Let's go, extracting the signal from the noise. It's theCUBE, covering VMworld 2015. Brought to you by VMware and its ecosystem sponsors. Now your hosts, John Furrier and Dave Vellante. Hello everyone, welcome back. This is theCUBE, live in San Francisco for day three of coverage of VMworld 2015. Again, live in San Francisco at Moscone North Lobby, right off the street. I'm John Furrier with SiliconANG. I'm Joe McCos, Dave Vellante. This is kicking off day three segments with two days of wall-to-wall coverage. Go to youtube.com slash siliconangle or siliconangle.tv for all the videos. We have special guests on this kickoff. George Gilbert, senior analyst at Wikibon on Big Data. Guys, Dave, big night last night, obviously it was the circuit party for all the action. VMworld is really known for their engagement, their communities, interactions, but also they're known for all their parties. And certainly tonight, the super party at AT&T Park. But we learn a lot at these parties. People love to talk and we love to listen. So we've got there pounding the pavement, getting the scoop. And interesting, a lot of venture capital parties. So you've got a lot of startups here trying to, VC's trying to find the startups, but certainly really the post-Gel-Singer day yesterday, really about the VM, where the future of VM, where what's going on with EMC, the Federation, a lot of conversation around the ecosystem. Everyone's engaged. I mean, it's kind of a lot of storage focus, but again, it's a free-for-all on the floor there. Ton of activity. It seems like people are waiting. It's like the calm before the storm, we're in the eye of the hurricane. Some stuff's going to happen. We're in this ecosystem, a lot of transformation. Dave, what's your take on what you learned last night on theCUBE? Yeah, so last night we were at Lightspeed, hit Highland Capital, Kleiner Perkins, Sequoia. We met a lot of entrepreneurs, a lot of VC's. I'll say this, there's investments in innovation going on virtually every part of the stack. And surprisingly, including storage, because you figure, okay, who needs another all-flash array company, but there are several in stealth. But also a lot of activity around security and networking. Tons and tons and tons of investment going into those spaces, and also still tons into the cloud management. So it was good. And then as well, George, a lot of big data action going on. We saw numerous people, I saw Ben Werther out last night. We saw Stefan from Datamere, a bunch of folks in the Hadoop and big data community. Not that this is a Hadoop and big data show, but everybody's here, and it's close. So we wanted to have this special segment with you, George, to sort of catch up on what you're seeing in the space, special big data segment here at VMworld 2015. So give us the update. What are you hearing in the ground? You've been focusing on Spark versus Hadoop. Hadoop 3.0, you've been talking to the practitioners, the technologists, what's your take on what's going on? So if I were to sort of make the drawing evolution of big data on this infrastructure and sort of in stages, Hadoop 1.0 was MapReduce. We all know that. And we built higher level things on MapReduce to hide its complexity, but you could only do one thing at a time. It was the DOS era. It was pretty low level. Sea prompt. Yes, exactly. With Yarn and HDFS, we had enough of a platform where we could have multiple compute engines working. We could have an Impala for sort of MPP SQL query. We could have machine learning going on. We could have essentially multiple applications working on the same data. What has been less clear is what Hadoop 3.0 is going to look like. And I think those threads are beginning to come together where Yarn will grow up from something that just allocates hardware resources to something that can also manage workloads. One of the sort of secret sauces and best kept secrets within Oracle is that you can run queries that are optimized for performance, but also that have like hundreds of other people competing for the Oracle resource. And it can manage that. The what's leftover resource. Yes. And the methods. So you're talking about your quality of service, if you will, for the main app. And then, OK, everybody else can go fight over it. Well, but it's also, which apps get how much in terms of processor and memory, things like that. But then there's the other layer, which is how each user, based on the role and their priority, how much of the workload gets distributed to them within the application and then between apps. So you're running Oracle, and you might be running a stream processor. And how do you keep everyone sort of happy? That's now a layer that's becoming a sort of either Yarn v2 or messes. And you're saying that's a harbinger of what we're going to see in Hadoop 3.0? As a Hadoop 3.0 as a collection of capabilities, that would be the new foundation in terms of resource management. So it's really kind of like an operating system, like Windows manages all the workloads within all the applications that are cooperating. Then, right now, HDFS is kind of limited as a storage medium because you can only add stuff. So it's sort of like an archive. And it's also pretty much disk-based. But we want to have a performance tier that's in RAM. And we also want to have sort of a higher performance kind of storage tier, like our analyst David Flurr talks about, which is sort of large, flash, but high speed. Low latency. Yes. OK. No, but doesn't Spark bring that? It's very intensive. Well, that's the thing. Some people think Spark will choke on the fact that it needs so much RAM. And then when it doesn't, it spills to disk and gets really slow. But if you have a richer storage tier where you have both RAM and this low latency persistent storage as well as traditional disk, then things like Spark and others can become much more cost-effective in very, very large scales. That's those are the first two layers. Then the third layer is we've got all these different vectors going off where we have the MPP SQL databases. We have these document databases like Mongo. We have the stream processing things, because whether it's the internet of things or whether we want analytics to happen really, really fast, not store it in the database and then do something with it. So this next layer that's coming together is putting together the storage where you could have key value and tables that you would get from HBase, the files that you would have from HDFS++ and a doc database like MongoDB. That would all be integrated in one layer. Then you would have your choice of streaming or storage. And then on another layer, you would pick your poison in terms of analytics, whether you want machine learning, whether you want SQL. So you have all these three things integrated. Then on top of the storage layer and with this resource mediation where you put those three together, you have a platform that customers can essentially go to their database vendors and say, OK, you're good for the transaction processing. We're not going to do that here. Everything else? Yes. But you're talking about totally reinventing the big data stack. I'm talking about evolving it. OK, so it's not reinventing it. All right, so it's specific. What vendors now are doing that? Well, it's reinventing what? Well, how does that relate to VMworld? OK, it relates to VMworld in the sense that we need sort of infrastructure as a service to make that work, because we have to abstract out the complexity of running the clusters. Well, it also relates in the sense that guys like Amazon, as you pointed out to me, George, are building that end-to-end data management stack and delivering it as a service. And that is a killer offering. Within the infrastructure. Everything from infrastructure to service all the way through platform as a service. And that is the how does VMware and others compete with that? OK, here's my take. So someone like a VMware, maybe OpenStack, maybe IBM with SoftLayer, if they can manage that infrastructure as a real service, like Amazon does it. And then you have the software vendors, like a MapR. MapR is really bringing these software layers together. But you'll see it coming from others. I think you'll see it coming together from them for an on-premise offering. If you put that on top of the VMware style infrastructure, and if MapR can manage it as a service, you're going to see essentially the TCO that you might get in the cloud, but without the proprietary lock-in that you would get in the cloud. Okay, so now let's come back to Spark V Hadoop. When Spark came out, you guys did the Spark Summit. There was a lot of excitement around Spark. IBM, I think they did their billion-dollar play or playbook, or maybe not, I can't remember about. But they put a lot of effort and resource into Spark.  But you, George, got very excited and said, hmm, you actually made the point to me, is maybe Dave, excuse me, Hadoop needs Spark more than Spark needs Hadoop. And we sort of had that conversation. What's your take now, having gone out and talked to people, maybe getting some arrows thrown at you, shot at you? What's your take, give us a sort of view of the Spark versus Hadoop, where does it fit debate? What I talked about in terms of these three layers, storage, sort of resource scheduling and mediation, and then the application analytics on top. Spark fits in that in the sense that you can have one engine that does your machine learning, your streaming, your graph processing, SQL, all on the same data set. And it's a single application model. It can rest on the mediation layer, whether it's yarn or even more sophisticated mesos. And it can rest on a storage layer though that has to evolve somewhat from HDFS, just like the broader Hadoop v3 I was talking about. We need a smart storage layer so that yarn, I'm sorry, so Spark knows where to move data around when you're doing the analysis. So a couple of little things we hear in this week, John. So the rumor was floating around that Microsoft was going to buy Docker. And supposedly that deal's done because Ben, they couldn't agree on a price because the price was too damn high. So a good way to go Docker is to stay private. So word is now they're going after mesos. That's because if you look at mesos and if Microsoft does some interesting work on storage, then they can lay claim to the foundation of the next generation data center operating system. And that makes Microsoft extremely relevant in that space, doesn't it? Right, also Microsoft needs have a play here and that's bottom line. Docker is going for, they have too much traction in my mind. Docker is going to be the winner in my opinion, not just because I like the company and like what they've done, but their lead is significant. Their mind share, their developer traction is significant, but that doesn't mean there's room for other guys. Categories being created, there's going to be a number two and number three. I agree with you. I love CoreOS, love Alex, but I mean Docker's got a lead, they've got the market momentum, they've got the mind share. The CoreOS, I mean I was talking with Alex last night at the Atlanta Perkins party with CoreOS and I'm a huge fan of CoreOS, been on theCUBE, he's been on theCUBE. Here's the problem that they have. They're running really hard right now, super hot, they're scaling up, they're looking for engineers, they got basically an open ticket item, any engineer they are trying to hire so fast, they're growing really, really fast. At the same time there's a lot of pressure coming at them, M&A pressure, everyone wants to do a reach around on them, everyone wants to grab them, they're the bride right now that everyone wants to dance with because Docker's saying, no, no, we're going alone, so that's a lot of pressure for an entrepreneur and team.  I run it too hot, but right now they're looking very good and I think it's a great company. Well, with a few points, there's definitely room for number two, there's always a room for an alternative, but the market pressure for an emerging startup is really hard to grow a company really fast and not self-induced blow up, that's the only thing that's going to stop CoreOS in my opinion, CoreOS is solid and with Docker's traction, there's totally that love, hate relationship going on between the two. So bottom line of the bottom line, Spark v Hadoop, Spark kills Hadoop, Hadoop evolves, there's a coexistence, what's your take? You know, I don't think it'll kill Hadoop, but there will be a certain class of customers the most sophisticated and potentially also the smaller ones where integrated on the high end where you can take advantage of features that sort of reinforce each other that no one else can offer and on the lower end, simplicity of having a one package where it's very compelling, but it's likely to involve some amount of the sort of Hadoop v3 infrastructure that can exist separately from it, the Databricks doesn't really require the Hadoop infrastructure itself, but you'll see a Venn diagram where there is some overlap and you'll see situations where the Hadoop vendors say, oh yeah, we'll run Spark as a job and you'll see the Databricks guys and perhaps IBM as well say, you know, you can run it self-contained. All right, let's wrap, John. Give us some predictions, you know, based on what you've seen here this week and then we'll wrap it up. Yeah, we've got a long day today but we want to kick off this segment by saying, look, we were on the streets last night, pound the pavement, year to the ground, listening to all the conversations and everyone's got their side of the story of what they want to promote. A lot of people promote in their agendas but here's the bottom line. We predicted on Silicon Angle, I predicted on theCUBE that EMC would not allow a reverse takeover. I'm still maintaining that prediction that EMC will create a new version of itself, maybe shed some product divisions off to the HPs of the world that really have good synergies, doing some M&A and VMware comes back in. You're predicting Federation 2.0? I'm predicting that VMware is so powerful that if EMC, you said this yesterday, I'm borrowing your line, is too powerful for EMC to give up and I think that legacy of the Federation is key and this Elliott Capital is the Gordon Gecko, modern hedge fund, he's evil and think about the damage that would happen if he raids that company. Thousands of jobs glossed in Massachusetts, how can it be a ghost town? I just don't see the individuals who run EMC, they're tough, they're not going to let that happen. So my opinion, I think you're going to see EMC reborn, rebooted, pivoted, whatever you want to call it, in a whole new way with VMware at the center, I just don't see VMware. But that story doesn't end there, right? Because Gordon Gecko's not going to go away. Well, lighting a fire into someone's butt is one thing but coming in and taking down the company just over short-term gain, I don't think EMC will let that happen. The founders and the DNA of that company is resilient. And then obviously the startup scene, Docker versus CoreOS, that is going to be a real battle. And then on the big data side, you're seeing big data conversation here at VMworld, but from an IT ops perspective, I think you're going to see the developer piece, got a big part of next year, where it's real developers, where infrastructure as code is going to be the DevOps ethos. And then you're going to start to see the end-to-end architecture. I think that's what the new EMC is going to look like. They're going to come out looking more and more like Oracle, more and more like an engineered system end-to-end with cloud to compete ultimately with Amazon. So, and big data is going to be sprinkled all throughout that fabric. It's that next layer. It's that next layer. Again, we're breaking it down here inside theCUBE day three. We're kicking off, we've got two sets. We have our director set, our new innovation. Go to siliconangle.com and wikibon.com for the free research and subscribe to some of the cutting edge research over there. Of course, siliconangle.tv, where all the videos are. And we're going to be more live from seven years ago after this short break.


study so usually what researchers do is they increase the sample by 20 percentage expecting that 20 percentage might leave the study so if we increase the sample size it will not affect the power of the study at the end so after follow up uh so attrition is lost to follow due to inevitable factors such as death migration or loss of interest so finally we do the assessment uh we take the values and we compare with control groups and we find out which is better and which is uh bad okay so randomized control trial are the true sense of experimental design so there are many types of randomized control trial what we are seeing is very basic uh details of experimental and type of trials are clinical trials preventive trials risk factor cessation experiment trial for trial of etiology agents and evaluation of health services all these are methods where we can uh conduct our experimental study designs okay so what we are saying is just clinical trial so this experimental study design is very vast uh we are not going very deep into it we are just seeing what is randomized controlled trial and the steps of it so preventive trials are like vaccine trials commonly the clinical trials what we have seen uh the drug trials like beta blockers and the trials for other aspirin trials so all this comes under the first category clinical trial then the preventive trials is most commonly known as vaccination trials so vaccination trial size going to happen in the covid case if they find a vaccine they have to be tested in group of people so risk factor trial is like the risk factors of coronary heart diseases are elevated uh blood cholesterol smoking hypertension and sedentary habits so we do experiment on people with risk factors and people without risk factors so that is also an trial uh where we are checking the effect of risk factors on a particular outcome that is heart disease so we keep two groups and one group it will be almost like a cohort study design one group with risk factors and one group without risk factors and we'll observe it for a period of time that is also be a longitudinal study design because it is the following cessation experiment also can be done using a trial that is smoking and lung cancer people who did uh cessation and who did not follow the and uh did not follow the habit and those one group with which continues the habit and other group which has given up and comparing the outcome that is lung cancer so that also can be done as a trial and the trial of etiological agent so this uh retro lental fibroplasia uh that uh is one of the study to confirm or refute an etiological hypothesis so uh this any type of particular disease can be tested using an experimental design and evaluation of health services so the effectiveness and efficiency of health services also can be uh assessed using uh uh trials that is randomized controlled trials so all these were trials uh types of um trials that is randomized controlled trial that is clinical trials um so most commonly we use randomized controlled double blind clinical trial double blind means the participant and the investigator will not be knowing which group the participants are being allocated if we do randomization with cheat method the participants will not be knowing if we keep an assistant to enter the cheat details uh how participants are being allocated the investigator also will not be knowing which participants are in which group so that will remove the selection bias in this process of blinding to allocation of participants from participant side and investigator side is known as a double blind clinical trial that is what uh that is the studies most commonly used in our city so these are the various examples clinical trial uh preventive trial risk factor trial cessation experiments trial for etiological agent and health services evaluation so clinical trial we have just seen for uh some drug trials preventive trial for some vaccine trial risk factors for that uh factors which affects coronary and disease one group without factor one group with factors cessation experiments one group with smoking and one group without smoking and checking the lung cancer and trial of etiological agents and lastly the health services evaluation can also be made uh by rct so the experimental design which there is no randomization involved which is known as non-randomized control trial there will be control group but there will not be any randomization so participants will not be blinded so participants will be know will be knowing which group they are allocated to whether they are in the study group or control group so it is like three types uncontrolled trials without control natural experiment before and after comparison studies and controlled trial means there will not be any control there will not be any comparison group that the pap test is effective in reducing mortality from cervical cancer so when we take pap test from participants they will be knowing they are part of a study group and maybe we will know we can't keep a control group for cervical cancer and pap test so in such type of studies it's very difficult to keep a control group so we keep only one group and check the effectiveness of one particular intervention that is pap test smear natural experiences are when we observe a group of people they naturally will be into that is smokers and non smokers and we had discussed this experiment johnson that city map i was showing so that cholera disease naturally divided the population into people with cholera and people without cholera so it becomes a natural experiment we are not dividing the participants into two groups the net itself creating two groups people with disease and people without disease so this is a johnson experiment so before and after comparison are two types one is without control and one is with control so we have to do the outcome measurement before and after the preventive measure that is before and after the introduction of preventive measure and we have to compare the incidence so that is what the third type of non randomized controlled trial the classic example is before and after comparison is the scurvy which i was talking in the beginning among the sailors okay that is before and after comparison before he checked the patient status that is the people with scurvy and how it was and after giving the fruits and how it came or the effect of this fruit so this before after comparison and johnson also that cholera experiment also before after comparison before removing the pipe and after once the pipe is removed from that street how the cholera is changed the incidence of cholera is changed so all these are before after comparison it can be done with and without control before the experiment where without control we can keep a control also through the experiment so the basic advantage of a experimental study design in our city it is scientifically it is an ideal method and it removes a large number of bias the least bias study design is our cities and it ensures that perfect the temporal relationship between exposure and outcome the temporal relationship means the exposure cost outcome we have to prove that smoking cost lung cancer that relationship this happened first and this happened second so that temporality we have to prove so it is very difficult to prove in analytical study cohort study can be used to prove temporality but this is the ideal design to prove temporality so i was talking about this lung cancer and smoking this cannot be conducted as a case control study if it is going into future because i was talking about risk factor trial so risk factor trial was like one group with risk factors other group without risk factors this is almost like a cohort study but what does the change in this thing is we are allocating the group this one we are allocating the group into two as like one with a this is and one without this is or one with risk factors and without risk factors the allocation is important randomly allocation or non-randomly allocation so this allocation is like becoming a trial and we are following up cohort study means we will be observing it we will not be doing any intervention from the investigator part okay here the investigation part is we are observing we are observing arrest factors and cohort study and this rct is almost follows the same pattern but the only thing is there will be allocation of participants into two groups there it becomes automatically changed into two groups okay people who are not having any disease they become exposed and non-exposed and later they'll develop disease in future period of time but in trial and random is controlled trial in the beginning only we are keeping two groups one with risk factors and one without risk factors and we are following it that's all about our study design controlled trials and controlled trials it's examples the basic thing is the steps in rcts and the advantages disadvantages advantages we have talked already like temporality reduce biases and it is scientifically proven and the disadvantages it needs a long time and it has ethical issues definitely it has ethical issues because in cases like disease cases like if I said that risk factor trials we know that people will develop disease in the experimental group people with risk factors will develop many will develop lung cancer in future period so in order to just prove the hypothesis we are keeping them to follow the habit which is detrimental to their health in spite of knowing that it is detrimental to and we are not stopping them just to prove our hypothesis just to gain some scientific knowledge we are sacrificing their health we can think in that way also that way it has become ethical so in most of the time getting approval for this trials which involves diseases is very rare nowadays so that's all about experimental study design so what is blinding and how blinding helps to prevent the biases so last class we had seen various types of biases biases which are seen in case control and cohort study so the most commonly biases are seen in case control and cohort are very rarely experimental study because in experiment we are keeping both participants in random sequence I mean both groups of participants we are allocating by random methods so the group itself are comparable the group itself are not producing any bias that the selection bias is completely removed from the study design that's why it is the most powerful study to prove hypothesis or prove and cause effect or the association between a cause and effect so exactly what is blinding and blinding is not only possible in case control or cohort study because the study case control study the participants will be definitely knowing their cases or their controls because people with the diseases they will be knowing their cases and people without the disease will be knowing their control so that is an inherent part of the studies in that bias cannot be eliminated in any way the cohort study also after a period of time they will be realizing they are becoming exposed to group and the other group is not exposed so some part of the bias was some types of biases unavoidable but the main bias selection bias is completely removable or avoidable in experimental study because of randomization so mostly this randomization is possible only in trials so as I had mentioned in experimental design the study designs randomization is possible only with drug trials or any new machineries or any new techniques any comparison of new and old trick in such scenarios we can do blinding not in any case study because of in the cases and such scenarios are ethically involved so we cannot keep any random allocation so let's come to the topic blinding so blinding in clinical trial is as the name suggests it is the concealing or masking the treatment regime or treatment allocation that is in any trial we have basically two groups one is treatment group and one is a control group or the two groups will be getting two different treatment or two different drugs one new trick and one old trick so the idea of blinding is the participants should not be knowing which drug they are getting or which group they are allocated to whether they are being allocated to the treatment group or control group they should not be knowing that is a basic idea of blinding the participants will not be knowing which group they are going that is allocation concealment so that is we do by randomization but which drug they are getting we can do only by blinding because we have to make sure that the participants is not have any clue about the intervention being applied so there are basically three types of blinding single blind double blind and triple blind studies the most commonly used is double blind studies so blinding refers to keeping the trial participants that is the first group they are the participants they get the intervention the investigator they are the people who give intervention or they are doing the manipulation or intervention they are the investigators or the usually the doctors or any other clinical trial people and the third part is assessors those collecting outcome data they will be checking the measurement if we are trying a drug on blood pressure so they'll be checking blood pressure before and after the study so we have three groups in any type of study that is first one is participants second one is investigators and third one is assessors okay so in blinding we have to blind one or three groups of these participants i mean these categories so that becomes primary i mean single double or triple blind study so they should be unaware of an assigned intervention they should not be knowing which what type of material they are getting what is the intervention they are being allocated to nothing they should be knowing so it actually prevents bias so the one bias we had seen in case control studies hot tone effect observer bias when they are being known as a case or they they know that they are being watched under study they'll automatically psychologically change the output so that will be eliminated because they don't know which group they are being allocated they know that they are just part of the study but they don't know which group they are being allocated and another bias was investigator bias the investigator who knows the hypothesis and who knows which group is getting which intervention so such bias also will be eliminated so investigator means in case control and it is about hypothesis but in experimental it is about intervention okay so blinding basically prevents bias so bias something which distorts the result of any study so we have only three types first one is single blind study so we can see the picture okay this is an open trial where the first category that is participants or patient next healthcare providers or investigator that is staff and the analyst people who measure the outcome data that is BP or anything all are open to know the intervention so that is an open trial so the single blind trial the participants they don't know which they don't know which drug they are getting or which type of intervention they are getting okay so allocation concealment is different and blinding is different allocation concealment is they'll be allocated to different groups by random checking or randomization so this blinding is the participants will be blinded to the treatment intervention or the allocation not allocation the particular intervention they are being given so here the patient is blinded or the participants is blinded okay that is a single blind study it is the one of the three categories that is participants rather than investigators usually we get out whether the participants or the investigator is blinded usually it is the participants okay that is single blind trial so when we come to double blind trial what happens is this is a double blind trial here the patient that is the participants and the staff or the investigator both are blinded both groups they don't know what they are doing what intervention they are giving to what group they are giving they don't know anything about the studies okay so this is known as a double blind trial so double blind trial participants and the investigators usually remain unaware of the interventions okay that is a double blind trial the triple blind trial is nothing but the double blind plus the analyst okay so all the three categories will be blinded the participants the participants the investigator and the outcome measure people that is analyst or the assessors are blinded so it is a double blind trial that also maintains a blind data analysis can say that the outcome measuring people or the data analyst the person who is doing that analysis so all the three categories of people are blinded here so this is triple blind study okay so most commonly you study is double blind study rather than triple blind because the triple blind study usually needs a very laborious framework because the participants has to be kept very cautiously they have to be blinded the investigator has to be blinded and again the assessors or analysts to be blinded then there should be another group of people who will be doing majority of the other works they have to do the allocation they have to give the treatment they have to make sure they have to do the outcome measure so there will be four groups of people in this study so usually will be the double blind study is most commonly happening triple blind study is quite rare in our trials so this is all about blinding so blinding is basically about preventing bias some released bias reported studies experimental study which is most trusted or it is at the top of the evidence based just under meta analysis and systematic reviews so what is the best study to prove causality or association so blinding is must in any type of trial so usually allocation concealment and blinding is different allocation concealment is the allocation of participants will be concealed blinding is different thing the intervention is masked so blinding is also known as masking so we have three type of blinding single blind double blind and triple blind single blind this patient or participant is blinded double blinded the investigator also will be blinded and the triple blind outcome measure and the analyst also will be blinded okay so that's all about blinding


The Red Lake Police Department is reminding the public that it is illegal to take moose on the Red Lake Indian Reservation due to a decline in the moose population throughout the region. The Red Lake Tribal Council has taken steps to protect the moose inhabiting Red Lake lands by implementing a moratorium on moose hunting on all tribal lands. This moratorium includes the diminished lands, seeded lands, and Northwest Angle. If you enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland PBS.


Yo, early in the morning about to make some coffee. Got my like, fake, easy slides that I got from Uniqlo. They're actually really comfortable. I'll make sure I link them down in the description box. But before we start, it's coffee time. Looks a little bit better. The motor's up. Just a tad. What's up everyone? Jossi here. I got a new setup, sort of. Not from what you can see, but basically kind of camera setup. But I finally got a new cord from my monitor so I can actually see myself and it's over there. I'll make sure I'm in frame and in focus. Got this new Sigma 24mm f1.4 lens, which just looks really incredible. The background is just really blown out. A very sharp lens and it's nice that it has those extra stops of light, which is great, especially since it's so gray here in the Midwest. I also got my microphone that I've been using to pass, I think, two videos. So the audio should sound much better. So I apologize for previous videos where the audio was bad. But with that being said, I'm really excited about today's video where I talk about the five reasons why you should learn how to code in 2021. Before we get started, I want to thank Ritual for sponsoring this video. So this video is kind of almost like a part two to why programming is the best job, or maybe not a part two, I won't say that, but maybe an extension from that video where I talk about why programming is the best job to pursue in 2021. So I talked about more so like the career opportunities that you have and a lot of the perks that you get for being someone who is in tech. So in today's video, I'm going to talk specifically about programming and the value it has in 2021. And with all the digital transformations and billions of dollars invested in digital, this is the perfect time to learn how to code. I don't care what age you are. I know a lot of you all asked me like, hey, is it too late to learn how to code? Never. If you give this video a thumbs up, that would be amazing. It helps out with the algorithm, which is going to help my channel. And I want as many people as possible to be able to view this video and hopefully get some good information that can help them with their career and skill sets that they're looking to gain. Also, subscribe to the channel. I love for you all to become a part of this amazing community. Hit that notification bell so you can know right away when I release a video. All right. So the first reason why you should learn how to code in 2021 is, of course, if you're someone who's looking to become an engineer or to pursue a career in tech. So even traditional engineering majors need to learn how to code. I know that because I graduated with the computer science degree, which was in the engineering college. So I have a lot of classes or a good amount of classes with other majors outside of my own. So I had classes with electrical engineers, civil engineers, mechanical engineers, and maybe even industrial systems engineering. Because the more traditional engineering majors actually had to take at least one programming course. And I know for a fact that electrical engineers had to take even more. I think they took like at least like three to five programming courses. And the reason being is because having an understanding of programming is huge, especially when you're a mechanical engineer using Matlab, or you're an electrical engineer who's interested in pursuing embedded systems. And obviously, if you want to pursue something like software engineering or computer science, you need to learn how to code. So my advice for someone who hasn't started their computer science program, software engineering program or bootcamp is to learn how to code to some capacity. You don't have to be an expert, but just get familiar with the syntax. I think that will be really, really helpful for you before you start your program. Had to get another batch of coffee. It gets really, really cold in here. Also, sometimes I find myself being unproductive or just kind of sleepy because it's really gray outside.  But before we continue this video, I just want to take a moment to talk about the sponsor of this video, Ritual. Do you have a perfect diet, day in and day out? Yeah, me neither. However, I've been taking my nutrition a lot more seriously, which is why I'm happy to be collaborating with Ritual in this video. Ritual helps people fill gaps in their diet, their multivitamins have no fillers, colorants or shady additives. These two easy take capsules provide 10 nutrients to support a strong foundation for your health. 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I also really appreciate that Ritual has a delivered to door subscription that makes buying vitamins easier. And honestly, there's less work for me to do because it's one only a dollar a day for 10 high quality nutrients your body needs. I don't have to worry about going to the store to get vitamins. Also, they're delivered straight to your door. Did I mention how amazing this bottle smells? It's like this perfect blend of like mint and like freshness. It smells amazing. Fill in the gaps in your diet with essential vitamins, which is just a small step that helps support a healthy foundation for your body. Ritual is also offering 10% off using this link for three months using this promo code and all of this will be in the description box. Once again, thank you to Ritual for sponsoring this video and giving me this amazingly minty, fresh bottle of multivitamins. The next thing is money. That's important. I mean, people make a lot of money in tech and that's definitely a driving factor for people to learn how to code. And I think we'll talk more about this later on in the video, but getting those skillsets can help you in many ways, even if your job isn't directly using programming in your day-to-day tasks. The average software developer makes a little over $100,000, which is a lot of money, especially if you're not living in major cities and those major cities are paying even more. And money is important because you can use it as a tool, especially for other interests outside of work. I use part of my salary from my YouTube channel because I love sharing information and making content and obviously travel and other things. Programming can help you do that and not only from like getting a programming job, but also freelance work, which is really dope because if you can understand the freelance market and get to the point where you always have work and always have some sort of customer, you can work for yourself and that can give you the opportunity to work remotely and it allows you to have your own schedule and really your own business, which isn't that like the American dream. Programming can help you do that. You can turn passion projects into actual startups and you can also do a bunch of freelance work and make really, really good money along with building your skillsets. I know money is the main topic, but it's more so about freedom, being able to work remotely along with creating your own schedule and working for yourself. So maybe you have some sort of full-time job that's maybe programming or maybe it isn't. And then on the side, you do freelance work, which you can invest in other passions, which is amazing. And especially those of you all who kind of feel stuck in the career path or job that you're in, programming can open up a lot of doors for you. The third reason is opportunity. Understanding how code works or having some programming experience can help you in a multitude of ways, even if your job isn't directly tied to programming. For one, it's going to increase the opportunity for you to land a job, whether it's programming, whether it's something within digital or something that isn't not necessarily tied to programming where you're doing that on a day-to-day basis. And the reason being is because you're showing recruiters and hiring managers that not only are you dynamic, you like to learn, you're a problem solver, and you're up for the challenge and ultimately have a better understanding of the digital world, which is really important, considering the fact that the pandemic has made a lot of businesses have to rethink how they're marketing themselves and investing into the digital space. Number four, just quickly want to just talk about having a better understanding of technology, web development, and ultimately how the internet works. If you're looking at like HTML and CSS and JavaScript or studying those things, that can help you a lot. So you could be more of like a dynamic person. So maybe your company is going to do some sort of digital transformation and there's a new project manager role that's opened up, but you have a programming experience and project management experience or business experience, and you are actually able to apply to that role because you spent time working on programming just so you can understand what in the world is going on because as we continue to transform digitally more and more as a society, more and more roles are going to require technical expertise, even if it isn't, you know, having all this programming experience, having just the knowledge and technical expertise can help a lot. As someone who works for a large company as a front-end dev, I work with a lot of business areas and they definitely need technical expertise. It allows us to work more fluently along with being able to move projects along a lot faster because we're on the same page. Number five and that's personal development. Personal development is something that I take really seriously and I think or I've at least seen that it seems like it's been pretty hard for people who have unfortunately lost jobs due to the pandemic. And I will say that one thing that I like to do is to focus on personal development even in my job because a lot of companies offer time for you to work on other skill sets. The reason why that's so important is because when, you know, unfortunate events like a pandemic happens and you get furloughed or laid off and all those, you know, unfortunate things, having skill sets outside of your job can really greatly benefit you because I've noticed that some people have been in like an industry for maybe 10, 20 years and never really worked on anything else outside of the specific job they have. And then they go out into the market to the job market and don't really have any skill sets that matter because nowadays you need basic computer skill sets. That's like basic. You need to know word. You need to know Excel and then some jobs require even more technical expertise and you're taking tests and, you know, aptitude tests for jobs that aren't even technical. So personal development can help you in many, many ways even if you're not necessarily looking to make a career change because you never know. So that concludes this video. I hope it was helpful. I wanted to make this video as early as possible in 2021. That way you can kind of, you know, get your mind thinking about, you know, programming even if you are maybe someone who's in marketing because you never know how important programming can be in your field or in your career, especially five, 10 years down the road and want to make sure you are prepared and can get into other roles that are, you know, have more opportunities or higher paying with more responsibility along with other things that offer a lot of benefits to people who know how to program. So that concludes this video. Once again, I'd really appreciate it if you give this video a thumbs up, subscribe to the channel so you can become a part of this amazing community of software developers, content creators, and of course the curious. And don't forget to hit that notification bell so you can know right away when I release a video. And as always, have a wonderful, blessed rest of your week. And once again, thank you to Ritual for sponsoring this video. Peace.


Alright, so how do you go about starting a crowd-sourcing project? Well, I started with a shawl. I was going through some old exhibition files when I found one that basically said, when Carrie McDonald was a young girl, she made this shawl with the help of her mother using some wool from their neighbor's sheep. How interesting. It's so rare we have something made by children in the collection. I wonder if it's any good. Here's a close-up of the shawl. That's a pretty intricate feather and fan pattern. I'm guessing this wasn't her first project. From the catalogue record, I found out that her mother's name was Harriet and her father's name was Donald. That's right, Donald MacDonald. I saw that the neighbor with the sheep was Mr. McKay and the donor of the shawl was Mrs. T. W. Dent. I was very pleased to find out her father's name because this meant I might be able to find out some more information about her. Canterbury Museum has a wonderful resource called the GR MacDonald Dictionary of Canterbury Biographies, or the MacDonald Dictionary, for short. I should note that there's no relation between GR MacDonald and Donald MacDonald. They spell their names differently. For those of you unfamiliar with the MacDonald Dictionary, you might be wondering why I wouldn't just look for Carrie's name in the dictionary. That's because the dictionary is mainly organized by the male heads of the household. The dictionary was presented to Canterbury Museum in 1964. This is a photo from the museum's annual report from that year. You can see a tall bespectacled George Rinald MacDonald reading one of the biography cards to a board member and archivist librarian John C. Wilson. George started the project back in 1952. At first, he just intended to write biographies for some of the portraits in our photographic collection, but the scope soon expand to include anyone he could find information about who lived in the 19th century in Canterbury. Twelve years later, and he had biographies for over 12,000 people. Some people had just one line, and others had biographies that span several index cards. There are over 22,000 index cards in the collection. And as you can see, they're originally stored in the metal drawers. I should mention that George volunteered his time for this project, receiving encouragement from our then director, Roger Duff. The dictionary was considered very progressive at the time it was donated because it included people from all social classes. But you have to keep in mind that MacDonald was writing about 19th century people in the 1950s and 1960s. So while people from all social classes are included, which is progressive, there are very few Maori and women who have their own biography card. In the early 1990s, when Canterbury Museum was preparing an exhibition to mark the centennial of women's suffrage, someone went through and counted all the women who have their own biography card. They came up with 64. No one's done account of how many Maori have their own individual biography card, but I'm sure it's equally as low. However, women aren't completely absent from the other thousands of index cards in the dictionary. They're just not indexed because they're not the head of the household or a public figure. And that's why I look for Kerry on her father's card. Here is page one of Donald MacDonald's biography. There's his wife Harriet mentioned twice on the first page. Since all the cards were written out by hand, George invented shortcuts to make it faster, such as M-A-R-R for married or D-A-U for daughter. Now this is where I got excited because I noticed that his daughter Flora married T-W Dent, which means she's Mrs. T-W Dent and Flora is the donor of the shop. On to page two of Donald's biography. There's Harriet again, and Flora, and Flora's husband. But who's this? Caroline Houston MacDonald. Could Kerry be short for Caroline? To figure that out, I could check some of George's sources, such as the family Bible referenced here on the second page, or at the bottom of the first page there are some newspaper references. There's a list that explains all these abbreviations. And using this list, I can see that these are newspaper references to the Littleton Times from the 20th and 21st of July 1906, and it happens to be Donald's obituary. Now the Littleton Times isn't digitized to 1906 yet, but while I was on papers past, I found Kerry's obituary in another newspaper, and I did in fact confirm that she was Caroline. Using the dictionary can be a bit like solving a puzzle between deciphering the handwriting and the abbreviations. In the past, people had to come to the museum to look at the dictionary, and they could look at the printed list of abbreviations and see the index with the list of women's biographies at the front. There was also a subject index, so you could find a list of all the people connected to city council or whatever it is that you were looking for. This was a good resource for the few people who could make their way to Christchurch and to the museum. Now we've put it online with Creative Commons licensing so that anyone with internet access can potentially look at the cards. So we've greatly increased our potential audience, but we've actually decreased access to the finding aids. You can still search for all the people that were indexed. That's under associated person now, but the list of abbreviations and the subject index isn't available online. Sure, we could put PDFs of these documents online, but that isn't really making the best use of collections online, and it doesn't really reflect how people want to do research today. They expect to be able to type in Carrie McDonald and come up with all the documents, objects, photos that are related to that person. Wouldn't it be better if you could just click on a link and go straight to Donald's obituary on paper's pass? It would be amazing. But then you remember that there are over 22,000 individual index cards and you get overwhelmed. But then you get excited because you realize that the McDonald dictionary is perfect for a crowdsourcing project. And a crowdsourcing project driven by volunteer contributions is very much in keeping with Donald's original project. I just hope it doesn't take us 12 years. Making the project a reality is where Chris and our project intern, Phoebe Fordyce, come in. So I'll hand over to Chris now. Kia ora koutou. Thank you for having us here to talk about this. So as Joanna said, we got excited about the possibilities for crowdsourcing some of this information and running it as a student project. And that conversation really began at one of a series of meet-ups that we have been running at the University of Canterbury for people interested in digital humanities and for those from the cultural heritage kind of areas in Christchurch and the region. We've been running these kind of monthly, except for the months where we don't run them. So they've been a little bit ad hoc throughout this year, but they have been a really fantastic way to kind of connect up people from different areas and sort of join up interests. They've been run by a couple of post-grad research associates of the Arts Digital Lab at Canterbury, including Kara who's pictured at the top here. She spoke at last year's NDF and she's gesturing to a cat meme here that says the future is meow, in case you can't read it. So we do some fun stuff. And this is really the sort of setting where Joanna and I kind of talked about in the McDonald dictionary as a potential sort of crowd sourcing project. We have a program called Professional and Community Engagement at the University of Canterbury, where students can do an internship that is both sort of underpinned by some academic work, but where they get to work with a partner from industry or the community on a specific project to do some practical hands-on kind of stuff. So we set up an internship project around this. But really we wanted to sort of ask, well, kind of critically examined, is this actually a good idea? What would we want to get out of it? What specifically would we want to transcribe? How would we want to make that available? And kind of answer some of those questions, as well as really answering how would this work as a teaching project? I sympathised or related to what Harkin will said about being a person who doesn't have very many colleagues around them who knows what they do. Sometimes I feel a little bit like that in teaching and doing research in digital humanities. And so we wanted to sort of see if this really was a good idea. And Canterbury Museum's interest in transcribing the many names of women mentioned in the dictionary also provided a natural, intellectual focus for the project and thinking about crowdsourcing both in practical terms but also in terms of feminist history and having a kind of a focused approach or a direction to our aim for our crowdsourcing work. So we found a great intern, Phoebe, and we made her do a number of things as part of the project. She had to transcribe a number of the index cards in full in order to get a sense of what this material was like, what the challenges would be with the handwriting, deciphering the abbreviations and so on. And we also asked her to then produce a scoping report that would kind of set out the kinds of things that we might do, how we might go about doing it, who would be involved, what the sort of considerations might be for Canterbury Museum in setting up something like this. So she wrote about sort of the suitability of the McDonald dictionary and kind of identifying the things that would be worth doing, what the potential issues might be, including thinking about sort of the any potential ethical or kind of copyright issues. She had to produce some sort of project management sort of scoping material and summarise what the options would be in terms of potential software that we could use for the project. She also had an academic side to the project. So she was taking a Stage 2 paper on feminist history, so she brought a lot of her interest from that into this and kind of asked what kinds of theoretical underpinnings would be most valuable to apply to the project through that work. We also, I should also acknowledge a lot of great work was done by our developer colleague of ours, Antoine, who is here giving us a dramatisation of web development in action. He's a digital project specialist in the Arts Digital Lab at UC and his main task was working out the data round trip, so taking material from Vernon and getting it into Pybossa, which is the platform we ended up using, which I'll say a little more about in a second, and then getting it back again. So kind of doing that plumbing work and setting up a web platform that we could sort of start to test some ideas out. It was sort of a difficult balance, I guess, that I was exploring with this project to do with how much could a student, in turn, realistically do here. How can we involve someone who's not done this before in a project team where they get a real sense of ownership and contributing something and learning new skills, but, you know, we don't want to do it for them. We want to challenge them but not challenge them too much, if that's a good way to put it. So we were trying to strike that balance and not having done a project like this before, that was really one of the things that I was seeking to find out how we would do that. So having Antoine's support to do a lot of the technical side of the setup and, you know, say no to things when I was suggesting maybe we should bigger the project was really valuable. So part of Phoebe's work with assistance from Antoine and interference from the others in the team was to assist different options for a technical platform, and we looked at three things. Scribe, which is a platform that's been used quite extensively by the New York Public Libraries for some big projects that many people will have heard of. I believe the old Weather project and measuring the ANZACs and some ones like that use it. And Pibosa, which is another Python based crowdsourcing framework, and Google Docs for a sort of more easier to get up and running quickly kind of approach. And there were sort of obvious trade-offs in the analysis that Phoebe was able to do of these. Scribe is very powerful, but we had some problems really understanding it, getting to terms of running properly. So it seemed like a bit of a racehorse in this kind of field, like it was perhaps a bit, had more features than we needed and a bit difficult to manage. Google Docs would have been very easy to work with, but would of course limit the ways that we could present the project and what we wanted to, if we wanted to build this into sort of more of a public facing thing, then we'd be limited. So Pibosa was a good middle ground in terms of the solutions that we looked at. It's open source, it was fairly easy to install, had reasonably good documentation that we could work with. They also have a platform called crowdcrafting.org that's powered by Pibosa that you can use to get up and running if you don't want to host your own platform. So that was quite good. So we made something that looks like this and started with Phoebe and Antoine, mainly started working through the issues of kind of how do we get this interface to work, how do we write instructions for people that the most people will understand, how do we make it clear for the widest audience. So we had a number of challenges getting the image big enough to do transcription. People are sort of going to be frowning at the screen trying to work out McDonald's handwriting. So we needed to make it as big as possible and we went for actually not showing the whole image at once, just having a wide image that could be scrolled in order that we could also have some form elements on the page and instructions on the page. Actually writing the instructions in a clear sort of direct way was a challenge. Capturing alternative forms of a name because particularly with the women listed in these cards often there are multiple forms of their names, they have their husbands names maiden names and name variants. So we wanted to be able to capture all of those if we were going to do this work and there's some browser testing stuff that we need to do. So Antoine did a great job of getting us started with a platform that we can use but at the moment it only works on Chrome. So yeah, future work there. We then did a bit of a trial to test our platform and this kind of expanded the scope of the project sort of a little more than I'd really anticipated when we started the earlier in the year. We did a Human Ethics Committee application in order to run this as a research project through the university and if any of you have done those sorts of ethics applications before that you know can be a fairly difficult process sometimes. We also initially targeted students to get students to participate in this and found that they are very difficult to motivate. So we sort of shifted tack and targeted colleagues and people whose arms we could twist much more easily to sort of get a group of people who we thought were an appropriate audience who would be likely to be motivated to participate in something like this for real and we wanted to find out really what they thought about the interface and doing the task but also why they thought the project was of interest and if this was something that they would do more of if they were interested in doing this because it had a feminist history perspective or for other reasons and in terms of our findings with this small study we got around 20 respondents who did the task, used the website to transcribe a card and gave us some feedback they found McDonald's handwriting difficult to read so we do need to manage the expectations of this and recognise that there are people who are very keen to do this and that's the hang of it so we want to do the best we can not to put people off early working with the dates is quite challenging I think our interface didn't allow people to just put a year date so they could put a month and a year or a day and a month and a year and so that's a problem that's sort of tied up with browser testing and other things but that was a challenge for us and something that users wanted to do better do more accurately interest in the feminist aspect of the project was strong there were a couple of people who didn't understand the question as we posed it but for the majority it was motivating or an interesting factor to be working on a project like that and really we need to do a bit more testing of our resources to kind of make sure that the clarity is there so there's quite a bit of future work that we've identified through doing this and as Joanna pointed out one of the things that we'd love to do here is to create links to other external resources it raises questions about exactly how we do that how we delimit McDonald's work as a historical document in relation to links out to other things that we might draw in that are relevant so there are a number of interesting related materials on papers passed that we could draw in interestingly the Littleton Times article that McDonald actually cites that Joanna mentioned to you earlier hasn't been digitised but you can find other materials that he didn't cite such as a bituary from New Zealand and carries a bituary from the New Zealand Herald so there's a range of other materials that we might draw in here and we'd be very interested to retest and refine the interface next year to be able to capture some of these references and consider how we can use these additional links that we might be able to create to papers passed and other such things and also think about the existing subject index and other finding aids that exist at the Canterbury Museum and three or four large ring binder folders and what we could do to connect those to the collections online as well so the state that we want to get to I'll ask Joanna if you want to say a little more about that so when everything's all done we hope that our collections online will look something like this you can see the list of names has been greatly expanded you can see all of Donald's family members including the neighbour that he brought some land from Mr. Jay McClellan the family bible that was referenced on the second page is held privately but you would be able to click on all these newspaper references here from the first page and clicking on any of these names will bring up anything in Canterbury Museum's collections online that relates to that person and hopefully people will also use this data to make collections past Canterbury Museum's collections online and all these names will become nodes in a network and who knows maybe that network will even end up resembling a feather and fan pattern thank you thanks it was a great presentation are you modelling the familiar relationships or just capturing names not currently in this run we did look at testing that but it proved to be very complicated and it's not always clear yeah Vernon has the capacity to store that data but would you use Pipe Oster again we're just trying to choose a platform now for some geocoding crowdsourcing and Pipe Oster sort of rose to the top of the list from my early research but good to get some feedback it seems to be quite stable quite easy to use quite easy to get up and running sorry yeah so it seems to be quite yeah quite stable quite easy to use and get up and running fairly quickly with we don't have a lot of technical expertise and we don't have a budget to kind of get a specialist to really dig into a new platform so we wanted to go with something that we felt was was going to be you know within our abilities and that seemed like a good match for us has it been around a lot? it's been a few years I don't know exactly when it started it's mostly developed by side fabric who do you know kind of crowdsourcing and citizen science provides sort of services on top of the platform I think they're based in the UK in Spain is it? all right did you find with the trial that you needed to have an editorial eye over the results like was there a need to kind of see what people were producing from the cards and make sure yep so we will be doing that we just haven't done that yet because we finished the trial right before the conference but we will do that and just another note the Linton times will be it is coming nice that fits perfectly I was just curious it's a great resource that you're doing here and I wondered whether there were thoughts to in future link through to you know archives New Zealand's references to those people or to other you know crush your city libraries references and that kind of thing there's definitely lots of opportunities and I think after we finish this first trial and get it working and out to a broader public for tagging I think that's when it's time to start thinking about other possibilities and those are definitely on the list you know with the scope of the cards that you had did you find it more less wine blowing bring that back down to a small portion of the woman aspect of it well we will be tagging all the cards as part of the broader crowdsourcing project that's just kind of like a theoretical focus for why we are approaching it and of course everyone's name will be tagged but many of those people are women as a McDonald well done hi I was wondering about recognition of the contributions from the crowd as a way like there's an outdated word at this point but gamification to encourage people to do it more so a leader board or levels that you can get to as a contributor yeah perhaps like a comment so the reasons people gave why in the trial that we did as to why they would participate were varied that sort of competitive aspect of it or the gamified aspect of it didn't come up but I don't think you'd expect that with the kind of question that we asked people talked about doing these kinds of projects because they find them because they're personally interested in the social history some people one person talked about they find it relaxing and kind of good for the clearing the mind to do some kind of this kind of work yeah Pibosa has a leader board kind of stuff that sort of built into it that we could make use of to do that so it's something we could look at at a future stage I was going to ask the same question as him but I'll now ask a different one what's the schedule for sort of public release or going live or announcing it to the world or moving to full function we don't have a schedule yet as you'll remember we finished the trial right before the conference so we have to after we get back to Christchurch we've got to go check that if anyone else has participated in between and very carefully review that feedback and then make a schedule and I guess I'd say we want to figure out how we can develop this as a teaching project so that's sort of been my motivation for and one of my motivations for doing this is working out how to integrate it with teaching and kind of keep it manageable and make work for the students and meet certain learning outcomes so we want to sort of design a piece of the next steps that could be done next year and maybe have two or three students work on it rather than one to be a little bit more ambitious about the size of what we'll do but that has to be kind of flexible that time line I think we're at lunch so one more round of things