Hey everyone, thanks for joining us at the open source Summit Latin America. I'm Karine Wallach. I am head of developer community at Star Tree and this is my colleague. Hi, my name is Mark. I'm a developer advocate at Star Tree. So I'm just going to share my screen. So hopefully that's working. Yeah, slideshow button. So, yes. So we're going to be talking about real time analytics going beyond stream processing with Apache peanut. So I'm going to let Karine get us underway. So just to kick it off, I'm going to start off with like some basic fundamentals about real time analytics and the trends and how where it's changing the dynamic of businesses. So to start off, there are three generally speaking three different types of analytics use cases. You can skip to the next slide. The first one is probably the most commonly known when people think of analytics as dashboards and bi tools. These are often used internally at organizations and they're often obsessed by bi analysts and other internal folks. You can go to the next slide. The next one is user facing analytics. So user facing analytics is essentially when organizations are providing their end users or their customers with their own real time analytics. And we'll dig into this a little bit further. This is probably going to be like a big chunk of the highlight of what we're going to be speaking about today. The third one is machine learning. These are often processed by some kind of system on the opposite end. So you know you're bringing the real time analytics and then there's some kind of system processing. So this could be like things like anomaly detection or like fraud detection that's automated by a machine any kind of like machine learning type of powered analytics. So going into further about user facing analytics and how it's kind of changed the dynamics of businesses over time. So back when I don't know what yours but back when it used to be that organizations were primarily using analytics for internal purposes like the bi analysts and they still are and it's still very very powerful. As time progressed, a lot of organizations started providing their end users with their own own real time analytics, and they're basically empowering their users to be able to make decisions and read into these analytics and so much so that this is actually transformed into services like they have actually product ties and created products providing their end users with real time analytics so there's like a lot of organizations that provide premium services where their end users have the ability to access their own real time analytics, which is really powerful. The other thing that the actionable insights. actionable insights I think if this is really important to kind of take away because this is kind of like where the power like how you can empower your end users or your customers. So actionable insights are essentially the real providing your end users with real time analytics, but then allowing them to take immediate action upon gaining those insights. So we could dig into this a little bit further with some examples, but essentially a user sees something this is action one site slide I wasn't sure we have this one. So essentially when a user gets some kind of information or insight, they have the ability to take action right away, which increases your user engagement, but also empowers your users and this is where some of the benefits of having these premium services that provide their end users with real time analytics and utilizing it can become essentially really powerful for the users. So we'll go into some a couple use cases. The first one is LinkedIn, they do a lot of end user real time analytics. The first one, the most popular one that LinkedIn has actually I think the total users now at LinkedIn is like over 800 million insane, but is who viewed my profile. So chances are, most of you guys have experienced the who viewed my profile experience. It does allow you to see in real time who viewed your profile. But what it also does is allows you the ability to slice and dice this data, based on like country or company and things like that. So the real time analytics experience for the end user is really dynamic, which increases the engagement people love the who viewed my profile feature. Another example of the real time analytics at LinkedIn, you can click. I mean, I guess the other thing to point out of this one is you notice it's got a little premium icon in the corner is that they've actually made a product that makes money out of the data that then adds on top of the free version that they everybody see. So that's pretty cool. Like it's data they had anyway, and they're able to package it back to you and say, Hey, here's what's happening right now and you're like, Oh, that's cool. It's a major moneymaker. I think I was reading somewhere. I don't know if this is accurate or not, but like I was reading that online. It said that like almost 40% or something have premium LinkedIn accounts, which is like crazy. Oh, okay. So another example of real time analytics for the end users, which is like a little bit of a twist of this, but it has like the same necessities to be able to build something like this is the newsfeed. So when a user goes to their homepage, they have to see relevant information, things that they haven't seen again. And ideally it's already been processed or some algorithm to make sure that it's actually relevant for them. And this system has to be built with the same kind of pipelines as, you know, providing your end users with real time analytics, you have to be able to take all this data in, which will explain how to systematize that. But that's another example of the real time analytics. You can click again. And then the big productized real time analytics that LinkedIn does is their talent insights, their recruiter facing products. This gives them like a big real time look on what's going on in the market, the trends, things like that, all these insights. Now, these insights are very powerful as you can imagine for their end users, they were able to see something and they could take action on it immediately, which is really, really powerful. So this is an example of a few different ways that LinkedIn has productized, as Mark was mentioning the real time analytics. Another example is Uber Eats. So Uber Eats has over 500,000 plus restaurant owners. And they provided these restaurant owners as part of like these premium features, their ability to access their own real time analytics. And on their analytics, they could see misorders and accurate orders, top selling items, things like that. And going back to speaking about things like the actionable insights where these end users have the ability to take action. If a restaurant owner sees that something is going wrong, there's a misorder or like maybe there's some item feedbacks that are like very thumbs down, they have to be able to act immediately, right? They can't wait until all the batch data comes in is queryable, like they need to know now. And that experience is much more positive when you can provide them with real time analytics, like things that are almost instantaneous. You can click to the next one. Another example is Stripe. I really like this use case because it's not necessarily user-facing analytics, but in terms of the components of what they need, it's very similar. So Stripe's use case is like this. They have dozens of different types of engineering teams that work on all different sides of the product. And a lot of this data is real time, constantly feeding in transactions and all this kind of stuff. Now, they have a lot of sensitivities with this data, right? They have to have like everything has to be like accurate and it has to be like legal and compliant and secure and all this other things. But essentially you can click on the next slide. Oh, no, actually go back one. Sorry. I think I thought we had another one for this slide, but I guess we don't have the other image for it. It doesn't matter. I can explain it. So essentially what they do is they have to take all this data that all these different engineering teams are working on. They have to be able to pull it in and then they have to provide it internally to a bunch of the different teams. So it's kind of combining this like user-facing analytics and the BI analytics kind of stuff together.  So like, so it's basically essentially the backend teams that are really working on the functionality of application and the people who need access to this real time data to be able to make decisions or even if it's automated, right?  They have to be able to do this in a streamlined way and it has to be all in real time. So I really like this use case. I think it's a good example. So let's talk about the properties of a real time analytics system. So there are three things that you substantially need to be able to build a solid real time analytics system. The first one is speed of ingestion. This means you have to pull all your data in very, very quickly. As soon as something happens, you have to be able to pull it inside and be able to record that data. The next one is speed of queries.  Right? So it's pulling it in and then also pulling it back out because you have to be able to show these analytics, right? And then the third part is you have to be able to do this at scale. So you have in some cases hundreds of thousands of users querying this real time data at any given second. And you need to be able to pull this data up. So pull it in, pull it out and show it at scale like in real time, which is kind of a complex problem. And we're going to talk about how to build those things. Yeah, so the heart of the system that we're going to use demonstrate all of this are Apache Pino and Apache Kafka. So those are sitting in the middle of the rectangle we've got there. And then you can kind of see around the outside of the rectangle the different properties that we've got. On the left hand side you can do real time ingestion of very high dimensionality like very wide column tables or lots of columns in the tables. We can get that data in very quickly. We can store it cost effectively and in a scalable way. We've got high availability on the actual data itself. And then once we've got it in, we need to be able to get it out again quickly, right? It's no good if we get it in really fast if you then have to wait 10 seconds for the query to come back. So we've got to do that quickly. And then we want to be able to have thousands of queries per second. And the reason for that is that we're going to have lots of either lots of users, we're going to be building some product for the users, or there's going to be some sort of maybe metrics dashboard but doing lots of queries from that dashboard like pulling in lots of different lots of different charts at the same time. So just to give a quick intro, we're not going to assume you roughly know what Kafka is. And it doesn't have to be Kafka in these systems. It pretty much can be any streaming engine. But they generally have a producer. So someone's generating some messages and I'm going to put them on to Kafka through this broker onto a topic. So a topic is like, but almost like a named name of some messages so it could have a topic for events and other topic for people and other something else. And then those can be split into partitions allowing you to scale the production and consumption of the messages and so consumption is again on the other side that's taking those messages off. And we'll be able to do those in parallel as well. Kafka also has the concept of consumer groups, but Pino doesn't actually use that concept. It has its own way of managing where exactly it's read up to in Kafka audition. And Kareen's going to do a quick explanation of Pino. Oh, you got muted. Yes, I did mute myself while you were talking about Kafka. Sorry. So I'm going to talk about what is Apache Pino like hopefully everyone can read because he was on the slide is what is Apache Pino. So Apache Pino is an OLAP distributed data store. Essentially what it does is it gives you the ability to pull in data from a variety of different sources. So like streaming and batch like S3 and then like as Mark was mentioning like you could do Kafka or Pub sub or kinesis things like that. So you can pull it into Pino and then Pino has a bunch of like different types of very powerful indexing capabilities and aggregation. It allows you to like merge all this data into one consolidated view, have a lot of these powerful indexes, and then make this data quickly accessible, like in real time to the end users at scale. It has a lot of dimensionality too. So as Mark was mentioning with like the very wide data is also like pretty important with these like complex analytical queries. Oh, yeah, okay. Cool. So let's just do a quick run through the architecture of how this works. So starting with the ingestion side. Let's go into what I've left on corners. It's coming in here's lots of data coming in. I've got a Pino controller sitting at the top of the sort of managing the whole cluster managing everything that's going on, handling any metadata working out where stuff's going to go. And then ingestion goes into Pino service a Pino service and serving, serving the data and hosting the data itself. So it will then be managing using zookeeper as the storage layer for the metadata. It'll be keeping on track like segments are Pino's way of Pino's partitions if you like. They have like a segment and it will be pointing to a server. So for example, in this diagram segment one is going on server one segment two on seven two and so on. And then if we have set a replication factor, each of those segments might then be on multiple multiple servers. So if one of them went down we'd still be able to serve it from the other one. So that's the sort of data ingestion side. And then on the query side, we have the concept of a broker so we send our query to the broker, and the broker then scatter gathers out that request to the servers that have the appropriate segments, and then it gets the results back aggregates together and then serves it back to the client. And so now we're going to have a look at a demo. So this is the architecture so we're going to build ourselves a real time analytics dashboard so I guess this in this would be kind of an example of a user facing tool so it could be a BI dashboard used internally but the way it's designed you could easily replace one of the components and make it a user facing one or maybe you could even use this as a user facing one. So the idea is we're going to have a streaming API. We're going to process that streaming API using Python will put the messages on to Kafka P no will then consume the messages from Kafka and finally will show how to visualize the data and query like query query that data via which is which we'll get we'll explain what it is and we got that. Okay, so let's, oh actually I forgot going on the slide just explaining what the data set is. So the data set that we're going to use is the wiki media recent changes feed. So this has a continuous stream of all the stuff that's being changed on wiki media so like all the pages that are being updated. So the changes being made in their metadata so everything is captured. And those messages are published. So they actually store internally in Kafka, and then they output it by over HTTP endpoint which uses the server site events protocol, which allows you to stream stuff. This is an example of what it looks like. So you get an event you get an ID and you get the data and the one we're particularly interested in is data and you can see in here we've got a schema it's got a metadata section. So we've got a length, we've got a length, and then what was the length before, what was the length after revision, and some other data as well. So let's now go over to the original studio code. Here we go. So what we're going to do is we're going to process this stream. So let's just first copy the URL in here. So we're going to have a look what does it actually look like. So there we go. So that's the same as what we saw before. It says loads and loads of messages coming in. All the messages. And you can see each one has an event, an ID, and data. And I'll just close that down so my Chrome doesn't hate me. And what we're doing in here is we're using the requests library. So we're saying hey, I want to create a request. It's a streaming request. And then once we've got the result from that request just here, we're going to wrap it inside this set aside event client. This is a Python library. And then it gives us an infinite stream of stuff. So we can, we can then just go and call our, call our, we call it here. We get up by. There we go. So streaming all the messages coming in. So you can see, I mean, it's the same as what we had before. So it's not any different than what you could see in the browser. There we go. We've got the messages. Next step is we want to get it into Kafka. So what we're going to do there, we're going to use this wiki to Kafka file to do that. So you can see most of this, a lot of this code is the same. But then we introducing and we're introducing a Kafka producer here, which indicates where Kafka is running since running locally on 1992. And then we're going to loop over all the events and publish them into Kafka and every, that will load them into one of the partitions, one of Kafka's partitions. And then every hundred, we're going to flush it. So if there's anything left in the queue, we're going to flush it and then it's going to make sure it makes its way to disk. Now, before we run this script, we're going to create a Kafka topic and it will actually create one for us. We don't do it, but it will create it with one partition whereas we want to see how we can, how we can do it if we have multiple partitions. So there we go. We've got our, we've got our topic set up. And now we can run this right here. So this is going to put data into Kafka from wikimedia into Kafka. So there we go. We've got 100 events.  You see it's going up. And we can also have a look at what the messages are if we want to. So we could copy this query here. So you can see here, there's the messages, they're coming in. They're really fast. So we've got lots of messages coming in. So the next, right, so we've got, we kind of got it. We've gotten, we've gone from the wikimedia into the Python script now into Kafka. So that's the whole middle of the application is working. So the demo is going well so far. So the next bit is, can we get that into Pino? Now, the first thing we need to do before we put it into Pino is we need to create a schema. So schema is just defining what does that table look like, that table that can take in high dimensionality data. We need to specify each column and then a data type to the column. So we've got an ID, we've got wiki, we've got user title. And then we've got datetime as well. That's a timestamp. You can also specify a type. So you can see we've got dimension fields and we've got datetime fields. And those are kind of just metadata to help the query optimizer when it's running. So we've got a schema and then the schema goes with a table. So you can see our tables here. It's called wiki events with stating that it's a real-time table and that indicates to Pino, hey, I'm going to be streaming data into this. So there's going to be, and it will then expect some sort of streaming config. If you don't provide it, you'll get an error. And if we scroll down here, you can see this is the Kafka config. So we're saying, hey, I want to connect Kafka. Kafka is running on Kafka-wiki. That's the Docker container name. 1993. So 1993 is where we've got it inside Docker and 1992 outside. And then it's the wiki underscore events topic. And then the only other interesting thing around this bit is down here, we're saying, I want you to flush to create a new segment every thousand rows. Now, obviously in a production app, you do it much higher. We're just doing this so that you can see what happens. And then the other thing is we specify the schema and then we need to specify a time column names. That's important for the naming of the segments and how Pino keeps track of what exactly is in each segment. So we need to specify a time column name as it's going to use that. And then the last thing is we've got some transformer transformation functions. So those are used. So if all the columns in your data source, i.e. on our Kafka topic matched up exactly with the schema names, we wouldn't have to do anything. But in this case, we've actually got some nested content. So we've got like some stuff under meta. So we've got meta ID, meta stream, meta domain. And so that's what we're doing here. So we're saying, you know, I want to go pull out meta domain and put that in under domain. And the same for the other things. And the last thing we're doing is the timestamp that we get from wiki media is in seconds, so epoch seconds, but Pino, all of its calculations assume that any timestamps are epoch milliseconds. So we do a multiple by 1000 on that field. So now we've gone through that, we can create the table. So we could put a slide, let's go over here. And then we'll paste that in down here and we'll run that. And so now we can go over to the browser. And if we click on the query console button here, you can see we've got a wiki events table. And at the moment we've got 45, so it's catching up, like loading in all the documents that were there while we were waiting. Oh, sorry, while we were running that command and you can see that there's more and more documents are loaded and we could even group that. So hey, you should find me the domain, find the number by domain, let's put in that. So hey, I want you to group by domain or by count of sending. And so we can see, most of the things that are being changed are on wiki data. And that's their metadata website. And then after that it's commons wiki media, which I think is images and then we get onto the English video. Okay, so that's cool. So we can use this UI to do exploratory queries and that works pretty well. But if we want to build an application, obviously we don't really want to be doing it through here. So what we want to do instead is we're going to look at a tool called streamlet. And streamlet is a Python web framework, I suppose is the best way of describing it. And it allows you to write everything in only Python. And then it generates effectively like an interactive web page for you. So we've got a few different versions of this dashboard. So we'll just start with the first one. So if we just run that streamlet run command, it'll open up streamlet dashboard. So there we go. So I'll just leave that on the screen at the moment. So you can kind of see at the top, we've got what they call metrics. So we've got a metric showing how many changes have been done in the last minute. And then the number below it is how many changes are done compared to the previous minutes are like two, like one to one last minute and then one to two minutes kind of before that. And then it shows the number of users who made changes in the number of domains as well. So this is all using, if I just minimize that window there, this is all using the Python clients. So you see we do an import from PinoDB, import connect, and then we create connection down here. And then we're running a query. So this is quite a cool thing that you can do. So you can actually do a count and a filter at the same time. So we've got our big filter down here on line 21. So that's like get all the records in the last two minutes. We've got like a, like another filter afterwards say, which says show me the stuff in the last minute and then show me the stuff in the minute before that we do the same total number of events that number of users who have made changes in the number of domains. And then it kind of just goes down the page and so we created metric using this code. And then the, the chart is kind of just doing a slightly different query. This one is grouping the data by minutes. It's funny, like the number of changes per minute. At the moment, if we want to see an update, we've got to manually go and refresh it. But if you make a code change streamlet will automatically pick it up. And obviously what we'd really like is that it should automatically refresh. And so that's the, that's what version two does.  And we laid version two instead. If I just come over here, change that to say version two. And that will open up in the browser as well. So I'll just come back here and show you what's the difference. So streamlet. Version two, there we go. So what all version two has is some, a couple of differences. So we've got this code here. So this is like checking whether or not we are sleepy or whether we've set like a sleep time, like how long should it wait before it refreshes. And then whether we are auto-refreshing or not. So we've kind of got the state that carries between page refreshes. And then down the bottom, all the way down the end, we check, are we already refreshing? If so, we're going to call this experimental rerun function. And then it will just run the whole page down from the top, down here, wait again, then do it again. Keeps on go. And so what that allows you to, or the feature that you get, you can see up here, last update. So 20, 29, 11, 20, 29, 14. And so it's changing. So like this is updating. And you'll notice that always the right hand side is always going to be slightly like the last minute is always going to be slightly lower. Because we're not all the way through that many, I would probably about 20 seconds through this minute. And so, yeah, so that's what we can do with streamlet. And we could choose, we could choose to substitute in a different dashboarding tool if you want it. But for now, we're going to go back to the slides and just sort of go through to the conclusion of our talk. So just quickly running you through. So how does this work? So how is this data being stored? So as we kind of looked at like a few slides before, this data is being stored in segments. And so we can have lots of different segments on a server. And we'll have copies of those across servers. And for us, we said that every thousand, it'll rotate to a new segment. So what does the segment look like? So Pino is a column store. So all the data is stored in columns. So all the data in one column is always next to each other. So all the data for countries next to each other, all the data for browser and then so on. And so that means that if we do like aggregation queries or like searching through there, it can very, very quickly do those types of things for a column. And so the idea is that we're not returning every column. That's the benefit of what happens when you're storing data by column is that you're only really loading the data for the columns that you're interested in. We can then apply indexes on top of it to make things even faster. So we can do a range index like a lot of these, I guess, are very common indexes on the database. So we can do full text search, we can do JSON index. And then there's kind of a quite a cool one called the starchy index, which is where the company where Karina and I work is named after. It's like a selective pre aggregation. We'll run through that in a second as well. So just some of the more basic ones first inverted indexes is the idea that we want to do a filter query to say find where the country is US. And so then inverted index means we'll have some sort of like a voice that you can think of as like a table. And it has each of the countries and then it has the document IDs or the row IDs where they are. And then it means if we look up the country, instead of having to scan the whole column, we just go, hey, country, oh, it's all these going to go and get the value. We've got the sorted index as well. So this one's actually automatically applied by Pino. So when you, when you, when the, when a segment gets flushed, so when it reaches its threshold gets flushed to this, it will, it will automatically look at which columns are sorted and then will create a sorted index on them. What it means is that it puts everything that is the same as each other next to each other. So in the country one, all the values of Canada will be next to each other, all the ones in Japan, all the ones in the US. And therefore if you have like quite a, it's like a low cardinality of values in a column, it means that you can save quite a bit of space, right? Because you can go country, Canada starts at zero, goes to 80. Japan starts at 81, goes to 100. So we're saving ourselves something to store Japan over and over again or US over and over again. So the start tree index, this is the, this is a quite, quite a neat one. So this is almost doing like partial pre aggregation. So you can choose how much pre aggregation do you want to specify which combination of fields you want to pre aggregate, and then you specify how many rows should there be. So you can kind of get almost like a kind of a consistency of query. So you say, I want the maximum number of rows scanned in a query to be 10,000. And so it will then build a tree of these, almost like a random forest type thing. So going down, splitting the data. And then when it does a query, you should get roughly the same performance regardless of what you're doing. And then sort of across the whole stack, Pino is doing optimizations to try and make things faster. So it's doing things, as we just talked about now. So at the data level, it's doing it when you're actually doing the filtering, it's also doing like clever optimizations at the storage level. And then of course, when the query planner is running as well, it's again trying to work out which one am I going to use, which index should I use, which storage facility should I use. And so yeah, so hopefully we've shown you so I hand over to Karim to conclude, this is a pretty good set of tools to build real time analytics applications. Yeah, that was awesome mark. Thank you so much. So yeah, I mean as the slide says it's like, you know, the combination of any kind of streaming system plus Apache Pino will help you build out pretty much any kind of user facing real time analytics use case. We can go to the next slide I think. So just a little bit of background about Apache Pino so Apache Pino was actually originally built at LinkedIn. And since has been widely adopted and it is a very mature product. It's used at over 100 companies, our community is constantly growing I think it's, I don't know probably 2800 or something like that now. And, you know, lots of GitHub stars which we love so if you are interested in giving us a GitHub star please go and do that. So in terms of performance, the largest Pino instances that's currently being run I think has as at LinkedIn, I believe and it has over a million events that are ingested per second with a query query performance of two actually this is incorrect it's 250,000 plus queries per second while maintaining millisecond latency. So it's highly performant when it comes to this ingestion and query with high throughput capability. And then we can go to the next slide. So that's the summary right so if you take away three things from the talk. I hope you've seen that real time analytics let's us build is kind of real fast applications where the user can quickly see like what's going on like the Wikipedia one if you were, imagine that was the Wikipedia editors and they were like why is there suddenly like a big increase in people doing stuff or why is there suddenly a decrease or why are people all focusing around this one topic this with this type of tool chain would allow us to be able to quickly identify that and go and do something about it now, rather than maybe finding out in like 10 minutes or 20 minutes time we're a bit too late to do something about it so that's the idea is it allows us to do some sort of action straight away. So that's Korean described on the slide before the whatever you use whatever tools change whatever set of tools you use you got you need three things right so data's got to be fresh like it comes into the streaming service we want to get it straight away be able to query it. Those queries then need to be fast they need to be like mobile app fast like web page refresh fast we don't be waiting for like 10, 10 15 seconds for the results come back, and then in order for lots people to use it obviously has to be able to scale. So Kafka and Pino is a great combination to achieve those, those things. So, thanks for coming to watch. I know Karina forgot to put you put you on this slide. That's okay. And you definitely green wallet because it's Karina's one we've got the Linux foundation there. If you're interested in, in learning more you can have a look at dev.starchy.ai we've got a bunch of recipes and guys there. Thanks to me and Karina come and come and join us on the selects. Yes, and if you have any additional questions about Apache Pino or want to talk to some of the PMC and committers directly. They're there too so yeah, that wraps it up. Yeah, so thanks for coming to our talk. And that's the end. Thanks. Bye.


I am aware that in the first episode, linked up there of the UTMB training series, I did tell you we were going to keep updating you on Victoria's training, because Victoria is also training for a run in Chamonix. She's training for the OCC. We're going to do that today, as well as looking back on week four of UTMB training. Now, this week is the final week of training at around 100K a week. This week, we did around 103, 104 kilometers split between Zwift on the treadmill and outdoors. We did about 54, 53, 54 kilometers on the treadmill and 50K outside. We did one interval session, and I'd raced the park run, but unfortunately, I didn't hit my elevation goal for the week, because I didn't manage to get a hill session in. In fact, the week started with two pretty lazy days, if I'm honest. Monday was just 10K on Zwift, in the evening, low heart rate, steady running. Then Tuesday, all I did was the film I run 500 on Zwift. That's only 4.2 kilometers. It is 500 meters of climbing all the way up, not an interval session, just climbing all the way. Fairly lazy start to the week on Monday and Tuesday. I'd finally sorted myself out by Wednesday and we got working. Wednesday morning, I did 21 kilometers with some elevation on the hill. Some good climbing, some good running, and good distance on Wednesday morning. Wednesday evening, I did my usual interval session on Zwift. It was 600 meters over threshold, 400 meters under threshold. You can see by my heart rate here how hard I worked during that session. So, a paltry 15 kilometers on Monday and Tuesday was made up for with 20 miles in total on Wednesday. Thursday morning, I was back on the hills of Zwift with a 500 meter climb. For the film, I run 500 with the Noble Pro treadmill. If you want a Noble Pro treadmill, the link is right down there. Get your discount code in the description below. Thursday evening, rather than 10, I did 13 kilometers on the treadmill with a little bit of progression at the end just to make it a bit more interesting. So, only two double days this week. Wednesday and Thursday were my double days. Friday, well, that was a very easy standard low heart rate 10K run on Zwift. By the way, if you're looking at the background thinking, he's going up the same bit all the time. I am doing hill reps. It's Monday morning, start of week five for me whilst filming this. And yeah, I'm just doing a little hill session. So, up and down the same stretch of concrete. And if you're saying, well, why is he walking up the hills? Well, I'm doing a bit of walk run. I'm going to be doing a lot more walking up the hills in Chamonix than I am running. So, it makes sense to practice what you're actually going to be doing. So, I am walking up some of these hills. Saturday, of course, is park run day. I've been a bit lax in this training block about getting regularly to park run, but I managed this Saturday. I went to Ifield Millpond in Crawley. It's kind of halfway between where I live in Worthing and London. And I raced, even though I'm not fast at the moment, I went as hard as I could on quite a twisty course. So, my time is nowhere near my PB. 2050, something like that, 2048, I think, for Ifield Millpond park run on Saturday. And that left the long run on Sunday. We did 21km, 13 miles on the hills with some of the guys from my running club. And got some good elevation in as well, about 450m of elevation gain. So, that made it up to 104km, 103km, 104km for the week, just missing my elevation goal of 2,000m. I came in at 1,900 or so meters. So, I did two hard efforts in the week, the park run and the Wednesday interval session. But no actual hill interval sessions this week. All my elevation was made up of running on the downs or running straight up on the treadmill. Stick around to the end because I'm going to tell you what I'm going to be doing for week five of the UTMB training block. But right now, we're going to have a little look at what Victoria has been doing over the past four weeks for her training. I do always need to remember how lucky I am, because I get to train pretty much whenever I want to. I can go out in the mornings, in the afternoons or the evenings, because my work schedule allows for that. But Victoria's work schedule is a lot more defined. At the beginning of the training block, Victoria and I agreed she should be looking at around about 60km for the week. And around about 1,000 to 1,500m of elevation during the week as well. So, that was loosely Victoria's training regime for the next 8 to 10 weeks. And Victoria's first week got off to a really good start. She did 65km and a good 1,500m or so, I think, of elevation gain with some hill reps thrown in. So, that was a great start for Victoria in week one. By the way, if you are enjoying the video, if you're finding it useful or interesting, as always, please do hit the like button. If you're not subscribed, like I said last week, we are a small channel and we are trying to grow. So, please do hit that subscribe button and the notifications bell. We'd really appreciate it. Thanks so much. Now, of course, Victoria works during the day. She finishes at 2.30 in the afternoon, and that often means that she can get out for a run in the late afternoon or early evening sometime. However, second week, Victoria wasn't feeling well. She did work, but once she'd finished work, she was absolutely shattered and just couldn't make it out. So, she didn't manage to get what she wanted for her second week of training. She only hit around 30km of running and all less than 500m of elevation gain for week two. So, a little bit of a low period for Victoria. But Victoria was back on it again for week number three. She did 55km or so, I think, and then around 1500m, a good 1500m of elevation gain with a hill rep session thrown in. So, really good to get those hill rep sessions in, climbing up the hill, working the legs, and then working the quads on the way down the hill as well. Week four was pretty decent for Victoria as well. She did around 50km, 52km or so last week, and a good 1500m once again, another 1500m of elevation gain. She did a really good hill session as well last week, seven times up this hill that I'm doing today, seven times up and down. It's around about an 800m distance climb and 80m elevation for each rep. So, she got in around 600m of elevation gain on one hill rep session yesterday. So, that's really good. You know, any training block is going to have its ups and downs. So, Victoria's training block hasn't gone perfectly, but mine hasn't gone perfectly either. I could have definitely done with a little bit more elevation gains so far. So, we're four weeks in. Victoria, as you know, is training for a 56km run called the OCC in Chamonix, and I'm training for the full 100m, it's over 100m UTMB race, but we are also training for the North Downsway 100m, which is part of our Centurion Grand Slam attempt. So, it's number three of the four, and that's in three weeks time. So, we need to make sure that we're on it for that as well. Whilst the North Downsway isn't our main A race, we do want to finish it. So, next week, Victoria and I will both be upping the mileage. So, I'm going to go up to 130 or so km for week five, and I think we'll probably try and get Victoria up to around 70km if we can. I'll definitely be continuing with the interval sessions on a Wednesday. I'll try and race a few more park runs. I think we even have some more West Sussex Fun Run League races to go to as well. I'm going to try and up the elevation gain a little bit, but definitely increasing the mileage this week. If you'd like to watch the video of me coming second in park run this week, then just click that link right there. In the comments below, do tell me, how do you manage your running life balance? You know, it's really difficult to fit work with running. How do you do it? Let me know in the comments, and if you're not subscribed to the channel, I'd really appreciate it if you could do that now. Thank you very much for watching, and we'll see you on the start line next time. Bye, guys.


With this engine Hello everybody and welcome back here to the channel. I'm your rabbit. We're here on Hoppegera on beat man G drive. We do have a gorilla here We're gonna go full send and see what we can do make sure you guys follow me and also show me all the Trifon's curse box below.  We're gonna go full send And drift we do have a silver top motor revving it 1200 or 12,000 rpms in this car. We're just gonna drift the mountains here.  Oh clutch damage Definitely loves how this corolla feels here on beam and G. It's a proper drift car And a track that I kind of am familiar with from a set of course in life. Oh, man, we're killing it now As I said, we almost oscillated into the wall No, we loosened up our rear bumper there Getting into the tight section Perfect to gay car though Not a hatch.  Definitely having loads of fun with this really tight section Might be might be a first gear ripper with this car this engine oh Hmm. Oh, yeah, we got some. Oh, we got some suspension damage and everything going on right now They get caught in the guardrail. I Think I did just keep caught in the guardrail. What? All right, we're gonna Get a fresh corolla there Transmission lock or how McGarrett's really tight Hang in the front bumper We got it now. Oh, don't lose it. Nice. Sure. We are now I'm gonna break drag. Oh, that's the end of the map. Oh, that was the end of the map So we're gonna turn ourselves around here Get it over. So we go Man, no worries about the look. I really got to get used to stay in the clutch a lot longer Game is not like when you get off clutch super early. We're in the wrong gear We may have to throw a turbo on This engine we do have the ability to Or just a gear ratio, maybe overall we are Doflin if I do say no, I do say so myself or not doing too bad Not the smoothest, but you know what? regression now one thing this game I really wish had was in car like drift cam Is unfortunately it only Does not rotate which is unfortunate So if it had like a real head motion or something there is let me know down in the comments It'd be sweet to use first-person stuff here. I figured we're gonna roll out to it. So get run Oh We don't need that bumper or that bumper He mentioned so much fun. It's the damage in everything. Oh Back to the part I know. Oh String lock was not there You do have the drift suspension or drift steering on this Corolla To go through in finding out how to modify cars. Oh hmm Kind of a drift missile now. We're gonna finish up the top of your run. We didn't need that bumper or a trunk That aspect a little wall scrape spreads. It's still an exhaust overall. We haven't totally killed the car yet Except that one time we got stuck Oh bmg All the fun with this he's used clutch. I can't got it not sure where we're going up this way It's this way. I never drove this way in a set. Oh We just trying to I knew this road. This would be so much better and a little more power than this Corolla a little wall tap very narrow section Of course, we haven't popped a rear tire yet Done it before We meant he definitely not an easy game to drift in But found this cars. Oh a lot nicer than Some of the other drift cars Guess remember that turbo drift girl out We tried in the other episode the same whoo cartola just not as Aggressive so knit we're dead hours ping pong and now Okay Okay, I think we're gonna spawn this car back at home and then we're going to try one of their factory drift cars grand This car has actually been one of my favorite cars That is currently in the game And I wish I could load my previously saved vehicle, but for some reason it doesn't want to load my previously saved vehicle, but Well v8 to end this episode off. This is much bigger. It's gonna be a lot harder Think the steering lock is this much all the left foot brakes though. Oh, oh, I think we bent our wheel I think we definitely bent that rear wheel on that curb did this much nicer with the Corolla Definitely more comfortable in that coral if you guys want to keep seeing beam and G here on the channel Let me know down in the comment section down below One of the hardest games I feel to drift in but also one of the most fun and most satisfying games With the damage and everything like that We're gonna have to try and get online and do some online drifting So if you guys follow me and also should be all that she's found in the squishbox below final down run down on half gara Popping the tail light no big deal We got this now. We're flowing Oh as I say that We go hard into the guardrail and uh Cripple in the back a little bit Nothing major. All right, we got this drift missile going. Oh, we're dead So I think that'll do it here for this episode here on beam and G. We did just lose our rear wheel Um, it went off the mountain So that being said like thank you guys for coming back and watching. I'm people rabbit. I'll see you guys on the track


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All right guys, welcome back to the channel if you're new man with Bobby guys since and rotate reverted You've been asking me to do a reaction video to him I didn't do it right away because guess what he just reverted to Islam and I said congratulations Now he has to start his life as a Muslim since then a lot of things happened Of course, he went to jail and there he was able to read the Quran every day and like that I'm certain he was able to strengthen his faith his iman in Islam even more so Therefore now it is interesting to hear what he has to say about Islam and why he became a Muslim on the absolutely viral Exclusive interview by Patrick bet David from value tamant if this is the first time for you seeing my face I myself come from an orthodox Christian background and I reverted to Islam justice and rotate here Therefore it is of course very interesting for me to hear why he left Christianity why he became a Muslim with no further do guys Let's have a look. What's been your evolution of your fate and where are you at today? Yeah? I think Newton's I think it's third law equal and opposite force what you just said there is absolutely true there has been equal and opposite force to all the evil in the world and That the equal and opposite force to all that evil has to be God and I've talked about this at length even with atheists And I try and say that Regardless of whether you're an atheist or not You're thinking of God as a man in the sky, but you need to think of God as a concept as a whole and once you do that It's impossible to accept that he doesn't exist right couple of things don't pack here He speaks of God as the opposite force to the devil. I would say that good is the opposite force to evil He's absolutely accurate here. However, if we're thinking about God We're thinking about it being that is absolutely transcendent of all He is the creator of all things and therefore he is the creator of the devil as well Therefore if we see evil in the world and we see goodness in the world, of course, those are manifestations Those are contrasts. This is the battle between good and evil However, we have to realize as believers that nobody is as powerful as God is because God is the creator So there for the devil is not in direct competition to God. He's not a direct adversary to God He's an adversary to us in comparison to God He is absolutely nobody and then he mentioned God as a concept and not a man in the sky I believe that Christianity did a lot of damage here with the trinity and with the understanding of God the father God the son, etc. etc. because like that you anthropomorphize God and yes in the end the atheist says I don't believe in a sky daddy, but thank God in Islam. We do not have that concept in Islam God is all transcendent. He is the creator of all things. He is indeed the necessary Being he is the necessary being for there to be the first course. I said to the atheists I said, let's say there was two islands your shipwrecked, right?    So again, I absolutely understand what he means here And I see the value in speaking about concepts Especially in this day and age where you have many atheists and he being an ex atheist himself He understands how to talk to the people However, what he is describing here is utilitarianism. He's speaking about what would benefit a society and therefore No matter if God is real or not as long as we believe in a God We all benefit through that and this is yet again something that Jordan Peterson has discussed in the past If you ask a Jordan Peterson, he cannot tell you if he really believes in God Yes, or no, he can only tell you about the benefits of believing in God. What do you mean do? What do you mean you what do you mean believe and what do you mean God? But if we look at John Peterson, and I do not mean to mock the man here at all He is severely depressed. So something is not in alignment with that worldview And I would say that no matter how utilitarian your worldview is if you do not really believe that there is a God You will suffer you will be depressed in fact We find this promise in the Quran and whoever turns away from my remembrance indeed He will have a depressed life and that's basically what it boils down to because yes We do have a better outcome if people believe in God however for this to be sustainable We need to cultivate a true belief in God We cannot just pretend to believe in God and then say are because of our value system Society is progressing this wouldn't be much different than communism if you look at China for example Their society is progressing and yes, they're fighting degeneracy actively as well However, they are a godless society. They are communists and they obey the rules They're playing by the rules and therefore they have a progressive society. It works on the surface level But within a religious mind we of course thinking about the afterlife as well We're not thinking only about this creation only about this dunya Of course, we're trying our very best to make this existence as productive as fertile as we can But the real focus has to be the afterlife and we are missing that point if we see God only as a concept The idea of opposing evil as a whole is a belief in God So you're either an evil person or you believe in God in some regard and then you believe in good I became once I understood that there has been equal and opposite force. I was raised Christian I live in a Christian nation. The Romania is actually I think the second or third most Christian country on earth It's very Christian country here. It's churches everywhere. They strongly believe and that's where I began my journey But I always had a very healthy respect for Islam because I understand that to a degree to have a religion at all You have to have an intolerance to a degree 100% Because without an intolerance you don't have rules. You don't have laws You don't have any like you said you have to earn God's respect Coming from an Orthodox Christian background and then reverting to Islam myself I have to absolutely agree here. We have to be intolerant but guess what not only as religious people but as people in general and Everybody is intolerant even the oh so tolerant leftist is Absolutely intolerant if you do not agree with their agenda you become a bigot So therefore they are discriminating against you think about it An outrageous example here if you talk to a leftist and you ask them is everybody included in your ideology They will tell you yes, of course come on over but if you ask them how about Nazis how about right-wing extremists Quote-unquote are Nazis welcome in this safe place as well Of course, they will get outraged and tell you no because yet again you are a bigot So therefore there is no real inclusion in any type of ideology even in the perceived Oh, so nice social justice movements are of course extremely intolerant and if we bring it back to religion Of course, we have to be intolerant as well because what is a religion a religion is a belief system a belief system of Values that you hold dearly So if your religion tells you that homosexuality is not permitted then you cannot be tolerant of Homosexuality you have to hold on to the rope of your religion so to speak and defend that value Just as the LGBT people defend their ideology is exactly the same and they're both intolerant They're competing ideas ultimately and you can only test a competing idea by defending it And if you can defend it properly then you win and guess what you win you win the future of your children Do you understand if leftist ideologies win? Then your children might believe that they are girls or boys or something in between if Islam wins your children will believe that there is one God worthy of worship This is the value behind it if you're a religion which is tolerant of everything then you don't have to earn God's respect You can be a bad person and do bad things God loves me. So it's fine. Well, no, it's not That's not the point of the religion So then you extrapolate that out and I'll sit in and thinking what's the primary function of a religion and the primary function I have to cut it off one more time because he said that's not the primary focus of religion However, I have to disagree here because within Christianity Unfortunately, it is the primary focus of the religion It is about forgiveness that God forgives you your sins through his son Sacrificing himself for you. This is the doctrine of Christianity The doctrine of Christianity tells you that your sins are forgiven because Jesus died on the cross for you for me This was extremely Irresponsible and I have to say that I have nothing but love for my Christian brothers and sisters But nevertheless, I understood as a man that I have to take full Responsibility I look back on my life and I realize that all the sins that I have committed I have committed and nobody can change it. Nobody can take that away from me But I myself have to stand up for my dignity. I have to repent in front of God I have to understand where it went wrong. It was my fault. I submit to God I repent to God. Please God forgive me only this mindset can purify your heart if I believe that Jesus died for my sins What is stopping me? I can go out. I can drink alcohol. I can commit Zinnah fornication Who cares?  My sins are washed away through the blood of Jesus Christ That is the doctrine of Christianity and this is why it is so problematic And I believe that this is why we see the West falling Religion I don't believe is the religion exists so I can live forever. I mean, that's it That's a nice thought But I think on a macro level the primary objective of a religion we do store and contain some degree of traditional Value within the site. How do you judge? We change the utilitarianism again? How do you judge the success of a religion? I don't think you can judge it by the number of people who join it because there's lots of people who are Catholic and don't act Catholic They don't act in any way particularly Christian anyone can say there's something you can walk into a strip club And everyone says Christian Christian doesn't mean anything, right? Thank you So how do you judge the successful religion? Well, I like to think the best way to judge the successful religion is how successful is it? Fighting evil. How successful is it at preserving the morality of a population in X parameter in X? Geographical it's a good productive pragmatic way of looking at it I do understand why he would take this approach because he simply looks at the evidence He looks at what works and what doesn't I get it That makes sense when you look at it from that way you can't say that Islam isn't the most successful religion on earth It's the most successful in regards to opposing evil It's the most successful in regards to opposing its differing viewpoints It's the most successful in regards to having people act within the limits and the confines of what it finds to be moral and good to God It's the most adhered to it's the most feared. It's the most respected in most forms And then also I'd like to think I would say it's the most fear. Yeah, it's the most feared, but what is respect without fear? Dude, do we respect men? We don't fear There's a man who could do absolutely nothing to you on any level at all You might be nice to him sure because we're not bad people But would you really respect it if you could take his chick in front of his face? Save car on fire and he wouldn't do a thing you wouldn't respect it You might be nice to him because we're not bullies, but you wouldn't respect it respect and fear are linked But not always the same thing and you can you can maybe have fear without respect But it's very hard to have respect about fear. He's pointing out something extremely important here The fear aspect and the fear aspect is of course frowned upon nowadays society has become very weak and very detached from nature as well You see for example anti bullying laws everywhere guys don't get me wrong Of course nowadays everything goes to extremes even the bullying however bullying in its natural state is a normal mechanism of Selection this how boys understand who is on top of the hierarchy who is the alpha male even from childhood out on It is completely natural, but society nowadays tries to destroy everything of course when you see sports nowadays Hey children, there is no winner anymore. Everybody wins no second places, but that is not real That is not true Just as it's not real that a man is a woman just because he puts on a woman's dress No, he's not a woman, but society goes along with it. Yes Yes, you go Queen and because society goes along with all of this stuff and becomes weaker and weaker and says yes And amen to everything. This is why they have a negative connotation when it comes to fear as well But he is absolutely accurate here fear and respect go hand-in-hand and Ultimately every society is built like that if you look at the United States for example You have a legal and justice system So you have to have some sort of fear of that legal and justice system to not commit any type of atrocities Of course, it is not bulletproof and therefore we see that people do not fear it enough However, try the same thing in Saudi Arabia. It's not gonna work because people fear the system So yet again, I understand people nowadays don't want to accept that I don't want to live in fear But the reality is you have to have a natural fear of something even a wife That is loving her husband has a natural fear of her husband because usually generally speaking naturally speaking The husband is stronger than the woman and he is her protector. So on some deep level She's actually afraid of him. She has a natural healthy fear for her husband that in turn is her protector That is good a normal human being that is not fallen has the fear of God instilled in his heart as well That is natural state and Islam is the only religion that has this fear of God still intact within it And therefore is able to defend it and by that standard It leads to fear in the hearts of the disbelievers and that is a good thing I would say because it leads to confrontation and it leads to respect because people try to attack the ideology But they see they cannot like this Islam is establishing a firm ground that cannot be shaken and like this Islam Establishers that it's here to stay but also I heard someone say once that we see the world as we see ourselves If you're a thief you think everyone's thief if you're a liar you think it was a liar and I kind of agree with that I understood that I Also like to think we see religion as we see ourselves. I like to see myself as a person who's respect you have to earn I like to see myself as a person who has strong rigid boundaries I like to see myself as a person who will stand up and say no that's wrong I like to see myself as a person who's not afraid of being shamed by whatever community for saying I don't agree with that particular ideal. I like seeing myself that way So if I'm gonna see myself that way then I'm gonna naturally align with a religion that operates within that form So when I say Islam is the last religion on earth, I say I have to add here and disclaimer I'm not an Islamic scholar that Islamically speaking. We talk about the fitra the fitra is the natural predisposition of men and therefore when we return to that when we return to God when we Return to our natural state as human beings. We Automatically line up with Islam and therefore the attributes that and rotate listed here as his own He's attributes that align with Islam I will go one step further and say that by him returning to his natural state He is in alignment with those God-given natural attributes. There is a right and a wrong remember So that means there is a right way of being a man as well And I believe of course as a Muslim speaking that the closer we get to God the closer we get to Islam The more we become the man that we should become under God because it seems to be the only one who will stand up and say no We don't care. No That's not what the book says. No, I Was raised Christian and and Muslims as a whole we have no problems with Christians none at all I don't want anyone to think I'm anti-Christian like I dislike Christians. We don't have problems Christians We believe in a lot of the same things. We believe in Jesus We put more respect on the name of Jesus than most Christians do I just don't like the idea of people saying they're a Christian and saying but because I'm a Christian I can do whatever I want and throw all the rules away None of it matters because once you have that level of tolerance you no longer have a religion again I don't want to go into too long tangent here But as I said already many Christians take the position that Jesus died for their sins and therefore the old law has been Overturned we don't have to go back into the Old Testament. We don't have to listen to those old laws We can do what we want. Essentially. We are saved by grace Islam seems to be left in life with parameters. We don't parameters You don't religion at all So the closer I found myself to God the closer I saw I found myself to Islam. That's just how it ended All right, this is it for this video in the end. Of course, I have to say belated welcome to Islam brother Andrew I believe that he's doing a fantastic job coming from the background that he comes from he of course has a huge audience That resonates with his lifestyle resonates with his speech resonates with his perspective and resonates with his path as a man Being born as a Christian becoming an atheist Ultimately losing faith then trying to get back to God through Christianity and ultimately finding God within Islam and I would say that he is of course not the first one to discover exactly that many people Myself included wanted to return to Christianity because they wanted to return to God I was never an atheist myself, but I dabbled in the new age drug culture and what not I want to get back to God and the only way for me was orthodox Christianity because I was born a Christian after all So there's the only option I guess but ultimately my yearning for God was not satisfied in Christianity Because in Christianity as I said throughout the video my sins were just forgiven and I do understand that some people Resonate with that. They simply want to be forgiven. They simply want to move on But move on to what you're gonna move on and do the exact same mistakes over and over again Because there is no real consequence, but in life. There's always a consequence Everybody knows that anybody that left their house once in their life will understand that there is a consequence To anything you do in life you have to look to the left look to the right so you don't get run over by a truck Everything you do has a consequence the education that you get in school or aside from that The interactions with people that you have the way that you think of yourself if you work out or not all of those parameters all of those actions have Consequences if you cheat on your wife that has a consequence as well You have to calculate everything and you have to be held accountable And this is what it boils down to if you want to be a man in this world You have to be held accountable if your religion doesn't hold you accountable Nothing will all right guys and this is it for today's video If you liked it leave the thumbs up if you haven't subscribed already guys Please do so and if you want to support this channel by patreon for example all the links are in the description box below Thank you so much for your support guys and as always make God bless you all much love and peace you


Good afternoon, welcome back to Chicago. Lisa Martin here with John Furrier. Day one of our coverage of Ansible Fest 2022. John, it's great to be back in person. People are excited to be here. We've had some great conversations with folks from Ansible and the community and the partner side. One of the things I always love talking about, John, is talking with organizations that have been around for a long time that maybe history, maybe around nearly 100 years. How are they embracing technology to modernize? Yeah, we've got a great segment here of the financial services leader, end user of Ansible, so it's a great segment. Absolutely. Please welcome Noor Shadeed to the program. The senior, SVP, excuse me, senior technology manager at Wells Fargo. Noor, it's great to have you on the Cube. Thank you for joining us. Of course, happy to be here. Talk a little bit about technology at Wells Fargo. I was mentioning to you, I've been a long time customer and I've seen the bank evolve incredibly so in the years I've been with it. But talk about technology, Wells Fargo was a technology driven company. Yeah, so I like to consider Wells, being a financial institution company. So I consider us a technology company that does banking. As a customer, like we were talking about, there's so much that we've been able to release over the couple of years. I mean, decades worth of automation and technology has been coming out. But lately, the way we provide for our customers, how fast at scale what we're doing for our customers, it's been significant. And I think our goal is always how can we enhance the process for our customers and how can we provide them the next best thing? And I think technology has really allowed us to evolve with our customers. The customers we're so demanding these days, right? I think one of the things in short supply in the last two years was patience and tolerance. Yes, yes. And I don't think that's coming gonna rubber band back. Yeah, no, I don't think so. Talk to us about how Wells is using automation to really drive innovation and surprising delight those customers on a minute by minute basis. Yeah, and so, you think about banking, we've been able, with automation, we've been able to bring banking into the 21st century. You do not have to go to a branch to manage your money anymore. You do not have to go into pause at your check inside of a branch. You can do it through your mobile app, right? That's driven by automation and innovation, right? And we've all of these back ends tools working for us to help get us to this next generation of banking. You, we can instantly send money to each other. We don't have to worry about, I need to go and figure out how I'm gonna get money to this person and I need to wait X amount of days. You have the ability and you feel safe being able to manage your money at the organization. And so, automation has really allowed us to get to this place where we can constantly enhance and provide features and reliability to our customers. It's interesting you mentioned that you guys are a technology, you can have it do banking. It reminds me of the old iPhone analogy. It's a computer that happens to make phone calls. So like, this is the similar mindset. How do you guys keep up with the technology? So we, it's tough, right? Because there's so much that comes out and I think the only thing that's constant in technology is change, right? Because it's constantly evolving. But what we do is we integrate very well with these new tools. We do proof of concepts where we try to, what's on the market? What's hot? How can we involve these new tools in our processes? How can we provide a better end result for our customers by bringing in these new tools? So we have a lot of different teams that bring their jobs are to do these proof of concepts and help us build and evolve our own strategies, right? So it keeps us on our toes and I think it keeps all these new things that are coming out in the market. We're a part of it. We want to evolve with the latest and greatest is and it's been working, right? As customers of financial services and us managing our money through banks. It's been great. The business is the application. Yes. And how do you guys make that happen when it comes down to getting the teams aligned? What's the culture like? Yeah, so at Welles, we have evolved so much over the last few years. The culture right now is we want to make changes. We are making changes. We want to drive through innovation. We want to be able to provide, it's a developer centric approach right now, right? We want to push to the next and the greatest. And so everybody is excited and everybody's adapting to all of what's happening in the environment right now. So it's been great because we're able to use all of these new features and tools and things that we were just talking about by allowing our developers to do that work and allowing people to learn these new skills and be able to apply them in their jobs, which is now creating this better result for our customers because we're releasing at such a faster pace and at scale. Talk about how you talked about multiple groups in the organization really investing in innovative technology. How do you get buy-in? What's that sort of pyramid like up to the top level because to your point, you're making changes very quickly and consumers demand it. You can do everything from home these days. You don't have to go into a branch. We just changed dramatically in the last few years. What's that buy-in conversation like in the organization? If you don't have leadership buy-in, it's very difficult to make those changes happen, but we at Wells have such a strong support from our leadership to be a part of the change and constantly evolve and get better. So the way we work is we're such a large organization. We bring in our business teams and we talk to them about what is it that's best going to better our customers? How do we also not just support external but internal? How do we provide these automated tools or processes for people to want to do this next work and do these new releases for our customers? And so we bring in our business partners and we bring in our leadership and our stakeholders and we kind of present to them, this is what we're trying to do. This is the return that you'll get. This is what our customers will also receive and this is how we keep evolving with that. How has the automation culture changed? Because big discussion here is reuse, team work, I call it multiplayer kind of organizations where people are working together because that's a big theme of automation. Reuse, leverage, can you explain how you guys look at that? Yeah, it's changed the way that we do banking because we now have, we're eliminating a lot of the repetitive tasks in the toil because we have partners that are developing these services so specifically with Ansible. We have these playbooks. Rather than having every customer write the same playbook but with their own little flavor to it, we're able to create these generic patterns that customers can just consume simply by just going into a tool, filling out that playbook template credentials or whatever it is that they need and executing it. They don't have to worry about developing something from scratch and it also allows our customers to feel safe because they don't have to have those skills out the box to be able to use these automation tools. They can use what's already been written and execute it for their jobs. So that makes things go faster? The benefits are what? Speed? Faster stability, right? We're now at speed, stability, scalability because we're now able to use this at scale or it's not just individual teams trying to do this within small spaces. We're able to, we're reliable, right? Automation allows us to be reliable internally and for our customers because you're not asking, there's no human intervention when you're automating, right? You have these opportunities now for people to just, it's one click solution or your end-to-end, you got self-healing involved. It's really driving the way that we do our work today. So automation sounds like it's really fueling the internal employee experience at Wells as well as the customer experience and those two things are like this to me, they're inextricably linked. 100% because if you need it, they need to be together, right? You want your internal to also be happy because they want to be able to develop these solutions and provide these automation opportunities for our teams, right? And so with the customers, they're constantly seeing these great features come out, right? We can, you know, with AIML today, we can, we're now able to detect fraud significantly quicker than what we would have, what we could have done a couple of years ago and developers are excited to be able to do that, right? To be able to learn all these new tools and new technologies. What's interesting, Wells is you guys are like an edge application, everyone's got the banking in their hand, fintech, obviously money's involved so there's people interested in getting that money, security hackers or whatnot. So when you got speed and you got the consistency, I get that as you look at securing the app, that becomes a big part of what's the conversations like there, because that's the number one concern. And it's an edge out, I got my mobile, I got my desktop, everything's in the cloud, I'm on premise. Yeah, and I think for us, security is number one. We want to make sure that we are providing the best for our customers and that they feel safe banking, whatever financial service you're working with, you want to feel like you can trust that your money with those services, right? So what we do is we make sure that our security partners are with us from day one. They're a part of the process, they're automating their pieces as well. We don't want to rely on humans to do a lot of the manual work and do the checking and the logging. You want it to be through automation and new tools, right? You want it to be done through trusted services. You don't, you know, security is right there with us. They're a part of our technology organization. They are in the technology org. So they're the ones that are helping us get to that next generation to provide more secure processes and services for customers. And that's key for trust. Yes. And trust is critical to reduce churn and to increase the customer lifetime value. But people, and especially with the amount of generations that are alive today in banking, you need to be able to deliver that trust intrinsically to any customer. Yes, 100%.  And so being able to create that environment and provide those services is everything, right? For our customers. What are some of the state of the art kind of techniques or tradecraft around building apps? Because, I mean, basically, you're digitally transformed.  Yeah. The app is the company. Yeah. That's the bank. How do you stay current? What's some of the state of the art things that you guys do that wasn't around just a few years ago? Yeah, I mean, right now, just using, we're using tools like Terraform and Ansible. We're making sure that those two are hand-in-hand working well together. So when we work on provisioning, when we're doing provisioning where it's all, you know, it's automated fully end-to-end, you know, AI ops, right? Being able to detect reoccurring issues that are happening. So if you have an incident, we want to make, we want to learn from that incident and we want to be able to create, you know, incident tickets without having to rely on a human to find that problem that was occurring and self-healing, right? All of this is starting to evolve and bringing in the proper alerting tools, bringing in the right automation tools to allow that self-healing to work. That's, you know, these are things that we didn't have, you know, a decade ago. This is all coming out now as we're starting to progress and really take innovation and, you know, automation in itself at scale. What's the North Star internally?  And what's the internal conversation like when you talk about a future state? Yeah, I think right now we're on our cloud transformation journey, right? We're moving, right now we have workloads into our two CSPs, our public cloud. Also providing a better service for infrastructure and being able to provide services internally at a faster space, right? So moving into the public cloud, making sure everything's virtualized, moving away from hard, you know, physical hardware or physical servers. That's kind of the journey that we're on right now, right? Also machine learning, we want to be able to rely on these, you know, bots. We want to be able to rely on things learning from what we're doing so that we don't make the same mistakes again. Where would you say the most value or the highest ROR that you've gotten from automation today? Where is that in the organization? There's so much, but what I mean, we're doing it because of all of the work that we're doing, there's a lot that I could list, but what I will say is that the ability to allow self-healing in our environments without causing issues is a very big return. Automating, failovers, right? I think a lot of our financial institutions have made that a priority where they want to make sure that their applications are active active and also that when things do go wrong, there is something in place to make sure that that incident actually doesn't take down any problems. I think it's just also investing in people. Right now, the market is hot and we want to make sure that people feel like they're being able to contribute, they're using the latest and greatest tools, they're able to upskill within our own environments at the firm. And I think our organization does an amazing job of prioritizing people. And so we see the return because we're prioritizing people. And I think a lot of institutions are trying people first, people first, but I can say that at Wells because we are actually driving this. We're enforcing that and we want our engineers to get certifications. We're providing vouchers so that people can get those cloud certifications. It's when you do that and you put people first, everything kind of comes together. And I think a lot of what we see in our industry, it's not really the technology that's the problem, it's process because you're so, we're working at large scales, our environments are massive. So my three years at Wells have seen a significant amount of change that has really driven us to be a lot better.  As you've been pointing out, what is the role's change as automation scales in? Is it the operator? I mean, we know it's going with devs. Devs are doing more IT in the CICD pipelines. We see that velocity check, good cloud native development. What's the ops scene look like? It seems to be a multi-tool role where the versatility of the skillset is the quick learner, you know. Yeah, what's your view on this new persona that's emerging from this new opportunity? Yeah, and I think that's a great question because if you think about where we're going and even the term DevOps, right, it means so many things to different people, but literally when you think about what DevOps is, allowing our developers and our operations to work together on one team, it's allowing, you know, our operation engineers aren't, you know, years ago, ops engineers were not doing the development work, they were relying on somebody to do the development work and they were just supporting, making sure our systems were always available, right? Our engineers, our ops are now doing the development work, they're able to contribute and to get, they're writing their own playbooks, they're able to take them into production and ensure that they're being used correctly. We are a change-driven execution organization, everything is driven through change and allowing our ops engineers or production support engineers to write their own playbooks, right? And they know what's happening in the environment is powerful. You're seeing DevOps become a job title. It used to be like a function of philosophy and then SRE, I'm the SRE, I'm like how many servers do you have? I'm like no, a cloud. I'm like yeah, what's next dad? I think with SREs, it's important that if you have site reliability engineers, you're working towards those non-functional requirements, making sure that you're handling those key components that are required to ensure that our systems and our applications and our integrations are up there and they're meeting the standards that we set for those that don't fall. And I think Red Hat Ansible nailed it here because infrastructure is code, we get that, infrastructure as configuration is code, but Opses code really is that SRE outcome. SRE obviously came from the Google background, but that means infrastructure is just doing its thing. The Opses automated, that's an interesting concept. Yeah, because it's still new, right? A lot of organizations used to see and they probably still see operations as being, their role is just to make sure that the lights are on and they have the specific access so they're not touching code. But the people that are doing the work and know the environment should really be the ones under creating the content for it. So yeah, I mean, it's crazy what's happening now. So I got an analogy that's going to be a banking analogy, but for tech, you know, back in the automation, oh, I'm going to put my job out of business. ATMs are going to put the teller out of business. There's more tellers now than there are before the ATMs. So that metaphor applies into tech where people are like, what's automating a way? Is it my job? And so people know it's not, but what does that free up? So if you assume, if you believe that's good, you say, okay, all the grunt work and the low level and differentiated heavy lifting gets automated away, great. What does that free up the talent to do? So when you, so, and that's great that you bring it up because I think people fear, you know, of automation, especially people that weren't doing automation in the past and now their roles are now, they're able to automate those roles out. They're fearful that they don't have a space, a role anymore, but that's not the case at all. What we prioritize is now that those new engineers have this new skill set, apply them, start using it to be a part of this transformation, right? We're moving from, we went from physical to virtual to now, you know, we're moving into the public, moving into the cloud, right? And that transformation, you need people who are ramping up their skill sets, you know, being a part of one of the tools that I own is Terraform at Wells. That, you know, right now our priority is we're trying to ramp up the organization to learn Terraform, right?  And it's, you know, people who have been automating some of the stuff that they're doing in their day to day and now trying to learn something new so that they can contribute to this new transformation. New functionality, higher value services. Yes, yeah. It brings tremendous opportunity for those folks involved, automation. Yes. On so many levels. Last question, Nor, for you, was what, you know, as we are rounding out calendar year 2022, entering into 2023, that patience is that we talked about is still not coming back. What's next for Wells as a technology company that does banking? I mean, you name it, we're working on it because we want to be able to deliver the best for our customers. And I think right now, you know, our digital transformation strategy and moving into the public cloud and getting our applications re-architected so that we're moving into microservice driven apps, right? We're moving these workloads into the public cloud in a seamless way. We're not lifting and shifting so that we're not causing more problems into the environment, right? And I think our goal is, right, like I was saying earlier, people and evolving with the technology that's coming out. We're not, you know, we are a part of the change and we are happy to be a part of that change and making those changes happen. Equal first, automation first, sounds outstanding and I will never look at Wells Fargo as a bank again. Yeah, perfect, great, that's all I know. It's been such a pleasure having you on the program, talking about how transformative Wells has been and continues to be. We appreciate your insights and your time. Thank you so much. It was a pleasure. Our pleasure, thank you guys. For our guest and John Furrier, I'm Lisa Martin. You've been watching theCUBE all day, I'm sure. Live from Chicago at Ansible Fest 2022. We hope you have a wonderful rest of your day and John and I will see you tomorrow morning.


Hello everyone, this is AliceKL. In this video, I'm going to continue discussing strategies to escape local optimals. In the previous video, I discussed two strategies for escaping flat local optimals, sideway moves and taboo list. In this video, let's look at how we can escape strict local optimals using random moves. Let's start by looking at two types of random moves. Random restarts and random walks. A random restart is a potentially big jump in the search space. If the algorithm is stuck in a particular region, we will generate and move to a random state in the space. Adding random restarts will improve the property of greedy descent significantly. I will discuss this in more detail later on. The second type of random move is a random walk. A random walk says that we will move to a random neighbor in our local neighborhood. By doing this, it is possible for us to move to a neighbor that's worse than the current state. Greedy descent does not allow random walks. In the next video, I will discuss another algorithm called simulated annealing, which makes use of random walks to explore the search space. Let's think about the two types of random moves. When is it a good idea to use one random move versus the other? Consider two different search spaces, A and B. Which type of random move is better for which search space? Pause the video and choose an answer. Then keep watching. Here are the correct answers. Random restarts are better for search space A, whereas random walks are better for search space B. Let me explain why. The main difference between the two search spaces is that A is smooth, whereas B is bumpy and jagged. A has few local optimals, whereas B has lots of local optimals. In the smooth search space A, greedy descent will perform quite well. Greedy descent will take us to the local optimum quickly, since there's a clear direction towards the local optimum. Random walks are not helpful, since they may take us further away from the local optimum. However, once we reach a local optimum and want to explore other parts of the search space, a random restart might help. A random restart can take us to a new region where we may be able to find a better local optimum. Therefore, for space A, a random restart is a better choice. Let's look at search space B. Since there are lots of local optimals, greedy descent will get stuck in one very quickly. After that, if we perform a random restart, we will jump to another region. Unfortunately, this new region is still bumpy, greedy descent will once again get stuck right away. As you can see, a big jump is not useful here. No matter which local neighborhood we're in, we will get stuck right away. However, a random walk can be helpful. Moving randomly in a local neighborhood is a better way of getting us out of a local optimum and possibly making progress towards a global optimum. Therefore, for space B, a random walk is a better choice. Here's some intuition behind the two random moves. A random restart is a global random move, since it allows us to make a big jump in the search space.  Let's look at how random restarts can help greedy descent escape strict local optimals. Recall that, greedy descent can find a local optimum fairly quickly. However, the first local optimum reached by greedy descent is often not the global optimum. What if we combine greedy descent with random restarts? Each time, we will execute greedy descent until it finds a local optimal and terminates. After that, we will generate a random state and start greedy descent again from the random state. After running greedy descent for a number of times, we will return the best state among all the local optimals found. This quote is a great intuitive description of greedy descent with random restarts. If at first you don't succeed, try try again. This is one of my favorite quotes. Also, this quote is a great way to approach life. If you have an important goal and you don't succeed the first time, then keep trying. How does random restarts improve the properties of greedy descent? Recall that, without random restarts, greedy descent is not complete even if we have unlimited time. It is not guaranteed to find the global optimum. In fact, greedy descent will not find the global optimum most of the time. Does this property change if we perform greedy descent with random restarts? Pause the video and think about this question for a minute. Then keep watching for the answer. The correct answer is yes. Greedy descent with random restarts is complete with probability 1. Given enough restarts, greedy descent is guaranteed to find the global optimum. There's a simple reason for this. Given enough time, the algorithm will eventually generate the global optimum as the initial state, and it will terminate immediately. In the past, students have raised an interesting question. We know that greedy descent can determine whether a state is a local optimum by comparing the state with its neighbors. The question is, how does greedy descent determine whether a state is a global optimal or not? I'll leave this as a thought question for you. The answer is different depending on whether we consider a constraint satisfaction problem or a pure optimization problem. That's everything on greedy descent with random moves. Let me summarize. After watching this video, you should be able to do the following. Describe the two types of random moves. Define which random move is better suited for which type of search space. Describe the greedy descent with random restarts algorithm and its properties. In particular, is greedy descent with random restarts complete? Thank you very much for watching. I will see you in the next video. Bye for now.


Hey, I'm Chantastic, this is React Holiday, day 24. Today we're talking about code splitting. Now, you'll notice that I've done quite a bit of work here to modularize all of the things that we had in this index file. You can explore this on your own time. The most important thing is that I've now put this Pokemon component in its own file. Now, that allows us to import it on its own. Now, the reason that's important is because code splitting happens on component boundaries. So we want to import a component dynamically. We need to have it in its own file. So to do that, to change from a static import to a dynamic import, we just change a few things. First, we're gonna do an assignment, const Pokemon, put our import over here and wrap that up in prens. This is the dynamic syntax. And we need to tell React what the heck's going on here. So we'll use react.lazy to do the communication work between the import promise and what that means for React. So that takes a function, so we'll give that there and boom, we're done. We see a little bit of an error here and that's just saying that all of our imports need to be together at the top of the file. So just switch the order there and you're good to go. So now we're importing our component dynamically, which is pretty sweet. Let's see what happens. Breakage, that's not good. Now, the reason we're seeing this is because anything that's imported dynamically may happen on a delay. So it might not happen right away because we don't have the code right away. And while we're waiting, what do we show to the user? That's what the suspense component comes in with a fallback. So we're gonna find our components, do what the error tells us to do to render suspense around it. So react.suspence, top and tail. And we'll give it some kind of fallback. So it's like, hey, while we're waiting, just render something like loading. We can put a component in here, whatever you want. Now, when we do this, everything works like a charm and we're dynamically importing this Pokemon component. We only pull this component from the internet where we use it, which is really, really nifty.