Personal Finance PowerPoint Presentation, Mortgage Points. Get ready to get financially fit by practicing personal finance. Most of this information can be found at Investopedia, How Mortgage Points Work, which you can find online. Take a look at the references, resources, continue your research from there. This is by Lisa Smith, updated January 14, 2022, How Mortgage Points Work. Mortgage points are used in the loan closing process and are included in closing costs. So clearly if we're in the purchasing process of a home, typically we can't put all the money down for cash upfront. Therefore, need some kind of financing, some type of loan. Points could be involved in the loan process. The term points can be a little bit confusing because they can refer to different things. And then we might have some options with regards to points. And that then is another thing that we would have to consider to see what would be most beneficial from our perspective. We might need to do some comparing and contrasting to do so. So we've got the origination points or mortgage points used to pay the lender for the creation of the loan itself. Whereas discount points or mortgage points used to buy down the interest rate of the mortgage. So oftentimes when people think about points, they're thinking about those discount points because you're talking about that decision-making process as to whether you want to put more money down upfront to pay for a lower interest rate. If you pay for a lower interest rate, then that will have a longer term effect on the payments that you're going to be making. So you've got that kind of trade-off. And it's a little bit tricky to think about what that trade-off would be to be comparing and contrasting loans that have different rates and then different kind of costs upfront. Because remember, if you're kind of adjusting the loans and you're comparing and contrasting them and you can adjust a few different things, right? You could say, well, if you have two loans and they're both the same years long, you could adjust the costs, the upfront costs, and then lower the interest rate, right? As opposed to having, you know, less upfront costs but higher interest rates, for example. And so you've got to be able to compare and contrast those two. Obviously you then have the time value of money that takes into consideration as well because you have the questions whether or not you have the cash flow upfront to pay for it upfront. And then whether or not it be worthwhile, when are you going to get basically, where's the break-even point in terms of time? When would you get paid back for buying down the rate and so on? So how mortgage points work? Mortgage points come in two variations, varieties. We have the origination points and the discount points. In both cases, each point is typically equal to 1% of the total amount mortgaged. So you can take a look at the amount and then you're going to have that 1%. So that's kind of what a point means to kind of figure what a point is. So for example, on a 300,000 home loan, for example, one point is equal to 3,000 or 1% of the 300,000. That's what it means to have that one point. Both types of points are included under closing costs in the official loan estimate and closing disclosure that come from the lender. Origination points. Origination points compensate loan officers. Not all mortgage providers require the payment or the origination points. And those that do are often willing to negotiate the fee. Origination points are not tax deductible and many lenders have shifted away from origination points with several offering flat fee and no fee mortgages. So you're seeing less of the origination points, which I think is kind of a good thing because it sounds a little bit confusing because there's a couple things that come into play here with the points. If you're purchasing a home, then sometimes you might think that the points are going to be categorized as interest in some way. And if they were interest, then you'd get possibly some tax benefits, some kind of deductibility of them. If they're not interest, then they're part of the closing cost. You might not get the same kind of tax benefits. So it's important to be able to distinguish between the two. But when you call them both points, it gets a little bit confusing. So a lot of times I think more and more they're moving away and just calling them fees or no fee mortgages. And so that would make it easier, I would believe, to be going through the closing statements for a lot of people that are looking for the tax consequences of points, for example, which for my experience or from my perspective as a tax person would be nice. So discount points are prepaid interest. The purchase of each point generally lowers the interest rate on your mortgage up to 0.25%. So now you've got the discount point. So now you are basically talking about interest that is in essence prepaid, right? So that's kind of the idea because you're paying down the interest rate. You're saying I'm going to put more money upfront to get a lower interest rate. So you can kind of think of that as basically paying kind of like the interest upfront. So most lenders provide the opportunity to purchase anywhere from a fraction of a point to three discount points. So that's the typical trend. You've got up to three points that you can pay each point representing 1% of the loan amount typically. So prior to the passage of the Tax Cuts and Job Act, the TCJA in 2017, which applies to tax years 2018 to 2025, were not tax deductible, but discount points could be deducted on Schedule A. So you've got that kind of deductibility concern or consequence. So you've got to take that into consideration for taxes. So going forward, discount points are deductible, but limited to the first 750,000 of a loan. In addition, there is a higher standard deduction. So it's advisable to check with a tax accountant to find out if you could receive tax benefits from purchasing points. So remember the whole home purchasing process, oftentimes you hear, well, you got to purchase a home because there's a tax benefit for it and so on and so forth. And it used to be a higher tax benefit because the standard deduction was kind of lower. So that means more people took an itemized. It would be easier to get over the threshold to the itemized deduction. And they actually increased the standard deduction, which is beneficial to most, you know, low to income, low to moderate income people because that should standardize the code. Because obviously most of the people that are benefiting from the itemized deductions are usually more wealthy people because you're more likely to itemize as your income level goes up. But that also means that there's less, there could be less, if you're taking a standard deduction and you buy a home and now you have the interest involved and the property taxes, there's going to be less of a benefit than you might otherwise think because you got to take into consideration the benefits you're already getting for the standard deduction and you want to make sure that you're understanding that when you go into the purchase of the home because you can falsely think, well, I'm going to get all this interest as a deduction, not realizing that the place you're starting at is one where you're taking a standard deduction. So you're not going to get quite, you know, the same kind of benefit. You're going to be able to deduct all that as an itemized deduction, but you only get the itemized or the standard.  So how do you figure that? You have to actually use tax software and run projections basically to figure that in. And as you're doing that, you can consider the deductibility of points in that process. And so we will focus here on discount points and how they can decrease your overall mortgage payments. Keep in mind that when lenders advertise rates, they may show a rate that is based on the purchase of points. Clearly, if you're advertising, you have the incentive to show the lowest rates that you could offer. And so you might show the rates after you have the discount points in that case, if you're allowed to do that. But that means that you paid for basically the points upfront by in essence paying for the interest upfront. So it's a little deceptive, you know, in a case, but you can see why they would do that. Should you pay for discount points? So this is a yes-no question. I expect a yes or a no. And no, no, they're going to say, let me guess, it depends. Here we go. There are two primary factors to weigh when considering whether or not to pay for discount points. The first involves the length of time that you expect to live in the house. So when you're when you're paying discount points, you're saying, OK, I'm going to say I'm going to put more money upfront. And then and then I'm going to get a lower rate, which could mean that I paid less when I make my actual payments.  So as time passes, the more the better that decision is. If you plan on selling the home, though, in a fairly short amount of time, then then the whole calculation is going to be different than it would be if you're planning a whole 30 year time frame. So the first involves the length of time in general, the lower you plan, the longer you plan to stay, the bigger your savings if you purchase discount points. So if you're saying this is my home, I'm plotting my roots, I'm going to be here for 30 years. I'm going to go right through this thing then and I have the money upfront to pay the points. Then then it might be a beneficial thing or often it's much more likely to be beneficial because you got that long time frame for the rate to to outpace and make it a better situation. Even though you paid the points basically upfront, if you're going to sell the home in a shorter time frame, then you paid the points upfront and you might not have as long as time for it to kind of recoup. So consider the following example for a 30 year loan. Let's do that. On a $100,000 mortgage with an interest rate of 3%, your monthly payment for a principal and interest is 421 per month. With the purchase of three discount points, your interest rate would be 2.75 and your monthly payment would be 382 per month. So clearly you're saying, okay, it was 3% and then I purchased the points and now I'm bringing that thing on down to the 2.75, which means that the payments that I'm making due to the lower interest on a monthly basis was at 421. Now it's going down to the 382. That's good. Purchasing the three discount points would cost you $3,000. Why does it cost $3,000? Because the loan is the 100,000 and they're each at that 1%. So you got the 3,000 in exchange for a savings of $39 per month, which is the difference between the 421 and the 382. So you're putting the $3,000 upfront, but then when you make the payments, you're paying $39 less per month. So you will need to keep the house for 72 months or six years to break even on the point purchase. So that's going to be the most basic calculation. You could get more complicated on this, but obviously if you put 3,000 upfront and then divide that by the savings that you're getting each month, which is $39 per month, then you've got 70, 76, 76. They're coming with 72, but that's the most basic kind of calculator. I can then divide that by 12, which would be the six years, six years. So you can get more detailed on this if you actually kind of map this out, but that would be the quickest kind of calculation you could do because a 30-year loan that lasts 360 months purchasing points is a wise move in this instance if you plan to live in your new home for a long time. So if you're going to be there for 30 years, you're clearly passing that point and then it would be more beneficial after that point of time. So there's, you know, got that timing difference that comes into play. If on the other hand you plan to stay only for a few years, you may wish to purchase fewer points or none at all. There are numerous calculators available on the internet to assist you in determining the appropriate amount of discount points to purchase based on the length of time you plan to own the home. So you can dive deeper into more detail on it with online calculations and tools. This is a subject of debate and interest amongst many, so tools have been developed. The second factor to consider with the purchase of discount points involves whether or not you have enough money to pay for them. So clearly, if you're in a situation you're strapped for cash, that's why I'm getting a loan in the first place. I'm putting as much money as I possibly can at this point down upfront. Then you might not be able to get the points because that would mean that you would need the cash flow upfront to pay for the points in order to pay down the rate. So it's a benefit that you could have that could be beneficial, but you got to take into consideration the cash flow component. So many people are barely able to afford the down payment and closing costs on their home purchases and there simply isn't enough money left to purchase points. On a 100,000 home, three discount points are relatively affordable, but on a $500,000 loan, three points will cost $15,000 and that gets a little hectic. So on top of the traditional 20% down payment of a $100,000 loan for that $500,000 home, another $15,000 may be more than the buyer can afford. I'm sure it could quite well be more than could be. Using a mortgage calculator is a good resource to budget these costs. Using APR to compare loans. So we got the good old APR comes into play here and that's supposed to give us some kind of standardization, even though we've got these differences in rates and upfront costs and whatnot. So comparing different loans with varying interest rates, lender fees, origination fees, discount points and origination points can be difficult. But sure can the annual percentage rate the APR figure on each loan estimate helps make it easier for borrowers to compare loans, which is why lenders are required by law to include it on all loans. So the APR is kind of, you know, again, you can have like a similar type of loan, like a 30 year loan and so on. But then you can have differences in the rates which are compensated for as we kind of seen here with the points are compensated for with the fees. So you could you could try to adjust your loans by increasing the fees in order to get a lower rate, for example, or you can have points involved that you could be purchasing the points in order to buy down the rate. But then the question is, well, how do I make these comparisons from one loan to another loan and so on and so forth. It gets a little confusing because I can't just compare the rate and the loan amount because now I've got these different upfront costs and that messes it all up. Well, you could try to roll everything into one number and that's basically kind of what the attempt of the APR is. So the APR is not the interest rate on the loan. It's an attempt to kind of roll in the costs and so on into one number. So you have a number that's comparable. It's not perfect. It's only one calculation. It's one tool. It's a good tool that you can kind of use to do comparisons of different loans that have different upfront costs and so on. So the APR on each loan adjusts the event at the advertised interest rate on the loan to include all discount points fees or origination points and any other closing costs for the loan. So the metric exists to make comparisons easier between loans with widely different discount points, interest rates and origination fees. So when you're trying to figure how much you're going to pay and so on, how much interest, then you got to use the interest rate. But when you're trying to say, is this loan comparable to this loan, which loan is better, even though they have different amounts of closing costs and points and whatnot, then you could try to use that APR as one tool for that comparative process. It's a little difficult still because you might, depends on how long you're going to hold the home and so on and all that kind of stuff too. So it's one tool that you can dig into though. Are mortgage points worth it? Again, I'd expect a yes or no, but no, you got this whole, we'll discuss it for a while. I want a yes or no. So though money is paid on discount points could be invested in the stock market to generate a higher return, the amount saved by paying for points. So in other words, people that are saying that points are not worth it, make the argument that you could take that money and put it into the stock market possibly and make a return higher than that that you would get on the points. But obviously there's more of a risk to put it in the stock market. So you got that kind of risk reward. The average home homeowner's fear of getting into mortgage, they can't afford outweighs the potential benefit they may accrue if they managed to select the right investment. So if you put it into the points, you're probably more on the on a like a conservative or risk averse kind of standpoint because you're probably saying, hey, look, if I can put the money down up front, and I can make my mortgage payments lower and in such a way that I know I can make my mortgage payments, then a lot of people might feel that I have more insurance myself that I can at least make the mortgage payments. And that might be worth more to people than putting it into the market and trying to get a higher return on the market than the interest rates would be because again, the higher return isn't guaranteed on the market. So it just it there's a risk risk tolerance reward and cash flow factor involved in many people's decision making process there. So in many cases, paying off the mortgage is more important. So also keep in mind the motivation behind purchasing a home, though most people hope to see their residents increase in value, few people purchase their home strictly as an investment. So when you're purchasing the home, you're not, you know, most people aren't going I'm going to I'm going to purchase the home because it's it's primarily an investment. It is primarily a home that you're going to be living in for a long period of time and using, you know, as the home, not basically as an investment tool. So you're trying to possibly buy some insurance on the home to make sure that the home will be there. And that would be more on the points side of things. If you are buying the home and property more on the investment side of things, and you're taking more of a risk kind of investment strategy, then you would think that more likely you'd be on the argument that that if I can get a better return on the point somewhere else, then I would be doing that from an investment perspective. If your home triples in value, you may be unlikely to sell it for the simple reason that you then would need to find somewhere else to live. So if you're thinking about if my home if my home goes up in value, great. But really that doesn't have a real big impact for most people if their home goes up or down in value as long as they can make the mortgage payments, because if they can make the mortgage payments and they're happy about where they are, then it's not going to change their cash flow. Although, of course, if the home goes up in value significantly, you could refinance you could try to dip in to some of that cash and so on in that way or sell the home. But again, for most people they're thinking the home is where I'm going to be for a long period of time. I've got the cash flow to pay for the mortgage. So as long as that's the case doesn't matter what happens with the market because you're locked in and you're fairly content in that case. Although increases and decreases in the housing market can cause havoc, you know, personally when you're considering when you're thinking about it. But if your home gains in value, it is likely that most of the other homes in your area will increase in value too. So that means if you like where you're at and your home goes up in value, great. But the only way to take advantage of that and still be in the same location you're at would be to sell the home there and then buy another home in your area, which would put you not in a better situation. You'd have to sell your home and then go somewhere else where the home was cheaper, but she oftentimes don't want to go somewhere else because that's why you bought the home. And the current place that you're at. So if that is the case, selling your home will give you only enough money to purchase another home for nearly the same price. Also, if you take the full 30 years to pay off your mortgage, you will likely have made nearly triple the home's original selling price in principle and interest cost. And therefore you won't make much in the way of real profit if you sell at the higher price. Bottom line then purchasing a home is a major financial decision. It sure is. We've been thinking about it a lot here. Plan carefully. Look at the numbers before you start shopping. Decide on the monthly payment amount that you can afford and determine exactly how you will get that payment, whether it's by making a large down payment, purchasing discount points or buying a less expensive home. Then be sure to shop around. Don't settle for the first mortgage package that you stumble across. You want to hold out and check things out, do comparisons and stuff. There are plenty of banks to choose from and numerous resources including real estate agents, mortgage brokers and the internet to help you shop for the best deal for your situation. Origination points are usually avoidable and negotiable, so don't spend too much on them. So those origination points, those are the other kind of points. And then so then you got the discount points can save you a lot of money over the life of the loan, but only if you can afford to buy them without lowering your down payment below the 20% and having to get a private mortgage insurance, the PMI. So I mean, if you have the 20% down payment and then you're going to buy the points, but you have to dip below the 20% down payment, which means then you'd have to buy the mortgage insurance, which kind of takes away from the benefit that you would get from paying down the points. So if you don't have the cash flow, then you might not be able to pay the points upfront. The option wouldn't be there. And then it is what it is.


We are going through the greatest technological and economic transition in human history and our political class is totally asleep at the switch about it. It's crazy to me. I mean, it's like an elephant in the center of the country just tearing things up and like devastating communities and taking whole like categories of livelihoods and making the moms to lead.  Like, I don't know why that happened. Adeo Resi was on our show a couple times, the founder, founder and student, he calls it the plane. We're all on the plane and the plane is the pilots have fallen asleep and we're just chilling on the back of the plane, sipping wine, looking out the window, oh, it's going down. But some of us, our lenses, our vision, we're able to perceive the plane going down. And so a lot of the people that are perceiving the plane going down are in New York and SF and LA, Seattle, these different metropolitan hubs where interestingly enough, you write about it in all these collegiate graduates, people from and especially from Detroit and Cleveland and Birmingham, these, these central areas in the US as well, myself included from Sioux Falls, South Dakota, I've migrated out to these technological hubs. I want to experience the network effect. I want to be around the leading scientists and entrepreneurs. And that's why I'm here. And we're seeing that migration happen and these people are now saying, hey, look around you. This is the major time that's coming up. Well, we know it because we're working on it. I have friends who are directly automating away many jobs. They know it. It's in their business plan. They have the line item. They say, we're going to save you this much money. Technology often sees human labor as a flaw to be fixed and done away with and ironed out. So it's the people that are working on it who are closest to the scene who will say, look, guys, this is not going to be some gentle transition. And even if you look at the Industrial Revolution, it had mass riots and violence and popular movements. Labor unions came into existence in 1886. Labor Day was inaugurated because of riots that killed dozens of people and caused billions of dollars of damage. I mean, that's why they have Labor Day. It's like, oh, snap. Laborers are freaking revolting. Let's give them a holiday. And we instituted universal high school in 1911. So in large part because of a response to what was happening. So the Industrial Revolution was really, really rough. And this time is going to be much, much worse. According to Bain, it's going to require labor absorption at between three and four times the rate of the Industrial Revolution. What does labor absorption mean? Well, what that means is that you have workers who are losing their jobs who manage to find new jobs. And so the Industrial Revolution, again, massive conflicts and strife. And according to Bain, this time you're going to need to somehow have reintegration of labor at a much, much higher pace. And if you look at the numbers right now, they're really discouraging because the efficacy rates of government-sponsored retraining are between zero and 37% really bad. I mean, 37 is being very, very generous, honestly, because that's just one study said 37% of people trained in this industry went into that industry. And so some of that would have happened without the retraining, presumably. So we're terrible at retraining. And right now, the rates of Americans moving from one state to another, for any reason, also at a multi-decade low. So you have a labor market that's not dynamic at all. And then you're saying, hey, it's time to have a transition happen at four times the rate of the Industrial Revolution. I mean, the odds of that happening magically, painlessly, seamlessly, zero, essentially. And our government is, again, the sleep of the switch where instead of putting to work massive incentives or really empowering people to be able to adapt, we're just hoping the market figures it out, which it will not.


Wrth i bawb, mae nhw ymddech chi'n gallu gyda'r hynny o gael y byd. Yn y fwy o mater, Richard Benwell. Mae wedi bod nhw wedi fath o'n meddwl ganddangos, a wneud o bryd yn cyfathio data ac yn ei centres, a maen nhw'n bwrdd yn lleion hefyd i'r cyffredinol i'r newid yn ymduno'r hanes gael, mae yna ddweud ond yn wir i ddweud a'i sgiliau newid rydych chi'n yn eu cyflwyng sy'n gyfathol sy'n ei data. Mae o armae, maen nhw'n meddwl ar gael y tawl yw'r opaith. The task will be great to wait for you and today my talk is on why large-scale observability needs graph. By large scale I mean not necessarily volume of data but the complex mesti kind of systems that we are trying to observe as we scale up our organisations. By graph I mean not line graph type of graph but the camera may have the same connectivity type of graph. So, yn y dŵr ydydd yw'r ddych chi'n gweithio y gweithio'r dda i'r ffag o fath i ddechrau gwahanol. Mae'r gwahanol yn ei gweithio'r ddod o'r fgaf amdano. Mae hyn yw ymdweithio'r ddod yn y ddiddorol ar gyfer y dyfodol ar gyfer dda'r gwasanaethau. O'r ddych chi'n gweithio, o'r ddych chi'n gweithio'r ddod o meddwl, a'r treisio, ac yn ddod i'n gweithio'r barthau, mae'n dda chi'n gweithio'r ddod, y three pillars maybe there's six pillars maybe maybe there's maybe there's a lot of pillars, but I'll use metrics logs and traces in this talk often when we talk about this data we really focus on how do I collect and store that data and many of the talks today are about how we do that at massive scale, how we store that data at massive scale and it's a huge engineering challenge the other challenge though is how we use that data collecting that data It does not necessarily actually offer any value to us. Sitting in a database is only valuable if we can actually use that to answer questions. How do we find and use the right data to answer questions. Where is the problem for this incident? How reliable are my components. Where's the weak spot? How is my application going to scale? With its performance and if I make a change to one component. How is it going to impact other components? How do I plan change? Ac mae'n gandd iawn ei gynhyrch yn cyflwyno erbyn chi'n bwysig beth i gael y cy paternau yn g lawsawd. Felly y gwiruriaethau sy'n tuustio'r gom residencell i by Designer, ac mae hynny o Bath yw deinech chi fydd ei gynnwys gynhyrch, os y gall Gwyrtaf jedcaf gwirawchten. Dw' telefonwch greu casodd yw ddweud yn g Damnodd a potwn hwn. Ac yna ll öz wrth mai am wneud hynys gwirioneddol os wedi gweld arigимеч hi. Is there anyone who hasn't read this Wikipedia page? So the Wikipedia page on observability. It's where the inspiration for observability came from was from the control systems domain. And so you go Google observability and you'll find this Wikipedia page. And right at the front at the top is the definition of observability that we're all very familiar with. Observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs. So how do we understand what's happening, give them what we are observing from the external outputs? So that's great. That's the bit we've taken from this Wikipedia page and we've put on every single blog about what is observability. It's where every single observability talk starts with that definition. And if you have been to this page, you've probably done like me the first time I got here, which was you read that and you think that's pretty cool. You maybe read some of the rest of the paragraph and then you start to scroll down and your eyes glaze over and your scroll finger kicks into action and you just scroll down the bottom of the page and move on. But I think there's more to be inspired from the control system's observability and that's further down the page. So the first instinct we have is collect the data but actually we scroll down the page, we hit some of these equations and again most of us kind of glaze over and move on. And I'm not going to talk in depth about equations, this isn't going to be a maths session but I did want to just talk about what those equations actually mean and what we can learn from them. So if you scroll down the page from control theory and observability, it talks about whether a system is observable. And these two equations brought in me in the first one is the x dot. So kind of the new state of the system is a function of the previous state of the system plus the inputs. So you've got x dot equals ax plus bu. So u is the inputs and x is the current state of the system. So that's the first equation. How does the system change over time with inputs? The second equation is what our outputs look like. Our outputs, our signals are some function of the current state of the system plus the inputs that are going into it. So that's what control theory talks about. It has the idea of the current state inputs and those outputs. And then it talks about this observability matrix. So when in control theory they say is a system observable, you need the outputs, sure you need the signals but you need something else. You need those matrices that define how a system works. How does a system change over time? How are the outputs a function of the state of the system? So in control theory they have these matrices that define the model of the system. And that raises the question, where's our system model? So we're talking about collecting the data. We have the signals, the outputs but where's our system model? How do we work out the internal state of the system? So just putting this into context if we take a control system observability case, this is like a textbook case, what is the internal state of the system? What's the voltage across this capacitor? We know these signals. We've got a voltage across the inductor there and an input voltage. So what's the voltage of C? Now it's pretty obvious we have absolutely no idea because we don't know what circuit it's talking about. So in a control systems case you have a circuit diagram, that's then model using those matrices and you can find out whether we can in fact understand what the voltage across that capacitor is if we have these output signals, right? And so you put those into that observability matrix equation and say yes it is observable. Given those outputs we can figure out what's happening within that system. So very obviously we need a model to interpret those voltages. Those voltages are meaningless without that model. So we hop over to something that's a bit more familiar, our control, so our software system observability. Maybe a sort of similar question where's the problem, where's the fault in component. We have our signals, the metrics, logs and traces. We can't use those unless we have a model of the system, right? Just if we had a database full of metrics, logs and traces we wouldn't know what we're looking at. And often that is the case in organisations that the data is collected but not many people actually know how to interpret that data. So of course we need a model as well, right? We have a model maybe of our microservice architecture or a model of our infrastructure and our clusters. So we absolutely need a model to interpret the observability data, the signals that we have. So we clearly do have some sort of model. Well, just to recap there, sorry. In terms of asking the question is our system observable, the proposition here is that actually we need the signals plus the model, right? We can't just say our system's observable because we're collecting the data. We need to put that data into context with a model. So clearly we do have a model of some sort because we do use this data.  So where is the model typically? Well, the model typically is in someone's head or in our heads in terms of informal knowledge. Maybe it's written down in a confidence page that's out of date or an out-of-date diagram but it's probably just informal knowledge shared within a team and across teams. Now this is absolutely fine if you're a two-pizza devops team, right? You build it, you run it. You know what's going on. Everyone knows how the system works. You know how to interpret all this data. But as you scale and become multiple teams and you're not just a single system, you're a system of systems, where's the model of the system of systems?  And that's when you start to see the pattern of something like an architect who's the kind of job it is to work out how everything fits together. Everything comes to them. Or maybe the unlucky SRE who's really trying to figure out how everything fits together and so all questions come to them. So that scales so far, right? You get maybe a few people who have this knowledge and understand the model and so know where to look and interpret that data. But at some point in a large-scale application, you get to this level of complexity. So this is the infamous AWS Death Star so when they mapped out their microservices they ended up with a diagram that looked something like this. Where's the knowledge of that? How do we interpret all of that data and put it into context when we have a system of that sort of complexity? Now, of course, we're not all necessarily like that. We don't have sort of massively complex systems like that but if you have, if you work in an organisation that's been around for a few years and has gone through technology platforms, has gone through developers, gone through scale, you will have a messy system with a lot of different dependencies that are probably not written down anywhere. And that's why it's so hard to use that data that we collect is we're not quite sure where to look when things go wrong or how to interpret the data that we have. So suggestion here is that we need to store a model if we're going to use our data and have true observability. So how do we capture, store and use a model? So that's why I want to talk about graph. I think the model is a graph. And like I said in the intro, when I say graph I don't mean the sort of line chart type graph, I mean the graph of connected things. These two diagrams are actually just from the Wikipedia pages. So line chart on the left and social graph on the right. So if you look up Wikipedia for social graph, that's what you see. And of course we're all quite familiar with that idea of a graph, the social network graph, you know interconnected things. And very clearly this maps very well on to our domain with our services that maybe call another service or maybe even that service puts a message onto a Kafka topic. So publish this to a Kafka topic and maybe another service subscribes that Kafka topic. So they are relating to have those relationships. Maybe that service is hosted on a particular cluster. Maybe those clusters have particular hosts in them. So a lot of connected data and graph is the way to model that data. So before I go on, I wanted to do a couple of quick slides on sort of graph 101 just so we kind of all understand what graph is. It's a technology I think that's trending for a lot of the moments and we're sort of seeing it more and more and I'll talk about how we are starting to see it in our observability platforms today. So the graph concept is this idea of the two dots in a line, right? The boxes and lines. So we have two nodes with a link in sort of mathematics graph term. They're called vertices and edges, which I never get used to. So I kind of often call them just some nodes and links. And so we have something like service A called service B. Okay, so we can capture that relationship as a graph of two nodes there. And then just sort of slightly academic, but just to demystify some terms you hear, you then start to hear about something called a property graph and a knowledge graph. And so a property graph is against something very familiar. It's when we have those nodes with relationships. So service A called service B, but we start to add properties to both the nodes and potentially the edges as well. So we can add the label of that service. It's called service A. It's owned by team, team alpha. It's in this particular repo. And we can put properties on the links if we want to as well. So we can mix properties and edges and just sort of kind of easily express what we want to express. And then just to, as I say, demystify a term if you've heard it. There's also this term called knowledge graph that's being used more and more. And you may have heard about it from Google's knowledge graph. So now when you Google something instead of just getting web pages as results, you get that sidebar, that knowledge panel that actually knows what you're interested in and starts to give you facts about what you're interested in. It could be a person, a famous person, or it could be the results of the football match last night. So Google used something called a knowledge graph and it's slightly academic, but the idea really is you sort of blow apart that graph to mean that everything is expressed as this subject, predicate object. So instead of having properties on a node, you say, well, that node has repo and the identity of the repo is a separate node and it's owned by a team. That team is a separate node and it also calls another service and that service is a separate node. It's a little bit like how we normalise a relational database.  And so within this sort of idea of knowledge graph, everything has a URI, a unique resource indicator, and those identifiers can be then shared across different domains and that's how you start to link data together. So when Google built their knowledge graph, they ingest all this data from different places but they correlate it all using these URIs. This sort of is very exciting. This idea of like, could you express every fact in the world as a graph? That's pretty cool. What could you do with it if you could? Unfortunately, it's typically very academic. It has the feeling of XML web services and sort of soap if you've ever worked with those back to 20 years ago. So it feels a little bit too academic, not very practical, which is why the property graph is generally how graphs are used today. And just another couple of aspects of graph, just so you know these terms if you hear about them. Just as a popular database is around graphs, first is AWS have Neptune. So that's their graph database. You have Azure who has Cosmos DB, which is a multipurpose graph, but can act as a graph database. And the open source world is dominated by Neo4j, which one of the earlier graph databases is now used very widely. So if you come across those databases and talk about graph, that's what they do. And you get something like this as an example query. They're very powerful if you've got all this data connected. You can ask questions like, what upstream front ends use this particular microservice?  You don't necessarily even know how that component is being used by all the applications within your organisation. You don't necessarily know who is calling you and what end user applications you're part of. So the Amazon Cosmos DB both use Gremlin, I think Neo4j supports Gremlin as well, but they have their own language called Cypher. And you can see there it's a little bit complex, but it's this sort of idea that you can express a query like that in this language. G represents the graph, V is those vertices, and then you just say, hey, I'm looking for a service that has the name service X, and I'm going to iteratively look at the incoming links to those services with a calls relationship until I find a node that has type front end. And then I'm going to map those out. So super powerful once all this data is connected. So that's graph, how can graph be used to model our systems that we're trying to observe? The good news is, is that it already is. Not necessarily in the form of those databases, but in terms of concepts, graph is already being used within many observability tools and this is kind of one of the reasons why I wanted to talk today about this topic because it is just a concept and a technology that is seeing a sort of increased adoption and I think as a community, we should look at how we can formalize that and really accelerate its adoption. So one system I wanted to quickly talk about is probably the most familiar, Jaeger. When we perform our tracing ostensibly, that's to allow us to identify performance issues with a particular transaction or type of transaction. But typically, I think one of the greater benefits is when it's first run is you understand what's talking to what and you end up with this graph and go, aha, I didn't realize that actually, this microservice is actually being called by so many other different services. So in Jaeger, they have a graph view and this is the graph you get, but it's just a visualization at the moment. That's not stored anywhere, but it is one of the most useful outputs of these traces. The other use of graph you may be less familiar with is in a tool called Backstage. Is anyone here familiar with Backstage? Yeah, just maybe a handful. So Backstage, actually have BackstageCon just a few doors down the hallway for the first time. It's a Spotify project that was open sourced a few years ago and is now in the incubator phase of the CNCF. And it came into life within Spotify by some platform engineering team there saying, well, we really think we need to understand what we have and how it all works together, right? Effectively the model. And they didn't know what to call it at the time, they called it System Z, or System Z. And it allows developers to effectively catalogue their services and metadata about those services, including then what services they depend on in a service catalogue. So those definitions are defined normally in the code repo. Backstage pulls in the ammo files for different code repos, stores it in a centralized catalogue and enables you to map out your entire environment. It's not specific to any particular infrastructure technology or deployment style or application style. And it's just a service catalogue. You might hear Backstage also called a developer portal. I think it's got more uses as a service catalogue than a developer portal. But again, the guys within Spotify weren't quite sure what to call it, which is why they call it System Z. But it's a very powerful concept which is a service catalogue that allows you just to dump all this metadata about your services and what they talk to into it in a single place. And so this is starting to provide people with a model of what's going on. When I saw Backstage within Spotify, they did have a demo of how they actually imported the output of Jager traces into Backstage as well. So you could actually spot anomalies or discrepancies between what your developers were declaring as their dependencies and actually what the dependencies were when you're looking at the code running for real. And it's kind of an interesting use case, right? So do your developers really know what's calling them and what applications they're part of? So Graph is already being used to model systems certainly sort of conceptually. I just wanted to also present on a couple of examples of where it's being used to combine those signals with the model. I think Backstage is very exciting. And just down the hallway there, I think there is great potential to combine what Backstage are doing with what we're seeing here in the open observability community. But a couple of examples of how we can then correlate observability data across services using tools like this. One is Keali on top of Istio service mesh. This is specific to Istio. But this is a really nice example where they use the Prometheus data to understand what services are connecting to what other services with Envoy there just tracing all incoming and outgoing calls they can create that map. And then they overlay on top of that other Prometheus data. So including alerts. So you can see the data in the context of your service map and the performance metrics as well. And I think Bartek also mentioned about Prometheus maybe having something similar as well. I haven't seen that yet, but I'm looking forward to seeing that. So starting to combine the model with the observability data I think really produces some powerful results. And I kind of won very far end of the scale of how we could use it. I just wanted to highlight a project that was internal to eBay. Something called Groot. There's a white paper and I think I think it may have been open sourced. There's an event graph based approach for root cause analysis. It's a little small on the screen there. But this idea is saying, okay, if we have on the left our dependency graph that we've got from either sort of declarative or tracing. And then we overlay on top of that the events that are going on within our system, then we can actually understand causality. We can understand that an issue or an event that occurred on some service has impacted some upstream service that's impacted a customer by correlating events across different tools. So there's definitely scoped for using this in some very powerful ways and starting to make useful that observability data we're collecting. I wanted just to finish with some ideas around like how can we use this sort of concept further? How can we adopt this? And how can we do more with our data? The first one is about extending the scope of observability. So as I say, we're talking a lot about metrics, logs and traces. And typically that's because we're focused on the system as we define it as our software. Like what is the software doing? What is the code doing right now? Or perhaps the infrastructure that code is running on? But really that software system is just running within a larger system that we really do care about. I've called it here the business system. But there are kind of two elements of that. One is when we're looking at how our software is performing we shouldn't be looking at that in isolation of what the business is actually doing and cares about or what our customers are experiencing. If we're just looking at the performance of the code then we're really blinkered to the bigger picture. So really as a system in terms of modelling the system we should be thinking more broadly than just our code. We should be thinking about the business that the code is running within. So for example, is the business hitting certain KPIs like conversions or adoption or revenue? Or are the customer events something that we need to observe as well? Like logon, purchases, service tickets even? That's what we should really be thinking about as our system that we're trying to observe. Not just the code. And on the other side, very importantly, as we all know, things break often because of changes. So when we're talking about the system that we're trying to observe we really need to include in that the development teams. Like pipeline deploys, code changes, the issues that they're tracking and fixing. And of course our third party dependencies, our external dependencies, the cloud providers that we're running on, some external API that we're depending on. And we need to understand the status of that. Potentially even cost as well. That's another aspect of observability. So when we're thinking about observability in the model, the system we're trying to model we really should think beyond just the software. We should look at it in the context of the broader system. And again, that's where graph can really help us because we're trying to find answers really by connecting the dots across everything that's going on. So for example, if we have a customer impacting incident that might be recorded in our service desk tool. And we might know which customer it is and it has some information about that ticket. And of course, first question we'll ask ourselves is where do I look? Where's the problem? But often within a larger organisation it's not so much about where do I look? It's like who do I get involved? I don't want to now A have to go and get some architect who understands how all pieces together and B have to try and pull in multiple dev teams and take them off what they should be doing which is delivering new features and value to try and resolve this incident. So by correlating that data across multiple different tools and domains we should more easily be able to relate something that's happening on the customer front with what our dev team are doing and changes happening to our system. And then just the final point in terms of adoption I've talked about a number of different tools that are really either defining graphs, creating graphs perhaps consuming data and graph form. But right now we don't have a way of connecting these things. And that's very frustrating given the whole idea here is that we should be able to extend that graph from one tool and one domain across multiple tools and multiple domains and that's when really we start to see the big picture. So for example, taking our service catalodes in back stage with the data we're seeing from our observability platforms with our dev systems like our CICD pipelines and JIRA and then even platform resources. We've got Azure Resource Graph, we have AWS. How can we stitch this all together? So potentially in the future Open Observability Day hopefully we can be talking about an open standard for something we might be calling the observability graph that allows us to really start to make sense of all this data that we're collecting. So that's all I had just to recap. Observability needs signal and model. Models are graphs already in use today across a number of tools but we should be thinking about extending the scope of what we think about as observability. We can use that to connect the dots to find those answers and do we need an open standard for the observability graph. So thank you very much for listening. If you do have questions I think we might have a few minutes otherwise grab me in the hallway, I'd love to talk to you about this if you're interested. Thank you.


My name is Lukas Klerks. I'm 25 years old and I studied the dual degree program at Tilburg University, the master's strategic management program and then the finance master in Germany. The dual degree program is a very nice opportunity for students, especially students from supply chain management and strategic management. They study for one year in Tilburg and then they continue studying one year abroad with one of our partner universities and at the end, so after two years, they get two diplomas. What is it like?  We see three main benefits. One is that it allows our students to gain additional knowledge and skills and attitude in an area that's complementary to the program that they're following at the moment. In addition, it allows them to gain more international experience, increase their international network and finally, it allows them to really stand out when they apply later on for a job at a nice company. There were definitely some stressful moments in this program. Studying in Germany is something that is really intensive, so they have really high expectations of your performance and all the people really strive to be the best they can but also strive to be the best in partying. We are highly selective because the number of places that our partners offer is limited, so we're looking for students who have a very international outlook and are very open-minded and eager to learn. There's a possibility to write a joint thesis, which really allows you to combine your specialisms into one thesis. Tuition fee is the Dutch tuition fee of around €2,000 instead of the high tuition fee that our partners generally charge. The total duration of the program is two years instead of the two and a half or three years that it would otherwise take to acquire two diplomas. I would definitely recommend the dual degree program to students. Since it offers you an international experience, you get the knowledge from two masters and it really allows you to develop yourself academically and you really get the performance standards that you need to thrive in your future career. Students start in our master's programs and at the beginning, at the start of the semester, they apply. They submit a letter of motivation and a resume, including their preference for a partner university. We invite them for an interview and after they've been selected, we propose the candidates to our partners. If you're interested in this program, we invite you to apply by sending us your letter of motivation and resume and be prepared for an interview.


What is up, my Cranky Crew? We did the Crank Gameplays and today we are here in Knockout City. Huge huge thank you to EA for sponsoring this video. They're allowing me to play it early, although when this video comes out it is launch day. So if you want to check out the game for yourself make sure to go down in the link in the description down below and get the game and play it and have a great time. I also just realized that I never explain what the game is because I'm dumb. It's a dodgeball game. Here I am. Look, it's me. Crank Gameplays. So I have it and I- Oh yeah. Hello? Whoa. No, I want to go to training for sure. For once I do actually want to do the tutorial because I have no idea how to play. Oh, that's an opponent. Get knocked out. Those will track their targets. No aim required. That's great. That's great. I love not having to aim. Get out of here. First to catch incoming throws. Try to catch the guy right before he hits you. Hey, throw it at me. Oh, there we go. Oh, perfect. Try it again. When a opponent locks onto you, you see a border warning. The border warning will also show you the direction of the opponent. They're off screen. Let's try this again. Hold on. Wait too early. Oh, okay. Hold on. Wait. I'm going to do this. I'm going to do this. I'm going to do this. Hold on. Wait. I will do this. Every map in Knockout City has a unique quick to learn and master. Unique quirk. Rooftop Rumble has high wind blowing between two buildings. You'll glide faster while inside high wind. Whoa. That's so cool. Can I play instruments? You need a ball? You can find them at designated spots like this. A ball falls off the level. It will respond to one of these spots. Once you've found all the bots, you're free to move to the next training. Great. Well, I did it. What if I play a match instead? I'm going to play a match and see what happens. Swap brawler. There's different brawlers. That kind of looks like me right now. This is going to be my brawler. What's this brawler's name? Do you have a name? Nope. Queben will be your name. Okay. Where are my enemies? Point me in the direction of the nearest enemy. Yeah. Did you see me? Papa. Did you see me? Oh, no. Oh, no. Anybody? Round lost. That's just because I wasn't in soon enough. I would have won. Easy peasy. Here we go. Round two. This is where we make the big box. First real round. Get it. Come here. Oh, yeah. You think you can get away? You can't. My poor boy. My poor, poor boy. What did I name him? It's okay. It's three to three. This is when we separate the... Can I have a ball? Okay. I'd love a ball. Dahino. Dahino. Dahino. So bad. All right. I have to get Ramiro Vren. Come on. Get me out of my cage. And I'm going to do it just fine. I'm so disappointed in myself. Gold team looks cool. But blue team? We're sexy. Here we go. I'm going to knock out every opponent in my path. Mark my words. Yes. Oh, look at me. I got a kill. I did it. A knockout? I don't know technically what it's called. Get him. Come on. Come on. Get him. Get him. Get him. I've been hit. I was hit. I was struck. No. Got him. All right. I got to be careful, though. I can't get too cocky. Why did you see me? Come on out. My pretty. Where are the others? Where are the other drugs going? But hey, fighter three and I've gotten two of those. I really like the sniper ball a lot. That was fun. Got him. Give me a ball. All tied up. I need a ball. For heaven's sakes, I need a ball. Yes. Nice. Oh, that was way too soon. Got him. Hit me. Hit me. Hit me. Not with that. Not with that. I don't think I can catch that. We can't lose. We can't lose. I've done so well. I've done so well. I've been putting the team on my back. Next point wins. Oh no. Do you hear? Do you hear it? The bell. The tolls.  I picked up my opponent, my teammate, I mean. Hey friends, I love a ball. Left click, not right click.  Sniper ball. Destroy him. Got him. Let me at him. Take a ball back up. Got. All right. We in the lead, but I need a ball. Top. Does that mean that I got the most kills? Final round. Two at once. They double team me. Throw me. I got him though. Or I got an assist at least. Got him. Nice. Nicely done. I was hit off the map.  Squander. Dead. No, they need one more point. 69. Half money. No, no, no. I want that. I'm so sorry, team. Come and get me. Come and get me. He got me. He got me. I got hit by a car. I'm dead. Oh, it's okay. We're catching up. I mean, we're, you know, very much in the lead, but it's a herb in the, not the, in the lose. I'm losing. I'm dead. I love a ball. Ow. Got him. Yeah. Give me a little guy. I've got something for you. It's called a knuckleset. Oh, I ran into a wall. We've got to win this one or the game's over. And we all go home. First blood. We're about to get double teams. Oh my God. Oh my God. Oh my God. I was cute. I was cute a lot of four. Come on. If we lose this, we lose it all out of town. They only need one more point.  I was knocked out and then I fell into a pit of water though and I drowned because I was already passed out. No one's coming down there to save me. I'm dead.  That's so sad. We've lost the game. Whoa. Whoa. Whoa. Gloves. I want gloves. Ultimate hologram. Hell yeah. Well, I'm going to end it here. Guys, this was Knockout City. Again, huge, huge thank you to EA for sponsoring this video. It is out now. Go in the link in the description to check out the game for yourself. What we did is just 3v3 team KO, but there's a bunch of different game modes as well, which I'm super excited to check out. I'm just at my head out right now where I can go and hang out and stuff. Look at this chair. Nice. Anyway, go in the link in the description. Check it out. Huge thank you to EA for sponsoring this video. Hope you liked it. I hope you check out the game. I genuinely had a lot of fun. That's very fast paced in a very, very good time. So thank you guys so much for watching. Hope you enjoyed it. If you did, make sure to slap that like button right in the face and I will see you guys in the next video. Love you all. Stay cranky. Bye.