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