Welcome to the 2017 NCHA Lucas Oil Superstakes and with Gary Rosenback, the winner of the unlimited amateur four-year-old final, he rode herding instincts and scored a 220.5. Congratulations to you, Gary. Thank you very much. Now, you're no stranger to this arena or winning titles here. How does this third title feel? Well, it's been a while in between the last ones. I think the last one I won was three, four years ago, but it's really exciting to be back in the winner's circle. What's about your other titles that you've got? So I won a Derby title on, it's evading me right now. His barn name was Spider, and I won a Superstakes title on Scooter's Daisy Duke. And so this is the first time in the unlimited amateur, like I said, but we're really happy to be here. Did you feel a difference in the competition because you're in the unlimited now? It felt a lot more competitive. There are a lot more competitive riders in here that go back and forth between non-pro and actually a lot of competitive seniors as well. Okay, so you had a big final, 33 in all, I think 17 or something in your set.  Well, you know, we spent a lot of time in the cow box. I was up there with R.L. and Michael Cooper, and we had four or five cows on our radar. The first one I cut was a brown bridge, and it was just perfect. And then we went back to the herd looking for a slick neck brown cow, but we couldn't find it. So I cut shape on another cow that we liked, which was a brown wicker pole cow, and that worked out pretty well. And when I walked back to the herd for the third time, we found the slick neck brown cow. Not easy for me to say. We found the slick neck brown cow, and it just worked perfectly. Just got them in a good spot. I put my hand down in the middle of the pen, and it just was great. Can you describe to us that feeling when you've got that horse moving under you like that and it's really in sync with the cow? You know, we only own him for about four months, but he's incredibly strong and powerful. And at times I got to remember to stay soft, because he literally throws me all over the place, but he was just really locked into the cow right now. One time I asked him pretty hard with my left leg to get across the cow, and he just listened, and he got brighter and brighter for the rest of the run. So herding instincts, I take it, has good herding instincts? I'm happy with them. You came by this horse fairly recently, is that right? Tell us. I bought him right after the Futurity. Okay. How often have you shown it? This is the second time for me walking to the herd on him. I showed him, I can't remember, I think at the Bonanza. Yes. Well, some would say that's excellent odds. Yes. I'll take one out of two every time. Now, you've got, I think, 118,000 or so in lifetime earnings. You've won another 11,000. I'm going to guess that you're going to keep going with this horse and keep pushing this winnings up. He's just a four-year-old, and we think there's a lot of upside to this horse. We really do. Thank you for joining us today. Pleasure. Thank you.


So what I'm going to talk about is Ray, which is a Python library, mostly although it's got a little C++ core for making it really easy to distribute Python applications across the cluster. It was inspired by the challenges of doing distributed machine learning, training neural networks and so forth. You can reach me at this information on the left, in my email address, I'm on Twitter at Dean Wampler. Ray.io is the site for Ray, and I work for any scale, the company developing Ray. We're having a Ray summit this fall, September 30th and October 1st. If you're interested, you can find out more at our events page on our website. So this will be a really fast talk because I have 30 minutes. I'll cover a lot. What I hope you'll get out of it is sort of the gist of what's going on with Ray and why you might be interested in it, and we can certainly take questions in the Discord channel afterwards. So let me start with a demo. I'll exit from this presentation and then switch to my browser where I have an application running. So what I'm going to do is just walk through first, what it's like to work with a core API. Whether you're doing machine learning or not, this is the API you might use for distributing your applications. And then I'll run through an example using reinforcement learning in a library for Ray called RLL, so you can get a sense of what Ray is doing behind the scenes as it were. So I went ahead and evaluated a few cells already. I did some imports. I initialized Ray, which here is just running on my laptop, but I could also tell it to connect to a cluster. And then there's this what we call the Ray dashboard that comes up so I can actually see what's going on if I'm trying to understand performance issues or see what's going on and so forth. We won't look at that again for time's sake, but just so you know it's there. So the example I'm going to simulate is the case where maybe I need to call an expensive data store, expensive in the sense that it takes a lot of time to compute relative to just regular computing. Just to make it a simple case, I've defined two dictionaries, one of which takes the keywords like reinforcement learning, hyper parameter tuning and so forth and returns the corresponding high level library in Ray for those particular tasks. And then there's a second one Ray URLs that takes the values returned from the first dictionary and gives you the links to the documentation for them. And you'll see why we have a second one in just a moment. I'll mostly start with the first one. So let me define it just a regular Python function that will take in one of these phrases like reinforcement learning and look up the value in the dictionary and return both that key and the value when it's done. And to kind of simulate doing something that's compute intensive, I'm going to sleep for a period of time equal to the length of the phrase divided by 10. So it turns out reinforcement learning, I believe is 22 seconds. So it'll sleep for 2.2 seconds when we call it. And then if I define this function that just iterates through all of the phrases in the dictionary and then calls them and time how long this takes, we'll find that it takes about 7.1 seconds because it'll do one at a time and sleep for each one. And it turns out there are 71 characters in the keywords. So that's why we slept for 7.1 seconds. Well, this kind of query could be done in parallel. There's nothing that's related between the queries that I did. So let's see how we could use Ray to turn something into a asynchronous process that we can then do in parallel or possible, but without doing a lot of low level parallel kind of programming. And the way we do that is we define something called a Ray task. We annotate a regular function with this annotation at Ray.remote and I can just turn around and call the other one. So I can have it both ways. I can have the original Python function or this Ray task and use whichever I want. And then when we actually call it, we add this dot remote to the invocation as a visual cue to us that we're actually doing something with Ray and not just making a regular call. And this thing that it's returning is actually a future. So we've fired off this asynchronous call somewhere in our cluster. And at some point we can retrieve the value using ray.get. And this ray.get will block until the value is ready. I waited long enough so it came back immediately and here's what we got. So let's actually do a similar search iteration that we did before. Notice what I'm gonna do. I'm gonna fire off all four queries at once, save those IDs and then call ray.get to get them all at once. And this will take 2.2 seconds, why 2.2? Because the longest key is reinforcement learning. So all of them, even though some of them finish sooner because they have shorter keys and we slept for a shorter period of time, the way I called ray.get actually means that we'll wait for all of them. I'll show you a workaround for that in just a second. And you can see that, yes, reinforcement learning is 22 characters long. Right, now here's why I have the second dictionary and it's a really nice feature of ray that lets us do tasks that actually depend on each other in a reasonably intuitive way. So what I'm gonna do is define a function that is gonna get the doc URL from. So recall that I queried the first one, I got something like ray.rlib. Now I can use that to query the second dictionary to get the URL for the documentation. The way I've written this function is it's actually gonna take the tuple that was returned from the first one. And that will actually be very convenient in what I'm about to do. But otherwise it's kind of identical to the first one. And also I'll create a task as well as a function. And then here, this method will actually first query the original dictionary with all four keys. Then it'll turn around and take those results and query for the doc URLs and then we'll finally return the results there. Let me go ahead and run this. It'll take like four seconds or something, but I want you to notice something crucial that's happening in line two. We have task dependencies now. I can't schedule the second, these doc URL tasks until the value from the first task is completed that corresponds to that lookup. So in a regular distributed system library I'd have to pull or something, wait for the first result, unpack it and then pass it to the next one. Ray is doing all that for me. This subject name is actually one of these IDs. It's not even a regular tuple. It's actually a future ID, but Ray knows that it needs to unpack that for me. And it also waits to schedule this task until the thing is ready. So I can have a graph of dependencies and Ray will handle that for me, but otherwise this code kind of looks like regular Python synchronous code where I'm not even thinking about distributed systems. So I think the reason I got excited about Ray when I joined any scale was I just loved the way that it kind of took normal concepts we like functions, added extensions that let us still kind of pretend like we're working with synchronous code, but now we have the magic of distributed concurrency across a cluster in the whole bit. So it's pretty nice for that reason. And sure enough, it took about 4.4 seconds to do that whole thing. I did mention that we were blocking for everything. There is an idiom with a function called ray.weight where we can loop getting the results that have already finished while we wait for more to go on. I won't take the time to walk through this code because it's a little complicated. I don't have a lot of time, but what we can see happening is as results finish, I go ahead and process them. You know, the first one came back in two seconds while other things are still running and then eventually I finished the loop as I've basically drained the queue, if you will. So that's a nice way to do work with things that are already finished while other work is running. And then lastly, really I'm finishing almost everything about the core API right now is you can also put objects into the distributed object store that Ray is using. So really in a real application, there's dictionaries I wrote, a reference data, I probably put them in the very object store. So tasks all over my cluster can query, pull out those objects and use them as needed. So this is a nice little sort of, let's call it the reverse, the dual if you will of Ray.get. All right, one thing I have not done yet is dealt with distributed state. These tasks that I've been writing have all been asynchronous, or sorry, well, they've been asynchronous, but they've all been stateless. What if I wanna keep some moving state and I wanna have that distributed over the cluster? Well, once again, we start with a familiar idea, something that we know already is a good way to park state, which is a class. So I'm gonna declare just a regular Python class that now is called a search service. It's gonna have these dictionaries, just for convenience that we mentioned. But one thing it's gonna do differently now is that it's actually going to remember how many times I asked for each key, as well as keys that don't exist in the dictionary. So it's actually keeping a state of, sort of metadata about how this service was used. And then we have a query method that does basically what we've done before, where we query for a given phrase. This one also handles the case that it'll determine which dictionary it needs to read and that kind of stuff. And then we'll have a convenience method for getting all of the keys from both dictionaries. But otherwise it's kind of the same code we've already had. As usual, you create a service. We can then do queries over it. And if we run this, we see that, yeah, it returns all of the, here's the known keys and an unknown one that I've appended to it because I'm now gonna try timing this thing. And this will take about 11 seconds because it's gonna one at a time go through each of these sleep for each call and so forth. It's important to note that I've actually gone back to synchronous execution in the sense that I have one actor, as we call it, it's gonna process one request at a time. It's not gonna do it in parallel the way we were doing it. The way you would get back to parallelism is have multiple instances of these, like a farm of them or something, or you can also do a concurrent invocation. So by default, we'll do one at a time, which is usually what you want because that means I'm not likely to corrupt the state in this class, which is the number of queries. So that's usually the way you wanna go. And you can see the results we got back and it took about 11 and a half seconds. And sure enough, we queried each of those keys once, including this one that had never seen before. Well, we can very much turn this into an actor now, just very much like we did before, we'll subclass our search service, we'll annotate it with ray.remote. The one other thing we have to do here is in an actor, you can't reach in and read objects or fields inside the actor. So we have to have getter methods or accessors to get at them. So I've added those to get the dictionaries and also to get that query object that's keeping track of how often we've called something. Notice how we construct one, it's serviceactor.remote. We'll use the same sort of function we used before, but now what we're gonna do is fire off all the queries we want. And again, that's the setup here for list. And then we'll use our weight loop to pull them off as they're done and print out the results. So if we time this and watch what happens, we can see that some of them are starting to come back, but this is synchronous again, because of the way we've now designed it, but at least it's completely thread safe and robust. So it did take 11 and a half seconds, but it worked as before. And we can see that once again, we called all of those keys once. All right, to finish this, let me just rip through very quickly an example of the high-level librarian Ray called RLib for reinforcement learning. If you don't know what reinforcement learning is, it's the thing you may have heard about that beat the world's best go player. It's the technology used to beat Atari games. Essentially, you have an agent. It's looking at an environment. It's making observations about the state of the environment. It's trying to guess what the best action to take is and then it observes the reward it receives, and then it tries to learn and get better and better at choosing actions based on the state. And what I'm gonna do actually is use a popular example of a reinforcement learning environment, which is called cart pole. And that's basically where I have a one-dimensional cart moving back and forth, and I wanna keep this vertical pole balanced as long as possible. And this is very easy to set up. And Ray, for time's sake, I can't go through all the details we're seeing, but I will say this, we are gonna train a two-layer, fully connected neural network, the two hidden layers with 50 weights on each one. And we'll actually see how I get smarter and better as it goes. And while I'm waiting for there it goes, that's finished. So this is just a loop. It's gonna do the training and it'll print out results as it goes and now we'll watch it happen. This will take a few seconds. But what we'll see is we're gonna print out, as it runs, it can do up to 500 points and that's when it just stops. But what we want is for most of these so-called episodes to get as close to 500 as possible. So the number we really care about is the middle number. That's the mean, so that on average, it will do this well at this level of training. And as you can see, this number is getting higher, excuse me. So it's the maximum score that it got when it was doing a training run. And that means it's getting smarter as it goes. Now the iterations, I think we're gonna do 20 and it turns out that it'll get good, but not great. If we let it go longer, it could actually get really good. Close to 500 every time. So we'll let this continue. I'll go ahead and evaluate the next cells so that they can load when we're ready. And what I'm gonna do is just show you the data which kind of reproduces in a nicer format what we're seeing printed and then we'll actually plot it just to see what it looks like. And then we'll do one last step and I'll be done with the demo. So it's up to roughly half of a maximum score. I think it gets up to about 350 or something when it's done for this particular size network and for 20 training steps. And then the last step we'll actually do when it's finished is we'll take that network that we've trained that's saved as a checkpoint and we'll actually try running it and we'll see how it works when it runs like five or six cycles. So there's the data. You can see that the reward mean which is the center column got up to about 305. And here's what it looks like when we plot. The max we very quickly got to the point where we could get a maximum score but the mean score still rose during that whole time. And now we'll try this rollout and you'll see a window pop up where it's actually running these so-called episodes where it's trying to balance the cart. Balance the pole on the cart rather. All right, there we go. So this does about five, I think, episodes. This one's not too bad. It's holding up pretty well. It'll also print out the score. I scroll this down a bit. So that first one, it actually got all the way to 500. This one it looks like it'll stop almost at 500. So this is actually doing pretty well even though our mean score isn't that high. So hopefully you can get a sense that it's actually pretty easy to use a high level domain specific library like this to do the work we wanna do. But at the same time, it's actually using this distributed compute framework under the hood. And the example we've shown would actually go much faster if we were using a real cluster.  So let me go back to the talk. All right, so why Ray? Well, it's kind of emerged out of two big trends. One is that the size of neural networks is growing enormously, which also translates to how much compute is required to train them. We're far outstripping Morris law in terms of growth. At the same time, Python, as you all know, it's been seen enormous growth of interest in the last decade or so driven in a large part by interest in machine learning and all the great libraries available in machine learning, data science and so forth written in Python. So that kind of means that we really need an easy way to distribute Python over a cluster if we're going to get past the limits of Morris law. But we want something that people who don't really want to care about distributed computing can do so easily. So there's a whole bunch of icons here about different steps that you have to do when you're doing like a typical machine learning pipeline, all of which typically requires some sort of distributed computation to meet the scalability requirements. And the vision of Ray is that we could have this sort of very generic framework. If you think about the original part of the demo there, it was really nothing about machine learning. It was just about scheduling tasks of arbitrary size, managing distributed state. And then on top of that, we can build these libraries that handle specific domains. These are four of them right now for that are part of Ray that are part of the machine learning space. There's others that are being written in neural rather natural language processing. And a lot of people are starting to write generic applications with Ray as well. So let me just talk briefly about these libraries. We actually just saw an example of RL Lib. See how I'm doing for time, pretty good. Here's a different icon or image than the one I showed you. But it's one of these huge spaces that's starting to see a lot of interest for a wide variety of reasons. The first big successes were in gameplay like beating the world's best go player. They've been used a lot in robotics. Actually this one in the middle, this bipedal robot is actually implemented with Ray. There's some interesting work being done, getting closer to regular business problems, industrial automation, workflow management, that sort of thing is becoming a hot area. As well as optimizing systems like your network topologies, HVAC systems, Netflix and YouTube have published some interesting papers about using reinforcement learning to improve their recommendation systems, which has been an old problem, but now they're finding new ways to do it. And then of course in the finance world, they always leverage everything. Finance is a time oriented problem and that's what our reinforcement learning is really good at. So peeling the onion a bit, what's going on in AlphaGo, the go player is that the observations in this case, so the state of the board, the actions are where you're gonna play stones and the rewards are really in this case, instead of a reward at each step, it's only win or lose, that's all. So you don't really know how you did until the end. And behind these things, they built this huge neural network that at various layers can identify different patterns in go play. Reinforcement learning is a very broad field and there's also lots of different ways that people are using it and building algorithms that work, that do reinforcement learning. So what Ray Aralib tries to do is to give you support for all of these different ways of doing things. Open AI Gym is where we got that cart example, for example, and then give you lots of different algorithms that are built in or the easy ability to define your own, lots of abstractions that can be glued together in different ways and then all of this running in a distributed way arbitrary scales using Ray. You can actually try it out in SageMaker. If you're already using SageMaker, there's Ray in the middle here of this picture and Azure just rolled out support for reinforcement learning with Ray as well. Back to why Ray was created. If you think about all of the different compute requirements that you need to be able to support in reinforcement learning, you've got these things like simulators and game engines that look a lot more like regular applications with complex distributed graphs of objects and memory. And the agent itself may be fairly complex, which are much different than what we're used to doing in data science and neural network training, but we have to do that too. And we have to do this over and over again as efficiently as possible because the only way we can train in reinforcement learning is to just play over and over again and learn as we play. So the kind of diversity of CPU requirements, memory access patterns and so forth is what drove the need to build something that's very flexible like Ray, but very efficient also, so that you can build on top of it tools like RLM. The second one I'll mention quickly is Tune. And this is for hyperparameter tuning. What is hyperparameter tuning? Well, it's become a research area in its own right because of problems like when you need to decide what's the best neural network architecture for my problem. Every number you see here is a hyperparameter, meaning before I even train anything, I have to decide on the structure of this neural network. So like how many layers am I gonna have? How big are they? If I'm doing convolution, how much am I doing, what's the size of the convolution? What about pooling and so forth? So the space of possible neural networks is enormous and you don't wanna naively just search all possibilities. You want some intelligence that makes it easy, hopefully to get to a good architecture relatively quickly without a lot of expensive compute time. So Tune is really great for being very concise in how you declare what you want. And then it integrates in lots of frameworks like our favorite neural network and machine learning libraries, as well as intelligent algorithms that try to optimize tuning process. In this case, I'm using Bayesian optimization. It's also optimized for neural network training because that's where the real problem of hyperparameter tuning exists. And it's designed to be easy to plug in new frameworks and new algorithms. Last thing I wanna talk about for those of you who aren't interested in machine learning is what about using Ray to build applications or microservices? I won't go into all the reasons we create microservices. I just wanna focus on one part which is the need to separately manage things. And if you think about it, we often have separate microservices because we might need lots of different instances of some things more than others for scalability reasons. Some microservices might be evolving much more quickly than others. So we need to swap them out very frequently. But the downside is that we have all of this stuff to manage ourselves. We have to have a lot of instances because no one machine is big enough probably. And we don't wanna rely on one machine that if it fails could bring down our application. So we have all of this manual stuff we have to do to deploy our applications. Well, the vision of Ray is that we could actually go back to thinking about one logical instance for each microservice. But behind the scenes, Ray is scheduling all of these actors and tasks across our cluster sort of transparently for us. And we don't have to do nearly as much of that management of instances that we had to do before. There's a lot more that needs to be done here to make this as fully powerful as we might want. But this to me is one of the exciting aspects of Ray. It's really what we've been doing with RLiv and these other tools, but it applies equally well to other general purpose applications as well. And also nicely integrates with systems like Kubernetes because Ray is working at a very fine grain. So it works at a lower level of granular or I guess maybe it's a higher level of granularity than whichever, than Kubernetes does. So it integrates nicely with these other frameworks. So if you're interested in adopting Ray, one thing you might look at is if you're already doing multi-processing with these libraries, JobLiv or multi-processing pool, Ray actually provides some drop-in replacements that break the single node boundaries. So with just changing the import statements, you can now do a scheduling across the cluster rather than just across the cores in a single machine. And it also integrates nicely with async.io if you like programming co-routines. There's a very nice way to use Ray that way as well. So check out ray.io for more information. I've been writing tutorials that you can find at AnyScale Academy at this URL. You're welcome to join the Ray Slack. That's the, excuse me, the best place to find out about Ray to ask questions and so forth. And there's even a Google group for it. Once again, please check out our Ray Summit conference this fall, it's free, it's online and we hope to see you there. Thanks for listening. I'll be happy to take your questions in the Discord channel. Hey, thank you so much. That's amazing. Actually, I really love it because, you know, I really hate setting up all these like pipeline, you know, like distributor thing, everything. I was doing data science, but that really bugs me. And this is actually really good that you have one thing for everything. So let me have a look at the, so because there's no Q&A in Zoom, so I'll have a look at the power of chat to see if anybody's firing any like questions in. If not, then I gotta maybe I would ask one question myself because I'm really interested. So, is it like, so you said that because it's for everything. So like, is it like, so is it difficult to set it up? Like, I really, I'm not good at deploying anything. So like, would you say that it's good for, you know, people to like independent, you know, for example, like my pet project, I just do it myself. I don't have my colleague. Like, is it okay for people to do it that way? Or is it usually for more like a corporate solution for that? So. Yeah, that's a really interesting question. Ray clusters when you go, like when your laptop isn't big enough and you need to go to a cluster, it can be as easy as just standing up some instances in Amazon and then running the initialization script on each one of them. And then it does a pretty good job bootstrapping itself from there. When you submit code to that cluster by, and it's transparent. You just do Ray and it, here's the address in my cluster, then it will actually upload libraries and so forth that you need. So it's generally, you know, I've used a lot of distributed system tools, you know, Spark and Hadoop and all this stuff. And it's about as easy as it can be in that way. It's not completely seamless. Some of the technology we're working out at any scale hopefully make it even easier where you don't have to think very much about it at all. But right now there's a little bit of setup but it's not too difficult and it can be as lightweight or as big as you want. Yeah, that's good. That's good. I love the flexibility. So thank you so much. I think it's really interesting.  And thank you so much. And I hope you enjoy the rest of the conference and the show show afterwards as well. Oh, thank you. Thank you. Thanks for having me.


This is the Bowmaker PA01, it's a 500 watt surround sound stereo speaker system, it's very portable, it has an internal rechargeable battery, it has high immersive stereo sound and it's so portable. Now I'm going to cover all of the accessories that come with this, how it sounds, all of the specs but the one thing I really like about it is the portability of using this wherever you like, it only weighs 5 kilograms as you can see, I can pick it up with one hand very easily. Speaking of the portability, the fact that it has a very nice grip handle here which is fixed, you can also carry this like a suitcase. So it has a two height adjustable handle like you do with all of your suitcases and then it has two wheels at the bottom there. So let's go ahead and see all of the accessories, the functionality and let's give it a sound test. Right so you have a microphone that comes with it, the lead on the microphone is 2.3 meters so it's not the longest but it gives you a very good distance anyway to be not so close to the speaker. You also have a auxiliary cable to connect this directly to a phone, a speaker or a TV. You also have a remote control and then you also have a DC adapter. So if you want to use this connected via the outlet then you can just connect this and it will also recharge the internal battery otherwise you can just use it without this plug and just run off of the battery. If you do decide to use this on the battery it has a 2200mAh battery and built into it, in perspective that depends on your usage. So you can get 11 hours of playback time if you have the volume on around 30% so quite low but if you have the volume at its maximum level then you can still get five hours which is more than enough to have an event or even a concert or whatever you'd like it's just the perfect thing to have. Now there's five ways you can connect to the speaker. The first one is with Bluetooth so it has Bluetooth 5.0 which is probably the way I'm going to use it. It has an auxiliary input so if you want to connect to multiple devices like I mentioned you can do that. It also has a guitar input so if you're a guitarist and you have one you can connect it directly to this playoff of the speaker. It also has a USB port so if you have a USB stick with some music files or anything like that connect it to the USB port and just play off of that. And finally you have FM radio so if you just want to listen to some music off the radio scan for your channel and just play it back like that. Now inside the box you have a three inch tweeter which is a treble speaker and you have a massive eight inch subwoofer which gives you a high quality bass. Let's go ahead and take a look at the top of the speaker and see some of the knobs and controls. So this is the USB port to put your USB stick in. You can also reverse charge your devices with the USB charge port there. This is where you have the auxiliary, the mic input, guitar input and this is your DC adapter. Now it does have a DC adapter that comes in the box with this but you can also buy an external one. This is universal and if you want to take this on the road then you can also get a 12 volt car outlet DC adapter as well so you can have this in your car or your van wherever you're going connect it to your car and keep it charged at all times. So as it power on the device just hold down the power button for a few seconds. The EQ source button this is where you'll be able to change the mode so between Bluetooth or auxiliary or FM radio etc you'll be able to do that. When you press scan if you are on FM radio this will scan the best radio channels and will display on the LED display there. If you wanted to skip a radio station you can go forward and backwards with these M buttons here. Now plus 10 and minus 10 if you wanted to skip maybe 10 stations because there is a lot of amount of frequency stations on this then you just hold it down for a few seconds and you can go 10 stations back or 10 stations forward. If you wanted to connect it via Bluetooth just hold down the pairing button it will go into pairing mode on your phone or your Bluetooth device just look for a LARC PA01 it will turn up and then just select that it will automatically be paired. Then you have these knobs here now these are very nice twist knobs you have the volume for the main speaker system you can control the treble and the bass. This is the microphone volume so once you do connect the microphone in the input there that volume will become redundant and this volume will take over. This is the guitar volume so you can adjust that if you've connected to the guitar input every other knob will be disabled in terms of volume and only that one will be available and this is to adjust the echo levels if you wanted to add an echo to it. So let's go ahead and turn this on. So it's currently on the auxiliary input if I select source just hold it down for a few seconds FM radio FM radio Bluetooth now it's on Bluetooth so you can see the battery percentage there at the moment is fully charged so I will have plenty of battery life to take this with me. So let's take a look at the remote control because I might not be close to the speaker at all times when I'm using it so you can use this remotely. So this is the remote control you can see you have all of the sources there along the top these are the different equalizer preset modes so depending on whether you're indoors or outdoors which is the in and out you can set this to have it on the music mode or party mode. Party mode obviously giving you a little bit more of a punchy base and depending on whether you're indoors and outdoors it will adjust the volume and the output accordingly so that's a very convenient thing to have of course you have the general controls there so you can adjust the treble the bass and the echo yourself manually you can also adjust the volume of the guitar and the volume of the microphone scan for your FM radio channels and then this is unique if you maybe connect this to Bluetooth and you Spotify you can shuffle play or repeat songs using this button so very straightforward easy to use so let's go ahead now and test the audio starting off with the microphone. So I currently have this on volume 10 but it goes up to maximum of volume 30 so the microphone is connected I've turned it on you have on and off switch for the microphone as well so if you just want to make sure you don't speak into the system you can manually turn it off from here turn it on hello this is testing the microphone now I think it's a little bit too low so I'm going to put it on to volume 20 and then I'll go to volume 30 so this is volume 20 I think it's quite loud I need to be a little bit closer to the microphone so you guys can hear the PA system if you're in an event hall this is perfect for that whatever it may be you can use it for weddings etc now let's go and put it to volume 30 so this is the max volume wow that is quite loud I reckon the range of this would be at least 100 meters especially in an indoor venue so it is quite powerful if I keep my volume a little bit away from the microphone it doesn't come too loud so I need to be a little bit closer just like this so that's the microphone I think it does a great job let's go ahead connect it back to my Bluetooth and play some music on it so I have it connected so I won't go too loud not to disturb a lot of people but let's go ahead and play some music back now that was only at volume 3 so let's go ahead and put it to volume 10 20 and see if we can put it very loud so this is volume 10 finally let's just try 20 because I think 30 is going to be a little bit too loud that is so good it's so punchy it's very loud the bass in it is amazing the adjustments on the equalizer that you can make yourself gives you the best type of audio that you need for whatever purpose you're going to use this for so I'm very impressed with this and it is an excellent purchase and to be honest guys whatever purpose you're going to be buying this for whether you want to use it as a speaker system a PA system I might even use this in my home gym as well just to play some music in the background while I'm training multiple different purposes with it you can get a you're going to get an excellent buyer from this so this comes in that 150 pounds on Amazon I'll leave a link down below where you guys can check it out in the meantime if you guys have any other questions drop them down below to me and I'll try to get back to you as soon as I can if you like this video and you want to see some more really cool tech gadget videos in the future make sure you go ahead and subscribe make sure you like this video and I'll catch you guys next time take care


Okay. I'm here to talk about slightly interesting thing than what you saw just now. I'm sure what you saw so far have been interesting in all other aspects. But I am told that your word animation in your presentation is liked by many people. So I have avoided that typically to just start about what am I doing here and what are the kind of projects which we'll be doing under this. So basically we do a lot of animation, so to say, but we do it for e-learning. At IIT Bombay, we had started this lab around five, six years back where animation is done only for e-learning. So we don't have this Tom and Jerry and other cartoon character animation here. We have beakers and test tubes and one year calipers and all that, which is jokingly I call it characterless animation because we don't have any character in that. But so to say, we have received very warm welcome from various strata of different education institutes and teachers especially and students who have liked that animation. And for that, we use something called a 3D tool called Blender. So now that you have heard enough, there's something to do now. So I'll show you something which is a top view and a side view of object. So what could be the 3D view of this object? So can you just doodle around and just show it up from there itself? It's like 15 second job. Time starts now. Side view, you need this, right? And top view, okay. So you mean to say this finger, okay, quite nice. Anything else? This is not allowed. Anything else is fine. So don't think of staircases. That is the simplest one I had got results at many other places. But yeah, this was quite interesting. So none of them, I think, I don't know whether you thought of it or not. But very few people thought of even this as an option, which can look. So a cut face from the top is also looking as a square if you look at from the top. So while making anything, I'm saying any object for that matter. But no need to think of that object could be like this from the side. Yeah, this is a perfect answer, actually. But you are still going with only straight lines nowhere you have thought of. So even this is a perfect answer, right? Do you agree? So it depends on how you think in terms of the third dimension. So most of the time we assume things as 2D. And that is why this is lacking. So 3D animation brings you that reality check immediately. Once you see something in 3D, you realize that it's very much real. Based on the same thing, so there are two pictures. One of them is a photograph. One of them is a 3D animated tool created image. Created actually using Blender. How many people feel that the right side image is a photograph? No? Okay, how many people feel left side image is a photograph? A lot of you, okay. This is a 3D animation output. Even this is Blender and to be specific, this part is 3D. The rest is a photograph. This was part of a competition organized by Blender community. Even this is Blender. This is Blender. Even this is Blender. And by the way, this is our in-house Blender. So artists from my lab have done this. And but we normally don't do this kind of stuff. We do these things. So the photograph is of the screw gauge and the big size images of Blender created screw gauge. And you can see the obvious advantages. You can use it any number of times. It doesn't break. It doesn't have any marks of getting D. So you don't have to calibrate it or it doesn't get out of calibration even after you use it for several years. Even this is Blender. So the right side is Blender. Left side is a photograph. And you can again see some obvious advantages. No need to clean them. Even if you put any chemical in that. So in your labs, people, I don't know how much experience you had with chemical labs. But if there was some stain of the earlier chemical in tests, your experience would go for a toss. Nothing of that happens here, actually. So you can still go ahead and do your experiments. What do we do here? Why is Blender so much of a big name here? Because we started very modestly with some like four or five people team here.  They were studying in some other colleges. And they came here for a final year project. And most of those projects have led into various outputs, what I'm just showing. We have over 200 such models related to e-learning domains. So there is a physics section where you have all the physics models and chemistry models. You have biology models. You have other models. You have animation of more than 20 concepts. Couple of animations have actually been so popular. We ourselves didn't believe that animation of the same thermodynamics, animation was created for a distillation cycle, actually, not in thermodynamics. Distillation cycle animation has got more than 2 lakh hits. So we are not really sure whether we are looking at this correct figure or we are not able to calculate the zeros properly. But that was it. And people are asking, how did you do that? And can you share? And obviously, this is all shared in open source creative commons license. We have a repository called Oscar. Somebody mentioned before me, I saw that slide. So oscar.itb is a resource where you can go there and download all these models and animations and interactive, whatever animations are available there. We have published more than seven or eight papers already about how to do animations in Blender, how to create interactivity in Blender, and so on, so forth. 20 odd workshops have been already conducted for students in comm science, IT, teachers in comm science, IT, and various places. For even professionals, we have conducted a couple of workshops to teach them Blender. And now this is a big need because the software is becoming costlier. So all these Maya and 3D Max users come to me saying, Samir, please get a workshop for us, because we are expecting a raid any time now. So better switch to Blender as early as possible. By the way, I come from the industry of film making. I'm a filmmaker myself. So I had those connections. So these guys keep on coming at various points whenever they get that hint. So in a nutshell, we have reached out to more than 1,000 people so far. We have a page on spoken tutorials. Professor Condon spoke about it just now. If you go there, you'll find a Blender tutorial. So this, actually, message is especially for these people. So those who have selected Blender Animation as their projects here, these guys should definitely go and look out for the spoken tutorials webpage and look for Blender tutorials. There are 10 of them of less than 10 minutes each. So you will have to go through those tutorials. And once you are done, you can just mail us and we'll take you through the rest of the thing. But in any case, for people who are not included in this list right now and wish to learn Blender, there is an option. You can just learn the basics of Blender at least using those tutorials and pass it on. Again, it's an open resource. So what are we going to do with these eight people? OK, so there are two projects, basically. The first one, which I'm showing here, is actually a part of what they have been doing already. So since we don't have time right now, probably, I'll just see if I can show you that video. So what we have here is a tablet version of animation to teach students the internal structure of human eye. So advantages, obviously, are that you can touch the eye and you can split it open. If you remember, when we were young and students and the teachers used to get a porcelain model of an eye, I don't know what happened to you guys when you were a student. But when I was a student, there used to be a porcelain model of an eye and she used to open parts of it, like this is retina and this is lens and all that. I had no chance in my lifetime to touch that porcelain model for whatever reason. But it was so very exciting to me to see that because basically it was so big as compared to your human eye. It was very big model. So it was very easy to understand what was happening. But given a chance, actually, I would have loved to do that myself, but I never had a chance. So I just took that inspiration and said, OK, let me do it in 3D now that I have blender with me. So we have a blender model like this where you can just take it apart. And you can understand parts of it by clicking on it and you'll get the definition working on it. All that will come on the tablet. What it does not have is the typical features of gesture control, which is normally available on any other tablet app, that you can swipe, you can pinch, zoom, you can twist and all that. That is not possible because we are using blender and its components, namely blender game engine, to construct these things. How we have got it in a separate way is by using something called libgdx. So this libraries actually are helping us to give that option but not within blender. So the task, first project, what we were thinking of is to get these gestures into blender game engine. Blender game engine, like other open source tools, is a completely free and open source tool. So we can easily try to do something. I said very easily that we'll try to do it, but I'm not sure how difficult it is. These guys have tried it and they couldn't find a solution for that. So we settled down for a mediocre option in between, which we will show you sometime later, but this should be enough for other people. That is one. The second option is slightly different and this is not dependent directly on Android but we are trying to see how we can make use of that. We have already developed a game which students can play using gestures. So that game is called Math Amazing. Again, this is the work done by BTEC students last year. So Math Amazing actually takes you, it's about a practice game to teach maths. So you see a maze here and there are three answers given to a question. And the user has to stand in front of the computer and a gesture recognition hardware, Kinect is used here to get the skeleton of the person like this. So once you know which is the answer and the clock is ticking already, it starts down from 40 seconds or something. If you know the eight is the correct answer, you have to make body gestures in order to move this Android guy up to the correct answer. And if you get there, you obviously go to the next level and so on and so forth. So this is already developed, you can see it working. We have Kinect here and we have all the source code for that. The problem is that we need to do couple of amendments here. We need to do the, so if you move out your right hand, the character will turn to your right and all that thing is already done. What is not done is based on the complexity level, the maze should change and give some new things. That is one option. The second is that we should have more gestures also incorporated in that. So how that can be more friendly because this bending down and we are just looking at whether can more gestures be incorporated. And third is that we use something called fast, which is a skeleton tracking tool available. It's a free and open source tool. So that is the third problem we have. So we just wanted to see if this can be made more interactive. Also we need to see how this logic can be applied to some other game. Maybe not for mathematics, but can we make some English game out of it? Can we make some history game out of it? And so on and so forth. So this is project B. These are two projects right now we have. So all the best. Thanks.


Live from Boston, Massachusetts, it's theCUBE at the HP Vertica Big Data Conference 2014. Brought to you by HP. With your hosts, John Furrier and Dave Vellante. Okay, welcome back everyone. We are live now in Boston, Massachusetts. This is theCUBE, our flagship program. We go out to the events and sometimes go on stage, like we were here at HP Vertica. I'm John Furrier with Dave Vellante and Jeff Kelly kicking off day two of wall-to-wall coverage of HP Vertica's Big Data Conference. And Dave, Vellante and myself were the keynote stage act, if you will. theCUBE was on stage, technically with no desk, but with chairs. Dave, great session. We basically did up there, did a CUBE interaction kind of thing with crowd chat on the big screen. So they took theCUBE and brought it up on stage to entertain the crowd, which we did and we informed. Yeah, I really was proud to be up there with you and Jeff and having crowd chat in the background. So let's talk about some of the things that were said and we can get into sort of recapping yesterday. I'm really concerned, John and Jeff, of two things. One is the schism between IT and business. IT seems to think that, okay, this big data thing, it's working great, we've gotten the maximum value out of our project and business guys are saying, no, less than 20% of the business folks that we talked to say that their big data projects are paying off to full value. That's number one. There is a big shift coming. The cost, the expense, the ROI on traditional enterprise data warehousing and analytics initiatives and business intelligence initiatives which have not lived up to their potential promise, compared to the potential for emerging analytics projects is big disparity. I think that there's a share shift that is going on now and will go on for the next 10 to 15 years and I'm concerned that this audience doesn't see it. Before we go to Jeff Kelly on this, I know he wants his data on this. Rephrase the first one, the schism, what specifically, slow that down a little bit. Go in slow motion, please.  Over half of the IT practitioners say, our big data projects are successful. We're realizing the full value. I think about 54%. Less than 20%, about 18% of the business people that you talk to, by the way, the guys who are funding it, the CMOs who are going to spend more than the CIOs say that their projects are successful. So 54% of the IT guys say it's a success, 18% of the business guys say it's a success. Big schism, that to me says there's misalignment. The IT guys are measuring success based on, oh, the technology works. Did my job. Check the box. So check the box. Yeah, check the box. That's not how IT people should be measuring success. That's certainly not how the CIO is measured and is going to be measured. You should measure successes. Did it increase revenue? Did it cut costs? Did it save lives?  You agree? I agree with that. From, I think there's a few reasons for that schism we're seeing. One, as we talked about, the way you measure success is different if you're an IT person versus a business person. Get the technology stood up and operating success versus a business person wants value from the data. They want insights from the data and striving value, whether, as you said, cutting costs, finding new sources of revenue, whatever that metric is. And I'm saying warning, warning, warning, that's misalignment. Right, I agree with you. And so that something has to be done to bring those two together so that IT and the people on the technology side understand the impact that their technology's having on the business side. The other issue, I think, is also that business people have to change their mindset. We talk a lot about becoming a data-driven organization. It's not as simple as deploying some technology and throwing up some dashboards or some data visualizations on it. Business people have to buy into the value, buy into the hype, if you will, that data, big data analytics is going to change the way they do business, improve their business. If they don't, it doesn't matter how much the IT team screams and kicks, hey, you guys got to start changing the way you operate to take into account data. It's not going to matter. The business people have to under, there's a shift that business has to have. So IT has to change as well, but the business people have to understand this is a new world we're living in. So basically, if you notice in that room, Dave Vellante asked a strategic question, Jeff, and he basically said, I love it. The analyst did this perfectly. I love how, and Dave's a master at it. How many people have Hadoob in all the hands go up? Majority of the hands. Let's say maybe half, practitioners. So they're all big data savvy or learning about columnar data stores with Vertica. And then we said, how many people are paying for it? Almost every hand went down. There were three hands that stayed up. I mean, I think those were plants from probably Hortonworks. Those are MapR customers, probably, right? Those are either MapR customers or Cloudera customers. When you talk about Hortonworks, are you paying, meaning, you're not paying for the distribution? No, I said it couldn't be paying for the distribution. The point is the hands went down. The hands went down. What does that tell you? Nobody's paying for it. That means it's early stage POCs and figuring out where to integrate this thing. And so I didn't get the red hat for Hadoob question that I wanted to bring up, but in the reality is big data is a variety of use cases. We've talked in marketing, they call it omnichannel marketing is the new normal for with social business. With Clou, you have different implementations, different stacks and all this stuff. You're seeing different versions of solutions that are actually really tailor made for a specific use cases at scale. That means general purpose software is gone. So I think people are really trying to figure out where to go with Hadoop. How do you customize it? How do I play with it? So what's your take on that, Jeff? What did you get in the survey? Are people really still scratching their head or is it stuck in the mud spinning its wheels? I think people are, look, when you've got a technology that's still developing as quickly as something like Hadoop is, the application for Hadoop is changing from where it was two years ago we talked about on stage from pure batch map reduce style analytics to what is potential is today with things like yarn for interactive in real time. So based on the feedback we're getting from both the survey data but also talking to practitioners on theCUBE and elsewhere. We did dozens of in-depth interviews as part of our survey work. I think people are still trying to figure exactly where Hadoop's going to fit. There's a reluctance to pay for something where you don't yet quite know what the value is going to be. And increasingly I think people understand that it's Hadoop while important is just one aspect of this modern day architecture. And I think there is going to be a significant market for the Hadoop vendors for Hadoop technology and services but where the real value is going to be as further up the stack in the analytics and ultimately the applications that you're packaging that are delivering insights and triggering actions based on analytics. That's where a lot of the value is going to come. So it rains to be seen how much people are going to be willing to pay for what is increasingly becoming a commodity layer. A very important foundational layer but a commodity nonetheless. I still think that the IPA window for a public company is going to open up in 2015 and the truth will be told whether that happens or not and it's going to come down to Dave what we talked about yesterday is will the technology be in place where people actually deploy practitioners to deploy the software and infrastructure to do the big data and where is the killer app? Is it analytics? So it's kind of a red flag for me that Cloudera takes massive amounts of funding from Intel. It's kind of a red flag that you don't see the tsunami of apps hitting the table outside of analytics and visualization which by the way are in my opinion the killer apps. So without that present I'm skeptical right now that the IPA window may or may not be open because if there's no value creation for the vendors that are going public and it's going to shift to the practitioners it's going to be a whole different landscape than what people are looking at. So what's your thoughts on that Dave? Well I wonder, so I've been thinking about this a lot I wonder if in the boardroom of Cloudera somebody said hey guys you know we could do IPA we're almost ready to do that we're cleaning up our act but we're really not going to be ready until let's say mid 2015 and we're worried. Interest rates are going to go up and the IPO window could close so here's an alternative like Mark Andreessen says going public sucks why not give up 20% of the company sell 20% of the company to Intel and create an exit for some of our early investors and maybe some of our senior management I don't know John you might know the inside baseball there and that way we have our options open if the market gets super frothy we can go public if the valuation's there. It's a fact I've confirmed I've confirmed just for the record fact investors cleared cash off the table in some cases all their investment has gone. What about Mike Olson? That's been confirmed that employees I'm not going to confirm names but I know that employees did take off the table that would imply that management did take some off the table whether it's all or not. So there's an incentive and now they have the flexibility if the market gets really frothy interest rates don't go up the bull market keeps cranking maybe they go public but they have that option. So a question that came in that I didn't bring up on the stage because it was kind of long-winded it was really pro-vertica I didn't want to pimp on Vertica because they had pimping already out there but it came in from a practitioner and I got an email they wanted to remain anonymous I love the anonymous stuff it's like don't use my name but ask this question and it was not a bad question and it was Hadoop is moving into the production phase clearly from POC and cost to operationalize it as a data warehouse is becoming apparent period. Does the upfront cost to purchase Vertica versus open source worth it if you get a low cost data lake and cost versus cheap thoughts do you agree or disagree? Well so my take on that is Vertica is a very specific use cases Vertica to me is not your data data your one size fits all data lake it is for loading data fast MPP doing things that are conducive to that type of architecture so you can't do that with a traditional enterprise data warehouse you're not necessarily going to be able to do that with Hadoop but at the same time it's probably not going to be as cost-effective to do your data lake with Vertica so you're going to have Vertica compliments the data lake it's you called it the Ferrari I think you nailed that John Vertica is like the Ferrari that you put on you know you drive on the weekends or you drive when you need it you drive on the roads that are really fast that are designed for that Facebook needs a Ferrari I mean it's a high performance vehicle basically in this case a data movement vehicle it's not a general purpose platform but it's a flagship what they're doing with Haven that's why the strategy is brilliant from an HP's part is that they have the flagship high performance Ferrari that's the core product and they're going down into the product positioning having an entry level in the mid-range it's really smart because as you saw in the room Jeff the practitioners are just now kicking the tires and one question is still people think it's a big data bubble which I'm shocked I was surprised by that as well you know I would say an HP is not positioning Vertica as your data lake HDFS is your data lake right but that's what the question was I think right was essentially should I just use you know Hadoop or should I use Vertica for my for everything or is it too expensive that's what I understood the question then if that is the question as we understand clearly that the answer is no HDFS is your data lake I mean you can decouple compute with storage when you're using Hadoop so you can I mean you can go to a Hortonworks get the distribution for free and store as much data as you want and basically you're paying for the hardware Pivotal gives away their Hadoop distribution same thing and they've changed their pricing structure to basically you're only paying for compute you're not paying for storage like I said nobody's gonna become a trillionaire selling Hadoop distribution absolutely which is another back to your other question about you know when when's this window for IPOs I think it's it's not gonna be a big window because I think investors as they start to figure out this is not a huge opportunity here that the opportunity is for the practitioners that's where the billion dollar companies are gonna come from not necessarily I mean look there may be one company maybe Hortonworks is the billion dollar company maybe Cloudera gets there but this is not a huge market in terms of Hadoop distribution revenue guys let's shift gears to the insights we gleaned from our esteemed panel Q on stage Dave I think we hit some really interesting points from the crowd and the crowd chat and I think two things that jumped out at me this conversation that we just introduced to the industry called born in big data is a really huge deal and what I mean by that is that being mobile first is great cloud first is great data first is which we coined in the cube a few cubes ago but born in big data means that you are born with no baggage you are born with you know a glean in your eye you got a twinkle in your eye for creating value you have no legacy to clutter your mind up you are either a new cultural force like DevOps was in the cloud and that interesting is a cultural shift I think that is a really important new trend that is very clear you either see it or you don't and you know there's a question that said oh there's every tool for the job but if you're a hammer everything looks like a nail so if you're old school IT and you think you're successful with big data projects you're going to hammer that nail and that's what you're going to look for whereas the business people don't see it as success with big data so I'm seeing this culture shift with born in big data what's your thoughts? I think you're right on I think there's a bifurcated market out there you're either born data driven born in big data and in that case you know you've probably got much more alignment to your IT guys and your business guys are probably much more aligned with regard to the objectives of the project and the success of the project and then the other end of the spectrum is and I think many people in this crowd surprised me when I asked are you shifting resources I 100% guarantee that there's going to be more spending and more spending growth going on in the new emerging big data space than there is in the traditional enterprise data warehouse space if you ask business people how's your enterprise data warehouse working out for you? You got self service BI you liked having to wait a week two weeks, a month, two months for data consistently business people tell you now you know we use it we rely on it it's the best that we have but it's not what we envisioned and now the big question I still have is will Hadoop and big data live up to that promise? What would you say? People process and technology so that's if you're born in big data break that down people process technology okay so your people are are new generate younger skilled people in process younger skilled in dev ops or whatever we're calling them software engineering they're data hackers and they're much more aligned from a process standpoint there's no such thing as an IT project in companies that are born data driven it's a business project with a revenue target yes, yes it's not going to get the list and get the form filled out in the technology the guy from Saber actually I think nailed it the definition we brought in Hadoop because we had too much data and we had to handle it differently that's big data integrated stack so integrated stack so the process is simple there's no process it's you know, move fast, deploying process technology is stacks Hadoop I would add to the process part and this was an interesting session yesterday while you guys were doing some great cube interviews I was able to go to a couple of sessions and the lead data scientist from Nimble Storage had a session about really the business value of big data and he talked about how you know, Nimble Storage has been around since I think around 2008 so they're you know fairly new companies so they have the I guess the luxury of doing this but they you know they instrumented their storage arrays and their products with the ability to create and create lots of data so when I say so the process part of this is understanding beforehand as you develop new products that data is going to be key to developing and servicing those products so not kind of going back and saying okay we're going to slap some sensors on our products which is one way to go about it and it's important for existing products but as you develop new products instrumenting them in a way that they create valuable data that you can then use to both improve the product and service your clients that's another process aspect that I think people have to wrap their heads around well I think the process is business process right it's again the alignment between business and IT and so my argument is that the guy it's like it's like cloud first it's like mobile first I mean that those are disciplines that every single company is going to be adopting and I think data first is also born and big data born and big data driven great insight here in the cube again another first original piece of content coming out of the the greatness of the crowd sourcing second observation Dave you asked about the bubble right the question and this is interesting we were talking about this on the panel in prior and I want to bring this up if you look at the big data I truly believe that looking at the data and talking to all the experts and looking at the customers we are not in a big data bubble there's just demand for big data action and born on big data the kind of personnel things we talked about so there's a rationale around big data that's justified you don't see people crazy big data stuff it's really pretty rational in markets that are growing projects are in play but the bubble shifting to other areas and this is where the storage connection comes in storage and cloud are very bubbly because of big data I believe so I think that the thesis here is don't get your thoughts Jeff Dave is that if big data is truly real does that justify the shift to the enterprise with cloud and storage so it's what I was saying the practitioner is going to create more value than the vendors having said that you know what practitioner is getting an obvious valuation like cloud era so I want to go back to Netscape you remember the Netscape bubble you remember well because it jacked up prices in Palo Alto that was sort of the and Marc Antriesen was the co-founder who's my Twitter buddy Twitter friend so what happened in the next day everybody thought Netscape was going to that the browser was going to be the next essential operating control point it didn't happen and then Netscape Jim Barks said no problem if Microsoft bundles that's okay because we're going to go up the stack and we're going to be a software vendor and that all died why did Netscape why was there a bubble because people thought that Netscape was going to create a lot more value than Netscape actually did they were looking for the next Microsoft and the next Intel and then everybody was looking for the next Microsoft and the next Intel it turns out it merged in the face of Google well no it was called the web Dave so Netscape actually had the browser had an opportunity to create some cohesiveness and somewhat lock-in spec if they wanted to with the public free web but what happened was combination of Netscape kind of getting too arrogant which well documented certainly and then making a move into software enterprise software and all this other stuff and then three Microsoft's monopoly chokehold with IE3 just crushed them so that was just a systematic dismantling of Netscape by Microsoft and their own blunders and so that's well documented but they missed it but the web was the value the web came in through practitioners so here's my point but here's my point there was no Microsoft essentially that emerged out of the web like Google right but Google kind of came out of it as a surprise so here's the question related to Cloudera somebody thinks investors think that Cloudera is going to be sort of the big next value play or maybe they just no no your web analogy is actually a good one and I think it's a more stethesis of Jeff but maybe they think it's greater fool than I there you know how VCs think hey if I can make money it doesn't matter but so the question is is Cloudera's valuation justified will somebody come out of the big data world with a valuation such that it is justified and will that be Cloudera or somebody else? I want to answer that one question this is really key so this is what's Bill Gurley from Benchmark who was an analyst before he became a VC was very ProBubble.com bubble so was Mary Meeker you know all the people who promoted it and they all got egg on their face but what Bill Gurley came back and said if you look at all the stuff that they were promoting actually happened so selling pet food the hype was justified was early but the market lived up to the hype how they were allocating the value to the players was incorrect but actually everything actually happened the tooling was slower all the automation came in put moving digital to digital from paper, email all that stuff happened and this is where the value shifted it shifted to the practitioners the people putting the websites up the software, the web, ad tech software so that is exactly the same thing that we are seeing with Jeff Kelly's report and what Jeff's promoting which is the value creation of practitioners the people using the technology that's the web so your investment now certainly Google and others made money and they were worth billions and that was the vendors of but I would argue they're a practitioner yes I would say the same thing Google's a practitioner Facebook's a practitioner Amazon was a practitioner before they became a vendor well then the question is is cloud era a practitioner and I would say no no definitely not and so this makes investing really hard in big data you have to find the companies that are going to transform and the industries that are going to be transformed and make bets based on that so that's retail that's healthcare that's the things we talked about there was a question today about you know when are we going to tackle the really big problems and impact society and you know those are where enormous value is going to be created so I want to bring up the third point so we're born in big data the bubble shifting the enabling of big data rationalization to other areas like storage and also the practitioners but let's talk about the practitioners I think the HP putting up the United States Postal Service was a genius move and I want to highlight that because I think that is representative in today's marketplace globally in the U.S. at least of a legacy vendor would you agree their legacy I think we can they're slow with their government but they have to compete now with privatized companies and they have all the elements of big data internet of things so this is going to be a great presentation this is actually a working case study of the modernization in action so if you look at what they do they have people process technology issues that are legacy and now trying to be born in the cloud born in mobile so to me what they're doing is very interesting they have all kinds of instrumentation issues but that to me is a great case study and I think that's worth watching look I think people naturally people are impatient that comes to especially when you've got a movement of an area of technology like big data that's got so much hype around and people are impatient to see the success stories where is it and then they're quick just as quick to jump on it and say well it's not living up to the hype these things take time it's not going to happen overnight the tooling itself is still developing but the impact that not just big data but the intersection of big data in cloud and mobile is going to have is going to be enormous where the winners though are not going to be in my opinion the vendors it's going to be the practitioners well but so who create value create valuable services that people will pay for that impact their everyday lives that help people run businesses better that's where the value is going to be so when vendors will win I'm not saying vendors won't win but the investing community saw what happened with nobody wanted to own Microsoft and Intel in 1990 when George Bush the elder was president you could buy Microsoft Intel at the time compact for a song if you invested you know fifty thousand dollars back then it would you know you'd be multi-millionaire now so nobody wanted to touch him so when the internet came around with Netscape we said we're not going to make that mistake again well guess what they got it wrong so now vendors will make money now where are they going to make money the woman from Singapore said it what about integration this stuff is too hard I'm a business person this stuff is too hard so developing solutions the question is is it going to be HP IBM EMC the big whales that are going to do it and which startups and pure plays are going to emerge let's use the last minute to address that so Bert also Bert Lattimore was watching has a question is the Red Hat next Microsoft how will the cloud era significantly larger using the same business model or will be bought by somebody basically it brings up the M&A question this is the interesting thing if you say that the practitioner will be the next Google someone will the next Google in this new world company will be a practitioner that has to emerge so the question is people these startups are getting bought left and right so when to sell will they even make it will the big guys actually buy out the next one so they don't want to make that same mistake twice so the question is entrepreneurially are we in a marketplace that's going to be set up for it's going to consolidate guarantee that who's going to get bought first M&A M&A if I were a betting man I'd say map are would be my first bet second would be Intel finishes their acquisition of cloud era Hortonworks I'd probably put last on that list mainly because a large part of their value proposition is being independent so they go public I would if there if I if there was only one company at those three that's going to go public and be an independent company it's going to be Hortonworks well the Hortonworks thing is interesting and I think the Red Hat for Hadoop never is never going to happen but I think they play that card I think Rob Bearden doesn't play it as much or even at all because it's not a comparable or Red Hat had other dynamics and so I just say it but what's different about Hortonworks now is that their business model still isn't playing my opinion because of the the critical mass goals that they want similar to to the old way but the global market place is a very interesting dynamic we brought the bubble up on stage here and we thought about China the global market is a huge issue so if you look at the distribution opportunity TAM and you factor in China and other countries Hortonworks has a run to be massively huge under their current business model by the way that's what I love about what Tableau is doing Tableau is nation building and I love that there's this huge demand for simpler visualization out there the global market is very important and a lot of times these smaller companies don't spend enough time building international because it's so hard and they're putting investments in there okay so we have a big day coming up here day two exciting got our juices flowing really early with the keynotes exciting Dave, Jeff great job for our first real cube we did want EMC world was a little bit different more scripted but this one was awesome HP did a good job we got Chris Zealand coming up we got Zynga we got the SVP of technology at Royal Philips autonomy SVPs we got the West Fox from Tallend Tom Davenport's coming in he's a Babson guy so look at the Babbo thing going for me I've got my where I got my MBA we got Tibco Les Bonnie CO Quilk Larry Schwartz great lineup day two here live coverage in Boston, Massachusetts the cube we'll be right back after this break stay with us