So the next presentation that we've got is from Zavier Lecumbidi from Green Instruments and the BASC Research Institute AZTI Hello, my name is Zavier Lecumbidi and I'm a computer scientist currently doing my PhD Honesty. I specialize in computer vision and in this presentation I'm going to tell you how we applied to the identification and measurement of tropical tuna. To do this we use cameras already installed on the boats that take images of the fishery. Our work begins by making a selection of the available images. Then, based on these images and annotations made by expert observers, we train and validate our prediction models. Finally, we compare our estimates with official data collected in ports. The two main workflows share two of their tasks, image preprocessing and segment preprocessing. The image preprocessing is divided into three parts. First, perspective correction and contrast enhancement. By following these two processes we achieve a more homogeneous image, which appear to have been taken by the same camera and which has been optimized to improve fish segmentation. Then estimating the lens dirtiness allows us to discard images that are so dirty that will not provide useful segments for our job. For the segment preprocessing we use the width of the belt as calibration method. Now we need size in meters and pixels allows us to calculate the size of an individual. Due to the nature of the images the fish may appear overlaid, so not all of them are suitable for measurement. The parameters that we have used to consider an individual as valid are as follows. A minimum size of 20 centimeters and that the length of the fish must be at minimum four times its width. By doing so we avoid a large number of segments that are not useful for this job. For the training workflow we have two main steps, image annotation and the training of the models. Image annotation can be done in two ways, manually or automatically. To do it manually we have used an annotation software called CBAT in which expert observers can delimit the fishes and assign a category to it. The levels that we have used are big eye, skipjack and yellow fin for each of the target species. Fish for those where the observer was able to delimit the fish but not to classify precisely. To do it automatically first we must train the segmentation model with the manual annotations. Once the model is trained we segment one specific set so we can safely say that all the fishes that are model is capable of segmenting belong to the same species. For the training of the models we have to differentiate what we have used in each one. The segmentation model uses the complete images with the annotations while the classification model uses already corrupt segments of individual fishes. Both have been implemented in TensorFlow using pre-training models for each work. We train all models has allowed us not to need as much data as we would have needed if we had to train them from the beginning. In this workflow we focus on obtaining the prediction of the models and seeing how they fit our data. First we try to evaluate the two models separately but while for the classification it is easy to have a precise estimation of the accuracy for the segmentation is not that easy. For the classification model we have constructed a confusion matrix in which the vertical axis represents the actual class of an individual and the horizontal axis the class predicted by our model. The confusion matrix of a model that classifies everything perfectly would be a matrix whose main diagonal has the values of one and the rest is filed with zeros. In our case we can observe how our model currently classifies approximately 3 out of 4 individuals. For segmentation however it's not that easy. How do we define the shape of a fish? One way to solve this and validate both models at the same time is adding new classes to our dataset. These new classes are head for only its head segments fin for only fin segments and art for other type of segments as the ones malformed. This makes our confusion matrix bigger but the real remains the same. We must classify fish segments as fish, green square and known fish segments as known fish, red square. This allows us to get rid of those bad predictions. It is preferable to lose some real fishes as artifacts top right corner than to add artifacts to what we are considering valid individuals, bottom left corner. This is due to the fact that we can extract many fishes from a set so losing a few is not a problem. Once we have the results of our models it's important to contrast them with official data so this is what we do in our last workflow. For the comparisons with official data we have four samples. The first two are monospecific yellow fin sets which were fished to free schooling and the other two are mixed set fished to floating objects. The free schooling ones have been very useful to increase the training set easily however we can see that their distribution is farther away than those of the floating object. This happens because the yellow fin tuna instead of completely overlapping they overlap only with their fins making the segmentation model count one individual as two different fishes. This problem has been identified and we are working on resolving it. At first the floating object seems to be more complicated since the species are mixed but from what we can see the distributions are estimated very well. The data we have just seen is the result of good quality images but the moment the image capture system is neglected the model is not able to work properly. In this other four samples the camera was very dirty so it was impossible to make an estimation. Thank you for listening to me if you have any questions please don't hesitate to ask. Thank you very much Xavier. Well there's a lot to unpack there in five or six minutes but let's try and get to some of the questions. You spoke to how AI enhances the visual recognition of marine species in electronic monitoring systems and your presentation is one of the first to really show fish on top of each other, underneath each other and trying to partition them out and do species recognition at the same time. So it's fascinating how you're slowly refining those models to help you. What are the main hurdles that you foresee to have this taken up across fish value chains? What are the hurdles you've spoken about the hurdles within your presentation but what other types of areas do you think there's opportunities to overcome to improve? So thank you for your question. The main problem that we have encountered working on this was mainly the maintenance of the overall electronic monitoring system because it's not thought to use in order to make an automatic work with these images. So the images were very, very, a lot of them were not very useful. So we have to work a lot in this pre-segmentation and pre-processing of them. So that's... And this was due to the quality of the images that were taken, the speed the fish were moving and how they were packed or due to other issues? Yes, it's mostly because they are very aggravated. So in a lot of the images, the cameras have a lot of blood and water splashes. So it's impossible to take anything from the images. Right, so it was a practical solution problem that we're also seeing on trying to monitor catches on boats. Yes. They're difficult places to work. Thank you very much. Matt, can I hand over the questions to you? Yeah, what's the plan with the work that you're doing? Is it in a specific domain or will it be... Do you plan on releasing it widely? Because I think it's a really important piece of work that relates to real-world scenarios that you've done and could be valuable for other people to use. I don't know if that's something that you're doing or it's very much in one specific domain. So this first assessment was mostly to assess the utility of the currently images, because we were working with historical images, but the idea is to implement more and more phishing vessels in order to make better estimations for environmental sustainability and so on. Thanks very much, Xavier. Thank you. Anton, do you have any questions? I think we're missing Anton. No, even if I'm unmuted, I'm not a specific one, but it's very interesting to see how you work to get the information also to more official authorities that have to validate probably the output of your approach and do you have an idea how long it will take before electronic systems like this will be accepted by authorities for monitoring of catch or even by vessel captains? We don't have an update or a precise timeline because now, as you can see, we have very small data to work on to compare with official data. So we are not working on making and asserting a good data pipeline to have more data to work and we don't have a precise timeline to implement it. These are the kinds of questions that we'll be reaching at the end and the opportunities for groups and administrations such as FAO who work closely with RFMOs to try and bring the signal backwards and forwards amongst developers and fishers and even valued people in the value chain trying to team together so that we can overcome these problems but also so we can get feedback across different groups about what's working and what isn't working. So thank you very, very much, Xavier.


Presidency backs Bahá'í and says he has delivered promises made to Nigerians. And the Nigerian Senate asks the United Kingdom government to pardon Ike Kuramadu in organ trafficking case. This is Plus Politics and I am Mary Anacorn. The presidency said yesterday that it was mischievous to paint a bleak pessimistic and frequently inaccurate picture of the state of the nation and the outgoing President Mohammed Buhari's leadership. A statement by the senior special assistant to the president on media and publicity, Garba Shehu, said in the post-election period of heightened tension and disinformation, he saw it fit to set the record straight. According to him, those who said President Buhari inherited an economy in disarray were right. And Nigeria had become an international terror cell where Burkha Haram controlled the size of Belgium within the country's borders. He also added that external reserves had maintained a healthy growth throughout these eight years and within two years of the Buhari administration, experts have more than doubled from the 2015 records. Joining us to discuss this is Dayak Ayadeh, a public affairs analyst and Ike Waiyeh, Sharafa Omweli, illegal practitioner. Thank you gentlemen for joining us. Yes, good evening. It's my pleasure being with you on this evening. Thank you for having me. Thank you. It's my pleasure to build your program. All right. Great. Thank you very much. Let me start with you, Mr. Ike Waiyeh. Let's look at the President's promises from 2015. The President did tell us that he was going to change a lot of things. He was going to deal with unemployment. He was going to deal with security, three top things and of course deal with corruption. How well would you score, Mr. President, in terms of those three top promises did he deliver? Mr. Sharafa, can you hear me? Yeah, I will too. Go ahead. Well, it's my pleasure to be on this program. As a matter of fact, President Obama-Buhari, I don't think as our President, when I was speaking, a lot of challenges. Bodies on security. Bodies on economy. And we didn't make the use of administration. We have been able to stabilize Nigerian nations. If you pass our mind back to President Mr. Buhari Kukua, Kukua Aram's urgency was actually assuming a very dangerous trend to the extent that they were in control of most states in the notice of this country. In fact, they were also there flagged in almost all the local governments in that part of the country. And they were much known to take over North Central and North West. Mr. Buhari Kukua was able to dismantle the sale that Kukua Aram was having in almost all the states in the notice. To the extent that today, Kukua Aram are no longer having the grip of a single chance, not a talk of look at their money. What we are having currently is a guerrilla warfare. And it's a difficult thing. It's a conventional way to fight a guerrilla warfare. It's a difficult thing not to deploy technology after that in fighting guerrilla warfare. So to my mind, President Buhari Kukua has security, a culturally downward. To hear of economy, if you look at our foreign research, it is robust. It is healthy from the time when it took over. I want to forget that this government has experienced some lengthy in politics, international politics, in economy. And don't forget, in your 20th century, the old world experienced a COVID pandemic.  So we should give it to the current president. I have tried to ensure that our economy is stable despite the challenges in the world economy. And as a matter of fact, the government has been able to stabilize too many things. And Nigeria is actually better off for this. If you look at the real policies, if you look at this government, we might not actually feel the impact now, but I can assure you, the incoming administration will definitely consolidate all the games the government has achieved in the area of security, area of economy, and areas of infrastructure. Look at our rules. Most of our rules have been reconstructed, repaired, so many. And not only that, rail transportation has taken its toll. Today, we are having rail transportation to keep Karima and Abuja, Legos and other fans of health work. And they are not planning to have rail from Cano to Potakot, which to my mind will boost our economy, will improve a lot of things. So I want Nigerians to believe that rule was not between a day. This government has actually laid foundation, solid one, welcoming administration to build on in the areas of infrastructure, areas of security, and areas of economy. We might be faced that we are experiencing some challenges. Yes, we must understand that it's not always easy to have a very good, robust administration without sacrifice. That's what we are experiencing now. And I can assure Nigerians, we have gone past Egypt, we are moving on to the deep scale of this country, and just willing all of us who love and be happy in the couple of months or years when we go back to Iran. Mr. Shafa, I'm interested in some of these assertions that you've made, especially when you say that a lot has been done and that we will laugh. So we're supposed to wait till the eight-year tenure is over for us to be able to laugh.  We couldn't laugh in December. We couldn't laugh in January or in February or in March when we had the Naira and the cash crunch. So we're going to wait until the president's hands over, and then we're going to be able to laugh really. How well? No, no, no, just hold on, because you've made very interesting points about dealing with insecurity and how things have changed and roads have an infrastructure has been put together. Out of all of the monies that we've borrowed, I mean, I'm sure that you know that our debt profile has almost hit the roof, if not literally taken the roof off, and we've had stolen crude every single day in this country, so much so that nobody can account for that crude. And the president sits as the Petroleum Minister. All of this is happening under his watch, and you're telling me where you are that he has been able to deliver on the dividends of democracy. All of this is happening under his watch. He's taking it where he wants. Mr. Kawaii, you need to turn off your television. We're having a feedback, please. Now. Mr. Kawaii, please turn off your TV so that we don't have that feedback again. Thank you. All right, I've got that. Now, I set the function now, and I want to repeat. Government is a consumer. When Mr. Kawaii and Mr. Kawaii took over in 2016, he took over both after the liability of the adjunct government, and what he inherited was a lot of issues which he tried to solve and resolve. Mr. Kawaii was used in laying a very good foundation for our economy, a very good foundation for our culture, a very good foundation for our security. Now, I mentioned earlier, the issue of Rukwara, this one, was becoming a very threatening national challenge when the government was in office. When it came on board, when the government was in office, it showed that if government totally demobilized Rukwara and its citizens in North Nigeria, today, we are not having, as we're having, we might have this thing. But we have other forms of insecurity, Mr. Kawaii. We have never in the history of this country could a moving train be stopped, but it was done under the Baha'i administration and people were kidnapped. So dealing with Rukwara, yes, all fine and well, but then there are other issues. Mr. Kawaii, please, disease from turning on your television because that's giving us a feedback. What I said was that what we are currently currently now is rather guerrilla warfare. And don't think because it is a conventional method to subdue a guerrilla warfare. That's why you are still having the purchase of Napa Haram there and they are striking the monetary of that means of starting our security apparatus as it is easy because our very proper government and family are poor. As I said, the government is not going to change that technology was destroyed in meeting the both security infractions in our state. And I cannot show you because the Mawangari administration has made a foundation of college security network. Like all the administration would have really been on to ensure that finally the issue of intelligence is nothing, foundation is also for Rukwara put to rest. But what I said is that Nigeria and Nigeria are enjoying very little peace for the people to be safe. And you are doing that our government activities in all regions of this country are going on as planned. We are not going to have any serious economic breaches in any part of the country except for Nigeria which is normal which is not going to be compared to Nigeria. In America there are a series of security breaches in Rukwara. There are series of security breaches which means no nation is at least free from security challenges. If you have security challenges you can have their massive technology to know how to show that Nigeria we are trying. And we are saying that so far so good because the things in Nigeria are being brought under control. Our autonomy is actually between all. All right. All right, Mr. Waiakaiwe. Let me go to Mr. Dayakairi now. Mr. Kairi, let me go back to what you said. All right. Let me go to what Mr. Waiakaiwe said. I would like to quote him directly. He said that the President inherited an economy that was in disarray. He said corruption was rampant. Our prizes were nose diving and outstandingly an international terror sale controlling a territory of a size of Belgium within the country. But my question is, the President obviously knew these things and he was very impressed. Isn't that why he was campaigning to deal with Boko Haram, to deal with the economy and give us the kind of government that we were asking for? Yes. Am I having this problem? Yes, we can hear you. Yes, you see, I'm going to give my condition for the future. And rather than to be safe for the future and for the future and factors. Now, let us take the purity as a positive point. When this comment was campaigned, it talked about insecurity. I was going to face it clearly. And interestingly, the candidate if he did, 2014 to 2015, used to be a soldier that would stop all its fate to face insecurity and other types of insurgency within our nation. But unfortunately, and he got the power the incident of insecurity in our country became so alarming. So the extent that it was sub-government started shouting that insecurity is making their state of government do for them. Now the question is this, the so-called community that the in quotes now, Moko Mara, in there, were they not exploiting the mineral resources there? So the extent that I also mentioned, all over the place there, some aircraft were supplying food items, water, and other things to all those abandoned places. That is why. Two, 29 news. A man, Paul, said on CNN sometimes ago and she said, the media that is unable to fight insurgent within its political entity within 24 hours is an I mean, I mean, it's part of that thing. It's not like in that office there, you are having peace, you have been operating for years that have known you peacefully and somehow a kind of a conflict now came in and you are all seeing it and you are not attacking it and you want to tell me your your money man is not in the public? Your money man is not in the public? Can that box of money be still on this security? And America was kidnapped somewhere in Africa and was brought down to Nigeria and the American president he warned that America released allies within 24 hours. All the way from America they came to Nigeria and they rescued that person. Is that what we talk about to leadership?  Anyway, let's leave that because of our technology let's come to economy let's come to economy What were the economic indices that made Nigeria the fastest growing economy in Africa since 2015 because the data are there not manufactured by me but even by our Sanjuku Guru in Nigeria alright? What were the indices then? What do we have now? I really want to use words that will not be easily described by listeners but rather I'm going to be basic in 2014 to 2015 that's number one that's number one what are the productive capacity of our industry that's number two when we're looking at when we're looking at the rate of employment what are we having then? How much was the bag of rice then? How much is it now? I'm talking about I'm talking about market price indices What do we have there? What do we have now? Are you talking about economy? What was the rate of of people, professionals learning people living in our country then I mean tomorrow it is just now that we're not having I mean very loud that kind of situation now Mr Khairi let me just come in there Mr Khairi let me come in there Mr Khairi let me come in there just hold on I want to correct an impression the Jack Wa syndrome that you're talking about this is every single time in the history of this country we have seen we keep seeing people leave this country in droves I do not exactly believe that it's just under the Buhari administration that people have left this country in droves I do not want to believe that I want to correct that impression there had to be before now there was Andrew and we don't leave the country and all that but I'm not talking about the rate the rate of president I'm talking about a great part of the top priority analysis not our research institutions and you see that a lot of researchers living in Nigeria working abroad have we ever seen in the history of our nation our national assembly discussing about the rate of emigration of our professionals before because of that reason I'm not even saying what happens all over the country that's what we call migration and all that now but the incidence now anyway let's leave the economy let us come to corruption I'm sure we will appear to have just approved approved another loan of $22.5 trillion not billion, trillion which which to be taken from the central bank what did that money mention tell me it's coming that is having just about three weeks to leave office no tell me and this is this is a money that was not allowed about a year ago so what are the facts that we consider to talk the money we must have a lot of money that is enough for a year project to not be approved what has happened to the ex-wife ex-wife ESC chairman that was unleashed a lot of corrupt activities what has happened to the NDBC NDBC and let corruption I'm talking about sub-demise sub-demise that happened in national assembly what has happened people be warning MTA the European authority whereby these are what are made what soldiers called before what has happened what did they make the alleged what happened now Mr. Karey let me call me tell me how has this government lost it yet unfortunately we have lost Mr. Sharif on this call but there are those who would score the president's administration pretty high in terms of infrastructure development and there are statistics to back that the president has not lost it or failed in every single aspect of his administration that he has been able to do well in terms of infrastructure development quickly let's talk about because we have just about two minutes to wrap up here let's talk about what the president can do to change things to prepare the grounds for this continuity that you're talking about as government being a continuum do we see President Buhari you know laying the foundation for an easier and better governance before May 29 is there even enough room for any changes to be made yes Mr. Buhari government is a continuum all right is that why we are having the most the biggest Nigeria that we used to have for that stand up stand up the incoming government the the president's the president's let of Nigeria we are going to continue on the legacy that this government is going to leave behind but my question now is what are those legacy yes government is a continuum for this say because government is a continuum we will not continue on things that are not that is not a defining of as a people whereby you go to schools universities the houses are presented whereby a lot of people are now into unemployment whereby our economy is going to change whereby our refineries are not working whereas are we putting money to make our refineries work we are not borrowing money we are not borrowing money so give to people because we want to remove some things that this particular government said was a fraud why the way it can be government is a continuum we find another way to make our nation to put our nation on the path of progress and work for us Mr. Daya Kaede is a public affairs analyst and Okawosha is a legal practitioner unfortunately we lost him in the middle of this conversation thank you so much for joining us Mr. Kaede yes this is my play up alright well up next we turn our attention to the senate's key to the United Kingdom government or by Kei Ikorimadu's organ trafficking case stay with us


Life in Liberia, City of Monrovia, History, People, Lifestyle, Traditions, and Music. Liberia was established in 1822 as a haven for freed slaves from the Americas. Originally named Christopolis, the settlement was later renamed Monrovia in honor of American President James Monroe. Over time, Liberia developed from a colony into a commonwealth and gained independence in 1847. The colony was formally known as Liberia, meaning the Freeland in Latin. Liberia is the only black state in Africa that was never subjected to colonial rule and is Africa's oldest republic. Liberia is located along the coast of West Africa. It is bordered by the Atlantic Ocean to the southwest, Sierra Leone to the west, Guinea to the north, and Côte d'Ivoire to the east. Liberia is made up of 15 political subdivisions called counties. It has a population of around 5 million and covers an area of 43,000 square miles, or 111,369 kilometers square. Monrovia, the capital city of Liberia, is located on Cape Mesurado on the Atlantic coast and as of the 2023 census had 1,678,000 residents, home to 32% of Liberia's total population. Liberia is a multi-party republic and enjoy a very stable political environment. The 1986 constitution, the head of state and government is the president who is directly elected for a six-year term. Members of the bicameral national assembly who serve six-year terms in the House of Representatives and nine-year terms in the Senate are also elected directly. The name of the current president of Liberia is George Apongwa, who is a former footballer. Liberia's first inhabitants were the Gola and Kisi peoples from north-central Africa who arrived as early as the 4th century. They were joined later by the crew and people moving in from the north and east. Liberia has a diverse population with 16 ethnic groups, including the Capelle, Bassa, Geo, Crew, Griebo, Mandingo, and more. The Kela people are the largest ethnic group. Polygamous marriages are common and over two dozen languages are spoken, with English being the official language. The Fanga Dance is a traditional Liberian invocation to the earth and sky, possibly originating in Liberia or Sierra Leone. Liberia relies primarily on commodity exports, mainly rubber, iron ore, gold, and diamonds, as major sources of export earnings. The Liberian dollar is the official currency of the Republic of Liberia. However, the United States dollar is a legal tender in Liberia and is used alongside the Liberian dollar currency. Liberians enjoy a vibrant lifestyle, with beautiful beaches and lively nightlife. Jolof Rice is a popular dish, and contemporary music like hip-co thrives. Football is a beloved sport, and Liberia has produced icons like George Weya. It's a culture that blends tradition with modernity, making Liberia a captivating and unique place. Liberia is an excellent destination for those seeking new adventures, culture, and natural beauty. As a country still largely untouched by mass tourism, Liberia has many untapped areas just waiting to be discovered from pristine rainforests to beautiful beaches. Liberia, a hidden gem on the West African coast, offers diverse landscapes, pristine rainforests, and stunning beaches. Modern airports provide easy access. Explore wildlife in Sappo National Park. Relax at coastal towns like Cape Mountain Robertsport, and discover cultural landmarks such as Providence Island and the National Museum. Immerse in warm hospitality, save a Liberian cuisine, and create unforgettable memories.


All right, thank you so much. Yeah, so I'm Todd Parriston, CEO and co-founder of AeroSoftware. Yeah, I wanted to talk to you all about observability data pipelines today. A relatively new concept, kind of from a nomenclature perspective and observability. We're also seeing some folks call them telemetry pipelines, but still an emerging space. And kind of wanted to talk about our perspective on what it means to build it yourself versus buying one of the emerging technologies on the market. So a little bit of background on me. I previously co-founded a company called Inflex Data.  That product interestingly started off as a SaaS product. We shifted to kind of an open source model. So I've been in the observability space in one form or another for about a decade. And then went to a company called Pivotal after that and worked on observability for the Cloud Foundry platform as a service. Started AERA in 2019, mid-2019. And we've been focusing on time series and observability use cases again. Yeah, and this is something I love talking about. So happy to chat with anybody here at any point. Feel free to reach out to me. Love talking about the space. So one of the things that we've been looking at as we, you know, as we talk to customers, as we go to market, we think about how we're positioning our own products or our primary focus in the observability space has been log management. That was a place where when we were starting AERA two and a half, three years ago, we saw a lot of opportunity for innovation and disruption primarily because the data volume of logs is so huge and the desire to keep logs around for long periods of time and have long, long retention of windows is high. And there's also a lot of complexity in logs. And so they've got a lot of these really difficult properties that make it hard to manage at scale versus metrics and traces, which have their own properties that make them difficult to manage, but it's I think somewhat less complex than logs if you just had to focus on one. So we've been looking at logs and log management. We obviously see lots of customers using Splunk, lots of customers using Elasticsearch. And as we started looking at, what does it mean to manage some of these growing data volumes? You know, we took an opportunity to survey a lot of practitioners in the IT world and ask a bunch of questions about observability, what those folks think about tooling, data volumes, data growth. And one of the places we asked some questions about was how do you think in your organization you're gonna see log volumes grow kind of as a way to get into people's mindset about how to think about managing growth over the next few years. And so this chart here is really breaking down by bucket, like how do folks think their log volumes are gonna grow year on year from where they stand right now? So if you kind of look at this, you can split the graph kind of in half. They're sort of the folks that think less than 50%. And the kind of like 10 to 50% range comprises what, 58% of the respondents. But it's interesting to note on the right-hand side of this graph, there are two pretty big bars there for people who think they're gonna see 50 to 100% annual growth, which effectively is like doubling year over year. And then there's almost 20% of the respondents think two to five X growth. So they're looking at significant growth in the not too distant future. And we're already hearing from customers that are seeing tens of terabytes a day, hundreds of terabytes a day, and the larger the organization, the more log volume they're generating. And especially with the adoption of tools like Kubernetes and containers, that there's just so many more sources for logs to come from. And so one of the things that we're seeing and kind of getting into observability pipelines a bit is just storing this data is actually hard. If you actually wanna do things with it, you wanna pre-process it, you wanna route data to multiple destinations or have kind of rules that decide what goes where, you actually need to start embracing a relatively sophisticated set of technology to figure out how you manage this incoming data volume if you wanna do anything with it. And then you still have to figure out what that storage piece looks like. So what we're really seeing is that the volume of observability data in general, and specifically logs growing is something that's on people's minds and is becoming a significant problem for a lot of the existing tools. And so pulled this quote kind of out of our paper, but 79% of the folks surveyed are concerned about the rising costs of observability and observability data management if we don't see some innovation in either the tools or the technologies that we currently have. And I think if you go back and look at the folks that are thinking about their ingestion going up two to five X over the next year, if you think about it just from like a hardware cost, if you are imagining, say you're spending a million dollars a year on hardware to manage logs right now and you're looking at that going to two to five million by next year. And that's a relatively significant increase, especially in the current climate where actually a lot of folks are starting to look at cost reduction and ways that they can use fewer tools and fewer resources to manage things going forward. And so this is pretty interesting to me that like while lots of folks are embracing observability there's a pretty significant desire to see some innovation. And so our kind of viewpoint on where we're gonna see the biggest I think differentiation is the adoption of observability pipelines or telemetry pipelines. And so one of the other survey questions we put out there was really asking have you thought about observability pipelines? Are you using them yet? Are you going to be using them yet? And so this chart here really shows kind of the blue bars are folks that have either deployed something or are deploying something. And then the brown and brown and brownie gray bars on the right hand side are folks that are looking at embracing a new technology. And so it's a pretty even split. I would say it probably is about 50-50 maybe less just a little bit less than half of actually made the decision to implement something and the other slightly more than half are evaluating now. And so it feels like looking at the data it's something that is familiar to folks in the space but something that still has a lot of room for adoption and standardization and really finding use cases in the market at scale that I think can really drive some of this innovation that we're seeing the need for in managing log data, observability data in general. And so our viewpoint here is that we think the observability pipeline space is something that will continue to be important to all sizes of organizations and will become a more important part of the stack that I think people are thinking about choosing when they start to roll out observability infrastructure in their organization. And so kind of breaking it down a little bit you've got a lot of choices when you think about what an observability pipeline is it can be a product that you buy off the shelf and bringing up some potential competitors there's like a Cribble of the world you can just like take that does a lot of the things that you might want their focus is on Splunk users primarily they do a lot of work there to try and help reduce the cost of Splunk. Again, Splunk is an expensive product Cribble is also an expensive product and it isn't necessarily the right fit for all sizes of data all shapes of data and all organizations. So that's probably one end of the spectrum and then the other is you can just grab some open source tools and you can build a processing pipeline it can be either you manage it yourself there are some obviously some cloud resources you could deploy but there are a handful of problems that you need to think about as you're deploying an observability pipeline and so really the first question is like what are the problems that you need to solve when you're thinking about what do you want to build or buy? So we highlighted a couple here the first is the rate of ingestion and this is not something that just applies to observability pipelines this is something that applies to all observability infrastructure and if you're looking at using something like I mean it could be Splunk it could be Elastic Search it could be any of these log management tools the rate of ingest is something that can be a real problem especially when you have outages and you see your log volume spike so it's not uncommon for some sort of error state to cause a lot of systems to start emitting error messages simultaneously in the event of a failure and you could see your log volume go up 10x and if the rate of ingestion of your storage system isn't able to scale up to handle that then you start either backing up writes or you start getting failures and when you're looking at trying to solve whatever the root cause is for an outage that's the time you most want ingestion. So it's a problem on the storage side but it's also a problem on the ingestion on the observability pipeline side if you have a system that's doing data processing data enrichment whatever you need to make sure that that system can also handle that burst in scale and so there's a bit of a trade off there cause some of it maybe is it simply about storage and queuing inside of the observability pipeline is it about rate of process cause if you've got workers that are dedicated to processing this data and they're expecting let's say on the order of like 10 gigabytes per day and suddenly it goes to a terabyte per day are you gonna have enough resources to actually process that data in real time or is your pipeline gonna start backing up and then you're gonna actually lose visibility into that data potentially and so rate of ingest is an important factor needs to be something that can be relatively flexible without causing you to have to deploy a bunch of like permanent resources to handle that max capacity cause otherwise those are gonna be sitting idle a lot of the time. So that's a relatively complex problem that's simple on the surface data transformation so if you think about log data in particular I mentioned this earlier it comes in a lot of different formats structures you may not even have the same fields from one system to another so being able to do initial data transformation do you standardize on a schema? Do you do a set of either like rewriting or modifying that data in flight so that you have a consistent set but you need to think about that so that when it lands in your destination systems it's in the format that you actually want it to be queried in so there are lots of different ways and different rules about how that data can be transformed and it may not be consistent from organization to organization so being able to have some flexible rules and flexible tooling so that you can do these transformations without each individual transformation having to be a new piece of code that somebody has to write or a rule that somebody has to write and test and manage is a significant part of thinking about what this observability pipeline implementation looks like. Data transport is another thinking about it's not necessarily just a input output stream there may be lots of inputs and lots of outputs there may be places in the middle like potentially dead letter queues and things like that where error state data goes so thinking about what's the global picture of all of these different input output streams and how do they tie together what are the rules for what gets routed where and how do those get managed so that's problem number three and then the last one is really data provenance which is something that we see a bit more of in kind of the data warehousing ETL space but really just making sure that you have some sort of way to track the flow like if you have data that's landing in a system is there some way either through looking at looking at configurations or configuration changes over time figuring out where did this data come from does it get tagged based on source like do you apply metadata to it so that you can know in the destination system what the origin was like so thinking about what does that mean maybe you don't even care about the problem is you just sort of want all the data to land wherever it lands that's fine but it's something that needs to be considered because once it passes through the pipeline has been touched you sort of have missed your opportunity if you then want to need to go back and do a lot of work to retag data in those destination systems down the road so each one of these things has its own concerns and considerations that you have to think about and add to the complexity of what it means to build an observability pipeline and so when you start to think about your build option so think through all those problems we talked about before and then you kind of look at the tools that are relatively standard in the space for open source tools or cloud managed tools but what are the things that you can use to compose an observability pipeline so sort of the foundation that you'll see most commonly is some sort of queuing system Kafka probably is the most common there's another newer one called Apache Pulsar and RabbitMQ is another open source option that's been around for a while most of these have some managed offering on AWS or GCP as well so if you're trying to build it on a particular cloud you may not necessarily have to manage those pieces yourself but the thing to keep in mind is that each of these has its own design trade-offs about what it means to be a queue it's not necessarily just a single pass through data processor there may be topics that you have to think about and how do you handle defining topics and what data sources go into what topics and what gets pulled from what topics and how do you actually resource individual topics to make sure that if there's one topic that's particularly noisy that it gets handled well and so each of these is sort of designed to be a very generic message bus that may or may not necessarily fit the logging workloads that you're trying to approach and then so you've kind of got those open source options there are others as well I could probably fill an entire talk just going through like queuing options but then you also have some vendor specific things like SQS on AWS or Kinesis and if you look at the design of SQS it's really kind of more designed to be a like a single really large queue that has a huge scalable processing volume but then there are limitations on payload sizes but it has nice things like dead letter queues which Kinesis does not so depending on how you want to think about think about those individual trade-offs that there are lots of very fine points that you have to go into thinking about the implementation of each of these as it relates to how you want to build your pipeline and so for the queuing piece there's a whole bunch of decisions and each one of them comes with its own potential issues if you don't get to factor them into your decision upfront and then when you look on the sort of processing side I mean you can look to some of these telemetry or metrics-based agents like if you look at Log Stash from Elastic, Telegraph we built at Influx Fluent D's been around for a long time from treasure data and then Fluent Bit more recently as more of like an edge collector and now there's a company called Calypcia around that's formed around Fluent Bit Vector was an open-source Rust tool that's now part of Datadog and each one of these has its own sets of inputs and outputs its own internal state management I guess decisions in how it's architected that affect performance and how it works, how it fails and so potentially the simplest thing can be pick a queue, pick a set of processors and you have data go into the queue and you can use these agents to pull off of Kafka for instance process data and then send it to another source it's fairly simple but then there are lots of ways where that can fail like what happens if you have a lot of like agent management, agent configuration management that you have to do and so as you start to actually deploy these you'll start to see the individual characteristics of each tool that you're choosing how it performs, how it scales what are your input and output options and you sort of have to you end up having a system that's gonna be the composite of the trade-offs that you have to choose around and so this works well for some folks but it really ends up putting a lot of the burden on you, the implementer to figure out how do you manage all of these other tools together and make it feel like a cohesive system and if you think about pushing out configuration changes like if you've got let's say you have a hundred vector agents running and you wanna make a configuration change how do you manage rolling that out to a hundred vectors how do you actually make sure that you don't have 99 that get updated and one that fails and is still putting data in the wrong place so you start to think about an ostensibly simple configuration of these tools but really it starts to come with a lot of complexity and again there are many, many other tools you could use for this this is not nearly an exhaustive list but the goal here is to get you to see there are lots of options and with each of those options come some baggage that you're gonna have to take on and so things that you wanna think about a little bit more broadly as you look at the tools or the option to potentially buy something off the shelf the simplest, I guess the first one is how do you manage this thing what does it look like to spin it up what does it look like to scale it down how hard is it to deploy in multiple places is there a centralized configuration management tool or is that something that you have to build a pipeline but build your own CICD pipeline to do configuration management so what does the operator experience look like back pressure performance this kind of comes back to the rate of ingest problem if the pipeline itself gets to the point where it's sort of reached its maximum processing potential what happens, does it give errors to clients does it have some way of buffering to disk for data that it can't process these are all like really important considerations and things that will affect your entire observability infrastructure because the pipeline now is gonna become that leading edge of all this data being fed into the system so just thinking about what happens because if you start to apply back pressure and give errors to clients then those clients that are sending data upstream then have to have their own durability and state management because you never know how long an outage is gonna last and how long the back pressure is gonna last so then you start having to think about what does it mean to have back pressure versus some sort of buffering or durability versatility is sort of just in general what are all the things that it can do if you look at the Fluent D versus Fluent Bit kind of trade off Fluent D has a lot more integrations a lot more inputs and outputs that it supports than Fluent Bit but Fluent Bit is much more lightweight so it's able to do again it's kind of more targeted at edge processing but if you needed to run with a lower footprint it's a much more efficient tool but again it comes with that trade off of it doesn't support as many native inputs and outputs so if there's something that you end up needing in one of these tools that you've chosen that you don't have you either need to build it find someone else to add it contribute back to open source whatever but just thinking about what are the set of inputs and outputs that you need to work with versus what each of these individual tools offers and again at this point I feel like a lot of them support a lot of the same things you know if you look at like LogStatch versus Vector things like that I think there are lots of they've been around for long enough that they have enough inputs and outputs that you're probably going to be well covered but you never know there may be things like how it handles a particular type of authentication for a certain input or output that may not work with the way that your system is designed so again lots of little nuance each of those things but it's an important point the next thing is sort of data manipulation what is the what are the tools that are available to you sort of within those agents to make changes to that data so for instance Vector uses Lua as a scripting language that allows you to rewrite data and I think you know LogStatch has its own and so is that is that sufficient for you to be able to make all the changes that you want is that efficient enough for you from a compute perspective to be able to do the scale of processing that you want without having to spend a ton on hardware something that's really hard it's really hard to know until you sort of test it at scale so there's a lot of work there that goes into figuring out what are your goals with it as a as a tool you know how much does it give you another piece to this sort of is configuration changes as you're deploying these agents sort of in a processing mesh or like a pool of workers whatever whatever architecture you choose how do you deploy this configuration changes do you have to worry about data loss when agents restart if you need to make lots of small changes what happens you know if you've got data sort of in an intermediate state in a pipeline like does that data get stale and get dropped or is there some way of tagging version changes between configurations so that that data continues getting processed before the new configuration kicks in so lots of again subtle nuance there but things that you need to think about how you're going to manage this from a configuration perspective and also once you start to get to the point where you start putting a significant amount of processing logic in these agent configurations now you essentially have code effectively like do you need to test that you need to test your Lua inside of your agent to make sure that it's doing what you wanted hasn't broken any previous changes and is you know ready for production or whatever do you test it in staging again lots of things to keep in mind there and then the last thing to really think about is error management so you know let's say you have a an output destination that goes offline or you know the credentials are wrong and you've been trying to write data like let's say it's datadog let's say one of your target destinations is datadog and somehow the API token that you're using to write or the credentials you're using are no longer correct well now you're going to back up a ton of logs that are going to be sitting in some sort of intermediate state potentially and it could be a dead letter queue something that's designed for these errors but how do you get alerted when that starts to fill up what happens when that actually becomes so big that that starts dropping logs so thinking about what these error states are like you know is it something that has to be manually remediated is it something that needs to have a code or configuration change to resolve but again it's a relatively important concept because errors can happen fast and errors can cause massive amounts of data and so it's you know I can imagine a single configuration change could cause an entire pipeline to stop working and then you've got potentially your entire real-time data volume getting backed up within the pipeline and you know if it goes unnoticed or if it isn't designed to be able to handle some amount of scale to store that data then you're looking at data loss and so there are lots of these choices implementation decisions that could potentially lead to undesirable side effects when you're thinking about how your pipeline operates so again things that you really need to do a lot to think about if you're building it yourself things that theoretically products that you may look to buy have thought through and solved for you and hopefully taken away a significant amount of that burden. And so getting a little bit to us and kind of how we think about observability pipelines so we started out building a product called Erasearch and the easiest way to think about Erasearch is really kind of a rewrite not necessarily a rewrite a competitor to Elasticsearch in the log management space it's a totally different architecture we wrote the entire thing in Rust it's designed to be cloud native managed with Kubernetes and the idea there was looking at the way the efficiency of Elasticsearch for log management isn't great kind of realizing that there are a lot of properties about time series data, observability data in general that need a relatively specialized storage technology to get optimal efficiency. And so again, kind of going to my background spent a lot of time thinking about this stuff at Influx a lot of time thinking about the stuff at Pivotal and looking at ways that we can optimize the storage problem for observability data and specifically log management. So we've been working on that Erasearch product for about two years now. We've tested it up to about a petabyte per day of ingest on a single cluster. And so really looking for ways that we can deliver kind of a foundational technology for storing log data that makes it cost effective and scalable for organization so that the storage piece is solved. And then as we started to spend more time talking to customers and looking for that the next set of pain points kind of leading into the storage, the observability pipeline problem kept coming up with customers and conversations. And we started working on a product called Erastreams again, written in Rust and thinking a lot about the efficiency and optimizing for these log volumes that are growing and growing. And so Erastreams really is our answer to the sort of all the problems with observability pipelines that I outlined. You know, you're going to need lots of input formats you're going to need a high degree of durability you're going to need the ability to take in lots of different formats and go to lots of different places. You're going to need the ability to have a dead letter queuing and visibility into what's required for remediation as soon as you start seeing those those error states. And so Erastreams really is a full a fully functional observability pipeline presently for log data. I see a lot of use cases coming up where I think metrics will also start to be a factor in observability pipelines right now. There isn't much yet that I think folks are doing from that perspective, but I think it's going to start to become a lot more common, especially we start to see more desire for a correlation of logs and metrics, more pre-processing, alerting things like that where I think logs and metrics can be there's some synergistic value there. I think if they can go through a single processing pipeline I think there's an opportunity to deliver more value. So we just launched a beta program for Erastreams last month. And so we're starting to work with customers now on figuring out how Erastreams can fit a lot of these workloads in larger organizations. And so broadly what we're seeing is that it's not just about logs and metrics and pre-processing. We're really seeing an evolving ecosystem that we're calling observability data management. And so we're thinking about Erastreams really as being this central hub for all of your observability data. If you as an organization are thinking about sources of metrics, sources of logs, how that data is flowing through a processing pipeline, how it's being durably delivered to these other destinations and it needs to be efficient and also needs to be fast. If you're looking for real-time observability and your processing pipeline is adding five or 10 minutes of processing latency, that's not acceptable. It needs to be sub-second processing times. It needs to be effectively processing data in real-time as it's coming through. And so what we sort of anticipate is going to happen is that as these data volumes continue to grow over the next few years, the observability pipeline or the telemetry pipeline piece is going to actually start to become a critical part of not only cost management, but of just operational management for observability platforms. And so this comes down to how do you manage configuration changes? How do you roll back? How do you look at who made what change when and how do you efficiently roll out these changes within your organization, either through GitOps or a CI CD kind of workflow because there's going to be a breaking point with just traditional agents where deploying all these configurations, managing all these configurations is no longer easy enough for organizations to operationalize. And it's going to start leading to more and more problems, more outages. And the most important part of observability from our perspective is that it needs to be the most reliable part of this entire system because if it's not, you can't make sure that your business is functioning, that your other tools are functioning, that your other systems are functioning. And so getting this piece right and getting this to be something that can be easily operationalized, scaled up effortlessly and having as close to 100% uptime as possible within realistic constraints in whatever environment you're running in is actually really critical. And so our view on it also is that the stream processing piece or the observability pipeline piece is great, but what we really want to be able to bring also is the storage technology as well. And that's where we feel like the interaction between era streams and era search is actually really important for us as we're talking to customers. The storage piece solves kind of a fundamental cost and scale issue of where do I put this data and how do I keep it around for as long as I need to without worrying about exponential cost increases. And that's something that we're starting to see with folks that have been running elastic search for a while is it's getting harder and harder to manage these growing data volumes because at a certain point, there's sort of, there are some fundamental limits to what that architecture is capable of handling without starting to introduce a lot of operational complexity and a lot of potential failures. And so the storage piece coupled with the observability pipeline piece really starts to create this holistic picture where you can have all of your data flowing in. We don't really care where you want it to go. If you want it to go into era search, that's great. We're happy to support that.  But there are a lot of customers we talk to who don't need to be putting all of the data into Splunk or don't need to be putting as much data into Splunk as they're currently doing. And so having a cost effective scalable storage technology starts to make it possible to think about what does it look like to stratify these logs in terms of value or business value and think about what's the right place for this data to live long-term and having a tool that can really manage all of the filtering, routing, transformation, aggregation, deduplication in a single tool makes that view of the world very possible. And so just looking a little bit at the era search piece and just sort of our very high level architectural overview. So we've done a lot of work to scale our ability to ingest data efficiently on the storage tier and that also relates to the same way that we think about ingest and efficiency in era streams as well and the observability pipeline piece. But the critical thing for us that we've embraced from the very beginning is having separation of storage and compute built in, being able to have the ability to store data whether it's for archives or compliance or security in some sort of cold storage tier like S3 or GCS or a Minio, being able to leverage high durability, low cost storage for data that gets relatively infrequently accessed but also have that tied into a dynamically rebalanced caching tier for hot storage. So in an era search, everything that comes in gets automatically persisted to object storage and is available immediately in our caching tier. And so you, the user don't need to think about what does it mean for data to go from hot to cold because in reality, all the data is already in cold. The only thing that gets managed for you is what data stays in hot, how much caching capacity is available, what rules you have set around time-based eviction and essentially that data just gets flushed out of hot storage when the time is appropriate. And then if you need to query against data that's in cold storage, that just automatically gets pulled back into hot storage through a rehydration process and is immediately available for querying. So that piece is a fundamental architectural separation but again, is essentially transparent to you as the user. And then having built a query engine on top of that that makes that entire process transparent to you as a user and then we've also done a lot of work to build some API endpoints that mirror what you'd see in Elasticsearch. So whether it's ingest or query, we can actually work with a lot of tools that work directly with Elasticsearch. So you can use our backend as a Grafana data source. You can use LogStash, ingest data, telegraph or vector. Any of these tools work automatically. And so again, this sort of, a lot of these properties are things that we've been backing up into the observability pipeline piece realizing that having that ability to support those inputs and outputs and be able to do that processing in the pipeline tier is really important. So I think finding a lot of these just sort of like design goals that we've built into the storage piece and focusing on those within the pipeline piece has been a big part of our design goals as we build this entire system. And so looking at era streams versus certainly versus building your own thing, we've tried to find the hardest parts of that and solve for those as we build era streams. So being able to scale easily. So this really comes down to if you don't have to think about managing your own fleet of agents, if you don't have to think about managing the capacity of your own queue, that's something that we've built into the product that is relatively straightforward from a management perspective and something that I think should be essentially table stakes for observability tooling. Like the scale should be there, the ability to handle burst and ingest and be able to provide reasonable and efficient back pressure when necessary, all is built into the product. Dynamic reconfiguration is probably the biggest. And so in this is when you apply a configuration change to era streams, the pipeline itself never goes offline. Those changes get updated automatically across every worker in the fleet. And you don't have to think about downtime, you don't have to think about what it means to make a change because the system handles that all automatically. And that includes intermediate processing that needs to be finished, anything that may come along with what you would consider normally having to restart a process for an agent to pick up a new configuration change. And so again, going back to this, there are definitely some tools will allow you to just do a non, just like a heads up kind of restart and trigger a configuration reload. But if you have to do that across a hundred different agents that are all running simultaneously, how do you manage the timing? How do you manage the synchronization? And that's something that we've put a lot of work into with era streams. And then really ease of use. We've got a UI that we've been working on, I'll show a screenshot of that in a minute. But thinking about what it's like for you as a user to define inputs and outputs for you to figure out what data flows where if you get insights into whether your pipeline is running, I think all of those things factor into how you as an operator gonna use this tool and how you wanna surface this to other folks inside of your organization. So being able to see potentially the ability to build these pipelines without having to think about really writing code or having to, I guess YAML for most people, it may or may not be considered code, but even being able to step one level above that and have a tool that lets you build some of these configurations for these pipelines visually. And so really the idea here is make this system sort of a data processing fabric really targeted observability data. So taking a lot of the other potential use cases off the table, focus on observability and make it something that scales like you would expect has changed management like you would expect and becomes a critical part of your observability infrastructure knowing that everything that's flowing in everything that's flowing out has strong durability guarantees and basically does what you would expect. And then I mentioned this earlier but having the integration with AeraSearch, again, it's not a mandated integration. We just have obviously done a lot of work to make sure that that's as seamless as possible but being able to have a low cost storage option is something we think is really important as you start to think about rolling out observability pipelines and thinking about managing your entire organizations observability data workloads. And then the ability to leverage that AeraSearch piece to really get petabyte scale search across all of your logs and having a place that, unlike just dumping data into S3 gives you the ability to run queries, use it in a real-time manner if you choose, you can use it for archival purposes, really whatever you need to do, AeraSearch opens that up for you and makes it easy to get data from AeraStreams into AeraSearch and have a tool that can let you manage all those workloads in a cost-effective way. And then just a couple of things on benefits for AeraStreams. This probably is all relatively obvious based on the things that we've said but a huge part of this is being able to reduce costs. You can do some work to either remove data in flight from your log streams. Some folks like the idea of removing data, some people just wanna be able to store everything and regardless you can do either with AeraStreams but you can use AeraStreams to route data to the platforms that are the most efficient for you. So again, coming back to Splunk, if you could reduce the data that you're sending to Splunk by half that could potentially be significant cost savings and by being able to bring something else whether it's Elasticsearch or whether it's AeraSearch or maybe you wanna use DataDog for some of that, being able to route the data to the right place and efficiently build those rules that decides what data goes where. But really with the goal of not necessarily not just saving money but being able to efficiently optimize how you're spending that money. Taking action on data in flight. Again, I mentioned that you really as an observability pipeline you wanna be able to have this data be effectively processed in real time so that you're not having a lot of latency to your observability data as it's coming into the system. So really being able to do transformations and enrichment, deduplication, whatever it is on the data as it's passing through the system before it lands in your storage destination. And then again, effortlessly managing data at scale. This is something that needs to work at high scale needs to be able to scale up for those organizations that are envisioning two to five X log volume growth in the next year. This needs to be something that works now and is gonna work at 10 X or 50 X its current size because that's the world that we're heading into is probably by the end of this decade I think the log volumes that we're doing now will seem silly in comparison. And everybody will probably be doing 10 times as much data as they're doing now. So we need to be able to build and deploy a set of tools that will scale with those data challenges as organizations grow. And I promised you a screenshot. I was gonna try and squeeze a demo in here but I don't think I left myself enough time but really this is just an overview of what the AeroStreams configuration UI looks like. Again, we're thinking about this as basically a flow chart. You have a set of input sources and on this one we basically just got a Splunk HTTP event collector input that can take data and drop some fields. You can forward the rest of that data onto AeroSearch or any other destination but then you can also fork the data off and you can have a second stream there that maybe goes through some additional filtering step and maybe the ultimate destination here is that you've dropped some fields and you store all the raw data in AeroSearch. Cool, that's efficient. But then you also wanna peel off some of that data and you wanna just strip out all of the logs that are log-level error and you wanna only store those in a different system. And so maybe that's a good thing for you to be putting into your Splunk system or maybe those are the ones that you send to Datadog but AeroStreams lets you efficiently decide and route all that data into whatever destinations you want and be able to quickly visualize what's going where, make changes on the fly and then once you've made your changes you can just hit save and deploy it automatically to your AeroStreams infrastructure and that configuration change will get picked up automatically. And there's another view here that I don't have a screenshot for but a relatively simple change history. So you can go back and look and see who made the most recent change, maybe something stopped working or maybe you stopped seeing logs that you were hoping to see in Datadog. You can go back and roll back to those previous configuration changes and deploy those automatically as well. And so the idea here is that this view gives you the ability to not only configure easily but also to make and visualize these changes, visualize pipeline health and rapidly configure and deploy these changes so that you can start to get data processing quickly. And then another thing we didn't touch on and I'll say one last thing is an important part of this is being able to do to apply some rules here for PII redaction and things like that. So if you know that you've got systems that maybe have more general access within your organization you can have one data source that has PII redacted data that a broader part of your organization has access to and then you can have one that's unredacted and maybe only a set of administrators or a set of higher authorization users have access to. And that's actually a great use for a tool like Aera Streams. It's not something that is super complicated but you wanna be able to visualize oh, this flow is going here and it's got my redaction steps and I wanna be able to see that those are working and then I've got this flow over here that's got my raw data. Make sure it's going to the right place and make sure that it's doing what you expect. And then down below we've just got a log viewer you can hop into really any stage in the pipeline and see a sample of what the logs are that are flowing through that part of the system. And I'm pretty close to time so I'm gonna wrap up here. So we have been, as I mentioned we open up a beta program for Aera Streams. There's a link here. I think you guys should get provided a link to that as well. If you're interested in checking out Aera Streams definitely reach out to us. Again, no shame if you wanna build your own but just keep in mind that there are a lot of things that you have to do and manage. We would love to help you have an easier life and help you manage those with Aera Streams. But definitely sign up if you're interested and we'll find some time to chat. And I actually don't see any questions in the Q&A box. So maybe I've just done a fantastic job talking for 50 minutes. I'll give people like one more second to see if anything pops up. And if there's nothing else I will hand it back over to the Linux Foundation. Thank you so much Todd for your time today and thank you everyone for joining us. As a reminder, this recording will be on the Linux Foundation YouTube page later today. We hope that you join us for future webinars and have a wonderful day.


You talk about schedule because you mentioned that multiple times. That's a big part of your week and what you do. You're really organized. I'm actually not that organized, so I'm really impressed that you are. Oh my gosh. I will give you a schedule that will change your entire everybody, everybody. It's a $25,000 a month schedule and that's really, that's like watering it down. That's watering it down. That's like recycled leads, old leads, three-year-old leads watered like it's like that. It's simple. By the way, at this current moment, I have zero staff and I have had three at one time before. Well, no, that's not true. I have a virtual assistant. They fill in the gap. This schedule is for somebody that has a ton of people under there on their team or the brand new agent coming in and it specifically will help brand new agents. Okay. Monday, I always write it out, so I don't forget anything. Monday is what we call dial day and this is where you do not get an appointment center. You don't have the luxury of having somebody book your appointments. You spent your money on the lead. Why would you have a stranger call it? They're not invested, so you're giving up $40. Go put it in the toilet and flush the toilet. Would you do that? No, right? Like you might, but I'm not. I probably would, but most people would not. No, it doesn't work that way. So you have to dial your own appointments. I start at 9am and I go until it's full. Today, I got done at 11.52 and I was dialing brand new leads.  So from 9.48 to like 11.48, I booked 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14. I booked probably 15 appointments and then did four for another agent. Jeez. Yeah. So two hours of solid, just solid. Oh, and did an app at the end. Got a whole application. And you do that face-to-face appointment? No, I was dialing. No, I mean face-to-face. You're booking face-to-face? Yeah, booking face-to-face appointments and then I sold an app over the phone. Jeez. At the end. So that was today, which gave me the rest of the day. Now I have the whole day off. I feel like I'm done. I had a conference call to get on and I had agents to call. But what it does is it frees up. So if I'm done by 12, I take a lunch break if I want. From 1 to 5, you can do all of your office stuff. Yep. And you can hit the gym. I get up at 5 a.m. So gym in the morning, shower, dial. Tuesdays and Wednesdays are field days or phone days, whatever you want. You're either on the phone booking appointments to go in the field or you're on the phone selling. So I do 8 to 10 on Tuesday, 8 to 10 on Wednesday, every hour on the hour. Thursday is the same exact schedule as Monday. And then Friday, you get 8 to 10. And Saturday is every other for me. And it's just that simple. And it's 25 appointments, minimally, a week. And I break that down. So basically, 25 appointments. This is for your new agent. You guys should definitely write this down. 25 appointments are sets, S-E-T-S. You set 25, which means you need, what, 40 leads or more. Yep. So if you have 25 sets, you'll get into 15 to 20. So 15, let's call, let's say we stake, right? And like, we're not that great. So we set 25, but only 15 let us in. So that's 15, six S-I-T-S. Yep. And then out of those sets, we were really bad and we only sold like, five to eight sales. We'll do the math. Good money. It's a $5,000 a week. It's $20,000 a month. Quarter mil. 25 appointments a week, baby. That's it. That's it. That's it. I love that. That's huge. They stick to that system. There's no way they don't make six figures. Yeah, the problem is sticking to the system. You got to show up to what? Dials. You have to show up to dials. If you don't, that's why I started live dialing publicly. Today was actually the first day we had Tom on. You know what? Yeah, Tom messaged me. He said I'm sitting here watching, he said I'm sitting here watching Leslie do her live dials. I'm like, how awesome is that? Did he put a little brain emoji with the splatter? Because that's how he was. He was brain splatter emoji today. Listening to live dials with Leslie, beast mode. He was blown away and we used go high level and he just got to see that and the benefit of having that. It was so valuable for Tom. I know it was. That's so huge. I freaking love that. Man. His wife. We should have charged. Yeah, Denise is amazing too. We should have charged for this. Yeah. We should have charged for this. It was so good. We do. Well, you do with six figure Academy, right? Yeah, but now it's sort of like, you know, again, I just, I just want you guys to sell because here's the thing.  That's right. Yeah. So on that note, if they want to reach out to Leslie and learn more or keep following your journey, how do they do that? Hello. Hello at Leslie d scofield.com. Hello at Leslie d scofield.com and then Instagram. I'm on Insta. Yes, I am Leslie underscore scofield. Yes. And I am on clubhouse. What? What? Six figure Leslie. Yes. Club. There's her Instagram. There's her Instagram for those that are wondering. Okay. I am Leslie underscore scofield. In case you want to spell her name, I was putting that up on Instagram earlier. So I am Leslie underscore scofield on IG. I get a lot of agents. I get a lot of agents DMing me on Instagram every single day because of stuff like this. So you'll definitely get some messages. It's at six figure Leslie on clubhouse and I have invites. Ah, you want in? You want in? Also email. Hello at Leslie d scofield.com. You've been unbelievable.  I'm fortunate that you spent some time with us.  Anything else you want to add before we close? Yes, Thursday, 9 15 a.m. Eastern. We will be doing live dials. If you go on my Facebook page, I always post my zoom link. It is public. Open to the public. You can come hear me dial with our with our with our team. We overcome every objection in advance so that you book everybody that answers the phone. Boom. That's a mic drop. She's willing to share her live dials. You're amazing. Thank you very much for your time and thank you for sharing with our audience today. Appreciate you. Thank you. I appreciate it. All right. Hey, make sure you guys go and continue to follow Leslie. Apply everything she's saying and make sure that you become Leslie scofield one day. Hey, if you enjoyed this, I got another one you're gonna love. It's right there. Click on it. See you in there. Clubhouse has been fun lately, right? Yes. Not just fun, but like, I mean, there's been some some extreme value, right? Yes, that's it. It's been so good that people have been dropping bombs. People have been asking questions.


recipe number SCP-5250 ease of preparation save formerly Ketter special cooking procedures growing up in rural Michigan nothing blocked out the cold like a big bowl of your mama's special SCP-5250 now you know some people would say there are a thousand and one ways to make SCP-5250 but they'd be worse off than the deer trapped beneath the ice you sigh as you've forgotten how to cook it it's been so long thankfully you've got the recipe right here how convenient one lake in rural Michigan one five meter tall security fence two armed guards for freshness make sure to switch these out frequently and a dash of sound dampening equipment how did you forget it's a pretty simple recipe all in all now you recall there's just a couple of things to avoid when making this you are never ever to step on the ice no matter what that silly deer is doing don't try and help him he'll be fine if you're not sharing a recipe don't share anything that crazy old deer will hear you no writing unless it's a recipe if he can't read the document you know he can't get out only use the second person no first person no passive voice no third person description to cook a splendid dish like SCP-5250 you first take the lake by the cabin as a base add in an unceasing appetite the lake's edges open and up like the gaping jaws of a deep sea fish crack open two sheets of ice by accidental it tossing a spoon on to it remember not stare deep deep down into its depths because what is in there is not human no matter how much it may cry you are not to look down there you are not to feed it with what tapestry of yourself you have weaved in your mind apply three pints of a taste that appears similar to what you used to feel when you swallowed five chili peppers whole the fire within almost calls reaches out to you the heat and warmth make you feel more comfortable you will need heavy amounts of seasoning with its umami flavor it's not as if it can understand taste eating or anything of remotely related to it it is safe whatever it is it is most certainly not a lake you frown being one would have been beneficial you think as the water would help with the sheer amount of spice you add in which even the most resilient chef would scoff at what it did with who it took is still unknown to you but the dish is still missing something you think it'd be hard for you to think of exactly what seen as you're quite poor in the culinary department adding the deer for a bit of a zesty kick taste it just to ensure you've added enough salt the taste is a familiar one it reminds you of the woods behind your house where you used to go and creep amongst the bushes in the trees and pretend that you were a hunter on the prowl it is strange being on the other side of that now to be the one breathing silently eyes in the back of your head silly deer you think so concerned about the exact location of whoever talks about it toss a cp50 250 with two salad forks then mix with a ladle to disorient the deer pouring in the liquid now into a large bowl you laugh as its lame leg trails behind it like a shriveled corpse it's not as if it could follow you or at least it couldn't yesterday you think today it is fine but it will not be the day after then the cycle repeats or are you just too stupid to actually recognize it solely being injured or not but you ignore the deer the mere thought of it or talking about it in any way but this is purely hazardous because it will find you then like a tempest whipped up in a frothy sea it will not let go of you as you've cycled down its gyre whisk gently at first then quickly to prevent the liquid and these thoughts from settling see if the liquid within the bowl will connect in a paper thin strand like melted cheese if not then continue to whisk as the lake and deer are definitely connected but why can't you simply do it on your first time wash your hands then continue preheat your oven and take out your colander ignore the lake's calls and let them drain through the colander's holes ignore the fact that anything that falls down into its depths is immediately taken someplace beyond ignore it all the bubbling stream is all you should focus on with its soft gentle calls repelling that of the siren song SCP 5250 wasn't thought of by you not like you could you're always in the mind that your grandma made it as a secret family recipe even if you don't have one nor are you always the same you however you find SCP 5250 pairs well with the same cabin from the lake you always think of and cheese the charcuterie board is made of the same wood that the cabin built in 1999 was it was owned by two men a mr. Josephus drudgery and a mr. Neil Williams both of them are deceased having died of natural causes in 2000 a shame you tell yourself as you remove the dish from the oven with an oven mitt plate the dish well ensuring that all the elements are equally presented in a way that is mysterious familiar and brand new all at once it's a difficult task are you sure you're even ready for it after all of that you're done cooking up a likely amazing stew of SCP 5250 the aromatic delectable aura the dish you may remind you somewhat of home of you even but ignore that as well the smell tickling your nostrils must not be given mind you've done better than you thought you could you're aware that it took testing for the cabin the deer and this beautifully plated bowl of SCP 5250 to be reclassified as safe after you discovered the appropriate way to talk about them which took you too long to find you incompetent wreck below you've decided to attach some relevant documentation you know to add a little more spice to the recipe and ensure that future cooks in training can serve up a dish just as decadent and spectacular as yours should be one that the lake and the deer are both allergic to they may as well be illiterate they hardly can understand what a good recipe is a danda you have done some tests with SCP 5250 adjusting ingredients here and there and seeing what would happen if you tasted what came out of it below you can find the results future chefs the following table is separated into three sections actions helpful ingredients and result action stepping into the lake helpful ingredient snowshoes to help out out his way result you saw him slipping under the ice despite this you could still see their heat signature for a little bit action getting the computer to write about the deer then reading what is there helpful ingredients your trusty keyboard result the deer didn't notice how delightful action looking at the deer helpful ingredient a pair of binoculars result the deer want you to keep on coming action attempting to kill the deer helpful ingredients some fire a gunpowder and two 45 caliber bullets with earthy undertones result a disappointing presentation as the deer was completely fine action preparing a nice beef jambalaya helpful ingredients a pinch of salt some vision blocking goggles thermal imaging systems and infrared sensors result the lake didn't quite like that you weren't supposed to see what lies beneath action waiting helpful ingredients a sprinkling of nothing result the wind blows the lake is hungry in the cabin when you and the others first came you found a journal with some entries written by mr. drudgery in the cabin there was not much else except for a gun found near the door you decide to attach the journals below with approval from your site director it is not as if there is much else you can do you are stuck in the snow that rages outside the deer seemed interested into him him being one of the gods outside start your stove set it to simmer january 20th 1999 i enjoy that what i can see right now is exactly what i have pictured in my mind the fire is crackling neil is snoring his ass off and i'm writing this we'll finish work on the cabin tomorrow is odd work but neil has been able to offer up some minor reassurances he was talking to fur traders a ways back managed to procure us cured meets the last almost two months so he shall not run low on supplies i admit i had some choice words for him when i found out what he traded for it but i suppose it doesn't matter now it's not as if i'd need the damn thing now that we plan to live up here you revise your recipes awaiting orders the snow is picking up there's nothing you really can do but wait cooking is the only escape really but you're thankful the caverns got quite the apparatus but you're a bit full aren't you haven't eaten so much of that scp 52 50 it's a really filling and the sort of flavor you'd only get by accidentally biting down on your own tongue it's a strange flavor certainly but nothing too strange perhaps a bit familiar really you sit and think think about deer their webs of antlers their majestic look they seem quite trustworthy don't they a buck pride and noble ruler of the woods would not hesitate to help you out so why did you leave the one out on the ice you quickly shake your head the recipe told you so junior researcher you open your computer staring at a smiling photo of yourself you close it must conserve your battery garnish with bits and pieces of thought january 27th 1999 neil has developed a taste for fish he's been spending his time ice fishing in the small pond out back he spent an awful lot of time back there after he said a bear eating our supplies an unfortunate start to our new life that's quite okay i asked him to stay close to me in bed tonight he agreed though mentioned he'd been getting splinters granted what we have isn't much of a bed but i suppose that it have to do he talked about what he caught it wasn't a lot just a boot a really old one i'm surprised that there's been up here but i guess the stream can bring anything i'll worry about him sometimes probably too much it's hard not to though he's a bit too trusting the snow picks up even further the winds howling outside your walls you look at the bookshelf of the cabin empty you open your computer nearly dead you don't recognize the person on your screensaver nor do you remember your password you shrug that's all right it's not like you'd have a connection up here add in two teaspoons of sugar february 1st 1999 we're running though on food neil went out to the woods yesterday came back this morning i never struck him as much of a mountain man but i can't deny the grizzled look was a bit attractive he said he managed to shoot a deer outside our cabin last night but he couldn't go out onto the ice to get it that it was too risky which of course i believed he then told me he'd kill it tomorrow only i'm a bit of a light sleeper i heard no gunshots yesterday neil's never been much of a liar was the first to defend me and actually wanted us to come up here when the town got angry he's never lied to me before i'm worried about him there's a storm brewing and he seems to only want to keep getting food the guards outside are gone you're not quite sure where they went but you think they may have left before the wind started to pick up jokes on them you've got food you've got the recipe book you found and you've got warm february 14th 1999 the deer's not dead the storm has picked up lately neil isn't himself still trying to fish he's not doing it as much which i suppose is a bit better than how often he was doing it i tapped him a couple of times last week it took him a bit to come to perhaps some of the herbs are hallucinogenic or something else who knows i asked him to stay close in bed again tonight he just looked at me and told me i was thinking about what he had said or something like that then turned around i don't think he realized what it was today he didn't even say anything besides that you feel the deer it comes closer and you want to go closer to it you can reason with the deer some it's a lot like you isn't it wounded worried just trying to survive out there on the ice you're quite surprised that things managed to survive this long it's a big buck though must be hardy he'd make for some good venison stew if you managed to kill the thing perhaps you try later let's sit for 30 minutes february 25th 1999 neil's been lying to me i would well leave him but the storms outside still i suppose the storm's a bit of an excuse i don't really want to i'm still doing what we used to going through the motions but i found the meats we traded for hidden under the floorboards and neil keeps telling me that i wonder how he found them even though i did that not him i don't sleep so well he doesn't even sleep with me anymore and the snow is starting to break into the cabin it's too cold he hasn't eaten in days either and i'm afraid he's gonna starve i hear hooves sometimes outside my window when i turn there's nothing there you shot it the ground rumbled and the deer flailed and went under the ice what an awful coincidence you lost your food and an earthquake you sigh whatever the lake is it's hungry it doesn't eat normally you know as much that's why you're writing like this in the first place does it even know how people normally eat it's only tipped off when you talk about it without using you so this starves it somewhat but it knows you're here add two cups of you i believe he is dead i don't know when i'm writing this i don't know what i'm doing here i don't at all the deer isn't dead it's completely fine i saw him step onto the pond to kill it and it he well fell in just like that and the deer did too and then the deer came back up it was wounded and then it was fine and the deer sounded fine and he didn't come back up and i'm he was a lion bastard but he was my lion bastard you walked over to the lake today the deer had returned in the small hole perfect for one to ice fish in you look down and you almost want to jump in but you don't you feel like it's drawing you it's calling you as if it's a part of you you've lost how awful awful it is you and the lake must be without each other how awful it is you and the deer must be without each other how great it would be to go walk over and join the deer however you are cold it would be so warm to kill the deer and steal its pelt but it doesn't have a pelt it's not quite itself just like you aren't yourself as if you were staring at it through a dirty mirror as if you were being reflected within a dirty mirror stirring a pinch of self-doubt the traders have been coming by every three days or so to give me food i'd be dead without them guess they took pity on me but they've gone now haven't come by in a week or so it's hard to tell because it's so dark outside i have to block out the windows because of all the snow and the holes that something punched through looks like a gun but i don't know i hear a knock at the door maybe it's them it was not them it was a deer the deer sounded like a man the deer sounded like them i heard it say help me i am stuck there were two more holes in the ice the pond is bigger i saw the deer enter their mouths then it left then they went into the deer's mouth and both went under the ice what do i do what do you do clip-clop clip-clop their noises outside your cabin then nothing then a phone rings but the phone is outside it's a godstone they're back how great for you but the god doesn't answer nothing does and then you notice the ringing comes and goes with the clip-clop cooked for 40 seconds on high heat you've been waiting silent for three days now you can only tell the passage of time via the birds crows and owls at night spares at morning the tapping is there you hope neil is there wait you hear a knock maybe it is neil you recognize there are no more journal entries disappointing because perhaps you know what the deer is however your thermal imagery has shown that the deer leads under the ice its hoof connects to a thin thin strand which leads to a large familiar mass waiting patiently underwater it rains it clips it clots it cries out to be heard for it is hungry your computer manages to eke out a single bar connection from the car which is running on its last legs you can hear something stabbing it running upon its hood destroying it down to its last metal bits before a loud splash can be heard then you lose your connection but it was enough time to send the document through at least you can remember at least that part of you but you are stuck the snow is barren down on your cabin you write this for your future chefs there is a knock at the door you have your gun ready locked and loaded to file maybe you are being rescued but you're hungry so so hungry and you want to cook up another bowl of scp fit to 250 a nice creamy warm and steaming bowl of scp fit to 250 stew the lake feels like that like one big part of it so maybe deep down you hope it is the deer maybe it is you at the door maybe you open it and voila your dish is finished