 From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. Snowflake Summit 2022 underscored the ecosystem excitement, which was once forming around Hadoop as being reborn, escalated and coalescing around Snowflake's data cloud. What was once seen as a simpler cloud data warehouse, good marketing with the data cloud, is evolving rapidly with new workloads, a vertical industry-focused data applications, monetization and more. The question is, will the promise of data be fulfilled this time around, or is it same wine, new bottle? Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this Breaking Analysis, we'll talk about the event, the announcements that Snowflake made that are of greatest interest, the major themes of the show, what was hype and what was real, the competition and some concerns that remain in many parts of the ecosystem and pockets of customers. First, let's look at the overall event. It was held at Caesar's Forum, not my favorite venue, but I'll tell you, it was packed. Fire Marshall full, as we sometimes say. Nearly 10,000 people attended the event. Here's Snowflake's CMO, Denise Pearson, on theCUBE, describing how this event has evolved. No, again, three years ago, we were about 1,800 people at the Hilton in San Francisco. We had about 40 partners attending. This week, we're close to 10,000 attendees here, almost 10,000 people online as well, and over 200 partners here on the show floor. Now, those numbers from 2019 remind me of the early days of Hadoop World, which was put on by Cloudera. But then, Cloudera handed off the event to O'Reilly as this article that we've inserted, if you bring back that slide, would say, the headline, it almost got it right. Hadoop World was a failure, but it didn't have to be. Snowflake has filled the void created by O'Reilly when it first killed Hadoop World, and killed the name, and then killed Strata. Now, ironically, the momentum and excitement from Hadoop's early days, probably could have stayed with Cloudera, but the beginning of the end was when they gave the conference over to O'Reilly. We can't imagine Frank Slutman handing the keys to the kingdom to a third party. Serious business was done at this event. I'm talking substantive deals. Salespeople from a host sponsor and the ecosystems that support these events, they love physical. They really don't like virtual because physical belly to belly means relationship building, pipeline, and deals. And that was blatantly obvious at this show. And in fairness, other CUBE events that we've done this year, but this one was more vibrant because of its attendance and the action in the ecosystem. Ecosystem is a hallmark of a cloud company, and that's what Snowflake is. We asked Frank Slutman on theCUBE, was this ecosystem evolution by design, or did Snowflake just kind of stumble into it? Here's what he said. Well, when you are a data cloud and you have data, people want to do things with that data. They don't want to just run data operations, populate dashboards, run reports. Pretty soon they want to build applications, and after they build applications, they want to build businesses on it. So it goes on and on and on. So it drives your development to enable more and more functionality on that data cloud. Didn't start out that way. We were very, very much focused on data operations. Then it becomes application development, and then it becomes, hey, we're developing whole businesses on this platform, similar to what happened to Facebook in many ways. So it sounds like it was maybe a little bit of both. The Facebook analogy is interesting because Facebook is a walled garden, as is Snowflake, but when you come into that garden, you have assurances that things are going to work in a very specific way because a set of standards and protocols is being enforced by a steward, i.e. Snowflake. This means things run better inside of Snowflake than if you try to do all the integration yourself. Now, maybe over time, the open source version of that will come out, but if you wait for that, you're going to be left behind. That said, Snowflake has made moves to make its platform more accommodating to open source tooling in many of its announcements this week. And I'm not going to do a deep dive on the announcements. Matt Silke's from Monte Carlo wrote a decent summary of the keynotes and a number of analysts like Sanjeev Mohan, Tony Bear, and others are posting some deeper analysis on these innovations, and so we'll point to those. I'll say a few things though. Unistore extends the type of data that can live in the Snowflake data cloud. It's enabled by a new feature called Hybrid Tables, a new table type in Snowflake. One of the big knocks against Snowflake was it couldn't handle transaction data. Several database companies are creating this notion of a hybrid where both analytic and transactional workloads can live in the same data store. Oracle's doing this, for example, with MySQL Heatwave and there are many others. We saw Mongo earlier this month add an analytics capability to its transaction system. Mongo also added SQL, which was kind of interesting. Here's what constellation research analyst Doug Henshin said about Snowflake's moves into transaction data. Play the clip. Well, with Unistore, they're reaching out and trying to bring transactional data in. Hey, don't limit this to analytical information and there's other ways to do that like CDC and streaming, but they're very closely tying that again to that marketplace with the idea of bring your data over here and you can monetize it. Don't just leave it in that transactional database. So another reach to a broader play across a big community that they're building. And you're also seeing Snowflake expand its workload types in its unique way and through Snowpark and its streamlet acquisition enabling Python so that native apps can be built in the data cloud and benefit from all that structure and the features that Snowflake has built in. Hence that Facebook analogy or maybe the App Store, the Apple App Store is app-reposed. Well, Python support also widens the aperture for machine intelligence workloads. We asked Snowflake Senior VP of Product, Christian Kleinerman which announcements he thought were the most impactful and despite the who's your favorite child nature of the question, he did answer. Here's what he said. I think the native applications is the one that looks like, I don't know about it on the surface but it has the biggest potential to change everything. Like create an entire ecosystem of solutions for within a company or across companies that I don't know that we know what's possible. Snowflake also announced support for Apache Iceberg which is a new open table format standard that's emerging. So you're seeing Snowflake respond to these concerns about its lack of openness and they're building optionality into their cloud. They also showed some cost optimization tools both from Snowflake itself and from the ecosystem notably Capital One which launched a software business on top of Snowflake focused on optimizing costs and eventually the roll out data management capabilities. All kinds of features that Snowflake announced at the show around governance, cross cloud what we call super cloud, a new security workload and they re-emphasized their ability to read non-native on-prem data into Snowflake through partnerships with Dell and Pure and a lot more. Let's hear from some of the analysts that came on theCUBE this week at Snowflake Summit to see what they said about the announcements and their takeaways from the event. This is Dave Meninger, Sanjeev Mohan and Tony Baer. Roll the clip. Our research shows that the majority of organizations, the majority of people do not have access to analytics and so a couple of the things they've announced I think address those or help to address those issues very directly. So Snowpark in support for Python and other languages is a way for organizations to embed analytics into different business processes and so I think that'll be really beneficial to try and get analytics into more people's hands. And I also think that the native applications as part of the marketplace is another way to get applications into people's hands rather than just analytical tools because most people in the organization are not analysts. They're doing some line of business function. They're HR managers, they're marketing people, they're sales people, they're finance people, right? They're not sitting there mucking around in the data. They're doing a job and they need analytics in that job. Primarily I think it is to contract this whole notion that once you move data into Snowflake it's a proprietary format. So I think that's how it started but it's hugely beneficial to the customers, to the users because now if you have large amounts of data in Park KFIs you can leave it on S3 but then you're using the Apache Iceberg table format in Snowflake you get all the benefits of Snowflake's optimizer. So for example you get the micro partitioning, you get the metadata and in a single query you can join, you can do select from a Snowflake table union and select from an Iceberg table and you can do store procedure, user defined functions. So I think what they've done is extremely interesting. Iceberg by itself still does not have multi-table transactional capabilities. So if I'm running a workload I might be touching 10 different tables. So if I use Apache Iceberg in a raw format they don't have it but Snowflake does. So the way I see it is Snowflake is adding more and more capabilities right into the database. So for example they've gone ahead and added security and privacy. So you can now create policies and do even cell level masking, dynamic masking but most organizations have more than Snowflake. So what we are starting to see all around here is that there's a whole series of data catalog companies, a bunch of companies that are doing dynamic data masking, security and governance, data observability which is not a space Snowflake has gone into. So there's a whole ecosystem of companies that is mushrooming, although you know, so they're using the native capabilities of Snowflake but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other relational databases you can run these cross-platform capabilities in that layer. So that way Snowflake's done a great job of enabling that ecosystem. I think it's like the last mile essentially in other words it's like okay you have folks that are basically that are very comfortable with Tableau but you do have developers who don't want to have to shell out to a separate tool and so this is where Snowflake is essentially working to address that constituency. To Sanjeev's point, I think part of it this kind of plays into it is what makes this different from the adobe era is the fact that all these capabilities, a lot of vendors are taking it very seriously to put this native and obviously Snowflake acquired Streamlit so we can expect that the Streamlit capabilities are going to be native. I want to share a little bit about the higher level thinking at Snowflake. Here's a chart from Frank Slutman's keynote. It's his version of the modern data stack if you will. Now Snowflake of course was built on the public cloud without if there were no AWS there would be no Snowflake. Now they're all about bringing data and live data and expanding the types of data including structured we just heard about that unstructured geospatial and the list is going to continue on and on. Eventually I think it's going to bleed into the edge if we can figure out what to do with that edge data. Executing on new workloads is a big deal. They started with data sharing and they recently added security and they've essentially created a PAS layer. We call it a super PAS layer if you will to attract application developers. Snowflake has a developer focused event coming up in November and they've extended the marketplace with 1300 native apps listings. And at the top, that's the holy grail monetization. We always talk about building data products and we saw a lot of that at this event. It's very, very impressive and unique. Now here's the thing. There's a lot of talk in the press in the Wall Street and in the broader community about consumption based pricing and concerns over Snowflake's visibility and its forecast and how analytics may be discretionary but if you're a company building apps in Snowflake and monetizing like Capital One intends to do and you're now selling in the marketplace that is not discretionary unless, of course, your costs are greater than your revenue for that service in which case it's going to fail anyway. But the point is we're entering a new era where data apps and data products are beginning to be built and Snowflake is attempting to make the data cloud a de facto place as to where you're going to build them and our view there well ahead in that journey. Okay, let's talk about some of the bigger themes that we heard at the event. Bringing apps to the data instead of moving the data to the apps. This was a constant refrain and one that certainly makes sense from a physics point of view but having a single source of data that is discoverable, shareable and governed with increasingly robust ecosystem options that doesn't have to be moved. Sometimes it may have to be moved if you're going across regions but that's unique and a differentiator for Snowflake in our view. I mean, I've yet to see a data ecosystem that is as rich and growing as fast as the Snowflake ecosystem. Monetization, we talked about that, industry clouds, financial services, healthcare, retail and media all front and center at the event. My understanding is that Frank Slootman was a major force behind this shift, this development and go-to-market focus on verticals. It's really an attempt, he talked about this in his keynote, to align with the customer mission, ultimately align with their objectives which not surprisingly, are increasingly monetizing with data as a differentiating ingredient. We heard a ton about data mesh, there were numerous presentations about the topic and I'll say this, if you map the seven pillars, Snowflake talks about Benoit d'Ajaville, talked about this in his keynote but if you map those into Jamak Degani's data mesh framework and the four principles, they align better than most of the data mesh washing that I've seen. The seven pillars, all data, all workloads, global architecture, self-managed, programmable, marketplace and governance. Those are the seven pillars that he talked about in his keynote. All data. Well, maybe with hybrid tables, that becomes more of a reality. Global architecture means the data is globally distributed, it's not necessarily physically in one place. Self-managed is key, it's self-service infrastructure is one of Jamak's four principles and then inherent governance. Jamak talks about computational, what I'll call automated governance built in and with all the talk about monetization, that aligns with the second principle which is data as product. So while it's not a pure hit, and to its credit by the way, Snowflake doesn't use data mesh in its messaging anymore, but by the way, it's customers do, several customers talked about it. Geico, JPMC, and a number of other customers and partners are using the term and using it pretty closely to the concepts put forth by Jamak Degani. But back to the point, they essentially, Snowflake that is, is building a proprietary system that substantially addresses some, if not many of the goals of data mesh. Okay, back to the list, super cloud, that's our term. We saw lots of examples of clouds on top of clouds that are architected to span multiple clouds, not just run on individual clouds as separate services. And this includes Snowflake's data cloud itself, but a number of ecosystem partners that are headed in a very similar direction. Snowflake still talks about data sharing, but now it uses the term collaboration in its high-level messaging, which is, I think smart, data sharing is kind of a geeky term. And also, this is an attempt by Snowflake to differentiate from everyone else that's saying, hey, we do data sharing too. Finally, Snowflake doesn't say data marketplace anymore, it's now marketplace accounting for its application market. Okay, let's take a quick look at the competitive landscape via this ETR XY graph. Vertical axis, remember, is net score, or spending momentum in the X axis is penetration, pervasiveness in the data center. That's what ETR calls overlap. Snowflake continues to lead on the vertical axis. They guided conservatively last quarter, remember, so I wouldn't be surprised if that lofty height, even though it's well down from its earlier levels, but wouldn't be surprised if it ticks down again a bit in the July survey, which will be in the field shortly. Databricks is a key competitor, obviously, and a strong spending momentum, as you can see. We didn't draw it here, but we usually draw that 40% line, a red line at 40%, and anything above that is considered elevated, so you can see Databricks is quite elevated. But it doesn't have the market presence of Snowflake. It didn't get to IPO during the bubble, and it doesn't have nearly as deep and capable go-to-market machinery. Now, they're getting better and they're getting some attention in the market, nonetheless, but as a private company, just naturally or not, more people are aware of Snowflake. Some analysts, Tony Bayer in particular, believe Mongo and Snowflake are on a bit of a collision course long-term. I actually can see his point. I mean, they're both platforms. They're both about data. It's a long ways off, but you can see them sort of in a similar path. They talk about kind of similar aspirations and visions, even though they're quite in different markets today, but they're definitely participating in similar time. The cloud players are probably the biggest, definitely the biggest partners, and probably the biggest competitors to Snowflake, and then there's always Oracle. Doesn't have the spending velocity of the others. It's got strong market presence. It owns a cloud, and it knows a thing about data, and it definitely is a go-to-market machine. Okay, we're going to end on some of the things that we heard in the ecosystem. Because look, we've heard before how a particular technology, enterprise data warehouse, data hubs, MDM, data lakes, Hadoop, et cetera, we're going to solve all of our data problems, and of course they didn't. And in fact, sometimes they create more problems that allow vendors to push more incremental technology to solve the problems that they created, like tools and platforms to clean up the no schema on right nature of data lakes or data swamps. But here are some of the things that I heard, firsthand from customers and partners. First thing is, they said to me that they're having a hard time keeping up sometimes with the pace of snowflake. It reminds me of AWS in 2014, 2015 timeframe. You remember that fire hose of announcements, which causes increased complexity for customers and partners. I talked to several customers that said, well, yeah, this is all well and good, but I still need skilled people to understand all these tools that I'm integrating in the ecosystem, the catalogs, the machine learning, observability. A number of customers said, like I just can't use one governance tool. I need multiple governance tools and a lot of other technologies as well. And they're concerned that that's going to drive up their cost and their complexity. I heard other concerns from the ecosystem that it used to be sort of clear as to where they could add value when snowflake was just a better data warehouse, but to point number one, they're either concerned that they'll be left behind or they're concerned that they'll be subsumed. Look, I mean, just like we tell AWS customers and partners, you got to move fast. You got to keep innovating. If you don't, you're going to be left either if your customer is going to be left behind your competitor, or if you're a partner, somebody else is going to get there or AWS is going to solve the problem for you. Okay, and there were a number of skeptical practitioners, really thoughtful and experienced data pros that suggested that they've seen this movie before. That's hence the same wine new bottle. Well, this time around, I certainly hope not, given all the energy and investment that is going into this ecosystem. And the fact is, Snowflake is unquestionably making it easier to put data to work. They built on AWS, so you didn't have to worry about provisioning compute, storage and networking and scaling. Snowflake is optimizing its platform to take advantage of things like Graviton, so you don't have to and they're doing some of their own optimization tools. The ecosystem is building optimization tools so that's all good. This firm belief is that the less expensive it is, the more data will get brought in to the data cloud. And they're building a data platform on which their ecosystem can build and run data applications, AKA data products without having to worry about all the hard work that needs to get done to make data discoverable, shareable and governed. And unlike the last 10 years, you don't have to be a keeper and integrate all the animals in the Hadoop Zoo. Okay, that's it for today. So today, thanks for watching. Thanks to my colleague, Stephanie Chan, who helps research breaking analysis topics sometimes. Alex Meyerson is on production and manages the podcasts. Kristin Martin and Cheryl Knight helped get the word out on social and in our newsletters and Rob Hoef is our editor in chief over at Silicon Angle does some wonderful editing. Thanks to all. Remember, all these episodes are available as podcast wherever you listen. All you have to do is search breaking analysis podcasts. I publish each week on wikibon.com and siliconangle.com and you can email me at david.pillante at siliconangle.com or DM me at dpillante. If you got something interesting, I'll respond if you don't, I'm sorry, I won't. Or comment on my LinkedIn posts. Please check out ETR.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE. Insights powered by ETR. Thanks for watching and we'll see you next time.