(bright upbeat music) >> Welcome to theCUBE. Welcome to this CUBE Conversation. I'm Dave Nicholson and with me today, I have Jen Bennett, technical director out of the office of the CTO at Google. Jen, welcome to theCUBE. How are you? >> I'm doing great, Dave. Thanks for having me. >> It's great to have you here. Now, some estimates have 1% of global electrical production. So all of the electricity produced globally, 1% of that is being used currently for the category we call ICT or Information Communications Technology. By 2030, the estimate is that if that goes unchecked, unmeasured, unmitigated, we could be at between 8% and 20% consumption of all of the electricity generated globally, at that point. The question I have for you to start this off is, is that sustainable and if not, what are you and the folks at Google thinking about in this area? >> Yeah Dave, that is a great question. And I think one that's top of mind for many of us in the industry, and there's unfortunately not a simple answer, but for sure, I think that sustainability has been something that's been a core value of ours from the very beginning at Google. And we've really focused on the impact that we have throughout, you know, our computation on climate and on other aspects of the environment. And so I think this is something that's a really important topic that we need to discuss. And so, you know, I want to give you a little bit of a history from a Google perspective. You know, in 2007, we were the first major company to become carbon neutral. So today, you hear a lot about companies talking about carbon neutrality and how important that is to reach, you know, some of these targets that we've been talking about from the Paris Climate Agreement, et cetera. In 2017, you know, as we were looking at this target of how do we become more carbon free, we became the first company, first major company to match a hundred percent of our electricity use associated with our computation that you talked about earlier with renewable energy. So this was a really big push for us. So with the question around, is it sustainable, has to kind of go back to, well, where does the energy come from and how is it generated? And renewable energy is one of those big pieces of the puzzle here. And so we invested very heavily in what was called Power Purchase Agreements. This is, you know, creating the mechanisms, the financial instruments to, you know, enable energy companies to transition from maybe things that are less carbon friendly to more renewable energy. So that was a big emphasis that we put on that first decade of climate action. Since then, and you may be familiar that in 2020, we announced probably our most ambitious goal yet, which is to operate on carbon free 24 by seven. So one of the things that you'll appreciate is that renewable energy, the sun doesn't shine 24/7, wind doesn't blow 24/7. And so we need more to become carbon free, 24/7 around the clock. We need storage techniques. We need a lot of other potential, you know, technologies that are evolving. And so we're very committed to investing in making sure that we advance those technologies just like we did with renewable energy. And so this is a really ambitious goal. We don't know how to get there yet. And that's why we often referred to is as a moonshot. >> Yeah, so yeah, that is a moonshot. And it's interesting because it sounds like you've decided to use a metric that is more difficult than the metric you could use, which is aggregate neutrality. You mentioned the sun doesn't shine 24 hours a day. Well, of course it does somewhere on the planet, but not on an individual facility. So if you're... If you're measuring carbon neutral at a certain physical location, that's a higher bar. So you talk about storage with batteries and things like that. So it's admirable that you're not taking the sort of easy way out, and looking at it just from an aggregate perspective. >> Yeah. And I guess I would say, you know, I think it's a journey. So you referred to it as sort of easy way out. And, I know, I certainly... I think this is one of the challenges is that oftentimes, action... Climate action can seem overwhelming and we have to find ways to advance and to get to impact. And so I think, you for us and our journey, you know, we started with how can we best make the use of the energy that we have. And so we applied machine learning in our data centers to optimize the energy utilization so that we could really bring down, you know, or optimize how we used energy in the first place. Of course the most renewable energy is the energy you don't use. But then I think the next point was where do we go next and how do we continue to advance to a place where we do get to a carbon free future, at least for us with our energy use as well. And I think, just as importantly, the next step is, "And how do we enable others?" And that's where we're at today. And so, you know, we've made some really exciting announcements and we're hoping that that's just the start of many more to come. >> Well, let's talk about that. So how do you enable others on this journey? >> Yeah, it's great. So when we talked a little bit about carbon free and that idea of energy in 24 x 7, one of the things that we started to focus on was how do we look at energy on an hourly basis, not on an annual basis or on a monthly basis. And so, as we started to pull the, you know, gather that data instrument up, we realized that this information is extremely valuable to our customers as well, who leverage our cloud platform. And so one of the big announcements that we made as part of our suite of sustainability offerings is really... It's called carbon footprint. And it's really providing that information to customers that use our cloud technology to give them a sense of where, how carbon friendly, the energy that they're using. Of course, we are carbon neutral and so effectively the neutrality is zero. But for those who really want to make sure that they're selecting locations where there's clean energy, making sure that their digital infrastructure is running as efficiently as possible. This new tool carbon footprint will give them the opportunity to see that just like you might see on your billing, you know, how much you spend, how much carbon emission there is associated with that as well. So that's one of our big announcements and we've worked very closely with customers like HSBC and Twitter and Salesforce as we've developed that capability so that we know that it helps meet their needs as well. >> So right now the part of your carbon footprint that is managed by this capability would be say GCP consumption or the Google part of a organization's footprint. Any plans in the future to integrate other data sources into a centralized capability? >> Yeah. So I think we have a lot of other exciting news we shared as well. You know, we recognize that our customer's digital footprint is a growing part of their business. As we've seen, sort of the pandemic has accelerated this shift to digital, and we want to make sure that we're bringing the most climate friendly cloud platform, to enable that. But a lot of our customers also have other parts of their business that aren't digital. And so being able to bring sustainability offerings to them, to help them with data, as you mentioned, but also with other tooling is really important. And so the other exciting announcement that we made is that, we are bringing Google Earth Engine to Google Cloud for our customers and Google Earth Engine has been around for over a decade used by, climate researchers, NGOs, It's really a trusted platform for earth observation. And so we're really excited to bring this to our Google Cloud customers as a mechanism, to look at the impacts of climate on their business, as well as see how their business is impacting the environment. And so this is an amazing platform that we're really excited and have partnered very closely with customers like Unilever, to leverage this platform in their business. >> So what are some practical examples of data sources that come out of the Google Earth platform and how they integrate from a sustainability perspective? What are some insights that organizations can gain by leveraging this? >> Super question. So, within Earth Engine, there is, over 50 petabytes of data and over 700 curated data sets. It's the world's largest for earth observation. Things like satellite imagery to look at land cover and deforestation for example, soil moisture, water, and availability of water in flood zones. There's just a... It's a really rich catalog and has been, you know, really curated over a matter of time. It brings also not just historical data, but is constantly refreshed. And so for someone who wants to look at changes over a period of time, it's really the perfect platform for that. And so for example, with Unilever, we have been looking at deforestation and the association with their supply chain because of course, many companies like Unilever have committed to a zero deforestation, And pit land conversion as part of their commitments to climate action. And so this is a really powerful capability. It's sort been kept within the research community and the NGOs for such a long time, that we're really excited to start to now evolve that into applications where we can make a real difference. I think this is part of our mission is to say, "How do we extend our reach in our climate action to not only the billions of users that use our platform, but also our customers who also reach many, many users through their products." So we're really excited about that opportunity. >> So imagine five, 10 years in the future. How does this play out? Give us a view into the enterprise of the future and the kind of information they're going to have at their fingertips. You gave a great example of deforestation within a supply chain, and we're not talking software supply chain here, we're physical supply chains, >> Physical supply chain, yeah. >> If think about a modern enterprise having hundreds or thousands of connections with other enterprises when you're trying to understand what the impact is that you're having on the earth, you have to take all of those things into account. So, can you kind of walk us through a scenario of what that might look like in the not too distant future, the kinds of things they'll have access to? >> You know, I wish I had that, the vision of five to 10 years from now, but if I look at what has evolved even over the last few years, you know, we've been... As consumers, we have so much information at our fingertips, that's both a blessing and a curse in many ways. And I think our job as technology providers is to make sure that the technology that we bring enhances the ability to make decisions quickly and that's... And effectively. And I think that's really what the power of these tools is enabling. So if I look into the future and I look at a little bit of the past and say, you know, "How do we navigate?" We often navigate no longer by paper maps. We navigate by using digital maps. And that same capability is what I'm talking about with some of this geospatial capability is being able to see that information in a way that's very meaningful, very powerful, but also helps you make decisions right now. And so I think that capability is going to be really critical and it's going to bring the complexity of information in a way that I believe is going to help us make better decisions. So today, if we take that... if we continue to take that example that we talked about, you know, often we talk about a lot of decision-making in terms of, you know, pricing, I think in the future, you're going to start to see the complexity of decision making, you know, sort of, you know, becomes more complex, but with the capability and the tools and some of the AI capabilities, we're going to be able to make that very tangible for people so that the decision making is fairly straightforward, but enables us to take into consideration the complexity of the ecosystem. Really excited about the future. I also believe that the future involves a lot more collaboration than maybe we see today, because I think one of the challenges with climate change is, we need to come to an agreed, you know, a shared truth, a shared sense of truth. And that really means we have to break down some of these silos we've created, you know, whether it be in business, whether it be in collaboration with the ecosystem but we're going to start to see a lot more collaboration around data. >> Yeah. Well, Those other things, those other costs that are sometimes hidden and hard to fair it through, in economics terms we refer to those as externalities and may have always been a challenge, how do you account for those real costs of your activity? Jen, I have to say that your passion for this comes through, despite the technology dividing us, it must be a delight working in an environment where you have the support of your organization. And you've got a clear mission that you can wake up every day and pursue that. Talk a little bit about that from a personal perspective. >> Yeah, I wake up every day, well, wake up, I think about this all the time, and I'm reminded of it, everything that I do, you know, whether it be turning off the lights, whether it be running the water too long. I mean, we're now experiencing droughts like we never have you know, historically. And so I think all these things impact us on a personal level. It's really... I feel so fortunate to be able to be thinking about this, to be able to take on something that really is one of the most pressing issues of our time. But I will say I'm also truly inspired by the customers I get to work with and their passion for the same topic. And I really feel like we've crossed this point of really moving to action. So it's no longer just about debating, you know, the science, if you will, we've really started to move into and what action can we take? And I think that is really what is going to both help move the technology and the ability to leverage technology and push us to continue to develop solutions that help make those actions real. But I also am very inspired by the fact that we have these large commitments across organizations to really make change. And I think this is what we all need. >> Well, Jen, thanks for joining us today in this CUBE Conversation and sharing some of the insights that you have along with Google and your customers. We appreciate the work that you do, it's critically important. With that, this is Dave Nicholson. Thanks for joining us for this CUBE Conversation. Until next time, see you then. (bright upbeat music)

>> Live from San Francisco, it's theCUBE covering Google Cloud Next 2018, brought to you by Google Cloud and its ecosystem partners. (techy music) >> Hey, welcome back, everyone, this is theCUBE Live in San Francisco coverage of Google Cloud Next '18, I'm John Furrier with Jeff Frick. Day three of three days of coverage, kind of getting day three going here. Our next guest, Sudhir, as the director of product management, Google Cloud, has the luxury and great job of managing BigTable, BigQuery, I'm sorry, BigQuery, I guess BigTable, BigQuery. (laughs) Welcome back to the table, good to see you. >> Thank you. >> So, you guys had a great demo yesterday, I want to get your thoughts on that, I want to explore some of the machine learning things that you guys announced, but first I want to get perspective of the show for you guys. What's going on with you guys at the show here, what are some of the big announcements, what's happening? >> A lot of different announcements across the board, so I'm responsible for data analytics on the Google Cloud. One of our key products is Google BigQuery. Large scale, cloud scale data warehouse, a lot of customers using it for bringing all their enterprise data into the data warehouse, analyzing it at scale, you can do petabyte scale queries in seconds, so that's the kind of scale we provide. So, a lot of momentum on that, we announced a lot of things, a lot of enhancements within that. For example, one of the things we announced was we have a new experience, new UI of BigQuery, now you can literally do the query, as I was saying, of petabyte scale or something, any queries that you want, and with one click you can go into Data Studio, which is our DI tool that's available, or you can go in Sheets and then from there quickly go ahead and fire up a connector, connect to BigQuery, get the data in Sheets and do analysis. >> So, ease of use is a focus. >> Ease of use is a major focus for us. As we are growing we want to make sure everybody in the organization can get access to their data, analyze it. That was one, one of the things, which is pretty unique to BigQuery, which is there is a real time collection of information, so you can... There are customers that are actually collecting real time data from click-stream, for example, on their websites or other places, and moving it directly into BigQuery and analyzing it. Example, in-game analytics, if in-game you're actually playing games and you're going to collect those events and do real time analysis, you're going to literally put it into BigQuery at scale and do that. So, a lot of customers using BigQuery at different levels. We also announced Clustering that allows you to reduce the cost, improve efficiency, and make queries almost two X faster for us. So, a lot of announcements other than the machine learning. >> Well, the one thing I saw in the demo I thought was, I mean, it was machine learning, so that's hot topic here, obviously. >> Yes. >> Is you don't have to move the data, and this is something that we've been covering, go back to the Hadoop, back when we first started doing theCUBE, you know, data pipeline, all the complexities involved in moving the data, and at the scale and size of the data all this wrangling was going on just to get some machine learning in. >> Yep. >> So, talk about that new feature where you guys are doing it inside BigQuery. I think that's important, take a minute to explain that. >> Yeah, so when we were talking to our customers one of the biggest challenges they were facing with machine learning in general, or a couple of them were, one, every time you want to do machine learning you are to take data from your core data warehouse, like in BigQuery you have petabytes of scaled data sets, terabytes of data sets. Now, if you want to do machine learning on any portion of it you take it out of BigQuery, move it into some machine learning engine, ML engine, auto-ML, anything, then you realize, "Oh, I missed some of the data that I needed." I go back then again take the data, move it, and you have to go back and forth too much time. There are analysis I think that different organizations have done. 80% of the time the data scientists say they're spending on the moving of data-- >> Right. >> Wrangling data and all of that, so that is one big problem. The second big challenge we were hearing was skillset gap, there are just not that many PhD data scientists in the industry, how do we solve that problem? So, what we said is first problem, how do we solve it, why do people have to move data to the machine learning engines? Why can't I take the machine learning capability, move it inside where the data is, so bring the machine learning closer to data rather than data closer to machine learning. So, that's what BigQuery ML is, it's an ability to run regression-like models inside the data warehouse itself in BigQuery so that you can do that. The second we said the interface can't be complex. Our audiences already know SQL, they're already analyzing data, these folks, business analysts that are using BigQuery are the experts on the data. So, what we said is use your standard SQL, write two lines of code, create model, type of the model you want to run, give us the data, we will just run the machine learning model on the backend and you can do predictions pretty easily. So, that's what we are doing with that. >> That's awesome. >> So, Sudhir, I love to hear that you were driven by that, by your customers, because one of the things we talk about all the time is democratization. >> Yeah. >> If you want innovation you've got to democratize access to the data, and then you got to democratize access to the tools to actually do stuff with the data-- >> Yes. >> That goes way beyond just the hardcore data scientist in the organization-- >> Yeah, exactly. >> And that's really what you're trying to enable the customers to be able to do. >> Absolutely, if you look at it, if you just go on LinkedIn and search for data analyst versus data scientist there is 100 X more analysts in the industry, and our thing was how do we empower these analysts that understand the data, that are familiar with SQL, to go ahead and do data science. Now, we realize they're not going to be expert machine learning folks who understand all the intricacies of how the gradient descent works, all that, that's not their skillset, so our thing was reduce the complexity, make it very simple for them to use. The framework, like just use SQL and we take care of the internal hyper-tuning, the complexity of it, model selection. We try to do that internally within the technology, and they just get a simple interface for that. So, it's really empowering the SQL analyst with an organization to do machine learning with very little to no knowledge of machine learning. >> Right. >> Talk about the history of BigQuery, where did it come from? I mean, Google has this DNA of they do it internally for themselves-- >> Yes. >> Which is a tough customer-- >> Yes. >> In Cloud Spatter we had the product manager on for Cloud Spatter. Dip Dee, she was, like amazing, like okay, baked internally, did that have the same-- >> Yes. >> BigQuery, take a minute to talk about that, because you're now making it consumable for enterprise customers. >> Yeah. >> It's not a just, "Here's BigQuery." >> No. >> Talk about the origination, how it started, why, and how you guys use it internally. >> So, BigQuery internally is called Dremel. There's a paper on Dremel available. I think in 2012 or something we published it. Dremel has been used internally for analytics across Google. So, if you think about Spanner being used for transaction management in the company across all areas, BigQuery, or Dremel internally, is what we use for all large scale data analytics within Google. So, the whole company runs on, analyzes data with it, so our things was how do we take this capability that we are driving, and imagine like, when you have seven products that are more than a billion active users, the amount of data that gets generated, the insights we are giving in Maps and all the different places, a lot of those things are first analyzed in Dremel internally and we're making it available. So, our thing was how do we take that capability that's there internally and make it available to all enterprises. >> Right. >> As Sundhir was saying yesterday, our goal is empower all our customers to go ahead and do more. >> Right. >> And so, this is a way of taking the piece of technology that's powered Google for a while and also make it available to enterprises. >> It's tested, hardened and tested. >> Yeah, absolutely. >> It's not like it's vaporware. >> Yeah, it's not. (laughs) >> No, I mean, this is what I think is important about the show this year. If you look at it, you guys have done a really good job of taking the big guns of Google, the big stuff, and not try to just say, "We're Google and you can be like Google." You've taken it and you've kind of made it consumable. >> Yes. >> This has been a big focus, explain the mindset behind the product management. >> Absolutely, there is actually one of the key things Google is good at doing is taking what's there internally used, but also the research part of it. Actually, Corinna Cortes, who is head of our AI side who does a lot of research in SQL-based machine learning, so again, the-- >> Yeah. >> BigQuery ML is nothing new, like we internally have a research team that has been developing it for a few years. We have been using it internally for running all these models and all, and so what we were able to do it bring product management from our side, like hey, this is really a problem we are facing, moving data, skillset gap, and then we were like, research team was already enabling it and then we had an engineering team which is pretty strong. We were like, okay, let's bring all three triads together and go ahead and make sure we provide a real value to our customers with all of that we're doing, so that's how it came to light. >> So, I just want to get your take, early days like when there was the early Google search appliance, I'll just pick that up, and that was ancient, ancient ago, but one of the digs was, right, it didn't work as well in the enterprise, per se, because you just didn't have the same amount of data when you applied that type of technique to a Google flow of data and a Google flow of queries. So, how's that evolved over time, because you guys, like you said, seven applications with a billion-- >> Yep. >> Users, most enterprises don't have that, so how do they get the same type of performance if they don't have the same kind of throughput to build the models and to get that data, how's that kind of evolved? >> So, this is why I think thinking about, when we think about scale we think about scaling up and scaling down, right? We have customers who are using BigQuery with a few terabytes of data. Not every customer has petabytes scale, but what we're also noticing is these same customers, when they see value in data they collect more. I will give you a real example, Zulily, one of our customers, I used to be there before, so when they started doing real time data collection for doing real time analytics they were collecting like 50 million events a day. Within 18 months they started collecting five billion a day, 100 x improvement, and the reason is they started seeing value. They could take this real time data, analyze it, make some real time experiences possible on their website and all, with all of that they were able to go out and get real valuer for their customers, drive growth, so when customers see that kind of value they collect more data. So, what I would say is yes, a lot of customers start small, but they all have an aspiration to have lots of data, leverage that to create operational efficiency as well as growth, and so as they start doing that I think they will need infrastructure that can scale down and up all the way, and I think that's what we're focusing on, providing that. >> You guys look at the possibility, and I've seen some examples where customers are just, like, they're shell-shocked, and you're almost too good, right? I mean, it's like, "We've been doing "Dremel on a large scale, I bought this "data warehouse like 10 years ago," like what are you talking about? (laughs) I mean, there's a reality of we've been buying IT, enterprises have been buying IT and in comes Google, the gunslinger saying, "Hey, man, you can do all this stuff." There's a little bit of shell-shock factor for some IT people. Some engineering organizations get it right away. How are you guys dealing with this as you make it consumable? >> Yeah. >> There's probably a lot of education. As a product manager do you see, is that something that you think about, is that something you guys talk about? >> Yes, we do, so I think I actually see a difference in how customers, what customers need, enterprise customers versus cloud native companies. As you said, cloud native companies starting new, starting fresh, so it's a very different set of requirement. Enterprise customers, thinking about scale, thinking about security and how do you do that. So, BigQuery is a highly secure data warehouse. The other thing BigQuery has is it's a completely serverless platform, so we take care of the security. We encrypt all the data at rest and when it's moving. The key thing is when we share what is possible and how easy it is to manage and how fast people can start analyzing, you can bring the data. Like you can actually get started with BigQuery in minutes, like you just bring your data in and start analyzing it. You don't have to worry about how many machines do I need, how do I provision it, how many servers do I need. >> Yeah. >> So, enterprises, when they look at-- >> Cloud native ready. >> Yeah. >> All right, so take a minute to explain BigTable versus, I mean, BigTable versus BigQuery. >> Yes. >> What's the difference between the two, one's a data warehouse and the other one is a system for managing data? What's the difference between Big-- >> So, it's a no-SQL system, so I will... The simple example, I will give you a real example how customers use it, right. BigQuery is great for large scale analytics, people who want to take, like, petabyte scale data or terabyte scale data and analyze historical patterns, all of that, and do complex analysis. You want to do machine learning model creation, you can do that. What BigTable is great at is once you have pre-aggregated data you want to go ahead and really fast serving. If you have a website, I don't expect you to run a website and back it with BigQuery, it's not built for that. Whereas BigTable is exactly for that scenario, so for example, you have millions of people coming on the website, they want to see some key metrics that have been pre-created ready to go, you go to BigTable and that can actually do high performance, high throughput. Last statement on that, like almost 10,000-- >> Yeah. >> Requests per second per node and you can just create as many as you want, so you can really create high scale-- >> Auto-scaling, all kinds of stuff there. >> Exactly. >> And that's good for unstructured data as well-- >> Exactly. >> And managing it. >> Absolutely. >> Okay, so structured data, SQL, basically large scale-- >> Yes. >> BigTable for real time-- >> Yes. >> New kinds of datas, different data types. >> Absolutely, yes. >> What else do you have in the bag of goodies in there that you're working on? >> The one big thing that we also announced with this week was a GIS capability within BigQuery. GIS is geographical information, like everything today is location-based, latitude, longitude. Our customers were telling us really difficult to analyze it, right, like I want to know... Example would be we are here, I want to know how many food restaurants are in a two-mile radius of here, which ones are those, how many, should we create the next one here or not. Those kind of analyses are really difficult, so we partnered with Earth Engine, Earth Engine team within Google with Maps, and then what we're launching is ability to do geospatial analysis within BigQuery. Additionally along with that we also have a visualization tool that we launched this week, so folks who haven't seen that should go check that out. One great example I will give you is Geotab, their CEO is here, Neil. He was showing a demo in one of the sessions and he was talking about how he was able to transform his business. I'll give you an example, Geotab is basically into vehicle tracking, so they have these sensors that track different things with vehicles, and then with, and they store everything in BigQuery, collect all of that and all, and his thing was with BigQuery ML and a GIS capability, what he's now able to do is create models that can predict what intersections in a city when it's snowing are going to be dangerous, and for smart cities he can now recommend to cities where and how to invest in these kind of scenarios. Completely transforming his business because his business is not smart cities, his business was vehicle tracking and all, he's like, but with these capabilities they're transforming what they were doing and solving-- >> New discoveries. >> New discoveries, solving new problems, it's amazing. I wonder if you could just dig at a little bit to, you know, the fact that you've got this, these seven billion activities or apps that you can leverage, you know, specific functionality or goals or objectives or priorities in those groups, and now apply those, pull that data, pull that knowledge, pull those use cases into a completely different application on the enterprise. I mean, is that an active process-- >> I don't think that's how people. >> Do people query? >> No, no. >> But how does that happen? >> No, we don't-- >> As a customer. >> As a customer completely different, right? Our focus in Google Cloud is primarily enabling enterprises to collect their data, process their data, innovate on their data. We don't bring in, like, the Google side of it at all, like that's their completely different area that way, so we basically, enterprises, all their data stays within their environment. They basically, we don't touch it, we don't get to access it at all, and they can know it. >> Yeah, yeah, no, I didn't mean that, I meant, you know, like say Maps for instance, it's interesting to see how Maps has evolved over all these years. Every time you open it, oh, and it's directions-- >> Yep. >> Oh, now it's better directions, oh, now it's got gas stations, oh, now it's where the... And it triggered because you said the restaurants that are close by, so it's kind of adding value to the core app on that side, and as you just said, now geolocation can be used on the enterprise side-- >> Yeah, yes. >> And lots of different things, so that-- >> Exactly. >> That's where I meant that kind of connection-- >> Exactly right, so-- >> In terms of the value of what can I do with geolocation. >> Absolutely, exactly, so like, that's exactly what we did. With Earth Engine we had a lot of learnings on geospatial analysis and our thing was how do you make it easy for our enterprise customers to do that. We've partnered with them closely and we said, "Okay, here are the core pieces of things "we can add in BigQuery that will allow you "to do better geospatial analysis, visualize it." One of the big challenges is lat longs, I don't think they're that friendly with analysts, like oh, numbers and all that. So, we actually will turn a UI visualization tool that allows you to just fire a query and see visually on a map where things are, all the points look like and all. >> Awesome. >> So, just simplifying what analysts can do with all these. >> Sudhir, thanks for coming on, really appreciate it and congratulations on your success. Got a lot of great, big products there, hardened internally, now-- >> Yes. >> Making consumable, it's clear here at Google Cloud you guys are recognized that making it consumable-- >> Yep. >> Pre-existing, proven technologies, so I want to give you guys props for that, congratulations. >> Thank you, thanks a lot. >> Thanks for coming on the show. >> Thanks for coming on. >> Thank you. >> It's theCUBE coverage here, Google Cloud coverage, Google Next 2018. I'm John Furrier with Jeff Frick, stay with us, we've got all day with more coverage for day three. Stay with us after this short break. (techy music)