Welcome to Amsterdam and KubeCon CloudNativeCon 2023. Join John Furrier, Savannah Peterson, Rob Streche, and UPSconn as the Kube covers the largest conference on Kubernetes, CloudNative, and open source technologies together with developers, engineers, and IT leaders from around the globe. Live coverage of KubeCon CloudNativeCon 2023 is made possible by the support of Red Hat, the CNCF, and its ecosystem partners. Hello, everyone, and welcome to KubeCon Europe. We are here in beautiful Amsterdam. In fact, it is so sunny, and we've got natural light inside the building. They're even letting me keep my shades on, although I will say that's probably because my co-host, Yup, and our first guest, Amit, are so bright and brilliant. I've got to keep my shades on for that reason. Yup, are you going to bring shades tomorrow? I need to. It is so bright in here. It is so bright in here. It is, it is, it is so bright. It might be Amit's fault.  We loved having you on the show in Detroit. We're over here in Europe. How are you doing today? How are you feeling? Fantastic. Couldn't get better with the sunlight here. I know people believe it. We're not under fluorescent lights. That in itself is pretty exciting. I haven't seen the sunlight for two weeks, so. So we'll see you outside in a few minutes. Precisely. Yeah. So we obviously know who you are and what you do, and we love having you. Love your energy. We, as a collective, have actually dubbed you the best hustler in the community, and we say that as a compliment. We love your hustle. Jesus, OK. I'll take it. So just in case the audience isn't familiar, give us just a quick pitch. Sure. Now that you're feeling awesome. I'm feeling amazing. So Kube, Kube, we're branded ourselves as chat GPT for DevOps. What that actually means without all the buzzwords is we're offering a conversational AI experience for end users who can go and consume DevOps functions and platform engineer functions very easily using natural language, prompting Slack teams or any type of chat interface, and very easily attributing their natural language to automations, knowledge base, and so forth. So very easily, you can go and consume DevOps function that typically would require a human in a loop with a chatbot, a smart chatbot that's very conversational, tells jokes, very actually lighthearted. So that's one. Just like us. Just like us. I feel like a chatbot now.  Maybe. So when we came out, that was kind of our initial thesis. Since then, we've rolled out, and we're actually announcing this today at KubeCon, the operator experience. Right. Tell us more about that. Sure, absolutely. So imagine one of the biggest challenges for platform engineering teams, DevOps, SRE teams is rolling out and maintaining a platform, adopting a new platform, whether it's an internal developer portal, a service catalog, or really any type of workflow automation. How do you go and create and maintain these workflows? And we've created LLM embedding with all of the new technology. Now you can go and prompt a workflow using natural language. It will go and generate that workflow with all of your business logic, with all of the access controls that are baked in. And now you can very much fine tune it, drag and drop, and the low-code interface. Play it back, simulate it, and then commit it to your organization within minutes. OK, so you set a magic buzzword, low-code. So this used to be a thing. Years ago, everyone was talking about this, right? I mean, I think a lot of people are still talking about it. And people are still talking about it. And the interesting part here is there was a concept called the citizen developer, kind of riding that same wave. And I see it making a comeback in the cloud native space as well, where a lot of the stuff that we do is still fairly complex. You need to know a lot about Kubernetes, about storage, about networking, about cloud. The list goes on to be able to even just operate something from day one. And so that complexity is still something that is sometimes too complex, too mind-boggling for users in an organization, especially if you factor in self-service, giving people the empowerment to do it themselves. So if I understand correctly, this is kind of the problem, space that you're solving that you're looking at? The two sides of the coin is really the end user experience. How do you go and extend things very naturally to them so they don't have to either flag one of the operators in the loop and get their help, or otherwise no code or low code in order to interact with the system? And for the operators, it's really velocity. How do you go and extend that without having all the maintenance and overhead, otherwise low code systems would typically, even if you want to go and create, as a citizen developer, if you use that term, let's use it, prompt engineering, as a new citizen developer, if you want to be honest about it, but that's a different story. If you want to go and talk about low code as a citizen developer, you still need to have all the business logic, all the organizational knowledge and domain expertise. Even if you're not technical in terms of coding, you still need to have that operational knowledge. What better way to go and bridge a gap than to express your intent? And to being able to have the system smart enough to recognize all that and align that and have you, with the reinforcement learning aspect of it, just fine tune it for your own needs. Yeah, exactly. Yeah, that makes total sense. And especially if you factor in kind of the amount of work that still needs to be done with boilerplate code with, you know, total, there is just so much work that still needs to be done every time, and it doesn't necessarily add value. And like you said, the velocity of it is kind of the key aspect of it. It's not necessarily, you know, an insurmountable mountain, but it is a lot of work. And just taking away that boilerplate, I think is very, very valuable. Sure. So what are the use cases that you see your solution being used for specifically? What's kind of a... Oh yeah, talk about trends. Yeah. Yeah. We'll give a few flavors. One of them, and really the low-hanging fruit is just access to organizational knowledge. You can go and prompt it. So we train on confidence, notion, your own documentation, structured, unstructured data. It's really a data catalog, if you want to call it. Now, doing our own embeddings, using vector database, we can go into that in a little bit. But the concept here is, without having to know anything, without having to really being thrown into Wikis who may or may not be outdated, just have a conversation with the interface, almost like a chat GPT-like experience, only here at Kubee, or Kubee is our mascot is called. And then it will either throw the documentation at you with links or precise, very concise answers, even with actionable prompts. So you really don't have to know anything. It's having conversation with a human being. That's the closest thing you can have. You're expecting short, concise answers. You don't want to have a Wiki thrown at you and read the Bible, right? That's not the point here. Or have to go to someone with a legacy knowledge in an organization every single time. Data that exists in silos. Yeah, exactly. So I love the velocity. I'm glad you just brought that up. And ever since we met, I've thought about it multiple times. You save people a lot of time. Yes. My God, yes. I mean, so talk more about that. How much time do you think you're really saving if you can quantify that? I feel like you might know. When people try to quantify transactions, they usually do machine-to-machine interactions. How much time did you trigger a CICD job or did you... Right. That's actually the smallest portion of the time saving. That's typically two to three or 5% of the time saving. What is a time saving? The context gathering, bridging and stitching different transactions, sub-workflows into each other, human in a loop. So you can go and use an example of one of our customers who's in a digital media services space. What is he doing? He's doing kind of a more of a legacy type of transaction where his project managers are going and bridging the gap with the technical team of having to go through file storage servers, going and getting an encrypted file, unencrypting it, giving kind of a proxy, a little small thumbnail of it, delivering it over Slack in a secure manner, uncorking it. All these things can take days because of the human element to it. Here you can do it within minutes with approval flows of everything. I just felt that. Wow, that's nice. And there's another aspect of this that I'm kind of interested in. Yes, you optimize existing workflows, but I'm sure this unlocks some new potential too. Like there's new use cases. There's something that we haven't thought of before that organization now can deal with this. Tell me more about that aspect. Great question. We're going straight to the punchline of where this is going. And let's start there, okay? It's always good to start with TLDR and go from backwards. What's the utopia in world of large language models? What, you know what? I'm going to come with a statement here. What have large language models actually done for us beyond all the buzz and all the cool little demos you're seeing? It's bridged the gap between human to machine interaction, okay? For the first time, and this is kind of where the thesis lies. Humans and machines can understand each other and rather than having the human need to know code or low code to interact with the machine, the machine is now smart enough to interact in natural language and convert that into code. So that's uncorked all of the business potential. Now, where are we going with this? If we can now bridge all the gaps and all the little gray matter that still exists in there, a human will be able to go into a chat interface and interact as if they're talking to their colleague on the other side. Hey, Fred, help me do XYZ. Now, whether it's an existing or non-existing workflow, the system will be able to build that workflow, fine tune it with the operator possibly and with all the access and permissions and then serve that to an almost real tie. That's incredible and it's all optimized. I mean, it's pretty amazing. Precisely. Wow, okay. So you mentioned that your booth is an experience. Tell us a little more about that. Savannah, don't get me in trouble. I'm not going to get you in trouble. You said that we can take photos and stuff. You've got t-shirts, you were a big part of our swag segment in Detroit. I'm going to keep it PG here, don't worry. I understand the rules. We have a dancing robot. Oh, that's why you're going, okay. No, that's not what's going to get me in trouble, but we do have a dancing robot. We do have a hot dog. Are you the dancing robot? I wish I could say I could dance like the robot, but a kid would tell you otherwise. We have t-shirts, we have a very fun experience. Show it off, give us a little chest here. Yes, baby, love that. And we do have a quite a unique experience with swag that all I can say, you're an Amsterdam, just be protective, okay? Yeah. You're stealing my lines, I love it, I love it. I mean, we all want everyone to stay safe here on theCUBE. You're going to get me in trouble here, so. And let's just say if you need to stay safe, folks, be sure and drop my Kibbe a booth. I'll be dropping by that booth later today. I can assure you of that. You probably should too, in fact, the whole staff should. No, only a few targets. I love it. What's next for you all?  So one of the very interesting ideas that we've kind of accumulated with all the human interactions and human-to-machine interactions. What an interesting message. It goes beyond DevOps. So it goes beyond just cloud operations. Today, that's really our focus. But a lot of the end users we're discovering are actually our customers' customers. So customer success, solution architects who are actually using this to interface with their customers and to give them a better experience. So that's kind of next in line is how do we go and allow our ecosystem to leverage conversational AI, highly-curated conversational AI in their own workflows and really partner up. And that's actually where we're going with this. I love that. So actually, now I'm curious, since you mentioned that, has there been a use case or users that have surprised you that you weren't expecting? Oh, gee. Yes. But see, this is really where it comes down. Every organization has their own domain expertise. So whether it's in a digital media space, as I mentioned to you, or in a dev tool space, a lot of customers are really seeing the benefits of the embeddings of really the large language models and how that interacts with the user base. Beyond the humor, that exists already in the bot. We're actually seeing a lot of internal organizational workflows are. People are interacting. Their CFO is interacting with them. So for example, one of the use cases is, and I have to be very careful because I don't want to call myself replacing or displacing other people's income, but cost savings around license management of our ISP. Oh, yeah. So this, I'm not going to use their name because they're not going to partner with me any longer. But imagine. Well then, they're not getting any free press. But imagine my users are saying, my next tier is going to cost me, instead of $20 per user or $100 per user, and I need to roll this across 20, 30 different users. Can we go and use Kubea to be the interface and then keep it in the licensing model to a lower tier? And now that's approval flow and attributes access, the request will go through a single point of contact with the smart approval flow. And that very easily optimize the workflows and more $7,000 under-existing SaaS products. So that actually extends into the FinOps movement as well, where we're now in a phase where cost is starting to matter. We're past, cloud can cost anything. We're rationalizing. And so instead of doing that work manually, there is a big opportunity for you to go in and help customers automatically save costs. And control. So think about how you can go and provision a machine through Slack, right? Like you would with the AWS interface. But all of a sudden, if there's a budget breach above $50 per day, that has to go through another approval flow. So now it's really guardrails and hygiene that you can really implement without having to overextend yourself, go into different consoles, context switch, all the things that typically happen in an organization. Too much effort means people aren't doing it, right? And that's the idea. We're trying to go into democratize that and give that type of sentiment across the organization. Well, we certainly see that you're democratizing a lot at your booth this time around. Long live democracy. Yeah. Well, so you're now a KubeCon veteran. We'll hope to have you back on the show when we're in Chicago. Well, will we see you in Chicago? Will we be there? 100%. 100%. I love this. What are you? So obviously you've got the big announcement. What else are you excited about at the show this year? Oh man. You know, there's so much back when sale wins. Even though we're in a very tough economy, I think everybody can relate to that, right? Nothing's going away in terms of the customer needs. The customer needs, if anything, are only extending themselves and increasing with the need for provisioning of infrastructure in a smarter way, with the need for access to requests and controls in order to go into tying the hygiene, the DevSecOps movement. None of that's going away. Even with that GPT movement, all of that gray matter is starting to float up, right? How do you go and fine tune models? How do you deal with the regulatory issues? All of these things we're really going to go and start attacking front and center. Question for you, and I know we're not going to apply buzzword bingo, but I'm curious about your particular angle on this. Do you think that chat GPT has made the conversation, and I realize what you do is very different, but has it made the conversation easier for you to get the message across? 100%. Yeah. It's done all the market education for me. It's also given all the tailwinds for the bandwagon to also increase the type of noise, and now you really have to go into rise above that noise, but really, it's done more harder than before. Yeah, a little bit of noise in the space right now, just a little bit. You know, it's very easy to separate yourself when you actually show, not just a demo environment, but actually prove it to me, and we have the credentials to prove it to folks, and we have a new playground, we just announced yesterday. Tell us more about the, I like Playgrounds, let's hear about it. The Playground is the end user experience, it's not yet the gendered ABI experience of the workflow automation, but you can go and really sign up. We had the first hour, a couple hundred signups, people playing around with the, having a conversation with our chatbot, rated PG of course. It's very sensitive. It has some jokes, but it's very sensitive to today's political issues and everything. Oh, we'll have to check that out. Yeah, yeah, yeah, we're gonna have to test that. Yeah, yeah, that feels like a test for us. Yes, sir, absolutely. For sure. Is there anything, this is actually, now that we're in this category, is there anything that makes you nervous about all the buzzword bingo that's going on right now? I think the, not the buzzwords per se, this is the first time I can remember, and I'm sure others can attest that. Large organizations, Microsoft, the world, AWS, Google have moved at the speed of startups. This is unbelievable, it's unheard of, so. That's a really good point. That's an excellent point, yeah. While we're still ahead of the curve, because we've already been in this space, six months ahead of the trend. Nice timing, just go ahead and dust your shoulders off on that one, well done. It's always a pause for hesitancy, do you partner, what do you expose, how much do you reveal, how much do you keep in house, in terms of domain expertise and knowledge, when you integrate and you overlay on top of their platforms. So that's always going to be some that we're going to have to do the trade off game with. I think that's a really, I love that just as a benchmark, the competition and the market have demanded the acceleration of some of the largest organizations on earth. It's an arms race and it's between themselves. They don't see startups as being a factor yet, they're just competing with each other. Well maybe they should start looking at the startups. If they view theCUBE, they'll know. Yeah, yes, I love that, on that note, on that note. Wow, what a good closing note, on that note. Thank you for being here next to me. And thanks for coming on again, Amit, I love this. I feel like it's not KubeCon unless you're here with us on theCUBE talking about Kubia. So, I mean, if that's not KubeCon, I don't know. I don't know what is. If that's not meta, I don't know what is. Wow, thank you all for joining us here in Amsterdam. My name is Savannah Peterson and we are the leading source for high tech coverage here on theCUBE. We'll see you this afternoon.


The selection of the most appropriate additive manufacturing process depends on several factors, including the material being used, the complexity of the part, and the desired design features. For example, if the part requires high strength and stiffness, then selective laser melting may be the best option. However, if the part needs to be lightweight and flexible, then binder jetting would likely be more suitable. Additionally, the design features of the part should be taken into consideration when selecting the right process. For instance, if the part has intricate details, then electron beam melting may be the best choice since it can create fine details with precision accuracy. This article was authored by Prashant Khanda Gokuldas, Sri Kala, and Jürgen Eckert.


I would like to invite our panelists over here. I would like to invite Mr. Ankit Paul, country lead monetization, strategy, and programmatic, Alibaba Yousi. Come on, everyone. Mr. Karan Shroff, head brand marketing, Xiaomi. Well, I hope I pronounced it correctly. Mr. Nareesh Krishnaswamy, head growth and marketing, secure.fit, Rahul Deora, VP, marketing, urban clap, and Ms. Somasri Bose, head marketing, Godrej consumer product. Well, joining the panel, we also have Mr. Bharat Rajamani, the partner advisory services, KPMG India. Welcome to all the panelists and a session chair. The panel is all yours. Thank you so much. So we'll get into it right away without the introductions are done. You know, this is a conversation we've all been having every day. It's not something which probably was the first time any of us are going to have within our own offices and outside, this is a challenge that we're all facing. So we'll spend the next 40 minutes trying to understand from an experienced group of marketers here, how they deal with this challenge of connecting with the millennial audience and the consumer every day. What are their own strategies and what's interesting of this group and I'm sure you would have noticed is everybody here comes from a very different industry and, but they're all talking to the same consumer. What would be interesting to understand is how different products and services coming from completely opposite ends of the spectrum, how do they connect with the consumer and how do they manage their digital interactions. And I want to throw this first to a very interesting, you know, a brand and my personal favorite, a very young brand and this is something which I'm really proud of what you've built at Urban Clap. 20,000-odd, you know, independent service providers who are many entrepreneurs, how do you manage to build that consistency and that engagement when you need to, you know, literally bring out the brand promise through people whom you don't even control directly. Thanks Ashwin. So I think to answer that question, I'll take 60 seconds to zoom out a little bit and maybe set the context of how the offline world works for services. So consider a typical beautician. She works out of a parlor, makes close to 14 to 18,000 rupees in a month, serves, works from nine to five, serves anywhere from eight to 10 clients a day. Whatever she makes, 80% of it is pocketed by the parlor owner. And the growth trajectory that she has is not looking very, very good right now. Now cut to, when she comes on board with a platform like Urban Clap, suddenly her avenues for growth multiply because now she's a master of her own destiny. She's serving at the max four to five clients a day at her own time and pocketing 80% of the earnings overall, minimum. So now what impact does this have on her? Let's think about it. Suddenly her income has doubled so she's now the largest breadwinner in her family. Her kids can start going to better schools. She maybe moves into a better house, she maybe buys a car. So to answer your question, Ashwin, there are three things that are needed to manage something like this, right? First is creating the right business model in which everyone wins including the customer, the service professional as well as the company. Second is ensuring that we give them all the tools to succeed right from market knowledge to a lot of training that standardizes the service. And third of course is the intent because a service professional sticks to Urban Clap yes for monetary reasons but the most important reason that comes out in our conversations is the respect they get from the society when they sort of tell them that they are working with Urban Clap. So are you saying that the experience that your consumer gets is in a way starts internally when you're able to inspire and to motivate so many professionals to because you're giving them the chance to grow. You're giving them the chance to do something with their lives. They're motivated, they're inspired and thus to a great extent, the service quality and delivery gets taken care of. Is that what you're saying? Absolutely, absolutely. I think you got right on point. Cool. Now similar kind of business for you Naresh, not similar, it's more in control for you but heavily on the services front right from food delivery to food that you yourself make and deliver to your fitness centers, very diverse range of services and products. And how do you manage the customer's expectations and how do you talk to them? Yeah, so I think on the first part which sort of Rahul mentioned which just starts with kind of what value you are offering to the partner.  So it really starts with the culture of the company and what the founders really emphasized from day one, what kind of behavior leaders show on a daily basis and having that loop where you're talking to the customer almost real time. So it's all good to do social media sentiments over a month and comment about how it's trended but on a daily basis in businesses like ours fires happen like things go wrong, customers have experiences that are not desired both on the positive front, they have great experiences as well as bad experiences. So the culture of the organization in an industry like this where you're touching the customer almost on a daily basis is what sets the context of what kind of experience you give to the customer because it just in a company of our size which is still only three years old, the connection between the endpoint to the top is not very large. I mean, information does flow very fast given the nature of our size and how everybody is anyway connected through email and the mobile phone. So just stressing on what kind of culture the founders set and what kind of behavior the leaders underneath show on a daily basis, what kind of behavior you reward and create that obsession around customer experience on a daily basis. And this can only happen when you can measure it almost real time. So our North Star has always been this metric called ratings which is a measure from the customer on every interaction that they've had whether it's a fitness class or whether it's a food delivery and how importantly you treat that metric, how importantly you treat positive initiatives that kind of drive that metric up is what sets the tone in a completely single owned sort of first party business like ours. What's interesting is that in both the examples that we've discussed it's about the experience that we're talking about we give the consumer. But how are we driving our communication with the consumer? Not necessarily through the experience but also independent of the experience there's a lot of communication that we are and engagement that we are driving. Does this feedback loop also go back into a communication in some form? Absolutely. So I think when you're in such a high-touch industry you understand what the consumer is. You may have a certain desire to build a certain brand and you may think the category is like this so therefore the communication needs to be like this but when you have that feedback loop you get that information real time from a consumer on what they're thinking of your brand what moves the experience in a positive and negative way. So that definitely feeds us back. So I'll give you a simple example. The gym industry has always been about showing extremely aspirational imagery, unattainable kind of imagery of people with really grotesque muscles or zero percent fat, et cetera. Right and that's been the norm for years I would say like 30, 40 years. But what we observe from our consumers is look you walk into a cult center on a daily basis you will not see one individual that looks like that and you will also realize that fit people don't need to look like that.  Now there will always be a delta between what kind of imagery drives CTRs drives performance versus what is really the ground experience. It's always somewhere in the middle, marketers do want to play on aspiration but you know where the line is. Bases what your consumers are telling you on a daily basis what's driving the light for them. So for us at least the brand and the customer experience is a very sort of, it's a continuum. It's a continuum. We do not look at it as separate entities that are trying to talk to the customer in different languages. So the same team talks to social media pretty much. The people who are resolving queries versus people who are doing marketing. The same people are on Twitter, responding to customer queries as well as trying to market on Twitter. So we try to keep that continuum so that who we are versus what we deliver. There isn't a gap that customers ever perceive. That's an interesting, the way you've organized it that you've brought these two together. As we have evolved in organizations, we have tended to keep these two separate. Quite interesting that you've brought them together. Karan, as Xiaomi you always looked at what is being spoken about in the air and you've built campaigns around that. So you always capitalized on conversations and then built your campaigns around that and the brand promise around that. Is that the DNA of the brand? Is that something that you do every time and how do you do it every time? So one of our core philosophies has always been from the day we entered the Indian market, incidentally we've just finished five years now, has always been to listen to the consumer. We always want to know what they are thinking, what they want, what direction they want the brand to move in. To the degree, our products are also sometimes designed by the UIs designed around what the consumer wants. There are features added based on what the consumer wants. So typically we are always listening to them. That's been one of our core philosophies. It's been in our DNA through and through. It's something we've carried from day one right all the way through till our launches even to the one that happened yesterday. What will be interesting is if you take us through how did the Naya note campaign get created because clearly it is contextual. It was a new product as well as it was in the air. So all our campaigns have been MeFan centric. You know our community in India, MeFan community in India right now on the Me Community app we have about 15 million monthly active users. So we realize that this audience that we are talking to is very glued onto the brand. They're following us very closely. They have a lot of expectations and we figured that we need to build campaigns that resonate with them, not telling them stories that they don't want to hear but telling them stories that they want to hear. So the Naya note campaign was sort of developed on that. There was this whole demonetization topic that had triggered that point in India. And we kind of figured that the new Red Me Note series that gets launched every year had a similar excitement. So maybe a few weeks before the launch there is that bus, oh there's a new note coming. There's a new note coming. Have you heard about it? What does it have? So we took the whole demonetization angle. We took our Naya note, which is the new Red Me Note 5 Pro at that point and we built a campaign around that. It did really well. The MeFans loved it. Another similar campaign was Kiska Baja. You know I used to travel a lot. I still travel a lot. And you know even in fact maybe 15 minutes back I heard the Me ring tone go off somewhere. And you know the typical thing, it's not been something that's probably in your role but being something that's been happening for years is when a phone goes off you say Kiska Baja. What we kind of realized was people buy our phones but they don't change the ring tone for some reason. Maybe they love it a lot. And there's that one common ring tone that's playing everywhere. And the communication that we wanted to roll out was we were the number one smartphone brand with about 30% market share. So we, one out of three phones is a Xiaomi phone typically. So that ring tones everywhere. So we built an ad campaign around that and what was interesting is the next day Twitter took to it and everybody said you know the ad player on TV and I thought my phone was ringing in the room. So our focus has always been consumer centric whether it's marketing, whether it's product, whether it's anything to do with even feedback. We're always listening to them and we react to them. So what I'm hearing consistently is that the millennial consumer obviously is very expressive. Talks about what he or she wants. And whenever you, as brands as you're evolving and you're building successful brands, you're not just listening but you're almost co-creating. Can we say that? Yeah, they're a part of our journey. You know it's very important to have them. I mean if you look back maybe 10 years ago if you had a complaint or a grievance against a brand you would look behind the box and then write to them. Today you have social media. They can reach you immediately. So we need to be on our toes, respond to them, understand them, listen to them more importantly. And from there build a brand. So it is co-creating. And I'm gonna jump to you so much Shree because a traditional product category, personal and home care. And I'm interested to know how things have changed for you in terms of the way you engage with consumer, how they engage with you. And how does it change the way you communicate the brand to them, whether it's in advertising or in any form, right from the pack shot to the advertising. What's changed for you? So before getting into the industry that I'm in I would just take a step back to maybe spend few seconds on telling you the, or rather understanding or sort of sharing with all our understanding of this millennial, right? I would say it's a digital know it all generation. And when I say know it all, essentially they make it their business that they actually get to know it all. So even before all the brands start talking about, let's say I talk about soap, they will possibly know about all the ingredients and functionality, et cetera, already by searching it online. Because this generation is also about very, very inquisitive nature. So they search it all. So the way we changed our communication, for example, I'm handling Senthol. And 2012, we actually relaunched our brand. Before that we were all about freshness. But freshness in the space of bathing inside the traditional toilet, bathroom, et cetera. In 2012 what we realized was that these millennials, if you want to keep talking to them you can't just be selling a product. You have to make them live an experience. And one of the best examples that I really hold close to myself is what I have read. And maybe if somebody over here belongs to that industry over here, can talk about is most of the destinations for holidays are decided now basis the experience created on Instagram, right? Maybe if somebody from the travel industry over here can vet it out. The understanding is that it's the Instagram of the world which is actually driving a lot of experience. So what we did for Senthol was instead of making it a freshening device just in your bathroom, we actually made bathing itself an experience and have exaggerated the whole bathing experience by taking outdoors, taking it to absolute exotic locals, making it an experience and then seeping in the brand functionality into it. Because of course that's the reason why you exist. You cannot do without the functionality. So creating that experience is important because millennial is all about living that experience rather than buying that product. Second thing is because we are not just in the service industry, we are offline as well. Creating that kind of experience offline is super important and ensuring that that experience happens when they want that experience. And not just bombarding every time with your message like a lot of time it happens for us when you're searching the Facebook and suddenly some message pops in you are not even interested in. For example, Godrej Air. We actually launched that air care brand by fragrancing newspapers. One is at the cheapest cost, I got lakhs of people experience, actually millions of people in a single day experience the newspaper and that happened first thing in the morning. The insight of there was people love waking up to a nice cup of coffee or tea and that fragrance of tea, coffee, et cetera, that is really freshening that literally wakes you up apart from the daily ritual of reading the newspaper. What better than getting you there and surprising you with a great aroma. So just few examples where we drove the experience across our brands. I'm sure a couple of my friends from the print industry will be extremely happy with what you've done and there are many of them here. So we expect you to do more of that. Thank you so much and we would love to discuss. We're dominated by the digital gang here. Yes, we would love to have that discussion offline. So what I'm kind of hearing you say is that the expectation of the consumer now is satisfy me now, right? When I want to, I would want it now. What you also said was that I research, I would ask around, I would talk to friends and family and influenced in a way, right? Whether it's by my own circle of cohorts or by somebody else whom I look up to. Doesn't need to be a legend to look up to anymore. It could be Saifu that I can look up to, right? So that seems to play an important role, the cohorts with whom I link up with and I look up to in terms of my decision making. And I want to talk to you because these are all coming through UC, right? There's a huge millions and millions of people are searching for content and accessing content through UC. What are the trends that you're seeing? Where do they access it from? How do they access? What kind of content? We are interesting for us to know what you have observed as UC browser. Well, so in regards with UC, yes. There's a lot of data which we collect in regards with making the unique personas of the user, understanding their behavior, what exactly they are doing on their mobile phone or on the UC browser when they come on it. We have done it differently now. In the last one year, we have changed a lot and we have started analyzing as to the kind of behavior user is doing. Not only on UC, but on the other apps which he is using on the mobile phones. Right now, we are sitting at approximately 140 million monthly active users, what we generally claim in the market and that's what we are getting. Now, from where we are getting those users is very important. We are getting these users from the perspective of them having to get all the information at one place. We are not letting the user go out of UC to any other place in regards with if I need to elaborate on that. We are working with some of the biggest advertising companies in the space, the biggest of clients in the space. Every time you come to UC, you can do everything inside it. You don't have to go to a third party or a third app or a third site to do things. When a user is able to do everything inside UC, I as a person who is actually managing it is able to read through the mind of the user. I'm able to define the unique personas of that user by analyzing what are they using inside the UC browser. What we have done recently is, earlier there was UC news as everyone knows, we have actually integrated UC news in the browser. So when you go on the UC browser to navigate, as soon as you scroll down, you have the new UC news feed starts coming in. So you have plethora of information, entertainment, politics, news, everyone at one place. You don't have to go anywhere outside. And Kit is not wanting to give the trade secret of what is the consumer doing and who is the consumer. So that's pretty confidential. I just can't get into that. You're very neatly moved away from what I wanted you to talk about. But yes, I would say one thing. So right now, I was actually reading a report, another Kanta report, the latest one. So India right now sits at approximately 450 million million years, and out of which 300 million are in tier two, tier three and tier four market. And we are pretty big on that, rather very big on that. Gave a little bit. Barat, you obviously talked to everybody across the media and the customer in the brand side, right from people who run brands to people who run the companies themselves. What's the general talk that you get to hear when people talk about connecting with this consumer? Is there a clear thought that you see emerging? Are there strategies that seem to be a common kind of a playbook? Is that something which you seem to be noticing that's happening across organizations? In India, if you drive 200 kilometers, you know you get to interact with people who talk different languages, and you know you talk about people who consume different kind of media, right? So the era of milleniums, it was always there. But the current milleniums, you know, the challenge is all the digital aspects here, right? So everybody is investing on digital, everybody is investing on analytics, and understanding the media consumption habits of all these milleniums people. So that's the whole ask from the audience. But good to hear that everybody's investing in it, but how am I expecting to kind of deploy this investment to connect with my consumers, and how am I expecting that this investment is going to really help me build a stronger brand? Is there a thought on that? See, as Anurag said, ROI is a big challenge. So how are we measuring the return on marketing investments is another aspect which we are all looking into. Having, analyzing your last 24 months media data and predicting your next 12 months is a question. So that is what the brands are looking for now. Deep dive into your last 12 months and 24 months data and understand this and predict your next 12 months. So given the fact that we all kind of agree that we're listening to consumer on a real-time basis, we are trying to co-create our communication with them, we also accept that because it's a connected world, it's real-time, not all experiences are great. What I would like to hear is, for example, how does Xiaomi manage a negative communication and how do you kind of spin it or how do you kind of address it? Not actually spin is not the right word, but probably how do you address it? And how do you then probably use it to build on your own communication? Because that's something which we all are facing every day, right? That's the new aspect of communication today. So how do you manage the worst-case scenario? Well, the easiest way that we know at Xiaomi is to always be honest. So if there is a flaw or a mistake from our end, we just own it. We just say, yes, this was wrong. We are fixing it. It'll be fixed by this and this state, right? And if there are expectations that are out there that aren't being met for any reason, and there are these times that do come up in a journey, right, you can't always have 100% positive response of sorts. You know, you just own it from your heart and tell them like, okay, this is what it is. You talk to the consumer, you don't leave it unaddressed. You do respond to them, you tell them, because that's what they want. They have brought into your brand philosophy, they've brought into your product, right? And you can't leave them unattended. It's very important to be honest. And that's what we follow at Xiaomi very closely. There are also times where there's an expectation mismatch, and you know, the brand is not wrong. There is a certain expectation that the consumer has, which is not something that is an industry requirement per se, hypothetically. In which case, you do give an explanation.  You always talk to your consumer, you listen to them, you respond to them, you tell them we're here for you. We need to be very, you know, I would say modest as a brand. We can't be a brand that doesn't, you know, take into the consumer's needs or requirements. We need to explain. It's a necessity. So given the fact that you're building the brand every moment, every day with every interaction that's happening across multiple, you know, platforms, it's not that one campaign that we do, the media campaign that we do, but it's a campaign that's 24 by seven on in a way, right? That's the life we're living now. Barat, is there, are you seeing a trend where companies are using automated tools and machine learning, et cetera, to kind of be always on and to manage this? Because it is a tough one to manage to be always be available all the time and do the right thing every time. Absolutely. In fact, I'll give you one live example. I landed in Bangladesh and I was just passing through customs and I just checked into Facebook and I got a call from a telecom player, you know, to buy a SIM card. You know, so it's so real time out there. You know, so I'm like fully excited to say that big data, artificial intelligence and robotics is what the brands are looking for in the next one year. That's a big statement. Robotics as well. Cool. So three, four things that come to my mind and I want to kind of do a sign off note from each of you. One thing that came up clearly is that the millenial consumer is looking to be a part of our brands and not necessarily be spoken to, but I would like to have a conversation. I think that's something which is very clearly coming out from all our experiences. The second one that's coming out is that the peer group and the people whom I influenced by as a consumer are very important for me. My decisions get driven by that, like you spoke about so much, that I would have already done my search even before I, you know, checked out the product. I had already spoken to probably a few people before I even came and tried it out. So that, and I can do it real time. I don't need to spend time doing it. So that's very important, that group of people whom I'm connected to and their views and opinions is very important. So I'm not just talking to an individual, I'm talking to a larger group. That seems to be a clear trend that's emerging. And finally, Naresh, what you kind of spoke about that the service delivery and communication in some sense needs to get integrated and it's the same thing. It can't be that communication is independent of service delivery. Both are integrated with each other and so in a way the business logistics and the communication logistics will need to probably come together to be able to address this consumer. These are the three or four things that key takeaways that I'm getting. Any observations you have, given that you also have a bird's eye view, you work with multiple brands and you also work with, you have an understanding of consumers as well? In regards to millennials, I can only say that we sit on almost 70% of the millennials which is my audience as of now. And as Bharat said, the robotics and the artificial intelligence and the machine learning which is coming up. Well, I somewhat, I'm sorry Bharat, but I somewhat disagree on that. Why, because as we discussed earlier as well, there is a very thin line between going ahead and helping a consumer and then going ahead and making him irritated as to by throwing him the same ads again and again. In regards with understanding, coming to your question, in regards with understanding the user behavior, yes, we do have our own machine learning platform, we do have our own DMPs which we work on and we understand what kind of content a consumer on UC is coming and consuming. We are able to understand what kind of behavior they have. We are able to understand what kind of age group they come from. For example, I'll give you an example. There was a certain brief from one of the big FMCG companies which came to us and which was like, they wanted to target only mothers. Now, how would you target mothers? So you can actually target mothers or people or ladies who are expecting by just understanding the kind of behavior they are using on a browser or the kind of content they are using. The data becomes small, but that becomes more than 90% precise. So that's how we generally go ahead and do that. Amongst the many set of services that are a part of the Urban Clap portfolio, they are the top three, right? What are those and why are they top three amongst consumers? And typically what's the kind of age group which takes on to these top three services that you have? So Urban Clap as a platform appeals more to folks in a certain life stage and beyond, which is 25 plus. I think our core TG is working women, it's 25 plus. So the top three services in Urban Clap are Beauty at Home, which is a high repeat category, super high play on convenience and excellence of service delivery. Second biggest service is appliance repairs because the current offline alternatives are just 240. Either you call an OEM and wait for them to show up. I mean, it's generally a broken experience. I don't want to generalize it. And the second option is you go to a nearby market and you find a guy. So appliance repairs as a category has really, really picked up for us. And the third emerging large category for Urban Clap is actually massages inspired home. Again, the consumer of today needs these services like a stress relief service or a simple pain relief service at home. And we realized how massively this is exploding in the top three markets. So actually I want to connect that to the whole millennial piece and build a little on what Nareesh said about and Karan said about authenticity, delivering what you promised. I think there's one more large aspect, very important aspect which appeals to millennials, which is a multi-channel marketing strategy. Digital actually has been generalized too far. I think digital is also very, very nuanced. Indian consumers are coming online for the first time in their lives on their mobile phones unlike the West where the laptop or the computer or the desktop was the first port of golf to go online. So on that screen, which is a four by five screen, there are still multiple channels of communication. And a lot of authenticity and brand loyalty or brand promise can be communicated through multiple channels. And being on multiple channels really appeals to a large variety of consumers because then they start believing in the brand promise depending on, of course, your messaging and depending on the real product delivery. So yeah, these are the two points. I think I've answered your question about the top three services. Absolutely. And what's interesting is, there's always been the fear about letting someone in to your home, right? And in all the services that you provide and the top three services that you provide, except for probably the appliance thing where you're used to someone coming to your home. The other two, traditionally you've not been used to people coming home and servicing you. It's interesting how you broke that barrier. Actually, it was important for us to identify that there is a need in the market, right? The multitasker woman of today doesn't really have time. She's juggling so many things from work to the home to the kids. There is just so much to juggle, right? So we realized there is a positive of time that's a problem in our life and also a promise of quality. When we combine those two, it was a seeming like a very obvious answer. Of course, exactly. I mean, Ashwin, the fact you mentioned about the possibility of things going wrong, right? They are immense because right from someone entering your home, your personal space inside your home, they're seeing how many kids you have, what time you're coming back, going in, et cetera, et cetera. So yes, all of those different points of interaction have to be managed extremely carefully. But yes, I think the Eureka moment for us was when the first consumer said that, yeah, this is something I would take and then we'll build from that. Brilliant. So a point that clearly came out from this conversation that you had was, this consumer is clearly consuming content and communication across multiple channels at the same time. So it's a multi-channel, omni-channel kind of a, consumption of content that's happening at the same time and you need to be there everywhere. The other thing, obviously, is that the attention span is also low. So how can our communication fit into that attention span, make it interesting for them? And a little what we spoke about earlier in terms of talking to the peers and having relevant conversations. Like you spoke about creating a product in the service which was needed, right? So if it's relevant, I'm short of time. Can you make my life easier and can you communicate that to me quickly? I would probably listen to you and start connecting with you as a brand. Is that, is the right thing to say? Cool. Any, any, Naresh, you have something to add now?  I just believe that word of mouth as a channel of marketing has just been amplified. Like while we talk about, you know, things, negative things traveling very fast or, you know, what the pitfalls of social media which generally tends to be the theme for marketers, I also believe this is just a huge opportunity to even amplify good things for people to back you as a brand. So there have been many moments even in CureFit and in my past history in Mintra where when things have gone wrong, there have been hordes of your loyal customers that have actually come and backed you. When, and in an industry like us, a lot of things can go wrong. You can get into trouble with some local authorities. You can get into trouble with a politician or his son. All kinds of things can happen when you're geographically distributed like this.  So I think that's the only thing I would add and I would think that that's very common with Urban Clap as well, that working woman who you're saving time for every week ultimately becomes a champion for your brand and that's a huge asset to have in this age. Great. So it's not a formula which can't be broken clearly and but I think we'll need to change the way we are probably thinking about a consumer. We will need to be available for the consumer real time always. We need to co-create not just our communication but our product as well with them and listen to them like you said. And more importantly, be sensitive to their likes, dislikes and their larger group of people that they work with and live with as we interact with them. And I think we should be able to figure out with the help of some machine learning how to be able to connect it to them on a real time basis every day. Cool, thank you so much for your time and insights on how you've been running your brands. And I hope it was entertaining as well. That was the brief that Anurag gave me saying that if you don't entertain, I'll kill you. But so thank you so much for being patient listeners and all the best for the evening.


If we go down the column in group two from easing to calcium, we see that the calcium is also stored in the air.  As you go down the column in group two, the metals tend to get harder. Calcium is malleable, but requires a great deal more force to shape it. Calcium is a metal and does conduct electricity. If we add the calcium metal to water, we get a much more rapid reaction of the calcium with water than the magnesium. The reaction is very exothermic. This is due to the lower ionization energy of the calcium.  We can test for the hydrogen gas by collecting some of the hydrogen gas in a test tube and testing its flammability. We can test for the presence of calcium hydroxide by adding phenithaline indicator. The indicator turns purple indicating that the solution is basic and that calcium hydroxide has been formed.


Well, hello and welcome to another Dev Nation Live. I'm excited today to have Gallauder with us. He's gonna deep dive us into big data. I'm hoping you guys are excited about big data. And of course, big data is one of those overloaded terms that has a lot of different technologies and techniques and ideas behind it. But Gallauder's got a really awesome presentation and our series of really awesome demonstrations I think you're gonna really enjoy. And again, for Dev Nation Live, we've run this in very short intervals, about 25 minutes of presentation and demonstration, then a few minutes at the end for questions. Please hit us on the chat with your questions, just type those right in, and then when we get towards the end, I'll be asking Gallauder those same questions. So at this point, let's turn it over to Gallauder. Are you ready to go? Yeah, I'm here. Awesome. Hello, welcome everyone. Thank you for attending this presentation on big data in action with UKinniSpec. So today we're gonna start a little bit looking at the problems of real time big data, the data growth that are happening at the moment and see how we can solve those with InfinisPan, which is a memory data grid. And then we'll be going through a couple of live coding demos that use InfinisPan Vertex and OpenShift. So my objective today is to show you how you can build infrastructure based on InfinisPan to store, search, process and near real time big data, how to do Calcune analytics as well. Now, dealing with real time streaming big data is quite a unique challenge. This is an offline processing of big data has been happening for a long time, whether it's via batching or similar technologies, but you can never really get it to be fully real time. And the reason why this is important is because even a few delay of a few seconds can mean the difference between either keeping or losing a customer or for a financial institution, it could be the difference between increased liquidity or big losses. So real time processing can be really crucial for certain businesses. We've also seen like a huge data growth happening and this is the result of a lot of the smartphones, IoT devices. So with all this big data that is happening, how can we handle it with how can we make some sense out of it? And for these two problems, we think that memory data grids are a perfect solution for this type of problems. So what are the memory data grids? Memory data grids are essentially a way to manage distributed data in memory. So what we have here is servers connected in a mesh with peer-to-peer communication style. So there's no master or slave categories. So there's no kind of single bottleneck or single point of failure. And this kind of grids, data grids are designed to run on commodity hardware. So you don't need expensive hardware. These data grids are linearly scalable. For that what we use, we use a smart data distribution technique. So even if you have like a big cluster, then we can divide the data such that only certain copies of the data are maintained. So what this gives you is this gives you that each node maintains a subset of the data. So by doing that, you get this kind of nice, implicit data problem, which is gonna be very important for us later on in this talk. Data grids are also elastic and handle failures transparently. So they're very well suited for cloud or platform as a service environment. And they can be backed by a database file system or their persistence source. So if you want to give them more life to your data. And they're accessible from any type of application. So this gives you the ability to have this kind of transparent sharing of application data. And we have connectors available for Java, NodeJSC, C++, or NetAccessible. In PhoenixPan can be used in many, many different ways. I'm not gonna go through all of them here because we don't have the time. So you can, for example, you see that distributed cache or as I can on distributed SQL database with querying, transactions, et cetera. But for this presentation, the two very interesting use cases are the one about how can we do data analytics using either distributed data streams or with Spark Hadoop integrations? Or how can we do event driven computations that allows us to do real time processing of data? And the talk today is gonna be focusing on these two last use cases. So the first use case, the one about event driven computation, it's a very important for us on there are many, many ways in which, well, there's several ways in which you can access in PhoenixPan, whether you go like a dictionary key value store or the current API. But if you want to do like real time processing, we need a little bit more advanced. This is what continuous query API gives us. It enables you to be reactive to data changes. So what it does is basically the query API is an extension allowing an application to receive entries that currently match a query and then be continuously notified of any changes that happen to the query data set. So what this gives you is, it gives your application the ability to receive a steady stream of bands instead of you having to all the time execute a query to see if there is any new data already or if there is any database not part of the query. So this leads you to a more efficient use of their resources. So the best way to understand how it works is to really see it in action. So for that, I'm gonna be showing you a demo today which is based around the Swiss rail transport system. Here what you see in the screen is a domain of the objects we're gonna be using. So we've got a train, which is a physical train and then represents its ID type, et cetera. Then we've got the station. So we've got information about the position geographically. And then for each station, we've got a station board. So this is like at this moment in a particular moment in time, which trains are coming at which platforms, et cetera, where are they going? And each of the entries in the station board we call that a stop. The demo that I'm going to show today runs on top of OpenShift. You probably, we've done several presentations already in definition. First shift is RedHats platform as a service that allows developers to pick lead, developed hosts and a scale, applications in the cloud environment. And this is gonna be all the applications are gonna be running on top of that. And the demo also uses Vertex, which is a toolkit for building reactive applications on top of the gradient. It's a bend-driven and not blocking. So it means that your application can handle a lot of concurrency using a very small number of threads. So the first demo that I'm going to show you, what we're going to try to achieve is we're gonna try to create, represent our data grid is storing the state of the station boards at a particular time. And then what we're gonna try to do is create this kind of a dashboard that shows us all the trains are delayed in the system. So of all the trains are going through the country, which ones are delayed? And then that way, say I'm someone in the management of Swiss transport system, I can see where which trains are getting delayed, et cetera.  And that's basically going to be communicating with a real-time component, which is already on the OpenShift. And then we're gonna have like an injector vertical. Vertical is this kind of concept in OpenShift that allows you to, it's like an actual, like a unit of processing. Then we're gonna be feeding our data grid with data about the station boards. And then we're gonna have on the left a data grid, which is three nodes, we distribute the data around. And then what we're gonna do is we're gonna work on the continuous query vertical, which is the one that allows us to say, okay, of all the station board data, give me the information about those trains that are delayed. So the best, so now I'm gonna show you all of these in action. So I'm just gonna switch to my screen, to my ID. And then what I have here for the dashboard is a JavaFX application. So I'm just gonna run it as is at the moment, so you can see what it looks like. Okay, can you see right now it's just a little dashboard, it's empty, the code is not complete, which is what I'm going to be doing right now. So from a continuous query perspective, the two things we need to do, one is define the query and then define the listener. Here, the most thing from a structural perspective, we've got a cache, which is kind of worse, our base storage. And then what we're doing is for each station, we've got the station board, it can stay. So what we want to do from our query is say, we want to do, from our query factory, we want to do a station forward query. Are we gonna say, of each station board, can we do those station boards, where there is at least one entry that has got more than one stop that is delayed. So the entries that is here comes from, if you look at station board, it's got a list of stops, which are main entries. So that's where this particular code is coming from. Okay, so we have our query. Now, the next part is the listener. Here what we need to say is, when we get a station board that contains this one entry, that is delayed, we get the entries, and we kind of need to pass them over to the front end. So what we first need to do is, we're gonna take out of each station board, we can multiple stops. So we say those stops whose delay is bigger than zero, so it's delayed. And then for each of those, we're gonna be basically pushing it to the event bus. The event bus is the part that underneath is gonna be making sure that this is shipped over a web socket to our front end. So we've got the query now, we've got the listener in place. The final thing we need to do is put the two together. So what we need to do is here is say, okay, continue to query, add for this query, add this listener. That's it. Now, the next step we need to do is we need to publish these changes. So what we need to do first of all is we need to, we're gonna just, so what we need to do is now push these changes over to OpenShift. There are multiple ways to do that. One that I've chosen here is one that just involves building it locally and then pushing it, that's kind of like a binder over to OpenShift. So what I do is I just build it first. It's a few minutes. And then what I can do is I can do called something called OCS style build, which allows me to say, okay, take this binary and push it over to OpenShift. So if we do that, we can now go to our OpenShift. And we start to see now here that we're starting to, this is a second version being built of the real time component. That's our component that's gonna have the update it. We see it here. It's now updating it. Okay. So now the version two of our application is up. So we just could go back to the IDE. From here, we run our effects application again. And within just a few seconds, we should start seeing delays appear. So yeah, we start to see our dashboard, like as we are feeding kind of a station board that we've kind of compiled about two, three hours worth of station board. Then we kind of feeding into the system as delays are happening, we kind of feeding them from OpenShift. The website gets over to our effects application. So we can see like delays are happening. So train system in Switzerland is really good, but as you can see, sometimes delays are still happening. So this is the part about the real time demo that I wanted to show you. So we're gonna move on to the analytics part. And Galdor, we do have a question on the demo that I think would be pertinent right now. Why is there a filter and a stream for minimum delay? Men delay? With, oh yes, because when I get the query, so the query is about a station board. So this first credit gives me giving a station board where at least one of the entries is delayed. So if I have, the station board has got train entries, or one of them is delayed, then I'm gonna have, I get as callback all the station board. So then afterwards I need to say, all the ten entries just extract the one that is really delayed. That's based on the structure of the cache. I could have a structure in a different way. So I didn't have to do the two filters. But at the moment, this is the kind of granularity that I get, understood? Yeah, and that's very cool. The concept of having that continuous stream of data and then being able to filter it and modify it and then react to it as it comes in. Yeah, at the moment it's only implementing the joining, but you could say, if another result leaves, then I should remove it also from the table, which is not yet implemented, but it could be done easily. Okay, so let me just stop this. Let's get back to my presentation. So in the next part, what I want to do is I want to show you a little bit about the analytics part of, in Infinispan, what you can do with big data analytics here. In Infinispan, we primarily have two ways of analyzing the data. On one side, we've got Spark-Hadoop integrations. So what that means is that you can use the very powerful APIs of Hadoop of Spark with an Infinispan backend. So what you do there, you combine the advanced data analytics APIs of Hadoop and Spark with memory data grid technology. So all the kind of benefits of data grid. So peer-to-peer communication, no master slave concepts, et cetera. Now, the kind of bigger problem of Spark or Hadoop is that they are big stacks that require independence resource management. So they have their own technologies for clustering, using ZooKeeper, et cetera. So they have very powerful APIs, but you should really only use them when you really need them. Alternatively, oops, sorry, I've gone too far. Alternatively, what you can do with Infinispan is you can alternatively use distributed data streams, which allows you to transform and analyze data storing a data grid. This is essentially an extension to the Java Streams API so that you can do filter, map calls on Java Streams by the distributed fashion. So this is quite cool. The way it works is that it, one of the key benefit of this is that it allows you for operations to run simultaneously on different nodes at the same time. This is because, as I mentioned earlier, one of the key aspects of Infinispan is that the data is distributed. So for each particular piece, a key value pair, there's always one node that is the owner. So what we do is each node is owner for that particular, for certain keys. So when the lambdas or the functions are executed to these nodes, they're executed locally, only for the subset for which I'm owner. So it means you get all these nodes working together like kind of executing these lambdas in simultaneously. And this is a very powerful way of kind of doing, distributed processing without the need to bring in a Spark or Hadoop. So the next time I'm going to show you is, it's going to use distributed Java streams to answer this question. What is the time of the day of the hour? The hour of the day where you get the biggest ratio of delayed trains in Switzerland. This is not a trivial thing to answer, as you'll see in the demo. Now the way this demo is structured on the front end, we're going to have like a Jupyter notebook. Jupyter is kind of a very cool technology for writing quick technology for writing data visualizations and data science related things. And then what I'm going to do from Jupyter, I'm going to take advantage of its diagramming. And then what I'm going to do from there is call into our OpenShift environment. It's going to go to the analytics vertical. This is like another vertical, which is going to try to calculate to get the data to answer this question. The way it works is, we've got again a data grid from three nodes, but in there we're going to have this kind of server task deployed, and that's where we're going to be writing our distributed Java strings. So the analytics vertical is going to make a call to one of the nodes, and the other node is going to be taking, it's going to, one of the nodes is going to take charts of distributing the strings operations to the other nodes, bringing them back and sending back to the analytics vertical. On the other, what we also have is an injector vertical. This is the one that pushes data into the data grid. Here we're talking about about two million, two and a half million entries, which are three weeks worth of data to basically analyze. So let's see this part in action. So what we need to do now is we're going to go to, the most important part is the server task. This is kind of like a little bit like a PLS curve kind of thing, where you can just deploy it and run it. Now what we need to do here is we need to do, to calculate what's the ratio where we get the biggest delayed trades, we need to get a couple of maps in place. We need to get a map saying, okay, between zero and one o'clock we've got maybe a thousand trades going through. Between one and two we've got maybe 800, et cetera. And then we need to do the same for delayed trades, but those which are only delayed. So maybe between midnight and one o'clock we've got 90, between one o'clock and two o'clock maybe we've got 20, et cetera. So how do we do this with distributed streams? What we need to do if you look at the cache here, the cache here we structure in a slightly different way. Here we're basically recording all the real stops that happened in our station. So what we're interested is about the stops. So what we need to do is we need to do, we need to collect them group in buy. So we need to say in buy. For it to stop, what we're gonna do is get an hour of the day for each of the, okay, amen, amen, amen, amen. So this is going to give us, it's gonna give us per hour how many stops or how many trades are going through. Now the thing that is a little bit tricky at the moment is that this collect takes a, this group in my returns an object that is not serializable. So a very easy trick that you can do is you can kind of force, if something is not serializable, what you can do, we can make the function that creates those collectors serializable. So instead of creating the object right here, send the function to create that object to other nodes and then execute it in its node local. This is one of the tricks that is part of Java 8 where we can say we can kind of, if you take an object and pass it to a method and then you say this method also takes serializable. So this is a supplier that we're passing here. If we say this method takes a supplier that is also serializable, the JVM kind of puts the two together. So it does a little bit of hard work for us without having to do the casting. Now for the delay part, it's pretty much the same but with the difference that we need to say, only those are delayed, we're interested here. Now the cool thing here, we don't have to do any magic here. This is the predicate that we pass to filter. It's already kind of forcing to be serializable. So it's already ready to be shipped to them. And this is all we have to do. And the final bit I need to do is I need to kind of go to the, I need to rebuild the server task and deploy it. The first thing we need to do is we need to build it and then to plug it. This is something that is still a bit that we're working through underneath. We can just create the car and push it into a particular place that then the container picks it from there. But there are all the ways that we could have done it. We could have done it with volumes or with other reps. But for this, I'm just going to do just a simple copy and then the server picks it up. Okay, so our server now contains the VR with the task that we want. So I'm just going to go for the last bit of the demo. I'm just going to go into the screen. So here now you see my, the Jupyter notebook. This allow, Jupyter allows me to kind of go and kind of execute individual calls so I can execute the imports, get the URL. And finally, in this third line, this is when I kind of do the calls. So you see it takes a few seconds and then once I receive the answer, then what I can do is plot. So we see, something that is a slightly surprising. We see like the biggest ratio of delayed trains is at two o'clock in the morning. And you see as the day progresses towards the end of the day, you see more trains delayed, which might surprise you, it also surprised me because this is one of the nice side effects of the Swiss train system is that for the last connections of the day, the connections wait for each other. So that means if you have the entire system, there's one train that is delayed that has a ripple effect through the entire system for the last connections. So if you're stuck somewhere and then you need to get a train and you need to get your last connection, if that train is delayed, then you can be sure they're gonna wait for it. So just for fun, let's have a look at the data so that you see what we're talking about. So even at a rush hour, which maybe will have been what you would expect, you see at the seven o'clock in the morning, you've got 99,000 trains running per hour. Like five o'clock, you've got 113,000. Not only 6,000 are delayed, whereas two o'clock in the morning, there's 2,300, 321 are delayed. So it's kind of a bigger ratio of the day trains at that time. That's all I had to show you today. I just hope, going back to the objective, I just wanted to show you a little bit how you can use InfiniSpan to create an infrastructure that can have the real-time data and run analytics using continuous queries, how we can use them for real-time data processing and InfiniSpan distributed JavaScript or some basic processing. Thanks a lot to these people who allow me to use their icons here. I think the links are going to be put up. This is the demo that I've shown today. They're in a repo, so you can go and try them. They're in instructions on how to do that and how to run them locally, et cetera. That's all I had to talk to say today. And we do have a question, at least we have several questions and more will be coming in. But one that in particular I think is important is could you also discuss again the relationship between Apache Spark and Hadoop and InfiniSpan? Does one include the other or how do you integrate those different platforms and different technologies? Essentially what we do is there is an integration point in Hadoop and Spark which allows you to where does it consume the data from? This is an integration point in the Spark in Hadoop that we've built and then we've got these modules that you can use them in your projects. They basically see that the data they feed it or they take it from InfiniSpan or they can store it into InfiniSpan and then consume it again. I think this is particularly interesting. If you have already data maybe on InfiniSpan then maybe you want to consume from an existing Spark or Hadoop project or maybe you want to take advantage of the elasticity that InfiniSpan gives you as opposed to other working memories for this project. So that's how it normally works. We've got some blog posts and documentation on how to use them and they're very popular as well. And they have very interesting APIs as well for us. And I do think the intersection where these guys integrate is very interesting. Hadoop of course being more disk-based, it's not in memory per se, it's firing off a Hadoop file system, Spark allowing for its mini, what do you call them, the concept where it has these little mini blocks of streams. Right, so it's still in memory but it's still blocked. Yeah, but also Hadoop, that's where you switch. So in Hadoop, instead of consuming your data out of ITFS, you can consume it out of InfiniSpan. So for example, if you already have InfiniSpan data that you've used for something else, like for keeping some live data, then you can hook it in Hadoop and then processing with Spark as well. And the APIs are very advanced.  The APIs are fantastic. So there was an interesting question you came up early on, was do you work for, have you ever worked for, profit bricks? No, no.  Is there, can you give us a little explanation on how to get started? So what you showed us is it's incredibly advanced, incredibly cool, I love the real time streaming, continuous query. I love how you compared it to PLSQL even, and of course using Vertex to do that, doing that reactive programming model on top. But how would I get started with the basics? You know, just setting up InfiniSpan, getting started, and then understanding how to apply this continuous query action. We, in our InfiniSpan.org, we've got an area with little, little small tutorials which allow you to quickly get started with a particular feature. We've got them for querying a continuous query. We've got them, for example, if you have a JavaScript application, how can I get started with these tutorials? If you still see my website, I can just, if you go to the community, in the learn, we've got these tutorials at the best starting point. Because they are very focused examples on how you can do querying, remote scripting, for example, to do these kind of server tasks. Which you, in my case, I've shown the server tasks written in Java, but you can also write them in JavaScript. And then you've got a lot of libraries to get started. But that's the first phase, too. They're very, very small, so very easy to kind of understand and go through that. All right, well, that is fantastic. And thank you very much for that. And we are out of time for today's session. Thank you guys so much for hanging out with us and spending this 30-minute window with us. The recording will be immediately available as soon as we get disconnected here. But do look forward to other DevNation Live sessions. The next one coming up will be more focused on reactive programming and another deep dive more into that reactive programming world with Vertex. And do look at the actual developersoverhead.com slash DevNation Live for other archives. And we're going to be scheduling many more of these things into October and November and other exciting content like you saw today. Galder, thank you so much. Awesome demonstration. Great introduction to those concepts. Thank you so much. Thanks, Bear. Thanks everyone for attending. And yeah, thank you. Yeah, absolutely.


Hello and thank you for enrolling in my course this is Hari Swaminathan and in this Udemy bonus lecture video I want to show you ways where you can take maximum advantage of my Udemy channel as well as all my Udemy courses so this is a very important video and I would recommend you watch this video until the end. First of all I want to give a brief background about the bonus lecture format in the Udemy system. Udemy allows a bonus lecture for all instructors and this bonus lecture must be the last lecture and must have the word bonus in it. So this works well for both the Udemy instructors as well as the students because the instructors are allowed to market their other products and the students benefit by getting some discounts which is not available to anyone who is not a student of my Udemy channel and so this can be a very important way for students to acquire these products at cost effective prices and so this is a very important feature of the Udemy system and so with that said let's talk about the Udemy coupon system many students may or may not have realized that Udemy changed their entire coupon system in October 2019 and today instructors cannot issue coupons that are more than 31 days valid and so this presents a big problem for instructors that have over 50 courses like myself because we have to change the coupon codes on a monthly basis every month and so this video will highlight the best way for students to avail of these specials. The specials will include offers for all my Udemy courses as well as include all of the products on my website which is optiontiger.com and of course if you have questions at any time please email info at optiontiger.com. Now I want to give a brief description of how my Udemy channel is structured first of all I have options courses and so these are in the beginner intermediate and advanced categories that there are over 35 options courses I also have a 47 course bundle then I have nine options mastery courses these are beyond advanced they are very elite intellectual property systems and they are called the max systems and you may have noticed I use the thinkorswim platform by TD Ameritrade and I have a very comprehensive tutorial on the thinkorswim platform which is over three and a half hours long and every month or two I conduct swing trading trade alert services and four times a year for earnings I conduct an earnings report trade services as well so you're going to find all of these mix of courses as well as trade services on my Udemy channel and on my website which is optiontiger.com I have a proprietary algorithm for swing trading which I use it for as a service as well as the algo is available for sale and you can use this for stocks and options I also have a proprietary day trading algorithm for stocks and options I have a deep market internals SPX intraday trading algorithm as well and another algorithm for custom RSI which maximizes profits on a winning or a losing trade and just like my Udemy channel I also run the same swing trading signals trade alert services on my website as well and I also run the four times a year earnings report trades alert services on my website the swing trading signal services is a set of daily videos which covers any new trade entries any adjustments to existing trades as well as trade exits it's a detailed video which does all the analysis and also shows how the algorithm is outputting the best trade candidates the earnings report service comes four times a year and we pick about 12 to 15 high volatility stocks and we pick the appropriate strategy for it earnings report trades when it comes to options are very unique and you have to structure your earnings trade in a very unique manner and so there is an earnings max product also behind it and we will use the tactics and techniques in this product to create very unique earnings reports strategies four times a year let me quickly show you these products on my website this is option tiger.com on the homepage itself you'll see the swing trading signals you'll see the SPX index day trading you'll see the custom RSI indicator as well as the algorithm based day trading signals below that you'll find the three beginner intermediate and advanced options modules and together there are the same 47 courses in my way on my website as well and below that I have the nine max systems these are all the max systems here you can come in and click on them to find out more you can also access all these products from under the products menu and I'll be putting up a new page for the earnings report service let's now come to the coupons and special offers for existing customers on my udemy channel you can get 25 to 50% off on all of my udemy courses as well as my max products and you can also get up to 25 to 50% off on all my option tiger products of course it depends on how many products you buy and of course the more you get it as a bundle the better the discounts will be as I mentioned before because udemy has changed their discounting and coupon system and we cannot have a coupon for more than 31 days it's impossible to put a coupon that will work at all times and therefore your best way to get these discounts is to email us at info at option tiger.com and we can send you the updated coupon codes so once again if you're interested in discounts for all the all my option tiger as well as udemy products please send us an email at info at option tiger.com and please include everything that you're interested in and as mentioned before the more the bigger bundle you purchase the more discounts I can give you finally I want to leave you with some important links so that you can have quick access if you're interested in the swing trading signals or the algo you can go to this bit.ly link bit.ly slash 25 swing if you're interested in the day trading signals algo you can go to bit.ly slash day algo similarly you can go to the spx also at bit.ly slash spx algo now all of these have playlists where we record all our past trades so the swing trading youtube playlists is there I have a spx sticks playlist a day trading playlist as well as the earnings trade service playlists you'll find this presentation as well as my udemy course order as part of a downloadable PDF in this lecture itself so thank you very much for watching this video and once again if you have any questions on any specials and offers please email us at info at option tiger.com thank you


Hey what's good y'all me and Chase are here in Cleveland of all places last time we're here was the Jake Paul fight How's the highlights put us up in hotel and look at this dude What even there's a post office, right? Yeah, we're here for all star weekend It's gonna be absolutely insane. I'll compete for a hundred K probably gonna get you guys a banger vlog whether or not I win it Cash is in a DDG Kenny kind of a hot chick I don't know her name Look brown brown white dunks. It's too easy.  Okay, listen I'm guilty All right, so next up we got walkthroughs the events not till tomorrow But today we're just gonna go tour the facility meet all the competitors talk through the production team It's gonna be a good time Out of the box Yeah, I'm a women's right here Hold on, hold on All right, let's see All right, three two one All right, we good Hey, we got the boys in here They're cating the event is that fine So what they told me is if I can get in the fireball it's a shot of fireball so we're going for that What is the oh, I got it Where's my shot I don't know how they got all the alcohol in there, but that's pretty cool I don't get it though. How does this get around my head? All right, I'm done. It's all bad.    I mean, I think they got one more game.  Hey, still can't stop it. That's tough This won't be one did go to a game of three and it's on house highlights youtube I don't want to steal their content. So if you want to check it out check it out from there But that's a wrap for us today tomorrow is game day. You boys know I gotta dial in Hey, what's good y'all is game day. Um, it's saturday We have the event in about four hours kind of nervous, but I want to get this stuff Uh, we're in this cute little strip of cleveland. So we're trying to go find food right now It's been pretty good so far. We had amazing night last night went to the gunna concert Spectacular met guy at the airy.    He actually sucked so much.  She's at daycare right now It's like a build your own blocks for you. It's actually really cool. We're gonna have to order more food though Yeah, what are you gonna get a bonus pizza? It looks really good I built it like lego's I built it I'm gonna give it a hard eight and a half All right, it's game time. We're fueled up ready to show down We're gonna go about an hour and a half early with everybody get some shots up and uh, hopefully get this done for the boys All right, yeah, we're back.   This is my first attempt. No cheating That's for you, Sam What I tell you all right, it's almost around Who said that Sam? I'm gonna hit another one I'll get you another one then Mark's behind me. Well, you're behind me. So we'll run it.  I got y'all. I got y'all You Hey, huge. Thank you to Hasa highlights. I'm so grateful even to get re-invited to these Let me get another banner up there next time This basketball too. I listen I did get a little bit of flak for golf because low key that was luck I shot 80% today. I did not miss a putback. I did not miss a layup. I think I really earned this one I'm really happy. I'm so happy Yeah Let's get all you guys together over there There you go, right good right here What did you do that? Oh What's your favorite month Oh No, all right, y'all so much has happened. I just wanted to wrap this video up What an incredible weekend. We got the dub so amazing got to go out with all the guys It was so much fun to hang out with them I've missed going out with mark and cash since the last uh the last tourney So I'm just trying to keep my thoughts together right now chase. I can barely do this I'm usually so happy. I put this on a top three absolute top three weekend of my last It's like five years so you can remember golf weekend was up there too Winning 100k is always great But honestly we we did that we went to all star games was so much fun And after that we went out with all the boys and meek mill showed up. Fuck. Yeah. Are you kidding? It was so sick.  It turns out delta is goaded. They ended up driving us to our flight This is so cool. Take us out. Oh my god So, um, our first flight got delayed Hey, thank you so much By the minute here, we've had so many delays and you know, it's like there's so much going on right now Anyway, you guys have a really tight connection Yeah, thank you so much. We've never driven in the delta portion before Yeah, this is amazing. What this is so cool. So do you guys have boarding passes on your phone? Yeah, okay, because I think what I'm gonna do is take picture. Those are those because I think they're already boarding And then I will let you guys go ahead and sneak in line and I will go up and scan you on with the gate agent Oh, that'd be cool. Thank you. Thank you so much. Beautiful. Thank you. So let's go up together I'm filming it all. Thank you guys But that's awesome Thank you That's why I fly down there


Welcome to this presentation of the STM32WB clock recovery system. It covers the main features of this module used to control the precision of the USB clock frequency. The goal of the clock recovery system is to obtain a precise enough clock signal for use by the USB module without the need for an external resonator component, just by simply using the USB traffic as a timing reference. The peripheral's main functions are its ability to trim the internal oscillator on the fly to benefit from its fine granularity in order to meet the USB protocol requirements and have enough information available for the user to track in early phases any potential issues. The key features are a selectable synchronization source with programmable pre-scaler and polarity. External pin, LSE oscillator output, or USB SOF packet reception. The possibility to generate synchronization pulses by software. An automatic oscillator trimming capability with no need for CPU action. A manual control option for faster startup convergence. A 16-bit frequency error counter with automatic error value capture and reload. A programmable limit for automatic frequency error value evaluation and status reporting. Maskable interrupts and events. Expected synchronization or ESYNC, synchronization OK or SYNC OK. Synchronization warning or SYNC worn. Or synchronization or trimming error or ERR. Three different sources can be selected for the clock recovery system. An external signal on a GPIO, the 32 kilohertz crystal, or the USB startup frame signals can be used as a clock source to create a reference signal to calibrate the HSI 48 megahertz oscillator. This reference signal called SYNC is used to reload the 16-bit counter and capture the value of the actual countdown. Depending on this value, the HSI 48 megahertz clock frequency, HSI 48, is fine tuned to reach the most accurate frequency. The CRS counter value is reloaded with the reload value on each sync event. It starts counting down till it reaches zero. Then it starts counting up to the outrange limit where it eventually stops if no sync event is received before and generates a SYNC miss event. A sync event received when the counter is below the outrange will eventually find trim, the HSI 48, depending on the philim 7 to 0 value. If the CRS counter value is below the philim limit, no trim actions are taken. If it is between three times philim and philim, the trim bit is incremented or decremented by one, depending on the counter direction. If the CRS counter is between 128 times philim and 3 times philim, the trim bit field is incremented or decremented by two trim steps. The following interrupts can be activated by the clock recovery system. The expected synchronization is set when the counter reaches zero and starts counting up. The synchronization OK is set when the sync event has been received within the expected time window. The synchronization warning is set when the sync event has been received within the margins of the OK window, but not yet in the error range. The synchronization or trimming error or trim OVF, sync miss or sync error is set when the sync event has been received too early, not received at all, or if the trim bit field overflows after an update. You can refer to peripheral training slides related to the USB and RCC modules for additional information.