(upbeat music) >> Every company needs great salespeople, it's one of the most lucrative professions out there. And there's plenty of wisdom and knowledge that's been gathered over the years about selling. We've heard it all, famous quotes from the greatest salespeople of our time, like Zig Ziglar and Jeffrey Gitomer, and Dale Carnegie and Jack Welch, and many others. Things like, "Each of us has only 24 hours in a day, "it's all about how we use our time." And, "You don't have to be great to start, "but you have to start to be great." And then I love this one, "People hate to be sold, but they love to buy." "There are no traffic jams on the extra mile, "make change before you have to." And the all time classic, "Put that coffee down. "Coffee is for closers." Thousands of pieces of sales advice are readily available in books, videos, on blogs and in podcasts, and many of these are free of charge. So why would entrepreneurs start a company to train salespeople? And how is it that sharp investors are pouring millions of dollars into this space? Hello everyone, and welcome to this Cube Video Exclusive, my name is Dave Vellante, and today we welcome Paul Fifield who's the co-founder and CEO of Sales Impact Academy who's going to answer these questions and share some exciting news on the startups. Paul, welcome to "The Cube" good to see you again. >> Yeah, good to see you again, Dave, great to be here. >> Hey, so before we get into the hard news, tell us a little bit about the Sales Impact Academy, why'd you start the company, maybe some of the fundamentals of this market, your total available market, who you're targeting, you know, what's the premise behind the company? >> Yeah sure. So I mean, I started the company, it was actually pretty organic in the way it began. I had a 10 year career as a CRO and it was, you know, had a couple of great hits with two companies, but it was a real struggle to basically, you know, operate as a CRO and learn your craft at the same time. And so when I left my last company, I kind of got out there, I wanted to kind of give back a little bit and I started doing some voluntary teaching in and around London, and I actually, one of the companies I started was in New York so I got schooled very much on a sort of US approach to how you build a modern you know, go to market and sales operation. Started going out there, doing some teaching, realized that so many people just didn't have a clue about how to build a scalable and predictable revenue function, and I kind of felt sorry for them. So I literally started doing some, you know, online classes myself, got my co-founder Alex to put curriculum together as well and we literally started just doing online classes, very live, very organic, just a Google Drive and some decks, and it really just blew up from there. >> That's amazing. I mean, so you've my, you know, tongue and cheek up front, but people might wonder, why do you need a platform 'cause there's so much free information out there? Is it to organize, is it a discipline thing? Explain that. >> Well, I think the way I sort of see this is that is that the lack of structured learning and education is actually one of the greatest educational travesties, I think, of the last 50 years, okay. Now sales and go to market is a huge global profession, right? Half the world's companies are B2B, so roughly that's a proxy for half the world's GDP, right? Which is $40 trillion of GDP. Now that 40 trillion rests on kind of the success of the growth and the sales functions of all those companies. Yet in its infinite wisdom, the global education system literally just ignored sales and go to market as a profession. Some universities are kind of catching up, but it's really too little too late. So what I sort of say to people, you imagine this Dave, right. You imagine if the way that law worked as a profession let's say, is that there's no law school, there's no law training, there's no even in work professional continuous professional development in law. The way that it works is you leave university, join a company, start practicing law and just use like YouTube just to maybe like, you know, where you're struggling, just use YouTube to like work out what's going on. The legal profession would be in absolute chaos. And that's what's happened in the sales and go to market profession, okay. What this profession desperately desperately needs is structured learning, good pedagogy, good well designed course and curriculum. And here's the other thing, right? Is the sort of paradox of infinite information is that just because all the information is out there, right, doesn't mean it's actually a good learning experience. Like, where do you find it? What's good? What's not good? And also the other thing I'd point out is that there is this kind of myth that all the information is out there on the internet. But actually what we do, and we'll come into it in a second is, the people teaching on our platform are the elite people from the industry. They haven't got time to do blog posts and just explain to people how they operate. They're going from company to company working at like, you know, working at these kind of elite companies. And they're the people that teach, and that information is not readily available and freely out there on the internet. >> Yeah, real opportunity, you made some great points there. I think business schools are finally starting to teach a little bit about public speaking and presenting, but nobody's teaching us how to sell. As Earl Nightingale says, "To some degree we're all salespeople, "selling our family on living the good life" or whatever. What movie we want to see tonight. But okay, let's get to the hard news. You got fresh funding of 22 million, tell us about that, congratulations. You know, the investors, what else can you share with us? >> Sure. Well, I mean, obviously, you know, immensely proud. We started from very sort of humble beginnings, as I said, we've now scaled very rapidly, we're a subscription business, we're a SaaS business. We'll come onto some of the growth metrics shortly, but just in a couple of years, you know, the last year which ended January, we grew 500% from year one, we're now well over 125 people, and I'm very, very, very honored, flattered, humbled that MIT, obviously one of those prestigious universities in the world, has taken a direct investment by their endowment fund, HubSpot Ventures. Another Boston great has also taken a direct investment as well. They actually began as a customer and loved what we were doing so much that they then decided to make an investment. Stage 2 Capital who invested in our seed round pretty much tripled down, played a huge role in helping us assemble MIT and HubSpot ventures as investors, and they continue to be an incredible VC giving us amazing, amazing support that their LP network of go to market leaders is second to none. And then Emerge Education, who is our pre-seed investor, they're actually based in London, also joined this round as well. >> Great, well actually, let's jump ahead. Let's talk about the metrics. I mean, if Stage Two is involved, they're hardcore. What can you share with us about, you know, everybody's chasing AR and NR and the like, what can you share with us? >> They are both pretty important. Well, I think from a headcount perspective, so as I mentioned our fiscal ends at the end of January, each year. We've gone from 25 to over 125 employees in that time. We've gone from 82 to 260 customers also in that time. And customers now include HubSpot, Gong, Klaviyo, GitHub, GT, Six Cents, so some really sort of major SaaS companies in the space. Our revenue's grown significantly with 5X. So 500% increase in revenue year over year, which is pretty fast, very proud of that. Our learning community has gone from over 3000 people to almost 15,000 professionals, and that makes us comfortably, the largest go to market learning community in the world. >> How did you decide when to scale? What were the sort of signals that said to you, "Okay, we're ready, "we have product market fit, "we can now scale the go to market." What were the signals there, Paul? >> Yeah. Well, I mean, I think for a very small team to achieve that level of growth in customers, to be kind of honest with you, like it's the pull that we're getting from the market. And I think the thing that has surprised me the most, perhaps in the last 12 months, is the pull we're getting from the enterprise. We're you know, I can't really announce, we've actually got a huge pilot with one of the largest companies actually in the world which is going fantastically well, our pipeline for enterprise customers is absolutely huge. But as you can imagine, if you've got distributed teams all over the world, we're living and working in this kind of hybrid world, how on earth do you kind of upscale all those people, right, that are, like I say, that are so distributed. It's impossible. Like in work, in the office delivery of training is pretty much dead, right? And so we sort of fill this really big pain, we solved this really, really big pain of how to effectively upskill people through this kind of live curriculum and this live teaching approach that we have. So I think for me, it's the pull that we're getting from the market really meant that you know, we have to double down. There is such a massive TAM, it is absolutely ridiculous. I mean, I think there are 20 million people just in sales and go to market in tech alone, right. And I mentioned to you earlier, half the world's companies effectively, you know, are B2B and therefore represent, you know, at its largest scope, our TAM. >> Excellent, thank you for that. Tell us more about the product and the platform. How's it work if I'm a customer, what type of investment do I have to make both financially? And what's my time commitment? How do you structure that? >> So the model is basically on a seat model. So roughly speaking, every seat's about a thousand dollars per year per rep. The lift is light. So we've got a very low onboarding, it's not a highly complex technical product, right? We've got a vast curriculum of learning that covers learning for, you know, SDRs, and the AEs, and CS reps, and leadership management training. We're developing curriculum for technical pre-sales, we're developing curriculum for revenue operations. And so it's very, very simple. We basically, it's a seat model, people literally just send us the seats and the details, we get people up and running in the platform, they start then enrolling and we have a customer success team that then plots out learning journeys and learning pathways for all of our customers. And actually what's starting to happen now, which is very, very exciting is that, you know, we're actually a key part of people's career development pathway. So to go from you know, SDR1 let's say to SDR2, you have to complete these three courses with Sales Impact Academy, and let's say, get 75% in your exam and it becomes a very powerful and simple way of developing career pathway. >> Yeah, so really detailed curriculum. So I was going to say, do I as a sales professional, do I pick and choose the things that are most relevant for me? Or are people actually going through a journey in career progression, or maybe both? >> Yeah, it's a mixture of both. We tend to see now, we're sort of starting to standardize, but really we're developing enough curriculum that over, let's say a 15 year period, you could start with us as an SDR and then end as a chief revenue officer, you know, running the entire function. This is the other thing about the crazy world of go to market. Very often, people are put into roles and it's sink or swim. There's no real learning that happens, there's no real development that happens before people take these big steps. And what this platform does so beautifully is is it equips people with the right skills and knowledge before they take that next step in their profession and in their career. And it just dramatically improves their chances of succeeding. >> Who are the trainers? Who's leading the classes, how do you find these guys, how do you structure? What are the content, you know, vectors, where's all that come from? >> Yeah. So the sort of secret source of what we do, beyond just the live instruction, beyond the significant amount of peer to peer learning that goes on, is that we go and source the absolute most elite people in go to market to teach, okay. Now I mentioned to you before, you've got these people that are going from like job to job at the very like the sort of peak of their careers, working for these incredible companies, it's that knowledge that we want to get access to, right. And so Stage 2 Capital is an incredible resource. The interesting thing about Stage 2 Capital as you know Dave, you know, run by Mark Roberge, who was on when we spoke last year and also Jay Po is all the LPs of Stage 2 Capital represent 3 to 400 of the most elite go to market professionals in the world. So, you know, about seven or eight of those are now on an advisory board. And so we have access to this incredible pool of talent. And so we know by consulting these amazing people who are the best people in certain aspects of go to market. We reach out to them and very often they're at a stage in their career where they're really kind of willing to give back, of course there are commercials around it as well, and there's lots of other benefits that we provide our teachers and our faculty, and what we call our coaches. But yeah, we source the very, very best people in the world to teach. >> Now, how does it work as a user of your service? Is it all on demand? Do you do live content or a combination? >> Yeah look, one of the big differentiators is this is a live delivery of learning, okay. Most learning online is typically done on demand, self-directed, and there's a ton of research. There's a great blog post on Andrew's recent site. A short time ago, which is talking about how the completion rates of on demand learning are somewhere between 3 and 6%. That is like, that's awful. >> Terrible. >> I was like why bother? However, we're seeing through that live instruction. So we teach two, one hour classes a week, that's it. We're upskilling very busy people, they're stressed, they've got targets. We have to be very, very cognizant of that. So we teach two, one hour classes a week. Typically, you know, Monday and a Wednesday, or a Tuesday and a Thursday. And that pace of learning is about right, it's kind of how humans learn as well. You know, short bursts of information, and then put that learning and those skills that you've acquired in class literally to work minutes after the class finishes. And so through that, and it sits in your calendar like a meeting, it doesn't feel overwhelming, you're learning together as a team as well. And all that combined, we see completion rates often in excess of 80% for our courses. >> Okay, so they block that time out- >> In the calendar, yeah. >> And they make an investment. Go ahead, please. >> Yeah yeah, exactly, sorry Dave. Yeah, yeah, exactly. So like, you know, we have course lengths. So one of our shorter courses are like four hours long over two weeks. And again, it's just literally in the calendar. We also teach what we call The Magic Learning Hour. And the magic learning hour is this one specific hour in the day that enables teams all over the western hemisphere to join the same class. And that magic learning hour is eight o'clock Pacific 11 o'clock Eastern, >> 4: 00 PM over in the UK, and 5:00 PM in the rest of Europe. And that one time in the day means that these enterprises have got teams all over the western hemisphere joining that class, learning together as a team, plus it's in the calendar and it's that approach is why we're seeing such high engagement and completion. >> That's very cool, the time zone thing. Now who's the target buyer? Are you selling only to sales teams? Can I as an individual purchase your service? >> Yeah, that's a good question. Currently it's a very much like a B2B motion. As I mentioned earlier on, we're getting an enormous pull from the enterprise, which is very exciting. You know, we have an enterprise segment, we have sort of more of a startup earlier stage segment, and then we have a mid-market segment that we call our sort of strategic, and that's typically and most of like venture backed, fast growth tech companies. So very much at the moment a B2B motion. We're launching our own technology platform in the early summer, and then later on this year we're going to be adding what's called PLG or a product led growth, so individuals can actually sign up to SIA. >> Yeah, I mean, I think you said $1,000 per year per rep, is that right? I mean, that's- >> Yeah. >> That's a small investment for an individual that wants to be part of, you know, this community and grow his or her career. So that's the growth plan? You go down market I would imagine, you talked about the western hemisphere, there's international opportunities maybe, local language. What's the growth plan? >> Yeah, I mean look, we've identified the magic learning hour for the middle east and APAC, which is eight o'clock in the morning in Istanbul, right. Is 5:00 PM in Auckland, it's quite fun trying to work out like what this optimum magic learning hour is. What's incredible is we teach in that time and that opens up the whole of the middle east and the whole of APAC, right, right down to Australia. And so once we're teaching the curriculum in those two slots, that means literally you can have teams in any country in the world, I think apart from Hawaii, you can actually access our live learning products in work time and that's incredibly powerful. So we have so many like axis of growth, we've got single users as I mentioned, but really Dave that's single users we'll be winning from the enterprise and that will represent pipeline that we could then potentially convert as well. And look, you make a very good point. You know, we've seen students are now leaving university with over $100,000 dollars in debt. We've got a massive, massive debt problem here in the US with student debt. You could absolutely sign up to our platform at let's say a hundred bucks a month, right. And probably within six months, gain enough knowledge and skill to walk into a $60,000 a year based salary job as an SDR, that's a huge entry level salary. And you could do that without even going to university. So there could be a time here where we become a really viable alternative to actually even going to university. >> I love it. The cost education going through the roof, it's out of reach for so many people. Paul, congratulations on the progress, the fresh funding. Great to have you back in "The Cube." We'd love to have you back and follow your ascendancy. I think great things ahead for you guys. >> Thank you very much, Dave. >> All right, and thank you for watching. This is Dave Vellante for "The Cube, we'll see you next time. (upbeat music)

(upbeat music) >> When Oracle acquired Sun in 2009, it paid $5.6 billion net of Sun's cash and debt. Now I argued at the time that Oracle got one of the best deals in the history of enterprise tech, and I got a lot of grief for saying that because Sun had a declining business, it was losing money, and its revenue was under serious pressure as it tried to hang on for dear life. But Safra Catz understood that Oracle could pay Sun's lower profit and lagging businesses, like its low index 86 product lines, and even if Sun's revenue was cut in half, because Oracle has such a high revenue multiple as a software company, it could almost instantly generate $25 to $30 billion in shareholder value on paper. In addition, it was a catalyst for Oracle to initiate its highly differentiated engineering systems business, and was actually the precursor to Oracle's Cloud. Oracle saw that it could capture high margin dollars that used to go to partners like HP, it's original exit data partner, and get paid for the full stack across infrastructure, middleware, database, and application software, when eventually got really serious about cloud. Now there was also a major technology angle to this story. Remember Sun's tagline, "the network is the computer"? Well, they should have just called it cloud. Through the Sun acquisition. Oracle also got a couple of key technologies, Java, the number one programming language in the world, and MySQL, a key ingredient of the LAMP stack, that's Linux, Apache, MySQL and PHP, Perl or Python, on which the internet is basically built, and is used by many cloud services like Facebook, Twitter, WordPress, Flicker, Amazon, Aurora, and many other examples, including, by the way, Maria DB, which is a fork of MySQL created by MySQL's creator, basically in protest to Oracle's acquisition; the drama is Oscar worthy. It gets even better. In 2020, Oracle began introducing a new version of MySQL called MySQL HeatWave, and since late 2020 it's been in sort of a super cycle rolling, out three new releases in less than a year and a half in an attempt to expand its Tam and compete in new markets. Now we covered the release of MySQL Autopilot, which uses machine learning to automate management functions. And we also covered the bench marketing that Oracle produced against Snowflake, AWS, Azure, and Google. And Oracle's at it again with HeatWave, adding machine learning into its database capabilities, along with previously available integrations of OLAP and OLTP. This, of course, is in line with Oracle's converged database philosophy, which, as we've reported, is different from other cloud database providers, most notably Amazon, which takes the right tool for the right job approach and chooses database specialization over a one size fits all strategy. Now we've asked Oracle to come on theCUBE and explain these moves, and I'm pleased to welcome back Nipun Agarwal, who's the senior vice president for MySQL Database and HeatWave at Oracle. And today, in this video exclusive, we'll discuss machine learning, other new capabilities around elasticity and compression, and then any benchmark data that Nipun wants to share. Nipun's been a leading advocate of the HeatWave program. He's led engineering in that team for over 10 years, and he has over 185 patents in database technologies. Welcome back to the show Nipun. Great to see you again. Thanks for coming on. >> Thank you, Dave. Very happy to be back. >> Yeah, now for those who may not have kept up with the news, maybe to kick things off you could give us an overview of what MySQL HeatWave actually is so that we're all on the same page. >> Sure, Dave, MySQL HeatWave is a fully managed MySQL database service from Oracle, and it has a builtin query accelerator called HeatWave, and that's the part which is unique. So with MySQL HeatWave, customers of MySQL get a single database which they can use for transactional processing, for analytics, and for mixed workloads because traditionally MySQL has been designed and optimized for transaction processing. So in the past, when customers had to run analytics with the MySQL based service, they would need to move the data out of MySQL into some other database for running analytics. So they would end up with two different databases and it would take some time to move the data out of MySQL into this other system. With MySQL HeatWave, we have solved this problem and customers now have a single MySQL database for all their applications, and they can get the good performance of analytics without any changes to their MySQL application. >> Now it's no secret that a lot of times, you know, queries are not, you know, most efficiently written, and critics of MySQL HeatWave will claim that this product is very memory and cluster intensive, it has a heavy footprint that adds to cost. How do you answer that, Nipun? >> Right, so for offering any database service in the cloud there are two dimensions, performance and cost, and we have been very cognizant of both of them. So it is indeed the case that HeatWave is a, in-memory query accelerator, which is why we get very good performance, but it is also the case that we have optimized HeatWave for commodity cloud services. So for instance, we use the least expensive compute. We use the least expensive storage. So what I would suggest is for the customers who kind of would like to know what is the price performance advantage of HeatWave compared to any database we have benchmark against, Redshift, Snowflake, Google BigQuery, Azure Synapse, HeatWave is significantly faster and significantly lower price on a multitude of workloads. So not only is it in-memory database and optimized for that, but we have also optimized it for commodity cloud services, which makes it much lower price than the competition. >> Well, at the end of the day, it's customers that sort of decide what the truth is. So to date, what's been the customer reaction? Are they moving from other clouds from on-prem environments? Both why, you know, what are you seeing? >> Right, so we are definitely a whole bunch of migrations of customers who are running MySQL on-premise to the cloud, to MySQL HeatWave. That's definitely happening. What is also very interesting is we are seeing that a very large percentage of customers, more than half the customers who are coming to MySQL HeatWave, are migrating from other clouds. We have a lot of migrations coming from AWS Aurora, migrations from RedShift, migrations from RDS MySQL, TerriData, SAP HANA, right. So we are seeing migrations from a whole bunch of other databases and other cloud services to MySQL HeatWave. And the main reason we are told why customers are migrating from other databases to MySQL HeatWave are lower cost, better performance, and no change to their application because many of these services, like AWS Aurora are ETL compatible with MySQL. So when customers try MySQL HeatWave, not only do they get better performance at a lower cost, but they find that they can migrate their application without any changes, and that's a big incentive for them. >> Great, thank you, Nipun. So can you give us some names? Are there some real world examples of these customers that have migrated to MySQL HeatWave that you can share? >> Oh, absolutely, I'll give you a few names. Stutor.com, this is an educational SaaS provider raised out of Brazil. They were using Google BigQuery, and when they migrated to MySQL HeatWave, they found a 300X, right, 300 times improvement in performance, and it lowered their cost by 85 (audio cut out). Another example is Neovera. They offer cybersecurity solutions and they were running their application on an on-premise version of MySQL when they migrated to MySQL HeatWave, their application improved in performance by 300 times and their cost reduced by 80%, right. So by going from on-premise to MySQL HeatWave, they reduced the cost by 80%, improved performance by 300 times. We are Glass, another customer based out of Brazil. They were running on AWS EC2, and when they migrated, within hours they found that there was a significant improvement, like, you know, over 5X improvement in database performance, and they were able to accommodate a very large virtual event, which had more than a million visitors. Another example, Genius Senority. They are a game designer in Japan, and when they moved to MySQL HeatWave, they found a 90 times percent improvement in performance. And there many, many more like a lot of migrations, again, from like, you know, Aurora, RedShift and many other databases as well. And consistently what we hear is (audio cut out) getting much better performance at a much lower cost without any change to their application. >> Great, thank you. You know, when I ask that question, a lot of times I get, "Well, I can't name the customer name," but I got to give Oracle credit, a lot of times you guys have at your fingertips. So you're not the only one, but it's somewhat rare in this industry. So, okay, so you got some good feedback from those customers that did migrate to MySQL HeatWave. What else did they tell you that they wanted? Did they, you know, kind of share a wishlist and some of the white space that you guys should be working on? What'd they tell you? >> Right, so as customers are moving more data into MySQL HeatWave, as they're consolidating more data into MySQL HeatWave, customers want to run other kinds of processing with this data. A very popular one is (audio cut out) So we have had multiple customers who told us that they wanted to run machine learning with data which is stored in MySQL HeatWave, and for that they have to extract the data out of MySQL (audio cut out). So that was the first feedback we got. Second thing is MySQL HeatWave is a highly scalable system. What that means is that as you add more nodes to a HeatWave cluster, the performance of the system improves almost linearly. But currently customers need to perform some manual steps to add most to a cluster or to reduce the cluster size. So that was other feedback we got that people wanted this thing to be automated. Third thing is that we have shown in the previous results, that HeatWave is significantly faster and significantly lower price compared to competitive services. So we got feedback from customers that can we trade off some performance to get even lower cost, and that's what we have looked at. And then finally, like we have some results on various data sizes with TPC-H. Customers wanted to see if we can offer some more data points as to how does HeatWave perform on other kinds of workloads. And that's what we've been working on for the several months. >> Okay, Nipun, we're going to get into some of that, but, so how did you go about addressing these requirements? >> Right, so the first thing is we are announcing support for in-database machine learning, meaning that customers who have their data inside MySQL HeatWave can now run training, inference, and prediction all inside the database without the data or the model ever having to leave the database. So that's how we address the first one. Second thing is we are offering support for real time elasticity, meaning that customers can scale up or scale down to any number of nodes. This requires no manual intervention on part of the user, and for the entire duration of the resize operation, the system is fully available. The third, in terms of the costs, we have double the amount of data that can be processed per node. So if you look at a HeatWave cluster, the size of the cluster determines the cost. So by doubling the amount of data that can be processed per node, we have effectively reduced the cluster size which is required for planning a given workload to have, which means it reduces the cost to the customer by half. And finally, we have also run the TPC-DS workload on HeatWave and compared it with other vendors. So now customers can have another data point in terms of the performance and the cost comparison of HeatWave with other services. >> All right, and I promise, I'm going to ask you about the benchmarks, but I want to come back and drill into these a bit. How is HeatWave ML different from competitive offerings? Take for instance, Redshift ML, for example. >> Sure, okay, so this is a good comparison. Let's start with, let's say RedShift ML, like there are some systems like, you know, Snowflake, which don't even offer any, like, processing of machine learning inside the database, and they expect customers to write a whole bunch of code, in say Python or Java, to do machine learning. RedShift ML does have integration with SQL. That's a good start. However, when customers of Redshift need to run machine learning, and they invoke Redshift ML, it makes a call to another service, SageMaker, right, where so the data needs to be exported to a different service. The model is generated, and the model is also outside RedShift. With HeatWave ML, the data resides always inside the MySQL database service. We are able to generate models. We are able to train the models, run inference, run explanations, all inside the MySQL HeatWave service. So the data, or the model, never have to leave the database, which means that both the data and the models can now be secured by the same access control mechanisms as the rest of the data. So that's the first part, that there is no need for any ETL. The second aspect is the automation. Training is a very important part of machine learning, right, and it impacts the quality of the predictions and such. So traditionally, customers would employ data scientists to influence the training process so that it's done right. And even in the case of Redshift ML, the users are expected to provide a lot of parameters to the training process. So the second thing which we have worked on with HeatWave ML is that it is fully automated. There is absolutely no user intervention required for training. Third is in terms of performance. So one of the things we are very, very sensitive to is performance because performance determines the eventual cost to the customer. So again, in some benchmarks, which we have published, and these are all available on GitHub, we are showing how HeatWave ML is 25 times faster than Redshift ML, and here's the kicker, at 1% of the cost. So four benefits, the data all remain secure inside the database service, it's fully automated, much faster, much lower cost than the competition. >> All right, thank you Nipun. Now, so there's a lot of talk these days about explainability and AI. You know, the system can very accurately tell you that it's a cat, you know, or for you Silicon Valley fans, it's a hot dog or not a hot dog, but they can't tell you how the system got there. So what is explainability, and why should people care about it? >> Right, so when we were talking to customers about what they would like from a machine learning based solution, one of the feedbacks we got is that enterprise is a little slow or averse to uptaking machine learning, because it seems to be, you know, like magic, right? And enterprises have the obligation to be able to explain, or to provide a answer to their customers as to why did the database make a certain choice. With a rule based solution it's simple, it's a rule based thing, and you know what the logic was. So the reason explanations are important is because customers want to know why did the system make a certain prediction? One of the important characteristics of HeatWave ML is that any model which is generated by HeatWave ML can be explained, and we can do both global explanations or model explanations as well as we can also do local explanations. So when the system makes a specific prediction using HeatWave ML, the user can find out why did the system make such a prediction? So for instance, if someone is being denied a loan, the user can figure out what were the attribute, what were the features which led to that decision? So this ensures, like, you know, fairness, and many of the times there is also like a need for regulatory compliance where users have a right to know. So we feel that explanations are very important for enterprise workload, and that's why every model which is generated by HeatWave ML can be explained. >> Now I got to give Snowflakes some props, you know, this whole idea of separating compute from storage, but also bringing the database to the cloud and driving elasticity. So that's been a key enabler and has solved a lot of problems, in particular the snake swallowing the basketball problem, as I often say. But what about elasticity and elasticity in real time? How is your version, and there's a lot of companies chasing this, how is your approach to an elastic cloud database service different from what others are promoting these days? >> Right, so a couple of characteristics. One is that we have now fully automated the process of elasticity, meaning that if a user wants to scale up or scale down, the only thing they need to specify is the eventual size of the cluster and the system completely takes care of it transparently. But then there are a few characteristics which are very unique. So for instance, we can scale up or scale down to any number of nodes. Whereas in the case of Snowflake, the number of nodes someone can scale up or scale down to are the powers of two. So if a user needs 70 CPUs, well, their choice is either 64 or 128. So by providing this flexibly with MySQL HeatWave, customers get a custom fit. So they can get a cluster which is optimized for their specific portal. So that's the first thing, flexibility of scaling up or down to any number of nodes. The second thing is that after the operation is completed, the system is fully balanced, meaning the data across the various nodes is fully balanced. That is not the case with many solutions. So for instance, in the case of Redshift, after the resize operation is done, the user is expected to manually balance the data, which can be very cumbersome. And the third aspect is that while the resize operation is going on, the HeatWave cluster is completely available for queries, for DMLS, for loading more data. That is, again, not the case with Redshift. Redshift, suppose the operation takes 10 to 15 minutes, during that window of time, the system is not available for writes, and for a big part of that chunk of time, the system is not even available for queries, which is very limiting. So the advantages we have are fully flexible, the system is in a balanced state, and the system is completely available for the entire duration operation. >> Yeah, I guess you got that hypergranularity, which, you know, sometimes they say, "Well, t-shirt sizes are good enough," but then I think of myself, some t-shirts fit me better than others, so. Okay, I saw on the announcement that you have this lower price point for customers. How did you actually achieve this? Could you give us some details around that please? >> Sure, so there are two things for announcing this service, which lower the cost for the customers. The first thing is that we have doubled the amount of data that can be processed by a HeatWave node. So if we have doubled the amount of data, which can be a process by a node, the cluster size which is required by customers reduces to half, and that's why the cost drops to half. The way we have managed to do this is by two things. One is support for Bloom filters, which reduces the amount of intermediate memory. And second is we compress the base data. So these are the two techniques we have used to process more data per node. The second way by which we are lowering the cost for the customers is by supporting pause and resume of HeatWave. And many times you find customers of like HeatWave and other services that they want to run some other queries or some other workloads for some duration of time, but then they don't need the cluster for a few hours. Now with the support for pause and resume, customers can pause the cluster and the HeatWave cluster instantaneously stops. And when they resume, not only do we fetch the data, in a very, like, you know, a quick pace from the object store, but we also preserve all the statistics, which are used by Autopilot. So both the data and the metadata are fetched, extremely fast from the object store. So with these two capabilities we feel that it'll drive down the cost to our customers even more. >> Got it, thank you. Okay, I promised I was going to get to the benchmarks. Let's have it. How do you compare with others but specifically cloud databases? I mean, and how do we know these benchmarks are real? My friends at EMC, they were back in the day, they were brilliant at doing benchmarks. They would produce these beautiful PowerPoints charts, but it was kind of opaque, but what do you say to that? >> Right, so there are multiple things I would say. The first thing is that this time we have published two benchmarks, one is for machine learning and other is for SQL analytics. All the benchmarks, including the scripts which we have used are available on GitHub. So we have full transparency, and we invite and encourage customers or other service providers to download the scripts, to download the benchmarks and see if they get any different results, right. So what we are seeing, we have published it for other people to try and validate. That's the first part. Now for machine learning, there hasn't been a precedence for enterprise benchmarks so we talk about aiding open data sets and we have published benchmarks for those, right? So both for classification, as well as for aggression, we have run the training times, and that's where we find that HeatWave MLS is 25 times faster than RedShift ML at one percent of the cost. So fully transparent, available. For SQL analytics, in the past we have shown comparisons with TPC-H. So we would show TPC-H across various databases, across various data sizes. This time we decided to use TPC-DS. the advantage of TPC-DS over TPC-H is that it has more number of queries, the queries are more complex, the schema is more complex, and there is a lot more data skew. So it represents a different class of workloads, and which is very interesting. So these are queries derived from the TPC-DS benchmark. So the numbers we have are published this time are for 10 terabyte TPC-DS, and we are comparing with all the four majors services, Redshift, Snowflake, Google BigQuery, Azure Synapse. And in all the cases, HeatWave is significantly faster and significantly lower priced. Now one of the things I want to point out is that when we are doing the cost comparison with other vendors, we are being overly fair. For instance, the cost of HeatWave includes the cost of both the MySQL node as well as the HeatWave node, and with this setup, customers can run transaction processing analytics as well as machine learning. So the price captures all of it. Whereas with the other vendors, the comparison is only for the analytic queries, right? So if customers wanted to run RDP, you would need to add the cost of that database. Or if customers wanted to run machine learning, you would need to add the cost of that service. Furthermore, with the case of HeatWave, we are quoting pay as you go price, whereas for other vendors like, you know, RedShift, and like, you know, where applicable, we are quoting one year, fully paid upfront cost rate. So it's like, you know, very fair comparison. So in terms of the numbers though, price performance for TPC-DS, we are about 4.8 times better price performance compared to RedShift We are 14.4 times better price performance compared to Snowflake, 13 times better than Google BigQuery, and 15 times better than Synapse. So across the board, we are significantly faster and significantly lower price. And as I said, all of these scripts are available in GitHub for people to drive for themselves. >> Okay, all right, I get it. So I think what you're saying is, you could have said this is what it's going to cost for you to do both analytics and transaction processing on a competitive platform versus what it takes to do that on Oracle MySQL HeatWave, but you're not doing that. You're saying, let's take them head on in their sweet spot of analytics, or OLTP separately and you're saying you still beat them. Okay, so you got this one database service in your cloud that supports transactions and analytics and machine learning. How much do you estimate your saving companies with this integrated approach versus the alternative of kind of what I called upfront, the right tool for the right job, and admittedly having to ETL tools. How can you quantify that? >> Right, so, okay. The numbers I call it, right, at the end of the day in a cloud service price performance is the metric which gives a sense as to how much the customers are going to save. So for instance, for like a TPC-DS workload, if we are 14 times better price performance than Snowflake, it means that our cost is going to be 1/14th for what customers would pay for Snowflake. Now, in addition, in other costs, in terms of migrating the data, having to manage two different databases, having to pay for other service for like, you know, machine learning, that's all extra and that depends upon what tools customers are using or what other services they're using for transaction processing or for machine learning. But these numbers themselves, right, like they're very, very compelling. If we are 1/5th the cost of Redshift, right, or 1/14th of Snowflake, these numbers, like, themselves are very, very compelling. And that's the reason we are seeing so many of these migrations from these databases to MySQL HeatWave. >> Okay, great, thank you. Our last question, in the Q3 earnings call for fiscal 22, Larry Ellison said that "MySQL HeatWave is coming soon on AWS," and that caught a lot of people's attention. That's not like Oracle. I mean, people might say maybe that's an indication that you're not having success moving customers to OCI. So you got to go to other clouds, which by the way I applaud, but any comments on that? >> Yep, this is very much like Oracle. So if you look at one of the big reasons for success of the Oracle database and why Oracle database is the most popular database is because Oracle database runs on all the platforms, and that has been the case from day one. So very akin to that, the idea is that there's a lot of value in MySQL HeatWave, and we want to make sure that we can offer same value to the customers of MySQL running on any cloud, whether it's OCI, whether it's the AWS, or any other cloud. So this shows how confident we are in our offering, and we believe that in other clouds as well, customers will find significant advantage by having a single database, which is much faster and much lower price then what alternatives they currently have. So this shows how confident we are about our products and services. >> Well, that's great, I mean, obviously for you, you're in MySQL group. You love that, right? The more places you can run, the better it is for you, of course, and your customers. Okay, Nipun, we got to leave it there. As always it's great to have you on theCUBE, really appreciate your time. Thanks for coming on and sharing the new innovations. Congratulations on all the progress you're making here. You're doing a great job. >> Thank you, Dave, and thank you for the opportunity. >> All right, and thank you for watching this CUBE conversation with Dave Vellante for theCUBE, your leader in enterprise tech coverage. We'll see you next time. (upbeat music)

(upbeat music) >> Oracle is in the midst of an acceleration of its product cycles. It really has pushed new capabilities across its database, the database platforms, and of course the cloud in an effort to really maintain its position as the gold standard for cloud database. We've reported pretty extensively on Exadata, most recently the X9M that increased database IOPS and throughput. Organizations running mission critical OLTP, analytics and mix workloads tell us that they've seen meaningfully improved performance and lower costs, which you expect in a technology cycle. I often say if Oracle calls you out by name it's a compliment and it means you've succeeded. So just a couple of weeks ago, Oracle turned up the heat on MongoDB with a Mongo compatible API, in an effort to persuade developers to run applications in a autonomous database and on OCI, Oracle cloud infrastructure. There was a big emphasis by Oracle on acid compliance transactions and automatic scaling as well as access to multiple data types. This caught my attention because in the early days of no SQL, there was a lot of chatter from folks about not needing acid capability in the database anymore. Funny how that comes around. And anyway, you see Oracle investing, they spend money in R&D We've always said that`, they're protecting their moat. Now in social I've seen some criticisms like Oracle still is not adding enough new logos, and Oracle of course will dispute that and give you some examples. But to me what's most impressive is the big name customers that Oracle gets to talk in public. Deutsche Bank, Telephonic, Experian, FedEx, I mean dozens and dozens and dozens. I work with a lot of companies and the quality of the customers Oracle puts in front of analysts like myself is very very high. At the top of the list I would say. And they're big spending customers. And as we said many times when it comes to mission critical workloads, Oracle is the king. And one of the executives behind the success is a longtime Cube alum, Juan Loaiza who's executive vice president of mission critical technologies at Oracle. And we've invited him back on today to talk about some news and Oracle's latest developments and database, Juan welcome back to the show and thanks for coming on today and talking about today's announcement. >> I'm very happy to be here today with you. >> Okay, so what are you announcing and how does this help organizations particularly with those existing Exadata cloud at customer installations? >> Yeah, the big thing we're announcing is our very successful cloud at customer platform. We're extending the capabilities of our autonomous database running on it. And specifically we're allowing much smaller configurations so customers can start small and grow with our autonomous database on our cloud customer platform. >> So let's get into granularity a little bit and double click on this. Can you go over how customers, carve up VM clusters for different workloads? What's the tangible benefit to them? >> Yeah, so it's pretty straightforward. We deploy our Cloud@Customer system anywhere the customer wants it, let's say in their data center. And then through our cloud APIs and GUIs they can carve up into pieces into basically VMs. They can say, Hey, I want a VM with eight CPUs to do this, I want a VM with 20 CPUs to that, I want a 500 CPUVM to do something else. And that's what we call a VM cluster because in Cloud@Customer, it is a highly available environment. So you don't just get one VM, you get a cluster of highly available VMs. So you carve it up. You hand it out to different aspects of a company. You might have development on one, testing on another one, some production sales on one VM, marketing on a different VM. And then you run your databases in there and that's kind of how it works and it's all done completely through our GUI and it's very, very simple 'cause they use it the same cloud APIs and GUIs that we use in the public cloud. It is the same APIs and GUIs that we use in the public cloud. >> Yeah, I was going to say sounds like cloud. So what about prerequisites? What do customers have to do to take advantage of the new capabilities? Can they run it on an Exadata cloud a customer that they installed a couple years ago? Do they have to upgrade the hardware? What migration pain is involved? >> Yeah, there's no pain, so it's just, (coughs) excuse me. I can take their existing system, they get our free software update and they can just deploy autonomous database as a VM in their existing Exadata cloud system. >> Oh nice okay what's the bottom line dollars? Our audience are always interested in cutting costs. It's one of the reasons they're moving to the cloud for example. So how does autonomous database on VM clusters, on Exadata Cloud at Customer? How does it help cut their cost? >> Well, it's pretty straightforward. So previous to this a customer would have to have dedicated a system to either autonomous database or to non autonomous data. So you have to choose one together. So on a system by system basis, you chose I want this thing autonomous, or I don't want it autonomous. Now you carve in the VMs and say for this VM I want that autonomous for that VM I want to run a regular database managed database on there. So lets customers now start small with any size they want. They could start with two CPUs and run an autonomous database and that's all they pay for is the two CPUs that they use. >> Let's talk a little about traction. I mean, I remember we covered the original Exadata announcement quite a long time ago and it's obviously evolved and taken many forms. Look, it's hard to argue that it hasn't been a big success. It has for Oracle and your target customers. Does this announcement make Exadata cloud a customer more attractive for smaller companies. In other words, does it expand the team for ADB? And if so, how? >> Yeah, absolutely. I mean our Exadata cloud platform is extremely successful. We have thousands of deployments, we have on our data platform we have about almost 90% of the global fortune 100 and thousands of smaller customers. In the cloud we have now up to 40% of the global 100 a hundred biggest companies in the world running on that. So it's been extremely successful platform and cloud a customer is super key. A lot of customers can't move their data to the public cloud. So we bring the public cloud to them with our cloud customer offering. And so that's the big customer is the fortune hundred but we have thousands of smaller customers also. And the nice thing about this offering is we can start with literally two CPUs. So we can be a very small customer and still run our autonomous data based on our cloud customer platform. >> Well, everybody cares about security and governance. I mean, especially the big guys, but the little guys that in many ways as well they want the capabilities of the large companies but they can't necessarily afford it. So I want to talk about security in particular governance and it's especially important for mission-critical apps. So how does this all change the security in governance paradigm? What do customers need to know there? >> Yeah, so the beauty of autonomous database which is the thing that we're talking about today is Oracle deals with all the security. So the OS, the hardware, firmware, VMs, the database itself all the interfaces to the VM, to the database all that is it's all done by Oracle. So, which is incredibly important because there's a constant stream of security alerts that are coming out and it's very difficult for customers to keep up with this stuff. I mean, it's hard for us and we have thousands of engineers. And so we take that whole burden away from customers. And you just don't have to think about it, we deal with it. So once you deploy an autonomous database it is always secure because anytime a security alert comes out, we will apply that and we do it in an online fashion also. So it's really, particularly for smaller customers it's even harder because to keep up with all the security that you you need a giant team of security experts and even the biggest customers struggle with that and a small customer's going to really struggle. There's just two, you have to look at the entire stack, all the different components switches, firmware, OS, VMs, database, everything. It's just very difficult to keep up. So we do it all and for small cut, they just can't do it. So really they really need to partner with a company like Oracle that has thousands of engineers that can keep up with this stuff. >> It's true what you say, even large customers this CSOs will tell you that lack of talent, lack of skill sets. They just don't have enough people and so even the big guys can't keep up. Okay, I want you to pitch me as though I'm a developer, which I'm not, but we got a lot of developers in our community. We'll be Cube con next month in Valencia, sell me on why a developer should lean into ADB on Exadata cloud as a customer? >> Yeah, it's very straightforward. So Oracle has the most advanced database in the industry and that's widely recognized by database analysts and experts in the field. Traditionally, it's been hard for a developer to use it because it's been hard to manage. It's been hard to set up, install, configure, patch, back up all that kind of stuff. Autonomous database does it all for you. So as a developer, you can just go into our console, click on creating a database. We ask you four questions, how big, how many CPUs how much storage and say, give your password. And within minutes you have a database. And at that point you can go crazy and just develop. And you don't have to worry about managing the database, patching the database, maintaining the security and the database backing up to all that stuff. You can instantly scale it. You can say, Hey, I want to grow it, you just click a button, take, grow it to much any size you want and you get all the mission critical capabilities. So it works for tiny databases but it is a stock exchange quality in terms of performance, availability, security it's a rock solid database that's super trivial. So what used to be a very complex thing is now completely trivial for a developer. So they get the best of both worlds, they get everything on the database side and it it's trivial for them to use. >> Wow, if you're doing all that stuff for 'em are they going to do on their weekends? Code? (chuckles) >> They should be developing their application and add value to their company that's kind of what they should focus on. And they can be looking at all sorts of new technologies like JSON and the database machine learning in the database graph in the database. So you can build very sophisticated applications because you don't have to worry about the database anymore. >> All right, let's talk about the competition. So it's always a topic I like to bring up with you. From a competitive perspective how is this latest and instantiation of Exadata cloud a customer X9M how's this different from running an AWS database service for instance on outpost, or let's say I want to run SQL server on Azure Stack or whatever Microsoft's calling it these days. Give us the competitive angle here. >> Yeah, there kind of is no real competition. So both Amazon and Microsoft have an at customer solution but they're very primitive. I mean, just to give you an example like Amazon doesn't run any of their premier database offerings at customers. So whether it's Aurora Redshift, doesn't run just plane does not run. It's not that it runs badly or it's got limited, just does not run. They can't run Oracle RDS on premise and same thing with Microsoft. They can't run Azure SQL, which is their premier database on their act customer platform. So that kind of tells you how limited that platform is when even their own premier offerings doesn't run on it. In contrast, we're running Exadata with our premier autonomous database. So it's our premier platform that's in use today by most of the biggest, banks, telecom to retailers et cetera in the world, thousands of smaller customers. So it's super mission critical, super proven with our premier cloud database, which is autonomous theory. So it couldn't be more black and white, this is a case where it's there really is no competition in the cloud of customer space on the database side. >> Okay, but let me follow up on that, Juan, if I may, so, okay. So it took you guys a while to get to the cloud, it's taken them a while to figure it on-prem. I mean, aren't they going to eventually sort of get there? What gives you confidence that you'll be able to to keep ahead? >> Well, there's two things, right? One is we've been doing this for a long time. I mean, that's what Oracle initially started as an on-prem and our Exadata platform has been available for over a decade. And we have a ton of experience on this. We run the biggest banks in the world already, it's not some hope for the future. This is what runs today. And our focus has always been a combination of cloud and on-prem their heart's not really in the on-prem stuff they really like. Amazon's really a public cloud only vendor and you can see from the result, it's not you can say, they can say whatever they want but you can see the results. Their outpost platform has been available for several years now and it still doesn't even run their own products. So you can kind of see how hard they're trying and how much they really care about this market. >> All right, boil it down if you just had a few things that you'd tell someone about why they should run ADB on Exadata cloud at customer, what would you say? >> It's pretty simple, which is it's the world's most sophisticated database made completely simple, that's it? So you get a stock exchange level database, you can start really small and grow and it's completely trivial to run because Oracle is automated everything within our autonomous data we use machine learning and a lot of automation to automate everything around the database. So it's kind of the best of both worlds. The best possible database starts as small as you want and is the simplest database in the world. >> So I probably should have asked you this while I was pushing the competitive question but this may be my last question, I promise. It's the age old debate It rages on, you got specialized databases kind of a right tool for the right job approach. That's clearly where Amazon is headed or what Oracle refers to is converge database. Oracle says its approach is more complete and "simpler." Take us through your thinking on this and the latest positioning so the audience can understand it a bit better. >> Yeah, so apps aren't what they used to business apps, data driven apps aren't what they used to be. They used to be kind of green screens where you just entered data. Now everyone's a very sophisticated app, they want to be have location, they want to have maps, they want to have graph in there. They want to have machine learning, they want machine learning built into the app. So they want JSON they want text, they want text search. So all these capabilities are what a modern app has to support. And so what Oracle's done is we provided a single solution that provides everything you need to build a modern app and it's all integrated together. It's all transactional. You have analytics built into the same thing. You have reporting built into the same thing. So it has everything you need to build a modern app. In contrast, what most of our competitors do is they give you these little solutions, say, okay here you do machine learning over here, you do analytics over there, you do JSON over here, you do spatial over here you do graph over there. And then it's left a developer to put an app together from all these pieces. So it's like getting the pieces of a card and having to assemble it yourself and then maintain it for the rest of your life, which is the even harder part. So one part upgrades, you got to test that. So of other piece upgrade or changes, you got to test that, you got to deal with all the security problems of all these different systems. You have to convert the data, you have to move the data back and forth it's extraordinarily complicated. Our converge database, the data sits in one place and all the algorithms come to the data. It's very simple, it is dramatically simpler. And then autonomous database is what makes managing it trivial. You don't really have to manage anything more because Oracle's automated the whole thing. >> So, Juan, we got a pretty good Cadence going here. I mean I really appreciate you coming on and giving us these little video exclusives. You can tell by again, that Cadence how frequently you guys are making new announcements. So that's great, congrats on yet another announcement. Thanks for coming back in the program appreciate it. >> Yeah, of course we invest heavily in data management. That's our core and we will continue to do that. I mean, we're investing billions of dollars a year and we intend to stay the leaders in this market. >> Great stuff and thank you for watching the Cube, your leader in enterprise tech coverage, this is Dave Vellante we'll see you next time.

(upbeat music) >> Oracle continues to pursue a multi-mode converged database strategy. The premise of this all in one approach is to make life easier for practitioners and developers. And the most recent example is the Oracle database API for MongoDB, which was announced today. Now, Oracle, they're not the first to come out with a MongoDB compatible API, but Oracle hopes to use its autonomous database as a differentiator and further build a moat around OCI, Oracle Cloud Infrastructure. And with us to talk about Oracle's MongoDB compatible API is Gerald Venzl, who's a distinguished Product Manager at Oracle. Gerald was a guest along with Maria Colgan on the CUBE a while back, and we talked about Oracle's converge database and the kind of Swiss army knife strategy, I called it, of databases. This is dramatically different. It's an approach that we see at the opposite end of the the spectrum, for instance, from AWS, who, for example, goes after the world of developers with a different database for every use case. So, kind of picking up from there, Gerald, I wonder if you could talk about how this new MongoDB API adds to your converged model and the whole strategy there. Where does it fit? >> Yeah, thank you very much, Dave and, by the way, thanks for having me on the CUBE again. A pleasure to be here. So, essentially the MongoDB API to build the compatibility that we used with this API is a continuation of the converge database story, as you said before. Which is essentially bringing the many features of the many single purpose databases that people often like and use, together into one technology so that everybody can benefit from it. So as such, this is just a continuation that we have from so many other APIs or standards that we support. Since a long time, we already, of course to SQL because we are relational database from the get go. Also other standard like GraphQL, Sparkle, et cetera that we have. And the MongoDB API, is now essentially just the next step forward to give the developers this API that they've gotten to love and use. >> I wonder if you could talk about from the developer angle, what do they get out of it? Obviously you're appealing to the Mongo developers out there, but you've got this Mongo compatible API you're pouting the autonomous database on OCI. Why aren't they just going to use MongoDB Atlas on whatever cloud, Azure or AWS or Google Cloud platform? >> That's a very good question. We believe that the majority of developers want to just worry about their application, writing the application, and not so much about the database backend that they're using. And especially in cloud with cloud services, the reason why developers choose these services is so that they don't have to manage them. Now, autonomous database brings many topnotch advanced capabilities to database cloud services. We firmly believe that autonomous database is essentially the next generation of cloud services with all the self-driving features built in, and MongoDB developers writing applications against the MongoDB API, should not have to hold out on these capabilities either. It's like no developer likes to tune the database. No developer likes to take a downtime when they have to rescale their database to accommodate a bigger workload. And this is really where we see the benefit here, so for the developer, ideally nothing will change. You have MongoDB compatible API so they can keep on using their tools. They can build the applications the way that they do, but the benefit from the best cloud database service out there not having to worry about any of these package things anymore, that even MongoDB Atlas has a lot of shortcomings still today, as we find. >> Of cos, this is always a moving target The technology business, that's why we love it. So everybody's moving fast and investing and shaking and jiving. But, I want to ask you about, well, by the way, that's so you're hiding the underlying complexity, That's really the big takeaway there. So that's you huge for developers. But take, I was talking before about, the Amazon's approach, right tool for the right job. You got document DB, you got Microsoft with Cosmos, they compete with Mongo and they've been doing so for some time. How does Oracle's API for Mongo different from those offerings and how you going to attract their users to your JSON offering. >> So, you know, for first of all we have to kind of separate slightly document DB and AWS and Cosmos DB in Azure, they have slightly different approaches there. Document DB essentially is, a document store owned by and built by AWS, nothing different to Mongo DB, it's a head to head comparison. It's like use my document store versus the other document store. So you don't get any of the benefits of a converge database. If you ever want to do a different data model, run analytics over, etc. You still have to use the many other services that AWS provides you to. You cannot all do it into one database. Now Cosmos DB it's more in interesting because they claim to be a multi-model database. And I say claim because what we understand as multi-model database is different to what they understand as multimodel database. And also one of the reasons why we start differentiating with converge database. So what we mean is you should be able to regardless what data format you want to store in the database leverage all the functionality of the database over that data format, with no trade offs. Cosmos DB when you look at it, it essentially gives you mode of operation. When you connect as the application or the user, you have to decide at connection time, how you want, how this database should be treated. Should it be a document store? Should it be a graph store? Should it be a relational store? Once you make that choice, you are locked into that. As long as you establish that connection. So it's like, if you say, I want a document store, all you get is a document store. There's no way for you to crossly analyze with the relational data sitting in the same service. There's no for you to break these boundaries. If you ever want to add some graph data and graph analytics, you essentially have to disconnect and now treat it as a graph store. So you get multiple data models in it, but really you still get, one trick pony the moment you connect to it that you have to choose to. And that is where we see a huge differentiation again with our converge database, because we essentially say, look, one database cloud service on Oracle cloud, where it allows you to do anything, if you wish to do so. You can start as a document store if you wish to do so. If you want to write some SQL queries on top, you can do so. If you want to add some graph data, you can do so. But there's no way for you to have to rewrite your application, use different libraries and frameworks now to connect et cetera, et cetera. >> Got it. Thank you for that. Do you have any data when you talk to customers? Like I'm interested in the diversity of deployments, like for instance, how many customers are using more than one data model? Do for instance, do JSON users need support for other data types or are they happy to stay kind of in their own little sandbox? Do you have any data on that? >> So what we see from the majority of our customers, there is no such thing as one data model fits everything. So, and it's like, there again we have to differentiate the developer that builds a certain microservice, that makes happy to stay in the JSON world or relational world, or the company that's trying to derive value from the data. So it's like the relational model has not gone away since 40 years of it existence. It's still kicking strong. It's still really good at what it does. The JSON data model is really good in what it does. The graph model is really good at what it does. But all these models have been built for different purposes. Try to do graph analytics on relational or JSON data. It's like, it's really tricky, but that's why you use a graph model to begin with. Try to shield yourself from the organization of the data, how it's structured, that's really easy in the relational world, not so much when you get into a document store world. And so what we see about our customers is like as they accumulate more data, is they have many different applications to run their enterprises. The question always comes back, as we have predicted since about six, seven years now, where they say, hey, we have all this different data and different data formats. We want to bring it all together, analyze it together, get value out of the data together. We have seen a whole trend of big data emerge and disappear to answer the question and didn't quite do the trick. And we are basically now back to where we were in the early 2000's when XML databases have faded away, because everybody just allowed you to store XML in the database. >> Got it. So let's make this real for people. So maybe you could give us some examples. You got this new API from Mongo, you have your multi model database. How, take a, paint a picture of how customers are going to benefit in real world use cases. How does it kind of change the customer's world before and after if you will? >> Yeah, absolutely. So, you know the API essentially we are going to use it to accept before, you know, make the lives of the developers easier, but also of course to assist our customers with migrations from Mongo DB over to Oracle Autonomous Database. One customer that we have, for example, that would've benefited of the API several a couple of years ago, two, three years ago, it's one of the largest logistics company on the planet. They track every package that is being sent in JSON documents. So every track package is entries resembled in a JSON document, and they very early on came in with the next question of like, hey, we track all these packages and document in JSON documents. It will be really nice to know actually which packages are stuck, or anywhere where we have to intervene. It's like, can we do this? Can we analyze just how many packages get stuck, didn't get delivered on, the end of a day or whatever. And they found this struggle with this question a lot, they found this was really tricky to do back then, in that case in MongoDB. So they actually approached Oracle, they came over, they migrated over and they rewrote their applications to accommodate that. And there are happy JSON users in Oracle database, but if we were having this API already for them then they wouldn't have had to rewrite their applications or would we often see like worry about the rewriting the application later on. Usually migration use cases, we want to get kind of the migration done, get the data over be running, and then worry about everything else. So this would be one where they would've greatly benefited to shorten this migration time window. If we had already demo the Mongo API back then or this compatibility layer. >> That's a good use case. I mean, it's, one of the most prominent and painful, so anything you could do to help that is key. I remember like the early days of big data, NoSQL, of course was the big thing. There was a lot of confusion. No, people thought was none or not only SQL, which is kind of the more widely accepted interpretation today. But really, it's talking about data that's stored in a non-relational format. So, some people, again they thought that SQL was going to fade away, some people probably still believe that. And, we saw the rise of NoSQL and document databases, but if I understand it correctly, a premise for your Mongo DB API is you really see SQL as a main contributor over Mongo DB's document collections for analytics for example. Can you make, add some color here? Are you seeing, what are you seeing in terms of resurgence of SQL or the momentum in SQL? Has it ever really waned? What's your take? >> Yeah, no, it's a very good point. So I think there as well, we see to some extent history repeating itself from, this all has been tried beforehand with object databases, XML database, et cetera. But if we stay with the NoSQL databases, I think it speaks at length that every NoSQL database that as you write for the sensor you started with NoSQL, and then while actually we always meant, not only SQL, everybody has introduced a SQL like engine or interface. The last two actually join this family is MongoDB. Now they have just recently introduced a SQL compatibility for the aggregation pipelines, something where you can put in a SQL statement and that essentially will then work with aggregation pipeline. So they all acknowledge that SQL is powerful, for us this was always clear. SQL is a declarative language. Some argue it's the only true 4GL language out there. You don't have to code how to get the data, but you just ask the question and the rest is done for you. And, we think that as we, basically, has SQL ever diminished as you said before, if you look out there? SQL has always been a demand. Look at the various developer surveys, etc. The various top skills that are asked for SQL has never gone away. Everybody loves and likes and you wants to use SQL. And so, yeah, we don't think this has ever been, going away. It has maybe just been, put in the shadow by some hypes. But again, we had the same discussion in the 2000's with XML databases, with the same discussions in the 90's with object databases. And we have just frankly, all forgotten about it. >> I love when you guys come on and and let me do my thing and I can pretty much ask any question I want, because, I got to say, when Oracle starts talking about another company I know that company's doing well. So I like, I see Mongo in the marketplace and I love that you guys are calling it out and making some moves there. So here's the thing, you guys have a large install base and that can be an advantage, but it can also be a weight in your shoulder. These specialized cloud databases they don't have that legacy. So they can just kind of move freely about, less friction. Now, all the cloud database services they're going to have more and more automation. I mean, I think that's pretty clear and inevitable. And most if not all of the database vendors they're going to provide support for these kind of converged data models. However they choose to do that. They might do it through the ecosystem, like what Snowflake's trying to do, or bring it in the house themselves, like a watch maker that brings an in-house movement, if you will. But it's like death and taxes, you can't avoid it. It's got to happen. That's what customers want. So with all that being said, how do you see the capabilities that you have today with automation and converge capabilities, How do you see that, that playing out? What's, do you think it gives you enough of an advantage? And obviously it's an advantage, but is it enough of an advantage over the specialized cloud database vendors, where there's clearly a lot of momentum today? >> I mean, honestly yes, absolutely. I mean, we are with some of these databases 20 years ahead. And I give you concrete examples. It's like Oracle had transaction support asset transactions since forever. NoSQL players all said, oh, we don't need assets transactions, base transactions is fine. Yada, yada, yada. Mongo DB started introducing some transaction support. It comes with some limits, cannot be longer than 60 seconds, cannot touch more than a thousand documents as well, et cetera. They still will have to do some catching up there. I mean, it took us a while to get there, let's be honest. Glad We have been around for a long time. Same thing, now that happened with version five, is like we started some simple version of multi version concurrency control that comes along with asset transactions. The interesting part here is like, we've introduced this also an Oracle five, which was somewhere in the 80's before I even started using Oracle Database. So there's a lot of catching up to do. And then you look at the cloud services as well, there's actually certain, a lot of things that we kind of gotten take, we've kind of, we Oracle people have taken for granted and we kind of keep forgetting. For example, our elastic scale, you want to add one CPU, you add one CPU. Should you take downtime for that? Absolutely not. It's like, this is ridiculous. Why would you, you cannot take it downtime in a 24/7 backend system that runs the world. Take any of our customers. If you look at most of these cloud services or you want to reshape, you want to scale your cloud service, that's fine. It's just the VM under the covers, we just shut everything down, give you a VM with more CPU, and you boot it up again, downtown right there. So it's like, there's a lot of these things where we go like, well, we solved this frankly decades ago, that these cloud vendors will run into. And just to add one more point here, so it's like one thing that we see with all these migrations happening is exactly in that field. It's like people essentially started building on whether it's Mongo DB or other of these NoSQL databases or cloud databases. And eventually as these systems grow, as they ask more difficult questions, their use cases expand, they find shortcomings. Whether it's the scalability, whether it's the security aspects, the functionalities that we have, and this is essentially what drives them back to Oracle. And this is why we see essentially this popularity now of pendulum swimming towards our direction again, where people actually happily come over back and they come over to us, to get their workloads enterprise grade if you like. >> Well, It's true. I mean, I just reported on this recently, the momentum that you guys have in cloud because it is, 'cause you got the best mission critical database. You're all about maps. I got to tell you a quick story. I was at a vertical conference one time, I was on stage with Kurt Monash. I don't know if you know Kurt, but he knows this space really well. He's probably forgot and more about database than I'll ever know. But, and I was kind of busting his chops. He was talking about asset transactions. I'm like, well with NoSQL, who needs asset transactions, just to poke him. And he was like, "Are you out of your mind?" And, and he said, look it's everybody is going to head in this direction. It turned out, it's true. So I got to give him props for that. And so, my last question, if you had a message for, let's say there's a skeptical developer out there that's using Mongo DB and Atlas, what would you say to them? >> I would say go try it for yourself. If you don't believe us, we have an always free cloud tier out there. You just go to oracle.com/cloud/free. You sign up for an always free tier, spin up an autonomous database, go try it for yourself. See what's actually possible today. Don't just follow your trends on Hackernews and use a set study here or there. Go try it for yourself and see what's capable of >> All right, Gerald. Hey, thanks for coming into my firing line today. I really appreciate your time. >> Thank you for having me again. >> Good luck with the announcement. You're very welcome, and thank you for watching this CUBE conversation. This is Dave Vellante, We'll see you next time. (gentle music)

(upbeat music) >> Hi, I'm Emily Freeman, author of DevOps for dummies. My talk revolution in DevOps discusses a wild idea that we should throw away the SDLC. That's right. The software development or delivery lifecycle. The thing we talk about all the time has been around since the 1960s. And I think it's time for a refresh. I hope you'll join me at the AWS startup showcase, where we discuss new breakthroughs in DevOps, data analytics, and cloud management tools. It's on September 22nd at 9:00 AM. Pacific. Hope to see you there. (cheerful music)

(bright music) >> Surprising many people, including myself, Oracle last year began investing pretty heavily in the MySQL space. Now those investments continue today. Let me give you a brief history. Last December, Oracle made its first HeatWave announcement. Where converged OLTP and OLAP together in a single MySQL database. Now, what wasn't surprising was the approach Oracle took. They leveraged hardware to improve the performance and lower the cost. You see when Oracle acquired Sun more than a decade ago, rather than rely on loosely coupled partnerships with hardware vendors to speed up its databases. Oracle set out on a path to tightly integrate hardware and software innovations using its own in-house engineering. So with his first, MySQL HeatWave announcement, Oracle leaned heavily on developing software on top of an in-memory database technology to create an embedded OLAP capability that eliminates the need for ETL and data from a transaction system into a separate analytics database. Now in doing so, Oracle is taking a similar approach with its MySQL today, as it does for its, or back then, whereas it does for its mainstream Oracle database. And today extends that. And what I mean by that is it's converging capabilities in a single platform. So the argument is this simplifies and accelerates analytics that lowers the costs and allows analytics, things like analytics to be run on data that is more fresh. Now, as many of you know, this is a different strategy than how, for example, an AWS approaches database where it creates purpose-built database services, targeted at specific workloads. These are philosophical design decisions made for a variety of reasons, but it's very clear which direction Oracle is headed in. Today, Oracle continues its HeatWave announcement cadence with a focus on increased automation as well. The company is continuing the trend of using clustering technology to scale out for both performance and capacity. And again, that theme of marrying hardware with software Oracle is also making announcements that focus on security. Hello everyone and welcome to this video exclusive. This is Dave Vellante. We're going to dig into these capabilities, Nipun Agarwal here. He's VP of MySQL HeatWave and advanced development in Oracle. Nipun has been leading the MySQL and HeatWave development effort for nearly a decade. He's got 180 patents to his name about half of which are associated with HeatWave. Nipun, welcome back to the show. Great to have you. >> Thank you, Dave. >> So before we get into the new news, if we could, maybe you could give us all a quick overview of HeatWave again, and what problems you originally set out to solve with it? >> Sure. So HeatWave is a in-memory query accelerator for MySQL. Now, as most people are aware, MySQL was originally designed and optimized for transactional processing. So when customers had the need to run analytics, they would need to extract data from the, MySQL database into another database and run analytics. With MySQL HeatWave, customers get a single database, which can be used both for transactional processing and for analytics. There's no need to move the data from one database to another database and all existing tools and applications, which are compatible with MySQL, continue to work as is. So in-memory query accelerator for MySQL and this is significantly faster than any version of MySQL database. And also it's much faster than specialized databases for analytics. >> Yeah, we're going to talk about that. And so obviously when you made the announcement last December, you had, I'm sure, a core group of, of early customers and beta customers, but then you opened it up to the world. So what was the reaction once you expose that to customers? >> The reaction has been very positive, Dave. So initially we're thinking that they're going to be a lot of customers who are on premise users of MySQL, who are going to migrate to the service. And surely that was the case. But the part which was very interesting and surprising is that we see many customers who are migrating from other cloud vendors or migrating from other cloud services to MySQL HeatWave. And most notably the biggest number of migrations we are seeing are from AWS Aurora and AWS RDS. >> Interesting. Okay. I wonder if you've got other feedback you're obviously responding in a pretty, pretty fast cadence here, you know, seven, eight month cadence. What are the feedback that you get, were there gaps that customers wanted you to to close? >> Sure. Yes. So as customers starting moving in to HeatWave they found that HeatWave is much faster, much cheaper. And when it's so much faster, they told us that there are some classes of queries, which could just not run earlier, which they can now with HeatWave. So it makes the applications richer because they can write new classes of queries with which they could not in the past. But in terms of the feedback or enhancement requests we got, I would say they will categorize the number one was automation. There've been customers move their database from on-premise to the cloud. They expect more automation. So that was the number one thing. The second thing was people wanted the ability to run analytics on larger sizes of data with MySQL HeatWave because they like what they saw and they wanted us to increase the data size limit, which can be processed by HeatWave. Third one was they wanted more classes of queries to be accessed with HeatWave. Initially, when we went out, HeatWave was designed to be an accelerator for analytic queries but more and more customers started seeing the benefit of beyond just analytics. More towards mixed workloads. So that was a third request. And then finally they wanted us to scale to a larger cluster size. And that's what we have done over the last several months that incorporating this feedback, which you've gotten from customers. >> So you're addressing those, those, those gaps. And thank you for sharing that with us. I got the press release here. I wonder if we could kind of go through these. Let's start with AutoPilot, you know, what's, what's that all about? What's different about AutoPilot? >> That's right. So MySQL AutoPilot provides machine learning based automation. So the first difference is that not only is it automating things, where and as a cloud provider as a service provider, we feel there are a lot of opportunities for us to automate, but the big difference about the approach we've taken with MySQL AutoPilot is that it's all driven based on the data and the queries. It's machine learning based automation. That's the first aspect. The second thing is this is all done natively in the server, right? So we are enhancing the, MySQL engine. We're enhancing the HeatWave engine and that's where all the logic and all the processing resides. In order to do this, we have had to collect new kinds of data. So for instance, in the past, people would collect statistics, which are based on just the data. Now we also collect statistics based on queries, for instance, what is the compilation time? What is the execution time? And we have augmented this with new machine learning models. And finally we have made a lot of innovations, a lot of inventions in the process where we collect data in a smart way. We process data in a smart way and the machine learning models we are talking about, also have a lot of innovation. And that's what gives us an edge over what other vendors may try to do. >> Yeah. I mean, I'm just, again, I'm looking at this meat, this pretty meaty preference, press release. Auto-provisioning, auto parallel load, auto data placement, auto encoding, auto error, auto recovery, auto scheduling, and you know, using a lot of, you know, computer science techniques that are well-known, first in first out, auto change propagation. So really focusing on, on driving that automation for customers. The other piece of it that struck me, and I said this in my intro is, you know, using clustering technology, clustering technology has been around for a long time as, as in-memory database, but applying it and integrating it. My sense is that's really about scale and performance and taking advantage of course, cloud being able to drive that scale instantaneously, but talk about scale a little bit in your philosophy there and why so much emphasis on scalability? >> Right. So what we want to do is to provide the fastest engine for running analytics. And that's why we do the processing in memory. Now, one of the issues with in process, in-memory processing is that the amount of data which you're processing has to reside in memory. So when we went out in the version one, given the footprint of the MySQL customers we spoke to, we thought 12 terabytes of processing at any given point in time, would be adequate. In the very first month, we got feedback that customers wanted us to process larger amounts of data with HeatWave, because they really like what they saw and they wanted us to increase. So if we have increased deployment from 12 terabytes to 32 terabytes and in order to do so, we now have a HeatWave cluster, which can be up to 64 nodes. That's one aspect on the query processing side. Now to answer the question as to why so much of an emphasis it's because this is something which is extremely difficult to do in query processing that as you scale the size of the cluster, the kind of algorithms, the kind of techniques you have to use so that you achieve a very high efficiency with a very large cluster. These are things which are easy to do, because what we want to make sure is that as customers have the need for like, like a processing larger amount of data, one of the big benefits customers get by using a cloud as opposed to on-premise is that they don't need to worry about provisioning gear ahead of time. So if they have more data with the cloud, they should be able to like process pool data easily. But when they process more data, they should expect the same kind of performance. So same kind of efficiency on a larger data size, similar to a smaller data size. And this is something traditionally other database vendors have struggled to provide. So this is a important problem. This is a tough engineering problem. And that's why a lot of emphasis on this to make sure that we provide our customers with very high efficiency of processing as they increase the size of the data. >> You're saying, traditionally, you'll get diminishing returns as you scale. So sort of as, as the volume grows, you're not able to take as much advantage or you're less efficient. And you're saying you've, you've largely solved that problem you're able to use. I mean, people always talk about scaling linearly and I'm always skeptical, but, but you're saying, especially in database, that's been a challenge, but you're, you're saying you've solved that problem largely. >> Right. What I would say is that we have a system which is very efficient, more efficient than like, you know, any of the database we are aware of. So as you said, perfect scaling is hard with you, right? I mean, that's a critical limit of scale factor one. That's very hard to achieve. We are now close to 90% efficiency for n2n queries. This is not for primitives. This is for n2n queries, both on industry benchmarks, as well as real world customer workloads. So this 90% efficiency we believe is very good and higher than what many of the vendors provide. >> Yeah. Right. So you're not, not just primitives the whole end to end cycle. I think 0.89, I think was the number that I, that I saw just to be technically correct there, but that's pretty, pretty good. Now let's talk about the benchmarks. It wouldn't be an Oracle announcement with some, some benchmarks. So you laid out today in your announcement, some, some pretty outstanding performance and price performance numbers, particularly you called out it's, it's. I feel like it's a badge of honor. If, if Oracle calls me out, I feel like I'm doing well. You called out Snowflake and Amazons. So maybe you could go over those benchmark results that we could peel the onion on that a little bit. >> Right. So the first thing to realize is that we want to have benchmarks, which are credible, right? So it's not the case that we have taken some specific unique workloads where HeatWave shines. That's not the case. What we did was we took a industry standard benchmark, which is like, you know, TPC-H. And furthermore, we had a third party, independent firm do this comparison. So let's first compare with Snowflake. On a 10 terabyte TPC-H benchmark HeatWave is seven times faster and one fifth the cost. So with this, it is 35 times better price performance compared to Snowflake, right? So seven times faster than Snowflake and one fifth of the cost. So HeatWave is 35 times better price performance compared to Snowflake. Not just that, Snowflake only does analytics, whereas MySQL HeatWave does both transactional processing and analytics. It's not a specialized database, MySQL HeatWave is a general purpose database, which can do both OLTP analytics whereas Snowflake can only do analytics. So to be 35 times more efficient than a database service, which is specialized only for one case, which is analytics, we think it's pretty good. So that's a comparison with Snowflake. >> So that's, that's you're using, I presume you got to be using list prices for that, obviously. >> That is correct. >> So there's discounts, let's put that into context of maybe 35 X better. You're not going to get that kind of discount. I wouldn't think. >> That is correct. >> Okay. What about Redshift? Aqua for Redshift has gained a lot of momentum in the marketplace. How do you compare against that? >> Right. So we did a comparison with Redshift, Aqua, same benchmark, 10 terabytes, TPC-H. And again, this was done by a third party. Here, HeatWave is six and a half times faster at half the cost. So HeatWave is 13 times better price performance compared to Redshift Aqua. And the same thing for Redshift. It's a specialized database only for analytics. So customers need to have two databases, one for transaction processing, one for analytics, with Redshift. Whereas with MySQL HeatWave, it's a single database for both. And it is so much faster than Redshift. That again, we feel is a pretty remarkable. >> Now, you mentioned earlier, but you're not, you're obviously I presume not, you're not cheating here. You're not including the cost of the transaction processing data store. Right? We're, we're, we're ignoring that for a minute. Ignoring that you got to, you know, move data, ETL, we're just talking about like the like, is that correct? >> Right. This is extremely fair and extremely generous comparison. Not only are we not including the cost of the source OLTP database, the cost in the case of the Redshift I'm talking about is the cost for one year paid full upfront. So this is a best pricing. A customer can get for one year subscription with Redshift. Whereas when I'm talking about HeatWave, this is the pay as you go price. And the third aspect is, this is Redshift when it is completely fully optimized. I don't think anyone else can get much better numbers on Redshift than we have. Right? So fully optimized configuration of Redshift looking at the one year pre-pay cost of Redshift and not including the source database. >> Okay. And then speaking of transaction processing database, what about Aurora? You mentioned earlier that that you're seeing a lot of migration from Aurora. Can you add some color to that? >> Right. And this is a very interesting question in a, it was a very interesting observation for us when we did the launch back in December, we had numbers on four terabytes, TPC-H with Aurora. So if you look at the same benchmark, four terabytes TPC-H HeatWave is 1,400 times faster than Aurora at half the cost, which makes it 2,800 times better price performance compared to Aurora. So very good number. What we have found is that many customers who are running on Aurora started migrating to HeatWave, and these customers had a mix of transaction processing and analytics, and the data sizes are much smaller. Even those customers found that there was a significant improvement in performance and reduction in costs when they migrated to HeatWave. In the announcement today, many of the references are those class of customers. So for that, we decided to choose another benchmark, which is called CH-benchmark on a much smaller data size. And even for that, even for mixed workloads, we find that HeatWave is 18 times faster, provides over a hundred times higher throughput than Aurora at 42% of the cost. So in terms of price performance gain, it is much, much better than Aurora, even for mixed workloads. And then if you consider a pure OLTP assume you have an application, which has only OLTP, which by the way is like, you know, a very uncommon scenario, but even if that were be the case, in that case for pure OLTP only, MySQL HeatWave is at par with Aurora, with respect to performance, but MySQL HeatWave costs 42% of Aurora. So the point is that in the whole spectrum, pure OLTP, mixed workloads or analytics, MySQL HeatWave is going to be fraction of the cost of a Aurora. And depending upon your query workload, your acceleration can be anywhere from 14,000 times to 18 times faster. >> That's interesting. I mean, you've been at this for the better part of a decade, because my sense is that HeatWave is all about OLAP. And that's really where you've put the majority, if not all of the innovation. But you're saying just coming into December's announcement, you were at par with a, in a, in a, in a, in a rare, but, but hypothetical OLTP workload. >> That is correct. >> Yeah. >> Well, you know, I got to push you still on this because a lot of times these benchmarks are a function of the skills of the individuals performing these tests, right? So can I, if I want to run them myself, you know, if you publish these benchmarks, what if a customer wants to replicate these tests and try to see if they can tune up, you know, Redshift better than you guys did? >> Sure. So I'll say a couple of things. One is all the numbers which I'm talking about both for Redshift and Snowflake were done by a third party firm, but all the numbers we is talking about, TPC-H, as well has CH-benchmark. All the scripts are published on GitHub. So anyone is very welcome. In fact, we encourage customers to go and try it for themselves, and they will find that the numbers are absolutely as advertised. In fact, we had couple of companies like in the last several months who went to GitHub, they downloaded our TPCH scripts and they reported that the performance numbers they were seeing with HeatWave were actually better than we had published back in December. And the reason was that since December we had new code, which was running. So our numbers were actually better than advertised. So all the benchmarks are published. They are all available on GitHub. You can go to the HeatWave website on oracle.com and get the link for it. And we welcome anyone to come and try these numbers for themselves. >> All right. Good. Great. Thank you for that. Now you mentioned earlier that you were somewhat surprised, not surprised that you got customers migrating from on-prem databases, but you also saw migration from other clouds. How do you expect the trend with regard to this new announcement? Do you have any sense as to how that's going to go? >> Right. So one of the big changes from December to now is that we have now focused quite a bit on mixed workloads. So in the past, in December, when we first went out, HeatWave was designed primarily for analytics. Now, what we have found is that there's a very large class of customers who have mixed workloads and who also have smaller data sizes. We now have introduced a lot of technology, including things like auto scheduling, definitely improvement in performance, where MySQL HeatWave is a very superior solution compared to Aurora or other databases out there, both in terms of performance as well as price for these mixed workloads and better latency, better throughput, lower costs. So we expect this trend of migration to MySQL HeatWave, to accelerate. So we are seeing customers migrate from Azure. We are seeing customers migrate from GCP and by far the number one migrations we are seeing are from AWS. So I think based on the new features and technologies, we have announced today, this migration is going to accelerate. >> All right, last question. So I said earlier, it's, it's, it seems like you're applying what are generally well understood and proven technologies, like in-memory, you like clustering to solve these problems. And I think about, you know, the, the things that you're doing, and I wonder, you know, I mean, these things have been around for awhile and why has this type of approach not been introduced by others previously? >> Right. Well, so the main thing is it takes time, right? That we designed HeatWave from the ground up for the cloud. And as a part of that, we had to invent new algorithms for distributed query processing for the cloud. We put in the hooks for machine learning processes. We're sealing processing right from the ground up. So this has taken us close to a decade. It's been hundreds of person-years of investment, dozens of patents which have gone in. Another aspect is it takes talent from different areas. So we have like, you know, people working in distributed query processing, we have people who have a lot of like background in machine learning. And then given that we are like the custodians of the MySQL database, we have a very rich set of customers we can reach out to, to get feedback from them as to what are the pinpoints. So culmination of these trends, which we have this talent, the customer base and the time, so we spent almost close to a decade to make this thing work. So that's what it takes. It takes time, patience, patience, and talent. >> A lot of software innovation bringing together, as I said, that hardware and software strategy. Very interesting. Nipun, thanks so much. I appreciate your, your insights and coming on this video exclusive. >> Thank you, Dave. Thank you for the opportunity. >> My pleasure. And thank you for watching everybody. This is Dave Vellante for theCUBE. We'll see you next time. (bright music)

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>>don't talk mhm, >>Okay, thing is my presentation on coherent nonlinear dynamics and combinatorial optimization. This is going to be a talk to introduce an approach we're taking to the analysis of the performance of coherent using machines. So let me start with a brief introduction to easing optimization. The easing model represents a set of interacting magnetic moments or spins the total energy given by the expression shown at the bottom left of this slide. Here, the signal variables are meditate binary values. The Matrix element J. I. J. Represents the interaction, strength and signed between any pair of spins. I. J and A Chive represents a possible local magnetic field acting on each thing. The easing ground state problem is to find an assignment of binary spin values that achieves the lowest possible value of total energy. And an instance of the easing problem is specified by giving numerical values for the Matrix J in Vector H. Although the easy model originates in physics, we understand the ground state problem to correspond to what would be called quadratic binary optimization in the field of operations research and in fact, in terms of computational complexity theory, it could be established that the easing ground state problem is np complete. Qualitatively speaking, this makes the easing problem a representative sort of hard optimization problem, for which it is expected that the runtime required by any computational algorithm to find exact solutions should, as anatomically scale exponentially with the number of spends and for worst case instances at each end. Of course, there's no reason to believe that the problem instances that actually arrives in practical optimization scenarios are going to be worst case instances. And it's also not generally the case in practical optimization scenarios that we demand absolute optimum solutions. Usually we're more interested in just getting the best solution we can within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for a computation. This focuses great interest on so called heuristic algorithms for the easing problem in other NP complete problems which generally get very good but not guaranteed optimum solutions and run much faster than algorithms that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem for which extensive compilations of benchmarking data may be found online. A recent study found that the best known TSP solver required median run times across the Library of Problem instances That scaled is a very steep route exponential for end up to approximately 4500. This gives some indication of the change in runtime scaling for generic as opposed the worst case problem instances. Some of the instances considered in this study were taken from a public library of T SPS derived from real world Veil aside design data. This feels I TSP Library includes instances within ranging from 131 to 744,710 instances from this library with end between 6880 13,584 were first solved just a few years ago in 2017 requiring days of run time and a 48 core to King hurts cluster, while instances with and greater than or equal to 14,233 remain unsolved exactly by any means. Approximate solutions, however, have been found by heuristic methods for all instances in the VLS i TSP library with, for example, a solution within 0.14% of a no lower bound, having been discovered, for instance, with an equal 19,289 requiring approximately two days of run time on a single core of 2.4 gigahertz. Now, if we simple mindedly extrapolate the root exponential scaling from the study up to an equal 4500, we might expect that an exact solver would require something more like a year of run time on the 48 core cluster used for the N equals 13,580 for instance, which shows how much a very small concession on the quality of the solution makes it possible to tackle much larger instances with much lower cost. At the extreme end, the largest TSP ever solved exactly has an equal 85,900. This is an instance derived from 19 eighties VLSI design, and it's required 136 CPU. Years of computation normalized to a single cord, 2.4 gigahertz. But the 24 larger so called world TSP benchmark instance within equals 1,904,711 has been solved approximately within ophthalmology. Gap bounded below 0.474%. Coming back to the general. Practical concerns have applied optimization. We may note that a recent meta study analyzed the performance of no fewer than 37 heuristic algorithms for Max cut and quadratic pioneer optimization problems and found the performance sort and found that different heuristics work best for different problem instances selected from a large scale heterogeneous test bed with some evidence but cryptic structure in terms of what types of problem instances were best solved by any given heuristic. Indeed, their their reasons to believe that these results from Mexico and quadratic binary optimization reflected general principle of performance complementarity among heuristic optimization algorithms in the practice of solving heart optimization problems there. The cerise is a critical pre processing issue of trying to guess which of a number of available good heuristic algorithms should be chosen to tackle a given problem. Instance, assuming that any one of them would incur high costs to run on a large problem, instances incidence, making an astute choice of heuristic is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This has certainly been pinpointed by researchers in the field is a circumstance that must be addressed. So adding this all up, we see that a critical frontier for cutting edge academic research involves both the development of novel heuristic algorithms that deliver better performance, with lower cost on classes of problem instances that are underserved by existing approaches, as well as fundamental research to provide deep conceptual insight into what makes a given problem in, since easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law and speculate about a so called second quantum revolution, it's natural to talk not only about novel algorithms for conventional CPUs but also about highly customized special purpose hardware architectures on which we may run entirely unconventional algorithms for combinatorial optimization such as easing problem. So against that backdrop, I'd like to use my remaining time to introduce our work on analysis of coherent using machine architectures and associate ID optimization algorithms. These machines, in general, are a novel class of information processing architectures for solving combinatorial optimization problems by embedding them in the dynamics of analog, physical or cyber physical systems, in contrast to both MAWR traditional engineering approaches that build using machines using conventional electron ICS and more radical proposals that would require large scale quantum entanglement. The emerging paradigm of coherent easing machines leverages coherent nonlinear dynamics in photonic or Opto electronic platforms to enable near term construction of large scale prototypes that leverage post Simoes information dynamics, the general structure of of current CM systems has shown in the figure on the right. The role of the easing spins is played by a train of optical pulses circulating around a fiber optical storage ring. A beam splitter inserted in the ring is used to periodically sample the amplitude of every optical pulse, and the measurement results are continually read into a refugee A, which uses them to compute perturbations to be applied to each pulse by a synchronized optical injections. These perturbations, air engineered to implement the spin, spin coupling and local magnetic field terms of the easing Hamiltonian, corresponding to a linear part of the CME Dynamics, a synchronously pumped parametric amplifier denoted here as PPL and Wave Guide adds a crucial nonlinear component to the CIA and Dynamics as well. In the basic CM algorithm, the pump power starts very low and has gradually increased at low pump powers. The amplitude of the easing spin pulses behaviors continuous, complex variables. Who Israel parts which can be positive or negative, play the role of play the role of soft or perhaps mean field spins once the pump, our crosses the threshold for parametric self oscillation. In the optical fiber ring, however, the attitudes of the easing spin pulses become effectively Qantas ized into binary values while the pump power is being ramped up. The F P J subsystem continuously applies its measurement based feedback. Implementation of the using Hamiltonian terms, the interplay of the linear rised using dynamics implemented by the F P G A and the threshold conversation dynamics provided by the sink pumped Parametric amplifier result in the final state of the optical optical pulse amplitude at the end of the pump ramp that could be read as a binary strain, giving a proposed solution of the easing ground state problem. This method of solving easing problem seems quite different from a conventional algorithm that runs entirely on a digital computer as a crucial aspect of the computation is performed physically by the analog, continuous, coherent, nonlinear dynamics of the optical degrees of freedom. In our efforts to analyze CIA and performance, we have therefore turned to the tools of dynamical systems theory, namely, a study of modifications, the evolution of critical points and apologies of hetero clinic orbits and basins of attraction. We conjecture that such analysis can provide fundamental insight into what makes certain optimization instances hard or easy for coherent using machines and hope that our approach can lead to both improvements of the course, the AM algorithm and a pre processing rubric for rapidly assessing the CME suitability of new instances. Okay, to provide a bit of intuition about how this all works, it may help to consider the threshold dynamics of just one or two optical parametric oscillators in the CME architecture just described. We can think of each of the pulse time slots circulating around the fiber ring, as are presenting an independent Opio. We can think of a single Opio degree of freedom as a single, resonant optical node that experiences linear dissipation, do toe out coupling loss and gain in a pump. Nonlinear crystal has shown in the diagram on the upper left of this slide as the pump power is increased from zero. As in the CME algorithm, the non linear game is initially to low toe overcome linear dissipation, and the Opio field remains in a near vacuum state at a critical threshold. Value gain. Equal participation in the Popeo undergoes a sort of lazing transition, and the study states of the OPIO above this threshold are essentially coherent states. There are actually two possible values of the Opio career in amplitude and any given above threshold pump power which are equal in magnitude but opposite in phase when the OPI across the special diet basically chooses one of the two possible phases randomly, resulting in the generation of a single bit of information. If we consider to uncoupled, Opio has shown in the upper right diagram pumped it exactly the same power at all times. Then, as the pump power has increased through threshold, each Opio will independently choose the phase and thus to random bits are generated for any number of uncoupled. Oppose the threshold power per opio is unchanged from the single Opio case. Now, however, consider a scenario in which the two appeals air, coupled to each other by a mutual injection of their out coupled fields has shown in the diagram on the lower right. One can imagine that depending on the sign of the coupling parameter Alfa, when one Opio is lazing, it will inject a perturbation into the other that may interfere either constructively or destructively, with the feel that it is trying to generate by its own lazing process. As a result, when came easily showed that for Alfa positive, there's an effective ferro magnetic coupling between the two Opio fields and their collective oscillation threshold is lowered from that of the independent Opio case. But on Lee for the two collective oscillation modes in which the two Opio phases are the same for Alfa Negative, the collective oscillation threshold is lowered on Lee for the configurations in which the Opio phases air opposite. So then, looking at how Alfa is related to the J. I. J matrix of the easing spin coupling Hamiltonian, it follows that we could use this simplistic to a p o. C. I am to solve the ground state problem of a fair magnetic or anti ferro magnetic ankles to easing model simply by increasing the pump power from zero and observing what phase relation occurs as the two appeals first start delays. Clearly, we can imagine generalizing this story toe larger, and however the story doesn't stay is clean and simple for all larger problem instances. And to find a more complicated example, we only need to go to n equals four for some choices of J J for n equals, for the story remains simple. Like the n equals two case. The figure on the upper left of this slide shows the energy of various critical points for a non frustrated and equals, for instance, in which the first bifurcated critical point that is the one that I forget to the lowest pump value a. Uh, this first bifurcated critical point flows as symptomatically into the lowest energy easing solution and the figure on the upper right. However, the first bifurcated critical point flows to a very good but sub optimal minimum at large pump power. The global minimum is actually given by a distinct critical critical point that first appears at a higher pump power and is not automatically connected to the origin. The basic C am algorithm is thus not able to find this global minimum. Such non ideal behaviors needs to become more confident. Larger end for the n equals 20 instance, showing the lower plots where the lower right plot is just a zoom into a region of the lower left lot. It can be seen that the global minimum corresponds to a critical point that first appears out of pump parameter, a around 0.16 at some distance from the idiomatic trajectory of the origin. That's curious to note that in both of these small and examples, however, the critical point corresponding to the global minimum appears relatively close to the idiomatic projector of the origin as compared to the most of the other local minima that appear. We're currently working to characterize the face portrait topology between the global minimum in the antibiotic trajectory of the origin, taking clues as to how the basic C am algorithm could be generalized to search for non idiomatic trajectories that jump to the global minimum during the pump ramp. Of course, n equals 20 is still too small to be of interest for practical optimization applications. But the advantage of beginning with the study of small instances is that we're able reliably to determine their global minima and to see how they relate to the 80 about trajectory of the origin in the basic C am algorithm. In the smaller and limit, we can also analyze fully quantum mechanical models of Syrian dynamics. But that's a topic for future talks. Um, existing large scale prototypes are pushing into the range of in equals 10 to the 4 10 to 5 to six. So our ultimate objective in theoretical analysis really has to be to try to say something about CIA and dynamics and regime of much larger in our initial approach to characterizing CIA and behavior in the large in regime relies on the use of random matrix theory, and this connects to prior research on spin classes, SK models and the tap equations etcetera. At present, we're focusing on statistical characterization of the CIA ingredient descent landscape, including the evolution of critical points in their Eigen value spectra. As the pump power is gradually increased. We're investigating, for example, whether there could be some way to exploit differences in the relative stability of the global minimum versus other local minima. We're also working to understand the deleterious or potentially beneficial effects of non ideologies, such as a symmetry in the implemented these and couplings. Looking one step ahead, we plan to move next in the direction of considering more realistic classes of problem instances such as quadratic, binary optimization with constraints. Eso In closing, I should acknowledge people who did the hard work on these things that I've shown eso. My group, including graduate students Ed winning, Daniel Wennberg, Tatsuya Nagamoto and Atsushi Yamamura, have been working in close collaboration with Syria Ganguly, Marty Fair and Amir Safarini Nini, all of us within the Department of Applied Physics at Stanford University. On also in collaboration with the Oshima Moto over at NTT 55 research labs, Onda should acknowledge funding support from the NSF by the Coherent Easing Machines Expedition in computing, also from NTT five research labs, Army Research Office and Exxon Mobil. Uh, that's it. Thanks very much. >>Mhm e >>t research and the Oshie for putting together this program and also the opportunity to speak here. My name is Al Gore ism or Andy and I'm from Caltech, and today I'm going to tell you about the work that we have been doing on networks off optical parametric oscillators and how we have been using them for icing machines and how we're pushing them toward Cornum photonics to acknowledge my team at Caltech, which is now eight graduate students and five researcher and postdocs as well as collaborators from all over the world, including entity research and also the funding from different places, including entity. So this talk is primarily about networks of resonate er's, and these networks are everywhere from nature. For instance, the brain, which is a network of oscillators all the way to optics and photonics and some of the biggest examples or metal materials, which is an array of small resonate er's. And we're recently the field of technological photonics, which is trying thio implement a lot of the technological behaviors of models in the condensed matter, physics in photonics and if you want to extend it even further, some of the implementations off quantum computing are technically networks of quantum oscillators. So we started thinking about these things in the context of icing machines, which is based on the icing problem, which is based on the icing model, which is the simple summation over the spins and spins can be their upward down and the couplings is given by the JJ. And the icing problem is, if you know J I J. What is the spin configuration that gives you the ground state? And this problem is shown to be an MP high problem. So it's computational e important because it's a representative of the MP problems on NPR. Problems are important because first, their heart and standard computers if you use a brute force algorithm and they're everywhere on the application side. That's why there is this demand for making a machine that can target these problems, and hopefully it can provide some meaningful computational benefit compared to the standard digital computers. So I've been building these icing machines based on this building block, which is a degenerate optical parametric. Oscillator on what it is is resonator with non linearity in it, and we pump these resonate er's and we generate the signal at half the frequency of the pump. One vote on a pump splits into two identical photons of signal, and they have some very interesting phase of frequency locking behaviors. And if you look at the phase locking behavior, you realize that you can actually have two possible phase states as the escalation result of these Opio which are off by pie, and that's one of the important characteristics of them. So I want to emphasize a little more on that and I have this mechanical analogy which are basically two simple pendulum. But there are parametric oscillators because I'm going to modulate the parameter of them in this video, which is the length of the string on by that modulation, which is that will make a pump. I'm gonna make a muscular. That'll make a signal which is half the frequency of the pump. And I have two of them to show you that they can acquire these face states so they're still facing frequency lock to the pump. But it can also lead in either the zero pie face states on. The idea is to use this binary phase to represent the binary icing spin. So each opio is going to represent spin, which can be either is your pie or up or down. And to implement the network of these resonate er's, we use the time off blood scheme, and the idea is that we put impulses in the cavity. These pulses air separated by the repetition period that you put in or t r. And you can think about these pulses in one resonator, xaz and temporarily separated synthetic resonate Er's if you want a couple of these resonator is to each other, and now you can introduce these delays, each of which is a multiple of TR. If you look at the shortest delay it couples resonator wanted to 2 to 3 and so on. If you look at the second delay, which is two times a rotation period, the couple's 123 and so on. And if you have and minus one delay lines, then you can have any potential couplings among these synthetic resonate er's. And if I can introduce these modulators in those delay lines so that I can strength, I can control the strength and the phase of these couplings at the right time. Then I can have a program will all toe all connected network in this time off like scheme, and the whole physical size of the system scales linearly with the number of pulses. So the idea of opium based icing machine is didn't having these o pos, each of them can be either zero pie and I can arbitrarily connect them to each other. And then I start with programming this machine to a given icing problem by just setting the couplings and setting the controllers in each of those delight lines. So now I have a network which represents an icing problem. Then the icing problem maps to finding the face state that satisfy maximum number of coupling constraints. And the way it happens is that the icing Hamiltonian maps to the linear loss of the network. And if I start adding gain by just putting pump into the network, then the OPI ohs are expected to oscillate in the lowest, lowest lost state. And, uh and we have been doing these in the past, uh, six or seven years and I'm just going to quickly show you the transition, especially what happened in the first implementation, which was using a free space optical system and then the guided wave implementation in 2016 and the measurement feedback idea which led to increasing the size and doing actual computation with these machines. So I just want to make this distinction here that, um, the first implementation was an all optical interaction. We also had an unequal 16 implementation. And then we transition to this measurement feedback idea, which I'll tell you quickly what it iss on. There's still a lot of ongoing work, especially on the entity side, to make larger machines using the measurement feedback. But I'm gonna mostly focused on the all optical networks and how we're using all optical networks to go beyond simulation of icing Hamiltonian both in the linear and non linear side and also how we're working on miniaturization of these Opio networks. So the first experiment, which was the four opium machine, it was a free space implementation and this is the actual picture off the machine and we implemented a small and it calls for Mexico problem on the machine. So one problem for one experiment and we ran the machine 1000 times, we looked at the state and we always saw it oscillate in one of these, um, ground states of the icing laboratoria. So then the measurement feedback idea was to replace those couplings and the controller with the simulator. So we basically simulated all those coherent interactions on on FB g. A. And we replicated the coherent pulse with respect to all those measurements. And then we injected it back into the cavity and on the near to you still remain. So it still is a non. They're dynamical system, but the linear side is all simulated. So there are lots of questions about if this system is preserving important information or not, or if it's gonna behave better. Computational wars. And that's still ah, lot of ongoing studies. But nevertheless, the reason that this implementation was very interesting is that you don't need the end minus one delight lines so you can just use one. Then you can implement a large machine, and then you can run several thousands of problems in the machine, and then you can compare the performance from the computational perspective Looks so I'm gonna split this idea of opium based icing machine into two parts. One is the linear part, which is if you take out the non linearity out of the resonator and just think about the connections. You can think about this as a simple matrix multiplication scheme. And that's basically what gives you the icing Hambletonian modeling. So the optical laws of this network corresponds to the icing Hamiltonian. And if I just want to show you the example of the n equals for experiment on all those face states and the history Graham that we saw, you can actually calculate the laws of each of those states because all those interferences in the beam splitters and the delay lines are going to give you a different losses. And then you will see that the ground states corresponds to the lowest laws of the actual optical network. If you add the non linearity, the simple way of thinking about what the non linearity does is that it provides to gain, and then you start bringing up the gain so that it hits the loss. Then you go through the game saturation or the threshold which is going to give you this phase bifurcation. So you go either to zero the pie face state. And the expectation is that Theis, the network oscillates in the lowest possible state, the lowest possible loss state. There are some challenges associated with this intensity Durban face transition, which I'm going to briefly talk about. I'm also going to tell you about other types of non aerodynamics that we're looking at on the non air side of these networks. So if you just think about the linear network, we're actually interested in looking at some technological behaviors in these networks. And the difference between looking at the technological behaviors and the icing uh, machine is that now, First of all, we're looking at the type of Hamilton Ian's that are a little different than the icing Hamilton. And one of the biggest difference is is that most of these technological Hamilton Ian's that require breaking the time reversal symmetry, meaning that you go from one spin to in the one side to another side and you get one phase. And if you go back where you get a different phase, and the other thing is that we're not just interested in finding the ground state, we're actually now interesting and looking at all sorts of states and looking at the dynamics and the behaviors of all these states in the network. So we started with the simplest implementation, of course, which is a one d chain of thes resonate, er's, which corresponds to a so called ssh model. In the technological work, we get the similar energy to los mapping and now we can actually look at the band structure on. This is an actual measurement that we get with this associate model and you see how it reasonably how How? Well, it actually follows the prediction and the theory. One of the interesting things about the time multiplexing implementation is that now you have the flexibility of changing the network as you are running the machine. And that's something unique about this time multiplex implementation so that we can actually look at the dynamics. And one example that we have looked at is we can actually go through the transition off going from top A logical to the to the standard nontrivial. I'm sorry to the trivial behavior of the network. You can then look at the edge states and you can also see the trivial and states and the technological at states actually showing up in this network. We have just recently implement on a two D, uh, network with Harper Hofstadter model and when you don't have the results here. But we're one of the other important characteristic of time multiplexing is that you can go to higher and higher dimensions and keeping that flexibility and dynamics, and we can also think about adding non linearity both in a classical and quantum regimes, which is going to give us a lot of exotic, no classical and quantum, non innate behaviors in these networks. Yeah, So I told you about the linear side. Mostly let me just switch gears and talk about the nonlinear side of the network. And the biggest thing that I talked about so far in the icing machine is this face transition that threshold. So the low threshold we have squeezed state in these. Oh, pios, if you increase the pump, we go through this intensity driven phase transition and then we got the face stays above threshold. And this is basically the mechanism off the computation in these O pos, which is through this phase transition below to above threshold. So one of the characteristics of this phase transition is that below threshold, you expect to see quantum states above threshold. You expect to see more classical states or coherent states, and that's basically corresponding to the intensity off the driving pump. So it's really hard to imagine that it can go above threshold. Or you can have this friends transition happen in the all in the quantum regime. And there are also some challenges associated with the intensity homogeneity off the network, which, for example, is if one opioid starts oscillating and then its intensity goes really high. Then it's going to ruin this collective decision making off the network because of the intensity driven face transition nature. So So the question is, can we look at other phase transitions? Can we utilize them for both computing? And also can we bring them to the quantum regime on? I'm going to specifically talk about the face transition in the spectral domain, which is the transition from the so called degenerate regime, which is what I mostly talked about to the non degenerate regime, which happens by just tuning the phase of the cavity. And what is interesting is that this phase transition corresponds to a distinct phase noise behavior. So in the degenerate regime, which we call it the order state, you're gonna have the phase being locked to the phase of the pump. As I talked about non degenerate regime. However, the phase is the phase is mostly dominated by the quantum diffusion. Off the off the phase, which is limited by the so called shallow towns limit, and you can see that transition from the general to non degenerate, which also has distinct symmetry differences. And this transition corresponds to a symmetry breaking in the non degenerate case. The signal can acquire any of those phases on the circle, so it has a you one symmetry. Okay, and if you go to the degenerate case, then that symmetry is broken and you only have zero pie face days I will look at. So now the question is can utilize this phase transition, which is a face driven phase transition, and can we use it for similar computational scheme? So that's one of the questions that were also thinking about. And it's not just this face transition is not just important for computing. It's also interesting from the sensing potentials and this face transition, you can easily bring it below threshold and just operated in the quantum regime. Either Gaussian or non Gaussian. If you make a network of Opio is now, we can see all sorts off more complicated and more interesting phase transitions in the spectral domain. One of them is the first order phase transition, which you get by just coupling to Opio, and that's a very abrupt face transition and compared to the to the single Opio phase transition. And if you do the couplings right, you can actually get a lot of non her mission dynamics and exceptional points, which are actually very interesting to explore both in the classical and quantum regime. And I should also mention that you can think about the cup links to be also nonlinear couplings. And that's another behavior that you can see, especially in the nonlinear in the non degenerate regime. So with that, I basically told you about these Opio networks, how we can think about the linear scheme and the linear behaviors and how we can think about the rich, nonlinear dynamics and non linear behaviors both in the classical and quantum regime. I want to switch gear and tell you a little bit about the miniaturization of these Opio networks. And of course, the motivation is if you look at the electron ICS and what we had 60 or 70 years ago with vacuum tube and how we transition from relatively small scale computers in the order of thousands of nonlinear elements to billions of non elements where we are now with the optics is probably very similar to 70 years ago, which is a table talk implementation. And the question is, how can we utilize nano photonics? I'm gonna just briefly show you the two directions on that which we're working on. One is based on lithium Diabate, and the other is based on even a smaller resonate er's could you? So the work on Nana Photonic lithium naive. It was started in collaboration with Harvard Marko Loncar, and also might affair at Stanford. And, uh, we could show that you can do the periodic polling in the phenomenon of it and get all sorts of very highly nonlinear processes happening in this net. Photonic periodically polls if, um Diabate. And now we're working on building. Opio was based on that kind of photonic the film Diabate. And these air some some examples of the devices that we have been building in the past few months, which I'm not gonna tell you more about. But the O. P. O. S. And the Opio Networks are in the works. And that's not the only way of making large networks. Um, but also I want to point out that The reason that these Nana photonic goblins are actually exciting is not just because you can make a large networks and it can make him compact in a in a small footprint. They also provide some opportunities in terms of the operation regime. On one of them is about making cat states and Opio, which is, can we have the quantum superposition of the zero pie states that I talked about and the Net a photonic within? I've It provides some opportunities to actually get closer to that regime because of the spatial temporal confinement that you can get in these wave guides. So we're doing some theory on that. We're confident that the type of non linearity two losses that it can get with these platforms are actually much higher than what you can get with other platform their existing platforms and to go even smaller. We have been asking the question off. What is the smallest possible Opio that you can make? Then you can think about really wavelength scale type, resonate er's and adding the chi to non linearity and see how and when you can get the Opio to operate. And recently, in collaboration with us see, we have been actually USC and Creole. We have demonstrated that you can use nano lasers and get some spin Hamilton and implementations on those networks. So if you can build the a P. O s, we know that there is a path for implementing Opio Networks on on such a nano scale. So we have looked at these calculations and we try to estimate the threshold of a pos. Let's say for me resonator and it turns out that it can actually be even lower than the type of bulk Pip Llano Pos that we have been building in the past 50 years or so. So we're working on the experiments and we're hoping that we can actually make even larger and larger scale Opio networks. So let me summarize the talk I told you about the opium networks and our work that has been going on on icing machines and the measurement feedback. And I told you about the ongoing work on the all optical implementations both on the linear side and also on the nonlinear behaviors. And I also told you a little bit about the efforts on miniaturization and going to the to the Nano scale. So with that, I would like Thio >>three from the University of Tokyo. Before I thought that would like to thank you showing all the stuff of entity for the invitation and the organization of this online meeting and also would like to say that it has been very exciting to see the growth of this new film lab. And I'm happy to share with you today of some of the recent works that have been done either by me or by character of Hong Kong. Honest Group indicates the title of my talk is a neuro more fic in silica simulator for the communities in machine. And here is the outline I would like to make the case that the simulation in digital Tektronix of the CME can be useful for the better understanding or improving its function principles by new job introducing some ideas from neural networks. This is what I will discuss in the first part and then it will show some proof of concept of the game and performance that can be obtained using dissimulation in the second part and the protection of the performance that can be achieved using a very large chaos simulator in the third part and finally talk about future plans. So first, let me start by comparing recently proposed izing machines using this table there is elected from recent natural tronics paper from the village Park hard people, and this comparison shows that there's always a trade off between energy efficiency, speed and scalability that depends on the physical implementation. So in red, here are the limitation of each of the servers hardware on, interestingly, the F p G, a based systems such as a producer, digital, another uh Toshiba beautification machine or a recently proposed restricted Bozeman machine, FPD A by a group in Berkeley. They offer a good compromise between speed and scalability. And this is why, despite the unique advantage that some of these older hardware have trust as the currency proposition in Fox, CBS or the energy efficiency off memory Sisters uh P. J. O are still an attractive platform for building large organizing machines in the near future. The reason for the good performance of Refugee A is not so much that they operate at the high frequency. No, there are particular in use, efficient, but rather that the physical wiring off its elements can be reconfigured in a way that limits the funding human bottleneck, larger, funny and phenols and the long propagation video information within the system. In this respect, the LPGA is They are interesting from the perspective off the physics off complex systems, but then the physics of the actions on the photos. So to put the performance of these various hardware and perspective, we can look at the competition of bringing the brain the brain complete, using billions of neurons using only 20 watts of power and operates. It's a very theoretically slow, if we can see and so this impressive characteristic, they motivate us to try to investigate. What kind of new inspired principles be useful for designing better izing machines? The idea of this research project in the future collaboration it's to temporary alleviates the limitations that are intrinsic to the realization of an optical cortex in machine shown in the top panel here. By designing a large care simulator in silicone in the bottom here that can be used for digesting the better organization principles of the CIA and this talk, I will talk about three neuro inspired principles that are the symmetry of connections, neural dynamics orphan chaotic because of symmetry, is interconnectivity the infrastructure? No. Next talks are not composed of the reputation of always the same types of non environments of the neurons, but there is a local structure that is repeated. So here's the schematic of the micro column in the cortex. And lastly, the Iraqi co organization of connectivity connectivity is organizing a tree structure in the brain. So here you see a representation of the Iraqi and organization of the monkey cerebral cortex. So how can these principles we used to improve the performance of the icing machines? And it's in sequence stimulation. So, first about the two of principles of the estimate Trian Rico structure. We know that the classical approximation of the car testing machine, which is the ground toe, the rate based on your networks. So in the case of the icing machines, uh, the okay, Scott approximation can be obtained using the trump active in your position, for example, so the times of both of the system they are, they can be described by the following ordinary differential equations on in which, in case of see, I am the X, I represent the in phase component of one GOP Oh, Theo f represents the monitor optical parts, the district optical Parametric amplification and some of the good I JoJo extra represent the coupling, which is done in the case of the measure of feedback coupling cm using oh, more than detection and refugee A and then injection off the cooking time and eso this dynamics in both cases of CNN in your networks, they can be written as the grand set of a potential function V, and this written here, and this potential functionally includes the rising Maccagnan. So this is why it's natural to use this type of, uh, dynamics to solve the icing problem in which the Omega I J or the eyes in coping and the H is the extension of the icing and attorney in India and expect so. Not that this potential function can only be defined if the Omega I j. R. A. Symmetric. So the well known problem of this approach is that this potential function V that we obtain is very non convicts at low temperature, and also one strategy is to gradually deformed this landscape, using so many in process. But there is no theorem. Unfortunately, that granted conventions to the global minimum of There's even Tony and using this approach. And so this is why we propose, uh, to introduce a macro structures of the system where one analog spin or one D O. P. O is replaced by a pair off one another spin and one error, according viable. And the addition of this chemical structure introduces a symmetry in the system, which in terms induces chaotic dynamics, a chaotic search rather than a learning process for searching for the ground state of the icing. Every 20 within this massacre structure the role of the er variable eyes to control the amplitude off the analog spins toe force. The amplitude of the expense toe become equal to certain target amplitude a uh and, uh, and this is done by modulating the strength off the icing complaints or see the the error variable E I multiply the icing complaint here in the dynamics off air d o p. O. On then the dynamics. The whole dynamics described by this coupled equations because the e I do not necessarily take away the same value for the different. I thesis introduces a symmetry in the system, which in turn creates security dynamics, which I'm sure here for solving certain current size off, um, escape problem, Uh, in which the X I are shown here and the i r from here and the value of the icing energy showing the bottom plots. You see this Celtics search that visit various local minima of the as Newtonian and eventually finds the global minimum? Um, it can be shown that this modulation off the target opportunity can be used to destabilize all the local minima off the icing evertonians so that we're gonna do not get stuck in any of them. On more over the other types of attractors I can eventually appear, such as limits I contractors, Okot contractors. They can also be destabilized using the motivation of the target and Batuta. And so we have proposed in the past two different moderation of the target amateur. The first one is a modulation that ensure the uh 100 reproduction rate of the system to become positive on this forbids the creation off any nontrivial tractors. And but in this work, I will talk about another moderation or arrested moderation which is given here. That works, uh, as well as this first uh, moderation, but is easy to be implemented on refugee. So this couple of the question that represent becoming the stimulation of the cortex in machine with some error correction they can be implemented especially efficiently on an F B. G. And here I show the time that it takes to simulate three system and also in red. You see, at the time that it takes to simulate the X I term the EI term, the dot product and the rising Hamiltonian for a system with 500 spins and Iraq Spain's equivalent to 500 g. O. P. S. So >>in >>f b d a. The nonlinear dynamics which, according to the digital optical Parametric amplification that the Opa off the CME can be computed in only 13 clock cycles at 300 yards. So which corresponds to about 0.1 microseconds. And this is Toby, uh, compared to what can be achieved in the measurements back O C. M. In which, if we want to get 500 timer chip Xia Pios with the one she got repetition rate through the obstacle nine narrative. Uh, then way would require 0.5 microseconds toe do this so the submission in F B J can be at least as fast as ah one g repression. Uh, replicate pulsed laser CIA Um, then the DOT product that appears in this differential equation can be completed in 43 clock cycles. That's to say, one microseconds at 15 years. So I pieced for pouring sizes that are larger than 500 speeds. The dot product becomes clearly the bottleneck, and this can be seen by looking at the the skating off the time the numbers of clock cycles a text to compute either the non in your optical parts or the dog products, respect to the problem size. And And if we had infinite amount of resources and PGA to simulate the dynamics, then the non illogical post can could be done in the old one. On the mattress Vector product could be done in the low carrot off, located off scales as a look at it off and and while the guide off end. Because computing the dot product involves assuming all the terms in the product, which is done by a nephew, GE by another tree, which heights scarce logarithmic any with the size of the system. But This is in the case if we had an infinite amount of resources on the LPGA food, but for dealing for larger problems off more than 100 spins. Usually we need to decompose the metrics into ah, smaller blocks with the block side that are not you here. And then the scaling becomes funny, non inner parts linear in the end, over you and for the products in the end of EU square eso typically for low NF pdf cheap PGA you the block size off this matrix is typically about 100. So clearly way want to make you as large as possible in order to maintain this scanning in a log event for the numbers of clock cycles needed to compute the product rather than this and square that occurs if we decompose the metrics into smaller blocks. But the difficulty in, uh, having this larger blocks eyes that having another tree very large Haider tree introduces a large finding and finance and long distance start a path within the refugee. So the solution to get higher performance for a simulator of the contest in machine eyes to get rid of this bottleneck for the dot product by increasing the size of this at the tree. And this can be done by organizing your critique the electrical components within the LPGA in order which is shown here in this, uh, right panel here in order to minimize the finding finance of the system and to minimize the long distance that a path in the in the fpt So I'm not going to the details of how this is implemented LPGA. But just to give you a idea off why the Iraqi Yahiko organization off the system becomes the extremely important toe get good performance for similar organizing machine. So instead of instead of getting into the details of the mpg implementation, I would like to give some few benchmark results off this simulator, uh, off the that that was used as a proof of concept for this idea which is can be found in this archive paper here and here. I should results for solving escape problems. Free connected person, randomly person minus one spring last problems and we sure, as we use as a metric the numbers of the mattress Victor products since it's the bottleneck of the computation, uh, to get the optimal solution of this escape problem with the Nina successful BT against the problem size here and and in red here, this propose FDJ implementation and in ah blue is the numbers of retrospective product that are necessary for the C. I am without error correction to solve this escape programs and in green here for noisy means in an evening which is, uh, behavior with similar to the Cartesian mission. Uh, and so clearly you see that the scaring off the numbers of matrix vector product necessary to solve this problem scales with a better exponents than this other approaches. So So So that's interesting feature of the system and next we can see what is the real time to solution to solve this SK instances eso in the last six years, the time institution in seconds to find a grand state of risk. Instances remain answers probability for different state of the art hardware. So in red is the F B g. A presentation proposing this paper and then the other curve represent Ah, brick a local search in in orange and silver lining in purple, for example. And so you see that the scaring off this purpose simulator is is rather good, and that for larger plant sizes we can get orders of magnitude faster than the state of the art approaches. Moreover, the relatively good scanning off the time to search in respect to problem size uh, they indicate that the FPD implementation would be faster than risk. Other recently proposed izing machine, such as the hope you know, natural complimented on memories distance that is very fast for small problem size in blue here, which is very fast for small problem size. But which scanning is not good on the same thing for the restricted Bosman machine. Implementing a PGA proposed by some group in Broken Recently Again, which is very fast for small parliament sizes but which canning is bad so that a dis worse than the proposed approach so that we can expect that for programs size is larger than 1000 spins. The proposed, of course, would be the faster one. Let me jump toe this other slide and another confirmation that the scheme scales well that you can find the maximum cut values off benchmark sets. The G sets better candidates that have been previously found by any other algorithms, so they are the best known could values to best of our knowledge. And, um or so which is shown in this paper table here in particular, the instances, uh, 14 and 15 of this G set can be We can find better converse than previously known, and we can find this can vary is 100 times faster than the state of the art algorithm and CP to do this which is a very common Kasich. It s not that getting this a good result on the G sets, they do not require ah, particular hard tuning of the parameters. So the tuning issuing here is very simple. It it just depends on the degree off connectivity within each graph. And so this good results on the set indicate that the proposed approach would be a good not only at solving escape problems in this problems, but all the types off graph sizing problems on Mexican province in communities. So given that the performance off the design depends on the height of this other tree, we can try to maximize the height of this other tree on a large F p g a onda and carefully routing the components within the P G A and and we can draw some projections of what type of performance we can achieve in the near future based on the, uh, implementation that we are currently working. So here you see projection for the time to solution way, then next property for solving this escape programs respect to the prime assize. And here, compared to different with such publicizing machines, particularly the digital. And, you know, 42 is shown in the green here, the green line without that's and, uh and we should two different, uh, hypothesis for this productions either that the time to solution scales as exponential off n or that the time of social skills as expression of square root off. So it seems, according to the data, that time solution scares more as an expression of square root of and also we can be sure on this and this production show that we probably can solve prime escape problem of science 2000 spins, uh, to find the rial ground state of this problem with 99 success ability in about 10 seconds, which is much faster than all the other proposed approaches. So one of the future plans for this current is in machine simulator. So the first thing is that we would like to make dissimulation closer to the rial, uh, GOP oh, optical system in particular for a first step to get closer to the system of a measurement back. See, I am. And to do this what is, uh, simulate Herbal on the p a is this quantum, uh, condoms Goshen model that is proposed described in this paper and proposed by people in the in the Entity group. And so the idea of this model is that instead of having the very simple or these and have shown previously, it includes paired all these that take into account on me the mean off the awesome leverage off the, uh, European face component, but also their violence s so that we can take into account more quantum effects off the g o p. O, such as the squeezing. And then we plan toe, make the simulator open access for the members to run their instances on the system. There will be a first version in September that will be just based on the simple common line access for the simulator and in which will have just a classic or approximation of the system. We don't know Sturm, binary weights and museum in term, but then will propose a second version that would extend the current arising machine to Iraq off F p g. A, in which we will add the more refined models truncated, ignoring the bottom Goshen model they just talked about on the support in which he valued waits for the rising problems and support the cement. So we will announce later when this is available and and far right is working >>hard comes from Universal down today in physics department, and I'd like to thank the organizers for their kind invitation to participate in this very interesting and promising workshop. Also like to say that I look forward to collaborations with with a file lab and Yoshi and collaborators on the topics of this world. So today I'll briefly talk about our attempt to understand the fundamental limits off another continues time computing, at least from the point off you off bullion satisfy ability, problem solving, using ordinary differential equations. But I think the issues that we raise, um, during this occasion actually apply to other other approaches on a log approaches as well and into other problems as well. I think everyone here knows what Dorien satisfy ability. Problems are, um, you have boolean variables. You have em clauses. Each of disjunction of collaterals literally is a variable, or it's, uh, negation. And the goal is to find an assignment to the variable, such that order clauses are true. This is a decision type problem from the MP class, which means you can checking polynomial time for satisfy ability off any assignment. And the three set is empty, complete with K three a larger, which means an efficient trees. That's over, uh, implies an efficient source for all the problems in the empty class, because all the problems in the empty class can be reduced in Polian on real time to reset. As a matter of fact, you can reduce the NP complete problems into each other. You can go from three set to set backing or two maximum dependent set, which is a set packing in graph theoretic notions or terms toe the icing graphs. A problem decision version. This is useful, and you're comparing different approaches, working on different kinds of problems when not all the closest can be satisfied. You're looking at the accusation version offset, uh called Max Set. And the goal here is to find assignment that satisfies the maximum number of clauses. And this is from the NPR class. In terms of applications. If we had inefficient sets over or np complete problems over, it was literally, positively influenced. Thousands off problems and applications in industry and and science. I'm not going to read this, but this this, of course, gives a strong motivation toe work on this kind of problems. Now our approach to set solving involves embedding the problem in a continuous space, and you use all the east to do that. So instead of working zeros and ones, we work with minus one across once, and we allow the corresponding variables toe change continuously between the two bounds. We formulate the problem with the help of a close metrics. If if a if a close, uh, does not contain a variable or its negation. The corresponding matrix element is zero. If it contains the variable in positive, for which one contains the variable in a gated for Mitt's negative one, and then we use this to formulate this products caused quote, close violation functions one for every clause, Uh, which really, continuously between zero and one. And they're zero if and only if the clause itself is true. Uh, then we form the define in order to define a dynamic such dynamics in this and dimensional hyper cube where the search happens and if they exist, solutions. They're sitting in some of the corners of this hyper cube. So we define this, uh, energy potential or landscape function shown here in a way that this is zero if and only if all the clauses all the kmc zero or the clauses off satisfied keeping these auxiliary variables a EMS always positive. And therefore, what you do here is a dynamics that is a essentially ingredient descend on this potential energy landscape. If you were to keep all the M's constant that it would get stuck in some local minimum. However, what we do here is we couple it with the dynamics we cooperated the clothes violation functions as shown here. And if he didn't have this am here just just the chaos. For example, you have essentially what case you have positive feedback. You have increasing variable. Uh, but in that case, you still get stuck would still behave will still find. So she is better than the constant version but still would get stuck only when you put here this a m which makes the dynamics in in this variable exponential like uh, only then it keeps searching until he finds a solution on deer is a reason for that. I'm not going toe talk about here, but essentially boils down toe performing a Grady and descend on a globally time barren landscape. And this is what works. Now I'm gonna talk about good or bad and maybe the ugly. Uh, this is, uh, this is What's good is that it's a hyperbolic dynamical system, which means that if you take any domain in the search space that doesn't have a solution in it or any socially than the number of trajectories in it decays exponentially quickly. And the decay rate is a characteristic in variant characteristic off the dynamics itself. Dynamical systems called the escape right the inverse off that is the time scale in which you find solutions by this by this dynamical system, and you can see here some song trajectories that are Kelty because it's it's no linear, but it's transient, chaotic. Give their sources, of course, because eventually knowledge to the solution. Now, in terms of performance here, what you show for a bunch off, um, constraint densities defined by M overran the ratio between closes toe variables for random, said Problems is random. Chris had problems, and they as its function off n And we look at money toward the wartime, the wall clock time and it behaves quite value behaves Azat party nominally until you actually he to reach the set on set transition where the hardest problems are found. But what's more interesting is if you monitor the continuous time t the performance in terms off the A narrow, continuous Time t because that seems to be a polynomial. And the way we show that is, we consider, uh, random case that random three set for a fixed constraint density Onda. We hear what you show here. Is that the right of the trash hold that it's really hard and, uh, the money through the fraction of problems that we have not been able to solve it. We select thousands of problems at that constraint ratio and resolve them without algorithm, and we monitor the fractional problems that have not yet been solved by continuous 90. And this, as you see these decays exponentially different. Educate rates for different system sizes, and in this spot shows that is dedicated behaves polynomial, or actually as a power law. So if you combine these two, you find that the time needed to solve all problems except maybe appear traction off them scales foreign or merely with the problem size. So you have paranormal, continuous time complexity. And this is also true for other types of very hard constraints and sexual problems such as exact cover, because you can always transform them into three set as we discussed before, Ramsey coloring and and on these problems, even algorithms like survey propagation will will fail. But this doesn't mean that P equals NP because what you have first of all, if you were toe implement these equations in a device whose behavior is described by these, uh, the keys. Then, of course, T the continue style variable becomes a physical work off. Time on that will be polynomial is scaling, but you have another other variables. Oxidative variables, which structured in an exponential manner. So if they represent currents or voltages in your realization and it would be an exponential cost Al Qaeda. But this is some kind of trade between time and energy, while I know how toe generate energy or I don't know how to generate time. But I know how to generate energy so it could use for it. But there's other issues as well, especially if you're trying toe do this son and digital machine but also happens. Problems happen appear. Other problems appear on in physical devices as well as we discuss later. So if you implement this in GPU, you can. Then you can get in order off to magnitude. Speed up. And you can also modify this to solve Max sad problems. Uh, quite efficiently. You are competitive with the best heuristic solvers. This is a weather problems. In 2016 Max set competition eso so this this is this is definitely this seems like a good approach, but there's off course interesting limitations, I would say interesting, because it kind of makes you think about what it means and how you can exploit this thes observations in understanding better on a low continues time complexity. If you monitored the discrete number the number of discrete steps. Don't buy the room, Dakota integrator. When you solve this on a digital machine, you're using some kind of integrator. Um and you're using the same approach. But now you measure the number off problems you haven't sold by given number of this kid, uh, steps taken by the integrator. You find out you have exponential, discrete time, complexity and, of course, thistles. A problem. And if you look closely, what happens even though the analog mathematical trajectory, that's the record here. If you monitor what happens in discrete time, uh, the integrator frustrates very little. So this is like, you know, third or for the disposition, but fluctuates like crazy. So it really is like the intervention frees us out. And this is because of the phenomenon of stiffness that are I'll talk a little bit a more about little bit layer eso. >>You know, it might look >>like an integration issue on digital machines that you could improve and could definitely improve. But actually issues bigger than that. It's It's deeper than that, because on a digital machine there is no time energy conversion. So the outside variables are efficiently representing a digital machine. So there's no exponential fluctuating current of wattage in your computer when you do this. Eso If it is not equal NP then the exponential time, complexity or exponential costs complexity has to hit you somewhere. And this is how um, but, you know, one would be tempted to think maybe this wouldn't be an issue in a analog device, and to some extent is true on our devices can be ordered to maintain faster, but they also suffer from their own problems because he not gonna be affect. That classes soldiers as well. So, indeed, if you look at other systems like Mirandizing machine measurement feedback, probably talk on the grass or selected networks. They're all hinge on some kind off our ability to control your variables in arbitrary, high precision and a certain networks you want toe read out across frequencies in case off CM's. You required identical and program because which is hard to keep, and they kind of fluctuate away from one another, shift away from one another. And if you control that, of course that you can control the performance. So actually one can ask if whether or not this is a universal bottleneck and it seems so aside, I will argue next. Um, we can recall a fundamental result by by showing harder in reaction Target from 1978. Who says that it's a purely computer science proof that if you are able toe, compute the addition multiplication division off riel variables with infinite precision, then you could solve any complete problems in polynomial time. It doesn't actually proposals all where he just chose mathematically that this would be the case. Now, of course, in Real warned, you have also precision. So the next question is, how does that affect the competition about problems? This is what you're after. Lots of precision means information also, or entropy production. Eso what you're really looking at the relationship between hardness and cost of computing off a problem. Uh, and according to Sean Hagar, there's this left branch which in principle could be polynomial time. But the question whether or not this is achievable that is not achievable, but something more cheerful. That's on the right hand side. There's always going to be some information loss, so mental degeneration that could keep you away from possibly from point normal time. So this is what we like to understand, and this information laws the source off. This is not just always I will argue, uh, in any physical system, but it's also off algorithm nature, so that is a questionable area or approach. But China gets results. Security theoretical. No, actual solar is proposed. So we can ask, you know, just theoretically get out off. Curiosity would in principle be such soldiers because it is not proposing a soldier with such properties. In principle, if if you want to look mathematically precisely what the solar does would have the right properties on, I argue. Yes, I don't have a mathematical proof, but I have some arguments that that would be the case. And this is the case for actually our city there solver that if you could calculate its trajectory in a loss this way, then it would be, uh, would solve epic complete problems in polynomial continuous time. Now, as a matter of fact, this a bit more difficult question, because time in all these can be re scared however you want. So what? Burns says that you actually have to measure the length of the trajectory, which is a new variant off the dynamical system or property dynamical system, not off its parameters ization. And we did that. So Suba Corral, my student did that first, improving on the stiffness off the problem off the integrations, using implicit solvers and some smart tricks such that you actually are closer to the actual trajectory and using the same approach. You know what fraction off problems you can solve? We did not give the length of the trajectory. You find that it is putting on nearly scaling the problem sites we have putting on your skin complexity. That means that our solar is both Polly length and, as it is, defined it also poorly time analog solver. But if you look at as a discreet algorithm, if you measure the discrete steps on a digital machine, it is an exponential solver. And the reason is because off all these stiffness, every integrator has tow truck it digitizing truncate the equations, and what it has to do is to keep the integration between the so called stability region for for that scheme, and you have to keep this product within a grimace of Jacoby in and the step size read in this region. If you use explicit methods. You want to stay within this region? Uh, but what happens that some off the Eigen values grow fast for Steve problems, and then you're you're forced to reduce that t so the product stays in this bonded domain, which means that now you have to you're forced to take smaller and smaller times, So you're you're freezing out the integration and what I will show you. That's the case. Now you can move to increase its soldiers, which is which is a tree. In this case, you have to make domain is actually on the outside. But what happens in this case is some of the Eigen values of the Jacobean, also, for six systems, start to move to zero. As they're moving to zero, they're going to enter this instability region, so your soul is going to try to keep it out, so it's going to increase the data T. But if you increase that to increase the truncation hours, so you get randomized, uh, in the large search space, so it's it's really not, uh, not going to work out. Now, one can sort off introduce a theory or language to discuss computational and are computational complexity, using the language from dynamical systems theory. But basically I I don't have time to go into this, but you have for heart problems. Security object the chaotic satellite Ouch! In the middle of the search space somewhere, and that dictates how the dynamics happens and variant properties off the dynamics. Of course, off that saddle is what the targets performance and many things, so a new, important measure that we find that it's also helpful in describing thesis. Another complexity is the so called called Makarov, or metric entropy and basically what this does in an intuitive A eyes, uh, to describe the rate at which the uncertainty containing the insignificant digits off a trajectory in the back, the flow towards the significant ones as you lose information because off arrows being, uh grown or are developed in tow. Larger errors in an exponential at an exponential rate because you have positively up north spawning. But this is an in variant property. It's the property of the set of all. This is not how you compute them, and it's really the interesting create off accuracy philosopher dynamical system. A zay said that you have in such a high dimensional that I'm consistent were positive and negatively upon of exponents. Aziz Many The total is the dimension of space and user dimension, the number off unstable manifold dimensions and as Saddam was stable, manifold direction. And there's an interesting and I think, important passion, equality, equality called the passion, equality that connect the information theoretic aspect the rate off information loss with the geometric rate of which trajectory separate minus kappa, which is the escape rate that I already talked about. Now one can actually prove a simple theorems like back off the envelope calculation. The idea here is that you know the rate at which the largest rated, which closely started trajectory separate from one another. So now you can say that, uh, that is fine, as long as my trajectory finds the solution before the projective separate too quickly. In that case, I can have the hope that if I start from some region off the face base, several close early started trajectories, they kind of go into the same solution orphaned and and that's that's That's this upper bound of this limit, and it is really showing that it has to be. It's an exponentially small number. What? It depends on the end dependence off the exponents right here, which combines information loss rate and the social time performance. So these, if this exponents here or that has a large independence or river linear independence, then you then you really have to start, uh, trajectories exponentially closer to one another in orderto end up in the same order. So this is sort off like the direction that you're going in tow, and this formulation is applicable toe all dynamical systems, uh, deterministic dynamical systems. And I think we can We can expand this further because, uh, there is, ah, way off getting the expression for the escaped rate in terms off n the number of variables from cycle expansions that I don't have time to talk about. What? It's kind of like a program that you can try toe pursuit, and this is it. So the conclusions I think of self explanatory I think there is a lot of future in in, uh, in an allo. Continue start computing. Um, they can be efficient by orders of magnitude and digital ones in solving empty heart problems because, first of all, many of the systems you like the phone line and bottleneck. There's parallelism involved, and and you can also have a large spectrum or continues time, time dynamical algorithms than discrete ones. And you know. But we also have to be mindful off. What are the possibility of what are the limits? And 11 open question is very important. Open question is, you know, what are these limits? Is there some kind off no go theory? And that tells you that you can never perform better than this limit or that limit? And I think that's that's the exciting part toe to derive thes thes this levian 10.

if Studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cute conversation hey welcome everybody Jeff Rick here with the cube we're in our Palo Alto studios today it's been a crazy couple of weeks but things seem to have settled and one of the results of what's happening is everyone now is sheltering at home working from home so we wanted to take a few minutes to talk about some of the best practices that we've seen when you are joining a video from home if we've got you scheduled for a cube interview in the next several months we'll probably be doing it remotely with you dialing and from your laptop I'm sure you're doing lots of zoom meetings and Skype meetings and WebEx meetings and all the other meetings so we wanted to go through really a couple key things to help you have a better quality video experience and there's really six things that we're gonna cover today number one you got to get a hard line bandwidth this super super important there's some other things we'll talk about in terms of firewalls etc number two camera position really really important it goes a long way and really improving the experience for you but also the people on the other side of the of the conversation number three will go into audio and really best practices on audio Audio is super important for background something that's often forgot about but really can make a big difference in what's going on five or talked a little bit about lighting and six clothing which is you know kind of at the end of the list in a situation like today one on bandwidth a hard line makes a huge difference go out get yourself a dongle if you don't have a dongle my favorite brand is anchor but when you have a consistent hard line it's going to make everything work a lot better at the same time you also want to plug your computer and plug the laptop in there's all kinds of battery saving functions and power functions that are disabled when you're running on battery power so plug it in talk about camera position really it's all about having the camera at eye level so that when you're looking at your laptop it looks like you're looking at the people you need to look into the camera that really helps experience from term in terms of you know not looking down or having the camera look up your nose which is not only an unflattering position but it's just not a good look the third thing we'll talk about is audio whether you have ear pods if you're a Apple person if you're a gamer and you've got a hard line with headset and a microphone this is not a place to skimp it you can use the microphone in your laptop but it's better if you have a standalone microphone third thing is background we'll wait till we get into the other room to show that and then lighting and clothing so with that let's get off this beautiful welded studio and go to an actual situation okay so the first thing we see all the time is people have their laptop on the table usually the tops tip back a little bit it's kind of an up to no shot not very flattering nobody wants to see that shot so a really simple way to get the camera eye level the same as your regular eyes are these handy-dandy things called books so what we'll do is we'll take a stack of books we'll slip it under the laptop and what that will enable me to do is get a really good shot and now I can look at the the laptop I can pay attention to the presentation and also look into the camera it's really close together and it's a much better experience okay the next thing that we wanted to talk about really is the audio so you can use the audio on the laptop it's usually not that great there's a lot of echo in the room potentially and there might be a little bit of a lag so we strongly recommend that you either go with Apple earbuds if that's what your thing is or you get a gamer headset you want one that has both the microphone and the over ear the next thing is what's going on in the background a couple things you really want to watch out for number one top secret whiteboards you want your whiteboard in the background make sure your background is clear of that type of material but more importantly is really the lighting what you want to do is make it easy for your camera light and the way you make it easy for your camera light is to have a minimum amount of super darks and super lights so one of the things we see all the time with really bright backgrounds is windows so if I swing my set up here and if I was to sit with my back to the window you can see much harder challenge for the camera it's really not a good look so if you have a window in your home office make sure you pull the curtains put some shade it's really tough for the camera now by simply switching either 90 degrees to the position where I was before or even 180 which is even better now I have the benefit of the light from the window coming through and not as a backdrop much better look much better look adjust the Headroom and here we are so the next thing I want to talk about is lighting and lighting is really really important so if you can have natural light coming in turn on all the lights in your room but you still might want a spotlight for the front of your face I'm a big fan of what's called a loom cube full disclosure I don't get paid by them and they've been paid by them I bought this myself but I like the Loom cube because it's really small it's really simple it's rechargeable and mainly because it's got a six-step give me a 10-step bright brightness function and I can get diffusers and filters and all this other fun stuff so what I could do is put this slightly off to the side I already had pretty good light coming in from the window and I can add a little fill with the limb cube you can see as I step that up it gets brighter and brighter try to position it so we don't have any any clear off the glasses but you can see that somebody's fill these things are not that expensive whether you get a loom cube or some other cube go get a little light it makes a huge difference some of them attach to laptops this one I have on we're called the Joby legs which are kind of fun little legs you can stick on any camera so get a light again this is not only for the cube interview that we look forward to having with you but it's also for all of your other online meetings your zooms your WebEx the last thing I want to talk about really is clothing these this clothes is actually a little bit dark I got the dark blue and black underneath again what you want to do is make it easy on the camera so you want to avoid tight patterns you want to avoid tight stripes you want to avoid green and try to have something that's pretty easy for the camera to deal with this not too bright not too dark it's something that that is really easy for the camera to pick up so hopefully you've enjoyed some of these tips hopefully this will help you be more productive in your in your zoom calls and your cube interviews and your skypes and webex's etc we look forward to catching up everybody hang in there this too will pass we'll get through these tough days and just help help help out your friends help everybody out great to see you we'll see you next time thanks for checking in [Music]

if Studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cute conversation hey welcome everybody Jeff Rick here with the cube we're in our Palo Alto studios today it's been a crazy couple of weeks but things seem to have settled and one of the results of what's happening is everyone now is sheltering at home working from home so we wanted to take a few minutes to talk about some of the best practices that we've seen when you are joining a video from home if we've got you scheduled for a cube interview in the next several months we'll probably be doing it remotely with you dialing and from your laptop I'm sure you're doing lots of zoom meetings and Skype meetings and WebEx meetings and all the other meetings so we wanted to go through really a couple key things to help you have a better quality video experience and there's really six things that we're gonna cover today number one you got to get a hard line bandwidth this super super important there's some other things we'll talk about in terms of firewalls etc number two camera position really really important it goes a long way and really improving the experience for you but also the people on the other side of the of the conversation number three will go into audio and really best practices on audio Audio is super important for background something that's often forgot about but really can make a big difference in what's going on five or talked a little bit about lighting and six clothing which is you know kind of at the end of the list in a situation like today but those are the things we want to talk about so Before we jump into it I just want to cover a few basic things and then we'll get into more detail one on bandwidth a hard line makes a huge difference go out get yourself a dongle if you don't have a dongle their way expensive my favorite brand is anchor but when you have a consistent hard line it's gonna make everything work a lot better at the same time you also want to plug your computer and plug the laptop in there's all kinds of battery saving functions and power functions that are disabled when you're running on battery power so plug it in second thing we'll talk about camera position really it's all about having the camera at eye level so that when you're looking at your laptop it looks like you're looking at the people you need to look into the camera that really helps experience from term in terms of you know not looking down or having the camera look up your nose with is not only an unflattering position but just not a good look the third thing we'll talk about is audio whether you have air pods if your Apple person or if you're a gamer and you've got a hard line with headset and a microphone this is not a place to skimp it you can use the microphone in your laptop but it's better if you have a standalone microphone third thing is background we'll wait till we get into the other room to show that and then lighting and clothing so with that let's get off this beautiful welded studio and go to an actual situation okay so the first thing we see all the time is people have their laptop on the table usually the tops tip back a little bit it's kind of an up to know shot not very flattering nobody wants to see that shot so a really simple way to get the camera eye level the same as your regular eyes are these handy-dandy things called books so what we'll do is we'll take a stack of books we'll slip it under the laptop and what that will enable me to do is get a really good shot and now I can look at the the laptop I can pay attention to the presentation and also look into the camera it's really close together and it's a much better experience so step 1 get your eyes in line with your camera ok the next thing that we wanted to talk about really is the audio so you can use the audio on the laptop it's usually not that great there's a lot of echo in the room potentially and there might be a little bit of a lag so we strongly recommend that you either go with Apple earbuds if that's what your thing is or you get a gamer headset you want one that has both the microphone and the over ear which seems a little extreme again you can go with the iPod but this is going to give you much better sound so people can hear what you're listening to now that you've got your audio set you've got your you can listen in you've got your mic at the right or excuse me your camera at the right level the next thing is what's going on in the background a couple things you really want to watch out for number one top secret whiteboards you don't want your whiteboard in the background make sure your background is clear of that type of material but more importantly is really the lighting what you want to do is make it easy for your camera light and the way you make it easy for your camera light is to have a minimum amount of super darks and super lights so one of the things we see all the time with really bright backgrounds is windows so if I swing my set up here and if I was to sit with my back to the window you can see much harder challenge for the camera it's really not a good look so if you have a window in your home office make sure you pull the curtains put some shades it's really tough for the camera now by simply switching either 90 degrees to the position where I was before 90 degrees does position where I was before or even 180 which is even better now I have the benefit of the light from the window coming through and not as a backdrop much better look much better look adjust the Headroom and here we are so the next thing I want to talk about is lighting and lighting is really really important so if you can have natural light coming in turn on all the lights in your room but you still might want a spotlight for the front of your face I'm a big fan of what's called a loom cube full disclosure I don't get paid by them I've never been paid by them I bought this myself but I like the loom cube because it's really small it's really simple it's rechargeable and mainly because it's got a six-step or give me a 10-step bright brightness function and I can get diffusers and filters and all this other fun stuff so what I could do is put this slightly off to the side I already have pretty good light coming in from the window and I can add a little fill with the link cube you can see as I step that up it gets brighter and brighter try to position it so we don't have any any clear off the glasses but you can see that somebody's fill these things are not that expensive whether you get a loom cube or some other cube go get a little light it makes a huge difference some of them attach to laptops this one I have on we're called the Joby legs which are kind of fun little legs you can stick on any camera so get a light again this is not only for the cube interview that we look forward to having with you but it's also for all of your other online meetings your zooms your WebEx the last thing I want to talk about really is clothing these this clothes is actually a little bit dark I've got the dark blue and black underneath again what you want to do is make it easy on the camera so you want to avoid tight patterns you want to avoid tight stripes you want to avoid green and try to have something that's pretty easy for the camera to deal with this not too bright not too dark it's something that that is really easy for the camera to pick up so hopefully you've enjoyed some of these tips hopefully this will help you be more productive in your in your zoom calls and your cube interviews and your skypes and your WebEx is etc we look forward to catching up everybody hang in there this too will pass we'll get through these tough days and just help help help out your friends help everybody out great to see you we'll see you next time thanks for checking in [Music]

yeah all right here we go hello and welcome to the LTA capability review here from the studio so our first capability of LTA is having one host and one guest we can have somebody host from the Palo Alto studio or we can have somebody host remotely from anywhere else in the world so we can have one host one guest from Palo Alto or Boston have a cute conversation the normal way next up we have two guests in the studio we can bring two guests from any location whether they are in the same room or not as long as they have two different computers into this into the studio again with a host from Palo Alto or with a host from Boston in in in this session we have we have the host with the first guest with the second guest we have the option to have the host in between two guests or if we have the option to have the host with the two guests next to each other again this host can be in Palo Alto or in Boston and we have a normal panel style cube conversation with two guests next up last and not least we can have a three guest call-in interview we can bring three guests into the show and have the three guests together in a panel like setting and more of a panel like setting all three of these guests can be in any location again for the last time we can have a host in palo alto or a host in boston it is interchangeable we'll have the host with guest number one closed with guest number two host with guest number three and a four shot of the host and all three of their guests that concludes the capabilities of LTA as of now me

(energetic music) >> Announcer: From our studios in the heart of Silicon Valley, Palo Alto, California, this is a Cube Conversation. >> Hi, welcome to our Palo Alto, California studios for another Cube Conversation, where we go in depth with thought leaders about some of the most pressing topics of the day in business and technology. I'm your host Peter Burris. One of the biggest challenges that any company faces is how to get more out of their people, even though we are increasingly distributed, we are increasingly utilizing digital means to interact and work together, and we are increasingly trying to do this with customers and with other third parties that are crucial to making business work, profitable, and grow revenue. A number of things have occurred in the last few years that are actually making it possible to envision how we can be more distributed and yet be more productive. And one of the most important ones is the use of video as a basis for connecting people. How're we going to to do that? Well, to have that conversation, we're here with David Maldow who's the CEO of, Let's Do Video. David, welcome to theCUBE. >> Hey, thanks for having me Peter, appreciate it. >> So, tell us a little bit about, very quickly, about, Let's Do Video, and then let's jump into it. >> Sure. Let's Do Video's, a boutique analyst blog on www.letsdovideo.com. We cover everything having to do with remote technology, anything that allows teams to be more productive whether they're working together or working across the country. >> All right, so in your name is, "video." Let's identify some of the key trends. What really is making it possible to utilize video in this way today where it really was nothing more than a promise made, put forward by a lot of companies 10 years ago. >> I think, well, there's been a lot of factors, but big part of that has been the cloud. A few years ago we had the big cloud software revolution in video conferencing. Before then you had to buy these expensive video appliances to have them at your workplace, and you really needed a team of experts to run them. By running the video in the cloud, all we need is our apps on our phones, and apps in our meeting rooms. And it makes it a lot easier, and it made it a lot more affordable. So, now it's available for everyone, and it was just a matter of whether we were ready for it, and appears that we are. >> So, we're getting the service that we need without having to worry about the technology that's required, the formats that are being employed, the operational complexities associated with video. Have I got that right? >> Yeah, actually there was a long list of reasons we weren't using video. Analyst like myself looked at the video conferencing industry and said, "Guys, you need to fix all of these things "or no one's going to use it. "It needs to be easier, one click to join. "It needs to be more affordable." The stuff was expensive. Needs to be reliable. Balls were dropping. It needs to use less bandwidth. It was taking over our networks. All of these things it needed to be, and they fixed all of that. And we promised if they fixed all of that, people would start to use it. Now we are seeing an absolute explosion in the market of people taking these apps into the workplace and actually using them. >> It seems to me, David, I want to get your take on this. That some of the early suppliers of some of these video related services were treating it largely as a means to an end, and typically that end was, what type of things can we put in the marketplace that's going to increase the amount of network bandwidth that's required so we can sell more networking equipment, or sell more networking services? Let me ask you a question. Because that has been fixed by utilizing the cloud. Does it now mean that we are getting a whole bunch of new technology companies that are stepping into the market place to provide video services as the end itself? And that's leading to better engineering, better innovation, and better customer experience? >> That's exactly what happened. We went from a top-down adoption model, to a ground-up adoption model. And what I mean by that is. It used to be a top-down thing, where these video conferencing companies would go talk to the CEO or CTO of a big company and do an amazing demo in the meeting room, and say, "look at this amazing video quality that you get." And they would show these studies that people like me help write (laughs), showing that if you do use video you'll be more productive. If you do use video you'll have more impact, and if you do use video you'll get all these benefits. So, buy this expensive stuff and then force your people to use them. And that didn't work 'cause they bought the stuff, and they tried to force people to use them. But, like we talked about, it was complicated. it was inconvenient. Now what's happening is, instead of the top-down we're getting the bottom-up. We're getting people walking into the workplace saying, "I'm using this app. I'm using this app. "I need video to talk to my teammates." And the boss CEO has to say, "Okay, okay, we'll accommodate that. "Don't use the consumer apps, though. "Let us find a nice business app that's secure for you." So instead of having, "You should use this "'cause we were sold on it." We're having a great new cloud video industry that's saying, "oh, let's give you what you want." >> So, when adoption happens from a bottom-up stand point, it means that the benefits have to be that much more obvious to everybody, otherwise, you don't get the adoption. So, what are some of the key productivity measures that this rank and file, this ground swell of interest in these technologies, are utilizing to evaluate and to judge how they want to use video within their business lives, workflows, engaging the customers, etc. >> For a long time it was just anecdotal. It just seemed obvious, if you, we all know that when you have a face-to-face meeting you get the work done. If it's a phone call, "oh, I'll explain to them why it's not done." We all know things get done more effectively in meetings. We all know a face-to-face meeting can last 20 minutes and get the work done. While a phone call can go on for hours. But now that we are starting to use it, instead of anecdotal, we're actually getting real data. Companies are reporting that they use to have a... Their web app development team used to take eight weeks before every release. Now they're doing it every six weeks. We're seeing real results. Frost & Sullivan, a big analyst firm in the space recently came out with some statistics. A survey of CEOs, CTOs, and they reported that using video among their team accelerated decision making. 86% of them agreed with that, 83% that agree, that it improves productivity, that's massive. 79% said it boosts innovation. So not only people getting more work done, more leading work, getting ahead of the competition, coming up with new things. And this is a huge one, 79%, this is self-reporting, believe that it improved their customer experience. We know, you know, the customer relationship is everything in sales. >> Why? >> Now we're actually measuring the results. >> Why is that, what is it about video that is so important to allowing us to not only accelerate workflows and achieve the outcomes, but also as we take on more complex workflows, even as we distribute work greater, what is it about video that makes the difference? >> There's a lot to it. I think a lot of it is that human connection. It's really hard to focus on a phone call. You lose track, I mean, you know, one of the reasons that my I named my company "Let's Do Video" is 'cause I'd be on the phone with a partner, a colleague, a teammate, and I'm like, "is he or she checking her email? "Did you hear, do I have to repeat what I just said?" We need to get work done, let's do video. And I think teams across all industries are finding that out now. Once they get on video, the work just gets done. >> But it's not just that they're on video, it's that they're utilizing video as a way of connecting with each other. That you can see whether or not somebody's paying attention to you at the most simple level. You can also register whether or not someone is a little bit agitated with what you're saying, even though you may not hear that on the phone. But video is being utilized as a way of adding to how other work gets done. It's not like we're suddenly, you know, putting a whole bunch of presentations up in the video. We're looking at faces, we're listening to people. We're having a connection as we work in other medium. Have I got that right? >> Exactly, yeah. I used to... When video conferencing first hit the scene 20 years ago, we were marketing it as a replacement for travel. Instead of flying across the country for that big meeting, you do it over a video. And what we realized is you still need to travel for that really, really big meeting once or twice a year, you still get on a plane. Video conferencing isn't getting rid of that niche meeting. It's not fixing that one big meeting, It's not cutting your travel costs. It's upgrading the phone call. It's upgrading the text message, the imChat. It's upgrading the e-mail. It's becoming, like you're saying, a part of how we're normally working. And it's changing the way remote workers see their teams. Let's Do Video, my team is completely remote. I've never met one of my teammates in person till we were two or three years in. We met up at an airport and said, "oh my God, I actually get to see you in three dimensions! "It's amazing!" And if we had started this company 10 years ago, I would say, I don't really have a team. I'm a sole guy, it's all me, I have some contractors. I send them an email, and a month later, they send me the result. But with video, I have a team, there's accountability. We're friends, we know what's going on with each other's lives. And there's a lot more motivation there, because instead of just, "Hey, you're my graphics person, "get this graphics for me. "You're my web person, fix the thing on the site." My colleagues, they're part of the team, and they want the company to succeed, 'cause they look at me in the face and they say, "I got this project done!" They feel good about it. It's a lot more of an investment, and it sounds like happy fluffy stuff, but it affects your bottom line. I don't think my... I know my company would not be as successful if I did not regularly meet with my team over video. >> Well, who doesn't want (laughing) a little bit of happy fluffy stuff every now and then? It's nice to bring a smile to your job. Let's pivot a little bit and just talk about the difference between internally to now externally. Because one of the other things that a lot of these video conferencing solutions offered, was they offered the opportunity to connect with video on a single network, your company's network with specialized end points. Now we're talking about trying to find new ways to enhance the experience that sales people have, service people have. Utilizing video to engage customers, to drive new types of experience, to drive new forms of revenue. How is video starting to alter the way we engage not just internally but also externally? >> That's more starting to happen than already happening. I think video in the workplace is becoming just a normal thing. I meet with my team over video. We're still finding ways to engage our externals. But the drive is definitely there, because we're seeing the results from working with our teams, and we know the impact. I think anyone in sales, they'll do anything to get that face-to-face meeting. They'll do anything to get you to come into their office or let you into their office to sit down. If you give a salesperson a choice between face-to-face or a phone call. That salesperson wants to be face-to-face. So, as we're getting the technology to make it easier for customers to get face-to-face with us, and partners, and externals. The demand will be there, and what's great is that the cloud enables that. The real problem is, like you said, they were on our own network. So, if I wanted to talk to a customer or a partner, I had to open a hole in my firewall, and let someone else into my network, and my IT people would go crazy. Now, the call's hosted up on whatever video conferencing company's cloud, it's safe. So, we're ready for that sort of thing. >> Lot of changes, lot of opportunities, tremendous potential. The types of changes we see in five years are going to dwarf the changes we've seen in the last five years. Again, as folks get used to using video internally, they're going to start demanding it as they engage each other externally as well. David Maldow, CEO of, Let's Do Video. Thanks for being on theCUBE. >> Thanks so much, this was fun. >> And once again, I'm Peter Burris. Until next time, thanks for watching. (upbeat music)

>> Live from Orlando, Florida its theCUBE covering Enterprise Connect 2019. Brought to you by Five9. >> Hello from Orlando, Lisa Martin with Stu Miniman theCUBE. We are live, day three at Enterprise Connect 2019. We have been in Five9's booth all week and we're very excited to welcome to the program for the first time Harry Moseley the CIO of Zoom Video Communications. Harry thanks so much for joining Stu and me on The CUBE today. >> Lisa, Stu its a pleasure to be here, thank you for having me. >> And you're a hall of famer, you have been inducted into the CIO Magazine's hall of fame and recognized as one of the world's top 100 CIO's be Computer World >> Yes that's right >> So we're in the presence of a VIP >> (chuckles) Well thank you for that it's, as I say its all credit back to the wonderful people that have supported me throughout my career. And I've worked with some amazing people and leaders and, who have supported me and the visions that I've created for their organizations. And so, I understand its about me but it's also about the great teams that I've worked with in my past. I can't make this stuff up, yep. >> Harry, we love talking to CIO's especially one with such a distinguished career as yours 'cause the role of CIO has gone through a lot of changes. IT has gone through a lot of changes. You know we've been doing this program for nine years. Remember reading Nick Carr's IT, does IT matter? And you know, we believe IT matters more than ever Not just IT, the business, the relationship maybe give us a little more of your view point as to the role of the CIO and technology, at a show like this. We hear about the CMO and the business and IT all working together. >> Yeah so its actually, in my opinion, there's never been a better time to be a CIO, irrespective of the company you are in, whether its a tech company like where I'm, you know Zoom Video Communications or any one of the prior companies I worked for, professional services, financial services. But even when you think about it like trucking, You think about trucking as an industry, you think about trucking as a company, its like it was a very sort of brick and mortars? But now its all about digital, right? A friend of mine runs a shipping container company and to think that they load five miles of wagons every day. And so I said to him, "how long does it take to load a wagon on a truck?" "It takes four minutes, and you know what Harry, "we're working that down to three. "And that'll increase our revenue by 20 to 25 percent.' And so its just fantastic. And the pace of change, you know it's just growing exponentially. It's just fascinating, the things that we can actually do today we only dreamed about them a year ago. And you think about it sort of' I can't wait to be back here next year, 'cause we're going to just lift the roof off this place in terms of the capabilities. And so its fantastic, yeah it's just absolutely fantastic. >> So looking at, a lot of us know Zoom for video conferencing and different things like that, but you said something very interesting in your fireside chat this morning that I hadn't thought about, and that is when, either going from audio to video, when you're on a video chat you really can't or shouldn't multi-task. So in terms of capturing peoples attention, enabling meetings to happen maybe more on time, faster, more productive. Thought that was an interesting realization, I thought, you're right. >> It just clicks, it just works. You know mobile, you know when I go back to my you know sort of' going back and again, thank you for the recognition from the key note. But when I go back earlier in my career it's like dialing that number, dialing that ten digit number, misdialing that number, what happened? I got to' hang up, I got to' get a dial tone, I got to' dial the numbers again. Now I'm like two minutes late and I know I'm late more often than I'd like, but when its late because of something like that, that's frustrating. That's really frustrating. And so the notion that you can just click on your mobile device, you can click on your laptop, I have no stress anymore, in joining meetings anywhere. I love telling the story about how I had a client meeting, I was in O'Hare Airport and I joined the client prospect meeting. I joined the prospect meeting on my phone using the free wifi service at O'Hare Airport. Put up my virtual background on my phone I just showed you this Stu, with our logo shared the content off of my phone 18 minutes into this 30 minutes call, the person I was talking to, the CIO for this firm called a halt to the meeting. This is what exactly what happened. Enough, I've heard enough. (announcement in background) >> Keep going. >> Keep going, okay. Enough, I didn't know what enough meant. And so I was a little spooked by that if you will. He goes, "you're on a phone, you're in O'Hare Airport, "you've got a virtual background, "you're sharing content, its all flawless. "Its like this is an amazing experience "that we can't get from all the technology "investment we've done in this space "for our company. "So guys, enough. "We're starting a proof of concept on Monday. "No more discussions about it. "Harry, looking forward to being a business partner." >> Does it get better than that? >> It doesn't get better than that. Its like you know, you hop through security, you get on a plane, and its cruisin' all the way home. >> Yeah I mean Harry, I do have to say, you know disclaimer, we are Zoom customers I'm actually a Zoom admin and its that simplicity that you've built into it is the experience, makes it easy. >> And then when you, and Stu, sorry to interrupt you but I got really excited about this stuff as you can tell. But, and then you look at the enterprise. So you're admin? You get into the enterprise management portal and its like Stu, I had a really bad experience. Oh let me look that up, oh yeah, okay. Where were you? You know, I was in outer Mongolia Ah okay, about five minutes into the call you had some packet loss, its like yeah it wasn't. But it still maintains the connection, right? So you can actually, so our Enterprise Management Portal is awesome. >> Yeah so that actually where I was going with the question, is you know I remember back, I actually worked for Lucent right after they spun out from AT&T. And we had videos talking about pervasive video everywhere, in my home in the business. Feels like we're almost there but still even when I have a team get together my folks that live in Silicon Valley, their connectivity's awful. You know when they have their, and its like oh well my computer or my phone don't have the cycles to be able to run. Maybe we have to turn off some of the video Are we getting there, will 5G solve some of these issues? Will the next generation of phones and computers keep up with it? Because it's, I'm sure you can guess we're big fans of video. It's a lot of what we do. >> Because video is the new voice, right. We like video. If I can only hear you and I can't see you, then when I make a statement I can't see you nodding. If I say something you like, you nod. So we get that concurrency of the experience Again it comes back Stu, where were we a year ago? The capabilities we had, where will we be a year from today? Whether its AI, whether its the power in the device in front of us whether its the network, you know, 5G is becoming a reality. It's going to take some time to get there but you've got sort of great technologies and capabilities, that you know, you look at the introduction of our real-time transcription services. I mean how cool is that? I'm sure there's lots of questions, so lots of people would ask about that real-time transcription in terms of, well what's next? I'm not going to talk about what's next. But as they say in life, watch this space. >> Yeah, just you made some announcements at the show with some partners I actually believe Otter AI is one of the ones you mentioned there. I got a demo of their thing, real time, a little bit of AI built in there. Can you talk about some of those partnerships? >> Yeah so we have great, we love our partnerships right? Whether its on the AI space, with Apple and Siri and Amazon and Otter. We also love our partnerships with Questron and Logitek and HP, and Polly of course. Again its the notion of, we have terrific software. You guys realize that, right? Its terrific software, proprietary QOS proprietary capabilities, its like its a fantastic experience every time on our software. These partners have great technologies too. But they're more on the hardware side, we are software engineers at our core. As Andreson said, I think it was about ten years go, "software is the easing thing in the world "so you take terrific software "you imbed it in terrific hardware "with terrific partners and what happens "is you get exceptional experiences." And that's what we want to deliver to people. So its not about the technology, its about the people. Its about making people happy, making easy, taking stress off the table. You go to the meeting, you light it up, you share the content, you record it, you can watch it later, its just terrific. >> So the people, the experiences you about we've been hearing that thematically for the last three days. As we know as consumers, the consumer behavior is driving so much of this change that has to happen, for companies to not just digitally transform, but to be competitive. We're in Five9's booth and they've mentioned they've got five billion minutes of recorded customer conversations. You guys can record, but its not just about the recording of the voice and the video and the transcription. Tell us about what you're doing to enable the context, so that the data and the recordings have much more value. >> Yeah so , I mean its the notion of being able to sort of rewind and replay. I'll give you another example if I may. Coming out of an office in Palo Alto jumped in the Uber, going back to San Jose for a client meeting. I'm a New Yorker as we talked about a few minutes ago and, I don't know the traffic patterns in Southern, in the Valley. And its about 5:00 o'clock, 5:15. San Jose meetings 5:45. Normally it would be fine, but its rush hour, what do I know about rush hour? I know a lot more now than then. I realize I'm not going to be able to make it on time. Put up the client logo, virtual background on the phone, in the Uber, client gets on the call, Harry where are you? I'm in the back of an Uber. Again, the same sort of experience. Then he asks the question, "well with this recording capability, "can I watch it at 35,000 feet?" Of course you can. And that was it. That was the magic moment for this particular client, because he said "I'm client facing all the time. "I don't get it in time, "I don't always make my management meetings "so I won't have to ask my colleagues what happened "and get their interpretation of the meeting. "I can actually watch the meeting "when I'm at 35,000 feet on a plane, going to Europe." So that's what this is all about. >> Alright, well Harry obviously this space excites you a bunch. Can you bring us back a little bit? This brought you out of retirement and the chase, the space is changing so fast. We come a year from now, what kind of things do we think we'll be talking about, and what's going to keep you excited going forward? >> So lets talk about the first part first and then sort of' break it into two. So yes I had a fantastic career and I retired and so when I met Eric and I met the leadership team at Zoom and I dug into the technology and I understood sort of' A, the culture of the company which is amazing. When I understood the product capability and how this was built as video first, and how we would have this maniacal focus if you will on sort of being a software company at our core. And how it was all about the people. That was sort of a very big part of my decision. So that was one. Two is, look we have a labor shortage right? We can't hire enough people, we can't hire the people, we have more jobs than we have people. So and so, retaining talent is really important. Giving them the technology and the studies that have been done, if you make an investment in the technology, that helps with retention. That helps with profit. It helps with, product innovation. So investment in the people. And the ability to collaborate. It's very hard to work if you don't collaborate, right? It just makes it really, very lumpy if you will. So the ability to collaborate locally, nationally, and globally, and people say, well what's collaborating locally? It's kind of like we can just walk down the corridor. Yeah, well if you're in two different buildings how do you get there? And then it gives us, a foot of snow between you, its makes it really hard. So collaborating locally, nationally, and globally is super important. So you put all that together that was the, what convinced me to say okay you know what, retirement, we're just going to put a pause button on that. And we're going to gave some fun over here. And that really has been, so I've, over a year now and its been absolutely amazing. So yes, big advances. What's in the the future? I think the future, you know there's been a lot of discussion around AI. We hear that its like, all the time. And we've seen from a variety of different providers this week in terms of their, their thoughts around how they're going to leverage AI. Its not about the technology, its about the end of the its about the user experience. And you look at the things that we started to do, we talked about real-time transcriptions a few moments ago, you look at the partnership that we have with Linkedin where you can hover over the name and their Linkinin profile pops up. You're going to see this, I just see this as an exponential change in these abilities. Because you have these building blocks today that you can grow on an exponential basis. So, the world is our oyster, is how I fundamentally think about it. And the art of the possible is now possible, And so lets, I think the future is going to' be absolutely amazing. Who would have, sorry Lisa, who would have thought a year ago, you could get on a plane using facial recognition? Let me just throw that out there. I mean, that's pretty amazing. Who would have thought a year ago that when you rent a car, you can just look at the camera on the way out and you're approved to go? Who would have thought that? >> So with that speed I'm curious to get your take on how Zoom is facilitating adoption. You mentioned some great customers examples where your engagement with them via Zoom Video Conference basically sold the POC in and of itself, with you at an airport >> That's a great questions. >> I guess O'Hare has pretty good wifi. >> What's that? >> O'Hare has pretty good wifi. >> A little choppy but, but it worked. >> It worked. >> Because of our great software, yeah. >> There you go, but in terms of adoption so as customers understand, alright our consumers are so demanding, we have to be able to react, and facilitate collaboration internally and externally. How, what are some of the tools and the techniques that Zoom delivers to enable those guys and gals to go I get it, I'm going to use it, And I'm actually going to actually use it successfully? >> This is a question, I don't know how many clients, CIOs, CTOs, C suite execs I talk to, and they all say, they all ask me similar sorts of questions. Like we're not a video first culture. Its like video, its kind of like we're a phone culture. And then I, so I throw that right back at them and I say and why is that? Because we don't have a good video platform. Aha. Now, when you have good video, when it just works when its easy, when its seamless, when its platform agnostic. IOS, Andriod, Mac, Windows, Linux, VDI, web. When you have this sort of, this platform when you're agnostic to the platform, and its a consistent high quality experience, you use it. So its the notion of, Lisa, it's the notion of would we rather get into a room and, would we rather get into a room and have a face to face meeting? Absolutely. So why would you get on a call and not like to see the people you're talking to. You like to see the people. Why, because its a video first. >> Unless its just one of those meetings that's on my calender and I didn't want to be there and I'm not going to listen. But I totally agree with you Harry. So, another hot button topic that I think we're at the center of here and that I'm sure you have an opinion on. Remote workers. So we watched some really big companies I think really got back in the dialogue a coupla' years ago when Yahoo was like okay, everybody's got to' come in work for us and we've seen some very large public companies that said you need to be in your workforce. and as I said, I'm sure you've got some pretty strong opinions on this >> I don't know what's going on here, quite honestly Stu but its like I think you're reading my brain because these are things I love talking about. So yeah, its. Sorry repeat the question? >> Remote workers. >> Remote workers, yeah. So first of all, I was at an event recently we talked about remote work. We didn't like the term. Its a distributed workforce. >> Yes. Because if you say you're a remote worker its kind like, that doesn't give you that warm feeling of being part of the organization. So we call it, so we said, we should drop calling people remote workers and we should call them a distributed work force. So that's one. Two is, I'm in New york, I'm in Orlando, I'm in Chicago, I'm in Atlanta, I'm in Denver. I'm on planes, I'm in an Uber. I don't feel disconnected at all. Why? Because I can see my colleagues, and its immersive. They share content with me. I'm walking down Park Avenue and I've got my phone and they're sharing content and I'm zooming in and I can see them and I can hear them and I'm giving feedback and I'm marking up on my phone, as I'm walking. So I don't feel, and then when I go to, its fascinating, and then I go to San Jose and I'm walking around the office and I'm seeing people physically. It doesn't feel like I haven't seen them, its really funny. I was in San Jose last week, Wednesday and Thursday in San Jose, took the red-eye back. Hate the red-eye but, I don't like flying during the day, I think it's inefficient, a waste of time. Took the red-eye back, now I'm on calls Friday morning from my office at home with my green screen, Zoom background and everybody's got, it's like I'm talking to the same people I was talking to yesterday but they were in the flesh, now they're on video. It's like Harry where are you, why didn't you come to the room? Well I'm back in New York. It's just just that simple, yep. >> That simple and really it sounds like Harry, what Zoom is delivering is a cultural transformation for some of these newer or older companies who, there is no reason not to be a video culture. We thank you so much for taking some time >> Thank you, thank you >> To stop by theCUBE and chat with Stu and me about all of the exciting things that brought you back into tech. and I'm excited to dial up how I'm using Zoom. >> Well we can take five minutes after this and I can show you some cool tricks >> Wow, from the CIO himself. Harry Moseley, thank you so much for your time. >> Thank you, thank you >> Great to have you on the program. For Stu Miniman, I'm Lisa Martin and you're watching theCUBE (upbeat tune)

welcome to the cube I'm Lisa Martin with Keith Townsend and we are in Orlando sa piece sapphire now 2018 we're very proud to be in the NetApp booth now that sa very long standing partnership with sa PA welcome to Cuba thank you we're so glad you guys are here over a million people are expected to engage with the SH the experience both in person and online that's enormous yes sa P is the cash register of the world 70% of the world's transactions go through si people most of us don't see it a lot of the SI p products like Hybris like Arriba success factors are built on meta meta is 26 years young now and has undergone a big transformation from traditional storage company to more cloud we're gonna be now that data management company for hybrid clouds every customer has a different rate of motion to the cloud that's why we have to spend an awful lot of time listening to our customers don't and then talkative the c-level executives in the business side to say what are your what are your expectations about the technology right whether if the reduction of labor improved quality again overall equipment effectiveness and help them understand what the treaty chuckles on choice we're hearing for customers is I need choice I need to move my data around on-prem into whatever hyper hyper scalar environment you want fast efficient with analytics readouts everybody looks at their phone when we make a deposit we expect to see that deposit instantaneously right the business needs to operate just as instantaneously and a company like NetApp could build this data fabric to connect them seamlessly so that the customers have choice it's interaction of sensors and to way taking IOT data in and then also feeding it back into signals but that's part of the interface of the software people can deploy much more effectively with a lower skill set right so there's not that hurdle really allows the administrators to configure dream workspace where you can get all the data that you need to work with in one place takes all that noise and makes it into one screen so that you can just simply make and change the data the way you would expect to on a spreadsheet sa P is serious about this C for Hana move of being able to say you know what we are going to create an ecosystem of truck if you have a developer and your enterprise and you say you know what I'm a big sa p user but I actually want to develop a custom map or are there some things I might do then s ap makes available to Leonardo a machine learning foundation and you can take advantage of that and develop a customized again not just a products company but an ecosystem company on C sapphire in Orlando is a great example of how they're expanding the brand is that say P can't do everything so we work with a lot of specialists we were critiques we couldn't do this without hardware partners with storage Annette app has proven you know to be one of those partners that could deal with a myriad of data types from a myriad of applications that forces the stretch into voice recognition that voices the data mining and data analytics and the like augmented intelligence to augment humanity this connection of humans and machines working together they're doing all this genomic research personalized medicine for cancer patients throughout Europe using Hana I even know about it public safety if you could think about that that's a big thing to focus on thinking about using drones for first responders smart farming throughout all the Netherlands reducing pesticide use water usage dramatically down and they increased yields by 10% helping customers change their business change industries save lives pretty cool stuff yeah SAV has a little ways to go yet that that's kind of you talk to any HDI customer validated and certified for Hana is a bad word today but s ap understands it in their there they're moving to certify the pot platform for HDI so I thought that was a great example of them listening to customers and continuing to transform over the years we'd love to hear you know from customers hey can I eat with a buddy could I put this object you know on that object together and build a process basically there's almost everywhere place where the net up product will fit but again we have to make session where's the place to start step back and look at what perhaps other competitors have done in their space or in completely different industries are compared to making great content the cute makes great content that content would be found people will take notice you make a great product that impacts people's lives it's no wonder that s ap is near the top of that brand recognition brand value seventeenth on the list so if you want to become a leader or a thought leader in your own specific industry join the SMP HANA community make the investments in SP Leonardo work with SP work with net after and like Bill says let's get it done thank you all for being here we're a static for having the cube in our booth Lisa Martin with Keith Townsend on the cube from the net out booth at SVP sapphire now 2018 thanks for watching [Music]

welcome back to the queue we are live at peer storage accelerate 2089 Lisa Martin with Dave Allen say we are here in San Francisco at the Bill Graham Civic Auditorium which is why we're sporting concert t-shirts through the WHO and the Quan Rocha [Music] I really view technology as being a three-legged store compute networking and storage and storage didn't keep up at the same time that data was exploding we spent a good chunk of time troubleshooting issues directly related to storage before whether it was storage creep where we had too much data versus of the capacity of the array or the input-output problems in terms of i/o latency those types of issues we don't see any of that anymore pure has always said we're gonna make flash cheaper than or as cheap as spinning disk and we're gonna drive performance and we're gonna differentiate from the market and we're gonna be first right now only about 1/2 of 1% of the data that companies have can even be analyzed because it's being kept in cold storage and at pure we believe in no cold storage you know it's all gonna be hot it's all got to be available able to be analyzed able to be mined and they talked about going all-in on shared accelerated storage it's gonna be multi cloud and so you wanted to provide an architecture a platform that removed the trade-offs of the bottlenecks while also being open and allowing customers to take advantage of container technologies and platform as-a-service technology was the CIO I would imagine you don't want to wake up every day and think about storage that's all without respect to our friends at pure storage should be self-driving infrastructure should be self-driving these are not things that you know in a boardroom people care about but they care about is how can they can change their business and have a competitive edge one of the things that really impresses me is their focus on sharing multiple workloads in one place we get a lot of virtual servers running on you know relatively small amount of storage we went from 40 you of old-school spinning disk lots of complexity and cabling administration down to 2/3 you m20 arrays they're more heat tolerant I have two power cables in each and two network cables so complexity is gone they're providing the pickaxes in the shovels and the basic tools but the real challenges of AI are where do I apply it how do i infuse it into applications how do I get ROI and then how do I actually have a data model where I can apply machine intelligence if you understand the way machine learning operates it has to practice on tens of thousands millions of samples it could take all year or it can take hours and what wanted to do is take minutes or hours those things will start to identify patterns and genomic sequences that humans aren't you know finding with their typical approaches our genomic platforms built up to the point where they have enough sequences in them to do that sort of analysis and you need you know big compute fast storage to do that you can add capacity and and upgrade your software and move to the next generation non-disruptive Lee why is this a big deal three decades you would have to actually shut down you know the storage array have planned downtime to do an upgrade so pure solve that problem with its evergreen model and its software capability every three years they swap out your controller as part of your support and maintenance agreement which is you know huge for us because we don't have a lot of money we have budget is very small for us it you have a green model was brilliant for us but simplicity was critical I can't just keep adding stuff to look after a new technology you need to look after itself no uplifts on pricing for nvme so everybody's gonna follow that the Evergreen model they can do these things and claim these things as we were first of course we know David for you were first to make the call but but pure was right there with you we've always wanted to be the pioneers you know we always wanted to be the innovators we always wanted to challenge convention the other piece that they've done really well is marketing and marketing is how companies differentiate today you've seen that sea of orange that's here it's a bit infectious people are become dedicated you know not to an entity they become it dedicated to a cause enabling so many 4,800 plus customers globally to really transform their businesses and that's one of the things that I think is cool about this event is not just the plethora of orange everywhere but the pride and the value of what they're delivering to their customers gratulations thank you we're just getting started and we really appreciate all the work you guys do so thanks for being here thanks so much David for joining me all day who to my awesome David thanks for joining us on the wrap we appreciate you watching the cube from pure storage accelerate 2018 I'm Lisa Martin for Dave and David thanks for watching [Music]

Jeff Rick here at the cube we're at the LA Convention Center at e3 is our first time coming to this convention is sixty eight thousand people and every single hall and outside inside hotels it's pretty crazy great to see you thank you so much for having me [Music] years ago it was really much more about a a trade show so that you know the big people who are gonna buy the disc could actually come to eat right right check out our games and place their disorders and now it's really much more of a consumer phenomenon right let's have a competition let a brand's find outdo each other but more of let's make this more about the games than the booth babes and those things it's funny everything changed in dubbings chains right people are always super excited there's always gamers that want to see the newest stuff that hasn't changed at all but just the sheer technology differences so we're doing this series as part of the Western Digital data makes possible and data is such a big part of what you guys do you can really start to understand who your players are and so if you're gonna do an upsell offer you know you can understand like oh this person has actually already purchased this type of material so I'm gonna give them this type of upsell versus this type of upsell or you know I see all my players are really struggling on level three and no one's making it through what's wrong with level three they're spending too much time in an area not knowing what they're doing will go OK right we need to change that we need to signpost back to serenity we need to turn around say how can we make it clearer to the players they know what they do but also keep the reward so that they feel like they've achieved it they feel like they've figured it out right we've placed people in front of the game in very early stages to receive him alcohol ideas of working and then based on that we then look at video footage interviews and all that stuff some kind of that feedback see into the design loop process previously years ago to get some of these insights you would have had to be one of the largest game company from them and now with you know the democratization of these different game engines and then the democratization of this type of like to lean and online services that are available it really creates an amazing opportunity for all developers everywhere we see these tremendous boots that are here fabulous graphics VR coming down the pike CPU and graphical chips are all over the place so basically power an internet and 5 G's coming mobility is gonna be way way faster the horsepower that you need to run this kind of game is actually pretty staggering we can compute a lot of stuff on the GPU the CPUs tons and tons of the objects get physics constraints and things that are costly for computation cycles and then there's like memory issues you know we have streaming that we have to kind of get better at these worlds are very large and so to store the things that you're gonna see and do takes a lot of actual you know harddrive space and the speed at which we can load and unload things is that critical factor in terms of you know unlocking the freedom of your experience right we really have a PC development technology that is easy to port the Xbox and PlayStation so we have a private cloud in Europe and a private cloud and we run this on your own inference we're on our totally on our own infrastructure and it has its advantages because we're completely in control but I think now just don't need to make the big investment in hardware upfront you can solve all the problems in a cloud solution right now and then deploy either privately or publicly it's much more flexible now than it was we know from our creator standpoint the biggest thing that they complain about is hey I want to grow right like I've been streaming for X amount of years I'm creating content how do I grow at twitch we have like the broadest means of ways to monetize but also the lowest barrier of entry to take advantage of them and our subscribers by the way they know that they're supporting you and proud to do so Joy's supporting the kind of courage do they know if they didn't support you you might not be streaming they love being playing a role in keeping their favorite creators around the content that you see here today much more diverse and much broader you know we still have a long way to go as an industry but it's very different than my first 17 years ago used to be gamers played games because of the technology and now they play games because of the games right because no one cares about the technology right because you could do almost anything on any device now and now so it's really important to us as game developers to hide the technology from players and just give them a great expression and every year you know new stuff rolls out slightly newer Xbox slightly newer PlayStation better pcs so we just stay up-to-date with the drivers and make sure that we support whatever crazy hardware is coming out right and it all works great you're watching the cube from e3 I like convention center thanks for watching [Music]

welcome to the Windy City everybody you're watching the cube the leader in live tech coverage my name is Dave Volante I'm here with my co-host Stuart men immense dude this is our second year of covering v-mon you've painted the town in green we certainly have we've been talking about for I can't even now tell you how long data is at the center of it all companies are taking a new look at what does it really mean to ensure that I know where all my data is that I ensure it's protected and that it's in compliance all this challenges are much bigger part of them not just the day that backup remedy company's overall data management pirate market which is much bigger and more and more companies are digitizing their organization and for us we're kind of the ones that keep that up and running and I think it was important for us to make sure that message gets out but some years we've been saying women's VMware only the most right there be only we will never do physical crappier would always say we're just gonna do virtual virtual well in the enterprise that can't be there's ten fifteen twenty percent of all these enterprises that are gonna stay physical so last year we introduced a comprehensive m2m platform now we can do virtual physical and cloud for our enterprise customers for everybody but we see it more in the entry box allowing workloads to move seamlessly across multi clouds Rob I they soon go play on glass to manage all your data it grows the cloud there's a dichotomy between what the businesses expect in terms of the levels of data protection the levels of orchestration and automation that exist and what IT can deliver and it seems like beam is trying to fill that gap they did what most of the big companies do they start off with a partner day beams all about their partner you cannot be a platform provider without an ecosystem that's embracing and extending the value merging that value proposition together with these companies and brought about a tremendous impact on just a customer success they are experienced in in leveraging our technology they want to make sure that if I'm buying something from you it'll integrate into my existing environment so I don't have to do a complete rip and replace that's a very expensive proposition this is a company that has grown from you know very small to quite large it's gonna be probably close to a billion dollars in bookings this year we're not losing kind of what made us great which get in the door just get in the door to any of these companies go into coca-cola just get in the door and then do a really good job and expand from there which is really what we've been doing since the beginning but they've taken over Chicago VM is famous for its parties part vmworld and other big events work hard way card them so VM is known for having the best parties pleasure seeing you and congratulations on all your success thank you very much this day Volante with stew minimun you're watching the cube live from Chicago v-mon 2018 [Music]

Jeff Rick here with the cube we're in downtown San Francisco at the OSI soft Thai world 2018 they've been doing it for over 15 years is about 3,000 people here from all types of industries using this software solution and the data that comes out of it to basically find in efficiencies it is about solving some [Music] we started in San Francisco in 1990 we had 68 of our closest friends and it's just been an amazing journey some new players have grabbed onto it but we've been doing this for 30 years and you know our goal is to collect operational data wherever it exists reliably and securely persist that and deliver it to whoever or whatever needs it whether it's human or physical asset everyone has the data everyone knows it's not being utilized and they're saying where can I get my next advantage from because it is a competitive advantage the world has changed for most of our energy companies because their business models are under attack and so they are forced to transform digital transformation and energy so we think obviously every nua Buhl's right is growing like crazy and and the wind turbines are all over all over the place what are some of the other ways that they're really kind of under fire you have changing of regulation that takes place so they need to accommodate that in very short notice but you also have a very interactive environment where it used to be one way we're now two way and now you have communication coming from all of its participants in the market we're using PI as a data hub or like a bus a data bus essentially so for them it's good because we're saying look we're gonna have this one point of you know point-to-point system instead of having all of these individuals we're gonna connect to one system which will be easier for them to manage and maintain and will instruct staff to go to PI to get the data so that's a selling point for IT right more secure that's more manageable you know cybersecurity is gonna be forefront everybody's mind right how do we secure all this data so that our customers can really trust that their IP is being protected as everybody shares this data right sometimes companies by companies who owns that data so data ownership is going to be critical and these are the things that internally we are already trying to you know build solutions for one of the beautiful things about this conferences we see our partners we see our customers we see hundreds and thousands of different technologies and applications built around this disinformation that hasn't changed customers are demanding specific types of energy you may have customers at what clean energy they may want the cheapest they may want hydro so that interaction real-time is the world that we are in right now information which initiative is not connected can now be connected you have now full visibility into your entire systems and you can actually be able to control things it's really in any environment right businesses are gonna get more benefits it's not about sensors it's not about data collection is about business benefits the bottom line right the ability to see it and get insights with it does it make sense to put something new just to get another two percent maybe not but what about if you can now predict not just real time a predict what's gonna happen six hours 12 hours two days a week ahead of time that's entirely brand new and the problem is looking at your data you have today there's just way too much data for you to humanly possibly do that if it takes me more time to do the analysis in the spreadsheet right or a kind of paper write to impact the outcome of the batch of mine I do it but against modern analytics hey I can get the insight quickly and I can make a change to what I'm doing and I or prevent something from happening and now it's worth doing with the rise of intelligent machines and artificial intelligence as you know other machines gonna take over the world but really consistency ly we hear it's really humans making better decisions with data that's provided by the machines and systems we're just automating your process make it better so that you could do more cool or better things so that you've actually analyzed the data set of inputting data right so that you can actually solve problems versus spending all your time trying to you know identify the data and collect information you take that natural intelligence that people have always had pushing that into some of those advanced tools doing what they couldn't do before and that's what's really exciting overlay some of these new technologies that are coming from you know the giants of you know Google and Amazon is these we could take advantage of a lot of those tools with the data we've collected for 30 years that really drive outcomes of course the energy efficiency of all the machines are getting better and better but at some point you know it needs to be optimized right and that's where the software components it removed it of the human-in-the-loop really to optimize that that heats distribution and remove one of the next things always the next thing and that's blockchain the exchange of value would in you know a blockchain network also makes the the monetization of data very possible we have you know some assumptions of where blockchain might make sense to us as a company but especially to our customers so this year we really want to validate some of those assumptions digital transformation is more cultural transformation you know we all have these cool gadgets and a lot of these we we use it in our daily lives but how we can use these effectively in the mining world things like in iPads wireless technology and bring that in as I mentioned before on the table of the operator so that they are empowered now right now other departments in the city one it Public Works is asking for the city manager's office so it's really picking up you know some good buzz right we're kind of working our way down discussion of smart cities Hawks we don't have to worry about it we got it right on day one it's updatable and we know that the right solution for one customer and the right data is not necessarily the right data for the next customer right so we're not going to make the assumptions that we have it all figured out we're just trying to design the solution so that it's flexible enough to allow customers to do whatever they need to do if you just show them a white paper it's hard for them to say right this is what I need right you see once you see a suit to say I don't like that's high I don't like that shirt but something close yeah but something like that it's one thing to have scale in a data center it's another thing to have scale across the globe and this is where PI excels the idea doesn't have to generate in your industry could generate somewhere else then you can bring it back and that's what this conference really helps are our customers do is share those successful right people have been doing pilots until now maybe or files up to now are actually they've actually stepped in and we're seeing real purchase orders for real production applications and is happening in every industry so much easier right to get those efficiencies versus rip and replace or leave the data where it is that you're into you involve you are watching the cube from OSI soft hi world 2018 in downtown San Francisco [Music]

welcome to Las Vegas everybody watching the cube the leader in live tech coverage my name is Dave Boehne on time student Leena man he with my co-host Keith Townsend I'm Lisa Meredith John Sawyer coverage of Dell technologies world 2018 thanks so much for having us here and thanks for joining us on the Q how great to be here thank you guys for all the great coverage you always do a wonderful job [Music] loads of people here 14,000 in attendance 6500 partners analysts press you name it it's here talking about all things transformation we have this incredible platform that's been built over the last thirty years but now there are all these new enabling technologies that are going to take it much further as super powers are coming together the compute is now big enough the data is now volume is enough that we can do things never possible before obviously a very good couple of years since the Dell EMC merger it's really helped us there companies have come together right and and the and the offerings have come together together in a much more integrated fashion one of the most funny shows I mean obviously it's important for us to set our vision but you see things like the bean bags and sitting out there as a therapy job they're working so to be able to take a break and just spend some time breathing with some animals really really good and it didn't really experience the fun in the solutions Expo I'm a car guy so you know and talking about the way that we're taking plastic trash out of ocean and making art with it topped off as a great DX rail customer we have gold control try to beat the AI and TVs for a goal and it's a very cool demos vector right behind me we have our partner lounge we're hosting over 800 one-on-one meetings bdellium see executives or the partner executives so it's a combination of technical training networking executive meetings obviously product launches and announcements that we're bringing to market the opportunity to really cultivate it work globally in our global partner summit so it's a pretty active week the power of all of our capabilities we're powering up the modern data center the magnitude shift and what this portfolio can now do for our customers it's mind-boggling we've been talking for years about data as the rocket fuel of the economy and a business transformation and now we're really talking about data combined with those emerging technologies so things like AI IOT blockchain which are really taking that data and unlocking the business value data is the precious metal ISTE it's the crucial asset the whole world is gonna be wired everything is gonna have sensors outside of data center environments that's where all the data is gonna be produced and that's where decisions are going to be made and be all kinds of data if you've got structured data unstructured data and now it's important that we actually get all the disparate data into a format that can now be executed upon the business strategy really is the IT strategy and for that to happen we really have to bring our IT talent up the stack into where it's really enabling the business and that's usually at that application layer makes it more agile removes cost reduces complexity makes the planet more green we think we've got a long way to go in just building a private cloud making the data center if you like a cloud that's part number one freightin number two extending to the hybrid cloud the benefit of the fact that it is hosted in the cloud means that customers don't have anything to deploy and just like your smartphone you get all of the latest upgrades with no effort at all seamless process to scale quickly when you have new hotels coming online for example from a storage administrator perspective you can focus on much more strategic initiatives you don't have to do the day-to-day management you have to worry about what data sending where you don't to worry about how much of the different media types you've put into that array you just deploy it and it manages itself you can focus on more tasks this is the realest first step of actually trying to be truly autonomous storage it took so much time to do it before that I'd have to run my guys ragged for you know two or three weeks I'm like all right stay up overnight make sure at all companies that means value to customers that's money that they're saving directly there's a portfolio effect where customers look across everything that we're doing you say you know I don't really want to deal with 25 little companies but I wouldn't have a bigger relationship with Dell technologies and of course the dirty secret is is that almost all of the cool new apps are some ugly combination of new and old you don't want to have to have some other interface to go to it just has to be a natural extension of what your day-to-day job is you'll get this dashboard kind of help score across the entire environment then you'll see the red yellow green type markings on what to next the isolation piece of the solution is really where the value comes in you can use that for analysis of that data in that cleanroom to be able to detect early on problems that may be happening in your production environment the alternative one one product for everything we've always chosen not to go that path give them the flexibility to change whether it is nvme drives or any kind of SSD drives GPUs FPGAs the relevance of what we are doing has never been greater if they can sustain a degree of focus that allows them to pay down their debt do the financial engineering and Tom Suites our study I want you to take economics out of your decision about whether you want to go to the cloud or not because we can offer that capacity and capability depends a lot around the customer environment what kind of skill sets do they have are they willing to you know help you know go through some of that do-it-yourself type of process obviously Dell UMC services is there to help them you can't have mission-critical all this consolidations without data protection if they're smart enough to figure out where your backups are you're left with no protection so we really needed to isolate and put off network all that critical data we have built into power max the capabilities to do a direct backup from power max to a data domain and that gets you that second protection copy also on a protection storage it's no longer just about protecting the data but also about compliance and visibility it's about governance of the data it's really about management making it available so those are trends in which I think this this industry is not basically evolved over time in comes the Dell technologies world and you see this amazing dizzying array of new things and you're like wow that sounds great how do I do it right train them enable them package it for them I know the guys offer you where you can go in and so classroom kind of sympathy for today and see it in action before you actually purchase and use it we want them to engage in the hundreds of technical sessions that we have but still come away with I wish I could have gone to some more right and and so we we have all those online and and you know for us this is also big ears we're listening and we're learning we're hearing from our customers no I'm a little maybe a little smaller than some of your others but you still treat me like I'm the head you still listen to me I bring you ideas you say this fits so it's very very exciting to have a partner that does that with you do all of your reference Falls see it for yourself I mean I think quite a number of reference calls if people are in the same boat I was you know I'll scream share with them if they want to see our numbers I'll show them this is the opportunity for all of us embrace whether it's in the cube or through the sessions learn adjust because everybody's modernizing everybody needs to transform this is a great opportunity for them to do that with their skill set in their knowledge in the industry if everything you did work perfectly you're not trying enough stuff you need a change agent need a champion most likely at the senior level that's gonna really ride through this journey first three months didn't make a whole lot of progress I was just yelling like a madman to say Weiss it's not getting done and then you have to go back into I have to hire the right people so let's talk a few thing I made changes to the leadership team need more role models you need to get rid of and totally eliminate the harassment and the bullying and the you know old boys kind of club you got to create places where women in and minorities feel like they can be themselves culture plays a huge huge huge role there's just a wealth of enormously talented people now in our company ultimately creating a shared vision and an inspiring vision for what we want to do in the future you either embrace it okay you either stand on the sidelines or you leave the most creative of people from Leonardo da Vinci to Einstein Ben Franklin but Steve Jobs all love of the humanities and the science they stand at that intersection of sort of liberal arts technology you've got to interview Ashton Kutcher yeah which was quite amazing he's an unbelievable people don't maybe don't know no he's an investor he's kind of a geek Yeah right even though he's engineer my training please know that when you bring together a diverse group of individuals Jules always get to better answer for your customer you do place your bets on dell technology that's the right partner for you it's gonna it's gonna move you and your company Michael's got the right vision of where this is going he's got the right technology to do it and we've got great team members to help you get there simple predictable profitable right right keep it it's really that simple we need a few more thousand salespeople so if you're if you're really talented you know how to sell stuff you know it come come come join us at Dell technologies work where I earn more salespeople the future as Bob Dickinson said today we can cool all right everybody that's it from Dell technologies world I love you guys it's always great to be on the cube you guys do a fabulous job they go for a live tech coverage and it really has been a lot of fun we appreciate you and your team being here the next year we're gonna go party for your 10 year anniversary the cube love it we want to thank you for watching the cube again Lisa Martin with John Turner I'm Stu Mittleman this is Keith Townsend thanks for watching everybody we'll see you next time [Music] [Music]

[Music] hello I'm John furry with the ques we're back walk chain week New York consensus 2018 sold-out show let's jump in [Music] blockchain decentralized applications what's the areas and people should focus on what's it like you're navigating the sea of AB Road that is exactly the question you should be asking as business person one sell me pulled me on the strip to some pushy faithless at that point we knew that this is going to change the world now we're entering the inner Canal you doing well in doing good at the same time and that's what crypto is all about right the discipline to mediates all of these industries filled with central governments are looking at in supporting enterprises getting into it and this is the future it's not just an American we signed a deal with the Indian government which is essentially going to the 50 million people out of poverty I think in the convenient and then we're going to Dubai and China my Kroger it's all up and down the west coast of Haiti to help these people lift themselves out of Darkness the blockchain phenomenon and crypto and gentle [Music] so many smart feet that are figuring that down one by one and getting involved flywheel here is the network offender and networks are powerful when I think of watches and watches I think of networks and digital cooperatives most open market I've ever seen where everybody's willing to talk to each other to try and share ideas to make this grow really democratize investing socrata its finances it's changing the landscape completely leaders of old paradigms often have trouble embracing winning the winners here are gonna be bigger than Google bigger than Apple because the market is bigger all the ingredients are there the capital markets are changing radically technology product markets that with regulatory landscape there is an intercultural between the old bankers we should really compete on the application layer and that we should collaborate on the code base think of I see us as we know them today as project finance not corporate the best ideas the winners have not yet been decided thanks much for coming have a great night everybody let's do I'm John Fourier your host thanks for watching [Music]

Jeffrey Kier with the Qbert Computer History Museum in Mountain View California for the 14th annual association of corporate growth Silicon Valley grow Awards we've been here for a couple years now and it's a big event 300 people coming in to talk about really an ecosystem that helping other companies grow always great to be on the cube [Music] essentially what we are is an organization that's dedicated towards providing networking opportunities educational opportunities programming for c-level executives and other senior level executives at companies to help them develop their career and also grow their businesses tonight it's about tech as a force for good and I'm gonna talk about what I call the four superpowers today mobile unlimited reach cloud unlimited scale ai unlimited intelligence and IOT bridging from the digital to the physical world and how those four superpowers are reinforcing each other today very sophisticated population I mean it's just wonderful living in this seventy some people our biggest thing that we see is just the whole better together message that all of the resources from the strategically line businesses all working together to support the customers technology is evolving at a remarkable speed you know that's being driven largely by the availability of increased processing power less and less expensive faster and faster digital transformation IT transformation security transformation and work force transformation those are the big things for us this year it's great to be able to have a computer that really understands how to generate meaningful realistic text it's our opportunity to improve the quality of lives for every human on the planet as a result of those superpowers and really how it's our responsibility as a tech community to shape those superpowers for good there are issues created operationally day to day that we have to sort of always be on the watch for like you know readiness distance or these technologies it's the two sides of the same point always you can use it for good or you can use it for bad and unfortunately the bads within the news more than the good but there's so many exciting things going on in medicine health care oh yeah agriculture energy that the opportunities are almost endless not just the first world problems those of us here in the Silicon Valley see every day but really open our eyes to what's happening in other parts of the globe the need for water clean water water filtration clean air having access to information education so these are some things that are you know really personally dear to me in the last 50 years we've taken the extreme poverty rate from over 40 percent to less than 10 percent on the planet we've increased the length of life by almost 20 years these are stunning things and largely the result of the technological breakthroughs that we're doing that's the beauty of this right that's all of these things actually create opportunities you just have to stick with it and look at solutions and there's no shortage of really talented creative people to go address these opportunities and it's so fun to be involved in it right now the scale that we're able to now conduct business to be able to develop software to reach customers and truly write to change people's lives there are in many ways the technology halves and the technology have not absolutely and a lot of it is not just about making the product but then taking the product you've made and then implementing it in various use cases that really make a change from about in the world as I say today is the fastest day of tech evolution of your life it's also the slowest day of tech devolution of the rest of your life the rest of your life I'm Jeff Rick you're watching the cube from the a cts-v Awards thanks for watching [Music]