(bright music) >> Hey, welcome back everyone, theCUBE coverage here at AWS re:Mars 2022. I'm John, your host of the theCUBE. re:Mars, part of the three re big events, re:Invent is the big one, re:Inforce the security, re:MARS is the confluence of industrial space, of automation, robotics and machine learning. Got a great guest here, Muhammad Faisal senior consultant solutions architect at Capgemini. Welcome to theCUBE. Thanks for coming on. >> Thank you. >> So we, you just we're hearing the classes we had with the professor from Okta ML from Washington. So he's in the weeds on machine learning. He's down getting dirty with all the hardcore, uncoupling it from hardware. Machine learning has gone really super nova in the past couple years. And this show points to the tipping point where machine learning's driving space, it's driving robotics industrial edge at unprecedented rates. So it's kind of moving from the old I don't want to say old, couple years ago and the legacy AI, I mean, old school AI is kind of the same new school with a twist it's just modernized and has faster, cheaper, smaller chips. >> Yeah. I mean, but there is a change also in the way it's working. So you had the classical AI, where you are detecting something and then you're making an action. You are perceiving something, making an action, you're detecting something, and you're assuming something that has been perceived. But now we are moving towards more deeper learning, deep. So AI, where you have to train your model to do things or to detect things and hope that it will work. And there's like, of course, a lot of research going on into explainable AI to help facilitate that. But that's where the challenges come into play. >> Well, Muhammad , first let's take, what do you do over there? Talk about your role specifically. You're doing a lot of student architecting around AI machine learning. What's your role? What's your focus. >> Yeah. So we basically are working in automotive to help OEMs and tier-one suppliers validate ADAS functions that they're working on. So advanced driving assistance systems, there are many levels that are, are when we talk about it. So it can be something simple, like, you know, a blind spot detection, just a warning function. And it goes all the way. So SAE so- >> So there's like the easy stuff and then the hard stuff. >> Muhammad : Exactly. >> Yeah. >> That's what you're getting at. >> Yeah. Yeah. And, and the easy stuff you can test validate quite easily because if you get it wrong. >> Yeah. >> The impact is not that high. The complicated stuff, if you have it wrong, then that can be very dangerous. (John laughs) >> Well, I got to say the automotive one was one was that are so fascinating because it's been so archaic and just in the past recent years, and Tesla's the poster child for this. You see that you go, oh my God, I love that car. I want to have a software driven car. And it's amazing. And I don't get a Tesla on now because that's, it's more like I should have gotten it earlier. Now I'm going to just hold my ground. >> Everyone has- >> Everyone's got it in Palo Alto. I'm not going to get another car, no way. So, but you're starting to see a lot of the other manufacturers, just in the past five years, they're leveling up. It may not be as cool and sexy as the Tesla, but it's, they're there. And so what are they dealing with when they talk about data and AI? What's the, what's some of the challenges that you're seeing that they're grappling with in terms of getting things integrated, developing pipelines, R and D, they wrangling data. Take us through some of the things. >> Muhammad: I mean, like when I think about the challenges that autonomous or the automakers are facing, I can think of three big ones. So first, is the amount of data they need to do their training. And more importantly, the validation. So we are talking about petabytes or hundred of petabytes of data that has to be analyzed, validated, annotated. So labeling to create gen, ground truth processed, reprocessed many times with every creation of a new software. So that is a lot of data, a lot of computational power. And you need to ensure that all of the processing, all of handling of the data allows you complete transparency of what is happening to the data, as well as complete traceability. So your, for home allocations, so approval process for these functions so that they can be released in cars that can be used on public roads. You need to have traceability. Like you can, you are supposed to be able to reproduce the data to validate your work that was done. So you can, >> John: Yeah >> Like, prove that your function is successful or working as expected. So this, the big data is the first challenge. I see that all the automotive makers are tackling. The second big one I see is understanding how much testing is enough. So with AI or with classical approach, you have certain requirements, how a function is supposed to work. You can test that with some test cases based on your architecture, and you have a successful or failed result. With deep learning, it gets more complicated. >> John: What are they doing with deep learning? Give an example of some of things. >> I mean, so you are, you need to then start thinking about statistics that I will test enough data with like a failure rate of potentially like 0.0, 0.1%. How much data do I need to test to make sure that I am achieving that rate. So then we are talking about, in terms of statistics, which requires a lot of data, because the failure rate that we want to have is so low. And it's not only like, failure in terms of that something is always detected, and if it's there, but it's also having like, a low false positive rate. So you are only detecting objects which are there and not like, phantom objects. >> What's some of the trends you're seeing across the client base, in terms of the patterns that they're all kind of, what, where's the state of their mindset and position with AI and some of the work they're doing, are they feeling, you feel like they're all crossed over across the chasm so to speak, in terms of executing, are they still in experimental mode in driving with the full capabilities is conservative or is it progressive? >> Muhammad: I mean, it's a mixture of both. So I'm in German automotive where I'm from, there is for functions, which are more complicated ones. There's definitely hesitancy to release them too early in the car, unless we are sure that they are safe. But of course, for functions which are assisting the drivers everyday usage they are widely available. Like one of the things like, so when we talk about this complex function. >> John: Highly available or available? >> Muhammad: I would say highly available. >> Higher? Is that higher availability and highly available. >> Okay. Yeah. (both laughing) >> Yeah, so. >> I know there's a distinction. >> Yeah. I mean >> I bring up as a joke cuz of the Jedi contract. (Muhammad laughs) >> I mean, in like, our architecture. So when we are developing our solution, high availability is one of our requirements. It is highly available, but the ADAS functions are now available in more and more cars. >> John: Well, latency, man. I mean, it's kind of a joke of storage, but it's a storage joke, but you know, it's latency, you got it, okay. (Muhammad laughs) But these are decisions that have to be made. >> Muhammad: They... >> I mean. >> Muhammad: I mean, they are still being made. >> So I mean, we are... >> John: Good. >> We haven't reached like, level five, which is the highest level of autonomous driving yet on public roads. >> John: That's hard. That's hard to do. >> Yeah. And I mean, the biggest difference, like, as you go above these levels is in terms of availability. So are they these functions? >> John: Yeah. >> Can they handle all possible scenarios or are they only available in certain scenarios? And of course the responsibility. So, it's, in the end, so with Tesla, you would be like, if you had a one you would be the person who is in control or responsible to monitor it. >> John: Yeah. But as we go >> John: Actually the reason I don't have a Tesla all my family would want one. I don't want to get anyone a Tesla. >> But I mean, but that's the sort the liabilities is currently on you, if like, you're not monitoring. >> Allright, so, talk about AWS, the relationship that Capgemini has with AWS, obviously, the partnerships there, you're here and this show is really a commitment to, this is a future to me, this is the future. >> Muhammad: Yeah. >> This is it. All right here, industrial, innovation's going to come massive. Back-office cloud, done deal. Data centers, hybrid somewhat multi-cloud, I guess. But hybrid is a steady state in the back-office cloud, game over. >> Muhammad: Yeah. >> Amazon, Azure, Google, Alibaba done. So super clouds underneath. Great. This is a digital transformation in the industrial area. >> Muhammad: Yeah. >> This is the big thing. What's your relationship with AWS >> Muhammad: So, as I mentioned, the first challenge, data, like, we have so much data, so much computational power and it's not something that is always needed. You need it like on demand. And this is where like a hyperscale or cloud provider, like AWS, can be the key to achieve, like, the higher, the acceleration that we are providing to our customers using our technology built on top of AWS services. We did a breakout session, this during re:MARS, where we demonstrated a couple of small tools that we have developed out of our offering. One of them was ability to stream data from the vehicle that is collecting data worldwide. So during the day when we did it from Vegas, driving on the strip, as well as from Germany, and while we are while this data is uploaded, it's at the same time real time anonymized to make sure it you're privacy aligned with the, the data privacy >> Of course. Yeah. That's hard to do right there. >> Yeah. And so the faces are blurred. The licenses are blurred. We also, then at the same time can run object detection. So we have real time monitoring of what our feed is doing worldwide. And... >> John: Do you, just curious, do you do that blurring? Is that part of a managed service, you call an API or is that built into the go? >> Muhammad: So from like part of our DSV, we have many different service offerings, so data production, data test strategy orchestration. So part of data production is worldwide data collection. And we can then also offer data management services, which include then anonymization data, quality check. >> John: And that's service you provide. >> Yeah. >> To the customer. Okay. Got it. Okay. >> So of course, like, in collaboration with the customer, so our like, platform is very modular. Microservices based the idea being if the customer already has a good ML model for anonymization, we can plug it into our platform, running on AWS. If they want to use it, we can develop one or we can use one of our existing ones or something off the shelf or like any other supplier can provide one as well. And we all integrate. >> So you are, you're tight with Amazon web services in terms of your cloud, your service. It's a cloud. >> Yeah. >> It's so Capgemini Super Cloud, basically. >> Exactly. >> Okay. So this we call we call it Super Cloud, we made that a thing and re:Invent Charles Fitzgerald would disagree but we will debate him. It's a Super Cloud, but okay. You got your Super Cloud. What's the coolest thing that you think you're doing right now that people should pay attention to. >> I mean, the cool thing that we are currently working on, so from the keynote today, we talked about also synthetic data for validation. >> John: Now That was phenomenal. So that was phenomenal. >> We are working on digital twin creation. So we are capturing data in real world creating a virtual identity of it. And that allows you the freedom to create multiple scenarios out of it. So that's also something where we are using machine learning to determine what are the parameters you need to change between, or so, you have one scenario, such as like, the cut-in scenario and you can change. >> John: So what scenario? >> A cut-in scenario. So someone is cutting in front of you or overtake scenario. And so, I mean, in real world, someone will do it in probably a nicer way, but of course, in, it is possible, at some point. >> Cognition to the cars. >> Yeah. >> It comes up as a vehicle. >> I mean, at some point some might, someone would be very aggressive with it. We might not record it. >> You might be able to predict too. I mean, the predictions, you could say this guy's weaving, he's a potential candidate. >> It it is possible. Yes. But I mean, but to, >> That's a future scenario. >> Ensure that we are testing these scenarios, we can translate a real world scenario into a digital world, change the parameters. So the distance between those two is different and use ML. So machine learning to change these parameters. So this is exciting. And the other thing we are... >> That is pretty cool. I will admit that's very cool. >> Yeah. Yeah. The other thing we like are trying to do is reduce the cost for the customer in the end. So we are collecting petabytes of data. Every time they make updates to the software, they have to re-simulate it or replay this data, so that they can- >> Petabytes? >> Petabytes of data. And, and physically sometimes on a physical hardware in loop device. And then this >> That's called a really heavy edge. You got to move, you don't want to be moving that around the Amazon cloud. >> Yeah. That that's, that's the challenge. And once we have replayed this or re-simulated it. we still have to calculate the KPIs out of it. And what we are trying to do is optimize this test orchestration, so that we are minimizing the REAP simulation. So you don't want the data to be going to the edge, >> Yeah. >> Unnecessarily. And once we get this data back to optimize the way we are doing the calculation, so you're not calculating- >> There's a huge data, integrity management. >> Muhammad: Yeah. >> New kind of thing going on here, it's kind of is it new or is it? >> Muhammad: I mean, it's- >> Sounds new to me. >> The scale is new, so- >> Okay, got it. >> The management of the data, having the whole traceability, that has been in automotive. So also Capgemini involved in aerospace. So in aerospace. >> Yeah. >> Having this kind of high, this validation be very strictly monitored is norm, but now we have to think about how to do it on this large scale. And that's why, like, I think that's the biggest challenge and hopefully what we are trying to, yeah, solve with our DSV offering. >> All right, Muhammad, thanks for coming on theCUBE. I really appreciate it. Great way to close out re:MARS, our last interview our the show. Thanks for coming on. Appreciate your time. >> I mean like just one last comment, like, so I think in automotive, like, so part of the automation the future is quite exciting, and I think that's where like- >> John: Yeah. >> It's, we have to be hopeful that like- >> John: Well, the show is all about hope. I mean, you had, you had space, moon habitat, you had climate change, potential solutions. You have new functionality that we've been waiting for. And, you know, I've watch every episode of Star Trek and SkyNet and kind of SkyNet going on air. >> The robots. >> Robots running cubes, robot cubes host someday. >> Yeah. >> You never know. Yeah. Thanks for coming on. Appreciate it. >> Thank you. Okay. That's theCUBE here. Wrapping up re:MARS. I'm John Furrier You're watching theCUBE, stay with us for the next event. Next time. Thanks for watching. (upbeat music)

>> Live from Bahrain, it's theCUBE. Covering AWS Summit Bahrain. (upbeat music) Brought to you by Amazon Web Services. >> Okay, welcome back everyone. We're here live in Bahrain for theCUBE's exclusive coverage here in the Middle East for AWS, Amazon Web Services', new region being announced and being deployed early 2019. I'm John Furrier your host. Our next guest is Faisal Hammad, Assistant Professor, Information Systems at the University of Bahrain. Welcome to theCUBE. >> Thank you very much. Thank you for having me and welcome to Bahrain. >> It's been a great pleasure. Our team has been blown away. It's been a very surreal experience. We're really excited. We've learned a lot and we're super impressed with the people and the culture. >> Yeah thank you very much. >> It's just Silicone Valley vibe. It's got community. It's got money and it's got, now, an ecosystem that's going to be flourishing. It really looks, really good. >> Yes, yes. As I told you, we'll have the little desserts of Silicon Valley soon, inshallah. >> Now Silicon Valley, I wanted to bring this up because one of the big success stories of Silicon Valley is they let the innovation flow. They have soil and they feed it with money and things grow and the entrepreneurs are out there making things happen, but they have two universities. They've got Stanford and University of California, Berkeley, Of course you've got UCLA in Southern California so research is really important and also at the role of academia is really important. Not in the sense of just being too hard core but creating a ground for free thinking, entrepreneurship, and then as the kids come out of school, sometimes dropping out, they just want to start companies. >> Alright. >> This is big. How are you guys looking at this massive wave of innovation coming because it's got to be taking you by surprise. You got, ya know the old way, get the computer science, here's some IT, like oh my god here comes cloud. All these new languages, data science. >> So, it didn't take us by surprise, if you say. We have been expecting this change for quite sometime. The thing is with the leadership of the government of Bahrain, as well as the leadership of the University, they want to make sure that we are able to produce talents to the economy. And, Bahrain, the University of Bahrain was involved from early on steps in the cloud first initiatives, or cloud first policy. So, we were aware that we have to change the ways that we are operating in order for us to produce these, not produce them but to shape these talents for the students to compete not just locally but internationally. >> So you see this coming, okay that fair, but the way this here, there's multiple waves coming in, it's going to be a 20, 30 year generation of waves. So you got to get the surf boards, to use the metaphor from California. Sorry, I'm from California. >> (laughing) There's no waves in the desert, the water's 91 degrees. But, as a metaphor, this is what's happening. So how has that shaped some of the curriculum, some of the interactions? Certainly the economic development board, the EDB has been gung ho supporting entrepreneurial resources. But when you're going to come in, you're going to be feeding the young kids the nutrients, what are giving them? New languages, new IT, what's the plan? >> Let me just, try to focus the, focus the discussion on the University and what the University is doing. So, what we are doing here at the University now, we have that partnership, with AWS. And now University of Bahrain is an AWS accredited academy. So we now provide curriculum, that is aligned with AWS, so that when our students take these courses, they will be able to take the certification and then be certified upon graduating. So, in that sense, we're providing the talents, and trained talents, to start working immediately with limited, or lower, training needed. As well as, in terms of research. If you say, it used to take us a long time if you want to research something. If you want, for example, the data centers, let's say for example some expert in artificial intelligence, it would take us a long time and a lot of effort to do so. >> Yeah >> But with AWS, all you need to do is, just log into the console. >> Amazon is doing all the research for you. They've got all the tools. >> Yes >> Yes, so if a student is, or even a researcher, is interested in, let's say for example, artificial intelligence, instead of waiting for the instructor to be knowledgeable, waiting for an instructor to be knowledgeable about that part, they could just start plugging in and playing with it. And then with that experimentation, they could do a lot of great stuff. >> What about software, let's get back to software, and I want to get to the IT in just a second because I know information technology is in your wheelhouse. But software is driving a lot of the dev-ops and the cloud native IT disruption. >> Yes Amazon is now winning a lot of that business, that's the main Amazon Web Services. But they started with developers. That's where the software developers are, how is that developing in the University? Are people taking to software programing, what's the curriculum like? >> So, in terms, >> What's the story? >> Yeah, so, we don't, we're not going to just focus on creating a curriculum for cloud computing. Cloud computing now is embedded throughout the all the curricular that we have in the University. So, in any let's say, program, whether it's in IT or even Arts, as well as Business, there's a small component of cloud computing telling them what is cloud computing, and what can it provide for them. >> [John} So you're focusing on cloud first? >> Yes, cloud first. >> And then we have these courses designed specially for IT students, as I told you before we are partners with AWS, the AWS academy, so now we'll be able to provide a curriculum that's actually updated by AWS and all we have to do is just deliver this material. >> How long have the courses been out there? Have they been released yet? Have they been out there for a while? >> They just has been released, and we have almost 50 students now, taking these courses. >> [John} Well, you know, University of California, in Berkeley, where my daughter goes, the number one class is Intro to Computer Science and Intro to Data Science. It seems that the younger kids are wanting that intro to programing >> Yes and intro to data science. Is there any data thing going on with Amazon? They do a lot of big data, you got Red Shift, Aurora, you got I.O.T. >> So in our, >> SageMaker, is one of the most popular features of Amazon, is like, I think it's going to be the most popular but... >> So, in our department, for example, the Department of Information Systems, Instead of just having a bachelors in Information Systems, now we have smaller tracks within the program itself. So if the student is, let's say interested in cloud computing, then he can take the cloud computing track and take all these cloud computing components as part of the curriculum. If he or she is interested in, >> Yeah let's say big data, we have a big data track within our program. >> And the government is really behind you on this right? >> Yes, yes, The government is behind us in the way that they want students, not just to rely on having to secure a white collar job. They want them to create the jobs for others. They are trying to create this culture of entrepreneurship. So you start your own business, you don't have to wait for opportunities, you make your own opportunities. With the help of, I think Temp Keen, EDP, all of them are giving them the platform to just flourish, to just go into the world and then create opportunities not just for themselves, as I told you, but for others. >> So, final question I want to ask you. Okay, personal opinion, what do you think is going to happen after the Amazon region gets deployed. You're going to get these training classes, people are going to be coming into the marketplace, graduating. What's the impact? What's your vision? >> What's my, I don't know! >> Any guesses? If you had to kind of project and connect the dots. >> I think there's going to be a huge move towards, small business. Because it used to cost a lot, owning a business, or starting a start-up used to cost a lot. Now, it doesn't cost that much if they choose, let's say, for example cloud computing, or if the choose AWS in particular. It's just going to cost them the operational expenditures, there's no huge capital expense that they have to pay. So my projection is that we're going to see a lot of small businesses, small newer apps, and newer ways to go around businesses because of these opportunities offered by... >> Yeah, it lowers the bar to get a new innovation going. And it certainly cost less than provisioning servers. >> Exactly, so if a company wants to start up a business, if it's a small business, they don't have that much time to spend on servers, spend on many things. >> Faisal, thanks for coming on theCUBE, we really appreciate it. >> Thank you very much, thank you for having me. >> We're looking forward to following what's going on in the University when we come back. We'll certainly be back here, >> Thank you very much. in the future covering you guys. It's certainly a lot of action, Dubai right around the corner. This is a new hot area for innovation. For theCUBE, covering our first time here, we're excited. I'm, John Furrier. You can reach me on Twitter @furrier, or find me anywhere online, all my channels are open. Stay with us for exclusive coverage of AWS's new region here in Bahrain, be right back. (upbeat music)

>>Hi, everyone. And thanks for joining us today for our session on the 2020 Data Drivers Award winners. I'm excited to be here today with you. I'm a lease. Bergeron, vice president, product marketing for snowflake. Thes rewards are intended to recognize companies and individuals for using snowflakes, data cloud to drive innovation and impact in their organizations. Before we start our conversations, I want to quickly congratulate all of our award winners. First in the business awards are data driver of the year is Cisco. Our machine learning master is you Nipper, Our data sharing leader is Rakuten. Our data application of the year is observed and our data for good award goes to door dash for the individual and team awards. We first have the cost. Jane, Chief Digital officer of Paccar. We have a militiamen, director of cybersecurity and data science winning our data science Manager of the Year award at Comcast for a date. A pioneer of the year. We have Faisal KP, who's our senior manager of enterprise data Services at Pizza Hut. And lastly, we have our best data team going to McKesson, led by Jimmy Herff Data and Analytics platform leader Huge congratulations to all of these winners. It was very difficult to pick them amongst amazing set of nominations. So now let's dive into our conversations. We'll start with the data driver of the year. Representing Cisco today is Robbie. I'm a month do director data platform, data and analytics. >>Let me welcome everybody to the wonderful. Within a few years before Cisco used to be a company, you know, in making the decisions partly with the data and partly with the cuts. Because, you know, the data is told in multiple places the trading is not done right and things like that. So we, you know, really understood it. You know what was a challenge in the organism? By then we defined the data strategy on we put in a few plants in place, and it is working very well. But what is more important is basically how we provide the data towards data scientists and the data community in Cisco. I'm making them available in a highly available scalable on the elastic platforms. That's where you know, snowflake came into picture really very well for arrest, along with the other data strategies that we have had in place more importantly, data. Democratization was a key. You know, you along with the simplification, something technologies involved in the past. Our clients need to be worrying, laudable the technologies involved, you know, for example, we used to manage her before we make it. Snowflake Andi Snowflake, in a solve all of these problems for us with the ease on it. Really helping enabling a data data given ordinances in our >>system. In the data sharing leaders category, Rockhampton was our winner. We have mark staying trigger VP of analytics here to share their story. I >>wanna thank Snowflake for the award, and it's an honor to be a today. The ease of use of snowflake has allowed projects to move forward innovation to move forward in a way that it simply couldn't have done on old Duke systems or or or other platforms. And I think the truth the same is true for us on a lot of the similar topics, but also in the data sharing space, data sharing is a part off innovation. Like I think, most of the tech companies we work with certainly are business partners, merchants, but also with a range of other service providers and other technology vendors, um on other companies that we strategically share data with 2 May benefit of their service or thio to allow data modeling or advanced data collaboration or strategic business deals using the data and evaluated with the data on. But I think if you look Greece snowflake, you would see a lot of time and effort money going to just establishing that data connection that often involved substantial investments in technology data pipelines, risk evaluation, hashing, encrypt encryption. Security on what we found with snowflakes sharing functionality is that we can not eliminate those concerns, but that the technology just supports the ability to share data securely easily, quickly in a way that we could never do >>previously. Now we have a really inspiring winner of the data for good award door dash with their Project Dash Initiative here to speak about their work is act shot near Engineering manager >>Thank you sports to snowflake for recognizing us for this initiative. Eso For those of you who don't know, Dash, the logistics technology platform company that connects people with the best in their cities and Project Dash, our flagship social impact program, uses the door dash logistics platform to tackle the challenges like hunger and food waste. It was launched in 2018 on over the first two years in partnership with food recovery organizations, we powered the delivery off over £2 million of surplus food from businesses to hunger relief agencies across the U. S. And Canada. Andi simply do Toko with tremendous need has a much we were ableto power. The delivery often estimated 5.8 million meals to food insecure communities and frontline workers across 48 states on the 3.5 million off. These meals have been delivered since much. We do all of our analysis for our business functions from like product development to skills and social impact in snowflake On the numbers I just provided here actually have come from Snowflake on. We have used it to provide various forms of reporting, tow our government and non profit partners on this snowflake. We can help them understand the impact, analyzed friends and ensure complaints in cases where we are supporting efforts for agencies like FEMA, our USDA onda. Lastly, our team is really excited to be recognized by snowflake for using data for good. It has reminded us to continue doubling down on our commitment to using our product and expertise to partner with communities we operated. Thank you again. >>The winner of the machine Learning Master's word is unit for Energy. Viola Sarcoma Data Innovation leader is here on behalf of unit for >>Hello, everyone, Thanks for having me here. It's really a pleasure. And we were really proud to get this award. It means a lot for you. Nipper. It's huge recognition for our effort since last couple of years assed part of our journey and also a celebration off our success now for you. Newport. It would not be possible to start looking at Advanced Analytics techniques, not having a solid data foundation in place. And that's where we invested a lot in our cloud data platform in the cloud back by snowflake. Having this platform allowed us to employ advanced analytics techniques, combining data from Markit from fundamental data, different other sources of data like weather and extracting new friends, new signals that basically help us to partly or even in some cases fully automate some trading strategy. And we believe this will be really fundamental for for the future off raiding in our company and we will definitely invest in this area in the future. >>Our data application of the year is observed. Observers recognizes the most innovative, data driven application built on Snowflake and representing observed today is their CEO, Jeremy Burton. >>Let me just echo the thanks from the other folks on the coal. I mean snowflakes, separation of storage. Compute. I can't overstate what a really big deal it is. Um, it means that we can ingest in store data. Really? For the price of Amazon s three on board, we're in a category where vendors of historically charged for volume of data ingested. So you can imagine this really represents huge savings. Um, in addition, and maybe on a more technical note, snowflakes, elastic architectures really enables us to direct queries appropriately, based on the complexity of the query. So small queries or simple queries weaken director extra small warehouses and complex queries. We can direct, you know, for Excel. Or I think even a six x l is either there are on its way. The key thing there is that users they're not sitting around waiting for results to appear regardless of the query complexity. So I mean, really? The separation storage compute on the elastic architectures is a really big deal for us. >>Turning to the data Pioneer of the Year Award, I'm excited to be here with Faisal KP, senior manager of Enterprise Data Services from Pizza Hut. >>First of all, thank you, Snowflake, for giving this wonderful person. I think it means a lot for us in terms of validating what we're doing. I think we were one of the earlier adopters of Snowflake. We saw the vision of snowflake, you know, stories. Russell's computer separation on all the goodies, right? Right from back in 2017, I believe what snowflake enabled us is to actually get the scale with very little manpower, which is needed to man the entire system. So on the Super Bowl day, we have, you know, the entire crew literally a boardroom where the right from the CME, most of the CEOs to all the folks will be sitting and watching what is happening in the system. And we have to do a lot of real time analytics during that time. So with snowflake, you know, way used the elasticity of the platform we use, you know, platform you know their solutions, like snow pipe to basically automate the data ingestion coming through various channels, from the commas, from the stores, everything simultaneously. So as soon as the program is done, you know, we can scale scale down to our normal volume, which means we can, you know, way can save a lot. Of course. So definitely it snowflake has been game changer for us in terms of how we provide real time analytics. Our systems are used by thousands off restaurants throughout the country and, you know, by hundreds of franchisees. So the scale is something we have achieved with a lot of ability and success. >>In the category of data science Manager of the Year Award, we have a mission Min, director of cybersecurity and data science at Comcast. >>So thank you for having me and thank you for this wonderful award. So one of the biggest challenges you see in this other security spaces the tremendous amount of data that we have to compute every day to find the gold haystack. So one of the big challenges we overcame with by uniting snowflake was how do we go from like my other counterparts on the panel have said Theo operational overhead of maintaining a large data store and moved to more of results driven and data focused environment. And, you know, part of that journey was really the tremendous leadership. Comcast saying, You know, we want Thio through our day to day lives by relying less on operational work and Maura on answering questions. And so you know, over the last year we've really put Snowflake at the center of our ecosystem, knowing that it's elastic platform and its ability scale infinitely have given us the ability to dream big and use it to drop five cybersecurity. And while it's traditionally used for cybersecurity, we're starting to see the benefits right away and the beauty of the snowflake. Ecos, Miss. We're now able to enable folks that not traditionally have big data skills, but they have standards, sequel skills, and they could still work in the snowflake platform. So, you know, the transition to cloud has been very powerful for us as an organization. But I think the end story, the real takeaways, by moving our secretary operation to the cloud, we're now been able to enable more people and get the results they were looking for. You know, as other people have said fast, people hate to wait. So the scale of snowflake really shines. >>Yeah. Now, let's hear from our data Executive of the year. The Cost. Jane. Chief Digital Officer Packer. >>Thank you very much, Snowflake, for this really incredible recognition and honor of the work we're doing it back. Are we began. The first step in this process was for us to develop an enterprise Great data platform in the cloud capable off managing every aspect of data at scale. This this platform includes snowflake as our analytics data warehouse amongst many other technologies that we used for ingestion of data, data processing, uh, data governance, transactional, uh, needs and others. So this platform, once developed, has really helped us leverage data across the broad pack. Our systems and applications globally very efficiently and is enabling pack are, as a result to enhance every aspect. Selfish business with data. >>Ah, big congratulations again to all of the winners of the 2020 Data Drivers Awards. Thanks so much for joining us for a great conversation. And we hope that you enjoy the rest of the data cloud summit