Google Cloud Next '24 | Yasmeen Ahmad | Google

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From insights to action: Google Cloud’s next-gen data analytics solutions

The transformative power of technology is driving innovation and unlocking new possibilities in data analytics.As businesses continue to navigate an increasingly complex digital landscape, the integration of AI and data analytics will undoubtedly play a central role in shaping success. With Google LLC at the forefront of this revolution, the future of data analysis has never looked more promising.“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, long and hard work,” said Yasmeen Ahmad (pictured), managing director of data analytics and AI at Google

play_circle_outline Exploring the Power of Gen AI: Seamless Integration with Enterprise Data and Gemini Experience in BigQuery and Looker

play_circle_outline Focus on security and governance in AI and automation

play_circle_outline Availability of public previews for Gemini and BigQuery, Gemini and Looker

play_circle_outline Simplifying data analysis for non-data engineers and empowering business analysts

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Yasmeen Ahmad | Google

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(upbeat music)

>> 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 Strechay. Gentlemen, what an a dizzying array of new product announcements, updates, the pace of innovation here is just mind blowing.

>> Yeah, yeah, the data is at 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 Ahmad 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 this as we've said, there've 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, and the next couple of days. It's really the time where enterprise data and gen AI 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 gen AI.

>> One of the things I wanted to observe with you is that okay, Thomas Curry laid a lot out, he covered a lot of ground, but he said a couple things that were very interesting. Ground in enterprise truth.

>> Yes. >> Okay. He also talked a lot about BigQuery, but you got the Gemini story is the big news. Then you got now the Vertex integration with Looker and BigQuery. So you got integration going on with Vertex and more Gemini. How's that impacting the analytics products that you are 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 or what's the big news?

>> Absolutely, so two different areas of innovation I would talk about. First is bringing gen AI 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 gen AI on top of their enterprise data. And then the second category is the whole Gemini experience, in BigQuery and Looker, providing assistant and agent like capabilities. And you saw on the keynote 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.

>> One of the biggest application successes we've seen in gen AI in developers and users is the RAG, retrieval-augmentation generation, has been the talk of the industry. Because it's easy to do if you got data 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 like 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 LoRA 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, 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 gen AI and 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.

>> And how do you see this really evolving? Because, I mean, 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, it's a unified foundation with one access control layer, one set of governance, across structured or unstructured data. And actually with BigQuery Omni, 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 gen AI on top of some of.

>> I just want to announce this real quick clarification. I know there's a lot, there's a GA of BigQuery integration with Gemini 1.0 Pro, that's available.

>> That's available today. >> Now the rest are previews, are 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 Looker. 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,

>> okay? >> Yes.

>> What has the feedback 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 are 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 segments.

>> It just gets it. >> Yeah, 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 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. Today, it's very mundane, it's very repetitive, it takes a long time to pull data, build pipelines, get to insights. And typically, the way we work today, it's not very iterative, 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 tasks, more of the understanding and outcomes of the business instead of trying to wrangle these data pipelines. So for me, I think, 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.

>> Yasmeen, I got to ask you about the BigQuery being the place where you can put the multi-models, you get the fine tuning and the grounding of the enterprise data, that's compelling as a platform. How do people get started? One and two, if I have a diverse environment outside of Google, how does that interaction work? Obviously you don't want to move data around if 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?

>> 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. Some of the feedback we heard historically was, Google, you're innovating so fast, but we can't adopt it all.

>> It's coming at us so quickly. >> Slow down.

>> So rather than slowing down- >> No, no, 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 are 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, today, data lives in lots of different places. So for us, we are very much focused on being open and cross-clouds. We know your data sits in AWS, Azure, many organizations of SaaS applications that are producing data in different clouds. So with BigQuery Omni, we are 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 leave your data where it is, you might be exploring, and at some point you might move it

>> or you might not move it

>> If there's value there, why not move it? 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 gen 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 graduated from college this year, he is like, "Dad, that's your cloud, Google's my cloud."

>> Not your father's cloud.

>> So the younger generation, they don't have the baggage, 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 need to have performance. How does the analytics tap into that? What's the connection down to the GPUs and the TPUs?

>> How do you spin up more power?

>> Yeah, so we are 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. 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 BigQuery was originally built, it was built for an internal use case at Google. It was built to run Google's internal services. We built in a serverless way that means we can scale 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 gen AI integrated in,

>> it's up to your imagination what you want to do.

>> Yeah, Rob and I were talking and during the research meetings we're having at our CUBE research team, AI with the agents, we see a future where, and some movement now and some I won't name names, but older applications lifting and shifting 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, so 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 were running either on-premise or in 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 gen AI and all of the automation and agent like capabilities we spoke about, today, that's in the cloud. And so 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 all, you don't need the traditional DBAs and so on. But also with gen AI, 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 they're 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 the analytics tools,

>> how easy it is, what do people do?

>> Yeah, and we are spending actually a lot of time doing, for example, integrations with our Looker stack and Workspace. Because actually the future of 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 my assistants 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 ahead.

>> Well, I just wanted 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. 2022 was the year ChatGPT 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 Prima, Priceline. they've pushed their first, second, third gen AI applications now into production. And so it's been incredible to see 'cause 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 hear.

>> You could say it, the nerds.

We're okay. >> We're nerds,

>> we're proud of that. >> Yes, very proud.

>> We're very proud. But with 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. It's not just the internal, right now there's a lot of kind 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?

>> 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. 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 at any point in time in tech, like now's the time, the innovation, et cetera. I think earlier we touched on young people are gravitating, I think they are seeing the excitement of this 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 if we look at all of the use cases and things coming out with generative AI and the creativity, that's something that we can also tap into to get more people interested in this space. We joked about being nerds and being proud of it, it's breaking down that misconception of what a nerd is-

>> It's mainstream. >> It's cool.

>> It's cool. >> Yeah, absolutely, Yasmeen.

>> That's why we're cool.

>> Terrific to have you on the show, a really fun conversation.

>> Thank you so much. >> Yeah thank you, thank you.

>> I'm Rebecca Knight for John Furrier and Rob Strechay, stay tuned for more of 'theCUBE's" live coverage of Google Cloud Next. You are watching "theCUBE," the leading source for enterprise news. (upbeat music)

Yasmeen Ahmad | Google

search

(upbeat music)

>> 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 Strechay. Gentlemen, what an a dizzying array of new product announcements, updates, the pace of innovation here is just mind blowing.

>> Yeah, yeah, the data is at 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 Ahmad 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 this as we've said, there've 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, and the next couple of days. It's really the time where enterprise data and gen AI 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 gen AI.

>> One of the things I wanted to observe with you is that okay, Thomas Curry laid a lot out, he covered a lot of ground, but he said a couple things that were very interesting. Ground in enterprise truth.

>> Yes. >> Okay. He also talked a lot about BigQuery, but you got the Gemini story is the big news. Then you got now the Vertex integration with Looker and BigQuery. So you got integration going on with Vertex and more Gemini. How's that impacting the analytics products that you are 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 or what's the big news?

>> Absolutely, so two different areas of innovation I would talk about. First is bringing gen AI 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 gen AI on top of their enterprise data. And then the second category is the whole Gemini experience, in BigQuery and Looker, providing assistant and agent like capabilities. And you saw on the keynote 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.

>> One of the biggest application successes we've seen in gen AI in developers and users is the RAG, retrieval-augmentation generation, has been the talk of the industry. Because it's easy to do if you got data 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 like 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 LoRA 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, 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 gen AI and 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.

>> And how do you see this really evolving? Because, I mean, 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, it's a unified foundation with one access control layer, one set of governance, across structured or unstructured data. And actually with BigQuery Omni, 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 gen AI on top of some of.

>> I just want to announce this real quick clarification. I know there's a lot, there's a GA of BigQuery integration with Gemini 1.0 Pro, that's available.

>> That's available today. >> Now the rest are previews, are 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 Looker. 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,

>> okay? >> Yes.

>> What has the feedback 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 are 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 segments.

>> It just gets it. >> Yeah, 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 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. Today, it's very mundane, it's very repetitive, it takes a long time to pull data, build pipelines, get to insights. And typically, the way we work today, it's not very iterative, 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 tasks, more of the understanding and outcomes of the business instead of trying to wrangle these data pipelines. So for me, I think, 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.

>> Yasmeen, I got to ask you about the BigQuery being the place where you can put the multi-models, you get the fine tuning and the grounding of the enterprise data, that's compelling as a platform. How do people get started? One and two, if I have a diverse environment outside of Google, how does that interaction work? Obviously you don't want to move data around if 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?

>> 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. Some of the feedback we heard historically was, Google, you're innovating so fast, but we can't adopt it all.

>> It's coming at us so quickly. >> Slow down.

>> So rather than slowing down- >> No, no, 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 are 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, today, data lives in lots of different places. So for us, we are very much focused on being open and cross-clouds. We know your data sits in AWS, Azure, many organizations of SaaS applications that are producing data in different clouds. So with BigQuery Omni, we are 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 leave your data where it is, you might be exploring, and at some point you might move it

>> or you might not move it

>> If there's value there, why not move it? 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 gen 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 graduated from college this year, he is like, "Dad, that's your cloud, Google's my cloud."

>> Not your father's cloud.

>> So the younger generation, they don't have the baggage, 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 need to have performance. How does the analytics tap into that? What's the connection down to the GPUs and the TPUs?

>> How do you spin up more power?

>> Yeah, so we are 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. 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 BigQuery was originally built, it was built for an internal use case at Google. It was built to run Google's internal services. We built in a serverless way that means we can scale 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 gen AI integrated in,

>> it's up to your imagination what you want to do.

>> Yeah, Rob and I were talking and during the research meetings we're having at our CUBE research team, AI with the agents, we see a future where, and some movement now and some I won't name names, but older applications lifting and shifting 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, so 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 were running either on-premise or in 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 gen AI and all of the automation and agent like capabilities we spoke about, today, that's in the cloud. And so 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 all, you don't need the traditional DBAs and so on. But also with gen AI, 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 they're 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 the analytics tools,

>> how easy it is, what do people do?

>> Yeah, and we are spending actually a lot of time doing, for example, integrations with our Looker stack and Workspace. Because actually the future of 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 my assistants 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 ahead.

>> Well, I just wanted 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. 2022 was the year ChatGPT 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 Prima, Priceline. they've pushed their first, second, third gen AI applications now into production. And so it's been incredible to see 'cause 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 hear.

>> You could say it, the nerds.

We're okay. >> We're nerds,

>> we're proud of that. >> Yes, very proud.

>> We're very proud. But with 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. It's not just the internal, right now there's a lot of kind 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?

>> 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. 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 at any point in time in tech, like now's the time, the innovation, et cetera. I think earlier we touched on young people are gravitating, I think they are seeing the excitement of this 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 if we look at all of the use cases and things coming out with generative AI and the creativity, that's something that we can also tap into to get more people interested in this space. We joked about being nerds and being proud of it, it's breaking down that misconception of what a nerd is-

>> It's mainstream. >> It's cool.

>> It's cool. >> Yeah, absolutely, Yasmeen.

>> That's why we're cool.

>> Terrific to have you on the show, a really fun conversation.

>> Thank you so much. >> Yeah thank you, thank you.

>> I'm Rebecca Knight for John Furrier and Rob Strechay, stay tuned for more of 'theCUBE's" live coverage of Google Cloud Next. You are watching "theCUBE," the leading source for enterprise news. (upbeat music)