The Exchanges

Q Great. Alright, so let's jump into use cases. Why do customers use you and how? Is it a consolidation type scenario of several data marts? Um, you know, is that a data lake type scenario?

A Every story with the customer is different. I mean, I, I think you see customers coming from different places. You know, we have some customers that Are, are trying to make Hadoop work, and are struggling with that to analyze machine generated data, and so they come to Snowflake from the machine generated side, and they use us for, for analytics associated with that, and, and those customers tend to think of us as kind of a big data solution. Um, they're, they're pretty much in the minority though, I'd say there's quite a few of them, but, but most of our customers come to us from some sort of, of relational data warehouse that they have, Which they, they want to have, they, which they, they are having some set of challenges with. Typically associated with the business teams not, not getting the performance or concurrency that they need, or the fact that the data is not able to be consolidated within a single system because of limitations associated with it. So they come to us from area, from, from those two different directions overall. Um, at first we saw more people who were already experienced with the cloud, And we're looking for the scalability that Snowflake can offer and the concurrency. So our first initial customers were really customers who already had experience in the cloud. They might already be running a, a database, a, a data warehouse in the cloud, and they wer…

AI assessment note: “most of our customers come to us from some sort of, of relational data warehouse”

Q So the Hadoop and Spark ecosystems are friend or foe? Are they repositories that feed into Snowflake?

A I think they're different. I think they're very different. I think what we're seeing is, is a movement of the open source community towards technologies like Spark, um, which are, which are not storage-oriented, um, in, in their history. Uh, Hadoop, Hadoop's history with HDFS very much makes it a storage-based system, And then the, the, the, the typical approaches that people have traditionally used with Hadoop, variations of MapReduce, um, are now being seen, I think, as, as, now that there are alternatives, such as Snowflake that are available, that allow you to work with large amounts of data, and to do so with a true relational database, I think many customers who have previously tried Hadoop are moving towards a solution like Snowflake. So I, I think Hadoop is, is, is a, is a past technology. I think it's, it's, it's, although it's still gonna, people will still use it, it still has a place, I think it's, it's not an area where there's gonna be a lot of, of incremental additional investment. Spark is different. Spark, I think, is being used very, very broadly for advanced analytics, machine learning, in some cases for streaming data, and those scenarios are all very, very complimentary to Snowflake. We have a lot of customers We have a Spark connector, and we have a lot of customers that are running Spark in combination with Snowflake, and we think that's a pretty common s…

AI assessment note: “those scenarios are all very, very complimentary to Snowflake.”

Q uh, AWS reported a, uh, ten billion run rate, uh, growing at 45%, and then Microsoft Azure, uh, reported a seven million dollar run rate accelerating at 98%. This is one of, it's incredible to see multi-billion dollar businesses, uh, that continue to, not just scale, but accelerate as they scale. What, what, what does that mean to you in terms of, of where we are with, with the cloud?

A Well, I think we're, we're at the, At that point in adoption where we're seeing mainstream organizations really begin to adopt the, the cloud in a big way. When I was here three years ago, this was, this was very early still. There were early adopters, ah, that had gone into the cloud in a big way, and that's really who we sold to. What we found was the initial customers that we saw in the cloud, which was a combination of companies that had already adopted the cloud and had a lot of data that they cared about, They tended to be in industries like advertising, media, entertainment, online gaming, and then we started to see SaaS companies begin to, to adopt Snowflake as well, but all of those were non-traditional enterprise companies, and what we're now seeing is, ah, is really the traditional enterprises move, and, and that seems to be pretty ubiquitous. Almost every organization we talk to has some cloud strategy associated with their IT, And most are actively moving. I mean, if you, if you were, 18 months ago, people were talking about and committing to move to the cloud, and now people are doing it. And we're even seeing that in, uh, regulated industries such as financial services, and, and I think that's a little bit more challenging for those organizations, because they need to, I was meeting with, with a British firm yesterday, and, and, you know, they were talking, talki…

AI assessment note: “we're at the, At that point in adoption where we're seeing mainstream organizations really begin”

Q that you're, uh, head-to-head with some pretty big names, um, and I guess... Who would that, who would that be? So, so starting with AWS, correct me if I'm wrong, but it feels from my perspective that, you know, Redshift is probably the, the biggest competitor in Redshift is an Amazon product. Meanwhile, I believe that you guys sit exclusively on AWS. How does it, how does that relationship work?

A Well, let me start by saying I think Amazon's been a very, very good partner of ours, um, and we work a lot with them. Um, they're, they're, I mean, they're a great cloud provider. I mean, they're, they've obviously been our cloud provider, and we like the technology a lot, and we get very good support from Amazon, so the team is great, and as I said, they've been a very good partner of ours. We do compete with Redshift. Um, I think Amazon has a 150 different services in their portfolio. We compete with maybe Three, depending on whether you want to include Athena and Spectrum in that. Um, but, but Athena's kind of a different product anyway, and, and, and so really, I think the product that we compete with is Redshift. I mean, we've, we've, we've done a fair number of Redshift conversions, and that, you know, in the early days in particular, what we saw was customers that, that wanted some of the scalability characteristics of Snowflake moving on to us, and we still see that. But more and more, we're working with Amazon to, To do those Teradata migrations, to do those Neteza migrations onto AWS, because those customers will be better served by a product like Snowflake running on Amazon. And I, I think that that's becoming more and more clear. Um, it's certainly clear to our customers. I think it's becoming more, it's clear to most, I think almost all of Amazon, the, the Redshif…

AI assessment note: “It's, you know, it's challenging at times. There definitely are elements of competition.”

Q the wealth creation and, and, and value creation happened in the second decade. Um, so, um, I think the numbers were that Microsoft grew to a billion dollar, uh, in its first decade, but then ended its second decade at twenty four billion. Then Oracle grew to one billion in its first decade, but ended its second decade with sixteen billion. I mean, is that the same thing happening here?

A I think, though, that you're going to have to compress that decade. I mean, I don't think it's a decade. I think it's more like a five to seven year period, and, you know, we can almost think about Amazon finishing that first period now, because it was, what, 2008 when they launched? It was, I guess, 10 years ago in that sense. It was 10 years ago. In a way, that way, it was a first decade. Um, And, but, but it was, it was very, it's very much ahead of us. I mean, the growth will all be ahead of us, so. And I think we'll see it go faster, for sure. It's gonna, it's gonna grow faster than client server did.

AI assessment note: “I think it's more like a five to seven year period”

Q So what happens in the next three to five years is just more growth, more companies embracing it?

A I think the thing that people have not really grasped fully is what a major sea change it is when an organization can work with data and, and make that ubiquitous across the organization. I think it's almost impossible to, to, for an organization, it's very difficult to imagine in a, a situation where The, the, the affordability is there and the, and the solution sets are there for an on-premises, or an organization that's running on-premises to, to be data-driven across their entire organization. I think it's just very difficult to orchestrate that because you, the technology didn't support it, doesn't support it very well, and, and so it, it's a massive amount of IT work and quite a lot of cost to, to enable an entire organization to be data-driven. And very, very few companies Have the ability to do that in the on-premises world. The cloud changes that totally because of the elasticity and the pricing structures that are possible in the cloud. You know, we now bill at a per second level of granularity, and we have customers, we have one customer that every Friday afternoon, um, goes from a really, relatively modest amount of continuous usage of Snowflake to literally thousands of nodes running simultaneously, and that happens in about a five minute period. They run it that way for about three hours, And then they drop it all back down. That would be, I mean, that would be im…

AI assessment note: “what a major sea change it is when an organization can work with data”

Q Uh, so what, what led you to becoming CEO of, uh, Snowflake? What was the

A Well, when I, when I left, uh, I joined Juniper, it was time for me to leave, to leave Microsoft. It was the right time. You know, Steve, I had worked directly for Steve. Steve's a fantastic, Balmer's a fantastic guy in many ways, and I learned a lot from him, but it was time, it was definitely time to leave, and so, and so I had to transition out of, out of Microsoft, and, uh, I spent a couple years at Juniper, and one, there were a couple things about Juniper that were interesting. One was that, at least when I was there, Juniper's Uh, systems were relatively nascent in a lot of ways, so I learned a lot from Juniper about, about the importance, and in some ways I was so spoiled at Microsoft, and at, at, in the fact that they were very good in general IT systems, and when I came to Juniper, there was definitely opportunities for that to improve. The company, by the way, is doing a lot of great stuff right now. Um, they're actually one of our customers now, so, so I'll, I'll say that for sure, but they've, so they've come a long, they've come a long way. Um, but, uh, but one of the things I saw while I was at Juniper was that this cloud thing Was just a massive disruption that was gonna struck, gonna run across the entire industry. And, and large companies, large tech companies that have a history on-premises were gonna have a very rough time transitioning their, their business…

AI assessment note: “one of the things I saw while I was at Juniper was that this cloud thing”

Q But just maybe to add something, and then that'll be the last question from me, I'll open it up to, to you guys right after this, uh, but I, I read some interesting stuff about, um, that you guys were doing some interesting stuff, which is machine learning and AI, both internally, but also externally, is that something you can talk about?

A Yeah, I mean, we're, mostly I'd say what we're doing, what we're doing mostly is frankly working with the ecosystem on, on machine learning. So we use, we, you know, we internally, uh, uh, we use R and Spark and things like that internally to do machine analytics. Um, and we've many, many dozens of customers that are doing, are doing a similar thing. One of the things that we are doing, which is incredibly unique to Snowflake, which I think is, it goes hand in hand with global, and will become very, very important to companies, is we're enabling data sharing between organizations. And what, what Snowflake can do, because of the architecture of Snowflake, it is possible for any organization to take data that they have and stored in Snowflake, And securely and fully under their control, in real time, share it with another organization. And that organization can be another division within a company, and we have, you know, if you look at all of our large customers, they're pretty much all using multiple accounts, snowflake accounts, and doing data sharing between them as a way to allow the data to land in one place, but to structure the access to the data in a way that is most appropriate for the business. You know, as a typical example of this, very typically you might have production separated from test dev, put those two in two separate accounts and share data from production as…

AI assessment note: “One of the things that we are doing, which is incredibly unique to Snowflake”

Q It's like this concept of like digital transformation, which is amazing to me that, isn't that what all of us have been doing for the last 30 years?

A Well, you know, it's a journey that I don't think ever totally ends. I mean, it certainly won't end in, in my career, and, and, and I think it's one where, where you're always at a certain point in time, And the thing that is, is, is amazed me, I mean, I, you know, I run this company, and we try and be as data-driven as we can be. I mean, all of our data, all of the data about Snowflake is in Snowflake. And, you know, it sort of should be, obviously. It's an obvious thing. And, and so all of a sudden, I mean, I can ask any question, and, and really, it is fairly remarkable how easy it is, in some senses, to get, to get some answers. And my engineers, when there's a problem with Snowflake, You know, they run a query, and they, they, the, the system tells them a lot about what's going wrong, and, and, and yet, it's too hard. I mean, it's still too hard, and, and one of the things that I find exciting is that we've now taken the, a problem, which is the bottleneck associated with the data warehouse, and the challenges associated, that require, that the, the scalability challenges, the performance challenges, I mean, all of these, the concurrency challenges, the ability to support multimodal types of data, we've taken all of those problems and effectively provide a solution for that within Snowflake. But what's fascinating to me is that it just, it just shifts the problem to other …

AI assessment note: “it's a journey that I don't think ever totally ends.”