>>from around the globe. It's >>the cube >>with coverage of >>Kublai khan and >>Cloud Native Con, Europe 2021 virtual brought >>to you by red hat, >>the cloud Native >>Computing foundation and ecosystem partners. Hello, welcome back to the cubes coverage of Kublai khan. Cloud Native Con 2021 part of the CNC. S continuing cube partnership virtual here because we're not in person soon, we'll be out of the pandemic and hopefully in person for the next event. I'm john for your host of the key. We're here with ricardo. Roach computing engineers sir. In CUBA. I'm not great to see you ricardo. Thanks for remote ng in all the way across the world. Thanks for coming in. >>Hello, Pleasure. Happy to be here. >>I saw your talk with Priyanka on linkedin and all around the web. Great stuff as always, you guys do great work over there at cern. Talk about what's going on with you and the two speaking sessions you have it coop gone pretty exciting news and exciting sessions happening here. So take us through the sessions. >>Yeah. So actually the two sessions are kind of uh showing the two types of things we do with kubernetes. We we are doing we have a lot of uh services moving to kubernetes, but the first one is more on the services we have in the house. So certain is known for having a lot of data and requests, requiring a lot of computing capacity to analyze all this data. But actually we have also very large community and we have a lot of users and people interested in the stuff we do. So the first question will actually show how we've been uh migrating our group of infrastructure into the into communities and in this case actually open shift. And uh the challenge there is to to run a very large amount of uh global websites on coordinators. Uh we run more than 1000 websites and there will be a demonstration on how we do all the management of the website um life cycle, including upgrading and deploying new new websites and an operator that was developed for this purpose. And then more on the other side will give with a colleague also talk about machine learning. Machine learning has been a big topic for us. A lot of our workloads are migrating to accelerators and can benefit a lot from machine learning. So we're giving a talk about a new service that we've deployed on top of Cuban areas where we try to manage to uh lifecycle of machine learning workloads from data preparation all the way to serving the bottles, also exploring the communities features and integrating accelerators and a lot of accelerators. >>So one part of the one session, it's a large scale deployment kubernetes key to there and now the machine learning essentially service for other people to use that. Right? Like take me through the first large scale deployment. What's the key innovation there in your opinion? >>Yeah, I think compared to the infrastructure we had before, is this notion that we can develop an operator that will uh, manage resource, in this case a website. And this is uh, something that is not always obvious when people start with kubernetes, it's not just an orchestra, it's really the ap and the capability of managing a huge amount of resources, including custom resources. So the possibility to develop this operator and then uh, manage the lifecycle of uh, something that was defined in the house and that fits our needs. Uh, There are challenges there because we have a large amount of websites and uh, they can be pretty active. Uh, we also have to some scaling issues on the storage that serves these these websites and we'll give some details uh during the talk as well, >>so kubernetes storage, this is all kind of under the covers, making this easier. Um and the machine learning, it plays nicely in that what if you take us for the machine learning use case, what's going on there, wow, what was the discovery, How did you guys put that together? What's the key elements there? >>Right, so the main challenge there has been um that machine learning is is quite popular but it's quite spread as well, so we have multiple groups focusing on this, but there's no obvious way to centralize not only the resource usage and make it more efficient, but also centralize the knowledge of how these procedures can be done. So what we are trying to do is just offer a service to all our users where we help them with infrastructure so that they don't have to focus on that and they could focus just on their workloads and we do everything from exposing the data systems that we have in the house so that they can do access to the data and data preparation and then doing um some iteration using notebooks and then doing distributed training with potentially large amount of gps and that storage and serving up the models and all of this is uh is managed with the coordinates cluster underneath. Uh We had a lot of knowledge of how to handle kubernetes and uh all the features that everyone likes scalability. The reliability out of scaling is very important for this type of workload. This is, this is key. >>Yeah, it's interesting to see how kubernetes is maturing, um congratulations on the projects. Um they're going to probably continue to scale. Remember this reminds me of when I was uh you know coming into the business in the 98 late eighties early nineties with TCP I. P. And the S. I. Model, you saw the standards evolve and get settled in and then boom innovation everywhere. And that took about a year to digest state and scale up. It's happening much faster now with kubernetes I have to ask you um what's your experience with the question that people are looking to get answered? Which is as kubernetes goes, the next generation of the next step? Um People want to integrate. So how is kubernetes exposing a. P. I. S. To say integration points for tools and other things? Can you share your experience and where this is going, what's happening now and where it goes? Because we know there's no debate. People like the kubernetes aspect of it, but now it's integration is the conversation. Can you share your thoughts on that? >>I can try. Uh So it's uh I would say it's a moving target, but I would say the fact that there's such a rich ecosystem around kubernetes with all the cloud, David projects, uh it's it's uh like a real proof that the popularity of the A. P. I. And this is also something that we after we had the first step of uh deploying and understanding kubernetes, we started seeing the potential that it's not reaching only the infrastructure itself, it's reaching all the layers, all the stack that we support in house and premises. And also it's opening up uh doors to easily scale into external resources as as well. So what we've been trying to tell our users is to rely on these integrations as much as possible. So this means like the application lifecycle being managed with things like Helmand getups, but also like the monitoring being managed with Prometheus and once you're happy with your deployment in house we have ways to scale out to external resources including public clouds. And this is really like see I don't know a proof that all these A. P. I. S are not only popular but incredibly useful because there's such a rich ecosystem around it. >>So talk about the role of data in this obviously machine learning pieces something that everyone is interested in as you get infrastructure as code and devops um and def sec ops as everything's shifting left. I love that, love that narrative day to our priests. All this is all proving mature, mature ization. Um data is critical. Right? So now you get real time information, real time data. The expectations for the apps is to integrate the data. What's your view on how this is progressing from your standpoint because machine learning and you mentioned you know acceleration or being part of another system. Cashing has always done that would say databases. Right. So you've got now is databases get slower, caches are getting faster now they're all the ones so it's all changing. So what's your thoughts on this next level data equation into kubernetes? Because you know stateless is cool but now you've got state issues. >>Yeah so uh yeah we we've always had huge needs for for data we store and I I think we are over half an exhibit of data available on the premises but we we kind of have our own storage systems which are external and that's for for like the physics data, the raw data and one particular charity that we had with our workloads until recently is that we we call them embarrassing parallel in the sense that they don't really need uh very tight connectivity between the different workloads. So if it's people always say tens of thousands of jobs to do some analysis, they're actually quite independent, they will produce a lot more data but we can store them independently. Machine learning is is posing a challenge in the sense that this is a training tends to be a lot more interconnected. Um so it can be a benefit from from um systems that we are not so familiar with. So for us it's it's maybe not so much the cashing layers themselves is really understanding how our infrastructure needs to evolve on premises to support this kind of workloads. We had some smallish uh more high performance computing clusters with things like infinite and for low latency. But this is not the bulk of our workloads. This is not what we are experts on these days. This is the transition we are doing towards uh supporting this machine learning workers >>um just as a reference for the folks watching you mentioned embarrassing parallel and that's a quote that you I read on your certain tech blog. So if you go to tech blog dot web dot search dot ch or just search cern tech blog, you'll see the post there um and good stuff there and in there you go, you lay out a bunch of other things too where you start to see the deployment services and customer resource definitions being part of this, is it going to get to the point where automation is a bigger part of the cluster management setting stuff up quicker. Um As you look at some of the innovations you're doing with machines and Coubertin databases and thousands of other point things that you're working on there, I mean I know you've got a lot going on there, it's in the post but um you know, we don't want to have the problem of it's so hard to stand up and manage and this is what people want to make simpler. How do you how do you answer that when people say say we want to make it easier? >>Yeah. So uh for us it's it's really automate everything and up to now it has been automate the deployment in the kubernetes clusters right now we are looking at automating the kubernetes clusters themselves. So there's some really interesting projects, uh So people are used to using things like terra form to manage the deployment of clusters, but there are some projects like cross playing, for example, that allows us to have the clusters themselves being resources within kubernetes. Uh and this is something we are exploring quite a bit. Uh This allows us to also abstract the kubernetes clusters themselves uh as uh as carbonated resources. So this this idea of having a central cluster that will manage a much larger infrastructure. So this is something that we're exploring the getups part is really key for us to, it's something that eases the transition from from from people that are used already to manage large scale systems but are not necessarily experts on core NATO's. Uh they see that there's an easier past there if they if they can be introduced slowly through through the centralized configuration. >>You know, you mentioned cross plane, I had some on earlier, he's awesome dude, great guy and I was smiling because you know I still have you know flashbacks and trigger episodes from the Hadoop world, you know when it was such so promising that technology but it was just so hard to stand up and managed to be like really an expert to do that. And I think you mentioned cross plane, this comes up to the whole operator notion of operating the clusters, right? So you know, this comes back down to provisioning and managing the infrastructure, which is, you know, we all know is key, right? But when you start getting into multi cloud and multiple environments, that's where it becomes challenging. And I think I like what they're doing is that something that's on your mind to around hybrid and multi cloud? Can you share your thoughts on that whole trajectory? >>Absolutely. So I actually gave an internal seminar just last week describing what we've been playing with in this area and I showed some demo of using cross plane to manage clusters on premises but also manage clusters running on public clouds. A. W. S. Uh google cloud in nature and it's really like the goal there. There are many reasons we we want to explore external resources. We are kind of used to this because we have a lot of sites around the world that collaborate with us, but specifically for public clouds. Uh there are some some motivations there. The first one is this idea that we have periodic load spikes. So we knew we have international conferences, the number of analysis and job requests goes up quite a bit, so we need to be able to like scale on demand for short periods instead of over provisioning this uh in house. The second one is again coming back to machine learning this idea of accelerators. We have a lot of Cpus, we have a lot less gPS uh so it would be nice to go on fish uh for those in the public clouds. And then there's also other accelerators that are quite interesting, like CPUs and I p u s that will definitely play a role and we probably, or maybe we will never have among premises, will only be able to to use them externally. So in that, in that respect, actually coming back to your previous question, this idea of storage then becomes quite important. So what we've been playing with is not only managing this external cluster centrally, but also managing the wall infrastructure from a central place. So this means uh, making all the clusters, whatever they are look very, very much the same, including like the monitoring and the aggregation of the monitoring centrally. And then as we talked about storage, this idea of having local storage that that will be allow us to do really quick software distribution but also access to the data, >>what you guys are doing as we say, cool. And relevant projects. I mean you got the large scale deployments and the machine learning to really kind of accelerate which will drive a lot of adoption in terms of automation. And as that kicks in when you got to get the foundational work done, I see that clearly the right trajectory, you know, reminds me ricardo, um you know, again not do a little history lesson here, but you know, back when network protocols were moving from proprietary S N A for IBM deck net for digital back in the history the old days the os I Open Systems Interconnect Standard stack was evolving and you know when TCP I P came around that really opened up this interoperability, right? And SAM and I were talking about this kind of cross cloud connections or inter clouding as lou lou tucker. And I talked that open stack in 2013 about inter networking or interconnections and it's about integration and interoperability. This is like the next gen conversation that kubernetes is having. So as you get to scale up which is happening very fast as you get machine learning which can handle data and enable modern applications really it's connecting networks and connecting systems together. This is a huge architectural innovation direction. Could you share your reaction to that? >>Yeah. So actually we are starting the easy way, I would say we are starting with the workloads that are loosely coupled that we don't necessarily have to have this uh tighten inter connectivity between the different deployments, I would say that this is this is already giving us a lot because our like the bulk of our workloads are this kind of batch, embarrassing parallel, uh and we are also doing like co location when we have large workloads that made this kind of uh close inter connectivity then we kind of co locate them in the same deployment, same clouds in region. Um I think like what you describe of having cross clouds interconnectivity, this will be like a huge topic. It is already, I would say so we started investigating a lot of service measure options to try to learn what we can gain from it. There is clearly a benefit for managing services but there will be definitely also potential to allow us to kind of more easily scale out across regions. There's we've seen this by using the public cloud. Some things that we found is for example, this idea of infinite, infinite capacity which is kind of sometimes uh it feels kind of like that even at the scale we have for Cpus But when you start using accelerators, Yeah, you start negotiating like maybe use multiple regions because there's not enough capacity in a single region and you start having to talk to the cloud providers to negotiate this. And this makes the deployments more complicated of course. So this, this interconnectivity between regions and clouds will be a big thing. >>And, and again, low hanging fruit is just a kind of existing market but has thrown the vision out there mainly to kind of talk about what what we're seeing which is the world's are distributed computer. And if you have the standards, good things happen. Open systems, open innovating in the open really could make a big difference is going to be the difference between real value for the society of global society or are we going to get into the silo world? So I think the choice is the industry and I think, you know, Cern and C and C. F and Lennox Foundation and all the companies that are investing in open really is a key inflection point for us right now. So congratulations. Thanks for coming on the cube. Yeah, appreciate it. Thank you. Okay, Ricardo, rocha computing engineer cern here in the cube coverage of the CN Cf cube con cloud, native con europe. I'm john for your host of the cube. Thanks for watching.

>>Hi, everyone. Good afternoon. Thank you for joining this session. I feel honored to be invited to speak here today. And I also appreciate entity research Summit members for organ organizing and giving this great opportunity. Please let me give a quick introduction. First, I'm a Takashi from Marvin American population, and I'm leading technology scouting and global ation with digital health companies such as Business Alliance and Strategically Investment in North America. And since we started to focus on this space in 2016 our team is growing. And in order to bring more new technologies and services to Japan market Thesis year, we founded the new service theories for digital health business, especially, uh, in medical diagnosis space in Japan. And today I would like to talk how health care has been transformed for my micro perspective, and I hope you enjoy reasoning it. So what's happened since the US identify the first case in the middle of January, As everyone knows, unfortunately, is the damaged by this pandemic was unequal amongst the people in us. It had more determined tal impact on those who are socially and economically vulnerable because of the long, long lasting structural program off the U. S. Society and the Light Charity about daily case rating elevator country shows. Even in the community, the infection rate off the low income were 4.5 times higher than, uh, those of the high income and due to czar straight off the Corvette, about 14 million people are unemployed. The unique point off the U. S. Is that more than 60% of insurance is tied with employment, so losing a job can mean losing access to health care. And the point point here is that the Corvette did not create healthcare disparity but, uh nearly highlighted the underlying program and necessity off affordable care for all. And when the country had a need to increase the testing capacity and geographic out, treat the pharmacies and retails joined forces with existing stakeholders more than 90% off the U. S Corporation live within five miles off a community pharmacy such as CVS and Walgreen, so they can technically provide the test to everyone in all the community. And they also have a huge workforce memory pharmacist who are eligible to perform the testing scale, and this very made their potential in community based health care. Stand out and about your health has provided on alternative way for people to access to health care. At affordable applies under the unusual setting where social distancing, which required required mhm and people have a fear of infection. So they are afraid to take a public transportacion and visit >>the doctor the same thing supplied to doctor and the chart. Here is a number of total visit cranes by service type after stay at home order was issued across the U. S. By Ali April patient physical visits to doctor's offices or clinics declined by ALAN 70%. On the other hand, that share, or telehealth, accounted for 25% of the total total. Doctor's visit in April, while many states studied to re opening face to face visit is gradually recovering. And overall Tele Health Service did not offset the crime. Physician Physical doctor's visit and telehealth John never fully replace in person care. However, Telehealth has established a new way to provide affordable care, especially to vulnerable people, and I don't explain each player's today. But as an example, the chart shows the significant growth of the tell a dog who is one of the largest badger care and tell his provider, I believe there are three factors off paradox. Success under the pandemic. First, obviously tell Doc could reach >>the job between those patients and doctors. Majority of the patients who needed to see doctors who are those who have underlying health conditions and are high risk for Kelowna, Bilis and Secondary. They showed their business model is highly scalable. In the first quarter of this year, they moved quickly to expand their physical physicians network to increase their capacity and catch up growing demand. To some extent, they also contributed to create flexible job for the doctors who suffered from Lydia's appointment and surgery. They utilized. There are legalism to maximize the efficiency for doctors and doing so, uh, they have university maintained high quality care at affordable applies Yeah, and at the same time, the government recognize the body of about your care and de regulated traditional rules to sum up she m s temporary automated to pay a wide range of tell Her services, including hospital visit and HHS temporarily waived hip hop minorities for telehealth cases and they're changed allowed provider to use communication tools such as facetime and the messenger. During their appointment on August start, the government issued a new executive order to expand tell his services beyond the pandemic. So the government is also moving to support about your health care. So it was a quick review of the health care challenges and somewhat advancement in the pandemic. But as you understand, since those challenges are not caused by the pandemic, problems will stay remain and events off this year will continuously catalyze the transformation. So how was his cherished reshaped and where will we go? The topic from here can be also applied to Japan market. Okay, I believe democratization and decentralization healthcare more important than ever. So what does A. The traditional healthcare was defined in a framework over patient and a doctor. But in the new normal, the range of beneficiaries will be expanded from patient to all citizens, including the country uninsured people. Thanks to the technology evolution, as you can download health management off for free on iTunes stores while the range of the digital health services unable everyone to participate in new health system system. And in this slide, I put three essential element to fully realize democratization and decentralization off health care, health, literacy, data sharing and security, privacy and safety in addition, taken. In addition, technology is put at the bottom as a foundation off three point first. Health stimulus is obviously important because if people don't understand how the system works, what options are available to them or what are the pros and cons of each options? They can not navigate themselves and utilize the service. It can even cause a different disparity. Issue and secondary data must be technically flee to transfer. While it keeps interoperability ease. More options are becoming available to patient. But if data cannot be shared among stakeholders, including patient hospitals in strollers and budget your providers, patient data will be fragmented and people cannot yet continue to care which they benefited under current centralized care system. And this is most challenging part. But the last one is that the security aspect more players will involving decentralized health care outside of conventional healthcare system. So obviously, both the number of healthcare channels and our frequency of data sharing will increase more. It's create ah, higher data about no beauty, and so, under the new health care framework, we needed to ensure patient privacy and safety and also re examine a Scott write lines for sharing patient data and off course. Corbett Wasa Stone Catalyst off this you saved. But what folly. Our drivers in Macro and Micro Perspective from Mark Lowe. The challenges in healthcare system have been widely recognized for decades, and now he's a big pain. The pandemic reminded us all the key values. Misha, our current pain point as I left the church shores. Those are increasing the population, health sustainability for doctors and other social system and value based care for better and more affordable care. And all the elements are co dependent on each other. The light chart explained that providing preventive care and Alan Dimension is the best way threes to meet the key values here. Similarly, the direction of community based care and about your care is in line with thes three values, and they are acting to maximize the number of beneficiaries form. A micro uh, initiative by nonconventional players is a big driver, and both CBS and Walmart are being actively engaged in healthcare healthcare businesses for many years. And CBS has the largest walking clinic called MinuteClinic, Ottawa 1100 locations, and Walmart also has 20 primary clinics. I didn't talk to them. But the most interesting things off their recent innovation, I believe, is that they are adjusted and expanded their focus, from primary care to community health Center to out less to every every customer's needs. And CBS Front to provide affordable preventive health and chronic health monitoring services at 1500 CBS Health have, which they are now setting up and along a similar line would Mark is deploying Walmart Health Center, where, utilizing tech driven solutions, they provide affordable one stop service for core healthcare. They got less, uh, insurance status. For example, more than 40% of the people in U. S visit will not every big, so liberating the huge customer base and physical locations. Both companies being reading decentralization off health care and consumer device company such as Apple and Fitbit also have helped in transform forming healthcare in two ways. First, they are growing the boundaries between traditional healthcare and consumer product after their long development airport available, getting healthcare device and secondary. They acted as the best healthcare educators to consumers and increase people's healthcare awareness because they're taking an important role in the enhancement, health, literacy and healthcare democratization. And based on the story so far, I'd like to touch to business concept which can be applied to both Japan and the US and one expected change. It will be the emergence of data integration plot home while the telehealth. While the healthcare data data volume has increased 15 times for the last seven years and will continuously increase, we have a chance to improve the health care by harnessing the data. So meaning the new system, which unify the each patient data from multiple data sources and create 360 degrees longitudinal view each individual and then it sensitized the unified data to gain additional insights seen from structure data and unable to provide personal lives care. Finally, it's aggregate each individual data and reanalyzed to provide inside for population health. This is one specific model I envision. And, uh, health care will be provided slew online or offline and at the hospital or detail store. In order to amplify the impact of health care. The law off the mediator between health care between hospital and citizen will become more important. They can be a pharmacy toe health stand out about your care providers. They provide wide range of fundamental care and medication instruction and management. They also help individuals to manage their health care data. I will not explain the details today, but Japan has similar challenges in health care, such as increasing healthcare expenditure and lack of doctors and care givers. For example, they people in Japan have physical physician visit more than 20 times a year on average, while those in the U. S. On >>the do full times it sounds a joke, but people say because the artery are healthy, say visit hospitals to see friends. So we need to utilize thes mediators to reduce cost while they maintained social place for citizens in Japan, the government has promoted, uh, usual family, pharmacist and primary doctors and views the community based medical system as a policy. There was division of dispensing fees in Japan this year to ship the core load or pharmacist to the new role as a health management service providers. And so >>I believe we will see the change in those spaces not only in the U. S, but also in Japan, and we went through so unprecedented times. But I believe it's been resulting accelerating our healthcare transformation and creating a new business innovation. And this brings me to the end of my presentation. Thank you for your attention and hope you could find something somehow useful for your business. And if you have any questions >>or comments, please for you feel free to contact me.

>>Hi, everyone. Good afternoon. Thank you for joining this session. I feel honored to be invited to speak here today. And I also appreciate entity research Summit members for organ organizing and giving this great opportunity. Please let me give a quick introduction. First, I'm a Takashi from Marvin American population, and I'm leading technology scouting and global ation with digital health companies such as Business Alliance and Strategically Investment in North America. And since we started to focus on this space in 2016 our team is growing. And in order to bring more new technologies and services to Japan market Thesis year, we founded the new service theories for digital health business, especially, uh, in medical diagnosis space in Japan. And today I would like to talk how health care has been transformed for my micro perspective, and I hope you enjoy reasoning it. So what's happened since the US identify the first case in the middle of January, As everyone knows, unfortunately, is the damaged by this pandemic was unequal amongst the people in us. It had more determined tal impact on those who are socially and economically vulnerable because of the long, long lasting structural program off the U. S. Society and the Light Charity about daily case rating elevator country shows. Even in the community, the infection rate off the low income were 4.5 times higher than, uh, those of the high income and due to czar straight off the Corvette, about 14 million people are unemployed. The unique point off the U. S. Is that more than 60% of insurance is tied with employment, so losing a job can mean losing access to health care. And the point point here is that the Corvette did not create healthcare disparity but, uh nearly highlighted the underlying program and necessity off affordable care for all. And when the country had a need to increase the testing capacity and geographic out, treat the pharmacies and retails joined forces with existing stakeholders more than 90% off the U. S Corporation live within five miles off a community pharmacy such as CVS and Walgreen, so they can technically provide the test to everyone in all the community. And they also have a huge workforce memory pharmacist who are eligible to perform the testing scale, and this very made their potential in community based health care. Stand out and about your health has provided on alternative way for people to access to health care. At affordable applies under the unusual setting where social distancing, which required required mhm and people have a fear of infection. So they are afraid to take a public transportacion and visit >>the doctor the same thing supplied to doctor and the chart. Here is a number of total visit cranes by service type after stay at home order was issued across the U. S. By Ali April patient physical visits to doctor's offices or clinics declined by ALAN 70%. On the other hand, that share, or telehealth, accounted for 25% of the total total. Doctor's >>visit in April, while many states studied to re opening face to face visit is gradually recovering. And overall Tele Health Service did not offset the crime. Physician Physical doctor's visit and telehealth John never fully replace in person care. However, Telehealth has established a new way to provide affordable care, especially to vulnerable people, and I don't explain each player's today. But as an example, the chart shows the significant growth of >>the tell a dog who is one of the largest badger care and tell his provider, I believe there are three factors off paradox. Success under the pandemic. First, obviously tell Doc could reach >>the job between those patients and doctors. Majority of the patients who needed to see doctors who are those who have underlying health conditions and are high risk for Kelowna, Bilis and Secondary. They showed their business model is highly scalable. In the first quarter of this year, they moved quickly to expand their physical physicians network to increase their capacity and catch up growing demand. To some extent, they also contributed to create flexible job for the doctors who suffered from Lydia's appointment and surgery. They utilized. There are legalism to maximize the efficiency for doctors and doing so, uh, they have university maintained high quality care at affordable applies Yeah, and at the same time, the government recognize the body of about your care and de regulated traditional rules to sum up she m s temporary automated to pay a wide range of tell Her services, including hospital visit and HHS temporarily waived hip hop minorities for telehealth cases and they're changed allowed provider to use communication tools such as facetime and the messenger. During their appointment on August start, the government issued a new executive order to expand tell his services beyond the pandemic. So the government is also moving to support about your health care. So it was a quick review of the health care challenges and somewhat advancement in the pandemic. But as you understand, since those challenges are not caused by the pandemic, problems will stay remain and events off this year will continuously catalyze the transformation. So how was his cherished reshaped and where will we go? The topic from here can be also applied to Japan market. Okay, I believe democratization and decentralization healthcare more important than ever. So what does A. The traditional healthcare was defined in a framework over patient and a doctor. But in the new normal, the range of beneficiaries will be expanded from patient to all citizens, including the country uninsured people. Thanks to the technology evolution, as you can download health management off for free on iTunes stores while the range of the digital health services unable everyone to participate in new health system system. And in this slide, I put three essential element to fully realize democratization and decentralization off health care, health, literacy, data sharing and security, privacy and safety in addition, taken. In addition, technology is put at the bottom as a foundation off three point first. Health stimulus is obviously important because if people don't understand how the system works, what options are available to them or what are the pros and cons of each options? They can not navigate themselves and utilize the service. It can even cause a different disparity. Issue and secondary data must be technically flee to transfer. While it keeps interoperability ease. More options are becoming available to patient. But if data cannot be shared among stakeholders, including patient hospitals in strollers and budget your providers, patient data will be fragmented and people cannot yet continue to care which they benefited under current centralized care system. And this is most challenging part. But the last one is that the security aspect more players will involving decentralized health care outside of conventional healthcare system. So obviously, both the number of healthcare channels and our frequency of data sharing will increase more. It's create ah, higher data about no beauty, and so, under the new health care framework, we needed to ensure patient privacy and safety and also re examine a Scott write lines for sharing patient data and off course. Corbett Wasa Stone Catalyst off this you saved. But what folly. Our drivers in Macro and Micro Perspective from Mark Lowe. The challenges in healthcare system have been widely recognized for decades, and now he's a big pain. The pandemic reminded us all the key values. Misha, our current pain point as I left the church shores. Those are increasing the population, health sustainability for doctors and other social system and value based care for better and more affordable care. And all the elements are co dependent on each other. The light chart explained that providing preventive care and Alan Dimension is the best way threes to meet the key values here. Similarly, the direction of community based care and about your care is in line with thes three values, and they are acting to maximize the number of beneficiaries form. A micro uh, initiative by nonconventional players is a big driver, and both CBS and Walmart are being actively engaged in healthcare healthcare businesses for many years. And CBS has the largest walking clinic called MinuteClinic, Ottawa 1100 locations, and Walmart also has 20 primary clinics. I didn't talk to them. But the most interesting things off their recent innovation, I believe, is that they are adjusted and expanded their focus, from primary care to community health Center to out less to every every customer's needs. And CBS Front to provide affordable preventive health and chronic health monitoring services at 1500 CBS Health have, which they are now setting up and along a similar line would Mark is deploying Walmart Health Center, where, utilizing tech driven solutions, they provide affordable one stop service for core healthcare. They got less, uh, insurance status. For example, more than 40% of the people in U. S visit will not every big, so liberating the huge customer base and physical locations. Both companies being reading decentralization off health care and consumer device company such as Apple and Fitbit also have helped in transform forming healthcare in two ways. First, they are growing the boundaries between traditional healthcare and consumer product after their long development airport available, getting healthcare device and secondary. They acted as the best healthcare educators to consumers and increase people's healthcare awareness because they're taking an important role in the enhancement, health, literacy and healthcare democratization. And based on the story so far, I'd like to touch to business concept which can be applied to both Japan and the US and one expected change. It will be the emergence of data integration plot home while the telehealth. While the healthcare data data volume has increased 15 times for the last seven years and will continuously increase, we have a chance to improve the health care by harnessing the data. So meaning the new system, which unify the each patient data from multiple data sources and create 360 degrees longitudinal view each individual and then it sensitized the unified data to gain additional insights seen from structure data and unable to provide personal lives care. Finally, it's aggregate each individual data and reanalyzed to provide inside for population health. This is one specific model I envision. And, uh, health care will be provided slew online or offline and at the hospital or detail store. In order to amplify the impact of health care. The law off the mediator between health care between hospital and citizen will become more important. They can be a pharmacy toe health stand out about your care providers. They provide wide range of fundamental care and medication instruction and management. They also help individuals to manage their health care data. I will not explain the details today, but Japan has similar challenges in health care, such as increasing healthcare expenditure and lack of doctors and care givers. For example, they people in Japan have physical physician visit more than 20 times a year on average, while those in the U. S. On the do full times it sounds a joke, but people say because the artery are healthy, say visit hospitals to see friends. So we need to utilize thes mediators to reduce cost while they maintained social place for citizens in Japan, the government has promoted, uh, usual family, pharmacist and primary doctors and views the community based medical system as a policy. There was division of dispensing fees in Japan this year to ship the core load or pharmacist to the new role as a health management service providers. And so I believe we will see the change in those spaces not only in the U. S, but also in Japan, and we went through so unprecedented times. But I believe it's been resulting accelerating our healthcare transformation and creating a new business innovation. And this brings me to the end of my presentation. Thank you for your attention and hope you could find something somehow useful for your business. And if you have any questions >>or comments, please for you feel free to contact me. Thank you.

(groovy music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Everyone, welcome to this special CUBE Conversation here in Palo Alto Studios for theCUBE. I'm John Furrier, host for theCUBE. We're here with special guest, Arijit Mukherji, who's the CTO of SignalFx, hot startup that's now growing very very fast in this cloud native world. Arijit, great to see you. Thanks for coming on. >> Great to see you too again, John. >> So cloud growth is changing the landscape of the enterprise. We're seeing it obviously, it's no real surprise, Cloud 1.0 has happened, public cloud. Cloud 2.0 as we're calling it is changing the game, where you're seeing enterprise cloud really the focus. We're seeing cloud native really move the needle. Kubernetes has kind of created that abstraction, kind of standard, defacto standard, if you will, people getting around. So you're seeing the game changing from how apps are built. >> Right. >> To security and everything in between. So a new set of services, web services at scale has certainly change the game. You guys are in the middle of this with monitoring and observability. And I want you to help us understand the core problem enterprises are having today because they know it's coming. They know it's here. They got investments out there. The cloud has changed the game for the enterprise, what's the problem? >> Yeah, so you're absolutely right, John. So everybody's moving to this sort of the new way of doing things, right. So monoliths are gone. Microservices are in, containers are in. And you're going to have to do that because we know if you don't do that, you're going to get lapped right, by the competition. And so the challenge right now is how do you make that successful? And the challenge there is these new environments are much much more complex as you mentioned. And the question is unless you understand how these systems behave, how will you be able to run them successfully? So the challenge as far as monitoring and observability is concerned is I think it's critical that it be there for it to be able to sort of do this cloud transformation successfully. But it's a far more complex and hard challenge than it used to be. >> We've seen the evolution. Yeah, we've been covering that for 10 years. It's the 10th year of theCUBE. We'll be celebrating that at VMworld this year. You've seen wave one, lift and shift, do some basic stuff, not a lot of heavy lifting. No tinkering with some of the tech in there. Monitoring is great. And then you've got rearchitect. Let's get some cloud native. Let's see some Kubernetes. And then the next path is this complete microservices. This is where everyone's really excited about. >> That's right. >> This is where the complexity is. So I got to ask you, that changes the notion of monitoring and observability. So given that this shift is happening, rearchitecting to full microservices, what is observability in that equation? >> Right, so there's a very interesting difference between I think, monitoring and observability that I think I would like to touch upon here. So you know, back in the days of the monolith, it was what we called classic monitoring. Monitoring is about looking at things, looking for things that you know might happen. So for example, if I know my server might fall down, I will run a probe to make sure that it's up or not, right? But when you move to this new world, I mean, have you, if you look at any cloud native environment with all the microservices and containers, for example Amazon's S3 has 120 different microservices powering it behind it. Now, if you were to go and ask an engineer like what is the map or how are the data flow happening in that environment? I guarantee you, no one person can probably do that well. So then, monitoring doesn't work because I don't even know what to look for. So what's important is I be able to gather telemetry, have the information available so that the unknowns, the kind of things that I'm not expecting because it's just too complex or just unanticipated. Like that data will allow us to figure out what went wrong. So observability is about gathering telemetry and information so that we can deal with that complexity, understand problems as they behave because the world is no longer simple anymore. >> So overall, observability is just monitoring in a dynamic environment, what you're saying. 'Cause monitoring used to be simple. You know it's going on. Static routes. >> That's right. >> Set policy, get some alarms. Network management, basic stuff. >> Exactly like Nagios checks and what not, yup. >> Now, you're saying there's unknowns happening, unexpected things going on around the services. What would that be just as an example? >> Yeah, so for example, again with microservices, why are we doing it? Because we want smaller teams to be able to innovate quicker, faster, right. So instead of my monolith, let's say I have whatever, SignalFx has 50 different microservices powering it. Now each of these teams, they are deploying software on their own because the whole idea of Cloud 2.0 is that we are able to move faster. So what that means is individual chunks of my overall service are adapting or changing over time or evolving. And so that's the complexity, like it's actually a changing landscape. Like my map does not stay the same on an ongoing basis. That is fundamentally a big challenge. The other challenge that I would mention too is that how ephemeral things are getting. So all these microservices that are themselves adapting, they're also being deployed in containers and by Kubernetes. Where these containers, they keep popping up and down all the time. Like even on infrastructure on which we are running it's extremely dynamic, right. Containers, Lambdas, sort of serverless is another great example of that. So it's a very shifting sands is what we're standing on, in some sense, right. >> And a lot of times, we cover a lot of real time. And you can't just throw in logs, you got to have that in there. This begs the question, okay, so I get the complexity. I'm a customer or I'm someone who wants to really go down this observability track with you. Why is it important? What's in it for me? >> So in the end, without it, how will you succeed? So it's almost like will a pilot with blinders on, will he be able to fly an aircraft? The answer is no. Similarly, I mean we may want to move to this modern awesome environment, which lets us move fast but unless you have visibility into it, unless you can find when problems are happening, unless you can, when those problems happen, be able to find the root cause and remediate them quickly, you're not really going to be successful. And so that's really why observability is important because it allows us to not only sort of run this well but it also allows us to understand the user experience because in the end, we are all service providers, we have users, right. And so understand what the user's experience is like. So that's important. Understand the key business metrics. If you look at a lot of the talk track that's been going around in the circuit around error budgets, and SLIs, and SLOs, which are sort of important things. The whole idea is that we want to measure and monitor what's important to the business, to the user. And that's kind of what observability allows us to get. >> You know gone are the days of a few application servers and a database. >> That's right. >> So on the why is it important, I got to follow up and say from an operations perspective, what is the new reality, okay? Because we know there's going to be a lot of databases out there, and a lot of different applications. You mentioned some of the containerization, dynamic microservices. But what's the impact and what's the importance to the operation side of the equation with observability? >> So what's happening now is again, back in the monolith days, the operators, the IT staff, who were running those infrastructure, they were the ones who would implement monitoring, right. But if you see the way now these environments are structured, these organizations are structured, it's the developers who are building tools. They are the ones who are also running them. And in order for an organization to be able to move fast, they need to give powerful tools to their developers to do their job. And because there is no one person who knows the right way of doing things. So it's really about sort of democratizing that capability. So you will need to give powerful observability tools to the developers, the operators, who are also the new operators, to sort of make with it what they will, in the sense that they are ones who best understand the meaning of the data that's being collected. Because it's all very specific to individual microservices. So that's really a powerful observability platform is one that allows you to easily collect a lot of information, allows you to analyze, visualize it, and sort of treat it in a way to sort of it helps you answer the questions you want to answer. >> So you're saying that okay, ops gets monitoring and observability. But a new persona, user is a developer. >> That is correct. >> And what do they care about? 'Cause they just want it to be abstracted away. They're not really probably wake up and say, "Hey, I can't wait to look at observability." So is it more of a use, so talk about the developer dynamic 'cause this is, that seems like a new trend. >> Yes, it totally is. So things are becoming less about black box testing, and more about sort of observability being an end-to-end process. So let me tell you what I mean. So back in the day, let's say, I implemented, I deployed a monolith. It was a Java server. There were standard ways to check them as a black box. I could run probes, et cetera, to run a health check end point and whatnot, life was great. But now, that's obviously not good enough. Because as I mentioned, because of interactions, because of complexity, a black box testing doesn't even work because like I said, the whole environment is very dynamic. So what the pattern now is that as I said, observability is an end-to-end process in the sense that developers care about observability when they're writing the software. It is not an afterthought anymore. So as I'm writing, as I'm developing software, I think about well, when this thing goes out into the wild, how will I monitor it? What are the things that I care about as a developer? Because I understand the system the best. And so you instrument, you build systems for observability is I think a big change that's happening. And once that happens, when you are the one person, who also is able to best read that data. >> So while they're developing, they get these benefits inherently right there on the spot. >> That is correct. >> This is kind of consistent with the live programming trend that's really popular in some languages. Rather than doing all the debugging, post event, coming back to it. >> That's right. >> So making it very efficient seems to be a use case. >> Yes, you are absolutely right. It's one of the things I'll actually talk about a lot actually is you know, observability, what is it for? Is it just for telling me when my production is not working well? The answer is no. Even when I'm developing, I may want to know well, did I have a performance degradation? How do I know that the code that I wrote is good? So I use again the same telemetry that I'm going to use later, even during the development process to make sure that the code that I wrote works well. We do the same thing during deployment. Again, I deploy a version or a canary or a few of them. Are they running well, right? So it is not just about what's happening in production, it's about end-to-end from development and deployment up to production. >> And that's what developers want. They want it in the moment, right when they're coding. >> That's right. >> Taken care of. >> It's instant gratification, like everybody else wants. >> And more efficiency. They know it's going to break, they know the consequences, they can deal with that. >> Yes. >> This is awesome, so the next question I have for you is how do you implement observability? >> That's a great question. So you can think of it as sort of in two ways. One is the means through which you get it. So you get observability through metrics, through logs, through traces, through probes, et cetera. That's one way. Another one is I think I alluded to a little bit earlier is what are your goals? Because everybody's goals are different, right? And if you think about in that sense, then the sort of the purpose of observability are a few. A, is it allowing your teams to move faster? So I spoke about some of the process just about earlier. Are they able to deploy code with confidence, faster? When problems happen, how quickly are we able to then triage them? So the whole incident review process. That's kind of important, observability better help me with that. The user experience is also something very important. As I mentioned, observability is going sort of more up the stack, so to speak. And so being able to understand what the user experience is, is very important. Similarly, understanding from the business point of view, what does the business care about? For example, when I had that outage, how much loss did I have? How many eyeballs did I miss on my side? So I think one way to think about it, you need to have good processes, good tools. At the same time, you need to be clear about what your goals are, and make sure that sort of whatever you're implementing, sort of furthers those to some extent. >> I'd like to play a little CUBE game here with you, and walk through the observability myths and reality. >> Sure. >> I'll say the myth, and you can tell me the reality. Myth number one, having observability reduces incidents. >> No, actually it might increase it. Let me put it that way, I'll tell you why. So it's almost like in a human, I may be measuring someone's temperature or pulse rate like every day. Does that make the person less or more prone to health problems? Chances are it's going to be the same. I might actually find things that I was not aware of, right. So in that sense, just having observability does not necessarily change anything about the process. But what it will do though is when a problem does happen, it I have this treasure trove of data, which I can then use to quickly isolate the problem. So what it does is it shrinks the outage time, which is in the end, what's very very important. So while it may not reduce your outages, it will definitely make them better from the end user point of view. >> Second myth, buying a tool means you have observability. Reality? >> No. Having a doctor doesn't mean I am healthy. In the sense that I think a tool is very important. It's a very very important step but the question is how well adopted is the tool? What kind of data am I sending to the tool? How are the users, my engineers, how well versed are they in using it, right? And so there's a lot of other stuff associated with it. So tool selection is very important but I think adoption and making it a success within the organization is also very very important. >> Okay, final myth, observability is free or cheap. >> I wish, so. >> Well you're a for-profit company. >> That's exactly right. No, I think, I really feel that observability is almost like a, it's an associated function. As I mentioned earlier, if I'm going to be successful flying this plane, I need commensurate amount of other services that sort of help me make that successful. So in a way, one way to think about it is it is it scales up as complex and as large as your environment gets and justifiably so. Because there's various other reasons too because in a way, adopting new technologies all the time. So tools are getting just more and more complicated. My requirements are getting more complicated. And then another thing I would add is the quality of my service like the level, the quality of service that I provide, the higher bar I want, I probably am spending more on observability. But it's a justified cost. So I think it's not a fixed cost obviously. It grows with your complexity and the kind of quality that you want to provide. >> Well, it's also, I mean I think the observability challenge with the complexity is there's a hidden cost though if you're not observing the right areas. >> Yes. >> The cost of not having that visibility, as a blind spot, could have business benefits, I mean not benefits, but consequences in a sense of outages, security, I mean there's a lot of different things that you got to have the observation space being enterprise. >> You totally have to do that. It's actually one thing we like to say is that instrument first, ask questions later. Now, coming from a for-profit vendor, it may sound sort of self-serving. But it's kind of not so too in the sense that I mean hindsight is 20-20. If I am stuck in a bad situation, I have no telemetry to sort of fall back on. Then where do I go with it? So I think we should be more conservative and sort of try and instrument the things that we think might be applicable, the kind of questions I may want to ask when a rainy day comes. So I think you're absolutely right. So it has to be something. It's a philosophy that developers, engineers, should sort of imbibe and they should then practice it as part of their sort of own workflows. >> I want to get into where company should invest in observability but I want to just throw a wildcard question at you, which is when you look at the big data space, even go back 10 years ago to now, cloud, there's always been they're talking about tool versus platform. And anything that's been data centric tend to be platform like conversations, not a tool. Tool can be like okay, it does a thing, does it really well, a hammer, everything's a nail with the hammer. But there's more dynamic range required 'cause you're talking about observation space, talking about cloud, horizontally scalable, hybrid on-premises. >> That's right. >> So again, it kind of feels like a platform technical challenge. >> You're absolutely right. So I think two factors at play if you ask my opinion. One is if you were interested in monitoring, a tool is perfect, right? Because you kind of know what you want. If a tool does that well, there's more power to it. But if you don't know what you want, if you are basically collecting stuff and you're depending on it as a way to answer questions on the unknown unknowns as Charity Majors like to say, then you do need a platform. Because that platform needs to be sort of inclusive. It must have data of different types, all be able to come into it. It's not really meant for a specific purpose. It's meant to be a generic tool. So we do see this trend in the industry towards more sort of a platform approach to this. Obviously, they will have tool-like capabilities because they're answering sort of particular use cases, et cetera. But the underlying platform, the more powerful it is, I think the better it is in the long run. >> Yeah and the argument there could be if it's an enabling platform, you can create abstraction layers for visualization. >> That's correct. >> Or APIs, other services. >> That's exactly right, yes. >> Okay, so now, I'm interested. I want to get started. How do I invest in good observability? I'm crossing the chasm, I'm going to full microservices. I've done a rearchitect, my team has got cultural buy-in. We're hiring, we're building our own stack, we're going to have on-premise, we'll be in the cloud. In some cases, fully cloud. What do I do, what do I invest? What's my play book? >> Sure, so I think we talked about the first one a little bit. So you have to choose the right tool. And the right tool in my opinion is not the one that does the best job now. When maybe I'm small, I'm not fully there yet. We have to think about what's the right tool or the right platform for when I'm, where for where I do want to go. Now, that may be commercial, that may be open source, that's not the point. But the point is that we need to have a very considered thought about what is it that we're betting the farm on, right. So that's number one. But that's not good enough. So as I mentioned, we need to make sure that there is a usage and understanding of the tool within the organization. So a big part of it is just around the practice and the culture of observability within the organization. So for example, good habits like every time we have an incident, you speak about these are the things that we measure, this is how we use our observability tool, here are the dashboards that we depended on. Sort of reinforcing those concepts over and over again so that those who are on board, they are obviously doing it right. Those who are not, they see the value and they start sort of using those good practices. That's kind of very important. The third thing I would say is start moving towards more higher level monitoring, observability. So measure the user experience, measure what's important to the business. Stuff like that are important. The fourth area that's sort of very very key is around sort of the whole incident management process. This is actually a very active topic. A lot of discussion going on out there right now is you know, it's great that I have this great tool, I have all this telemetry. I found there was a problem. But when that incident happened, there are a lot of again good practices. This is part of the whole culture and process of observability is how do we make that process smooth and standardized, and sort of become more efficient at it, right? And let's say you were to do that, the final sort of end goal is as we like to call is a self-running or a self-automated cloud, where do we really need humans in all of this? How can we sort of run remediation in a more and more automated fashion? At least for the stuff that maybe does not require a human intelligence, right? Actually, you'll find that 80, 90% of issues probably don't. If you think hard about it, don't require human. So I think this move towards automation is also another sort of very fantastic trend. I've seen that being very successful in the past. Some of my old companies. And I think that's going to be a trend in later stage. >> Yeah, for known processes, people and process. >> Yup. >> That's where problems come. Bad process or code and people mistakes. You can automate that. Mundane tasks or on differentiated kind of heavy lifting. >> That is exactly right because you know, there was this interesting study done that was commissioned by Stripe, where they found that among the CXOs, they value engineers and developers more than money. Because getting them, because they are such a scarce resource. So if you do get them, you probably don't want them to sort of run and do mundane things. That's not what you hired them for. So you want them to do the work of the business, and the more you can isolate them from sort of the mundane and they have automation come into play, I think it's just better across the board. >> Oh, they're investing more from the CSOs and the CIOs we talked to. >> Right. >> There's more investments in-house now for real development, real software development, real projects. And those top talent, they want to work on the toughest problem. >> That's exactly, that's why we're moving to a SASS future right? Because all the function that are not core to my business, I want to farm off and have somebody else take care of them. >> So you got me sold on observability, I'm a big believer in the observation space. And I certainly think cloud as they're horizontally scalable, elastic resource, and certainly, the Kubernetes trends with service measures and all the stuff going on, Kubeflow, and a bunch of other things. More and more services are going to be very dynamic. >> Yes. >> So you're going to have a lot of unknown and unusual patterns. >> That's right. >> That's just the way the internet works now. So you had me sold on that. Now, I need to get my team to the next level. They bought into devops. How do I take the temperature of where our IQ is in the life cycle of observability? Because I got to know where I am. Is there a way I can track my maturity or progress? >> Yes, yes, it's a topical question because I go out and I meet a lot of prospects and customers. And it's part of my job. And a lot of times, because sort of we are sort of in the leading edge, they will ask us, they look to us to tell them like are we doing it right? Or how did you guys do it? So just so that sharing of information, how can we get better? So as part of that, we actually, at SignalFx, we actually built a maturity model. It's a way for us to sort of evaluate ourselves across various dimensions to see how well are we doing? Not only, it's not just the score, it's also about how well are we doing? But how can we improve? Like if you wanted to go to the next level of maturity for example, like what are some of the things that we can do? And it spans multiple dimension. It starts with how are we even collecting data, right? How easy is it, in other words, for somebody starting a new service or using a new software to get to the business of observability? That's kind of important. You got the data, how will am I able to visualize it? Because effective visualization is as you can understand, very important. The next part comes with alerting. So well, things are running. I know how they're going. How well can I detect when problems are happening? How soon can I detect when problems are happening? What kind of items can I monitor? Can I monitor the low-level things? Or can I monitor the higher-level constructs? When the problem happen, let's talk about remediation. How quickly can I triage the problem to find out where it was? What kind of tools and slice-and-dice capabilities do I have? That's an important part of it. Let's say I did it. After that comes things like remediation. So I found the problem, how well can I remediate so like we talked about automation. So there is multiple different categories where we sort of, we talked about what, we've seen in the field in terms of what people are doing as well as some of the best practices. >> So you're going to make this tool available to customers? >> Yes, we have, it's actually available on our website. And if you come to our website, you'll be able to sort of run the assessment, as well as sort of see all of it yourself. >> Well, we've been following you guys since you launched. You've got a great management team, great technical chops, we've covered that. And this observability is a real trend as it moves into more complexity as we talked about. Most customers that are getting into this are trying to sift this from the signal, from the noise, and trying to think, decide who is the leader, and who is not. So how would you describe what a leader in enterprise observability looks like from a supplier's standpoint. You guys are one, you want to be the leader. You're the market leader. >> Right. >> What does a leader look like from a customer's standpoint? What are the things that have to be in place? What are the table stakes to be that leader? >> Sure, that's a great question. And yeah, so we did, SignalFx, we did build SignalFx to be a leader in this space, frankly. And there's a lot of different aspects that goes behind like what creates a good supplier in this space. One is I think you have to be open and flexible. Like you have to be, it's a platform play. You better be able to collect data from all the systems that are out there. The kind of the quality of the integration is very important. Another big thing we're finding is scale. A lot of these systems might not work when you move to sort of large numbers. And the problem that we are seeing is while I may have a hundred servers, I may be running 10,000 containers in those hundred servers. So now, everybody is a scale player, right. So the question is will your platform really be able to handle the complexity and the load? So that's an important one. Analytics as we mentioned is another very very important capability. I'll like to say that the ability to do analytics is not just good enough. How easy is it to use? Like are you developers and engineers, are they even using it? So the easy and the capability of analytics is important because that's kind of what allows us to measure those KPIs, those SLIs, those business metrics. And so that's kind of important. Slice-and-dice capability like how fast is the tool? Because when that outage happens, I don't want to run a five-minute query to sort of find some suspicion or you know to. And so the question is how quickly will it answer these ad hoc questions for me when the problem happens? So sort of the whole triage process that I talked about. The ability to support automation is one. The ability to, as I mentioned to take in different types of data, traces, metrics, be able to play with logs. All of those are sort of important aspects of it, yeah. >> Final question for you. If a customer says, "I'm going to cross the bridge "to the future with SignalFx." What's some of the head room? What's some of the futures that you would expect the customer to imagine or expect down the road as observability becomes more scalable? I can imagine the metrics are going to be all over the place. >> Yes. >> A lot of unusual patterns. New apps could come in that could be hits. And new data comes in. So as you take them today in observability, what's the next level, to cross that bridge to the future? >> Sure, sure. >> What's the next expectation? >> I think one thing will be to expect the unexpected. Because the world is changing so fast, I think you would probably be running things that you won't expect later. But a few things, I would say yes. So I think the proliferation of metrics and traces is like a big trend, where we see there used to be this dependence on sort of these monoliths and APM that sort of is transforming a little bit. There is this also this concept of using data science and artificial intelligence to come to bear on this space. So that's actually an interesting trend we see, where the idea is that it's hard because it's a complex system. It's hard for humans to define exactly what they want. But if the system can help them, can help identify things, that's actually really fantastic. Another one that we sort of briefly touched upon is automation or self-automated systems, where I think well, the time you're going to see that platforms like ours are going to help you automate much of this in a safe manner, because these are controlled systems, where if you, things can go awry and that's not a good position to be. So these are some exciting areas, where I think you will see some development down the road. >> And we've been seeing a lot of conversation around correlation and causation, and the interplay between those as these services are being stood up, torn down, stood up, torn down. You can look at the numbers all day long but you got to know causation, correlation. >> You bet, you bet because I think a lot of times, we naively think about this as a data problem, right? Where I find the kink in the graph, and if I go looking, I'll probably going to find a hundred different things that were sort of also correlated. Some of them may or may not be related to it. So I think a good tool is one that sort of gives you a sense. It sort of creates a boundary around the data set that it needs to look at, that is sort of relevant to your problem, and able to give you clues to causation. That you're exactly right because again, complexity is a hard problem to deal with. And anything that we can do to sort of help you short-circuit some of the pain is awesome. >> And I think you're on the right track with this developer focus because devops has proven that the developers want to code, build apps, and abstract away the complexity. And certainly, it's complex. >> That's right, that's right. It's fairly complex. >> Arijit, thanks for coming on. Arijit Mukherji, the CTO of SignalFx here inside the special CUBE Conversation breaking down the future of observability, where monitoring is going to the next level, certainly with cloud, impact to enterprise cloud. I'm John Furrier, here on theCUBE. Thanks for watching. >> Thank you. (groovy music)

>> Announcer: Live from the Computer History Museum in Mountain View, California. It's theCUBE, covering DevNet Create 2018. Brought to you by Cisco. >> Hey, welcome back, everyone. This is theCUBE, live here in Mountain View, California, heart of Silicon Valley for Cisco's DevNet Create. This is their Cloud developer event. It's not the main Cisco DevNet which is more of the Cisco developer, this is much more Cloud Native DevOps. I'm joined with my cohost, Lauren Cooney and our next guest is Christine Yen, who is co-founder and Chief Product Officer of Honeycomb.io. Welcome to theCUBE. >> Thank you. >> Great to have an entrepreneur and also Chief Product Officer because you blend in the entrepreneurial zeal, but also you got to build the product in the Cloud Native world. You guys done a few ventures before. First, take a minute and talk about what you guys do, what the company is built on, what's the mission? What's your vision? >> Absolutely, Honeycomb was built, we are an observability platform to help people find the unknown unknowns. Our whole thesis is that the world is getting more complicated. We have microservices and containers, and instead of having five application servers that we treated like pets in the past, we now have 500 containers running that are more like cattle and where any one of them might die at any given time. And we need our tools to be able to support us to figure out how and why. And when something happens, what happened and why, and how do we resolve it? We look around at the landscape and we feel like this dichotomy out there of, we have logging tools and we have metrics tools. And those really evolved from the fact that in 1995, we had to choose between grep or counters. And as technology evolved, those evolved to distribute grep or RDS. And then we have distribute grep with fancy UIs and well, fancy RDS with UIs. And Honeycomb, we were started a couple years ago. We really feel like what if you didn't have to choose? What if technology supported the power of having all the context there the way that you do with logs while still being able to provide instant analytics the way that you have with metrics? >> So the problem that you're solving is one, antiquated methodologies from old architectures and stacks if you will, to helping people save time, with the arcane tools. Is that the main premise? >> We want people to be able to debug their production systems. >> All right, so, beyond that now, the developer that you're targeting, can you take us through a day in the life of where you are helping them, vis a vis the old way? >> Absolutely, so I'll tell a story of when myself and my co-founder, Charity, were working together at PaaS. PaaS, for those who aren't familiar, used to be RD, a backend form of mobile apps. You can think of someone who just wants to build an iOS app, doesn't want to deal with data storage, user records, things like that. And PaaS started in 2011, got bought by Facebook in 2013, spun down very beginning of 2016. And in 2013, when the acquisition happened, we were supporting somewhere on the order of 60,000 different mobile apps. Each one of them could be totally different workload, totally different usage pattern, but any one of them might be experiencing problems. And again, in this old world, this pre-Honeycomb world, we had our top level metrics. We had latency, response, overall throughput, error rates, and we were very proud of them. We were very proud of these big dashboards on the wall that were green. And they were great, except when you had a customer write in being like, "Hey, PaaS is down." And we look at our dashboard we'd be like, "Nope, it's not down. "It must be network issues." >> John: That's on your end. >> Yeah, that's on your end. >> John: Not a good answer. >> Not a good answer, and especially not if that customer was Disney, right? When you're dealing with these high level metrics, and you're processing tens or hundreds of thousands of requests per second, when Disney comes in, they've got eight requests a second and they're seeing all of them fail. Even though those are really important, eight requests per second, you can't tease that out of your graphs. You can't figure out why they're failing, what's going on, how to fix it. You've got to dispatch an engineer to go add a bunch of if app ID equals Disney, track it down, figure out what's going on there. And it takes time. And when we got to Facebook, we were exposed to a type of tool that essentially inspired Honeycomb as it is today that let us capture all this data, capture a bunch of information about everything that was happening down to these eight requests per second. And when a customer complained, we could immediately isolate, oh, this one app, okay let's zoom in. For this one customer, this tiny customer, let's look at their throughput, error rates, latency. Oh, okay. Something looks funny there, let's break down by endpoint for this customer. And it's this iterative fast, highly granular investigation, that is where all of us are approaching today. With our systems getting more complicated you need to be able to isolate. Okay, I don't care about the 200s, I only care about the 500s, and within the 500s, then what's going on? What's going on with this server, with that set of containers? >> So this is basically an issue of data, unstructured data or have the ability to take this data in at the same time with your eye on the prize of instrumentation. And then having the ability to make that addressable and discoverable in real time, is that kind of? >> Yeah, we've been using the term observability to describe this feeling of, I need to be able to find unknown unknowns. And instrumentation is absolutely the tactic to observability of the strategy. It is how people will be able to get information out of their systems in a way that is relevant to their business. A common thing that we'll hear or people will ask, "Oh, can you ingest my nginx logs?" "Can you ingest my SQL logs?" Often, that's a great place to start, but really where are the problems in an application? Where are your problems in the system? Usually it's the places that are custom that the engineers wrote. And tools need to be able to support, providing information, providing graphs, providing analytics in a way that makes it easy for the folks who wrote the code to track down the problem and address them. >> It's a haystack of needles. >> Yeah, absolutely. >> They're all relevant but you don't know which needle you're going to need. >> Exactly. >> So, let me just get this. So I'm ducking out, just trying to understand 'cause this is super important because this is really the key to large scale Cloud ops, what we're talking about here. From a developer standpoint, and we just had a great guest on, talking about testing features and production which is really the important, people want to do that. And then, but for one person, but in production scale, huge problem, opportunity as well. So, if most people think of like, "Oh, I'll just ingest with Splunk," but that's a different, is that different? I mean, 'cause people think of Splunk and they think of Redshift and Kinesis on Amazon, they go, "Okay." Is that the solution? Are you guys different? Are you a tool? How do I understand you guys' context to those known solutions? >> First of all, explain the difference between ourselves and the Redshifts and big queries of the world, and then I'll talk about Splunk. We really view those tools as primarily things built for data scientists. They're in the big data realm, but they are very concerned with being 100% correct. They're concerned with fitting into big data tools and they often have an unfortunate delay in getting data in and making it acquirable. Honeycomb is 100% built for engineers. Engineers of people, the folks who are going to be on the hook for, "Hey, there's downtime, what's going on?" And in-- >> So once business benefits, more data warehouse like. >> Yeah. And what that means is that for Honeycomb, everything is real time. It's real time. We believe in recent data. If you're looking to get query data from a year ago we're not really the thing, but instead of waiting 20 minutes for a query over a huge volume of data, you wait 10 seconds, or it's 3:00 AM and you need to figure out what's happening right now, you can go from query to query, to query, to query, as you come up with hypotheses, validate them or invalidate them, and continue on your investigation path. So that's... >> That makes sense. >> Yeah. >> So data wrangling, doing queries, business intelligence, insights as a service, that's all that? >> Yeah. We almost, we played with and tossed the tagline BI for systems because we want that BI mentality of what's going on, let me investigate. But for the folks who need answers now, an approximate answer now is miles better than a perfect one-- >> And you can't keep large customers waiting, right? At the end of the day, you can't keep the large customers waiting. >> Well, it's also so complicated. The edge is very robust and diverse now. I mean, no-js is a lot of IO going on for instance. So let's just take an example. I had developer talking the other day with me about no-js. It's like, oh, someone's complaining but they're using Firefox. It's like, okay, different memory configuration. So the developer had to debug because the complaints were coming in. Everyone else was fine, but the one guy is complaining because he's on Firefox. Well, how many tabs does he have open? What's the memory look like? So like, this a weird thing, I mean, that's just a weird example, but that's just the kinds of diverse things that developers have to get on. And then where do they start? I mean. >> Absolutely. So, there's something we ran into or we saw our developers run into all the time at PaaS, right? These are mobile developers. They have to worry about not only which version of the app it is, they have to worry about which version of the app, using which version of RSDK on which version of the operating system, where any kind of strange combination of these could result in some terrible user experience. And these are things that don't really work well if you're relying on pre-aggregated 10 series system, like the evolution of the RDS, I mentioned. And for folks who are trying to address this, something like Splunk, these logging tools, frankly, a lot of these tools are built on storage engines that are intended for full text search. They're unstructured text, you're grepping over them, and then you're build indices and structure on top of that. >> There's some lag involved too in that. >> There's so much lag involved. And there's almost this negative feedback loop built in where if you want to add more data, if on each log line you want to start tracking browser user agent, you're going to incur not only extra storage costs, you're going to incur extra read time costs because you're reading that more data, even if you're don't even care about that on those queries. And you're probably incurring cost on the right time to maintain these indices. Honeycomb, we're a column store through and through. We do not care about your unstructured text logs, we really don't want them. We want you to structure your data-- >> John: Did you guys write your own column store or is that? >> We did write our own column store because ultimately there's nothing off the shelf that gave us the speed that we wanted. We wanted to be able to, Hey, sending us data blogs with 20, 50, 200 keys. But if you're running analysis and all you care about is a simple filter and account, you shouldn't have to pull in all this-- >> To become sort of like Ferrari, if you customize, it's really purpose built, is that what you guys did? >> That is. >> So talk about the dynamic, because now you're dealing with things like, I mean, I had a conversation with someone who's looking at say blockchain, where there's some costs involved, obviously writing to the blockchain. And this is not like a crypto thing it's more of a supply chain thing. They want visibility into latency and things of that nature. Does this sounds like you would fit there as a potential use case? Is that something that you guys thought of at all? >> It could absolutely be. I'm actually not super familiar with the blockchain or blockchain based applications but ultimately Honeycomb is intended for you to be able to answer questions about your system in a way that tends to stymie existing tools. So we see lots of people come to us from strange use cases who just want to be able to instrument, "Hey I have this custom logic. "I want to be able to look at what it's doing." And when a customer complains and my graphs are fine or when my graphs are complaining, being able to go in and figure out why. >> Take a minute to talk about the company you founded. How many employees funding, if you can talk about it. And use case customers you have now. And how do you guys engage? The service, is it, do I download code? Is it SaaS? I mean, you got all this great tech. What's the value proposition? >> I think I'll answer this-- >> John: Company first. >> All right. >> John: Status of the company. >> Sure. Honeycomb is about 25 people, 30 people. We raised a series A in January. We are about two and a half years old and we are very much SaaS of the future. We're very opinionated about a number of things and how we want customers to interact with us. So, we are SaaS only. We do offer a secure proxy option for folks who have PII concerns. We only take structured data. So, at our API, you can use whatever you want to slurp data from your system. But at our API, we want JSON. We do offer a wide variety of integrations, connectors, SDKs, to help you structure that data. But ultimately-- >> Do you provide SDKs to your customers? >> We do. So that if they want to instrument their application, we just have the niceties around like batching and doing things asynchronously so it doesn't block their application. But ultimately, so we try to meet folks where they're at, but it's 2016, it was 2017, 2018-- >> You have a hardened API, API pretty much defines your service from an inbound standpoint. Prices, cost, how does someone engage with you guys? When does someone know to engage? Where's the smoke signals? When is the house on fire? Is it like people are standing around? What's the problem? When does someone know to call you guys up at? >> People know to call us when they're having production problems that they can't solve. When it takes them way too long to go from there's an alert that went off or a customer complaint, to, "Oh, I found the problem, I can address it." We price based on storage. So we are a bunch of engineers, we try to keep the business side as simple as possible for better, for worse. And so, the more data you send us, the more it'll cost. If you want a lot of data, but stored for a short period of time, that will cost less than a lot of data stored for a long period of time. One of the things that we, another one of the approaches that is possibly more common in the big data world and less in the monitoring world is we talk a lot about sampling. Sampling as a way to control those costs. Say you are, Facebook, again, I'll return to that example. Facebook knew that in this world where lots and lots of things can go wrong at any point in time, you need to be able to store the actual context of a given event happening. Some unit of work, you want to keep track of all the pieces of metadata that make that piece of work unique. But at Facebook scale, you can't store every single one of them. So, all right, you start to develop these heuristics. What things are more interesting than others? Errors are probably more interesting than 200 okays. Okay. So we'll keep track of most errors, we'll store 1% of successful requests. Okay. Well, within that, what about errors? Okay. Well, things that time out are maybe more interesting than things that are permissioning errors. And you start to develop this sampling scheme that essentially maps to the interesting ness of the traffic that's flowing through your system. To throw out some numbers, I think-- >> Machine learning is perfect for that too. They can then use the sampling. >> Yeah. There's definitely some learning that can happen to determine what things should be dropped on the ground, what requests are perfectly representative of a large swath of things. And Instagram, used a tool like this inside Facebook. They stored something like 1/10 of a percent or a 1/100 of a percent of their requests. 'Cause simply, that was enough to give them a sketch of what representative traffic, what's going wrong, or what's weird that, and is worth digging into. >> Final question. What's your priorities for the product roadmap? What are you guys focused on now? Get some fresh funding, that's great. So expand the team, hiring probably. Like product, what's the focus on the product? >> Focus on the product is making this mindset of observability accessible to software engineers. Right, we're entering this world where more and more, it's the software engineers deploying their code, pushing things out in containers. And they're going to need to also develop this sense of, "Okay, well, how do I make sure "something's working in production? "How do I make sure something keeps working? "And how do I think about correctness "in this world where it's not just my component, "it's my component talking to these other folks' pieces?" We believe really strongly that the era of this single person in a room keeping everything up, is outdated. It's teams now, it's on call rotations. It's handing off the baton and sharing knowledge. One of the things that we're really trying to build into the product, that we're hoping that this is the year that we can really deliver on this, is this feeling of, I might not be the best debugger on the team or I might not be the best person, best constructor of graphs on the team, and John, you might be. But how can a tool help me as a new person on a team, learn from what you've done? How can a tool help me be like, Oh man, last week when John was on call, he ran into something around my SQL also. History doesn't repeat, but it rhymes. So how can I learn from the sequence of those things-- >> John: Something an expert system. >> Yeah. Like how can we help build experts? How can we raise entire teams to the level of the best debugger? >> And that's the beautiful thing with metadata, metadata is a wonderful thing. 'Cause Jeff Jonas said on the, he was a Cube alumni, entrepreneur, famous data entrepreneur, observation space is super critical for understanding how to make AI work. And that's to your point, having observation data, super important. And of course our observation space is all things. Here at DevNet Create, Christine, thanks for coming on theCUBE, spending the time. >> Thank you. >> Fascinating story, great new venture. Congratulations. >> Christine: Thank you. >> And tackling the world of making developers more productive in real time in production. Really making an impact to coders and sharing and learning. Here in theCUBE, we're doing our share, live coverage here in Mountain View, DevNet Create. We'll be back with more after this short break. (gentle music)

Yeah, I'm Aala, They're the co-founder back to back. This is the cube silicon angle.com, Silicon angle dot TV's production of the cube, our flagship telecasts. We go out to the event. That was a great conversation. I was really just, just cool. I could have, we could have probably hit on a few more things, obviously well read. Awesome. Co-founder of Cloudera a. You were, you did a good job teaming up with that co-founder, huh? Not bad on the cube, huh? He's not bad on the cube, isn't he? He, >>He reads the internet. >>That's what I'm saying. >>Anything is going on. >>He's a cube star, you know, And >>Technology. Jeff knows it. Yeah. >>We, we tell you, I'm smarter just by being in Cloudera all those years. And I actually was following what he was saying, Sad and didn't dust my brain. So, Okay, so you're back. So we were talking earlier with Michaels and about the relational database thing. So I kind of pick that up where we left off with you around, you know, he was really excited. It's like, you know, hey, we saw that relational database movement happen. He was part of that. Yeah, yeah. That generation. And then, but things were happening or kind of happening the same way in a similar way, still early. So I was trying to really peg with him, how early are we, like, so, you know, as the curve, you know, this is 1400, it's not the Javit Center yet. Maybe the Duke world, you know, next year might be at the Javit Center, 35,000 just don't go to Vegas. So I'm trying to figure out where we are on that curve. Yeah. And we on the upwards slope, you know, down here, not even hitting that, >>I think, I think, I think we're moving up quicker than previous waves. And actually if you, if you look for example, Oracle, I think it took them 15, 20 years until they, they really became a mature company, VM VMware, which started about, what, 12, 13 years ago. It took them about maybe eight years to, to be a big company, met your company, and I'm hoping we're gonna do it in five. So a couple more years. >>Highly accelerated. >>Yes. But yeah, we see, I mean, I'm, I'm, I've been surprised by the growth. I have been, Right? I've been told, warned about enterprise software and, and that it takes long for production to take place. >>But the consumerization trend is really changing that. I mean, it seems to be that, yeah, the enterprises always last. Why the shorter >>Cycle? I think the shorter cycle is coming from having the, the, the, the right solution for the right problem at the right time. I think that's a big part of it. So luck definitely is a big part of this. Now, in terms of why this is changing compared to a couple of dec decades ago, why the adoption is changing compared to a couple of decades ago. I, I think that's coming just because of how quickly the technology itself, the underlying hardware is evolving. So right now, the fact that you can buy a single server and it has eight cores to 16 cores has 12 hards to terabytes. Each is, is something that's just pushing the, the, the, the limits what you can do with the existing systems and hence making it more likely for new systems to disrupt them. >>Yeah. We can talk about a lot. It's very easy for people to actually start a, a big data >>Project. >>Yes. For >>Example. Yes. And the hardest part is, okay, what, what do I really, what problem do I need to solve? How am I gonna, how am I gonna monetize it? Right? Those are the hard parts. It's not the, not the underlying >>Technology. Yes, Yes, that's true. That's true. I mean, >>You're saying, eh, you're saying >>Because, because I'm seeing both so much. I'm, I'm seeing both. I'm seeing both. And like, I'm seeing cases where you're right. There's some companies that was like, Oh, this Hadoop thing is so cool. What problem can I solve with it? And I see other companies, like, I have this huge problem and, and, and they don't know that HA exists. It's so, And once they know, they just jump on it right away. It's like, we know when you have a headache and you're searching for the medicine in Espin. Wow. It >>Works. I was talking to Jeff Hiba before he came on stage and, and I didn't even get to it cuz we were so on a nice riff there. Right. Bunch of like a musicians playing the guitar together. But like he, we talked about the it and and dynamics and he said something that I thoughts right. On money and SAP is talking the same thing and said they're going to the lines of business. Yes. Because it is the gatekeeper that's, it's like selling mini computers to a mainframe selling client servers from a mini computer team. Yeah. >>There's not, we're seeing, we're seeing both as well. So more likely the, the former one meaning, meaning that yes, line of business and departments, they adopt the technology and then it comes in and they see there's already these five different departments having it and they think, okay, now we need to formalize this across the organization. >>So what happens then? What are you seeing out there? Like when that happens, that mean people get their hands on, Hey, we got a problem to solve. Yeah. Is that what it comes down to? Well, Hadoop exist. Go get Hadoop. Oh yeah. They plop it in there and I what does it do? They, >>So they pop it into their, in their own installation or on the, on the cloud and they show that this actually is working and solving the problem for them. Yeah. And when that happens, it's a very, it's a very easy adoption from there on because they just go tell it, We need this right now because it's solving this problem and it's gonna make, make us much >>More money moving it right in. Yes. No problems. >>Is is that another reason why the cycle's compressed? I mean, you know, you think client server, there was a lot of resistance from it and now it's more much, Same thing with mobile. I mean mobile is flipped, right? I mean, so okay, bring it in. We gotta deal with it. Yep. I would think the same thing. We, we have a data problem. Let's turn it into an >>Opportunity. Yeah. In my, and it goes back to what I said earlier, the right solution for the right problem at the right time. Like when they, when you have larger amounts of unstructured data, there isn't anything else out there that can even touch what had, can >>Do. So Amar, I need to just change gears here a minute. The gaming stuff. So we have, we we're featured on justin.tv right now on the front page. Oh wow. But the numbers aren't coming in because there's a competing stream of a recently released Modern Warfare three feature. Yes. Yes. So >>I was looking for, we >>Have to compete with Modern Warfare three. So can you, can we talk about Modern Warfare three for a minute and share the folks what you think of the current version, if any, if you played it. Yeah. So >>Unfortunately I'm waiting to get back home. I don't have my Xbox with me here. >>A little like a, I'm talking about >>My lines and business. >>Boom. Water warfares like a Christmas >>Tree here. Sorry. You know, I love, I'm a big gamer. I'm a big video gamer at Cloudera. We have every Thursday at five 30 end office, we, we play Call of of Beauty version four, which is modern world form one actually. And I challenge, I challenge people out there to come challenge our team. Just ping me on Twitter and we'll, we'll do a Cloudera versus >>Let's, let's, let's reframe that. Let team out. There am Abalas company. This is the geeks that invent the future. Jeff Haer Baer at Facebook now at Cloudera. Hammerer leading the charge. These guys are at gamers. So all the young gamers out there am are saying they're gonna challenge you. At which version? >>Modern Warfare one. >>Modern Warfare one. Yes. How do they fire in? Can you set up an >>External We'll >>We'll figure it out. We'll figure it out. Okay. >>Yeah. Just p me on Twitter and We'll, >>We can carry it live actually we can stream that. Yeah, >>That'd be great. >>Great. >>Yeah. So I'll tell you some of our best Hadooop committers and Hadoop developers pitch >>A picture. Modern Warfare >>Three going now Model Warfare three. Very excited about the game. I saw the, the trailers for it looks, graphics look just amazing. Graphics are amazing. I love the Sirius since the first one that came out. And I'm looking forward to getting back home to playing the game. >>I can't play, my son won't let me play. I'm such a fumbler with the Hub. I'm a keyboard controller. I can't work the Xbox controller. Oh, I have a coordination problem my age and I'm just a gluts and like, like Dad, sorry, Charity's over. I can I play with my friends? You the box. But I'm around big gamer. >>But, but in terms of, I mean, something I wanted to bring up is how to link up gaming with big data and analysis and so on. So like, I, I'm a big gamer. I love playing games, but at the same time, whenever I play games, I feel a little bit guilty because it's kind of like wasted time. So it's like, I mean, yeah, it's fun and I'm getting lots of enjoyment on it makes my life much more cheerful. But still, how can we harness all of this, all of these hours that gamers spend playing a game like Modern Warfare three, How can we, how can we collect instrument, all of the data that's coming from that and coming up, for example, with something useful with predicted. >>This is exactly, this is exactly the kind of application that's mainstream is gaming. Yeah. Yeah. Danny at Riot G is telling me, we saw him at Oracle Open World. He's up there for the Java one. He said that they, they don't really have a big data platform and their business is about understanding user behavior rep tons of data about user playing time, who they're playing with. Yeah, Yeah. How they want us to get into currency trading, You know, >>Buy, I can't, I can't mention the names, but some of the biggest giving companies out there are using Hadoop right now. And, and depending on CDH for doing exactly that kind of thing, creating >>A good user experience >>Today, they're doing it for the purpose of enhancing the user experience and improving retention. So they do track everything. Like every single bullet, you fire everything in best Ball Head, you get everything home run, you do. And, and, and in, in a three >>Type of game consecutive headshot, you get >>Everything, everything is being Yeah. Headshot you get and so on. But, but as you said, they are using that information today to sell more products and, and, and retain their users. Now what I'm suggesting is that how can you harness that energy for the good as well? I mean for making money, money is good and everything, but how can you harness that for doing something useful so that all of this entertainment time is also actually productive time as well. I think that'd be a holy grail in this, in this environment if we >>Can achieve that. Yeah. It used to be that corn used to be the telegraph of the future of about, of applications, but gaming really is, if you look at gaming, you know, you get the headset on. It's a collaborative environment. Oh yeah. You got unified communications. >>Yeah. And you see our teenager kids, how, how many hours they spend on these things. >>You got play as a play environments, very social collaborative. Yeah. You know, some say, you know, we we're saying, what I'm saying is that that's the, that's the future work environment with Skype evolving. We're our multiplayer game's called our job. Right? Yeah. You know, so I'm big on gaming. So all the gamers out there, a has challenged you. Yeah. Got a big data example. What else are we seeing? So let's talk about the, the software. So we, one of the things you were talking about that I really liked, you were going down the list. So on Mike's slide he had all the new features. So around the core, can you just go down the core and rattle off your version of what, what it means and what it is. So you start off with say H Base, we talked about that already. What are the other ones that are out there? >>So the projects that we have right there, >>The projects that are around those tools that are being built. Cause >>Yeah, so the foundational, the foundational one as we mentioned before, is sdfs for storage map use for processing. Yeah. And then the, the immediate layer above that is how to make MAP reduce easier for the masses. So how can, not everybody knows how to learn map, use Java, everybody knows sql, right? So, so one of the most successful projects right now that has the highest attach rate, meaning people usually when they install had do installed as well is Hive. So Hive takes sequel and so Jeff Harm Becker, my co-founder, when he was at Facebook, his team built the Hive system. Essentially Hive takes sql so you don't have to learn a new language, you already know sql. And then converts that into MAP use for you. That not only expands the developer base for how many people can use adu, but also makes it easier to integrate Hadoop through all DBC and JDBC integrated with BI tools like MicroStrategy and Tableau and Informatica, et cetera, et cetera. >>You mentioned R too. You mentioned R Program R >>As well. Yeah, R is one of our best partnerships. We're very, very happy with them. So that's, that's one of the very key projects is Hive assisted project to Hive ISS called Pig. A pig Latin is a language that ya invented that you have to learn the language. It's very easy, it's very easy to learn compared to map produce. But once you learn it, you can, you can specify very deep data pipelines, right? SQL is good for queries. It's not good for data pipelines because it becomes very convoluted. It becomes very hard for the, the human brain to understand it. So Pig is much more natural to the human. It's more like Pearl very similar to scripting kind of languages. So with Peggy can write very, very long data pipelines, again, very successful projects doing very, very well. Another key project is Edge Base, like you said. So Edge Base allows you to do low latencies. So you can do very, very quick lookups and also allows you to do transactions. So you can do updates in inserts and deletes. So one of the talks here that had World we try to recommend people watch when the videos come out is the Talk by Jonathan Gray from Facebook. And he talked about how they use Edge Base, >>Jonathan, something on here in the Cube later. Yeah. So >>Drill him on that. So they use Edge Base now for many, many things within Facebook. They have a big team now committed to building an improving edge base with us and with the community at large. And they're using it for doing their online messaging system. The live mail system in Facebook is powered by Edge Base right now. Again, Pro and eBay, The Casini project, they gave a keynote earlier today at the conference as well is using Edge Base as well. So Edge Base is definitely one of the projects that's growing very, very quickly right now within the Hudu system. Another key project that Jeff alluded to earlier when he was on here is Flum. So Flume is very instrumental because you have this nice system had, but Hadoop is useless unless you have data inside it. So how do you get the data inside do? >>So Flum essentially is this very nice framework for having these agents all over your infrastructure, inside your web servers, inside your application servers, inside your mobile devices, your network equipment that collects all of that data and then reliably and, and materializes it inside Hado. So Flum does that. Another good project is Uzi, so many of them, I dunno how, how long you want me to keep going here, But, but Uzi is great. Uzi is a workflow processing system. So Uzi allows you to define a series of jobs. Some of them in Pig, some of them in Hive, some of them in map use. You can define a series of them and then link them to each other and say, only start this job when these other jobs, two jobs finish because I'm waiting for the input from them before I can kick off and so on. >>So Uzi is a very nice framework that will will do that. We'll manage the whole graph of jobs for you and retry things when they fail, et cetera, et cetera. Another good project is where W H I R R and where allows you to very easily start ADU cluster on top of Amazon. Easy two on top of Rackspace, virtualized environ. It's more for kicking off, it's for kicking off Hadoop instances or edge based instances on any virtual infrastructure. Okay. VMware, vCloud. So that it supports all of the major vCloud, sorry, all of the me, all of the major virtualized infrastructure systems out there, Eucalyptus as well, and so on. So that's where W H I R R ARU is another key project. It's one, it's duck cutting's main kind of project right now. Don of that gut cutting came on stage with you guys has, So Aru ARO is a project about how do we encode with our files, the schema of these files, right? >>Because when you open up a text file and you don't know how to what the columns mean and how to pars it, it becomes very hard to work for it. So ARU allows you to do that much more easily. It's also useful for doing rrp. We call rtc remove procedure calls for having different services talk to each other. ARO is very useful for that as well. And the list keeps going on and on Maha. Yeah. Which we just, thanks for me for reminding me of my house. We just added Maha very recently actually. What is that >>Adam? I'm not >>Familiar with it. So Maha is a data mining library. So MAHA takes some of the most popular data mining algorithms for doing clustering and regression and statistical modeling and implements them using the map map with use model. >>They have, they have machine learning in it too or Yes, yes. So that's the machine learning. >>So, So yes. Stay vector to machines and so on. >>What Scoop? >>So Scoop, you know, all of them. Thanks for feeding me all the names. >>The ones I don't understand, >>But there's so many of them, right? I can't even remember all of them. So Scoop actually is a very interesting project, is short for SQL to Hadoop, hence the name Scoop, right? So SQ from SQL and Oops from Hadoop and also means Scoop as in scooping up stuff when you scoop up ice cream. Yeah. And the idea for Scoop is to make it easy to move data between relational systems like Oracle metadata and it is a vertical and so on and Hadoop. So you can very simply say, Scoop the name of the table inside the relation system, the name of the file inside Hadoop. And the, the table will be copied over to the file and Vice and Versa can say Scoop the name of the file in Hadoop, the name of the table over there, it'll move the table over there. So it's a connectivity tool between the relational world and the Hadoop world. >>Great, great tutorial. >>And all of these are Apache projects. They're all projects built. >>It's not part of your, your unique proprietary. >>Yes. But >>These are things that you've been contributing >>To, We're contributing to the whole ecosystem. Yes. >>And you understand very well. Yes. And >>And contribute to your knowledge of the marketplace >>And Absolutely. We collaborate with the, with the community on creating these projects. We employ committers and founders for many of these projects. Like Duck Cutting, the founder of He works in Cloudera, the founder for that UIE project. He works at Calera for zookeeper works at Calera. So we have a number of them on stuff >>Work. So we had Aroon from Horton Works. Yes. And and it was really good because I tell you, I walk away from that conversation and I gotta say for the folks out there, there really isn't a war going on in Apache. There isn't. And >>Apache, there isn't. I mean isn't but would be honest. Like, and in the developer community, we are friends, we're working together. We want to achieve the, there's >>No war. It's all Kumbaya. Everyone understands the rising tide floats, all boats are all playing nice in the same box. Yes. It's just a competitive landscape in Horton. Works >>In the business, >>Business business, competitive business, PR and >>Pr. We're trying to be friendly, as friendly as we can. >>Yeah, no, I mean they're, they're, they're hying it up. But he was like, he was cool. Like, Hey, you know, we know each other. Yes. We all know each other and we're just gonna offer free Yes. And charge with support. And so are they. And that's okay. And they got other things going on. Yes. But he brought up the question. He said they're, they're launching a management console. So I said, Tyler's got a significant lead. He kind of didn't really answer the question. So the question is, that's your core bread and butter, That's your yes >>And no. Yes and no. I mean if you look at, if you look at Cloudera Enterprise, and I mentioned this earlier and when we talked in the morning, it has two main things in it. Cloudera Enterprise has the management suite, but it also has the, the the the support and maintenance that we provide to our customers and all the experience that we have in our team part That subscription. Yes. For a description. And I, I wanna stress the point that the fact that I built a sports car doesn't mean that I'm good at running that sports car. The driver of the car usually is much better at driving the car than the guy who built the car, right? So yes, we have many people on staff that are helping build had, but we have many more people on stuff that helped run Hado at large scale, at at financial indu, financial industry, retail industry, telecom industry, media industry, health industry, et cetera, et cetera. So that's very, very important for our customer. All that experience that we bring in on how to run the system technically Yeah. Within these verticals. >>But their strategies clear. We're gonna create an open source project within Apache for a management consult. Yes. And we sell support too. Yes. So there'll be a free alternative to management. >>So we have to see, But I mean we look at the product, I mean our products, >>It's gotta come down to product differentiation. >>Our product has been in the market for two years, so they just started building their products. It's >>Alpha, It's just Alpha. The >>Product is Alpha in Alpha right now. Yeah. Okay. >>Well the Apache products, it is >>Apache, right? Yeah. The Apache project is out. So we'll see how it does it compare to ours. But I think ours is way, way ahead of anything else out there. Yeah. Essentially people to try that for themselves and >>See essentially, John, when I asked Arro why does the world need Hortonwork? You know, eventually the answer we got was, well it's free. It needs to be more open. Had needs to be more open. >>No, there's, >>It's going to be, That's not really the reason why Warton >>Works. >>No, they want, they want to go make money. >>Exactly. We wasn't >>Gonna say them you >>When I kept pushing and pushing and that's ultimately the closest we can get cuz you >>Just listens. Not gonna >>12 open source projects. Yes. >>I >>Mean, yeah, yeah. You can't get much more open. Yeah. Look >>At management >>Consult, but Airs not shooting on all those. I mean, I mean not only we are No, no, not >>No, no, we absolutely >>Are. No, you are contributing. You're not. But that's not all your projects. There's other people >>Involved. Yeah, we didn't start, we didn't start all of these projects. Yeah, that's >>True. You contributing heavily to all of them. >>Yes, we >>Are. And that's clear. Todd Lipkin said that, you know, he contributed his first patch to HPAC in 2008. Yes. So I mean, you go back through the ranks >>Of your people and Todd now is a committer on Edge base is a committer on had itself. So on a number >>Of you clearly the lead and, and you know, and, but >>There is a concern. But we, we've heard it and I wanna just ask you No, no. So there's a concern that if I build processes around a proprietary management console, Yes. I'm gonna end up being locked into that proprietary management CNA all over again. Now this is so far from ca Yes. >>Right. >>But that's a concern that some people have expressed. And, and, and I think one of the reasons why Port Works is getting so much attention. So Yes. >>Talk about that. It's, it's a very good, it's a very good observation to make. Actually, >>There there is two separate things here. There's the platform where all the data sets and then there's this management parcel beside the platform. Now why did we make the management console why the cloud didn't make the management console? Because it makes our job for supporting the customers much more achievable. When a customer calls in and says, We have a problem, help us fix this problem. When they go to our management console, there is a button they click that gives us a dump of the state, of the cluster. And that's what allows us to very quickly debug what's going on. And within minutes tell them you need to do this and you to do that. Yeah. Without that we just can't offer the support services. There's >>Real value there. >>Yes. So, so now a year from, But, but, but you have to keep in mind that the, the underlying platform is completely open source and free CBH is completely a hundred percent open source, a hundred percent free, a hundred percent Apache. So a year from now, when it comes time to renew with us, if the customer is not happy with our management suite is not happy with our support data, they can, they can go to work >>And works. People are afraid >>Of all they can go to ibm. >>The data, you can take the data that >>You don't even need to take the data. You're not gonna move the data. It's the same system, the same software. Every, everything in CDH is Apache. Right? We're not putting anything in cdh, which is not Apache. So a year from now, if you're not happy with our service to you and the value that we're providing, you can switch. There is no lock in. There is no lock. And >>Your, your argument would be the switching costs to >>The only lock in is happiness. The only lock in is which >>Happiness inspection customer delay. Which by, by the way, we just wrote a piece about those wars and we said the risk of lockin is low. We made that statement. We've got some heat for it. Yes. And >>This is sort of at scale though. What the, what the people are saying, they're throwing the tomatoes is saying if this is, again, in theory at scale, the customers are so comfortable with that, the console that they don't switch. Now my argument was >>Yes, but that means they're happy with it. That means they're satisfied and happy >>With it. >>And it's more economical for them than going and hiding people full-time on stuff. Yeah. >>So you're, you're always on check as, as long as the customer doesn't feel like Oracle. >>Yeah. See that's different. Oracle is very, Oracle >>Is like different, right? Yeah. Here it's like Cisco routers, they get nested into the environment, provide value. That's just good competitive product strategy. Yes. If it they're happy. Yeah. It's >>Called open washing with >>Oracle, >>I mean our number one core attribute on the company, the number one value for us is customer satisfaction. Keeping our people Yeah. Our customers happy with the service that we provide. >>So differentiate in the product. Yes. Keep the commanding lead. That's the strategist. That's the, that's what's happening. That's your goal. Yes. >>That's what's happening. >>Absolutely. Okay. Co-founder of Cloudera, Always a pleasure to have you on the cube. We really appreciate all the hospitality over the beer and a half. And wanna personally thank you for letting us sit in your office and we'll miss you >>And we'll miss you too. We'll >>See you at the, the Cube events off Swing by, thanks for coming on the cube and great to see you and congratulations on all your success. >>Thank >>You. And thanks for the review on Modern Warfare three. Yeah, yeah. >>Love me again. If there any gaming stuff, you know, I.