Google Cloud Marketplace Marvels | Google Cloud & Confluent

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Stephen Orban

VP of Migrations, ISVs & Marketplace

Google Cloud

Shaun Clowes

Chief Product Officer

Confluent

Confluent’s real-time data engine accelerates AI innovation via Google Cloud Marketplace

The rapid evolution of artificial intelligence and cloud computing is reshaping how organizations manage and utilize data. In this dynamic landscape, real-time data streaming has emerged as a critical enabler for operational agility and AI readiness.By allowing companies to process, analyze and act on data as it flows, this technology transforms traditional batch-based systems into flexible, event-driven architectures. Such capabilities are crucial for businesses striving to break down silos, enhance decision-making and stay competitive in an increasingly data-driven world.Confluent Inc

play_circle_outline Real-Time Data Insights: Apache Flink and Confluent Stream Processing Technology

play_circle_outline Confluent Cloud: Real-Time Data Streaming with Apache Kafka, Connectors, and Governance Tools

play_circle_outline Confluent's partnership with Google Cloud for customer transformations and migrations

play_circle_outline Confluent's intentional approach to marketplace integration for customer benefits

play_circle_outline Confluent's product integrations with Google Cloud products for seamless use

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Google Cloud & Confluent

Stephen Orban

VP of Migrations, ISVs & Marketplace Google Cloud

Shaun Clowes

Chief Product Officer Confluent

John Furrier hosts the Google Cloud Marketplace Marvels Series with Stephen Orban and Shaun Clowes. Stephen praises Confluent's innovation in real-time data streaming and its role with Apache Kafka. Connector and governance tools enhance data movement and processing on Confluent Cloud across various cloud providers. Confluent transforms IT estates and breaks down data silos through real-time processing, aligning with Google's infrastructure. Integration with Google's core products like BigQuery and Vertex AI delivers high-quality data for AI models. Business ... Read more

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What is Confluent doing as a company and how are they contributing to the modern infrastructure and preparing for the gen AI wave? add

What features and tools does Confluent offer to enable efficient and secure access, movement, and processing of data across different systems and platforms? add

What benefits does Confluent provide for customers looking to transform their IT estate with Google Cloud? add

What are the benefits and advantages of the partnership between Confluent and Google around the marketplace for their data streaming platform? add

What technologies and innovations make up the foundation of Confluent's cloud-native Apache Kafka engine, including Kora and the fully managed connector portfolio? add

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>> Hello, everyone. Welcome to this special CUBE presentation. I'm John Furrier, your host of theCUBE here in our Palo Alto Studios with the Google Cloud Marketplace Marvels Series. Got two great guests here. I'm joined by Stephen Orban, vice president, Migrations, ISVs, and Marketplace with Google Cloud, and Shaun Clowes is the chief product officer at Confluent. Gentlemen, thanks for joining us here for the Marvels Series. Good to see you.

Stephen Orban

>> Thanks for having us as always, John. Appreciate it.

>> Shaun, let's get into Confluent's relationship with Google. Talk about Confluent. You guys are driving innovation, real-time data innovation. This is the top topic in today's world as we talk about setting the modern infrastructure and preparing for the gen AI wave.

>> I couldn't have said it better myself. What a fantastic introduction. Confluent, what are we doing at Confluent? Ultimately, we're building a really new category of data infrastructure that we like to call the data streaming platform. What is it? Why do you care about it? Well, ultimately, we're trying to enable people to evolve data from a world where everything is kind of batch-based and it's designed for a world of data at rest where you're moving data around from operational systems and analytical systems. And then to work with it, you always have to keep moving it. We're moving to a world where companies can harness that data and their insights in real time. And think about turning a database inside out, literally working with data as it moves all throughout all of your different parts of your enterprise and then being able to bring that to bear on customer experiences, employee experiences, decision-making, business intelligence. So we built basically a fully managed service around Confluent Cloud, which is also based on Apache Kafka, which is perhaps the world's most popular open-source project for real time data streaming. And that means that you can move data from anywhere to anywhere at any scale in real time or in batch if you need it for that particular type of use case, and you can decouple the producers and consumers of data in your enterprise. So that's a pretty great foundation. We then augmented that with a series of connectors, which make it really easy to connect to various different data sources and destination systems and move all of that data in real time streaming. Then we made it possible to process the data, so think about the types of queries and processing you might otherwise do in a database or in some sort of custom coded system. We made that easier using our stream processing technology, which is powered by Apache Flink, which is a very popular open-source project in its own right. And then we added a bunch of governance tools to enable secure data access, visibility, security, reuse. And then we took all of that and we made it available on all three major cloud providers, and that means that Confluent can be easily procured and used no matter where your data is through the marketplaces of the leading hyperscalers. And obviously, we're here today to talk about our partnership with Google.

>> Stephen, weigh in on the marvel of Confluent and how they relate to your business.

Stephen Orban

>> Yeah, I mean, it's an amazing product that Shaun and his team have built up over the course of the last many years, and when you really think about some of the things that we're up to with our customers, a lot of customers are leaning into Google to do a complete transformation of their entire IT estate. And as Shaun mentioned, Confluent really helps them break down the silos that often appear across their many different types of data states that could sit all over the organization and really break that down, not just with all those great connectors, but giving them a much more agile way to respond and react to things that happen within that data by giving the real-time processing and ways to look at that. And when you really think about it, we have an 86, $87 billion backlog of all the customers who have committed to moving to Google Cloud and they have two ways to be able to retire those commitments. One, of course, is consuming Google Cloud services and the other is to consume some of our partners off of our marketplace. And Confluent has done a wonderful job by building not just great technology, but leaning in with us to help our customers who are offered on these big transformation journeys make use of Confluent to help them get to their destination faster.

>> Shaun, talk about how Confluent thinks about the marketplace specifically as you see these consumption models changing data streaming, for instance, very dynamic. How have you looked at the go-to-market? How are you growing your business? How has the marketplace helping you guys go faster? Can you weigh in on the marketplace and how that's impacted your customers?

>> Yeah, we've been super excited by our partnership with Google around the marketplace and it's actually a key driver of our success and the adoption of this data streaming platform. We designed Confluent from the ground up, sorry, Confluent Cloud from the ground up as a cloud-native serverless application that runs seamlessly in Google Cloud. And that means that people can put Confluent to work with all of their Google data or Google applications inside GCP instantly, trivially, really easily, and so they can take advantage of all of that data that's moving. That's kind of our reason for existing, to be able to trivially and easily move all types of data from wherever it is to wherever it needs to go. And there's actually been a few different things that have made that really great for us. First, transacting through the marketplace solves a bunch of customer pain points for our customers, our joint customers. So centralization of IT purchasing and budgeting decisions is obviously easier. You heard Stephen talk about the backlog of investment that customers are making with Google and being able to enable customers to take advantage of that investment to get on these joint offerings is clearly much better from a customer perspective. They get a unified billing experience across all of the different technology providers including Confluent. They get the ability to draw down against those customer commits that Stephen referred to a little bit. So from a customer pain point thing, it definitely helps, but for us as an independent vendor, the marketplace means that we can do a few things too. We can simplify billing and administration on our side. The sales cycle is easier because there's less complexity in procurement. You spend less time having budgeting discussions. You can align your incentives across organizations, so the customer benefits from buying and using our software. And the Google reps who are working with those Google customers, they can also benefit when customers buy and use our software. And of course, the Confluent reps on our side and the company benefit when customers buy and use our software. So it's kind of a win-win-win in terms of benefiting joint customers, benefiting those customers, benefiting Confluent, and benefiting Google. Now, you kind of don't get all of that unless you're a little bit intentional. There's no free lunch in the world. You have to be intentional. I think our Google partnership has really been a great example about that. We have designed like our go-to-market efforts to achieve awareness and you'd almost say preference of the marketplace as the transaction channel. A few different ways we've done that. We've made flexible billing models in marketplace easy to take advantage of, and that means there's flexible models support our standard GTM model, which is customer-led, consumption oriented, and it appeals to customers who are early in their journey who might prefer a pay-go type of model, as well as more mature customers who are looking for commit type of relationships and increase utilization and efficiency of their performance spend. We provided compensation neutrality for sellers when they're transacting through the marketplace. We can provide a number of deal-level incentives to end customers when they transact through the marketplace, and we spent a bunch of time building our marketplace integration to be on par with the degrees of freedom that customers might expect if this was a direct transaction scenario. So broadly speaking, it's like a win-win-win for joint customers, Confluent, and for Google, but only if you're intentional and you forge a strong partnership like we have with Google.

>> Stephen, I love the line, I always love the line, meet the customers where they are. Where they are, wherever they are, they're in integrations, they're doing things, trust is huge. And if they're integrating with Google Cloud, it becomes a huge factor to know that they can buy right there on the spot. Trust is huge. This is a big part of why the joint motion seem to work in the marketplace.

Stephen Orban

>> Yeah, look, Shaun really nailed it when he talked about the intentionality. And I think he said there's no such thing as a free lunch or something to that extent. And Confluent has really leaned in across all the best practices that we see our largest and most successful ISV partners take advantage of when they partner with us. First and foremost, they've made a number of unique integrations into some of our marquee products, so that's connectors within BigQuery, it's connectors within Vertex AI platforms. So if you think about data analytics and AI, some of Google's core differentiators, some of the reasons customers pick Google over other providers, Confluent has made Confluent Cloud work really, really, really well and in a differentiated way with those products. So that was kind of step one. And then Shaun also talked about making sure that they're meeting the customer where they are, not just where they are, but in the way they want to buy, whether that be as a pay-as-you-go consumption model or having a commitment-based contract or maybe they can get a discount for having a committed set of usage over a longer period of time, and they've integrated that not just in their co-sell process, which I'll come onto in a moment, but actually in their product. So it works really well in both of those scenarios. And then as Shaun outlined, we work on doing account mapping and sharing what we are doing with each other's accounts so that we can not just meet the customer where they are, but meet them intentionally with a shared understanding of what that customer is trying to achieve and really trying to make sure that it's a win-win-win across the board.

>> Shaun, talk about the product integrations and technology specifically because we're in an innovation world where you got joint market motions with sales side, which is great. Customers get benefit there, check. That's a good box to check certainly for sure because it's good business. But we're in a connected ecosystem now with hyperscale cloud computing. Technology innovation is a key part of this because of the platforms or modernizing right now in real time, pun intended. Talk about the integrations, because it's not just a business deal, there's more to it on your side. Can you weigh in on what you guys are doing on the integration front?

>> Yeah, I would love to. I think Confluence business, like we shared at the start, is really all about helping customers move their data from wherever it is to wherever it needs to go and really drive a business advantage out of that data, real time rich experiences. And so we're kind of no stranger to delivering deep experiences that bring together different systems and very complex infrastructure. Our data streaming platform, it really suits well Google Cloud's advanced analytics, the AI capabilities that Stephen talked about. And together, they enable organizations to process, analyze, and act on data instantly. You literally just talked about that. And that means that we can deliver scalable, secure solutions that drive faster insights, people can make smarter decisions, you can build better experiences that make you more competitive as an organization. And these platforms together can get all the way to on-premises environments, edge environments, into Google Cloud and beyond. Now, from our perspective, a lot of that is from the foundation of Confluence innovation, including Kora, which is our cloud-native Apache Kafka engine that enables elasticity, reliability, performance, optimized costs, and 100% compatibility. So it's the Kafka Foundation. Then we've enabled this fully managed connector portfolio that I referred to earlier. We have almost 100 of those just in general at Confluent, but we've made 10 GCP specific connectors that have rich connectors to all of the most important GCP services. Then with our Connect with Confluent Program, we've enabled direct integration into BigQuery with BigQuery's partner center. Obviously, BigQuery is a tremendous technology as part of the GCP platform, so we have the foundational Kafka operating at enormous scale in GCP. We have these connectors across all the major GCP services. We have first-class integration with BigQuery. And recently, we've offered our new Confluent Flink AI model inference capability, which means that you can now directly integrate into Google's Vertex AI as well. So you can take all of your most important data, you can join it, enrich it, make it exactly what you need, the first-class, real-time, high-quality data. You can power your AI models with fresh continuous data for faster insights, better decision making, and to power really great AI experiences. You can automate, you can enhance your processes, you can deliver great customer experiences. It's kind a pretty amazing story, very seamless. Very, very seamless, enables you to put your data to work. But it even goes beyond that actually. Those are the product-level innovations about the ways the product work better together, but we're also working just specifically to make the marketplace integration feel natural. I mean, if you think about it, as a data infrastructure vendor, we want our infrastructure to feel hand in glove with all of the tremendous business infrastructure that joint customers are standing up in GCP. We want to feel literally native, first party, all the way up to billing and commercials and everything else. And so we've actually done a whole bunch of engineering innovation to support flexible payment structures, different billing periods, support for prepay and postpay, which Stephen talked about just a second ago. Flexibility for contract dates, term renewals, I mean, you name it. Literally trying to feel first party with our Google partners.

>> Share your thoughts, Shaun, if you don't mind, because you brought up the inference. That's the killer app. We're hearing that. If you have all that flexibility, you have the scale, how's that inference piece fit in? Because the developers with Vertex are going to love this because you're in the data business, right? That's the foundation for the best AI. Talk about the inference piece. How do you guys see that playing out?

>> Yeah, people ask me a lot about AI, and obviously, being in the data business, it's a good time to be in the data business in the current world, world of AI. I think that there's two different things going on. So people talk a lot about inference and they talk a lot about these very important foundational models, which I completely agree with, and obviously, Google has some unbelievable models going on, but really, at the end of the day, for AI to be really smart, you have to bring it quality data, otherwise, it's only as smart as the data it was originally trained on. Now, in our case, we literally transport the most important data that our customers work with and we do so in real time. Now, that is a smart thing. That data is really the gold needed to put these very smart models to work. So there's really two phases to that. People talk about RAG architectures or retrieval augmented generation architectures, but broadly speaking, you need our process by which data is brought in, it's enriched and you get this high quality, reliable, rich data, preferably as close to instant as possible and you need to be able to store that. So basically, you want to calculate embeddings and then you want to store that in a vector database. We make that super easy in GCP. Then you have the inference time, although both are technically inference. But then you have the inference time thing of where you're building a bot or you're building a co-pilot or you're building an agentic architecture where you want to take in a request or a question or a instruction. You want to take the data of your business and put it to work on that question to decide what to do or how to do it. And so to do that, you need to do context window management managing the question. Then you need to combine it with the rich data you stored in your RAG pipeline and then provide that to the model, take the results back, and act on it. And so this Flink integration that I was talking about earlier, this Flink model inference, it does both. It makes it very easy for you to take the data you're already moving in real time, high quality, make it even richer, then store that for later use in a RAG architecture. And then at inference time, to immediately retrieve the most important context and to get really dynamite answers from the AI if you're using a chatbot type of use case or to get way more accurate next steps and actions from an agentic type of architecture as well.

>> Stephen, this is where I think the Google Cloud relationships really shines because reliability, we heard, we heard about scale. Yeah, low-hanging fruit projects get off the ground, they get some wins, and then you want to get scale going. You have to have high quality data, high quality data with total reliability integrated into the system. Share that aspect of it because that's where the technology one plus one is like five.

Stephen Orban

>> Yeah, there's two things that Shaun just riffed on that I'd highlight. One is around artificial intelligence and he's 100% right that many of these models, including the ones like Gemini that we offer to customers are fantastic because they've been trained on large corpuses of data. But those large corpuses of data are what's publicly available out there on the internet, and most customers that we work with want to take those models and the power that they bring to their business and make sure that they're either trained on or at least grounded, in the case that Shaun mentioned, RAG, retrieval augmented generation, grounded in their own data. So rather than just tell me what's happened with a particular, say, financial services scenario, tell me what's happening within my customer base of what they're doing with their trades or what sort of financial policies I have or what's going on with the records that I have in my own database and making sure that that's available for the model. And the second thing that I would just sort of highlight on is Shaun talked about Confluent Cloud connects to on-premises as well and helps customers take that data that may have otherwise been siloed in systems that aren't scalable like a cloud-based system like, let's say, a BigQuery or a Vertex AI are. And it can move that data seamlessly very quickly, make it available to whether it be an agentic or AI workload or a traditional analytics workload very quickly. So a lot of our customers are quite eager to get to the cloud very quickly, but are often hamstrung by the legacy tech investments that they have in their existing data centers across on-premises. So one of the big centers of gravity we see for Confluent Cloud is helping customers unshackle what they have in their on-premises systems, get them into the cloud very quickly to power their AI or analytics use cases, and that's a big advantage for customers who are trying to get to the cloud quickly.

>> Okay. Let's talk about customers. How is this joint solution bringing value to them? Can you share some names and some use cases?

>> Sure. I mean, a recent one that I'd love to talk a little bit about is Nuuly, which is Urban Outfitters' subscription offering, online subscription offering, and they enable customers to rent clothing items from the different Urban Outfitters brands. They were tasked with building a new IT infrastructure to support that brand new service, this whole new product from the ground up, and they had to do so in a few months. And so when the Nuuly team began working on the technology platform that was going to underpin all of these operations, they thought, "Okay, well, we're going to have to develop a new architecture that's going to be more flexible and faster to deploy than traditional architectures." And so that's how they landed on event streaming. It's a pretty cool story because they came to Confluent and to Google and they thought, okay, we're going to use Confluent Cloud on Google Cloud to build a reliable event-driven architecture that they could use as their core operating platform. So the whole platform, this new thing that they built operates via this underlying data substrate. Then a team of engineers and developers at Nuuly, they designed and deployed this event streaming architecture using Confluent Cloud and Apache Kafka, and they did so in about six months, so literally from ID to standing up a whole new kind of line of business in about six months, and that architecture then serves as the IT foundation for all of that company's operations. As you guys would know, the operation of this type of subscription service with logistics is actually incredibly complicated, and they were able to take advantage of a whole bunch of Google Cloud Platform services to make it easier. They use Google Cloud storage, obviously, they use BigQuery, they use Google's Kubernetes engine and then Confluent Cloud instead of having to manage their own Kafka cluster, for example. They use Kafka Connect to immediately link all of this streaming data into BigQuery to be able to use it immediately. When I spoke with them, they highlighted to me that being able to take data from their Kafka topics and have it appear in BigQuery just with no effort whatsoever had been really beneficial because it meant that they didn't have to have any new technology stacks. They don't have to duplicate data, they're not creating more data silos. All of their data is available in this real-time underlying substrate. They shared with me a quote that I thought was pretty cool. They said, "In January, we had an empty plot of land, and in July, we launched with not only a new DC, distribution center, but with fully operational production event-driven applications." Pretty cool. Six months, empty plot of land. I like how they put it in physical terms too. This is logistics. It's the real world.

>> Yeah, I mean, that's the real benefit. Can you share any metrics on benefits? Was it overhead in terms of deployment cycles? Obviously, the build time to value on the physical is a great example. Is there any metrics on data management, just cost structure and speed? What are some of the metrics?

>> Yeah, there's a ton of different metrics around data streaming platforms in general that have been investigated by IDC, Forrester, and others. In general, people who are taking advantage of event streaming architectures have been able to see a five-times return on their investment with dramatically accelerated data access and much faster time to market. We've seen customers have 25% reduction in their data management and total cost of ownership. Actually, about 70% of all of the Fortune 500 companies rely on data streaming now for their most mission-critical use cases. You can accelerate development cycles back to 75%, reduce management costs by 60%. It's kind of like a fundamental re-architecture of how data moves and the way you can drive value from your data. And so you kind of get win-win-win because each new use case you layer on top of this underlying infrastructure gets easier, not harder. You've been doing this a long time, I've been doing this a long time. It's kind of funny how every project is today's shiny object and it's tomorrow's legacy. The more work you do, it seems to be the slower that you go. And one thing I think is really interesting about just cloud environments in general, obviously, Google's cloud in this case, but also these new architectural approaches, these foundational approaches, it's kind of interesting how we're going the other way. We're trying to undo some of that kind of repetitiveness, point-to-point repetition of the same work over and over again to just have a much more agile way to think about data as a first-class citizen. Imagine if you, instead of thinking about links between database A and database B and system Y and system K, you could instead think about your customers and their purchases and debits and credits and deliveries and employees and literally think about the nouns of your business and how you can get value out of them rather than bits and bytes and systems and databases. I think that's a really exciting for future we're all building towards.

>> Yeah, and you get the keys to the kingdom over there making the product better. Data movement's always been a hotspot, cost, hassle, wrangling infrastructure. Stephen, on your side, your customer, I mean, they get the marketplace benefits. We cover that so many times, the benefits in the business side is just off the charts great. On your side, your customers are getting this kind of integrations capability. What are they seeing in terms of benefits? Can you share your thoughts?

Stephen Orban

>> Yeah, I mean, look, if you just take the newly example that Shaun outlined just a second ago and you really listened to how he described the architecture that that customer created where they were using BigQuery as their analytics engine, storage as their storage layer, and Confluent Cloud as the way that they were processing all of this data in real time and then doing analytics on top of it as well, it really appears to that customer as though Confluent is just a native part of Google Cloud, and I think that's emblematic of what Shaun was outlining before, is that we're working really hard together, make sure that our solutions work so well together, that from the customer's perspective, it's indistinguishable from who's providing which service to who. And I think it's a testament to all the work that we've done over the course of the last many years to do all the integrations and make sure that the marketplace capabilities are wired up in the right way, but we won't be satisfied until we can continue to have every customer that use our mutual products to have that type of seamless experience.

>> Well, you guys have got a great collaboration between Confluent's data streaming platform and Google Cloud's advanced analytics, and obviously, the awesome AI capabilities. We're into transformation right now. Business transformation AI is highlighting that. It's not just the old classic digital transformation. Real-time data and the insights with actionable results is the top line story we're hearing every day and it's only getting more and more focus as businesses are looking at that transformation. How would you guys share your thoughts on someone watching this and saying, "Hey, I'm in the middle of modernizing my platform and trying to usher in the agents that I want to deploy." This is a big part of the focus, is top down from the boardroom, bottom up from the developer, and the platform engineers at the same time. This is a unique time. Shaun, we'll start with you. Your thoughts on the folks out there who are really working hard to re-architect, whether they had machine learning workloads coming into GenAI, they got the frameworks for resilience all being built out on a large scale enterprise to the small medium-sized enterprises too. They're looking for this. So what's your thoughts?

>> Yeah, thanks, John. It's a really good question. I think at the end of the day, it's becoming increasingly clear to everybody that getting access to timely, reliable rich data and having it be literally something you could pick off the shelf at your organization is a competitive rocket view. I mean, really, at the end of the day, what many people who are standing up AI projects, for example, today are discovering is that no amounts of really great AI will save them from what is really a data management problem, a data movement problem, a data agility problem. And data, being able to apply data, apply those electrons and convert into business advantages, to some degree, what most of us do for a living. And so I think that if at the end of the day, data is an asset, data in real time, reliable data, and having it be something that you can trivially access is a business advantage and something we're trying to build towards, we need to design an architecture that enables that. I think that's what this data streaming platform is all about, but we don't have to big bang this. You heard Stephen talk about this earlier. At the end of the day, you can think about this brick by brick by brick. You can think about, okay, how do I set some of the most important data in motion in a reusable way? So it might be I need some payments data or I need some customer record data or I need some delivery data or some other type of data. Whatever your business is trying to do, you could think about in terms of some of your most important data assets. And then it can be like, okay, wherever they are, no matter what, how do I set them free? How do I set them in motion? And bricks, as you add more and more of these data products into your architecture and you set them free, then you can then build new experiences that take advantage of them. You can put them to work with AI, you can put them in your analytics stack and do all sorts of business decision making. You can build really great mobile applications, web applications, the exact same data. Fundamentally, you do it once and it's like this asset that keeps giving back money rather than something you have to solve over and over and over again. And I think a lot of us are kind of in that process. We're trying to obviously continue business on top of architectures that have grown up over a very long period of time, so we're not going to big bang anything but brick by brick by brick, data product by data product, we can move a lot more quickly than we could otherwise.

>> Building that technology allows companies to build that brain, the AI brain, but you guys are like the central nervous system of the organization. Great stuff.

>> What's really interesting to me is when a new project starts at a big financial services organization, for example, what's step one? It's like we want to build this. Step two is, okay, what data do we need? And then step three is, okay, how am I going to build a whole bunch of code to get at that data? It's like we kind of end up having to repeat this pattern over and over and over again. Why does step three even exist? If you think about your data as ultimately these moving assets inside your organization, then step three doesn't need to exist. It's now envision some cool new thing you could build, understand what data I need to power it, pull that data off the shelf, and build it. And then you get into this really dynamic self-improving system you can think about as a data nervous system. But either way, it's a little bit different than the world we used to think about where all that databases and systems and silos and how am I ever going to get access to that data in the first place? Is it accurate? How old is it? All of the problems we've all experienced with data in the enterprise complex enterprises.

>> Data's going to have its own brain. Stephen, take us home on your side. Business transformation with AI is really the top conversation. Again, you get the platform at scale. You're making your customers move faster in the marketplace, but also the technology innovation's there too.

Stephen Orban

>> Yeah, there's really no time like the present. As Shaun laid out, you can do it brick by brick. And if you're really after the freedom that Shaun outlined about being able to take an idea and action it by having your data readily available for any application or agentic AI or other use case, there's no time like the present just to get started because you can get started with one very simple use case. And once you have that brick, it's much easier to lay the second brick and then easier to lay the third brick, et cetera. So we're super excited about the work that we continue to do with Confluent to make them indistinguishable from any other Google Cloud service that our customers use, and very excited to see what customers are going to continue to innovate using our collective tools together.

>> Brick by brick, building the next generation platform for AI. Gentlemen, thank you, Stephen. Thank you, Shaun. Google Cloud Marketplace Marvels Series. Thanks for joining me today. Appreciate it.

Stephen Orban

>> Thanks, John.

>> Thanks, John.

>> I'm John Furrier here in theCUBE in our Palo Alto Studios for the Google Cloud Marketplace Marvels Series. Thanks for watching.