short term liquidity. So it means there is long term liquidity also. So currently we are looking short term for long term purposes we have some other ratios which we are not going to discuss here because we are simply short term liquidity. Liquidity is a company ability to raise cash in short term to meet its obligation cash flow working capital. Simply in current year your operational cash flow is coming from operating activity. For one year as much as you want, is it available to pay off your liabilities? Current ratio is current assets divided by current liabilities. This is number one. Total current assets divided by current liabilities. Normally we do not adjust the current assets in the balance sheet and divide the current liabilities. But sometimes we take something out of the current liabilities. For example, if you have taken assets on lease then you have to pay off the lease on the current portion. Because you have to add that in the long term. Sometimes we do adjust the current assets and current liabilities. Current ratio may use the company ability to pay its current obligation. That means in one year the payments you have to pay in short term can be paid. So it means that current assets should be greater than the current liabilities. And then they will fall due. Current ratio is a quick measure of the liquidity of a firm. It is an index of firm's financial stability. It is also an index of technical solvency. Those companies having current ratio problem, they will not survive. Usually current ratio may have two or one ratio. Your current assets should double. If one-one is there then that is also dangerous. And if it is less then it is also more dangerous.  Acceptability criteria. General acceptability criteria is two is two-one. As I said, it should double due to your current assets' current liabilities. And that is why it is called a bank ratio. If you go to the bank and want to arrange funds, the very first thing they will see is what is your current ratio. Can you regularly pay installments or not? It means that the current asset should be double than the current liabilities. Then another immediately. In the current assets, we take all assets total. But what do we do in the asset test ratio or in the quick ratio? We minus two things. We minus two things from total assets. Number one, the inventory and number two, the prepaid expenses. Because prepaid expenses are not available to pay. They have already been paid. Inventory will be sold first, then the receivable will be made and then the money will come. That is why we minus two things. The remaining assets will be divided by current liabilities. And in this ratio, it is very useful in measuring the liquidity position of the company. It measures the company's ability to pay off its current liabilities quickly and immediately. When you do the first thing, all your craters come and say, if you return the money, then you should have it. This is not the money in the stock. Cash should be so available that you can pay off. Acceptable criteria is generally one is to one. At least you should have quick assets so that you can pay off your liabilities. Because it is very important that the company should not have any problem in its working capital. But if you cannot pay off your salaries for a month, then that is the problem. That is why you should not have so much money in the form of cash that you can pay off your immediate liabilities. So it is one is to one. It means that the company should possess quick assets of one rupee against the current liability of one rupee. At least this is the minimum. If it is more than this, then it is better. But it should be this much. Thank you very much.


Chinese Canadians want to tell a story. How we lived and what we had to go through in order to get to today. Our ancestors, they had to work hard and they had to prove themselves. And today we're going to have a museum that we'll be proud of. The fact that this is going to become a reality is gratifying, it's emotional, it's moving. And I'm just very thankful for all the Chinese Canadians who paid this way to where we are today.


at Big Data SV 2014 is brought to you by headline sponsors WAN Disco. We make Hadoop invincible and Actian, accelerating Big Data 2.0. Okay, welcome back everyone. We are here live in Silicon Valley. This is the Cube, our flagship program. We go out to the events, extract the signal from the noise, talk to the thought leaders, the tech athletes, the people making it happen, entrepreneurs, VC, CEOs, anyone who has knowledge, we want to share that with you. I'm John Furrier, the founder of Silicon Angle. I'm joined by co-host Jeff Kelly from wikibon.org, just published a Big Data Survey. Again, on the path of $50 billion market, total available market. And our guest here is Cube alumni. And been there from the beginning is Abhi Mada with Truseta. Abhi, welcome back again. And another Hadoop world, we'll call this Strata. It's in California, Silicon Valley. Big Data SV is the event here. All the Big Data happening in Silicon Valley is about innovation and no other, the best person to talk to than you about that. Being an entrepreneur, building a business in this market, challenging technically, a lot of business growth, what to shoot at, there's so many opportunities. Welcome back. Well, thank you so much. And I'm especially proud of coming back today because this is our first sponsorship of the Cube. And hopefully we'll keep this on and continue it. We've lived the Big Data journey together. I'm incredibly proud to be here again. Thank you. And you believe in the Cube. You saw, you were one of those magical people who saw what we were doing with the Cube, saw our mission of opening up the data and the underwriting support. I know anything is always appreciative. And our goal is to have that underwriting support from Truseta and folks like you guys for our mission to get the data and share it. So thank you very much. Absolutely, anytime. So let's talk about what's happening, right? Obviously Hadoop is here to stay. That's the theme again. Hadoop is real, it's in all the customers. Big Data just isn't Hadoop. It's a lot of other things hitting the stage. You got legacy vendors, you got data science. It really is an innovation party here at Big Data SV. What's your take? And what are some of the new technologies? Like we see Spark, which I know you're really interested in. What's happening? What is your take on the current market? I think the take remains the same, John, which is the thing I'm the most proud about, having built the business the way we have. You know, and there's this interesting metric where people equate success to how much money you've raised. I equate success to how much money we have made. Our customers are funding our CDSA. We had an awesome year. The report that Jeff released talks about the revenues we were able to hit. And I think the theme hasn't changed, which is the market is seeing the light of what you, Dave, and I spoke about four years ago, which is data factories, the ability to actually monetize data in ways never possible before. But the ability to do that depends squarely on domain knowledge and science and technology coming together seamlessly, right? What we have since called advanced analytics applications. That hasn't changed. I think the era, you made an interesting point. Big data is a bigger ecosystem than just Hadoop. Completely agree. The reality though is the era of building and commercializing tools is fast behind us. You can't, if you're building a database in the big data market, don't worry.  BI is commoditized. Democratization that everybody talks about and commoditization are two sides of the same coin. And as big data technologies democratize, they will commoditize. The only way to extract value is going as high up the stack as possible and finding problems to solve with big data. That hasn't changed. And as much as you push people, I love that you push people, to talk to you about that, I am disappointed that the market has moved slower in there. But I'm excited because it's moved slower, I have success. So it's this interesting, interesting part of it. I'll ask you, because again, this is an interesting point. The commoditization angle is great. So we were at the Open Compute Summit just recently here and there's a revolution happening at a hardware level. It's a whole nother conversation. It's like the homebrew club of the future. The modern version of what's going on with the cloud hardware. But the comment we were talking about there is someone said on Twitter to us during your crowd chat, if you're going to commoditize infrastructure and you're going to commoditize the middleware and you're going to commoditize the application, what's left, where's the value? So the conversation was interesting. At the end of the day, you mentioned value. So value can never be commoditized because at some point people will pay for it. So if the stack becomes commoditized, where does the value shift in your opinion or can apps never be commoditized? No, I think it's a good question. I think, again, if you have to break it down into its elements, we have to realize as an industry that companies that are hundreds of billions of dollars in revenue on a stack that includes databases, storage, BI, analytical software are under attack, under massive attack, both with their own legacy stacks, as well as this revolution called open source. The track record isn't hard to find. Lilix did it to one of the biggest technology companies for one of the smartest guys on the coastline in the world. So the track record is there to see. The question that you ask is, does commoditization keep going up the stack? The answer is absolutely yes. However, that's why I don't think the big data market is a technology market. I think you've heard me say multiple times. This is a business revolution. Where you add value is in technology, is not data, is not science, is the combination of the three.  Everybody will say absolutely yes. But understanding customer behavior is an incredibly hard thing to do. It's a combination of bringing in open data, open tech, and open science together. When you bring those three components that are fundamentally open together, you build something that's unique. That's where the value is. That can never be commoditized because the expertise that Tristata has in financial data is very hard to replicate. And no one will replicate exactly what Splunk has done. So yes, commodization will rapidly expand up the stack. We have to remember that the value at stake is $2.4 trillion, right? IT, IT is a $3 trillion market. 80% of it is enterprise software, right? Something that you guys cover and we are in. We can't forget that. We can't forget the value at stake. Isn't the trillions, not in the billions? And we've spoken about that quite often. Let's talk about the business revolution. I want to get your take on this.  But I want to ask you about it. The current crop of startups, are you impressed with them? Are you seeing some goodness there? What are you seeing in the market around some of these startups? Are they solving hard problems? Because the enterprise market has always been a hard market, right? But now what does that mean? What does enterprise mean? It's all business. It's a business revolution. That's the enterprise. You got a consumerization angle going on here. We got the consumerization of IT and a business with mobile, data, tsunami everywhere. So it's that enterprise problem is still there. No one can just say, hey, I want to be an enterprise entrepreneur. What are you seeing in the crop of startups here in the big data world, the strata conference? It's interesting. I love the fact that there's so much confusion and lack of quality startups. Because it, in a way, helps to set out in an obtuse kind of way. We saw the market so early, it is actually shocking to me that there are three clear trends, John, that we all have seen. And it surprises me when I ask people like yourself, I ask everybody, so where are the interesting startups solving business problems in big data? Tell me, because I want to learn from them. The answer, the room goes silent. The three trends are clear. Trend number one, the days of selling databases for hundreds of millions of dollars or analytical tools is over.  BI is free. Database functionality is free. But he's struggling with it, trying to figure out, how do I take those open source components and find value, right? That's number one. So enterprise, we've got to give credit to enterprise buyers that they're smart enough to realize that open source works at scale in the enterprise, right? Not just in the operating system level, all the way to analytics. Number two, the buyer itself is changing. You're not selling to the CIO anymore. You're selling to the CMO, you're selling to the CRO, you're selling to the CFO, you're selling to the CEO. I was with the CEO of a large bank in North America talking about big data.  This is important enough as an initiative that for I need to run it. Not my CIO. I personally am going to guide this conversation. How many startups do you know who have talent and people who can sit and have a CEO conversation on how big data will revolutionize their business? Nobody. That's what fundamentally differentiates, the domain knowledge differentiates, we say that. The last and most important part is applications I hate to say. There is a suite of software we're calling Next Generation Predictive Analytics Software. They are delivering actionable intelligence at scale at a segment of one for a variety of problems that you can monetize tomorrow. You know the funniest part was when we tried, we have trademarked the term monetize big data. I was shocked it was available. Because why is someone not talking about monetization of big data? It's all around big data tech and big data investments. So why aren't they, why aren't it starts working on it? Is it just lack of awareness? Hard. It's incredibly hard. You have lived the Tristella journey with me. Tristella was announced at theCUBE, right? Three years ago. And here we are with $10 million in revenues funded by our customers. It's incredibly hard. It's incredibly hard to combine domain knowledge, technology expertise, and the leading edge science to solve problems. Not easy to do. And when you've tried to focus and build a company on tech, and just domain, and just science, individually, I'll kick butt for all three of those guys, right? So I think it's just not easy to do. It's not easy to pull together business leadership, thought leadership, with deep technical expertise, and the best algorithms in the world, and say, I'll apply this particular solution to solve this problem. A great example is, now that we've had the initial success, and I always say, we're still changing the odds. I like my chances. With initial success, we are picking up a audacious goal to go after every year. So this year we picked fraud to go after. Financial fraud in the world is a $100 billion problem. Even if we together made a 10% dent in it, that's $10 billion. Why isn't anybody applying big data to fraud? Because I think the challenge becomes, do we understand the elements of fraud? Do we understand the network? Fraud is a network problem. Can we do it real time, right? A big announcement we made this week was announcing the first real-time graph traverse a network discovery engine we call NET on Spark. So can you actually take those capabilities, deliver them real time for massive problems by applying frameworks like energy-based models, which are brand new artificial intelligence frameworks in big data. That is just hard to do. Well, let me push back on that a little bit. So it is definitely difficult to do, but why can't a company like IBM do that? They've got all this technology. They've got the services arm. They've got the research arm. Why haven't they been able to do it? But I think, you know, I think IBM gets a bad rep for the wrong reasons. Smarter Planet is, in my mind, by far the smartest marketing genius campaign ever. And I think IBM is trying very, very hard to do exactly that, right? Their big push into healthcare, their big push into looking at disease diagnoses and prevention is a step in absolutely the right direction. I think the whole picture on Watson and Margini came out and said the big investment they're making is set in the right direction. So I think I actually don't believe that no one else is doing it. I think the reality is, what technology stack do you build it on to deliver advantages of scale, speed and accuracy at a cost that's feasible? So if I want to build a platform to eliminate financial fraud in the ecosystem, it needs to work across four billion different nodes on the planet. So I think that's where IBM's technology will, I think, evolve. Because what they're lacking is the scale and the speed, not the intent. I think they actually, absolutely understand the smarter planet is the future. So they've got that legacy business and that legacy technology, which, you know, it's the innovator's dilemma. Do they, they don't want to give up too much of that revenue, of course, at the expense of some of the new things that are happening, but they're going to have to make this transition if they want to survive and thrive. Well, that's our bet. I think the reality is that this is a Darwinian moment in our lifetime. I think I've said multiple, this is a 50 year boom cycle where data as the fuel, right? We spoke about this four years ago. Data as the fuel will fundamentally transform how we measure, evaluate, and act on consumer behavior across a host of problems, from education all the way to fraud or disease prevention and disease management. And I think that journey will take time, but the ecosystem is rapidly, rapidly evolving to solve exactly those things. So I think that, I agree with you. I don't think that they will have a choice but to jump on the band line. So another thing you mentioned that struck me was talking, you've got to be able to talk the language of business and not necessarily the language of tech. And that's a challenge, I think, one of the, to John's point about why aren't some of these startups having more success? And part of it is because they speak the language of tech. They don't necessarily speak the language of business. They think, well, we're going to build these technologies and we'll put them in the hands of customers and they'll figure out how to use them. And that's a hard thing to do because frankly, we've seen a lot of the enterprises don't know yet how to do it. They're begging for leadership and thought leadership and some direction about how to use this technology. So talk a little bit about how you go about building a company who can, you know, not just the sales but the whole company who can speak the language of business while at the same time bringing in the science and the tech. I mean that, as you said, it's an incredibly hard problem. How do you go about finding these people? Because I'm sure others would love to find similar people. Absolutely, not giving any secret sources away but I think, you know, building a company is hard and you guys are entrepreneurs yourself and we have learned a lot of lessons along the way around and we've made mistakes. We made this new transition last year where we made a decision that a bottom third of our talent, we will rotate out, right? To the bottom third of our talent, we will proactively rotate out because the only way we can achieve our ambition to be the next billion dollar company is if we have the best people on the table having the right conversations. So what, the way we solve the problem, we put data at the center of every conversation. So when you come to me, if you come to Truseta and say, well, let's think about understanding how you can underwrite risk or think around how can you deliver omni-channel offers at a segment or one. We always start with the data and get such a deep understanding of transactional data or social data or interaction data, understand what elements, what triggers, what observations can inform the right answer before we do anything else. So they bring data to the party? They bring data to the party. But I think we will have to, I'm also surprised that we're the only big data startup with a chief data officer. So when me and my co-founder, you know, starts putting more structure on the company, the most important function in a big data company should be the chief data officer. You know, so why is it that while every single financial services company, every single retailer, every single healthcare company announcing chief data officers, the startups in the space who are trying to monetize data and don't have a chief data officer is the most important function. So I think that's a key, key tenet. Bring data to the party because the tools exist, the technology exists, the real value, the secret sauce is this beautiful, magical combination of data coming together and making love for technology and the algorithm. If you can pull that together, you know, tomorrow's Valentine's Day, so we'll keep going with your love and allergy, the answers are beautiful and wonderful, right? You will propagate little minions of insight, like there's no tomorrow. So I think that's the secret sauce to say that we always start with the data. If we want to be the only company or the first company to truly understand customer behavior, we have a read on customer data like no one else, like no one else in the market today. So let's talk a little bit about the announcement you alluded to a moment ago. So you guys have built a new application on Spark, which is getting a lot of attention at this show, kind of, you know, we've heard from Cladera's made an announcement around Spark. Tell all our viewing audience a little bit about Spark, what it is and kind of what it enables and use your application kind of illustrate some of the power of the technology. This is great. You know, that's why I love coming here. I mean, go from business to deep tech. Yeah, we're all over the place, isn't it? This is great. We covered the Spark Summit this year. Oh, awesome. With the crowd chat and really intimate group. Amazing technology. Absolutely. Charmele Mulligan from ClearStory. Absolutely. Supporters to go there. Amazing. Amazing ecosystem. Yeah, I want to drill down on this a good. Absolutely. So I think, again, we should take credit for this. So when we spoke in New York at Big Data NY, you guys asked me what I'm at the most British about, right? And we did the news as to, I said Spark, by far. And the reason I was the most British about this was, two years ago when we had the conversation, I used to get asked the question, well, Hadoop is great. It's awesome, but it's batch. And I said, don't underestimate the open source community, right? Don't underestimate the power of the common man as they say. And Spark was the genesis of that. So it's been a project that we actually have been following and contributing to for 18 months. And it brings to Hadoop an in-memory real-time computational capability. So Spark is, in a simplistic way, an in-memory database-like framework that allows algorithms like ours, advanced algorithms to run in memory at scale. The cool part about Spark is, it works natively with HDFS. So unlike other in-memory systems and engines, where you have to go and repopulate data, they're trying to find insight on different populations, Spark will do this dynamically. There's something that they're proud of us about. So we have this in-memory graph traversal engine at scale. So say you're looking at a particular segment, a particular cluster. Say it's the telecom cluster. And now you want to say, well, I don't want to look at the telecom cluster. I want to look at the energy cluster. In traditional systems, you have to go back to the database, resample, recut, wait, hours, days sometimes, and get the information back. Spark does it natively from memory to disk real-time. So it completely changes. So we were asked the question often, well, why is it that you guys are building this beautiful ecosystem and beautiful software? Why don't you build visualization? And we said, we would only build visualization when scale is the insight. In graph network application, which is the killer application in big data, graph today is scale is the insight. So that is a easy to use, beautiful visualization tool, already deployed at a massive bank to understand money flows across the world at scale. And Spark is the framework behind that. So I got to ask you on the analysts, you guys are just a great job. Where are people missing the boat on analytics? I mean, I'll say some great examples of greatness, but in your opinion, where are people, where's the blind spot for the folks doing analytics? You know, it's this question, it's a great question. I was debating this yesterday. The term that I've grown to not like in big data is this way it was being defined in the past. There's two terms I'll critique today. One is allowing people to ask questions they did not know how to ask before. Well, that's kind of yes. The questions are very well known, right? It could be anything from if a particular, if a particular supplier of yours goes bankrupt, right? What will the impact do you be? You know the question, you got to find the impact, right? So it's not like you're searching in this mountain of data. Maybe it's a question they couldn't ask before, is the right one, because they all know their question. Correct. When am I going to go out of business, when am I going to lose a customer? Exactly, right? The language of business. Absolutely, the language of business is quite simple, right? Every business has three core goals, right? Get customers, grow customers, keep customers. You have that, I have that, everybody else has that, right? ExxonMobil, Walmart, they all have the same goals. The questions are already very well known. How do you apply new, bigger, better data? So newer data, bigger data, better data. To those existing existential questions for every corporation is what people should be worried about. But people don't talk like that. I come over here and I see these beautiful visions and rainbows, but the pot of gold is missing. And I go, well, the pot of gold exists if you ask the right question, if you know where to look. It's a medical marijuana cards while you're out here too. They're smoking a lot of, you know, Z-er sense. Silicon Valley always has always been dreamy in the sense of inventing the future. And that's good. And you gotta dream the future before you can invent it. Absolutely. However, at some point. You gotta apply the dream. You gotta make money, right?   But I think the second thing is this misnomer. Seattle and Denver, they won the Super Bowl. All right, I'll go.  Getting back to subject. The second big issue that the industry needs to realize is this wrong focus on lack of talent. I don't think we have a lack of talent issue in data science. In fact, data scientists have become the most confusing term ever invented by man for any profession, you know? Because what does it mean? What does a data scientist do? Unfortunately, when you call somebody a data scientist, they chase the sexy part of data science and not the real value of data science. We believe the challenge that we have as an industry is not to create the talent. It's to actually deliver intelligence to existing talent to make better decisions. So I wanna ask you on the talent point. So this comes up a lot when we talk about hyperscale, right? Facebook, Google, Amazon, huge hyperscale, talk about scale, they're at scale and they're kicking butt doing it. So the issue was why didn't they just buy Oracle licenses? Why don't they just buy off the shelf software? Well, because they had smart talent that didn't wanna pay the money. So they used the talent to build their own, hence the history of what's going on with HBase and Hadoop and Facebook and other environments. So the question is, everyone's saying, I wanna be like Facebook, I wanna be like Google, I wanna be like Amazon. Build my own computers, write my own software. But the problem is not everyone can do that. So the talent gap is huge between the mid-range hyperscale, the enterprise. So how do you talk about people saying, I wanna be like Facebook, but the reality is they just don't have the talent and might not get the talent. But here's the good news though. I think what people forget is this is the beauty of open source. The world is developing for you. You can be like Facebook. Guess what? Facebook's entire backend is this thing called Hadoop and it's open source. You can absolutely leverage it. What is lacking? What, the way you drive the hyperscale growth, the way you transform businesses, is there need to be more companies like Trisada that are making existing talent more intelligent by delivering them the knowledge. That's what's missing. If we keep talking about data scientists and their 1.5 million jobs that are waiting to be filled, we'll always chase tail because five years from now, the talent gap will look different.  When we can build tools to make the population make Earth a smarter planet, no pun intended for IBM, that's the way to win it. So the only way you'll get hyper growth is you can automate it. You can absolutely, as I've always said, machine learning can automate 99% of the work done around data analytics. The last 1% is human, giving better information to you and me to make the right decision at the right time. On the machine learning, obviously machines have to learn, so you have to teach it. Correct. So if you don't have the knowledge, you can't teach the machines. So how do you fill that gap? And you guys are doing some work in this area. Correct. Obviously in memory speeds up and accelerates some compute power, but assume compute might go to zero cost and be infinite. Right. If you believe that, I mean it's a thesis, but let's just stay there for a minute. Right. Compute is infinite and the cost is zero. Where's software go? So here's how do you break it, right? There are four key components to building analytics application, right? Data, so you gotta store it. You gotta make sure you can catalog it, right? Which is what databases do. Then you gotta make sure there are algorithms you have available to find the insight, right? And the last piece is delivery of the insight. You gotta make sure you can actually act on it, right? Decision tools. Let's look at those components. Storage, free, open source, commoditized. Data processing, free, open source, commoditized. Algorithms, free, open source, commoditized. In fact, we're talking to a large customer. He says, you're the only company, Avi. Trisita is the only company that I have seen and who has been able to maximize the algorithmic power in Hadoop. Can you give me the underlying algorithm to your application? We said, sure. We have an open source product called Gneth. We're open sourcing them. Go get them. They were into pay us for it. We said, no, algorithms are free. Why should you pay me for it, right? The last part is decision tools. Decision tools have two components, technology and human. That's where we play. We are building the next generation decision tools to enable intelligence by combining all those elements. So that's where it comes in, John, is the open source community in the next 50 years is going to dramatically commoditize and democratize access to intelligence tools for the world, not just the enterprise. We were talking yesterday around everything being free. How do you make money? So there's the customer support and the red hat and Horton was just doing that. And one of our guests from InfoObjects, Rishi said, the get effect, the network of trust. So you have a trust equation now developing. What's your vision on trust? Because what you're talking about here is ultimately you're offering algorithms for free, which I would argue is good move on one end, but it might be risky, some would say. However, if you do it for free and you aren't, how do you make that a differentiation for you? Is it the trust? Is it the halo effect? Is there an element there? What's your vision on trust? It's quite simple. It is the combination of those elements that are fundamentally free, coming together to offer a business solution, right? We had a customer sit down with us and said, I said, how would you characterize Trasada? In your mind, our average deal size is seven figures. Trasada's average deal size is seven figures, right? If you do the math on how we hit the numbers, we don't have hundreds of customers. We're not setting BI software for $10,000 a pop, right? We're doing seven-figure deals. And that's based on this very simple premise, which is if I can take... There's 10 customers.  No wonder I love you even more now. But if you take, John, if you take the elements around data, a business problem, combine what even we will push on democratizing. The algo isn't the secret sauce. It's the combination of the algo, the data with the domain knowledge, solving up specific problems.  It cost me $2 billion a year. And I said, John, I can reduce it by 10%. And you only pay me.  Do the math. You say, where do I sign? You won't ask me technology I use, because you don't care if it's a do or not. You won't ask me if the algo underlying it is open source. You will say, if you can prove to me that you can take my burden. And typically, so it has a metric field. Typically in a large enterprise to manage financial fraud, it takes a combination of an act of God, thousands of people, and some software with 98% false positives. If I can just walk in and say, I'll drop the false positives to 90%, you and I will sell that software, sitting in our drawing rooms in our pajamas, with no sales, with zero sales, because who will not come and say, that is value for me? You have to figure out what are the pain points? Where is the value delivered? And as long as we can do that, you can extract a piece of it. That gets back to what you were saying earlier. You've got to speak to the business. You've got to speak about the business problem. They don't care what the technology is. Business people will open up the checkbook if you can solve a business problem and show them savings or money that they're going to make.  Life is short. You got to remember, so are enterprises. You know, enterprises are no different than our own human interactions. They all have limited lifespans. They realize it, right? Competition drives them. They don't have enough resources. They don't have enough intelligence. They don't have enough software. They don't have enough time to build everything themselves. That is the nugget. If anybody wants to enter the open source ecosystem of big data, right? And drive value from it. Drive value from it. And derive value from it for themself. Don't focus on databases, BI tools, you know, storage. You've got to be able to add value at a differentiated tier in that stack. Okay, we got a break here but I want a great conversation. Always great to have you on. It's intoxicating and informative for all the folks involved, including us. Great to share and, you know, kind of riff back like kind of musicians. It's fun. It's really great. Thank you. The final question I want to ask you is, I want you to summarize for the folks watching about this moment in history here in Silicon Valley, big data SVR event that we're having here with the Stratoconference. At this point in time, what is the most important thing that's happening that's the big story in this moment right now? I think the VR in the middle of the second industrial revolution it started five years ago. I would hate to say the time was when you and I spoke at that summit. And the most important driving factor is a massive change in consumer behavior that is forcing every single enterprise to rethink their business model. It is across every single industry. Every industry vertical is struggling to figure out how do they go from no growth where GDP is growing at 2%, how do you go from no growth to fundamentally redefining how products and services are understood, designed and built to meet customer needs and wants, not to exploit customer needs and want to meet customer needs and wants. So we call it becoming customer advocates. Enterprises who understand that can leverage data to become a customer advocate are the winners of the future. It is a Darwinian moment in time. That when we look back, when you and I look back 40 years from now and you will be probably on ESPN and doing 24-hour something fun. Curling. Yeah, curling. Check curling. I think we'll look back and we'll say, wow, I never thought a bank would look like this. I never thought a retailer is delivering products and services when they ask me before they design it. I think that is the most exciting part for me. We'll go back to the footage. Look at what Abbie met.  Now he's got his mansion and the next Microsoft. Well, Abbie, great to have you on theCUBE, as always. Thank you so much. Thanks for hanging on. This is theCUBE. We'll write back here. We're live in Silicon Valley here in Santa Clara, the Hilton Live, broadcasting Big Data SV and the Stratoconference. We'll write back.


Aloha and welcome to this week's episode of Think Tech. I'm Dave Molinaro. I'm a project manager with the Hawaii Center for Advanced Transportation Technologies filling in for Stan, the energy man who is on the mainland right now in an epic quest for new renewable technologies. An interesting guest for the show. He should be back next week. We've got an interesting show today. We're going to be talking about microgrids and specifically microgrids in Hawaii, but I would like to introduce my guest for today's show. John Badoff. He is with the architecture and engineering firm Burns and McDonald. John and his firm have got loads of experience and are probably some of the best engineers in the country dealing with microgrids. So again, we're going to talk about microgrids. And before we get into the conversation, I just want to do a, got to have a textbook definition of what microgrids are and according to the Department of Energy, the microgrid grid is a group of interconnected loads and distributed energy resources with clearly defined electrical boundaries that act as single controllable entities with respect to the grid. A microgrid can be connected and disconnected from the grid to enable it to operate from both grid and island and mode. So that's a pretty heavy definition according to the Department of Energy. I'm going to turn to John and your expertise, John. Tell me what does a microgrid mean to you? You know, I think the Department of Energy actually does a pretty good job of defining what a microgrid really is. The keys to that long definition really are it's a system of interconnected loads and energy resources that can connect and disconnect to the grid. You know, there's a lot of different definitions. Microgrids really become a buzzword in the industry that you know about and everybody comes out and says, well, we can do microgrids and we can set this up. But at the heart of what you're trying to do with a microgrid is use different distributed energy resources and loads and control both the resources and the loads to stabilize your grid and to be able to connect to a utility grid and actually help stabilize that utility and at the same time, if needed, disconnect from that utility and island yourself and secure your own loads and resources within your microgrid. Okay, great. That's a great start into microgrids.  You talked about it being a controllable entity to provide power and energy distribution and security. Tell me a little bit more. How are they used? What are they used for specifically? Mainly if you're asking me and you're looking for my definition, I'm going to tell you that first and foremost, microgrids are about energy security. They're about providing a reliable energy source to the user. There's a lot of different pieces and parts that you can do with a microgrid, but at the end of the day, your goal is energy security because we can add resources to the grid. We can put renewables within your system and you can have financial paybacks associated with that. You can put in generation associated with your system, have financial payback with that. The question of why are you islanding and why are you designing a microgrid is because your goal is to give yourself security above and beyond that that is provided by a traditional backup generation or the utility on top of that. When you're defining a microgrid, you're looking to either make the problem of, and I use the term problem, but the disturbance of renewables that can happen on the macrogrid, if you take it down to a smaller level, you can make it simpler. We try to do that and that's part of security. If I can take the problem down and manage it at a lower level, I'm securing both the loads within the microgrid and the external loads that are in the utility in the other world. I can secure both sides. It's a security platform for both entities. So both the producer and really the consumer have a stable, secure flow of electrons for lack of a better term. Why do you think microgrids, and we hear about microgrids a lot, not only in Hawaii but worldwide, why do you think they're becoming more and more relevant, more important? We're changing the expectations of our grid as the flow of electrons become more important, not just from a power standpoint but from a computing standpoint. We ask our computers, we ask our systems to be far more reliant on data and that data transmission and that's becoming more and more a part of our society. And so those centers of critical distribution of that data and or defense of that data become very, the power to that system becomes very critical in its mode and in its use. And so the question becomes how do you protect those critical assets and that data infrastructure for the world, number one, and I would say number two portion is we're focusing on a change in our grid. There's more renewables being introduced into our grid because we're trying to back ourselves off of diesel and coal and some of the more traditional generation assets. And in order to do that, you have to take intermittent sources that are things like solar, wind, and those types of resources that aren't as predictable and can't be brought up and brought down when we need them and we have to figure out how to move those resources to the place that they're best utilized. And so again, it's about breaking the system down into parts and pieces that are more manageable and make the problem more simplified on the micro grid level. You talked, you touched on something predictability in a grid and with the power source. That is a critical element. Renewable energy is a wonderful entity, but it is not predictable. How does that affect the design of a grid? It makes the design of a grid a challenge. Utilities today have a real, it is a real problem in dealing with the intermittency of the new technologies that are coming in. And utilities have actually started in some manner, started calling them disruptive technologies because they disrupt the way the grid works, but they disrupt the mode of thinking of utilities over the last few years. It's made it fun, quite honestly, from my standpoint, being an electrical engineer and trying to solve that problem. But what essentially happens is every day the utility is trying to match the demand to the load. That's how they hold the voltage and frequency that make our grid stable and that keep your lights running and keep your hair dryer going when it needs to turn on. So there's a predictability curve that they have that utilities have over time that say this is how our grid is going to perform. Well, as you start adding renewables to it and when you have something, and I'll use a solar array as an example, if a cloud passes over an array, just a cloud, you can see a drop in the output of production of that array of 30, 40, 50%. And as that array gets a deeper and deeper penetration into the grid, that becomes a bigger impact. Well, you have to have the generation on the backside to match that. And so, and it happens very quickly, happens on a very quick ramp down or ramp up. And you have to have generation that matches that, that can compensate for that ramp up and that ramp down. And if you can't, you potentially lose the grid. It's much easier to do with an asset that you can predict and move. So a traditional generator, we can ramp up and ramp down as we need it. Well, now I have, I've always had slightly unpredictable loads. Now we've added slightly unpredictable generation and as those, and that being in photovoltaic renewables, all of those things, there's an aura of predictability, but it's not as predictable as before. And now you have two unpredictable things you're trying to match up and hold stable. So Hawaii does, it's kind of a mixed blessing that we do have a high ingress of photovoltaic because of the nature of the weather here, but it is also a engineering design issue if you want to say problem. It does present challenge because it is relatively unpredictable and you've got to balance the load with power generation source. So, well, let's segue into, let's talk a bit, a bit about how microgrids could be utilized in Hawaii. And I know we've got, you've been involved with several projects here on the islands, as well as the mainland as well too, but talk a little bit about what you see with microgrids here in Hawaii. Yeah. This is kind of a passionate opinion of mine. I know you've heard of it a bunch, but microgrids again are about energy security and doing that. And there are a lot of very important, very important defensible positions in Hawaii where we are in the middle of the Pacific. It makes us vulnerable, but also makes us a strategic area. And so providing, there are strategic areas in which microgrids provide and they provide another layer of defense against the vulnerability to our grid. On the mainland, you have multiple interconnected utilities. Between states. Right. Between states and all around. So essentially the mainland is one big giant interconnected grid. In Hawaii, if the utility goes down, the island goes down. There's nobody else that we can call. So if major generation station drops, you have a risk of loss of utility. If you have multiple failures, you can lose the island. And so on the mainland, they pick up the phone and just call somebody else and tell them, help us out, pump us more power. Here you can't do that. And so a microgrid really gives us a second level of protection on our critical assets, our harbors, our fuel depots, our airports, our Department of Defense acquisitions and things. That is a big part of it. Similarly, microgrids in Hawaii really can help and they'll help with penetrations of renewables. It's like I kind of hinted on it before. You can take problems that are on the macro level when you're designing an entire grid. If you try to deal with the entire grid issues and the things that the total solar penetration has on a grid or in the case of Hawaii, let's just talk solar. The penetration that solar has on the grid can destabilize you a little bit. And so what we look for, what we really look for is to break those down into smaller segments and make the grid more controllable on a smaller scale. It's literally biting the elephant one bite at a time to resolve the problem. That's a perfect example. Let's leave it there right now. Try to take a break, John, but I know when we get back from break, we'll talk a little bit more on a fascinating subject. So we'll be back in a few minutes. Aloha. My name is Carl Campania. Hello. This is Martin Despingh. I want to get you excited about my new show, which is called Humane Architecture for Hawaii and Beyond. And it's going to be on Think Tech Hawaii from downtown Honolulu on Tuesday afternoon at 5 p.m. And we're going to talk about to make architecture more inclusive on the islands, which is one of the definitions of humane, which is being tolerant of many people, of nature, of many other influences. So we're going to have some great guests, like today's guest, for example, my collaborator David Rockwood, who is the author of the awesome manifestation of humane architecture in the background. So see you on Tuesdays, 5 p.m. and look forward to it. All right. You're welcome back to Think Tech. Let's continue our discussion about microgrids. And we are honored today to have one of the premier engineers and experts on microgrids with us today on the show, John Bada from the A&E firm of Burns and McDonald. First half of the show, we talked a little bit about microgrids writ large. Let's get into something that a topic that is near and dear to both of our hearts. That is the renewable energy microgrid that we are building in a joint effort at Joint Base Pearl Harbor Hickam in conjunction with the Air Force Research Lab. The project is called Pearl, the Pacific Energy Assurance and Resiliency Laboratory. Real quick, it is a Air Force funded program to basically provide renewable energy in a microgrid format to provide energy assurance and energy resiliency to the Hawaii Air Guard. And John and I are both involved in the project. And let's get into a little bit more about what Pearl is. And I'll turn that over to you, John. Talk to me a little bit about what Pearl means to you and let's tell our audience about what it's really about. Yeah. You know, it goes back to kind of what we were talking about earlier. Pearl is about energy security for a major mission that happens here in defense of the islands and of the United States. It's on a critical asset for the state of Hawaii and so focused on that. But the secondary piece of Pearl that makes it really interesting is the research and laboratory part of Pearl. It's where it makes my job fun and I know your job fun and challenging, right? But we really get a focus on finding technologies that are pushing the boundary a little bit, but that we can bring in and test at a scale that really hasn't been tested before and really push and move things to where we haven't taken traditional microgrids. My company has been fortunate to be involved with and I've been fortunate to be involved with a lot of different microgrids using both traditional generation and more newer renewable type generation and energy storage assets and this one pushes the boundary farther than we've ever gone before and so we're really excited to take on that challenge and do that and at the same time provide the security that we know we can but also provide a platform for technologies to show off what they can do. That's a great point and it should go without saying that this is a unique one-of-a-kind grid in the DOD right now and it does, it will compose, be composed of about six different grids right now centering on a portion of the Hawaii Air Guard campus that it's, again, you talked about the criticality of the mission there and this being a research lab type setup or development is also really important as well too so as an engineer, as an A&E would you talk to the audience a bit more about what your role is in the development of Pearl in particular? Right, so the first day honestly was studying the reality of can it be done. Are the electrical circuits set up right? Is there enough land to do what we need to do and is the atmosphere there and essentially what we do from there is we start putting a planning process together to say what's reality, what can we really do, what types of assets can we use, how's the electrical system configured and what upgrades and changes are we going to have to make to the infrastructure in order to really make the problem simpler, to break the problem down into a simple, into a more simple problem to solve and then to focus on, and to help people focus the mission really is one of the other things that I would say. We look at it and we say okay, here's what the overall goals are, here's what the realities are and kind of bring the two together and then help everybody get from A to B. My job is to make the state of Hawaii and the Air Guard successful, provide them what they're looking for in the mission and that's what we try to do is help everybody get from A to B and get to a final microgrid. Okay, again you alluded to the fact that you have been involved with both rather conventional grids and parole being a completely renewable, so what are the nuances between those types of grids? There's a lot of nuances between those two types of grids. That's a throw it off question. No, that's alright, that's what we're here for, right? Generators, for the most part we got generators figured out, right? We've got the controls figured out, we've got the pieces figured out, we can put them in and we can make them work. We understand the fuels, we understand how the fuels react, we understand how the generator is going to react. We're going to seek to use technologies that we think we know how they're going to act and we've tested them, they've been tested at small scales, they've been shown at small scales and that's one of the biggest things about parole is we're going to take the idea of using hydrogen and batteries and wind and all of that and we're going to take it and actually distribute it from kind of the building level where microgrids have kind of been done at that, at the building level and we're going to take it and we're going to put it out on the distribution level, we're going to pass it throughout the entire, throughout the entire fighter campus and push power where we need it, give the ability to select buildings that are the critical buildings and really make decisions with your power and that's a new thought process in today's design and in today's world is okay, I've got intermittent power generation that's not predictable, it goes back to what we were talking about, it's not predictable but I'm going to make it predictable and I'm going to make it usable. That, at that inverter based type technology where we see solid state power coming out that is a new concept to be taken care of at the megawatt level that we're at. So just to touch base with you a little bit on some of the technology, there's, you mentioned hydrogen wind, photovoltaic, there's a waste to energy system there that we think is pretty remarkable, that can literally burn different types of waste streams from construction to use tires to potentially use of hazmat such as boiler rigs and so on and so forth. That again is a type of R&D aspect that we're looking at as well too. So also on a touch base, you talked about diesel generator, well we've used generators, why is that unique and you know what's different, do you think we'll be different with this grid with respect to diesel generators? I know we talked a little bit about fuels though. Right, so with regards to diesel generators, we are going to put some diesels in and that's in the interest of reliability. The goal is when we're done to be able to turn them off and we're putting batteries in and having large scale batteries react with generators and really try to use the batteries and the renewables that are there in order to offset those generators and be able to turn them off and essentially use them as standby, true emergency, a third level emergency generation. You know when people hear batteries they think of Tesla, they think of 9 volt battery packs, what's the difference without getting too technical on some of the batteries that we're getting into for the use of on Perl? Yeah, asking me not to get technical stuff, right? You've had too many conversations with me, right? So the batteries that we're going to use are going to be large scale batteries. We're talking in the 3 meg and an uprange with large megawatt hours. When you're doing that there's limitations to how fast you can charge and discharge those batteries and there's many different technologies in the world. As you know, we're going to do the process of vetting some of them and everybody's got a new solution out there. Vetting through those and trying to figure out, okay, whose solution best matches my load profiles? Whose solution best matches what I expect to have out of this grid is a is a major challenge. And that's what we're looking for out of these batteries and it's going to be fun to work with these companies and see them perform because we're going to ask it to do a lot of things that you don't traditionally always ask batteries to do and not just be their standby but actually act almost like a generator. And we really are. We collect collaboratively with the Air Force Research Lab and some of the companies are really pushing the envelope on the technology that we are going to adapt on parole to. So we've talked a lot about security. I want you to just delve into when we talk security, we're not only talking stability in the grid itself, there's a cyber security aspect of it. And that I think I don't know, explain that a little bit to our audience about what we really talk about with respect to cyber security. Yeah, you couldn't have said it better. Cyber is one of our main concerns as far as reliability in the grid today. Like I said earlier, our grid is more and more reliant on data and information and our data and information is more and more reliant on the grid. And so we have to, in order to provide the resiliency that you're looking for out of a microgrid and the energy security you're looking for out of a microgrid, you have to secure the data that's being transmitted within that microgrid. If you can't keep the data secure, then you can't keep the microgrid secure. If I can send in, if I can send in bad data to the microgrid and shut the generator down without ever having to be, I can be on the other side of the world and shut the grid down. And if I can do that, if I can shut your microgrid down with ones and zeros without even having to do anything else to the system, then all the work you've done to secure everything else is null and void. And so we've made a, we've made a massive effort in securing microgrids at a cyber level and making sure that they, you know, nothing's, nothing's foolproof when it comes to cyber and defending something against cyber, but making it really tough and making it really hard to get into and really disrupt what we're trying to do at a microgrid level. So you've got the standards that are set for utility from a cyber standpoint. And we've been using utility and DOD standards to top that off. So not only would a cyber attack have to get through the utilities, but they'd have to get through on these bases and attack these systems too. So not that it can't be done, but our goal is to make it pretty hard in the cyber world. Well, and that's not a, that's not a what if it happens, that has happened already. I believe a year or two ago in the Ukraine, one of utility was hacked into and I think there were a couple hundred thousand customers in that utility that lost power for several days. That was an eye opener, I think, for a lot of the utility companies. And that was definitely interpreted to a cyber attack as well. So, you know, John, in the last few minutes we've got, let's, let's talk about what Pearl means to, you know, the Air Force has already defined the requirements for Pearl to provide energy assurance, energy resilience, reliability. How do you think Pearl as a laboratory will be useful to the state of Hawaii in its endeavor to reach the 2045 renewable energy mandate? Right. As you know, we're working, we're working with a lot of people on Island, along with the Air Force Research Laboratory, we're working with, we're actually working with HECO, we're talking with them, we're sharing information between the Air Guard and the state of Hawaii, HCAT, and HECO as well. And that fact, thank you. And working with all of those parties to deal with the real challenges of going 100% renewable in 2045, there's quite a bit of data gathered off of these micro grids. And in my talks with a lot of the utilities in the area and in the world, quite honestly, they're all interested in the decisions that we're making, because what's happening is a lot of the decisions we make on the micro grid level are the same ones that they're making. How do you decide on a ratio between generators and batteries? What's the right percentage of battery storage versus typical generation? And how much storage do you need to back up with a battery? What can you do with hydrogen? What are the percentages that are right? What is this technology good at that this technology is not good at? And how can I marry those two together? The data that comes off of this scale of battery, or of this scale of micro grid correlates. It's not direct one for one, but it correlates and it goes up. And a lot of the decision points that we make on the micro level are translating to the macro level. And so we stay in touch with a lot of our utility clients to share that information. And that is definitely going to play a role in the 2045 goal. The states get a lot of valuable information and quite honestly learning how to where the real risks are, and they're getting to do it on their island. We don't have to learn from we in Oahu don't have to learn from people on the mainland as to what what happened in their micro grid. We're going to stand up and say, here's what we're seeing. And here's how our solar is reacting here. And what's happening on our grid and what's best and what's best for the state of Hawaii. Great. So there is definitely a connection between Pearl and micro grids and utilization for the state of Hawaii. That's all the time we've got today. I'd like to say thank you, John, for your expertise and looking forward to collaborating with you more and your company on Pearl. And that's all we've got for again for the time for the show today. Stan should be back shortly. And as said is worldwide and never actually on the mainland for finding some renewable technology. But until then Aloha and thanks again for joining us.


Hey, this is Brett and this 2019 Ram 3500 Crew Cab Short Box Single Rear Wheel Laramie Level 1 is stock number 13104Z. I am here at Summit Automotive in Fond du Lac, Wisconsin, you're new and used heavy duty truck and Ram headquarters. This 2019 Ram 3500 has the 6.7 liter Cummins diesel. It's paired up with the 6 speed automatic 68 RFE transmission. This truck has been fully safe deed and inspected by our service shop per the state of Wisconsin inspection process. It has a fresh oil and filter change, all the fluids have been checked and topped off. This truck is 100% ready to go. I'm going to go all the way around in this video, inside, underneath, start it up and take a look under the hood. This truck has a max tow capacity of 20,170 pounds and it has a max payload of 3,969 pounds. I've found all those stats on Ram's tow guide. It's a pretty cool website, I'll link it in the description below. As you go down this side, you can see just how clean that body is, how reflective that paint is and if you like the video and how I do things here at Summit Auto, you can subscribe to my YouTube channel and then click the bell notifications, you'll get updates on the videos I do each and every day. This one comes with the 20 inch polished aluminum rims. I believe these were a $1,595 option brand new and it has Firestone Destination XT tires. These are LT28560R20s and these tires have, I would say, probably 70 to 80% of the tread left. The frame and underbody is in nice condition and everything is stock under there. Front fender is in nice shape, I didn't see any major dents or dings. It does come with the LED headlamps, LED running lights and the LED fog lamps. We'll turn all those on at the end of the video, so definitely stay tuned for that. This will be really quick. We're just going to do an overview of the truck, go inside, look underneath and make sure everything is right. And the hood is in very nice condition as well. Passenger side front fender looks really good. And the passenger side front rim, no major scuffs or scrapes on there. Max Steel, one of my favorite colors on the Rams, got a lot of metal flake in it. Blue, gray. At night it looks black, it's really a cool color. And as you go down this side of this 2019 Ram 3500, take note of how clean the body is, how reflective and mirror-like that paint is. I take these HD videos so if you are far away or even if you're close by and you just cannot make the trip down but you're still interested in purchasing the vehicle, you can see the truck, hear the truck, and have confidence in the vehicle that you're looking at before you even get here. So when you do get here, there's absolutely no surprises and you can make a smart and informed buying decision from wherever you're at. And if this video helps you make that buying decision, let your salesman know that you saw the video, that it was helpful, and that Brett sent you. Back rim is in excellent shape as well. And once again, if you want to subscribe to that YouTube channel in the upper right hand part of the screen, there's a link to do that.  I can still see the little wear knobs. I would say probably 90% of the tread or more on those back tires. Frame and underbody is in nice shape. Has all the remaining factory exhaust, another of them being a little dusty under here. It's in really nice shape. Lower rockers look pretty good, you can see corrosion on them, and it does have a nice set of Erie's chrome step bars. Pass in the side box, I didn't see any major dents on there. And then around to the back, the rear bumper is in nice shape. Does have the backup parking sensors, full towing package, which includes the receiver hitch, four pin, and seven pin wiring. Tailgate is in very nice condition. Has the shock down assist tailgate, so it's never going to slam on you. Spray and bed liner, fifth wheel, and gooseneck prep, and it does come with a gooseneck hitch. And you do get seven pin wiring back here as well as some tie downs. This one also has the cargo cam, and I'll show you that when we get inside the truck. Tailgate shuts nice and solidly, and as you go down the driver's side, just as clean as that passenger's side. No major dents or dings on the box here, looks really good. And for full disclosure, this back rim is in very nice shape as well, no scuffs or scrapes on there. You can see just how new that tire is as well. On the rest of this side of the truck, doors and cab all look really good.  Get the Laramie stitched into the back rest, you get the gray piping on there, and a lumbar as well. You get the Berber floor mats, auto headlamps, power pedals, power windows, power locks, power mirrors. These mirrors do power fold in, I always like showing both sides, so that you know that both sides are working properly. Memory driver seat, and then you can control that power or that blind spot mirror there or the tow mirror, the little one from the buttons right there, which is really nice because if you have to adjust the passenger side one, you'd have to get out and do it manually, normally, but now they make those power back seats. No rips or tears back here, does have the power side mirror window with the built in mirrored frost. It's kind of a leather and suede mixture on there. These seats fold up, you do get a load floor, which is really nice if you've got muddy boots or tools, put them on there, you're not going to get your seats dirty. Or if you've got pats, you can put both sides down, they've got a nice flat place to lay. Looks like it comes with the winter front, some tie downs for the fifth wheel prep package, and then in-floor storage, one on each side with removable bins for easy cleaning to put ice and drinks in there.  Back here you get two USBs, two USB C's, and a 115 volt 400 watt plug-in. Add safety locks on the back doors and the bottoms of the doors, all look really good. We'll hop inside, check out the miles, the radio and everything that this truck has to offer on the interior, and then we'll check out all those LED lights. This one has 56,530 miles, you do get that 7-inch LCD display, digital speedometer, and that instrument cluster is very nice and clean. It comes with the heated leather-wrapped steering wheel, cruise controls and gear selector on the right, Bluetooth and information center controls on the left, and audio controls on the back. This one has the UConnect 4C 12-inch radio, you get AM, FM, and Sirius XM radio capabilities on here. Here's your heated and cooled seat buttons, you can see it has the factory navigation system, and if I turn that on then we can check out the surround camera and everything. You can see the surround camera there, that's your backup cam, you can do a wide view of the back, wide view of the front, narrow view of the front, you can check out the cargo cam, you can check out your tow cam, so if you're backing up a trailer I could imagine this would be the most handy thing ever, especially if you have like a big camper or something, you can choose the different sides, and so that's that, you know the backup cam you can zoom in over your receiver hitch, get hooked up to your boat camper trailer the first time, every time. So really cool stuff that you can do there, if I go to the apps, you'll have projection manager where you can project your cell phone to the screen, I actually did a demo on Android Auto with an 8.4 radio which is the same system as this, you can check that out, that's in the right-hand part of the screen.  Passenger side, floor mat, and seat are in excellent condition, no rips or tears on there, smells very clean inside this truck, you do get the speakers in the ceiling part of that Alpine premium sound system, assistant SOS buttons, map lights and power sign, window button, home link buttons for your garage door security systems and lighting systems, and we will start it up and take a look under the hood, starts right up, no check engine lights or anything like that, and let's go take a look under the hood to check out all those lights, so there are those LED side lights, they are super bright, I would personally like to thank you for checking out the video today, and hopefully from this HD video you'll been able to verify the quality condition options and cleanliness of this truck all the way around, you can see all those lights are working nicely, and under the hood we have the 6.7 liter Cummins diesel engine, engine bay is very clean, it runs very smooth, once again this truck has been fully safety and inspected by our service shop, has a fresh oil and filter change, all the fluids have been checked and topped off, this truck is 100% ready to go, there is your emissions sticker, and I did want to show the door sticker because everybody always asked me to show that, the entire, alright, Max steel one of my favorite colors, really sweet truck, really nice condition and thanks for checking out the video, I'd highly recommend this truck from a quality and condition standpoint, and to see more pictures of this truck or one of the 400 and 50, actually 500 new and used cars, trucks, SUVs, minivans, ringlers, halftones, three-quarter tons, one tons, you name it we got to go to that website right there is summitauto.com, and if you want to check out more HD videos you can go to youtube.com slash summitauto, remember to like, subscribe, and share on this video and all the videos that you see there, click the bell notifications you'll get updates on the videos I do each and every day here at Summit Auto, in fact in a second you will see a link to subscribe to my YouTube channel on the upper left, I'll link to more Ram 3500 truck videos like this one the upper right link to this vehicle on our website in the lower left and a link to one of our latest YouTube videos in the lower right, click those check us out, there's a full description in the description of this video so check that out too, and thanks again for checking out the video, we're super excited to help you with this ultra clean 2019 Ram 3500 true cab short box Laramie level one and max steel without thanks again for checking out the video remember like, subscribe, and share on my YouTube channel, I really appreciate it thanks again


To get to the idea that this is a bit of a fad, I think it's a real one. Some of the programs we hear sound a little bit, to me, like they're just measuring learning outcomes, which their accreditor probably was requiring them to do anyhow. So that's kind of the CBE light side of things. And then on the more aggressive side, you have direct assessment where, do folks are familiar with direct assessment? What that is, I think most of you are well. This basically means if you know it and you can do it, you can test out of it and move on. There's completely untethered from the credit hour, completely untethered from time, and frankly freaks out a lot of people in traditional higher education and regulators as well. So, but in that range, it seems to me, no grades, real mastery, that seems to me to be kind of core of the definition of something different and emerging. And I wonder in that, if you agree with that, and also, when we talk about competency-based education, it's often these adult students who can really cruise through things they've already learned, already know how to do. What happens if you can't get past a competency? I think that that side of it, I mean, this is not a gentleman C world that you're talking about here.  So what happens in that scenario and what do you think about my kind of definition of what really is competency-based education?


Why are people in LA not eating oysters right now? This weird story starts with hundreds of people in SoCal recently getting violently ill from raw oysters. And that comes after a really bad PR year for oysters. With many food safety scientists warning it might never be safe to consume them raw. So why is this happening now? Dangerous bacteria that infect shellfish thrives in water over 68 degrees. And those LA oysters came from the Gulf of California, which like all bodies of water is getting hotter. And experts think those temp changes mean that months where it was previously safe to eat oysters might now be dangerous in certain areas. If you're not willing to give them up all together, only eat them in months that have an R, order them fully cooked, and make sure you know exactly where they came from.