Hi, I'm Katrina. I work at SunBasket. Today I'm going to tell you about how I used R to drastically increase the reliability of our forecast process. Slide 2. I'll start by telling you our forecast challenges. Then I'll share how we solve these challenges by re-architecting our forecast pipeline using R. Finally, I'll talk about the impact of these changes. Slide 4. SunBasket is an e-commerce food company. This is what our customer-facing website looks like. There are different meals on the menu each week which we create in-house, and our challenges to predict how many of each item will be ordered in each week. We publish a file that gets fed into procurement systems, and buyers purchase thousands of dollars worth of perishable ingredients based on these numbers. Slide 5. There's a substantial financial impact of both over and under forecast, and if the forecast process fails entirely, then the buyers have no guidance on what to buy and how much. So how do we generate this forecast? Slide 6. This file used to be produced primarily using spreadsheets. Infra from different teams across the company was combined with a lot of formulas and spreadsheet linking. This diagram represents 1-200 Google Sheets that are all inputs to the process. So literally thousands of cells and a mistaken edit in any of one of them could cause a breakage. Also, formulas constantly needed to be manually maintained and extended to show more future dates. Slide 8. Our first step to get this situation under control was to start moving the logic to R and SQL. In this version of the process, we had a handful of scripts that ran in sequence. Each script would read from Google Sheets or a database table, do some basic manipulations, and then write back to another Google Sheet or database table. The nice part was everything we did put into code was now under version control. However, with this method, although we cut down on some Google Sheet usage, it was still relying on quite a few sheet formulas to execute business logic. Also, when we ran these scripts, the intermediates were overwritten, and there was no way to test changes locally without potentially impacting the production job. We also had no way to definitively troubleshoot. If a job failed and then later succeeded, there was no way to know the state of the system during the failure. Slide 9. To solve these issues, I created a system where each forecast went through a standardized set of steps. Each step had predefined inputs and outputs, and every output gets automatically checked at the subsequent stage. Slide 10. What does this look like in practice? We have one large YAML file with most of the forecast configuration. For each product line, there's a separate forecast and a separate entry in this file. Here I'm showing you one example entry. On the left, we've defined what dates we need to forecast for and where we need to send the output. Under inputs, we've defined what we need for the first two stages, input snapshot and input check. We have exactly where to find the data who is responsible for the data and checks defined by rules. Here we're using syntax defined by the validate package. These checks are especially important for human provided inputs. We use the checks like these to check the output of each stage, not just inputs. Slide 11. In addition to the YAML, there is a few stages in each pipeline where we need to execute arbitrary business logic. For these, we encapsulate the R code into a Drake plan. Each of these plans starts with pulling input data from snapshots. This is the one and only place where we can get input data. We do not execute arbitrary SQL queries or API calls during this plan. Therefore, given input data, the result is deterministic. And the input data that we get from this function has been checked and stored in case we need to troubleshoot later. In the middle, we put the actual business logic. At the end of each plan, we store the output, either features or predictions. We use helper functions to ensure they are stored consistently so they can also be checked. Slide 13. The number one benefit of this system is reliability. Daily flow forecast is no longer interrupted by Google formula issues. If there's any issue with a manual input, we know exactly which input was the issue. We've also sped up development time. Since the majority of the business logic is now in R, rerunning is relatively quick and does not risk interrupting the production process. Finally, with snapshots, we no longer have mysteries about the past state of the system. We know exactly what code and what input data went into each run of the pipeline. Ultimately, with all this free time, data scientists can focus more on building models instead of troubleshooting process issues. Thanks for joining.


The Independent National Electoral Commission, INEC, Adamo Estates says it has concluded arrangements to expand polling units following the directives by the national chairman of the commission. The resident Electoral Commissioner in Adamo Estates, Kasim Ghidam, who announced this in Yola during the stakeholders meeting, said the engagement is mainly targeted at taking the polling units closer to the voters. Here's the report as presented from our studios. Since the completion of the 2019 January elections in Adamo Estates, this is the first time that INEC is organizing such an event which brought about all stakeholders together to inform them of the need for the expansion of the polling units. The resident Electoral Commissioner, Kasim Ghidam, said that they are taking such steps to decongest the polling unit with many voter points and to address the big challenge of poor turnout during elections. The polling units we are talking about are the 2,609 polling units in Adamo Estates, as well as the voting points. In Adamo Estates for the 2019 January election, we had 1,495 polling units, voting points, and it is these 1,495 voting points that would now be combated into stand-alone polling units. Some of the participants who uploaded the effort of the INEC, describe the event as timely as it will go a long way in current people at the grassroots along. It's long awaited because this has to do with the election, the forthcoming general election. It is the creation or expansion of more polling units. I think INEC has done the needful for organizing such an interactive session. As you can see, so many questions are being raised and hopefully we are trying to see how we can get the solutions to the questions raised. In most cases, try as much as possible to get in touch with security agencies. Take permission before you can hold on to it because the security agency is there to give you protection. If there is any problem, you can easily run to security. So do not run away from us because we are partners in progress. Well, this meeting at Damawa State will now have additional polling units of 4,104 which will give those at the grassroots the opportunity to participate fully in the coming 2023 general elections without much comment. Hello. Hope you enjoyed the news. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


OK. Good afternoon. Unfortunately Boris is still not here. I wanted to check whether he recognizes this place. This is part of Norway, Spitzbergen. Very, very high above the polar circle. And this is nighttime. I'm thankful to organizers. For me it's a great pleasure to be here and to congratulate Boris, who is absent at present time. But still, I would recall our contacts. Actually, it's very hard for me to believe Boris is 60. Because according to Shakespeare classification, I know him since he was a schoolboy. And our contacts started in 1976 when my friend Arkady Aronov asked me to write evaluation for his master diploma thesis. Probably he found out something that I was not too happy because I had to write something and he told, don't worry, this boy is unusually good. And he was completely right. And since then I think benefited a lot working together with Boris.  So I'm very grateful to him. We produce something like about a dozen papers together and always he was the person who taught me how to do things. So mostly we collaborated on what is now called nano physics. And to illustrate it, I will show you some picture, how we did it. And I'm showing this picture because the name of this dog is nano. So this dog belongs to Boris and his name is nano. So we did nano physics and he was very helpful. And we did several things with Boris and what I will tell about today is somehow continuation of the line about the coherence. So we wrote some paper, review paper about that and all participants of this team are here. I will talk about relationship between thermodynamics with simple driven quantum system and its coherence. And the questions which I would like to address is the following. Is thermodynamics of a driven quantum system related to its coherence? And which protocol can be used to determine this relationship? So it will be not a review, it will be one simple story. So why I started being interested that I am newcomer to this field? And mainly a driving force of this work is Yuko Pecala from Helsinki. And a Japanese group doing experiments, trying to find non-equilibrium thermodynamic of qubits. And everything is initiated by support from European Union project in fairness, which is spelled out as information, fluctuation and energy control in small systems. So it means that people are interested to study works made on some quantum system, try to find statistics of this work and try to relate it to some general principles. It's a very tricky business because if people work with small systems and low temperatures, it's not easy to measure actual temperature to see whether it does exist and so on. So people are hunting for some exact relationship which allowed to check validity of experimental data, consistency. And this project is more or less about that. This is a brief plan of what we're going to do. First of all, I started total motivation and I will tell something about the relationship which people quite come in to check at present time is Jarzynski equalities. And try to see how you can check this equality in a simple quantum devices when they can keep track of what is going on. This is my program. I wanted to give some examples but I put it in gray because most probably will not have enough time to go for that. Well, before starting that, I was very confused because I was grown up, educated in Soviet system. Everything was based on one doubt textbooks and according to conventional thermodynamic, I didn't understand how we can even speak about thermodynamics of a driven system. Usually what you can, if you open textbook, you can read that you have two states say A and B, both are equilibrium. You can define free energy between, for these both states, calculate difference. And if you make some work, this work should be at least bigger than difference in free energies. And you get equality if you do it very slowly across aesthetically. So you have only inequality and equality only one situation and you cannot propose any relationship which you can check. So auto-equilibrium according to conventional thermodynamics one has to treat each system individually, do some non-equilibrium science about that. And I can tell that that was my attitude at all that like happy families are all alike, every unhappy family is unhappy in its own way. So you have to discuss non-equilibrium systems just separately for each system. Then appeared this kind of formula which is called Jarzynski equality. And this is a question which relates free energy difference between some equilibrium systems and average special averages of the works needed to transfer a system from one state to another. So it's like that that you have to beat this inverse temperature different. So you have the system A which exists at some given temperature. Then you change external conditions and you construct another equilibrium system at the same temperature but the system corresponds to external conditions. For that system you can define free energy. So difference in free energies should be average of all ways of changing external conditions. So it doesn't matter how fast it takes place, still this should work. So you relate equilibrium states with non-equilibrium processes. And more or less there are several derivations which are more or less strict for classical system and somehow controversial for quantum system. I will not go through this derivation but they are mostly based on the reversibility of dynamical equations both in classical and quantum mechanics. So this is one of the examples. Suppose you have gas and a piston and this is state A. Then you push the piston and make, change the volume, arrive at system B and then you construct artificial system, equilibrium system at the same temperature which corresponds to this position of the piston. It's a lot of philosophy about that which I don't exactly understand. People tell that it's temporary violation of second law of thermodynamics because they speak about big fluctuations. There are no miracles here. This is the matter of correct, choice of correct set of representative fluctuations and taking them into account. There is a very beautiful paper by Luan Grossberg which they discussed, this generic classical case where you have a piston and molecules of the gas and they explicitly calculate distribution function and show which fluctuations are responsible for the Georgian's inequality in classical gas. It's not so simple because to keep track of that you have to take into account not typical fluctuations, rare fluctuations, you have to wait a long time and if the system volume is large and the piston is moving with great speed compared to some velocity for a very short time you have to wait exponentially long to pick up the fluctuations, the proper fluctuations. Any way you have exact relationship and people who work with a driven system are very much interested to make a cross check to see whether they get things correct or not and what I'll tell about one of the protocols how to make it with a quantum system. Regarding quantum system it's much more difficult because there are different definitions of working and people don't agree how to define work for rather complicated arrangements so I will do it for a simple system. So suppose you have two level system and then the total work contains a change of internal energy. Internal energy is just, if you measure it, you have initial state, a final state and the difference is the difference in internal energy sometimes it's called useful work in this business and also you can emit photon or phonon and then you have dissipation. So if you have a cyclic process, the Georgiansky inequality equality requires that if you take into account all the processes and all the works you have to get this average for one. That you can check and there are many papers today even in good journals where people really check this and claim that everything is consistent with this inequality. So how to do it with a quantum system having something simple? So we decided to go along the proposals, so-called two measurement protocol where the state of the system is measured so you know where you start, then you wait a bit and then apply drive. After some special drive you leave system alone and then it can decay here then you do one more pulse projected on initial state and then measure. Actually in the experimental setup which we discussed there is also some very precise colorimeter so they can also identify the quantum jump and they can discriminate between absorption and emission. So even from this what was initial state? It was emitted and then it was excited state it was sort of ground state. So for a closed system if you don't have any jumps and any inelastic processes then you have to get the average of U equal to 1 and this is not the case for realistic system because it's open and you can emit or absorb some quantum degrees of freedom, photons or photons. So what I'm discussing can we extract useful information from this average? This average should not be one it should be something different but is it reasonable, is it possible to extract something? My answer will be yes and I will show how people do it experimentally. So this is so-called quantum jump approach which was developed for quantum optics and that's what we use to calculate this average. So the idea is following suppose you study a system for a very short period and the probability to have a jump is very low either zero jumps or one jump then you can, what you have to do you have to analyze density matrix for the system but there is a simplified method which works on the level of wave functions and you construct some non-Hermitian motion and some non-Hermitian Hamiltonian which takes into account the possibility to have a jump quantum mechanics Hermitian so this is a model equation and you have to derive it if you study the dynamic of a system for a short period but you don't make a jump you have to take into account the possibility some final probability to have a jump and in this way you create this non-Hermitian Schrodinger equation what you have to do you can show that it's fully equivalent to a block rate equation for density matrix so this is Hamiltonian and now Hamiltonian we have this diagonal Hamiltonian this is a possibility to have noise or the coherence which you take into account which just moves levels in time and this is the part which takes into account final probability to have a jump and this is the drive of the system so we can do it and if you work with that we can represent the wave function as a linear combination of ground state and excited state and analyze motion of this amplitude this is technology and I will probably this is where the system has advantages compared to the density matrix because you can easily write diagrams for different process here and make it systematic was analytically and numerically okay, so let me tell answers suppose you are very long times in your protocol much longer than inverse rate of quantum jump then what you get all these occupation numbers are equilibrium and you get for the difference between exponentials of the work and one depending on temperature so if you have a long time if temperature is low then you have big differences if temperature is high you don't have differences and this is a way to check what actual temperature system has you can trace that you know what is the temperature of your device not of your thermal bars but of your device now let me answer the difference doesn't depend on the coherence fully thermodynamics okay, now let's discuss the protocol the protocol is following we do measurement of the system on the time t1 we apply power over 2 pulse then keep it for time t2 and then put another power over 2 pulse and then after some time do one more measurements this is the protocol which we use let me depict it, so if there is no coherence I do the following I start with some eigenstate for example spin up rotate it then nothing happens and then in the rotating frame and then you finish a measurement system in some other state if you have the coherence what you get you get the following you start with some state, rotate it when it's parallel there is no coherence due to pure defacing so we can split the time when you have defacing and then you have a trace of this defacing when you do measurements this is the measurement protocol and we can do calculations which we don't have time to explain but let me give you the answer the answer is the following so if we have at most one jump this average we can measure we can measure, we can measure since we measure system we know the difference in internal energy it's about a million repetitions of this experiment so statistics is made and according to the statistics this average depends on the times if these times go to zero you get one, system is closed and an elastic process doesn't take place for finite times you have this expression which depends on the temperature and it depends on the amount and mechanism the coherence because delta phi2 is the only phase shift which is accumulated during middle part of the protocol so this is our central result, very simple but it allows to make experiments and it allows to study this distribution function why it's possible, why don't have to wait so long because only few states is two level system so you can have either there is jump or no jump and probability for more than one jump is low so we did some numerics and this numeric shows that at small times it gives exactly the same analytic result but we can do it for longer period and study more jumps and what's good that we can also have we can calculate distribution function and distribution functions of the energies is measurable so this is cartoon of the experiment is made in Japan and this is Yasunakamura and his student and his student they have a transmount qubit and they do exactly the same protocol which I explained so these are characteristics of the qubit it's quite a good qubit so they made full spectroscopy of this qubit so they know left-hand side of this equation and right-hand side of this equation there are no fitting parameters so this is a check of the consistency and the quality of how this device works it's a lot of experiments because they have made many repetitions they do many repetitions this is why I'm still not allowed to demonstrate the experimental data because they are still in progress and they do double check but the first results are extremely encouraging so we get proper numbers of parameters and if you properly define temperature so the temperature turns out to be few percent more than what they estimate from the direct measurement of a qubit so this is consistent system and actually people now think that the system is fully characterized and they want to modify the system in order to make feedback related experiments so to make something like quantum Maxwell-Giman based on fully characterized device you have to characterize it otherwise it's very difficult to speak about distribution of the values entropy and so on so this is how it stands for today what is important and I will not spend a long time on that you have average of the cosine and delta-phi to it the same as you get for free induction signal if you work with magnetic resonance arrangements so you can study the mechanism of the coherence you can study how this average depends all the time and this is just in progress and I did it for a very simple model which is not physical I assume that distribution of phase shift is Gaussian and then you get the result which contains exponential of tau 2 squared we work a lot on the case of 1 over f noise distribution function is different it's not Gaussian I don't want to remove all this skip all this business because I don't have time for that otherwise I will miss dimmer but I will show you the distribution function this is a distribution function for 1 over f noise it's far from being Gaussian so then depending on different situation different function of time delay between two pulses they are working at right now and try to check how does it work I think I am approaching the end of my talk but I want to emphasize the following so the central result summary first two measurement protocol turns out to be an independent tool for studying some dynamic relations in driven systems and the central result is the average between this quantity there is a relationship between this quantity and the coherence temperature and it depends on the protocol on the protocol which people use if the right hand sign is known then jersinski equality can be checked only by measuring statistics of the internal energy you don't need to measure photons or phonons right now you are preparing system precise colorimeter which will allow measuring some quantum jumps for energy which is transferred to the heat and then it will be another functionality to check this relationship and another point if you can measure these statistics do many measurements and make average and then after this average know it as a function of all times you can find out the same information about the coherence and qubit from direct spectroscopic measurements or measurements in real time so this if you know these statistics you can use this relation to find out the coherence and the qubit well I think I can stop on that and thank you for your attention and of course this Boris happy birthday you haven't seen the pictures I showed in the very beginning but I will show you thank you thank you very much


13. Reasons You Should Start Your Own Small Business Today Stephen C. Hogan said you can't have a million-dollar dream with a minimum wage work ethic. We all hope to achieve our dreams someday. Some way, right? We watch successful people and study their lives to find out what they do differently. We also ensure that we put on our best to become one of the best employees at our workplace so that we can get promoted and get higher pay. However, over the years we find out that these aren't enough to get us close to achieving our dreams. If anything, they push us farther because with promotion comes more responsibilities. In the end, your excuse becomes, I couldn't make enough more to fuel my vision. Well, while this may be an excuse, it is equally true.  So, does this mean that the only way to achieve one's dream is to start a business like most of the millionaires we know? Well, you're about to find out. Plus, in this video, we'll be sharing with you 13 reasons you should start your own small business today. If you're new here, consider subscribing so that you won't miss other interesting videos like this. 1. You'll build the right connections. Starting your own business will give you a platform to network, which so many people from different industries. You'll begin to build the right connections because, unlike before, you're now beginning to offer value to people. And who wouldn't like to connect with a person of value? Of course, no one. However, in building these connections, you have to understand that relationships don't thrive when you are a user. You have to give first before expecting to receive.  2. You can challenge yourself to learn new skills. Let's admit it. When you work for someone else, there's a high tendency that you'll spend most of your time making out excuses to slack off on learning new skills. It's like this. You have a job where you are required to do certain tasks. Now, you have gotten used to doing them as you do them so well and effortlessly. Why then do you need to learn new skills? Denise Waitley, an American motivational speaker, writer and consultant said, and I quote, All of the top achievers I know are lifelong learners, looking for new skills, insights and ideas. If they're not learning, they're not growing and not moving towards excellence. As an entrepreneur, however, you'll continually see the need to improve and develop yourself. If you want your business to grow and generate revenue. 3. You'll become an expert. Nils Bohr defines an expert as a man who has made all the mistakes which can be made in a narrow field. According to Nicholas M. Butler, an American philosopher and diplomat, an expert is one who knows more and more about less and less. Becoming an expert in your field means you've had a prolonged or intense experience through practice and education in that field. As a result, people begin to seek your expertise on that subject. 4. You can network in your industry. Remember what we mentioned earlier about being able to build the right connections? Well, not only will you be able to build the right connections, you will be able to network with people in your industry. The wider your network gets, the more your influence rises in your industry because more people will want to network with you. Owning your own business means you have a lot more to offer when it comes to collaboration and negotiations and it means you will have more insight and knowledge in your industry. 5. You'll create new jobs. Owning your business means creating new jobs for people because whether you like it or not, you're going to need people to work with you at some point. You'll need a team for either production, distribution or marketing. 6. You'll gain financial independence. To create financial independence, you'll have to learn how to create and accumulate wealth for yourself, not for anyone else. When you work for yourself, you will be calling the shots when it comes to taking major decisions, such as taking risks, deciding on investments and expenditures. The good thing about this is that when it comes to enjoying the rewards and benefits of your work, you'll be at the receiving end, not the mercy of your boss or the company. 7. You'll earn recognition.  As a business owner, you're a member of society that can contribute something meaningful and lasting. As a result, people would celebrate your accomplishment and respect you. 8. You'll follow your passion. Following your passion means doing what you love and living a purposeful life. This will make you feel fulfilled. And do you know what makes it even more interesting? You'll get paid doing what you love to do best. Anthony Robbins, an American author and life coach said, there is no greatness without a passion to be great. Whether it's the aspiration of an athlete or an artist, a scientist, a parent or a business person. 9. You can inspire others. Growing a successful business isn't one of the easiest things to do. It takes a lot of hard work, perseverance and consistency. However, if you can sum out every obstacle and then go ahead to build a successful business, then you can be sure that you'll begin to inspire younger entrepreneurs who aspire to someday become like you. 10. You can innovate. Get creative. Don't be restricted. But how can you do this when you work for someone? You always have to follow the rules and procedures. Working for yourself, on the other hand, allows you to express your flexibility and creativity. You can innovate anything you can think of. For instance, when Facebook was created, nothing like that had been in existence before that time. However, the man who created the app, Mark Zuckerberg, was innovative enough to come up with his plan. 11. You'll learn to face challenges and take risks. A big part of running a business is learning to take risks. It can be tempting to give up at some point, especially when faced with obstacles and challenges. However, as time goes on, you'll begin to learn how to overcome those tough times and take risks. 12. You'll be in charge of your life. Working for someone means being in charge of your life. You get to choose what to do, where to go and what time to go wherever. 13. You'll change the world. Because entrepreneurs are people who offer value to others at an agreed rate, their influence is stronger. With this influence, you can change the world and make it a better place. That's it!  Thank you very much for watching our videos. We'd like to give you another interesting video for you to enjoy next. But before then, our team will be very happy if you can like this video and share it with your friends on social media. If you're new here, don't forget to subscribe so you won't miss other interesting videos like this. Look at your screen now to see two other videos we handpicked for you to enjoy next. We love you.


What's up everybody Jason here for jazby's case breaks.com just sold out jazby's 10 box slam dunk basketball mixer random teams Well, we're also giving away $2,000 Tatum prism giveaway. So guys 30 total spots Remember no veterans common ship out of this mixer But we're gonna do the break at the end of the break though will randomize the 30 customer names top name wins The Jason Tatum blue prism fast break a disco Rookie which is out of 175 PSA 10, which I have here to show you guys right there Well, very nice And again most two recent sales were 2249 and 1700 So kind of in the middle of 2000 around there and remember no veterans common ship So we have some pretty solid boxes guys from a prism choice all the way down an optic blaster So we did a lot of cellopax guys we did four of them I believe so customers that bought it straight up from Greg Nick Adam with to pass well with one Asa John Joshua Daniel so men and John inner in and then Parker down to guy You want those from a six L pack 175 176 177 and 178? And there's the teams so good luck. Let's roll the dice and we got ourselves snake eyes. Just two times. Well, all right easy dice roll two times one Two guy Down to ASA. All right two times One two Show the pistons down to the Houston Rockets So now you have the pistons Chris with the clippers Ryan You have the bulls guy with the thunder shone the jazz guy with the Suns Joshua You have the spurs Adam with the calves Daniel L with the Hawks Jeremy with the Raptors Raymond with the Kings So men straight up with the Lakers Ryan Shackler the Mavericks Nick with the Celtics Adam with the Knicks Parker the 76er Daniel Lynch. You have the Milwaukee Bucks. That's well with the Magic Darren McKenzie DMACC. You got the Grizzlies Paul with the Wizards Sean with the trailblazers Greg. You have the Golden State Warriors guy with the nuggets Mike You have the pelicans Sean with the heat Paul you have the T-wolves Jonathan last moment your Brooklyn net straight up Paul with the Pacers Brian crowd for the horn It's an AC of the Houston Rockets So again, I'll give you guys a little trade window if you guys would like I want to trade I guess I'm not gonna take too long for the mixer But I'm just gonna start ripping maybe some Some of the hoops boxes All right, does anybody want to trade guys if not when I finish looking this hoops box we'll start the very Officially, I'm just ripping these packs. Okay, they shouldn't take too long this mixer It's a couple of heavy-based stuff the rest are pretty quick and if it is heavy-based, it's pretty nice stuff though All right, TWC guys All right, good luck us carry Irving Marty Rosen numbered Jeremy Lin hops they can choose to 25 Lana Hawks going to Daniel Betray young triple J Honest Wow that would have been an awesome right there Luca, but this is cost us onto the combo rook ink There you go Mavericks that's going to Ryan Oh They returned the money that Who's that What about it Now it's just those other two Yeah All right, so there you go two out of those little tree young all right next one we'll go Chronicles Blast logo fast Oh look at that I got a little classic That is numbered 60 out of 99 Luca Chronicles I'm doing the mixer man, so you will have You have the Orlando matter They're checkerboard Zach Levine bulls And express lane Calari Stop Curley Darje Barrett and a nice your little hollow Troublasers Bible All right, let's go fast break 1718 optic. Good luck Who's that That's A good question Steven Well Sometimes I feel like the boxes are a little bit bigger depending on the products are like mega boxes. I guess You kind of have to have or say what you have which is a mega box So I show it so I feel like mega boxes are usually big because of the word mega I know I Know like these boxes feel like this prism is not too big But I'm assuming it does have to do with protecting the product a little bit We got Trevor Booker for the 76ers When a Parker, but for the most part I just think I don't know I just think the company likes to design a certain type of box And if they think it's good then they run the production box of it Reggie Jackson, but obviously you'll notice heavier base products. There's really not that much protection So that's probably why sometimes Some of the cards may come damaged But I think obviously for like, you know What nice to kill no champions here for like Immaculate and NT obviously it's well protected You know place perfectly around like some Some styrofoam and stuff Tyler Dorsey But yeah, it's kind of I just feel like sometimes it's just a waste of cardboard You know you don't need that much cardboard to To protect some of these it's nice down to Mitchell LeBron James Drexler, bless you Jordan Clarkson, Al Horford Disco LeBron James. Oh, that's nice number to 59 out of 155 for the Cavaliers What is the Cavs? Adam Kelly does I'm sure that LeBron will sell pretty well LeBron is nicely centered Emma DeBio Russell And Amir Johnson to 199 So Sean with that Raider Ricky, Banna DeBio Our next one. I think we'll go Mosaic, Prism 17-18. We'll stick with 17-18. And after this guys then the rest of it's all 1920 TJ Leaf Rookie We got Markels Foltz Miles Turner Billy Colestine Bully Monk Jabari Parker Tony Bradley And Joel Embiid Jonathan Isaac Rookie Donna Schroeder Nice, we got a Tatum. There you go Prism Mosaic. Nice photo too Celtics, that's going to Nick DJ Wilson Thomas Jeremy Lin The 25 Justin Patton Eric Blexio We really appreciate the opportunity Alright, ooh nice little Zion Dike right there Laurie Markerman, Romain Linkford Sonia Williamson As far as the Pelicans here Mike Yandere Hunter Nick Alexander Walker And a Zach Levine number 25 That's your little Yandere Hunter. Nice Alright let's go with Mosaic's cello box. I'm sure you have to see the base run on Mosaic first And I'm sure they have every pink camel version, so I'm sure there's like over 300, 400 of them probably Yeah we've done Pick Your Team Mixers Steven for sure Definitely I think when we've done Pick Your Team Mixers, I don't know if we've given away Like a big hit like this Tatum, but But we've done Pick Your Team Mixers before I just think for like a giveaway like this, it should be all fair. It should be the same price And everybody has a fair shot at everything I'm Nikolai Jokic Caleb Zander Walker Even Giltrey Young Cam Reddish Porzingis Valentine B.P.'s Nice Rui Darren Jackson Jr The Silver Turns Ferguson MVP Steph Old Blue Chris Weber And a Jamerant Ooh and a Silver Jamerant Who has the Memphis Grizzlies D-Mac Darren Mackenzie Dunboya See how it comes over Dr. Hunter Alright so all these new penises right now guys Let's go with the pinks Same thing for those Fernando Garland Rikki Kobe White And Tatum Schofield Nice Zion Pink Camel What are these like 200 bucks now Mike G with the Pelicans Won that in Mosaic Selo 177 This has been a great Selo box Terrence Mann Pashall And Bovapitatsu Alright let me just penny-seval this really quick And we'll continue on guys three more boxes so We're gonna do Obsidian X then we'll do Mosaic Javi And then we're gonna end off a Prism Choice Alright let me just finish this guys and then we'll continue on Alright let me just finish this guys and then we'll continue on Alright guys cool let's continue on So again we'll leave these two To be the last ones we'll go to Obsidian now Nice little jam around base Minda Robinson Jemon Green And Alex Caruso Number 250 We got a die cut James Harden Pitch Black We got James Johnson The last one The last one The last one The last one James Harden Pitch Black We got James Johnson For the Tee Wolves Out of 75 And for the Hawkes Cam Reddish 27 out of 90 Mosaic Javi now guys Good luck And we'll end off a choice Kevin Hawkes Is the autograph that it looks like Here we got a Gold Norman Poell One out of 10 for the Raptors Kim O'walker Stage Lou. 92 out of 99. Look at that. Right time to pull this. Yes, Stephen. I pulled that last week for Brandon on Instagram. It's a good time to get a bull bull. Who has the Denver Nuggets? Guy does. So these will all be plenty of sleep there. Move these out of the way. Last box. Oh, also too, guys. It looks like, just look at his buddy. It looks like it's kind of dented already in. Obviously, it has nothing to do with me opening it. It looks like it's already had been dented. So I'm still going to penny sleep it, but just let you guys know, buddy healed for the Kings, which we're going to ram them.  All right, you guys, Prism Choice coming up. It's the last box. Yeah, seen the photo this morning. Hey, someone asked if we're going to do, have we done a filler for that one before? Yeah. Yeah, Ryan, if you have some bull bull stuff, I don't know, man. I really believe in the guy. I just don't know about his health. I think he'll be really good, but can you stay healthy? I would have gotten rid of him, like within this week probably, just because I probably would have got a super chief. I wonder where like an NC patch autographs are going for now. Four grand. Oh, wait, really? And they were like, what, maybe a couple hundred? Yeah. All right, you guys, last box. Good luck. Jim O'Keeke to start off. A little silver. I don't even say that. Well, I guess we could say that we haven't seen much LeBron. We got a green scope coming up out of eight. And again, not the biggest rookie, but still a nick. It is Ignas Brazdaikis, green scope, which these are out of eight. So that'll be six out of eight. And a nice one there for the Knicks. You know, someone collecting Ignas and Knicks stuff, would love to have a green scope. And then they only make eight of them. So still pretty solid. So that'll be for the Knicks and Adam Kelly. The rest of this. We got Miles Turner Otto for the Pacers. You see him being pretty decent around. Nice. Indiana one of Paul. And nice. Another rookie. And this time Red Scope, Tramon Waters. Dan, yeah, you did, man. You have the bucks, I believe. I got Jordan Clarkson, Ricky Rubio, and Javel McGee. I'll do a little recap right now, Daniel. So maybe at least for the bigger stuff. Let's see what you got. A lot of Yanis stuff adds up. You know what I mean? All right, guys, with a go. No Zion auto, but you got a couple nice Zion rookies. Same thing with Javel and some other great stuff. So a little quick recap before we get away to Tatum. So look at this is all mosaic, multi-pack, which is retail and hobby. That'll be all be shipping as well. So that's all mosaic right here in this stack. All the pinks, every single thing right there for mosaics. And here was all the other stuff from the other products that will be shipping. And you see the rookies to be top loaded. And then here were all the top loaded hits right here. Go through them there. Tramon Waters, you got a seven or out of eight Ignace, Kiki, Daival, Gold, Poel, John Moran debut, Peter Washington Silver, Jackson A's Red, Bowl Bowl to 99, 92 out of 99, Kevin Knox auto, Kam Reddish patch auto, John Moran bass from Obsidian, Zion pink Camel, Colby White pink Camel, Jason Tatum pink Camel, Dunboyard Green, Silver, John Moran, Rui Hachimura, Curry, John Der Hunter patch auto, Zion there, Jeremy Lin, Tatum, Bam, LeBron number to 155 bass, Donovan Mitchell, Luca, Trey to 99 classics, Postos, Trey Young bass, Mark Jeremy Lin to 25. And you got a lot of other stuff in there guys. But again, everybody has a chance to win this if you didn't hit much. Good luck. $2,000 hit right here guys. I'm going to somebody right now. Good luck. So here are the customer names in the break. You're ready to roll the dice. And we've got a two and a four for six times. Good luck. Number one gets the Tatum. One, two, three, four, five, and six. Six times. Ryan, man. You got some good luck with me and welcome back. Vacation, welcome back. Mosaic cella 176. Add number one after six times. Look at that, man. Get some good luck with me. So there you go, that Tatum. We'll be going out to you a little later this week. Appreciate it. Jasby's Case Breaks dot com.


We're back, this is Dave Vellante of Wikibon.org and this is The Cube, I'm here with Jeff Kelly, my co-host. SiliconANGLE comes to these events, we extract the signal from the noise. We go out, we scour these events for the very best guests that are at these shows and bring them to you. They share their opinions, their thought leaders, their practitioners, their technologists. Anjul Bambri is here, she's a multi-time Cube guest. She's the vice president of Big Data and Stream, something we're going to talk about. Stream is a very interesting product at IBM. Anjul, welcome back to The Cube. Thanks. It's a pleasure to see you. We're here at Hadoop Summit. A lot of discussions going on about various things that we're going to get into, but let's start with big insights. That is your flagship offering for the Big Data Group at IBM.  Sure, and it's always a pleasure to be here. So we just announced big insights, our next release, a couple of weeks back, and a lot of the capabilities that we have made generally available, it's interesting, have also been discussed as capabilities needed by the enterprise for Hadoop to really stick in the enterprise throughout the day here. So I think that's really good. The first thing there is that we have brought SQL to Hadoop, and by that what I mean is that now we are providing, via our big SQL capabilities, complete ANSI SQL 2010 support. So it's all ANSI SQL 2010 compliant, very feature-rich, and we are also bringing the processing, the MPP processing of data, structured data to Hadoop. So from a performance standpoint, this is something that is, the enterprise customers are really going to be wanting to, that as they are using SQL, they are expecting the same kind of performance that they were getting when they were using SQL on structured data and relational databases. So this is something, both from a feature function standpoint, as well as from performance, I think people will really like what we have released there. And we are doing this by not moving data out of Hadoop into any database on the side. The data stays in the Hadoop file system, we don't move the data around, and we are able to run SQL, whether it is data which is in Hadoop file system, it may be in Hive, HBase, comma-separated files, whatever the case might be. The other key part of the big SQL support is that we have our BI tools like Cognos and MicroStrategy all supporting big SQL, which is really important for customers that are doing, or augmenting their warehouse to bring in different kinds of data into Hadoop, that their existing investments that they have made in Cognos, or maybe MicroStrategy and others, that they can continue to leverage those, just because they have data in Hadoop. And now that we are giving them SQL, it's equally important that tools like Cognos continue to work. So there's very tight integration that we are providing with the likes of the BI tools like Cognos. Obviously, both on the distributed platform as well as, you know, there is still a world out there which people don't talk about that much, which is the mainframe world. And now we are, you know, the next month we are going to be releasing this whole big insights and Cognos support for our customers who are on mainframe, right? So big data is not something that is only on a certain platform, right? There are big transactional systems that are still running on the mainframe. And it's equally important for those customers and the applications to be able to bring the polystructured store called Hadoop in those enterprises and be able to use tools where they are accessing data which is in Hadoop as well as transactional systems which are on mainframes like DB2Z. So, you know, with big SQL, we are really tying all of this together. Yeah, so in fact, of course, you guys made the announcement, the announcement in April, Blue Acceleration, we had Bernie Spang on at Wikibon. We've beaten that one pretty hard and this is good. But so important, our customers want instant gratification, essentially. You know, they want the same experience in Hadoop that they have in the transactional world and that's obviously a challenge for your business. So you have this offering of big insights and it essentially comprises IBM's distribution of Hadoop. Talk a little bit more about what's inside of big insights. What is that all about? So, big insights is our offering which is completely based on Apache Hadoop. So there is no forking or there is no, it's pure Apache Hadoop. And what we are providing on top of that is capabilities like big SQL which are also ANSI SQL compliant so that customers can access data. Once it reaches a structured form in Hadoop using SQL which they have been used to, as well as we are providing from the standpoint of security, there is capabilities that we've added where you can secure your data, govern your data, bringing in things like data encryption, masking. So which is where we are providing integration with like Guardian where you can now look at, have a complete audit trail of who has accessed what data, who has run which map reduce jobs. So having that audit trail is very important because otherwise the data is just out there for anybody to access which is okay when you are maybe doing some sandbox. But there is always in the enterprise, there is a governed side of the enterprise and that's really important that you don't lose control of the governed side of the data. So RACF for Hadoop basically, resource access control facility for all you made from yourself there. See, we love the main frame. That's a really important point especially when you are moving. When you want to actually use Hadoop to support some mission critical applications and this exposing customer data for example to different parts of your organization or potentially even outside your organization, clearly you've got to have the governance and the rules apply to that. I wanted to ask you a question a little bit about the impact of Hadoop on IBM's overall big data business. So we hear a lot about one of the use cases for Hadoop is moving some of the workload from your, what can be an expensive data warehouse into Hadoop where you can still have access to it but it's less expensive to store and things like that. So we know certainly IBM has kind of the IBM pure data system for the analytics based on some of the Netiza technology more of the data warehousing technology. You've also got pure data for Hadoop. So I'm curious, how is that impacting your business? Are you seeing that, are people looking to use Hadoop and potentially move off of, move some of their data from Netiza into Hadoop? And how do you see that over the long term impacting your business? So it depends on, obviously what Hadoop is doing is it is enabling many new classes of applications which were maybe expensive just because the cost of putting so much data in a warehouse could clearly, would not be a cost effective way for some of the scenarios. But just as an example, right, like, you know, from when we spoke specifically about Netiza and Hadoop and we have customers that are using Hadoop, you know, by putting Hadoop in next to their warehouses and really augmenting their warehouse. And the usage that we are seeing is that since Hadoop lets you bring in all kinds of data which is also very noisy data into, you know, the Hadoop file system, obviously you can do that by in a very cost effective manner and you don't have to really process a lot of your data, just dump it in there and then you can see, you know, what do you really care about there? So to that extent, I think it's a great technology for all warehouse customers to be able to bring in data which doesn't really belong in a relational database initially at least, right? And once they have done the filtering and analysis, they may identify that there is now a subset of the data which they were not leveraging initially to make decisions and they may decide actually to move that data back into the warehouse. So because for certain kinds of applications, the warehouse is probably the highest performant way to access that data or analyze that data. So leaving that, so in some sense, it's also the reverse that is happening, right? That if they were only making decisions based on the data that's in the warehouse, you know, you can only squeeze it that many ways. I call it that you can only, you know, just because you keep torturing the same data, you're not going to get different results. So you've got to stop torturing the data, right? And now you don't have to torture the data, but you bring new types of data and get new insights, figure out what else you were not capturing and bring that into the warehouse so that you can actually do more and have better insights than, you know, what you were trying to accomplish by torturing the data. So stop the torture, bring in the new data in Hadoop, find out what's useful, move it back into the warehouse and you'll get new kinds of correlations and insights. So you're bringing these worlds together. I mean, IBM's always done, and of course, there's a lot of tweets going on today around IBM serving up data at Wimbledon, you know, Pond intended, IBM's always done the Olympics.  In a nice way. Are they? That's my question. Is it all harmonious or is there some dissonance? What are you seeing in the customer base? So, you know, whenever there is new technology, there is always going to be some disruption and which is fine, right? In the end, I mean, from an IBM standpoint, we want to bring to the customers what's best for them, right? We want to make sure that they are able to leverage the technology that is the right one to solve their business problems. So we would never discard any technology just because it's going to cause disruption to something which has prevailed for the last decade or so. So looking ahead, we firmly believe that, you know, we have to embrace this, which is what we have done. And it's not that, you know, we've embraced this, we are getting it ready for the enterprise by really helping not just our customers, but you know, there's a whole world of SQL programmers out there and really helping them cross the chasm, making sure that the tools and the skills that they have invested in, they can continue to leverage that, not just for, you know, what they've had traditionally, but also for these new technologies. I mean, that's where we are really surrounding Apache Hadoop and building an ecosystem of capabilities around it so that the customers really get the best of both worlds. And they can now actually implement new classes of applications. And really, you know, providing a way that you run your workload where it suits and fits the best. And you know, it's not an either-or, it's an extension and an augmentation. HiBM has always had the ability to interact with, sell to the upper, you know, senior management audience. But it especially strikes me, Anjul, in this big data world. You've got, like Merv was saying today, you've got the guys in the hoodies, the guys in the suits. Who do you interact with? Who are the customers of Big Insights? Are they hoodies? Are they the suits? Are they both? They're both, you know, because we are obviously, you know, the customers that have been with us for a long time.  We are looking at the trends in the market both from a technology standpoint as well as what are the new insights that they need so that, you know, they can serve their customers better. So we are bringing to them, here's the new technology, here are the new possibilities that were not there before.  And then, of course, we also have, you know, the new customers where maybe they don't have a lot of legacy to, you know, that they have to bring to the next decade, for example. And they are finding it also very attractive because they know that we are embracing the open source and we are building a good ecosystem around it so that they'll be able to solve their problems quickly with us. And which is important for them because they have to survive, right? They're just starting out. So for them to really become, you know, the next bigger companies, they have to bet on companies which will take them forward in the next decade. And we don't have much time but I got to ask you about Streams because I'm so fascinated with this product. It's a product that is organic out of IBM's research labs. There's been some startups. I know some of the folks spun out of there. There's a new startup we're actually going to have them on later, H-streaming. So it's a really interesting space being able to make decisions, allow machines to make decisions on data before persisting it truly in real time. What's happening with that product? So, you know, Streams is, we've seen so many implementations of where people are leveraging Streams in the telco industry to analyze call data records at volumes and throughputs that were just not possible before to predict customer churn, to gain insights into their consumer and offer them the right promotions and services at the right time without waiting for all this data to be persisted somewhere and then take action on it. We've seen deployments of this in healthcare. You know, you've all heard me talk about data baby many times. But, you know, it was same data that was before not being able to be processed at the right time to be able to predict certain things, right? In this case, infection in a newborn baby. But the same data, because of Streams technology, is now saving lives. I mean, talk about that as an impact. We are seeing, you know, marketing and ad agencies use Streams where every interaction that the customer is having has to be captured. The context has to be captured and the context has to be understood amidst, you know, many different kinds of interactions and then the right action needs to be taken. Three-letter government agencies, no comment. Yeah, and, you know, for security. I mean, this is, I mean, you know, like, yeah, in the open source, there's a lot of talk now happening about real-time analytics. We've had real-time analytics for now the last 10 years. And as a platform, the throughput, the consumability of Streams, you know, the development environment that we offer to the application developers is something that is really cool and I think something that people should try out. You know, we have quick start editions of Big Insights as well as Streams now available. That way, for development and test, people can download those and try it for free and, you know, hopefully form the same opinions that I have about these products. IBM is really getting it done in open source. You guys have been doing that for well over a decade, two decades really. Lou Gershner called you were covering alcoholic. I owe you that one. All right. Thanks very much for coming back on theCUBE. It's great to see you as always. All right, we'll be right back. This is Dave Vellante. We're live, Silicon Angles theCUBE. I'm with Jeff Kelly and John Furrier. We're here at Hadoop Summit in San Jose. We're right back after this word. Thank you.


This video is brought to you by our user-supported Vitamix blog Life is No Yoke. Go to LifeIsNoYoke.com to learn more. So I was going to start this video by saying, you know, I'm not really an apron kind of guy. And then I read this article or headline that said, you're an adult. Get yourself a nice apron. So let me show you why this apron might be a good fit for you. Or me, because I'm an adult. It's the Vitamix apron by Hedley and Bennett. Here we go. One, it's gender neutral. And this goes both ways. Some aprons, it feels like they came from the boys club for chefs or mixologists. So you've seen the bear. 25 minutes to open chefs. Yes, sir. Yes, sir. Menu. And then maybe like 10 years ago, I would envision an apron being white with flowery, frilly.  I'm taking it to a picnic. That looks great on any two. It's super easy to put on the neck strap. Once you get it sized, right? You just pop it on and then there's straps that you can tie in the front or the back. I like mine being almost baggy. So there's less pressure on my neck. I don't know if this would be accepted by culinary schools in researching this. I found a lot of people that like cross back bib aprons ones where it's a strap that goes over your shoulders around the back. So the pressure is on your shoulders and not your neck. It'd be nice if Vitamix had an option for this type of apron. I feel like people are really divided on this. If it was an option, there'd be people like, Oh, no, I'm not doing that. That's way too much work. The over the next step is way easier.  If you feel strongly one way or the other over the neck versus cross back. Number three, this apron is comfy. It's soft and it's heavy duty. Somehow it's all three. It's denim. It's like a pair of jeans. That's what jeans are supposed to be. Wear them every day, durable. And the stitches, if you look closely, you can tell that they're going to remain intact through wear and tear in the kitchen, but also in the wash machine straps, the pockets, they're well built. And this is number four. The pockets are amazing. You have two huge pockets and your thighs. You can use them for utensils and tools. And then you have one small one kind of on the chest area. If you look at the pictures on the website, it kind of looks like two tiny ones. So place for a thermometer and a pen separate. It's just the way that the stitching is. It is one pocket on the chest. Speaking of perfect, this is number five. It's the perfect color. There's only one choice. Dark denim. I love the line about black socks. They never get dirty. It's the same with dark denim. Aprons are made for you to keep your clothes from staining. It's going to get food on it and it's going to stain. But it also kind of feels like that pair of jeans that you wash maybe once a month. Not going to look as dirty as it actually is. And when you do like jeans, it's going to change color a teeny tiny bit. And number six, the apron is premium. So it's not like expensive for the sake of being expensive. I saw aprons for $175. Why? This apron's not cheap, but it's designed for a pro kitchen to withstand the wear and tear. It's going to last a long time. Just like your Vitamix. Just like you. Thanks for watching. Life is New York. Dot com.


Alabama boasts a diverse population of bird species, making bird feeding a popular activity in our state. The bird population includes migrant birds flocking to warmer areas for the winter as well as birds that make Alabama home. To try bird feeding at your home, place feeders in readily visible areas. Feeders near shrubs and trees have the most visitors. Offer birds food, habitat, nest boxes, and water to ensure that they visit your yard year-round. Birds attracted to your yard change with the seasons. Make your feeders and food suitable for each season's group of birds.  Try mixtures of sunflowers and whole peanuts and platform and hopper feeders to entice more species. Also offer fruits, mealworms, and nectar. Incorporating suet feeders in the winter months will attract species of birds that normally eat insects. Keep the birds safe, move feeders at least three feet from windows to avoid collisions, remove hiding places for cats, and keep feeders debris-free and filled only with seeds. Learn more about the birds you attract by using helpful tools such as backyard bird guides and binoculars.