Good afternoon, and thank you for joining us for Snowflake's first Investor Day. My name is Jimmy Sexton, and I'm the Head of Investor Relations here at Snowflake. We hope everyone had the opportunity to attend Summit this week to 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, and 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 billion in product revenue. Next, we will hear from Christina Bieniek at Deloitte, who will sit down with Colleen Kapase, 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. Lastly, before Q&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&A, please submit your question in the box on the right-hand side of your screen. Now with that, I'll pass it over to Mike.
Thanks, Jimmy. As Jimmy said, today we're going to talk about the path to $10 billion. Fiscal 2021 was a great year for Snowflake. We achieved many milestones that deserve recognition. We continued to deliver exceptional growth at meaningful scale. We reported more than $590 million of revenue, representing more than 120% growth year-over-year. We reported more than $1.3 billion 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%+ 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. Lastly, the biggest event of the year was our debut as a publicly traded company. Raising more than $3 billion during our IPO will allow us to continue to invest in the business. The impact of the event can be seen in our hiring and our customer additions as the IPO success advanced Snowflake's brand recognition around the world. We like to celebrate our many achievements, we're focused on the road ahead. Our next target is $10 billion of product revenue, and 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 billion.
This market centers around workload-specific use cases and is measured by revenue per customer in each of one of our sales segments, multiplied by the addressable customers 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 Data Cloud opportunity increase to approximately $90 billion. We believe that there's still room for the Data Cloud 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 Data Cloud 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 grow meaningfully, and data management represents a large portion of that market. Snowflake is perfectly positioned to benefit from three tailwinds. One, workloads moving to the cloud. Two, data volumes growing. Three, 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 structure our sales organization as such. Majors, largest 250 potential accounts. 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 months product revenue greater than $1 million. 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 months product revenue greater than $5 million, up from 12 just one 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, we believe we have a great line of sight to seeing more customers becoming million-dollar or more in the future. Increasingly, due to Snowflake's innovative product, $1 million customers can come from customers of all sizes. 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 multi-million-dollar relationships with Snowflake.
As you can see, 2% of our $1 million customers are associated with our inside sales team, meaning 2% of our $1 million customers represent organizations with fewer than 500 employees. Even more powerful is the data point that only 25% of million-dollar 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 Fortune 500 customers. We believe we can significantly grow the average spend in these accounts beyond the current average of $1 million. 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 seven-day consumption run rate to exceed their contracted annual amount. This shows that on average, it took a customer 212 days, or roughly seven 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. 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 second 12 months to the first 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 an important component of our ability to expand margins.
As we move upmarket, we continue to sell more enterprise and business-critical editions, as seen on this graph. We recognize higher contribution margins on these premium editions. The benefit of this evolution can be seen in the leverage inherited in our long-term financial model and is what is driving the gross margin improvement. Let's discuss our path to $10 billion 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. We stress-test these models to ensure they are reasonable. This path to $10 billion is intended to be used as a way for our investors to track our progress annually against this milestone using publicly available metrics.
Let's dive in. We believe a great way to track our success against our large opportunity is to monitor our million-dollar-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 1,400 customers with trailing 12-month product revenue of $1 million+. They will, on average, have recognized approximately $5.5 million 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. Let's take a look at the long-term operating model. At $10 billion of product revenue, we believe we will still be growing at approximately 30%+ year-on-year. On a non-GAAP basis, our product gross margin will be in the mid-70%s at approximately 75%. We will show continued leverage from economies of scale to achieve 15% of sales for R&D and 10% for G&A. We will see meaningful leverage in sales and marketing between now and then, ultimately landing at 40% of sales. This will lead to 10% + operating margin and 15% + free cash flow margins. Okay. Our key priorities to achieve this target, in this order are, invest for durable growth. This is not investing at all costs. Show continued product gross margin expansion with our move upmarket.
March towards meaningful free cash flow generation. Continue to show operating leverage year-over-year. Lastly, a couple of modeling points to consider before wrapping up. Less than 3% year-over-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. 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's Colleen Kapase. I'm the SVP of Partner and Alliances here at Snowflake. I am joined by Christina Bieniek, who is the Chief Commercial Officer and Principal at Deloitte Consulting. Christina, welcome.
Thanks, Colleen. 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. 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 perspective, especially.
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, I feel like everything is smart and intelligent. All fueled by data in the cloud and the application of AI to generate the insights and really render autonomous actions. 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.
Across all of our clients, whether it's businesses in the private sector, public companies, national governments, are all retooling, pivoting, and preparing for this new, smarter, connected world, and to thrive in what you could call the modern economy. 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. This really means that massive amounts of data that is generated, connected, stored, and comprehended to be acted upon, and it requires that data. Data then in many ways you could say is the lifeblood. You ask about business transformation, that doesn't happen without data. The data is really what's accessible 24/7 via the cloud. That data is then acted upon by AI, protected by cyber.
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, 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. 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?
Yeah. Colleen, if you take a step back on that business transformation that I just talked about and how it's really fueling the modern economy, what's really needed, the data resides in the cloud. If you just think about that, everything that happens, so the machine learning algorithms learn from that data and provide that competitive edge that organizations are seeking. When I think about the clients we work with together, we're seeing this across the board on that business transformation journey. This is fueled by 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. AI's really difficult to apply across fragmented data across the organization.
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 the growing data sets in a secure, governed, compliant way. The Data Cloud serves as the true central data hub for all your data types. 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. 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. 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. 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 excited, both near term, short term, about the vision for the Snowflake practice. 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 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. 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 with respect to when, where, and how data is stored.
There's need for training for machine learning algorithms, really just this enabling of this jumping-off point. We see the demand from clients in so many different ways, and we like to think about Snowflake as the endpoint of migrating data to the cloud, but it's also the starting point for the application of AI. Organizations are really recognizing the fact that migrating large volumes of data to the cloud and establishing a Data Cloud is almost an unlock. It enables them to begin that harnessing of 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. Just the application of AI on the connected enterprise data sets to really get after that business transformation. The relationship between us building this practice and the partnership together, we're really looking to help our clients with all of that. 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 see 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 magic. It truly is magic, and they really experience 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 with the trust and the security. I'd just love to hear from you, especially 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 it 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. Data loss before and during the migration. Oftentimes, we have source versus destination compatibility challenges, and even something that you think, wow, just technology restraints that could be with the wrong software or hardware mix to support that end state. 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.
In other words, we've pulled together our million-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. We think about it across the strategy and planning dimension, rehosting, enhancing optimization, and then just the data operations. Look, our approach has been shaped, and I would argue the approach is one that we're continuously improving because it's based on feedback and it's based on 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. One of the challenges we often hear is migration is an expensive throwaway effort. You sometimes hear that, and I think we have demonstrated in our work together that modernization programs lend themselves to a strong business case.
If done right, and this is where taking the factory approach, the project pays for itself. We had a recent client where the client will realize $25 million in annual savings on the completion of a Teradata migration project. Another challenge to put in the second bucket is modernizing the data platform is disruptive to the business. I know you would agree, and that's certainly something that you often hear is you've got to minimize the disruption to the business. This is where that early engagement in the factory approach of users so that they have a long runway, that they have the time that's needed for the upskilling of associates rather than replacing them. Really, oftentimes it's also the automation to replace user effort to test and mitigate the risk of human error.
There are many things in that approach that really help with the mitigation there. The third challenge is really choosing the lowest cost vendor to drive mission-critical modernizations. 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 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. 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.
This is where that playbook, those best practices, and what we've really built together in what we call our Deloitte Migration Factory approach, I think is really critical and has been extremely helpful with the risk that often comes up when working with our clients.
I have to just ask you, as we go back to looking at your Snowflake practice, what goals have you set out for the team and for yourselves as success? 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. How we get there, in many ways, is by clients in the market choosing to work with the two of us and seeing the value of Snowflake and Deloitte together, and the value that we will collectively help those clients to realize. We have prioritized the relationship with Snowflake, and we'll look to 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. As you can imagine, we work with many partners.
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 existing and new ecosystems. Whether those are Deloitte ecosystems or Snowflake ecosystems, and then they come together, we want to be more than just a one plus one relationship, and want to collaborate on the Snowflake product roadmap and future innovations.
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.
I couldn't agree with you more. I think here at Snowflake, I can say 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. Walking in jointly, along with Snowflake professional services and your expertise and your trust that you've built up with your customer and your industry expertise, I think we just make a powerful pair, frankly. We couldn't be happier with the relationship and how it's progressed and the growth we're seeing together. 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 is just exciting. We thank you for your investment.
We thank you for your belief in us and for you extending what we're able to do together. It's just been a phenomenal journey. I can't wait to see what's ahead of us.
Colleen, thank you. I have a huge ditto to everything you just said. Really looking forward to that journey together.
Great. Well, thank you for joining us. 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. In this next section, I want to do a quick recap of our major product announcements at Snowflake Summit, and then we're going to go look a little bit farther out in the horizon, what's next, and then how we think about the bigger vision for what we're doing. Before we do that, I want to share with you, I want to show you a data visualization. You may have seen in our website, in the title slide, in much of the content on Snowflake Summit, what looks like a network graph, a connection of entities. 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 represents a Snowflake customer.
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. We wanted to share this to show two parts. One, the year-over-year change. You can see on the left, April 30th of last year. On the right-hand side, a much more populated visualization with April 30th of this year. 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.
The overall set of announcements, we organized them on these five innovation pillars. I have 1 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. Our goal in the 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 listings available in the marketplace. For clarity, a listing represents, from the perspective of a data provider, a data product.
We have effectively 500+ data products available to our customers to enrich their data, complement their data, put the data in context. 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. 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 it available for business and monetizing it.
I do not want to invest in billing systems or sales teams or the entire distribution of my data product. It was with that lens, with that feedback, 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. We showed a demo at Snowflake Summit. It's in development. It will be in preview later this year. What we aspire is to enable organizations of any shape, size, and industry to bring data onto the Snowflake Data 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? 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 anonymized or a partially redacted data set that can be tried by consumers, validated, and if they like it, then they can commit and do the actual purchase. That usage-based business models are the ones that best correlate with in the interest of consumers, where you pay for what you consume. From that perspective, the business models that we're enabling are all around usage-based. Is it based on the number of rows or number of days, or a combination of the different business models?
Monetization in the marketplace, try before you buy, and tremendous progress with existing providers and data listings. 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. We had, as part of our keynote, Andy Markus, Chief Data Officer at AT&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 second 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.
Obviously, there is increasing concern around cyberattacks and other types of compromises that are chasing or looking at the data. Very important, we have been investing in data governance since day one of Snowflake. We have thought about security from the entire life cycle of data at rest, data in motion, data when it's being queried. We continue to advance the capabilities of Snowflake. In the last six months, we did the general availability of data masking for us. We also brought row access policies. One of the big announcements that we made at the conference this week is the integration with Alation. Alation is enterprise data 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. Many organizations may be 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. As part of that, what we announced at the conference is an effort around privacy that has two prongs. One is on classification and identification of sensitive data, as well as potentially identifying data. 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.
As part of the keynote, we have Vibha Ahlawat from Capital One, and she was just sharing how governance is a primary reason why Capital One leverages Snowflake and hosts data inside Snowflake. Pillar number three is the topic of platform optimization, and this is a little bit of a catch-all of many efforts that are going throughout Snowflake product engineering teams. 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 is the topic of interactive use cases, where we have a business intelligence dashboard or an application that requires interactive experiences.
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 x better to 8 x better in terms of both concurrency as well as reduction in 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 you might recall that 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. 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 it, or not read 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 and how we've built the platform and how we continue to innovate. 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 had virtual warehouses as a compute model. In the last several years, we've introduced many serverless tools to do ingestion and automatic clustering and other capabilities. What we provided 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. Over time, it lays the foundation for us to provide optimization and insights into that consumption. The fourth pillar, which probably carries the most weight in terms of new workloads that are addressable by Snowflake now, is the topic of data programmability. Effectively addresses how I can program data, how I can transform and get more value out of data. It's applicable to data engineering, it's applicable to data science. 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 in public preview Java and Scala as programming language choices. Later this year we will match it with Python support.
This is a dramatic change in the appeal that Snowflake provides to a variety of use cases and developer preferences. What we see is data 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 programming 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 private preview and will roll out into a public preview later this year. That completes the rough support for all the data in an organization under a single system. The last topic, we announced a Snowpark Accelerated partner program. It is fairly easy to say that the value of any platform is actually from the solutions and ecosystems that are built on top of that platform. We're delighted to see that over 50 different partners have committed to delivering Snowpark solutions. Some of them have it up and running and are headed to customers already. Some of them are starting their journey on Snowpark.
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 by 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 Pandya from Adobe sharing how they're building the new campaign management application all within the Snowflake capabilities. That is the recap of Summit. I am not doing justice to the tens of other announcements that we had. Obviously, as Jimmy alluded, we have the recording for all of you to cover, I've done a quick recap of the high-level announcements. Here's the question on where are we headed? What is the bigger picture? I'd like to start by thinking through this reality of on-premises data silos.
The most interesting thing to me is that with all the momentum that you're seeing from Snowflake, with all the momentum that you're seeing from the cloud in general, this is 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, "Oh, we're using everything. We have a little bit of everything." It's a little bit of, I have 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. The opportunity to go and centralize and consolidate this is as big as it gets. 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. What used to be a long purchasing procurement 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. The insight for us is we obviously are 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.
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. It's global in nature, has geographic reach, but it's also cross-cloud in nature. I hear this consistently, that our customers love the idea that data can be in different clouds in different regions, but Snowflake's bringing the single analytical capability across all of that. It's very interesting to me that a lot of the things that we can talk about in terms of new capability for Snowflake, many of the benefits for customers today reside in this, "Can I look at data across my businesses or business units? Can I look at the full picture of my user, my customer?" This is the foundation for that.
Obviously, putting all the data in a single place and having it interact through a 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. We look at our broad vision, our broad direction through these three lenses. One is the skill set of our users. We want to embrace diversity of skill set. If a customer wants to use a declarative programming language or an imperative programming language, we're happy to do that. If it's SQL, we're happy to do that. We introduced a SQL Snowflake Scripting language.
Also, if they are comfortable with Java or Python, they're also part of that, and this is where the significance of Java user functions in Snowpark is maximum. We also look at the lens of diversity of workloads. Even though I'm not going to go into every single use case and workload that we intend to support, I think suffice it to say that we're looking at the entire life cycle of data, from birth all the way to archival. How is the data being ingested? How does it come from transactional systems? How does it get transformed? Is it batch? Is it streaming? In 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. 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, I can tell you some of those investments have been going on for a year, some of them even two years already, because we understand that a single platform for all these workloads is what our customers value quite a bit out of Snowflake. 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, not necessarily about talking the capabilities of the technology. We're using that understanding to inform our product roadmap and how do we maximize the transformation of data into value. Once we have a platform that enables our customers to have all their data in a single place, 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. Obviously, these two 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. 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 announcement at Summit. 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. Also, something that Frank and others were talking at the Snowflake Summit is it's enabling us to do things like multi-party computation. How do we bring data from different elements and go and deliver results? Privacy and security front and center on everything we do, alongside with performance and economics. Once we have this solution, Snowflake, for each one of our customers, high priority for us is to enable the collaboration through data. 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. 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. 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. Obviously what you saw in the momentum of the Snowpark Accelerated program is that we have many companies wanting to be part of the Snowflake ecosystem. One of the most common things that we hear is, "You know what? My sales cycle," for these partners, "involved 90% of the time getting through governance and security and legal teams to allow me to get data into my application or service." 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 life cycle for our customers as well as for all of these vendors? 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. One of the big directions, though, that we're pursuing is how do we embrace all of this and make it even simpler for a 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 data.
Then to cap the journey on all this, the Snowflake Marketplace, you will 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. What's driving the best-in-class net revenue retention rates? These are very high percentages quarter-on-quarter for a company of our scale.
Yeah. We find that people are often puzzled, like, "How does that work? Where does that come from?" I think it's worthwhile just pointing out there's very strong undercurrents that drive those numbers. It's not just like, "Oh, they got Snowflake. They're kind of likely to use some more." The reality is when they land on Snowflake, they find out that they can run many clusters concurrently against the same data. Before, they couldn't do it, so they just said, "Well, it's not even an option. Why even try that?" That expands the consumption right away. They run processes much more frequently because they now can. Maybe they were recomputing out 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.
They can massively provision workloads than before, when they were limited to the size of the cluster. Now they can really increase the size of the cluster, run the workload much faster. These are just things that we sort of unlocked 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 net 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.
Yeah. That's interesting.
Yeah.
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?
Yeah. I actually thought Christian did a great job articulating what are the key underpinnings. The central thing about Snowflake is that it is a platform. 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. There is no scalability limitations, both in terms of the number of workloads and how fast they can run. It's an incredible canvas for people to develop data operations. Also, 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 workloads to run incredibly fast. I'm always sort of mesmerized by Snowflake being able to run these incredibly highly scaled, 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 a platform does. 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 your data, who's doing it, making sure that we bring a security model that has to be ironclad, but also privacy compliance. 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 a single byte of data until the governance people say, "Yeah, go. We're good." 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 that does all that stuff for you right out of the box. There was a box.
These all feel like TAM expanders. Mike and I get a number of questions on trying to pinpoint a specific number. How do you think about the opportunity for Snowflake?
Yeah. Snowflake, it unfolds very rapidly. 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. What investors often do, and a lot of our customers, quite frankly, do the same thing, they look at their historical workloads, and then what does it take to move these workloads to Snowflake? That becomes the initial scoping and scaling of what's going on. 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 a large oil and gas company.
They're eyeballing an energy cloud, not just for oil and gas, but also for alternative fuels, and they're starting to think about the data relationships that could make that up. They've never thought about that before because it never really was an option, but now it is. It's very difficult to pinpoint very specific parameters to the scale of the opportunity.
Yeah, that's very interesting. We heard a number of announcements from Christian coming out of Snowflake Summit. As the CEO, what do you think are the most important announcements that the investor community should focus on?
Yeah. It's almost like, which ones are your favorite children? Those are hard questions. Some of our product managers might be upset if I don't mention them. The data programmability set of announcements, especially Snowpark, are extraordinarily strategic to the platforms. These really represent huge evolutions in platforms. It may look to investors like, "Oh, this is great. This will expand the scope of workloads. We now can bring programmers onto the platform." That's all true. The reality is, there's now such a thing, not just Snowflake data, but Snowflake applications. 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.
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. A couple of 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?
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 went after existing workloads, and we sold architectural distinction. We benchmarked the old way, the new way, and 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. They have interesting challenges in terms of enriching their data, because obviously they're big advertisers. In order to get the efficiency and effectiveness of their advertising dollars, they need to enrich the data. That is not that easy, and the reason is data sharing is inhibited by compliance and privacy requirements.
Snowflake is incredibly good to bring the data clean room concepts and the real infrastructure to allow people to data share in a completely governed manner. Now, these media companies can really optimize their advertising spends, as well as, for example, the much more familiar walled garden solutions by Google and Facebook and so on. They have an opportunity to compete against very established advertising platforms.
Interesting.
Yeah. Financial, obviously huge. We made the announcement with BlackRock a quarter ago. They have tremendous data gravity around asset management data, and they were quite visionary in really appreciating the opportunity in front of them. Retail is a very, very active one. Healthcare, life sciences, very, very active. I mean, there's not a day doesn't go by that we don't get. By the way, each vertical is really comprised of the institutions that make up those verticals, but then also the data providers that are specific to those verticals. 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 when we meet with customers. In the beginning, they look at us like, "I have no idea what you're talking about." Until we start to really wrap our heads around it, and then all of a sudden, ideas start coming. It's, in other words, these are the killer apps of the Data Cloud. That's what we call them. They just start to stimulate and ignite opportunities we never thought would be possible.
That's very interesting. Before we close, any final comments that you'd like to give to the investor community?
Yeah. I think today was really, Mike did a great job laying out a longer-term view. Snowflake is a journey. 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 five 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. Today it's really about understanding what is the long-term mission, because I think Snowflake is a journey. 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 as the longer-term trajectory that we're on. That's why we're here.
Yeah. Very well said. Thank you, Frank.
You bet.
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. We'll be back in a second. Welcome back. I'm now joined by Frank Slootman, Mike Scarpelli, and Christian Kleinerman. We appreciate everyone submitting your questions. We'll tick through those right now. 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 most customers using competitive solutions right now, or is that largely a greenfield opportunity?
I'll take an answer here. I think it broadens us in the space of data engineering and data science. It is 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 those compute cycles to Snowflake. Same thing for data engineering, cleansing, deduping. Those are the two. 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 Keirstad at UBS. "Mike, what are the factors driving your outlook for 10% operating margins in FY 2029? Why can't they be higher than that?
I said it was 10%+ in 2029, and we're very focused on 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 billion+ in fiscal year 2029, and there's a lot of unknowns on the margin side, and I think this is the worst-case scenario on the margin side.
That's great. Next question from Gregg Moskowitz at Mizuho. How do you think about the trajectory of that growth to 3`0%, 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. 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. We don't see that in the usage patterns today. Clearly, we're going to grow as fast as we can.
Yep. Next question from Derrick Wood at Cowen. How do we think about government or public sector playing an impact on the $1 million-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. For most companies like ours, we should be aiming for public sector to end up being 10%-15% of the mix. We're 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. 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 million-dollar-plus revenue customer in the public sector today.
Yeah. True.
It's only upside.
Yeah.
Mike, to drive this point home, Keith Weiss from Morgan Stanley asks, does the $10 billion target assume entering adjacent markets like transactional data stores, or is it based on current functionality?
It's based upon current functionality. We don't need to do big M&A. There's no big adjacent markets we have to go into to get there.
Yeah. 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. Probably more important, if you want to contrast it to an open protocol, the protocol they're going to announce is file-based protocol, an FTP of sorts, slightly more newer. The key thing is some of the use cases that you heard us enable, like secure multi-party 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 J.P. Morgan. 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 two clouds involved, and sometimes three. Sometimes there's balances of trades between customers and the cloud providers, and they're really sort of required to do business with all three. It's more typical to see multiple clouds than a single cloud posture. 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, and 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, something that Christian talked about in his section. If anything, yeah, you can drill some sort of vertical cylinders. You've got Google over here, you've got Amazon over there, the data layer better be straddling them across.
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 the data, and we monetize the underlying storage and compute.
Makes sense. From Itay at Oppenheimer. We've mentioned decision-making as one of the three drivers for our business. Would we ever introduce our own ML platform?
ML platform is really broad. If you look at the life cycle of ML, there's a lot of getting the right data in the first place, data preparation. What most people think of ML platform is training and model development, and then there's operations and 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 where 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'll be a function of partnerships and how the ecosystem evolves.
Makes sense. From Stefan at Exane. European data regulations. 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. One thing is for certain, we are going to enable our European business with whatever it takes. We're very, very committed to that.
Yeah. Makes sense. From Camille at William Blair, I think this one's 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 private preview were the ones that just couldn't wait. They need to enrich relational data, some of them with images, some of them with speech. Very, very positive. Even though they had not seen the piece where Snowpark brings the programmability into that, which just lights up the use case, but right now all that I have is positive.
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 two different systems for different data, there's a silo, and again, customers love the breaking down the silos.
Makes sense. Mike, on the call a couple 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 two 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?
Yeah. Well, we're in Europe especially, but also in Japan and in Australia and New Zealand. We really need to catch up in terms of our sales campaigning, our selling motions to reach a 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 one to three years slower typically than the U.S. 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 the health. I personally will spend quite a bit of time in Europe this year to help that cause along.
In the $10 billion target, when we think about an M&A strategy or inorganic growth, is that factored into that $10 billion number?
There is no big M&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.
It looks like the final question that we have is from Kash at Goldman Sachs. Do we have any assumptions on fiscal 2029 revenue breakout by either direct partners versus partners product type or our market share overall?
Nothing that we're disclosing right now.
Yeah, 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