 From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. A better than expected earnings report in late August got people excited about Snowflake again, but the negative sentiment in the market has weighed heavily on virtually all growth tech stocks and Snowflake is no exception. As we've stressed many times, the company's management is on a long-term mission to dramatically simplify the way organizations use data. Snowflake is tapping into a multi-hundred billion dollar total available market and continues to grow at a rapid pace. In our view, Snowflake is embarking on its third major wave of innovation, data apps, while its first and second waves are still bearing significant fruit. Now for short-term traders focused on the next 90 or 180 days, that probably doesn't matter. But those taking a longer view are asking, should we still be optimistic about the future of this high flyer or is it just another overhyped tech play? Hello and welcome to this week's Wikibon Cube Insights powered by ETR. Snowflake's quarter just ended and in this Breaking Analysis, we take a look at the most recent survey data from ETR to see what clues and nuggets we can extract to predict the near-term future in the long-term outlook for Snowflake, which is going to announce its earnings at the end of this month. Okay, so you know the story. If you've been investor in Snowflake this year, it's been painful. We said at IPO, if you really want to own this stock on day one, just hold your nose and buy it. But like most IPOs, we said there will be likely a better entry point in the future and not surprisingly, that's been the case. Snowflake IPOed at a price of 120, which you couldn't touch on day one unless you got into a friends and family dealio. And if you did, you're still up 5% or so. So congratulations, but at one point last year, you were up well over 200%. That's been the nature of this volatile stock. And I certainly can't help you with the timing of the market. But longer-term, Snowflake is targeting $10 billion in revenue for fiscal year 2028. A big number. Is it achievable? Is it big enough? Tell you what, let's come back to that. Now, shorter-term, our expert trader and breaking analysis contributor, Chip Simington, said he got out of the stock a while ago after having taken a shot at what turned out to be a bear market rally. He pointed out that the stock had been bouncing around the 150 level for the last few months and broke that to the downside last Friday. So he'd expect 150 is where the stock is gonna find resistance on the way back up. But there's no sign of support right now, he said. Maybe at 120, which was the July low and of course the IPO price that we just talked about. Now, perhaps earnings will be a catalyst. The announcement when they, Snowflake announces on November 30th, but until the mentality toward growth tech changes, nothing's likely to change dramatically, according to Simington. So now that we have that out of the way, let's take a look at the spending data for Snowflake in the ETR survey. Here's a chart that shows the time series breakdown of Snowflake's net score going back to the October 2021 survey. Now at that time, Snowflake's net score stood at a robust 77%. Remember, net score is a measure of spending velocity. It's a proprietary network and ETR derives it from a quarterly survey of IT buyers and asks the respondents, are you adopting the platform new? Are you spending 6% or more? Is your spending flat? Is your spending down 6% or worse? Or are you leaving the platform decommissioning? You subtract the percent of customers that are spending less or churning from those that are spending more and adopting or adopting and you get a net score. And that's expressed as a percentage of customers responding. In this chart, we show Snowflake's N out of the total survey, which ranges, the total survey ranges between 1200 and 1400 each quarter. And in the very last column, oh, sorry, very last row, we show the number of Snowflake respondents that are coming from in the survey from the Fortune 500 and the Global 2000. Those are two very important Snowflake constituencies. Now what this data tells us is that Snowflake exited 2021 with very strong momentum and a net score of 82%, which is off the charts and it was actually accelerating from the previous survey. Now by April, that sentiment had flipped and Snowflake came down to earth with a 68% net score, still highly elevated relative to its peers, but meaningfully down. Why was that? Because we saw a drop in new ads and an increase in flat spend. Then into the July and most recent October surveys, you saw a significant drop in the percentage of customers that were spending more. Now notably the percentage of customers who are contemplating adding the platform is actually staying pretty strong, but it is off a bit this past survey. And combined with a slight uptick in planned churn, net score is now down to 60%. That uptick went from 0% and 1% and then 3%. It's still small, but that net score at 60%, it's still 20% percentage points higher than our highly elevated benchmark of 40% as you'll recall from listening to earlier breaking analysis. That 40% range as we consider a milestone, anything above that is actually quite strong. But again, Snowflake is down and coming back to churn, while 3% churn is very low in previous quarters, we've seen Snowflake 0% or 1% decommissions. Now the last thing to note in this chart is the meaningful uptick in survey respondents that are citing, they're using the Snowflake platform. That's up to 212 in the survey. So look, it's hard to imagine that Snowflake doesn't feel the softening in the market like everyone else. Snowflake is guiding for around 60% growth in product revenue against the tough compare from a year ago with a 2% operating margin. So like every company, the reaction of the street is going to come down to how accurate or conservative the guide is from their CFO. Now earlier this year, Snowflake acquired a company called Streamlet for around $800 million. Streamlet is an open source Python library and it makes it easier to build data apps with machine learning, obviously a huge trend. And like Snowflake, generally its focus is on simplifying the complex. In this case, making data science easier to integrate into data apps that business people can use. So we were excited this summer in the July ETR survey to see that they added some nice data and pickup on Streamlet, which we're showing here in comparison to Snowflake's core business on the left-hand side. That's the data warehousing, the Streamlet pieces on the right-hand side. And we show again NetScore over time from the previous survey for Snowflake's core database and data warehouse offering, again in the left as compared to Streamlet on the right. Snowflake's core product had 194 responses in the October 22 survey. Streamlet had an end of 73, which is up from 52 in the July survey. So significant uptick of people responding that they're doing business and adopting Streamlet. That was pretty impressive to us. And it's hard to see, but the NetScore's stayed pretty constant for Streamlet at 51%. It was 52%, I think in the previous quarter, well over that magic 40% mark. But when you blend it with Snowflake, it does sort of bring things down a little bit. Now there are two key points here. One is that the acquisition seems to have gained exposure right out of the gate as evidenced by the large number of responses. And two, the spending momentum. Again, while it's lower than Snowflake overall and when you blend it with Snowflake, it does pull it down. It's very healthy and steady. Now let's do a little peer comparison with some of our favorite names in this space. This chart shows NetScore or spending velocity on the y-axis and overlap or presence pervasiveness, if you will, in the data set on the x-axis. That red dotted line, again, is that 40% highly elevated NetScore that we like to talk about. And that table inserted informs us as to how the companies are plotted, where the dots set up, the NetScore, the ends. And we're comparing a number of database players, although just a caution oracle includes all of oracle, including its apps. But we just put it in there for reference because it is the leader in database. Right off the bat, Snowflake jumps out with a NetScore of 64%. The 60% from the earlier chart, again included Streamlet. So you can see its core database data warehouse business actually is higher than the total company average that we showed you before because Streamlet has blended in. So when you separate it out, Streamlet is right on top of Databricks. Isn't that ironic? Only Snowflake and Databricks in this selection of names are above the 40% level. You see Mongo and Couchbase, they are this solid and Teradata Cloud actually showing pretty well compared to some of the earlier survey results. Now, let's isolate on the database data platform sector and see how that shapes up. And for this analysis, same XY dimensions, we've added the big giants, AWS and Microsoft and Google. And notice that those three plus Snowflake are just at or above the 40% line. Snowflake continues to lead by a significant margin in spending momentum, and it keeps creeping to the right. That's that end that we talked about earlier. Now here's an interesting tidbit. Snowflake is often asked, and I've asked it myself many times, how are you faring relative to AWS, Microsoft and Google, these big whales with Redshift and Synapse and BigQuery? And Snowflake has been telling folks that 80% of its business comes from AWS. And when Microsoft heard that, they said, whoa, wait a minute, Snowflake, let's partner up, because Microsoft is smart and they understand that the market is enormous and if they could do better with Snowflake, one, they may steal some business from AWS, and two, even if Snowflake is winning against some of the Microsoft database products, if it wins on Azure, Microsoft is gonna sell more compute and more storage, more AI tools, more other stuff to these customers. Now, AWS is really aggressive from a partnering standpoint with Snowflake. You know, they're openly negotiating, not openly, but they're negotiating better prices, they're realizing that when it comes to data, the cheaper that you make the offering, the more people are gonna consume. At scale economies and operating leverage are really powerful things at volume that kick in. Now, Microsoft, they're coming along, they obviously get it, but Google is seemingly resistant to that type of go-to-market partnership. Rather than lean into Snowflake as a great partner, Google's field force is kind of fighting fashion. Google itself had cloud next, heavily message what they call the open data cloud, which is a direct rip-off of Snowflake. So what can we say about Google? They continue to be kind of behind the curve when it comes to go-to-market. Now, just to brief aside on the competitive posture, I've seen Frank Slutman, CEO of Snowflake in action with his prior companies and how he depositioned the competition. At data domain, he eviscerated a company called Avamar with what he called their expensive and slow post-process architecture. I think he actually called it Garbage, if I recall, at one conference I heard him speak at. And I started to destroy BMC when he was at service now, kind of positioning them as the equivalent of the Department of Motor Vehicles. And so it's interesting to hear how Snowflake openly talks about the data platforms of AWS, Microsoft, Google and Databricks. I'll give you this sort of short bumper sticker. Redshift is just an on-prem database that AWS morphed to the cloud, which, by the way, is kind of true. They actually did a brilliant job of it, but it's basically a fact. Microsoft, they sell a collection of legacy databases, which also kind of morphed to run in the cloud. And even BigQuery, which is considered cloud native by many, if not most, is being positioned by Snowflake as originally an on-prem database to support Google's ad business, maybe. And Databricks is, for those people, smart enough to get it to Berkeley and love that love complexity, and how Snowflake does it, they don't mention Berkeley as far as I know, that's my addition, but you get the point. And the interesting thing about Databricks and Snowflake is a while ago in theCUBE, I said that there was a new workload type emerging around data, where you have AWS cloud, Snowflake, obviously for the cloud database, and Databricks data for the data science and the ML, you bring those things together and there's this new workload emerging that's going to be very powerful in the future. And it's interesting to see now the aspirations of all three of these platforms are colliding, that's quite a dynamic. Especially when you see both Snowflake and Databricks putting venture money and getting their hooks into the loyalties of the same companies like DBT Labs and Calibra. Anyway, Snowflake's posture is that we are the pioneer in cloud native data warehouse, data sharing and now data apps and our platform is designed for business people that want simplicity. The other guys, yes, they're formidable but we Snowflake have an architectural lead and of course we run in multiple clouds. So it's pretty strong positioning or depositioning, yeah, you have to admit. Now I'm not sure I agree with the big query, knockoffs completely, I think that's a bit of a stretch but Snowflake as we see in the ETR survey data is winning. So in thinking about the longer term future let's talk about what's different with Snowflake, where it's headed and what the opportunities are for the company. Snowflake put itself on the map by focusing on simplifying data analytics. What's interesting about that is the company's founders are as you probably know from Oracle. And rather than focusing on transactional data which is Oracle's sweet spot, the stuff they worked on when they were at Oracle, the founders said we're going to go somewhere else, we're going to attack the data warehousing problem and the data analytics problem. And they completely reimagined the database and how it could be applied to solve those challenges and reimagine what was possible with if you had virtually unlimited compute and storage capacity. And of course Snowflake became famous for separating the compute from storage and being able to completely shut down compute so you didn't have to pay for it when you're not using it and the ability to have multiple clusters hit the same data without making endless copies and a consumption slash cloud pricing model. And then of course everyone on the planet realized, wow, that's a pretty good idea. Every venture capitalist in Silicon Valley has been funding companies to copy that move. And that today has pretty much become mainstream and table stakes. But I would argue that Snowflake not only had the lead but when you look at how others are approaching this problem, it's not necessarily as clean and as elegant. Some of the startups, the early startups, I think get it and maybe had an advantage of starting later which could be a disadvantage too. But AWS is a good example of what I'm saying here is its version of separating compute from storage was an afterthought and it's good. Given what they had, it was actually quite clever and customers like it but it's more of a, okay, we're going to tear storage to lower cost. We're going to sort of dial down the compute, not completely, we're not going to shut it off. We're going to minimize the compute required. Really not true a separation is like for instance, Snowflake has but having said that, we're talking about competitors with lots of resources and cohort offerings. And so I don't want to make this necessarily all about the product but all things being equal, architecture matters, okay? So that's the cloud S curve, the first one we're showing. Snowflake's still on that S curve and in and of itself it's got legs but it's not what's going to power the company to $10 billion. The next S curve we denote as the multi-cloud in the middle and now while 80% of Snowflake's revenue is AWS, Microsoft is ramping up and Google, well, we'll see. But the interesting part of that curve is data sharing and this idea of data clean rooms. I mean, it really should be called the data sharing curve but I have my reasons for calling it multi-cloud. And this is all about network effects and data gravity and you're seeing this play out today, especially in industries like financial services and healthcare and government that are highly regulated verticals where folks are super paranoid about compliance and they're not going to share data if they're going to get sued for it or they're going to be in the front page of the Wall Street Journal for some kind of privacy breach and what Snowflake has done is said, put all the data in our cloud. Now of course, now that triggers a lot of people because it's a walled garden, okay? It is, that's the trade-off. It's not the Wild West, it's not Windows, it's Mac, it's more controlled but the idea is that as different parts of the organization or even partners begin to share data that they need, it's got to be governed, it's got to be secure, it's got to be compliant, it's got to be trusted so Snowflake introduced the idea of, they call these things stable edges. I think that's the term that they use and they track a metric around stable edges and so a stable edge or think of it as a persistent edge is an ongoing relationship between two parties that last for some period of time, like more than a month, it's not just a one-shot deal, one and done type of, oh, I shared it for a day, done. It's an FTP, it's done, no. It's got to have trajectory over time, four weeks or six weeks or some period of time that's meaningful and that metric is growing. Now I think, sort of a different metric that they track, I think around 20% of Snowflake customers are actively sharing data today and then they track the number of those edge relationships that exist so that's something that's unique because again, most data sharing is all about making copies of data, that's great for storage companies, it's bad for auditors and it's bad for compliance officers and that trend is just starting out, that middle S curve, it's going to hit the base of that steep part of the S curve and it's going to have legs through this decade, we think and then finally the third wave that we show here is what we call super cloud, that's why I called it multi-cloud before so it could invoke super cloud. The idea that you've built a pass layer that is purpose built for a specific objective and in this case it's building data apps that are cloud native, shareable and governed and this is a long-term trend that's going to take some time to develop, I mean application development platforms can take five to 10 years to mature and gain significant adoption but this one's unique, this is a critical play for Snowflake, if it's going to compete with the big cloud players it has to have an app development framework like Snowpark, it has to accommodate new data types like transactional data, that's why it announced this thing called Unistore last June at Snowflake Summit and the pattern that's forming here is Snowflake is building layer upon layer with its architecture at the core, it's not currently anyway, it's not going out and saying all right, we're going to buy a company that's got another billion dollars in revenue and that's how we're going to get to 10 billion. So it's not buying its way into new markets through revenue, it's actually buying smaller companies that can complement Snowflake and that it can turn into revenue for growth that fit into the data cloud. Now, as to the 10 billion dollars by fiscal year 28 is that achievable? That's the question. Yeah, I think so. Would the momentum resources go to market, product and management prowess that Snowflake has? Yes, it's definitely achievable. And one could argue that 10 billion dollars is too conservative. Indeed, Snowflake CFO Mike Scarpelli will fully admit his forecaster built on existing offerings. He's not including revenue, as I understand it, from all the new stuff that's in the pipeline because he doesn't know what it's going to look like. He doesn't know what the adoption is going to look like. Doesn't have data on that adoption, not just yet anyway. And now of course, things can change quite dramatically. It's possible that his forecast for existing businesses don't materialize or competition picks them off or a company like Databricks actually is able to in the longer term replicate the functionality of Snowflake with open source technologies which would be a very competitive source of innovation. But in our view, there's plenty of room for growth. The market is enormous. And the real key is, can and will Snowflake deliver on the promises of simplifying data? Of course, we've heard this before from the data warehouse, the data markets, data lakes and master data management, ETLs and data movers and data copiers and Hadoop and a raft of technologies that have not lived up to expectations. And we've also, by the way, seen some tremendous successes in the software business with the likes of ServiceNow and Salesforce. So will Snowflake be the next great software name and hit that $10 billion magic mark? I think so. Let's reconnect in 2028 and see. Okay, we'll leave it there today. I want to thank Chip Simington for his input to today's episode. Thanks to Alex Morrison who's on production and manages the podcast. Ken Schiffman as well. Kristen Martin and Cheryl Knight helped get the word out on social media and in our newsletters and Rob Hoef is our editor-in-chief over at Silicon Angle. He does some great editing for us. Check it out for all the news. Remember, all these episodes are available as podcasts wherever you listen, just search Breaking Analysis Podcasts. I publish each week on wikibon.com and siliconangle.com or you can email me to getintouchdava.valante at siliconangle.com, DM me at dvalante or comment on our LinkedIn posts. And please do check out etr.ai they've got the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, thanks for listening and we'll see you next time on Breaking Analysis.