Etcher and James n- woah, woah woah, woah, woah, woah! What in the fuck...? What in the fuuu- What in the fuck...  I obviously I don't know what they're gonna be quite yet But just look at this. We've got this extravagant egg right here a lot of people believe this is prime time It's got the clock the clocks at 8 p.m. Which is the eastern standard time for NFL prime time games So it's got to be Dion Sanders right my only concern is there's already a gold 99 Dion Sanders And he's already insanely good So this might actually be wide receiver Dion Sanders on top of that I damn near back-to-back to this egg bro this low key might be the best egg in the game a lot of people are speculating that this is Golden ticket out of position Derek Henry Quarterback reason being somebody who pulled the golden ticket made a Derek Henry out of position quarterback Holy shit. I'm not playing Madden for the first week that that comes out because that's gonna be broken It's got the crown, you know King Henry I also have like four of this egg right here undoubtedly This is Chris Westry a corner for the Ravens. I have no idea why he's getting a card My best guess is it is a developer golden ticket Who knows also pulled this egg Which I have to assume is Charles Woodson because there's that trophy right there Which I assume is like alluding to the Heisman trophy and it's a claw a Wolverine claw and it's blue and yellow It's got to be Charles Woodson right tons of super good eggs.  I was like boys.    I will be on a cruise on Easter Sunday I know not very religious. I kind of sort of want to bring my Xbox on the cruise I'm on vacation, but I'm hoping I can take like 30 minutes out on that Sunday and find some Wi-Fi Find a TV hook up my Xbox hook up my recording software bring the camera and shred those Easter eggs for you boys This will be undoubtedly the most Easter eggs or Christmas presents.  Look anyway.  It's just started.  What did I tell you? I could feel it, bro It's like electricity in the air. I could feel the packs. All right. Let's get it boys. Let's get it Hey, this is racist recording. I just back to back it. Oh my god I don't even need to open up the Easter players at all. What is going on? This back opening just started I went ahead and grabbed a six times bundle as well Just cuz I knew that our pack luck was stupid, but we just I Can't believe this is real. Don't you dare do it again. I was gonna freak out if I saw that package. Oh my god All right, we're good. I am human. This was our worst pack so far has a 90 to a 90 and 89 It is our worst pack. All right, that started that pack opening was like a nuclear explosion. Let's calm down Let's cool it down here. That's like virtually exactly what I'm talking about to at 94 earth Smith I got candy as well. I got candy early. This is exactly what I like I wanted like we already got the gold 99s, but I want like 95 overalls. So we're gonna quick sell for 26,000 training That's a lot of players right there race John Jenkins Jim Kelly Matt Ryan Charles Haley 292s ain't bad and Troy Hill think I want Troy Hill Vince will fork a Keem Hicks Training fodder, you know, I love to see that 91 edge. You're in James 88 John Hanna Tredavious white Ryan Jensen. My heart rate is returning to a normal humans heart rate. This is a guaranteed 95 96 It's literally every single 95 96 that's out there like you got Aaron Glenn corner Michael Vic corner So really all I'm gonna do here is take a 96 and quick sell it I got one more ultimate legends fans back and then the topper of the 91 plus edge. You're in James I almost should like block it from my view Ted Hendricks Ozzie Newsome sure it's just training. That's all it is I'm gonna block this just in case this is something good. I'm gonna block it with my hand I mean, I'll know if I see the gigantic rings animation. I don't see the gigantic rings, but that is why I don't gamble kids All right, we got one more three times bundle I personally believe that this is how packs should always be like this shouldn't be like a God-tier cracked opening like I think this is what you should always do and I'm gonna block every single one This one's blocked. I think that was Ozzie Newsome 88 Newsome. Mel Renfro the Garrett Blunt struggle boss, dude You know, it's funny. I still believe it in these two packs and the topper I think I have one really good pole left key Shawn Johnson and a team of the weak player Xavier McKinney I still feel like there's one really good thing just waiting in these ultimate legends backs here Roger Thomas Justice Simmons, you know, maybe I was wrong. Maybe there's not super heat still in here.    Yeah, hey, really.  Oh my god. What a pull dude seeing those big rings. That just gets you going All right. Hey, let's see if my my Ridley Esk mannerisms paid off.  Oh my god. Oh my god. What is it? Yeah, bro, I am a god right now 297s to gold 99 limited by the way this Dickerson is the best hat back online right now It might not look like it's speed accept agility wise.   We're gonna go open the Easter packs Hopefully get a gold 99 in there too if we get a gold 99 awesome if we don't that's fine We'll quick sell everything for more candy and we'll open more large eggs to make our Easter opening even better 443,000 training. I'm kind of torn. I pulled gold 99 Jalen Ramsey on stream out of the 88 plus But off stream I pulled gold 99 Lamar out of the 92 plus So I'm gonna start out with just 10 92 plus and I'll probably do the rest 88 plus We'll just see how it goes. Also, it is glaringly obvious.   I have pulled 192. This is packed number six Stay stay with me. Hey, okay second 92 and our last one is Sam Martin puncher All right, let's go to 88 pluses We'll be able to open a lots of these and that's kind of the goal here So this reminds me of my two million training opening from last year because in that opening You know, I'm sitting there waiting to get a golden ticket So I go like 30 minutes and pull nothing and then all of a sudden there's this crazy golden ticket So I think that's gonna be me right now. I think I'm gonna be I'm just gonna be laid back opening packs drinking my coffee And then we're just gonna pull some heat 96 John who smith out of the 88 plus we take those also This will be good I guess as a small sample size for you guys to know how much training will turn into how much candy I started this opening with 443,000 training and at the end of it when I'm at zero training will see exactly how much candy that converted to We're starting to run down some training here. No limited yet But with how insane my luck has been so far. I genuinely believe I'm gonna pull another gold 99 I just think it's gonna happen kind of letting off the letting off the gas here I'm not pulling anything too crazy right now My only concern is it no longer says on the front page of madden that the limited's are in packs But yesterday it said their impacts until the 10th. So I assume they are impacts There's an easy way to find out and that's by pulling one. I got six 88 plus is left wish me luck boys Ooh 96 Metcalf out of the 88 plus we take those it's no limited, but we take those oh michael carter. I take it blank and ship I have enough for almost exactly two more. Uh-uh not gonna get it All right, so let's quick sell everything we got here that we didn't yet sell the good amount of stuff in here, too puts us at 244,000 candy So that's gonna put us at about nine large eggs and I'll probably do some random ones in there too I think a lot of people are gonna want to know the random egg just how good it is I open five I got two extravagant two small one medium do with that information what you will and in fact Actually, let's make that sample size a little bit better. I'm gonna open five right here five random eggs Let's see if we can get some sauce if I get five smalls here I would just be getting collapsed So let's hope that doesn't happen a medium although I don't really give too much of a shit about a medium egg A medium is 9,200 candy and these are 8,000 so that is a good pull It's medium and that's a redux player. You can very obviously see probably I'd say 91 or 92 plus redux for a medium egg And what do we got here another medium egg looks like playoffs if I had to guess Okay, so we're getting all medium eggs so far We have two more random eggs till I've opened what I said I'll take a large redux Don't mind if I do this large egg quick sells for 16,000 candy and a 94 overall sugar rush player quick sells for 9,500 so that kind of makes me think that these eggs are just crazy good Like this might be a 94 or like 94 plus redux player So random eggs three mediums one large and I'm do a small egg, right? How am I gonna get no small eggs in these?  I swear every other egg. I was getting an extravagant Let's see how it goes right now. I get a big large egg that looks like playoffs probably all right I said 50 50 right so that would mean this should be an extravagant unless I'm scamming all of you I'm scamming all of you Large egg number three is extravagant and I think we just got a double up on charles woodson. Love to see that I'll take two large egg number four. We've got one extravagant two larges. Come on, baby. Give me a new one That large egg.  Does this not look exactly like limited I don't know if that's what it is I do know I want extravagant eggs and there's a few that I'm missing. I'm definitely missing a few So let's see if we can stack on this five It's the extravagant redux egg quick sells for 30,800 k So, you know that boy's a heater. I want a new extravagant. I want to try something new Damn under most circumstances. I don't want to quick sell any of them But I will quick sell that one there so that I can open two more I do have a lot of those large eggs So you guys will definitely get to see it get opened and a few of them I just said I wasn't gonna do it But I just did it again because I I also have multiple of that one This is called a gambling problem ladies and gentlemen Shit this is that's madden machines. No doubt that's madden machines. Damn. I was on fire yesterday I I could tell you that This one is so good, bro. This one quick sells for 37,700. Is it guaranteed ultimate legend? That's my opinion.    I love that Gabe neighbors is in this promo Which makes it all the better.  I was literally I was literally looking at the lyrics to the song I'm listening to Chris Godwin 96. Let's get it out of the 88 plus two not the 92 plus Dude, there's nothing that like shoots my adrenaline higher than seeing that rainbow pop up in the back and the pink like Sprinkled muck card. Oh, I want to see it so bad. It's so I see you boy I see you Chris Godwin almost a back-to-back and there's a 92 in the 80 a we we're looking good right now At the very least I can tell you we're gonna pull a new large egg something we haven't got before 92 Michael Carter This boy is cranking right here. I think this is our last one right here. I don't think I have the training for another What's the sauce canard? So that was 216,000 training turns into how much Can it looks like it's a damn near one to two ratio if you took if you have 500,000 training on average That's gonna turn into 250,000 candy. It's all large eggs, baby I think I get four and then I'll be able to do a few randoms. Let's see if we can get something new That is my fifth. It's my fifth chris westerie. But bro. I low-key like this egg Why is chris westerie in the game? It makes me think it's actually gonna be good Uh, and I can get exactly three more of these here's one. It's a large egg. I can't tell what that is either Is that a strategy item?  This is our third one It's a madden machine's large egg and our final large egg. Let me see that salad no dress Let me see the salad It's an extravagant egg, baby and this one quick sells for a hot 30,000 So, you know, it's a heater. That's gonna be like, bro That one's gotta be like a 97 plus reductor 96 plus read that one's gonna be a heater.   I have no the upside down blue egg. I don't have no clue I got four chris west street.  I got two charles woodson's I got three of whatever the hell this is two more reduxes three whatever the hell that is Three madden machines one of whatever that is I got like seven medium eggs and eight small eggs And then I've also got a hidden egg of voucher.  This is a super heat opening Thank you for watching as always. I will see you guys in the next video Oh, you got a banger wheel in my tomorrow. Enjoy boys. Peace


Hello and welcome to this session. This is Professor Farhad and this session we would look at the report on internal control over financial reporting. This topic is covered in auditing and attestation course and obviously covered on the CPA auditing exam. As always I would like to remind you to connect with me on LinkedIn if you haven't done so. YouTube is where you would need to subscribe. I have 1600 plus accounting, auditing, tax and finance lectures. This is a list of all the courses that I cover. If you like my lectures, please like them. Click on the like button. It doesn't cost you anything. Share them. Put them in playlists. If they benefit you, it means they might benefit other people and please connect with me on Instagram. On my website you'll have additional resources such as PowerPoint slides through false multiple choice if you're studying for your CPA exam, 2000 plus CPA questions. You can look at my website. It's worth checking it out. So the first thing we're going to look at is the auditor's report about the financial statements. The auditor's report about the financial statements and you might be asking why do we have to do so. Well for one thing we looked at this report in the prior session and when we looked at the report in the prior session we had a paragraph and I said we'll come back to that and it read well let's go over the report real quick. We have the report title. It mentioned the word independent. It's who's it addressed to the board of directors and the stockholders. In the auditor's opinion we talked about the financial statements and those statements present fairly in all material respect. If you remember we talked about this and when I get to the third paragraph so this was the first, the second, the third. Under the third paragraph I said the auditor will also have to audit in accordance with the PCAOB the company's internal control over financial reporting. As of December 31st, 2019 based on criteria established by COSO criteria the committee of sponsoring organization and I said I will look at this report in a separate recording and this recording is we're going to be looking at this report. So simply put the auditor will have to basically for publicly traded companies they'll have to issue to report. One on the financial statements which we already looked at that too on the internal control and this is the internal control report that we're going to be looking at today or right now in this recording. So let's go ahead and take a look at the report just like with the other report. I'm going to break it down into separate components go over each component separately then we will look at each one and take it from there. The first thing is again we have report title and the report title must include the word independent. Just like when we did the financial statement we had to be independent we're going to see when we are auditing the internal control we have to be independent. Again who is it addressed to the board of directors and the stockholder those are the same. Then we're going to get to the auditor's opinion and in a sense it's the same concept we also have an auditor's opinion and notice the auditor's opinion when we looked at the report let's take a look at the report here the auditor's opinion comes first just like with the financial statements the report because it's important the opinion is important so I'm not going to be flipping back and forth but there we go we have the auditor's opinion so let's take a look at the opinion it's listed first so let's see what they said in the opinion first. In the opinion it says we have audited audited notice the word internal control over financial reporting of the company they have the company name what else do you need to basically be aware of? The audit for the internal control cover only one period December 31st 2019 if you notice the financial statements they covered multiple period the the auditing report covers only one period then they tell us what criteria they used which criteria they used the criteria as the internal control integrated framework issued by the committee of sponsoring organization the COSO treaty in our opinion now this is the most important part the company maintained in all material respect effective internal control over financial reporting basically they're given them an unqualified opinion. Now we have something called cross-reference paragraph and what is a cross-reference paragraph is to tell you we did mention that we're going to be auditing this report and the other and the others in the other report which is right here and they're cross referencing to this paragraph now they're just saying we also we also have audited in accordance with this PCAOB the consolidated financial statement December 31st 2019 and 2018 notice the audit of the financial statements is for multiple period the the audit for the internal report is only for one period so just telling you just like in this report we cross-reference the internal control in this report we cross-reference the financial statement okay in the related consolidated statement of stockholders equity cash flow for each three period ending actually three period ending December 31st and expressed an unqualified opinion on those so we also expressed an unqualified opinion so those are the first part of the report the second part is the basis for the opinion how did you how did we come up with our opinion what what are the basis well before we talk about the basis we need to also explicitly state what responsibility do we have as auditors what responsibility do management have okay the company's management is responsible for maintaining an effective internal control and for its assessment of the effectiveness of the internal control so management is responsible for that management is responsible for the internal control our responsibility as auditors is to express an opinion now our responsibility is only to express an opinion we are a public company firm registered with the PCAOB and are required to be independent so we're just kind of telling the users that we are independent and we are registered with the PCAOB okay now how did we conduct the audit how do we how did we conduct the audit we conducted the audit in accordance with the standard of the PCAOB so we are part of the PCAOB and the and the framework we follow is the PCAOB this is how we conducted the audit these standards require that we plan and perform the audit to obtain notice reasonable assurance we never give any guaranteed any absolute assurance we always give reasonable assurance about whether effective internal control over financial reporting was maintained in all material respect now we talk a little bit more about our audit it includes obtaining an understanding of the internal control so we did understand it assess the risk that a material weakness exists test and evaluate the design and operate an effectiveness of the internal control based on the assessed risk and perform such procedures as we consider necessary in the circumstances we believe that our audit report provide a reasonable reasonable basis for our opinion so again we're explicitly stating how we conducted the this internal control in this internal control testing in this paragraph this is how did we come up with the basis of our opinion this is what we did we're just explicitly saying so then we have a definition paragraph and this is basically a boilerplate definition basically we need to tell the users what is internal control what is internal control okay a company's internal control is a process designed to provide reasonable assurance regarding the reliability of the financial reporting and the preparation of financial statements for external purposes in accordance with gap a company's internal control over financial reporting include these policies and procedures just basically the finding internal control this is the definition of internal control when you study internal control and then accounting one-on-one course pretend that the maintenance of record that that and that unreasonable detail accurately and fairly reflected the transaction and disposition of the asset of the company provide reasonable assurance that the transaction are recorded as necessary provide reasonable assurance regarding prevention or timely deduction of unauthorized acquisition use or disposition of the company's assets so basically just telling you what is internal control basically the definition of internal control we don't only stop with the definition we have to let the users that there's always inherent limitation in other words the internal control are not perfect something could go wrong and they may not work as as appropriate so because of the inherent limitation internal control over financial reporting may not prevent or detect misstatement so we're saying we did the testing it's working it's if it's working but we cannot eliminate the possibility that it may not work for many reasons also projection of any evaluation the future period are subject to risk that the control may become inadequate because of change changes in condition so we're saying we're saying what we did now it looks good but we cannot we cannot predict anything about the future because of changes in condition maybe the employees that are using the internal control that are maintaining the internal control might change maybe their situation might change and may start to to defeat the internal control so we don't know what's going to happen in the future or that the degree of compliance with the policies and procedures may deteriorate so they were complying but now in the future they may not comply because of changes maybe personal changes people who are working at the company or changes at the company itself the company is now coming under a tremendous pressure then now we have we might have to fudge the numbers to look good so we don't know about the future and we don't know we don't know what would happen in future period so we can only say when we tested the internal control they were effective that's that only for that specific period this is basically the report about the internal control i broken down on three on three on three slides it's basically this is all basically one one report on one page in the next session we would look at the unmodified opinion but we're going to be looking at that report with an emphasis of a matter paragraph or non-standard report wording if you like this lecture please click on the like button subscribe as always i would like to remind you to visit my website for additional resources you're going to be studying for your cpa once it's a 20 to 30 year investment in your career do it properly study hard it's worth it and stay motivated


Welcome trade hackers. Today's Tuesday, February 18th. Good to be back after a closed market on Monday for President's Day. So let's take a look at what's going on in the markets to start with. S&Ps got about 45 minutes left in the cash market. S&Ps down about 7. They were down as much as 20. Just another day, another bounce where we see the markets going down and then rally back up. In fact, the Nasdaq actually turned green, so it's up on the day. Other movers, big moves in commodities. If we look at wheat up over 4%, we went ahead and booked profits in one of our wheat trades. We had an iron condor on in wheat. It was kind of hanging out near the lower end of the range, rallied up. We booked profits there. Some other big movers in commodities, natty gas. And that gas up over 7%, making a big bounce off of its lows there. And then silver, which we don't trade a lot. The markets aren't great in silver, but silver futures up over 2.5%. Twice as much as gold. So big move in silver as well. What did we do today? Well, we booked some profits. We took profits in SPX. So we had an iron duck on in SPX. Took some profits there. The other thing I wanted to point out was a couple of the post earnings plays that we did. So NVDA. Anytime price opens up above the expected move, which is marked by this line after their earnings announcement, just like we teach in our earnings course, got a nice move higher booked profits on our Nvidia trade. We've also got one in Shopify, which did the same thing. And here's the expected move line right here. And look at how the market is just respecting that. We actually put this on up here, so we're still down on this trade a little bit. But if we can get a move higher at some point here in the next couple of days, we should be able to book profits. But you can see price came right down to that expected move line. Bounce Tire came down. Bounce Tire came down. Bounce Tire. And it continues to do that. I'm not a big support and resistance person. I think they work about 50% of the time, but just look how price is respecting that expected move line. Interesting. So hopefully it doesn't break through and hopefully we get a little bit of a continuation to the upside in Shopify. Other movers, Apple. Apple was down over about 2.5%. It also with the rest of the market has started to rally back still down 1.73% at this point. They came out with some guidance saying, you know, due to the coronavirus, they are going to see some reduction in profits. And so market opened up down, but has since rallied. Apparently they decided the coronavirus isn't as big of a deal. Well, let's see what else. Tesla is on the move again. If we look at our friend Tesla TSLA up 7% today. So another big move in Tesla. And on the earnings front, not a whole lot going on today or this week in stocks that we like to trade. The one that does announce before the bell on Friday is John Deere. So we will potentially be looking at earnings trades in ticker DE. But other than that, not much interesting as far as earnings plays the remainder of this week. Everybody have a great day. Talk to you tomorrow.


So yeah, so here's what the really neat part is that the thing is that Joe said we're directly from our customers. This is the insight that we get by running Total Customer Experience. We feel the customer's pain and we have to develop technology, solutions, and services to address those customer needs. So what Joe said earlier today and the challenge is about disruptive technology, trust, security, flexibility. These are areas that our customers are continually pointing out as areas of concern. Our challenge is how do we deliver today, tomorrow, and in the future to address cloud and big data? But Total Customer Experience is the basis by which we do it. So Total Customer Experience has a couple parts. The first part is we listen. We have different listening posts throughout the company available to our customers and partners to have them understand exactly what are their current challenges? What are the things that they're top of mind for them? Then we make investments. We actually take their feedback. We prioritize it based on the impactful things that are most important to customers' success. And then we make investments. And we deliver. And we deliver consistently. And it is an official TCE program office that we run this model basically every week. And so it represents the voice of the customer. It represents our listening posts. It represents our investments. And hopefully our customers are seeing exactly what we're delivering now and in the future based on today and tomorrow. Yes, we are. That's it. Right, exactly. Well said. Yeah, so we are, we actually, I'm not sure when we acquired Green Plum as within the last year, but we have a fully integrated environment with Green Plum now, fully enabled. So we are living, we are the, I'll take the guinea pig scenario, that's fine. We deal between quality and voice of customer data. We deal with hundreds of millions of records on a regular basis. So we have the same challenge our customers do. How do we take this massive amount of data, create some intelligence out of it, drive information or recommendations back to the business that represent the customer. So we are a living use case and we are virtualizing a lot of our environments. And we're recognizing that when customers speak to us about the challenges, we live it every day. And that's the best use case, I think you could possibly imagine. So we're in good shape. We'll share, Mike. Okay, I guess we'll share, Mike. Little technical difficulties.  We can get the mic on here. The big data scenario though really hits your wheelhouse though. Let's talk about the analytics. That's your job, you deal with data. So talk about some of the things that you're seeing now with servicing the clients. You need to have that customer experience. We've talked with, I think actually a Green Plum customer, ClickFox, which is in the business of essentially using big data to provide a better customer experience. Talk about what you're seeing inside EMC. I mean, we talked privately before we started that it's at the beginning your team's coming together, but you have essentially data scientists on your team. And we're going to hear about that theme here at EMC World where data science is now probably the hottest job title available. And then the summit about it. Yeah, it's great because I always speak about when you run a total customer experience program, what is the most challenging thing that we have to do? One of them is analyzing the massive amounts of data we have. And again, creating some intelligent information about it. I can give you just one quick little case study. It used to take us upwards of six hours to run a customer report before the integration of Green Plum. It's now down to a little under eight minutes. So here's a great example of the type of data that we have internally. We received a US patent last November. And the US patent was based on the amount of quality data that we manage in the business and the amount of voice a customer we manage. We run a correlation between those two data elements and we drive recommendations that are based on multiple data points. So here's your example of big data. How much data do we have to deal with on a daily basis? You think of our internal systems. Look at the complexity of just the parts that we have to look at from a quality perspective. Then take all the voice a customer data, run that correlation engine. I mean, that's a massive amount of data and we have to turn it into information. Executives, when they listen to us in terms of where should they make their investment our ROI model. What is it based on? It's based on big data. It's based on turning data into recommendations.  Well, I mean, you bring up a good point. I mean, you're talking about the running reports from hours to days to hours. Yeah, hours to minutes. Hours to minutes. This is the value proposition of the cloud. So better economics, better time to market. Sure. And data is the center of it. But I mean, are there challenges and where are we in the life cycle? I mean, in how early are we? I see there's some demonstrable benefits you have now. What are you seeing? Yeah, I think we're pretty early on right now. I mean, I think one of the challenges one is figuring out what data elements you have to integrate into big data, right? The disparate teams that are working on multiple data elements, they're disparate systems. So one of our big challenges we have right now is how do we take, you know, there's two, I look at it as three separate challenges. One is how do we look at the data that we have to turn into intelligent information? Turning that into intelligent information takes a set of analytics, right? I think as you pointed out, data scientists and tag on some analytical capabilities on top of that is just one of the hottest things in the industry. But even more important than that is how do you deliver that information, right? At what facility?  So some of the challenges are what's the data you need to manage? Where does this exist? How do you analyze it? And I think most importantly is does the recommendations you make make sense to the business in a communicable, actionable format? What's EMC's approach for total customer experience and what's different about what you guys are doing? Yeah, an incredibly disciplined approach. Starts at the top, right? Joe Tucci's a firm believer of paying attention to customers and partners and enabling future technology. It's a very disciplined approach where we take a set of collection mechanisms for our customers worldwide. We take a set of competitive data. We look at quality data. We drive recommendations and we're developing an ROI model now to say, you know, we're in competition with voice of the business, voice of the market, voice of the economy. So when we produce the recommendations to our internal stakeholders, we have to produce an ROI model because what's the investment worth to them? So I think that the unique differentiators are we take quality and voice of the customer together. We have a formal program, a disciplined approach and a proven, we're actually starting a program called TCE proven. So over the last couple of years we've been listening to customers. We're delivering results. Wouldn't it be nice for our own customers and partners to understand exactly what we're delivering? It's so fun to watch the green plum in influence on EMC and the Isilon and the acquisitions and some of the things Joe Tucci is talking about because, you know, Silicon Angle and Wikibon, we talk about, you know, cloud meets big data all the time and that's really a relevant and trend that's totally going to impact IT. Oh, sure.  So one of those touch points that people can relate to is the iPhone, Android, Facebook, social media, Twitter, blogging and your customers profiles change their expectation of their user experience on the internet and how they communicate with their peer groups has changed. So that's fundamentally a social media phenomenon. Sure. How do you view that? And that's, the customers now can talk to themselves like online in a high velocity frictionless environment and you can't control that anymore. So what's your strategy there? Well, there's a couple of things I think going back to your point that customers can talk to one another. I mean, we started a couple of years ago in the global services division, the chat program, right? Customers are helping solve each other's problems. They're not even involving us, right? So there's a great use of technology but you bring up an interesting point of what is social media presenting in terms of TCE and voice to the customer?  How do we discover what customers are saying about us in a social media content, a less formal, it's not a survey or a structured approach. It's a very open approach and we're looking to people like yourselves that can help us in terms of understanding what our customers are saying. And again, more importantly here, it comes back to big data and cloud again. How do we take that data and make sense of it to our internal stakeholders to drive recommendations representative of our customers? So it's a huge challenge. Have we got it all figured out yet? No, but it's a fun journey to be on, especially with EMC IT as our major enabler. We're here at EMC World 2011 inside the Blogger Lounge and we're talking about big data meets cloud. That's the theme for the show, we are Jim Bampos and this is a really groundbreaking event for EMC because last year during the private cloud was the core theme. Now it's cloud meets big data. That's the next step in their evolution transformation as Joe Tucci says. So it's important and cloud has been in the news with a lot of Amazon going down, RSA got hacked, PlayStation Network got hacked. And then you got emerging sandboxes of innovation like Hadoop and Open Source. And these are powerful dynamics. And so EMC transformation has been interesting as a storage company to an information infrastructure company and the M&A activities have been interesting. Talk about EMC's discipline that you just talked about with regard to, as they say, dog food and eating their own dog food. We've had Sanjay on, who's the CIO, and they talk about we have the biggest virtualized infrastructure. You now have them talking about having an IT infrastructure, no one else has with Vblock and Vplex and all that good stuff. So how are you guys dog fooding big data? So can you share any insights specifically around initiatives in the company that are going to leverage big data and cloud to provide new value? Yeah, I think there's a couple of good case studies being happening right now. I think one is the one we talked about already in total customer experience. But I'll go back to the eating your own dog food. I mean, Paul Moritz is the guy who, according to his own lord, coined that phrase. But it exists in EMC and I think Sanjay is a great representative of, when we look at acquisitions, they're two sides of the business. One side is, how do we bring the acquisition into the sort of a normal play of EMC way of doing business? That's a longer term and I think Joe has a very nice way of acquiring companies and bringing them into the fold. On the flip side, Sanjay's got a bigger part. I'd say it was probably four or five days after we bought Green Plum that the email came down to Sanjay to say, okay, when are we enabling a Green Plum in our IT infrastructure? And we happen to be one of the early guinea pigs two weeks after that. So I would confidently say that when EMC says it eats its own dog food, I would be absolutely confident that we are the ones. Let me put it another way too. You know, we bring up an interesting challenge with the quality aspect of EMC because when you bring in these type of acquisitions, quality is a different challenge. Look at Hadoop, open source. There's an extraordinary challenge with managing quality to that because you've got an open source set of components. You're compiling it. You're bundling it in your own enterprise systems. You're offering it to your customers. That's an extraordinary challenge. So here's another example of using big data around the industry to collect all of this data about how customers are using our products, where they see our challenges, where they see the opportunities for growth. And again, this is where we're taking and eating our own dog food, leveraging, you know, a lot of infrastructure, VCE, Vblocks is another area that we're leveraging within TCE program. So huge future ahead of us. It's really nice to be able to sit in EMC, use our acquisitions components and portfolio, look at how we're doing the same things to address our customer challenges. I'll tell you one quick little other story. Recently we went through a whole analysis of what technology and what vendors we ought to use in terms of our total customer experience program. And we actually used EMC's customer feedback to determine how we should judge our vendors for when they come up for proposal. So we took our own customers, we looked at their challenges and we looked at our own and said, wow, look at that, they actually match. What do we learn from them? How can we use what we do internally at EMC based on the challenges of our customers? And then you've got Pat Gelsinger from last year, he's going to have some footage later, he's going to come by today. So Pat Gelsinger will be here at 6.30, we're going to do a special with Pat. He talked last year about being humble and you guys have changed a lot of the product mix. How does that affect your job in terms of, you guys are much more services focused than you've ever been, being an infrastructure company versus a storage company. Right. You've got VCE out there, Joint Venture, you've got some acquisitions, they're doing a lot of services. A lot of your customers, your deliverables are changing. How does that change your TCE and your view of your organization? Well, it changes in a couple of ways. One, managing quality is a little different than a traditional box that gets put into a customer environment and you manage that box.  I think the biggest change for the Total Customer Experience Program is recognizing the different layers of customers that we have to deal with in our accounts. The challenges for a CIO versus the challenges for the people on the floor, the traditional storage guy is no longer the traditional storage guy. He's no longer our traditional, I mean, there's certainly a lot of customers that are still following that footprint, but you hit it dead on. Pat comes in, he's phenomenally connecting the dots, that's the best way I'll put it, connecting technology and innovation with acquisition, with services led, with customer needs. And Total Customer Experience has changed a little bit around a core technology play to a solutions, to an integrated solutions component to our customers. So managing quality, managing Total Customer Experience has some challenges, but believe it or not, we flip right back to our internal organization and look at how our internal IT department helps solve our customer's problems and take advantage of that. Talk about your vision for the future. I mean, obviously you're in an important part of EMC, TCE and your whole world is about data. Share with the folks out there your vision for how big data analytics is going to change for you and for society. Yeah, for us, I think the challenge is extraordinary because we have so many data elements we're dealing with now and the complexity of what quality means to a customer. Traditional quality is my box running or my disk spinning. Can I get access to my storage? Versus now it's an integrated solution. So what does quality really mean to a customer? So that's one extraordinary challenge which manages data 10x to what we normally, traditionally manages quality. There are two future components of Total Customer Experience as it relates to our customers. One I think we talked about already, which is the social media aspect. What are customers saying? How can we help them in terms of integrating better solutions toward the future? The second component is something we call customer value. Customer experience is really good around the elements of a disciplined formal approach. But let me put a value proposition out there for you for value described as price you pay for the quality of product you get based on the image you're buying from them. Now think about how that manages itself into the new world of EMC. What is our image? What is, I mean, product quality bar none with most reliable products in the world. But again, it's a little different flavor and customer value means- And your marketing's changing too.  Really, we've had full control of the messaging and it's pretty clear that its cloud meets big data. I mean, we can't really deny it's everywhere. So he's done a good job. And he said last year on the cube that messaging was important to him. But your image is changing. So now your expectations, does that change your results? I mean, how do you factor that in? Well, I think this is the transition of value. This is the transition from a traditional sort of loyalty metric, if you will, into more of a value metric. What is the value we're providing our customers more than just a simple box? It is a solution. So it is changing TCE. There's no question about it. It's changing the way in which we manage data, the way in which we drive recommendations because we're driving more toward the value. And I'll ask the challenging question. I think we clearly understand what loyal customers mean to EMC. What does EMC mean to loyal customers? This is the value component. What value are we providing? Because we have to get better at that. And your touch points are changing too. You have VCE, you're very partner focused. So you have implementations with partners to size and channel partners. And you've got VNX out there now in the lower end and mid-range. So you bought up a great point. We're launching TPE total partner experience too. So we understand the landscape is changing quickly in terms of our dependencies on partners. So we're going to be doing a very formal discipline approach around partners too. Good deal, good deal. So Jim, thank you for coming into the queue. Appreciate it. Any final words you want to share with the folks out there before we go to a package? Yeah, the final words I'd like to share really are around. We do listen. We do make investments. We do deliver. It's more important now than ever that we understand customers' challenges of getting to big data and cloud. And I think, you know, I encourage our customers to participate in any program we have. Big cloud, big cloud works. Big cloud works, big cloud little, yeah. All kinds of integrate. Big cloud and data. And data. So come and continue to share with us. That's the most important message I can say is we are listening. Share with us your challenges and let us try to help you and let us try to learn with you. As Joe said, we're here to learn as much as you are. So it's not just about liking it on Facebook. I mean, that's normal marketing. Hey, like it on Facebook. We see that everywhere around here. Text-like, I mean, that's gestural data. That's not real data. I mean, that's an applause. It's a clap. It's important because you're getting them into a community. But that's just the beginning. How do you look at beyond Facebook? You got the likes. You got a little aggregation of a community going. But you want more data. You want them to share all of it. Anything in particular that you guys like to see from your customers? If you could have a magic wand and saying, hey customers, share with us. I think there are two things. There are challenges. There are successes with EMC. And I think, what does the future bring to that? What are their biggest areas of concern? So we can really listen and understand that. I think we have some great facilities to do that. But it's not just about today and what we're delivering today, it's about our challenges in the future. And I think the program run by the executive team in terms of total customer experience, they are in fact listening. And we just have to be able to understand all of the challenges and all of the arenas that customers can speak to us about. So we're listening. What's your final question is, what is your plan for the year? What do you hope to accomplish by EMC World next year? I think better demonstrated results in the TCE Proven Program. I think a demonstrated value metric in terms of our customers being able to really understand the value that we provide and they provide us. And I think finally to be able to come back a year from now with a solid set of results and say, here's exactly where we're making the investments today and tomorrow and here's how we're accepting your challenges in the future. All right, Jim Bampost with EMC, Vice President of Customer Quality, which has run the Total Customer Experience Program. Thanks for coming on theCUBE. Appreciate it.


Hello, everyone. My name is Qi Wenbo. You can call me Gaios. I'm the maintainer of the drone fly. Another speaker is Jiang Han. He's work for the Ant Group. And I hope my introduction can let you know about the current state of the drone fly. And I hope many developers can be interested in the drone fly. Okay, the first page. I will introduce what is drone fly. Drone fly provides image acceleration and file distribution on the P2P technology to be the best practice and the standard solution in cloud-native architectures. It is hosted by the SynCef as an incubating project and it is designed to improve the speed of the file distribution in the large-scale clusters. And it is used in the fields of the application distribution, log distribution, cache distribution, image distribution, AI data site distribution, and AI model distribution. Okay, let me see the contemporary gesture of the SynCef land skip. There are two graduation-level and incubation-level projects. One is the harbor as an advocate hub. The other is the drone fly, provides the image acceleration and the file distribution. Now we have more than 100 contributors and the maintainers come from the Ant Group, Alibaba Group, ByteDance, Intel, Baidu AI Group, and Jeep AI, and Dalian University of Technology. Next page, I will introduce the NADUS. NADUS is a sub-project of the drone fly and it provides a file system on the REFS format and it can remove the duplicate trunk in the building and download on demand by the trunk. So NADUS can reduce the end-to-end code launching of the container from the minutes to the seconds and the maintainers comes from Ant Group, Alibaba Group, and ByteDance. Okay, let me introduce the important milestones of the drone fly. The drone fly has been selected and put into the production by many internet companies since it is open source in 2017. And it joins the SynCef as a sandbox project in 2018 and became the incubating project in 2020. And in 2020, NADUS became the drone fly sub-project and widely used in the image acceleration. In the 2021 drone fly released version 2 and now drone fly is not only used in the image acceleration but also can use it in the file distribution and AI data distribution. Now drone fly maintainers believe that the project is ready for graduation. So we submitted the drone fly graduation prop source. Now drone fly focus on three parts. The first part is image acceleration. Drone fly supports the container clients such as Docker, Kennedy, and so on. And it provides three solutions in the image acceleration. The first solution is to use drone fly to distribute the container image which is suitable for the large scale cluster. The second solution is to use drone fly and NADUS to distribute accelerated image which is suitable for the large scale cluster and faster container launching. The third solution is to use NADUS to distribute the accelerated image which is suitable for the faster container launching. And in the fields of the file distribution, drone fly supports the protocols include HTTP and ADFS. It also supports the object storage protocols include S3, OSS, OBS, and so on. So we have been working for the image acceleration and file distribution for almost six years. I think this drone fly is the standard and the best practice in these fields. In the AI info, drone fly supports to distribute the model and this site through the drone fly based on the P2P technology. Now drone fly supports the Triton server and TorchSoul to distribute the model through the drone fly. And it also supports to distribute this site and the model from the hugging face by the passenger SDK through the drone fly. In the future, we will pay more attention to the AI info because we believe the P2P technology is the best solution to resolve the model distribution. Okay, next part I will introduce why we use the drone fly. For example, we have Kubernetes clusters and this cluster has 1,000 nodes and each node will download a large file or large image or large model at the same time. For the storage, there are 1,000 concurrent download requests so the storage can easily reach the bandwidth limitations and this will cause the slower file downloads and the slower container launching. So how to resolve this? We have three solutions. The first solution is to increase the bandwidth of the storage but no matter how to increase the bandwidth of the storage as I'm back on the storage, it must have limitations so this is not a better solution. The second solution is to use the P2P technology to use the idle bandwidth of the nodes to eliminate the impact of the storage bandwidth limitation. So drone fly uses the P2P technology to eliminate the bandwidth limitation. The second solution is to reduce the download files. We can remove the duplicate content in the building and download on demand in the running to reduce the file size. So NIDUS will remove the duplicate content by the trunk and download on demand by the trunk in the file system. So drone fly includes the second solution and the third solution. Okay, in this sharing I will focus on the AI inference. One way download a large model file in large scale clusters we must face the problem of the bandwidth limitation of the storage. So drone fly support, hugging face SDK, gate LFS protocols, threatened server, and the torch serve to distribute the model through the drone fly. So we can easily use drone fly in the AI inference. That's the best solution to use drone fly to forward the download model traffic to the P2P network. So we can use drone fly to distribute the model based on the P2P technology. So users can upload the model to the model registry and the interface serving will download the model from the model registry through the drone fly. So we can use the idle bandwidth of the node to emanate the bandwidth limitation. Okay, this performance pricing of the hugging face to use the drone fly distributed the model on this site. For detail, we can find the document in the drone fly website. The next part is performance pricing of the gate LFS protocols to download the AI model through the drone fly. Okay, this is the performance pricing of the torch serve to use drone fly to distribute the AI model by the torch service native plugin. So you can also find the document in the drone fly website. Okay, in the future, we will release the new version in the first half of this year. And we will focus on the AI inference. So there are two important features we will release in the new version. The first important feature is that drone fly will support to write the large file into the peer. For large file write to the peers, this peer need only report the metadata to the drone fly scheduler. So this file can be downloaded by the other peers without uploading the model to another storage just like S3. So this feature will support the AI model and AI this site to distribute read and write faster in the P2P network. Okay, the second feature is that drone fly will support the RDMA for the faster network transmission in the P2P network. Okay, so it also can load the model into the memory directly and the CPU offload and the zero copy. Okay, the following we are shared by the Jiang Han. Okay, thanks. Bonjour, everyone. Well, it looks like everyone is talking on AI. So let's take a look on something else. Well, I will start the background of the whole project we built. We built a system called HUSE, which means HyperUnitified Service Engine. And we built this in order to hand both service function and the spark drop from the big data application. And we also supported the task from the flink. And while we built this, the answer is easy that we want to standardize the allocation of resource. Well, you can see that we have lots of scheduler in different systems like we have scheduler in Kubernetes and they are used to schedule the port and we have scheduling the yarn that we use that to deploy the task from the spark. So we want to build a unified engine to schedule everything. Oh, that's the background. Okay, next I will give a brief statement of the system. You can see that the whole architecture is just like the lambda from AWS. And I will focus on the node level in order to reduce the redundancy. I had lots of details. Okay, let's take a look at that. If a user invokes the function service, we will download the files from different storage registry by Dragonfly. And we have a node worker on the node side. It's just like a Kulvet in Kubernetes. And you can see that we have downloaded different files from the different backend that we have downloaded the spark jar packages for the big data. And we will download the user code and the runtime images for the service function. And when we download them, we will start a container, something like a container, but it's not actually. Yeah, and that's the whole architecture. Next, I will explain why we choose Dragonfly as the downloading utility in the whole system. Well, you can see that the first reason and the most important, we need to download files from different backend. First, just like the GateLogic file storage and something from the Gate directory and also HDFS and OSSS. Well, I have to say that the Dragonfly inherently support all those backend. And so it saves us a lot of time when we develop the whole system. And I just showed the data from the benchmark website for the AWS Lambda. And you can see that the time cost of the code start is just under 150 milliseconds. That's very fast. And what was the time cost? They download the user code and the starter container in the node. And the time underscores the necessary for a swift download that you cannot spend too much time on downloading the thing you need when you want to build a serverless architecture. Yeah. And the last reason why I chose the Dragonfly is because the maintainer guy was just said by me. So if I got some problem in the production cluster, you know, I can find him. Okay. And next, I will show you guys some special scenario that we meet in the cluster. The first is there are significant difference in the file size in the system because we need to download the huge images for the service functions and for the Spark tasks. But sometimes the Spark tasks file is very small. It could be 50 MPs. And let me give an example that you have two types of tasks. The first one is for the, they need to download the file like 50 MPs. And the second type, they need to download the 10 GBs task. And in such scenario, that you need to schedule when you need to think about the file size when you schedule. And you cannot schedule all type ones task in the one node. And you need to consider about this. And the next question is that in some scenario, we need to download a large number of files just like Spark. Well, many tasks in Spark, they contain lots of files just like configuration and some initial files. And the file number can reach 1,000. And in such scenario, where I gave a benchmarker test that you can see that if the file numbers come to the 2,000 and the time cost becomes over two minutes. And we've dive into the reason why the file numbers can cost such a result. Because more file will cost more damage to the DragonFly's schedule. So the solution is that we compress all the files in the Spark job and then we will decompress them after we're downloading. It's pretty easy, right? Okay, next, I will give an instruction about how we deploy the whole system focusing on the node level. We bound the necessary components in an image just like the Walker demon and the DragonFly demon and some runtime binaries. We put all of this into an image. And then we deploy the image as a port in the native Kubernetes. And you can see that we allow this port can run in different quality of storage. As an example, we make the port running under the batch state group which is low levels in the online state group. And by this deployment method that we can improve the resource utilization in the single node. Next, the whole deployment method can bring us lots of benefits. The first one is the whole cluster is scalable. We have a schedule to detect the lack of resource and when it finds we need to scale out some new port it will call the API server to create a new port. And the secondary is the whole system supports multi-tenants that we support different configuration for different tenancy in a cluster. We will make different configuration by config map and we will mount the different config map into different ports. And the whole system is configurable. That's because if you modify the config map and you apply it, it will immediately get into the cluster and you don't need to restart the port. Okay, in order to give you a brief insight of our scale, I just feature a data panel from the hour when a foreigner and you can see that during last week we the maximum task create can reach 1.44 million and it's pretty big, right? Okay, and in order to show the performance of drawing a fly, I just feature the data for last 24 hours and you can see that there was a spike around 10 AM because this is an active time for the online business. And during this interval, the batch state group will be strained and the whole port performance will be strained. And apart from this interval, you can see the dragonflies downloading speed is very stable across the whole day, yes. And this is data in a single day, but I have to point out additionally that dragonfly has supported the stable download speed during the last three years and it's pretty good. Okay, that's all container I wanna show and if you're interested, you can scan the QR code to access the dragonflies official website and that's all. Well, if you have any questions, I'm happy to answer. Thank you. Okay, thank you. And to work, I'm Charles from Huawei and just one question for the peer-to-peer software image distributions and it is very important from my point of view to decide that the first node to pull out the image and it can distribute this image to the third one and to the second one and the second one and the third one could be another source of peer-to-peer. Yes? So how to choose the very first one? No, and do you have some schedulers and do you have some algorithms related to the schedulers and it is open-source it and could you give me our audience more information on that, thank you very much. Okay, okay, I got your question and we do have the scheduler in dragonfly system and we have all about the technology details in our website and you can find the documentary in the website, it's very detailed and I trust you can find the answer by yourself and we actually, I contributed later to the dragonfly so if you want more details, I can find the guide to answer the questions. Do you want that or you just want to find the answer by yourself? Is it okay to... Gals, okay, come on. Thank you very much. The peer-to-peer, okay, the first image from the origin, the storage. Dragonfly selected just like the BT tracker. If you know about the BT protocols, you'll know about in the first BT have our tracker to do the schedule, okay. Dragonfly have the schedule in the clusters and we can select, Dragonfly have the two types of peers. The first is the peer, the second is the seed peer. So in our clusters, if the file download in the peer-to-peer, first schedule will target the seed peer and download from the source origin and the peer will download from the seed peer. So we can deploy the seed peer in some, in some of the nodes, it have the better network. Okay, so we can pull the first quickly, okay. Well, thanks for the question. And additionally, I just mentioned in the slide that if you download too much files and it will cause pressure to the Dragonfly's schedule, so yeah, you need to take care of that. So anything else, guys? Okay. Okay, thank you guys. That's all. Enjoy.


Welcome to NPTEL course on groundwater hydrology and management. This is week 12, lecture three. We are nearing the end of the entire NPTEL groundwater lecture wherein we have been focusing on different data sets to use in your groundwater studies, models, and research. Please understand that most of the data that I share has its own spatial and temporal limitations for which there is a need to use remote sensing data. So I do not want to leave that rock unturned. So what we are doing today is we will be focusing on the available remote sensing data, which is free that you could use and understand the groundwater behavior. There are limitations on this side also. As I said, you have to take your observed data and remote sensing data and try to mix it to a good new product. To be focusing on something new, let me tell you how IMB works. So IMB has the Indian Meteorological Department has rainfall gauges to monitor rainfall. However, they felt that the spatial and temporal resolution may be limited and could be improved by augmenting with remote sensing data. So they joined hands with NRSE and other ISRO data products and NASA products and made a rainfall grid rather than a point data. They made a grid. How did they do it by merging the observation data and your satellite data to get at good products? Like this, there are multiple other users of satellite data and it is good to see how that works for the groundwater system. So today we'll be looking at the most important groundwater instrument in the satellite that can monitor gravity. So gravity can be monitored and from gravity you can estimate the groundwater hydrology, at least the change. So this mission, very special mission is called GRACE which stands for Gravity Recovery and Climate Experiment. It is the only remote sensing satellite that can measure both terrestrial water and groundwater change. Terrestrial is any water on top of the land and including that and below the land under the ground also it can monitor and measure. It was not made for that purpose but the discovery is such that when they saw the gravity changes and they understood that gravity cannot change within months without a big phenomena happening. For example, if you have earthquake then the mass is shifted to one place. From here the plates move and maybe they get overlapped and so the mass is changed. When mass changes or the weight, the mass changes the gravity pull is more. We know this from physics that as the mass changes the gravity pull on an object changes. So knowing this, these smart NASA and German space scientists what they discovered is they made this GRACE satellite which just monitors how the gravity changes across the globe and those gravity changes along with other data can give you the groundwater change. We will see how through some examples and we'll also see the website and how it works. But please understand this is not a standalone neither is the observation data it's good to merge these two data together and make good products out of it. What are the benefits? The benefits are it produces higher spatial and temporal resolution because your point data is only a well at a particular location and it doesn't tell you the full picture of how the entire aquifer system changes and also the depth because as I said, you have drilling wells and you have shallow wells. Most of the monitoring is in the shallow wells not the drilling wells. However, farmers are also using water from the drilling wells. So that is where GRACE data plays a very vital role. Then you have higher spatial and temporal resolution. The groundwater data is monitored once every quarter which is once in four months. However, the satellite data GRACE is monitored every month and they're also thinking about bimonthly data. So this high resolution would eventually be very, very helpful for monitoring groundwater change. So we have global scale now. Globally, we can monitor because some of the aquifers are too big like example, the Ganges flame aquifers and then monthly you need to see the changes because suddenly there is a big pumping happening and we may miss it if we don't look at it at once. However, this data since it's a very specific specialized satellite data it needs some post-processing and cleaning to arrive at a tangible data for groundwater. So that is one limitations. There are some calculations that you would need to do based on the water balance calculations to arrive at groundwater. What you get is a gravity change. How do you change the gravity anomaly? Anomaly is the change per unit time. How do you change the anomaly to a monthly reading or a groundwater reading is the question. That we will show in a very simple water balance equation. Accuracy is good per millimeter also we'll get accuracy but it needs tremendous amount of ground grouping. So this is the catch. I'm saying that you can use this where you don't have data. So but now on the other hand I'm saying you do have data that can validate the grace mission. So how do you do this? Basically you should be able to merge both the data sets where the locations are known and the groundwater observations are available. Then we could actually merge these products and make predictions for groundwater anomaly groundwater storage change at across other regions. Basically what you get here is a net groundwater thickness or volume change between months. And so if you have a long time period and you know that the post monsoon season the groundwater change is positive and then from the post monsoon the groundwater is going to be depleted either by base flow or by pumping. And then it comes to the groundwater least level or point before the summer or during summer. We call it pre monsoon season. We have post monsoon and pre monsoon. So post monsoon will have the highest groundwater level whereas the pre monsoon would have the least. Let's take Maharashtra case. You have a good groundwater levels post monsoon for example, July, August because monsoon starts in June and then you have very low levels in March, April, May. May is really low. So that is where you need water to irrigate for crops and people use tremendous amount of water but the evaporation is high, transpiration is high. Okay, so therefore we do need good observation data for groundwater. So the good part here is it is not going to compete with observation data. It's going to liase or you get united unison with observation data to produce good groundwater storage change values. Okay, I will show you the mission, how it works. So satellites are normally called missions. So this grace mission theory is given by NASA. So what you see here is the grace mission on and how it works. So this grace mission is launched by a combined effort of NASA and the German JPL and JGFZ. So you would see all these different partners come together and work on a common satellite, which is good for the planet. It is one of a very unique system but let's talk about the satellite and how it measures. Okay, so you have a mass. Okay, the mass in the first stage, it is two satellites. This is the only few missions where the satellite is launched in pairs. You have two satellites and as the name suggests, one is called Tom and then the other is called Jerry. So basically Tom runs and Jerry catches. Tom is the cat. Sorry, Tom is the mouse. Is it Tom? Yes. So you have Tom and Jerry story, right? Tom is the cat and Jerry is the mouse. So Jerry runs and Tom catches. Okay, so here Jerry is running first. Okay, and when the satellite goes on top of the earth, it gets pulled by the mass. So when the satellite sees a bigger mass, then the satellite moves faster. Okay, so this satellite is not recording anything. Only it is moving around the planet and it speeds up and slows down depending on the mass. So the next satellite, which is your Tom is catching with Jerry and whenever the first satellite goes fast, it records how fast it goes. Then when it slows down, it records how slow it goes. This changing accelerometer because it accelerates and then de-accelerates. So basically it is not pulled by the body but mostly pulled by the attraction towards the first satellite. Okay, so now what happens is you have the satellite going in, the first satellite going around the earth and when it goes faster, the second satellite goes faster and then when it goes slow, this goes slow. So this change in speed is measured and the speed is then converted to a gravity change. Because the gravity changed, the speed changes. So this is how the gray system works in a very simplistic manner. There's much more physics in it but I'm just explaining the simplistic manner. So the first forward speed is there and then the gravitational pull is acting on the first satellite. So it goes faster. Then when it goes faster, the second satellite doesn't know that this guy is going too fast. So it catches up, okay? It catches up by the attraction towards here and also the gravity mass and then goes in. So this change in speed is recorded and it is taken as a change in gravity. That's it. There's no other force that can influence the speed of a satellite. It is purely gravity. So here, why doesn't other satellites do it? Because they don't carry a very sensitive accelerometer. Here, this satellite has no fancy things. It is only the only fancy because there's no camera, nothing. The only fancy thing is this accelerometer and it is very, very sensitive which means a very highly accurate accelerometer and that can be changed to a gravity pull. Now, you have the theory. So let's see what would change between months. So this is the first month. The mass is there, the satellites go past and it goes past this one, two, three, four is done. The satellites are gone. Then the next month, again, the satellite comes. So every month, once the satellite comes to the same location. Now, if the mass has changed, why would the mass change here? Because you have snow on the top, maybe the snow melts down. And when the snow melts down, there is less mass on the earth on that point. So if the pull is different, okay? The pull is not the same. So there's less mass, less full and less speed. So now this difference between months is accounted for the snow melt. So think about a region where there is no surface water and there is no change in mass, no snow, no nothing that melts between months. What changes is the water under the ground, okay? So let me draw it just as an example. So we have this as the earth, okay? And this has some water bodies like a lake and then there is trees, et cetera, et cetera, okay? So when the satellite goes on this part and the lake value changes between months, then it is influenced by the lake also, okay? Because the gravity, everything on top of the earth and under the earth influence the gravity on this satellite. What about in a place like this, where in a pixel like this, there is no change in surface water because there's no surface water. All it is is soil moisture, okay? And groundwater. And if there is a pump which is pumping groundwater into the aquifer, from the aquifer into the agricultural fields, then a mass is lost. You are losing groundwater mass. When you pull water out, the weight of water is lost and this can be accurately estimated by grace. So now you think that water which you take out is a level which is depleted and that level is also equivalent to a mass. It has a mass, right? 1000 liters is equal to, you know, the calculations for a weight depending on the density of water, okay? So you have this as one aspect to measure and monitor the groundwater change using the change in mass of groundwater which has been extracted and put outside. So now the extracted water can be covered with evapotranspiration or taken away to cities for drinking water, et cetera. But now it has been caught as a mass change. So when the satellite flow flies on that particular location and there is no change in lakes, there's no change in trees because trees are standing. And also if there's a building, if the building doesn't change, suddenly within a month, two, four, five flows extra added, then the pure change is due to soil moisture and groundwater change. Now we should just remove the soil moisture. I'll come to that part when I discuss the equation. Okay, so this is how the satellite looks at the planet. It does not have an optical sensor where like other data we saw, it is taking hyperspectral images, different bands coming in for assessing the properties of land, properties of leaves, color, et cetera. Here it is purely the gravity. So now you see how the grace looks at Earth. It is not a smooth sphere because it is having differences in masses. Suddenly you have the Himalayas with a high mass and then it goes around the Himalayas and then it dips down into the oceans and lakes and other areas. So it is not a common surface, okay? So what you see here is a changing surface of the Earth and most of the changes are attributed to soil moisture and groundwater change. Unless and otherwise it is a big change in the season. You have snowfall or snow melt and then those masses have been accounted for. So here is the equation that we use. Groundwater change, okay? Groundwater storage as a volume change. I will draw it also for explaining purpose. So think about this compartment which the grace is getting pulled every month, once a month, okay? So right now what you get is a total gravity change because from here, a total gravity change which is now equated to a groundwater by this formula. They give you the gravity change is converted to terrestrial water storage. So what you get from grace is this output, terrestrial water storage change which is any water on the top surface and ground also. The atmospheric cloud precipitation doesn't come. It is purely the Earth. Under the Earth, whatever storage is there and on the surface of the Earth, what is we have? Okay, so what we have here is the gravity is converted, anomaly change is converted to a terrestrial water storage change, which is this, which is the grace real output. Now to get groundwater out. So I'm going to say what is TWS. So this whole change is TWS. Okay, so this data is TWS. Now we know that a soil moisture is up to 0 to 400 centimeters maybe, okay? So 400 centimeters, we have soil moisture data. Below that it is shallow aquifer and deep aquifers. So now what happens is I will subtract the soil moisture change, change, not the exact soil moisture. I will subtract the soil moisture change. So now what happens is the TWS change and soil moisture change is taken out. The remaining is only the groundwater change. It's a very simple hydrological balance that you have because when you have the hydrological balance, let me type it here just so that we can quickly look at it. We had storage change is equals to precipitation minus Q, which is a runoff, minus ET and then plus the groundwater net. Or you can say, yeah, plus groundwater net, okay? So the storage can also be taken to the other side which is your surface plus soil plus groundwater, right? It's equivalent to the precipitation minus the runoff, minus the ET. Here we have in this particular example here, what we have is this part estimate, all this is estimated now, okay? Because precipitation minus Q, minus ET is the terrestrial water storage, the total storage, S, okay? So now this part is taken to be as TWS because the storage change includes the top surface, okay, and then the ground also. So that is the net storage that is becoming TWS as per the satellite's definition, okay? Let me see if I could clear it, okay, good. So what has happened is we have seen that TWS, we write it again, okay? TWS is equal to your groundwater storage, all storages together, okay? Just the storage change, the total storage change. And now just to get groundwater, you subtract it with your surface, is the storage plus the total storage, right?  However, surface water, we said it's not happening. It's only your soil and groundwater. So now you can reduce it to just the groundwater. So the theory is now clear, I hope. You take the terrestrial water storage from Grace, change, it's all a change. They will not give you the raw data, monthly data. They will run it across five years and then remove the average, okay? So what you see is an anomaly, not the actual data.  So moving on, let's see how the data comes. The data comes as grids from which you can take a point and then say this is the value of groundwater storage in that space. How do you compare it with an observation well? You can take an observation well. You can know the thickness of the aquifer by different data. Now you merge the thickness of aquifer data with the level data to get a volume and that is what Grace data gives you at the end. It is an increase or a decrease in volume and how much for it. So you can get it as grids or mass forms, mass concentration blocks. Five to 10 years ago, it was very detailed work you need to do to get this data out. But now they have given you a visualizer from which you can easily download the data and use it. If you don't want to fully download the data, at least you can take these kind of estimates from which you can clearly understand what is the trend of terrestrial water storage. And from terrestrial, if you know the soil moisture, you can actually get a groundwater, okay? So your soil moisture also follows your seasonal pattern and that data is also available in Google Earth, Engine and NASA websites. You can take it and use it. Even the other data sets that I've shown, Moon and ISRO, you can use those data sets to take soil moisture, subtract that from terrestrial waters change and then you will get the net water availability. I'm going to show you the website now. Let's start by a Google page on seeing how this works. Okay, so I've opened a new Google page and all I'm going to type is grace, okay? Data visualizer. I've given the link in the slide, so you can also look at it, okay? So the first one is Grace interactive browsers is there. You can click it to view the different data sets that are available. There are two solutions for the data. There is a JPL interactive and you use the Colorado. The mask on is a recent one. So I'm going to click the mask on and you have two links, as I said, the GSFC, which is a different center that actually gives the solutions are JPL. Most of the Indian data is based on the JPL, so I'm just clicking the JPL. For you to understand, you can take any solution. So basically you have the satellite data and the satellite data is converted to a solution, to a value and that value, lot of multiple centers work on it. Each center has their own algorithm, okay? So the JPL is what a lot of Indian literature has used, so we're using that in our study now. So what you see here is that India and all the other countries of the world, it's you can measure it as regions. So now the regions are Asia, Europe, Middle East, Africa, Australia, so it is based as regions. You can do it as basins because basins would make more sense. As I earlier mentioned, the grace works on large scale. So you need to have large scale area to monitor the groundwater, change an anomaly. Okay, so you see here, you have the basin as a Ganges basin, okay? I'm just going to click the Ganges basin. So the mouse, you just click there and it picks the mask on that particular grid. So I hope you could see the grid. So this is what the satellite, each time it measures one value for the entire grid, okay? So that is where you can see that it is not a very small scale district or a block level analysis. It is a massive analysis. And from there, you can try to understand what is happening in the district, but not accurately, okay? So that is one limitation of grace. But as I said, for large scale basins and Ganges studies, Kaveri basin studies, this data has worked. And I'm going to show you the Ganges basin here, okay? So within the Ganges basin, there are multiple grids. So this grid might have a different trend line, but all of it, since we are using it as the basin, the basin value is given. So JPL mask on Ganges Brahmaputra basin is the name. The trend is minus 1.53 centimeters per year. The terrestrial water storage change is going negative. So this includes the Ganges and the Brahmaputra. People would think that there's so much water in that basin. Why is the terrestrial water going down? The flow is not coming down. So what else should come down the groundwater? There has been extensive pumping in this region. So if you multiply this by the area of the basin, you will get how much water we are taking out. And that could equate well to your 265 kilometer cube per year analysis, much more than that for the entire India. So you could have this trend. You could see how the water, terrestrial water storage changes. As I said, it is an average. So above the average or below the average is what is going to show. So in the net, it increases slightly above, goes down. So this seasonality is because of the monsoon. Whenever you have the monsoon rainfall and goes up and down, when there's a big drought here, you don't see a big push in the upper direction. But with the seasonality, if the level comes down, then it is a pumping scenario. Because the season is going like this. You have a summer, you have a summer in the bottom and then you have rainfall, summer, rainfall, summer. Okay? But if the overall trend is depleting, that means that your groundwater and other water resources there is depleting. Because the rainfall is actually trying to push it back, but it's not getting enough strength. It's not having enough data. So there is a loss. Okay? So and every month, when you move this mouse on the data point, you could see that this is September 13, 2015, August 21. So the peak monsoon seasons are captured eventually. And the value is given and how much it is given. You can change the units. You can have some other trends on and off if you want. And also you can take a different point in the location if you want. You can close this and take another trend. So this trend is by pixel. So the pixel is big, as you can see. And you can just click that pixel and you can see, for example, here, this mask on. This whole grid, which is covering right in the center of Rajasthan is declining. So the recording has started again. So the recording of data is going down, right? So then what we can see is we also noted that somewhere the groundwater is going up, right? So let's take this pixel, for example. Okay? So you can see it is going up. So this actually relates very closely to the CGW media data that we saw. Okay? But what is the difference here? The difference here is it is monthly and it covers the entire whole area in one go. And it also includes deep and shallow aquifers because it is terrestrial and down. Anything that down that changes is going to impact your grace gravity record. So you could also save the data as an image. You can take this as an image for your studies or you can also get the data as a CSV file and then we can change it. So as I said, you could easily get the data and then subtract the soil moisture to greater groundwater. This course, this particular lecture is to introduce this topic. There are a lot of literature, a lot of tutorials on this on YouTube and free open source platforms. Please go there. And if you're interested in Grace, have a look at it. I've introduced the topic. You could learn more. They'll give you a step-by-step how to do these calculations. With this, I will stop today's lecture. Thank you.