>> Announcer: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Partner Awards Brought to you by Amazon Web Services. >> All right, welcome back to "the Cube's" coverage here from Palo Alto, California in our studio with remote interviews during this time of COVID-19 with our quarantine crew. I'm John Furrier, your host of "the Cube" and we have here the award winners for the best EDU solution from NorthBay Solutions, Jim Keller, the president and from Harvard Business Publishing and the University of Pittsburgh, Chad Burton, PhD and Data Privacy Officer of University of Pittsburgh IT. Thanks for coming on gentlemen, appreciate it. >> Thank you. >> So, Jim, we'll start with you. What is the solution that you guys had got the award for? And talk about how it all came about. >> Yeah, thank you for asking and it's been a pleasure working with Chad and the entire UPitt team. So as we entered this whole COVID situation, our team really got together and started to think about how we could help AWS customers continue their journey with AWS, but also appreciate the fact that everyone was virtual, that budgets were very tight, but nonetheless, the priorities remained the same. So we devised a solution which we called jam sessions, AWS jam sessions, and the whole principle behind the notion is that many customers go through AWS training and AWS has a number of other offerings, immersion days and boot camps and other things, but we felt it was really important that we brought forth a solution that enables customers to focus on a use case, but do it rapidly in a very concentrated way with our expert team. So we formulated what we call jam sessions, which are essentially very focused two week engagements, rapid prototyping engagements. So in the context of Chad and UPitt team, it was around a data lake and they had been, and Chad will certainly speak to this in much more detail, but the whole notion here was how does a customer get started? How does, a customer prove the efficacy of AWS, prove that they can get data out of their on premises systems, get it into AWS, make it accessible in the form, in this case, a data lake solution and have the data be consumable. So we have an entire construct that we use which includes structured education, virtual simultaneous rooms where development occurs with our joint rep prototyping teams. We come back again and do learnings, and we do all of this in the construct of the agile framework, and ideally by the time we're done with the two weeks, the customer achieves some success around achieving the goal of the jam session. But more importantly, their team members have learned a lot about AWS with hands on work, real work, learn by doing, if you will, and really marry those two concepts of education and doing, and come out of that with an opportunity then to think about the next step in that journey, which in this case would be the implementation of a data lake in a full scale project kind of initiative. >> Chad, talk about the relationship with NorthBay Solutions. Obviously you're a customer, you guys are partnering on this, so it's kind of you're partnering, but also they're helping you. Talk about the relationship and how the interactions went. >> Yeah, so I would say the challenge that I think a lot of people in my role are faced with where the demand for data is increasing and demand for more variety of data. And I'm faced with a lot of aging on premise hardware that I really don't want to invest any further in. So I know the cloud's in the future, but we are so new with the cloud that we don't even know what we don't know. So we had zeroed in on AWS and I was talking with them and I made it very clear. I said "Because of our inexperience, we have talented data engineers, but they don't have this type of experience, but I'm confident they can learn." So what I'm looking for is a partner who can help us not only prove this out that it can work, which I had high confidence that it could, but help us identify where we need to be putting our skilling up. You know, what gaps do we have? And AWS has just so many different components that we also needed help just zeroing in on for our need, what are the pieces we should really be paying attention to and developing those skills. So we got introduced to NorthBay and they introduced us to the idea of the jam session, which was perfect. It was really exactly what I was looking for. We made it very clear in the early conversations that this would be side by side development, that my priority was of course, to meet our deliverables, but also for my team to learn how to use some of this and learn what they need to dive deeper in at the end of the engagement. I think that's how it got started and then I think it was very successful engagement after that. >> Talk about the jam sessions, because I love this. First of all, this is in line with what we're seeing in the marketplace with rapid innovation, now more than ever with virtual workforces at home, given the situation. You know, rapid agile, rapid innovation, rapid development is a key kind of thing. What is a jam session? What was the approach? Jim you laid a little bit about it out, but Chad, what's your take on the jam sessions? How does it all work? >> I mean, it was great, because of large teams that NorthBay brought and the variety of skills they brought, and then they just had a playbook that worked. They broke us up into different groups, from the people who'd be making the data pipeline, to the people who then would be consuming it to develop analytics projects. So that part worked really well, and yes, this rapid iterative development. Like right now with our current kind of process and our current tool, I have a hard time telling anybody how long it will take to get that new data source online and available to our data analysts, to our data scientists, because it takes months sometimes and nobody wants that answer and I don't want to be giving that answer, so what we're really focused on is how do we tighten up our process? How do we select the right tools so that we can say, "We'll be two weeks from start to finish" and you'll be able to make those data available. So the engagement with NorthBay, the jam session scheduled like that really helped us prove that once you have the skills and you have the right people, you can do this rapid development and bring more value to our business more quickly, which is really what it's all about for us. >> Jim, I'll get your thoughts because, you know, we see time and time again with the use cases with the cloud, when you got smart people, certainly people who play with data and work with data, They're pretty savvy, right? They know limitations, but when you get the cloud, it's like if a car versus a horse, right? Got to go from point A to point B, but again, the faster is the key. How did you put this all together and what were the key learnings? >> Yeah, so John, a couple of things that are really important. One is, as Chad mentioned, really smart people on the U-PIT side that wanted to really learn and had a thirst for learning. And then couple that with the thing that they're trying to learn in an actual use case that we're trying to jointly implement. A couple of things that we've learned that are really important. One is although we have structure and we have a syllabi and we have sort of a pattern of execution, we can never lose sight of the fact that every customer is different. Every team member is different. And in fact, Chad, in this case had team members, some had more skills on AWS than others. So we had to be sensitive to that. So what we did was we sort of used our general formula for the two weeks. Week one is very structured, focused on getting folks up to speed and normalize in terms of where they are in their education of AWS, the solution we're building and then week two is really meant to sort of mold the clay together and really take this solution that we're trying to execute around and tailor it to the customer so that we're addressing the specific needs, both from their team member perspective and the institution's perspective in total. We've learned that starting the day together and ending the day with a recap of that day is really important in terms of ensuring that everyone's on the same page, that they have commonality of knowledge and then when we're addressing any concerns. You know, this stuff we move fast, right? Two weeks is not a long time to get a lot of rapid prototyping done, so if there is anxiety, or folks feel like they're falling behind, we want to make sure we knew that, we wanted to address that quickly, either that evening, or the next morning, recalibrate and then continue. The other thing that we've learned is that, and Chad and entire U-Pit team did a phenomenal job with this, was really preparation. So we have a set of preliminary set of activities that we work with our customers to sort of lay the foundation for, so that on day one of the jam session, we're ready to go. And since we're doing this virtually, we don't have the luxury of being in a physical room and having time to sort of get acclimated to the physical construct of organizing rooms and chairs and tables and all that. We're doing all that virtually. So Chad and the team were tremendous in getting all the preparatory work done Thinking about what's involved in a data lake, it's the data and security and access and things our team needed to work with their team and the prescription and the formula that we use is really three critical things. One is our team members have to be adept at educating on a virtual whiteboard, in this case. Secondly, we want to do side by side development. That's the whole goal and we want team members to build trust and relationships side by side. And then thirdly, and importantly, we want to be able to do over the shoulder mentoring, so that as Chad's team members were executing, we could guide them as we go. And really those three ingredients were really key. >> Chad, talk about the data lake and the outcome as you guys went through this. What was the results of the data Lake? How did it all turn out? >> Yeah, the result was great. It was exactly what we were looking for. The way I had structured the engagement and working with Jim to do this is I wanted to accomplish two things. I wanted to one, prove that we can do what we do today with a star schema mart model that creates a lot of reports that are important to the business, but doesn't really help us grow in our use of data. So there was a second component of it that I said, I want to show how we do something new and different that we can't do with our existing tools, so that I can go back to our executive leadership and say "Hey, by investing in this, here's all the possibilities we can do and we've got proof that we can do it." So some natural language processing was one of those and leveraging AWS comprehend was key. And the idea here was there are, unfortunately, it's not as relevant today with COVID, but there are events happening all around campus and how do students find the right events for them? You know, they're all in the calendar. Well, with a price of natural language processing using AWS comprehend and link them to a student's major, so that we can then bubble these up to a student "Hey, do you know of all these thousands of events here are the 10 you might be most interested in." We can't do that right now, but using these tools, using the skills that that NorthBay helped us develop by working side by side will help us get there. >> A beautiful thing is with these jam sessions, once you get some success, you go for the next one. This sounds like another jam session opportunity to go in there and do the virtual version. As the fall comes up, you have the new reality. And this is really kind of what I like about the story is you guys did the jam session, first of all, great project, but right in the middle of this new shift of virtual, so it's very interesting. So I want to get your thoughts, Chad, as you guys looked at this, I mean on any given Sunday, this is a great project, right? You can get people together, you go to the cloud, get more agile, get the proof points, show it, double down on it, playbook, check. But now you've got the virtual workforce. How did that all play out? Anything surprise you? Any expectations that were met, or things that were new that came out of this? 'Cause this is something that is everyone is going through right now. How do I come out of this, or deal with current COVID as it evolves? And then when I come out of it, I want to have a growth strategy, I want to have a team that's deploying and building. What's your take on that? >> Yeah, it's a good question and I was a little concerned about it at first, because when we had first begun conversations with NorthBay, we were planning on a little bit on site and a little bit virtual. Then of course COVID happened. Our campus is closed, nobody's permitted to be there and so we had to just pivot to a hundred percent virtual. I have to say, I didn't notice any problems with it. It didn't impede our progress. It didn't impede our communication. I think the playbook that NorthBay had really just worked for that. Now they may have had to adjust it and Jim can certainly talk to that, But those morning stand-ups for each group that's working, the end of day report outs, right? Those were the things I was joining in on I wasn't involved in it throughout the day, but I wanted to check in at the end of the day to make sure things are kind of moving along and the communication, the transparency that was provided was key, and because of that transparency and that kind of schedule they already had set up at North Bay, We didn't have any problems having it a fully virtual engagement. In fact, I would probably prefer to do virtual engagements moving forward because we can cut down on travel costs for everybody. >> You know, Jim, I want to get your thoughts on this, 'cause I think this is a huge point that's not just represented here and illustrated with the example of the success of the EDU solution you guys got the award for, but in a way COVID exposes all the people that have been relying on waterfall based processes. You've got to be in a room and argue things out, or have meetings set up. It takes a lot of time and when you have a virtual space and an agile process, yeah you make some adjustments, but if you're already agile, it doesn't really impact too much. Can you share your thoughts because you deployed this very successfully virtually. >> Yeah, it's certainly, you know, the key is always preparation and our team did a phenomenal job at making sure that we could deliver equal to, or better than, virtual experience than we could an on-site experience, but John you're absolutely right. What it forces you to really do is think about all the things that come natural when you're in a physical room together, but you can't take for granted virtually. Even interpersonal relationships and how those are built and the trust that's built. As much as this is a technical solution and as much as the teams did really phenomenal AWS work, foundationally it all comes down to trust and as Chad said, transparency. And it's often hard to build that into a virtual experience. So part of that preparatory work that I mentioned, we actually spend time doing that and we spent time with Chad and other team members, understanding each of their team members and understanding their strengths, understanding where they were in the education journey and the experiential journey, a little bit about them personally. So I think the reality in the in the short and near term is that everything's going to be virtual. NorthBay delivers much of their large scale projects virtually now. We have a whole methodology around that and it's proven actually it's made us better at what we do quite frankly. >> Yeah it definitely puts the pressure on getting the job done and focusing on the creativity in the building out. I want to ask you guys both the same question on this next round, because I think it's super important as people see the reality of cloud and this certainly has been around, the benefits of there, but still you have the mentality of "we have to do it ourselves", "not invented here", "It's a managed service", "It's security". There's plenty of objections. If you really want to avoid cloud, you can come up with something if you really looked for it. But the reality is is that there are benefits. For the folks out there that are now being accelerated into the cloud for the reasons with COVID and other reasons, What's your advice to them? Why cloud? What's the bet? What comes out of making a good choice with the cloud? Chad, as people sitting there going "okay, I got to get my cloud mojo going" What's your advice to those folks sitting out there watching this? >> So I would say, and Jim knows this, we at Pitt have a big vision for data, a whole universe of data where just everything is made available and I can't estimate the demand for all of that yet, right? That's going to evolve over time, so if I'm trying to scale some physical hardware solution, I'm either going to under scale it and not be able to deliver, or I'm going to invest too much money for the value I'm getting. By moving to the cloud, what that enables me to do is just grow organically and make sure that our spend and the value we're getting from the use are always aligned. And then, of course, all the questions about, scalability and extensibility, right? We can just keep growing and if we're not seeing value in one area, we can just stop and we're no longer spending on that particular area and we can direct that money to a different component of the cloud. So just not being locked in to a huge expensive product is really key, I think. >> Jim, your thoughts on why cloud and why now? Obviously it's pretty obvious reasons, but benefits for the naysayer sitting on the fence? >> Yeah, it's a really important question, John and I think Chad had a lot of important points. I think there's two others that become important. One is agility. Whether that's agility with respect to if you're in a competitive market place, Agility in terms of just retaining team members and staff in a highly competitive environment we all know we're in, particularly in the IT world. Agility from a cost perspective. So agility is a theme that comes through and through over and over and over again, and as Chad rightfully said, most companies and most organizations they don't know the entirety of what it is they're facing, or what the demands are going to be on their services, so agility is really, is really key. And the second one is, the notion has often been that you have to have it all figured out before you can start and really our mantra in the jam session was sort of born this way. It's really start by doing. Pick a use case, pick a pain point, pick an area of frustration, whatever it might be and just start the process. You'll learn as you go and not everything is the right fit for cloud. There were some things for the right reasons where alternatives might be be appropriate, but by and large, if you start by doing and in fact, through jam session, learn by doing, you'll start to better understand, enterprise will start to better understand what's most applicable to them, where they can leverage the best bang for the buck, if you will. And ultimately deliver on the value that IT is meant to deliver to the line of business, whatever that might be. And those two themes come through and through. And thirdly, I'll just add speed now. Speed of transformation, speed of cost reduction, speed of future rollout. You know, Chad has users begging for information and access to data, right? He and the team are sitting there trying to figure how to give it to them quickly. So speed of execution with quality is really paramount as well these days. >> Yeah and Chad also mentioned scale too, cause he's trying to scale up as key and again, getting the cloud muscles going for the teams and culture is critical because matching that incentives, I think the alignment is critical point. So congratulations gentlemen on a great award, best EDU solution. Chad, while I have you here, I want to just get your personal thoughts, but your industry expert PhD hat on, because one of the things we've been reporting on is in the EDU space, higher ed and other areas, with people having different education policies, the new reality is with virtualized students and faculty, alumni and community, the expectations and the data flows are different, right? So you had stuff that people used, systems, legacy systems, kind of as a good opportunity to look at cloud to build a new abstraction layer and again, create that alignment of what can we do development wise, because I'm sure you're seeing new data flows coming in. I'm sure this kind of thinking going on around "Okay, as we go forward, how do we find out what classes to attend if they're not onsite?" This is another jam session. So I see more and more things happening, pretty innovative in your world. What's your take on all this? >> My take, so when we did the pivot, we did a pivot right after spring break to be virtual for our students, like a lot of universities did. And you learn a lot when you go through a crisis kind of like that and you find all the weaknesses. And we had finished the engagement, I think, with NorthBay by that point, or were in it and seeing how if we were at our future state, you know, might end up the way I envisioned the future state, I can now point to these specific things and give specific examples about how we would have been able to more effectively respond when these new demands on data came up, when new data flows were being created very quickly and able to point out to the weaknesses of our current ecosystem and how that would be better. So that was really key and this whole thing is an opportunity. It's really accelerated a lot of things that were kind of already in the works and that's why it's exciting. It's obviously very challenging and at Pitt we're really right now trying to focus on how do we have a safe campus environment and going with a maximum flexibility and all the technology that's involved in that. And, you know, I've already got, I've had more unique data requests come to my desk since COVID than in the previous five years, you know? >> New patterns, new opportunities to write software and it's great to see you guys focused on that hierarchy of needs. I really appreciate it. I want to just share with you a funny story, not funny, but interesting story, because this highlights the creativity that's coming. I was riffing on Zoom with someone in a higher ed university out here in California and it wasn't official business, was just more riffing on the future and I said "Hey, wouldn't it be cool if you had like an abstraction layer that had leveraged Canvas, Zoom and Discord?" All the kids are on Discord if they're gamers. So you go "Okay, why discord? It's a hang space." People, it's connective tissue. "Well, how do you build notifications through the different silos?" You know, Canvas doesn't support certain things and Canvas is the software that most universities use, but that's a use case that we were just riffing on, but that's the kind of ideation that's going to come out of these kinds of jam sessions. Are you guys having that kind of feeling too? I mean, how do you see this new ideation, rapid prototype? I only think it's going to get faster and accelerated. >> As Chad said, his requests are we're multiplying, I'm sure and people aren't, you know, folks are not willing to wait. We're in a hurry up, 'hurry up, I want it now' mentality these days with both college attendees as well as those of us who are trying to deliver on that promise. And I think John, I think you're absolutely right and I think that whether it be the fail fast mantra, or whether it be can we make even make this work, right? Does it have legs? Is it is even viable? And is it even cost-effective? I can tell you that we do a lot of work in Ed tech, we do a lot of work in other industries as well And what the the courseware delivery companies and the infrastructure companies are all trying to deal with as a result of COVID, is they've all had to try to innovate. So we're being asked to challenge ourselves in ways we never been asked to challenge ourselves in terms of speed of execution, speed of deployment, because these folks need answers, you know, tomorrow, today, yesterday, not six months from now. So I'll use the word legacy way of thinking is really not one that can be sustained, or tolerated any longer and I want Chad and others to be able to call us and say, "Hey, we need help. We need help quickly. How can we go work together side by side and go prove something. It may not be the most elegant, it may not be the most robust, but we need it tomorrow." And that's really the spirit of the whole notion of jam session. >> And new expectations means new solutions. Chad, we'll give you the final word. Going forward, you're on this wave right now, you got new things coming at you you're getting that foundation set. What's your mindset as you ride this wave? >> I'm optimistic. It really is, it's an exciting time to be in this role, the progress we've made in the calendar year 2020, despite the challenges we've been faced with, with COVID and budget issues, I'm optimistic. I love what I saw in the jam session. It just kind of confirmed my belief that this is really the future for the University of Pittsburgh in order to fully realize our vision of maximizing the value of data. >> Awesome! Best EDU solution award for AWS public sector. Congratulations to NorthBay Solutions. Jim Keller, president, and University of Pittsburgh, Chad Burton. Thank you for coming on and sharing your story. Great insights and again, the wave is here, new expectations, new solutions, clouds there, and you guys got a good approach. Congratulations on the jam session, thanks. >> Thank you, John. Chad, pleasure, thank you. >> Thank you. >> See you soon. >> This is "the Cube" coverage of AWS public sector partner awards. I'm John Furrier, host of "the Cube". Thanks for watching. (bright music)

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>Welcome back to the Cube's coverage here from Palo Alto, California in our studio with remote interviews during this time of covert 19 with our quarantine crew. I'm John Furrier, your host of the Cube, and we have here the award winners for the best CDU solution from North based loses. Jim Keller, the president and from Harvard Business Publishing and University of Pittsburgh, Chad Burden PhD in data privacy officer of University of Pittsburgh. Thanks for coming on, gentlemen. Appreciate it. >>Thank you. >>So, Jim, we'll start with you. What is the solution that you guys have got the award for and talk about how it all came about? >>Yeah. Thank you for asking. And, uh, it's been a pleasure Worldwide chat and the entire you pitch team. So? So as we as we enter this this this whole covitz situation, our team really got together and started to think about how we could help AWS customers continue their journey with AWS, but also appreciate the fact that everyone was virtual. The budgets were very tight, but Nonetheless, the priorities remained the same. Um, So So we devised a solution which which we call jam sessions, AWS jam sessions and the whole principle behind the notion is that many customers go through AWS training and AWS has a number of other offerings, immersion days and boot camps and other things. But we felt it was really important that we brought forth a solution that enables customers to focus on a use case but do it rapidly in a very concentrated way with our expert team. So we formulated what we call jam sessions, which are essentially very focused, too. Weak engagements, rapid prototyping engagements. So in the context of Chad on the pitch team, it was around a data lake and they had been channels certainly speak to this in much more detail. But the whole notion here was how do you How does the customer get started out? Is how does a customer prove the efficacy of AWS proved that they can get data out of their on premises systems, get it into AWS, make it accessible in the form in this case, a data lake solution, and have the data be consumable. So we have an entire construct that we use, which includes structured education, virtual simultaneous rooms where development occurs with our joint sap prototyping teams. We come back again and do learnings, and we do all of this in the construct of the agile framework. And ideally, by the time we're done with the two weeks, um, the customer achieves some success around achieving the goal of the jam session. But more importantly, their team members have learned a lot about AWS with hands on work, real work. Learn by doing if you will, um, and really marry those two concepts of education and doing and come out of that with an opportunity then to think about the next step in that journey, which in this case be Thea implementation of a data lake in a full scale project kind of initiative. >>Talk about the relationship with the North based solutions. So your customer, you guys were partnering on this, so it's kind of your partnering, but also your they're helping you talk about the relationship and how the interactions went. >>Yeah, so I was faced with a challenge that I think a lot of people in my role is faced with where the demand for data is increasing and demand for more variety of data. And I'm faced with a lot of aging on premise hardware that, um I really don't want to invest any further. And so I know the clouds in the future, but we are so new with the cloud that we don't even know what we don't know. So it has zeroed in on AWS and I was talking with them and I made it very clear. I said, you know, because of our inexperience, you know, we have talented data engineers, but they don't have this type of experience, but I'm confident they can learn. What I'm looking for is a partner who can help us not only prove this out, that it can work, which I had high confidence that it could, but help us identify where we need to be putting our still skilling up. You know what gaps do we have? And you know, aws has so many different components. But we also needed help zeroing in on or our need. You know, what are the pieces we should really be paying attention to and developing those skills. So we got introduced to North Bay and they introduced us to the idea of the jam session, which was perfect. It was really exactly what I was looking for. Um, you know, we made it very clear in the early conversations that this would be side by side development, that my priority was, of course, to meet our deliverables. But it also for my team to learn how to use some of this and learn what they need to dive deeper in at the end of the engagement. I think that's how we got started on then. It was very successful engagement after that >>talk about the jam sessions because I love this. First of all, this is in line with what we're seeing in the marketplace, with rapid innovation now more than ever, with virtual workforces at home given situation, rapid, agile, rapid innovation, rapid development is a key kind of thing. What is a jam session was the approach. Give me a little bit about of it out, but what's your take on the jam sessions? Had it all has it all work? >>It was great because of the large team that north a broad and the variety of skills they brought and then they just had a playbook that worked, right? They broke us up into different groups from the people who be making the data pipeline to the people who then would be consuming it to develop analytics projects. Um, so that part works really well. And, yes, this rapid iterative development, You know, right now, with our current kind of process in our current tools, I have a hard time telling anybody how long it will take to get that new data source online and available to our data analysts who are data scientists because it takes months sometimes and nobody wants that answer. And I don't want to be giving that answer. So what we're really focused on is how do we tighten up our process? How do we still like the right tools so that we can pay, you know, will be two weeks from start to finish and you know you'll be able to make the data available. So the engagement with North of the jam session scheduled like that really helped us prove that. You know, once you have the skills and have the right people, you can do this rapid development and bring more value to our business more quickly, which is really what it's all about. We're out, >>Jim. I want get your thoughts because, you know, we see time and time again with the use cases with the cloud When you got smart people, certainly people who play with data and work with data, they're not. They're pretty savvy. They know the limitations. But when you get the cloud, it's like a car versus a horse or, you know, get a go from point A to point B. But again, the faster is the key. How did you put this all together And what were the key learnings? >>Yeah. So, uh, John, you know, a couple of things that are really important. One is, as Chad mentioned, really smart people, um, on the it side that wanted to wanted to really learn and had had a thirst for learning. Um, and then couple that with the thing that they're trying to learn in the actual use case that we're trying to jointly jointly implement a couple of things that we've learned that they're they're really important. One is, although we have structure, we have a Silla by and we have sort of a pattern of execution. We never lose sight of the fact that every customer's different. Every team members different and in fact chat in this case that team members some had more skills on AWS than others, so we had to be sensitive to that. So what we did was we sort of use our general formula for for the two weeks one week one is very structured, focused on getting folks up to speed and normalize in terms of where they are in their education of aws solution we're building, um, and then we two is really meant to sort of multiple together and really take this the solution that we're trying to execute around, um, and tailor it to the customer. So they were addressing the specific needs both from their team member of perspective and, uh, and the institutions perspective, Um, in total. We've learned that starting the day together and ending today with the recap of that day is really important in terms of ensuring that everyone's on the same page, that they have commonality of knowledge. And then we were addressing any concerns. You know, this stuff we move fast, right? Two weeks is is not a long time to get a lot of rapid prototyping done. So if there is anxiety or folks feel like they're falling behind, you want to make sure we knew that we want to address that quickly that evening or the next morning, recalibrate and and then continue. The other thing that we've learned is that and Chad, the entire Cube team did a phenomenal job of this was really preparation. So we want to We we We have a set of preliminary set of activities that we that we work with our customers sort of lay the foundation for, so that on day one of the jam session, we're ready to go. And with this we're doing this virtually. We don't have the luxury of being in a physical room and having time to sort of get acclimated to the physical constructive of organizing rooms and shares and tables. All of that, we're doing all that virtually so. Joe and the team were tremendous and getting all the preparatory work done. The thing about was involved in a data lake. It's the data and security and access of things Our team needed to work with their team and the prescription that in the formula that we use is really 33 critical things. One is our team members have to be adept that educating on a white board in this case. Secondly, we want to do side by side element. That's that's the whole goal. And then we want team members to to build trust and relationship side by side and then, thirdly and importantly, we want to be able to do over the shoulder mentoring. So as Chad's team members were executing, UI could guide them as we go. And those really those three ingredients really >>talk about the Data Lake on the outcome. As you guys went through this, what was the results of the Data Lake? How did it all? How'd it all turn out? >>Yeah, the result was great. It was exactly what we're looking for. The way I had structured the engagement and working with Jim to do this is I wanted to accomplish two things. I wanted to one prove that we can do what we do today with a star schema Martin model that creates a lot of reports that are important to the business but doesn't really help us grow in our use of data. There was a second component of it that I said, I want I want to show how we do something new and different that we can't do with our existing tools so that I can go back to our executive leadership and say, Hey, you know, by investing in this year's all the possibilities we can do and we've got proof that we can do it. So some natural language processing was one of those and leveraging aws comprehend with key and And the idea here was there are unfortunately relevant today with Cove it. But there are events happening all around campus. And how do students find the right events for them? You know, they're all in the calendar will live pricing national language processing using AWS comprehend and link them to a student's major so that we can then bubble these up to a student. Hey, you know of all these thousands of events here and you might be most interested in you can't do that right now, but using these tools using the skills that north they helped us develop working side by side will help us get there, >>you know, beautiful thing is with these jam sessions. You want to get some success, You go for the next one. You get this Sounds like another jam session opportunity to go in there and do the virtual version as well. As the fall comes up, you have the new reality. And this >>is >>really kind of What I like about this story is you guys did the jam session. First of all, great project, but right in the middle of this new shift of virtual, so it's very interesting. So I want to get your thoughts, Chad, You know, as you guys look at this, I mean on any given Sunday, this is a great project. You get people together, you have the cloud get more agile, get the proof points, show it double down on it. Playbook check. But now you've got the virtual workforce. How did that all play out? Anything surprise you any expectations that were met or things that were new that came out of this? Because this is something that everyone is going through right now. How do I come out of this or deal with current Cove it as it evolves and when I come out of it. I don't have a growth strategy in a team that's deploying and building. What's your take on? >>Yeah, so, yeah, you know, it's a good question. And I was a little concerned about it at first, cause when we had first begun conversations with North Bay, we were planning on a little bit on site and a little bit virtual. And of course, Cove. It happened. Our campuses closed. Nobody's permitted to be there. And so we had to just pivot to 100% virtual. I have to say I didn't notice any problems with it. It didn't impede our progress that didn't impede our communication. I think the playbook that North they had really just worked for that. Now they may have had to adjust it, and Jim can certainly part of that. But you know those morning stand ups for each group that's working the end of day worn out right? That's what those were the things I was joining in on, you know, it wasn't involved in it throughout the day, but I wanted to check in at the end of the day to make sure things are kind of moving along and the communication the transparency that was provided with key, and because of that transparency and that kind of schedule, they already have set up North Bay. We didn't see we didn't have any problems having a fully virtual engagement. In fact, I would probably prefer to do for two engagements moving forward because we can cut down on travel costs for everybody. >>You know, Jim O. Negative thoughts that I think is a huge point that's not just representing with here and illustrate with the example of the success of the EU solution. You guys got the award for, but in a way, covert exposes all the people that are been relying on waterfall based processes. You got to be in a room and argue things out. Our have meetings set up. It takes a lot of time when you when you have a virtual space and an agile process, you make some adjustments. But if you're already agile, it doesn't really impact too much. Can you share your thoughts because you deployed this very successfully? Virtually. >>Yeah, I know it is. Certainly, um, the key is always preparation and on our team did a phenomenal job of making sure that we could deliver equal to or better than virtual experience than we could on site and on site experience. But, John, you're right. You're absolutely right. But it forces you to really do is think about all the things that come natural when you're when you're in a physical room together, you can't take for granted virtually, um, even even interpersonal relationships and how those were built and the trust that's built in. And this whole, as much as this is a technical solution and as much as the teams did you really phenomenal aws work, foundational Lee. It all comes down to trust it, as Chad said, transparency, and it's hard, often hard to to build that into a virtual experience. So part of that preparatory work that I mentioned, we actually spend time doing that. And we spent time with Chad and other team members understanding each of their team members and understanding their strengths, understanding where they were in the education journey and experiential journey a little bit about them personally, right? So so I think. Look, I think the reality in the short and near term is that everything is gonna be virtual North Bay delivers much of their large scale projects. Virtually now, we have a whole methodology around that, and, um, and it's proven. Actually, it's made us better at what we do. >>Yeah, definitely puts the pressure on getting the job done and focusing on the creativity the building out. I want to ask you guys both the same question on this next round, because I think it's super important as people see the reality of cloud and there certainly has been around the benefits of there. But still you have, you know, mentality of, you know, we have to do it ourselves, not invented here. It's a managed services security. You know, there's plenty of objections. If you really want to avoid cloud, you can come up with something if you really look for it. Um, but the reality is, is that there are benefits for the folks out there that are now being accelerated into the cloud for the reasons we cove it and other reasons. What's your advice to them? Why cloud, what's the what's the bet? What comes? What comes out of making a good choice with the cloud? Chad? Is people sitting there going? Okay, I got to get my cloud mojo going What's your What's your What's your advice to those folks sitting out there watching this? >>Yeah, so I would say it. And Jim does this, you know, we have a big vision for data, you know, the whole universe of data. Where does everything is made available? And, um, I can't estimate the demand for all of that yet, right, That's going to evolve over time. So if I'm trying to scale some physical hardware solution, I'm either going to under scale it and not be able to deliver. Or I'm gonna invest too much money for the value in getting what? By moving to the cloud. What that enables me to do is just grow organically and make sure that our spend and the value we're getting from the use are always aligned. Um And then, of course, all the questions that you have availability and acceptability, right? We can just keep growing. And if we're not seeing value in one area, we can just we're no longer spending on that particular area, and we contract that money to a different components of the cloud, so just not being locked into a huge expense up front is really key, I think, >>Jim, your thoughts on Why Cloud? Why now? It's pretty obvious reasons, but benefits for the naysayers sitting on the fence who are? >>Yeah, it's It's a really important question, John and I think that had a lot of important points. I think there's two others that become important. One is, um, agility. Whether that's agility with respect to your in a competitive marketplace, place agility in terms of just retaining team members and staff in a highly competitive environment will go nowhere in particularly in the I t world, um, agility from a cost perspective. So So agility is a theme that comes through and through, over and over and over again in this change, right? So, he said, most companies and most organizations don't they don't know the entirety of what it is they're facing or what the demands are gonna be on their services. The agility is really is really key, and the 2nd 1 is, you know, the notion has often been that you have to have it all figured out. You could start and really our mantra and the jam session was sort of born this way. It's really start by doing, um, pick a use case, Pick a pain point, pick an area of frustration, whatever it might be. And just start the process you learn as you go. Um, and you know, not everything is the right fit for cloud. There are some things for the right reasons where alternatives might be might be appropriate. But by and large, if you if you start by doing And in fact, you know the jam session, learn by doing, and you start to better understand, enterprise will start to better understand what's most applicable to that where they can leverage the best of this bang for the buck if you will, um, and ultimately deliver on the value that that I t is is meant to deliver to the line of business, whatever that whatever that might be. And those two themes come through and through. And thirdly, I'll just add speed now. Speed of transformation, Speed of cost reduction, speed of feature rollout. Um, you know, Chad has users begging for information and access to data. Right? And the team we're sitting there trying to figure how to give it to him quickly. Um, so speed of execution with quality is really paramount as well these days >>and channels. You mentioned scale too, because he's trying to scale up as key and again getting the cloud muscles going for the teams. And culture is critical because, you know, matching that incentives. I think the alignment is critical. Point point. So congratulations, gentlemen. On great award best edu solution, Chad, While I have you here, I want to just get your personal thoughts. Put your industry expert PhD hat on because, you know, one of the things we've been reporting on is a lot of in the edu space higher ed in other areas with people having different education policies. The new reality is with virtual virtualized students and faculty alumni nine in community, the expectations and the data flows are different. Right? So you you had stuff that people use systems, legacy systems, >>kind of. >>It's a good opportunity to look at cloud to build a new abstraction layer and again create that alignment of what can we do? Development wise? I'm sure you're seeing new data flows coming in. I'm sure there's kind of thinking going on around. Okay. As we go forward, how >>do >>we find out who's what. Classes to attend if they're not on site this another jam session. So I see more, more things happening pretty innovative in your world. What's your take on all this? >>Um, I take, you know, So when we did the pivot, we did a pivot right after spring. Great toe. Be virtual for our students, Like a lot of universities dead. And, um, you learn a lot when you go through a crisis kind of like that. And you find all the weaknesses And we had finished the engagement. I think north by that point, or it were in it. And, um, seeing how if we were at our future state, you know, the way I envision the future state, I can now point to the specific things and get specific examples of how we would have been able to more effectively on when these new demands on data came up when new data flows were being created very quickly and, you know, able to point out to the weaknesses of our current ecosystem and how that would be better. Um, so that was really key. And then, you know, it's a This whole thing is an opportunity. It's really accelerated a lot of things that were kind of already in the works, and that's why it's exciting. It's obviously very challenging, you know, and that if it were really right now trying to focus on how do we have a safe campus environment and going with a maximum flexibility and older technology that's involved in that? And, you know, I've already got you know, I've had more unique data requests. >>My desk >>is coded and in the previous five years, you know, >>new patterns, new opportunities to write software. And it's great to see you guys focused on the hierarchy of needs. Really appreciate. I want to just share a funny story. Not funny, but interesting story, because this highlights the creativity that's coming. I was riffing on Zoom with someone in Higher Ed University out here in California, and it was wasn't official. Business was just more riffing on the future, and I said, Hey, wouldn't it be cool if you have, like an abstraction layer that had leverage, canvas, zoom and discord and all the kids are on discourse, their game received. Okay, why discord? It's the hang space people are his connective tissue Well, how do you build notifications through the different silos? So canvas doesn't support certain things? And campuses? The software. Most companies never say years, but that's a use case that we were just riffing on. But that's the kind of ideation that's going to come out of these kinds of jam sessions. You guys having that kind of feeling to? How do you see this new ideation? Rapid prototyping. I only think it's gonna get faster. Accelerated >>It was. Chad said, you know, his requests are multiplying. I'm sure on people are you know, folks are not willing to wait, you know, we're in a hurry up. Hurry up. I wanted now mentality these days with with both, um college attendees as well as those of us. We're trying to deliver on that promise. And I think, John, I think you're absolutely right. And I think that, um, whether it be the fail fast mantra or whether it be can we may even make this work right? Doesn't have lakes, is it is even viable. Um, and is it even cost effective? I can tell you that the we do a lot of work in tech. We do a lot of work in other industries as well. And what what the courseware delivery companies and the infrastructure companies are all trying to deal with and as a result of coaches, they've all had to try to innovate. Um, so we're being asked to challenge ourselves in ways we never been asked to challenge ourselves in terms of speed, of execution, speed of deployment, because these folks need answers, you know, tomorrow, Today, yesterday, not not six months from now. So the the I'll use the word legacy way of thinking is really not one that could be sustained or tolerated any longer. And and I want Chad and others to be able to call us and say, Hey, we need help. We need help quickly. How we go work together, side by side and go prove something. It may not be the most elegant. It may not be the most robust, but we need. We need it kind of tomorrow, and that's really the spirit of the whole. The whole notion of transition >>and new expectations means new solutions that will give you the final word going forward. You're on this wave right now. You got new things coming at you. You get in that foundation set. What's your mindset as you ride this wave? >>I'm optimistic it really It's an exciting time to be in this role. The progress we've made in the county or 2020 despite the challenges we've been faced with with, um cove it and budget issues. Um, I'm optimistic. I love what I saw in the in the jam session. It just kind of confirmed my I believe that this is really the future for the University of Pittsburgh in order to fully realize our vision of maximizing the value of data. >>Awesome. Best Edu solution award for AWS Public sector Congratulations and North based solutions. Jim Keller, President and University of Pittsburgh Chadbourne. Thank you for coming on and sharing your story. Great insights. And again, the wave is here. New expectation, new solutions. Clouds There. You guys got a good approach. Congratulations on the jam session. Thanks. >>Thank you, John. Pleasure. Thank you. Through >>the cube coverage of AWS Public Sector Partner Awards. I'm John Furrow, your host of the Cube. Thanks for watching. Yeah, yeah, yeah, yeah

from galvanized San Francisco extraction signal from the noise it's the kue cover the apache spark community event brought you IBM now your host John free George okay welcome back everyone we are live in San Francisco for this special q presentation with the IBM sparkman the event here live at galvanized in San Francisco workspace incubator great place for developer education IBM's big announcement today their commitment to spark they didn't see any numbers but I'm counting in the hundreds of millions of years to quote Papa Chiana on my call with him on Friday with rod $17 fuck yeah holler last for hundreds of millions yeah hundred millions of dollars getting late in the day going to be your coming rod Smith's our next guest rod welcome to the cube thank you very much with a catalyst behind spark at IBM worked hard on it yeah you guys tell a story what's the story well we worked on big data and I have a group of folks that go out and work with customers all the time and what we were doing Hadoop we would do these cool applications that sometimes you know small clusters 20 minutes you get a result and a customer would say can you do that in a couple seconds kind of look around and go what changed it means it did the business problem and they couldn't tell us but it's one of those data points in your head that go something's not quite right you know what's what's changing or what are they trying to tell me that they can't and that's when we started learning you know customers were looking for technology that they could iterate on quickly you know open-ended questions it wasn't the give me a problem do the game pew pew output I'm done this was oh gee there's the journey I now see some interesting insights I have other questions was it was something not right the data that they got didn't match their hypothesis or was it the expectation that if I can do it fast on google and find a Thai restaurant down the block well so I can it went that way something doesn't right what was with me that said why can't you tell me what you're really trying to accomplish what I learned is that as we go through these kind of digital transfer mation real real time they were thinking about how their business is going to change so fast and so the problems always been for technologists and vendors like IBM tell us the problem we pick out the technology and you're pretty well stuck with it it stays that way and they wanted more flexibility open-ended questions lots of different data sources on demand when they had to have it on this they wanted to see results along the way and they would rather have analytics be approximation that they could use quickly rather than after the fact and more accurate okay so you know when you went through that it wasn't they couldn't find a bi person to talk bad about and I couldn't find a data person so you know it was fun to try to put piece puzzles together and that's where spark came into this so I see a lot of other trends are kind of vectoring into that convergence which is in-memory databases you know the community flash for persistence store on the storage side so this you as a close to all that action what was the aha moment for for within IBM is han hey you know what this spark thing is the next Linux me we got to get out in front of this and help the community go faster and then kind of rising tide floats elbows what was that flash point flow we we had two of them one was that in our commerce group there's ways that they work on online pricing and there's a vendor stander which takes about a week when you get data off of a site or retail site they analyze that they correct the analytics they put it back up again takes about a week but we showed them a spark we could do it in about four hours a week down to four hours and now they started to think oh you know what do we offer customers now we have ways to have not just one product many products let's bring in other data location data traffic data weather data social data so that kind of exploded internally on this is a big change this is something that we can relate to cus of multiple data source of the need for unification and speed and and speed speed first because be first that's a heck all the speed i want to bring other data sets and it's time to value i mean if you're going to be a digital business and look at real time where it's going Netflix others have really set the standard on ok so then i'm a so let's take a next level so rod you're crazy we can't do that it would disrupt all these other businesses we have so how does that conversation happen within IBM the way that happens in IBM is rod you are crazy and you're going to cause me odds it up so please go away and I don't go away easily but you keep pushing on this and part of my job is to work with customers can I show value so I can take the product team saying you need to take this more seriously I've got currency now and then as you just said the marketplace starts to light up spark is on the front page as people are talking about how they're using it well Hadoop is growing too at the same time so it loop does it seeds the market seats the Mars you see you're playing ahead do but if you see the customer challenges and you're like you guys just connect the dots and and then it's back to the customer is talking about what their problems they want to use or the solutions are looking for so yeah it takes time because it's it's risky meaning that all of us have quarterly is what we're doing but how do we now make it safer for people in IBM jump in the water so that eventually they don't hate me so what's your what's your comment when a friend says hey rod you know linux was great but it's a different era oh you know here with cloud and mobile open source with the patch he's evolved to the point where it's very manageable for vendors to be contributed as well with with non company contributors how do you guys see the difference between those two worlds because really this is a Linux moment but there's no big bad main many many computer companies name frames out there but their specialized for like the Z systems are great but like this is scale out commodity hardware a dupe now that's growing how do you how do you describe that because there is a Linux correlation what linux was for open source then operating systems now this is kind of distributed analytics I think you're you're you know the the part of this is kind of real-time digital business transformations and while there is not a you know bad company out there you know amazon and others have shown how they can be online businesses and use analytics and be very effective but i'm a brick and mortar company and an online business how do i do the same thing and spark starts to really show that no they don't have a corner on the market we can compete so that's the big factor on this is well it's not one company doing this it's I need to be able to compete at the speed the businesses that didn't have to see that Amazon started kind of post recession or you know Dom bubble bursting you know web services was just kind of kicking through if we remember our history lessons and what happened was they really had no traction they built some building blocks right they made a good decision to integrate to core building blocks compute and storage and they built from there so in a way you guys can enable companies to have their own amazon like extensive experience because it's a fresh clean cute paper right it is and I think we're spark it's interesting is like you said in two verticals what do i do to retail what do I do in health care what are we doing finance right very specialized I we've shown in Watson you can do Watson for cancer research you can do Watson for cooking right but they're very vertical now so specialized domain expertise becomes really interesting right that's the big part and that's the part I really liked about spark they were the community really thought about solution developers you know they stayed away kind of middle ground I you don't have to be a deep dated person or a deep analytics API person what's the problem you want to solve how can I help you do that I think that's a you know that's interesting is that that's because most people go Jay this is speeds and feeds software we look at the solutions more holistic but then you're really talk about customer problems right the so-called outcomes that go on well that's what and I think that's the part that I've enjoyed is I want to talk to you you know about what your problem is I don't want to talk technology I you know I don't want to have to make a technology choice from stay one spark helps me with that I don't notify programming while all those things come together so I can concentrate we can concentrate on talking to the customer but you know learn from them what are you trying to accomplish so you watch the next things on your list good I just gonna say you know looking at your LinkedIn page i love this at BP emerging technologies for 20 some odd years so you see here you've seen a lot of technology's come a lot of emerging technologies and the acceleration of these technologies is only going more right you have a whole lot more in your portfolio you have to look at today then then you did yesterday or five years ago yeah why is sparks a special in the cornucopia of technologies that you've seen coming over the years it's a good question and and as I've done merging technologies I've learned that I have to you know listen to customers very carefully on it and when I hear those kind of repeatable business patterns do I see an economic change a transformation that really sticks with me and sometimes the old things have start really big you know they start out good and then they fade away but I always look for technologies that seem to have lots of dimensions to them from a business value standpoint that's what attracted me to spark and my team working with some customers on pocs we could do them quickly you know I really like to get to the point where you know we an industry we with notebooks and others we can do solutions in less than four hours for a customer what better thing to take your you know employee to lunch and spat them on the back for you know something that you didn't expect for weeks well one of the exciting things that you guys have done is you shine the spotlight on spark and you opened up the conversation globally around IBM is making a big move spark was a little bit of an outlier and the mainstream press I mean the press we're picking up spark oh yeah berkeley some credibility of great people behind it but now it's like wow it's going to get the attention of CX cxos out there and they're going to be like hmm if ibm's looking at it must be relevant because of the history you guys have with innovation but they're going to ask you the question I'm going to ask you which is it's not baked out yet where are we with this what are you guys going to do how does IBM work with the community to continue to bake out spark because a lot of people are using it bringing it in but it's evolving super fast and that's going to be the question is it baked and how does it get baked faster so I think there is lots of areas that if we just talked about if I'm doing retail or health care or fine it's going to be lots of specialized analytics because that's what spark for me is is enabling custom analytics on this second part is as you think about how you want to look at bigger problems I think that many times are learning is to try to you know once we got a technology lets make everything fit it rather than starting to separate it by business problems and I think we can do that now or we can bring to the table technology learning best practices around this and solutions I think you know at the end of the day it's house part can be integrated into a business solution and our customers very quickly and hopefully those customers see it broadly from interoperability standpoint of what they're going to do so the final question I have for you is what was the biggest learning that you've taken away from this process that was magnified through this whole journey of a taking IBM from being a participant in the as a citizen in the community early on as a founding member of spark this is back in two thousand nine so it wasn't like no one knew he was going on and you know we bird cover on Hadoop from the beginning so we'd love to watch these ecosystems grow but from from the early days to now today mmm what was the biggest thing that you learned that was magnified out of all the reactions all the feedback all the customers what can you share I I think for me when we did a spark hack you know our hackathon piece when 28,000 IBM ER showed up with ideas that told us twenty eight thousand 28,000 so now you stopped and 28,000 people who were focused on the customer so they had a thought of how this could be relevant this is great I mean this isn't like back talking for this isn't one little vein with a little stream it's big and it big was what we can do for our customer when was that um about two months ago how did you pull that off just out an email blast all the IBM's put on the message board to a crowd chat what did you do well when you put out an email blast the second one is you put on a webcam to explain to people what you're going to do with it what you'd like them to do and I'll we're setting it up and and then you step back and you know kind of cross your fingers hope people show up and then when you know you invite ten thousand and twenty eight thousand show up you kind of know that we're turning a corner as a company on understanding how we can use that for this this also highlights this whole connectedness apps internet of things and people are things to so their mobile device when you have that kind of people close to the action the creativity is there right there on the front lines and they don't feel like that the work they do is going to be taken by the machinery in the old days I got to go back all these hurdles I gotta jump now they could instantly be there with some solutions so that's that's super compelling the next question is security and how does how do you see that leaving in because now one of the things that came up will first meeting let me back up but I get this you think about security question for a second last week ahead dupe summit we were talking with the Hadoop ecosystem Hortonworks ODP conversations etc but when you looked at kind of like reading the tea leaves it was sparked that was kind of stealing the show the subtext was smart all the spark sessions were packed the developers had was salivating over sparks like to hear that I did why why is that why are the Hadoop developers salivating over spark is it because they wanted to go faster do they see extensions any thoughts I think that I've say it two ways one is I think there was and since I did who do for quite a while I think people thought for a while Hadoop was going to be an analytics platform and it it kind of went down the path of being immoral generalized platform so you can do more than MapReduce jobs so there's been this pent-up demand for really analytics focus and spark offered that focus and the performance side I think that's the parts in Hadoop sold kind of a false dream or it didn't materialize fast but I don't think of material out of false treaty I'm saying if they promise them around yeah it well and people set those you know well the fresh maybe yeah I don't think the vendors all I think was more than well vendors you know it did to unstructured data does that unstructured data does that storing data and I didn't be able to act on it creates some interesting dynamics I mean I've worked with customers who you know started to put data in Hadoop but to have put data dupe you know we're only going to do a year's worth of data and then putting three years of data because they want to do monte pucker up my Carlo simulations against a Monty Python it's time you threw water on us and we love yours we on the cube but the problem says we're talking about before like you know our internal use we can produce you know interesting innovations in days that's going to attract audiences because now they can show their you know business people what they can do for them that's what's really driving this I mean if you gotta see XO you know CMO says you know show me what you can do you know do segmentation on my population for these products they want it in in minutes not so you know going to run it in different jobs and the over a certain period of time I was just talking with the CEOs of docusign box 18 1018 Syrian kinky was executive director and then EVP a platform that Salesforce the common thread amongst those executives was the new digital transformation has such a dynamic or impactful economic impact yes I mean dr. Sanyal using examples how literally Deutsche Telekom saved 230 million dollars on one process yes one process yes with analytics and yeah process improvements extreme it sounds funny but it's extremely low hanging fruit they haven't had technology and the economics and be able support it now we do and now you're seeing the solution developer go I think I can make a business result faster yeah and if they can show it then businesses react and I think that's the beautiful thing about what Hadoop is done I mean I brought that up earlier trying to tease that out with reality we're seeing is that that mark is continuing to grow but there's a world beyond Hadoop yep I mean Hortonworks this public company I mean IBM is massive so you got Hadoop and then sparks a beautiful extension to that that enables so much more well I think spark will go further because it's more to me is another dimension it's an integration technology so i can have sparked up to legacy systems without hadoop you know in there doing analytics in there being an avenue for doing joins on data doing analytics on unstructured and transactional data whether data pulling it all together and I think that's the again talking about multi-dimensional that's what that was hard even five years ago so any relational database that's a nightmare yeah and you're asked about security so you want to touch on yeah okay go ahead so part of the things that I like about spark is the technology is called resilient distributed data sets r dds so I read data from a source and I make it into this r DD I can work on it that gives me a great data point or a great interaction with a Cassandra datastax did a really great job of a spark driver so you think about this in businesses for a db2 or something now I know where I can put my security and my governance I can put those at certain endpoints now as i'm reading in my application writing these things out so again back to my point of an integration it's not something that i'm trying to get around a business i'm at integrating extending their life and/or capabilities that's right so I got to ask you the internal IBM question my last question is it what's the vibe like at IBM because you know I've been you know I worked at IBM way back in the day back in the 80s and the cultures changed right so much mm-hmm but there's still a huge technical group of people at IBM so I got to ask you the question with all this new cloud innovation all this new capabilities to do stuff differently what's it like for all the technical guys at IBM right now because they got to be like Hayden we can now do this we can so new capabilities are emerging what's the what's the vibe like and what are some of the things that that are low-hanging fruit that are that our game change because low-hanging fruit is game-changing today oh yes I what's the vibe eternally at idea I've internally is very hot I mean the guys and gals at this you look at cloud computing look we've done with bluemix it got is getting you know great recent press it's getting great results with customers back to this time to value piece it's new to us I mean there's only a small group that started that so now the rest of the IBM arts are going this is really cool how do we do it now you've got analytics that you know we're starting you've been you know competencies are on this now you can take the real-time aspect so yeah the five is really all those little silos you know identity system here I got to build all the software now you can gotta go horizontal yeah so you know that's kind of a new thing that's kind of exciting it's gonna be fun to watch my final question I guess is my final final question is have you been keeping track this is the sixth and final time analytics well rods great to have you on the cube you're awesome great great commentary great great insight spark in the cloud is what data bricks announce what about an on-premise i'm a customer i want i want on prem I don't necessarily want to do what's next I 40 s or other stuff oh I think you're going to see you know like hybrid models for cloud where spark as a service is there on prem i think one of the really exciting parts to me is that one the unified program model to the portability of the analytic models so let's say I start on prom because I'm worried about security and other things and then I want to move it to a cloud service well I don't have to go rewrite it I can just move the analytics over from a model standpoint so I think you're going to see this evolved very fast as people want to do either on prem or hybrid or you know dedicated cuz of the integration capabilities and the distributed nature of it that's the point yep awesome well I'll let you get the last word on the segment share what the folks who's not or aren't watching what is this all about today why is in San Francisco today IBM's announcement what's so groundbreaking about it I know you're part of it a little bit biased but share the folks why what why now what's this all about what's what's what's going on here well we think that the kind of epicenter for spark innovation is here in San Francisco amp lab with data bricks and others are doing here and we want to be a part of that and I think spark technology senator setting up is about how we can contribute and learn and you know help the community grow we think this is gonna you brought some food to the party I mean you are I said earlier beer right you bring a you know the ml yeah you got them back other wine napa valley of course you got to go to wine well craft beers good north north bay thanks so much for coming on the cube really appreciate the insight because it is a great color from an expert IBM here we're on the ground this is the cube special presentation live in San Ruby back with more with live coverage of the breakouts in the event tonight IBM spark community event here in san fran at the galvanized workspace education center we write back