Major sponsors for Ableton on Air include Green Mountain Support Services, empowering people with disabilities to live home in the community, Washington County Mental Health, where hope and support come together. Media sponsors for Ableton on Air include Park Chester Times, Muslim Community Report, WWW, this is the Bronx.info, Associated Press Media Editors, New York Powered Online Newspaper, U.S. Press Corps Domestic and International, Anchor FM and Spotify. Partners for Ableton on Air include the HOD of New York and New England, where everyone belongs, the Orthodox Union, the Division for the Blind and Visually Impaired of Vermont, the Vermont Association for the Blind and Visually Impaired, Center Vermont Habitat for Humanity, and Montpelier Sustainable Coalition, Montefiore Medical Center of the Bronx, Roosevelt Kennedy Center of Bronx, New York, Albert Einstein College of Medicine of the Bronx, Ableton on Air has been seen in the following publications, Park Chester Times, WWW, this is the Bronx.com, New York Powered Online Newspaper, Muslim Community Report, WWW.H.com, and the Montpelier Bridge. Ableton on Air is part of the following organizations, the National Academy of Television Arts and Sciences, Boston, New England Chapter, and the Society of Professional Journalists. Welcome to this edition of Ableton on Air, the one and only program that focuses on the needs, concerns and achievements of the different label. I'm Lauren Seiler for Ableton on Air. Before we get started, we would like to say thank you to our sponsors, Washington County Mental Health, Green Mountain Support Services, and many others, including the support of the Muslim Media Corporation, Park Chester Times, and many, many, many others, including Albert Einstein College of Medicine of the Bronx, the Rose of Kennedy Center of the Bronx, Montefiore Medical Center of the Bronx, and the Vermont Division for the Blind, and Visually Impaired, and many others as well. We would like to welcome Shike Musa Jame, who is part of the Park Chester Times, and many other things he's doing with the Muslim Media Corporation. Let's talk about the Bronx fire that happened about a week and a half ago, where 17 families, including eight children, had passed away from a horrible fire and aftermath of this, which it was caused by a space heater and a door not working within the building. So why don't you talk about what happened, how it happened, and how you have been involved helping these families and victims and families of this horrible tragedy? Thank you so much, Larry. First of all, I would like to say greetings to you and your crew and your listeners. My name is Shike Musa Jame. I am the founder of Muslim Media Corporation, which publishes Muslim Community Report, New York Parrots, and Park Chester Times. I am also, I would say, the full-time community organizer within the African and Muslim community in New York City. You're also a Muslim Imam, correct? Yes, I'm also an Imam of a Masjid in Coab City. So I wear many hearts, but they are all intertwined into community work. So thank you for the question concerning the fire tragedy that took place two Sundays ago. I'm here to give an update as to what has happened and what is going on. First of all, we are mourning the deaths of 17 family members, including, as you said, eight children. Among them, two-year-old boy. So this is very, very painful, very, very tragic. Was there anybody with special needs that perished within this tragedy as well? That is among the many, many things that we're going through in terms of investigating who was what, who had any type of disability or special needs. And soon, we will get that information out publicly. But I just want to say that, unfortunately, we cannot bring back the death. We cannot turn the clock back. However, the overwhelming majority of New Yorkers took this tragedy at heart, and they have been providing all kinds of services and donations. They have done above the call of duty, and the families are very, very gratified. As far as I understand, also the Jewish community has, when above and beyond, with the Jewish community that was rocked in Texas and the problems they've been having, they've been helping you as well with donations. You know, I have been saying throughout the week, nobody wishes to be in such calamity. But if you have to be in such calamity, New York is the best place for that calamity to be for you. And I said the reason for that is because of the Jewish community. They have the infrastructure for anything humanitarian. From day one, the Jewish groups were among the force on the ground. They were among the force to provide food, even halal food, to the affected. They have been among the largest donors financially to discuss. They have been the power behind the scene, and the families and the community and the Muslim community in general could not have been more grateful for the Jewish neighbors that we have that came among all the other New Yorkers, but the prominence of Jewish services and assistance and donations cannot be understated. So can you explain a little bit about how the fire happened? It happened through a space heater. How exactly did it? So people can have the backstory. How exactly did it happen? Larry, for the past 30-something years, the worst fire tragedies in New York City happened in the Bronx. And all these fire tragedies were preventable. All these deaths were preventable. The last fire tragedy, which happened Sunday, January 9, was a result of overheated space heater. No, but just the other day. We were told by people who are extremely impoverished conditions living in dilapidated buildings that are not being provided with the services that they are entitled to and deserve. What we're saying to our local elected officials, to our mayor and the governor, and our congresspeople is that these tragedies are preventable and this tragedy that happened Sunday will be the closure for such tragedies ever be falling to such people. And we have launched a grassroots campaign in making sure that such tragedy will not be repeated under any circumstances or under any conditions. Space heaters are used because people do not have adequate heating systems in their apartments. That will be changed immediately. That goes also for people in, I don't mean to bring them up, it also goes to people who are in the New York City Housing Authority because the New York City Housing Authority for years has not been providing adequate heat for people. When you pay rent, and I'm sure you agree with me, when you pay rent and you pay your utilities or utilities are included, it should include adequate heat. Why do people have to use or conform to using a space heater? First of all, space heaters are extremely dangerous, extremely dangerous, and then in this building you had a door that did not work properly. So when you have nothing, when you pay rent, but then you have things that don't work properly, things like this are going to happen. And then the other day, a couple of days after this, there was another incident on Fox Street where a house exploded. I don't know if you heard about that one. But that was a gas leak, that was a different thing. So these buildings that are horribly maintained, there has to be somebody accountable. You can't just say, sorry, this happened. This is thousands of lives. So these are hundreds of lives that are at stake here. Go ahead. Thank you, Larry, for highlighting the heart of the issue. We live in the tale of two realities. Two cities, basically. Yeah, two cities and two realities. We have the most privileged people who happen to own the corporations that own this housing building. Let's take bronze, for example, over 80% of bronze residents are renters. Bronze is the place where 81% of the residents are rented. And poor, correct? Well, yeah, that's why mostly they rented. So we have a county who suffers so long for injustice, for inadequate and for neglect from the hands of absentee landlords who lives in some of the most luxurious neighborhoods and buildings and areas who do not suffer the consequences of eating and any kinds of public safety or quality of life issues. That is why the funeral of the 17 individuals were publicized. We invited the media and elected officials in the Masjid inside so that the whole world will see the injustice that caused their death and to vow never ever to repeat it again. Now, we welcome people to buy buildings. We welcome investors. We welcome people to have tenants. But what we do not tolerate anymore is that you invest on buildings and then you neglect your tenants and you neglect the quality of life and you neglect the basic services that your tenants need because you can neglect it without facing the consequences. Those days are gone. We're going to make sure that the residents of the bronze, whether they're tenant or whether they're owner occupants, they will get the best services that are available to them and we're going to insist on it. Right now, we are forming grassroots coalition of community organizers to making sure that we are no longer going to be perished in such negligible conditions, such injustice, and such extreme poverty so that we have to resort to all these heating units in housing development, whether they are nature or whether they are privately owned buildings, we're going to make sure that such a tragedy will not be repeated again without being challenged. And that has started since the fire tragedy. So what type of before we get to fire education, what has been some of the donations that have been given, obviously food, clothing, what money for hotels, correct? So what type of donations have come across since this fire tragedy? You know, from day one, donations have been trickling. You name it, they donated, including toys to making sure that families will not have interruption to their children's toy enjoyment. Every single items that are used in our households have been donated to a point where they are in the storage facilities, they are in massages and community centers, New Yorkers came in droves and brought everything you can think of, the family is very grateful. And also, monetarily, they have been doing so much fundraising online that millions of dollars have already been raised and I am sure millions of dollars more will be raised because now we have celebrities who are taking the course as they own and making sure that they raise as much money as possible so that the people who lost loved ones will not suffer financially afterward. As you know, after the burial, what's left is long-term consequences of losing the loved ones, some of whom were the break winners. So we need to be there for them, we need to provide them with all the services that they will need forever and ever. Some of these services are short-term while others are permanent because some people are still in a hospital with bad conditions so we don't know whether they will make it or whether if they do make it under what condition. So we are preparing to making sure that we meet them wherever they are because New Yorkers came and provided everything that they will need in short-term and long-term. As far as fire education, okay, because you had a door that did not work, okay. So is there anything that your organization is working with the New York City Fire Department or other fire departments in and around different counties to give fire education or help institute fire education when it comes to things like this not happening again? You know, New York City is a place where every service that you can think of is there. The problem has always been people not knowing the existence of those services and how to utilize them. So because of what happened, we have dedicated our activism toward education for that which can save lives. Whether it is a fire safety, whether it is a public safety, whether it is emergency response, you name it, we will collaborate with the government, with private agencies, community-based organizations and experts to making sure that all the residents in the city get the training that they need so that when such a tragedy happens, at least you can minimize the death and the pain and the suffering because this tragedy was a result of one door that remained open. If that door had closed shut, we will not be funeralizing 17 individuals. Maybe no one would have died or maybe one or two. But because of the door remain open, unfortunately the building was engulfed with smoke. All the death died because of smoke inhalation, not the fire, but smoke. I also understand that somebody from Lehman College had passed away, one of the students had passed away because of this. So explain now, so let's go into what your organization really does. What does your organization really do for helping communities such as the tragedy that happened? But go into more and what your organization really does. Absolutely. During Mayor Bloomberg's administration, we had started an organization we call African Rapid Relief Mobilization. African Rapid Relief Mobilization. And we had trained so many people into CPR, fire safety, emergency management, and disaster relief services. Unfortunately, the subsequent years, we did not do the right thing by continuing the efforts until this tragedy. Now we have decided that the African Rapid Relief Mobilization that was launched over 10 years ago will become a centralized organization that deals with all kinds of tragedies, relief services, fire, disaster, you name it, on behalf of the African community in the city. So this Sunday, we're having a meeting with African leaders so that we can actualize the African Rapid Relief Mobilization. As of now, we are working with different organizations from Red Cross to the Office of Emergency Management to Bronze Works and to Community Board 5, various organizations to Gambian Youth Association, Islamic Cultural Center. But we will now centralize all the operations concerning disaster fire or you name it, or emergency to the African Rapid Relief Mobilization. So that something like this happen, the city, the agencies, the affected individual, the community will just go to the African Rapid Relief Mobilization and get the services that are needed on a timely basis. Services that are culturally and religiously appropriate. Do these fires or these tragedies happen? Well, obviously, somewhat it was an accident with the space heaters. But is this because of lack of education, like with the doors or lack of education per se? No, no, this was the result of injustice. New York City is the wealthiest city in the nation. New York City is home to more billionaires and millionaires in the nation. For New Yorkers to be hard-working New Yorkers, yet they live in such impoverished condition, this is injustice. Space heating is used by people who do not have adequate heating system in their apartments and homes. And we should not tolerate it anymore because we have the resources to provide to every New Yorker, especially hard-working New Yorkers. So this is the result of injustice and we are going to get to the bottom of it so that we can prevent it. It is happening too frequently. It is happening too deadly and it is happening under our watch. And we are not going to allow it to continue to happen. We're going to use this tragedy to making sure that justice is done for not only Bronxites, for the residents of New York City at large. It is all about injustice, whether it is injustice from our government, whether it is injustice from our, you know, building owners, or whether it is injustice from the agencies that really regulate the inequality that exists in our housing arrangements. So what you're saying is that people don't care. They just let things happen. Correct? Well, because these people bought these buildings in a very cheap amount and then they get all the government programs and house New Yorkers and then they live in luxurious, safest, most affluent neighborhoods and communities and yet they do not give a banana to the people that they are renting because to them the people are not really human beings. They're just there so that they get the checks from the government or from the people. We're going to change it up. If you have a tenant, you're going to treat that tenant the same way you treat your loved ones. In terms of discriminating against poor people because they live in such a zip code or such a neighborhood will no longer be tolerated. So basically it's a small house after house, building after building program after program, neighborhood after neighborhood to making so that every New Yorker deserves dignity in their housing, safety in their community and equity in the economic condition. That is what our commitment is and that is what we're going to be doing in collaboration with all the stakeholders including the government. Now, the class system has been for years from the Titanic first class, second class, third class and then also the Holocaust, ghetto A, ghetto B, ghetto C. Now, during the Holocaust Jewish people were put into deplorable conditions. They were not fed well, not clothed well, etc. Now, do you think, I don't mean to say this but your opinion, do you think New York right now is going through its own class system where the poor people are not or people in general are being put into groups that they're not being treated the right way and they should? Absolutely. Now, we live in the greatest wealthiest nation on the face of the earth and in this wealthy nation we live in the wealthiest city in the nation. The reason why New York is a great place when tragedy happened to people is because of the experience of the Jewish communities. As you said, the ghettos of the Jewish world had given them the ability and the understanding and the experience of not creating these ghettos where people are not regarded as equal, as worthy, as valuable. And because of the experience the Jewish community has developed humanitarian infrastructure that can be activated at a minute's notice. And that's what has happened the moment the fire took place. The first group from Brooklyn that came was a Jewish group. Guess what they brought? Halal food so that everybody affected will be fed so that they can at least go on with their lives and dealing with the Red Cross and others because hungry people, silly people unfortunately and people who just come out of a worse tragedy in 30 years there's nothing they can do right but when you feed them and then you're there and then you provide guidance then they can talk and they can talk with relevance and that's what the Jewish community did and that comes from the background of suffering injustice for so long to a point where they have been able to build the infrastructure to mitigate the pain and suffering. What is Halal food? So what is Halal food? You mentioned Halal food. So what is Halal food for those that don't know in terms of like giving resources? So Halal food is food that is permissible for Muslims to eat meaning that there's no alcohol there is no pork product and it was let's say if it had meat the slaughtering was done in such a merciful manner that the animal was not tortured the animals was not butchered and the animal's right was given and the name of God was proclaimed upon slaughtering the animals so that is what constitutes Halal food. Okay so we have a little time left so the African community that you're a part of or the Muslim community that you're a part of now you're an Imam so I understand that the funeral was held last week or this past week and your your center or the center couldn't hold the amount of people you know because you had COVID and you had other so explain how the community came together with the burial of these individuals. Absolutely you know the place where the funeral took place is called Islamic Cultural Center of the Bronx and it's on 166th Street the good thing is that that place was recently integrated they built it brand new from ground up they raised over three million dollars to build it so it only opened a couple of months ago but as big as it is it could not you know accommodate one third of the people that sort of therefore we immediately work with the city to provide heating from block to block they planted the whole community with thousands of people in the tent but even the tent as huge as they were there were more people outside the mosque and the tent than they were in so people came from all areas of New York New Jersey and Connecticut because everybody Muslims and non-Muslims wanted to be part of it to show their solidarity and to show their fellowship the same thing they did with donations we are so extremely grateful for how New Yorkers handle and manage and really so support for the cost we are dealing with right now so everybody was there almost who paid for the funerals the city of New York? you know millions of dollars have already been raised so the preliminary payment was made by the city of New York but obviously when everything is done all the expenses will be deducted from what is raised and whatever left will be given to affected families okay so explain a little bit more about you know if you don't mind let's go to a slightly other topic since we have a little time left to promote it what exactly is going on because you're going to Africa maybe next year on a pilgrimage explain a little bit about that and your African the African part of your organization yeah thank you I don't know if it's very appropriate to combine the two but I think they are pretty much interconnected this September 3 2022 for the first time we have been communicating with the African governments asking them to create a week long open house on the continent meaning that every African nation on the continent will use September 3 to September 10 as an African open house we call it Daylight Africa Week and we ask people from all walks of life all backgrounds all geography to go to Africa and visit Africa motherland it doesn't matter whether you're black white, you live in Europe, you live in Asia you live on the continent or not America according to what we know so far we are all the descendants of Africa Africa is motherland to blacks and whites and everybody but unfortunately the overwhelming majority of the global citizenry does not have the knowledge they need to have about Africa what they see about Africa is so distorted it's so one sided it's so dark, so gloomy so empowering so we tell the governments open the door let everybody come and see for themselves and see the beauty and see how huge Africa is and see the environment and see the reality and if some of them are investors there are government opportunities if some of them are researchers they can do the research if some of them are philanthropists they can see areas where charities are needed but open Africa on September 3 for everybody that's one thing, number two we have over 43 million African-American we have over 43 million African-Americans in the United States majority of them very small percentage of them have any connection to the continent that they came from so we're also calling it in addition to calling it Daylight Africa Week we're also calling it transatlantic family reunification transatlantic family reunification so it's going to be powerful it's going to be huge but it's going to be very very beneficial both to the continent and to everybody in the world Africa is a place that is mystery to the majority of world citizenry and September 3 that mystery will begin to fade away and into the understanding of a continent that every person is part of so let's um if anyone wants to donate anything in terms of this tragedy whether it be diapers, food, clothes where do they turn can you give a website? absolutely, if anybody is donating material non-monetary then you can bring it to the Islamic Cultural Center the address is 371 East 166 Street Bronx, New York 1-0-4-5-6 again Islamic Cultural Center address is 371 East 166 Street Bronx, New York 1-0-4-5-6 if somebody is donating money please go to the mayor's fund just google mayor's fund and donate that all the monetary donations New York City mayor's fund thank you is there a website for the Islamic Cultural Center? no there is a website but the best thing is to use me because I am the spokesperson and the coordinator and my number is<PHONE_NUMBER><PHONE_NUMBER> I am the community coordinator spokesperson anything related to the fire tragedy? ok well we would like to thank Shikému Sadrame for joining us on this edition of Ableton On Air for more information on donating now this is extremely important for more information on donating anything clothes, food diapers baby food anything materialistic that people can use during this fire tragedy please call Shikému Sadrame at the following number<PHONE_NUMBER> that number once again is<PHONE_NUMBER> Shikému Sadrame is the community coordinator and he is also the one of the people in charge of the Partisanist of Times and the Muslim Media Corporation the address again that people can turn 371 is 166th Street 371 is 166th 166th Street Bronx, New York 10456 ok so for more information on that address to send any packages in relation to the Bronx fire that's 371 East 166th Street that's 166th Street, Bronx, New York 10456 that address once again is 371 East 166th Street Bronx, New York 10456<PHONE_NUMBER> Shikému Sadrame who is a community with liaison in terms of this Bronx fire and we thank you for all the work I know you're extremely exhausted and you know and recuperating but we thank Ableton and I thanks you for all the work that you've been doing and we thank this a reality because losing a loved one is not a good thing but prayer and having the community together is extremely important when it comes to these things so we thank you for helping the community out and we thank you for joining us on this edition of Ableton on Air Arlene is not here today we would like to thank of the Muslim Media Corporation and all his work that he has done for helping these families get back on their feet with housing food and many other one last thing is the American Red Cross involved American Red Cross is involved Office of Emergency Management involved and many many other agencies but I will end with this part of our advocacy is adequate services for individuals with special needs I just want to let you know that your platform and the people you advocate for are part of our advocacy to making sure that the housing the logistics, the transportation the services are also adequate for the special needs New Yorkers thank you for joining us on this edition of Ableton on Air again one last time that phone number is<PHONE_NUMBER><PHONE_NUMBER> the address is 371 East 166 Street on the envelope do they put your name well how do they do it no no no they can put the fire tragedy or the fire victims okay fire victims 371 East 166 Street Bronx, New York 104-56 Bronx, New York 104-56<PHONE_NUMBER> again this we put an end to this edition of Ableton on Air our leading is not here today thank you to our sponsors and supporters I'm Lauren Seiler see you next time on the next edition of Ableton on Air stay safe during a fire and we must work together to make these tragedies not happen again I'm Lauren Seiler see you next time major sponsors for Ableton on Air Green Mountain Support Services empowering people with disabilities to live home in the community Washington County Metal Health where hope and support come together media sponsors for Ableton on Air include Park Chester Times Muslim Community Report WWW this is the Bronx.info Associated Press Media Editors New York Power Online Newspaper U.S. Press Core Domestic and International Anchor FM and Spotify Partners for Ableton on Air include Yachad of New York and New England where everyone belongs the Orthodox Union the Division for the Blind and Visionary and Parallel Vermont the Vermont Association for the Blind and Visually Impaired Central Vermont Habitat for Humanity and Montpelier Sustainable Coalition Montefere Medical Center of the Bronx Rose of Kennedy Center of Bronx, New York Albert Einstein College of Medicine of the Bronx Ableton on Air has been seen in the following publications Park Chester Times WWW this is the Bronx.info WWW this is the Bronx.com New York Power Online Newspaper Muslim Community Report WWW .h.com and the Montpelier Bridge Ableton on Air is part of the following organizations the National Academy of Television Arts and Sciences Boston, New England chapter and the Society of Professional Journalists


But I believe there is. The ally's area. But I believe there is some discussion and the, and the save the ally. Brooke. And I'm sure the town manager. Discussing possibly using them closer towards. Magnolia thawndike, but. But anyway, so I'm going to talk to the chair. an agenda item, two no more than three speakers, three minutes each, regarding the ALWIFE, regarding their DEP submission, regarding the money that the legislative delegation got, as well as regarding the legislation that Dean Benson is the author and saved the ALWIFE focus filed in the State House. It has a docket number and they'd like to present it to us for our thoughts and hopefully maybe endorsement. So thank you. I know that was a long way to say. Mr. Diggins, Mr. Chair, please remind me when I call you, why did I want to talk to you? It was that ALWIFE agenda item. Thank you. You had me at three speakers, three minutes each. And so that's the ticket. So I am sorry. I have a number of items. I'm going to go through them quickly, talk quickly. I'll talk fast. I'm going to start off with the difficult one. I mean, I just wanted to acknowledge what happened in LA.  They're getting worse. And in regards to where you fall, I mean, with respect to the weapons that are being used, I mean, it's just that I think, I mean, Leslie and Arlington try to look out for each other in respect to if you see someone that's in trouble, having a hard time. I mean, even if you can't be the one to help them, I mean, just try to look out for them in a way that can get them some help. I mean, and so there's some kind of anger, I mean, that's driving this. I mean, I think if we can do anything in our community to help me minimize that, then hopefully we can continue to be fortunate. So there's that. So everything else is going to be a lot lighter. So the equity audit is going to be sent to us for our next meeting. We can improve it. They would like to do a longer presentation, mean to the community, mean, and so I am working with them to do a forum on the 13th, that would be the Monday after our meeting on the 6th, being if three of us participate in it, it can be held as a select board meeting. Otherwise, we can just hold it being as a forum, maybe sponsored by a select board or something, mean, but it won't be an official select board meeting. So right now the thinking is that we'll do it around 6.30 and right now it's also looking like it'll be hybrid. So just want to flag that, but that'll be an opportunity for them to do a much longer presentation with a lot longer audience participation because, yeah, so I'll leave it at that. So on the 6th, the town manager now is planning on doing his budget proposal, so we will give him a fair amount of time on that and CDBG will be doing their presentation. That will be a fairly long one, two, so there's not going to be too much room on the agenda for much else at that meeting. We have one other meeting on the 27th, so there's that. And I mentioned when we're talking about the overnight parking that potentially we could do a forum on the 15th, so I will circle back to Mr. Horsey, being to see how he feels about that. And I also mentioned when we're talking about the town manager search that we might want to do our meeting in our interview process during that vacation week, so let me know your thoughts about that and we'll probably maybe settle on some dates at the next meeting. I talked with the vice chair on this meeting, and also town manager, we reduced from four positions in the select board's office to three and a half. But we're going ahead and filling that half is more of a half-time clerk position with the expectation that if we do have to do more stuff with respect to overnight parking, that person can help. With that, certainly, TAC could use some assistance, even the case before we lost our senior transportation planner, but I also talked with Ms. Meyer and there's no shortage of work. And also, we hope that my thoughts is that that person can develop more relationship with planning, especially from transportation angle. And last two items I'd like to thank Mike Rademacher for coming to East Arlington Liberal Street's meeting, where we just learned a lot about what goes on with respect to transportation plans. And he's definitely working on creating this transportation improvement program, which will give residents a little more insight into how projects mean or progressing along what people can expect will happen. And finally, I was invited to talk to the Audison 8th grade students for their civic action project. It's a long history, has how it happened for this year. But I'll just say the kids are wonderful. One of the questions from them was, are their articles mean for affordable housing? And I just could have reached to the screen and hugged them all. And I was just thinking like, who are the parents and the teachers that are raising these kids mean? And it's us. I mean, in the context was that they were trying to determine what they could do in town meeting. And I was expecting like the sustainable stuff, the environment, but the affordable housing one. It was just, I was delighted to hear that. And so it's a good community that really cares and the next generation is amazing. It just makes me want to work even more on their behalf. So thank you for listening, indulgence. And with that, I'll take a motion to adjourn. Move to adjourn. Second. I'll hear a motion. Ms. O'Han is seconded by Mr. Corsi. Mr. O'Han. Mr. Hurd. Yes. Mr. Corsi. Yes. Mr. Helman. Yes. This is Mohan. Yes. Mr. Diggs. Yes. Santa Ms. Ville. Take care, everybody. Good meeting. Bye-bye.


hi guys welcome to a new vlog it is sunday and i'm trying to get caught back up with videos i just finished filming my stanley cup video which i'm gonna edit and upload today i was going to just post that like five minute vlog you saw i want to put up a better video especially because i haven't posted in a while and i just i don't know what it is like during the week i don't want to do anything except for like school when like do the homework go to cycle bar because i don't get like a lot of physical activity obviously and then go to sleep like shower go to sleep so i am sorry about the lack of uploads it's so unlike me and that's why i'm trying to get back on schedule i didn't really like get too too ready for the stanley cup video i just like kind of curled the bangs which are already falling out now as you can see they're kind i don't know next time i get bangs i'm gonna have her cut it to like the chin i absolutely hate not being able to like put it behind my ear and have it stay behind my ear um don't get me wrong like i love them but once they grow out to here they're gonna like stay here because i can still curl it and get the achieved or the desired look you know what i mean but i cannot be having all these hairs like keep falling in my face half of the time i'm like this and then they're like pulled back um because like it's just anyway um but i'm not wearing too much makeup uh i haven't put on makeup in a while because my face is finally like getting better like there was a time like you guys know my face is just so freaking bad and i'm like what is going on so i don't know it's finally like gotten to a point that i'm like oh good like my face is clean i cleaned all the brushes and stuff so when i do wear makeup again like hopefully that won't cause any sensitivities but i swear i think it was because i was wearing makeup more and like i wasn't letting my face like have its time but during the week i never wore makeup so i don't know i think it was the bacteria honestly so far so good right now with the face so no makeup is going on this ish anytime soon um but right now i'm going to get to editing the stanley cup video i think i'm gonna bring my computer up here um just because it's just quieter up here and i don't necessarily like editing with with headphones so yeah also too i want to show you guys the outside because they come a long way so let me go show you that while i get my computer look at this beautiful view it's come a long way as you can see there's like grass now there's pool furniture everything looks so good it's beautiful okay so i just got done printing off some photos of the cranial nerves because we need to like learn these and stuff so our teacher gave us one but it was like black and white and kind of pixelated so basically i just found the colored version because she got it from google so she just wants us to get like familiarized so i printed out this colored version and then i made these with all of the names covered in white so we could like quiz ourselves and then these are like the categories of the nerves so i just got done printing this out i just i kind of ruined the one that she gave me because the lines like were kind of hard to understand because i don't know i think i showed you guys but they were like blown up all the way here and then there was like a line and then it showed where it went but like i don't know it was messing with my mind like i did not like that but this one's just a lot more clear okay so i wanted to update you guys on my room we got a lot of boxes out we only have two more these are like pewter stuff and then this is just my juicer don't know what i'm gonna do with it honestly but as you can see it's really good there's barely anything in the shot but yeah also two guys i got one of these headbands i will link them down below it was like 12 bucks amazon and i'm like obsessed like now i always wear it because my bangs i don't know if i already said this but i am not going to have her cut the bangs so short like i'm gonna have to be able to tuck it behind my ear because these bangs are just i love them but like i can't tuck them behind the ear and it's just really difficult oh so this headband helps all right valerie where are we going for a family dinner we are going to well i'm not sure what it's called it's called the downtown diner downtown diner that's where we'll go downtown diner in celebration personally the other day that said that um it had different owners he was correct because i read on the website that it has now been repurchased by the family that owned it when it was first opened in celebration wow the name of max's really oh because see this uh location has had different owners and names so that's really cool okay what did you get i got um tuna milk tuna milk fries the fries and then what did you get that is a she loves it valerie also got some crispy bacon and eggs over there extra fries because i'm having the summer for fries okay so i got the crispy chicken tenders because i wanted to try them and the waitress said that they were really good so um they're breaded in corn flakes apparently that's how they used to do it so that's cool now we're gonna get ice cream here at kill wins oh my gosh it's so cold in here got the goods nice little vanilla tone going on here okay so i'm in class now the only one here right now but i wanted to say i swear mondays are always great because i'm not like tired as much but then as the week progresses it gets worse and worse like friday is a struggle getting up i don't know what it is it's probably because i like actually go to bed as early as i can like on sunday but still man who valerie's putting together our ornament sherry's started this great process and i was singing we're almost we're not really almost done but we're in the process of taking down our christmas ornaments and i'm singing this is the most worst time of the year because there is this one time we had our stuff up and it was valentine's day you know it's always the biggest struggle taking this thing down like i legit hate it but we're here okay so i'm just getting all these clips like different times so i'm still vlogging though it's so funny um i went to winter garden yesterday to like do some studying and then i went to cycle bar and this is the second starbucks that is under reconstruction or construction or whatever i'm like maybe i'm like putting a curse on these like starbucks places because this is the second time so crazy um but i just sat outside and studied it was fine but yeah it is hot let me tell you whoop almost fell also two guys i think i'm gonna end the vlog here because thursday and friday i actually have plans um like going out to drinks with some friends and everything so i will go ahead and end this here i hope you guys enjoyed make sure to give it a thumbs up don't forget to subscribe and i'll see you guys in my next video bye


Well, welcome everyone. I know it's almost beer o'clock on a Friday, so I'm grateful that you showed up. Welcome to the North Pole. I put a little thermometer on the board here to remind you how cold it is. But at least you won't fall asleep, I guess, or maybe it'll be hypothermia anyway. So my name is Dean Wampler. I have this pretentious made-up title at LightBend VP of fast-data engineering. Basically, I lead the development team that's building a distribution of streaming technologies on top of DCOS. Spark, Kafka, MESO, well MESO is underneath, obviously, Flink eventually, Aka Streams, and so forth. I'll talk a little bit about the pieces in this, you know, I know this, you don't want a marketing talk and that's not what this is about. But I did want to, you know, having played with these frameworks for a while in MESOS and actually made the choice of MESOS over alternatives, that's really what this talks about and what we've learned from that. By the way, if you look for the slides on the website, I forgot to check if you could even get them yet, but I uploaded them like an hour ago, so they are up there if you want to get the slides. And there's some general themes I want to hit on. I'm not going to do this in order, but just some takeaways is... First, talk a little bit about how applications and application architectures are changing. You know, why are they different today versus, say, five years ago or pick some time frame and how that's influenced everything else that I'm going to talk about. You know, why we picked MESOS and some of the particular things about it that have proven really valuable for our needs and obviously for our customer needs. And then talk about a few specific insights. It won't be a highly technical talk by any means, but hopefully you'll get some useful stuff out of it. All right, so first, why do we care? Well, I'm leading the team that's developing this product, we're calling the FastData Platform. It's in beta now, and it'll come out this fall. Just two slides about it. So this is sort of a block diagram of the pieces. Well, I don't know how useful this actually is. Let me just show you the interesting bits, which is what's under the little blocks. Mostly we're going to talk about the sort of middle right top, which is the streaming engines and then also Kafka and kind of what we've learned from using those. But also, you know, I'll get into a little bit how it turns out we're seeing a lot of need to build microservices that integrate with these other tools, much more so than maybe in a classic dupe environment. And then there's other pieces you need like management monitoring tools, machine learning and all that and deployed on DCOS for, you know, having a commercially supported MESOS distribution. The bottom, actually, that's sort of a little small to read is bring her on storage, although we are actually supporting HDFS ourselves in this case. Okay, so enough architecture. Well, the first question that we came up with, say, three years ago when we really started thinking about what we should do in big data was, well, should we use Hadoop? So let me step back a second and just to provide a little context. So I'd been working in, like, the Spark, rather the Scala community for a long time and actually knew everybody at LightBand called TypeSafe at the time when they got started. But I wasn't actually that interested in working on microservices, which has been kind of the focus. LightBand was founded by the creator of the Scala programming language and the guy who created Acca. You probably heard about Acca because it's the A in smack. Actually, there was a really good talk. I just went to by a couple of guys from Adobe who've been using OpenWisk, which is the serverless framework that is written in Acca.  And I'll briefly talk a little bit about it as we go along. But anyway, so, you know, three or so years ago, it turned out Scala was starting to get very interesting in the big data world because Spark and Kafka are written in Scala. So people started talking to LightBand about it because it was kind of driving them to use Scala. And then I got involved with LightBand to think about, well, how do we actually take what we've learned about microservice development and Scala programming and functional programming and apply that to the big data world. If you know anything about me, I have a lot of talks out there on YouTube and Twitter. And I have a few where I rant and rave about how the Hadoop world is screwed up because it's not functional. You know, we're using crappy languages and all this. But anyway, not to get into too much about that. So fast forward a little bit. And, you know, a couple of years ago, when we sort of crystallized this idea of, well, you know, the world is kind of moving towards more stream-oriented versus batch-oriented. Not that batch is going away, but people need answers faster.  If I can extract value more quickly from data, then that's better. So that's been kind of one of the driving forces behind streaming apps. The other one is just the pragmatics of how do I serve mobile? How do I serve, you know, like map applications and all that stuff? You can't do that in batch, right? Well, at least not completely. You might do some data processing offline to have the data ready for real-time serving. But as we started thinking about how to support customers doing stream processing at scale, it became clear that Hadoop just wasn't really cut out for it. This is sort of my schematic diagram of a Hadoop cluster. Really, there's only three important pieces here, and that is there's some sort of distributed storage, which is HDFS, the Hadoop file system. There's some sort of compute, which used to be map-reduced, and now it's mostly Spark. And then there's this thing called YARN, which is yet another resource negotiator that's sort of the analog of MAISO's, roughly speaking, that knows what to do. And you say, I want to run this job, and the data's over here, and it figures out how to partition that into tasks, which are basically processes, JVM processes. And run them over the cluster, over all this data.  And if you buy a Hadoop distribution today, you can get some streaming technologies, like Storm and Spark, and, sorry, I actually meant to say Kafka, Storm and Kafka, Spark Streaming, and so forth. But I want to make the case that that's not really good enough for what we need today, and that's one of the reasons MAISO's is much better positioned for this. So, but what's that to like? Well, three things. Limitations in YARN, the sort of batch orientation, and this idea of, well, what if I need to write other services, and I'm just going to use microservices, that buzzword to talk about the other services I might need to write in my environment. So let's talk about YARN first. And I have to be honest to the Hadoop community, a lot of these issues are trying to fix to some level, but it's relatively old software, so it can be difficult to bring it up to what we have today in MAISO's. Well, YARN, yet another resource negotiator, was always designed with this idea that you're going to submit a job to me, and I'm going to partition it based on what resources are available into tasks, and I'm going to run them, and they're going to have some finite duration. Because I'm assuming it's a batch job, that means I kind of know in advance how much data I'm going to actually be working with, so I kind of know roughly how many tasks to schedule. Why is that important? Well, because in a streaming world, I don't know how much data I'm going to be processing. Super Bowls happen, but Justin Bieber tweets, these kind of things happen, and suddenly you get these big spikes in traffic, you get network partitions if you run long enough, and so forth. So it's not terribly good if all I understand is batch jobs. In fact, it's so limited, it can't even run the demons for HDFS, like the name node and data node. So in fact, this is kind of a theme. If you're running Kafka, if you're running Storm, if you're running HDFS, you're not really going to be using the sort of Hadoop resource management. When did that happen? There it goes. You're going to be kind of hardwiring these things a little bit on your own, and then only getting the resource negotiation that we're used to, that we really love about mesos, just when you run your data analysis jobs. The other thing that's really bad these days is they do now support containers. There is, in fact, I Googled this right before to make sure that I wasn't lying about this. There is actually additions for container support, but it's way behind what we're used to, like the Open Container Initiative and CNI, and those kind of things. And once again, because of that batch orientation, it's kind of ideal for big, fat JVMs that are reading big chunks of file systems, but not so great if I made relatively small jobs for some things, big jobs for others. And so that means that if we don't want to do batch, if we want to do streaming, it's a little bit more like we're going to kind of roll our own services, we're going to kind of work around the limitations of yarn and do things our own way, like running Kafka, Cassandra, and so forth. Now, one of the things I want to argue as we go along is that increasingly what we see customers doing is they're not only building, like, you know, Spark streaming jobs and they're running HDFS, but they're also writing a lot of microservices that interoperate with these things. I'll give you an example just from a lunchtime conversation I had where I might work for a healthcare company, and even though I'm writing maybe classic data warehousing jobs with Spark, I'm also writing, like, microservices that embed the rules for, like, regulatory enforcement of data privacy, and I don't know all the terms that they use for this stuff, but basically anything I might need to know about healthcare billing and record management and all that, and I really want those services to be accessible from my streaming job so that as data's coming through, I can properly manage it with these requirements. I'm sorry about the flickering display here. So this kind of mixed workload is not so great in Hadoop either. Really, Hadoop is kind of focused on, I'm gonna do lots of batch jobs, and if I want to mix in microservices, you kind of have to sneak them in on the side or partition your cluster in some way.  Okay, so that brings us to why we picked Mezos, and I know some of this is preaching to the choir, so I won't belabor these points, but just to sort of catalog them. Well, I was thinking about, what do I like about Mezos? And I thought, you know, I'm gonna Google, I'm just gonna go to the homepage for Mezos for inspiration, and I liked what I saw so much, I just figured I'd do a screen capture. So this is just Mezos at Apache.org, and I'm only gonna talk about three of these. I don't want to go through all of them, but three that I think really are important for the problems that we're facing in streaming and mixed microservice architectures is container support, and all of the benefits we get from that for isolation, for running concurrent versions, people who deploy a container, and then it runs for a few seconds, and then it goes away, or they're deploying a service that's gonna run for months, and all of that just kind of works. Sort of at a lower level is really actually fine-grained management of my cluster resources so that when I really do need to optimize usage of CPU, GPU, et cetera, I get to do that just for free. It just comes with my ecosystem, and this list of resources that you can manage is more than Yarn is capable of today, for example. Like, they still haven't gotten GPU resource management done. I had a chat recently with a guy from Skymine, the company that does deep learning for J, and those machine learning guys, especially in the AI world, are all about GPUs now, so if you can't manage your GPU resources, it's a bad deal. Oh, and this reminds me of something else. I can never, I always find this amazing when I think back to the 90s, so I've been around a little while, as you can tell, from my platinum blonde hairstyle. But back in the 90s, we would stand up servers, and it was a bad deal if the server got more than 30% utilized, you'd start worrying, right? Because that was all your capacity was to, and these were like massive sun boxes too, so we only had a few of them. But these days, we want to get much closer to 100% without falling over for all the reasons of economy. And the last one is the famous two-level scheduling in Mesos. I love the simplicity of the Meso scheduler model that they've actually partitioned the notion of what scheduling means in sort of this tandem dance between the framework itself, well, I used the wrong word, between Mesos itself and your application, which is a framework, meaning that Mesos does not have to know everything there is to know about how to schedule a Spark job. Instead, it offers resources to Spark, and then Spark has the knowledge inside to know what resources it needs to take to do whatever it is it wants to do. So if I want to add my own custom database or whatever, then I can write the scheduler that knows how to accept resource offers, and Mesos remains relatively ignorant about what it is I'm going to do with those resources. That's the opposite of the way Yarn works, where they've actually hard-coded information in Yarn about what it means to be a Spark job or whatever. And that has some advantages, but the disadvantage is that Spark doesn't, sorry, Hadoop doesn't have the flexibility to run other weird things like file systems and databases. So it's those things that lets us do crazy stuff like add these new frameworks and then have them work in a fairly optimal way. So this is just a diagram of how Spark actually works. When you submit your job, the scheduler talks to the Mesos master, and this stance goes on about allocating resources on different nodes under the ages of a Mesos executor, and inside is a Spark executor. I just pronounced that word differently for no good reason.  So here's a little story. I don't know if everybody knows this, but maybe you do. If you read the original Mesos research paper that Ben wrote, there's this hilarious paragraph in there where he says, in order to prove our ideas about two-level scheduling, we invented this little framework called Spark to actually test it. So yeah, I figured most of you had heard that story before, but obviously Spark took off on its own. All right, let's dive into streaming a little bit more specifically then. So what are some of the characteristics at a high level that streaming systems have to support? One is, well, I'm going to go into these in detail, but continuous processing, variable lifespans of these streaming jobs, resiliency, and scalability. So what's the deal here? Well, continuous processing in a stream, hopefully the input isn't ever going to stop coming, and if it does stop coming, it probably means you went out of business or something. And similarly, you're going to have to keep feeding output to downstream consumers, storage, whatever. And one of the implications of that is that you need your framework to really scale dynamically on demand like for those Super Bowl events or Justin Bieber tweets or whatever. I'm going to come back to this one in a little bit in just a minute. The other one, though, that I think is kind of a cool thing in terms of optimization are tools like the container network interface that let you fine-tune how that your container networking works so that you can optimize transport between various components. So you really could fine-tune if you got this high bandwidth stream coming and you could actually fine-tune how that is configured in the cluster and maybe take resources away from other things. So stuff like that, I think, is going to further help us optimize how we use our clusters. Variable lifespans. So normally a stream is going to run for weeks to months. This is a big contrast with a batch job which might run for hours maybe overnight. But there's only so much optimization you have to do or dynamism you need if you're going to be done in a few hours. But if this thing is going to have to stand up and be resilient for weeks or months maybe, then it's a new ballgame. And of course, now why would you... You wouldn't necessarily have a streaming job that only lasts for a few minutes or a few hours normally, as I just said, this is data that's supposed to never stop coming. But in fact, and this is something Jay Kreps from Confluent likes to harp on a lot. If you have a stream processing system then you can treat batch as just a finite stream in principle. So I could take my batch stream processor if I realized I made a mistake in a calculation but I only want to rerun yesterday's data then I can just start up a stream job that's actually going to have a finite stream which is yesterday's data and I basically have one system that does it all in principle. So that's why I'd still put minutes on here. And of course, Mesos is really good at managing very short-lived containers because it's fairly lightweight. You wouldn't do seconds necessarily because there is some overhead with resource negotiation and offer management but you could do minutes without too much pain. And if you're actually running services that are going to last for ever, like the demons that run HDFS, the name nodes and so forth, that just works. So coming back to this scalability resilience and scalability or whether those last two high-level bullets, you just get a lot of stuff for free that's been really useful like a federation through ZooKeeper. Actually, in the open WISC talk there was discussion of not using ZooKeeper but using ACA clustering instead for federation within WISC the way they had modified it. But there's a lot of full-tolerant facilities like if you've submitted through a marathon you can have the job restarted if it fails but there is one important point that I'll come back to kind of at the end and that is if you're building stateful streaming services then you have to have some mechanism for checkpointing in the state so that if it goes down you can bring it back up where it was. In fact, it turns out there's kind of a bad bug in spark streaming. The older spark streaming versus the new structured streaming if you know that distinction. The older spark streaming could successfully start up again with a checkpoint but there's some particular kinds of data that actually gets lost. Basically, they have this notion of counters that you can build in. Those actually get reset to zero. It turns out in spark streaming if you restart. The reason I'm mentioning this is because I've been dealing with a customer issue the last few days on that particular point. But apparently the new structured streaming is much better at handling stuff like that. So this is a mechanism that you end up having to solve some way yourself or the tool that you're using like spark has to handle it for you. And then the scalability, the fact that it's not that hard actually but this is something that you have to build into your streaming system to actually do dynamic scaling up and down. And we've got all kinds of facilities in Mezos now to support this like optimistic offers and dynamic resource allocation that lets us do this kind of stuff as long as it's built as long as your framework takes advantage of this we've actually worked with the Mesosphere for Spark for example to support these kind of newer capabilities so that spark jobs can be more dynamic to scale and demand. There's still actually work to do though I have to be honest. Okay so that's kind of the high-level picture of the sort of things you have to worry about with streaming apps in general. I wanted to take a few minutes and talk about some particulars of some of the streaming engines that we've used. So we actually focus on four in this fast data platform and we picked these four because we felt that they covered a reasonable percentage of kind of the 100% of things you might need to do but it kind of sucks that you have four. I'd rather have like one or maybe two but kind of that's where the world we live in is that if you really want to cover everything you kind of need to be able to pick from a list of four in our opinion and some other criteria that we use that I won't get into in a lot of detail here are pragmatic things like are these actually viable projects? And somebody wrote this great blog post a couple of years ago listing like 11 or 12 Apache projects that claim to be streaming engines. So you know it's right you have this paradox of choice problem. It's sort of like going into Best Buy to buy a refrigerator and you see this line that goes down you know it'll a football field of refrigerators so you kind of walk out of there because you're afraid to buy the wrong refrigerator. So I don't really like giving people too many choices because of that problem but what I've tried to do is distill it down to four that cover the spectrum and then I'll talk briefly about how each one works for particular kinds of problems and then discuss how they fit into Meizos and how Meizos supports them. But to go over these sort of characteristics that you might use so now I'm at the level of I'm going to write an application and I need to figure out which of these engines I want to pick. So the first thing you might ask is what's my latency budget? This is actually pretty important because if you're going to do things like authorized credit cards the sort of the rule of thumb that I've heard in the banking world is if for usability reasons I have 200 milliseconds to like refresh a web page you know you've heard that number probably it turns out of all the things that have to happen between me clicking buy and getting a response the bank gets about 10 milliseconds to make a decision with your credit card purchase. That is way too short for something like Spark Streaming which is still mostly kind of a mini batch model where it likes to get data in chunks and then use the efficiency of the cluster to process in mass. But for something like ACA or Kafka Streams or one of those you could reasonably do 10 millisecond individual processing of events you know complex event processing. Sorry about that again. Volume is also important so some of the engines I'll talk about really don't scale horizontally so they're really great at low latency maybe but if you need to do a lot of stuff like process the Twitter fire hose maybe I'll just hold this thing I think this is a bit of an old Mac and it might be the connector screen. Oh it does? Okay all right well I won't touch it then maybe that's better. But volume so if you are processing the Twitter fire hose and something like Spark or Flink is really great because they can partition that stream as it's coming in and then do things in parallel but you know if you don't have quite that scale but you need lower latency then maybe another tool is better. What kind of processing are you going to do? It seems like everybody is layering SQL on top of their streaming engine. You may know that Kafka the Confluent guys just announced this at Kafka Summit like a week ago or two weeks ago. If you want to do machine learning and here there's a really interesting problem in machine learning in the streaming context and that is I'd kind of like to be training models incrementally because you know let's just say spam is actually evolving more quickly than it really is. I'd like to be adjusting my spam filters to the evolving threat and there's this notion of concept drift in machine learning where my model is getting stale over time. I had a one of the best managers I ever had used to say that software has a half life you know whether you're touching it or not it's kind of decaying maybe because it's growing irrelevant or something and I think that's true in machine learning. So back to the point we'd like to be able to train models but also serve them with lower latency than we can possibly train them so that kind of forces sometimes a choice between maybe I'll train with Spark because it's pretty good at that kind of batch or you know mini batch stuff like I might do maybe every hour I'm going to update my models but then how do I take that model and actually serve it from my low latency stream engine that may not be Spark so there's some interesting problems there and if I'm just doing simple filtering and transformations you know like for say ETL kind of problems extract transform and load then then I have a lot more options and some things are going to be better than others for other reasons all right moving on to the third page of these trade-offs this goes back to that thing I mentioned a minute ago where I might actually need to look at each event individually like authorizing a credit card doing like fraud detection that kind of stuff or maybe I'm just doing bulk processing where everything is coming in each of these records is actually kind of anonymous I just want to clean it up maybe join it with some other side band data and then spew it out to its downstream consumers I don't need to process it individually I could kind of actually exploit the efficiency of doing things sort of in groups and then the last one is what kind of interoperability do I need to other tools actually the reason this point is here is because it turns out Kafka Streams is really designed to read and write Kafka topics and it does that very very well but if you wanted to have it process like be a hook for restful input then you'd have to use something else along with it but other tools have a lot more flexibility in terms of just direct connection to this that and the other thing like writing to databases so let's talk about these four tools that I've sort of already mentioned and how they fit into this picture and then what Mesos does for us to make them you know really good tools to work with and they're actually fall into two groups there's Kafka Streams and Aka Streams have a lot of synergies and various ways they'll get into and then Flink and Spark kind of fit into their own little subgroup so if I'm going to do Kafka Streams this again is the library that sits on top of Kafka and actually to be more precise this is actually important you write an application that embeds Kafka Streams as a library and so you manage your application your microservice whatever any way you want the exception of that it turns out if you do use this Kafka SQL KSQL library that sits on top of Kafka Streams there are some services that you do run in that case but anyway it's it can be pretty low latency latency here is actually more limited by how long you let your queues get your topic length become in Kafka that's going to be your kind of latency budget in Kafka Streams it's not designed for you know it's not a tool you use to shard this massive pipe of data across a cluster into partition so you can run in parallel it's not really designed for that it's more like medium volume which I don't want to say low volume because that sounds bad but you know not volumes like Spark can do it's fantastic it has a lot of really good primitives written in to make a lot of common problems easy to do like extract transform and load transformation kind of stuff where I'm just gonna my favorite example of this is maybe I'm in chesting raw log data and I want to parse it into some sort of record format write it to a new Kafka topic and then downstream consumers aren't parsing strings they're actually reading records that represent the log data but they also have some cool table abstractions so if I just need to do aggregations I don't actually need to see every record I want to see like the average over the last minute or something they have some nice facilities for that and so you can sort of if you think about how what that means in terms of individual record process and I kind of wrote this last bullet backwards in a way ETL would be like looking at each event one at a time whereas the table abstraction would be more like I'm doing sort of a data flow that's doing aggregations sort of anyway now Aka streams is so again we're talking about the smack stack a placeholder Aka streams is actually a DSL domain specific language on top of Aka actors so rather than having to write low level actor primitives you can write things as data flows and then it will materialize actors for you it also is a library that you embed in your applications so it's sort of analogous to Kafka streams in that way it can be very low latency so even though there is a bit of overhead sending messages between Aka actors it's pretty good down into the millisecondish range I mean you wouldn't use it for like controlling SpaceX rockets when they're landing but it's pretty good for things like fraud detection credit card authorization and stuff like that again sort of medium volume not designed to partition your data it actually of all these tools that has the most sophistication in terms of the kind of event flows you can define it's the only one of these for example that lets you do like feedback loops although I really have no idea what that would look like I'm not sure I would try but you could do feedback loops if you wanted and so forth and it's Aka's kind of famous for doing this so-called complex event processing like you know fraud detection or credit card authorization and so forth so this is I drew this diagram like maybe two hours ago so it's not very good but it's sort of the idea of what you might what I mean by how you might embed Aka or Kafka streams into an application that's actually the application here is represented as microservices that you'd be running that are all processing the stream of events the thing about this really the reason this slide mostly is here is to talk about how this is supported in Mezos and once again because we have really good support for containers for you know lightweight containers as well as you know fairly big things it's pretty easy to have that event stream coming out of Kafka whether it's Aka or Kafka streams and then I've got these microservices that are reading the data maybe really this diagram is better for Aka because it implies that I'm going to route this over to other microservices and do manipulations of some kind depending on what the events are and I'm not really showing output but you would imagine that maybe it goes back to a Kafka topic or a black screen or something like that now the other two that sort of fit into a different category Spark and Flink are actually deployed as systems that are running their own services and then you submit jobs to them that they figure out how to partition and to work over the cluster and that's really actually even true for streaming it's true for queries etc okay so medium latency though because at least as of today and this is sort of going away over time Spark actually is doing mini batches behind the scene so when you say I want to run a stream job it's actually going to box up some amount of data and then do some processing over it so there's some latency there in the old Spark streaming API the latency was maybe 200 to 500 milliseconds as a minimum so that like that credit card authorization example no way you can it can use Spark streaming for that because you don't have 200 milliseconds to wait but it is great if you're training machine learning models and you don't mind like having minutes or whatever windows where you just accumulate data then you know do an incremental training of the model and then do something with the data downstream they will eventually get rid of this minimum latency and make it more of a true streaming engine that's work that's actively being done but this is kind of the state of the art today I really love the SQL support in Spark it's now ANSI like 2002 compatible so it's kind of impressive and then of course if you've written your logic for Spark streaming you can use it for batch mode processing too so a lot of your offline warehousing apps can be done this way but it isn't designed for single event processing that's what I mean by unmask processing more like you know just chunks so the flink is the fourth one and a year ago I wouldn't have included flink here but I really decided that there were two important reasons why I wanted to have flink in as part of our product one is that it is designed as one of these high volume tools but it was designed initially as a streaming engine from the get go so it does actually do low latency really well so if you need to do like this partitioning of data but still have reasonably low latency then flink is often picked by teams over Spark for that reason it's really more of a true streaming engine from the get go the other reason is that there is this Apache project called BEAM that's Google data flows sort of top side and the reason I put it that way is Google did something rather clever they open sourced the data flow definition part of data flow I'm overloading terms a little bit I know but not the runner part the thing would actually materialize that data flow and run it so if you're in Google Cloud you get data flow and you can run it that way but if you're not you need something else to run it and as of today at least Apache flink is the best tool for running BEAM data flows and what that mean why is that important well BEAM to me is like the small talk of our era I'm really dating myself here but small talk was like the the programming language that everyone aspired to back in the 80s okay yeah I know the 80s actually existed for most of you it's just you know this thing that people the people my age claim actually existed but the thing was nobody actually used small talk but they all talked about it it's like the gold standard well I feel that BEAM might sort of fall into the same category it's influencing everything that everybody else is doing the reason being that they're they've done a really good job thinking about all the weird things that happen when you're processing data and you want actual accuracy not approximate numbers so for example suppose that you want to for your accounting purposes you need to know like let's say for every 10 minutes how many units of a particular you know skew did I sell in my stores you know around the country you know I work for a physical retailer let's say well that sounds like not too bad I'll just set up this sort of batch or mini batch job or something in streaming that looks at 10 minute windows and then it just does these rollups and bang right into the accounting system well not so fast unfortunately because light has a finite speed chances are pretty high that most of those at least a lot of those numbers are actually going to show up later just because of the time it takes data trans it traverses the network but even worse if I get a network partition and data doesn't show up for maybe 10 minutes or an hour you know how do I handle this late arrival of data do I decide all right I'll do some approximate provisional calculations and send those down but I have this mechanism for sending corrections if I get data arriving late these are the kind of questions you end up asking yourself if you want to build something that doesn't just do approximate aggregations over windows but tries to actually be you know sort of really really accurate like the kind of stuff you'd want in an accounting system so Google's thought about all of this the beam does a really sophisticated job of presenting this and that's why I really like Flink because they're ahead of everybody else in terms of supporting these semantics if you need that kind of capability okay I'm running out of time so let me just quickly finish I showed this diagram before but just to emphasize both Spark and Flink they run services in the cluster you submit jobs and then they figure out how to partition it into tasks as opposed to embedding these tools in your applications the last point I want to get into a little bit that I've alluded to is the sort of merging of architectures that's kind of happening so if you go back a few years ago that there was the big data people who mostly worried about data availability and you know scaling to big data sets but didn't worry too much about you know high availability high resilience scalability those kind of things it wasn't as big a problem for them as it was for the guys building the web servers in your organization which I just lumped under services but I think today as we've moved to microservices and fast data they're kind of converging a little bit and let me just quickly make the case and then I'll quit so if you think about like a classic microsystems architecture it's usually I've got each little microservice does its own thing has a one responsibility I can evolve them independently I can you know scale them independently I can drop in new ones and this fits the masos model really well because masos supports containers really well and I can easily you know deploy multiple things and do this kind of stuff pretty nicely that may not seem to represent what's going on is streaming very much but actually I think most of the time people are building very similar things in streaming architectures I've got whether I've got spark or aca streams or whatever I'm usually writing an app that does one thing I'm going to deploy it I may need to deploy a lot of them I may need concurrent versions so I have a lot of the same concerns and I'll just skip past this the synergy stuff just to get to the point but I think what's actually happening is that it's kind of forcing these architectures to look more like than different if I'm a microservice person and I'm used to building I was building three tier apps now I'm building microservices well if I'm successful then my data is going to become dominant as my business grows and so now I'm going to be worried about building more like stream data processing you know if you think about what the Twitter architecture must have looked like from 2007-ish or eight to today and that's sort of the evolution that they went through conversely if I'm now going from Hadoop to streaming architectures now I'm going to have to learn how to write services that will live for months and resist network partitions and stuff okay the last thing is one thing I think we need to still solve I'd love to see this solve sort of generically in the masis world is like common mechanisms other than maybe zookeeper where I could have stateful apps that can persist state in a globally available way so that if some of those processes go down I can easily reconstitute them and not lose where I was and right now like I said everybody spark flank etc they all do it their own way or they don't do it at all okay that's it any questions like where's the beer so I'll repeat the question for the video if flank is so much better kind of the low latency stuff and the kind of sophisticated semantics why would I use spark I think it's a couple of things one is I still think spark is probably your better choice for a lot of if you still need to do a lot of batch stuff and if you have an organization that's already bet on spark like maybe you have a established Hadoop cluster a Hadoop organization that's using spark then it wouldn't make sense to use it and not everybody needs those semantics that I described you know briefly with fling yeah so I don't think anybody's going to use all four of these unless you're a really big organization where you're one of those places that uses everything in the world but I think that's one of the challenges is people are going to see a lot of overlap in some of these tools and just kind of make an arbitrary or semi-arbitrary choice between them and maybe have two like I'll use Kafka streams and flink or I'll use Aka streams and spark and but I won't use more than that yeah yeah some questions what about SAMHSA and there's some others you could throw in that I've heard about or people ask about like Apex Apache Apex and I think I think SAMHSA and storm kind of fall into the same category for example of tools that kind of sort of were pioneers in this space but have kind of suffered a little bit from oh we kind of did that wrong we kind of did this wrong and now maybe we need to start over and you know build the next generation so I think that they both kind of suffer from that a little bit although if you're a storm user you should really look at Twitter here and which is like their complete rewrite of storm I can't say I've actually used SAMHSA but I think in general from what I've seen of it it sort of falls into that camp of this was a really good first generation but we probably need to move on to a second generation technology maybe time for one more okay thanks a lot


What's good Josh your boy Ross back again with another video. So we're gonna check out. Oh my god, that's on highlights part one clips This is from BDWJ forever. I remember him having this guy having a channel like maybe a Few years ago, I would check out some of his clips on the interclutch page Well, we would check out some of his clips on the interclutch page and on my page And then I think his channel got taken down when I saw his it's good to see that Broadest channel back in what make this Channel cool to me is they would show a lot pretty much primarily in the Company wrestling like indie wrestling companies and their highlights and clips and I'm all for checking that out There's something different on the channel. It's not a aw or WWE It's more of the Indies indie scene and some of my favorite wrestlers obviously come from the independent scene So it's cool to just see these different promotions have their clips on there And you know be from you know Introduced or get familiar with some of these independent companies So if y'all want me to check out some more of these independent wrestling highlight clips hit that like button Now, let me know that you guys are interested in this and I'm actually you know looking forward to checking out some of these These different companies and the highlights they have that they've Accumulated so appreciate all love and support roll to Seventy K.  I love it fantastic Here he comes To qualify Respect those stripes Takes to be champion here push-up aeroplane He deserved so much better than WWE. He just did you just did Trying to break that out of this match with some incredible early offense, what does Teddy have in mind? Can control spring balls. Oh, he's trying to break his ribs I believe El Generico is Sami Zayn y'all I know y'all gonna correct me if I'm wrong I believe that is Sami Zayn before he went to WWE. Oh, what not? And it looks like I'm not sure if his contract supposed to be ending soon But what they did with him Brock Lesnar? I Feel like he deserves better, but that's needed here nor there Great Canadian hero Oh That gotta be This is fun everybody diving in the same spot. Oh Man, but this video was dope. I enjoyed this. I definitely want to check out some more clips from this channel So if y'all want me to y'all enjoyed this video y'all thought this was definitely worth the oh my god Moniker oh my god saying hit that like button. Let me know down below in the comment section I want me to check out some more of these type of independent wrestling clips like from the indie scene So appreciate all the love and support road to said you can't appreciate y'all kicking me see our next


Dressed in my Tuesday best. It's a Tuesday when I'm filming this Ready for some work Well, wonderful community of Kenora, Ontario, Canada, which is close to the Manitoba border Close to the center of Canada south down by the US border Picking up a load here. I got a tarpon and take it down to Brainerd, Minnesota Which is about six hours south of here into the United States They only unload there until 4 30 I have to get there by 4 30 to get unloaded Which means I have to leave here by about 10 o'clock to get there by 4 If I get here like I got here at 7 a.m. This morning And if I get loaded right away and get into the tarp shed right away get my load tarped I Can get get out of here by 10, but it looks like we might be Unloading tomorrow morning. I've gotten everything I need all ready to go I've gotten all my paperwork set ready to go ready to send in as soon as I get my load paperwork my BOL My bill of ladings Send those in get that clear at the border. So they have a tarp shed here and I have to tarp in there I'm not allowed to climb up on top of my load here for safety reasons Which is they supply a nice tarp shed here. That's nice, but there's only two bays So only two guys can tarp at a time No, the way it's supposed to work is you go into the tarp shed You use their crane to get the tarps over your load and you tack it down on all four corners and then you move out of the tarp shed and Finish bungeeing it down outside so that the next person can get in and use that crane because that's the whole point Of that tarp shed is just to get your tarps over your load because you're not supposed to climb on top of your load So you're supposed to just you know throw your tarps over move out of the tarp shed go bungee it outside So many guys they don't do that and they put every single bungee on they complete their whole tarp job Inside the tarp shed and then the lineup builds up behind them And uh, you know it could take out. I've waited six hours here before Because guys in front of me were taking three hours in the tarp shed Usually if you do it correctly for me, it takes me about 20 minutes They're 20 to 30 minutes tops but 20 minutes just to throw the tarps over and you're out, but The world doesn't work that way. It doesn't always work so smoothly. Not everybody's on board with Moving along quickly so Once I have the The load tarp usually from what I'm sitting where I am now they move me into the staging area now So usually from this point if there's no one in my way in the tarp shed I can get out of here in about two hours less than two hours. That's that's everything done rolling away The time is now 843 So even if everything went perfect right now I'd be leaving here to about 10 30 10 45 Which I don't think I'm going to make it before 4 30 down in Brainerd then Which is fine. I can just unload it in the morning then but I like to get unloaded the same day because then I can start moving towards my reload and be at my reload First thing in the morning instead of being at my unload first thing in the morning, you know what I mean? Either way, we'll make it work We'll make it work You always try and shoot for the best plan, right? You make a plan, but this is trucking. You have to remember This is trucking Plan for the plan not to work out Some of you have been asking about my bunk heater s bar situation my diesel bunk heater underneath my sleeper It's an airtronic d2. I believe It might be a d4. I'm pretty sure it's a d2 I got to double check that because I'm looking for a rebuild kit. I'm going to buy It looks like they're about like 30 35 bucks on amazon But it includes a new fuel filter a new burner screen and new gaskets for on there I think my fuel filter on there's a tiny little fuel filter on the fuel pump underneath my truck That takes diesel fuel from my tank and puts it through to the burner I think it's just a little bit plugged up. I think I just need to even maybe I can just take it out and clean it You know, maybe I should try that first before spending money Yeah, I looked at buying a new heater. They're about $1200 for a new heater I'm talking in canadian dollars here. Sorry for my american friends Just take a few hundred dollars off and you got your price $1200 canadian is probably what like $900 american Something off something like that. I don't know not exactly but Instead of spending all that money on a new heater I'm going to try to keep this one running because it still heats well. It's just I don't think it's getting fuel properly I ran it through several cycles of just straight kerosene some diesel injector cleaner some diesel cleaner Which helped a lot. I can get it running now, but it's still not running at 100% So that's just my little update for those of you who've been asking Still working on it. It's it's working, but it's just it's it's not working as great as it should So I still got to my next step will be to clean out that filter see if that'll work And if that doesn't work, then I'm gonna take the whole thing apart and buy one of those rebuild kits and spend a day or an afternoon whatever Oh, let's let's not let's not kid ourselves. It'll take me a full day Take a date fixing that thing up because then I can get it fixed for like Under 50 bucks instead of buying a new $1200 heater And if that still doesn't work, then I got to buy a new heater I Said a new record for myself It's 10 15 and I'm out of here So that's from 8 37 from when I got to the staging area 9 37 What is that? It's less than two hours. That's for sure 8 37 to 10 15. I'm rolling. I think I might make it Just getting out of their yard here right now. I am gonna have to Laser focus myself and hopefully hit no delays I don't have to stop for fuel I think I might make it and get loaded today yet What is that an hour and 23 plus 15 33 38 An hour and 38 minutes. Am I right with that math? I'm just Getting that off the top of my head hour and 38 minutes A new record Next time we try to beat hour and a half even The thing is you still got to do a proper job tarping too, right? I mean, I don't know if I could save any more time than I did Start right now. I'm still gonna be waiting here and I got zero minutes available to wait So right now it's looking like I'm not gonna make it now I got about 15 minutes to play with that's all I got If I can't get cleared in the next 15 minutes right till the morning And that means all of this rushing this morning was for nothing I'm in port frances now and by some miracle from above My customs just got cleared now That is The fastest I've ever seen it done that must have been a fluke They hadn't even started it when I was an emo and that was just 10 minutes behind me in 10 minutes They they opened the file and got it cleared and customs certified Still looks like I should be able to get unloaded today as long as there's no other delays And as long as they're okay with unloading me at about 415 and see what they say they say no That's too late. Well We'll sleep at the gate again and make sure our batteries don't die this time Slide right on trans Canada highway highway 11 The office let them know. Hey, I'm gonna have an empty trailer tonight. What do you want me to do? Let's go I want to go somewhere. I want to do something They got me headed over to minneapolis Pick up there tomorrow morning one at 7 a.m. One at 9 a.m Go to the same place. I believe in red lake ontario north of canora where we came from today Maybe a full day drive from minneapolis tomorrow all day tomorrow We'll deliver that first thing in the morning the next day run back down to canora grab another one of these loads Or delivery the following morning Maybe I'll get a reload on the way home yet after that That'll be a nice full week and every week like this. This is it looks great on paper We'll see what it looks like when uh, I don't pay day rolls around looks like it should be good Mine is 409. Oh, yeah 409 p.m. That is some pretty good time Just under six hours to get here That was probably the quickest pick up and drop I've ever done on this route So how long did I say I took before to get loaded? Not counting when I got to the gate. I got to the gate at 7 but when they started on me It was just about let's say 840 right just before 840 So eight and around to 840 I was out of there 10 15 940 so an hour and 35 minutes I was rolling down there. I got here just under six hours So that's seven hours 35 minutes and then 45 minutes no less than that 40 minutes. I was unloaded here So seven and a half eight ten Like just over eight eight and a quarter hours from the time I started getting loaded The time I was delivered and empty That's not bad. All right. Now we got to go to minneapolis Pick up a load there tomorrow morning. Like I was telling you before there's two pickups on this load So first one's at 7 a.m. Second one's at 9 a.m. I think I'm gonna go and park as close to the customer there as I can looks like they have parking on the street Uh, double check that we're gonna stop at st. Cloud minnesota for fuel fuel. There is the cheapest on my route today And I'll double check everything once I get there. I still don't know why st. Cloud minnesota has so much cheaper fuel then everywhere else around It's always the cheapest by a long shot 40 50 cents cheaper sometimes Must be the local taxes or something. I don't know But here we are Grabbing some cheap juice destination on the left side into 100 meters Actually, it's right here. Karen. What are you talking about? Look at that. No lineups. Wow