Okay, we're back live, packed coverage day here at the SiliconANGLE's exclusive coverage of Stratoconference. Go to siliconangle.com for all the reference point of innovation around big data, cloud mobile, social, analytics, we got it covered. My co-host is Dave Vellante, the chief scientist at wikibon.org, and go to wikibon.org for all the free research. Go to wikibon.org slash big data. We have the most comprehensive set of data research on the big data segments than any other competitor or any other company, so go there. Our next guest is Peter Wang, co-founder and president of Continuum Analytics. Welcome to theCUBE. Thank you.  I'll see it's open source base, but when you start to get into the big money players like EMC, we're all now essentially data warehousing type products. You got a data business, data processing, which is not necessarily looked at in a DevOps or developer kind of way, and we've been introducing this concept of computing, software defined networking, software defined infrastructure is all colliding. So, what we talked to you about, your take of the big data industry as it evolves from the bottom up with developers and top down with business kind of impact and kind of where the role of the developer, where's the role of analytics, stack wars are going on, platform wars are happening, so just what's your take of the current ecosystem here at Strata and the big data world? So, my take is actually that there's been fundamental disruption in the storage and ETL of the big data or the business analytics space, and I think people are pushing up market and there's down pressure from up market I guess on trying to reinforce and show up those infrastructure pieces. But I think what's interesting is that both of these competing forces I guess are all, they're competing in a climate that's changing and that the big data wave that's coming is actually exceeding I think the disciplines for doing business analytics that most companies are used to and that what we'll see over time is that business computing and business analytics is going to trend more and more closely towards traditional scientific computing and scientific simulation sort of needs. And so the intersection of those particular ecosystems is going to be very interesting to watch. Yeah, we've heard some of that theme this week. We had a company on a different space, they do storage infrastructure, but they're in the HBC world and their premises, those two worlds are colliding. The HBC and the big data worlds are coming together. What are the similarities specifically? Well, I guess the thing is that scientists for a long time have had data sets so large and budgets so tight that they couldn't afford to not squeeze every ounce of performance out of their infrastructure. And so as a result, they've developed software stacks and technologies and computing paradigms that are, they seem esoteric I think to the business world but that are extremely, extremely performant. But, go ahead, sorry. Well, and so what happens is that I think now as businesses are faced with sort of very, very critical analytical challenges in the large volumes of data that are swarming them and swapping them, they're going to have to have more of that kind of scientific computing discipline. Yeah, so there are differences as well though. I mean, I think a lot of HBC data is more structured as opposed to loosely structured. And that's actually changing a little bit as well. One thing you'll see a lot in scientific computing conferences is people talking about data management, data provenance, all these things that are very, concerns that are very, that you see in a traditional data warehouse, right? Master data management, data governance quality, things like that. Because they do so many simulations, so many data acquisition runs, just managing that stuff becomes very difficult. Now how do you guys exploit that and take advantage of that? Well, so what we're doing is actually we're taking open source technology, Python, and the ecosystem of fantastic tools that have in the last 10, 12 years really been kind of forged in fire in the HBC and scientific computing space. And we're taking those tools and really looking at how to adapt them better for the business computing needs. Okay, so where are you as the company now? I think you can give us some background there. So we're actually just a year old. We started just about 14 months ago, but we're all veterans of the industry and we actually, a lot of us have written open source tools in the Python space that are very popularly used on Wall Street, oil and gas, weather simulation, you name it. And so we're looking at building the next generation of tools that will sort of serve as a foundational layer for Python in both business computing as well as scientific computing. And that's Anaconda and that's your platform. Yes. Peter, one of the things I want to chat with you about is first of all, we totally love what you're doing. So just disclosure, we're biased. We think the tools of the trade and data science are evolving obviously from DBA kind of analyst role to outside developers and that's colliding. And Python developers all know that, but so we agree that instrumentation is happening because now data is 100% available if you know what you're looking for. So you talk about Wall Street, the scientific community simulation, those game theory meets behavioral economy kind of things we were talking about yesterday. So what is the state of developers right now and data? And for example, we were just talking in the last segment with Sean Connelly from Hortonworks around the future business value is going to evolve from not structured purpose-built data warehouses, but data marts that evolve and are adaptive and are learning data as code as we introduce it this morning. So all the elements of data processing that IT is used to goes out the window. So I want you to comment on that. Data processing kind of grew up from a mainframe mindset and you haven't heard that term around, but now we're hearing data processing, data pipelines in mainstream IT. That's not in IT's DNA, traditionally. So how does that evolve into business value and data sets that's taking place? Well, I think that's a really good point that essentially data is at this point a first class concern, right? And data is mass now. Like when you have enough data you can't just willy nilly move it around. You have to really think about where did it come from? How am I going to view it? How do I want to transform it into those most useful views? And do it in a way that doesn't incur more data movement. And I think so data movement as a first class concern is something that the HPC world is very aware of, right? They actually look at computation in terms of flops per watt. Well movement, what about transformations? Well transformations, traditionally I think in a traditional data warehouse view, transformations almost implied copies of movement, right? Okay, got it. Versus just actually moving the computation to the data, changing the description of the data around talking about how you might want to change the representation and moving more and more of the analytical code close to where the data is. So treating data locality as a first class concern, treating the size and the volume of the data as really having a respect for that and not saying I'm just going to build another cluster here where I can move the data over and analyze it here. That's the thing about why can't I analyze it in place where it's stored? I think you see that a lot now with all the vendors here at Stata. So do you agree with EMC's approach by bolting on pivotal HD on top of HDFS? I think that sort of thing is moving in the right direction. Moving more and more analysis engines closer to the data is absolutely the right way to do it. You're also, I know it's bungling in there. Wise I.O., we saw them last night, John, and the startup comes in. So you're using sort of new techniques, in memory techniques, smarter use of L1, L2 cache, much more efficient from a performance standpoint. So architecturally, can you talk about that trend and what it means to customers? I think the memory trend actually that we've seen in the last 12 months or so is maybe more than that. But the memory is sort of thing is what everyone's talking about now, right? And we're actually focused on more than just that. We're working on basically providing a more uniform approach to in memory, out of core, or what I call heterogeneous computing. So whether you have GPUs, whether you have Fusion I.O. boards, whether you have a Xeon Phi Intel mic architecture, whether you have a cluster of machines hooked up to a sand, you have basically a very heterogeneous storage and compute infrastructure and needing to address the data and the compute or what I call the meta compute and the metadata in a uniform way is a first class problem that really not very many people are tackling. They tend to give you a whole built, purpose built solution. Put your thing in our silo, put your thing on our platform versus saying whatever you might have, we have an engine, we have a way of describing that data, describing the compute infrastructure and you can write your analytics in a high level way that's independent of that. Yeah, so you're talking about, you mentioned metadata, a lot of times locked into that silo. So in this model that you're prescribing, I presume that metadata is accessible. Absolutely, our Blaze project, which is our next generation NumPy approach, that's actually essentially a open source tabular data descriptor framework and so you can actually describe data, whether it's in files, whether it's in a database, over ODBC, wherever, and you can write Fortran or MATLAB like array code that does a very efficient linear algebra, very efficient predictive modeling over that sort of thing. And so the Y's IO guys are great and they're doing next generation sort of things and they're doing it in Python and that's, you know, there's a reason for that. There's a lot of engineering going on. So we want to, we're talking about this concept called data as code is kind of just a way to put our arms around kind of what infrastructure as code is for DevOps and cloud, but the big data world, you know, code as part of first class citizen, if you will. But there's a lot of issues going on around unstructured data, for example, like life cycle governance of a corporation. Like, do I store this legal issue? So obviously everything is storeable. So there's a variety of issues to be solved that are unsolved at this point. So as a developer who deals with data, what do you think the future of that direction is going to look like?  Right, right. I think that's absolutely right, that essentially the days when you can view data as a static thing that you just handle and manipulate is very much over, I think, at this point. And the transformation of that data, how that data got there, I saw a great quote the other day that there's no such thing as raw data. Right, at the end of the day, there's some firmware and a sensor that actually gave you that data. And as you transform that data over time, if you only view it as transformation steps on static data, you're going to constantly be plagued with this problem of describing that data and so on and so forth. If you actually view the data and the code together holistically as a unit and you're able to actually view subsequent transformations, subsequent derived data products as code transformations on that data, that's a much more holistic and much more reasonable way, I think, of tackling that kind of problem. Yeah, I mean, and so how does this change the computer science business?  Yeah, I mean, because you start, there are some great old paradigms and concepts that are integrating into, but it's not exactly the same. AI, for example, machine learning, learning machines, learning, so all this is now kind of coming together. Viewing comments on that is what you're seeing in terms of what innovative cool things are coming out of the big institutions and the players and how computer science is changing because it's not just write code, build monolithic systems, it's a much more holistic approach. Well, I think that more and more we're going to see even a greater bifurcation than there already is between practical industry oriented computer science versus theoretical computer sciences mathematics, right? So there's that. But even the way that the industry practice the software development, I think is having to change over time and I think that what we're seeing essentially is that to Peter Norvig's quote about the unreasonable effectiveness of data, right? I think that's absolutely right, that it's data is disrupting how we view science, how we view mathematics, how scientists approach their problems. And the idea that actually data can tell us a lot more than we used to think about interrogating it and now we're actually trying to learn from it. And I think that's a very positive step. So interrogation old way, new way is learning from the data. Yes. So what kind of impact do you think the mobile apps, because obviously the big joke that we always say is that machine, the low hanging fruit is recommendation engines and kind of cool things and some predictive analytics. As you climb the tree and put your ladder up and get the top fruit on the tree, what are the big applied, big data applications that you're seeing? So predictive analytics is sort of a very big term, right? And that's basically sort of like it's from here to outer space, it could be anything, right? So I think that predictive analytics is absolutely the order of the day and probably in five years' time we're going to be talking about things that are non-predictive analytics as essentially just simple reporting and drill down kind of things. But overall the mobile revolution is centralizing all of our activities, all the data around our activities, all the data exhaust is centralizing. And so that's the big effect of I think cloud and mobile computing. And so it just creates more opportunities for those predictors. Well Peter we got a break but this is like we could talk for hours, we love data gravity, data physics, data as code. We are living in a new world and that new world is measurable, the simulations is learning and it's totally disruptive. Any kind of pre-existing process, keys and interactions. So thanks for coming on theCUBE and we'll keep in touch, a great guest here and go check out Continuum Analytics. Thanks for coming on theCUBE, we'll be right back with our next guest after this short break. All the programs out there and I identified a gap in tech news coverage. There are plenty of tech shows that provide new gadgets and talk about the latest in gaming but those shows are just the tip of the iceberg and we're here for the deep dive. There's a difference between technology consumers and those who live the business day today and our viewers recognize that. The market begged for our program to fill that void. We're not just touting off headlines, our goal is to provide you with a story but we also wanna analyze the big picture and ask the questions that no one else is asking. Our guests aren't just here to provide commentary, we work with analysts who know the industry from the inside out. The tech business isn't new but many networks treated as if it is and really barely scratched the surface on technology coverage. We follow the expansion of the cloud and the evolution of big data. We're covering new enterprise from startup to IPO and every move in between. So what do you think was the source of this misinformation and so you mentioned briefly there are several other. If that's the case then why does the world play? I like to think of us as a companion to theCUBE. We're here every morning trying to extract the signal from the noise where theCUBE excels in event coverage. We're working to bring that experience to you consistently every morning. We use the top stories of the day to provide you with breaking analysis so that you can forecast future trends.  We're creating a fundamental change in news coverage. Laying the foundation and setting the standard and this is just the beginning.


Qtile has long been one of my favorite tiling window managers. I've been using Qtile on and off Probably for 10 years or so. I've been a long time Qtile fan And one of the things is over the years each time I go and play around in Qtile living it for a while Configure it. I always discover new things and today I wanted to make a quick video Demonstrating some really cool built-in features to Qtile that most people probably don't know exist one of the things though Qtile has a fantastic website with fantastic Documentation and when you go and read their documentation Your mind will be blown by some of the stuff that is available for you in Qtile if you want it The first feature I want to talk about is actually one of the built-in layouts that's available for you within Qtile If you go to their documentation So these are probably about 15 layouts that you could enable within Qtile And one of the really cool ones and very unique is the tree tab layout and what the tree tab layout does it basically Has like a little side panel on one side of the screen that actually lists all your windows in a tree view And you can organize them into groups and you can have the windows Kind of stacked on each on top of each other and a particular order and you can have them indented depending on I guess The importance of a particular test. It's kind of Unusual and let me show you this in action. So I'm going to open four terminal windows right now I'm in the monad tall layout, which is the master and stack layout But let me cycle through my layouts until I get to tree tab So this is the tree tab layout So I've got those four windows open and I've created three groups by default in my tree tab layout And I've got them named. I simply named them one two and three But you can actually name the groups more descriptive names depending on like workspace names or you know The types of applications that are available within each of the tree tab groups essentially though It's a max layout, right? We've got the windows max for the most part on top of each other in a stack The only difference is I've got the the sidebar here that's actually got the tree view But if you do a super J or super K to cycle back and forth through the stack, you know, you can go through the stack As you would in the max layout But if I do super shift J or super shift K, I can actually move the windows through the groups So I if I do super shift J I just moved to this first terminal window that has focus down to Workspace or the group two here inside the tree tab view and let's change the title of this by actually running a program So it's titled H stop, right? Super shift J. I can move it to group three super shift K I can move it back up to two super shift K. I can move it back To workspace one here. Now what's really interesting? Watch what happens when I do super shift L for right, right? We're doing the Vem navigation keys L is right. So super shift L And you can see the window indented over a spot, right? You can say it's I guess less important than the window above it or maybe it's part of a Order of tasks that I maybe I need to do this window before I do this window By the way, I could click on this window with the mouse instead of cycling with super J and K But let's indent this one. Let me do super shift L on that one And you can see I indented that one over and because that one The h top window was below it and was already indented over it indented over a second time And you know, that's just kind of neat the way you can order these things if I Super shift L move that over now. They're all indented one two three four spots finally for the h top window But what happens if I do super shift h to move that back? I can super shift h again I could even move it all the way over so this I guess it's not part of This kind of grouping you you can think of it that way, but you can order them in whatever method you want But what's really cool? Let's go back to this very first terminal window. What happens if I do super shift j To move that to the next group It moves that window and all the windows that were indented over as part of its Tree it's on group, right? So that is just really really neat I don't know if I would ever personally live in tree tab and use it all the time But I think it's such an interesting kind of layout. I think some people for some use cases will really Enjoy the tree tab layout now. Let me pull up my cutal config so we can actually see Some of the code associated with that tree tab layout So i'm going to scroll down through my config until I get to the layout section here And you can see I've got this layouts section here and you can see I've got all of the built-in layouts that are available I've got it here But most of them are commented out like I'm not going to use the bsp layout the ratio tile layout the vertical layout So I've got a lot of those Commented out but the ones that I do use monad tall max stack columns and then tree tab and you know I've got all of these uncommented and you can see there are the options that are available for you to play with For tree tabs. So you got a lot of coloring options depending on Active window inactive window you can name the section g So I just named my sections one two three you can have as many or as few sections as you want And finally the the big thing is how wide is the panel you see panel underscore width That is the setting for that now one feature that probably most cutal users do know is available because if you've ever used the default Standard config for cutal, you know that super r brings you up a run prompt You can see I've got this little run prompt here spawn colon and then we've got a blinking cursor, right? It's essentially I think it's a wrapper around d menu, which honestly d menu the proper d menu program It's just a better program. So I always default to d menu I imagine most cutal users probably do use either d menu or rophy for their run launcher rather than the built-in Prompt there with super r that said using their built-in d menu wrapper That's the little run prompt here does have some advantages One of the advantages is with tree tab and because tree tab has these sections, right? Well, what if you want to dynamically add Sections or or delete sections to the tree tab layout? Well, you can actually do that There are commands available for you to do that you could actually set them to key bindings But really what you probably want to do is you want to create a new Function here in python and this simple little function I I grabbed this off of the internet because I saw someone else Using something similar But basically we're going to have this new function add tree tab section And then once we have that created we're actually just going to key bind that function So if I scroll down here, I've got this here Mod shift a so super shift a for add tree tab section prompt to add new section in tree tab And you see it's a prompt. So I'm going to get a prompt similar to this run prompt, right? Except this prompt will be prompting me to add a new section To tree tab. So let's see this in action. So let me once again, I'll open a few windows and let me cycle back over to tree tab And now I have one two and three for the group names for the tree tab But what if I do super shift a now I get a prompt it says section name and I could add four all caps and now I have a new section here that you know You could just randomly on the fly anytime you need a new section in your tree tab add that kind of a cool feature that's really kind of a neat thing that That built-in prompt for qtile Does integrate really well with some of these custom python functions that you may end up creating along the way And if you dive a little deeper into the qtile documentation, you'll actually find That qtile they've got a ton of built-in extensions and specifically they have a lot of built-in extensions that are designed around D menu because so many tiling window manager users and qtile users in particular here They they use d menu so it makes sense that they've built kind of these extension Programs built around d menu and one of the cool things is this built-in extension called command set It basically allows you To define almost like you're defining a key binding in your your standard key bindings in your config, right? Except this particular key binding using this command set extension basically allows you to quickly Create a d menu script. So let me show you this because I actually copied The example that they were using here And I gave it this particular key binding here super z And then one of the things you define is commands equals and then the braces inside the braces You give it a list of options. These are the options that are D menu options. It's this the options that d menu will show. So if I do super z you can see Play paul's next previous quit open shuffle repeat. Let me escape out of that play paul's next previous quit open shuffle repeat You give it a name, right? So that's the listing and then colon and then you give it the command that should execute if you Enter on that now for me. I do have mo cp. That's a Music on console a music player. I do have that installed on my system. I'm actually not going to play any music on this video urx vt I don't have that particular terminal open But you know what I could do really quick just to show you this in action instead of executing anything with mo cp I can just have it open The alacrity terminal. Let's open h top within alacrity. So i'm just going to change that Let's restart cutal and now i'm going to do Super z and if I go to the open option if I hit enter it should open h top in alacrity That's exactly what happens So that is a really quick and easy way to get A d menu script instead of having to actually write a bash script doing everything with d menu They've really streamlined the process now if you were doing something really complicated with d menu where you had to string a whole bunch of things In succession like you choose an option in a menu and then you get another d menu that you choose another option And you know if you're doing something really complicated You probably should do that as a separate bash script But for something really simple like this is an example of just a media player right Play pauls quit open shuffle repeat That makes sense and I could see people using this for even like a logout menu So you have you know logout reboot shutdown whatever So some really neat stuff there those features with the the d menu extensions the tree tab layout and again If you want to really Have your mind blown man. Just go read the the cutal documentation You'll find all kinds of things that these guys have already Set up right they've already got Extensions for you'd be amazed i've been back living in cutal for a few weeks now I've been daily driving cutal on my main production workstation here at the office as well as my home computer and I've got to say It's always been one of my favorite window managers and it still remains that way every time I revisit it I'm just so impressed with what the developers behind cutal have accomplished now before I go I need to think a few special people I need to think the producers of this episode Daniel Gabe James matt paul royal west armor dragon commander angry George lee met those nader yon paul peace arch and fedora realities for less red profit rolling Solastry tools deviler wards into an abunto and willy these guys They're my highest tier patrons over on patreon without these guys This quick little episode about really neat features available in cutal It wouldn't have been possible The show is also brought to you by each and every one of these fine ladies and gentlemen All these names you're seeing on the screen right now. These are all my supporters over on patreon I don't have any corporate sponsors. I'm sponsored by you guys the community If you like my work and want to see more videos about free and open source software like cutal subscribe to distro tube over on patreon these guys


War and Peace, Book 13, Chapter 17, read for LibriVox.org by Larry Ann Walden. Kutuzov, like all old people, did not sleep much at night. He often fell asleep unexpectedly in the daytime, but at night, lying on his bed without undressing, he generally remained awake, thinking.  Since Benixon, who corresponded with the Emperor and had more influence than anyone else on the staff, had begun to avoid him, Kutuzov was more at ease as to the possibility of himself and his troops being obliged to take part in useless, aggressive movements. The lesson of the Taratino battle and of the day before it, which Kutuzov remembered with pain, must, he thought, have some effect on others, too. They must understand that we can only lose by taking the offensive. Patience and time are my warriors, my champions, thought Kutuzov. He knew that an apple should not be plucked while it is green. It will fall of itself when ripe, but if picked unripe, the apple is spoiled, the tree is harmed, and your teeth are set on edge. Like an experienced sportsman, he knew that the beast was wounded, and wounded as only the whole strength of Russia could have wounded it. But whether it was mortally wounded or not was still an undecided question. Now by the fact of Laristan and Bartelamy having been sent, and by the reports of the guerrillas, Kutuzov was almost sure that the wound was mortal, but he needed further proofs, and it was necessary to wait. They want to see how they have wounded it. Wait and we shall see. Continual maneuvers, continual advances, thought he. What for?  They are like children from whom one can't get any sensible account of what has happened, because they all want to show how well they can fight. But that's not what is needed now. And what ingenious maneuvers they all propose to me. It seems to them that when they have thought of two or three contingencies, he remembered the general plan sent him from Petersburg. They have foreseen everything, but the contingencies are endless. The undecided question as to whether the wound inflicted at Borodino was mortal or not had hung over Kutuzov's head for a whole month. On the one hand, the French had occupied Moscow. On the other, Kutuzov felt assured with all his being that the terrible blow into which he and all the Russians had put their whole strength must have been mortal. But in any case, proofs were needed. He had waited a whole month for them, and grew more impatient the longer he waited. Lying on his bed during those sleepless nights, he did just what he reproached those younger generals for doing. He imagined all sorts of possible contingencies, just like the younger men. But with this difference, that he saw thousands of contingencies instead of two or three, and based nothing on them. The longer he thought, the more contingencies presented themselves. He imagined all sorts of movements of the Napoleonic army as a whole or in sections, against Petersburg, or against him, or to outflank him. He thought too of the possibility, which he feared most of all, that Napoleon might fight him with his own weapon and remain in Moscow awaiting him. Kudasov even imagined that Napoleon's army might turn back through Medin and Yukhnov. But the one thing he could not foresee was what happened, the insane, convulsive stampede of Napoleon's army during its first eleven days after leaving Moscow. A stampede which made possible what Kudasov had not yet even dared to think of, the complete extermination of the French. Dorkhov's report about Bruciay's division, the guerrillas' reports of distress in Napoleon's army, rumors of preparations for leaving Moscow, all confirmed the supposition that the French army was beaten and preparing for flight. But these were only suppositions, which seemed important to the younger men, but not to Kudasov. With his 60 years experience, he knew what value to attach to rumors, knew how apt people who desire anything are to group all news so that it appears to confirm what they desire, and he knew how readily in such cases they omit all that makes for the contrary. And the more he desired it, the less he allowed himself to believe it. This question absorbed all his mental powers. All else was to him only life's customary routine. To such customary routine belonged his conversations with the staff, the letters he wrote from Taratino to Madame Dyshtyle, the reading of novels, the distribution of awards, his correspondence with Petersburg, and so on. But the destruction of the French, which he alone foresaw, was his heart's one desire. On the night of the 11th of October, he lay leaning on his arm and thinking of that. There was a stir in the next room, and he heard the steps of Tolle, Konovnitsyn, and Bokovitanov. Eh? Who's there? Come in, come in. What news, the field marshal called out to them. While a footman was lighting a candle, Tolle communicated the substance of the news. Who brought it, asked Kutuzov, with a look which, when the candle was lit, struck Tolle by its cold severity. There can be no doubt about it, your highness. Call him in, call him here. Kutuzov sat up with one leg hanging down from his bed and his big paunch resting against the other, which was doubled under him. He screwed up his seeing eye to scrutinize the messenger more carefully, as if wishing to read in his face what preoccupied his own mind. Tell me, tell me, friend, said he, to Bokovitanov, in his low, aged voice, as he pulled together the shirt which gaped open on his chest. Come nearer, nearer. What news have you brought me, eh? That Napoleon has left Moscow? Are you sure, eh? Bokovitanov gave a detailed account from the beginning of all he had been told to report. Speak quicker, quicker. Don't torture me, Kutuzov interrupted him. Bokovitanov told him everything and was then silent, awaiting instructions. Tolle was beginning to say something, but Kutuzov checked him. He tried to say something, but his face suddenly puckered and wrinkled. He waved his arm at Tolle and turned to the opposite side of the room, to the corner darkened by the icons that hung there. O Lord my Creator, thou hast heard our prayer, said he in a tremulous voice with folded hands. Russia is saved. I thank thee, O Lord. And he wept. End of chapter 17. This recording is in the public domain. War and Peace, Book 13, Chapter 18, read for Librovox.org by Anna Simon. From the time he received this news to the end of the campaign, all Kutuzov's activity was directed toward restraining his troops by authority, by guile, and by entreaty, from useless attacks, maneuvers, or encounters with the perishing enemy. Doctorov went to Maloyarslavets, but Kutuzov lingered with the main army and gave orders for the evacuation of Kaluga, a retreat beyond which town seemed to him quite possible. Everywhere Kutuzov retreated, but the enemy without waiting for his retreat fled in the opposite direction. Napoleon's historians described to us his skilled maneuvers at Tartino and Maloyarslavets, and made conjectures as to what would have happened had Napoleon been in time to penetrate into the rich southern provinces. But not to speak of the fact that nothing prevented him from advancing into those southern provinces. The Russian army did not bar his way. The historians forget that nothing could have saved his army, for then already it bore within itself the germs of inevitable ruin. How could that army, which had found abundant supplies in Moscow and had trampled them in their foot instead of keeping them, and, on arriving at Smolensk, had looted provisions instead of storing them? How could that army recuperate in Kaluga province, which was inhabited by Russians such as those who lived in Moscow, and where fire had the same property of consuming what was said ablaze? That army could not recover anywhere. Since the Battle of Borodino and the Pillage of Moscow it had borne within itself, as it were, the chemical elements of dissolution. The members of what had once been an army, Napoleon himself and all his soldiers fled, without knowing wither, each concerned only to make his escape as quickly as possible from this position, of the hopelessness of which they were all more or less vaguely conscious. So it came about that the Council at Maloyarslavets, when the generals pretending to confer together expressed various opinions, all mouths were closed by the opinion uttered by the simple-minded soldier Mouton, who, speaking last, said what they all felt, that the one thing needful was to get away as quickly as possible, and no one, not even Napoleon, could say anything against that truth which they all recognized. But, though they all realized that it was necessary to get away, there still remained a feeling of shame at admitting that they must flee. An external shock was needed to overcome that shame, and this shock came in due time. It was what the French called l'horre de l'Empereur. The day after the Council at Maloyarslavets, Napoleon rode out early in the morning amid the lines of his army, with a suite of marshals and an escut, on the pretext of inspecting the army and the scene of the previous and of the impending battle. Some Cossacks on the prowl for Bouty fell in with the Emperor, and very nearly captured him. If the Cossacks did not capture Napoleon then, what saved him was the very thing that was destroying the French army, the Bouty on which the Cossacks fell. Here, as a tarotino, they went after Plunder, leaving the men. This regarding Napoleon, they rushed after the Plunder, and Napoleon managed to escape. When les enfants du don might so easily have taken the Emperor himself in the midst of his army, it was clear that there was nothing for it but to fly as fast as possible along the nearest familiar road. Napoleon, with his forty-year-old stomach, understood that hint, not feeling his former agility and boldness, and under the influence of the fright the Cossacks had given him, he at once agreed with Mouton and issued orders, as the historians tell us, to retreat by the Smolensk road. That Napoleon agreed with Mouton, and that the army retreated, does not prove that Napoleon caused it to retreat, but that the forces which influenced the whole army and directed it along the Mojaisk, that is, the Smolensk road, acted simultaneously on him also. CHAPTER XIII A man in motion always devises an aim for that motion. To be able to go a thousand miles, he must imagine that something good awaits him at the end of those thousand miles. One must have the prospect of a promised land to have the strength to move. The promised land for the French during their advance had been Moscow. During their retreat it was their native land. But that native land was too far off, and for a man going a thousand miles it is absolutely necessary to set aside his final goal and to say to himself, Today I shall get to a place twenty-five miles off where I shall rest and spend the night. And during the first day's journey that resting place eclipses his ultimate goal, and attracts all his hopes and desires, and the impulses felt by a single person are always magnified in a crowd. For the French retreating along the old Smolensk road, the final goal, their native land, was too remote, and their immediate goal was Smolensk, to which all their desires and hopes enormously intensified in the mass urged them on. It was not that they knew that much food and fresh troops awaited them in Smolensk, nor that they were told so. On the contrary, their superior officers and Napoleon himself knew that provisions were scarce there. But because this alone could give them strength to move on and endure their present privations, so both those who knew and those who did not know deceived themselves, and pushed on to Smolensk as to a promised land. Coming out onto the high road the French fled with surprising energy and unheard of rapidity to what the goal they had fixed on. Besides the common impulse which bound the whole crowd of French into one mass and supplied them with a certain energy, there was another cause binding them together, their great numbers. As with the physical law of gravity, their enormous mass drew the individual human atoms to itself, and their hundreds of thousands they moved like a whole nation. Each of them desired nothing more than to give himself up as a prisoner to escape from all this horror and misery. But on the one hand the force of this common attraction to Smolensk, their goal, drew each of them in the same direction. On the other hand an army corps could not surrender to a company, and though the French availed themselves of every convenient opportunity to detach themselves and to surrender on the slightest decent pretext, such pretext did not always occur. Their very numbers and their crowded and swift movement deprived them of that possibility, and rendered it not only difficult, but impossible for the Russians to stop this movement to which the French were directing all their energies. Beyond a certain limit, no mechanical disruption of the body could hasten the process of decomposition. A limp of snow cannot be melted instantaneously. There is a certain limit of time in less than which no amount of heat can melt the snow. On the contrary, the greater the heat, the more solidified the remaining snow becomes. Of the Russian commanders, Kutuzov alone understood this. When the flight of the French army along the Smolensk road became well-defined, what Konofnitsyn had foreseen on the night of the 11th of October began to occur. The superior officers all wanted to distinguish themselves, to cut off, to seize, to capture, and to overthrow the French, and all clamoured for action. Kutuzov alone used all his power, and such power is very limited in the case of any commander in chief, to prevent an attack. He could not tell them what we say now. Why fight? Why block the road, losing our own men, and inhumanly slaughtering unfortunate wretches? What is the use of that, when a third of their army has melted away on the road from Moscow to Vyazma, without any battle? But drawing from his aged wisdom what they could understand, he told them of the Golden Bridge, and they laughed it, and slandered him, flinging themselves on, and exulting over the dying beast. Ermolov, Milordovitch, Platov, and others, in proximity to the French near Vyazma, could not resist their desire to cut off and break up two French corps, and by way of reporting their intention to Kutuzov they sent him a blank sheet of paper in an envelope. And try as Kutuzov might to restrain the troops, our men attacked, trying to bar the road. Infantry regiments, we are told, advanced to the attack with music and with drums beating, and killed and lost thousands of men. But they did not cut off or overthrow anybody, and the French army, closing up more firmly at the danger, continued, while steadily melting away, to pursue its fatal path to Smolensk. End of Chapter 19. End of War and Peace Book 13 by Leo Tolstoy. This recording is in the public domain.


What's up everyone it's your girl Kenty here from Layton Sports Cards and I'm ripping it personal here for Matthew G. We've got a 21-22 Panini Revolution basketball hobby box. All right Matthew, best of luck. Here we go. Get all this Bowman dust out of the way. Got Groove, Carmelo Anthony, and rookie Corey Kispert. Nice. How about a Ray Allen auto from the box? Here you go Matthew. Nice Ray. That is not numbered but sick hard. We'll mag that. Got Julius Randall on the cosmic to 99 and Friends of Vox. A rookie of Calisacus and Jalen Green rookie. I think who's using about how to with the mags today y'all. Julius Randall Astro and rookie Revolution James Book Knight. Got OG Anoby on the impact to 149. James Book Knight rookie and Isiah Tomerick. Got Miles McBride rookie fractal and liftoff of LeBron James. LeBron James. Astro rookie James Book Knight and Usman Garuba rookie.  Astro of Carmelo Anthony and Vortex of Devon Booker. All right Matthew, that'll do it for your box. Congrats again on your hits. Thanks for hopping in and we'll get these right out to you.


Good afternoon everybody. Yes. Yes, we do. Good morning. Good morning. Good morning. You think it's morning? It's almost afternoon. All right. I'm Brad Topol. This is Guanyang Lu. We are fear from IBM and we're here to introduce everybody to CIG IBM Cloud, which is our CIG community community group to get input from the community. And so just a look at the agenda here. We'll do do an overview of the CIG and the structure and the activities. We do have code out in open source that folks can contribute to. So we'll talk about the projects we have there. And for those of you who hopefully are aware, we have a public cloud, a private cloud, hybrid cloud, and multi-cloud fact. So we'll be providing an overview of all of those as well. And so what this CIG looks at is, you know, how we worry about building, deploying, maintaining, supporting, and using Kubernetes on our public and private clouds. We have many developers, many of the developers that work on both public cloud and private cloud involved in this community. And so you can give input directly to the developers on any requirements, any features, whatever you may have, questions. So they're directly involved in our CIG community. And you can follow the evolution of our public cloud, which includes the IBM Kubernetes service and our IBM Cloud private platforms with respect to how they're adopting different aspects of the CNCF. Both are based on Kubernetes. And again, a great opportunity to directly interact with our key members of the IBM Cloud. The co-chairs couldn't be here today, but it's Khaled Ahmed and Richard Thysse and Saad of Zalla. Like all CIGs, we do have our charter. So we have our scope and our governance in our charter. We have a mailing list. And there's a document here. We can get more information about CIG IBM Cloud. We have meetings. We meet every other week at 2 p.m. Eastern Standard. The meetings are recorded, so you can see any ones that you miss. There's presentation on various topics. We've had a lot of cool topics. We've also had presentations from our partners, partners like SysDig that are involved in the CIG. We do have Slack discussions on CIG IBM Cloud. So you can reach us there. We do, as required, provide our quarterly updates to the Kubernetes community. And we have sub-projects. We do have sub-projects with open source code. And Guanyin is going to talk about this. This was all his genius. He's been doing a lot of our implementation for the cluster API provider and made that open source incredibly quickly. So he will be covering that in the talk today. Now, there has been some work going on, and you'll see this happening with all the CIGs, not just CIG IBM, but there's a consolidation to bring us all together, CIG IBM, CIG AWS, all those CIGs. And so you'll see that we're all getting grouped together as sub-projects, but nothing's really changing. We still have our group and doing what we do. Again, we've got two projects here, and Guanyin is going to go in a lot more detail about them, the work we've been doing in cluster API and Kubernetes federation. And let me hand off now to Guanyin, who is going to switch over to Chinese and cover things in a little more detail. All right. So I will give my talk in Chinese. So, this is the main reason I'm going to introduce you to the two most important projects. The most important project is class API and core file. And then we can take a look at why we're going to do these two projects. And one of the very important reasons is we can see that the latest version of KBS is 1.15. And then if you take a look at the last version of this log, you can see that in it, there's not a lot of bigger features. And then there's basically some bug fixes and some patching. So we can see that KBS has become very stable. So we can see that a lot of people are now trying to go to the top. So what we can see is that a lot of people are putting on some innovative services, such as SeriesMesh, Serverless, AI, are all trying to go through KBS to manage it. So this is what we can see. A lot of people are moving up. So in addition to moving up, a lot of users are also trying to expand to KBS. Because if there's a cluster today, it's not enough. So we may need to use this multi-function multi-function to handle some of these. When my machine is relatively big, I may need to use this multi-function multi-function to manage this cluster. So we can see that the future of KBS is that it will be based on multi-function multi-function. And then on this technology, we will run a lot of other innovative services such as AI, Serverless, SeriesMesh, etc. So this is why we spent a lot of time focusing on these two projects, including ClassiAPI and CoreFi. So let's take a look at ClassiAPI. This project is a relatively new project. This project is mainly through KBS-native, and then comes to KBS's cluster to manage it. So we can see that the first thing we need to do in this project is to use ClassiAPI as a cluster as a cattle. So we can easily bring different cloud services to KBS-native to manage. So the management here includes I can use this cluster to create, delete, upgrade, etc. So I can easily use this KBS-native method to manage KBS-native in different cloud services. So from this picture, we can see that the cloud we are supporting now includes AWS, Adder, Google Cloud, IBM Cloud, etc. So if you want to use ClassiAPI to manage this cluster, in fact, it's very simple. It just needs to define a YAML file. And then in here, it just needs to define some of my cluster pages. For example, I need a few master, a few worker, and then some of my work-node pages, for example, my CPU memory, etc. I just need to go to the YAML document, go to the Pages tab, and then I can use a command, Crop Control, apply. And then you can run this cluster in different cloud services very quickly. So the biggest advantage of ClassiAPI is that it can help users to manage KBS-native in the original way through Kubernetes. And then, ClassiAPI now has one problem. The problem is that it can only manage this S-cloud. From here, we can see that ClassiAPI can generate cloud, including AWS, Adder, Google Cloud, IBM Cloud, etc. These are all S-clouds. The main purpose is to help users to ask about VMs. Another feature is that it wants to manage Kubernetes through ClassiAPI for example, it wants to manage RKS, AKS, EKS, etc. through ClassiAPI. And then the last project is IBM Cloud Provider. This project is a brand new project that we just launched last month. The main project is that I use this red arrow to mark it. The main thing about this project is that users can easily deploy a KBS group on the cloud. And then this is some details of our ClassiAPI IBM Cloud Provider. From here, we can see that if we want to run a cluster on the IBM Cloud, it's actually very simple. The only thing a user needs to do is using the cluster control command. Then you can run the KBS group on the IBM Cloud. But before using the cluster control to create this command, including cluster.yaml, machine.yaml, and provider.components.yaml. The cluster.yaml mainly defines some users of the cluster. Including the POD, HealthService, CIDR, etc. The cluster.yaml mainly defines how many nodes I have, how many masters, how many workers, and the master and workers. I can do some pages through machine.yaml. The provider.component.yaml. mainly uses it to define some CRD resources, including how to define the cluster, how to define the machine. In addition to these results, there are some controllers. These controllers are mainly used to interact with IBM Cloud. They help users to position the VM on the IBM Cloud. So we can easily use the cluster control command on the IBM Cloud to position a KBS cluster. Let's take a look at CRD.yaml. This cluster is very similar to ClassAPI. Both of them use it to help users to operate the cluster. The biggest difference between CRD.yaml and ClassAPI is that ClassAPI is mainly focused on the bottom layer. The bottom layer is used to help users to operate the KBS cluster and focus on the service and the application. From this picture, we can see that CRD.yaml. has three main components, including template, placement, and all that. Template is mainly used to define the resources and how to define them. For example, I can define the deployment and service. Then I can put the deployment and service on different clouds. For example, when I control the cluster or the cloud, I may need some strategies to determine the results of the cloud. At this time, I need a placement policy to help me make some adjustments and to help me put the resources on different clouds. Another policy is Override. Its main function is that when I put all the resources on the cloud, I can make some customized pages. At this time, I need to use Override to make some customized pages on each cluster. I can use template, placement, and Override to make sure that the services can be deployed on different clouds on different clusters. At the same time, I can use Override to make some customized pages. Currently, CRD.yaml. In September of this year, we will enter Beta. As long as we can enter Beta, we can try to use this feature on PUC. Next, I will share with you the most important products of IBM Cloud. As you can see, we have public, priority, and hybrid cloud. The public cloud is IBM Cloud Corporate Service. It is called IKS. Prior cloud is IBM Cloud Parade. Apart from these two, we also have hybrid cloud, multi-cloud, and the most important product is IBM Multi-Cloud Manager. First, let's take a look at IBM IKS. IKS is a service provided by the public cloud to run a KBS cluster. From this, we can see that the biggest advantage of using IKS is that it can use a lot of IBM services, such as IoT services, AI services. On IBM Cloud, it is very convenient to use new services related to IBM. Apart from this, we can also see that there are a lot of IKS data centers. We can see that there are Asia, Europe, and North America in the data center. If we want to manage our services in IBM Cloud we need to choose a closer data center so that we can ensure that it can achieve better performance when we provide it with this service. We mainly share the main features of IKS. In fact, IKS is mainly for managing the KBS cluster. The biggest advantage of IKS is that it allows users to customize their cluster. For example, how many masters know and how many workers know and how many masters and workers can use it. In the end, we can also enable a lot of IBM Cloud services, such as IoT services, IBM Watson services, and AI services. We can also use IKS for this service. The key point is that we need to ensure that users can easily transfer their services from the KBS cluster to the cloud. IKS has also passed the KBS Confirmation Test. We can ensure that users can transfer their services from the cloud to the cloud. What we just saw is IBM's public cloud. The other thing I want to share with you is the IBM Cloud Parade. This product is a new product that we just launched in June. It's a 3.2 version. From this product, we can see that the IBM Cloud Parade is the core of KBS. It helps users to listen to a business platform. Let's take a look at this product from the bottom. From the bottom, we can see that the IBM Cloud Parade platform can run on many different platforms. It can run on many different platforms. Other than this, we can also support a lot of different cloud. We can also support OpenStack, VMware, GKE, etc. We can also support ICP. The top part is the most important component of the IBM Cloud Parade. From this, we can see that the core component, including Kubernetes, is mainly used to manage users' services. Other than this, we can also see that we have a new concept called call service. Call service means that if a user only has KBS, it's not enough. We have to help the user to provide many tools that can help the IBM Cloud Parade to run. Call service means that we can use this service to manage log, monitor, and other services. We can use this service to manage DevOps. This is mainly used to help the user to use this platform. This is an IBM Google Lift that was launched a thousand years ago. The latest version is a 1.12 version. It can be used in production. It was just launched. It was mainly used to help the user to provide a service platform. It is mainly used by Google, IBM, and then it was launched together. But if you use this project directly, there may be some problems. Because in short, it mainly uses a platform function. If you really want to provide this kind of function you need to go to LabryRef. Through Ref, there is definitely such a platform that can help the user to use this kind of function. The other component is ClassAPI. We mainly use this project to help the user to deploy some KBS cluster. So we can easily use ClassAPI platform to create a multi-use environment. This is our IBM Cloud Parade platform. In addition to IBM Cloud Parade, as we mentioned before, KBS is a multi-use environment based on KBS. So the multi-cloud manager has just launched a new product for multi-use environment management. From this point of view, IBM multi-cloud manager has a lot of cloud platforms. In fact, we don't have any requirements for KBS. As long as we use this platform, we can use IBM multi-cloud manager to manage it. From this point of view, we can see that the management platform including IBM Cloud Parade is the ICP platform of our previous industry. In addition to this platform, we can also manage OpenShift. These two are private cloud. In addition to these two, we can also manage public cloud, including IBM Cloud and KBS. Through IBM multi-cloud manager, it is very convenient to use to build a multi-use cluster. Our multi-cloud manager has three main functions including visibility, application management and compliance. Visibility is that from this point of view, I can see that through the multi-cloud manager, there are a lot of resources to look at. Through IBM multi-cloud manager with visibility, it is very convenient to view. Now, the multi-cloud manager is managing the cloud platform. The example here is AWS, Adder, Google Cloud, IBM Cloud. Through the multi-cloud manager, it is very convenient to view the cloud platform. In addition to this, another function of the multi-cloud manager is that it is very convenient to use application management. As mentioned earlier, we can also help users to use application management. But now, there is a problem with the application. The most basic function in it is that it can help users to use application management. The recovery in it is that the application is worse compared to the cloud. So we have made a lot of improvements here. It is very convenient for users to use application management to use application management. In addition to this, we have made some improvements. There are a lot of advanced policies in it. We can use these policies to help users to choose a platform to run the service. We can also use CPU memory or cost. We can also use different metrics to define our policies. We can also use this service to make a deployment on different cloud. In addition to the application management, we can also use a function called compliance. The so-called compliance is similar to the previous session called OPA. The most important thing we have done is that we can take a look at an example. When the application management cloud is relatively large, it may have some misunderstandings on different cloud. For example, it may delete some results. The most important function of our compliance is that we will define some policies in each cluster. This policy will monitor each group of resources. For example, it deleted some resources. We will use the compliance policy to help users to restore the resources. We can quickly restore the resources. On the right side of the map, we can see the main function of our application. It is very convenient for users to run a Vail. It can check all the resources on the machine. In addition to this, users can also use the application management. It can define a file of YAML and schedule a policy. It can also define how to sign between different groups. We can see that we have a lot of policies. It is very convenient for users to view all the dependencies of the application. It is very convenient to use this map. The main thing about this is the future. How will IBM Cloud look at the project together? From this point of view, our future is like this. In the future, KBS will be more cloud. We will use it as a base. We will use IBM as a base. In addition to this, we will implement the core file project as the foundation of IBM multi-cloud manager. All the functions of the application and service management will be implemented through the core file project. We will use IBM multi-cloud manager to enter the management of different groups. As I just mentioned, we will accept Cluster as a category project. This project will help us to enter the management of different KBS development boards in different Clouds. We can also enter the management of OpenStack, AWS, error, IBM Cloud and so on. We can use Cluster API to build a multi-cloud environment. In addition to this, we can see that there are many different logos and we can take a look at each logo. This is the Google Flow logo. Now, Cloud Native is slowly getting closer. Cloud Native's future will be KBS Native. All the applications will be implemented through KBS. As you can see, there is a new project called Google Flow. This project can be used through KBS Native to enter the management of AID tasks. I can also use Google Flow to manage the training and so on. In the future, we will add a component on KBS to build an AID project. In addition to this, we can see a logo called KBS World. This project is mainly used through KBS Native to enter the management of VMs. This project is mainly used by Red Hat. The biggest advantage of this project is that it can be used through KBS Native to enter the management of AID tasks. However, when users use their service from VM to KBS Native to enter the management of AID tasks, they may find some problems. There may be some services that are not suitable to enter the management through KBS Native. At this time, we need to contact the KBS World project to build a mixed service. In this service, we need Azure Edge. This project should be Azure Cloud. This project is mainly used by some of the editors to enter the management. We can see that the key point in Azure is that we need to build a very light VM to build a very light KBS. From here, we can see that the IBM Cloud Project can provide a very light version. It only needs 500 million memory, so it can easily build an ACP-led environment. It is very similar to the KBS of Rancher. So we can use the multi-cloud manager to help users to have more resources. We can help users to provide AID, mixed education, service mesh, service function, etc. This is the KBS of the IBM Cloud open source future. Now I will transfer to Brad. He will make a summary and talk something about Brad. Thank you, Gwanya. IBM Public Cloud will actively manage tens of thousands of clusters of active clusters at any given time. There is a lot of management and a lot of pushing out of resources. And so the team has gathered up a lot of expertise on how you do that, how you manage all those clusters. And they've actually come up with an approach which is a pull-based approach and having the clusters be able to dynamically describe what's installed and provide a history in a dashboard. It's based on open source. We've taken all this great expertise on managing tens of thousands of clusters and we've made it open source, made it available. So it's called RAISI and it is our multi-cluster continuous delivery tool for Kubernetes. It does automate the roll out of Kubernetes resources and provides support across multiple clusters, multiple environments all over the world. And it's designed for scale. So if you're looking for a tool that's going to help you with huge scale, it's got pull-based deployment, it's got self-updating clusters and we've made that all available. And so we've made this available as open source and you can go check it out at HTTPSRaisi.io and we can also talk about it in the CIG IBM cloud group as well if need be. And so that's what this intro is really about is making sure everyone's aware of the CIG IBM cloud, letting you know that we do meet every two weeks and for folks that are interested in learning more about our cloud offerings and what we've made open source with regard to our cloud providers and our continuous deployment tools for managing large number of clusters like RAISI. Please come talk to us. We'd love to hear more from folks and what they'd like to see and we are there ready to talk to you. So in summary, CIG IBM cloud is for our ability to reach out to the community and discuss the building and deploying, maintaining supporting and use of using Kubernetes in both our public and private cloud offerings and with our new deployment tools as well, making those available. And again, we'd love you to contribute to the CIG. We have lots of partners on our cloud like Sysdig that do participate. So we'd love more participation. So come talk to us. We are a sub-project and we do have our cluster API provider cloud code out there that Guanyan and his team made available. So please go look at it. It uses all the latest features that if you look at all the Kubernetes best practices the code that his team put together is using all of them. So in a very small small amount of code you can see a lot of Kubernetes best practices and the latest tooling and cube builders and what have you. So we highly recommend you look at that. You'll learn a lot. And at this point, I'd just like to say thank you and Guanyan, I'd be happy to take any questions. Yes. What is a managed service or a non-managed service? My question is about the public cloud. So is the managed the IBM Kubernetes service or non-managed service? Yeah, we provide a managed Kubernetes service. How many clusters you've managed now? Is there any problem you managed at the last scale? So that's a great question. We have actively over 10,000 clusters that we'll manage but are you familiar with the weather company? The weather channel weather company? Sorry. Okay. I'm not hurt. So in the United States we have a weather company that has all the forecasts for all over the world. Generates forecasts on two-kilometer grids. So the amount of data that it generates and the amount of scale so when there's a hurricane the amount of scale and processing that it needs to do all the forecasts will go up five times. So that is one of the applications that we host on our public cloud and that's what we've used to test our scalability. And if you look at the amount of weather forecasting and we have other applications, we have airlines and we have car rental companies that require large amounts of scale as well but the one that really tests the scale was the weather company and the forecasting for all over the world. And that's what we used to do our scalability. Okay. Thank you. Welcome. Another question there. Microphone. Okay, I'm Mike from IBM Dalian site. I have one question. In PPT continuous delivery tools called RISI I want to know is it available now? Yes. How can we to get the documentation and some practice for it? Yeah. So start here and that'll get you to the GitHub repo and it's all available or contact me if you want afterwards and we'll make sure you can get it for you. But it's all available open source. Okay. Okay. Thank you. Thank you. Any other questions? Nira? Yes, please. I like the open source of the monitoring part and how about installing behind the scenes what you do to install the tens of thousands of clusters or put them on VMs basically or wherever they are. Is that still coming? You should be able to find and raise it for the continuous deployment part of it. No, I mean actually installing the clusters. What IBM use on its cloud to install the clusters? Is that open source? I'm going to go ahead and take that one. Okay. You mean the classical product or which product? I mean IKS. That was a serious customized Yeah, right. I suppose great. Yeah. The work that the future work that Guanyin is leading is with the cluster API provider, which is open source. Actually, we do have plan to enable using class API to manage some managed community service like IKS, IKS, IKS, etc. And this is also the plan of class API. Okay. Yes. Thanks to the multi-cluster management too. Yeah. And what's the difference between this one, this two and the Rancher? I think Rancher is also a multi-cluster management too. So what's the difference between the two? So the question is so what is the difference of multi-cloud management and Rancher? Yeah. So I did some tests about Rancher and I found that currently the Rancher and the multi-cloud manager has some similar functions like they can deploy the application across different clouds and they also have the visibility for the cluster in different clouds but I found that now Rancher has some drawback. Currently, they don't have the policy like compliance. They don't have the government policy which can enable any other to do some policy checking in different, in some member clusters in different clouds. Which means that if you have some mistake actions to delay some resources or some other kind of policies and then the cluster will be broken but the IBM compliance will have to recover the cluster back but Rancher don't have that function now I think. So do you plan to provide more advanced features that can be used in more critical scenarios such as in the finance scenarios I think if a company use the multi-cloud or multi-classes that means we need the disaster recovery in some times. So do you have any advanced features about this? Great question. Let me think. Or we can talk offline. Yeah, actually IBM multi-cloud manager is going to provide some function like so we can look at some detail about it. So we can take the managed node at the half cluster. So this is the managed cluster of the multi-cloud manager. So we are not trying to do some functions for the half cluster HA which can make sure that the customer will always have a high availability multi-cloud manager. So if one half cluster is done and another half cluster will grow up and take the role of the new master node. Yeah, new half cluster. So this is some basic function of the half cluster HA so we can make sure that the customer will always have a available multi-cloud. And another thing I think you mentioned is how to recover or sync up some data between different clouds. So is this a question? Yes, thanks. Yeah, so currently we are working on a product for storage across different clouds and I think that currently it was still under development and we will have betaware in September I think. So and then it will be integrated into multi-cloud manager and then we will have the backup restore for different clusters. Hi, well thank you all for attending. We do appreciate it. Yeah, thank you.


This meta-analysis and systematic review compares the overall effect and safety of microsurgery versus laparoscopic surgery in treating varicoselle, using 23 studies with 1178 patients. The results show that microsurgery significantly decreases complication rate, hospital stay, and recurrence rate while increasing sperm concentration compared to laparoscopic surgery. However, there is no significant difference in operation time or sperm motility between the two groups. Overall, microsurgery is a better alternative therapy for varicoselle treatment than laparoscopic surgery. This article was authored by Hai Wang and Ji Gang Ji.


Yep up there to the left hand corner Appreciate it


Hey everyone, and welcome to this CUBE Conversation featuring RudderStack. I'm your host, Lisa Martin, and today we are very excited to be joined by Eric Amuega, VP of Marketing and Operations at RudderStack. Eric, thank you so much for joining us today. Thank you for having me, Lisa. Tell us a little bit about RudderStack. I see from the home page, RudderStack makes it easy to collect and send customer data to the tools and the teams that need it. But give us the real story here. Fantastic. So RudderStack is a warehouse native customer data platform that enables companies to collect, to unify and activate their customer data. Right? I'll talk more about what each of those pieces mean. Warehouse native, what that means is that instead of storing data, RudderStack actually turns a customer's data warehouse or data lake into a customer data platform and handles all of the collection, modeling, i.e. unification and the activation of that data. Nice. So talk to me about how RudderStack enables customers to handle things like event data specifically. As we all know, we create all these events with apps and things like that and we want this experience to be like real-time. How do you guys enable customers to deal with event data? Absolutely. So when the company started, the first product that we built was our event stream pipeline. And the event stream pipeline, what it does is that it collects customer data from your website or your app, right? So each interaction that a customer has with your web property, with your app property, is an event that's being created. Now, after collection, instead of us storing the data, which is what traditional CDPs do, we actually send that data in real-time to downstream destinations. So for example, if you want to activate real-time use cases, that data is collected and set in real-time to downstream destinations. So downstream destination could be an email marketing platform. It could be, you know, a Facebook ads platform. It could be any sort of downstream tool that business teams use to activate customer data. In addition to that, we also have the ability to send that data into your warehouse or data lake, whether it's AWS S3 or Redshift. And we expose that data for you to be able to model that data to build whether it's ML models, whether it's, you know, AI use cases, you can do all of that modeling in your warehouse and then use our reverse ETL pipeline to activate that data as well. So we have both the real-time pipeline to downstream destinations, but then also sending that data to your warehouse or data lake where you can do additional modeling and create more value from your customer data and activate it using our reverse ETL pipeline. Is that all done simultaneously? So the best way to think about it is there's both real-time and batch use cases. So real-time is done in real-time, right, with very limited lag. This batch use case is typically when you're sending data, for example, to a data warehouse. You probably don't want to do that in real-time. You can configure it down to five minutes, but for warehouse or data lake use cases, typically, you know, you're okay with a little bit of lag. You can configure it down to five, 15, 20, 30 minutes. And because when you're doing the modeling, chances are you want to run, you may want to run the data in batch, right? You're doing modeling overnight, creating those audiences, creating those user features, and then activating those downstream. But then there are also additional real-time use cases that don't involve just the event stream pipeline, sending data from web or app to downstream destinations. You may also want to activate real-time use cases, for example, personalization. When a customer is interacting with the app or website, already having pre-computed features, a feature could be something like a customer likelihood of churning, right? If you have a churn score and you have that customer who's been flagged as having a high likelihood to churn, you could actually send a notification in real-time. You know, maybe it's a 20% of coupon in real-time and the customer is engaging with the app. So that's an example of a real-time use case that Radistar can activate. Real-time is one of those interesting use cases, Eric, that we've been talking about a lot for the last few years. It's something that became no longer a nice to have for organizations in every industry as consumers, customers, business, personal, have this expectation that we're going to have a real-time experience. And to your point, it's going to be personalized. The expectation of personalization is only increasing. Talk a little bit more, expand on the personalization. You talked about a real-time use case, but enabling your customers to deliver that personalized experience, their customers are demanding 24 by 7. Exactly. And actually, I was looking at a study the other day by McKinsey. Basically, they looked at what value does personalization drive in terms of top line? It actually drives 15% to 25% in terms of revenue left. That's revenue per customer, right? And that goes to your point. Customers increasingly are looking for personalized experiences to engender greater brand loyalty. And it's something that you have to provide, especially if you're working with B2C-type brands. So that's the first thing. So this is an important use case that a lot of B2C companies need to unlock to win market share, to drive revenue growth. Specifically, how we enable that. We talked about the real-time activation using our event stream pipeline, sending that to downstream destinations. That's kind of like the most basic level, right? Where you're just collecting data and based off of customer activity within a certain browsing session, you can tailor that experience to the customer. But then when you start to think about the more advanced customers get, for example, they want to calculate machine learning features in real-time. There's kind of a number of different steps. The first step is the data collection. There's a number of different data sources for customer data. We have talked about the event stream, which is data from your website or your app. That's event stream data. You also have SAS data that lives within other SAS tools that you're using, whether it's a CRM platform, for example, Salesforce or customer service platform, for example, Zendesk, right? So you have two types of data, behavioral data and relational data. You collect that into your data lake or your warehouse, that's step one data collection. The second one is unification, right? You need to resolve the identity of that data so that you can say this specific customer, this is their behavioral data, right? And this is the additional data we have on them. For example, how much they've spent on the platform, right? The last time they logged on to your app or your website. Once you have that rich customer profile, you can now calculate, you can enable features and calculation of those features. A feature could be what I talked about, revenue over the last six months, last time I used a logged in. You calculate those values, right? So now in your warehouse, you have a user, the entire customer journey from behavioral data and additional features that could either be deterministic or probabilistic. And then now you can unlock true personalization where you're using all of the data that you have on the customer to deliver the experience that they need to make that next purchase. So the warehouse native CDP you said helps data teams collect data, unify it, activate it. You talked about some of the key features. Talk a little bit about your warehouse native CDP in terms of differentiators versus competition. Absolutely. So I'll be the first to admit that the customer data platform landscape is extremely crowded and it's very difficult to figure out who's within the customer data space. The way we like to think about it right is like there's three primary types of customer data platforms. You've got your traditional customer data platforms. You've got a new entrance. It's called Composable Customer Data Platform. And you've got the warehouse native customer data platform of which we're the leading warehouse native CDP. And I'll talk about each of those. A package CDP typically, traditional CDP typically, they collect data, they store data in their own infrastructure.  So what ends up happening with a traditional CDP, you basically have a parallel architecture that's completely separate from where the rest of your customer data lives. That is your warehouse. That is your data lake. That's your Redshift. That's your S3. Now you have this parallel architecture. And what that does is that you essentially end up creating two problems. One, you've got data silos, right? You've got data sitting in your warehouse. And then you've got data sitting in SAS black box. That's a traditional CDP. That's the first problem. You've created data silos where you may have different versions of the same data. Second problem is that you're paying for storage twice, right?  And then you're paying another SAS platform to store your data as well, right? And then obviously you're locked into whatever features the traditional CDP has. If they don't have real-time activation, you just have to use whatever features that they have. That's the traditional CDPs. They've been around since 2012. They're about the second one is composable CDP. This is a new model, which is kind of, you know, cobbled together a number of different SAS solutions to kind of build the CDP, right? So you have somebody doing a vendor for event stream. You have a vendor doing extract, transform load into your warehouse. And then you have one of the traditional, one of the reverse ETL players activating that data. The challenge with that is that you actually have three different vendors that you have to manage. The data schemas are different depending on the data source. And you're having to do a ton of work to integrate all of those tools together to ensure that you have end-to-end data that you trust. That can be extremely costly from a resource standpoint. Rattastack is a warehouse-native CDP, right? And the question our founders asked themselves when they were building Rattastack back in 2019, 2020, is if you were to build a best-in-class CDP with the evolution technology since the CDP, the first CDP tools were put in place, how would you do it? The first answer was obvious, right? You've got a proliferation of data warehouse and data leaks. You know, there's the decoupling of storage and compute. They have a ton of security features and they've kind of become the gravity where all of operational data is moving to. So the question was how the challenge for our founders was can you build a CDP on top of a customer's warehouse to avoid the data silo issue to reduce the cost from having to store data into different places, and hence Rattastack was born, right? So that is the warehouse CDP. Now, the advantage that we have is that we actually have the end-to-end feature set. We've got the data collection with our, what are considered best-in-class events to pipeline for behavioral data. We have our ETL pipeline, specifically focused on customer data. That's data collection. We also have a product in beta right now called profiles that enables the unification so unifying all of your behavioral data and transactional data or relational data in the warehouse. And then we also have reverse ETL to activate that data. So it's kind of the end-to-end story with data collection, unification, and activation, which are really the three components that you need for a customer data platform. Right, and you did a great job really describing the differences between traditional CDPs and what Rattastack has built. It's almost as if they deconstructed the traditional CDP and understood some of the challenges. So you must have a favorite customer story. You run marketing that really articulates the value. And some of the things I was hearing as you were describing the differentiators is there's got to be massive cost savings, ROI, maybe TCO reduction. Tell me your favorite customer story that you really think articulates to Rattastack's clear value. Absolutely. I would say, and this one is not public yet, but we work with a leading shoe retailer. And initially when we engage with them, they had a team of engineers that were working to build connectors for their ad platform. So essentially figuring out how to ingest data from Facebook ads, from Instagram ads into the warehouse doing modeling on that data to identify, to model the performance and analyze the performance of the ad campaigns. And then based off of that sending data out back to the ad platforms to run new campaigns. And we had an interesting model with them. We essentially had a POC, a proof of concept where we had a number of different yardsticks that we had to hit. And one of the things they wanted to do, they wanted to prove actual value from the product. So it's a paid POC over a period of three months to prove the value of Rattastack. And they ran the POC for three months and they saw a 400% increase in return on ad spend by using Rattastack. And the reason we're able to unlock that is, one, we had native out-of-the-box connectors for most of the major ad platforms like Facebook ads, Google ads, ETC. We exposed the data for them to model, for modeling and analytics in their warehouse. And then we also had the reverse CTL pipeline to actually send the results of those campaigns down to the ad platforms. And so you have that essential loop in terms of we're running a set of ad campaigns we are pulling that data into the warehouse. We're doing identity resolution in terms of who isn't interacting with those apps, unifying that data and then figuring out the ROI and then investing in the best performing ads. So that was just a simple story where in three months we're able to, one, unlock, basically enable them to get data flowing across the entire data stack, which is something that a team of five engineers have struggled with over a year to do. We're able to do that in three months. And then two, time to value, actually demonstrating the fact that we could have a positive ROI in a very short time period. So 400% return on outspend is an incredible start. That's nothing to scoff at, especially in this environment when there's a lot of pressure on ad budgets, being able to unlock significant return on outspend is really invaluable. Wow, what a substantial customer story. I'm sure that was one of many that you had that really shows the value and how you're able to show customers ROI that quickly, especially in today's climate, incredibly important. So you guys are a young company founded back in 2019 and 2020 or so, what's next? Here we are almost halfway through calendar year 23. What are some of the things that we can expect if we keep our eyes on Matterstack? Absolutely. So I think the vision has been clear from the very beginning. As we talked about the data activation lifecycle, the goal is to continue to beef up each and every step of that. We have a best in class data collection with an event stream pipeline. We have our unification product called profiles that is in close beta right now. And then we have a reverse ETL product. Once all of those three are live over the next few months, you essentially have the end-to-end warehouse native customer data platform that will enable our customers to unlock similar value and even more value similar to the case study that I mentioned earlier. So you'll continue to see continued innovation across the data activation lifecycle. And then there's two areas where we're going to focus on from a feature standpoint. One is data compliance. You're seeing increasing activity from regulators, not just in EMEA, everybody talks about EMEA, but also in the United States, California with CCPA. So we have an exciting set of data compliance features that we will be launching, that we have in place and we'll be launching and beefing up over the balance of the year. And data governance as well. So when you look at customer data is extremely important. It's the most valuable data that a company has. But then it's not, it shouldn't be, you shouldn't be hovering up all the data that you can get. You need to be intentional about it, that you're collecting the data that you want in the specifications that you want that data in to ensure that you can unlock value from that data downstream. So the three areas to look forward to over the next six months. One is excited about our unification product profiles. So more to come on that. And then we're going to have data compliance features and data governance features that will unlock our next phase of growth. And really as we continue to grow with large enterprises, these are features that increasingly becoming non-negotiable, that a lot of other CDPs cannot provide. Awesome. Eric, we better let you go. You must be really busy. We so appreciate you coming on theCUBE, talking about Ridershack's warehouse-native CDP. You did a great job of articulating what it is, its value, how it's different. We so appreciate you for coming on theCUBE. Eric, thank you. It's a pleasure talking with you. Absolutely. Thank you so much, Lisa. Appreciate your time as well. My pleasure. Keep it right here for more action on theCUBE, your leader in hybrid tech event coverage.


Ladies and gentlemen, the President of the United States. Please be seated. Good afternoon and welcome to the White House. Forgive me for being a few minutes late on a hot day like this, but by this time in the afternoon, I'm usually behind schedule. Secretary Bennett, Rana Romney, guest here, ladies and gentlemen, especially you are presidential scholars from 1986. It's an honor to be able to help celebrate all that you 141 young people have achieved. Congratulations as well to your teachers and parents. They're the ones with the ear-to-ear smiles. We celebrate on your own behalf taking pride in your individual achievements, but we celebrate as well on behalf of the nation that nurtured you and afforded you the opportunities to develop your talents to such an impressive degree. As my assistant, Mitch Daniels, himself a former presidential scholar, pointed out in his talk a few minutes ago, your country is proud of you now and we are certain to be proud in the future. The inspiration that you as presidential scholars provide to all young Americans is of immense importance, especially now that we're working so hard to improve American education. You may remember that during our first term, the Department of Education published a report called A Nation at Risk. The report concluded that the decline in our educational standards had become a crisis if a foreign power had somehow done the damage to our schools that we ourselves had permitted, the report said, we might have considered it an act of war. Well, since then, things have gotten better, not by themselves but through hard work. Today, all 50 states have task forces on education. Over 40 have stiffened their graduation requirements and 31 have enacted or are considering plans to encourage excellence among their teachers, perhaps most telling, SAT scores. The scholarship aptitude tests have started to rise after almost two decades of decline and now here you are today, 141 of you, proof positive that American schools and students can indeed achieve great things. I can't acknowledge each of you but perhaps by mentioning a few, I can indicate how outstanding you all are. There's Neil Minkoff of Maine at 14, the youngest presidential scholar ever. There's Keith McKay of New Hampshire who likes math so much he's opened his own computer business and Anne Handberg here comes from right here in Washington and at her young age, she's already fallen in love with teaching. Indeed, Anne spent last summer teaching in a village in the African nation of Zimbabwe. Kim Redlinger of Texas is the great-granddaughter of an Irish immigrant who was orphaned as his parents made the passage to America. Listen for a moment to Kim's own words. Today, I go to a great school and have everything I need. A far cry from a little Irish orphan on a leaky immigrant ship. In fact, the greatest legacy that my family has given me is an unshakable belief in hard work and the knowledge that I must give something back to a country that has given me so much. Kim's comments on her family bring to mind a few thoughts about education that I'd like to share with you. We Americans are becoming more and more aware of these days of the importance of a good education for the family. A strong family makes it so much easier to raise children and give them a good education. What can we do about it? Well, first, speak out. Just as children need a nurturing environment, so do families. Men and women in positions of influence, and here I mean not just government officials, but teachers, ministers, and yes, actors and rock stars need to demonstrate respect for family life. Certainly, Secretary Bennett and others in our administration have been speaking out on behalf of the family unceasingly. But I draw perhaps even greater encouragement from signs that the wider culture is once again beginning to respect, even to celebrate, family life. It's no accident that family ties is one of my favorite TV shows. And then we in government have a special responsibility, a responsibility to see to it that government programs are structured in a way that makes family life easier, not harder. Here at the federal level, I've established a working group on the family to study the relationship between federal programs and family life. The working proof group will give me its report in November, and I expect it to recommend a number of important changes. This will represent only a first step in the effort to make the federal government more responsive to the family. But I'm convinced no effort in this second term will carry greater importance. In the words of Michael Novak, the family is the original and most effective department of health, education, and welfare. So my friends, congratulations to you all once again. You've shown what we can achieve in two words, the best. And in doing so, you've given us heart. So thank you. God bless you all, and I'll now do what the little 11-year-old girl who wrote me a letter told me to do after I finished her letter, she said, now get to the Oval Office and get to work. And Secretary Bennett, while I do that, will be handing out the awards. And again, congratulations. God bless you all. Ladies and gentlemen.