>> Announcer: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Partner Awards. Brought to you by Amazon Web Services. >> Hi friend, welcome to this CUBE coverage of AWS Public Sector Partner Awards Program. I'm John Furrier your host of theCUBE. We've two great guests here, Travis Hartman Director of Analytics and Weather at Maxar Technologies, and Vijay Tallapragada who's the Chief Modeling and Data Assimulation Branch at NOAH. Tell us about the success of this. What's the big deal? Take us through the award and why Maxar. What do you guys do? >> Yeah, so Maxar is an organization that does a lot of different activities in earth intelligence as well as space. We have about 4,000 employees around the world. One side of the economy works on space infrastructure actually building satellites, and all the infrastructure that's going to help get us back to the moon, and things like that, and then on the other side we have an earth intelligence group which is where I sit, and we leverage remote sensing information, earth science information to help people better understand how and what they do might impact the earth, or how the earth, in its activities, might impact their business mission or operations. So what we wanted to set out to do is help people better understand how weather could impact their mission, businesses, or operations. A big element of that was doing it with speed. So we knew NOAH had capabilities of running numerical weather prediction models and very traditional on-prem, big, beefy, high performance supercomputers, but we wanted to do it in the cloud. We wanted to use AWS as a key partner. So we collaborated with Vijay and NOAH and his teams there to help pull that off. They gave us access, public domain information but they showed us the right places to look. We've had some of our research scientists talkin' and yeah, after a pretty short effort, it didn't take a lot of time, we were able to pull something off a lot of people didn't think was possible. And we got pretty excited once we saw some of the outcomes. >> Travis, Vijay was just mentioning the relationship. Can you talk about the relationship together? Because this is not your classic Amazon Partner client relationship that you have. You guys have been partnering together, Vijay and your team, with AWS. Talk about the relationship and how Amazon played because it's a unique partnership. Explain in more detail, that specific relationship. >> Yeah, with Maxar and AWS, our partnership has gone back a number of years. Maxar being a fairly large organization, there's lots of different activities. I think Maxar was the first client of AWS Snowmobile where they had the big tractor trailer backed up to a data center, load all the data in, and then take it to an AWS data center. We were the first users of that 'cause we had over a hundred petabytes of satellite imagery in an archive that just movin' it across the internet it'd probably still be goin'. So the Snowmobile was a good success story for us but just with the amount of data that we have, the amount of data we collect every day, and all the analytics that we're running on it, whether it's in an HPC environment or the scalable AIML, we're able to scale out that architecture, scale out the compute, the much easier dynamic and really cost-effective way with AWS 'cause when we don't need to use the machines, we turn 'em off. We don't have a big data center sittin' somewhere where we have to have security, have all the overhead costs of just keeping the lights on, literally. AWS allows us to run our organization in a much more efficient way. And NOAH, they're seeing some of that same success story that we're seeing, as far as how they could use the cloud for accelerating research, accelerating how the advancement of numerical weather prediction from the United States can benefit from cloud, from cloud architecture, cloud compute, and things like that. And I think a lot of the stuff that we've done here at Maxar, with our HPC solution in the cloud is something that's pretty interesting to NOAH and it's a good opportunity for us to continue our collaboration. >> If I could drill down on that solution architecture for a minute, how did you guys set up the services and what lessons did you learn from that process? >> We're still learnin' is probably the short answer, but it all started with our people. We have some really strong engineers, really strong data scientists that fundamentally have a background in meteorology or atmospheric science, so they understand the physics of, you know, why the wind blows the way it does and why clouds do what clouds do. But we also, having a key strategic partnership with AWS, we were able to tap into some of their subject-matter experts, and we really put those people together and come up with new solutions and new, innovative ideas, stuff that people hadn't tried before. We were able to steer a little bit of AWS's product roadmap as far as what we were tryin' to do and how their current technology might not have been able to support it, but by interacting with us, gave them some ideas as far as what the tech had to move towards, and then that's what allowed us to move in a pretty quick fashion. It's neat stuff, technology, but it really comes down to the people. I feel very honored and privileged to work with both great people here, at Maxar, as well as AWS, as well as bein' able to collaborate with the great teams at NOAH. It's been a lot of fun. >> Well Travis, got a great example, I think it's a template that can be applied to many other areas, certainly even beyond. You've got a large scale, multi-scale situation, there. Congratulations. Final question, what does it mean to be an award winner for AWS Partner Awards? As part of the show, you're the best-in-show for HPC. What's it like? What's the feeling? Give is a quick stub from the field. >> Yeah, I mean, I don't know if there's really a lot of good words that can kind of sum it up. I shared the news with the team last night and you know, there were a lot of, lot of good responses that came from it. A lot of people think it's cool, and at the end of the day, a lot of people on our team took a hobby or a passion of weather and turned it into a career. And being acknowledged and recognized by groups like AWS for best solution in a particular thing, I think we take a lot of that to heart and we're very honored and proud of what we're able to do and proud that other people recognize the neat stuff that we're doin'. >> Well, certainly takin' advantage of the cloud which is large scale, but you're on a great wave, you've got a great area. I mean, weather, you talk about weather, it's exciting, dynamic, it's always changing, it's big data, it's large scale. So you got a lot of problems to solve and a lot of impact too, when you get it right. So congratulations on an excellent-- >> Thank you very much. >> Great mission. >> Thank you. >> Love what you do, love to followup again and maybe do another interview, and talk about the impact of weather and all the HPC kind of down the road. Travis, thank you very much. >> Thank you, appreciate it. >> Good to see you. >> Thank you, glad to be here. >> So NOAH, National Oceanic Atmospheric Administration, National Weather Center, National Center for Environmental Predictions, Environmental Modeling Center, that's your organization. You guys are competing to be the best in the world. Tell us what you guys do at a high level, then we'll jump into some of the successes. >> So the National Weather Service is responsible for providing weather forecasts to save lives and property, and improve the economy of the nation. And as part of that, the National Weather Service is responsible for providing data and also the forecast to the public and to the industry. We are responsible for providing the guidance on how they create the forecasts. So we are, at the Environmental Modeling Center, the nation's finest institute in advancing our numerical weather prediction modeling, government, and a nucleation of all the data that's available from the world to initialize our models and provide the future state of the atmosphere from hours all the way to seasons and years. And that's the kind of the range of products that we download and provide. Our key for managing the emergency of services and hazard management and mitigation, and also improve in the nation's economy by preparing well in advance, for the future events. And it's a science-based organization and we have world-class scientists working in this organization. I manage about 170 of them at the Environmental Modeling Center. They're all PhDs from various disciplines, mostly from meteorology, atmospheric sciences, oceanography, land surface modeling, space weather, all weather-related areas, and the mathematics and computer science. And we are at the stage where we are probably the most doubled up, advanced modeling center that we use almost all possible computational services available in the world, so this is heavily computational in terms of use of data, use of computers, use of all the power that we can get, and we have a 3.5 protoflop machine that we use to provide these weather forecasts. And they provide these services every hour for some census like we see the weather outbreaks and for every three hours for hurricanes, and for every six hours for the regular weather like precipitation, the temperature forecasts. So all the data that you see coming out from either the public media or the government agencies, they all are originated in our center and disseminated in various forms. And I think NOAH is the only center in the world that provides all this information free of cost. So it is a public service organization and we pride in our service to the society. >> Well, I love your title, Chief Modeling and Data Assimulation title, branch over all these organizations. This is, weather's critical. I want to get your thoughts 'cause we were talking before you came on about how the hurricane Katrina was something that really kind of forced everyone to kind of rethink things. Weather is an evolving system so it's always changing. Either there's a catastrophe or something happens, or you're trying to be proactive, predicting say, whether it's a fire season in California, all kinds of things goin' on. It's always hard to get a certain prediction. You have big jobs, there's a lot of data, you need horsepower, you need computing, you need to stand up some HPC. Take us through the thinking around the organization and what's the impact that you see, because weather does have that impact. >> So traditionally, you know, as you mentioned there are various weather phenomena that you described like the fiber of the hurricanes, the heavy precipitation, the flooding, so we download solutions for individual weather phenomena. And we have grown in that direction by downloading separate solutions for separate problems. And very soon, it became obvious that we cannot manage all these independent modeling systems to provide the best possible forecasts. So the thinking had to be changed. And then there is another bigger problem is that there's a lot of research going out in the community, like the academic institutes, the universities, other government labs. There are several people working in these areas and all their work is not necessarily a coordinated government act duty, that we cannot take advantage, and there are no incentives for people to come and contribute towards the mission that we are engaged in. So that actually prompted to change the direction of thinking, and as you mentioned, hurricane Katrina was an eye-opener. We have the best forecasts, but the dissemination of that information was not probably accurate enough, and also there is a lot of room for improvement in predicting these catastrophic events. >> How are you guys using AWS? Because HPC, high performance computing, I mean, you can't ask for more resources than the massive cloud that is Amazon. How has that helped you? Can you take a minute to explain, walk us through AWS partnership? >> There are a few examples I can cite, but before then, I would really like to appreciate Travis Hartman from Maxar who is probably the only private sector partner that we had in the beginning. And now, we are expanding on that. So we were able to share our immunity cords with Maxar and with our help, they were able to establish this entire modeling system as it is done in operations at NOAH. They were able to reproduce our operational forecasts using the cloud resources and then they went ahead and did even more by scaling the modeling systems as they can run even faster and quicker than what NOAH operations can do. So that gives you one example of how the cloud can be used. You know, the same forecast that we produce globally, which will take about eight minutes per day, and Maxar was able to do it much faster, like 50% improvement in the efficiency of the cords. And now, the one advantage of this is that the improvements that Maxar or other collaborators are using our cords, that they're putting into the system, are coming back to us. So we take advantage of that in improving the efficiency in operations. So this like a win-win situation for both of what part is fitting in the R&D and what using in operations. And on top of it, you can create multiple conflagrations of this model in various instances on the cloud where you can run it more efficiently and you can create an ensemble of solutions that can be catered to individual needs. And the one additional thing I wanted to mention about the user cloud is that this is like when you have a need, you can surge the compute, you can instantiate thousands of simulations to test a new innovation, for instance. You don't need to wait for the resources to be done in sequential manner. Instead, you can ramp up the production of these equipments in no time, and without worrying about, of course, the cost is a factor that we need to worry about, but otherwise the capacity is there, the facilities are there to take advantage of the cloud solutions. >> Well Vijay, I'm very impressed with your organization. I'd love to do a followup with you. I love the impact that you're doing. Certainly, the weather impacts society from forecasting disasters and giving people the ability to look at supply chain, whether it's planning for potentially a fire season or a water shortage, or anything goin' on, there. But also it's a template. You are succeeding a new kind of way to innovate with community, with large scale, multi-scale data points, so congratulations. >> Thank you. >> Thank you very much. I'm John Furrier here, part of AWS Partner Awards Program, best HPC solution. Great example, great use case, great conversation. Thanks for watching. Two great interviews here, as part of AWS Public Sector Partner Awards Program. I'm John Furrier. The best-in-show for HPC solutions, Travis Hartman, Maxar Technologies, and Vijay Tallapragada at NOAH, two great guests. Thanks for watching. (soft electronic music)

>>from around the globe. It's >>the Cube with digital coverage of >>AWS Public Sector Partner Awards >>brought to you by >>Amazon Web services. Everyone, welcome to this cube coverage of AWS Public Sector Partner Awards program. I'm John Furrow, your host of the Cube with two great guests here. Travis Department director of analytics and Weather at Max. Our technologies and VJ teleplay Gotta Who's the chief? Modeling and data a simulation branch at Noah. Tell us about the success of this. What's the big deal? Take us through the award and why Max are what you guys do. >>Yeah, so Macs are is an organization. Does a lot of different activities unearth intelligence as well as space? We have about 4000 employees around the world. One side of the economy works on space infrastructure, actually building satellites on all the infrastructure that's going to help us get us back to the moon and things like that. And then on the other side we have a north of intelligence group, which is where, I said, and we leverage remote sensing information for science information to help people better understand how, how and what they do might impact the Earth or have the earth, and it's activities might impact their business mission. Our operation. So what we wanted to set out to do was help people better understand how weather could impact their mission, business or operations. And a big element of that was doing it with speed. Ah, so we we knew? No. I had capabilities running America weather prediction models and very traditional on Prem. Big, beefy ah, high performance compute supercomputers. But we wanted to do it in The cloud we want to do is AWS is a key part. So we collaborated with B. J and Noah and his team is there to help pull that off. They gave this access public domain information, but they showed us the right places to look. We've had some of the research scientists talking, and after pretty short effort, it didn't take a lot of time. We were able to pull something off that a lot of people didn't think was possible. I'm we got pretty excited. Once we saw some of the outcome >>Travis to be, Jay was just mentioning the relationship. Can you talk about the relationship together because this is not your classic Amazon partner client relationship that you have. You guys have been partnering together V. J and your team with AWS. Talk about the relationship and that and how Amazon plays because it's a unique partnership plane in more detail at specific relationship. >>Yeah, with Max or in AWS. You know, our partnership has gone back A number of years on Macs are being a fairly large organization. There's lots of different activities. I think Max Star was the first client of AWS Snowmobile, where they have the big tractor trailer back up to a data center, load all the data in and then take it to an AWS data center. We were the first users of that because we had over 100 petabytes of satellite imagery and archive that just moving across the Internet would probably still be going. Um, so the snowmobile is a good success story for us, but just with >>the >>amount of data that we have, the amount of data we collect every day and all the analytics that we're running on it, whether it's in an HPC environment or, you know, the scalable Ai ml were able to scale out that architecture scale out that compute the much easier, dynamic and really cost effective way with AWS, because when we don't need to use the machines, we turn them off. We don't have a big data center sitting somewhere. We have to have security, have all the overhead costs of just keeping the lights on. Literally. AWS allows us to run our organization and a much more efficient way. Um and Noah, you know, they're They're seeing some of that same success story that we're seeing as far as how they can use the cloud for accelerating research, accelerating how the advancement of numerical weather prediction from the United States can benefit from cloud from cloud architecture, cloud computer, things like that. And I think a lot of the stuff that we've done here, Max our with our HPC HPC solution in the cloud. It's something that's pretty interesting to know, and it's it's a good opportunity for us to continue our collaboration. >>If I could drill down on that solution architecture for a minute. How did you guys set up the services, and what lessons did you learn from that process? >>We're still learning. It was probably the the short answer, but it all started with our people. Uh, you know, we have some really strong engineers, really strong data scientists that fundamentally have a background in meteorology or atmospheric science, you know? So they understand the physics. So you know why the wind blows is the way it doesn't. Why Cloud's doing clouds to do, Um, but we also having a key strategic partnership with AWS. We really have to tap into some of their subject matter experts. And we really put those people together, you know, and come up with new solutions, new innovative ideas, stuff that people hadn't tried before. We're able to steer a little bit of AWS is product roadmap for is what we were trying to do and how their current technology might not have been able to support it. But by interacting with us gave them some ideas as far as what the tech had to move towards. And then that's that's what allowed us to move pretty quick fashion. Um, you know, it's it's neat stuff technology, but it really comes down to the people. Um, and I feel very honored and privileged to work with both great people here. Attacks are as well as aws, um, as well as being able to collaborate with your great teams. That power, it's been a lot of fun. Well, >>Travis gonna create example? I think it's a template that could be applied to many other areas, certainly even beyond. You've got large scale, multi scale situation there. Congratulations. Final question. What does it mean to be an award winner for AWS Partner Awards as part of the show? You're the best in show for HPC. What's it like? What's the feeling? Give us a quick side from the field? >>Yeah. I mean, I don't know if there's really a lot of good words that kind of sum it up. It's Ah, I shared the news with the team last night, and you know, there are a lot of a lot of good responses that came from a lot of people think it's cool. And at the end of the day, a lot of people on our team, you know, took a hobby or a passion of weather and turned it into a career. Ah, and being acknowledged and recognized by groups like AWS for best solution in a particular thing. Um, I think we take a lot of that to heart. And, ah, we're very honored and proud of what we were able to do and proud that other people recognize the need stuff that we're doing well, >>Certainly taking advantage. The cloud, which is large scale. But you you're on a great wave. You've got a great area. I mean, whether you talk about whether it's exciting, it's dynamic. It's always changing. It's big data. It's large scale. So you get a lot of problems to solve in a lot of impact to get it right. So congratulations on ECs. >>Thank you very much. Great mission. Thank you. >>Love what you do love to follow up again. Maybe do another interview and talk about the impact of weather and all the HPC kind of down the road. But, Travis, thank you very much. >>Thank you. Appreciate it. >>Good to see you. >>Thank you. Good to be here. >>So Noah, National Oceanic Atmospheric Administration, National Weather Center, National Center for Environmental Predictions, Environmental Modeling Center year. That's your organization? You guys are competing to be best in the world. Tell us what you guys do at a high level. Then we'll jump into some of the successes. >>So the national Weather Service is responsible for providing weather forecast to save lives and property and improve the economy of the nation. And that's part of that. That the national weather services responsible for providing data and also the forecasts to the public and the industry and be responsible for providing the guidance on how they create the forecasts. So we are at the Environmental Modeling Center, uh, the nation's finest institute in advancing our numerical weather prediction modelling development, and you play it off all the data that's available from the world to initialize our models and provide the future state of the atmosphere from hours all the way to seasons and years. That's that's the kind of a range of products that we don't lock and provide are our key for managing the emergency services and patch it management and mitigation and also improving the nation's economy by preparing well in advance for the future events. And it's it's a science based organization, and we have ah well class scientists working in this organization. I manage about 170 of them at the moment of modeling center. They're all PhDs from various disciplines, mostly from meteorology, atmospheric sciences, oceanography, land surface modelling space weather, all weather related areas and the mathematics and computer science. And we are at the stage where we are probably the most. Uh huh. Most developed, uh, advanced modelling center that we use almost all possible computational resources available in the world. So this is a really computational in terms of user data, user computer seems off. Uh, all the power that we can get and we have a 3.5 petaflop machine that we use to provide these weather forecasts, and they provide the services every hour. For some sense is like the CDO rather our rates for every three hours for hurricanes and for every six hours for the regular, Rather like the participation, uh, the temperature forecast. So all the data that you see coming out from either the public media, our department agencies, they are originated in our center and disseminated in various forms. I think no one is the only center in the world that provides all this information for your past. So it is, ah, public service organization and we riding on a visa with society. >>We'll I love your title, Chief modeling and data, a simulation title branch of a lot of these organizations. This >>is >>whether it's ever critical. I want to get your thoughts cause we were talking before we came on about how the Hurricane Katrina was something that really kind of forcing you to rethink things. Whether it is an evolving system, it's always changing. Either the catastrophe or something happens. Were you trying to proactive predicting, say, whether it's a fire season in California, all kinds of things going on that's not It's always hard to get a certain prediction. You have big job. It's a lot of data you need. Horsepower need computing. You need to stand up. Some HPC take us through like like the thinking around the organization. And what was The impact is that you see, because whether does have that impact. >>So traditionally, you know, as you mentioned, there are radius weather phenomenon that you describe like the five rather the Americans, every presentation, the flooding. So we developed solutions for individual weather phenomena, and, uh, we have grown in that direction by developing separate solutions for separate problems. And very soon it became obvious that we cannot manage all these independent modeling systems to provide the best possible forecasts. So the thinking has to be changed. And then there is Another big problem is that there's a lot of research going out in the community like the academic institutes, the universities, other government labs. There are several people working in these areas, and all their work is not necessarily a coordinated, uh, development activity that we cannot take advantage. And they have no incentive for people to come and contribute towards the mission that we are engaged in. So that actually prompted to change the direction of thinking. And as you mentioned, Hurricane Katrina was an eye opener. We had the best forecasts, but the dissemination of that information waas not probably accurate enough, and also there is a lot of room for improvement in predicting these catastrophic events. How are >>you guys using AWS? Because HPC high performance computing I mean you can't ask for more resources in the massive cloud that is Amazon. How is that help to you? Can you take a minute to explain, but walk us through? >>What? >>Aws? There >>are a few example. Second site. But before then, I would like to really appreciate a Travis Hartman from Max. Are you know who is probably the only private sector partner that we had in the beginning. And now we're expanding on. That s so we were able to share our community. Cores with Max are and without how they were able to establish this and drive modeling system as it is done in operations that Noah and they were able to reproduce operational forecast using the cloud resources. And then they went ahead and did even more by scaling the modeling systems is that it can run even faster and quicker them are what insert no operations can do. So that gives us one example of how the cloud can be used. You know, the same forecast that we produce, ah, globally, which will take about eight minutes per day. And, uh, Max I was able to do it much faster, like 50% improvement and in the efficiency of the colors. And now the one piece of this is that the improvements that matter are other collaborators are using, or cords that they're putting into the system are coming back to us. So we take advantage of that, improving the efficiency in operations. So this is that this is like a win win situation for both, uh, who are participating in the R and D on who are using it in operations, and on top of it, you can create multiple configurations of this model in various instances on the cloud when you can run it more efficiently and you can create an ensemble of solutions that can be captured toe individual needs. And the one additional thing I want to mention about User Cloud is, is that you know, this is like when you have a need, you can search the compute you can. Instead she 8000 sub simulations to test a new innovation. For instance, you don't need to wait for the resources to be done in a sequential manner. Instead, you can ramp up the production off these apartments in no kind and without Don't worry about. Of course, the cost is the fact that we need to worry about, but otherwise the capacity is there. The facilities are reacting to take advantage of the cloud solutions. If I'm a >>computer scientist person, I'm working on a project. Now I have all this goodness in the cloud, how's morale been and what's the reaction been like from from people doing the work. Because usually the bottleneck has been like I gotta provision resource. I gotta send a procurement request for some servers or I want to really push some load. And right now, I got a critical juncture. I mean, it's got a push morale up a bit, and you talk about the impact to the psychology of the people in your organization. >>Um, I haven't. I have two answers to this question. One from a scientist perspective like me. You know, I was not a computer scientist from the beginning, but I became a software engineer, kind of because I have to work with these software and hardware stuff more more on solving the computational problems than the critical problems. So people like us who have invested their careers in improving the science, they were not care whether it's ah, uh hbc on premise Cloud, what will be delighted to have, uh, resources available alleviate that they can drive. But on the other hand, the computer computational engineers are software engineers who are entering into this field. I think they are probably the most excited because of these emerging opportunities. And so there is a kind of a friction between the scientific and the computational aspects off personnel, I would say. But that difference is slowly raising on and we are working together as never before. So the collective moral is very high to take advantage of these resources and opportunities. I think way of making the we're going in the right direction. >>It's so much faster. I mean, in the old days, you write a paper, you got to get some traction. Gonna do a pilot now It's like you run an experiment, get it out there. VJ I'm very impressed with the organization. Love to do a follow up with you. I love the impact that you're doing certainly in the weather impact society from forecasting disasters and giving people the ability to look at supply chain, whether it's providing for potentially a fire season or water shortage or anything going on there. But also it's a template. You're exceeding a new kind of waiting to innovate with community with large scale, multi scale data points. So congratulations and >>thank you. >>Thank you very much. I'm John Furrier here part of AWS partner Awards program. Best HPC solution. Great. Great Example. Great use case. Great conversation. Thanks for watching two great interviews. Here is part of AWS Public Sector Partner Awards program. I'm John Furrier. The best in show for HPC Solutions. China's Hartman Max, our technologies and Vijay tell Apartado at Noah. Two great guests. Thanks for watching. Yeah, Yeah, yeah, yeah, yeah, yeah