>>welcome back. And thanks for joining us for our second session. External data, your new leading indicators. We'll be hearing from industry leaders as they share best practices and challenges in leveraging external data. This panel will be a true conversation on the part of the possible. All right, let's get to >>it >>today. We're excited to be joined by thought spots. Chief Data Strategy Officer Cindy Housing Deloitte's chief data officer Manteo, the founder and CEO of Eagle Alfa. And it Kilduff and Snowflakes, VP of data marketplace and customer product strategy. Matt Glickman. Cindy. Without further ado, the floor is yours. >>Thank you, Mallory. And I am thrilled to have this brilliant team joining us from around the world. And they really bring each a very unique perspective. So I'm going to start from further away. Emmett, Welcome. Where you joining us from? >>Thanks for having us, Cindy. I'm joining from Dublin, Ireland, >>great. And and tell us a little bit about Eagle Alfa. What do you dio >>from a company's perspective? Think of Eagle Alfa as an aggregator off all the external data sets on a word I'll use a few times. Today is a big advantage we could bring companies is we have a data concierge service. There's so much data we can help identify the right data sets depending on the specific needs of the company. >>Yeah. And so, Emma, you know, people think I was a little I kind of shocked the industry. Going from gardener to a tech startup. Um, you have had a brave journey as well, Going from financial services to starting this company, really pioneering it with I think the most data sets of any of thes is that right? >>Yes, it was. It was a big jump to go from Morgan Stanley. Uh, leave the comforts of that environment Thio, PowerPoint deck and myself raising funding eight years ago s So it was a big jump on. We were very early in our market. It's in the last few years where there's been real momentum and adoption by various types of verticals. The hedge funds were first, maybe then private equity, but corporate sar are following quite quickly from behind. That will be the biggest users, in our view, by by a significant distance. >>Yeah, great. Thank um, it So we're going to go a little farther a field now, but back to the U. S. So, Juan, where you joining us from? >>Hey, Cindy. Thanks for having me. I'm joining you from Houston, Texas. >>Great. Used to be my home. Yeah, probably see Rice University back there. And you have a distinct perspective serving both Deloitte customers externally, but also internally. Can you tell us about that? >>Yeah, absolutely. So I serve as the Lord consultants, chief data officer, and as a professional service firm, I have the responsibility for overseeing our overall data agenda, which includes both the way we use data and insights to run and operate our own business, but also in how we develop data and insights services that we then take to market and how we serve our dealers and clients. >>Great. Thank you, Juan. And last but not least, Matt Glickman. Kind of in my own backyard in New York. Right, Matt? >>Correct. Joining I haven't been into the city and many months, but yes, um, based in New York. >>Okay. Great. And so, Matt, you and Emmett also, you know, brave pioneers in this space, and I'm remembering a conversation you and I shared when you were still a J. P. Morgan, I believe. And you're Goldman Sachs. Sorry. Sorry. Goldman. Can you Can you share that with us? >>Sure. I made the move back in 2015. Um, when everyone thought, you know, my wife, my wife included that I was crazy. I don't know if I would call it Comfortable was emitted, but particularly had been there for a long time on git suffered in some ways. A lot of the pains we're talking about today, given the number of data, says that the amount of of new data sets that are always demand for having run analytics teams at Goldman, seeing the pain and realizing that this pain was not unique to Goldman Sachs, it was being replicated everywhere across the industry, um, in a mind boggling way and and the fortuitous, um, luck to have one of snowflakes. Founders come to pitch snowflake to Goldman a little bit early. Um, they became a customer later, but a little bit early in 2014. And, you know, I realized that this was clearly, you know, the answer from first principles on bond. If I ever was going to leave, this was a problem. I was acutely aware of. And I also was aware of how much the man that was in financial services for a better solution and how the cloud could really solve this problem in particular the ability to not have to move data in and out of these organizations. And this was something that I saw the future of. Thank you, Andi, that this was, you know, sort of the pain that people just expected to pay. Um, this price if you need a data, there was method you had thio. You had to use you either ftp data in and out. You had data that was being, you know, dropped off and, you know, maybe in in in a new ways and cloud buckets or a P i s You have to suck all this data down and reconstruct it. And God forbid the formats change. It was, you know, a nightmare. And then having issues with data, you had a what you were seeing internally. You look nothing like what the data vendors were seeing because they want a completely different system, maybe model completely differently. Um, but this was just the way things were. Everyone had firewalls. Everyone had their own data centers. There was no other way on git was super costly. And you know this. I won't even share the the details of you know, the errors that would occur in the pain that would come from that, Um what I realized it was confirmed. What I saw it snowflake at the time was once everyone moves to run their actual workloads in this in the cloud right where you're now beyond your firewall, you'll have all this scale. But on top of that, you'll be able to point at data from these vendors were not there the traditional data vendors. Or, you know, this new wave of alternative data vendors, for example, like the ones that eagle out for brings together And bring these all these data sets together with your own internal data without moving it. Yeah, this was a fundamental shift of what you know, it's in some ways, it was a side effect of everyone moving to the cloud for costs and scale and elasticity. But as a side effect of that is what we talked about, You know it snowflake summit, you know, yesterday was this notion of a data cloud that would connect data between regions between cloud vendors between customers in a way where you could now reference data. Just like your reference websites today, I don't download CNN dot com. I point at it, and it points me to something else. I'm always seeing the latest version, obviously, and we can, you know, all collaborate on what I'm seeing on that website. That's the same thing that now can happen with data. So And I saw this as what was possible, and I distinctly asked the question, you know, the CEO of the time Is this possible? And not only was it possible it was a fundamental construct that was built into the way that snowflake was delivered. And then, lastly, this is what we learned. And I think this is what you know. M It also has been touting is that it's all great if data is out there and even if you lower that bar of access where data doesn't have to move, how do I know? Right? If I'm back to sitting at Goldman Sachs, how do I know what data is available to me now in this this you know, connected data network eso we released our data marketplace, which was a very different kind of marketplace than these of the past. Where for us, it was really like a global catalog that would elect a consumer data consumer. Noah data was available, but also level the playing field. Now we're now, you know, Eagle, Alfa, or even, you know, a new alternative data vendor build something in their in their basement can now publish that data set so that the world could see and consume and be aligned to, you know, snowflakes, core business, and not where we wouldn't have to be competing or having to take, um, any kind of custody of that data. So adding that catalog to this now ubiquitous access, um really changed the game and, you know, and then now I seem like a genius for making this move. But back then, like I said, we've seen I seem like instant. I was insane. >>Well, given, given that snowflake was the hottest aipo like ever, you were a genius. Uh, doing this, you know, six years in advance. E think we all agree on that, But, you know, a lot of this is still visionary. Um, you know, some of the most leading companies are already doing this. But one What? What is your take our Are you best in class customers still moving the data? Or is this like they're at least thinking about data monetization? What are you seeing from your perspective? >>Yeah, I mean, I did you know, the overall appreciation and understanding of you know, one. I got to get my house in order around my data, um, has something that has been, you know, understood and acted upon. Andi, I do agree that there is a shift now that says, you know, data silos alone aren't necessarily gonna bring me, you know, new and unique insights on dso enriching that with external third party data is absolutely, you know, sort of the the ship that we're seeing our customers undergo. Um, what I find extremely interesting in this space and what some of the most mature clients are doing is, you know, really taking advantage of these data marketplaces. But building data partnerships right there from what mutually exclusive, where there is a win win scenario for for you know, that organization and that could be, you know, retail customers or life science customers like with pandemic, right the way we saw companies that weren't naturally sharing information are now building these data partnership right that are going are going into mutually benefit, you know, all organizations that are sort of part of that value to Andi. I think that's the sort of really important criteria. And how we're seeing our clients that are extremely successful at this is that partnership has benefits on both sides of that equation, right? Both the data provider and then the consumer of that. And there has to be, you know, some way to ensure that both parties are are are learning right, gaining you insights to support, you know, whatever their business organization going on. >>Yeah, great one. So those data partnerships getting across the full value chain of sharing data and analytics Emmett, you work on both sides of the equation here, helping companies. Let's say let's say data providers maybe, like, you know, cast with human mobility monetize that. But then also people that are new to it. Where you seeing the top use cases? Well, >>interestingly, I agree with one of the supply side. One of the interesting trends is we're seeing a lot more data coming from large Corporates. Whether they're listed are private equity backed, as opposed to maybe data startups that are earning money just through data monetization. I think that's a great trend. I think that means a lot of the best. Data said it data is yet to come, um, in terms off the tough economy and how that's changed. I think the category that's had the most momentum and your references is Geo location data. It's that was the category at our conference in December 2000 and 12 that was pipped as the category to watch in 2019. On it didn't become that at all. Um, there were some regulatory concerns for certain types of geo data, but with with covert 19, it's Bean absolutely critical for governments, ministries of finance, central banks, municipalities, Thio crunch that data to understand what's happening in a real time basis. But from a company perspective, it's obviously critical as well. In terms of planning when customers might be back in the High Street on DSO, fourth traditionally consumer transaction data of all the 26 categories in our taxonomy has been the most popular. But Geo is definitely catching up your slide. Talked about being a tough economy. Just one point to contradict that for certain pockets of our clients, e commerce companies are having a field day, obviously, on they are very data driven and tech literate on day are they are really good client base for us because they're incredibly hungry, firm or data to help drive various, uh, decision making. >>Yeah, So fair enough. Some sectors of the economy e commerce, electron, ICS, healthcare are doing great. Others travel, hospitality, Um, super challenging. So I like your quote. The best is yet to come, >>but >>that's data sets is yet to come. And I do think the cloud is enabling that because we could get rid of some of the messy manual data flows that Matt you talked about, but nonetheless, Still, one of the hardest things is the data map. Things combining internal and external >>when >>you might not even have good master data. Common keys on your internal data. So any advice for this? Anyone who wants to take that? >>Sure I can. I can I can start. That's okay. I do think you know, one of the first problems is just a cataloging of the information that's out there. Um, you know, at least within our organization. When I took on this role, we were, you know, a large buyer of third party data. But our organization as a whole didn't necessarily have full visibility into what was being bought and for what purpose. And so having a catalog that helps us internally navigate what data we have and how we're gonna use it was sort of step number one. Um, so I think that's absolutely important. Um, I would say if we could go from having that catalog, you know, created manually to more automated to me, that's sort of the next step in our evolution, because everyone is saying right, the ongoing, uh, you know, creation of new external data sets. It's only going to get richer on DSO. We wanna be able to take advantage of that, you know, at the at the pacing speed, that data is being created. So going from Emanuel catalog to anonymous >>data >>catalog, I think, is a key capability for us. But then you know, to your second point, Cindy is how doe I then connect that to our own internal data to drive greater greater insights and how we run our business or how we serve our customers. Andi, that one you know really is a It's a tricky is a tricky, uh, question because I think it just depends on what data we're looking toe leverage. You know, we have this concept just around. Not not all data is created equal. And when you think about governance and you think about the management of your master data, your internal nomenclature on how you define and run your business, you know that that entire ecosystem begins to get extremely massive and it gets very broad and very deep on DSO for us. You know, government and master data management is absolutely important. But we took a very sort of prioritized approach on which domains do we really need to get right that drive the greatest results for our organization on dso mapping those domains like client data or employee data to these external third party data sources across this catalog was really the the unlocked for us versus trying to create this, you know, massive connection between all the external data that we're, uh, leveraging as well as all of our own internal data eso for us. I think it was very. It was a very tailored, prioritized approach to connecting internal data to external data based on the domains that matter most to our business. >>So if the domains so customer important domain and maybe that's looking at things, um, you know, whether it's social media data or customer transactions, you prioritized first by that, Is that right? >>That's correct. That's correct. >>And so, then, Matt, I'm going to throw it back to you because snowflake is in a unique position. You actually get to see what are the most popular data sets is is that playing out what one described are you seeing that play out? >>I I'd say Watch this space. Like like you said. I mean this. We've you know, I think we start with the data club. We solve that that movement problem, which I think was really the barrier that you tended to not even have a chance to focus on this mapping problem. Um, this notion of concordance, I think this is where I see the big next momentum in this space is going to be a flurry of traditional and new startups who deliver this concordance or knowledge graph as a service where this is no longer a problem that I have to solve internal to my organization. The notion of mastering which is again when everyone has to do in every organization like they used to have to do with moving data into the organization goes away. And this becomes like, I find the best of breed for the different scopes of data that I have. And it's delivered to me as a, you know, as a cloud service that just takes my data. My internal data maps it to these 2nd and 3rd party data sets. Um, all delivered to me, you know, a service. >>Yeah, well, that would be brilliant concordance as a service or or clean clean master data as a service. Um, using augmented data prep would be brilliant. So let's hope we get there. Um, you know, so 2020 has been a wild ride for everyone. If I could ask each of you imagine what is the art of the possible or looking ahead to the next to your and that you are you already mentioned the best is yet to come. Can you want to drill down on that. What what part of the best is yet to come or what is your already two possible? >>Just just a brief comment on mapping. Just this week we published a white paper on mapping, which is available for for anyone on eagle alfa dot com. It's It's a massive challenge. It's very difficult to solve. Just with technology Onda people have tried to solve it and get a certain level of accuracy, but can't get to 100% which which, which, which makes it difficult to solve it. If if if there is a new service coming out against 100% I'm all ears and that there will be a massive step forward for the entire data industry, even if it comes in a few years time, let alone next year, I think going back to the comment on data Cindy. Yes, I think boards of companies are Mawr and Mawr. Viewing data as an asset as opposed to an expense are a cost center on bond. They are looking therefore to get their internal house in order, as one was saying, but also monetize the data they are sitting on lots of companies. They're sitting on potentially valuable data. It's not all valuable on a lot of cases. They think it's worth a lot more than it is being frank. But in some cases there is valuable data on bond. If monetized, it can drop to the bottom line on. So I think that bodes well right across the world. A lot of the best date is yet to come on. I think a lot of firms like Deloitte are very well positioned to help drive that adoption because they are the trusted advisor to a lot of these Corporates. Um, so that's one thing. I think, from a company perspective. It's still we're still at the first base. It's quite frustrating how slow a lot of companies are to move and adopt, and some of them are haven't hired CDO. Some of them don't have their internal house in order. I think that has to change next year. I think if we have this conference at this time next year, I would expect that would hopefully be close to the tipping point for Corporates to use external data. And the Malcolm Gladwell tipping point on the final point I make is I think, that will hopefully start to see multi department use as opposed to silos again. Parliaments and silos, hopefully will be more coordinated on the company's side. Data could be used by marketing by sales by r and D by strategy by finance holds external data. So it really, hopefully will be coordinated by this time next year. >>Yeah, Thank you. So, to your point, there recently was an article to about one of the airlines that their data actually has more value than the company itself now. So I know, I know. We're counting on, you know, integrators trusted advisers like Deloitte to help us get there. Uh, one what? What do you think? And if I can also drill down, you know, financial services was early toe all of this because they needed the early signals. And and we talk about, you know, is is external data now more valuable than internal? Because we need those early signals in just such a different economy. >>Yeah, I think you know, for me, it's it's the seamless integration of all these external data sources and and the signals that organizations need and how to bring those into, you know, the day to day operations of your organization, right? So how do you bring those into, You know, you're planning process. How do you bring that into your sales process on DSO? I think for me success or or where I see the that the use and adoption of this is it's got to get down to that level off of operations for organizations. For this to continue to move at the pace and deliver the value that you know, we're all describing. I think we're going to get there. But I think until organizations truly get down to that level of operations and how they're using this data, it'll sort of seem like a Bolton, right? So for me, I think it's all about Mawr, the seamless integration. And I think to what Matt mentioned just around services that could help connect external data with internal data. I'll take that one step beyond and say, How can we have the data connect itself? Eso I had references Thio, you know, automation and machine learning. Um, there's significant advances in terms of how we're seeing, you know, mapping to occur in a auto generated fashion. I think this specific space and again the connection between external and internal data is a prime example of where we need to disrupt that, you know, sort of traditional data pipeline on. Try to automate that as much as possible. And let's have the data, you know, connect itself because it then sort of supports. You know, the first concept which waas How do we make it more seamless and integrated into, you know, the business processes of the organization's >>Yeah, great ones. So you two are thinking those automated, more intelligent data pipelines will get us there faster. Matt, you already gave us one. Great, Uh, look ahead, Any more to add to >>it, I'll give you I'll give you two more. One is a bit controversial, but I'll throw that you anyway, um, going back to the point that one made about data partnerships What you were saying Cindy about, you know, the value. These companies, you know, tends to be somehow sometimes more about the data they have than the actual service they provide. I predict you're going to see a wave of mergers and acquisitions. Um, that it's solely about locking down access to data as opposed to having data open up. Um to the broader, you know, economy, if I can, whether that be a retailer or, you know, insurance company was thes prime data assets. Um, you know, they could try to monetize that themselves, But if someone could acquire them and get exclusive access that data, I think that's going to be a wave of, um, in a that is gonna be like, Well, we bought this for this amount of money because of their data assets s. So I think that's gonna be a big wave. And it'll be maybe under the guise of data partnerships. But it really be about, you know, get locking down exclusive access to valuable data as opposed to trying toe monetize it itself number one. And then lastly, you know. Now, did you have this kind of ubiquity of data in this interconnected data network? Well, we're starting to see, and I think going to see a big wave of is hyper personalization of applications where instead of having the application have the data itself Have me Matt at Snowflake. Bring my data graph to applications. Right? This decoupling of we always talk about how you get data out of these applications. It's sort of the reverse was saying Now I want to bring all of my data access that I have 1st, 2nd and 3rd party into my application. Instead of having to think about getting all the data out of these applications, I think about it how when you you know, using a workout app in the consumer space, right? I can connect my Spotify or connect my apple music into that app to personalize the experience and bring my music list to that. Imagine if I could do that, you know, in a in a CRM. Imagine I could do that in a risk management. Imagine I could do that in a marketing app where I can bring my entire data graph with me and personalize that experience for, you know, for given what I have. And I think again, you know, partners like thoughts. But I think in a unique position to help enable that capability, you know, for this next wave of of applications that really take advantage of this decoupling of data. But having data flow into the app tied to me as opposed to having the APP have to know about my data ahead of time, >>Yeah, yeah, So that is very forward thinking. So I'll end with a prediction and a best practice. I am predicting that the organizations that really leverage external data, new data sources, not just whether or what have you and modernize those data flows will outperform the organizations that don't. And as a best practice to getting there, I the CDOs that own this have at least visibility into everything they're purchasing can save millions of dollars in duplicate spend. So, Thio, get their three key takeaways. Identify the leading indicators and market signals The data you need Thio. Better identify that. Consolidate those purchases and please explore the data sets the range of data sets data providers that we have on the thought spot. Atlas Marketplace Mallory over to you. >>Wow. Thank you. That was incredible. Thank you. To all of our Panelists for being here and sharing that wisdom. We really appreciate it. For those of you at home, stay close by. Our third session is coming right up and we'll be joined by our partner AWS and get to see how you can leverage the full power of your data cloud complete with the demo. Make sure to tune in to see you >>then

>>Yeah, >>yeah. Hello and welcome to Day two at Beyond. I am so excited that you've chosen to join the building a vibrant data ecosystem track. I might be just a little bit biased, but I think it's going to be the best track of the day. My name is Mallory Lassen and I run partner Marketing here, a thought spot, and that might give you a little bit of a clue as to why I'm so excited about the four sessions we're about to hear from. We'll start off hearing from two thought spotters on how the power of embrace can allow you to directly query on the cloud data warehouse of your choice Next up. And I shouldn't choose favorites, but I'm very excited to watch Cindy housing moderate a panel off true industry experts. We'll hear from Deloitte Snowflake and Eagle Alfa as they describe how you can enrich your organization's data and better understand and benchmark by using third party data. They may even close off with a prediction or two about the future that could prove to be pretty thought provoking. So I'd stick around for that. Next we'll hear from the cloud juggernaut themselves AWS. We'll even get to see a live demo using TV show data, which I'm pretty sure is near and dear to our hearts. At this point in time and then last, I'm very excited to welcome our customer from T Mobile. They're going to describe how they partnered with whip pro and developed a full solution, really modernizing their analytics and giving self service to so many employees. We'll see what that's done for them. But first, let's go over to James Bell Z and Ana Son on the zero to search session. James, take us away. >>Thanks, Mallory. I'm James Bell C and I look after the solutions engineering and customer success teams have thought spot here in Asia Pacific and Japan today I'm joined by my colleague Anderson to give you a look at just how simple and quick it is to connect thought spot to your cloud data warehouse and extract value from the data within in the demonstration, and I will show you just how we can connect to data, make it simple for the business to search and then search the data itself or within this short session. And I want to point out that everything you're going to see in the demo is Run Live against the Cloud Data Warehouse. In this case, we're using snowflake, and there's no cashing of data or summary tables in terms of what you're going to see. But >>before we >>jump into the demo itself, I just like to provide a very brief overview of the value proposition for thought spot. If you're already familiar with thought spot, this will come as no surprise. But for those new to the platform, it's all about empowering the business to answer their own questions about data in the most simple way possible Through search, the personalized user experience provides a familiar search based way for anyone to get answers to their questions about data, not just the analysts. The search, indexing and ranking makes it easy to find the data you're looking for using business terms that you understand. While the smart ranking constantly adjust the index to ensure the most relevant information is provided to you. The query engine removes the complexity of SQL and complex joint paths while ensuring that users will always get thio the correct answers their questions. This is all backed up by an architecture that's designed to be consumed entirely through a browser with flexibility on deployment methods. You can run thought spot through our thoughts about cloud offering in your own cloud or on premise. The choice is yours, so I'm sure you're thinking that all sounds great. But how difficult is it to get this working? Well, I'm happy to tell you it's super easy. There's just forced steps to unlock the value of your data stored in snowflake, Red Shift, Google, Big Query or any of the other cloud data warehouses that we support. It's a simple is connecting to the Cloud Data Warehouse, choosing what data you want to make available in thought spot, making it user friendly. That column that's called cussed underscore name in the database is great for data management, but when users they're searching for it, they'll probably want to use customer or customer name or account or even client. Also, the business shouldn't need to know that they need to get data from multiple tables or the joint parts needed to get the correct results in thought spot. The worksheet allows you to make all of this simple for the users so they can simply concentrate on getting answers to their questions on Once the worksheet is ready, you can start asking those questions by now. I'm sure you're itching to see this in action. So without further ado, I'm gonna hand over to Anna to show you exactly how this works over to you. Anna, >>In this demo, I'm going to go to cover three areas. First, we'll start with how simple it is to get answers to your questions in class spot. Then we'll have a look at how to create a new connection to Cloud Data Warehouse. And lastly, how to create a use of friendly data layer. Let's get started to get started. I'm going to show you the ease off search with thoughts Spot. As you can see thought spot is or were based. I'm simply lobbying. Divide a browser. This means you don't need to install an application. Additionally, possible does not require you to move any data. So all your data stays in your cloud data warehouse and doesn't need to be moved around. Those sports called differentiator is used experience, and that is primarily search. As soon as we come into the search bar here, that's what suggestion is guiding uses through to the answers? Let's let's say that I would wanna have a look at spending across the different product categories, and we want Thio. Look at that for the last 12 months, and we also want to focus on a trending on monthly. And just like that, we get our answer straightaway without alive from Snowflake. Now let's say we want to focus on 11 product category here. We want to have a look at the performance for finished goods. As I started partially typing my search them here, Thoughts was already suggesting the data value that's available for me to use as a filter. The indexing behind the scene actually index everything about the data which allowed me to get to my data easily and quickly as an end user. Now I've got my next to my data answer here. I can also go to the next level of detail in here. In third spot to navigate on the next level of detail is simply one click away. There's no concept off drill path, pre defined drill path in here. That means we've ordered data that's available to me from Snowflake. I'm able to navigate to the level of detail. Allow me to answer those questions. As you can see as a business user, I don't need to do any coding. There's no dragon drop to get to the answer that I need right here. And she can see other calculations are done on the fly. There is no summary tables, no cubes building are simply able to ask the questions. Follow my train or thoughts, and this provides a better use experience for users as anybody can search in here, the more we interact with the spot, the more it learns about my search patterns and make those suggestions based on the ranking in here and that a returns on the fly from Snowflake. Now you've seen example of a search. Let's go ahead and have a look at How do we create a connection? Brand new one toe a cloud at a warehouse. Here we are here, let me add a new connection to the data were healthy by just clicking at new connection. Today we're going to connect Thio retail apparel data step. So let's start with the name. As you can see, we can easily connect to all the popular data warehouse easily. By just one single click here today, we're going to click to Snowflake. I'm gonna ask some detail he'd let me connect to my account here. Then we quickly enter those details here, and this would determine what data is available to me. I can go ahead and specify database to connect to as well, but I want to connect to all the tables and view. So let's go ahead and create a connection. Now the two systems are talking to each other. I can see all the data that's available available for me to connect to. Let's go ahead and connect to the starter apparel data source here and expanding that I can see all the data tables as available to me. I could go ahead and click on any table here, so there's affect herbal containing all the cells information. I also have the store and product information here I can make. I can choose any Data column that I want to include in my search. Available in soft spot, what can go ahead and select entire table, including all the data columns. I will. I would like to point out that this is important because if any given table that you have contains hundreds of columns it it may not be necessary for you to bring across all of those data columns, so thoughts would allow you to select what's relevant for your analysis. Now that's selected all the tables. Let's go ahead and create a connection. Now force what confirms the data columns that we have selected and start to read the medic metadata from Snowflake and automatically building that search index behind the scene. Now, if your daughter does contain information such as personal, identifiable information, then you can choose to turn those investing off. So none of that would be, um, on a hot spots platform. Now that my tables are ready here, I can actually go ahead and search straight away. Let's go ahead and have a look at the table here. I'm going to click on the fact table heat on the left hand side. It shows all the data column that we've brought across from Snowflake as well as the metadata that also brought over here as well. A preview off the data shows me off the data that's available on my snowflake platform. Let's take a look at the joints tap here. The joint step shows may relationship that has already been defined the foreign and primary care redefining snowflake, and we simply inherited he in fourth spot. However, you don't have toe define all of this relationship in snowflake to add a joint. He is also simple and easy. If I click on at a joint here, I simply select the table that I wanted to create a connection for. So select the fact table on the left, then select the product table onto the right here and then simply selected Data column would wish to join those two tables on Let's select Product ID and clicking next, and that's always required to create a joint between those two tables. But since we already have those strong relationship brought over from Snow Flag, I won't go ahead and do that Now. Now you have seen how the tables have brought over Let's go and have a look at how easy is to search coming to search here. Let's start with selecting the data table would brought over expanding the tables. You can see all the data column that we have previously seen from snowflake that. Let's say I wanna have a look at sales in last year. Let's start to type. And even before I start to type anything in the search bar passport already showing me all those suggestions, guiding me to the answers that's relevant to my need. Let's start with having a look at sales for 2019. And I want to see this across monthly for my trend and out off all of these product line he. I also want to focus on a product line called Jackets as I started partially typing the product line jacket for sport, already proactively recommending me all the matches that it has. So all the data values available for me to search as a filter here, let's go ahead and select jacket. And just like that, I get my answer straight away from Snowflake. Now that's relatively simple. Let's try something a little bit more complex. Let's say I wanna have a look at sales comparing across different regions, um, in us. So I want compare West compared to Southwest, and then I want to combat it against Midwest as well as against based on still and also want to see these trending monthly as well. Let's have look at monthly. If you can see that I can use terms such as monthly Key would like that to look at different times. Buckets. Now all of these is out of the box. As she can see, I didn't have to do any indexing. I didn't have to do any formulas in here. As long as there is a date column in the data set, crossbows able to dynamically calculate those time bucket so she can see. Just by doing that search, I was able to create dynamic groupings segment of different sales across the United States on the sales data here. Now that we've done doing search, you can see that across different tables here might not be the most user friendly layer we don't want uses having to individually select tables. And then, um, you know, selecting different columns with cryptic names in here. We want to make this easy for users, and that's when a work ship comes in. But those were were sheet encapsulate all of the data you want to make available for search as well as formulas, as well as business terminologies that the users are familiar with for a specific business area. Let's start with adding the daughter columns we need for this work shape. Want to slack all of the tables that we just brought across from Snowflake? Expanding each of those tables from the facts type of want sales from the fax table. We want sales as well as the date. Then on the store's table. We want store name as well as the stay eating, then expanding to the product we want name and finally product type. Now that we've got our work shit ready, let's go ahead and save it Now, in order to provide best experience for users to search, would want to optimize the work sheet here. So coming to the worksheet here, you can see the data column that we have selected. Let's start with changing this name to be more user friendly, so let's call it fails record. They will want to call it just simply date, store name, call it store, and then we also want state to be in lower case product name. Simply call it product and finally, product type can also further optimize this worksheet by adding, uh, other areas such as synonyms, so allow users to use terms of familiar with to do that search. So in sales, let's call this revenue and we all cannot also further configure the geo configuration. So want to identify state in here as state for us. And finally, we want Thio. Also add more friendly on a display on a currency. So let's change the currency type. I want to show it in U. S. Dollars. That's all we need. So let's try to change and let's get started on our search now coming back to the search here, Let's go ahead. Now select out worksheet that we have just created. If I don't select any specific tables or worksheets, force what Simply a search across everything that's available to you. Expanding the worksheet. We can see all of the data columns in heat that's we've made available and clicking on search bar for spot already. Reckon, making those recommendations in here to start off? Let's have a look at I wanna have a look at the revenue across different states for here today, so let's use the synonym that we have defined across the different states and we want to see this for here today. Um yesterday as well. I know that I also want to focus on the product line jacket that we have seen before, so let's go ahead and select jacket. Yeah, and just like that, I was able to get the answer straight away in third spot. Let's also share some data label here so we can see exactly the Mount as well to state that police performance across us in here. Now I've got information about the sales of jackets on the state. I want to ask next level question. I want to draw down to the store that has been selling these jackets right Click e. I want to drill down. As you can see out of the box. I didn't have to pre define any drill paths on a target. Reports simply allow me to navigate to the next level of detail to answer my own questions. One Click away. Now I see the same those for the jackets by store from year to date, and this is directly from snowflake data life Not gonna start relatively simple question. Let's go ahead and ask a question that's a little bit more complex. Imagine one. Have a look at Silas this year, and I want to see that by month, month over month or so. I want to see a month. Yeah, and I also want to see that our focus on a sale on the last week off the month. So that's where we see most. Sales comes in the last week off the month, so I want to focus on that as well. Let's focus on last week off each month. And on top of that, I also want to only focus on the top performing stores from last year. So I want to focus on the top five stores from last year, so only store in top five in sales store and for last year. And with that, we also want to focus just on the populist product types as well. So product type. Now, this could be very reasonable question that a business user would like to ask. But behind the scenes, this could be quite complex. But First part takes cares, or the complexity off the data allow the user to focus on the answer they want to get to. If we quickly have a look at the query here, this shows how forceful translate the search that were put in there into queries into that, we can pass on the snowflake. As you can see, the search uses all three tables as well shooting, utilizing the joints and the metadata layer that we have created. Switching over to the sequel here, this sequel actually generate on the fly pass on the snowflake in order for the snowflake to bring back to result and presented in the first spot. I also want to mention that in the latest release Off Hot Spot, we also bringing Embraced um, in the latest version, Off tosspot 6.3 story Q is also coming to embrace. That means one click or two analysis. Those who are in power users to monitor key metrics on kind of anomalies, identify leading indicators and isolate trends, as you can see in a matter of minutes. Using thought spot, we were able to connect to most popular on premise or on cloud data warehouses. We were able to get blazing fast answers to our searches, allow us to transform raw data to incite in the speed off thoughts. Ah, pass it back to you, James. >>Thanks, Anna. Wow, that was awesome. It's incredible to see how much committee achieved in such a short amount of time. I want to close this session by referring to a customer example of who, For those of you in the US, I'm sure you're familiar with who, Lou. But for our international audience, who Lou our immediate streaming service similar to a Netflix or Disney Plus, As you can imagine, the amount of data created by a service like this is massive, with over 32 million subscribers and who were asking questions of over 16 terabytes of data in snow folk. Using regular B I tools on top of this size of data would usually mean using summary or aggregate level data, but with thoughts. What? Who are able to get granular insights into the data, allowing them to understand what they're subscribes of, watching how their campaigns of performing and how their programming is being received, and take advantage of that data to reduce churn and increase revenue. So thank you for your time today. Through the session, you've seen just how simple it is to get thought spot up and running on your cloud data warehouse toe. Unlock the value of your data and minutes. If you're interested in trying this on your own data, you can sign up for a free 14 day trial of thoughts. What cloud? Right now? Thanks again, toe Anna for such awards and demo. And if you have any questions, please feel free to let us know. >>Awesome. Thank you, James and Anna. That was incredible. To see it in action and how it all came together on James. We do actually have a couple of questions in our last few minutes here, Anna. >>The first one will be >>for you. Please. This will be a two part question. One. What Cloud Data Warehouses does embrace support today. And to can we use embrace to connect to multiple data warehouses. Thank you, Mallory. Today embrace supports. Snowflake Google, Big query. Um, Red shift as you assign that Teradata advantage and essay Bahana with more sources to come in the future. And, yes, you can connect on live query from notable data warehouses. Most of our enterprise customers have gotta spread across several data warehouses like just transactional data and red Shift and South will start. It's not like, excellent on James will have the final question go to you, You please. Are there any size restrictions for how much data thought spot can handle? And does one need to optimize their database for performance, for example? Aggregations. >>Yeah, that's a great question. So, you know, as we've just heard from our customer, who there's, there's really no limits in terms of the amount of data that you can bring into thoughts Ponant connect to. We have many customers that have, in excess of 10 terabytes of data that they're connecting to in those cloud data warehouses. And, yeah, there's there's no need to pre aggregate or anything. Thought Spot works best with that transactional level data being able to get right down into the details behind it and surface those answers to the business uses. >>Excellent. Well, thank you both so much. And for everyone at home watching thank you for joining us for that session. You have a few minutes toe. Get up, get some water, get a bite of food. What? You won't want to miss this next panel in it. We have our chief data strategy off Officer Cindy, Housing speaking toe experts in the field from Deloitte Snowflake and Eagle Alfa. All on best practices for leveraging external data sources. See you there

>>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.

>>Hello. Nice to meet you. My name is yes. Um Escuela. I'm a professor in a university in Japan. So today I want to introduce my research. That title is a really time emotional detection using e g with riel time knowing the reduction. First of all, I want to introduce myself. My major is system identification and signal processing for large removed and by American signal process for owner off them. A common technique. It's most magical. Modern by you creation using this opportunity identification method. So today topic it's e easy modern by the Barriers Council with heavy notes. We call this technique the concept moody. Now what is a concept? I mean, the concept is Japanese world because studies are first in Japan. So consider is similar to emotion and sensibility, but quite different. The commercial nous sensibility is innate ability. The concert is acquired after birth, so concept is similar to how to be So we focus on this can see using the brain signals. As for the brain Sina, there is ah, many way to know the brain. For example, the optical leading X c T m i m e g e g optical topography um, function and my by using these devices, we have three areas off research, for example, like neural engineering area for obligation, including new market neuroscience area for understanding the mechanism a medically oil area for treatment. So but it's very important to use, depending on the purpose. So what did they can be obtained? Uh, in the case of e g, we can see the activity of neurons that scalp the case of in years so we can attain the river off oxygen bar Pratt The case off natural and safe Alagem we can see the activity of new uh, that contact is neck case off position. Martian topography. We can get activity off reception by the contact list. If we use that, I we can measure the amount of blood by the contractors. These devices are showing these figures. So our motivation is to get the concept question using their model by system identification where it's not removed on. The second motivation is to theorize that's simple and small cancer X election using the each information when we use the ever my the large scale and the expensive on binding. So it is unuseful. So we focus on the EEG because the e g iss Moscow inexpensive a non binding on to use. So we focus on the energy. So e g is actually a potential from the major from the scalp that detective data is translated to the pregnancy domain. And if you can see domain that their point to 44. We call the data death of it 4 to 6. We called a cedar with on 17. 14 were called the Alfa Hour and 14 to 26. We called a better work in a conventional method we want if we want use the cats a deep sleep, we use that death of it in a case of light sleep we used a secretive and so but this is just only the sensible method. So we cannot use that for all the film Actuary accuracies under the 20%. So we need to define the situation original. So recall this technique council modeling. So these are the block diagram Kansi the concept What? So this field this part eyes for the noise, this part for the mathematical model. So we calculate this transfer function like this. This is a discrete time water, and, uh, this time, uh, is continuous time model. So then we really right this part Thio Discrete time water. So we cull Create, uh, this part us like this This'll first part on the second part is calculated by the party application so we can get this the argumentative model. So then that we were right this part by using that the transfer function transport formation. So we right this argument ID model like this. So the off about the inverse and better off the inverse is the point as this equation. So each the coefficient is corrugated by this equation on. But then we calculate a way too busy with beaver by using this because of a least squares algorithm. So we call this identification method the self joining identification method. Um, that this is an example of stories modeling. The first of all, we decide we gather the data like a story. It's moving. So we move the small beans, try to trade at 41 hour. So last 10 minutes we used as stories and we measure that culture soul for sliced levin Onda. We associate the egg and we measure the 8000 data. Uh, in 17 years we? Yeah, that's a 17 years. So in the case, off the simple, easy universes that there are many simply devices in the world like this so many of them the There we calculate the signal nodes. Lazio, The signal means the medical easy system on the each device made it sn Lazio. And we investigate 58 kinds off devices on almost off All devices are noise devices. So I'm also asked about to various parts more device that best. So my answer is anything. Our skill is, you know, processing on def. With love. Data can be obtained from the device. No, but what device? He may use the same result commission. Our novelty is level Signal processing on our system is structured by 17 years Data for one situation. So the my answer is what? Anything. So we applied this system to Arial product. We call this product concern Analyzer. In a concept analyzer, you can see the concept that right the our time a concept dinner influence Solis sickness concentration on like so that we combine that this can't say analyzer And the camera system We made the euro system your account so pretty show it this is in Eureka. Well, this is, uh, e g system and we can get can say by using the iPhone on the, uh, we combine the camera system by the iPhone camera and if the cancer is higher than the 6% 60% so automatically recorded like this. Mhm. So every time we wear the e g devices, we can see the no awareness, the constant way. That's so finally we combine the each off cancer. So like that this movie, so we can see the thes one days. Can't say the movie s Oh, this is a miracle. On the next example, it's neuro marketing using a constant analyzer. So this is a but we don't know what is the number one point. So then we analyze the deeds CME by using concert analyzer so we can get the rial time concept then that we can see the one by one situation like this. So this is the interest level and we can see the high interest like this. So the recorded a moment automatically on the next one is really application. The productive design. Ah, >>Japanese professor has come up with a new technology she claims can read minds, she says. The brainwave analysis system will help businesses better understand their customers, needs workers at a major restaurant chain or testing a menu item that is being developed. This device measures brain waves from the frontal lobes of people who try the product. An application analyzes five feelings how much they like something and their interest, concentration, stress and sleepiness. >>The >>new menu item is a cheese souffle topped with kiwi, orange and other fruit. The APP checks the reaction of a person who sees the souffle for the first time. Please open your eyes. When she sees the souffle, the like and interest feelings surge on the ground. This proves the desert is visually appealing. Now please try it. After the first bite, the like level goes up to 60. That shows she likes how the dessert tastes. After another bite, the like level reaches 80. She really enjoys the taste of the souffle. It scores high in terms of both looks and taste, but there's an unexpected problem. When she tries to scoop up the fruit, the stress level soars to 90. I didn't know where to put the spoon. I felt it was a little difficult to eat. It turned out it was difficult to scoop up the fruit with a small spoon. So people at the restaurant chain are thinking of serving this a flavor with a fork instead. Green well. How could be the difference with the device? We can measure emotional changes in minute detail in real time. This is a printing and design firm in Tokyo. >>It >>designs direct mail and credit card application forms. The company is using the brainwave analyzing system to improve the layout of its products. The idea is to make them easier to read during this test, The subject wears an eye tracking device to record where she's looking. In addition to the brainwave analyzing device, her eye movements are shown by the red dots on the screen. Stress levels are indicated on the graph on the left. Please fill out the form. This is a credit card application form. Right after she turns her eyes to this section, her stress levels shoots up. It was difficult to read as each line contained 60 characters, so they decided to divide the section in two, cutting the length of the lines by half 15 a Hong Kong. This system is very useful for us. We can offer differentiated service to our clients by providing science based solutions. The brain wave analyzed. >>Okay, uh, now the we construct a concert detection like this. Like this. Like concentration, interest sickness stories contain, like comfortable, uncomfortable. I'm present the rats emotion, deadly addictive case lighting, comfort, satisfaction and the achievement. So finally we conquer more presentation. So in this presentation, we introduce the our such we construct the council question Onda we demonstrate that c street signal processing and we apply the proposed method to Arial product. Uh, we named the constant riser. So this is the first in the world, that's all. Thank you so much.

>>from around the globe. It's the Cube covering HP Discover virtual experience brought to you by HP. >>Welcome to the Cube's coverage of HP Discover 2020. This is the virtual experience. I'm Lisa Martin with the Cube, and I'm joined by a couple of guys who were gonna talk through one of HPC ease. Longest partnerships. We've got shells. No Ryan and the Senior director Ecosystem Sales or North America at HP And Dr Rajesh, It's really a Boston. The global head of sales and solutions for the TCS. Gentlemen, welcome to the Cube. >>Yeah, Thank you. >>So, first question for you is I mentioned HP and TCS have been partners for over 30 years. Talk to our audience about the partnership and how it has evolved to where it is today. >>Yeah. Thank you, Lisa. Firstly, you know, I'm pretty excited to be part of this Cube interview with garages. You know, I know him personally for over five years through various interactions globally and this new role for North America. This is our strategy and global system integrator partner. And this is a longstanding partnership between HP and this years has grown multi falls over the last 30 years. Ah, we you know, pretty much enjoyed every single I would say transactions or the business engagements, what we've had so far. And we liberate each other for our internal I T requirements and also to drive joint, go to market initiatives across the world. That's making this a truly 3 60 degree partnership. There is a lot of heritage, a mutual trust and respect between both organizations at all levels and the complimentary offerings. You know what you will hear a lot more in the next couple of questions. Uh, we bring to the table together are very unique and very differentiating to the clients which are >>excellent. Dr. Rajesh, walk us through some of those joint offerings that TCS cloud in h e or delivering. >>Yeah, so far. So far. Thanks. I just want to thank the HP team for giving me the opportunity to up to a larger audience. Andi, This new normal. This is the first time I'm doing an interview like this. Thanks for that experience. Actually, as Jules mentioned, this relationship goes a long way. I am talking about the larger PCs were a long relationship, Andi, specifically on the easiest load we started this journey in a very, very practical way. Five years back it was it was started in a very, very small trial and error basis. We started this relationship RPC explode. But at this point in time after So yes, we have taken this into ah, new norm, actually. So I'll give you a couple of examples. One of the examples We have a very major retailer in Germany, which we work so that it was a $1,000,000 deals. Our busiest on GHB. Yes, you wanna unique offering to the customer s AP and a space on that is really growing a lot. And that's the one offering I would like to tell the audience that really has picked up and spent on the relationship in the German region. Right now we are trying to take that up, offering across on other other regions also, so that is one of the key offerings that we are doing it. The other offerings are multiple offerings we are doing. But again, I want to highlight the storage as a service offering. Great. It's everybody in the industry today, Andi, we are experimenting that in the initial stages in Australia we started in Australia a small offering. And now we are expanding it in the US geography in a big way. And this year we are going to make that as a unique offering. And we're going to offer they're all over cloud customers as a storage as a service offering. Also multiple other offering, Lisa. But I just thought that I like this tool which are making our business. We're making a lot of business together with these two offerings >>is the, uh, s AP opportunity that you mentioned is that the Hana as a service that TCS is delivered? >>That's correct. So it's ah, it's a service. But the uniqueness of that particular offering is we jointly created the architecture so that the customer can use that, like a database as a service model. Right? So it was It was not available that time in the industry so easily like what we offered at that point in time to do enough years back. We offer that particular said we spoke a summer and interestingly, that particular offering the customer was using s AP themselves as a service initially, and they migrated their to us actually from Maybe that's a reason they bought HP and TCS. There is like a summer on this API and a platform. So that's the That's the interesting story under, >>if we didn't do that just a little bit further, I wanted the audience to understand the impact that this partnership has H p E and TCS delivering Hana as a service or your customers. What are the benefits there than what the customer, as you said was doing previously? >>Yeah, yeah, I think I just want to highlight the three or four points that make that this offering very unique, and that helps the customer number one is associated with model. So the customer has got the complete flexibility off going up and down like a true cloud model, right? And so it is a really a unique proposition at that point in time, where the customer not a story about using less for some time and then using more sometimes so it's kind of a complete, flexible model that we offered at the time. Number two is, it's a complete customization is possible. It is not like a fixed architecture. The architecture is so flexible so that the customer business needs can be met through the architectural changes. So it's not like normally people think that lotus highly standardized architecture, right? So that has gone out, and we were given a flexible architecture for the customer. That is the number two number three, obviously the cost end of the day. There's a business case which we need to make it work right for the customer. So obviously, with the PCs and HP coming together, we were able to do the costarred, want age with a customer that is the third advantage of that. The last, but not the least, is the quality of service it is it is all about. I always used to tell my partners that selling is easy. Delivering it is what it's important it is, which will make the customer to stick with you, right? The were given and delivery quality experience who our customer s so that I think that makes a very unique proposition from a technology perspective from a pricing. But but from an architecture and also from the delivery perspective. So those are the few few things I just thought that I violated. >>Excellent. So a couple of words that you mentioned popped into my mind as really even more well, have a different meeting as we're in summer 2020 flexibility and unique offering chills back to you from a go to market perspective. How is that relationship with HP? And he says, changing in the Koven era. >>Yeah, it's pretty interesting, and I would like to call it an example off. You know, what we see is is that you themselves during the corporate times, you know, it also came in the in the pets close to 90% of the workforce. We're 100% productive. Uh, and, uh, they have a plan to go 75% of the employees, you know, go being remote by 2025. Right? So that's the journey they're taking on. And another thing that you notice there's a lot of the, you know, During the corporate times, many of the customers were looking for solutions like virtual desktop infrastructure. So they wanted their employees to be productive, bi directional and in the other area of focus was like a TCP, you know, how do I kind of make sure on the applications are available to you? The customers and also do their internal organizations. So we've seen a lot off. I would say engagement with that is I could picture team and also the solution team toe address This requirements off the market joint >>when we look at certain things that now might even be more important with this new normal, if you will, that the fact that most companies are still in phase one of this work, everyone works from home trying to get to a phase to that might see some some maybe by function groups coming back to the office and then getting to this third. Maybe it's the new nirvana of some hybrid workforce, where there's gonna be some that come back permanently, and some that Don't and Tony Unirea chose, I saw was quoted last month as saying he thinks that 50% of the workforce will only 50% will come back. So in this new not only hybrid I T environment in which your customers love it now, this new pending hybrid workforce environment how are you addressing some of the concerns together with respect to the network connectivity security, >>I will just take the cost anything. It's a very, very interesting at least when we all ended up in this pandemic in March. We really very, very nervous, actually, because everyone has to operate remotely on we are. We are dealing with the customer data. It's ah, it's very, very important that we have a secure environment to access the information and at the same time maintain the integrity of the data and also the quality off the plate. Those other two primary objective for us. We don't want to compromise on quality. We don't want to compromise on security from a cloud perspective. So the solution we have put in really, I just give you one example there was on the airline Ah, UK based the airline industry airline company which they need that workforce overnight. They want everybody to go remote because you know you cape on. They just put up condition that nobody can work from the office overnight and then terror ports as toe work from home purchases, implement the solution for them on our clothes overnight and make that 1000 employees store from home the next day morning. All of them started working with full quality of services and also with a full security aspect of it, has been taken care ornate on the solution. We are deployed. Very interesting case study on The important thing we have done is use the technology to the port. Use all kinds of technology to make sure that the employees that work from home we took care of the network connectivity. We took our eye off the security aspects off the data from security aspects. We've implemented all the security functions from really APIs. But people, Children stop perspective. Andi, make the workforce enable that. But now you are talking about millions off millions of workers going to work from home. Right? Because it is one example for one company we have done that now the easiest themselves has got more than 400 1000 employees. And we are talking about millions off our pores, going to work from home on going forward. So that is I'm seeing this as a big opportunity. It's not that everybody has are just this at this point in time, I'm seeing this as an opportunity where on the cloud easiest cloud kind off. The solution is going to help them to achieve this. And this is a great opportunity for not only for PCs, but also for HP because the solution we're putting together with the HP is more on the digital or course how we can enable the people to work from home, not compromising on as I mentioned from a security you're in from millions perspective. So I'm seeing this as an opportunity for both the organization, and it's a long way to go is we need to work on this. It's not. We don't have a magic want to make the millions off workers to work from home, but it is going to have all soon and probably in the next step. Yeah, so we may achieve this, impair people off. The workforce is going to go remotely on this list. So that's that. And my take on this >>is so the impact that HP and TCS Herb being able to make for customers who have had to massively transform their entire workforce overnight, as he said, to work from home to talk about some of the new maybe new solutions or new business opportunities that HPC is partnering with TCS shells, we'll start with you in this new era, >>Yeah, so if you look at it, I just taking it again on extension off of the projects. What he just mentioned about the percentage of employees going remote Lisa across industries today. I would say less than 20% of the employees are actually working remote or they have the ability. But the organizations have the ability to support the employees going, and if you have to take it to 50% so you can look at the kind of opportunity we have both as HP and as PCs. So we bring in a lot of best in breed infrastructure from for enabling the employee workforce to know where it is. I would say capacity off workloads and it's all workload specific. And what business does this or when people Pretty easy as we kind of bundle that creating a reference architecture or a giant architect picture addressing the customers by industry word. So because one what suits for one vertical may not be really suiting well for a different world, right? For example, if you take a banking sector are playing, a workstation solution would look very different from somebody's doing remote work in retail, so we kind of continuously engage with the PCs, and that's where both of us have joint lab as well, where our technologies and pieces technologies come together, working on joint solutions and assisting the market in terms of the opportunity lights. And we offer this as part of A C is our digital workplace offerings. >>Are your conversations Dr Additional go to you or your conversations when you're jointly selling, changing in terms of who your audience is? Is this now a C level conversation? Since these leaders and we've heard leaders of Google and Facebook already last month saying Work from home extended still 2021. Is this now at the C suite level, where you guys are helping them really understand how to completely change and digitize their entire way of doing business? >>Absolutely. I think it's a great question, and it's actually the opportunity goes beyond the work from home solution. As you rightly I want to know that it is. It is all about digitization. It is all about digitizing their whole business process. It is not anymore infrastructure. Our application solution. It is more about really finding that business process be defending. The way the business is going to operate in future is the discussion we are having so a lot of these discussions are happening at a very, very high level and with the business team also directly so earlier, you used to interact with the technology partners off our organization. But now we are interacting directly with the head of business are the C level except of the company. And that is the reason the exact reason is Ah, you. If you want your ports to be productive remotely, you can't just offer them on network on. You can't offer them just a solution to work from home. But you need to really find your whole business process you need. You need to digitize your infrastructure. You need to digitize your application. You need to rethink your whole process off. You're operating on it, so that's what I'm seeing. It's not only an opportunity for our players like a business cloud, but it is the opportunity for a bigger opportunity for PCs. And it should be not only in terms off on infrastructure in our cloud business, it goes beyond that. So that is that is the kind of an opportunity we're seeing, especially in the in the sectors of healthcare you're seeing major reforms are happening in the healthcare industry as we speak, and obviously manufacturing is going to go through a lot of changes. Also from that. And retail obviously has gone through a lot of changes already in terms of online, uh, stuff, but know that also going to go through changes in this new era? Yes, >>I have to ask you shelled, talking about redefining? That's a word that we've seen so many years in a row at tech conferences, right, this technology redefining this business or that industry. And now, of course, we're being redefined by an invisible virus. But how? How is the sales process being redefined? Is it a lot more accelerated because businesses have to put together new plans to continue operations? >>Yeah, again, a great question. Is this how you have? You know, I would say it's divided by industry body. It's not a uniform thing by, as not British was saying, every industry has got its own, its own set of challenges and its own set of opportunities, and some of them are really actually doing well even in times like and some of them have seen, Really. I mean, like, travel our transportation or you know some of those industries are, and even hospitality that's kind of affected big time. So our view of you know, the entire sales engagement of the processes we're spending more time on there. We really need to focus and which can help improve the businesses. Right? So the conversation's ready from How do I take the cost out in terms of how can I make a little more investment to get greater returns from the business? So it's like it's completely, I would say, an interesting pain and engaging compositions and decisions are happening. So we, if you look at us from an automatic perspective, the Internet to the sales team is armed with various virtual tools like we know you zoom views Skype using SMS teens. So all the tools available to make sure that we're able to connect with all our partners and customers on do enable joint business together. >>I just want oh, I add to it, Lisa, 111 point. I want bad, Really interesting change I'm seeing on the sales is normally we respond to it. I asked from a customer that is a sales happens. I want this many days. Do it and then what you can do with a solution that is a normal sales process. What I have seen that has changed completely. Yes, we go and tell the customer, Is this what you need Actually, to make you yourself your business? Better? This is the new offerings I'm having good. And this offering is going to help you to solve the problem what you are having today. So we are engaging a different level off sales conversation today with our customers. We know the problem of the customer because we are working with them for many years and we know exactly what they're going through. And we also know what new offerings we are having in this. So we are engaging the discussion with the customer doing that. This is my new offering. This is going to help you to solve this problem. But that is a different angle of sales we have seen nowadays they spend on it. >>The last question shells to you. We started our interview today talking about the HP TCS relationship. You talked about how it's evolved. Last question. You talked to me about H B's strategy. How does it match TCS Alfa Cloud offering. >>Yeah, so again, a great question, Lisa, if you look at our strategy, is to accelerate the enterprises with it. Centric and cloud enable solutions which are workload up, optimized and delivered everything as a service. And whatever you heard from Dr Rogers through this entire conversation was about how do we give as a service model you gave an example of honor. You gave an example off, you know, going how optimizing workloads for video and getting employees to be able to be productive remotely and all of that kind of extremely resonate well with, you know, what we see is confined to price. Cloud offering is bringing to the table for the customer and the underlying platform. You know, we kind of elaborate extensively and closely with the easiest architecture. Seem to have the HP portfolio off. You know, the compute and storage portfolio integrated as part of their offering, and we go together to market, you know, and addressing and kind of an ask service model. 1,000,000,000. >>Excellent. Well, shells Dr. Rajesh, pleasure talking with you both today about what UCS and H e are doing together and some of the ways that you're really helping businesses move forward in these uncertain times, we appreciate your time. >>Thank you. Thank you for represents. Thanks. Thank >>you. Dr Rajesh. >>My guest. I'm Lisa Martin. You're watching the Cube's coverage of HP Discover 2020. The virtual experience. Thanks for watching. >>Yeah, yeah, yeah.

>>from around the globe. It's the Cube covering HP Discover virtual experience brought to you by HP. >>Welcome to the Cube's coverage of HP Discover 2020. This is the virtual experience. I'm Lisa Martin with the Cube, and I'm joined by a couple of guys who were gonna talk through one of HPC ease. Longest partnerships. We've got shells. No Ryan and the senior director Ecosystem sales for North America at HP and Dr Rajesh Boston, the global head of sales and solutions for the TCS. Wow. Gentlemen, welcome to the Cube. >>Thank you. >>So, first question for you, as I mentioned, HP and TCS have been partners for over 30 years. Talk to our audience about the partnership and how it has evolved to where it is today. >>Yeah. Thank you, Lisa. Firstly, you know, I'm pretty excited to be part of this Cube interview with garages. I know. I know him personally for over five years through various interactions globally and this new role for North America. This is our strategy and global system integrator partner. And this is a longstanding partnership between HP and this years has grown multi falls over the last 30 years. We you know, pretty much enjoyed every single I would say transactions or the business engagements, what we've had so far. And we liberate each other for our internal I T requirements and also to drive joint, go to market initiatives across the world. That's making this a truly a 3 60 degree partnership. There is a lot of heritage, a mutual trust and respect between both organizations at all levels and the complimentary offerings. You know what you will hear a lot more in the next couple of questions we bring to the table together are very unique and very differentiating to the clients, which are >>excellent. Dr. Rajesh walk us through some of those joint offerings that TCS cloud in h e or delivering. >>Yeah, so far so far. Thanks. I just want to thank the HP team for giving me the opportunity to off to a larger audience. Andi, This new normal. This is the first time I'm doing an interview like this. Thanks for that experience. Actually, as James mentioned, this relationship goes a long way. I am talking about the larger PCs were a long relationship. Andi, specifically on the easiest flowed. We started this journey in a very, very practical way. Five years back it was it was started in a very, very small trial and error basis. We started this relationship RPC explode. But at this point in time after So yes, we have taken this into, ah, new norm, actually. So I'll give you a couple of examples. One of the examples We have a very major retailer in Germany, which we work so that it was a multi $1,000,000 deals, our busiest on GHB. He has been a unique offering to the customer s AP, and a space on that is really growing a lot. And that's the one offering I would like to tell the audience that really has picked up and spent on the relationship in the German region. Right now we are trying to take that up, offering across on other other regions also, so that is one of the key offerings that we are doing it. The other offerings are multiple offerings we are doing, but again, I want to highlight the storage as a service offering. Great. It's everybody in the industry today, Andi, we are experimenting that in the initial stages in Australia, we started in Australia a small offering. And now we are expanding it in the US geography in a big way. And this year we are going to make that as a unique offering. And we're going to offer they're all over cloud customers as a storage as a service offering. Also, multiple other offering. Lisa. But I just thought that I like this tool which are making our business. We're making a lot of business together with these two offerings >>is the, uh, s AP opportunity that you mentioned is that the Hana as a service that TCS is delivered? >>That's correct. So it's ah, it's a service. But the uniqueness of that particular offering is be jointly created the architecture so that the customer can use that, like a database as a service model. Right? So it was It was not available that time in the industry so easily like what we offered at that point in time to do enough years back. We offer that particular said we spoke a summer and interestingly, that particular offering the customer was using s AP themselves as a service initially, and they migrated their to us actually from Maybe that's a reason they bought HP and PCs. That is like a summer on this API and a platform. So that's the That's the interesting story under, >>if we didn't do that just a little bit further, I wanted the audience to understand the impact that this partnership has H p E and TCS delivering Hana as a service for your customers. What are the benefits there than what the customer, as you said was doing previously? >>Yeah, yeah, I think I just want to relay the three or four points that make that this offering very unique, and that helps the customer number one is associated with model. So the customer has got the complete flexibility off going up and down like a true cloud model, right? And so it is a really a unique proposition at that point in time, where the customer not a story about using less for some time and then using more sometimes. So it's kind of a complete, flexible model that we offered at the time. Number two is, it's a complete customization is possible. It is not like a fixed architecture. The architecture is so flexible so that the customer business needs can be met through the architectural changes. So it's not like normally people think that lotus highly standardized architecture, right? So that has gone out, and we were given a flexible architecture for the customer. That is the number two number three, obviously the cost end of the day. There's a business case which we need to make it work right for the customer. So obviously, with the PCs and HP coming together, we were able to do the costarred, want age with a customer that is the third advantage of that. The last, but not the least, is the quality of service it is it is all about. I always used to tell my partners that selling is easy. Delivering it is what it's important it is, which will make the customer to stick with you, right? The were given and delivery quality experience who our customer s so that I think that makes a very unique proposition from a technology perspective from a pricing, but from an architecture and also from the delivery perspective. So those are the few few things I just thought that I violated. >>Excellent. So a couple of words that you mentioned popped into my mind as really even more well have a different meeting as we're in summer 2020 flexibility and unique. Offering chills back to you from a go to market perspective. How is that relationship with HP? And he says, changing in the Koven era. >>Yeah, it's pretty interesting, and I would like to call it an example off. You know, what we see is is that you themselves during the corporate times, you know, it also came in the pets close to 90% of the workforce. We're 100% productive. Uh, and, uh, they have a plan to go 75% of the employees, you know, but go being remote by 2025. So that's the journey they're taking on. And another thing that you notice there's a lot of the, you know, During the corporate times, many of the customers were looking for solutions like virtual desktop infrastructure. So they wanted their employees to be productive, bi directional and in the other area of focus was like a TCP, you know, how do I kind of make sure on the applications are available to you, the customers and also do their internal organizations? So we've seen a lot off. I would say engagement with that is I could picture team and also the solution team toe address This requirements off the market jointly >>when we look at certain things that now might even be more important with this new normal, if you will, that the fact that most companies are still in phase one of this work, everyone works from home trying to get to a face to that might see some some maybe by function groups coming back to the office and then getting to this third. Maybe it's the new nirvana of some hybrid workforce, where there's gonna be some that come back permanently, and some that Don't and Tony Unirea chose, I saw was quoted last month as saying, I think that 50% of the workforce will only 50% will come back. So in this new not only hybrid I T environment in which your customers love, but now this new pending hybrid workforce environment, how are you addressing some of the concerns together with respect to the network connectivity security, >>I I'll just take the cost anything. It's a very, very interesting at least when we all ended up in this pandemic in March. We really very, very nervous, actually, because everyone has to operate remotely on we are. We are dealing with the customer data. It's ah, it's very, very important that we have a secure environment to access the information and at the same time maintain the integrity of the data and also the quality off the plate. Those other two primary objective for us. We don't want to compromise on quality. We don't want to compromise on security from a cloud perspective. So the solution we have put in really I just give you one example there was on the airline Ah, UK basically are living in the spirit of the company which they need that workforce overnight. They want everybody to go remote because you know you cape on. They just put up a condition that nobody can work from the office overnight on the entire or ports as toe work from home, PTC is implemented the solution for them on our clothes overnight and make that 1000 employees store from home the next day morning all of them started working with the full quality of services and also with a full security aspect of it has been taken care or made on the solution. We are deployed. Very interesting case study on The important thing we have done is use the technology to the poor. Use all kinds of technology to make sure that the employees that work from home we took care of the network connectivity. We took our eye off the security aspects off the data from security aspects. We've implemented all the security functions from a media perspective. Actually stop perspective, Andi. Make the workforce enable that. But now you are talking about millions off millions of workers going to work from home. Right, Because it is one example for one company we have done that note easiest themselves has got more than 400 1000 employees, and we are talking about millions off work force going to work from home on going forward. So that is, I'm seeing this as a big opportunity. It's not that everybody has are just this. At this point in time, I'm seeing this as an opportunity where on the cloud easiest cloud kind off. The solution is going to help them to achieve this, and this is a great opportunity for not only for PCs but also for HP because the solution we're putting together with the HP is more on the digital or course how we can enable the people to work from home, not compromising on as I mentioned from a security you're in from millions perspective. So I'm seeing this as an opportunity for both the organization, and it's a long way to go is we need to work on this. It's not. We don't have a magic want to make the millions off workers to work from home, but it is going to have all soon and probably in the next step. Yeah, so we may achieve this. Impair people's off. The workforce is going to go remotely on this list. So that's that. And my take on this >>is so the impact that HP and TCS herb being able to make for customers who have had to massively transform their entire workforce overnight, as he said, to work from home to talk about some of the new maybe new solutions or new business opportunities that HPC is partnering with TCS shells, we'll start with you in this new era, >>Yeah, so if you look at it, I just taking it again on extension, offered up by just what you just mentioned about the percentage of employees going Lisa across industries today. I would say less than 20% of the employees are actually working remote or they have the ability. But the organizations have the ability to support the employees going, and if you have to take it to 50% so you can look at the kind of opportunity we have both as HP and as PCs. So we bring in a lot of best in breed infrastructure from for enabling the employee workforce to know where it is. I would say capacity off workloads and it's all workload specific. And what business does is over when people pretty easy as we kind of bundle that creating a reference architecture or a giant architect architecture addressing the customers by industry body. So because one what suits for one vertical may not be really suiting well for a different world, right? For example, if you take a banking sector, our traded workstation solution would look very different from somebody's doing remote in a retail. So we kind of continuously engage with the PCs, and that's where both of us have joint lab as well, where our technologies and pieces technologies come together, working on joint solutions and assisting the market in terms of the opportunity lights. And we offer this as part of A C is our digital workplace offerings. >>Are your conversations Dr Additional go to you or your conversations when you're jointly selling, changing in terms of who your audience is? Is this now a C level conversation? Since these leaders and we've heard leaders of Google and Facebook already last month saying Work from home extended still 2021. Is this now at the C suite level, where you guys are helping them really understand how to completely change and digitize their entire way of doing business? >>Absolutely. I think it's a great question, and it's actually the opportunity goes beyond the work from home solution. As you rightly I want to know that it is. It is all about digitization. It is all about digitizing their whole business process. It is not anymore infrastructure. Our application solution. It is more about really finding that business process be defending. The way the business is going to operate in future is the discussion we are having so a lot of these discussions are happening at a very, very high level on with the business team. Also directly, so earlier you used to interact with the technology partners off our organization. But now we are interacting directly with the head of business are the C level except of the company. And that is the reason the exact reason is Ah, you. If you want your ports to be productive remotely, you can't just offer them on network on. You can't offer them just a solution to work from home, But you need to really find your whole business process you need. You need to digitize your infrastructure. You need to digitize your application. You need to rethink your whole process off. You're operating on it, so that's what I'm seeing. It's not only an opportunity for our players like PCs cloud, but it is the opportunity for a bigger opportunity for PCs and be not only in terms off on infrastructure in our cloud business, it goes beyond that. So that is that is the kind of an opportunity we're seeing, especially in the in the sectors of healthcare you're seeing major reforms are happening in the healthcare industry as we speak on, obviously, manufacturing is going to go through a lot of changes. Also from that. And retail obviously has gone through a lot of changes already in terms of online, uh, stuff, but know that also going to goto changes in this new era? Yes, >>I have to ask you shelled talking about redefining? That's a word that we've seen so many years in a row at tech conferences, right, this technology redefining this business or that industry. And now, of course, we're being redefined by an invisible virus. But how is how is the sales process being redefined? Is it a lot more accelerated because businesses have to put together new plans to continue operations? >>Yeah, again, a great question. Is this how you have? You know, I would say it's divided by industry body. It's not a uniform thing. By, as the British was saying, every industry has got its own, its own set of challenges and its own set of opportunities, and some of them are really actually doing well even in times like and some of them have seen, Really. I mean, like, travel our transportation or, you know, some of those industries are and even hospitality that's kind of affected big time. So our view of you know, the entire sales engagement of the processes we're spending more time on there. We really need to focus and which can help improve the businesses. Right? So the conversation's ready from How do I take the cost out in terms of how can I make a little more investment to get greater returns from the business? So it's like it's a completely I would say, an interesting pain and engaging compositions and decisions are happening. So we, if you look at us from an automatic perspective, the sales team is armed with various virtual tools, like We know you zoom views Skype using SMS teens. So all the tools available to make sure that we're able to connect with all our partners and customers on do enable joint business together. >>I just want oh, I add to it, Lisa, 111 point. I want to ride Really interesting change I'm seeing on the sales is normally we respond to ask from a customer that is a sales happens. I want this many days do it and then what you can do with a solution That is the normal sales process. What I have seen that has changed completely. Yes, we go and tell the customer, Is this what you need Actually, to make you yourself your business? Better? This is the new offerings I'm having good. And this offering is going to help you to solve the problem what you are having today. So we are engaging a different level off sales conversation today with our customers. We know the problem of the customer because we are working with them for many years and we know exactly what they're going through. And we also know what new offerings we are having in this. So we are engaging the discussion with the customer doing that. This is my new offering. This is going to help you to solve this problem. But that is a different angle of sales we have seen nowadays in this. A friend of it, >>the last question shells to you. We started our interview today talking about the HP TCS relationship. You talked about how it's evolved. Last question. You talked to me about H B's strategy. How does it match TCS Alfa Cloud offering? >>Yeah, so again, a great question, Lisa, if you look at our strategy is to accelerate the enterprises with it. Centric and cloud enable solutions which are workload optimized and delivered everything as a service. And whatever you heard from Dr Rogers through this entire conversation was about how do we give as a service model you gave an example of Hana? You give an example off, you know, going optimizing workloads for VD I and getting employees to be able to be productive remotely and all of that kind of extremely resonate well with you know, what pieces are defined to. Price cloud offering is bringing to the table for the customer and the underlying platform. You know, we can have yeah, extensively and closely with the easiest architecture being tohave the HP portfolio off. You know, the compute and storage portfolio integrated as part of their offering, and we go together to market, you know, addressing and kind of an ask service model. 1,000,000,000. >>Excellent. Well, shells Dr Rajesh, pleasure talking with you both today about what UCS and H e are doing together in some of the ways that you're really helping businesses move forward in these uncertain times, we appreciate your time. >>Thank you. Thank you. For instance. Thanks. >>Thank you. Dr Rajesh. >>My guest. I'm Lisa Martin. You're watching the Cube's coverage of HP Discover 2020. The virtual experience. Thanks for watching. >>Yeah, Yeah, yeah, yeah, yeah.

>>from around the globe. It's the Cube with digital coverage of Red Hat. Summit 2020 Brought to you by Red Hat. >>Hi, I'm stupid, man. And this is the Cube's coverage of Red Hat Summit. Of course, it's happening digitally. We're interviewing Red Hat executives, customers and partners from around the globe who can are gonna be part of this event. And happy to welcome back to the program. One of our Cube alumni, Joe Fitzgerald, who is the vice president and general manager of the management business unit at Red Hat. Last time I caught up with Joe is that answerable fest last year. And, uh, Joseph, fourth year in a row, You've been on the Cube here at Red Hat Summit. Thanks so much for joining us again. >>Thanks for having me back. Still, I'm happy to be here, >>All right, So, Joe, I think it actually makes sense for us to kind of pick up the conversation where we left off and answer both best last year, so answerable fast. It's all about automation. You're really helping with digital transformation. What companies are going through in today's day and age automation and being able to be more agile. Of course, everybody, for the most part, is working from home, being able to enable things remote. The adoption of cloud is even more consideration. So what part there? And since we last talked, you know, obviously things have changed for everyone some, but give us the latest from your organization. >>Thanks to you know, when we met and so fast in Atlanta last fall, we were talking about strategic use of our nation. Um, in today's current crisis, if you will. There's a lot of folks who are leaning on automation in a much more immediate and tactical way. We're seeing a lot of automation being used by folks to the boy, either the infrastructure they need to deal with capacity and surge demand that they have. Right now, we're seeing people use it for things like working from home because they can't get access to gear we're seeing for bursting to public clouds because they can add more physical equipment, perhaps in the data center. So last year we were excited to talk to you about strategic automation. That's still really important. But right now a lot of folks have more pressing matters in terms of automating to get through the current crisis coming to you from northern New Jersey, which is certainly a hot spot on, but certainly a lot appreciation for the folks on the front lines. They're taking care of us and protecting us and things like that. We want to do everything we can as a company, as red hat, to enable folks to do whatever they need to do to be able to get through this crisis. >>Yeah, absolutely. Joe Ah, very important topics there. In the keynote for Red Hat Summit, there's discussion of your group, and of course, management has always been a critical piece of how we look at overall i t. When I first became an analyst, the joke was, Well, you know, security and management. We can always kind of okay, those pillars is two things we need to do is an industry to make things better. Ah, specifically, we've been talking for years about the growth of container ization and kubernetes. Of course, Red hat, strong leadership position with open shift. My understanding that if I heard right from the keynote, it's the advanced cluster. Management is the new piece. Can you give us a little bit about, you know, the team, the technology, how this fits into the overall red hat portfolio. >>Sure, so we're super excited around advanced cluster management. It turns out that you know, we have a lot of customers that are running open shift to other container based applications, and as they evolve, they inevitably end up with multiple clusters based on separation of duties or lines of business, perhaps for distributed availability zones and things like that with their clusters, so they inevitably back into a multi cluster scenario. What we've done is working with IBM would develop some very rich technology around advanced management for multi cluster environments built from scratch for container environments and kubernetes. We worked with IBM. We move that technology over to Red Hat. But when the process of doing two things one is we're announcing tech preview here at Summit of that technology and where the process we're open sourcing that technology because we're red hat and everything we do is open source. We're going to take some of the most advanced container management cluster management technology in the world that we've gotten from IBM, and we're going to open source that we're excited here is that we're gonna provide this red hat, offering advanced cluster management to help people who are struggling with managing clusters. >>Yeah, Joe, absolutely a super important point point. Anybody that's watched this space for the last few years, simplicity has not been the word that people have used for it. And over the last year there's been a lot of announcements from some of the major players in the industry about how do I manage those multiple cluster environment? So, Joe, if I think back two years ago Ah, it was, you know, here's the best way to run kubernetes. And when you talk to a lot of customers, it was well, they were starting with often spinning their own because that was what was available. And the number one choice that I usually heard from customers was, Oh, if I'm a Red Hat customer, started using open shift and start using open ships everywhere, fast forward to where we are today. Of course, you have lots of customers running open shift, but also in the public clouds. If I'm using Amazon, Google, Microsoft, other platform environments, often there's a native kubernetes, and I need to manage across those environments. So do I understand right? ACM Is that going toe? Help me not only with my open shift but as it moves forward. Also, manage some of those other kubernetes environment. And how does Red Hat approach this kind of the same or differently? From what I hear from from Microsoft with Arc with VM Ware with Andrew >>So ACM vessels Management from Red Hat supports any standard kubernetes environment. One of the advantages we have working in an open shift environments Open shift has a lot of functionality sides Kuban aids. In other words, it's already a layer of sophistication built on top of kubernetes, so open shift itself provides a lot of management automation. Now you had advanced cluster management on top of that, which will be able to import other communities clusters from other environments. But the ability for its take advantage of the sophistication that's already in open shift and then leverage things like Hansel Automation and then some of the management. SAS Services cloud at red dot com We're connected customer experience the ability to proactively look at open shift clusters and be ableto some cases tell people about problems they're having before they even realize they have the problem. That combination of management automation on top of the already rich open shift environment really puts us. A couple of you know, runs up in terms of capabilities. I'll be on a standard kubernetes vanilla. Our >>yeah, so one of the reasons I was looking forward to this conversation is one of the things that we've been looking at for the last few years is how is multi cloud the same or different from what we have done back, You know, 10 15 years ago with multi vendor. And I think anybody that's been around long enough and you talk about management in a multi vendor environment and you think about the leading tools from a software standpoint. We're out there and it gives us a little bit of flashbacks, and it's not. Not in a good way. So what have we learned as an industry? And, you know, you talked about it, you know, integration with answerable all the automation, you know, how do we make sure that we aren't repeating the sins of the past with these new generation of management tools? >>Well, what we've seen is that enterprises are inherently going to be hybrid and multi cloud red has been talking about open hybrid cloud for almost eight years. Right? So our CTO Paul Cormier, you know, sort of anticipated this, which was pretty insightful eight years ago because everybody thought and people gonna move exclusively cloud it would have any data centers and maybe hardware anywhere. That's why you've got data centers edge multiple public clouds with services that are all over those different footprints. We believe that, you know, unlike the past when you had heterogeneous systems management, right where you have different platforms that we're trying to manage is the lowest common denominator is a common platform. Now what Red hat is offering is open shift, which will run on all the public clouds, as well as on your physical and virtual hardware in the data centers at the edge. So it basically provides the consistency, which means that the management can then talk to a consistent environment, provided much higher level hybrid cloud management and trying to either have silos of different management tools by cloud by vendor by environment, um, and then try to Federated at the lowest common denominator. You'll see kubernetes management tools, for example, that have to use the lowest, you know, sort of common denominator, which is the straight kubernetes AP eyes. We could take advantage of those, but also the additional functionality That open shift brings in, for example, with the other kids abilities. It allows us to have a higher level of management but provide that consistency by having the same hybrid cloud platform this case. Coburn's shift run across those different environments. >>Yeah, so what? One of the things that also consumer concerns me a little bit as the industry when they talk about kubernetes. It's very much a discussion of the infrastructure piece, but we know this move to cloud native is very much about the application and the application development. So help me understand a little bit how that overall story for kind of the app Dev see I CD all those pieces fit into your story. I was one of the major points of discussion. You know, the best. >>Yeah, so So it is really all about the application. People really don't want to think about the infrastructure. They don't think about the application. That's really what's driving their business and their differentiation in the case of open shift, but open shift provides application. Lifecycle management for kubernetes environments are advanced. Cluster management sort of takes that a step further and allows you to extend that life cycle so that you can deploy applications based on policy to different environments based on your needs. Keeps compliance. All those things enforce regardless of how many different places the traction application. So it's not just a Z Z as taking an application to going into one location. People want to be able to continuously update their applications and deploy it to all of the places that it needs to be there based on availability, a proximity security environments and things like that represents a hard challenge. And so that's why some of the tools, like Advanced cluster Management, are exactly designed to help those kind of new applications. Yeah, >>all right, Joe, you talked about that. Some of the technology for for ACM came from the IBM side. Give us the update. When you look at the IBM Cloud portfolio, how is your group really interacting and supporting and working with the overall IBM solutions? >>So IBM has a very robust portfolio and they have you know a number of the cloud packs in their portfolio that address things like applications and data management, things like that. So IBM, in this case, I developed some advanced cluster management technology, but it was not open source. It wasn't available to other folks. One of the challenges with that is that we believe, as red Hat that the innovations happening in open source. If you develop something in a closed, proprietary way, maybe the best thing in the world today, a year, two years, three years from now there are other projects and there are other technologies have being collaborated on open that are going to make. But we leave you behind, right? So we think open is the future. So in this case, and working with IBM, we took some very advanced technology. We moved it over to Red Hat, and now we're in the process of open sourcing it, as well as providing an enterprise consumable version. More technology in Red Hat Advanced Cluster Management for kubernetes, IBM again has to support a much broader, diverse environment, right in terms of everything from mainframes to edge and containers and V M's and physical machines applications that span decades, So they have a much bigger sort of, you know, target environment that they have to work in. Red Hat's focused on the future. We're really sort of skating to where the puck is, if you will use a you know, hockey analogy where basically, we're trying to anticipate what enterprises are going to need and address that with not only the platforms with management automation, you're going to need to be successful with the cloud. >>Yeah, Joe, I want I want you to bring it into your customers and you talk about all these changes that are happening in the landscape and how they manage it. Any insight you can give as to, you know, organizational structures. You know, I remember last year at Summit I talked to a number of companies going through digital transformation. And, you know, we know that there is as much if not more organizational change that needs happen along with the technology pieces. So from your world, you know who's kind of leading the charge, what skill sets do people need to either, you know, bring to it or learn new on And you know, our companies, you know, taking advantage of >>Well, as they say, developers are sort of the new kingmakers, right? In some ways. And so you know the tools that you've always said people process and technology, right? And I know as software companies get very, very excited about technology, but it turns out that the people, the process here way people building their applications, Way Dev ops and see I CD. It's a very, very sort of different environment for management automation tools, you know and sort of. The relationship between teams has changed and will change more, by the way. And so one of things we're trying to do. You see this with answerable, but you're also seeing this with Advanced Cluster Management is ability to delegate and give different kinds of operational and management capabilities to the teams, whether it's like business developers, QE teams. So it's fundamentally changing the way that the processes were working. That requires that the tools map to those new team structures. There's no new processes on, and that's what I think's going on it fundamentally different, and one of things I think you're going to see is management tools that were built in the past for these were the old style organizing are not going to fare well in the new World, where these processes and the team structures are changing. >>All right, So, Joe, before I want to get some feedback from you on how your technologies and teams they're helping with the code 19 piece. But let's just wrap up the ACM discussion before we do that. So you said it's a tech preview. S 01 of things that really nice is when you move things to open source, the community gets pretty good visibility as to when things were getting releases. New features down the pike. So what should we be looking for as an industry for ACM? When that rolls out, how two people start getting their hands on it and you know, what does that look like? >>So there's really two paths there. One is from a tech preview point of view. You know, customers can get access to the technology right and see it in their environment and give us feedback. The fact that it's been developed for the past two years probably constitutes hundreds of years of developer, um, you know, time in it. It's not Alfa technology. It's pretty robust. So even though we're calling a tech preview, we anticipate that it's going to be production ready in short fashion. It will take us a little bit of time to open. Source. The technology's red hat has a history of open sourcing technologies that we acquire. Each one varies in terms of what's in the code licenses, how it's structured, how it should be open source. We just don't back the truck up and take a bunch of code and put in a repository is actually a thoughtful process about the way that's projects or set up a communities they should be in. We're going through that process now, but customers will be able to take advantage of it in short fashion, and I think they're gonna find a very high level of maturity, given how long and how much. I mean it's work of this, >>you know, a really important piece is there. The other one closed the discussion with how we started off. Obviously, you know, workers and companies are having to make changes and be more flexible than ever in response to the Kobe 19 endemic. What are some of the pieces of technologies and services Ah, that that you want to highlight as toe that are helping companies really adjust to what is happening in today's world. >>Well, Red Hat is always been a very conscientious company. And in my particular area, one of things we're doing is with sensible. We're trying to enable folks to use automation providing free workshop, free workshops and access to code playbooks and things for different environments. If you think about the different kinds of industries right now, some are struggling with no smaller workforces work at home. Other ones are under tremendous pressure to deliver services to help keep us safe and protect us. So we're trying to provide as much a so we can in terms of automation, enabling people to use free, open source innovation on automation to enable work from home to do everything from creative TVNZ toe, you know, set their statuses and communicate between teams in this new environment, but to burst into a lot of clouds in some cases because somewhere trying to scale because their business is now change but is under tremendous pressure. You see that delivery services and things like that. So we're trying to to help as much as we can with automation is something that could be immediately helpful and has been some of these other projects. You know, somebody's doing a transformation, and they're designing new applications as much longer. Burn to it. Whereas automation is needed today by companies under duress, you can help them accelerate, um, and connect the their their new work at home environments. Sweetie Automation. Helping a lot. The other thing I want to mention is that we have free capabilities like red hat insights that can actually access systems for security. The last thing you need is a security breach or some other problem. Why you're dealing with fighting fires. There are bad actors out there. We've seen a few already eso insides ability to look at systems and tell people what their current posture is. So they immediately, quickly, whether with our tools or some other tool they have. We're trying to do as much as we can to help our customers for this really tough time. >>Well, Joe, thank you so much for the updates. Ah, congratulations to the team on the progress and absolutely very important topics to help customers that need to react even faster than ever in today's time. Extra funding. I'm stew. Minimum lots more coverage from Red Hat Summit on the Cube. Check out the cube dot net. And thank you for watching. >>Yeah, yeah, yeah.

>>Live from San Diego, California It's the Q covering Koopa and Cloud Native Cot brought to you by Red Cloud, Native Computing Pounding and its ecosystem March. >>Welcome back to the Cube here at Q. Khan Club native Khan, 2019 in San Diego, California Instrumental in my co host is Jon Cryer and first of all, happy to welcome back to the program. Diane Mueller, who is the technical of the tech lead of cloud native technology. I'm sorry. I'm getting the wrong That's director of community development Red Hat, because renew. Goodbye is the technical lead of cognitive technologies at in video game to the end of day one. I've got three days. I gotta make sure >>you get a little more Red Bull in the conversation. >>All right, well, there's definitely a lot of energy. Most people we don't even need Red Bull here because we're a day one. But Diane, we're going to start a day zero. So, you know, you know, you've got a good group of community of geeks when they're like Oh, yeah, let me fly in a day early and do like 1/2 day or full day of deep dives. There So the Red Hat team decided to bring everybody on a boat, I guess. >>Yeah. So, um, open ships Commons gathering for this coup con we hosted at on the inspiration Hornblower. We had about 560 people on a boat. I promised them that it wouldn't leave the dock, but we deal still have a little bit of that weight going on every time one of the big military boats came by. And so people were like a little, you know, by the end of the day, but from 8 a.m. in the morning till 8 p.m. In the evening, we just gathered had some amazing deep dives. There was unbelievable conversations onstage offstage on we had, ah, wonderful conversation with some of the new Dev ops folks that have just come on board. That's a metaphor for navigation and Coop gone. And and for events, you know, Andrew Cliche for John Willis, the inevitable Crispin Ella, who runs Open Innovation Labs, and J Bloom have all just formed the global Transformation Office. I love that title on dhe. They're gonna be helping Thio preach the gospel of Cultural Dev ops and agile transformation from a red hat office From now going on, there was a wonderful conversation. I felt privileged to actually get to moderate it and then just amazing people coming forward and sharing their stories. It was a great session. Steve Dake, who's with IBM doing all the SDO stuff? Did you know I've never seen SDO done so well, Deployment explains so well and all of the contents gonna be recorded and up on Aaron. We streamed it live on Facebook. But I'm still, like reeling from the amount of information overload. And I think that's the nice thing about doing a day zero event is that it's a smaller group of people. So we had 600 people register, but I think was 560 something. People show up and we got that facial recognition so that now when they're traveling through the hallways here with 12,000 other people, that go Oh, you were in the room. I met you there. And that's really the whole purpose for comments. Events? >>Yeah, I tell you, this is definitely one of those shows that it doesn't take long where I say, Hey, my brain is full. Can I go home. Now. You know I love your first impressions of Q Khan. Did you get to go to the day zero event And, uh, what sort of things have you been seeing? So >>I've been mostly I went to the lightning talks, which were amazing. Anything? Definitely. There. A number of shout outs to the GPU one, of course. Uh, friend in video. But I definitely enjoyed, for example, of the amazing D. M s one, the one about operators. And generally all of them were very high quality. >>Is this your first Q? Khan, >>I've been there. I've been a year. This is my third con. I've been accused in Europe in the past. Send you an >>old hat old hand at this. Well, before we get into the operator framework and I wanna love to dig into this, I just wanted to ask one more thought. Thought about open shift, Commons, The Commons in general, the relationship between open shift, the the offering. And then Okay, the comments and okay, D and then maybe the announcement about about Okay. Dee da da i o >>s. Oh, a couple of things happened yesterday. Yesterday we dropped. Okay, D for the Alfa release. So anyone who wants to test that out and try it out it's an all operators based a deployment of open shift, which is what open ship for is. It's all a slightly new architectural deployment methodology based on the operator framework, and we've been working very diligently. Thio populate operator hub dot io, which is where all of the upstream projects that have operators like the one that Reynolds has created for in the videos GP use are being hosted so that anyone could deploy them, whether on open shift or any kubernetes so that that dropped. And yesterday we dropped um, and announced Open Sourcing Quay as project quay dot io. So there's a lot of Io is going on here, but project dia dot io is, um, it's a fulfillment, really, of a commitment by Red Hat that whenever we do an acquisition and the poor folks have been their acquired by Cora West's and Cora Weston acquired by Red Hat in an IBM there. And so in the interim, they've been diligently working away to make the code available as open source. And that hit last week and, um, to some really interesting and users that are coming up and now looking forward to having them to contribute to that project as well. But I think the operator framework really has been a big thing that we've been really hearing, getting a lot of uptake on. It's been the new pattern for deploying applications or service is on getting things beyond just a basic install of a service on open shift or any kubernetes. And that's really where one of the exciting things yesterday on we were talking, you know, and I were talking about this earlier was that Exxon Mobil sent a data scientist to the open ship Commons, Audrey Resnick, who gave this amazing presentation about Jupiter Hub, deeper notebooks, deploying them and how like open shift and the advent of operators for things like GP use is really helping them enable data scientists to do their work. Because a lot of the stuff that data signs it's do is almost disposable. They'll run an experiment. Maybe they don't get the result they want, and then it just goes away, which is perfect for a kubernetes workload. But there are other things you need, like a Jeep use and work that video has been doing to enable that on open shift has been just really very helpful. And it was It was a great talk, but we were talking about it from the first day. Signs don't want to know anything about what's under the hood. They just want to run their experiments. So, >>you know, let's like to understand how you got involved in the creation of the operator. >>So generally, if we take a step back and look a bit at what we're trying to do is with a I am l and generally like EJ infrastructure and five G. We're seeing a lot of people. They're trying to build and run applications. Whether it's in data Center at the and we're trying to do here with this operator is to bring GPS to enterprise communities. And this is what we're working with. Red Hat. And this is where, for example, things like the op Agrestic A helps us a lot. So what we've built is this video Gee, few operator that space on the upper air sdk where it wants us to multiple phases to in the first space, for example, install all the components that a data scientist were generally a GPU cluster of might want to need. Whether it's the NVIDIA driver, the container runtime, the community's device again feast do is as you go on and build an infrastructure. You want to be able to have the automation that is here and, more importantly, the update part. So being able to update your different components, face three is generally being able to have a life cycle. So as you manage multiple machines, these are going to get into different states. Some of them are gonna fail, being able to get from these bad states to good states. How do you recover from them? It's super helpful. And then last one is monitoring, which is being able to actually given sites dr users. So the upper here is decay has helped us a lot here, just laying out these different state slips. And in a way, it's done the same thing as what we're trying to do for our customers. The different data scientists, which is basically get out of our way and allow us to focus on core business value. So the operator, who basically takes care of things that are pretty cool as an engineer I lost due to your election. But it doesn't really help me to focus on like my core business value. How do I do with the updates, >>you know? Can I step back one second, maybe go up a level? The problem here is that each physical machine has only ah limited number of NVIDIA. GPU is there and you've got a bunch of containers that maybe spawning on different machines. And so they have to figure out, Do I have a GPU? Can I grab one? And if I'm using it, I assume I have to reserve it and other people can't use and then I have to give it up. Is that is that the problem we're solving here? So this is >>a problem that we've worked with communities community so that like the whole resource management, it's something that is integrated almost first class, citizen in communities, being able to advertise the number of deep, use their your cluster and used and then being able to actually run or schedule these containers. The interesting components that were also recently added are, for example, the monitoring being able to see that a specific Jupiter notebook is using this much of GP utilization. So these air supercool like features that have been coming in the past two years in communities and which red hat has been super helpful, at least in these discussions pushing these different features forward so that we see better enterprise support. Yeah, >>I think the thing with with operators and the operator lifecycle management part of it is really trying to get to Day two. So lots of different methodologies, whether it's danceable or python or job or or UH, that's helm or anything else that can get you an insult of a service or an application or something. And in Stan, she ate it. But and the operator and we support all of that with SD case to help people. But what we're trying to do is bridge the to this day to stuff So Thea, you know, to get people to auto pilot, you know, and there's a whole capacity maturity model that if you go to operator hab dot io, you can see different operators are a different stages of the game. So it's been it's been interesting to work with people to see Theo ah ha moment when they realize Oh, I could do this and then I can walk away. And then if that pod that cluster dies, it'll just you know, I love the word automatically, but they, you know, it's really the goal is to help alleviate the hands on part of Day two and get more automation into the service's and applications we deploy >>right and when they when they this is created. Of course it works well with open shift, but it also works for any kubernetes >>correct operator. HAB Daddio. Everything in there runs on any kubernetes, and that's really the goal is to be ableto take stuff in a hybrid cloud model. You want to be able to run it anywhere you want, so we want people to be unable to do it anywhere. >>So if this really should be an enabler for everything that it's Vinny has been doing to be fully cloud native, Yes, >>I think completely arable here is this is a new attack. Of course, this is a bit there's a lot of complexity, and this is where we're working towards is reducing the complexity and making true that people there. Dan did that a scientist air machine learning engineers are able to focus on their core business. >>You watch all of the different service is in the different things that the data scientists are using. They don't I really want to know what's under under the hood. They would like to just open up a Jupiter Hub notebook, have everything there. They need, train their models, have them run. And then after they're done, they're done and it goes away. And hopefully they remember to turn off the Jeep, use in the woods or wherever it is, and they don't keep getting billed for it. But that's the real beauty of it is that they don't have to worry so much anymore about that. And we've got a whole nice life cycle with source to image or us to I. And they could just quickly build on deploy its been, you know, it's near and dear to my heart, the machine learning the eyesight of stuff. It is one of the more interesting, you know, it's the catchy thing, but the work was, but people are really doing it today, and it's been we had 23 weeks ago in San Francisco, we had a whole open ship comments gathering just on a I and ML and you know, it was amazing to hear. I think that's the most redeeming thing or most rewarding thing rather for people who are working on Kubernetes is to have the folks who are doing workloads come and say, Wow, you know, this is what we're doing because we don't get to see that all the time. And it was pretty amazing. And it's been, you know, makes it all worthwhile. So >>Diane Renaud, thank you so much for the update. Congratulations on the launch of the operators and look forward to hearing more in the future. >>All right >>to >>be here >>for John Troy runs to minimum. More coverage here from Q. Khan Club native Khan, 2019. Thanks for watching. Thank you.

>>live from Orlando, Florida It's the cue covering Microsoft Ignite Brought to you by Cohee City. Welcome >>back, everyone to the cubes Live coverage of Microsoft IC night here at the Orange County Convention Center in Orlando, Florida I'm your host, Rebecca Night, along with my co host Stew Minutemen were joined by Parham Cologne. He is the chief product officer at you. I path. Thank you so much for coming on the Cube. >>Thank you so much for >>coming back on the cute. >>Thank you. >>So I I was just a u IE path with you in Vegas a couple of weeks ago and the U AI Path tagline is a robot for every employee Microsoft tagline is employing empowering every employee to be a technologist, empowering citizen developers. Does it strike you that do the two missions are are similar in their way? >>That's that's absolutely right. I think we have so much in common their companies together on I think we're working very closely together and not just our technology, but also in what we're trying to achieve, which is to make people achieve more in amplifying human achievement is a core mission of our company and very excited that Microsoft so shares the same emission. >>Yeah, it really does connect with Mace onto this morning. Talked about that 61% of job openings for developers air outside the tech sector. And of course, you AI path is really trying to help. But this is productivity overall, with everything you're doing, >>absolutely, and productivity's where we focus our technology primarily on. In fact, a lot of focus is around. How do we actually get people to do more with less time so they can have more time to do the things that they could do with the creative parts of their time, as opposed to doing a Monday in part? So, yeah, productivity's is really important to us. The company. That's what we think about every day. >>Could you bring us inside the relationship with Microsoft and you? I passed? >>Yeah, so we're deeply partner that Microsoft's and today one we've most of our technology is built on Microsoft's stack on dot net miran. Our databases all run on sequel server or cloud service runs on Microsoft Azure. So we are very deeply partner to be health Microsoft Bill. A lot of a I service is around document extraction. The forms recognize her with one of the first customers that we work together with Microsoft and Chevron on so very deep partnership with Microsoft. Okay, >>so let me ask you a question. Actually, as a customer of Microsoft, you know what? Why, why everything built on Microsoft from, you know, the dot net through the infrastructure of the service. What, what? Why did you bypass choose Microsoft? >>I think it made a lot of sense. Microsoft's focus on productivity Microsoft's focus on enabling developers do stuff quickly on it also helped a lot of the founders, myself included, came through with Microsoft to be a lot of experience with Microsoft's. I think part of that helped as well. >>Does it help or hurt when you are then pitching your service? Is that that it is that it is a much more Microsoft focused company, >>So I think we've grown over the years to actually have a much broader ecosystem, so we have more than 500 partners now we work with Google. Google is a customer, it's an investor. It's also very deep partner. A lot of very I service is we're welding on it with Google were be partnered with AWS as well. So I think we're working with all the way our customers are today. But I think we're still have a very close relationship with Microsoft, given our agitated given where we started. >>Yeah, I actually I I went to the passport event last year and had not realized how deep that connection was with Microsoft. I see you. I path across all the clouds. So there's a little mention of our p A. That this morning in the keynote theme, the power automate solution coming out from Microsoft. Of course, everyone seems tohave an R p A. Out there, you know all the big software houses out there. Tell us what this means in the marketplace. >>Yes, Listen, our P a is a very fast growing market. Is the fastest growing enterprise category today, And when you grow so fast, it's good for the business but also attracts attention, I think getting somebody like Microsoft to sort of say that we're in it as well. Only help sort of solidify the foundation, solidify the category and brings a lot more, you know, credibility to this category. So I think we're excited to have Microsoft here as well. >>And in terms of a CZ, you were saying to companies that are very much focused on workplace productivity, employee collaboration, and being able to be more creative with the time that you have. How much is that cultural alignment? How much does that help your partnership? >>I think it helps a partnership a lot. So you know, when we, for example, of when you meet with the office team, they think deeply about helping people do more with last time. You know, we think about the same things as well. So if you notice some of the newer products that we've launched our very deeply integrated into office, in fact to do a lot of inspiration from products like Excel to be able to say business people that are able to, you know, do some very sophisticated, complex business models and excel should be able to do similar stuff with their products as well. So we continue to work with Microsoft and across collaboration across the steams, anything in general, our message. We have a close relationship with Microsoft, So when Microsoft bring this into opportunities and it closes, it actually retired Dakota for Microsoft Sellers as well. So I think all of that alignment really helps. >>I would love to hear you know what? What? Joint customers. You know what brings customers to you? I path at a show here. What? What are some of the key drivers for their discussions that you're having this week? >>Yeah. I mean, we've got you know, through through the years, we've got over 5000 customers that work with us large enterprises in a very large banks to companies like Chevron. Chevron in particular, is one of those customers. You know, that's a very, very deep customer of Microsoft, but also a very strong customer of ours and a specific use case at my at Chevron. Chevron wanted to extract data from their oil field service reports. They were getting more than 1000 oil. Regular reports coming in every day with about 300 pages for average. For report on. Somebody had to manly go in and physically read those reports. Put him into that s a P system so that you could predict if there was a pretty prevent amendments appear that was acquired, you know, working together with Microsoft, we were able to take service that Microsoft was building an A. I called forums recognize ER and take it to pre bid on Alfa with customers so that Chevron is now able to have all of those reports read by you. I path robots and automatically punch it into, you know, the SNP preventive maintenance applications so that you can actually ship the engineer on side before you know that something happened to the old Greg. So I think that's a pretty cool a scenario. >>Another's another similarity between AI Path and you, AI Path and Microsoft is this customer obsession. And this is something that you talked a lot about at your path forward. This spending time with customers, learning how they would use our p A and then also thinking, thinking ahead of them and in terms of how they could use our p A. How do you work with customers and Microsoft together in partnership in terms of how do you find out exactly what their needs are and the joint solutions you could provide? >>Yeah, and then that's a really good question. Microsoft has been very obsessed with, you know, driving customer obsession and all parts of the organization we culturally have a really deep obsession about working closely with customers. And I think so that Microsoft has empty sea, meet the customer sessions around around the world on We were close living Microsoft to make sure that our technology can be showcased by Microsoft people in those empty see sessions so that when customers come in, they able to not only see Microsoft technology, but also our technology. And if they're interested, then our sales teams work elaborately together to make sure we can, you know, have a joint session than planning and working with customers. >>So I had a chat earlier this year with your CMO Bobbi Patrick talking about how a I and r p a go together. You on the product? So will I. I be able to allow our p A to get into more complex configuration, give us where we are and you know what? What's what's new in that space? >>Yeah, No, absolutely. So like the first wave of our p A was all about taking sort of structured processes, you know, deluding data from excel sheets, reading data for maybe eyes and be able to process it in different systems now in the humans don't always work with that. 10% of what >>we do >>on a daily basis, a structure, data right, spreadsheets and stuff, 90% of what we d'oh reading spread shades, extracting information from papers responding Thio. You know Chad conversations. All of that unstructured information can now be processed by AI algorithms to be able to extract the intent off the chat conversation to be to extract the data. That's in that unstructured document that we just received to be able to use computer vision to detect what is on the computer screen so that you're able to detect that control, whether rendered the browser or renders in a window start to application of that. So I brings the possibility to automate a lot more complex processes within the organization, you know, mimicking sort of MME. Or human like behavior. So the robots are not just doing the numbers and structured data but be able to process unstructured information. It's >>well, well, the way I help it all, trying to understand, what can I automate? >>Absolutely. And that's the other piece off being able to use process, understanding capability. So what we've done is we've built capability that's able to follow human activity logs and how people are using systems, but also how the databases air getting updated by different applications and be able to mind that information to understand how work is getting done and the enterprise and be able to understand what are the scenarios and possibilities for automating mawr business processes that's hold onto the key benefits of how a I and process mining can be can be applied to the context of the R P. A. >>There's so many product announcements today. On the main stage is an 87 page book that we that we were sent from the Microsoft calms team. What is it? What's the most exciting things you've seen here today? >>I think I'm really excited about some of the innovation that Microsoft is doing in the analytic stock to be able to report on the, you know, the data warehouse, but also big data together and one stack. I think that's really powerful. That is something that our customers have have be very interested in, because robots process structure log, but also in structure logs. I'm also excited about some of the eye investments that Microsoft is making, I think some of the eye capabilities and are really coming to practical use. A lot of companies tuck Brody I For a long time. We've applied a I practically in our technology, but I think a lot more technology is now available for us to be used in our products. >>Okay, parm. There's a recent acquisition process. Gold was. The company could tell us a little bit about that. What what? What are the plans for that >>absolutely process Goal is a company that's basically all in Germany and nine home and in bed. Ireland. On this is the company that was focused on process, understanding of process. Mining's essentially, what they had was that connectors a different line of business applications and be able to sit and study logs of how work was getting done over long periods of time. So what happened is if you went to a line of business owner and he asked them, What is your process for procure to pay look like, in order to cash look like chances out, they'll draw you a straight line. That's a haze with the processes, However, when you look at how work is getting done, it's typically not a straight line. And depending on how many variations you're looking at, you can get up to, like, you know, 15 or 20 different variations, the same process being done. So what process gold does is identifies. What are the different ways in which processes air getting done? Identify where the bottlenecks exist in the process, right? How long is the step one? How long is the time? But we step two and step three, right? Is that taking 25% of what the total time is? And is there a way to optimize that process by eliminating that bottleneck? And once you've optimized the process, it also gives you the ability to go automate that optimized process right? You don't want to automate a process that is sub optimal. You want to go understand the process, see how work is getting done, optimized the bottlenecks and eliminate the bottlenecks, optimize the process and then go out of made that and process go. It really helps us sort of cater to that need, which is go automate. You know, the best possible way to optimize the process >>in terms of Microsoft's use of things like a I and ML And now we have not really talked a lot about ML here. I mean, it was mentioned on the main stage, but not a lot. How? What? What do you think the future holds in terms of Microsoft in the next 5 to 10 years? >>Yeah. I mean, I think I see Microsoft investing a lot in data and really being able Thio get all kinds of data because ML is useful only after it's able to reason over tons of data. And Microsoft is in a rightfully investing and the data repositories in stores so that it has the ability to store that data to process that data. And once that's got the data on the data assets over it, then it's able to go Korea the algorithms that can reason over data on and create that stuff. And I think that's really exciting because Microsoft has a lot of the horsepower to be able to not only store that data process that data efficiently said can be used in machine learning. And I >>hope our um thank you so much for coming on the Cube. It was a pleasure talking to you. >>Thank you. Pleasure to have you here. Thank you very much. >>I'm Rebecca Knight. First to minimum. Stay tuned for more of the cubes. Live coverage of Microsoft ignite.

>>from New York. It's the Q covering Escape. 19. >>Welcome back to the Cube coverage here in New York City for the first inaugural multi cloud conference called Escape, where in New York City was staying in New York, were not escaping from New York were in New York. It's all about multi Cloud, and we're here. Lisa Marie Nancy, developer advocate for Port Works, and Eric Conn, vice president of Products Works. Welcome back. Q. >>Thank you, John. Good to see >>you guys. So, um, whenever the first inaugural of anything, we want to get into it and find out why. Multi clouds certainly been kicked around. People have multiple clouds, but is there really multi clouding going on? So this seems to be the theme here about setting the foundation, architecture and data of the two kind of consistent themes. What shared guys take Eric, What's your take on this multi cloud trend? Yeah, >>I think it's something we've all been actively watching for a couple years, and suddenly it is becoming the thing right? So every we just had ah, customer event back in Europe last week, and every customer there is already running multi cloud. It's always something on their consideration. So there's definitely it's not just a discussion topic. It's now becoming a practical reality. So this event's been perfect because it's both the sense of what are people doing, What are they trying to achieve and also the business sense. So it's definitely something that is not necessarily mainstream, but it's becoming much more how they're thinking about building all their applications. Going forward, >>you know, you have almost two camps in the world. Want to get your thoughts on this guy's Because, like you have cloud native and people that are cloud native, they love it. They born the cloud that get it. Everything's cracking along. The developers air on Micro Service's They're agile train with their own micro service's. Then you got the hybrid I t. Trying to be hybrid developer, right? So you kind of have to markets coming together. So to me, I see multi cloud as kind of a combination of old legacy Data center types of I t with cloud native, not just ops and dead. But how about like trying to build developer teams inside enterprises? This seems to be a big trend, and multi club fits into that because now the reality is that I got azure. I got Amazon. Well, let's take a step back and think about the architecture. What's the foundation? So that to me, is more my opinion. But I want to get your thoughts and reactions that because if it's true, that means some new thinking has to come around around. What's the architecture? What are you trying to do? What's the workloads behavior outcome look like? What's the work flows? So there's a whole nother set of conversations that happened. >>I agree. I think the thing that the fight out there right now that we want to make mainstream is that it's a platform choice, and that's the best way to go forward. So it's still an active debate. But the idea could be I want to do multi club, but I'm gonna lock myself into the Cloud Service is if that's the intent or that's the design architecture pattern. You're really not gonna achieve the goals we all set out to do right, So in some ways we have to design ourselves or have the architecture that will let us achieve the business schools that were really going for and that really means from our perspective or from a port works perspective. There's a platform team. That platform team should run all the applications and do so in a multi cloud first design pattern. And so from that perspective, that's what we're doing from a data plan perspective. And that's what we do with Kubernetes etcetera. So from that idea going forward, what we're seeing is that customers do want to build a platform team, have that as the architecture pattern, and that's what we think is going to be the winning strategy. >>Thank you. Also, when you have the definition of cod you have to incorporate, just like with hybrid I t the legacy applications. And we saw that you throughout the years those crucial applications, as we call them People don't always want them to refer to his legacy. But those are crucial applications, and our customers were definitely thinking about how we're gonna run those and where is the right places it on Prem. We're seeing that a lot too. So I think when we talk about multi cloud, we also talk about what What is in your legacy? What is it? Yeah, I >>like I mean I use legacy. I think it's a great word because I think it really puts nail in the coffin of that old way because remember, if you think about some of the large enterprises, these legacy applications, they've been optimized for hardware and optimize their full stack. They've been build up from the ground up, so they're cool. They're running stuff, but it doesn't always translate to see a new platform designed point. So how do you mean Containers is great fit for their Cooper names. Obviously, you know is the answer. We you guys see that as well, but okay, I can keep that and still get this design point. So I guess what I want to ask you guys, as you guys are digging into some of the customer facing conversations, what are they talking about? The day talking about? The platform? Specifically? Certainly, on the security side, we're seeing everyone running away from buying tools to thinking about platforms. What's the conversation like on the cloud side >>way? Did a talk are multiplied for real talk at Barcelona? Q. Khan put your X three on Sudden. Andrew named it for reals of Izzy, but we really wanted to talk about multiplied in the real world. And when we said show of hands in Barcelona, who's running multi cloud? It was very, very few. And this was in, what, five months? Four months ago? Whereas maybe our customers are just really super advanced because of our 100 plus customers. At four words, we Eric is right. A lot of them are already running multi cloud or if not their plan, in the planning stage right now. So even in the last +56 months, this has become a reality. And we're big fans of communities. I don't know if you know Eric was the first product manager for Pernetti. Hey, he's too shy to say it on Dhe. So yeah, and we think, you know, and criminal justice to be the answer to making all They caught a reality right now. >>Well, I want to get back into G, K, E and Cooper. Very notable historic moment. So congratulations, But to your point about multi cloud, it's interesting because, you know, having multiple clouds means things, right? So, for instance, if I upgrade to office 3 65 and I kill my exchange server, I'm essentially running azure by their definition. If I'm building it, stack on AWS. I'm a native, this customer. Let's just say I want to do some tensorflow or play with big table or spanner on Google. Now >>we have three >>clouds now they're not. So they have work clothes, specific objectives. I am totally no problem. I see that like for the progressive customers, some legacy be to be people who like maybe they put their toe in the cloud. But anyone doing meaningful cloud probably has multiple clouds. But that's workload driven when you get into tying them together and is interesting. And I think that's where I think you guys have a great opportunity in this community because if open source convene the gateway to minimize the lock in and when I say lock and I mean like locking them propriety respect if his value their great use it. But if I want to move my data out of the Amazon, >>you brought up so many good points. So let me go through a few and Lisa jumping. I feel like locking. People don't wanna be locked >>in at the infrastructure level. So, like you said, if >>there's value at the higher levels of Stack, and it helps me do my business faster. That's an okay thing to exchange, but it is just locked in and it's not doing anything. They're that's not equal exchange, right, So there's definitely a move from infrastructure up the platform. So locking in >>infrastructure is what people are trying to move away from. >>From what we see from the perspective of legacy, there is a lot of things happening in industry that's pretty exciting of how legacy will also start to running containers. And I'm sure you've seen that. But containers being the basis you could run a BM as well. And so that will mean a lot for in terms of how V EMS can start >>to be matched by orchestrators like kubernetes. So that is another movement for legacy, and I wanted to acknowledge that point >>now, in terms of the patterns, there are definitely applications, like a hybrid pattern where connect the car has to upload all its data once it docks into its location and move it to the data center. So there are patterns where the workflow does move the ups are the application data between on Prem into a public cloud, for instance, and then coming back from that your trip with Lisa. There is also examples where regulations require companies to enterprise is to be able to move to another cloud in a reasonable time frame. So there's definitely a notion of Multi Cloud is both an architectural design pattern. But it's also a sourcing strategy, and that sourcing strategy is more regulation type o. R. In terms of not being locked in. And that's where I'm saying it's all those things. I'd >>love to get your thoughts on this because I like where you're going with this because it kind of takes it to a level of okay, standardization, kubernetes nights, containers, everyone knows what that is. But then you start talking about a P I gateways, for instance, right? So if I'm a car and I have five different gateways on my device, I ot devices or I have multiple vendors dealing with control playing data that could be problematic. I gotta do something like that. So I'm starting. Envision them? I just made that news case up, but my point is is that you need some standards. So on the a p I side was seeing some trends there. One saying, Okay, here's my stuff. I'll just pass parameters with FBI State and stateless are two dynamics. What do you make of that? What, What? What has to happen next to get to that next level of happiness and goodness? Because Bernays, who's got it, got it there, >>right? I feel that next level. I feel like in Lisa, Please jump. And I feel like from automation perspective, Kubernetes has done that from a P I gateway. And what has to happen next. There's still a lot of easy use that isn't solved right. There's probably tons of opportunities out there to build a much better user experience, both from the operations point of view and from what I'm trying to do is an intense because what people aren't gonna automate right now is the intent. They automate a lot of the infrastructure manual tasks, and that's goodness. But from how I docked my application, how the application did it gets moved. We're still at the point of making policy driven, easy to use, and I think there's a lot of opportunities for everyone to get better there. That's like low priority loving fruitcake manual stuff >>and communities was really good at the local food. That's a really use case that you brought up. Really. People were looking at the data now and when you're talking about persistent mean kun is his great for stateless, but for state full really crucial data. So that's where we really come in. And a number of other companies in the cloud native storage ecosystem come in and have really fought through this problem and that data management problem. That's where this platform that Aaron was talking to that >>state problem. Talk about your company. I want to go back to to, um, Google Days. Um, many war stories around kubernetes will have the same fate as map reduce. Yeah, the debates internally at Google. What do we do with it? You guys made the good call. Congratulations on doing that. What was it like to be early on? Because you already had large scale. You were already had. Borg already had all these things in place. Um, it wasn't like there was what was, >>Well, a few things l say one is It was intense, right? It was intense in the sense that amazing amount of intelligence amazing amount of intent, and right back then a lot of things were still undecided, right? We're still looking at how containers or package we're still looking at how infrastructure kit run and a lot of service is were still being rolled out. So what it really meant is howto build something that people want to build, something that people want to run with you and how to build an ecosystem community. A lot of that the community got was done very well, right? You have to give credit to things like the Sig. A lot of things like how people like advocates like Lisa had gone out and made it part of what they're doing. And that's important, right? Every ecosystem needs to have those advocates, and that's what's going well, a cz ah flip side. I think there's a lot of things where way always look back, in which we could have done a few things differently. But that's a different story for different. Today >>I will come back in the studio Palop of that. I gotta ask you now that you're outside. Google was a culture shock. Oh my God! People actually provisioning software provisioning data center culture shock when there's a little >>bit of culture shock. One thing is, and the funny thing is coming full circle in communities now, is that the idea of an application? Right? The idea of what is an application eyes, something that feels very comfortable to a lot of legacy traditional. I wanna use traditional applications, but the moment you're you've spent so much time incriminates and you say, What's the application? It became a very hard thing, and I used to have a lot of academic debates. Where is saying there is no application? It's It's a soup of resources and such. So that was a hard thing. But funny thing is covered, as is now coming out with definitions around application, and Microsoft announced a few things in that area to so there are things that are coming full circle, but that just shows how the movement has changed and how things are becoming in some ways meeting each other halfway. >>Talk about the company, what you guys are doing. Take a moment. Explain in context to multi cloud. We're here. Port works. What's the platform? It's a product. What's the value proposition? What's the state of the company. >>So the companies? Uh well, well, it's grown from early days when Lisa and I joined where we're probably a handful now. We're in four or five cities. Geography ease over 100 people over 150 customers and there. It's been a lot of enterprises that are saying, like, How do I take this pattern of doing containers and micro service is And how do I run it with my mission? Critical business crinkle workloads. And at that point, there is no mission critical business critical workload that isn't stable so suddenly they're trying to say, How do I run These applications and containers and data have different life cycles. So what they're really looking for is a data plane that works with the control planes and how controlled planes are changing the behavior. So a lot of our technology and a lot of our product innovation has been around both the data plane but a storage control plane that integrates with a computer controlled plane. So I know we like to talk about one control plane. There's actually multiple control planes, and you mentioned security, right? If I look at how applications are running way after now securely access for applications, and it's no longer have access to the data. Before I get to use it, you have to now start to do things like J W. T. Or much higher level bearer tokens to say, I know how to access this application for this life cycle for this use case and get that kind of resiliency. So it's really around having that storage. More complexity absolutely need abstraction >>layers, and you got compute. Look, leading work there. But you gotta have >>software to do it from a poor works perspective. Our products entirely software right down loans and runs using kubernetes. And so the point here is we make remarries able to run all the staple workloads out of the box using the same comment control plane, which is communities. So that's the experiences that we really want to make it so that Dev Ops teams can run anywhere close. And that's that's in some ways been part of the mix. Lisa, >>we've been covering Dev up, going back to 2010. Remember when I first was hanging around San Francisco 2008 joint was coming out the woodwork and all that early days and you look at the journey of how infrastructures code We talked about that in 2008 and now we'll get 11 years later. Look at the advancements you've been through this now The tipping point. It's just seems like this wave is big and people are on it. The developers air getting it. It's a modern renaissance of application developers, and the enterprises it's happening in the enterprise is not just like the nerds Tier one, the Alfa Geeks or >>the Cloud native. It's happening in the >>everyone's on board this time, and you and I have been in the trenches in the early stages of many open source projects. And I think with with kubernetes Arab reference of community earlier, I'm super proud to be running the world's largest CNC F for user group. And it's a great community, a diverse community, super smart people. One of my favorite things about working for works is we have some really smart engineers that have figured out what companies want, how to solve problems, and then we'll go creative. It'll open source projects. We created a project called autopilot, really largely because one of our customers, every who's in the G s space and who's running just incredible application. You can google it and see what the work they're doing. It's all there publicly, Onda We built, you know, we built an open source project for them to help them get the most out of kubernetes. We can say so. There's a lot of people in the community system doing that. How can we make communities better halfway make commitments, enterprise grade and not take years to do that? Like some of the other open source projects that we worked on, it took. So it's a super exciting time to be here, >>and open source is growing so fast now. I mean, just think about how these projects being structured. Maur and Maur projects are coming online and user price, but a lot more vendor driven projects to use be mostly and used, but now you have a lot of vendors who are users. So the line is blurring between Bender User in Open source is really fascinating. >>Well, you look at the look of the landscape on the C N. C f. You know the website. I mean, it's what 400 that are already on board. It's really important. >>They don't have enough speaking slasher with >>right. I know, and it's just it. It is users and vendors. Everybody's in this community together. It's one of things that makes it super exciting. And it it's how we know this is This was the right choice for us to base this on communities because that's what everybody, you guys >>are practically neighbors. So we're looking for seeing the studio. Palo Alto Eric, I want to ask you one final question on the product side. Road map. What you guys thinking As Kubernetes goes, the next level state, a lot of micro service is observe abilities becoming a key part of it, Obviously, automation, configuration management things are developing fast. State. What's the What's the road map for you guys? >>For us, it's been always about howto handle the mission critical and make that application run seamlessly. And then now we've done a lot of portability. So disaster recovery has been one of the biggest things for us is that customers are saying, How do I do a hybrid pattern back to your earlier question of running on Prem and in Public Cloud and do a d. R. Pale over into some of the things at least, is pointing out that we're announcing soon is non series autopilot in the idea, automatically managing applications scale from a volume capacity. And then we're actually going to start moving a lot more into some of the what you do with data after the life cycle in terms of backup and retention. So those are the things that everyone's been pushing us and the customers are all asking for. You >>know, I think data they were back in recovery is interesting. I think that's going to change radically. And I think we look at the trend of how yeah, data backup and recovery was built. It was built because of disruption of business, floods, our gains, data center failure. But I think the biggest disruptions ransomware that malware. So security is now a active disruptor. So it's not like it after the hey, if we ever have, ah, fire, we can always roll back. So you're infected and you're just rolling back infected code. That's a ransomware dream. That's what's going on. So I think data protection it needs to be >>redefined. What do you think? Absolutely. I think there's a notion of How do I get last week's data last month? And then oftentimes customers will say, If I have a piece of data volume and I suddenly have to delete it, I still need to have some record of that action for a long time, right? So those are the kinds of things that are happening and his crew bearnaise and everything. It gets changed. Suddenly. The important part is not what was just that one pot it becomes. How do I reconstruct everything? What action is not one thing. It's everywhere. That's right and protected all through the platform. If it was a platform decision, it's not some the cattlemen on the side. You can't be a single lap. It has to be entire solution. And it has to handle things like, Where do you come from? Where is it allowed to go? And you guys have that philosophy. We absolutely, and it's based on the enterprises that are adopting port works and saying, Hey, this is my romance. I'm basing it on Kubernetes. You're my date a partner. We make it happen. >>This speaks to your point of why the enterprise is in. The vendors jumped in this is what people care about Security. How do you solve this last mile problem? Storage. Networking. How do you plug those holes in Kubernetes? Because that is crucial to our >>personal private moment. Victory moment for me personally, was been a big fan of Cuban is absolutely, you know, for years. Then there were created, talked about one. The moments that got me that was really kind of a personal, heartfelt moment was enterprise buyer. And, you know, the whole mindset in the Enterprise has always been You gotta kill the old to bring in the new. And so there's always been that tension of a you know, the shiny new toy from Silicon Valley or whatever. You know, I'm not gonna just trash this and have a migration za paying that. But for I t, they don't want that to do that. They hate doing migrations, but with containers and kubernetes that could actually they don't to end of life to bring in the new project. They can do it on their own timetable or keep it around. So that took a lot of air out of the tension in on the I t. Side because they say great I can deal with the lifecycle management, my app on my own terms and go play with Cloud native and said to me, that's like that was to be like, Okay, there it is. That was validation. That means this Israel because now they can innovate without compromising. >>I think so. And I think some of that has been how the ecosystems embrace it, right. So now it's becoming all the vendors are saying my internal stack is also based on community. So even if you as an application owner or not realizing it, you're gonna take a B M next year and you're gonna run it and it's gonna be back by something like awesome. Lisa >>Marie Nappy Eric on Thank you for coming on Port Works Hot start of multiple cities Kubernetes big developer Project Open Source. Talking about multi cloud here at the inaugural Multi cloud conference in New York City. It's the Cube Courage of escape. 2019. I'm John Period. Thanks for watching

>> fly from San Francisco. It's the Cube covering Google Club next nineteen Tio by Google Cloud and its ecosystem partners. >> Hey, welcome back. Everyone live here in San Francisco, California is the cubes coverage of Google Cloud Next twenty nineteen star Third day of three days of wall to wall coverage. John for a maiko stupid demon devil on things out around the floor. Getting stories, getting scoops. Of course, we're here with Sadeer has Bay. Who's the director of product management? Google Cloud. So great to see you again. Go on Back on last year, I'LL see Big Query was a big product that we love. We thought the fifty many times about database with geek out on the databases. But it's not just about the databases. We talked about this yesterday, all morning on our kickoff. There is going to be database explosion everywhere. Okay, it's not. There's no one database anymore. It's a lot of databases, so that means data in whatever database format document relational, Unstructured. What you want to call it is gonna be coming into analytical tools. Yes, this's really important. It's also complex. Yeah, these be made easier. You guys have made their seers announcements Let's get to the hard news. What's the big news from your group around Big Queria Mail Auto ml Some of the news share >> the news. Perfect, I think not. Just databases are growing, but also applications. There's an explosion off different applications. Every organization is using hundreds of them, right from sales force to work today. So many of them, and so having a centralized place where you can bring all the data together, analyze it and make decisions. It's critical. So in that realm to break the data silos, we have announced a few important things that they went. One is clouded effusion, making it easy for customers to bring in data from different sources on Prum Ices in Cloud so that you can go out and as you bring the data and transform and visually just go out and move the data into Big query for for analysis, the whole idea is the board and have Dragon drop called free environment for customers to easily bring daytime. So we have, like, you know, a lot of customers, just bringing in all the data from their compromise. The system's oracle, my sequel whatever and then moving that into into big Query as they analyze. So that's one big thing. Super excited about it. A lot of attraction, lot of good feedback from our customers that they went. The second thing is Big Query, which is our Cloud Skill Data warehouse. We have customers from few terabytes to hundreds of terabytes with it. Way also have an inline experience for customers, like a data analyst who want to analyze data, Let's say from sales force work, they are from some other tools like that if you want to do that. Three. I have made hundred less connectors to all these different sense applications available to our partners. Like five Grand Super Metrics in Macquarie five four Barrel Box out of the box for two five clicks, >> you'LL be able to cloud but not above, but I guess that's afraid. But it's important. Connectors. Integration points are critical table stakes. Now you guys are making that a table stakes, not an ad on service the paid. You >> just basically go in and do five clicks. You can get the data, and you can use one of the partners connectors for making all the decisions. And also that's there. and we also announced Migration Service to migrate from candidate that shift those things. So just making it easy to get data into recipe so that you can unlock the value of the data is the first thing >> this has become the big story here. From the Cube standpoint on DH student, I've been talking about day all week. Data migration has been a pain in the butt, and it's critical linchpin that some say it could be the tell sign of how well Google Cloud will do in the Enterprise because it's not an easy solution. It's not just, oh, just move stuff over And the prizes have unique requirements. There's all kinds of governance, all kinds of weird deal things going on. So how are you guys making it easy? I guess that's the question. How you gonna make migrating in good for the enterprise? >> I think the one thing I'll tell you just before I had a customer tell me one pain. You have the best highways, but you're on grams to the highway. Is that a challenge? Can you pick that on? I'm like here are afraid. Analogy. Yeah, it's great. And so last year or so we have been focused on making the migration really easy for customers. We know a lot of customers want to move to cloud. And as they moved to cloud, we want to make sure that it's easy drag, drop, click and go for migration. So we're making that >> holding the on ramps basically get to get the data in the big challenge. What's the big learnings? What's the big accomplishment? >> I think the biggest thing has Bean in past. People have to write a lot ofthe court to go ahead and do these kind of activities. Now it is becoming Click and go, make it really cold free environment for customers. Make it highly reliable. And so that's one area. But that's just the first part of the process, right? What customers want is not just to get data into cloud into the query. They want to go out and get a lot of value out off it. And within that context, what we have done is way made some announcements and, uh, in the in that area. One big thing is the B I engine, because he'd be a engine. It's basically an acceleration on top of the query you get, like subsequently, agency response times for interactive dash boarding, interactive now reporting. So that's their butt in with that. What we're also announced is connected sheets, so connected sheets is basically going to give you spreadsheet experience on top ofthe big credit data sets. You can analyze two hundred ten billion rose off data and macquarie directly with drag drop weakened upriver tables again. Do visualizations customers love spreadsheets in general? >> Yeah, City area. I'm glad you brought it out. We run a lot of our business on sheep's way of so many of the pieces there and write if those the highways, we're using our data. You know what's the first step out of the starts? What are some of the big use cases that you see with that? >> So I think Andy, she is a good example of so air. Isha has a lot of their users operational users. You needed to have access to data on DH, so they basically first challenge was they really have ah subsequently agency so that they can actually do interact with access to the data and also be an engine is helping with that. They used their story on top. Off half now Big Quit it, Gordon. Make it accessible. Be engine will vote with all the other partner tooling too. But on the other side, they also needed to have spread sheet like really complex analysis of the business that they can improve operation. Last year we announced they have saved almost five to ten percent on operational costs, and in the airline, that's pretty massive. So basically they were able to go out and use our connective sheets experience. They have bean early Alfa customer to go out and use it to go in and analyse the business, optimize it and also so that's what customers are able to do with connected sheets. Take massive amounts of data off the business and analyze it and make better. How >> do we use that? So, for a cost, pretend way want to be a customer? We have so many tweets and data points from our media. I think fifty million people are in our kind of Twitter network that we've thought indexed over the years I tried to download on the C S V. It's horrible. So we use sheets, but also this They've had limitations on the han that client. So do we just go to Big Query? How would we work >> that you can use data fusion with you? Clicks move later into Big Query wants you now have it in big query in sheets. You will have an option from data connectors Macquarie. And once you go there, if you're in extended al far, you should get infection. Alfa. And then when you click on that, it will allow you to pick any table in bickering. And once you link the sheets to be query table, it's literally the spreadsheet is a >> run in >> front and got through the whole big query. So when you're doing a favour tables when you're saying Hey, aggregate, by this and all, it actually is internally calling big credit to do those activities. So you remove the barrier off doing something in the in the presentation layer and move that to the engine that actually can do the lot skill. >> Is this shipping? Now you mention it. Extended beta. What's the product? >> It's an extended out far for connected sheets. Okay, so it's like we're working with few customers early on board and >> make sure guys doing lighthouse accounts classic classic Early. >> If customers are already G sweet customer, we would love to get get >> more criteria on the connected sheets of Alfa sending bait after Now What's what's the criteria? >> I think nothing. If customers are ready to go ahead and give us feedback, that's what we care of. Okay, so you want to start with, like, twenty twenty five customers and then expanded over this year and expand it, >> maybe making available to people watching. Let us let us know what the hell what do they go? >> Throw it to me and then I can go with that. Folks, >> sit here. One of the other announcements saw this week I'm curious. How it connects into your pieces is a lot of the open source databases and Google offering those service maybe even expand as because we know, as John said in the open there, the proliferation of databases is only gonna increase. >> I think open source way announced lot of partnerships on the databases. Customers need different types of operational databases on. This is a great, great opportunity for us to partner with some of our partners and providing that, and it's not just data basis. We also announced announced Partnership with Confident. I've been working with the confident team for last one place here, working on the relationship, making sure our customers haven't. I believe customers should always have choice. And we have our native service with Cloud pops up. A lot of customers liked after they're familiar with CAFTA. So with our relationship with Khan fluent and what we announced now, customers will get native experience with CAFTA on Jessie P. I'm looking forward to that, making sure our customers are happy and especially in the streaming analytic space where you can get real time streams of data you want to be, Oh, directly analytics on top of it. That is a really high value add for us, So that's great. And so so that's the That's what I'm looking forward to his customers being able to go out and use all of these open source databases as well as messaging systems to go ahead and and do newer scenarios for with us. >> Okay, so you got big Big query. ML was announced in G. A big query also has auto support Auto ml tables. What does that mean? What's going what's going on today? >> So we announced aquarium L at Kew Blast next invader. So we're going Ta be that because PML is basically a sequel interface to creating machine learning models at scale. So if you have all your data and query, you can write two lines ofthe sequel and go ahead and create a model tow with, Let's say, clustering. We announced plastering. Now we announced Matrix factory ization. One great example I will give you is booking dot com booking dot com, one of the largest travel portals in the in the world. They have a challenge where all the hotel rooms have different kinds off criteria which says they have a TV. I have a ll the different things available and their problem was data quality. There was a lot of challenges with the quality of data they were getting. They were able to use clustering algorithm in sequel in Macquarie so that they could say, Hey, what are the anomalies in this data? Sets and identify their hotel rooms. That would say I'm a satellite TV, but no TV available. So those claims direct Lansing stuff. They were easily able to do with a data analyst sequel experience so that's that. >> That's a great example of automation. Yeah, humans would have to come in, clean the data that manually and or write scripts, >> so that's there. But on the other side, we also have, Ah, amazing technology in Auto Emma. So we had our primal table are normal vision off thermal available for customers to use on different technologies. But we realized a lot of problems in enterprise. Customers are structured data problems, So I have attained equerry. I want to be able to go in and use the same technology like neural networks. It will create models on top of that data. So with auto Emel tables, what we're enabling is customers can literally go in auto Emel Table Portal say, Here is a big query table. I want to be able to go out and create a model on. Here is the column that I want to predict from. Based on that data, and just three click a button will create an automated the best model possible. You'LL get really high accuracy with it, and then you will be able to go out and do predictions through an FBI or U can do bulk predictions out and started back into Aquarian also. So that's the whole thing when making machine learning accessible to everyone in the organization. That's our goal on with that, with a better product to exactly it should be in built into the product. >> So we know you've got a lot of great tech. But you also talk to a lot of customers. Wonder if you might have any good, you know, one example toe to really highlight. Thie updates that you >> think booking dot com is a good example. Our scent. Twentieth Century Fox last year shared their experience off how they could do segmentation of customers and target customers based on their past movies, that they're watched and now they could go out and protect. We have customers like News UK. They're doing subscription prediction like which customers are more likely to subscribe to their newspapers. Which ones are trying may turn out s o those He examples off how machine learning is helping customers like basically to go out and target better customers and make better decisions. >> So, do you talk about the ecosystem? Because one of things we were riffing on yesterday and I was giving a monologue, Dave, about we had a little argument, but I was saying that the old way was a lot of people are seeing an opportunity to make more margin as a system integrated or global less I, for instance. So if you're in the ecosystem dealing with Google, there's a margin opportunity because you guys lower the cost and increase the capability on the analytic side. Mention streaming analytics. So there's a business model moneymaking opportunity for partners that have to be kind of figured out. >> I was the >> equation there. Can you share that? Because there's actually an opportunity, because if you don't spend a lot of time analyzing the content from the data, talk aboutthe >> money means that there's a huge opportunity that, like global system integrators, to come in and help our customers. I think the big challenges more than the margin, there is lot of value in data that customers can get out off. There's a lot of interesting insights, not a good decision making they can do, and a lot of customers do need help in ramping up and making sure they can get value out of that. And it's a great opportunity for our global Asai partners and I've been meeting a lot of them at the show to come in and help organizations accelerate the whole process off, getting insights from from their data, making better decisions, do no more machine learning, leverage all of that. And I think there is a huge opportunity for them to come in. Help accelerate. What's the >> play about what some other low hanging fruit opportunities I'LL see that on ramping or the data ingestion is one >> one loving fruit? Yes, I think no hanging is just moving migration. Earlier, he said. Break the data silos. Get the data into DCP. There's a huge opportunity for customers to be like, you know, get a lot of value. By that migration is a huge opportunity. A lot of customers want to move to cloud, then they don't want to invest more and more and infrastructure on them so that they can begin level Is the benefits off loud? And I think helping customers my great migrations is going to be a huge Obviously, we actually announced the migration program also like a weak back also way. We will give training credits to our customers. We will fund some of the initial input, initial investment and migration activities without a side partners and all, so that that should help there. So I think that's one area. And the second area, I would say, is once the data is in the platform getting value out ofit with aquarium in auto ml, how do you help us? It must be done. I think that would be a huge opportunity. >> So you feel good too, dear. But, you know, build an ecosystem. Yeah. You feel good about that? >> Yeah, way feel very strongly about our technology partners, which are like folks like looker like tableau like, uh, talent confluence, tri factor for data prep All of those that partner ecosystem is there great and also the side partner ecosystem but for delivery so that we can provide great service to our customers >> will be given good logos on that slide. I got to say, Try facts and all the other ones were pretty good etcetera. Okay, so what's the top story for you in the show here, besides your crew out on the date aside for your area was a top story. And then generally, in your opinion, what's the most important story here in Google Cloud next. >> I think two things in general. The biggest news, I think, is open source partnership that we have announced. I'm looking forward to that. It's a great thing. It's a good thing both for the organizations as well as us on DH. Then generally, you'LL see lot off examples of enterprise customers betting on us from HSBC ends at bank that was there with mean in the session. They talked about how they're getting value out ofthe outof our data platform in general, it's amazing to see a lot more enterprises adopting and coming here telling their stories, sharing it with force. >> Okay, thanks so much for joining us. Look, you appreciate it. Good to see you again. Congratulations. Perfect fusion ingesting on ramps into the into the superhighway of Big Query Big engine. They're they're large scale data. Whereas I'm Jeffers dipping them in. We'LL stay with you for more coverage after this short break

>> fly from San Francisco. It's the Cube covering Google Club next nineteen, right Tio by Google Cloud and its ecosystem partners. >> Okay, welcome back, everyone. We are here live in San Francisco for cubes. Coverage of Google next twenty nineteen. Hashtag Google. Next nineteen, Google's Cloud Conference, where their customers, developers all come together Cubes. Three days of coverage. Day one. I'm John forward, my Coast, Dave Aloft as well. Astute many men Who's out there doing some reporter? Next guess Allison. Wagon filled is the CMO of Google Cloud. Great to see you. Thanks for joining us. >> Thanks for having me. I'm glad to be here, >> so I got to say, looking out on the floor here, we're in the middle of the floor. Great demographics. A lot of developers, lot of enterprise customers. A lot of you know, sea levels will also enterprise architects and cloud architects. So this is not just a developer fest. This is a business developer conference. >> Yes. So that's been a real change this year. Not only have we increase the numbers I think I mentioned earlier that we have thirty thousand people are actually able even more than that. We had a cap registration we sold out last week. But the composition is different this year because this year we have over seventy percent from enterprise companies and then within enterprise Cos it's Dev's decision makers, business leaders. And then we have a whole executive track of leader Circle program as well. So it's been a really great mix of different energy, different questions in different sessions. >> You guys do a great job in event kudos to the team original Google Io was a great event that continues to be the consumer side on Google. You guys have that same kind of grew swing going on a lot of sessions. Take him in to explain the theme of the show. What's going on around the events? Breakouts? What's the focus? >> Yes, so the focus? Well, there's a theme and a couple different levels. The broad theme is a cloud like no other, because we've introduced a lot of new, different features and products and programs. We introduced Antos this morning, which was really revolutionary way of using containers broadly multi cloud, high but cloud. So it's from a product standpoint, but it's also a cloud like no other, because it's about the community that's here, and it's truly a partnership with our customers and our partners about building this cloud together, and we see the community as a really key part of that. It's really corta Google's values around openness, open source technology and really embracing the broader community to build the cloud together. >> And I thought was interesting. The Kino was phenomenal. You had the CEO of Google come out Sundar Pichai and the new CEO on the job for ten weeks. T K >> Sommers. Korean. Yes. Lot of action >> going on a Google right now. >> Yeah, it's been great to have Thomas. Diane was phenomenal and building the business. It's wonderful. Have Sundar here. He's got a lot of commitment, really engaged with our customers. And so it's a lot of energy and a lot of excitement. A Google. >> I thought the vory class act of Thomas Curry and his first words on stage at the CEO was to give props. The Diane Green very, very respected, that was >> great, was very gracious of, Thomas >> said. Sorry, he said. The press, sir, that one of things I really like about Google is not afraid of hard problems, So I wanted to ask you a CMO I always asked the most about brand promise. What's the brand promise? That you want customers and the community to take away from an event like this? >> So the brand promise has a couple different areas. First and foremost, we want our customers to be successful with their customers. And so we think, really holistically about lessons. Make sure that we're delivering the cloud technologies so that customers can really serve everyone that they want to serve, whether it be a retailer that wants to create a wonderful, offline and online experience, whether it's a health care provider that wants to ensure that every doctor, it knows all of the right data about all the patients or within a hospital. And so that's the way we're always thinking is how do we ensure that we help our customers set up to be successful? >> So one of the big teams we heard this morning was the industry focus, and you just referenced that again. It seems to be an increasingly important part of the messaging and the technologies that you're creating, and it ties into digital transformation. You seeing every industry transform data is at the heart of that transformation. You're seeing big companies traverse different industries. So what if you could talk about the industry focus? Uh, where'd that come from? Where do you see it going? >> Yes, So there's really three core parts of what we've been talking about today. First and foremost is the infrastructure and ensuring that we have the world's best infrastructure. Then, on top of that, it's ensuring that we have all the right applications to help with digital transformation. And then, as part of that further, is the industry solutions. Because in our six focus industries, we want to make sure that we're really developing the right applications with the right solutions and half a deep expertise that companies are looking for so that we can really part with partner with them and really, truly be innovative. And we could feel much more comfortable being innovative. But we really understand our customer problems >> keep Part of that is the global s eyes. You look out here, you see all the big names I won't name because I'll forget one. But there's two obvious ones right there because once you start to see those guys come into the ecosystem, that's when you can partner and get really deep industry expertise globally, >> I agree. And so we do have a great partnerships that said here with Accenture in tow, Lloyd and Antos or three of them, many more that we were working really closely with. And there really are an extension of what we want to build because we know that we will not be able Teo create every single last mile industry solution and every single industry, and working with those companies really helps us. >> I was on the plane last night watching the game. Of course, I love you guys got to see it. You're probably appear busy, but I focused. Google was all over the this year, >> so this is our second year of our partnership with the law, and it's been great. There's a couple dimensions to that partnership. First and foremost, we help them analyze eighty years worth of data. And through all of that analysis, we've been working with him about making predictions about games in helping them understand players and coaches and teams better. Everything from creating brackets. Teo, how do you fan experience? And then as part of that, we also had opportunity to do some advertising within their games. So you may have seen some of the TV spots that we did, which was about analyzing that data. We put ourselves on the line by making predictions during the game about what we thought would happen based on all of our analysis. And then the Big Chef this year was we included students, so it was really studies. Last year we created all these models, but we did it within Google. We had Google, Debs and Google engineers creating prediction models. We said, like, What if we brought students in tow? Help us? So we recruited thirty or so all star students around the country from their schools, brought them together. They learned DCP like that. It was awesome. And then they started working together doing predictions. And so a lot of what you saw in the Games and on our hub was actually students using Google Claude platform to make predictions about the games. >> So just get this right. The reference on stage by T K students. So you had data from the that was exposed to the students. They had a hackathon. How much lead time that they have? What was that >> did everything with thirty days. So they hack it on was about two months ago or so. But within the last thirty days, they did all of these different projects and they were actually doing really creative things about trying to come up with new types of stats like explosiveness. What does that mean? Does that mean that you move in closer to the basket or does it mean that here they're coming up, the stats around pace of game and different elements of the place? It was really fun. >> How many slam dunk this, Miss Fowles? So >> question, Who do you who you're rooting for? I was >> writing from Virginia. You know, Let's say I >> was right for >> Virginia after my bracket got busted, so I was allowed to kind of change a little bit. And they're Michigan. Once they were gone, I was like, >> So I use no way. I but I hit ninety ninth percentile. So you go. I had Michigan in Michigan State rather in Virginia in my Final Four for Michigan State. Lost, but still, I would have been >> That's pretty good >> night, point nine. So what is with what kind of predictions were the students doing well, >> predictions about everything from, well, last night we had some predictions about the number two point last. We had about how many different times we're going to exchange like the ball will go back and forth between teams. We had predictions about three pointers and one game everything. So it's been really fun. Teo work with >> that kind of in game predictions. To see that a lot. >> You probably saw some stats real >> probability of, ah, victory, which of course, last night. Forget it. I mean, it's changed so quickly. >> Great program. One of those I want to ask you change gears is you have a book in the press room called customer Voices. So this has been a focus, and I think a lot of people have been Lego Google's great tact, but not a lot of customers, which you guys air debunking with. Not only this, but here to show shown the logo slide really kind of showing the traction from a customer's standpoint. >> Yes, about >> the focus on the customer. How does that change? How you doing your job? How is the tech rolling out? Can you share some insight into customer focused. >> Yeah, this has been a really big step change this year. We have over four hundred customers speaking throughout this event, and then we have a number of them that are on stage in the keynotes telling real stories. Two years ago, we had some customers speaking and they would say, I'm looking. I'm dabbling and this But now they're making rial kind of bet The company decisions using our technology. And so this customer voices is looking at those companies. We have something called the customer innovation serious this afternoon, where the CIA of HSBC will be talking about their evolution and Gogo Cloud. Two years ago, Darrell West was on stage talking about just kind of what they will be getting. Two Dio with Google Cloud Platform And now here we are two years later, when they've made a lot of progress and we'LL be sharing their stories that the custom innovation Siri's is one of my favorite parts. It next, >> you know, we cover a lot of events. David eyes were like two ESPN of tech or game day. We've gotten the shows, we see a lot of events and you kind of hear the key words over and over again. Soon these events here we're hearing scale, which we've heard all the time. Google scales, scales, scales solve all our problems. But we're hearing more about customers. OK, this has been a big focus. How have you guys shifted internally? Because this seems to been around for a while. Like you said, I think it's a step function from what we're seeing as well. What's going on internally. How you guys mobilizing, How you guys taking this to the mark? Because you've got great partition. So Cisco onstage VM wears even up there. You got an ecosystem developing a lot of momentum. >> So we're truly this year Enterprise ready to use a buzz word that comes up. So two years ago, we still had some holes in some of our technology stack, and we're still really building to go to market teams. We still vastly scaling that so absolutely growing there. But we're in a whole different place as a business where we are able to serve really large enterprises at scale. McKesson just announced sixth largest company that they are moving and working with us a Google cloud. I mean, so these air major companies that are making big decisions to work with us. And so it's at a whole different level this year, and we're really proud that the customers have chosen to work with us, and we're building the organization to ensure that their successful. So that's our customer success program. That's ensuring we have the right kind of customer engineers working hand in hand with our customers. So it's a big focus ever. Whole group. It's a focus where Thomas Kurian has a lot of background serving enterprise customers at Oracle for twenty years, bringing that expertise. So you'LL see that everywhere. So I'm glad you picked up on that and feel it because it's really permeates everything we're doing at Google clouds, >> and it's been a good, positive change. The results of their What's the focus for you As you look forward, It's a lot to do. You guys are a great opportunity. I always say Google's dark horse now Samson's got a good lead out there being first in, but you guys have a lot of tech. You got the customer focus. You got a lot of momentum on the tech side. Cloud native Open source. Partner ecosystem Developing customer ecosystem. So kind of ball's in your court, so to speak. >> You feel really well, position we It's early. So in the whole market, people seem to think that I like all these decisions, but it's really still eighty percent of workload Zoran data centers of these big enterprises, everybody who's here with us right now. And most companies were choosing a multi club strategy this morning. We announced a major product and those that really enables the multi cloud strategy so enables Google to really be at the center of that multi cloud and provide the services using containers and a lot of the biggest best advances right now. And so as we scale our go to market, we can really bring this technology that way here, over and over again, is the best technology in the business. Yeah, we had it really had to go to market in place to bring it to customers. And this is really where we're taking it so we can help get this awesome technology. It's so fun is a marketer to them, bring it to everybody. >> I always say it so early. The wave is just getting started more ways behind it. I'm very impressed. That intrigue also by the rebranding of the Google Cloud platform what you guys announced last kind of hybrid and those is interesting because it's a rebrand slash new set of integration points Sisco again on stage kind of integrating with your container platform is a key key story that I think is nuanced but kind of points to a whole new Google. What was behind the rebranding? Can you just share some insight that what the commerce she's like Google Cloud Platforms is descriptive. But I mean, >> sister, thanks >> Cloud Services platform when we chose that name last year is when we wanted to Alfa with a product and frankly, within the marketing team, he kind of knew was always a placeholder name. And then the debate was, What do we change the name when you go to Beta, which we did a couple months ago? Or when we go to went to Gaea and we decided this would be a great opportunity to change the name, so we always knew it was going to change the name. Picking a name is always complicated, and so we spent a lot of time thinking about what way wanted that name too mean and what we wanted to stand for. And we really liked Anthros. It's a Greek word. It is a nod to the Greek aspects of the history of the product. With Cooper, Netease, Andhis, Teo and other areas. It means the blossom it means to grow. It means all. And so you many words like Anthology and things like that. So we'd liked both what it meant, And we also liked that with all Namie decisions, it's easy to spell. It's easy to find. It's all great, >> and it's super >> booming in California. Here as we speak. Well, ironic. >> It has an international flavor to it. But you guys, you guys are taking this show overseas, right? They've got a big show in London in November, I know and yes, >> be in Tokyo in July at next and then London in November. And then we do it between all of these. What we call Clouds Summit Siri's, which are in country slightly smaller. But we bring a lot of the same technology, and speakers and sessions just have a slightly scaled down version. >> Intimate. We really appreciate your support. We love doing the Cube hearing a lot of Czech athletes, as we say here on the show floor. Lot of knowledge, good customer converses. Alison's Thanks for sharing the inside congratulates on the great >> show, so I left be here. Thanks >> for rebranding as the market shifts. Great time to have a rebrand, certainly when it means something more. Multi cloud hybrid cloud Google Cloud Platform now and those that cube bring you live coverage here from the floor at Google next twenty nineteen. Stay with us for more after this short break.