Good afternoon, and thank you for joining us for Snowflake's 1st Investor Day. My name is Jimmy Sexton, and I am the Head of Investor Relations here at Snowflake. We hope everyone had the opportunity to attend Summit this week and hear from our customers on the impact the data cloud is having on their businesses. All of the recordings from Summit will be available online, We urge you to watch them if you haven't already. Before we jump in, I'd like to note that we will make forward looking statements during today's presentation, including relating to our long term operating model and future product features and releases.
These statements are subject to risks and uncertainties, which are further detailed in our safe harbor provisions. In addition, we will present both GAAP and non GAAP financial measures. Non GAAP measures are presented in addition to and not as a substitute for GAAP measures. A reconciliation of historical non GAAP measures is provided in the appendix of today's presentation. Let's dive into the agenda.
We have a great lineup for you today. First, we will begin with our CFO, Mike Scarpelli, who will give a detailed view of our longer term outlook of $10,000,000,000 in product revenue. Next, We will hear from Christina Bienic at Deloitte, who will sit down with Colin Kapsay, our Head of Alliances, to discuss how the combination of Deloitte and Snowflake drives customer success. We will then hear from Christian Kleinerman, our SVP of Product Management, who will highlight announcements from Summit and frame the future of the data cloud. And lastly, before Q and A, I will sit down with our CEO and Chairman, Frank Slootman, to hear what is top of mind for him right now.
If you would like to ask a question during the broader Q and A, Please submit your question in the box on the right hand side of your screen. And now with that, I'll pass it over to Mike.
Thanks, Jimmy. Okay. As Jimmy said, today we're going to talk about the path to $10,000,000,000 So fiscal 2021 was a great year for Snowflake. We achieved many milestones that deserve recognition. So we continue to deliver exceptional growth at meaningful scale.
We reported more than $590,000,000 of revenue, representing more than 120% growth year over year. We reported more than $1,300,000,000 of RPO And now have more than 4,000 customers, including more than 185 of the Fortune 500. We also continued to hire And now have more than 2,000 employees worldwide. While we are very proud of our growth, we're equally proud of our progress towards profitability goals. We cut our cash burn by 60 plus percent last year.
We showed 600 basis points of product gross margin leverage year on year And guided to free cash flow neutral for fiscal year 2022. We also pioneered a new market by announcing the Snowflake data cloud. And lastly, the biggest event of the year was our debut as a publicly traded company. Raising more than $3,000,000,000 during our IPO Will allow us to continue to invest in the business. However, the impact of the event can be seen in our hiring and our customer additions As the IPO's success advanced Snowflake's brand recognition around the world.
While we like to celebrate our many achievements, we're focused on the road ahead. Our next target is $10,000,000,000 of product revenue. I would like to spend our time today discussing how we will get there. The market we address seems to be growing every single day. At the time of our IPO, we sized the cloud data platform market at approximately $81,000,000,000 This market centers around workload specific use cases As measured by revenue per customer in each of one of our sales segments multiplied by the addressable customers in each of our in each of those segments.
Since that time, we've seen our average revenue per customer increase as we continue to displace existing solutions and address new use cases. Because of that increase we have seen, our cloud data platform opportunity increased to approximately $90,000,000,000 We believe that there's still room for the cloud data platform opportunity to grow as we continue to address new customers and use cases. However, we still view the data cloud opportunity as being significantly larger and currently immeasurable. The results I will discuss today are rooted in the cloud data platform market success. You will hear from Christian and Frank speak later about our plans for the future, But it is important to point out that the revenue impact from these initiatives today is minimal.
Snowflake is becoming core infrastructure to the digital economy. Our market opportunity is growing amid a massive generational shift in workloads to the cloud. Industry analysts predict that annual cloud spend is expected to be grow meaningfully and data management represents a large portion of that market. Snowflake is perfectly positioned to benefit from 3 tailwinds. 1, workloads moving to the cloud.
2, Data volumes growing. 3, data driving decision making. If companies do not take advantage of these opportunities, they will fall significantly behind competitively in their respective industries. Because of these trends, we have structured our sales organization to address All potential opportunities in front of us. Our product uniquely scales up and scales down to address the smallest and largest organizations globally.
This is why we structured our sales organization as such. Majors, largest 250 potential accounts, Enterprise, enterprise customers not included on the majors and corporates inside sales team who address companies with less than 500 employees. The sales structure has yielded great success since its inception. As of Q1, we had over 100 customers with trailing 12 Product revenue greater than $1,000,000 We believe this is very impressive as we are also very excited about the progress we are seeing with even larger customers. We now have 19 customers with trailing 12 month product revenue greater than $5,000,000 up from $12,000,000 just 1 quarter ago.
I would like to remind everyone that this is a number that looks at the last 12 months of product revenue actually recognized, which was arguably more impressive. We monitor consumption trends on a daily basis and we believe we have a great line of sight to seeing more customers becoming $1,000,000 Or more in the future. Increasingly, due to Snowflake's innovative product, dollars 1,000,000 customers can come from Unlike traditional enterprise software companies, we do not rely on large employee counts to drive spend. For example, customers who have very few employees but rely on large volumes of data to drive their business can have multimillion dollar relationships with Snowflake. As you can see, 2% of our $1,000,000 customers are associated with our inside sales team, meaning 2% Of our $1,000,000 customers represent organizations with fewer than 500 employees.
Even more powerful is the data point That only 25 percent of $1,000,000 customers are on the Fortune 500 list. This indicates a significant runway ahead with the largest enterprises in the world. As you can see, we are just scratching the surface of our enterprise opportunity with our existing 500 customers. We believe we can significantly grow the average spend in these accounts beyond the current average of 1,000,000. While we know these relationships can expand, it takes time for the largest accounts to get up and running on Snowflake.
It should also be noted that there's a natural headwind to this figure Because of the ramp time to get up and running on Snowflake. This is why we are Extremely focused on shrinking the time to value for our customers. This graph shows the average number of days it takes a customer For their 7 day consumption run rate to exceed their contracted annual amount, this shows that on average, it took a customer 212 days or roughly 7 months to get up to a consumption rate that they were contracted for. You will hear from Deloitte on their efforts to help our joint customers realize value faster. But at the end of the day, Database migrations take time.
I can't stress that enough. And we are doing all we can to help our customers deploy Snowflake successfully. We have prioritized these initiatives because we know that once a customer gets up and running, their growth doesn't end at their original contracted amount. As evidenced by our world class net retention rate, we see customers replacing existing solutions and growing beyond their legacy provider use cases. Snowflake creates new opportunities that customers previously never thought of.
While we believe our retention rate will remain high for the near to Medium term, we do not expect it to decrease linearly over time while still remaining best in class for the foreseeable future. I would like to remind everyone that we calculate net revenue retention by looking at the cohort of customers who have been consuming Snowflake for the last 24 months. We compare the 2nd 12 months to the 1st 12 months to reach our calculated rate inclusive of churn. Looking beyond our growth drivers, we remain focused on our path to profitability. Our success with Fortune 500 And other large enterprise accounts is important component of our ability to expand margins.
As we move up market, We continue to sell more enterprise and business critical additions as seen on this graph. We recognize higher contribution margins on these premium Additions, the benefit of this evolution can be seen in the leverage inherent in our long term financial model and is what is driving the gross margin improvement. Now let's discuss our path to $10,000,000,000 in product revenue. Before we detail how and when we will get to this milestone, I would like to clarify that this is not how we forecast revenue internally. Our revenue forecast is built using historical data patterns From our existing customer base coupled with data science to predict future trends.
However, we stress test these models to ensure they are reasonable. This path to $10,000,000,000 is intended to be used as a way for our investors to track our progress annually against this milestone using publicly available Let's dive in. We believe a great way to track our success against our large opportunity A lot is to monitor our $1,000,000 plus customers. We expect that we will continue to add these customers in a meaningful way And that they will grow their spend over time to represent a significant portion of our product revenue. If you look at our current customer base, Our addressable customers and the potential size of our customer relationships, we believe it is reasonable to assume that by fiscal year 2029.
We will have approximately 1400 customers with trailing 12 month product revenue of $1,000,000 plus. They will on average have recognized approximately $5,500,000 in product revenue for the trailing 12 months And that they will represent a large percentage of our overall product revenue at approximately 77%. This framework underscores our extreme focus on moving upmarket and growing our relationships with the largest customer opportunities. If we execute, we can become the fastest enterprise software company to reach this milestone. Now let's take a look at the long term operating model.
At $10,000,000,000 of product revenue, we believe we will still be growing at approximately 30% plus year on year. On a non GAAP basis, our product gross margin will be in the mid-70s at approximately 75%. We will Show continued leverage from economies of scale to achieve 15% of sales for R and D and 10% for G and A. And we will see meaningful leverage in sales and marketing between now and then, ultimately landing at 40% of sales. This will lead to 10% plus operating margin And 15% plus free cash flow margins.
Okay. Our key priorities to achieve this target In this order, our invest for durable growth, this is not investing at all cost, show continued product gross margin expansion With our move up market, march towards meaningful free cash flow generation and continue to show operating leverage year on year. Lastly, a couple of modeling points to consider before wrapping up. Less than 3% year on year dilution is assumed in this forecast, And we will continue to see free cash flow seasonality similar to the past year with Q1 and Q4 being our strongest quarters. I'm now very excited to transition to the next segment of the show.
We have seen a significant increase in the engagements from GSIs in the last year, Most meaningfully from Deloitte. These commitments from our partners marks an important inflection point for the business. So with that, I will now pass it To Colleen for her fireside chat with Deloitte. Thank you.
Welcome to our 2021 Investors Day. My name is Colleen Kopsey. I'm the SVP of Partner and Alliances here at Snowflake, and I am joined by Kristina Bienic, Who is the Chief Commercial Officer and Principal at Deloitte Consulting. Christina, welcome.
Thanks, Colleen. So excited to be here today and excited about the conversation and dialogue we're going to have. As our Chief Commercial Officer for Deloitte, it's really about driving growth For our business and in this capacity, I have sales, marketing, our client teams and our ecosystems and alliances. So All of our partnerships and relationships in the market, and that's one of the reasons why I'm here today. Snowflake is really big and important for us As we think about moving forward together, just excited to spend some time with you today.
Great. Well, you definitely have a unique perspective. I'm really excited to hear about what you're seeing with clients who want to do business transformation and what trends you're seeing out there from a data Especially.
Yes. So Colleen, if you take a step back and we start at the highest level, we live in a world that's just transforming right before our eyes. Our world is getting smarter and more connected by the day. Smart, intelligent machines, appliances, I look around my house and I feel like everything is smart and intelligent, But all fueled by data in the cloud and the application of AI to generate the insights and really render autonomous actions. And this world is one that we live in, but inherited by our children, where language, conversation, intellect, judgment, actions Are really transcending from purely humans doing these to smart, intelligent, hyper connected machines and systems With humans as well.
And across all of our clients, whether it's businesses in the private sector, Public companies, national governments are all real retooling, pivoting, and preparing for this new, smarter, connected world And to thrive in what you could call the modern economy and the beating heart of this modern economy is underpinned by cloud As the always on infrastructure and AI as the always embedded cognitive engine and cyber as the always required means to safeguard us. So this really means that massive amounts of data that is generated, connected, stored and comprehended to be act upon And it requires that data and data then in many ways you could say is the lifeblood. You asked about business transformation, that doesn't happen without data. And the data is really what's accessible 20 fourseven via the cloud. And that data is then acted upon by AI, Protected by cyber.
And I think and believe this is the opportunity in front of Snowflake and particularly how we see To be that data cloud that powers the modern economy and for businesses to thrive in it, a data cloud that's really the embodiment of this always on infrastructure, Sure. Making data available for machine learning algorithms to generate the insights so businesses make better decisions, To transform engagement, to render autonomous actions and really fuel the innovation that all of our clients are striving for When they think about transforming their businesses.
Oh, my gosh. I love it. You know, I'd I'd love to hear from you a little bit more about what you're seeing from our Clients, what are they asking for? And what makes the combination of Snowflake and Deloitte the answer for so many of them?
Yes. Pauline, if you take a step back on that business transformation that I just talked about and how it's this really fueling the modern economy, What's really needed, the data resides in the cloud. So if you just think about that, everything that And so the machine learning algorithms learn from that data and provide that competitive edge that organizations are seeking. And so when I think about Clients we work with together, we're seeing this across the board on that business transformation journey. And this is fueled by a recognition that look, Legacy on premise data solutions or solutions are expensive to maintain.
Data silos make harnessing the value and the power of that data Too time consuming and again expensive. And AI is really difficult to apply across fragmented data across the organization. So this is where together Snowflake and Deloitte, we have a major role to play. The data cloud is the network that connects The customers, the partners, the data providers, the service providers really enabling them to share rapidly Growing data sets in a secure, governed, compliant ways. The data cloud serves as the true central data hub for all your data types.
So whether our clients are talking about structured or unstructured data, organizations can leverage the data cloud to really reduce silos, to mitigate risk And to simplify what is oftentimes cumbersome data sharing methods, the data cloud also serves as that starting point for applying AI On really large sets of data to propel what we like to call the modern economy. So the opportunity and what they're asking us for is Help us to do that. Help us go after all of those different areas to ultimately drive financial benefits. And oftentimes, it's not just the financial benefits. It's what we're enabling and what's being unlocked.
I love how you're seeing the power of the data cloud And what we've termed the network effect and how it's just so transformative for clients, so that's I think that's Perfect that we're both seeing that same impact out there. When you look ahead at Deloitte, from a practice Standpoint around Snowflake, what do you see as sort of the short and long term investments?
Colleen, we're really I both near term, short term about the vision for this Snowflake practice. And see, really the horizon is Filled with possibilities and a really strong vision of what long term looks like as well. Let me tell you a little bit about kind of here and now today what we're really seeing from our clients. Just robust demand is what I would say and we're making tremendous investments to implement a data cloud. And we're seeing that from clients who are saying, hey, I need this because I have to have a centralized hub for all types of data across the enterprise.
I've got to have a way and development for a repository that's easy to access data for all my different use cases. Clients need to create the elasticity respect to when, where and how data is stored. There's need for training for machine learning algorithms And really just this enabling of this jumping off point. So we see the demand from clients in so many different ways and we like to think about Snowflake is the endpoint of migrating data to the cloud, but it's also the starting point for the application of AI. And so organizations are really recognizing the fact that migrating large volumes of data to the cloud and establishing a data cloud It's almost an unlock.
It enables them to begin that hardest thing in the data that I talked about and really making it a strategic asset. It makes the data accessible. We often say it helps you to democratize the data, to put it in the hands of the end users And not just the data engineers who we love, but we've got to get it throughout the business so that it can really be consumed and used. And then just The application of AI on the connected enterprise data sets to really get after that business transformation. So the relationship between us building this Practice and the partnership together, we're really looking to help our clients with all of that.
And this is where we are Investing heavily in things we're building together, solutions we're bringing to market together, training and really making heavy investments because we just The high growth in the near term demands, but really as we think about unlocking for our clients, How they see their businesses transforming in the future.
I couldn't agree with you more that when we see the transformation happening and the Unlocking together, it's just it's magic. It truly is magic, and they've really experienced the power of data. Now, That said, some customers and you have such a long and trusted history with so many of your customers. Some of them have Challenges migrating to the cloud and with the trust and the security. And, you know, I'd just love to hear from you, Large organizations struggling with legacy solutions.
What do you see are the biggest roadblocks to them moving to the data cloud?
There are difficulties In data migration. So let's just call Kaldi is what it is. It can be sometimes a lack of an action plan, Whether it's incomplete or confusing, oftentimes it's incomplete, duplicative, unnecessary data that can make it difficult. You know, data sometimes being data loss before and during the migration. Oftentimes, we have like source versus destination compatibility challenges.
And even something that you think, wow, just technology restraints that are could be with the wrong software hardware mix to support that end state. So, yes, we see these difficulties, but what we've done that really helps our clients and I can share some of how we've Overcome some of those challenges is we have this migration factory approach where we bring market leading automation Across each of the implementation phases to really successfully deliver those engagements. And in other words, we've pulled together Our 1,000,000 plus hours of data migration work to build this playbook of best practices to guide our clients together, To help avoid the hurdles that can really derail a migration. And we think about it across the strategy and planning dimension, Re hosting, enhancing optimization, and then just the data operations. And look, our approach has been And I would argue the approach is one that we're continuously improving because it's based on feedback and it's based on kind of always what is The next best, the newest automation, the newest thing that we can bring to help with these really large scale modernization efforts.
And One of the challenges we often hear is migration is an expensive throwaway effort. And so you sometimes hear that. And I think we have demonstrated in our work together that monetization programs lend themselves to a strong business case. And if done right, and this is where kind of taking the factory approach, the project pays for itself. And we had a recent client Where we'll realize the client will realize $25,000,000 in annual savings on the completion of a Teradata migration project.
Another challenge to Kind of put in the 2nd bucket is modernizing the data platform is disruptive to the business. And I know you would agree and that's certainly something that You often hear is, I've got to minimize the disruption to the business. And so this is where that early engagement In the factory approach of users so that they have a long runway, that they have kind of the time that's needed For the upskilling of associates rather than replacing them and really oftentimes it's also the automation to replace user effort to Test and mitigate the risk of human error. So there are many things in that approach that really help with the mitigation there. And the third challenge It's really choosing the lowest cost vendor to drive mission critical modernizations.
And we have a differentiated approach That's anchored on a few things. We have this joint approach in how we automate and we really align Snowflake Professional Services where we work Closely together to deliver kind of a more complex and complete and large scale migration With all of the things that we need to do together and proven automations at every phase that help to reduce and minimize that risk. And I think we have another really great example where the client saw an acceleration of the migration by 50% in terms of scope and timeline And it wasn't through just cost choices. And so this is where that playbook, those best practices and what we've really built together in this, What we call our Deloitte migration factory approach, I think is really critical and has been extremely helpful with You know, the risk that often comes up, when working with our clients.
I have to just ask you, you know, as we go back to looking at your Snowflake Practice, sort of what goals have you set out for the team and for yourselves of success? Like, how do you measure, Hey, we're doing great or we could do more or we're right on target for you and your team.
Colleen, I love that you asked that question. In full transparency, We want to be your number one partner. And how we get there in many ways is by clients in the market Choosing to work with the 2 of us and seeing the value of Snowflake and Deloitte together and the value that we will collectively help those clients to realize. And so we have prioritized the relationship with Snowflake and we'll look to kind of continue to make the investments that you're talking about. We're investing in all aspects of building our Snowflake practice and we believe in the opportunity to work together and really What our end clients are asking for, we see this relationship as a great way to help all of our clients together on business transformation.
And as you can imagine, we work with many partners. And why Snowflake and some of the things specifically that we think about doing with Snowflake, We want to make sure we're building and driving industry solutions and really putting ourselves at the heart of those industries together so that we can answer The tough challenges for each of the industries that we serve, bringing bold plays and ideas together. We want to enable the data cloud through in new ecosystems, whether those are Deloitte ecosystems or Snowflake ecosystems, and then they come together, We want to be more than just a 1 plus one relationship. I want to collaborate on the Snowflake product roadmap and future innovations. And I think this is where We can bring the best of the expertise that we're seeing broadly throughout our client base and the expertise that your teams have And bring that together to continue to drive innovation, all with driving our clients' value And helping clients to really realize the ambitions that they have.
Ultimately, the definition of success for both of us is in that client dimension.
Couldn't agree with you more. I think here at Snowflake, I can say with with relative authority, We don't believe that we can do it on our own. We need our partners. We need our ecosystem. We need you.
And walking in jointly, Along with Snowflake Professional Services and your expertise and your trust that you've built up with your customer and your your industry expertise, I think we just make a powerful payer, frankly. And we couldn't be happier with the relationship and how it's progressed and the growth we're seeing together. But As you said, our North Star is happy customers. And as we watch folks transform to the data cloud and really understand the power that they're harnessing with the network effect, it's it is Stand the power that they're harnessing with the network effect. It's it is just exciting.
So And we thank you for, you know, your belief in us and for you extending what we're able to do together, and it's just been a phenomenal journey, and I can't wait to see what's ahead of us.
Colleen, thank you. And I have a huge, ditto to everything you just said, really looking forward to that journey Together.
Great. Well, thank you for joining us, and, hope everyone got an opportunity to learn some new things about us and Deloitte and how we're going to market together.
Thank you, Christina and Colleen, very helpful. Now let's dive into the product. I'd like to introduce now Christian Kleinerman, our SVP of Product Management.
Thank you, Jimmy. So in this next section, I want to do a quick recap of our major product announcements at Summit. And then we're going to go look a little bit farther out in the horizon, what's next and how we're thinking about the bigger vision for what we're doing. But before we do that, I want to share with you I want to show you a data visualization. And you may have seen in our website, in this slide, in the title slide, In much of the Content and Summit, what looks like a network graph, a connection of entities.
And that came From this data visualization and let me explain it for you. I know that the scale is small, but every single dot On the diagram represent a Snowflake customer and every single link, You won't be able to appreciate it, but it's directional. And it talks about or represents an edge or a data sharing relationship. And we wanted to share this to show 2 parts. 1, the year over year change.
You can see on the left, April 30th last year. On the right hand side, a much more populated visualization with April 30th of this year. And the message behind this is even though I'll share lots of features, lots of visions, we did announce at Summit, Some features are in current preview. Some features are in future preview. But the most important takeaway is the data cloud is happening today.
The data cloud is real, and we see tremendous momentum on all of this. With that, let me get into The recap of what we announced at Summit. And the overall set of announcements, We organize them on these 5 innovation pillars. And I have one slide for each of them that sort of Capture the bulk of announcements and our product investments. Let's start with the topic of Connected Industries.
And our goal on this series of investments is to bring to life the data cloud. This is how we're looking at every single industry. -What are the data flows between players? How do we generate more value for business users for the specific use cases By unlocking and unleashing data to flow between organizations. The headline announcement on this Topic of connecting industries or the connected industries is the momentum that we have in the Snowflake data marketplace.
We've announced that we have over 500 Things available in the marketplace for clarity, a listing represents from the perspective of a data provider, a data product. So we have effectively 500 plus data products available to our customers to enrich their data, complement their data, Put the data in context. And of course, we continue to add many providers as well as many new listings onto the marketplace. Today, the commercial part of hosting data in the marketplace and having it be consumed by Snowflake customers, That is transacted out of the platform. And what we hear on a very regular basis from our customers is, I have some data.
I know that is valuable. I may be interested and open to making available For business and monetizing it, but I do not want to invest in billing systems or sales teams or the entire Distribution of my data product. And it was with that lens, with that feedback, highly, heavily validated by many customers, That we decided to take on the effort to bring the ability to do monetization of data into the platform. So this is we showed a demo at Summit. It's in development.
It will be in preview later this year. And what we aspire is to enable Organizations of any shape, size, and industry to bring data onto the marketplace and create a new revenue stream for them. Important as well is we heard from consumers, one of the most, difficult areas of Acquiring and consuming data is the process of iterating and validating, is this data good for me or is this data not that useful for me? Does it join well with my own data? And as part of our monetization effort, we are introducing a try before you buy experience where we make it simple for data providers To provide a subset or an anonymize or a partially redacted data set that can be Try by consumers, validate it.
And if they like it, then they can commit and do the actual purchase. And That usage based business models are the ones that best correlate with the, in the interest of consumers where you pay for what you can consume. And from that perspective, the business models that we're enabling are all around Usage base, is it based on the number of rows or number of days or a combination of the different business models? So monetization in the marketplace, try before you buy And tremendous progress with existing providers and data listings. And another part that we announced in this pillar of connecting industries Was a ServiceNow connector that will seamlessly enable our mutual customers to bring data from ServiceNow into Snowflake.
And we had, as part of our keynote, Andy Marcus, Chief Data Officer at AT and T, not only talking about the broader vision that he has, But how Snowflake is enabling that vision for them and the ServiceNow integration was an important aspect of bringing their data into Snowflake. Moving on to the 2nd pillar. The topic of data governance is Front and center for every single organization, not only because the importance of data has increased, but also there's now a regulation that is, Raising the bar of what is expected out of every organization. And obviously, there is increasing concern around Cyber attacks and other types of compromises that are chasing or looking at the data. So very important, we have been investing in data governance since day 1 of Snowflake.
We have thought about security From the entire lifecycle of data, data at rest, data in motion, data when it's being queried. But we continue to advance the capabilities of Snowflake. In the last 6 months, we did the general availability of data masking for us. We also brought row level policies. And one of the big announcements that we made at the conference this week is the integration with Alation.
Alation is Enterprise wide catalog, they have done a very nice job integrating the user interface and the user experience To manage those policies in Snowflake. And of course, we continue to partner with a variety of players and other partners. A very important topic of governance is the subject of privacy. And many organizations -Maybe willing to exchange data more freely with one another if the concern of PII or PHI data leading to re identification of patients or individuals not being there. And as part of that, what we announced at the conference is an effort around privacy that has 2 prongs.
1 is on classification and identification of Sensitive data as well as potentially identifying data. And the other one is we introduced the concept of anonymized views, -Which simplify the entire process of taking a data set and anonymizing it in such a way that it retains the analytical value, But it reduces the risk or prevents the risk of identification of individuals. We think that this is going to be transformative and accelerate Our data cloud motion of helping organizations share with one another. And as part of the keynote, we have had Viba Helu from Capital 1. And she was just sharing how governance is a primary reason why Capital One leverages Snowflake and hosts data inside Snowflake.
Pillar number 3 is the topic of platform optimization. And this is a little bit of a catchall of many efforts that are going throughout Snowflake product and engineering teams. And the message here is we're constantly investing and deliver better performance, better economics for our customers, -And also to help them make better decisions on how to get the most out of Snowflake. One such use case that has become prominent It's the topic of interactive use cases where I may have a business intelligence dashboard or an application that requires interactive experiences. And What we've done is dramatically improved those types of workloads that are usually short running queries, very large volumes, high concurrency, And those are the ones that power these applications or dashboards.
We saw improvements on the 6 times better to 8 times better in terms of both Concurrency as well as reduction on latency. And we have customers that have started to replace Serving systems, serving layers of data with just additional queries running on Snowflake because the performance is that much better. The other announcement that we did broadly is the improvement that we did in the storage format or the storage representation of data in Snowflake. Some of it Mike called out the earnings call a couple of weeks ago that it had a material impact On the economics that we present to our customers, some customers have seen 5%, 10%, 15%, 20%. We have many that are in the up to 30% Storage efficiencies relative to where they were.
But what I would like to emphasize the most is not those Economic benefits and storage benefits that will come also with performance benefits, but is the seamlessness and transparency Of how all of this was done, all of our customers are benefiting from that innovation, and they didn't have to Think about it, worry about it, talk about and upgrade about it. All systems, all queries are upgraded. And that is at the heart of the architecture of Snowflake And many of our design choices on how we've built the platform and how we continue to innovate. And last but not least on this list is the topic of, We introduced a usage dashboard. One of the most common pieces of feedback we've heard from our customers is I want to be able to understand The consumption overall that I have in Snowflake, when we started, we only have virtual warehouses as a compute model.
In the last Several years, we've introduced many serverless tools to do ingestion and automatic cloud clustering and other capabilities. So what we provide is now a single pane of glass To provide visibility into usage, we have controls for our customers to govern the costs and the usage of Snowflake. And over time, it lays the foundation for us to provide optimization and insights into that consumption. The 4th pillar, which probably carries the most weight in terms of New workflows that are addressable by Snowflake now is a topic of data programmability and effectively addresses how I can Program data, how it can transform and get more value out of data. It's applicable to data engineering.
It's applicable to data science, But it's also applicable to generic data powered applications. The bigger announcement on this pillar is the topic of Snowpark That comes with language choice. We are introducing public preview, Java, and Scala as programming language choices. And later this year, we will match it with Python support. And this is a dramatic change In the appeal that Snowflake provides to a variety of use cases and developer preferences, What we see is that engineers may have a preference to use Java or Python.
They will be able to leverage the exact same engine That has the great performance, the great economics of SQL, but from a language or program model of choice. The other announcement here is the topic of unstructured data. Snowflake was born with structured and semi structured data as first class support. And what we hear from customers is, we like your vision. We like this notion of putting all my data in a single system that doesn't have the scalability limitations of the past.
Unstructured data was a missing piece in being able to provide that single storage for all data. And now it's in prior preview and will roll out into a public preview later this year. That completes the rough Support for all the data and organization under a single system. And the last topic, we announced a Snowpark Accelerated Partner Program. It is fairly easy to say that the value of any platform is from the solutions and ecosystems that are built on top of that platform.
And we're delighted to see that over 50 different partners I have committed to delivering Snowpark solutions. Some of them have been up and running and are headed to customers already. Some of them are starting their journey on Snowpark, but the most important thing is there's a lot of use cases. There's a lot of new computation that is coming To natively running Snowflake by leverage of these extensibility mechanisms. The last pillar that we announced is a program called Powered by Snowflake.
For the longest time since the early days of Snowflake, we've had customers that are building applications on Snowflake. Analytics are increasingly becoming an important part of what end users expect from an application. It's no longer a transactional system where I can just, place orders or do itemized decisions. But I want to see aggregate. I want to see reporting.
And Snowflake has been doing that since the early days, as I mentioned. What Powered Buy does is now it gives A dedicated focus, a dedicated program with better technical support, better technical guidance, architectural guidance for our customers To build applications that leverage the capabilities of Snowflake, at the keynote we had Sandeep Parsa from Adobe Sharing how they're building the new campaign management application all within the Snowflake capabilities. So that is the recap of Summit. I am not doing justice to the tens of other announcements that we had. And, obviously, as Jimmy alluded, we have the recording for all of you to cover.
But I've done a quick recap of the higher level announcements. So here's the question on where are we headed? What is the bigger picture? And I'd like to start By thinking through this reality of on premises data silos. And the most interesting thing to me is that With all the momentum that you've seen from Snowflake, with all the momentum that you've seen from the cloud in general, this It's still the most common reality on the largest enterprises in the world.
When you ask, well, what are you using for analytics? What you hear is, We're using everything. We have a little bit of everything. And it's a little bit of I have a large enterprise data warehouse, but I have a lot of data marts on the side. Many of them took on the elephant and put Hadoop in the mix because it promised to Eliminate scalability issues, and it just added to the complexity.
So the opportunity to go and centralize and consolidate this -It's as big as it gets. But here's the other interesting insight. The Cloud in and of itself is not the answer to how do we eliminate silos. If anything, I've started hearing from customers that the cloud is making silos easier. Now what used to be a long purchasing Process to buy a new appliance.
Now I can go in a matter of minutes, spin up a new service in a cloud, and voila, I have a new silo. And the insight for us is we obviously are we're built for the cloud, born for the cloud, -But cloud does not mean no silos. We've been using this moniker of silos 2.0 because it's starting to happen not only across regions and Across systems, but across clouds. And the goal for us is to obviously provide a Single platform for all of our customers where they can go and put all of the data in a single unified system. -And it's global in nature, has geographic reach, but it's also Cross cloud in nature, and I hear this consistently that our customers love the idea that data can be in different clouds in different regions, But Snowflakes bring the single analytical capability across all of that.
And it's very interesting to me that A lot of the things that we can talk about in terms of new capabilities for Snowflake, many of the benefits for customers today reside in this. Can I look at data across my business businesses or business units? Can I look at the full picture of my user, my customer? And this is the foundation for that. But obviously, putting all the data in a single place and having it interact Through global mesh, we have interconnected all clouds.
That's not enough. The most important thing, once you have the prerequisite Of putting all the data in a single place is to be able to transform the data into value or extract value and extract insight from the data. And we look at our broad vision, our broad direction through these three lenses. 1 is the skill set of our users. We want to embrace diversity of skill set.
If a customer wants to use the clarity of programming language or an imperative programming language, we're happy to do that. Is it SQL? We're happy to do that. We reintroduced a SQL Snow scripting language. But also, if you're comfortable with Java or Python, they're also part of that.
And this is where the significance of Java user functions and Snowpark is maximum. We also look at the lens of diversity of workloads. And even though I'm not going to go into every single Use case and workload that we intend to support. I think it suffices to say that We're looking at the entire lifecycle of data from birth all the way to archival. How is the data being gist?
How does it come from transactional systems? How does it get transformed? Is it batch? Is it streaming? And every single step, we're looking at Broad diversity of workloads to bring more support under a single unified product, which our customers tell us all day long.
They love the fact that they don't have to learn 100 or 200 different products. It's one product with all these capabilities. We just to give you a sense of The types of efforts going on in this diversity of workloads, we largely know what are the announcements that we'll make A year from now at Summit, and I can tell you some of those investments have been going on for a year, some of them even 2 years already, because we understand That a single platform for all these workloads is what our customers value, quite a bit out of Snowflake. And the last lens is this topic of industries. We are developing deep industry insight and understanding on how do we meet business users, Where they are, how do we help them deliver solutions to their problems?
How do we talk with retail companies about Inventory replenishment or how we're talking about with, healthcare organizations about clinical trials and not necessarily about talking The capabilities of the technology and we're using that understanding to inform our product roadmap and how do we maximize the transformation of data into value. So once we have a platform that enables our customers to have all their data in a single place And with a variety of programming models, programming language and workloads, super important, everything we do has These two attributes as an invariant. One of them is the topic of performance. And at all points in time, we are looking at what are the choices that can lead to the best performance for our customers, which usually translates to Low time to insight and also the economics part of it. And obviously, these 2 are related.
We are very aware that each time that we make a performance improvement to the system, not only our customers benefit from the faster insights, But by virtue of our business model, the economics are also getting better, so customers doubly win. This encompasses everything we do. But also something that encompasses everything we do is this topic of data governance. We talked about it in the context of -One of the big pillars of our announcements at Summit. But everything that we do, we do it thinking of one thing.
We should not present Trade offs to our customers so that they can get value out of the data, but not have to go and compromise on security or privacy Or anything that is related to understanding the data, controlling the data and governing their data. This is where the announcements on privacy are so significant. We continue to advance The state of the platform on this front and also is something that Frank and others were talking at the summit is It's enabling us to do things like multiparty computation. How do we bring data from different elements and go and deliver results? So privacy and security front and center on everything we do alongside with performance and economics.
So once we have this solution, Snowflake, for each one of our customers, High priority for us is to enable the collaboration through data. And this is where you see our data sharing technology, our data exchange, our marketplace As ways to have organizations be able to collaborate with one another. Very important For us is that this collaboration is not just about data. It is true that it has been predominantly about data today, But we have enabled already in the last couple of years things like shared functions, where I can share small pieces of business logic that may have access to my data without having to give you data. And the reason to mention this is we understand collaboration.
We understand some of the platform choices that we've made to enable that collaboration. And I would say we're only getting started Relative to what is possible in terms of use cases, when we talked about clean rooms, that is powered by this type of shared function capability. And then there's a very interesting dynamic. And watching Colleen talk about ecosystem and partners, There is a rich ecosystem of data services and data applications out there. And obviously what you saw in the momentum of a Snowpark Accelerate Program is that we have many companies wanting to be part of the Snowflake ecosystem.
But one of the most common things that we hear is, you know what, my sales cycle for these partners Involve 90% of the time getting through governance and security and legal teams to get me to Allow me to get data into my application or service. And that led to a very interesting insight for us, which is, what if we can enable All of those applications to run closer to the data within the security and governance perimeter of Snowflake And simplify the lifecycle for our customers as well as for all of these vendors. And you see a lot of this starting to happen. We have many customers or many partners talking about how they're developing solutions for Snowflake to already run-in the customer Snowflake instance. And one of the big directions that we're pursuing is how do we embrace all of this and make it even simpler for Variety of data service providers, data application providers to bring the computation, the logic, the insight Into Snowflake.
There's no end of small companies that have a very interesting application of machine learning for a specific problem in a specific industry. And we think that we're going to go and simplify how do all of those companies come and deploy solutions closer to Snowflake, Snowflake Data. And then to cap the journey on all of this, the Snowflake marketplace, you'll see it continue to evolve To provide an even more important role on discovering, distributing and monetizing not just data, which is where we are today, But also data services and eventually data applications. Think of how all of this comes together, where Application developers can leverage extensibility, Snowpark, language choice, bring experiences closer to the data and leverage the marketplace To not only discover, distribute, but monetize. All of this that we just covered, a single platform Where customers can store all their data, where we have diversity of workloads, diversity of skills, diversity of industry solutions, Where we bring performance, cost, governance, where we enable collaboration, where we enable rich applications In the platform and all of this enabling distribution for our customers through the marketplace, that is what keeps us super excited.
That is Snowflake, -And that is the Data Cloud. With that, thank you, and I think we're going to take a 10 minute break, and then we'll be right back.
Welcome back, everyone. I'm now joined by our Chairman and CEO, Frank Slootman. Thanks for joining us, Frank.
You bet.
I want to spend some time talking about some hot topics since the IPO and especially coming out of Q1 earnings, so I'm going to dive into a few questions, if you don't mind. So what's driving the best in class net revenue retention rates? These are very high percentages quarter on quarter for a company at our scale.
Yes. We find that people are often puzzled, like, how does that work? Where does that come from? And I think it's worthwhile just pointing out there's very strong Undercurrents that drive those numbers, not just like, oh, they got Snowflake, but kind of like it, they use some more, right? The reality is, when they land on Snowflake, They find out that they can run many, many clusters concurrently against the same data.
Before, they couldn't do it, right? So they just said, well, it's not even an option. Ma, you can try that. So that expands the consumption right away. They run processes much more frequently because they now can.
Maybe they were recomputing auto loans Once a month, maybe they're doing it every night. Right now, we've heard that from some of our financial customers. That increases consumption. And they can massively provision workloads that before when they were limited to the size of the cluster. Now they can really, really increase the size of the cluster, run the workload much faster, right?
These are just things that we sort of unlock the demand that's already there, and then we sort of stimulate the thinking around all the possibilities That can happen next. That's really what's behind these high revenue retention rates. I wish we could take credit for it and say, we're just so good at positioning the product. Customers are really figuring out how to enable the demand they've always had, but could never really empower.
Yes, that's interesting. And there's a lot of ways to address these different workloads. Our space, it feels like there's news every single day. There's a number of data management products in the market. What makes Snowflake different?
Yes. I actually thought Christian did a great job articulating what are the key underpinnings. But the central thing about Snowflake Is that it is a platform, right? And it starts with the architecture, the fact that we can separate the storage from the clusters, we can run a multi cluster architecture. That is incredibly powerful, but there is no scalability limitations, both in terms of the number of workloads and how fast They can run.
So it's an incredible canvas for people to develop, data operations. But also, you know, when we ingest data into Snowflake, We go through these massive optimizations in terms of storage as well as setting up the data to enable a wide variety of work Close to run incredibly fast. I'm always sort of mesmerized by Snowflake being able to run these incredibly highly scaled, very, very complex batch processes, very typical Data warehousing processes, but then also being able to run highly concurrent, the snappy dashboards for thousands of people at the same time, As well as Global Search, which is like looking for needles in a haystack. How does one platform do all those things equally well without any tuning or tweaking? Just out of the box, it's right there.
That's what the platform does. And then the third thing that we've talked a lot about at Summit is the notion of governance. What the platform really does, it really brings a measure of control over what happens on the platform, what comes in, what comes out, What happens to the data, who's doing it, making sure that we bring a security model that has to be ironclad, but also compliance and privacy compliance is just a huge deal. We've really seen over the last couple of years that governance went From being a bit of a sideshow to really being the main show, we now see the business really not getting access to the single byte of data until, you know, the governance people So, yes, go. We're good, right?
And so, so governance has become a really big deal in terms of having a data platform. It's much harder when you run data lake operations. Every developer sort of has to reproduce these benefits a single job at a time versus having a platform Does all that stuff for you right out of the box, because there was a box.
So these all feel like TAM expanders. Mike and I get a number of I'm trying to pinpoint a specific number. How do you think about the opportunity for Snowflake?
Yes. Snowflake is Just, it unfolds very, very rapidly, right? It is not very useful to no spam call. It's not very useful to look at the business, what it has been. As I just said, it just unfolds.
It's very fluid. The business is really limited to people's budgets and people's imagination. But what investors often do, and a lot of our customers, quite frankly, do the Quite frankly, they do the same thing. They look at their historical workloads and then, you know, what does it take to move these workloads to Snowflake. And that sort of becomes sort of the initial scoping and scaling of what's going on.
But the reality is that the data cloud really changes the positioning and the Scale and scope, what people eventually end up doing. I cannot tell you how many data cloud conversations we have every week. I mean, yesterday, I talked to Large oil and gas company, they're eyeballing an energy cloud, right, not just for oil and gas, but also for alternative fuels. They're starting to think about the data relationships, right, that could make that up. They've never thought about that before because it never really was an option, But now it is, right?
So it's very difficult to pinpoint very specific parameters to the scale of the opportunity.
Yes, that's very interesting. And we heard a number of announcements from Christian coming out of Summit. As the CEO, what do you think are the most important announcements that the investor community should focus
Yes. So it's almost hard to wonder which ones are your favorite children, and that's the hard question. And some of our product managers might be upset if I don't manage them, but mention them. But the data programmability set of announcements, especially Snowpark, are extraordinarily strategic To the platforms, these really represents huge evolutions in platforms. And it may look to investors like, oh, this is great.
This will expand the scope of workloads. We're not going to bring programmers onto the platform. That's all true, right? But the reality is there's no such a thing, Not just Snowflake data, but Snowflake applications. So we're going to be a growing part of the application stack because we now have Literally, Snowflake data applications, they also become the currency of the marketplaces and the monetization models.
All you can see, this is going to become A rapidly growing opportunity, and that's really our ultimate vision, as Christian described in the previous section.
Interesting. And a couple quarters ago, we heard about the shift To an industry vertical focus, what have you learned or what has the company learned since going down this path?
You know, it's been incredibly interesting journey To be on because we're now learning industries and what their specific data issues and challenges are, whereas previously, we kind of went after existing workloads, and we kind of Architectural distinction, we benchmarked the old way, the new way, and sort of people made up their minds from there. Now we're involved in very industry specific Challenges. For example, our largest vertical is the media and entertainment cloud. So we work with a lot of the media streaming companies. And they have interesting challenges in terms of enriching their data because, obviously, they're big advertisers, right?
In order to get Seeing the effectiveness of their advertising dollars, they need to enrich data. That is not that easy. And the reason is data sharing It's inhibited by compliance and privacy requirements. So Snowflake is incredibly good, you know, to bring your data cleanroom concepts And the real infrastructure to allow people to data share in a completely governed manner. So now, you know, these media companies can really Optimize their advertising expense as well as, for example, the much more familiar walled garden solutions by Google and Facebook and so on.
So they have an opportunity To compete against very established advertising platforms. Interesting. Yes. And financial, Obviously, huge. We made the announcement with BlackRock a quarter ago.
They have tremendous data, gravity, You know, we're on asset management data, and they were quite visionary and really appreciating the opportunity in front of them. Retail is a very, very active one. Health Care Life Sciences is very, very active. So I mean, there's not a day doesn't go by that You know, we don't get it. And by the way, each vertical is really comprised of the institutions that make up this vertical, but then also the data There are specific to those verticals, right?
And then it's really a matter of understanding what are the data networking opportunities in that particular Industry, and they're all different.
Do you feel like we are in the early innings of that transition at this moment?
We're super early innings. It's really fascinating to me, you know, when we meet with customers in the beginning, they kind of look at us like, I have no idea what you're talking about, right, until we start to really get to wrap our heads around it. And then all of So ideas start coming. And it's in other words, these are the killer apps of the data cloud. That's what we call them, right?
They start to stimulate and ignite Opportunities I never thought would be possible.
That's very interesting. But before we close, any final comments
is a journey. You know, when we were getting ready to become a public company, we were really seeking out an institutional ownership That could really sign on to our mission for 5 to 10 years, build significant positions, over time And really believe in what we're doing. I know on a quarterly basis, we get into excruciating details about what happened that quarter and the next quarter and the rest of the year. But today, it's really about understanding what is the long term mission, because I think Snowflake is a journey. And to the extent that we can convey that And investors have more appreciation for that.
I think it's really important because the quarterly noise is not nearly as interesting You know, as a longer term trajectory that we're on, that's why we're here.
Yes, very well said. Thank you, Frank.
You bet.
And with that, we're going to take another quick break to give you an opportunity to submit your questions on the right hand side of your screen. So we'll be back in a second. Welcome back. I'm now joined by Frank, Mike and Christian. We appreciate everyone submitting your questions.
So we'll tick through those right now. And again, if you have any more questions, feel free to submit them on the right hand side of your screen. The first question comes from Kirk Materne at Evercore. Can you give us an idea of how Snowpark broadens the market opportunity for you all? Are the most customers using competitive solutions right now?
Or is that largely a greenfield opportunity?
I'll take an answer here. So I think it broadens us in the space of data engineering and data science. It is sort of well known that a lot of the compute cycles that go into data science, go into transformation of the data, and Snowpark Simplifies and brings all of those compute cycles to Snowflake. Same thing for data engineering, cleansing, deduping. So Those are the 2.
And then there's the third one, which is around data applications, and that is almost unbounded on what can happen.
Thanks, Christian. The next question from Karl Keirstead at UBS. Mike, what are the factors driving your outlook for 10% operating margins in FY 'twenty nine?
Getting leverage in our model, but we're not going to sacrifice, our growth for that leverage prematurely. This is a great opportunity in front of us. And we're just trying to lay out a framework for all of you. I feel very good about $10,000,000,000 plus in fiscal year 2029 and There's a lot of unknowns on the margin side and I think this is a worst case scenario on the margin side.
Great. Next question from Greg Moskowitz at Mizuho. How do you think about the trajectory of that growth to 30%, you know, being a triple digit grower last year?
Well, The numbers are getting really large and there has been no other company that's grown at these levels. And I want to stress, This is looking at our historical usage patterns of our customers today for how they use Snowflake. There's upside in the model as well too with a lot of these new announcements we have today because we just don't know and we don't see that in the usage patterns today. So, Clearly, we're going to grow as fast as we can.
Yep. Next question from Derrick Wood at Cowen. Do we think about government or public sector playing an impact on the $1,000,000 plus customers? We didn't hear a breakout of that vertical. Is that an opportunity with a lot of upside?
Well, I think it is. I mean, for most companies like ours, we should be aiming for Public sector to end up being 10%, 15% of the mix are obviously nowhere near those kind of numbers today, and there's a whole bunch of reasons for that, but we're Chipping away very quickly, taking away those impediments to that business. And that business is going to develop. It's going to be a big contributor to our overall mix.
I'll just add to that. I don't think there is a $1,000,000 plus revenue customer in the public sector today.
So
So it's only
upside. Mike, to drive this point home, Keith Weiss from Morgan Stanley asks, does the $10,000,000,000 Target assume entering adjacent markets like transactional data stores, or is it based on current functionality?
It's based upon current Functionality, we have not. So we don't need to do big M and A. There's no big, adjacent markets we have to go into, to get there.
Yes. Next question is Brent Bracelin from Piper. Data sharing has and continues to be one of the biggest product differentiators for Snowflake. A competitor recently unveiled a new data sharing initiative alongside several other industry partners. Can you refresh us on the foundational architecture that enables data sharing for Snowflake and how it may or may not differ from these competing alternatives.
Yeah, the key insight in how we do data sharing Is that you need a Snowflake endpoint on both sides. And by virtue of that, we control the full experience. We control the quality of the experience. And probably more important, if you want to contrast it to an open protocol, the protocol that they're going to announce is File based protocol and FTP of sorts, slightly more newer. But the key thing is some of the use cases that you've heard us enable, like Secure multiparty computation or data clean rooms, those are enabled by sharing functions, not data.
That is inherent to our architecture and is not Possible with an alternative approach.
Thanks, Christian. Mark Murphy from JPMorgan. When we think about multi cloud and how important that is in our Sales cycle, do we have an idea of how many customers are actually taking advantage of that benefit at this moment?
I don't have an exact metric on that, but I will tell you just from my visceral anecdotal everyday exposure Talking to customers, there is usually at least 2 clouds involved and sometimes 3. And sometimes there's big balances of trades Between customers and the cloud providers, and they're really sort of required to do business with all 3, but it's more typical to see multiple clouds Then a single cloud posture. Now, at the same time, there tends to be a center of gravity. In other words, they're more on this cloud versus that cloud. It's also often done by business units.
This business runs on this cloud. That business runs on that cloud. It's still Very fluid, I would say, in developing. Our positioning in a multi cloud environment is, look, the data layer better be straddling these clouds. Otherwise, we are Building the silos of the future is something that Christian talked about in his section.
So if anything, yes, you can drill some sort of vertical cylinders. You got Google over here. You got Amazon over there. But the data layer better be straddling them across,
you know? Yeah. Makes sense. Christian, from Tyler at Citi, how do you expect to monetize the data marketplace piece? Are you expecting to charge a take rate and monetize the storage and compute consumption?
Yeah, the obvious one is through the consumption of the storage and compute. We will take some small fees, and there are some processing costs involved, But the bulk of the business model is we help organizations connect through data, and we monetize the underlying storage and compute.
Makes sense. From Itay at Oppenheimer, we've mentioned decision making as one of the 3 drivers for our business. Would we ever introduce our own ML platform?
So ML platform is really broad. If you look at the lifecycle of ML, There's a lot of getting the right data in the 1st place, data preparation. Then there's what most people think of ML platform is training and model development. And then there is operations of life cycle. We're focused for now on the first part and the last part of this life cycle because we think that's where the real challenges are.
That's what the real opportunity is. We believe that the extensibility Snowpark that we announced will make enabling such a platform Fairly easy. Whether we do it or not, I think it will be a function of partnerships and how the ecosystem evolves.
Makes sense. From Stefan at Exane, European data regulation. So European governments are increasingly promoting sovereign clouds where the underlying infrastructure is owned and operated by a local provider. Would we ever consider any infrastructure partnerships outside of Microsoft, AWS and GCP in order to be compliant with these initiatives?
We would definitely consider it. It's not sort of out of the question type of a topic. I was on a call this week With a lot of our European customers, and that topic keeps on coming up. And one thing is for certain that we are going to enable Our European business with whatever it takes. So we're very, very committed to that.
Makes sense. From Camille at William Blair, I think this one is for Christian. Can we talk about initial feedback from our customers who are in private preview with the unstructured data support?
Very positive. The customers that were in the prior preview were the ones that just couldn't wait. So they needed to enrich relational data, some of them with images, some of them with speech, very, very positive. And even though they had not seen the piece where Snowpark brings the probability into that, which just lights up the use case. But right now, all that I have is positive.
And is this driven by cost, performance, or what would you say the main driver is?
It's the combination of simplicity and governance. The fact that you have to have 2 different systems for different data, there's a silo. And, again, customers love the breaking down the silos.
Makes sense. So, Mike, on the call a couple of weeks ago, Christian alluded to it in his section the performance improvements that we see on compression. Can we talk a little bit about what we're doing on that front? How frequently does this happen? Should we expect this in the model?
Or is this assumed? We
assume in the model that every 2 years we come out with a major improvement in our storage compression. And so that has an impact on revenue. That's something we've modeled into that guidance we gave. Whether we see a 30% improvement, that's A big unknown. I think we've been adequately conservative in our model going forward.
Makes sense. Frank, on the European front, we clearly saw growth in the prior quarter. What are we doing to expand internationally? And how do we view our enterprise sales efforts?
Yes. Well, we're in Europe especially, but also in Japan and in Australia, New Zealand, we really need to catch up In terms of our sales campaigning, our selling motions to sort of reach the level of maturity and sophistication that we have in the U. S, We are behind there, but those markets are also behind themselves, right? Their adoption cycles are 1 to 3 years slower typically than the U. S.
So, we're going to make we have made significant investments in leadership over there. It's really important that we get the right people in the right places in all these Geographies. And we're going to be supporting it up to health. I mean, I'm personally going to spend quite a bit of time in Europe, you know, this year To help that cause along.
Makes sense. And then the $10,000,000,000 target, when we think about an M and A strategy or inorganic growth, is that factored into that $10,000,000 number.
There is no big M and A in that number to get to that. That is all organic. Yes, we're going to continue to do these small Acquihire tuck in acquisitions, but it's not a new product line or anything like that we need to buy.
And it looks like the final question that we have is from Kash at Goldman Sachs. Do we have any assumptions on fiscal 'twenty nine revenue breakout by either direct partners versus partners, product type or our market share overall.
Nothing that we're disclosing right now.
Yes, and that wraps up all of the questions. So I appreciate you guys taking the time. As a reminder, The presentation that we walked through today will be posted on our Investor Relations website later this evening. Thanks, everyone, for tuning in.