>>Okay, We're now going into the technical deep dive. We're gonna geek out here a little bit. Ben Wa Dodgeville is here. He's co founder of Snowflake and president of products. And also joining us is Christian Kleinerman. Who's the senior vice president of products. Gentlemen, welcome. Good to see you. >>Yeah, you that >>get this year, they Thanks for having us. >>Very welcome. So it been well, we've heard a lot this morning about the data cloud, and it's becoming my view anyway, the linchpin of your strategy. I'm interested in what technical decisions you made early on. That that led you to this point and even enabled the data cloud. >>Yes. So? So I would say that that a crowd was built in tow in three phases. Really? The initial phase, as you call it, was it was really about 20 minutes. One regions Teoh, Data Cloud and and that region. What was important is to make that region infinity, infinity scalable, right. And that's our architectural, which we call the beauty cross to share the architectural er so that you can plug in as many were clues in that region as a Z without any limits. The limit is really the underlying prop Provide the, you know, resource is which you know, Cal provide the region as a really no limits. So So that z you know, region architecture, I think, was really the building block of the snowflake. That a cloud. But it really didn't stop there. The second aspect Waas Well, it was really data sharing. How you know munity internets within the region, how to share data between 10 and off that region between different customers on that was also enabled by architectures Because we discover, you know, compute and storage so compute You know clusters can access any storage within the region. Eso that's based off the data cloud and then really faced three Which is critical is the expansion the global expansion how we made you know, our cloud domestic layers so that we could talk You know the snowflake vision on different clouds on DNA Now we are running in three cloud on top of three cloud providers. We started with the ws and US West. We moved to assure and then uh, Google g c p On how this this crowd region way started with one crowd region as I said in the W S U S West, and then we create we created, you know, many you know, different regions. We have 22 regions today, all over the world and all over the different in the cloud providers. And what's more important is that these regions are not isolated. You know, Snowflake is one single, you know, system for the world where we created this global data mesh which connects every region such that not only there's no flex system as a whole can can be aware of for these regions, But customers can replicate data across regions on and, you know, share. There are, you know, across the planet if need be. So So this is one single, you know, really? I call it the World Wide Web. Off data that, that's, you know, is this vision of the data cloud. And it really started with this building block, which is a cloud region. >>Thank you for that. Ben White Christian. You and I have talked about this. I mean, that notion of a stripping away the complexity and that's kind of what the data cloud does. But if you think about data architectures, historically they really had no domain knowledge. They've really been focused on the technology toe ingest and analyze and prepare And then, you know, push data out to the business and you're really flipping that model, allowing the sort of domain leaders to be first class citizens if you will, uh, because they're the ones that creating data value, and they're worrying less about infrastructure. But I wonder, do you feel like customers air ready for that change? >>I I love the observation. They've that, uh, so much energy goes in in in enterprises, in organizations today, just dealing with infrastructure and dealing with pipes and plumbing and things like that and something that was insightful from from Ben Juan and and our founders from from Day one WAAS. This is a managed service. We want our customers to focus on the data, getting the insights, getting the decisions in time, not just managing pipes and plumbing and patches and upgrades, and and the the other piece that it's it's it's an interesting reality is that there is this belief that the cloud is simplifying this, and all of a sudden there's no problem but actually understanding each of the public cloud providers is a large undertaking, right? Each of them have 100 plus services, uh, sending upgrades and updates on a constant basis. And that just distracts from the time that it takes to go and say, Here's my data. Here's my data model. Here's how it make better decisions. So at the heart of everything we do is we wanna abstract the infrastructure. We don't wanna abstract the nuance of each of the cloud providers. And as you said, have companies focus on This is the domain expertise or the knowledge for my industry. Are all companies ready for it? I think it's a It's a mixed bag. We we talk to customers on a regular basis every way, every week, every day, and some of them are full on. They've sort of burned the bridges and, like I'm going to the cloud, I'm going to embrace a new model. Some others. You can see the complete like, uh, shock and all expressions like What do you mean? I don't have all these knobs. 2 to 3 can turn. Uh, but I think the future is very clear on how do we get companies to be more competitive through data? >>Well, Ben Ben. Well, it's interesting that Christian mentioned to manage service and that used to be in a hosting. Guys run around the lab lab coats and plugging things in. And of course, you're looking at this differently. It's high degrees of automation. But, you know, one of those areas is workload management. And I wonder how you think about workload management and how that changes with the data cloud. >>Yeah, this is a great question. Actually, Workload management used to be a nightmare. You know, traditional systems on it was a nightmare for the B s and they had to spend most a lot of their time, you know, just managing workloads. And why is that is because all these workloads are running on the single, you know, system and a single cluster The compete for resources. So managing workload that always explain it as explain Tetris, right? You had the first to know when to run. This work will make sure that too big workers are not overlapping. You know, maybe it really is pushed at night, you know, And And you have this 90 window which is not, you know, efficient. Of course, for you a TL because you have delays because of that. But but you have no choice, right? You have a speaks and more for resource is and you have to get the best out of this speaks resource is. And and for sure you don't want to eat here with her to impact your dash boarding workload or your reports, you know, impact and with data science and and And this became a true nine man because because everyone wants to be that a driven meaning that all the entire company wants to run new workers on on this system. And these systems are completely overwhelmed. So so, well below management was, and I may have before Snowflake and Snowflake made it really >>easy. The >>reason is it's no flag. We leverage the crowds who dedicates, you know, compute resources to each work. It's in the snowflake terminology. It's called a warehouse virtual warehouse, and each workload can run in its own virtual warehouse, and each virtual warehouse has its own dedicated competition resources. It's on, you know, I opened with and you can really control how much resources which workload gas by sizing this warehouses. You know, I just think the compute resources that they can use When the workload, you know, starts to execute automatically. The warehouse, the compute resources are turned off, but turned on by snowflake is for resuming a warehouse and you can dynamically resized this warehouse. It can be done by the system automatically. You know if if the conference see of the workload increases or it can be done manually by the administrator or, you know, just suggesting, you know, uh, compute power. You know, for each workload and and the best off that model is not only it gives you a very fine grain. Control on resource is that this work can get Not only workloads are not competing and not impacting it in any other workload. But because of that model, you can hand as many workload as you want. And that's really critical because, as I said, you know, everyone in the organization wants to use data to make decisions, So you have more and more work roads running. And then the Patriots game, you know, would have been impossible in in a in a centralized one single computer, cross the system On the flip side. Oh, is that you have to have a zone administrator off the system. You have to to justify that. The workload is worth running for your organization, right? It's so easy in literally in seconds, you can stand up a new warehouse and and start to run your your crazy on that new compute cluster. And of course, you have to justify if the cost of that because there is a cost, right, snowflake charges by seconds off compute So that cost, you know, is it's justified and you have toe. You know, it's so easy now to hire new workflow than you do new things with snowflake that that that you have to to see, you know, and and look at the trade off the cost off course and managing costs. >>So, Christian been while I use the term nightmare, I'm thinking about previous days of workload management. I mean, I talked to a lot of customers that are trying to reduce the elapsed time of going from data insights, and their nightmare is they've got this complicated data lifecycle. Andi, I'm wondering how you guys think about that. That notion of compressing elapsed time toe data value from raw data to insights. >>Yeah, so? So we we obsess or we we think a lot about this time to insight from the moment that an event happens toe the point that it shows up in a dashboard or a report or some decision or action happens based on it. There are three parts that we think on. How do we reduce that life cycle? The first one which ties to our previous conversation is related toe. Where is their muscle memory on processes or ways of doing things that don't actually make us much sense? My favorite example is you say you ask any any organization. Do you run pipelines and ingestion and transformation at two and three in the morning? And the answer is, Oh yeah, we do that. And if you go in and say, Why do you do that? The answer is typically, well, that's when the resource is are available Back to Ben Wallace. Tetris, right? That's that's when it was possible. But then you ask, Would you really want to run it two and three in the morning? If if you could do it sooner, we could do it. Mawr in time, riel time with when the event happened. So first part of it is back to removing the constraints of the infrastructures. How about running transformations and their ingestion when the business best needs it? When it's the lowest time to inside the lowest latency, not one of technology lets you do it. So that's the the the easy one out the door. The second one is instead of just fully optimizing a process, where can you remove steps of the process? This is where all of our data sharing and the snowflake data marketplace come into place. How about if you need to go in and just data from a SAS application vendor or maybe from a commercial data provider and imagine the dream off? You wouldn't have to be running constant iterations and FTP s and cracking C S V files and things like that. What if it's always available in your environment, always up to date, And that, in our mind, is a lot more revolutionary, which is not? Let's take away a process of ingesting and copying data and optimize it. How about not copying in the first place? So that's back to number two on, then back to number three is is what we do day in and day out on making sure our platform delivers the best performance. Make it faster. The combination of those three things has led many of our customers, and and And you'll see it through many of the customer testimonials today that they get insights and decisions and actions way faster, in part by removing steps, in part by doing away with all habits and in part because we deliver exceptional performance. >>Thank you, Christian. Now, Ben Wa is you know, we're big proponents of this idea of the main driven design and data architecture. Er, you know, for example, customers building entire applications and what I like all data products or data services on their data platform. I wonder if you could talk about the types of applications and services that you're seeing >>built >>on top of snowflake. >>Yeah, and And I have to say that this is a critical aspect of snowflake is to create this platform and and really help application to be built on top of this platform. And the more application we have, the better the platform will be. It is like, you know, the the analogies with your iPhone. If your iPhone that no applications, you know it would be useless. It's it's an empty platforms. So So we are really encouraging. You know, applications to be belong to the top of snowflake and from there one actually many applications and many off our customers are building applications on snowflake. We estimated that's about 30% are running already applications on top off our platform. And the reason is is off course because it's it's so easy to get compute resources. There is no limit in scale in our viability, their ability. So all these characteristics are critical for for an application on DWI deliver that you know from day One Now we have improved, you know, our increased the scope off the platform by adding, you know, Java in competition and Snow Park, which which was announced today. That's also you know, it is an enabler. Eso in terms off type of application. It's really, you know, all over and and what I like actually needs to be surprised, right? I don't know what well being on top of snowflake and how it will be the world, but with that are sharing. Also, we are opening the door to a new type of applications which are deliver of the other marketplace. Uh, where, You know, one can get this application died inside the platform, right? The platform is distributing this application, and today there was a presentation on a Christian T notes about, >>you >>know, 20 finds, which, you know, is this machine learning, you know, which is providing toe. You know, any users off snowflake off the application and and machine learning, you know, to find, you know, and apply model on on your data and enrich your data. So data enrichment, I think, will be a huge aspect of snowflake and data enrichment with machine learning would be a big, you know, use case for these applications. Also, how to get there are, you know, inside the platform. You know, a lot of applications led him to do that. Eso machine learning. Uh, that engineering enrichments away. These are application that we run on the platform. >>Great. Hey, we just got a minute or so left in. Earlier today, we ran a video. We saw that you guys announced the startup competition, >>which >>is awesome. Ben, while you're a judge in this competition, what can you tell us about this >>Yeah, >>e you know, for me, we are still a startup. I didn't you know yet, you know, realize that we're not anymore. Startup. I really, you know, you really feel about you know, l things, you know, a new startups, you know, on that. That's very important for Snowflake. We have. We were started yesterday, and we want to have new startups. So So the ends, the idea of this program, the other aspect off that program is also toe help, you know, started to build on top of snowflake and to enrich. You know, this this pain, you know, rich ecosystem that snowflake is or the data cloud off that a cloud is And we want to, you know, add and boost. You know that that excitement for the platform, so So the ants, you know, it's a win win. It's a win, you know, for for new startups. And it's a win, ofcourse for us. Because it will make the platform even better. >>Yeah, And startups, or where innovation happens. So registrations open. I've heard, uh, several, uh, startups have have signed up. You goto snowflake dot com slash startup challenge, and you can learn mawr. That's exciting program. An initiative. So thank you for doing that on behalf of of startups out there and thanks. Ben Wa and Christian. Yeah, I really appreciate you guys coming on Great conversation. >>Thanks for David. >>You're welcome. And when we talk, Thio go to market >>pros. They >>always tell us that one of the key tenets is to stay close to the customer. Well, we want to find out how data helps us. To do that in our next segment. Brings in to chief revenue officers to give us their perspective on how data is helping their customers transform. Business is digitally. Let's watch.