Hey, Hwee Bee, thanks for the introduction. I think you're on mute. You might need to unmute yourself. Hey, cool. You're all set. Have a good one. Yep. Hi, everyone. A warm welcome to SBM Pals. Let me start over. Right, I forgot to unmute myself today. I'm Hwee Bee. I'm the Head of Partner Marketing for APJ at Snowflake and Partners. Thanks for your time. It's been an incredible week following our summit in San Francisco where we welcomed over 20,000 attendees, doubling our growth this year. We're excited to bring that momentum back and continue our journey with all of you today. Today's session is all about what's new, what's next, and how we can continue to grow and win together this year. You'll hear directly from our leadership, Michael Gartner today, our CRO from Snowflake, and learn from the inspiring journey from our customer, Spark New Zealand, as well as Relational AI, how they're transforming and leading the way with us today. We also have our latest product innovations that is brought to you at Summit. We're having Amanda Kelly to share that with you later. Stay tuned to the very end of this session. To kick off all the things, I'm delighted to hand over to Ash Willis, our VP of Partner and Alliance for APJ today. Over to you, Ash. Great. Thanks, Hwee Bee. Good to see you. Good to see everyone. Hey, Hwee Bee. What. What shirts that you're wearing? Is that the L.A. Olympics? Right? Good stuff. I missed out on grabbing one of those. For everyone's benefit, we announced last week that Snowflake are a sponsor for the L.A. 28 Olympics, which is super exciting. I'm waiting for the announcement around Brisbane 32 as well, just to get ahead of things. Good stuff. Thanks. Hwee Bee and Mike, welcome to APJ SPN Pulse. Good to have you here. It's great to be here. Thank you for having me. Exciting. I know you're joining from the East Coast of the U.S., so quite late, and you know, we appreciate you jumping on board. Glad to do it. I'm getting used to these late nights to talk to all my Asia Pacific leadership team and customers. Glad to be here. The joys of being the CRO of an international company. We're super excited to have you on board, Mike. Just over three months at Snowflake now. Yeah, I know you've been super busy with a whole bunch of activity. I do want to talk about last week at Summit in a moment, but let's kick off with our latest earnings announcement. Great time to come into Snowflake. I know you had the opportunity to do the earnings call from the New York Stock Exchange. Maybe share a couple of, you know, observations in terms of Q1 results and quick intro, you know, to yourself, your background and, you know, why you chose to come to Snowflake. Yeah, sure. For those that have not, you know, looked at my LinkedIn profile or had the chance to look at me virtually, I joined Snowflake 90 days ago as Chief Revenue Officer. I've spent the better part of the last 29 years really in two companies. The first 15 years of my career was with EMC Corporation and I started my career just as the third industrial revolution was kind of taking shape and the Internet for the most part was changing the way that, you know, we were operating business to business as well as business to consumer. I had a lot of great experience and fun driving transformation and building out infrastructure for companies that were starting to deal with more e-commerce applications and transforming the way they operated from a technology perspective. I then took a shift over to VMware and I just wrapped up the General Manager of the Americas at VMware. I went over there because I started to see that software was starting to eat the world and people were delivering more value through software-defined data centers. Obviously as the leader of virtualization, we started to draw a much greater expansion of virtualization beyond compute into storage and networking and really helping customers build private clouds. Inevitably we built out partnerships with all the major hyperscalers because we recognized customers wanted to move their applications to the hyperscaler. We created this VMware private cloud in each one of these hypers, giving customers the ability to vMotion applications between On Prem and the hyperscalers. I certainly got a lot of experience watching customers trying to modernize their applications to move it to modern cloud. I think that was certainly a very difficult challenge. After 12 years of VMware it became apparent to me that customers started spending more time and effort to modernizing their data because they recognized they were going to get much more faster path to value by driving a new data architecture as opposed to trying to lift the legacy application and moving it to the cloud, which wound up being much more difficult than people had ever anticipated. As I started to look at really the next 10-15 years of my career, I knew I had to get into the data space. I was afforded the opportunity to interview for this role which fortunately enough I was able to get. I'm 90 days into the role. You know, we kicked off the year with some tremendous earnings. You know, we had our first billion dollar revenue quarter, which represented 26% growth year on year, which was a fabulous way to start the year. Our net revenue retention is 124%. And that metric basically suggests that customers are not just doing a static renewal, they're expanding their contracts with Snowflake. Demand continues to expand with all of our customers. You know, what's resonating well is, I'd say, our remaining revenue obligation, which represents really the backlog, is $6.7 billion, and that's a 34% year on year increase. Customers are signing up for longer term contracts. That backlog obviously is an addressable market for everyone on this phone call. To go help figure out how we can drive migrations, analytics, AI, that's really the attack surface that we've left open for our partner community to come in and help customers drive consumption and get value out of it. Our remaining revenue obligation continues to be very strong. Of the 11,200 customers that we have total, we added about 451 in Q1 and approximately half of those customers today, so about 5,200 customers, so about half are actively using our AI and ML products. That to me is a really strong leading indicator of how customers are looking at Snowflake, not just from a data perspective, but really trying to simplify their AI initiative. We're seeing great adoption around the AI. Great way to kick off the year, great way for me to learn what customers love about VMware. You know, again, of all the customer meetings I've been in, I consistently hear, you know, we're easy, we're trusted, and we have this great connected capability of allowing zero copy data shares between two different Snowflake instances. We're starting to create this market network effect around Snowflake. Really just great, great opportunity for me to see the company on display in Q1 and see some fantastic results. I think as I've always said, the market votes with their wallets every day, and right now they're voting on Snowflake, and it's exciting time to be here. Yeah, it's an incredibly exciting time to join, Mike. I think, you know, anytime a company sort of celebrates their first billion dollar quarter is amazing. As you touched on, you know, this momentum that's building around data and having been at Snowflake now for just on three years. When I first joined, Snowflake was doing some amazing stuff, but it was very much pitched in this technology space. I think this trend around driving business impact and the value that we can add to business is really impressive and really becoming very front of mind. Hey, one thing that I just want to unpack a little bit more that you spoke about there, so $6.7 billion RPO, maybe just dig into that a little bit more and you know what that means in terms of opportunity for partners. Because I harp on this all the time, but I think it's good for this audience to hear from you in terms of what that opportunity actually looks like. Yeah, so we have a unique compensation structure at Snowflake. We don't get paid when we book a contract. Our sales teams only get paid when we drive consumption. That means we've convinced the customer that Snowflake is an essential data and AI platform. They buy into, you know, a multi year contract with us, which is what we're seeing trending wise as customers are moving to multi year contracts. Then we start looking for use cases, you know, what data sets are we going to help them modernize? You know, the low hanging fruit for us continues to be legacy traditional warehousing technologies like a Teradata, Netezza, Oracle, Exadata, SAP, HANA, legacy warehousing that, that frankly isn't scaling to the needs of the companies. We're doing a lot to drive migration. There's an unbelievable opportunity for us to partner with this ecosystem to help drive and accelerate migrations onto the platform. When I say that we've got this large backlog pending, you know, you're going to find a very receptive audience at Snowflake that wants to partner with organizations that can help accelerate migrations onto the platform because that's going to drive consumption and that's when our sales team gets paid. It's a very unique, you know, structure from that perspective. You know, the thing that's really landing on me when we talk about AI, most customers look at us bringing, you know, a legacy, transactional, very structured data set into Snowflake. I'll give you a great use case example of where we did this. For a very large construction supplier in the U.S., we had already brought a very structured data set into Snowflake and we're helping them running some advanced analytics around, you know, typical structured data. One of the things that they had as a challenge was they've got several people that run their task is they, they build bids. People will send them a blueprint, an architectural blueprint, and you will have a person accept that blueprint. Over the next three days, they will count how many windows and doors and trusses and two by fours, and they build out a bill of materials and they submit their bid. They have about a 20% success rate where people will contract them for supplying the builder for that architectural blueprint. We were able to showcase in a pre sales motion when we were able to bring that unstructured data, that blueprint into the Snowflake environment and map it up to their structured data. We were able to basically use an AI model to go through and analyze 100 blueprints per day as opposed to one every three days. When a customer sees the unlocking that value, and now that person can submit 300 bids a day versus one over three days just by bringing an unstructured data set and a structured data set together and using a basic AI SQL query, it became a very powerful unlocking mechanism. What I think most customers are really starting to recognize is when they bring these structured and unstructured data sets together, they get a much richer look at their data ecosystem and they're leveraging us to really simplify the business outcome. That to me was really an unlocking moment where the customer wasn't sure we could do it. Most customers lack the awareness that this is the powerful platform we can deliver. That to me became an eye opening moment for me to say this is where customers are struggling is they don't think this stuff is capable, but we can really deliver some pretty significant outcomes just if we ask the right questions. I think what I'm asking my team to do is let's assess the business outcome first and then figure out how the technology will deliver the outcome. That to me is a lot of that's what I'm working on with my team is making sure we identify the business opportunities and the business outcomes. The technology is the easy part, really. Yeah, I think it's a trend that we see out here as well, helping sort of customers imagine the art of the possible. Everyone wants to talk about AI. Everyone kind of recognizes that technology can add a bunch of value to their business, but really trying to understand what those key use cases are. I love that one that you just described because not only is it improving productivity, but I would imagine that it's more accurate than somebody trying to count out windows by hand and all that sort of stuff as well. Yeah, but I think to your point, it's not a job elimination. It's not AI taking over the role, it's making that one person far more productive. I think that's really the unlocking moment right now is everyone's scared AI is replacing jobs. Listen, eventually it may, but the first iterations of what we're seeing is we're really just unlocking productivity and allowing customers to accelerate revenue streams and potential. You know, the first unlocking moment, I will say, is really leveraging AI to drive productivity. I think what's going to happen is you're going to see a skills shift. Right. What might be, you know, a radiologist is doing today? Maybe we don't need to hire 30 new radiologists in the healthcare market. I can scale my business more with the existing headcount I have. That to me is, I think, the power of AI, at least within its first iteration. I was chatting to a radiologist at a forum earlier this week actually, and they were saying I always, hey, what's AI doing? And they were talking about it in terms of improving accuracy and to diagnosis, not reducing the number of roles, but providing better outcomes. At the end of the day, Mike, I'm conscious of time and I've got so many more questions. Let's hit a couple more. Let's do a speed round, let's switch to Summit. Last week we were joined by more than 20,000 of our great friends in San Francisco. What were some of your initial thoughts? Other than wow? Yeah, it was eye opening for me on a few. Again, I'd shared. I just left VMware after 12 years and I think VMware started their user conferences in about 2003 and at our peak, just before the Broadcom acquisition, we had about 18,000 people there. To see Snowflake only after the sixth year of having our conference, right, we doubled our capacity from last year at 11,000 to 20,000 this year. Unbelievable amount of people. I was not expecting that big of a show, I was a bit blown away by that. I'd say the quality of people that were showing up to the event was really eye opening to me. I mean, we had Chief Data Officers, we had Presidents of AI, we had people that were truly trying to lean in and understand how Snowflake can unlock value for them and deliver better outcomes. You know, obviously we had a large degree of, you know, analysts and architects and AI engineers. I also saw a pretty good concentration of business minds coming to Snowflake and trying to look for the art of the possible, talking to other customers about what they were doing with Snowflake. There was an unbelievable network effect of customers learning from customers. What's really interesting is 70% of the content that was delivered at Summit last week was delivered by our customers. Right. It is one thing to hear a very biased opinion coming from us, but when you hear how customers are leveraging the technology, that to me was an incredibly powerful statement. I was super impressed by the content. I thought the logistics was fabulous, the energy level was high. To see how our customers were contributing to the Summit, I thought was just awesome. Yeah. So a couple of customer highlights, like seeing Canva, like a very big customer that we're incredibly proud of out here in APJ. To be featured as part of a keynote was amazing. I'm not sure if you're aware, Mike. Like, we had so many submissions from customers and partners that wanted to join us to advocate for Snowflake at Summit, to present that we could only take such a small portion of those submissions. So, yeah, it's an unfortunate byproduct of having a popular show is you can't have everybody. There's only so many hours and sessions we can hold. I do think next year we're going to have a bit of a problem. You know, if we get to 30,000 people, the Moscone Center is going to get really, really crowded. You know, we are excited to take our world tour. For those that could not travel in to San Francisco, we've got a world tour going all around the world. We're going to bring Summit to you. I'm going to try and hit a few of them as well. We're excited to bring the content for you to Asia. For those Asia Pacific that couldn't travel in, we're coming to a market near you soon. Look at our registration sites and get yourself registered. We're going to have some great events on the road. Yeah, we're excited to welcome you out here for a few of those events. Hey, last point on Summit, we were chatting at the start of this call in the green room, and you were saying, you know, you had an opportunity to spend some time with the Snowflake investor community. I think there's always some really, you know, interesting insights that come out of those conversations. Are there any, you know, any kind of key things that you could share with this audience? You know, they had quite a bit of, they had a lot of questions. I was on the panel. We had 200 investors in the room. It was myself, Sridhar, and Christian, our Head of Products. There were a lot of questions that were certainly around product and strategy in the future. The questions I was receiving were really relative to, you know, Mike, how are you looking at the go to market function? As I explained to the investor community, there are really two things I'm focused on, which is one, how do we scale our business efficiently, and number two is velocity. How do I remove friction from our selling motion? What I shared with the investor community was my desire, a very strong desire, to build an incredibly strong partner ecosystem. Right. Our global alliance and channel is going to be a big part of our future go to market motion. In order for me to scale this business, I can't keep hiring direct sales reps. We're going to be making a major investment in our channel and distribution. We've got wonderful relationships with the hyperscalers. There's some emerging needs for sovereign clouds in Europe so we have to make some investments and some unique investments in Europe and there's probably some of that even in Asia Pacific where we have to build sovereign clouds. We talk a lot about the ISV marketplace. These are people that are building businesses on top of Snowflake. That's an incredibly rich channel for us as well. We've got incredibly strong systems integrator market where companies are leaning on systems integrators to help them drive modern data architectures and platforms. The fourth and what I'd say frankly is, is an under invested leg of our channel is our distribution network with traditional resellers. I'm spending a lot of time what I'd say ramping or rebuilding out what I think is going to be a very rich partner resale program. Coming soon you're going to see a big investment from us in resale. We're excited to launch this. This to me is, you know, coming from EMC, coming from VMware, I saw the power of the channel. I believe in the channel and I'm going to be making some big investments there. That was a really big part of my presentation to the analyst community is how we're going to scale this business. We're doing quite well already. We're going to grow the business 26%. As we've mentioned to Wall Street, we raised our guidance at 26%. We should be doing well north of 35%. If I activate this channel, I shouldn't say if. I'll say when I activate the channel. You know, the investors obviously like that message. Yeah, the stock went up that day. Stuff went up and ratings, ratings went up as well. Yeah, Mike, you know, just to close out, you know, in the last three months you have already made some pretty decisive moves to make us a more partner friendly organization. Some things around, you know, comp neutrality for Marketplace and as you said, there is a bunch of other really exciting stuff to come. I could keep asking questions for a long time, but Hwee Bee is going to boot us out. You know, thank you so much for joining us. Thanks for investing in this ecosystem and we hope to see you out here very soon and get you back on a pulse maybe in six months time and, you know, get a few more updated reflections once you have had a bit more time in role as well. Yeah, I would love to do it. Thank you so much. It's an honor and privilege to be on the show with you all tonight. I look forward to getting into the market soon and making my trip through APJ and seeing as many of you as possible face to face. Thank you so much. Pleasure meeting you all virtually. We're great hosts, Mike. We'll show you a good time. Very good. See you later. Thanks. All right, take care. Bye. Bye. Hwee Bee, you're on mute if you're trying to do an introduction. No, I'm just saying good one for Mike. We have our customer and partner. Anshuman is here and Marco is here. Good stuff. Hey guys, thanks. Thanks for joining us. Thank you for having us. Great to be here. Looking forward to it. Good to see you both. I know that there's some really good work going on between Snowflake, Spark New Zealand, and Relational AI. Before we jump into that, Anshuman, why don't you give the audience a quick introduction to Spark New Zealand? It's obviously a brand and a company that's very familiar to me, but maybe not all of our audience are familiar with you guys. Thank you, Ash. So Spark New Zealand is the largest telco in New Zealand. New Zealand is not a huge country. It's a little country just off the side of Australia, right? It is, right? You can miss it easily. We should have a bar, you know, beyond the size of Australia. I'm going to get a lot of grief about that statement from my Kiwi colleagues. Yeah, the largest telco, we have the highest share on mobile in New Zealand, but we are also significantly into the business and small and medium enterprises in New Zealand. Small and medium enterprises make up 97% of New Zealand businesses and we play a significant role in there, and 45% approximately of our revenue comes from enterprise and small and medium enterprises. We are an agile organization overall and we went agile quite some time back, about 10 years back as an entire organization. We are really, really agile in the sense that we are very open to using technology, trying out things, experimenting, and you know, interested to innovate and try new things out. I'll probably stop there. That's good. That's a super helpful overview. Marco, over to you, mate. Tell us a little bit about Relational AI and your role. Yeah, thank you. Hi everybody. Look, Relational AI, so technology company, headquartered originally actually in Palo Alto. We've been partnering with Snowflake since very, very early on because there are a lot of actually ex-Snowflakers part of the team. The goal of the company is really, in my opinion, very simple, is to enable organizations, partners, and customers of Snowflake to empowering them to make better decisions on Snowflake. Okay. There is a true inner belief that organizations pretty much live and die by the quality of decisions that they make. What we want to do is to make the process of making decisions within Snowflake across a variety of different use cases and questions as easy as possible to the end user. We are approximately 200 people globally, so still quite small, but growing. We are fully remote and so we are spread across the globe, I think 50-50 between the US and the rest of the world. My role is really to lead the go to market team globally and that really encompasses sales, delivery, marketing, and partnership. It is really a real pleasure to be good. We have, as you said, got some really good work going on together, so good stuff. Anshuman, like, you mentioned, you know, Spark's an agile company. You know, that's been my experience working with you guys as well. I know that you guys also have a strong vision for AI and, you know, leveraging AI as kind of a strategic lever to drive business growth. Could you tell us a little bit more about that and what that looks like? Okay. I think when most organizations start doing AI or start getting into AI, it is generally for incremental benefits. The way we look at it is more an organizational level, right? Can you look at the potential of AI to change the way the organization works? Am I reimagining the organization in a world with AI? Am I tweaking the current processes incrementally or taking a more holistic approach? Am I building for the future? Now when we build our architecture and we build for our use cases, we are generally taking a lens, which is a longer term lens, a problem statement. Can we take that problem statement? Let's say an example of it is provisioning a complex product, right? And there are multiple steps to it. That problem statement might come to us from provisioning team, but we extend it out a bit further to say at some point can we enable customers, right, to come in via self service and actually provision a complex product themselves, which is extending it beyond the immediate customer who's asking for it and taking it to a level where I can go and make it really, really streamlined for customers. Now there might be multiple steps to that journey. Step one might be that we build it for provisioning because the stakeholders are from provisioning team. Step two might be taking that same thing and making it easier for our sales teams to sell the product. Step three might be actual customer enablement and self service of that particular outcome. When we build the architecture and we build that use case, we take a lens as to where can AI go, what's the potential of this use case? We build for that use case so we can extend it over a period of time in the next 6, 8, 10, 12 months. At the same time, the other thing that we also consider is that to be able to do it, we need to have skills of the future. As we can see, what's happening in the world of AI is a lot of skills which are about coding, a lot of skills which are about functional knowledge on a particular topic. They are becoming more transient. You can acquire those skills easily. Core skills are tending to go more towards creativity, problem solving, etc. Are we bringing in those people into Spark, the best of those people in with us on this journey? At the same time, are we also providing training to everyone else in the organization in terms of how to use AI, how to survive in the world of AI? Now, we are on a journey on all of this. We are not, we are not the best or the perfect in this, but that is the lens that we take when we look at the potential in a world which is where AI will play a significant role in the life of each one of us going forward. Okay? Hey, I'm going to jump around a little bit because I think that you're touching on a few interesting topics there, but can you share maybe one or two examples of where Spark New Zealand have already adopted or already built certain AI powered tools or initiatives? I think one of the examples which I'm really proud of is something which is about call summarization. I'll give you the context of this problem statement, which is we get X million calls in our contact centers and every call used to require an agent to spend two minutes to summarize the call at the end of the call before they log it into CRM. You mean a real agent in this instance? Yeah. Now we do classify very carefully when we are talking about humans versus robots, robotic agents. Yeah, and I think taking a call is important because that's the value add. Providing customer experience is important, but logging a call with your interpretation of it is not the most value added thing. We had an industry of compliance around it to say our agents doing it, et cetera. What we did was we brought AI in the life path of a call where as soon as the call ends, AI summarizes the call. My personal idea was summarize the call and log it. We have to work with stakeholders in all of these areas. Our ask from contact centers was you cannot log it. You have to play it back to the agents for them to validate if it's correct or not. We had to play it back on a live call path and they update it and then log it into CRM. What we observe is the number of times people actually updated is nearly zero. I'm waiting for a day when we'll flick the switch and it'll get automatically logged into CRM. That's a real example, a live example of AI in a live call path which we have implemented, which is generating business value. The other impact benefit of such things, which you don't anticipate, is that the quality of data that we are generating about each of these calls is of a much higher quality compared to what was getting logged earlier, which has downstream benefits in terms of root cause analysis and then reducing the actual calls or issues that customers face. That's a really interesting example and it sounds like it's returning some pretty impressive business results. Marco, talk to us a little bit, because I know you have a great partnership with the team at Spark. Talk to us a little bit about the work that you're doing there. I know there's some stuff around Relational Knowledge Graphs that are involved in this project as well. Maybe you could share a little bit of context. Yeah, absolutely. I'll start maybe by briefly telling you, tell the people here why we think it's really, really crucial, actually, this concept of capturing knowledge, which really links to what Anshuman was saying here, capturing really data knowledge. Look at the core of it again. There is a belief that every organization is different and knowledge is captured in a variety of different ways. Within organizations, you find knowledge across spreadsheets, you find knowledge in databases, you find knowledge in application code, you find knowledge in documents, you find knowledge in people's head. Structured and unstructured. Structured and unstructured. As we say in call logs, knowledge is spread really, really everywhere. That knowledge fragmentation generates a lot of pain actually, because it is really difficult to provide a unified, cohesive view of your enterprise. Suddenly when somebody asks a very simple question, what were my sales last week? Now teams crumble because they are like, okay, is a week five days, Monday to Friday? Is it a week six days? Do we include all the products here? Is this flag should be true, should be false? How should we interpret that question? By the way, that is a problem. That is true for people within the organization, but it is also true for the newcomers into the organization. They need to learn how the organization operates. Then the era of agents. Agents are like, I call agents little interns, little employees in your organization. That is true also for agents. They need to learn, they need to understand how enterprises operate. Really, the point of a Relational Knowledge Graph is to solve this challenge of these knowledge silos. It is interesting, back in the days I remember Snowflake would say, hey, our common enemy are the data silos. I think now I make the statement, our joint common enemy beyond the data silos are the knowledge silos. Right? What that does then is enable organizations to describe, to store, to share, to discover, and then infer new knowledge. Now, why is that important? This now links to some of the work we are starting to do with, of course, with our friends at Spark here. It is in just a few, just to give you example here, in just a few weeks, the team from management here leverage Relational AI knowledge graph to build a reliable Digital Twin of their network to simulate then complex users and network behaviors. That now implies that actually you get LLMs, they now can become crazily smart, they can be actually fine tuned to their knowledge graph. They can understand the business domain. Then thanks to a set of what we call reasoners in Snowflake, fully embedded within Snowflake, like a SQL reasoner, a predictive reasoner to reason about the future, a prescriptive reasoner to reason about what to do about the future. Now this system can really answer all sorts of questions. They can answer questions about the past, they can answer questions about the present, the future, and then what to do about it. That is why such a technology and reasoners together are becoming important and really, really central to, I think, every organization will become more and more important moving forward. Yeah, it's a, you know, it's an incredibly interesting space and like this, you know, this idea of knowledge centric and really understanding those different perspectives yields some pretty powerful results. Marco, just a little bit around, like how do you guys integrate with Snowflake to help make some of these outcomes a reality for customers such as Spark. Yeah, look again, the belief here is that we need to meet users and organizations where they are and really be able to complement their past, existing, and future investments. Where users are nowadays and where enterprises are, they're on Snowflake, they're actually storing all sorts of data on relational systems. It is important to be able to then provide support for users and enterprise within those relational systems to build and deploy AI at scale and being able to actually build intelligent applications. What I'm talking here is really being able, together with Snowflake and with partners like ourselves, or with more partners, thinking through what I call a unified Relational AI infrastructure. Apologies here for the play on words, but you see what I mean when I say relational, it's because it's built on a relational paradigm that has a set of key characteristics really as a data centricity. You get all the data types being able at your disposal. As you said, Ash, you get structured data, you get unstructured data, you get images, you get documents, you get graphs, you get tables, but it's all there for you. You get a set of reasoners available for you again baked in into the infrastructure that helps you to actually reason on that data. You get your SQL reasoners, you get your vector search reasoner, you get your graph reasoner, you get your predictive reasoner, your prescriptive reasoner. These are ways for you to interact with your data and then you get a semantic layer on top of it, which is in our case, it's a Relational Knowledge Graph that enables you to model the business and then express rich semantics that go beyond the type of things you would do with BI or dimensional semantics. Last but not least, because nowadays everybody's talking about the ability to just question, you know, ask questions to your data, a post, you know, trained large language model that is really fine tuned to that domain so that now it understands like how the business operates and can use those reasoners to ask questions to the data. This is really a paradigm shift, as you called it out, Ash, because, you know, in the past we will be pretty much application centric, right? We will take data and ship data to the application. That's what we'll be doing. What we're saying here, if you have now a unified AI infrastructure, you are much more knowledge and data centric, okay? Where you are actually sending, not sending, but really bringing compute to the data. The infrastructure is now providing you with the capabilities that historically you would either buy via point solutions or you need to build and maintain yourself. This approach really dramatically simplifies infrastructure, accelerates innovation and unlocks, you know, the full value of really data and knowledge together. That's, you know, that's a really comprehensive answer, Marco, but I think that you touched on a key point there right at the end, which is, oh, there were lots of great points, by the way, but you spoke about accelerating innovation. Anshuman, to close out, how has this partnership between Spark, Relational AI, and Snowflake helped you guys accelerate innovation? I think I'll just probably touch upon a few of the points which Marco mentioned, which I really like. It's the fact that at the end of it, I mean, for any AI or any of the things that we are doing, data is the oil and whatever AI we are doing on top of it, that is again, creating more data and more information. For us, moving into the journey of moving our data into the cloud with Snowflake was the starting point of a lot of the AI and innovation that we've been able to do. We started off with the general idea of moving data into the cloud from why people move into it from a cost, scalability, et cetera perspective. That has kind of put us on the path on this, of this journey, on exploring and experimenting with this data and creating new things. The things which Snowflake is bringing to the table in the form of partners and partnerships is really, really interesting and important for us because I have a feeling that the world of the future will be a world of partnerships because there is so much innovation which is possible now because, you know, again, AI is making innovation possible to quite an extent. I see us having a lot more partnerships going forward than we have now. The ability to onboard such partners, right, using the Snowflake AI and data cloud easily, seamlessly, and bring them where the data is and then use that in a meaningful way, I think is of immense, immense value to us. The other thing I wanted to also mention is I think we've gotten an awesome account team from Snowflake which works with us because we are very demanding sometimes. We have asked for the Cortex functionality three years back because we thought this was possible. I keep pushing Richard and the. Team. To put us in touch with your product team and partners. They are extremely helpful and they bring the teams in, which I really, really appreciate as well. It is a great working relationship for us. I think it is a very strong relationship, and I see it growing with new partners coming in and the ability to work with partners, like Relational AI, is great for us and my teams. That's good. You know, we talk a lot about accelerating time to value, and I think hearing those stories is really valuable. I'm super pleased to hear that you pushed Richard and the team hard down there, and the results are definitely showing. We're at time. Weebly's going to boot us out in a moment, but Anshuman, Marco, thank you both for joining SPN Pulse. A lot of great insights, and I really enjoyed it. Oh, there we go. Some photos from last week. It's great. Amazing. Awesome. Yeah, thanks. I'm the most handsome in this group, no doubt. It is good to see some of those shots and looks like you got to do a couple of videos there as well. Fantastic. Good stuff. Thanks, folks. And over to you. Yes. Cool. Thank you, Amanda. Amanda will be joining us. Yeah. See, I think, Amanda, you need to click the button on the right hand. Yeah. Yes. Cool. Hi, Amanda. How are you? Hello. I am doing well. Excited to be here. Okay, awesome. Marco and Anshuman, you can, you know, on the back, you can enter the back stage, and then we can have Amanda share with us. Over to you, Amanda, to share with us the latest innovation of Summit. Yes. I'm so excited to be here. I was actually just in Korea and Japan a few weeks ago. I know I need to make it out to Singapore, too, but I was at Snowflake Summit. We announced so many amazing things for Snowflake and for you. As partners and customers, I'm really excited to talk to you about really the future of AI-driven data platforms, which is what we're building at Snowflake. We truly are evolving from a data cloud to a fully integrated AI application platform that's redefining how products, applications, and intelligent systems are being built. We're giving you just the download today of some of the top things. Really, you should go, you should watch a bunch of the videos online. So many cool announcements, so many new quick starts, things in our solutions center that you can get started with and try today. I wanted to talk to you just a little bit about our mission. Just kind of fit in, you know, where we're investing and how these things are working. Right. Obviously, you know, we talked about, we're becoming the AI data cloud and we are still focused on giving you that one unified managed platform that's going to make it easy for you to connect, bring all your data together, to trust it, right. To make sure it's governed, it's secure, and then to do some pretty cool things with it, which I'll show you about in a second. Next slide. This is kind of a simplified view of a lot of the things that we're investing in, especially on the data side. Right. You've got all these different data sources, many of them you've already brought together in Snowflake. There's probably a lot more that you want as well, right. I often think about, you know, as a product lead, I often only see a slice, right, of what we're doing and it's frustrating because you don't have the full picture of what your customers are doing, what's happening in the market, or you can only get that by bringing in more data, right, and more applications that help you join these views together, right. Of course, right, you have the hard part. You got to get all that data processed, you got to get it into a place that you can actually do things with. And then, you know, once you have that there's. Right. We can use AI in so many interesting ways on the BI layer for data science, for apps, for sharing. That makes it really easy for you to actually drive decisions and outcomes in your company. Next slide. As we look at kind of the overall Snowflake platform architecture, right, it always starts at the bottom, right. It starts, you know, on the cross cloud, right. You know, no matter what cloud you're in, what region, right, we are able to support you, we're able to. That's why we're able to support so many of the largest companies, right, in the world, globally, right? We are a unified AI ready data platform that starts at the data layer, right? Unstructured data, semi structured, structured. It doesn't matter, right, what data type it is, it doesn't matter what format it is. You know, Iceberg, right? Is it, is it hybrid? Is OLTP, right? We're going to support it, right? We have Data Lakehouse, we have Data Warehouse, we have ways to work with, process that then to interact with it, right? To transform. Is it SQL, is it Python? It really doesn't matter. That layer on top of it, we're democratizing AI across the stack, right? That goes everything from the SQL embedded AI to fully agentic AI experiences that we're helping you make accessible to not just the builders in your org, but to all the users as well. Ultimately that's what's going to help you not only drive efficiency on your data side and through your stack, but also have that business impact. We're making sure that everything top to bottom is designed for that. Better economics, faster development for you and overall lower total cost of ownership. All right, so let's go into some of the amazing announcements that we made and again this is just a slice, right, of what we're doing. Next slide. Okay, so on that kind of base layer, right, how are we making sure when you bring your data in, right, that you feel confident, right, that we are giving that lower total cost of ownership. So here we're making advancements that are really going to help you as partners, not only simplify your operations and governance, but free up those resources so that you can focus on more, better value added things. Figuring out what the heck's going on with AI right now. So at the very kind of basic level, bullet point two, I'll start there, right, we've unveiled the standard warehouse generation 2 and that's going to give you 2.1 times faster analytics performance. That's going to accelerate the insights that you can bring to customers, right? We all love faster. Faster is better. It's awesome. We all know, right, managing warehouse is not always the most fun thing to do, right? Sizing that correctly, making sure you have that right size compute to scale. That's why we're also introducing adaptive compute. This makes warehouses even easier for you to use. It's taking that burden of platform management that nobody really loves off of your hands so that you can deliver faster results also at a lower cost. It's really exciting. It's in private preview, you should go and you should check that out. Right. Similarly, simplified ingest pricing. A lot of you are using Snowpipe, right, to bring that data in. Thank you. Do more of it. Right. We have more coming up about how you can even bring more data in. We're making lower pricing for you. Right. It's going to give you faster data onboarding, again, better price performance, and you can be confident in that overall total cost of ownership. You've got this data, right? We've got you the good price performance. How do you make sure, right, it's secure, it's governed, right, the right people are having access to the right things, right? That's where our total Horizon, what we're doing on the catalog, but also with the governance, is going to be so helpful, right? We're doing enhanced interoperability, AI-powered security and governance in our Horizon catalog. That includes a Copilot for Horizon catalog as well as AI-led monitoring. We're taking again, across the board, more of this off of your plate, right. You don't have to focus and you don't have to worry as much about kind of the performance and the compute in the warehouse layer, right. Even the governance. We're helping bring AI, we're helping bring that lower cost, right. You can focus on, right, the next stuff. How do you bring in more interesting data, your unstructured data, how do you. Put it to use? All right, next slide. All right, that's what we have. Smarter infrastructure and governance. Next slide. Bring the slides back up. We've got more to talk about. All right, next one. Okay, here we go. Okay. Accelerating development. All right, tools for builders. This is, this is. I was one of the co-founders of Streamlit. This is where I get really excited about how do we make it easier for you to bring that data in and do interesting things with it. So we're investing in so many new development tools and that's really going to speed up your project delivery, help you build more differentiated offerings, and drive innovation back there. Don't give away the good stuff on AI. Okay, so first, right, you've got a lot of data, right, that's sitting in SaaS tools, it's sitting in lots of other places. You want to bring it together, right? You want to get that great price perfect performance that I was talking about. You're going to want to do interesting things with the two that we're about to show you with the AI side, so getting that connectivity, managing things in OpenFlow is really going to help you with that, right? It facilitates this open interoperable architecture, right? Moving your data of choice around your data lakes and lake houses, it makes it really, really easy for you to adapt to all these new industry standards like Iceberg, right? Bring that data in or write back, right? Unifying structured, unstructured, batch, streaming data into a single platform, all with these kind of connectors out of the box, you can get all of this data in. We have Workspaces, and built on top of Workspaces we have dbt projects. This is two exciting announcements. Workspaces is a modern developer environment. We'll show you that in a minute. It makes it really easy for you to edit things, starting with SQL files and then we're adding in Notebooks and Streamlits. It'll all be together, kind of one central command system for you to be working with your data and your code, and then dbt projects. You can run dbt now natively inside of Snowflake, right? If you're already using dbt, this is great. You should go, you should try it out running your pipelines there. If you're not, you should try it. d bt is an amazing open source tool. You know, speaking of open source, in Postgres we announced a major acquisition at Summit, the Crunchy Data acquisition. Go and check it out. This is going to make it easy for you to do transactional workloads inside of Snowflake. Go take a look at that. We will not demo it today here, but you should check out the recordings on that. What I will give you demos of in just a second: Cortex, AI, SQL, and Semantic Views, right? These are two important building blocks that are going to help you build even more interesting things. I am not going to say too much about them now because we will jump into a demo in just a second. All right, let's round out some of the big announcements and then we will go over to the demos, last slide. All right, you have got your data in, you have got this solid foundation, right? You have transformed it, you are ready to put it to use, right? How do we help you put it to use? First, we have an Agentic Framework, right, with Cortex agents. We have Snowflake Intelligence, which is an interface that you can be giving to your business, right, to surface those agents, right, and put them to use, right. This is really exciting for all of you that are really embracing, right, these new AI capabilities who want to go to this next wave of intelligent applications. These are two really powerful things, both to build the agents, right, and to surface them to your customers, then back to the builders. Right. We're giving you a lot more tools in terms of Copilot. We'll show you inline Copilot and Workspaces, which helps you find data, edit it, give suggestions, fix, explain your queries. Again, really awesome ways that we're helping make you more productive. Cortex Knowledge Extensions are a really cool thing that we're introducing in the Snowflake Marketplace that allows you to share your Cortex search services as private listings or organizational listings. That's going to make things like RAG, bringing in your articles, market research, right into your agents more possible and easy. What we want to do is, we're bringing in all these models, bringing in all these AI capabilities. It makes it really easy for you to build agents and apps that are going to deliver the insights that you have from all that unstructured data. Right. That structured data that you're bringing together. Right. For that next level of insights for your company. All right, let's switch over and I'm going to talk even faster as we go through some demos and again, we won't get through all of it. You are going to have to go check out, right, some of those videos online. All right, let's go here super quickly. This is OpenFlow. What does OpenFlow do? There are tons of different connectors. Just look at all of the connectors that we have here. If you don't see a connector that you want, this isn't even showing all of them. You can go and you can make your own with NiFi. It's a great open source project. When you have them, then it's really easy to set up these runtimes and these deployments. We've got runtimes here. We're showing one for Kafka, for Postgres, for SharePoint. Right. This is allow you to bring in structured, unstructured, batch, streaming again. Right. It can do it all. And then you can have this running. You can have it inside your VPC. Right. It could be on AWS or you could be running it yourself. Right. In Snowflake, on Snowpark Container Services, we're giving you a lot of flexibility. I won't go into the full way that you make these, but it's a lot of great things that you can do and it'll help you bring your data into Snowflake. All right, say you brought your data, you know, into Snowflake right now. What can you do with it? One thing is, right, you've got your dbt pipelines. That's going to help you, right, make sense of a lot of this transformation. You can see here, I'm in a workspace, little one we call Summit Fest. Here you can see I have a dbt project. It's actually connected to Git. You can see some of the changes. I can push them if we wanted to take a look at them right now. Here are some of the diffs right here in terms of the side by sides. If I go back. Oops. Oh, no. Accidentally click something. This is how, you know, demos are live, folks. Okay, but going back in here, we can see we've got my models, we've got my YAML files, I can run this. It's going to compile. We can see the DAG from the last time. I compiled it all here inside of Snowflake, inside of a workspace, making it even easier than ever for you to manage your data. What else do we have? AI SQL. I skipped over this earlier, but this is really, really cool. I'm running this in a notebook here and what I want to show you is how you can use Cortex AI SQL to embed generative AI directly into your queries. That's going to help you analyze all types of data with just the familiar SQL syntax. Here we're going to show we're trying to find some customer issues across text, across image, across audio data that would have been really, really impossible, very hard in SQL before. Here, using AI Complete, we are able to do this with some multimodal prompts. Very little amount of code that we're doing that's going to allow us to consolidate that data across all of these different formats and start to put it to use. As we have that, we're going to also be able to not only consolidate it across the text, the image, the audio, we're going to use the power of AI to semantically join those customer complaints to the solutions. Right? Again, we're just using AI here in SQL. It's allowing us to work with this data, it's allowing us to do new amazing things so that we can get these aggregated insights across all of these different types of data. We're doing all of this in just a few lines of SQL on the Snowflake platform, getting direct access, the best frontier models that we have. This is an amazing kind of ability that we're providing you out of the box. It's going to give you amazing performance, productivity gains. If you don't want to take my word for it, right, check out these amazing benefits that we're measuring here for you, right? In terms of performance and cost benefits, sometimes three-seven times performance benefits. It's really amazing. You should go and you should try that out now. All right, what else do we have? Let's say you're saying, well, you know what, that's great, but I actually want to be doing my own ML. Notebooks, now we have them, they're GA. You can use them on containers, you can be using them with GPUs. Here I am, I'm running something in the background here. We're predicting diamond prices. I've been running this GPU for a while. You can be managing your CPU, right? You can be looking at your memory. You can do a lot of really sophisticated things now, both with our ML platform and with our notebooks. You can do a lot of these things too with Copilot, which if we have time in a minute, I'll show you as well. Super cool ways we can share these now. Getting so much you can do. All right, semantic models. I promised we would talk about that too. All right, so hopefully you know what a semantic model is and you know why it's important. Semantic layers help you unlock, right, these AI powered analyst experiences, right? You can create consistency across your AI and BI. It's really important as you start to build out these agents and these models. It can also take the semantic models out of the multiple BI tools that you use right now and that every customer has, right? You move it into a single data layer, right? We can see here we've got our tables in a schema and now, right, we can just very quickly create a V iew, right? Here's our DDL for the Semantic View. We're just doing this notebook, we're doing it in SQL. Very easy stuff that you're familiar with. Then here we can describe it, right? Since this is stored as a native object, we can describe it just like we do a table or a view, right? Was that easy. Now we could go over to cortex Analysts and we can ask it a question like, I'll copy this question here. What are the top 10 brands for the books category in the state of Texas? If I go over here and I ask that, right? Now it's going to use that Semantic View. We're going to use cortex Analysts. It's going to help us interpret that question, generate the SQL, run the SQL for us, right? And get an answer. You know, this is really cool. I'm starting to talk faster because of all of the things I still want to show you, right? Here's the semantic query. We can look at the physical query. We could go back here. If we ran it here, right, in SQL, you'd get the exact same result, right? This is a new and very powerful thing that all of you are going to want to explore. It's going to give you a lot of power, especially as you move into more of these AI things. Okay, couple more things, right? This is a workspace again. Amazing things that you can do. We can pull charts in for you, you can filter them in, you can do these fast. You can look at things side by side. Let's do a new query here. I can find things for you. Look at this. I can say, find my COVID 19 data set and give me a sample, right? It's going to go, it's going to use our search under the hood, look across, you know, the data sets that I have available. It's going to pull that for me, right? Give me the answer, I can go ahead, I can run that. It all works again, we're just weaving all of this AI things in for you. Whether you're a builder, whether you're somebody building those Semantic Views, whether you're trying to govern your data, it's all there for you. Last but not least, Snowflake Intelligence. This is how we help you bring these agents together, those semantic layers. Everything is coming together here. We can ask a question here, we can ask directly in chat. These are things that you can be giving to the business users in your company. It's going to do everything that we just showed you, right? It's building all of that together, right? The agents, the semantic layer. Right, the search, all of that's coming together in order for it to reason about it, to go into your data, to actually provide you these direct answers for it. I don't know if I'm out of time yet. We'll keep it on this while I kind of close as it's doing the reasoning and thinking about it. I hope you've gotten from this. Snowflake is no longer, right, just where your data lives, right? It's where your AI applications are born. With these innovations, you now have that toolkit at your fingertips to deliver the smarter, more personalized and more scalable customer solutions. Opportunities really just wide open for you as partners. Right. Those who move the fastest with these new innovations are going to have so many more things that they can bring to their business to deliver more value to your customers and to deepen those strategic relationships. Please go online, check out the summit replays. There are even more better, exciting things. Go to our solutions center, see how some of these things are coming. Turn on those previews in your accounts. We're really excited to see what you build. Thanks Amanda. Really, really cool demos. Yeah, we have a lot of questions that come at the Q and A. Team, just keep coming, keep asking. We have SA that's helping with the solutions and the questions. Go ahead. Thank you so much Amanda for all your sharing today. Exciting. Thank you. It was exciting. I look forward to coming back to Asia soon. I always love everything that's being generated here. I will say goodnight since I'm in the U.S. Thank you so much Amanda. Bye. Hi team. We love to have your feedback. So give us your poll. In about two more minutes we will close. Yep. We also have up and coming our Snowflake World Tour. So happy to get your post and you know if you have interested to join our sponsorship of our World Tour that's coming to our region, you know, do check it out and share your interest as well. Next up, our poll, which is what you guys are feeling. Really, really appreciate all the feedback that you have given us to make our show even better, the next one. We'll come back definitely in quarter three and quarter four. On a quarterly basis, stay tuned for all the updates that we will share with you guys. I'll be online for another two more minutes for every one of you to fill in your post and we really, really appreciate any feedback from you. Hope all of you have an amazing and all the partners, I really appreciate you taking your time to spend this morning with us or afternoon. Yeah, really appreciate it. We will close the webinar in about one minute's time. Hey. Team, thank you so much and we will end, pals. Thank you.