Look at Harry Wilson conjure up here. Left-footed! Oh! Freekicks don't come much better than that! And the Vitality Stadium is lit up in the dying moment to the first half. What a strike from the Loney. Two-in-two, form of back in it. Well, absolutely amazing. What a free kick. I can't think of many better freekicks. We've seen here at Diencourt. Kelsen with his left foot right into the top corner. Off the post. Game on again. Two-one just before halftime.


President Joe Biden spoke at a DNC fundraiser and he basically warned everyone that things are going to get very bad in this country because of the Supreme Court and how relentless they will be in going after our rights. And he says, mark my words, marriage equality is on the chopping block. Let's look at what he said specifically. CNN reports it's not just the brutality of taking away a woman's right to her body, but it also, if you read the opinion, basically says there's no such thing as the right to privacy. If that holds, mark my words, they are going to go after the Supreme Court decision on same-sex marriage, said Biden, speaking at a Democratic National Committee fundraiser in Chicago, according to pull reports. The court, he said, would do the same for Griswold v. Connecticut, the 1965 ruling in which the Supreme Court said the Constitution protects the right to marital privacy against the state restrictions on contraception. The High Court's decision on abortion, Biden suggested, could generate some enthusiasm at the ballot box in November and help Democrats pick up seats in the Senate and House. But the president acknowledged troubling economic news that has put his administration on the defensive in recent days. We can't let this happen, guys. And it's going to be hard because inflation is going to scare the living hell out of everybody, Biden said. All right, so there's a lot to unpack there. First and foremost, he's absolutely correct on the substance. The Supreme Court will target all of our rights. And that really makes for a horrifying situation because this country and all of its institutions will continue to hemorrhage legitimacy. And when that happens, we see the breakdown of democracy, the breakdown of the regime itself. So if we get all of these rights taken away from us, that generations before me and my generation has fought for decades to win, I mean, think about what that does. Think about what that tells us about this country. And even if we fight and win these rights back, it tells us that nothing is sacred. It's going to be a constant battle even in the future. And anything is possible. Social security can be taken away from us. Medicare can be taken away from us. There's a current plot to privatize it that Trump started and Biden is continuing currently. We can win Medicare for all one day. And then the Supreme Court or, you know, these rogue Republicans, they can take it away from us in some way, shape or form. So anything that we fight to accomplish can be taken away from us like that. And that does not make for a healthy system. That does not make for long-lasting institutions. It signals the breakdown of not just democracy, but society itself. So it's horrifying to think about this and think about what the Supreme Court is going to do to this country. But when it comes to Joe Biden, the president of the United States, who currently holds power saying this, you can't just tell us that bad things are going to happen to us and then not commit to fight for us. You can't just say, hey, it's going to get bad. Wish I could do something about it, but vote harder. Because he's admitting here, look, we want to expand the majority that we have, even though it's not really a majority because you have Kirsten Sinema and Joe Manchin, who are effectively Republicans. But he knows because of inflation, you know, Republicans will try to run on that, and they'll try to deflect and make the election not about abortion or issues that voters care about. But I mean, as president, he's admitting that, look, the window where we have power is closing and it's going to get bad. So, man, I'm crossing my fingers and I hope that the election turns out my way. I mean, he's not saying this obviously, but that's the implication. And you can't do that. If you actually know what's at stake and you are acknowledging how bad it's going to get, now that you're in power, use this opportunity today, not tomorrow, not the next day, not a month from now, not after November, but today to do absolutely everything in your power to prevent that from happening. You can't just tell us it's going to get bad and then say nothing else after that. There has to be a but. It's going to get bad, but as president of the United States right now, I'm going to do everything in my power to not just stop the Supreme Court and Republicans from taking away your rights, but I'm going to make sure that we win this election. So I'm going to start by doing things that improve the lives of the American people. Now, the question is, how does he get anything done if you have people like Joe Manchin and Kirsten Sinema in your own party sabotaging your agenda? Well, he can do a lot with his pen. He could take executive action unilaterally and it would make a gigantic difference. So the first and most important thing that he needs to do is galvanize young people because they usually are make or break for every single election. So what can he do to get out young people? We all know he can cancel student debt. As truth that explains, a recent morning console poll found Biden's extended pause on federal student loan interest and payments is popular, but that the president could reap rewards by going even further according to another poll. Nearly half of voters in the battleground states of Arizona, Georgia, Pennsylvania and Wisconsin would be either somewhat more likely or much more likely to vote in November if president Biden canceled $10,000 in student loan debt. That likelihood of voting increases by 11 points when asked if all student loan debt should be canceled. So let's just pause right there. An 11 point jump would be massive that could actually change this entire election. It could turn it around for Democrats, but he's not going to do that. He said he's not even going to cancel 50,000. So canceling all of it is out of the question, apparently. So think about how insufferable it is. He's admitting to us that he has the authority to cancel student loans, but he's just going to cancel 10,000. He knows that you want him to cancel all of it. He knows that there's going to be political benefits to canceling all of it, but he's choosing to not do that. Does that sound like a president who actually wants to hold on to this majority or expand it? But there's more that he can do. So two thirds of Americans want cannabis legalized for recreational purposes under federal law. Biden can remove cannabis from schedule one with executive action, and then he can call on Congress to put a bill on his desk to make it legal in all 50 states. If you can do that, like if you did that, can you imagine how motivated people would be in all of these states? Not just young people, older people as well.  He already, you know, showed us that he's serious about this by descheduling cannabis. So I mean, if he's serious and he just needs more senators who will go along with this, then sure, I'm going to vote for Democrats. Like this is not like just to be clear. It's not a guarantee that all of these strategies will work. But if you actually believe that your majority is in danger, then you have to do everything in your power to deliver. And let's be very clear, you shouldn't be doing this just because your majority is in danger. You should be doing these things to benefit the American people because it's just morally right. It's objectively good. Delivering for the people is what you should be doing as a public servant. But still, if you want to do it for political reasons, I don't care. What I care about is you do it and that could save this election. It could turn it around, but he's not going to do it. But let's assume for a second that he did do this. He did everything in his power. And you know, it's still looked as if he's going to lose this election. Well, do you just tell us that bad things are going to happen and then you do nothing about it? No, you say, look, as president of the United States, I'm not going to stand idly by and watch your rights be taken away from you. We saw what they did with Rome. So we know what's going to happen with marriage equality and contraception. So here's what I'm saying. I want to bill on my desk right now so we can codify marriage equality into law before it's too late. And also let's sign the equality act into law because that's something that I promised I would do right when I was elected and I never fulfilled that promise. Now, that's probably going to fail too. But explain it to the American people. Listen, we didn't have the votes, but I tried. I spoke with Joe Manchin and Kirsten Sinema. I threatened them. I tried to, you know, bring them on board of my agenda using carrots. That didn't work. I tried sticks, so it failed. But I'm still not going to give up. Now we're going to hold rallies across the country to galvanize the American people because we all agree that marriage equality should remain the law of the land. So all of us can agree that the Supreme Court should absolutely not impose their theocratic views on all of us. So let's hold rallies across the country and make sure that the momentum for this issue doesn't die, that we put pressure on these saboteurs in my party who are stopping us from codifying Obergefell the Hodges. There is nothing stopping him from doing these things. Trump held rallies when he was still president and that kept the base consistently engaged. It's probably one of the most, you know, savvy political things that Trump did. But we don't see that from Biden. Even if you fight and you fail, that still does wonders because it shows us that you have it in you to fight. But currently in the White House, there's just no sign of life there. There's nothing. Biden's just saying, hey, they're going to take away more of your rights. Good luck. You can't say that to us when you're the president and you have power right now. Again, it's not like they have a real majority with Kiersten Sinema and Joe Manchin. But you've given up on issue after issue, you couldn't even pass voting rights, which is the bare minimum thing that you can do to maintain this democracy that puts weight behind the words vote when you tell constituents to vote time and again whenever they're frustrated with what's happening in this country.  You ran for this job. You wanted the job. Now fight. Don't just tell us, it's going to get bad. Fight. And if you lose, at least we know that you tried, but I don't even see you trying. I don't even see a spark there. Show us that you're willing to fight and people will reward you for that. Do you enjoy watching independent news shows like the Humanist Report, the Rational National and the Majority Report, but oftentimes YouTube doesn't deliver our videos to your subscription box? Well, I've got a solution for you. It's called the Optout app available right now in the iOS app store coming soon to Android. Optout is an app made by and for progressives where they take all of the most popular independent news shows and they put them in one convenient location. You'll find all your favorites on there like the Humanist Report, the Rational National, the Majority Report, and the app is updated multiple times per day, so your news feed is constantly up to date. If you enjoy watching independent media, this is the app to get downloaded today.


On many distributions of Linux, you'll have no problem with emojis because packages are already installed. But if you're starting with more of a minimal system, when you go to look at emojis, you may get emojis that look like this on this webpage, or if I cat out this text file that's full emojis where it's just a very basic monochrome emoji, or you might not even get that.  Well, how do you get nice color emojis? I'm on a Debian-based system, so I'm gonna use my Debian package manager, but check your package manager for these packages. And what we're going to do is I'm gonna use aptitude, which is a package manager on Debian. And I'm gonna search for notonoto, which you can see right here stands for notofu, for some reason. There might be a few of the packages. We're gonna choose the one here that says font noto color emojis. I'm gonna sudo apt install that.  And you should see those emojis take effect. So I'm gonna log out real quick here and log back in. Okay, I've logged out and I've logged back in. And if I cat out that same emoji text file, you can see that I now have color emojis for most emojis. There's a few that were missed. And I go back to that website I was looking at earlier. You can see that it now has nice color emojis because the website and the shell don't have the emojis built in. Those, that text contains the information for what emoji display and your system will display whatever emojis it has on it. That's why Android looks different than iPhone and different browsers they might look different to. But if you're on a Linux system, again, just look for a package in your package manager that says noto and look for the one that says, I much prefer aptitudes output. It's easier to read. Again, a package fonts noto color emojis might be named slightly different on a different distribution but look for something like that, install it, log out, log back in, and you should be all set. Thanks for watching, filmsbychrist.com. That's Chris with a K, there's a link in the description and I hope that you have a great day.


For more videos and people's struggles, please subscribe to our YouTube channel. The first week of Julian Assange's extradition hearings in London came to a halt on Thursday, September 10th, after a COVID-19 scare. The proceedings will now resume from Monday, September 14th. Shortly before this phase of the trial began on September 7th, the previous extradition request was withdrawn and a fresh indictment was issued by the U.S. in August. Judge Vanessa Baritzer confirmed that Assange now stands trial for the new extradition request. Assange did not get the opportunity to review these new submissions against him. Assange's defense made a request to either remove these submissions or adjourn the trial until January so that Assange can review these additions. But this was denied by the judge. The judge also excluded over 40 applicants for remote access to the trial without any reasons. This includes WikiLeaks editor-in-chief Kristin Harafson, journalist John Pilger, Amnesty International and dozens of civil society and political monitors. The prosecution has been stating that Assange is only being tried for those leaked documents which named intelligence officials. This view was not shared by the witnesses, castifying in defense of Assange. Clive Stafford Smith, founder of Reprieve, stated that during a potential trial in the U.S., all documents could be used against Assange. The witnesses also sought to bring out the political nature of his indictment. International security expert Paul Rogers pointed out that a large number of federal officials in the U.S. are political appointees, including those in the U.S. Department of Justice, that is currently pursuing the case. The publication of leaked documents also altered the predominant narratives of U.S. wars in Afghanistan and Iraq. The expert witnesses also pointed the thread to press freedom posed by this case. Trevor Tim, co-founder of the Freedom of the Press Foundation, stated how the prosecution of Assange could lead to prosecution of other journalists and publishers. He said while WikiLeaks may not have had a sound editorial judgment, it was not for the government to advise media houses on editorial judgments. In the expanded indictment charges, Assange is accused of encouraging Chelsea Manning to share classified documents. Critics have dismissed this charge as this is a common media practice. This is the first time that the Espionage Act is being used against a publisher. Several U.S. administrations have threatened to prosecute journalists for publishing classified information in the past, but never did it. The fact that President Donald Trump's administration is the first to do so is indicative of his strained relationship with the media. His administration has declared the press to be their enemies since the beginning. Several voices of support came together in support of Assange. Reporters without borders tried to submit a petition signed by more than 80,000 activists and groups to the British government to dismiss the U.S. extradition request. But the Prime Minister's office turned them away. Demonstrations were held in several cities across the world in support of Assange. Iconic musician Roger Waters declared that he too had shown the collateral murder video. He said, if Julian is guilty, so am I. Julian is the contemporary equivalent of the head on a pipe as a warning to others. Ex-Brazilian President Louis Nassio Lula de Silva also defended Assange's innocence. He said, Assange should have been treated like a hero for having denounced to the world the lives of the U.S. State Department, investigating the world, investigating the government. All of the democratic countries should cry for his freedom.


Hi and how are you all today? I'm Priyanka and let us do the question. It says list all the elements of the following set Now these are the parts of the question which are given to us and we need to list the Elements of the following set so that means in the question sets are written in the set builder form and To list all the elements we'll write it in roster form in roster form all the elements of the set are Identified and listed separated by commas and closed within brackets places as you know, so Let us proceed on with our solution first Set is given to us as a is equal to x is to x is an is an odd Natural number Right, so there is no condition attached to it So that's all the odd natural numbers will be the elements of set a and that will be written as a is equal to 1 3 5 7 9 and since there is no upper limit given to us we'll End it at not a particular Element right so this is the answer of our first part proceed on with the second part Now we're given B is equal to x is to x is an Integer minus 1 by 2 is X is greater than minus 1 by 2 is less than 9 by 2 2 right so We'll write all the integers between minus 1 by 2 and 9 by 2 So that will be if we write it in decimal This is minus 0.5 and this is 4.5. So moving on In our number line we have zero one two three Four and we cannot write five because x need to be less than 4.5 That means five is not included over here. So this becomes the answer of our second part Proceeding on with the third part We have C is equal to x is to x is an integer x square is less than equal to 4 Right, so we know that squares of number minus 2 minus 1 0 1 2 are 4 1 0 1 and 4 respectively so The answer will be Minus 2 as its square is 4 that means it is equal to 4 minus 1 whose square is 1 which is Less than e less than 4 then 0 whose square is 0 and hence less than 4 1 and 2 we cannot have minus 3 or 3 because its square is 9 and we need to have a number which is less than equal to 4 right, so this completes our third part solution Proceeding on to the fourth part We have D is equal to x is to x is a letter in the word Loyal so our answer will be as we know in roaster form We do not repeat the letters So we'll have L oh by A and since L is already written we'll end it here. So this becomes the answer of the fourth part Proceeding on to the fifth part We have set E is equal to x is a month of a year not having 31 days So let us think which are the months of the year which are not having 30 days And I think I don't need to answer this for you. You can tell me and yes, I'm waiting for your answer It is E is equal to good February April June September The last one is right November So this completes the answer to the fifth part Let us proceed on with the sixth part which says F is equal to x x is a consonant in the english alphabet Which proceeds so We should be well versed With what is a consonant? Consonant are those english alphabets which Are except the vowels that means the all the alphabets except a e i o u are called Consonants and preceding which comes before k. So I hope you remember your alphabets And you can make a set. We will not start with a as it is a consonant. Then the next will be b c d e is also a vowel f g H We cannot take i as it is also a vowel J and we need to take it preceding k means before k. So we will end up our discussion over this This becomes the answer of our Solution and this ends the question which was given to us. I hope you enjoyed the session And Bye for now


This 2017 Chevy Silverado 2500 Crew Cab Short Box in Summit White is stock number 11836Z. We are here at Summit Automotive in Fontalek, Wisconsin. You're new and used heavy duty truck headquarters and today we might be checking out the cleanest 2017 Silverado that is on the road right now. This truck has the 6 liter V8 motor. All of our vehicles go through 130 point inspection per the state of Wisconsin inspection process before being made available to our customers. They get a fresh oil and filter change. All the fluids get checked and topped off before being made available. This one is Summit White. We shoot all our videos in 1080p 60 frames per second so if you have HD capabilities on your computer tablet or smartphone device or television turn them on right now because it is definitely your best way to check out the quality and condition of this amazingly clean truck before seeing it in person. And if you want to subscribe to our YouTube channel in the bottom right hand part of the screen is a link or a button to subscribe. It's gold. Click that to see daily videos that we do every day. Now this one has the 20 inch polished aluminum rims. No scuffs or scrapes on these rims and it has Goodyear Wrangler LT265 60R20 tires with just about all the tread left. This truck only has 900 some miles on it. You can see just how clean the frame and under body is. I've never seen a Chevy frame that clean in my life. And this is a 2017. Comes with the projector lamp headlamps painted front bumper. Front bumper parking sensors. No dents, dings, scuffs, rock chips or anything on there. Factory fog lights. The hood is absolutely perfect. And you do get the chrome trimmed grill. Passenger side front fender. Absolutely perfect as well. And the passenger side rim. No scuffs or scrapes on there. As you go down this side, the truck tick note of how clean the body is, how reflective and mirror like the paint is. We take these HD videos so if you are far away or even if you're close by and just cannot make the trip down, but you're still interested in purchasing the truck, you can see the truck, hear the truck and have confidence in the vehicle that you're looking at before you even get here so that when you do get here there's absolutely no surprises. And you can make a smart buying decision from the comfort of your own home. I didn't see a single dent or ding down that side. It comes with some really nice factory painted step bars. His back rim is in nice shape as well. And the back tires have just as much tread as the front tires. Take a look at that frame and on your body. I don't see any corrosion even on the drill holes there. It's usually where it starts, but absolutely perfect. Has the Z71 Rancho shocks. I mean even the exhaust looks brand new. Lower rockers look great. Has a Paragon fold-up tonneau cover. That's a pretty cool tonneau cover. I'll show you how that works. Your bumper is absolutely perfect. Has the bumper steps, parking sensors. Tailgate is an excellent condition. Has the full towing package which includes a receiver hitch four pin and seven pin wiring. Has the shock down assist tailgate, spraying bed liner. And to get this tonneau cover you got to press this middle piece down a little bit and then it'll slide. It goes just like that and then it just keeps folding up and then to get it back you just pull this strap like so. Works really nice and smooth and that tailgate shuts so nice and solidly. Just an amazingly clean, very nice condition truck. This back rim is absolutely perfect as well and on the rest of the side of the truck no dents, no dings, no scuffs, no scrapes. Get the telescopic tomeas. Telescope out like so. They fold in. They might even power fold in. Built in directional signals, LED side lights, blinkers. Inside the LTZ package gives you the black leather interior, no rips, no tears in the seats. They are like new as well. All weather formats, power windows, power locks, power mirrors, memory driver seat and these mirrors do power fold in. I always like showing both sides so that you can see that both sides are working properly. Factory brake controller turned out four-wheel drive and auto headlamps. We will hop inside here, turn the ignition on so you can see that this truck indeed only has 969 miles. You do get the digital speedometer, compass display, the Z71 instrument cluster, comes with the leather wrapped steering wheel, Bluetooth and information center controls on the right, cruise controls on the left, audio controls on the back, comes with the six-speed automatic transmission with the optional tap shift, the Chevy MyLink system, AM FM and SiriusXM radio capabilities on here. It also has the projection manager where you can project your cell phone to the screen and it does have a CD player there as well. This is also where your backup camera shows up. We'll start the truck up in just a second. You have your dual climate controls there as well. Your heated seat buttons, stability control. This is for your cargo lamps. That's also how you turn on your LED side lights in the mirrors. Parking sensors, downhill assist control, two USBs, two 12-volt power points and then a 110 volt, 150 watt plug-in. You do get the dual glove boxes there, passenger side format and seat are in excellent condition. I don't think this truck's ever been smoked in and the headliner is absolutely perfect. Power SunRough. Up here you get your home link buttons for your garage door security systems and lane systems, power side and rear window button and your SunRough controls. You also have on-star and SOS capabilities in the mirror. This one also has the cell phone charge pad. We'll take a quick look at the back seats and then we'll start it up and take a look under the hood. The back seats are just as clean as the front seats. I don't think they've ever been sat in. Child safety locks or latch child safety system I should say. On those back seats power slide and rear window with the built-in rear defrost. These seats fold up for extra storage, carpeting and floor mats are absolutely perfect. Side curtain airbags and of course the bottoms of the doors are going to be perfect as well. We'll start it up and then take a quick look under the hood. No check engine lights or anything like that. And I would personally like to thank you for checking out the video today and hopefully from this HD video you have been able to tell just how clean this truck is all the way around. I kind of got to see it to believe it. It's one of those rare finds. Under the hood we have the 6-liter V8 motor. Engine bay is very clean, runs very smooth. Once again all of our vehicles go through 130 point inspection for the state of Wisconsin inspection process. Get a fresh oil and filter change, all the fluids get checked and tapped off before being made available to our customers. And I would highly recommend this truck from a quality and conditioning standpoint. And to see more pictures of this truck or one of other 450 new and used cars, trucks, SUVs, minivans, Wranglers, halftones, three-quarter tons, one tons, you name it. We got to go to that website right there at www.summitauto.com. Full pictures and descriptions of every single vehicle from two locations all at summitauto.com. And if you'd like to check out more HD videos you can go to youtube.com slash summitauto. Number two, like, subscribe and share on this video and all the videos that you see there. In fact in a second you will see a link to subscribe to our YouTube channel in the upper left. A link to more Chevy Silverado 2500 truck videos like this one in the upper right. A link to this vehicle on our website in the lower left and a link to one of our latest YouTube videos in the lower right. Click those, check us out and we're super excited to be offering this ultra clean, really low mileage 2017 Chevy Silverado 2500 Crew Cab Short Box LTZ in Summit White. Thanks for checking out the video. Remember to like, subscribe and share.


Hello everyone. Good afternoon. Thanks for joining me as I talked to you guys about M3 and Prometheus and then how the two of these open source solutions can work together to create a metric monitoring solution at a global scale. So a little bit about myself before we get started. My name is Gebs Cullen. I am currently a developer advocate at Chronosphere where we provide a hosted metric monitoring solution built on top of M3. Before Chronosphere, I was a product manager at Amazon and AWS for a few years and then also included my GitHub and Twitter handles here in case you want to connect. So just going to go over the agenda for today's talk. So we're going to start out talking about monitoring with Prometheus and some of the challenges it has when reaching a certain scale. From there, we're going to talk about monitoring with Prometheus and M3 followed by a quick demo on how to get started with M3 and then there'll be plenty of time at the end for Q&A. All right. So monitoring with Prometheus. So just to give a quick overview of Prometheus, Prometheus is a single binary metric solution. It comes with a tag-based metric ingestion format in query language called PromQL. It is a scraped-based metric ingestion solution. So it is pool-based. It's a push-based. It comes with an efficient metric storage solution. So it's very efficient at storing metrics within itself. It also comes with the ability to visualize and graph metrics. Many users do, however, use Grafana for this, but Prometheus does have its own out-of-the-box solution as well. And finally, you can create alerts using Prometheus alert manager and metric roll-up rules. So you can aggregate sets of metrics using Prometheus as well. So as you can see, Prometheus has really risen to popularity over the past few years, and it's kind of taken over the title from graphite as being the de facto or go-to open-source metrics monitoring solution. And this is mostly due to a lot of these advantages that we'll discuss here. So first one being that it's very easy to get started. So there's basically just one single binary that's used for ingestion, storage, and query. And then there's another binary that's used for alerting as well. So very simple out-of-the-box setup. It is also the CNCF-recommended monitoring tool, which means that most of the software projects within the CNCF ecosystem also expose metrics in the Prometheus format. So this makes it really the de facto tool for getting started. It also has a wide range of discovery mechanisms. So it's easy to use on any platform you're running, including Kubernetes. This also makes it easy to integrate and start discovering particular metric client endpoints to scrape your metrics from. And then finally, it has a wide ecosystem of exporters, which are basically just custom integrations with particular software projects. So most major software projects, especially open-source ones, have these existing integrations. So this really just means that there's a great kind of community around these integrations when you are wanting to implement Prometheus. So getting started with Prometheus, as I mentioned, it's very easy to get started. And this is a pretty standard setup here. So in this diagram, we have our services exposing metrics via their Prometheus endpoints. From there, we have our Prometheus instance setup to discover these endpoints and to start scraping and adjusting the metrics from these endpoints and to the Prometheus instance. From there, you can choose to run an instance of Grafana and point it to the Prometheus instance to visualize your metrics. And you can also do the same for alerting with alert manager. So basically just also pointing your alert manager to your Prometheus instance in order to get alerts on your data. All right, so now we're going to get into some of the pain points with Prometheus. These pain points arise as you start to scale out your Prometheus instances, as well as when you start caring more about reliability. So from a reliability perspective, kind of by default, all of your data will kind of go into a single Prometheus instance, as you can see here. So if it does go down, you not only lose all active real-time monitoring of your services, but you also lose access to all of your historical data of that very of that particular service. So this is really a significant point of failure with the out-of-the-box setup of Prometheus. So the recommended solution to get around this is to have two or multiple instances of Prometheus and having them both scrape the same client endpoint. So that if one does go down, one Prometheus instance does go down, you will still have a copy of your metrics. And then when it comes to alerting, since the alert manager is also a single binary, you would need to run two instances as well, and then they would trigger alerts for their respective Prometheus instances. So when it comes to viewing data from a dashboard with this setup, that's when it gets a little bit trickier. So typically in this setup, you would put a load balancer between your two Prometheus instances and point the Rafauna instance to the load balancer. So from there, all read requests would get balanced between the two Prometheus instances. So if one does go down, you're still able to fulfill these requests. And this generally works well for reliability in the sense that you get one copy, at least one copy of your data. However, the problem is that if you are doing rolling restarts of your Prometheus instances for any reason, whether it be maintenance or for upgrade purposes, then you'll start coming across a gap in your data as the instance is down or kind of restarting. And you can see that here in this diagram. So in this example, let's say we did a rolling restart of both of our Prometheus instances, you can see that each instance is missing metrics from when that instance was down. So neither instance has a full image of the CPU usage over the, you know, over the skin and time period. So from a final standpoint, if you have a load balancer in front of it, you will really only see one of these instances or one of the two copies of your data here. So if you do refresh your graphs, you'll get either one gap or the other in your graph. And this can really lead to inconsistent results. Unfortunately, there's no really out-of-the-box solution at the moment with Prometheus to merge the two sets of data. The second set of kind of pain or, you know, the second major pain point that we will be discussing is around scaling up your Prometheus instances. So common use case here is if you are monitoring certain services with a single Prometheus instance, unless they all are set and one of your services starts producing a lot more metrics, then maybe that one instance can no longer manage a load and can become overwhelmed. So the recommended way to get around this is to create a separate Prometheus instance and then have that instance store and scrape metrics from the particular service while having the original instance store and scrape metrics from the other services. So by doing this, you are manually starting the load across your fleet of Prometheus instances. However, this does get a bit tricky for a few reasons. So the first is from a dashboarding and an alerting perspective. So you need to tell each dashboard or alert in this scenario which Prometheus instance to point to in order to get the data you're looking for. So originally, all the dashboards were pointed to the kind of this first Prometheus instance, but now that the data set is started across the two, you not only need to change the data source in Grafano when you are kind of wanting to look across the two different instances, but you also will kind of lose any of the historical data of service A when you do kind of, you know, spin up the second instance. And then the same thing happens with alerting as well. So there's another scenario or instance when a single dashboard or alert needs data from both of your Prometheus instances. So kind of summing up the data across your various services here. In this case, you would need to make sure all your data goes into a single place. So in order to do this, you will create a third instance through a process called federation. And this will allow you to see, you know, see your data across or view your data across both instances. By federating, by creating this federated node, you get a subset of data from your original instances. And then this allows you to query the kind of query this federated or third instance, you know, with that subset of data from the various services. The problem with this, however, is that you still only have a subset of data stored in your federated instance. So if you do need more data than what's in that node, you need to also query and point your alert manager to the perspective instances where that data is being stored. So as you can imagine the management of this and knowing which instance has which data and which ones have an overlap of the data can get very hard to manage at scale. So as you can see here, scaling, scaling this up leads to many more federated nodes, which can leave you with a very complicated Prometheus structure. So similar as the kind of previous slide. If you wanted to look across your instances or in this case across regions or zones, you would need to federate the data in another Prometheus instance and combine that across both zones or regions. However, you still need to maintain awareness and understanding of which Prometheus instance contains the data that you're looking for. All right. And so the third pain point that we're going to discuss is around efficiency. So Prometheus is not the most efficient when storing long-term data. And this is mostly because there's no built-in down sampling capability. So for example, if you're storing data for six months at a scrape interval of 30 seconds, it ends up taking up about 8,000 kilobytes. But if you're able to down sample the same dataset at a one-hour resolution for the six-month period, it would only use about 68 kilobytes. And then that's just for one instance. So you can see if you were to scale up to 100 instances of Prometheus, this discrepancy becomes much larger. So as you store more and more longer-term data, down sampling becomes a very valuable asset for efficiency. And just one thing to note is that as you start to look at metrics for that six-month period, even in dashboards, there's no really great way of viewing your data at a 30-second granularity because there aren't enough pixels on the screen. So it's much more common to store your data at a higher granularity anyways. But regardless, Prometheus does not have a great out-of-the-box way of doing this. So the way to get around it is to kind of federate your data so that you would have a second Prometheus instance as you see here. And this instance would read the raw 30-second data and then down sample and store it in a separate instance at a higher resolution. However, by doing this, the down sample data needs a new metric name. So when you do query the data, you need not only need two separate queries to look across both resolutions, but you also have to kind of switch between dashboards within Grafata to look across the two use of data. All right, so now we're just going to kind of recap the major pain points that we've discussed regarding scaling up Prometheus. So from a reliability perspective, Prometheus is not really designed to handle these availability zone or region failures, especially with a high level of consistency in terms of scalability. As we discussed, the management overhead of sharding the various data sources can become very painful over time. And in addition, the management of federation and configuration becomes difficult at scale as well. And then finally in terms of efficiency, while the platform is really great at storing short-term metrics, without the down sampling capability, there's no great solution for storing longer term data. Not to worry though, Prometheus is very aware of these pain points. And they intentionally built Prometheus to be a very easy out-of-the-box solution. So rather than kind of going in and creating solutions for these pain points themselves, they introduced this concept called remote storage that basically has remote write and remote read APIs that it exposes. And then these APIs can be implemented by other technologies such as M3, Thanos, and Cortex, which can then be used to solve for these pain points at scale. So for the rest of the talk, we're going to dive into M3 in particular and then how M3 and Prometheus can work together to solve for these particular pain points that we've discussed. Okay, so monitoring with Prometheus and M3. So just going to give a quick overview of M3. So what is M3? It is a open source metrics engine. It's comprised of three main components. One is a custom built time series database called M3DB, which is used to efficiently store all metrics data. Then there is a ingest and down sampling tier. And finally, we do have a query tier that is used to provide optimized queries and fetches for all of the data. M3 was built in open source from day one, having its first kind of check in and get hub in around April 2016. It was also originally built to solve the metrics and monitoring use cases at Uber, which it successfully did. And since then it has helped many other companies such as Walmart with their various metrics and monitoring solutions and use cases. And it is continuing to be maintained by Uber as well as Chronosphere. And as I mentioned, it is designed to be Prometheus remote storage compatible. So some of the key features of M3 as they tie back to the main pain points with Prometheus that we discussed. From a reliability perspective, M3 does keep consistent copies of all its data. It does have a default replication factor of three. It's also designed to tolerate single node availability zone and region failures out of the box. It's also designed to be highly scalable. So each tier is horizontally scalable. And it has already been proven to store billions of metrics time series data at a time. In addition, it was built with a very simple operation in mind. And this was to kind of help reduce the need for a complicated management overhead as you scale up. And then finally, in terms of efficiency, it does have a built-in down sampling capability. And then all data is optimized with a special compression algorithm that was designed specifically for metrics and time series data. So going into the architecture overview of M3, we have our three tiers here that we mentioned. You know, the first being the ingest down sampling tier called the M3 coordinator. From there, we have a distributed time series database called M3DB. And then finally, there's the query tier called M3 query, which is used to fetch all data. On the right side of things, we have our Prometheus instance pointing to M3 via the Prometheus remote right endpoint. So the coordinator from there will then implement this Prometheus endpoint. And then on the read side of things, we have Grafana, an instance of Grafana set up, and it's pointed directly to the M3 query tier, as you can see, which has a built-in Prometheus or built-in promql read endpoint and can execute any read requests by fetching data from the M3DB tier. So diving into the M3 coordinator and the right path. As you can see, we have a Prometheus instance here writing to the coordinator using Prometheus remote right. Every data point that goes into the coordinator will get replicated three times, and then these data points will then get written to three different places across the M3DB nodes. When writing these metrics to the M3DB nodes, the coordinator does use quorum right, which means that at least two of three copies of data have to successfully write over in order for it to be called a success. So if this doesn't happen, then the coordinator will retry until it does become a success. The coordinator is also in charge of down sampling. So what this means is the coordinator will generate down sample data inside itself. So for example, when a raw data point comes in, it will be in charge of not only writing that raw data point to M3DB, but it will also be in charge of down sampling that data, dating data point. And then from there, it'll write that down sample data to the various M3DB nodes using quorum right. So zooming in a little bit inside one of one of the M3DB nodes, just a couple concepts here that we're going to discuss. The first is a namespace, which is most similar to a table in most other databases. It is generally recommended that you store data with the same resolution and retention periods within a single namespace. And then that namespace will then be spread across all M3DB nodes within a cluster. So within a namespace, all data is sharded and replicated. So as I mentioned, there are going to be three replicas of each data point. In terms of the sharding, it does occur on the metric ID. And these metrics are then randomly sharded based on their metric IDs, which are essentially just a combination of a metric name and a metric tag key value. So inside this shard, there are two components of time series data. The first is an in-memory metric index. This is going to be an inverted metric index index that uses a tri-like structure to fulfill certain queries. And it can also read regex and glob patterns. In addition, all metric time series data is going to be stored against the metric IDs themselves. And they're also going to be stored based on time and blocks that go back over time. So for example, in this diagram, we have the metrics being stored at two-hour blocks, and these two-hour blocks are being compressed into these two-hour blocks by our compression algorithm so that the two-hour blocks are stacked up next to each other. And then finally, on the query and read set of things, we see that the query tier here can implement both promql and graphite or stat-steam metrics. In this particular diagram, we have a instance of Grafana pointing to our M3 query tier, just as you would with Prometheus instance. From there, the M3 query tier will fetch all of the data being requested using quorum reads. So this ensures that at least two of three copies are being fetched successfully so that you can get a consistent read. From there, it will apply any functions on top of the data before then returning it back to Grafana. So one of the good things about the M3 query tier is that it is a stateless tier. So you can put a load balancer in front of M3 query tier and have any of the instances that you have set up be able to fetch the data for you. So for example, when pointing your Grafana instance to the M3 query tier, you just have to point it to one of the single read endpoints and then you don't have to worry about which data source you're connected to within Grafana. And then one final thing to note about the M3 query tier is that it is designed to be 100% compatible with promql. So scaling up M3, as you can see, we still have a similar setup here where all metrics are being scraped by a single promql instance, which will then forward along the metrics to M3 via promql. So scaling up your various tiers here is just a matter of putting a load balancer in front of them and then adding additional instances. However, the way of doing this varies across the different tiers. So for the coordinator in the query tier, because they are stateless, it's much more simple and you can just basically add additional instances. However, for the M3 DB tier, it is designed to be aware of when you are adding or removing instances to the node or to the tier. So it is a stateful tier. So when you are wanting to scale up this tier, you do need to make sure that any sort of configuration changes are being made. All right, so now we're going to look into how M3 works across multiple zones. So in this example, we spread out our M3 cluster across three zones. As you can see, we have the coordinator query and M3 DB tiers here spread across these three zones. However, you will notice that for the M3 DB tiers, we have kept a single replica of each tier within a particular zone.  Also in this example, you have the coordinators here writing to all three replicas of M3 DB, as well as the query instances here are also going to be reading from all replicas across the zones. And looking on the right side of things, you can put a load balancer in between your Prometheus instance and the coordinator, and then have the Prometheus remote write go to any of the coordinators. And then as an alternative, if you didn't want this setup, you could also run your coordinator as a side card next to your Prometheus instance. And then looking over to the right side of things, you would also put a load balancer here in front of all of your M3 query instances, and then you could point your Grafana instance to that single endpoint, which would then evenly distribute your query requests across the zones. So basically, if you do, if you did lose an entire availability zone, the cluster will still be up and running, and you'll still have two copies of your data intact, so that you can kind of maintain that core reading, core write logic that we discussed earlier. In terms of the load balancers here, if a zone did go down, they would simply just reroute the data to the zones that were still up and running, and then they would do this until the zone that went down comes back online and recovers. In general, it's kind of to help with the management of this multi-zone setup. Really, the M3DB tier is the hardest part in terms of scaling up in management. So because of this, and that's because it is a stateful tier, as I mentioned. In order to kind of help with this, we do have a Kubernetes operator in open source. So if you do want to run M3 on Kubernetes, all you have to do is tell it the number of instances you want in the cluster, and then from there, the operator will take care of scaling everything up and down for you. Now we're going to look kind of across two regions. So in this example, it's going to have a similar setup as the previous screen, having the various tiers here spread across three zones. Obviously, you don't see the zones in this example because we have zoomed out a bit. But in terms of this setup, the recommendation for having multiple regions is to have all of the data that's being stored and produced in a particular region stay in that region. However, if you do want to kind of look across regions, we do, the recommended setup is to have your M3 query tiers here connected so that they can talk to each other across regions. And then that's how you can make any sort of fetch requests across your multiple regions. So for example, if you did want data across your various regions here within a single Grafana dashboard, how this would work is a query request would come in to your local query tier, which will then fan the query out to all other query tiers in the other regions you have set up. From there, the query tiers will fetch data from their local M3DB cluster and then send the results back to the original query tier for that request. From there, that original query tier will then combine the data across all regions and then perform any functions on that data locally inside that particular query node. And it'll do this before sending back the requested data to the dashboard or to your instance of Grafana. But the main point to get across here with this setup is that when you do have multiple regions, it is not recommended to replicate data across your multiple regions. Instead, you want to store the data within its respective region and then simply connect the query engine so that they can fetch data across your regions as needed. Okay, so now we're going to get into a quick demo of getting started with M3 and M3DB. And then just a couple of things to note before I switch over my screen to the demo. You know, we did talk about Prometheus remote write and we will kind of show that in the demo. I also already have three instances up and running just for the purposes of time and for the demo. We have two Prometheus instances up and running and then we do have one instance of M3DB. And in addition, you want to make sure to check out the documentation we have around M3 as there are other ways of deploying M3 if you were interested in going about those as well. Okay, so now we have the demo up and running here on my screen. So it is a pre-recorded demo. So I'll just talk through the different stages here. So you can see I've already kind of cd-ed into the repo where I have my various config files that we'll be using to kind of get the different instances up and running. So from here you can see all I did was just a single kubectl command and this from here created the cluster for me. So super simple just one kubectl command to get the cluster up and running. And then here is the, you know, kind of the configuration file for that cluster. So you can see we have the rectification factor of three. And now I'm just going to show you kind of the operator is already up and running in the back end, but I'm just going to like run this command here to show that to you. So as you can see here, pod status says we do have it up and running. Then now I'm going to do a quick kubectl command to get pods to show you the various instances that I've already have up and running that I mentioned. So here are the two Prometheus instances I mentioned, A and B. Those are already up and running for me. And then also just one thing to note here is I do already have an instance of Grafana up and running as well. All right, so now we're going to kind of go to Grafana. So we're going to port forward to our Grafana instance with port 3000. Cool. So now we're in Grafana UI. I'm going to go through the dashboard, go to the dashboard portion. So now, yeah, so I've gone ahead and selected the Prometheus A data source, which is set up to scrape from the Prometheus B instance. So running the up command here, you can see that we only are getting data for that one instance of, you know, Prometheus B. I'm zooming in a little bit now at the 15 minute interval. So doing the same thing, switching over to the Prometheus B instance. Also with up command, you can see that we're only seeing data here for the Prometheus A instance. So now if we were to go in and add the remote right configuration to our Prometheus config map configuration and kind of apply that. Now switching over to our M3DB instance and data source, you can see we have, you can see both Prometheus instances here in the same view. So rather than having to switch between the data sources, you can just see everything in one snapshot. And then this is with the up command. I think I'm going to show one more command here. I'm just getting a different perspective. I'm going to scrape duration seconds metric. So again, you know, you can see here the two instances or metrics across both instances are being shown together here in a single dashboard. So that concludes the demo. All right. Well, so now that we've included the demo, that kind of wraps everything up for my presentation. So thanks everyone for joining. And before we get to Q&A, I did want to kind of go over a few different resources here for M3. So, you know, if you're interested in kind of getting involved with our community, we have various active Slack channels. So make sure to sign up there. You know, if you have any additional questions on some of the topics or components I touched on today, you know, you can go to our documentation to dig more into that as well. And then on top of that, we do host monthly office hours, which you can sign up for with this link. And then we also do have a community meetup group where we try to host meetups every month. So yeah, thanks again. And now we will open it up to Q&A.


Live from Fisherman's Wharf in San Francisco. It's theCUBE, covering IBM Chief Data Officer Strategy Summit, Spring 2017. Brought to you by IBM. Hey, welcome back everybody. Jeff Frick here with theCUBE. We are wrapping up day one at the IBM CDO Strategy Summit Spring 2017 here at the Fisherman's Wharf Hyatt new venue for us. Never been there. It's kind of a cool venue. We're joined by Peter Burris, Chief Research Officer from Wikibon and we're excited to have practitioners. We love getting practitioners on. So we're joined by this segment by Bruce Tyler. He's a VP Data Analytics for IBM Global Business Services. Bruce, nice to see you. Thank you. And he's brought along Fawad Butt, the Chief Data Governance Officer for Kaiser Permanente. Welcome. Thank you, thank you. So Kaiser Permanente, regulated industry, healthcare, a lot of complex medical issues, medical devices, electronic health records, insurance, you are in a data cornucopia, I guess. It's data heaven all the way.  Kaiser Permanente is. And as such, the opportunity for us is the fact that we have access to a tremendous amount of data, right? So we sell insurance, we run hospitals, medical practices, pharmacies, research arm, labs, you name it, so it's an end-to-end healthcare system. That generates a tremendous amount of data set. And for us, the real opportunity is to be able to figure out all the data we have and the best uses for it. And I guess I never really thought of it from the vertical stack perspective. I used to think it was just the hospital, but the fact that you have all those layers of the cake, if you will, and can operate within them, trade data within them, kind of gives you a lot of kind of classic vertical stack integration. Very myself. And I haven't finished. I mean, I didn't give you the whole stack. I mean, we were actually building a medical school in Southern California. We have a residency program, in addition to everything else we've talked about. But yeah, the vertical stack does provide us access to data and assets related to data that are quite unique. And on the one side, it's a great opportunity. On the other side, it has to be all managed and protected and served in the best interest of our patients and members. And just to hold just electronic health records by themselves that people want access to that. They want to take them with, but then there's all kinds of scary regulations around access to that data. So the portability, I mean, I think what you're talking about is the medical record portability, which is becoming a really new construct in the industry because people want to be able to move from practitioner to practitioner and have that access to records. There are some regulation that provide cover at a national scale, but a lot of this also is impacted by the states that you're operating in. So there's a lot of opportunity to sort of reconcile some of the regulation in this space over time. And I think that will, then we'll see a lot more adoption in terms of these portability standards, which tend to be a little one-off right now. Right, right. So I guess the obvious question is, how the heck do you prioritize? I mean, you got a lot of things going on. You know, I think it's really the standard blocking tackling sort of situation, right? So one of the things that we've done is taken a look at our holistic data set end to end, right? And broken it down into pieces, right? How do you solve this big problem? You solve it by piecing it out a little bit. So what we've done is that we put our critical data sets into a set of what we call data domain. So patient, member, providers, workers, HR, finance, you know, you name it. And then that gives us the opportunity to not only just say how good is our data holistically, but we can also go and say how good is our patient data versus member data versus provider data versus HR data. And then not only just know how good it is, but it also gives us the opportunity to sort of say, hey, there's no conceivable way we can invest in all 20 of these areas at any given point. So what's the priority that aligns with business objectives and goals? So if you think about corporate strategy in general, it's based on customers and demand and availability and opportunities, but now we're adding one more tool set and giving that to our executives. As they're making decisions on investments and longer term, and this isn't just KP, it's happening across industries, is that the data folks are bringing another lens to the table, which is to say, what data set do we want to invest in over the course of the next five years, right? If you had to choose between 20, what are the three that you prioritize first versus the other? So I think it's another lever, it's another mechanism to prioritize your strategy and your investments associated with that. You got, but you're specifically focused on governance. Now, in the healthcare industry, software, for example, is governed by a different set of rules than software is in other areas. Data is governed by a different set of rules than data is governed in most other industries. Correct. Finances is still a sort of thing, so there's most. What does data governance mean at KP? Which is a great company, by the way, a Bay Area company. Absolutely. What does it mean at KP? You know, I think it's a great question, first of all. Every data governance program sort of has to be independent and unique, because it should be trying to solve for a set of things that are relevant in that context. For us at KP, there are few drivers, right? So first is, as you mentioned, regulation, right? So there's increased regulation, there's increased regulatory scrutiny and pressure, some things that have happened in financial services over the last eight or 10 years are starting to come and trickle into the healthcare space. So there's that. There's also a changing environment in terms of how, at least from an insurance standpoint, how people acquire health insurance, right? So it used to be that your employer provided a lot of that. Those services and those insurances. Now you have private marketplaces where a lot of people are buying their own insurance. And you're going from a B2B construct to a B2C construct in certain ways. And these folks are walking around with their Android phones or their iPhones, and they're used to accessing all sorts of information. So that's the customer experience that you have to deliver to them. So there's this digital transformation that's happening that's driving some of the need around governance. The other areas that I think are front and center for us are obviously privacy and security, right? So we're custodians of a lot of data sets that relate to patients' health information and their personal information. And that's a great responsibility. And I think from a governance standpoint, that's one of the key drivers that define our focus areas in the governance space. There are other things that are happening, there's obviously our mission within the organization, which is to deliver the highest coverage and care at the lowest cost. So there's the ability for us to leverage our data and govern our data in a way which supports those two mission statements. But the bigger challenge in nuts and bolts terms for organizations like ours, which are vertically integrated, is around understanding and taking stock of the entire data set first. Two, protecting it and making sure that all the defenses are in place. But then three, figuring out the right purposes to use the data, right? So data production is great, but data consumption is where a lot of the value gets captured. So for us, some of the things that data governance facilitates above all is what data gets shared for what purposes and how, right? Those are things that in an organization of our size deliver a tremendous amount of value both on the offensive and the defensive side. So in our research, we've discovered that there are a lot of big data functions around functions that fail because they started with the idea of setting up the infrastructure, creating a place to put the data and then they never actually got to the use case or when they did get to the use case, they didn't know what to do next and what a surprise. No returns, a lot of cost, boom. The companies that tend to start with the use case, independent of the individual technologies actually have a clear path and then the challenge is to create knowledge, create experience and turn into knowledge. So from a governance standpoint, what role do you play at KP to make sure that people stay focused in use cases, that the lessons you learn about pursuing those use cases then turn them with general business capability within KP? I mean, again, I think you hit it right on the head, right? So data governance, data quality, data management, they're all great words, right? But what do they support in terms of the outcomes, right? So from our standpoint, we have a tremendous amount of use cases that if we weren't careful, we would sort of be scattered brain to rock, right? So you can't solve for everything all at once, right? So you have to find the first set of key use cases that we're trying to solve for. For us, privacy and security is a big part of that, right? To be able to, there's a regulatory pressure there. So in some cases, if you lose a patient record, it may end up costing you $250,000 for a record, right? So I think it's clear and critical for us to be able to continue to support that function in an outstanding way. The second thing is agility, right? So for us, one of the things that we're trying to do with governance and data management in general is to increase our agility, right? So if you think about it, a lot of companies go on these transformation journeys, right, whether it's transforming HR or trying to transform their finance functions or their business in general. And that requires transforming their systems. And a lot of that work, people don't realize, is supported and around data, right? It's about integrating your old data with the new business processes that you're putting on. And if you don't have that governance or that data management function in place to be able to support that from the beginning or have some maturity in place, a lot of those activities end up costing you a lot more, taking a lot longer, having a lower success rate. So for us, delivering value by creating additional agility for a set of activities that as an organization we have committed to is one of our core use cases, right? So we're doing a transformation, we're doing some transformation around HR. That's an area where we're making a lot of investments from a data governance standpoint to be able to support that as well as in patient care and membership management. Great, great lessons. Really good feedback for fellow practitioners. You're kind of perspective, you're kind of sitting on the other side of the table. As you look at the experience at Kaiser Permanente, how does this equate what you're seeing with some of your other customers? Is this leading edge or are we, you know? Clearly on point, in fact, we were talking about this before we came up and I'm not saying that you guys led, we led the witness here, but really, you know, how do you master around the foundational aspects around the data? Because at the end of the day, it's always about the data, but then how do you start to drive the value out of that and go down that cognitive journey that's going to either increase value onto your insights or improve your business optimization? And so I've made, we've done a healthy business within IBM, the helping customers go through those transformation processes. I would say like five years ago or even three years ago, we would start big. Let's figure out, let's solve the data aspect of it. Let's build the foundational management processes around there so that it ensures that level of integrity and trusted data source that you need across an organization like KP because they're massive, you know, because of all the different types of business entities that they have. And so, you know, those transformation initiatives, you know, they delivered, but it was more from an IT perspective. And so the business partners that really need to adopt and are going to get the value out of that were kind of in a waiting game until that came about. So what we're seeing now is looking at things around from a use case driven approach. So, and let's start small. So whether you're looking at trying to do something like within your call center and looking at how to improve automation and insights in that spec, build a proof of value point around a subset of the data, prove that value, and those things can typically go from 10 to 12 weeks. And once you've demonstrated that, now how do you scale? But you're doing it under your core foundational aspects around the architecture and how you're going to be able to sustain and maintain and govern the data that you have out there. It's a really important lesson. All three of you have mentioned now that, you know, that old method of let's just get all the infrastructure in place is really not a path to success.  Unless that's right and they're shoes too. I mean, build it and they will come is, you know, sounds great, but in the data space, it's a change management function, right? So one of my favorite lines that I use these days is, data management is a team sport, right? So this isn't about IT, or this isn't just about business. And you can't call business one monolith. So it's about the various stakeholders and their needs and your ability to satisfy them through the changes you're about to implement. And I think that gets lost a lot of times, but it turns into a technical conversation around just capability development versus actually solving and solutioning for the business problems that are at hand. But you got to do both, right? You have to. Absolutely, yeah. So can I ask you, can we talk for another couple minutes? So really quickly, Afwad, do you have staff? I do. Tell us about the people on your staff, where they came from, what you're looking for. So one of the core components of the data governance program are stewards, right? Data stewards. So to me, there are multiple dimensions to what stewards, what skills they should have, right? So for stewards, I'm looking for somebody that has some sort of data background, right? They come from design, they come from architecture, they come from development. It doesn't really matter as long as they have some understanding. As long as you know what a data structure is and how you do data modeling. Absolutely. The second aspect is that they have to have an understanding of what influence means, right? So be able to influence outcomes, to be able to influence conversations and discussions way above their pay grade. So to be able to punch above your weight, so to speak, in the influence game. And that's a science. That's a very, very definitive science. Yeah, we've heard many times today that politics is an absolutely crucial game. It is part of the game, and if you're not accounting for it, it's going to hit you in the face when you release expected, right? And the third thing is, I look for people that have some sort of an execution background. So ability to execute. It's great to be able to know data and understand data and go out and influence people and get them to agree with you. But then you have to deliver. So you have to be able to deliver against that. So those are the dimensions I look at, typically when I'm looking at talent as it relates particularly to stewardship talent. In terms of where I find it, I try to find it within the organization because if I do find it within the organization, it gives me that organizational understanding and those relationship portfolios that people bring to the table, which tend to be part of that influence building process. I can teach people data. I can teach them some execution. I can't teach them how to do influence management. That just has to. You can't teach them the social network. That's exactly right. Are they like the frustrated individuals that have seen the data that they're like, ah, this is human rights. They come from a lot of different backgrounds. So we have people, so I have a steward that is an attorney. Is a lawyer. She comes from that background. I have a steward that used to be a data modeler. I have a steward that used to run compliance function within HR. I have a steward that comes from a strong IT background. So it's not one formula. It's a combination of skills and everyone's going to have a different set of strengths and weaknesses and as long as you can balance those out. So I think- People who had an operational role but now are more in an execution setup role. Yeah, everybody myself. They probably have a common theme though across some of that. They understand the data. They understand the value of it and they're able to build consensus to make an action. That's correct. That's great. That's perfect, perfect close. They understand it and they can influence and they can get the action. Pretty much sums it up. Yeah. All right. All right, thank you. Well thanks a lot Bruce. And if I want to stop and buy great story, love all the commercials on The Warriors. I'm a big fan of watch K and B Hark. Anyway, but really a cool story and thanks for sharing it and continued success. Thank you for the opportunity. Absolutely. All right, with Peter Burris, I'm Jeff Rake. You're watching theCUBE from the IBM Cheap Data Officer Strategy Summit Spring 2017 from Fisherman's War of San Francisco. We'll be right back after this short break. Thanks for watching.


Hi everyone, we're back, this is Dave Vellante, I'm with Wikibon.org, I hope you enjoyed the live streaming keynotes from the Tableau Customer Conference. Kristen Shabo gave what I thought was a phenomenal keynote, one of the better keynotes I've seen at events like this, we go to a lot of events like this, you know, typically you get a lot of big, you know, strong vendor messages talking about their products and their new innovations and so forth. This CEO chose to talk about, in my view, why they launched Tableau and why they started the company, what their philosophy is, what their mission is, it was very, very powerful. We're going to talk to Kristen Shabo tomorrow and I encourage you to tune into that. One of the more dynamic speakers that I've seen in a long, long time. But right now we're going to take a quick break in the action and we're going to talk to Burt Latimore. Burt Latimore's here, he's a writer for Silicon Angle Wikibon, spends a lot of time with our user and he writes the peer insight newsletters and Burt has been roaming the floor at the Tableau user conference, talking to customers, talking to people at the event. So Burt, welcome to the Cube. Thank you Dave. So what have you seen at the show? What's exciting you? Well, first of all, the feeling of the show is so, people are excited. I mean, in the general session, people were cheering all the time. Chris Schulte was cheering for product features that were being announced. Yeah, cheering for product features. I mean, Chris Schulte said, well, we've added box plots and people started cheering. I mean, these are people who are excited about this. And there's almost 4,000 of them here. So there's a pretty good turnout for a, for what is really not a gigantic company. Right, yeah, $200 million in revenue and 4,000 people, pretty good user conference. Yeah, comparable in size and enthusiasm to the Service Now conference. Oh yeah. So what have you seen? Give us a quick rundown. We only have about two minutes. Well, the exciting things, of course, happened in the, or exciting announcements, happened in the general session that's just getting out. And the first thing was that Schulte promised an accelerated pace of development in general with version 8.1 coming out in the fourth quarter, version 8.2 in early 2014, and then version 9.0, a major version in 2014. And then he introduced a series of people who talked about specific things that they're doing. And there's too many to go through here. But they are going, they are working for seamless access to data in 8.1 and 8.2. Basically get any data from any source into Tableau with simple visual operations. They've announced, I think one of the really exciting ones is they've announced integration with R, full integration with R in version 8.1. Of course, R is used a lot for doing advanced analytical predictive analysis. And instead of having to have R open and Tableau open, you simply select the elements in your data plot that you want to put into R, select external analysis and it happens in an instant. They were doing it up there with records of data flows from Rock Creek Park, which goes through Washington, D.C. and it happened instantly. It was amazing.  Dan Jewett is here. He is the head of product management for Tableau. We got to go. But the other thing I'll add is they announced support for Macintosh, Tableau for Mac. Yes, they announced Tableau for Mac. Thanks for coming on. We'll maybe have you on later and run it down. Keep it right there everybody. We'll be right back with Dan Jewett right after this.


hello in this lecture we're going to work some test type problems problems that could be short enough to fit into a multiple choice type format so we have here a company uses the FIFO method for inventory costing at the beginning of a period the production department had 30,000 units at the beginning in working process support accounting instruction by clicking the link below giving you a free month membership to all of the content on our website broken out by category further broken out by course each course then organized in a logical reasonable fashion making it much more easy to find what you need than can be done on a youtube page we also include added resources such as excel practice problems pdf files and more like quickbooks backup files when applicable so once again click the link below for a free month membership to our website and all the content on it inventory which were 45 complete the department completed the transfer 170,000 units at the end of the period 27,000 units were in the ending working process and 80 complete compute the number of equivalent units produced by the department so what we're doing here of course is transferring from the working process to finished goods and in order to do that we have to determine how much of the goods have been completed in terms mainly of conversion costs which means the amount of labor and the amount of overhead that's going into these units we're going to assume a first and first out meaning that the stuff that was in there at the beginning has you know been transferred out we finished those first before we started on some other units that's the assumption that we're making on the flow assumption so if we take a look at this we're going to break this out this way we could say beginning working process we had 30,000 units now those units are of all areas of completion and we're going to come to some kind of determination on how complete they're done meaning we got probably the raw materials in there how much completed have been done in terms of the work to the raw materials and the overhead applied to it the conversion type stuff we're going to say 0.45 45 percent if we go to the home tab we go to the numbers we add decimals or we make it a percentage we're going to say that those 30,000 units are equivalent to the 30,000 times 45 the equivalent units of 13,500 now the trick to this is to have the units that are both started and completed and if we could do that we can say okay those units are 100 completed throughout the process because we started and completed them now they're not going to include this one this 30,000 of course because those were started last period last month probably and therefore those are going to be completed but they weren't started and completed they were just finished here and therefore we're going to try to figure out how much were started and completed we could do that by saying how many units were transferred out in total 170,000 total units were transferred from the working process to the ending therefore we can take the 170,000 that were transferred out and we're going to subtract from that the 30,000 because those are included in the 170,000 and they weren't started and completed so that means that 140,000 were started and completed and of course those are one or a hundred percent completed and started during the time period so if we calculate the equivalent units 140 times 100 percent will be 140 and then we have the ending working process so the ending working process they say are 27,000 those are the stuff that's still in there not quite done and they're going to have to give us some kind of rate of completion on that we're going to say 80.8 so I'm going to go back here home tab numbers make it a percentage therefore the equivalent units would be equal to the 27,000 times the 80 percent or 21.6 okay let's read back here we're going to say compute the number of equivalent units produced by the department now it's going to be one other piece we're going to need to figure out here we've produced the amount that was finished in this beginning working process as of the end of last time meaning there was 45 units that were completed equivalent units of 13.5 that means we're going to have to finish those units so there's a couple ways we can calculate the amount that will be done at this period one is we could say well if we did 45 percent last time we're going to have to take 100 percent minus 45 percent and if we make that a percentage that would mean 55 so if it was 45 percent done last time how much is it going to be done this time 30,000 times the remaining 55 or of course we could have also said if there's 30,000 units and we did 13.5 equivalent units that means we got the 16.5 we're going to have to do this time so this was done in this period this was done in this period and this is the beginning inventory that will be will be completed this time therefore if we sum these up then we get the 16.5 plus the 140 plus the 21.6 gives us the 178.1 equivalent unit next one says that during the month the production department of a process operations system completed and transferred to finished goods 20,000 units that were in process at the beginning of the month and 180,000 that were started and completed in the month the month's beginning inventory was 100 complete with respect to materials 62 complete with respect to conversion at the end of the month 37,000 additional units were in process in the production department and were 100 complete with respect to materials 21 complete with respect to conversion compute the number of equivalent units with respect to both materials and conversion respectively so this is a very long ugly looking problem here but if we get the format of these types of problems they're not so bad so what we're going to do is we're going to set up kind of our table here and we're going to have two parts to it we got the material and we got the conversion and and so conversion something like that and we know that the materials obviously is pretty straightforward that's what's going to go into the stuff that the conversion remember is that's the stuff that goes into inventory that converts the material to the finished goods including the direct labor or the well the labor in the overhead okay so let's take a look at this we're going to break this out and we're going to start with the beginning to be completed so this is the stuff that was in you can think of it kind of it's still in the mixing pot whatever we're doing if we're making cookies it's still in the mixing department we haven't finished it it's in there as of the beginning of the month so we're trying to figure out how much of it needs to then be completed at this point in time so what needs to be done to it this month as it's already been started last month there's 20 000 units in there and we we're making the assumption that the materials are going to be in there at the beginning that's going to be a general assumption that's usually made they'll have to state that but usually that's going to be the assumption if it's in if there's something in process the material is already in there it's just the conversion we need to put in so we just got to kind of memorize for these types of problems that the beginning for this month it's going to be zero for the material again why because even though there's 20 000 units in there that all went into the process that we're working on last month therefore none of it is going into the process this month it was accounted for last month on the conversion half of it we're going to have to say okay how much of it was done completed in terms of direct labor and overhead and they're going to have to give us some kind of percentage on that and they're going to say that uh 62 percent was completed last month therefore how much was completed this month it's going to be equal to 100 percent minus 62 or 1 minus 0.62 so if I hit enter and I'm going to go up to the home tab and add decimals it's 48 I mean 38 percent if we add the percentage 38 percent so if 62 was completed last time what do we got to do this time we have to finish it we have to complete the remaining 38 percent of conversion to finish up the product so we're going to say of those 20 000 units times the 38 we have equivalent units of 7006 that we're going to do this month and then we're going to break this out by the start and complete units the units that we started and completed and in this case they actually just gave us that number they're going to say that the beginning of the month and we had 180 that were started and completed sometimes they give us the amount that was transferred out and we have to take that number and subtract the beginning amount from it assuming that this was started but they just gave us the 180 this time 180 000 and if it was started and completed the whole point of us determining this number breaking it out separately is because we know that 100 percent of that equivalent units were started and completed so for both materials and conversion 100 percent of those are there for equivalent units then we've got the ending inventory that this is the stuff that's not quite complete yet and so we're going to say that at the end they say that 37 000 units are still in there now on the material side similar logic that we did up here where we said we're not going to do anything to the materials it's already in there in this case if we started it this time even if we haven't completed it that means that all the materials went in there because that's the first thing we do to start it if we're going to you know bake the cookies we put all the cookie dough in there first so that's already done as far as conversion if we started them this month they're all in there i mean as far as material if we start them and that's all in there as far as conversion we're going to have to say okay how much of the work is still done and how much is not done they're going to give us the amount that is done so 21 percent of it has been done this month that's what we're calculating therefore of the 37 000 total units times 21 percent completed with respect to conversion means that we have 7 770 equivalent units with regard to conversion so if we add these up then we're going to sum up the materials 0 plus 180 000 plus 37 000 gives us the 217 000 and the conversion equals the sum of 7600 plus 180 000 plus 7 770 gives us the 195 370