 Welcome back everyone and welcome back to day two of theCUBE's live coverage of Google Cloud Next here in beautiful Las Vegas, Nevada. I'm your host, Rebecca Knight, along with my two co-hosts, John Furrier and Rob Stretche. Gentlemen, what a dizzying array of new product announcements, updates, the pace of innovation here is just mind-blowing. Yeah, the data is the center of the action. Data analytics, our next guest from Google will break it down for us. Excited to dig into this one. Rob and I have been waiting for this session. Yes. Don't be afraid. I want to welcome Yasmeen Ahmed to theCUBE. She is the managing director of data analytics at Google. Thanks so much for coming on the show. It's a pleasure to be here and just sharing everything that we're doing in data. And truly the pace is just mind-blowing. Yes, so tell us more. I mean, as we said, there have been so many new announcements and updates. What is really standing out to you as we are here, day two of this conference, 30,000 people here. For me, it's just amazing to see the momentum with our customers and what they're doing with our technology. And with all of the innovations in the data analytics space, we have 40 plus new announcements that we are making over the course of yesterday in the next couple of days. It's really the time where enterprise data and JennyI are coming together and changing the game for how business users experience data, how data teams work with data, really game-changing and how the whole industry is being disrupted with JennyI. One of the things I wanted to observe with you is that, okay, Thomas Curry laid a lot out, he had to cover a lot of ground, but he said a couple of things that were very interesting. Ground and enterprise truth. Yes. Okay, he also talked a lot about BigQuery, but you've got the Gemini story is the big news. Okay, then you've got now the Vertex integration of the Looker and BigQuery. So you've got integration going on with Vertex and more Gemini. How's that impacting the analytics products that you're involved in? Because that's going to be the key enabler for the user experiences and the value that's going to come out of that. What's the big change of, what's the big news? Absolutely, so two different areas of innovation I would talk about. First is bringing JennyI to enterprise data. For the customers we work with, they don't want to take their data out of their secure data cloud. They want the BigQuery Vertex AI direct integration, meaning you've got access to large language models on top of multimodal data now. It's not just structured. It's images, documents, videos. So that direct connection between BigQuery and Vertex AI really enables enterprises to make use of JennyI on top of their enterprise data. And then the second category is the whole Gemini experience in BigQuery and Looker, providing assistive and agent-like capabilities. And you saw on the Kino, various agents, including the data agent, really it's powering the users to be able to do more with data, but also automating parts of the data analytics lifecycle that has historically been super repetitive, mundane, and long and hard work. You know, one of the biggest application successes we've seen in JennyI in developers and users is the rag where retrieval, augmentation generation has been the talk of the industry, because it's easy to do if you get data and to work with, not super easy. I mean it's kind of easy compared to other things. It's easier than building machine learning algorithms. The vectors have been a huge support. You guys, that's a big part of your announcements. Why is everyone going crazy for vector support? And what does it mean to have the vector support in your portfolio versus other opportunities out there? It's all the craze right now, the vector database and the vector embeds. Yeah, so when you talk about retrieval, augmented generation, it's being able to ground models, being able to tune models. You can do that now in BigQuery. And so with the various techniques, whether it's rag or doing Laura adapters, it enables you to use your enterprise data to ground a model so it knows your business. So that's super powerful and vectors are really taking off because today, you know, enterprises typically have 90% of their data unstructured. It's documents, it's videos, it's images. And historically, you could not search that data. You couldn't really do anything with that data. But with JennyI in vector embeddings now in BigQuery, with our unified data foundation, you've got the ability to create embeddings, do vector searches, compare documents. If you're a HR team, you can match job descriptions to resumes. If you're looking at various financial reports in document format, you can extract those financial metrics and compare them. So the possibilities now of unlocking that 90% of unstructured data in enterprise, that's the major thing that is the attraction, right? How do you see this really evolving? Because you mentioned it, people don't want to move the data around, data has gravity. How do you look at it from a security perspective as well? And how has that really been a talk track with your customers and how they're interested in that? Yeah, and I think there's a lot of concern about security and also governance. Because in the world where you're bringing in more AI and you're doing more automation with AI, you need to know that the data on which it's operating is trusted, it's secured, and it's governed. And with BigQuery, we are the AI-ready data foundation. So we are building the, it's a unified foundation with one access control layer, one set of governance across structured, unstructured data. And actually with BigQuery Omni, it extends across clouds because we know many of the enterprises we work with are multi-cloud. And so being able to have that single unified access layer, single governance across that data means for our customers, they feel like they have a trusted foundation to deploy journey AI on top of. Some of the announcements, real quick clarification, I know there's a GA of BigQuery integration with Gemini 1.0 Pro, that's available now. The rest are previews, they public previews or describe the status of these projects? How do people get in on it? What's the preview process? Yeah, so our customers can sign up today for example, Gemini and BigQuery, Gemini and Lucas. Those are some of the areas that are in preview today. This is where you're seeing the assistive conversational analytics, you can now chat with your business data. This is where you're seeing data canvas, which is this entirely new graphical workflow experience for data engineers to build data pipelines, for data scientists and analysts to explore data. All of these items, customers can sign up today and we're very much looking forward to seeing our users get hands-on with these technologies. Just to be clear, public preview is a public preview. Yes, what has the feedbacks have been so far? Because I know we are here, people are getting their hands a little dirty with this, experimenting with it. What has the feedback been particularly on the conversation side? Because I think that that's a lot of skepticism with AI is that it doesn't feel real or human. It feels like a robot. So I think on the conversational side and in particular with enterprise data, what's really important is you need the Gemini models, the large language models need to be able to understand your business to give trusted answers. Because otherwise it's a generic model. So what we're doing, for example, with Looker is we have our semantic layer and that semantic layer contains your business data definitions. And so we tune the models so that they have access to semantic business data definitions. They have access to metadata usage history. So that gives the model a grounding in your business. So when you're doing conversational analytics, it actually comes back with answers that are very relevant. You don't have to explain to the model what you mean by customer segment or target. It just gets it. So the feedback on that has been super because context is going to be critical for really that accuracy and for adoption by enterprises on that tech. And building the trust, as you said. Absolutely. I was going to say, do you see a lot of organizations leaning into this because they're looking for non-data engineers to be able to use Looker more at the business analyst side versus the data engineering side to go and create these outputs and understand the data. Is that really why we're leaning into this? So a huge, I think absolutely, and a huge reason why organizations are leaning in is data has been a hard discipline. It has today, it's very mundane. It's very repetitive. It takes a long time to pool data, build pipelines, get to insights. And typically the way we work today, it's not very intuitive. So it really slows organizations down. And if you just look out there at how many job adverts there are for data engineers, scientists, analysts, the industry doesn't have enough talent. There's not enough people. So with Gemini and BigQuery, Gemini and Looker, it's ways of accelerating and having more people in the organization and having access to data and being able to do it faster and quicker. But I also think there's an interesting, we can have humans working on more of the creative task, more of the understanding and outcomes of the business instead of trying to wrangle these data pipelines. So for me, I think it's, when I look at it, it's not necessarily a, it's eliminating roles or so. It's actually creating time to focus on thinking about the outcomes for the business and how you get to value. I'm going to ask you about the BigQuery being the place where you can put the multi-models, you get the fine-tuning in the ground, you get the enterprise data. That's compelling as a platform. How do people get started, one? And two, if I have a multi-diverse environment outside of Google, how does that interaction work? Obviously, you don't want to move data around. You don't have to. That's preferred by most customers. Talk about that, the one, how they get started with BigQuery. And then two, if I have other data outside of Google, how does that work? So with everything we're doing with BigQuery, the unified platform from data to AI, we're really bringing everything under one banner and making it much more seamless and easy for users to get started. What some of the feedback we heard historically was, Google, you're innovating so fast, but we can't adopt it all. It's, you know, it's coming at us quickly. Slow down. So rather than slowing down. You don't want to slow down. Keep going faster. But we're bringing it all together in this unified platform stack that means for a user getting started, we're just GA'ing BigQuery Studio. There's one entry point and you get access to streaming data, whether it's real time, you're doing machine learning AI all from that single environment. So that's really making it much more easy for enterprises to get started. And to your point that, you know, today data lives in lots of different places. So for us, we're very much focused on being open and cross clouds. We know your data sits in AWS as your many organizations of SaaS applications that are producing data in different clouds. So with BigQuery Omni, we're connecting that data because we don't think you have to move it all. And so it's very easy to get started because it's not the historical, you must move everything together and then start doing analytics. It's, you know, leave your data where it is, you might be exploring and at some point you might move it or you might not move it. But the- If there's value there, why not move it? You know, it depends on the trade-off. It depends on the trade-off and it depends on the use case. And then with the seamless integration with our Vertex AI platform, you don't have to be an AI engineer to take advantage of any AI. You've got access to Gemini Pro, you're able to run really sophisticated large-language model operations just from SQL. So it's really empowering those everyday analyst users to make use of this technology. I got to ask you, Rob and Savannah and Rebecca and I were talking yesterday about the next generation developers that are coming in, like my son's graduate from college this year. I was like, Dad, that's your cloud. You know, Google's my cloud. Okay. So- Not your father's cloud. So the younger generation, they're also, they don't have the bags yet. They're like fresh thinkers. They just want more compute. So they're coding away. So I can see this being attractive to younger developers, but they're also thinking, I need horsepower. So that's also coming up in the enterprise. I want to turn on performance. So I got it, I need to have performance. How does the analytics tap into that? What's the connection down to the, to the GPUs and the TPUs? How do you spin up more power? Yeah. So we're super lucky. We have these, we have the stack. We've got the GPU, TPU technology. We have the AI platform. We've got the data platform. We have the business intelligence layer. So the way that Google is architected because we've got access to all of that, we can really leverage that to the most. You know, we very much could integrate across those layers to get the most out of the entire platform. But we're also serverless architecture. The way that Google scales and how, you know, BigQuery was originally built, it was built for an internal use case at Google. It was built to run, you know, Google's internal services. We built in a serverless way that means we can scale super, to super large degrees in very small increments, separating storage and compute. And that's why enterprises are coming to Google because we have the scalability, we have the security, we have the performance. And now with the JennyI integrated in, it's, you know, it's up to your imagination what you want to do. Yeah, Rob and I were talking during the research meetings, we're having our CUBE research team. AI is with the agents. We see a future where, in some movement now, and some I won't name names, but older applications lift and shift in those workloads and putting AI agent around it to either manage it or in some cases projects where the person left the company who's documented the code. So you're seeing new use cases where go document everything to a lot of mundane tasks and also lift and shift workloads into Google Cloud. So can you share any insight there how you see that evolving? Because we're predicting that you'll see Google take on new workloads that we're running either on-premise or in the other clouds and just wrapping AI around it for the lack of a better description. Yeah, and we're seeing a huge, actually, acceleration in workloads moving from on-premises into the cloud. Because actually, if you want to leverage the benefits of GenEI and all of the automation and agent-like capabilities we spoke about, today that's in the cloud. And so we're seeing, we've actually seen an acceleration in customers looking to move legacy workloads, pipelines up into the cloud. And to your point, you don't need humans managing them in the cloud. Cloud just works differently, first of all. You don't need the traditional DDAs and so on. But also with GenEI, you've got the assistive capabilities to set up, operational monitoring of existing workloads. You just need them to run. You're not necessarily doing anything with them while you have space and time to do more innovation. What about the consumption side? Obviously, visualization's always been hot. We've also seen data science, business intelligence groups. I won't say losing power in the organization, but with AI becoming much more software engineering, system-oriented, the roles of business intelligence and data science teams are being augmented with AI, that's changing the role of what applications are going to be consuming. So if I got all this data, I want to present it, I want to integrate it into the user experience. Can you share how that works with Analytics Tools, how easy it is, what do people do? Yeah, and we're spending actually a lot of time doing, for example, integrations with our looker stack and workspace, because actually the future data is meeting the user where they are. Today, users have to come out of the slide or documents that they're working in and go to their BI tool, whereas the future is, I'm working in slides or I'm writing a report, I want my assistant there and providing me the data or insights I need to plug into this report. So I do see that world infusing of where it's not a separate data environment, it's not a separate place to go, it's actually meeting the user where they are and where they need those data or insights. Yeah, oh, sorry, go on. Well, I just want to ask, one of the things we hear a lot is that we are really still in the early innings of this gen AI revolution. If 2022 was the year Chachi BT was released and you said 2023 was the year that people started searching for these proof of concepts, and now we're here where it is being fully integrated into the enterprise. I'm curious what you think is next for this. I mean, where do we go from here now that it is fully integrated? When are we going to start seeing this creativity explosion and this great ROI for the enterprise? And I think we're already seeing it. So some of the actually stellar customer examples we've seen here at Next, you've got customers in production, the likes of Pruma, Price Line, they've pushed their first, second, third gen AI applications now into production. And so it's been incredible to see because it's not just been the pace of innovation on the technology side, but actually the pace at which our customers have been able to adopt and actually push things into production. I've never seen it be so fast before. And I think part of the reason there is AI used to be the domain of the AI engineers who lived in a floor and it was really hard to do. You could say it, the nerds, yeah, we're okay. We're nerds, we're proud of that. It's very proud. We're very proud. But with the gen AI and all of the innovation, it's actually taken a discipline that was actually fairly mature, like AI models, et cetera, and made it now available to the masses. So the ability, the technology has always been there. It's actually just the unlock has been now opening that to the masses and making it, as you said, integrated and seamless to use. So where do I see us going from here? I think we're going to see more and more production use cases coming out. And we've already got customers talking about all of the things that they're now able to do and the efficiency that they're getting that is creating the time and space to go after more ambitious use cases. And it'll be exciting to see how industries change with this, right? It's not just the internal, right now there's a lot of internal within organizations driving efficiency. But once you get that internal organizational efficiency, what can those companies do externally and what offerings or products or services, how do they evolve? Yeah, yeah. And then finally, I just want to return to something you've been talking about throughout this interview, which is the dearth of data science talent out there. As particularly as a woman in tech, and I'm a woman asking you kind of a sexist question, but how do we get more young people into this and in particularly women more into this industry? Yeah, I mean, for me, like this industry has never been more exciting. Like if you wanted to be at any point in time in tech, like now's the time, the innovation, et cetera. I think earlier we touched on, you know, young people are gravitating. I think they are seeing the excitement of the space and what it can do. So I think we need to capitalize on that momentum. I think getting more women into tech, super, super important and we don't have enough females in tech. But I do think, you know, if we look at all of the use cases and things coming out and with generative AI and the creativity, that's something that we can also tap into to get more people interested in this space. You know, we talked about being nerds and being proud of it. It's breaking down that misconception of what a nerd is. A nerd, it can be- It's mainstream. Nerds are cool. Nerds are cool. Nerds are cool. Absolutely. That's me. That's why we're cool. Terrific to have you on the show. A really fun conversation. Thank you so much. Thank you. I'm Rebecca Knight for John Furrier and Rob Streche. Stay tuned for more of theCUBE's live coverage of Google Cloud Next. You are watching theCUBE, the leading source for enterprise news.