From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Hi, and welcome to this CUBE Conversation. I'm Stu Miniman coming to you from our Boston Area Studio. We've been digging in with the Pensando team, understand how they're fitting in to the cloud, multi-cloud edge discussion. Really thrilled to welcome to the program, first time guest. Silvano Guy, he's a fellow with Pensando. Silvano, really nice to see you again. Thanks so much for joining us on the CUBE. Stuart, it's so nice to see you. We used to work together many years ago and that was really good and it's really nice to come to you from Oregon, from Bend, Oregon, beautiful town in the high desert of Oregon. Yeah, I do love the Pacific Northwest. I miss the planes and the hotels. I don't necessarily, I should say actually I don't miss the planes and the hotels, but going to see some of the beautiful places is something I do miss in getting to see people in the industry I do like. As you mentioned, you and I cross-pass back through some of these spin-ins, back when I was working for a very large storage company. You were working for Cisco. You were known for writing the book. You were a professor in Italy, many of the people that worked on some of those technologies were your students. But Silvano, my understanding is you'd retired. So maybe share for our audience, what brought you out of that retirement and into working once again with some of your former colleagues and on the Pensando opportunity? I did retire for a while. I retired in 2011 from Cisco, so if I remember correctly. But at the end of 2016, beginning of 2017, some old friend that you may remember and know called me to discuss some interesting idea, which are basically the seed idea that is behind the Pensando product. And the idea were interesting. What we built, of course, is not exactly the original idea because you know, product evolve over time. But I think we have something interesting that is adequate and probably superb for the new way to design the data center network more for enterprise and cloud. All right, and Silvano, I mentioned that you've written a number of books really the authoritative look when some new products had been released before. So you've got a new book, Building a Future-Proof Cloud Infrastructure. And look at you, you've got the physical copy. I've only gotten the soft version. The title, really interesting. Help us understand how Pensando's platform is meeting that future-proof cloud infrastructure that you discussed. Well, network have evolved dramatically in the data center and in the cloud. You know, now the speed of a classical server in enterprise is probably 25 gigabit. In the cloud, we are talking of 100 gigabit of speed for a server going to 200 gigabit. Now the backbone are ridiculously fast. We no longer use spawning tree and all the stuff. We no longer use access core aggregation. We switch to closed network. And this closed network have a huge, enormous amount of memory. And that is good, but it also imply that it's not easy to do services in an decentralized fashion. If you want to do a service in a centralized fashion, what you end up doing is creating a giant bottleneck.  If you try to funnel all this traffic through that bottleneck and it's not really going to work. The only place that you can really do services is at the edge. And this is not an invention. I mean, even all the principle of cloud is move everything to the edge and maintain the network as simple as possible. You know, so we approach services with the same general philosophy. We try to move services to the edge as close as possible to the server and basically at the border between the server and the network. And when I mean services, I mean three main category of services. The networking services, of course, there is the basic layer, two-layer, three-stuff plus the bonding, you know, the MLog and what is needed to connect the server to a network. But then there is the overlay. Like the Exlan or Geneva are very, very important basically to build a cloud infrastructure. And that are basically the network service. We can have others, you know, but that sort of is the core of the network services. Some people want to run BGP there. Some people don't want to run BGP. There may be EVPN or kind of things like that, but that is the core of the network services. Then of course, and we go back to the time we worked together, there are the storage services. At that time we were discussing most about fiber channel. Now the buzzword is clearly NVME, but it's not just a buzzword, it's really a new way of doing storage. And it's very, very interesting. So NVME kind of services are very important. And NVME has a version that is called NVME OF over fabric, which is basically a sort of remote version of NVME. And then the third least but not last most important category is probably security. And when I say that security is very, very important, you know, the fact that security is very important is clear to everybody nowadays. And I think security has two main branches in services. There is the classical firewall and micro segmentation, which you basically try to enforce the fact that only who is allowed to access something can access something. But you don't at that point, care too much about the privacy of the data. Then there is the other branch, which is encryption, which you're not trying to enforce to decide who can access or not access the resource, but you're basically caring about the privacy of the data encrypting the data so that if it is hijacked, snooped or whatever, it cannot be decoded. Excellent. So, Silvano, absolutely. The edge is a huge opportunity when someone looks at the overall solution and say you're putting something in the edge, they could just say this really looks like a nick. You talked about some of the previous engagement we worked on, you know, host bus adapters, smart nicks and the like. There were some things we could build in, but there were limits that we had. So, you know, what differentiates the Pensando solution from, you know, what we would traditionally think of as an adapter card in the past? Well, the Pensando solution has two main multiple pieces, but in terms of hardware has two main pieces. There is an ASIC that we call CAP internally. You know, that ASIC is not strictly related to be used only in an adapter form. You can deploy it also in other form factor, in other part of a network, in other embodiment, et cetera. And then there is a card. The card has a PCIe interface and sit in a PCIe slot. So yes, in that sense, somebody can call it a nick. And since it's a pretty good nick, somebody can call it a smart nick. You know, we don't really like that two terms. We prefer to call it DSC, domain-specific card. But the real term that I like to use is domain-specific hardware. And I like to use domain-specific hardware because it's the same term that NSC and Patterson use in a beautiful piece of literature that is the Turing Award lecture. It's on the internet, it's public. I really ask everybody to go and try to find it and listen to that beautiful piece of literature, modern literature on computer architecture, you know, the Turing Award lecture of NSC and Patterson. And there they introduce the concept of domain-specific hardware. And they explain also the justification for why now is important to look at domain-specific card. And the justification is basically in a nutshell, and we can go more deep if you are interested, but in a nutshell is that the specking that is the single thread performance measurement of the CPU is not growing fast at all. It's only growing nowadays like few point percent per year, maybe four percent per year. And with this slow grow of the specking performance of the core, you know, the core need to be really used for user application or customer application. And all what is non-essential can be moved to some domain-specific hardware that can do that in a much better fashion. And by no mean I imply that the DSC is the best example of domain-specific hardware. The best example of domain-specific hardware is in front of the eye of all of us and our GPUs. And no GPUs use for graphic processing, which are also important, but GPU use basically for artificial intelligence, machine learning, inference. You know, that is a piece of hardware that has shown that something can be done with performance, but there are other processes. Yeah, it's interesting, right? If you turn back the clock 10 or 15 years ago, I used to be in arguments and you say, you know, do you build an offload or do you let it happen in software? And it was always like, oh, well Moore's law will mean that, you know, the software solution will always win because if you bake it in hardware, it's too slow. It's a very different world today. You talk about how fast things speed up. From your customer standpoint, though, often, you know, some of those architectural things or something that, you know, I look for my suppliers to take care of that. Speak to the use case. What does this all mean from a customer standpoint? What are some of those early use cases that you're looking at? Well, as always, you know, you get a bit surprised by the use cases in the sense that you start to design a product thinking that some of the most cool thing will be the dominant use cases. And then you discover that something that you have never really thought of the most interesting use case, you know. One that we have thought about since day one, but it's really becoming super interesting is telemetry. Basically measuring things in the network and understanding what is happening in the network. And I was speaking with a friend the other day and the friend was asking me, oh, but we had SNMP for many, many years, which is the difference between SNMP and telemetry, no. And the difference is to me, the real difference is in SNMP on many of this management protocol, you involve a management plane, you involve a control plane, and then you go to read something that is in the data plane. But the process is so inefficient that you cannot really get a huge volume of data and you cannot get it practically enough with enough performance. Doing telemetry means thinking a data path, building a data path that is capable of not only measuring everything in real time, but also sending out that measurement without involving anything else, without involving the control path, the management path, so that the measurement become really very efficient and the data that you stream out become really usable data, actionable data in real time. So telemetry is clearly the first one is important. One that you honestly, we had built, but we weren't thinking it was going to have so much success is what we call bidirectional ER span. And basically it's just the capability of copying data and sending data that the card see to a station. And that is very, very useful for replacing what are called TAP network, which is just network that many customers put in parallel to the real network, just to observe the real network and to be able to troubleshoot and diagnose problem in the real network. So these two features, telemetry and ER span that are basically troubleshooting features are the two features that at the beginning are getting more traction. You're talking about real time things like telemetry, the applications and the integrations that you need to deal with are so important. Back in some of the previous startups that you've done, it was getting ready for say, how do we optimize for virtualization? Today you talk cloud native architectures, streaming very popular, very modular, often container based solutions and things change constantly. You look at some of these architectures, it's not a single thing that goes on for a long period of time, but it's lots of things that happen over shorter periods of time. So what integrations do you need to do and what architecturally, how do you build things to make them as you talk future proof for these kind of cloud architectures? Yeah, I mean, what I mentioned were just the two low-hanging fruit, if you want the first two low-hanging fruit of this architecture, but basically the two that come immediately after and where there is a huge amount of value are distributed state for firewall with microsegmentation support. That is a huge topic in itself. So important nowadays that is absolutely fundamental to be able to build a cloud. That is very important. And the second one is wire rate encryption. There is so much demand for privacy and so much demand to encrypt the data. Not only between data center, but now also inside the data center, you know. And when you look at the large bank, for example, the large bank is no longer a single organization. A large bank is multiple organizations that are compartmentalized by law that need to keep things separate by law, by regulation, by FCC regulation, you know. And if you don't have encryption and if you don't have distributed firewall, it's really very difficult to achieve that. And then, you know, there are other application, you know, we mentioned storage and DME and it's a very nice application. And then we have even more if you go to look at lot balancing between server, you know, doing compression for storage and other possible application. But I sort of lost your real question. So just part of the piece is when you look at, you know, integrations that Pensando needs to do for really maybe some of the applications that you would tie into any of those that come to mind. Yeah, well, for sure, it depends, you know. I see two main branches again. One is the cloud provider and one are the enterprise. In the cloud provider, basically, this cloud provider have a huge management infrastructure that is already built and they want just the card to adapt to this, to be controllable by this huge management infrastructure. They already know which rule we want to send the card. They already know which feature we want to enable on the card. They already have all that, they just want the card to provide the data plan performance for that particular feature. So we are going to build something particular that is specific for that particular cloud provider that adapt to that cloud provider architecture. We want the flexibility of having an API on the card that is like a REST API or a GRPC in which they can easily program, monitor and control that card. When you look at the enterprise, the situation is different. Enterprise is looking to two things, two or three things. The first thing is a complete solution. They don't want to, they don't have the management infrastructure that they have built like a cloud provider. They want a complete solution that has the card and the management station and that's all what is required to make from day one a working solution which is absolutely correct in the enterprise environment. They also want integration and integration is with two that they already have. If you look at many enterprise, one of the dominant presence is clearly VMware virtualization in terms of ESX and vSphere and NSX. And so most of the customer are asking us to integrate with VMware, which is a very reasonable demand. And then of course, there are other player not so much in the virtualization space, but for example, in the data collection space and the data analysis space. And for sure, Pensando doesn't want to reinvent the wheel there. Doesn't want to build a data collector or a data analysis engine and whatever. There is a lot of work and there are a lot of out there. So integration with things like Splunk, for example, are kind of natural for Pensando. Excellent, so right. You talked about some of the places where Pensando doesn't need to reinvent the wheel. Talked through a lot of the different technology pieces. If I had to have you pull out one, what would you say is the biggest innovation that Pensando has built into the platform? Well, the biggest innovation is this P4 architecture. And the P4 architecture was a sort of gift that was given us in the sense that it was not invented for what we use it. P4 was basically invented to have programmable switches. And the first big P4 company was clearly Barefoot, but then was acquired by Intel and Barefoot built the programmable switch. But if you look at the reality of today, the network, most of the people want the network to be super easy. They don't want to program anything into the network. They want to program everything at the edge. They want to put all the intelligence and the programmability at the edge. So we borrowed it before architecture, which is a fantastic programmable architecture. And we implemented at the edge. You know, it's also easier because the bandwidth is clearly more limited compared to being in the core of the network. And that P4 architecture give us a huge advantage. If you tomorrow come up with the Stuart encapsulation super duper technology, I can implement in the Capri easy, the Stuart whatever was called super duper encapsulation technology. Even when I designed the easy, I didn't know that encapsulation exists. It's the data plane programmability, it's the capability to program the data plane and programming the data plane while maintaining wire speed performance, which I think is the biggest benefit of Pensando. All right, well, Silvano, thank you so much for sharing your journey with Pensando so far. Really interesting to dig into it and absolutely look forward to following progress as it goes. Stuart, it's been really a pleasure to talk with you. I hope to talk with you again in the near future. Thank you so much. All right, thank you for watching theCUBE. I'm Stu Miniman. Thanks for watching.


Hello in the last two videos you have seen how we can take an existing Docker image and Deploy into OpenShift. We looked at an example of an application where You took a MySQL database Docker image and deployed it into OpenShift and then we took a PHP application which was also built as a Docker image and then deployed into OpenShift, right? Now in this video what we'll be seeing What we call S2I process which is S2I stands for source to image conversion And this is a process where OpenShift will take your application source code and Build a Docker image for it. It you'll see how simple it is to do so. I'm going to do this using a web console So let me log on to the web console first. This is an OpenShift beta 4 installation I'm logging on to the web console Now let me first create a project. A project is a placeholder that it's it's kind of a namespace which includes all the The application that you want that you want to run So I'm going to create a project Now that the project is created you can see that the project is empty. That is nothing inside it So now let's Add our first component to it just like before we are going to create a MySQL database in this project and this time instead of Doing it in the vein which we did before where we were pulling an existing Docker image by using a command line by deploying an By deploying a part that runs MySQL Database this time what we are going to do is create it from what we call a template A template It describes a set of resources that are intended to be used together So when we are trying to quickly spin up a new application It's it's easier to use templates. So I'm going to select the MySQL template from here We are this template provides some parameters that you can pass So I'm going to edit these parameters I am going to give a password for my MySQL database a database name Let's call it sample and a user name I'll just call it here You can also set up some labels and I'm going to add some labels here. I Will say the name of this component is a database So Add the labels and then I'll say create now this this will be created as part of the my php application project So what it will do is it will spin up a MySQL database that runs as part of this project So as you can see on the console, it's actually spinning up a new MySQL database spin up a pod that runs a MySQL database and it frontends that part with a service a database service and this database service is routing traffic on This IP address and on port 54 34 by default MySQL uses 3 3 0 6 and the part 54 34 is forwarding from 3 3 0 6 so as you can see the database is now running and It is frontended by this service now Let's explore this web console a little bit if you click on this browse button You'll see different kinds of things that you can look at Let's look at what pods are running. It's running a database part with this name database one XNS 8x I can also view this from my Command line if I say OSC get parts like before it will show all the parts that are running So the same database part is running here. You could also see the service and The IP address of the service here and the ports Exposed by the service now I'm going to get inside this part and add some data to this database like before this time I'm going to use OSC exec command This will allow me to enter inside the part I'll supply the pod ID and I want to enter the bash bomb so now from my local machine I am entering inside the part and I have the bash prompt displayed here now Let's access the my sequel database to the IP address of the service the port number exposed by the services 5 4 3 4 Now I'm locked to the my sequel database If I say show databases here It should show The sample database that we wanted it to create and then I'll use the sample database Let's add a table and insert some data Exit this part. This is just to show you an example of how you can enter Into a part and from your desktop now that we added the database It will now add a front end to a front end application to access this database I have my source code in a git repository As you can see the source code is in the form of a PHP file. I have a simple PHP. This is the same as what we have used in the previous example it will connect to the database and It will display the list of users from the users table as simple as that. So what we are going to do is Supply the source code to open shift and let it Build the image from this source code That's the reason why it's called source to image conversion select the github's URL and I'll go back to console and This time I'll create by using this GitHub URL. I am selecting a builder image and since this is a PHP application I'm going to select the PHP builder image. I have to name this application I'm I'm good with dv test as the name and There are a few other parameters that you can set and this this is still evolving As of now you can create a route for this application, but you you can't edit it from the console yet So we are going to add that route manually outside You can provide some deployment configurations We are saying that it's it's going to auto deploy when the new image is available the build information It has configured that the source code is the github repository URL that we provided and it'll automatically build a new image when the code changes its CS and It's going to automatically build a new image when the builder image changes as well So whether the source code changes or the PHP itself changes It will trigger a new build and Let's all let's add a label here We'll call it PHP front-end And then I'm going to Say create Now what this does is it will spin up this DB test now if you look at the Builds here you have a new build called DB test creator and It is pointing to the source source repository, which is on github and I can start a new build now This process has started a new build with the name DB test one and the build is running here From the command line if I say OSC get builds, I should see the same build again here And if I want to see what is going on I can say where see build logs to look at the build logs it has actually pulled the source code it has converted the The source code and took the builder image and it Built an image for for my application now. It is trying to push this darker image for this application into a Local registry that is running in my open-shift environment. This is the local Docker registry that's in my environment. This is taking going to take a minute. You can see this is this has successfully pushed the image and Once this image has been pushed the application image has been deployed into a pod with this ID that you're seeing here and This application is now running in a pod on node 2 right now So this is the process of source to image where you give a source code and that source code has been converted into a Docker image it has been registered inside the Docker registry that is running on my open-shift environment and It has automatically taken that image and deployed it into a separate part Now this part should now talk to the database part But before we check that we'll have to add a route to this application. Let's look at the services here You can see that the database service from before is still there and there is a service for the data DB test, which is the Frontend that we created but It by default it adds a route here, but this is not this is not useful at this point So what we'll do is add an additional route for this application that is externally accessible I'm going to add this route manually for now and this application will be accessible using this URL by issuing this command I have Created a route for this application now now You can see that the route is now added now. Let's access this application using this route and Here are the two records from the database What this means is the front end code that we just added the DB test dot PHP is connecting to the database And it has accessed the two records that we added before so to summarize what we have learned in this video today we have created a project on using open shifts web console and We have added a database in that project by using templates then we have Created an application front end that talks to this database by using the source code and we Open shift by using the source to image conversion process Open shift has converted that source code into a Docker image It pushed it into a Docker registry running on open shift and then it deployed that Docker image as an application on running as a single part in OpenShift Thanks for watching


Hi everybody. Our next talk is starting. It's called I++. Run your fuss off and Please welcome rule standard Okay, can you hear me? Okay So once a year the students of Leuven gather to run their asses off For 24 hours long. I'm talking about the 24 hour run. It's in late October. It starts On a Tuesday at 8 p.m. And it goes on till Wednesday at 8 p.m. Again It's organized by local sport, which is the sports division of the general student organization in Leuven TSA, which is a technical support division They do all of the technical stuff that isn't IT and Ulysses that do all the IT related stuff And of course the participating teams so Ulysses is a student organization based in Leuven and They provide IT students for other services service Students that provide IT services for other students and it's a fast-minded organization. So that means that They Try to use as much open-source software as possible and that you try to promote it as much as possible They offer cheap web hosting for students and personnel at K-Leuven This is our server room. It's mostly second-hand servers or like old desktops Sometimes we like to hack into our own servers because we locked ourselves out We do workshops like workshops about Git or latex or like other More advanced system administration workshops. This is one about latex And we do a yearly job fair. So you can check out the job corner for this poster For open-source minded companies And you can spot me on that picture there representing Mweb. That's company I work for and So at a 24-hour run Ulysses provides a network a wired network and Wi-Fi and since 2013 Ulysses has also been responsible for doing lab counting So this is kind of what the track looks like. It's 520 meters long so it's outside of the normal 400 meters athletic start track and There are three checkpoints. So they start at the main checkpoints and they run counterclockwise to checkpoint 2 and then checkpoint 3 These extra checkpoints are mostly for redundancy Not so much to make sure that they run in the right direction Because we will assume if you go from a checkpoint 1 to checkpoint 3 that you passed checkpoint 2 But we just missed it So if you then go from 3 to 2 we assume that you passed 1 and if you go from 2 to 1 We also assume that you passed 3 so you'll have two laps But usually there are enough eyeballs on people to make sure that they don't do that So this checkpoint kind of looks like this it's a box We call that the reader and inside of that box is a UHF RFID reader a Raspberry Pi Type-B and a switch connecting those two and connecting that to all the other checkpoints and the checkpoint has two antennae in Opposite polarisation And this is kind of what the main checkpoint looks like so The to check one two and three are unmanned, but the first one there we're actually sitting there Looking out to work to the runners that are coming towards us and then you can see the in you can really Finally you can see the box Somewhere on the left and then the antenna there and we sit in there so and they We use RFID tags So these are belts type tags and we put them inside of These batons that we make out of PVC tube with caps on either side and then we put handlebar tape around It's that's used for bicycles So they're a bit nicer to hold and nicer to pass So the software that we use it's called I++ and that's the source code there not really It's a bit more complicated than that than just incrementing every lap so it's written in Java which Takes 30 seconds to start on the readers, but still it runs quite well on these Raspberry Pi's So there's the reader. That's what runs on the Raspberry Pi the processor where all of the reads are accumulated and the score is calculated and the UI is What we use to and there's a UI component and a publisher that publishes the scores live So this is how the reader and the processor kind of come communicate. So there's This UHF RFID reader that can be and the reader that's on the Raspberry Pi and That one communicates using LRP. That's the low level reader protocol with this RFID reader and then puts all of the updates onto a Redis database So it has a list of updates. Those are all in JSON and then they are published to And then they are published on a channel called updates and then a number because Bups ups channels are global in Redis and Then the processor subscribes to that channel and then whenever it gets a New update it will retrieve that from the server from the reader So this means that if the connection is broken between the processor and the reader Then at a later time the processor can still go ahead and get all of those old updates that it missed And so this processor it gets all these updates and they stores them as events in a post-crescue all database and this and Next to these events it stores snapshots. So it starts with an empty snapshot and then everything Is affected by these events and this means because we're keeping all these snapshots This means that if there's an event that happened in the past We can still apply that and still recalculate the states from that point and you might think that's a lot of data, but Really, it's only 24 hours. We're keeping your megabytes of data, but that's still fine and then The UI only communicates with the processor using these commands that it sends also on a Redis pops up channel and then notifications are also sent out to do a UI via Redis and Then the UI retrieves all of the events and the snapshots from post-crescue all database So this is kind of what the UI looks like. I know it's really pretty But I wrote it in like a weekend. I use Jwt for that. That's what we make at MEP that's a Java web framework and So if you look at the cyan bars the cyan bars are Our prediction of where the runners are at that particular time and then the red bars They indicate the last checkpoint that they passed So when we're sitting in this main checkpoints, we can keep an eye out Like it's if the bars almost full we can know like number two is almost gonna arrive and we keep an eye out for that and then also from this UI we can Updates messages so we can always push the status of the events out So like if there is an emergency or the event is over stuff like that We can immediately update from this UI and we have a start in the stop button To indicate to start in the stop time. It's not fixed And we have a safety for that so that we don't accidentally click that they click it and Then for every team you can look at three charts or Just as many charts as there are readers and this indicates how long it was since the last read so Looking at these charts you can kind of gauge whether Things are going wrong because normally a team that like up alone. It's a sports Student organization, so they run at a pretty constant speed So if you see like a peak there or you see like a low there You'll know that maybe we missed a read or maybe We have a read too many which happens rarely like Pretty much never but anyway And then the publisher is the components that publishes the the score So it that can take it from Redis and from our PostgreSQL database and push it out to a JSON file But it can also receive it over HTTPS and push it out over HTTPS And this means that we can chain these publishers after each other So we put one publisher inside of our local counting system network, which is firewall No one can access it and It gets all the updates from Redis and PostgreSQL and then publishes it over HTTPS to our web server in our Ulysses basements And that pushes it out to a JSON file and that is then so served using just a regular web server We use Nginx and there's just a static HTML page there with some JavaScript that pulls the JSON every three seconds and then It can show like some live bars and like people are actually often Think that we're really tracking in real-time where they are Because the bars seem so accurate But that's just because people just run at a pretty constant speed In 2013 sadly the server the software wasn't really working well the software was working with the hardware had some problems But then 2014 it was working flawlessly and This is our and even though the weather was really terrible that year towards a lot of winds 2015 was also a good addition 2016. I don't have a picture of that when he's 17. That's the team for the last time and This 24-hour run is not the only n-hour run Living is the only one that I know of that's 24 hours long But for example in Antwerp, there's a 10-hour run Which is also done by Ulysses, which is good because then Ulysses can test this system twice a year on real on a real event and then in Ghent there was a 12-hour run or friends of these guys they do it there and So Zeus They're called and they use a Bluetooth 2.0 for that and so Even my hometown of medics plus. I don't know if anyone has ever heard of medics plus. It's a tiny town Even they have like a six-hour run and I have nothing to do with that one So what if everyone could easily and cheaply organize events like this that would be great Now we would like to use I plus plus for that But at the moment there are some problems with this system still so it's not user-friendly enough yet So we want to move towards where maybe it's easy enough for someone who can like install a blog and share it hosting Can install it, you know, that's like I think the thresholds so little I don't know like your cousin who kind of knows knows computers can use it It's relatively expensive because these readers they're they can cost like thousands of euros So it would be nice to use like hardware like for example I beacon like these blue blue-toothed low energy kind of beacons that just send out a pulse every A few times per second and put those in our batons And then the checkpoints could just be old or cheap Android phones that would make it a lot cheaper and At the moment, it's kind of specialized for a trusted land So red is not really suitable for like other kinds of network deployments and VPNs are hard to configure for just someone who kind of knows only a little bit about computing so If you want to contribute or if you want to organize something similar you can go to our get up page and also check out the source code I will I have some time for questions, but I'll also hang out outside sites if you have more questions and thank you


Okay, welcome back to IBM's Information on Demand Live in Las Vegas. This is theCUBE's SiliconANGLE and Wikibon's flagship program. We go out to the events, extract the student from the noise, talk to the thought leaders, get all the data, share that with you. You can go to siliconangle.com or wikibon.org to get all the footage. And if you want to participate with us, we're rolling out our new innovative crowd-activated innovation application called CrowdChat. Go to crowdchat.net slash IBM IOD. Just log in with your Twitter handle or your LinkedIn and participate and share your voice. It's going to be on the record transcript of theCUBE Conversations. I'm John Furrier with SiliconANGLE. I'm joined by my co-host. Hi, everybody. I'm Dave Vellante of Wikibon.org. Thanks for watching. Anshul Bambri is here. She's the vice president of Big Data and Analytics at IBM. Many-time CUBE guest. Anshul, welcome back. Good to see you again. Thank you. So we were both down at New York City last week for the Hadoop world. Really amazing to see how that industry has evolved.  You've super glued your big data or your analytics business to the big data meme and really created a new category. I don't know if that was by design or not, but it certainly happened. Certainly by design. Well, congratulations then, because I think that, again, even a year and a half ago, those two terms, Big Data and Analytics were sort of separate. Now it's really considered as one, right? Yeah, yeah. I think because initially as people or businesses started getting really flooded with big data, right? Dealing with the large volumes, dealing with structured, semi-structured, unstructured data. They were looking at that, how do you store and manage this data in a cost-effective manner?  And now obviously it's, people realize that they need, and there is insights from this data that has to be gleaned, and there's technology that is available to do that. So customers are moving very quickly to that it's not just about cost savings in terms of handling this data, but getting insights from it. So Big Data and Analytics is becoming, it's becoming synonymous. You know what's interesting to me, Anjula, is just following this business, it's like there's a zillion different nails out there, and everybody has a hammer, and they're hitting the nail with their unique hammer, but it's like IBM has a lot of different hammers. So I wonder if you could talk about that a little bit. You've got a very diverse portfolio. You don't try to force one particular solution on the client. It's sort of in its depends sort of answer. We can talk about that a little bit. Yeah, sure. So in the context of Big Data, when we look at just, let's start with transactional data, right? That continues to be the number one source where there's very valuable insights to be gleaned from it. So the volumes are growing, that we have retailers that are handling now 2.5 million transactions per hour, right? Telco industry handling 10 billion call data, detailed records every day. So when you look at that level, that volume of transactions, obviously you need engines that can handle that, that can process, analyze, and gain insights from it that you can do ad hoc analytics on this, run queries and get information out of this at the same speed at which this data is getting generated. So we announced the blue acceleration, right? Which is our in-memory column store, which gives you the power to handle these kinds of volumes and be able to really query and get value out of this very quickly. So, but now when you look at, you go beyond the structured data or beyond transactional data, there is semi-structured unstructured data, that's where, which is still data at rest is where we have big insights which leverages Apache Hadoop open source, but we've built lots of capabilities on top of that where we get, we give the customers the best of open source, plus at the same time, the ability to analyze this data. So, we have text analytics capabilities, we provide machine learning algorithms, we have provided integration with that customers can do predictive modeling on this data using SPSS, using open source languages like R, and in terms of visualization, they can visualize this data using Cognos, they can visualize this data using MicroStrategy. So, we are giving customers, like you said, it's not just, you know, there's one hammer and they have to use that for every nail. The other aspect has been around real time, and we heard that a lot at Strata, right? In the, like, I've been going to Strata since the beginning, and those, that time, even though we were talking about real time, but nobody else was. No, it's true, nobody was talking about it. Nobody was talking. Back in the Hadoop World days, it was just one big, you know, batch job. Yeah. In real time, it's now the hotbed of the conversation, you know, you're talking about Storm, these new technologies coming out with, and with yarn is done, it's been interesting. Yeah. You've seen the same thing? Yeah, so, and of course, you know, we have a very mature technology in that space, you know, in first phase streams for real time analytics has been around for a long time. It was, you know, developed initially for the U.S. government, and so we've been, you know, in the space for more than anybody else, and we have deployments in the telco space where, you know, these tens of billions of call detail records are being processed, analyzed in real time, and, you know, these telcos are using it to predict customer churn, to prevent customer churn, gaining all kinds of insights at an extremely high, you know, very low latency. So it's good to see that, you know, other companies are recognizing the need for it and are, you know, bringing other offerings out in the space. Yeah, so as we were talking before, somebody says, oh, I want to go, you know, low latency and I want to use Spark, you say, okay, no problem, we could do that, and Streams is interesting because if I understand it, you're basically acting on the data, producing analytics prior to persisting the data. So it's all in memory. So it's all in memory, and yet at the same time, my question is, is it evolving where you now can blend that sort of real time activity with maybe some batch data and talk about how that's evolving? Yeah, yeah, absolutely. So, Streams is for, you know, whereas data is coming in, it can be processed, filtered, patterns can be seen in Streams of Data by correlating, connecting different Streams of Data, and based on certain events occurring, actions can be taken. Now, it is possible that, you know, all of this data doesn't need to be persisted, but there may be some aspects or some attributes of this data that need to be persisted. You could persist this data in a database that is used as a way to populate your warehouse. You could persist it in a Hadoop-based offering like Big Insights, where you can, you know, bring in other kinds of data and enrich the data. It's like data learns from data and a different picture emerges. Jeff Jonas's puzzle analogy, yeah. So that's very valid. And so when we look at the real time, it is about taking action in real time, but there is data that can be persisted from that in both the warehouse, as well as on something like Big Insights Hadoop. I want to throw a term at you and see what this means to you. We're actually doing some crowd chats with IBM in this topic. Data economy. I was going to ask the same question. You asked it. No, no, no, no, no, no, no. So, data economy, what does data economy mean to you? What are customers doing with the data economy? Okay, so my take on this is that there are two aspects of this. One is that the cost of storing the data and analyzing the data, processing the data has gone down substantially. But the value in this data, because you can now process, analyze petabytes of this data, you can bring in not just structured but semi-structured unstructured data. You can glean information from different types of data and a different picture emerges. So the value that is in this data has gone up substantially. Previously, a lot of this data was probably discarded without people knowing that there is useful information in this. So to the business, the value in the data has gone up. What they can do with this data in terms of making business decisions, in terms of making their customers and consumers more satisfied, giving them the right products and services, and how they can monetize that data has gone up. But the cost of storing and analyzing and processing has gone down, which I think is fantastic, right? So it's a huge win-win for businesses. It's a huge win-win for the consumers because they are getting now products and services from the businesses which they were not before. So that to me is the economy of data. So this is why, John, I think IBM's really going to kill it in this business because they've got such a huge portfolio. They've got, if you look at where IOD has evolved, data management, information management, data governance, all the stuff on privacy, security, these were all cost items before. People looked at them and I got to deal with all this data. And now there's been a bit flip. And IBM is just in this wonderful position to take advantage of it. Of course, Ginny's trying to turn the battleship and trying to get everybody aligned. But the moons and stars are aligning. And really, there's a tailwind. Yeah, we have a question on Twitter from Jim Lundy, analyst, former Gartner analyst has his own firm now. Shout out to Jim. Jim, thanks for watching, as always. I know you're a Cube alum and also avid watcher and now a loyal member of the CrowdChat community. The question is, blue acceleration helps drive more data into actionable analytics and dashboards. Can IBM drive more new deals with it? Absolutely. Yeah, of course it answers yes. Yes. And can you elaborate on that for Jim? Yeah, with blue acceleration, we have had customers that have evaluated blue and against SAP HANA and have found that what blue can provide is way ahead of what SAP HANA can provide. So we have a number of accounts where people are going with the performance, the throughput, what blue provides is very unique and it's way ahead of what anybody else has in the market. Including SAP. Including SAP. And ultimately, it's value to the business, right? And that's what we are trying to do, that how do we get our customers the right technology so that they can deal with all of this data, get their arms around it, get value from this data quickly. I mean, that's really of a sense here. So I wonder if part of Jim's question is, you guessed, is driving new deals, for sure. New product, new deals. Can it drive new footprints? Is that maybe what he's asking, right? In other words, traditional IBM accounts are doing deals. Are you able to drive new footprints? Yeah, yeah, we, you know, there are customers that, I'm not going to take any names here, but which have come to us, which are new to IBM, right? So it's a, it's that to us. And that's happening. That's net new business. That's net new business. And that's happening with us for all our big data offerings because you know, the richness that is there in the portfolio, it's not that we have, like you were saying, Dave, it's not that we have one hammer and we are going to use it for every nail that is out there. You know, as people are looking at blue, big insights for Hadoop, streams for real time. And with all this comes the whole life cycle management and governance, right? So security, privacy, all those things don't go away. So all the stuff that was relevant for the relational data, now we are able to bring that to big data very quickly. And which is I think of huge value to customers. And as people are moving very quickly in this big data space, there's nobody else who can just bring all of these assets together from and, you know, provide an integrated platform. What use cases to Jim's point, I know you don't want to name names, but can you name, can you talk about some use cases that these customers are using with blue? Like what use cases are they solving? So, you know, I, from a use cases standpoint, it is really like, you know, people are seeing performance, which is, you know, 30, 32 times faster than what they had seen when they were not using an in-memory column store. You know, so eight to 25, 32 times performance gains is, is, you know, something that is huge and is getting more and more people attracted to this. So let's take an industry, take financial services, for example. So the big ones in financial services are risk, people want to know, you know, are they a credit risk? There's obviously marketing, serving up ads. Fraud detection, you would think is another one that in more real time, are these, you know, some things that you're working on? These would be the segments and of course, you know, retail, where again, you know, there is like I was saying, right, that the number of transactions that are being handled is growing phenomenally. I gave one example, which was around 2.5 million transactions per hour, which was unheard of before. And the information that has to be gleaned from it, which is, you know, to leverage this for demand forecasting, to leverage this for gaining insights in terms of giving the customers the right kind of coupons, to make sure that those coupons are getting, you know, are being used. So it was, you know, before the world used to be, you get the coupons in your email, then the world changed to that you get coupons after you've done the transaction. Now, where we are seeing customers is that when a customer walks in the store, that's where they get the coupons based on which aisle they are in. So it's a combination of the transactional data, the location data, right? And we are able to bring all of this together. So it's blue combined with, you know, what things like streams and big insights can do that makes the use cases even more powerful and unique. So I like this new format of the crowd chat. Normally it's a one-hour crowd chat where it's kind of like thought leaders just going a pound in the way, but this is more like Reddit, AMA, but much better. Question coming in from Grant Case is, one of the themes to you is one of the themes we've heard about in the keynote was the lack of analytical talent. What is going on to contribute more value for an organization skilling up the workforce or implementing better software tools for knowledge workers? So skills is definitely an issue that has been a challenge in the industry with, and it got pretty compound with big data and the new technologies coming in. From the standpoint of what we are doing for the data scientists, which is, you know, the people who are leveraging data to gain new insights, to explore and discover what other attributes they should be adding to their predictive models to improve the accuracy of those models. So there is a very rich set of tools which are used for exploration and discovery. So we have, which is both from, you know, Cognos has such capabilities. We have such capabilities with our data explorer. So basically tooling for the predictive on the modeling. So right now the efforts on the modeling and the predictive analytics. And descriptive analytics, right? I mean, there's a lot of, when you look at that, when there's petabytes of data before people even get to predictive, there's a lot of value to be gleaned from descriptive analytics. And being able to do it at scale at petabytes of data was difficult before. And now that's possible with excellent visualization, right? So that it's taking things to, that the analytics is becoming interactive. It's not just that, you know, you are able to do this in real time, ask the questions, get the right answers. Because the models running on petabytes of data and the results coming from that is now possible. So interactive analytics is where this is going. So another question is Jim was asking, I was wondering if IBM's going around doing blue accelerator upgrades with all its existing clients. Loan origination is a no brainer upgrade. I don't even know it's loan. Yeah, so that was the kind of follow up that I had asked is it new accounts, is it new footprint or is it just sort of extending existing? It's both, it's both. What is the characteristic of a company that is successfully, or characteristics of a company that is successfully leveraging data? Big data? Yeah, yeah. So companies are thinking about now that their existing EDW, which is their enterprise data warehouse, needs to be expanded. So before, if they were only dealing with warehouses, which were handling just structured data, they are augmenting that. So this is from a technology standpoint, right? They're augmenting that and building their logical data warehouse, which takes care of not just the structured data but also semi-structured and unstructured data, bringing augmenting the warehouses with Hadoop-based offerings like Big Insights, with real-time offerings like Streams, so that from an IT standpoint, they are ready to deal with all kinds of data and be able to analyze and gain information from all kinds of data. Now from the standpoint of how do you start the big data journey, the platform that at least we provide is a plug and play. So there are different starting points for businesses. They may have started with warehouses. They bring in a polystructured store with Big Insights slash Hadoop. They are building social profiles from social and public data, which was not being done before, matching that with the enterprise data, which may be in CRM systems, master data management systems inside the enterprise, and which creates quadrilands of comparisons and they are gaining more insights about the customer based on master data management, based on social profiles that they are building. So this is one big trend that we are seeing. But to take this journey, they have to take smaller bites, digest that, get value out of it, and eat it in chunks rather than try to eat the whole pie. In one chunk. So a lot of companies starting with exploration, proof of concepts, implementing certain use cases in four to six weeks, getting value, and then continuing to add more and more data sources and more and more applications. So there are those who would say those existing EDWs, many people, some people would say they should be retired. You would disagree with that piece. No, no, I think we very much need that experience and expertise. Businesses need that experience and expertise because it's not an either or. It's not that that goes away and there comes a different kind of a warehouse. It's an evolution, right? But there's a tension there, wouldn't you say? There's an organizational tension between the sort of newbies and the existing, you know, EDW crowd. I would say that maybe, you know, three years ago that was, there was a little bit of that. But there is, I mean, I talk to a lot of customers and there is, I don't see that anymore.  They are moving with the times and they know that this evolution is where the market is going, where the business is going and where the technology is going. They know they're going to be made obsolete if they don't embrace it, right? Yeah, I think, yeah. So as we get on time, I want to ask you a personal question. What's going on with you these days within IBM? Honestly, you're in a hot area. You were at just the New York last week. Tell us what's going on in your life these days. I mean, things going well. I mean, what are things you're looking at? What are you paying attention to? What's on your radar when you wake up and get to work before you get to work? What are you thinking about? What's the big picture? So obviously, you know, big data has been very fascinating, right? Lots of different kinds of applications in different industries. So working with the customers in Telco, in healthcare, banking, financial sector has been very educational, right? So a lot of learning, and that's very exciting. And what's on my radar is we are obviously now seeing that we've done a lot of work in terms of helping customers develop and their big data platform on premise. Now we are seeing more and more a trend where people want to put this on the cloud. So that's something that we have now a lot of, I mean, it's not like we haven't paid attention to the cloud, but in the coming months, you are going to see more from us where, you know, how do we build, how do we help customers build both private and public cloud offerings and, you know, where they can provide analytics as a service to different lines of business by setting up the cloud. So cloud is certainly on my mind. The software acquisition was big. It was a hole in the portfolio and that filled it. You guys are going to drive that hard. So both software and then, of course, OpenStack, right, from an infrastructure standpoint for what's happening in the open source. So we are, you know, leveraging both of those and like I said, you'll hear more about that. Well, OpenStack is key, as I see it for you guys because you have street cred when it comes to open source. I mean, what you did in Linux and you made a great business out of that. So everybody will point it, you know, whether it's Oracle or IBM and HP say, oh, they just want to sell us our stack. You've got to demonstrate that you're open and OpenStack is a great way to do that and other initiatives as well. So like I say, I think that's great. So be excited about that, yeah. Yeah, okay. All right, Joel, well, thanks very much for coming on theCUBE. It's always a pleasure to see you. Yeah, see you here. Great having you back. Thank you very much. Okay, we'll be right back live here inside theCUBE here in IBM Information on Demand. Hashtag IBM IOD, go to crowdchat.net slash IBM IOD and join the conversation where we're going to have a on the record crowd chat conversation with the folks out who aren't here on site or on site. We're here live in Las Vegas. I'm John Furrier with Dave Vaughan through right back.


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