more widely accessible to all users in all types of roles, our GSI and our solution provider partners have asked for these cloud capabilities at scale, and they're excited to better support our customers, cloud and analytic >>Are. How about you go to market strategy? How would you describe your joint go to market strategy with snowflake? >>Sure. It's simple. We've got to work backwards from our customer's challenges, right? Driving transformation to solve problems, gain efficiencies, or help them save money. So whether it's with snowflake or other GSI, other partner types, we've outlined a joint journey together from recruit solution development, activation enablement, and then strengthening our go to market strategies to optimize our results together. We launched an updated partner program and within that framework, we've created new benefits for our partners around opportunity registration, new role based enablement and training, basically extending everything we do internally for our own go-to-market teams to our partners. We're offering partner, marketing resources and funding to reach new customers together. And as a matter of fact, we recently launched a fantastic video with snowflake. I love this video that very simply describes the path to insights starting with your snowflake data. Right? We do joint customer webinars. We're working on joint hands-on labs and have a wonderful landing page with a lot of assets for our customers. Once we have an interested customer, we engage our respective account managers, collaborating through discovery questions, proof of concepts really showcasing the desired outcome. And when you combine that with our partners technology or domain expertise, it's quite powerful, >>Dark. How do you see it? You'll go to market strategy. >>Yeah. Dave we've. Um, so we initially started selling, we initially sold snowflake as technology, right? Uh, looking at positioning the diff the architectural differentiators and the scale and concurrency. And we noticed as we got up into the larger enterprise customers, we're starting to see how do they solve their business problems using the technology, as well as them coming to us and saying, look, we want to also know how do you, how do you continue to map back to the specific prescriptive business problems we're having? And so we shifted to an industry focus last year, and this is an area where Altrix has been mature for probably since their inception selling to the line of business, right? Having prescriptive use cases that are particular to an industry like financial services, like retail, like healthcare and life sciences. And so, um, Barb talked about these, these starter kits where it's prescriptive, you've got a demo and, um, a way that customers can get off the ground and running, right? >>Cause we want to be able to shrink that time to market, the time to value that customers can watch these applications. And we want to be able to, to tell them specifically how we can map back to their business initiatives. So I see a huge opportunity to align on these industry solutions. As BARR mentioned, we're already doing that where we've released a few around financial services working in healthcare and retail as well. So that is going to be a way for us to allow customers to go even faster and start to map two lines of business with Alteryx. >>Great. Thanks Derek. Bob, what can we expect if we're observing this relationship? What should we look for in the coming year? >>A lot specifically with snowflake, we'll continue to invest in the partnership. Uh, we're co innovators in this journey, including snow park extensibility efforts, which Derek will tell you more about shortly. We're also launching these great news strategic solution blueprints, and extending that at no charge to our partners with snowflake, we're already collaborating with their retail and CPG team for industry blueprints. We're working with their data marketplace team to highlight solutions, working with that data in their marketplace. More broadly, as I mentioned, we're relaunching the ultra partner program designed to really better support the unique partner types in our global ecosystem, introducing new benefits so that with every partner, achievement or investment with ultra score, providing our partners with earlier access to benefits, um, I could talk about our program for 30 minutes. I know we don't have time. The key message here Alteryx is investing in our partner community across the business, recognizing the incredible value that they bring to our customers every day. >>Tarik will give you the last word. What should we be looking for from, >>Yeah, thanks. Thanks, Dave. As BARR mentioned, Altrix has been the forefront of innovating with us. They've been integrating into, uh, making sure again, that customers get the full investment out of snowflake things like in database push down that I talked about before that extensibility is really what we're excited about. Um, the ability for Ultrix to plug into this extensibility framework that we call snow park and to be able to extend out, um, ways that the end users can consume snowflake through, through sequel, which has traditionally been the way that you consume snowflake as well as Java and Scala, not Python. So we're excited about those, those capabilities. And then we're also excited about the ability to plug into the data marketplace to provide third party data sets, right there probably day sets in, in financial services, third party, data sets and retail. So now customers can build their data applications from end to end using ultrasound snowflake when the comprehensive 360 view of their customers, of their partners, of even their employees. Right? I think it's exciting to see what we're going to be able to do together with these upcoming innovations. Great >>Barb Tara, thanks so much for coming on the program, got to leave it right there in a moment, I'll be back with some closing thoughts in a summary, don't go away. >>1200 hours of wind tunnel testing, 30 million race simulations, 2.4 second pit stops make that 2.3. The sector times out the wazoo, whites are much of this velocity's pressures, temperatures, 80,000 components generating 11.8 billion data points and one analytics platform to make sense of it all. When McLaren needs to turn complex data into insights, they turn to Altryx Qualtrics analytics, automation, >>Okay, let's summarize and wrap up the session. We can pretty much agree the data is plentiful, but organizations continue to struggle to get maximum value out of their data investments. The ROI has been elusive. There are many reasons for that complexity data, trust silos, lack of talent and the like, but the opportunity to transform data operations and drive tangible value is immense collaboration across various roles. And disciplines is part of the answer as is democratizing data. This means putting data in the hands of those domain experts that are closest to the customer and really understand where the opportunity exists and how to best address them. We heard from Jay Henderson that we have all this data exhaust and cheap storage. It allows us to keep it for a long time. It's true, but as he pointed out that doesn't solve the fundamental problem. Data is spewing out from our operational systems, but much of it lacks business context for the data teams chartered with analyzing that data. >>So we heard about the trend toward low code development and federating data access. The reason this is important is because the business lines have the context and the more responsibility they take for data, the more quickly and effectively organizations are going to be able to put data to work. We also talked about the harmonization between centralized teams and enabling decentralized data flows. I mean, after all data by its very nature is distributed. And importantly, as we heard from Adam Wilson and Suresh Vittol to support this model, you have to have strong governance and service the needs of it and engineering teams. And that's where the trifecta acquisition fits into the equation. Finally, we heard about a key partnership between Altrix and snowflake and how the migration to cloud data warehouses is evolving into a global data cloud. This enables data sharing across teams and ecosystems and vertical markets at massive scale all while maintaining the governance required to protect the organizations and individuals alike. >>This is a new and emerging business model that is very exciting and points the way to the next generation of data innovation in the coming decade. We're decentralized domain teams get more facile access to data. Self-service take more responsibility for quality value and data innovation. While at the same time, the governance security and privacy edicts of an organization are centralized in programmatically enforced throughout an enterprise and an external ecosystem. This is Dave Volante. All these videos are available on demand@theqm.net altrix.com. Thanks for watching accelerating automated analytics in the cloud made possible by Altryx. And thanks for watching the queue, your leader in enterprise tech coverage. We'll see you next time.

>> We're here at the Data Cloud Summit 2020, tracking the rise of the data cloud. And we're talking about the ecosystem powering the next generation of innovation in cloud, you know, for decades, the technology industry has been powered by great products. Well, the cloud introduced a new type of platform that transcended point products and the next generation of cloud platforms is unlocking data-centric ecosystems where access to data is at the core of innovation, tapping the resources of many versus the capabilities of one. Casey McGee is here. He's the vice president of global ISV sales at Microsoft, and he's joined by Colleen Kapase, who is the VP of partnerships and global alliances at Snowflake. Folks, welcome to theCUBE. It's great to see you. >> Thanks Dave, good to see you. Thank you. >> Thanks for having us here. >> You're very welcome. So, Casey, let me start with you please. You know, Microsoft's got a long heritage, of course, working with partners, you're renowned in that regard, built a unbelievable ecosystem, the envy of many in the industry. So if you think about as enterprises, they're speeding up their cloud adoption, what are you seeing as the role and the importance of ecosystem, the ISV ecosystem specifically, in helping make customers' outcomes successful? >> Yeah, let me start by saying we have a 45 year history of partnership, so from our very beginning as a company, we invested to build these partnerships. And so let me start by saying from day one, we looked at a diverse ecosystem as one of the most important strategies for us, both to bring innovation to customers and also to drive growth. And so we're looking to build that environment even today. So 45 years later, focused on how do we zero in on the business outcomes that matter most to customers, usually identified by the industry that they're serving. So really building an ecosystem that helps us serve both the customers and the business outcomes they're looking to drive. And so we're building that ecosystem of ISVs on the Microsoft cloud and focused on bringing that innovation as a platform provider through those companies. >> So Casey, let's stay on that for a moment, if we can. I mean, you work with a lot of ISVs and you got a big portfolio of your own solutions. Now, sometimes they overlap with the ISV offerings of your partners. How do you balance the focus on first party solutions and third-party ISV partner solutions? >> Yeah, first and foremost, we're a platform company. So our whole intent is to bring value to that partner ecosystem. Well, sometimes that means we may have offers in market that may compliment one another. Our focus is really on serving the customer. So anytime we see that, we're looking at what is the most desired outcome for our customer, driving innovation into that specific business requirement. So for us, it's always focusing on the customer, and really zeroing in on making sure that we're solving their business problems. Sometimes we do that together with partners like Snowflake. Sometimes that means we do that on our own, but the key for us is really deeply understanding what's important to the customer and then bringing the best of the Microsoft and Snowflake scenarios to bear. >> You know, Casey, I appreciate that. A lot times people say "Dave, don't ask me that question. It's kind of uncomfortable." So Colleen, I want to bring you into the discussion. How does Snowflake view this dynamic, where you're simultaneously partnering and competing sometimes with some of the big cloud companies on the planet? >> Yeah, Dave, I think it's a great question, and really in this era of innovation, so many large companies like Microsoft are so diverse in their product set, it's almost impossible for them to not have some overlap with most of their ecosystem. But I think Casey said it really well, as long as we stay laser focused on the customer, and there are a lot of very happy Snowflake customers and happy Azure customers, we really win together. And I think we're finding ways in which we're working better and better together, from a technology standpoint, and from a field standpoint. And customers want to see us come together and bring best of breed solutions. So I think we're doing a lot better, and I'm looking forward to our future, too. >> So Casey, Snowflake, you know, they're really growing, they've got a pretty large footprint on Azure. You're talking hundreds of customers here that are active on that platform. I wonder if you could talk about the product integration points that you kind of completed initially, and then kind of what's on the horizon that you see as particularly important for your joint customers? >> You have to say, so one of the things that I love about this partnership is that, well, we start with what the customer wants. We bring that back into the engineering-level relationship that we have between the two companies. And so that's produced some pretty incredibly rich functionality together. So let me start by saying, you know, we've got eight Azure regions today with nine coming on soon. And so we have a geographic diversity that is important for many of our customers. We've also got a series of engineering-level integrations that we've already built. So that's functionality for Azure Private Link, as well as integration between Power BI, Azure Data Factory, and Azure Data Lake, all of this back again to serve the business outcomes that are required for our customers. So it's this level of integration that I think really speaks to the power of the partnership. So we are intently focused on the democratization of data. So we know that Snowflake is the premier partner to help us do that. So getting that right is key to enabling high concurrency use cases with large numbers of businesses, users coming together, and getting the performance they expect. >> Yeah, I appreciate that Casey, because a lot of times I'll, you know, I'll look at the press release. Sometimes we laugh, we call them Barney deals. You know, "I love you. You love me." But I listen for the word engineering and integration. Those are sort of important triggers. Colleen, or Casey too, but I want to start with Colleen. I mean, anything you would add to that, are there things that you guys have worked on together that you're particularly proud of, or maybe that have pushed the envelope and enabled new capabilities for customers where they've given you great feedback? Any examples you can share? >> Great question. And we're definitely focusing on making sure stability is a core value for both of us, so that what we offer, that our customers can trust, is going to work well and be dependable, so that's a key focus for us. We're also looking at how can we advance into the future, what can we do around machine learning, it's an area that's really exciting for a lot of the CXO-level leadership at our customers, so we're certainly focused on that. And also looking at Power BI and the visualization of how do we bring these solutions together as well. I'd also say at the same time, we're trying to make the buying experience frictionless for our customers, so we're also leveraging and innovating with Azure's Marketplace, so that our customers can easily acquire Snowflake together with Azure. And even that is being helpful for our customers. Casey, what are your thoughts, too? >> Yeah, let me add to that. I think the work that we've done with Power BI is pretty, pretty powerful. I mean, ultimately, we've got customers out there that are looking to better visualize the data, better inform decisions that they're making. So as much as AI and ML and the inherent power of the data that's being stored within Snowflake is important in and of itself, Power BI really unlocks that and helps drive better decisions, better visualization, and help drive to decision outcomes that are important to the customer. So I love the work that we're doing on Power BI and Snowflake. >> Yeah, and you guys both mentioned, you know, machine learning. I mean, they really are an ecosystem of tools. And the thing to me about Azure, it's all about optionality. You mentioned earlier, Casey, you guys are a platform. So, you know, customer A may want to use Power BI. Another customer might want to use another visualization tool, fine, from a platform perspective, you really don't care, do you? So I wonder Colleen, if we could, and again, maybe Casey can chime in afterwards. You guys, obviously everybody these days, but you in particular, you're focused on customer outcomes. That's the sort of starting point, and Snowflake for sure has built pretty significant experience working with large enterprises and working alongside of Microsoft to get other partners. In your experience, what are customers really looking for out of the two joint companies when they engage with Snowflake and Microsoft, so that one plus one is, you know, much bigger than two. Maybe Colleen, you could start. >> Yeah, I definitely think that what our customers are looking for is both trust and seamlessness. They just want the technology to work. The beauty of Snowflake is our ease of use. So many customers have questions about their business, more so now in this pandemic world than ever before. So the seamlessness, the ease of use, the frictionless, all of these things really matter to our joint customers, and seeing our teams come together, too, in the field, to show here's how Snowflake and Azure are better together, in your local area, and having examples of customers where we've had win-wins, which I'd say Casey, we're getting more and more of those every day, frankly, so it's pretty exciting times. And having our sales teams work as a partnership, even though we compete, we know where we play well together, and I see us doing that over and over again, more and more, around the world, too, which is really important as Snowflake pushes forward, beyond the North America geographies into stronger and stronger in the global regions, where frankly, Microsoft's had a long, storied history at. That's very exciting, especially in Europe and Asia. >> Casey, anything you'd add to that? >> Yeah. Colleen, it's well said. I think ultimately, what customers are looking for is that when our two companies come together, we bring new innovation, new ideas, new ways to solve old problems. And so I think what I love about this partnership is ultimately when we come together, whether it's engineering teams coming together to build new product, whether it's our sales and marketing teams out in front of the customers, across that spectrum, I think customers are looking for us to help bring new ideas. And I love the fact that we've engineered this partnership to do just that. And ultimately we're focused on how do we come together and build something new and different. And I think we can solve some of the most challenging problems with the power of the data and the innovation that we're bringing to the table. >> I mean, you know, Casey, I mean, everybody's really quite in awe and amazed at Microsoft's transformation, and really openness and willingness to really, change and lean into some of the big waves. I wonder if you could talk about your multi-platform strategy and what problems that you're solving in conjunction with Snowflake. >> Yeah, let me start by saying, you know, I think as much as we appreciate that feedback on the progress that we've been striving for, I mean, we're still learning every day, looking for new opportunities to learn from customers, from partners, and so a lot of what you see on the outside is the result of a really focused culture, really focusing on what's important to our customers, focusing on how do we build diversity and inclusion to everything we do, whether that's within Microsoft, with our partners, our customers, and ultimately, how do we show up as one Microsoft, I call one Microsoft kind of the partner's gift. It's ultimately how do our companies show up together? So I think if you look multi-platform, we have the same concept, right? We have the Microsoft cloud that we're offering out in the marketplace. The Microsoft cloud consists of what we're serving up as far as the platform, consists of what we're serving up for data and AI, modern workplace and business applications. And so this multi-cloud strategy for us is really focused on how do we bring innovation across each of the solution areas that matter most to customers. And so I see really the power of the Snowflake partnership playing in there. >> Awesome. Colleen, are there any examples you can share where, maybe this partnership has unlocked the customer opportunity or unique value? >> Yeah, I can't speak about the customer-specific, but what I can do and say is, Casey and I play very corporate roles in terms of we're thinking about the long-term partnership, we're driving the strategy. But hey, look, we'll get called in, we're working a deal right now, it's almost close of the quarter for us, we're literally working on an opportunity right now, how can we win together, how can we be competitive, the customers, the CIO has asked us to come together, to work on that solution. Very large, well-known brand. And we're able to get up to the very senior levels of our companies very quickly to make decisions on what do we need to do to be better and stronger together. And that's really what a partnership is about, you can do the long-term plans and the strategics and you can have great products, but when your executives can pick up the phone and call each other to work on a particular deal, for a particular customer's need, I think that's where the power of the partnership really comes together, and that's where we're at. And that's been a growth opportunity for us this year, is, wasn't necessarily where we were at, and I really have to thank Casey for that. He's done a ton, getting us the right glue between our executives, making sure the relationships are there, and making sure the trust is there, so when our customers need us to come together, that dialogue and that shared diction of putting customers first is there between both companies. So thank you, Casey. >> Oh, thanks, Colleen, the feeling's mutual. >> Well, I think this is key because as I said up front, we've gone from sort of very product-focused to platform-focused. And now we're tapping the power of the ecosystem. That's not always easy to get all the parts moving together, but we live in this API economy. You could say "Hey, I'm a company, everything's going to be homogeneous. Everything is going to be my stack." And maybe that's one way to solve the problem, but really that's not how customers want to solve the problem. Casey, I'll give you the last word. >> Yeah, let me just end by saying, you know, first off the cultures between our two companies couldn't be more well aligned. So I think ultimately when you ask yourself the question, "What do we do to best show up in front of our customers?" It is, focus on their business outcomes, focus on the things that matter most to them. And this partnership will show up well. And I think ultimately our greatest opportunity is to tap into that need, to that interest. And I couldn't be happier about the partnership and the fact that we are so well aligned. So thank you for that. >> Well guys, thanks very much for coming on theCUBE and unpacking some of the really critical aspects of the ecosystem. It was really a pleasure having you. >> Thank you so much for having us. >> Okay, and thank you for watching. Keep it right there. We've got more great content coming your way at the Data Cloud Summit.

>>We're here at the Data cloud Summer 2020. Tracking the rise of the data cloud. We're talking about the ecosystem powering the next generation of innovation in cloud. You know, for decades, the technology industry has been powered by great products. Well, the cloud introduced a new type of platform that transcended point products. And the next generation of cloud platforms is unlocking data centric ecosystems where access to data is that the core of innovation tapping the resource is of many versus the capabilities of one. Casey McGee is here. He's the vice president of Global I S V. Sales at Microsoft in He's joined by Colleen Capsule, who is the VP of partnerships and global alliances that snowflake folks, welcome to the Cube. It's great to see you. Thanks. >>Very good to see you. Thank you. >>You're >>very welcome. So, Casey, let me start with you, please. Microsoft's get a long heritage. Of course, working with partners renowned in that regard built a unbelievable ecosystem, the envy of many in the industry. So if you think about as enterprises, they're speeding up their cloud adoption. What are you seeing is the role and the importance of ecosystem the i s v ecosystem specifically in helping make customers outcomes successful. >>Yeah, let me start by saying, we have, ah, 45 year history of partnerships. So from our very beginning as a company, we invested to build these partnerships. And so let me start by saying from day one we looked at a diverse ecosystem as one of the most important strategies for us, uh, both to bring innovation to customers and also to drive growth. And so we're looking to build that environment even today. So 45 years later, focused on how do we zero in on the business outcomes that matter most to >>customers usually >>identified by the industry that they're serving and so really building an ecosystem that helps us serve >>both the >>customers and the business outcomes They're looking to drive. And so we're building that ecosystem of SVs on the Microsoft cloud and focused on bringing that innovation as a platform provider through those companies. >>Okay, so let's let's stay on that for a moment if we can. I mean, you work with a lot of I s V s and you got a big portfolio of your own solutions. Now, sometimes they overlap with the I S V offerings of your partners. How do you balance the focus on, you know, First Party Solutions and third party I E S p Partner Solutions? >>Yeah, First and foremost, we're a platform company. So our whole intent is to bring value to that partner ecosystem. While sometimes that means we may have offers in market day that may complement one another. Our focus is really on serving the customer. So anytime we see that we're looking at what is the most desired outcome for a customer driving innovation into that into that specific business requirements? So for us, it's always focusing on the customer and really zeroing in on making sure that we're solving their business problems. Sometimes we do that together with partners like snowflakes. Sometimes that means we do that on our own. But the key for us is really deeply understanding what's important customer and then bringing the best of the Microsoft and Snowflakes scenarios to bear. >>You know, Casey, I appreciate that a lot of times people say Dave, don't Don't ask me that question. It's kind of uncomfortable. So, Colleen, I wanna bring you into the discussion. How does snowflake view this dynamic? Where you simultaneously partnering and competing sometimes with some of the big cloud companies on the planet? >>Yeah, Dave, I think it's a great question. And really, in this era of innovation, so many large companies, like Microsoft are so diverse in their products, said it's almost impossible for them to not have some overlap with most of their ecosystem. But I think Casey said it really well to long as we stay laser focused on the customer. Um, and there are a lot of very happy snowflake customers and happy as your customers, we really win together. And I think we're finding ways in which we're working better and better together, uh, from a technology standpoint and from a field standpoint. And customers want to see us come together and bring best of breed solution. So, um, I think we're doing a lot better, and I'm looking forward to our future to >>So Casey Snowflake, you know, they're really growing. They got a pretty large footprint on on Azure because they're gonna hundreds of customers here, you know, that are active on that platform. I >>wonder if you >>could talk about the product integration points that you kind of completed initially on then kind of what's on the horizon that you see is particularly important for your joint customers. >>You have to say so. One of the things that I love about this partnership is that while we start with what the customer wants, we bring that back into the engineering level relationship that we have between the two companies. And so that's produced some pretty, incredibly rich functionality together. So let me start by saying, You know, we've got eight azure regions today, with nine coming on soon on. So we have a geographic diversity that is important for many of our customers. We've also got a Siris of engineering level integrations that we've already built. So that's functionality for Azure privately because well, as integration between power bi I, Azure data factory and Azure data, like all of this back again to serve the business outcomes that are required for our customers. So it's this level of integration that I think really speaks to the power of the partnership, so were intently focused on the democratization of data. So we know that snowflake is the premier partner to help us do that so getting that right >>is >>key to enabling high concurrency use cases with large numbers of businesses, users coming together and getting the performance they expect. >>I appreciate that case because a lot of times, you know, look at the press release. Sometimes we laugh. We call them Barney deals. You know I love you, You love me. But I listened for, you know, the word engineering and integration. Those air sort of important triggers Colleen or Casey, too. But I want to start with Colleen. Anything you would add to that. Are there things that you guys have worked on together that you're particularly proud of, or maybe that have push the envelope and enabled new capabilities for customers Would have given you great feedback Any any examples you can share >>Great question on beer, definitely focusing on making sure stability is a core value for both of us, and so that what we offer that our customers can trust eyes going to work well and be dependable. So that's a key focus for us. Um, we're also looking at How can we advance into the future? What can we do around machine learning? It's a an area that's really exciting for a lot of the sea XO level leadership at our customers. So we're certainly focused on that. Um, and also looking at power bi I and the visualization of how do we bring these solutions together as well? I'd also say, at the same time, we're trying to make the buying experience frictionless for our customers. So we're also leveraging and innovating with azure is market place so that our customers can easily acquire Snowflake together with azure. And even that is being helpful for our customers. Casey, what are your thoughts too? Let me add to >>that. I think the work that we've done with power bi I is >>pretty >>pretty powerful. I mean, ultimately, we've got customers out there that are looking to better visualize the data better informed decisions that they're making so as much as a i n m l. And the inherent power of the data that's being stored within snowflake, um is important in and of itself. How r b I really unlocks that and helps drive better decisions, better visualization. Onda helped drive to decision outcomes that are important to the customer. So I love the work that we're doing on power by on stuff >>like, Yeah, >>you guys both mentioned, you know, machine learning. I mean, there really are an ecosystem of tools. And the thing to me about azure, it's It's all about Optionality you mentioned earlier case. You guys are a platform. So, you know, customer A may want to use power. Bi I. Another custom might want to use another visualization tool. Find from a platform perspective. You really don't care, do you? So I wonder, Colleen, if we could and again maybe case you can chime in afterwards. You guys, obviously everybody these days, but you particularly focused on customer outcomes. That's the sort of starting point and snowflake for sure, is built pretty significant experience Working with large enterprises and working along the side alongside of Microsoft. You get other partners in your experience what a customer is really looking for out of the two joint companies when they engage with Snowflake and Microsoft, so that one plus one is, you know, much bigger than 2 may be calling. You could start. >>Yeah, I definitely think that what our customers are looking for is both trust and seamlessness. They just want the technology to work. The beauty of snowflake is our ease of use. Um, so many customers have questions about their business. More so now in this guy, um, you know, pandemic world than ever before. So the seamlessness, the ease of use, um, the frictionless. All of these things really matter to our joint customers and seeing our teams come together to in the field to show. Here's how Snowflake and Azure are better together, um, in your local area and having examples of customers where we've had wind winds, which I'd say, Casey, we're getting more and more of those every day, frankly, so pretty exciting times Onda having our sales teams work as a partnership. Even though we compete, we know where we play well together on guy. See us doing that over and over again, more and more around the world to which is really important as snowflake pushes forward, you know, beyond the North America, geography ease into stronger and stronger in the global, um, regions where frankly, Microsoft had a long, storied history at, so that's very exciting, especially in Europe and Asia. >>Okay, so anything you would add to that >>Yeah, >>calling it's well said, I think it ultimately, what customers are looking for is that when our two companies come together, we bring new innovation, new ideas, new ways to solve old problems. And so I think what I love about this partnership is ultimately when we come together, whether it's engineering teams coming together to build new product, whether it's our sales and marketing teams out in front of the customers across that spectrum, I think customers looking for US toe help bring new ideas. And I love the fact that we've engineered this partnership to do to do just that. But ultimately we're focused on how do we come together and build something new and different? And I think we can solve some of the most challenging problems with the power of the data on the innovation that we're bringing to the table. >>I mean, you know, Casey, I mean, everybody is really quite an odd and amazed that Microsoft's transformation, um and really openness and willingness to really, really change and lean into some of the big waves. I >>wonder if you >>could talk about your multi platform strategy and what problems that you're solving in conjunction with snowflake. >>Yeah, let me start by saying, You know, I think as much as we appreciate that that feedback on on the progress that we've been striving for. I mean, we're still learning every day, looking for new opportunities to learn from customers from partners. And so, ah, lot of what you see on the outside is the result of a really focused culture really focusing on what's important to our customers focusing on how do we build diversity and inclusion to everything we do, whether that's within Microsoft with our partners or customers on. Ultimately, how do we show up? Aziz? One Microsoft. I call one Microsoft kind of the partners gift. It's ultimately how do our companies show up together? So I think if you look multi platform, we have the same concept, right? We have the Microsoft cloud that we're offering out in the marketplace. The Microsoft Cloud consists of what we're serving up. A Sfar is the platform consists what we're serving up for data and AI modern workplace on business applications. And so this multi cloud strategy for us is really focused on how do we bring innovation across each of the solution areas that matter most to customers And so I see, Really, the power of the snowflake partnership playing in there. >>Awesome calling. Are there any examples you can share Where, you know, maybe this partnership is unlocked. The customer opportunity or unique value? >>Yeah. I can't speak about the customer specific, but what I can do and say is, um you know, Casey and I play very corporate roles in terms of we're thinking about the long term partnership. We're driving the strategy. Um, hey, look, we'll get called in. We're working a deal right now. It's almost close of, uh, of the quarter for us who are literally working on an opportunity right now. How can we win together? How can we be competitive? The customers? The CEO has asked us to come together to work out that solution. Um, very large, well known brand and were able to get up to the very senior levels of our customer era companies very quickly to make decisions on what do we need to do to be better and stronger together? And, uh um, that's really what a partnership is about. You could do the long term plans in the strategic, and you can have great products But when you're executives, come pick up the phone and call each other toe work on a particular deal for particular customers need, uh, I think that's where the power of the partnership really comes together. And that's where we're at. And that's been a growth opportunity for us. This year's wasn't necessarily where we were at. And I really have to thank Casey for that. He's done a ton, Um, you know, getting us the right glue between our executives, making sure the relationships air there and making sure the trust is there. So when our customers needs to come together, that dialogue and the that shared addiction of putting customers first is there between both companies. So thank you, Casey. >>No, thanks. Coming. Feeling's mutual. >>Well, I think this is key because as a cent upfront, we've gone from sort of very product focused the platform focus. And now we're tapping the power of the ecosystem. That's not always easy to get all the parts moving together. But we live in this. A P I economy you could say is, Hey, I'm I'm a company. Everything is gonna be homogeneous. Everything is gonna be my stack and maybe That's one way to solve the problem. But really, that's not how customers want to solve the problem. Okay, so I'll give you last word. >>Yeah, let me just end by saying, You know, first off, the cultures between our two companies couldn't be more well aligned. So I think ultimately, when you ask yourself the question, what do we do? The best show up in front of our customers. It is focused on there. This is outcomes focused on the things that matter most to them. And this partnership will show up well, I think ultimately our greatest opportunity eyes to tap into that need that interest on. I couldn't be happier about the partnership on the fact that we are so well aligned. So thank you for that. >>Well, guys, thanks very much for coming in the Cube and unpacking some of the really critical aspects of the ecosystem was really a pleasure having you. >>Thank you so much for having us. Alright, >>Keep it right there. Everybody, this is Dave Volonte for the Cube were powering on with data Cloud Summit 2020. Keep it right there.