from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hey welcome back here writer jeff rick here with the cube we're in our Palo Alto studios you know kind of continuing our leadership coverage reaching out to the community for people that we've got in our community to get their take on you know how they're dealing with the Kovach crisis how they're helping to contribute back to the community to to bring their resources to bear and you know just some general good tips and tricks of getting through these kind of challenging times and we're really excited to have one of my favorite guests he's being used to come on all the time we haven't had them on for three years which I can't believe it sabi Mehta the CEO of true SATA founder to say to obby I checked the record I can't believe it's been three years since we last that down great to see you Jeff there's well first of all it's always a pleasure and I think the only person to blame for that is you Jeff well I will make sure that it doesn't happen again so in just a check-in how's things going with the family the company thank you for asking you know family is great we have I've got two young kids who have become video conferencing experts and they don't teach me the tricks for it which I'm sure is happening a lot of families around the world and the team is great we vent remote at this point almost almost two months ago down and can't complain I think their intellectual property business like you are so it's been a little easier for us to go remote compared to a lot of other businesses in the world and in America but no complaints it'll be very fortunate we are glad that we have a business and a company that can withstand the the economic uncertainty and the family's great I hope the same for the queue family I haven't seen Dave and John and it's good to see you again and I hope all of you guys are helped happy and healthy great I think in we're good so thank you for asking so let's jump into it you know one of the things that I've always loved about you is you know really your sense of culture and this kind of constant reinforcing of culture in your social media posts and the company blog post at true SATA you know celebrating your interns and and you really have a good pulse for that and you know I just I think we may even talked about it before about you know kind of the CEOs and leadership and and social media those that do and that and those that don't and you know I think it's it's probably from any kind of a risk reward trade-off you know I could say something group it versus what am I getting at it but really it's super important and in these times with the distributed workforce that the the importance and value of communicating and culture and touching your people frequently across a lot of different mediums and topic areas is is more important than ever before share with us kind of your strategy why did you figure this out early how have you you know kind of adjusted you know your method of keeping your team up and communicating absolutely like I guess I owe you guys a little bit of gratitude for it which is we launched our company and you know I'm showing a member on the cube it was a social media launch you know if you say that say it like that I think there are two or three things that are very important Jeff and you hit on all of them one is the emphasis on information sharing it becomes more important than times like these and we as as a society value the ability to share a positive conversation of positive perspective and a positive outlook more but since day zero at the seder we've had this philosophy that there are no secrets it is important to be open and transparent both inside and outside the company and that our legacy is going to be defined by what we do for the community and not just what we do for our shareholders and by its very nature the fact that you know I grew up in a different continent now live and call America now a different continent my home I guess I was it's very important for me to stay connected to my roots it is a good memory or reminder that the world is very interconnected unfortunately the pandemic is the is the best or worst example of it in a really weird way but I think it's also a very important point Jeff that I believe we learned early and I hope coming out from this is something that we don't lose the point you made about kindness social media and social networking has a massively in my opinion massively positive binding force for the world at the same time there were certain business models it tried to capitalize on the negative aspects of it you know whether they are the the commercialized versions of slam books or not so nice business models that capitalize on the ability for people to complain I hope that people society and us humans coming out of it learn from people like yourself or you know the small voice that I have on social media or the messages we share and we are kinda in what we do online because the ability to have networks that are viral and can propagate or self propagate is a very positive unifying force and I hope out of this pandemic we all realize the positive nature's of it more than the negative nature's of it because unfortunately as you know that our business models built on the negative forces of social media and I really really hope they're coming out of this are positive voices drown out the negative voices that's great point and and it's a great I want to highlight a quote from one of your blog's again I think you're just a phenomenal communicator and in relationship to what's going on with kovat and and I quote we are fighting fear pain and anxiety as much as we are fighting the virus this is our humble attempt to we'll get into what you guys did to help the thousands of first responders clerks rockstars but I just really want to stick with that kindness theme you know I used to or I still joke right that the greatest smile in technology today is our G from signal FX the guys are gonna throw up a picture of him he's a great guy he looks like everybody's favorite I love that guy but therefore signal effects and actually it's funny signal FX also launched on the cube at big data a big data show I used to say the greatest smile intact is avi Mehta I mean how can I go wrong and and what I when I reached out to you I I do I consciously thought what what more important time do we have than to see people like you with a big smile with the great positive attitude focusing on on the positives and and I just think it's so important and it segues nicely into what we used to talk about it the strata shows and the big data shows all the time everyone wanted to talk about Hadoop and big data you always stress is never about the technology it's about the application of the technology and you focus your company on that very where that laser focus from day one now it's so great to see is we think you know the bad news about kovat a lot of bad news but one of the good news is is you know there's never been as much technology compute horsepower big data analytics smart people like yourself to bring a whole different set of tools to the battle than just building Liberty ships or building playing planes or tanks so you guys have a very aggressive thing that you're doing tell us a little bit about is the kovat active transmission the coat if you will tell us about what that is how did it come to be and what are you hoping to accomplish of course so first of all you're too kind you know thank you so much I think you also were the first people to give me a hard time about my new or Twitter picture I put on and he said what are you doing RV you know you have a good smile come on give me the smile die so thank you you're very kind Jeff I think as I as we as you know and I know I think you've a lot to be thankful for in life and there's no reason why we should not smile no matter what the circumstance we have so much to be thankful for and also I am remiss happy Earth Day you know I'm rocking my green for Earth Day as well as Ramadan Kareem today is the first day of Ramadan and you know I I wish everybody in the world Ramadan Kareem and on that friend right on that trend of how does do we as a community come together when faced with crisis so Court was a very simple thing you know it's I'm thank you for recognizing the hard work of the team that led it it was an idea I came up with it you know in the shower I'm like there are two kinds of people or to your you can we have we as humans have a choice when history is being made which I do believe I do believe history is being made right whether you look at it economically and a economic shock and that we have not felt as humanity since the depression so you look at it socially and again something we haven't seen sin the Spanish blue history is being made in in these times and I think we as humans have a choice we can either be witnesses to it or play our part in helping shape it and coat was our humble tiny attempt to when we look back when history was being made we chose to not just sit on the sidelines but be a part of trying to be part of the solution so all riddled with code was take a small idea I had team gets the entire credit read they ran with it and the idea was there was a lot of data being open sourced around co-ed a lot of work being done around reporting what is happening but nothing was being done around reporting or thinking through using the data to predict what could happen with it and that was code with code we try to make the first code wonder oh that came out almost two weeks ago now when you first contacted us was predicting the spread and the idea around breaking the spread wasn't just saying here is the number of cases a number of deaths and know what to be very off we wanted to provide like you know how firefighters do can we predict where it may go to next at a county by county level so we could create a little bit of a firewall to help it from stop you know have the spread of it to be slower in no ways are we claiming that if you did port you can stop it but if he could create firewalls around it and distribute tests not just in areas and cities and counties where it is you know spiking but look at the areas and counties where it's about to go to so we use a inner inner in-house Network algorithm we call that Orion and we were able to start predicting where the virus is gonna go to we also then quickly realize that this could be an interesting where an extra you know arrow and the quiver in our fight we should also think about where are there green shoots around where can recovery be be helped so before you know the the president email announced this it was surrender serendipitous before the the president came and said I want to start finding the green shoes to open the country we then did quote $2 which we announced a week ago with the green shoots around a true sailor recovery index and the recovery index is looking at its car like a meta algorithm we're looking at the rates of change of the rates of change so if you're seeing the change of the rates of change you know the meta part we're declining we're saying there are early shoots that we if as we plan to reopen our economy in our country these are the counties to look at first that was the second attempt of code and the third attempt we have done is we calling it the odd are we there yet index it got announced yesterday and now - you're the first public announcement of it and the are we there yet index is using the government's definition of the phase 1 phase 2 phase 3 and we are making a prediction on where which are the counties that are ready to be open up and there's good news everywhere in the country but we we are predicting there are 73 different counties that ask for the government's definition of ready to open are ready to open that's all you know we were able to launch the app in five days it is free for all first responders all hospital chains all not-for-profit organizations trying to help the country through this pandemic and poor profit operations who want to use the data to get tests out to get antibodies out and to get you know the clinical trials out so we have made a commitment that we will not charge for code through - for any of those organizations to have the country open are very very small attempt to add another dimension to the fight you know it's data its analytics I'm not a first responder this makes me sleep well at night that I'm at least we're trying to help you know right well just for the true heroes right the true heroes this is our our humble attempt to help them and recognize that their effort should not go to its hobby that that's great because you know there is data and there is analytics and there is you know algorithms and the things that we've developed to help people you know pick they're better next purchase at Amazon or where they gonna watch next on Netflix and it's such a great application no it's funny I just finished a book called ghost Bob and is a story of the cholera epidemic in London in like 1850 something or other about four but what's really interesting at that point in time is they didn't know about waterborne diseases they thought everything kind of went through the air and and it was really a couple of individuals in using data in a new and more importantly mapping different types of datasets on top of it and now this is it's as this map that were they basically figured out where the the pump was that was polluting everybody but it was a great story and you know kind of changing the narrative by using data in a new novel and creative way to get to an answer that they couldn't and you know they're there's so much data out there but then they're so short a date I'm just curious from a data science point of view you know um you know there there aren't enough tests for you know antibodies who's got it there aren't enough tests for just are you sick and then you know we're slowly getting the data on the desk which is changing all the time you know recently announced that the first Bay Area deaths were actually a month were they before they thought they were so as you look at what you're trying to accomplish what are some of the great datasets out there and how are you working around some of the the lack of data in things like you know test results are you kind of organizing pulling that together what would you like to see more of that's why I like talking to you so I missed you you are these good questions of me excellent point I think there are three things I would like to highlight number one it doesn't take your point that you made with the with the plethora of technical advances and this S curve shift that these first spoke at the cube almost eleven years ago to the date now or ten years ago just the idea of you know population level or modeling that cluster computing is finally democratized so everybody can run complicated tests and a unique segment or one and this is the beauty of what we should be doing in the pandemic I'm coming I'm coming I'm quite surprised actually and given the fact we've had this S curve shift where the world calls a combination of cloud computing so on-demand IO and technical resources for processing data and then the on-demand ability to store and run algorithms at massive scale we haven't really combined our forces to predict more you know that the point you made about the the the waterborne pandemic in the eighteen eighteen hundreds we have an ability as humanity right now to actually see history play out rather than write a book about it you know it has a past tense and it's important to do are as follows number one luckily for you and I the cost of computing an algorithm to predict is manageable so I am surprised why the large cloud players haven't come out and said you know what anybody who wants to distribute anything around predictions lay to the pandemic should get cloud resources for free I we are running quote on all three cloud platforms and I'm paying for all of it right that doesn't really make sense but I'm surprised that they haven't really you know joined the debate or contribute to it and said in a way to say let's make compute free for anybody who would like to add a new dimension to our fight against the pandemic number one but the good news is it's available number two there is luckily for us an open data movement you know that was started on the Obama administration and hasn't stopped because you can't stop open movements allows people companies like ours to go leverage know whether it's John Hancock Carnegie Mellon or the new data coming out of you know California universities a lot of those people are opening up the data not every single piece is at the level we would like to see you know it's not zip plus 4 is mostly county level it's available the third innovation is what we have done with code but not it's not an innovation for the world right which is the give get model so we have said we will curate everything is available lie and boo cost anybody is used but they're for purposes and computations you want to enrich it every organization who gives code data will get more out of it so we have enabled a data exchange keep our far-off purple form and the open up the rail exchange that my clients use but you know we've opened up our data exchange part of our software platform and we have open source for this particular case a give get model but the more you give to it the more you get out of there and our first installations this was the first week that we have users of the platform you know the state of Nevada is using it there are no our state in North Carolina is using it already and we're trying to see the first asks for the gift get model to be used but that's the three ways you're trying to address the that's great and and and and so important you know in this again when this whole thing started I couldn't help but think of the Ford plant making airplanes and and Keiser making Liberty ships in in World War two but you know now this is a different battle but we have different tools and to your point luckily we have a lot of the things in place right and we have mobile phones and you know we can do zoom and well you know we can we can talk as we're talking now so I want to shift gears a little bit and just talk about digital transformation right we've been talking about this for ad nauseam and then and then suddenly right there's this light switch moment for people got to go home and work and people got to communicate via via online tools and you know kind of this talk and this slow movement of getting people to work from home kind of a little bit and digital transformation a little bit and data-driven decision making a little bit but now it's a light switch moment and you guys are involved in some really critical industries like healthcare like financial services when you kind of look at this not from a you know kind of business opportunity peer but really more of an opportunity for people to get over the hump and stop you can't push back anymore you have to jump in what are you kind of seeing in the marketplace Howard you know some of your customers dealing with this good bad and ugly there are two towers to start my response to you with using two of my favorite sayings that you know come to mind as we started the pandemic one is you know someone very smart said and I don't know who's been attributed to but a crisis is a terrible thing to waste so I do believe this move to restoring the world back to a natural state where there's not much fossil fuels being burnt and humans are not careful about their footprint but even if it's forced is letting us enjoy the earth in its glory which is interesting and I hope you don't waste an opportunity number one number two Warren Buffett came out and said that it's only when the tide goes out you realize who's swimming naked and this is a culmination of both those phenomenal phrases you know which is one this is the moment I do believe this is something that is deep both in the ability for us to realize the virtuosity of humanity as a society as social species as well as a reality check on what a business model looks like visa vie a presentation that you can put some fancy words on even what has been an 11-year boom cycle and blitzscale your way to disaster you know I have said publicly that this the peak of the cycle was when mr. Hoffman mr. Reid Hoffman wrote the book bit scaling so we should give him a lot of credit for calling the peak in the cycle so what we are seeing is a kind of coming together of those two of those two big trends crises is going to force industry as you've heard me say many for many years now do not just modernize what we have seen happen chef in the last few years or decades is modernization not transformation and they are different is the big difference as you know transformation is taking a business model pulling it apart understanding the economics that drive it and then not even reassembling it recreating how you can either recapture that value or recreate that value completely differently or by the way blow up the value create even more value that hasn't happened yet digital transformation you know data and analytics AI cloud have been modernizing trends for the last ten years not transformative trends in fact I've also gone and said publicly that today the very definition of technology transformation is run a sequel engine in the cloud and you get a big check off as a technology organization saying I'm good I've transformed how I look at data analytics I'm doing what I was doing on Prem in the cloud there's still sequel in the cloud you know there's a big a very successful company it has made a businessman out of it you don't need to talk about the company today but I think this becomes that moment where those business models truly truly get a chance to transform number one number two I think there's going to be less on the industry side on the new company side I think the the error of anointing winners by saying grow at all cost economics don't matter is fundamentally over I believe that the peak of that was the book let's called blitzscaling you know the markets always follow the peaks you know little later but you and I in our lifetimes will see the return to fundamentals fundamentals as you know never go out of fashion Jeff whether it's good conversations whether it's human values or its economic models if you do not have a par to being a profitable contributing member of society whether that is running a good balance sheet individually and not driven by debt or running a good balance sheet as a company you know we call it financial jurisprudence financial jurisprudence never goes out of fashion and the fact that even men we became the mythical animal which is not the point that we became a unicorn we were a profitable company three years ago and two years ago and four years ago and today and will end this year as a profitable company I think it's a very very nice moment for the world to realize that within the realm of digital transformation even the new companies that can leverage and push that trend forward can build profitable business models from it and if you don't it doesn't matter if you have a billion users as my economic professor told me selling a watermelon that you buy for a dollar or fifty cents even if you sell that a billion times you cannot make it up in volume I think those are two things that will fundamentally change the trend from modernization the transformation it is coming and this will be the moment when we look back and when you write a book about it that people say you know what now Jeff called it and now and the cry and the pandemic is what drove the economic jurisprudence as much as the social jurisprudence obvious on so many things here we can we're gonna be we're gonna go Joe Rogan we're gonna be here for four hours so hopefully hopefully you're in a comfortable chair but uh-huh but I don't I don't sit anymore I love standing on a DD the stand-up desk but I do the start of my version of your watermelon story was you know I dad a couple of you know kind of high-growth spend a lot of money raised a lot of money startups back in the day and I just know finally we were working so hard I'm Michael why don't we just go up to the street and sell dollars for 90 cents with a card table and a comfy chair maybe some iced tea and we'll drive revenue like there's nobody's business and lose less money than we're losing now not have to work so hard I mean it's so interesting I think you said everyone's kind of Punt you know kind of this pump the brakes moment as well growth at the ethic at the cost of everything else right there used to be a great concept called triple-line accounting right which is not just shareholder value to this to the sacrifice of everything else but also your customers and your employees and-and-and your community and being a good steward and a good participant in what's going on and I think that a lot of that got lost another you know to your point about pumping the brakes and the in the environment I mean we've been kind of entertaining on the oil side watching an unprecedented supply shock followed literally within days by an unprecedented demand shock but but the fact now that when everyone's not driving to work at 9:00 in the morning we actually have a lot more infrastructure than we thought and and you know kind of goes back to the old mob capacity planning issue but why are all these technology workers driving to work every morning at nine o'clock it means one thing if you're a service provider or you got to go work at a restaurant or you're you're carrying a truck full of tools but for people that just go sit on a laptop all day makes absolutely no sense and and I'd love your point that people are now you know seeing things a little bit slowed down you know that you can hear birds chirp you're not just stuck in traffic and into your point on the digital transformation right I mean there's been revolution and evolution and revolution people get killed and you know the fact that digital is not the same as physical but it's different had Ben Nelson on talking about the changes in education he had a great quote I've been using it for weeks now right that a car is not a is not a mechanical horse right it's really an opportunity to rethink the you know rethink the objective and design a new solution so it is a really historical moment I think it is it's real interesting that we're all going through it together as well right it's not like there quake in 89 or I was in Mount st. Helens and that blew up in in 1980 where you had kind of a population that was involved in the event now it's a global thing where were you in March 20 20 and we've all gone through this indeed together so hopefully it is a little bit of a more of a unifying factor in kind of the final thought since we're referencing great books and authors and quotes right as you've all know Harare and sapiens talked about what is culture right cultures is basically it's it's a narrative that we all have bought into it I find it so ironic that in the year 2020 that we always joke is 20/20 hindsight we quickly found out that everything we thought was suddenly wasn't and the fact that the global narrative changed literally within days you know really a lot of spearhead is right here in Santa Clara County with with dr. Sarah Cody shutting down groups of more than 150 people which is about four days before they went to the full shutdown it is a really interesting time but as you said you know if you're fortunate enough as we are to you know have a few bucks in the bank and have a business that can be digital which you can if you're in the sports business or the travel business the hotel business and restaurant business a lot of a lot of a lot of not not good stuff happening there but for those of us that can it is an opportunity to do this nice you know kind of a reset and use the powers that we've developed for recommendation engines for really a much more power but good for good and you're doing a lot more stuff too right with banking and in in healthcare telemedicine is one of my favorite things right we've been talking about telemedicine and electronic medicine for now well guess what now you have to cuz the hospitals are over are overflowing Jeff to your point three stories and you know then at some point I know you have you I will let you go you can let me go I can talk to you for four hours I can talk to you for but days my friend you know the three stories that there have been very relevant to me through this crisis I know one is first I think I guess in a way all are personal but the first one you know that I always like to remind people on there were business models built around allowing people to complain online and then using that as almost like a a stick to find a way to commercialize it and I look at that all of our friends I'm sure you have friends have lots of friend the restaurant is big and how much they are struggling right they are honest working the hardest thing to do in life as I've been told and I've witnessed through my friends is to run a restaurant the hours the effort you put into it making sure that what you produce this is not just edible but it's good quality is enjoyed by people is sanitary is the hard thing to do and there was yet there were all of these people you know who would not find in their heart and their minds for two seconds to go post a review if something wasn't right and be brutal in those reviews and if they were the same people were to look back now and think about how they assort the same souls then anything to be supportive for our restaurant workers you know it's easy to go and slam them online but this is our chance to let a part of the industry that we all depend on food right critical to humanity's success what have we done to support them as easy as it was for us to complain about them what have we done to support them and I truly hope and I believe they're coming out of it those business models don't work anymore and before we are ready to go on and online on our phones and complain about well it took time for the bread to come to my table we think twice how hard are they working right number one that's my first story I really hope you do tell me about that my second story is to your have you chained to baby with Mark my kids I'm sure as your kids get up every morning get dressed and launch you know their online version of a classroom do you think when they enter the workforce or when they go to college you and me are going to try and convince them to get in a oil burning combustion engine but by the way can't have current crash and breakdown and impact your health impact the environment and show up to work and they'll say what do you talk about are you talking about I can be effective I can learn virtually why can't I contribute virtually so I think there'll be a generation of the next class of you know contribute to society who are now raised to live in an environment where the choice of making sure we preserve the planet and yet contribute towards the growth of it is no longer a binary choice both can be done so I completely agree with you we have fundamentally changed how our kids when they grew up will go to work and contribute right my third story is the thing you said about how many industries are suffering we have clients you know in the we have health care customers we have banking customers you know we have whoever paying the bills like we are are doing everything they can to do right by society and then we have customers in the industry of travel hospitality and one of my most humbling moments Jeff there's one of the no sea level executives sent us an email early in this in this crisis and said this is a moment where a strong David can help AV Goliath and just reading that email had me very emotional because they're not very many moments that we get as corporations as businesses where we can be there for our customers when they ask us to be their father and if we as companies and help our customers our clients who area today are flying people are feeding people are taking care of their health and they're well if V in this moment and be there for them we we don't forget those moments you know those as humans have long-term memories right that was one of the kindest gentlest reminders to me that what was more important to me my co-founder Richard you know my leadership team every single person at Reseda that have tried very hard to build automations because as an automation company to automate complex human process so we can make humans do higher order activities in the moment when our customers asked us to contribute and be there for them I said yes they said yes you said yes and I hope I hope people don't forget that that unicorns aren't important there are mythical animals there's nothing all about profits there's nothing mythical about fortress balance sheet and there's nothing mythical about a strong business model that is built for sustainable growth not good at all cost and those are my three stories that you know bring me a lot of lot of calm in this tremendous moment of strife and and in the piece that wraps up all those is ultimately it's about relationships right people don't do business I mean companies don't do business with companies people do business with people and it's those relationships and and in strong relationships through the bad times which really set us up for when things start to come back I me as always it's I'm not gonna let it be three years to the next time I hear me pounding on your door great to catch up you know love to love to watch really your your culture building and your community engagement good luck I mean great success on the company but really that's one thing I think you really do a phenomenal job of just keeping this positive drumbeat you always have you always will and really appreciate you taking some time on a Friday to sit down with us well first of all thank you I wish I could tell you I just up to you but we celebrate formal Fridays that to Seder and that's what this is all so I want to end on a good on a positive bit of news I was gonna give you a demo of it but if you want to go to our website and look at what everything we're doing we have a survival kit around a data survival kit around kovat how am I using buzzwords you know a is let's not use that buzzword right now but in your in your lovely state but on my favorite places on the planet when we ran the algorithm on who is ready as per the government definition of opening up we have five counties that are ready to be open you know between Santa Clara to LA Sacramento Kern and San Francisco the metrics today the data today with our algorithm there are meta algorithm is saying that those five counties those five regions look like I've done a lot of positive activities if the country was to open under all the right circumstances those five look you know the first as we were men at on cream happy Earth Day a pleasure to see you so good to know your family is doing well and I hope we see we talk to each other soon thanks AVI great conversation with avi Mehta terrific guy thanks for watching everybody stay safe have a good weekend Jeff Rick checking out from the cube [Music]

>> Jeff: Hello, everybody and thank you for Joining us today for the virtual "Vertica VBC" 2020. Today's breakout session has been titled "Optimizing Query Performance and Resource Pool Tuning" I'm Jeff Ealing, I lead Vertica marketing. I'll be your host for this breakout session. Joining me today are Rakesh Banula, and Abhi Thakur, Vertica product technology engineers and key members of the Vertica customer success team. But before we begin, I encourage you to submit questions or comments during the virtual session. You don't have to wait. Just type your question or comment in the question box below the slides and click Submit. There will be a Q&A session at the end of the presentation. We'll answer as many questions we're able to during that time. Any questions we don't address, we'll do our best to answer them offline. Alternatively, visit Vertica forums at forum.vertica.com to post your questions there after the session. Our engineering team is planning to Join the forums to keep the conversation going. Also a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of your slides. And yes, this virtual session is being recorded, will be available to view on demand this week. We'll send you a notification as soon as it's ready. Now let's get started. Over to you Rakesh. >> Rakesh: Thank you, Jeff. Hello, everyone. My name is Rakesh Bankula. Along with me, we have Bir Abhimanu Thakur. We both are going to cover the present session on "Optimizing Query Performance and Resource Pool Tuning" In this session, we are going to discuss query optimization, how to review the query plans and how to get the best query plans with proper production design. Then discuss on resource allocations and how to find resource contention. And we will continue the discussion on important use cases. In general, to successfully complete any activity or any project, the main things it requires are the plan. Plan for that activity on what to do first, what to do next, what are things you can do in parallel? The next thing you need, the best people to work on that project as per the plan. So, first thing is a plan and next is the people or resources. If you overload the same set of people, our resources by involving them in multiple projects or activities or if any person or resource is sick in a given project is going to impact on the overall completion of that project. The same analogy we can apply through query performance too. For a query to perform well, it needs two main things. One is the best query plan and other is the best resources to execute the plan. Of course, in some cases, resource contention, whether it can be from system side or within the database may slow down the query even when we have best query plan and best resource allocations. We are going to discuss each of these three items a little more in depth. Let us start with query plan. User submits the query to database and Vertica Optimizer generates the query plan. In generating query plans, optimizer uses the statistics information available on the tables. So, statistics plays a very important role in generating good query plans. As a best practice, always maintain up-to-date statistics. If you want to see how query plan looks like, add explain keyword in front of your query and run that query. It displays the query plan on the screen. Other option is BC explained plans. It saves all the explained plans of the queries run on the database. So, once you have a query plan, once you're checking it to make sure plan is good. The first thing I would look for, no statistics are predicted out of range. If you see any of these, means table involved in the query, have no up to date statistics. It is now the time to update the statistics. Next thing to explain plans are broadcast, three segments around the Join operator, global re segments around a group by operators. These indicate during the runtime of the query, data flow between the nodes over the network and will slow down the query execution. As far as possible, prevent such operations. How to prevent this, we will discuss in the projection design topic. Regarding the Join order, check on inner side and outer side, which tables are used, how many rows each side processing. In (mumbles) picking a table, having smaller number of rows is good in case of as shown as, as Join built in memory, smaller the number of rows, faster it is to build the hash table and also helps in consuming less memory. Then check if the plan is picking query specific projection or default projections. If optimizer ignoring any query specific projection, but picking the default super projection will show you how to use query specific hints to follow the plant to pick query specific projections which helps in improving the performance. Okay, here is one example query plan of a query trying to find number of products sold from a store in a given state. This query is having Joins between store table, product table and group by operation to find the count. So, first look for no statistics particularly around storage access path. This plan is not reporting any no statistics. This means statistics are up to date and plan is good so far. Then check what projections are used. This is also around the storage access part. For Join orders check, we have Hash Join in path ID 4 having it In Path ID 6 processing 60,000 rows and outer is in Path ID 7 processing 20 million rows. Inner side processing last record is good. This helps in building hash table quicker by using less memory. Check if any broadcast re segments, Joins in Path ID 4 and also Path ID 3. Both are having inner broadcast, Inners are having 60,000 records are broadcasted to all nodes in the cluster. This could impact the query performance negatively. These are some of the main things which we normally check in the explained plans. Still now, We have seen that how to get good query plans. To get good query plans, we need to maintain up to date statistics and also discussed how to review query plans. Projection design is the next important thing in getting good query plans, particularly in preventing broadcasts re segments. Broadcast re segments happens during Join operation, random existing segmentation class of the projections involved in the Join not matching with the Join columns in the query. These operations causes data flow over the network and negatively impacts the query performance particularly when it transfers millions or billions of rows. These operations also causes query acquire more memory particularly in network send and receive operations. One can avoid these broadcast re segments with proper projection segmentation, say, Join involved between two fact tables, T1, T2 on column I then segment the projections on these T1, T2 tables on column I. This is also called identically segmenting projections. In other cases, Join involved between a fact table and a dimension table then replicate or create an unsegmented projection on dimension table will help avoiding broadcast re segments during Join operation. During group by operation, global re segment groups causes data flow over the network. This can also slow down the query performance. To avoid these global re segment groups, create segmentation class of the projection to match with the group by columns in the query. In previous slides, we have seen the importance of projection segmentation plus in preventing the broadcast re segments during the Join operation. The order by class of production design plays important role in picking the Join method. We have two important Join methods, Merge Join and Hash Join. Merge Join is faster and consumes less memory than hash Join. Query plan uses Merge Join when both projections involved in the Join operation are segmented and ordered on the Join keys. In all other cases, Hash Join method will be used. In case of group by operation too, we have two methods. Group by pipeline and group by Hash. Group by pipeline is faster and consumes less memory compared to group by Hash. The requirements for group by pipeline is, projection must be segmented and ordered by on grouping columns. In all other cases, group by hash method will be used. After all, we have seen importance of stats and projection design in getting good query plans. As statistics are based on estimates over sample of data, it is possible in a very rare cases, default query plan may not be as good as you expected, even after maintaining up-to-date stats and good projection design. To work around this, Vertica providing you some query hints to force optimizer to generate even better query plans. Here are some example Join hints which helps in picking Join method and how to distribute the data, that is broadcast or re segment on inner or outer side and also which group by method to pick. The table level hints helps to force pick query specific projection or skipping any particular projection in a given query. These all hints are available in Vertica documentation. Here are a few general hints useful in controlling how to load data with the class materialization et cetera. We are going to discuss some examples on how to use these query hints. Here is an example on how to force query plan to pick Hash Join. The hint used here is JTYPE, which takes arguments, H for HashJoin, M for MergeJoin. How to place this hint, just after the Join keyword in the query as shown in the example here. Another important Join in this, JFMT, Join For My Type hint. This hint is useful in case when Join columns are lost workers. By default Vertica allocates memory based on column data type definition, not by looking at the actual data length in those columns. Say for example, Join column defined as (mumbles) 1000, 5000 or more, but actual length of the data in this column is, say, less than 50 characters. Vertica going to use more memory to process such columns in Join and also slow down the Join processing. JSMP hint is useful in this particular case. JSMP parameter uses the actual length of the Join column. As shown in the example, using JFMP of V hint helps in reducing the memory requirement for this query and executes faster too. Distrib hint helps in how to force inner or outer side of the Join operator to be distributed using broadcast or re segment. Distrib takes two parameters. First is the outer site and second is the inner site. As shown in the example, DISTRIB(A,R) after Join keyword in the query helps to force re segment the inner side of the Join, outer side, leaving it to optimizer to choose that distribution method. GroupBy Hint helps in forcing query plan to pick Group by Hash or Group by Pipeline. As shown in the example, GB type or hash, used just after group by class in the query helps to force this query to pick Group by Hashtag. See now, we discussed the first part of query performance, which is query plans. Now, we are moving on to discuss next part of query performance, which is resource allocation. Resource Manager allocates resources to queries based on the settings on resource pools. The main resources which resource pools controls are memory, CPU, query concurrency. The important resource pool parameters, which we have to tune according to the workload are memory size, plan concurrency, mass concurrency and execution parallelism. Query budget plays an important role in query performance. Based on the query budget, query planner allocate worker threads to process the query request. If budget is very low, query gets less number of threads, and if that query requires to process huge data, then query takes longer time to execute because of less threads or less parallelism. In other case, if the budget is very high and query executed on the pool is a simple one which results in a waste of resources, that is, query which acquires the resources holds it till it complete the execution, and that resource is not available to other queries. Every resource pool has its own query budget. This query budget is calculated based on the memory size and client and currency settings on that pool. Resource pool status table has a column called Query Budget KB, which shows the budget value of a given resource pool. The general recommendation for query budget is to be in the range of one GB to 10 GB. We can do a few checks to validate if the existing resource pool settings are good or not. First thing we can check to see if query is getting resource allocations quickly, or waiting in the resource queues longer. You can check this in resource queues table on a live system multiple times, particularly during your peak workload hours. If large number of queries are waiting in resource queues, indicates the existing resource pool settings not matching with your workload requirements. Might be, memory allocated is not enough, or max concurrency settings are not proper. If query's not spending much time in resource queues indicates resources are allocated to meet your peak workload, but not sure if you have over or under allocated the resources. For this, check the budget in resource pool status table to find any pool having way larger than eight GB or much smaller than one GB. Both over allocation and under allocation of budget is not good for query performance. Also check in DC resource acquisitions table to find any transaction acquire additional memory during the query execution. This indicates the original given budget is not sufficient for the transaction. Having too many resource pools is also not good. How to create resource pools or even existing resource pools. Resource pool settings should match to the present workload. You can categorize the workload into well known workload and ad-hoc workload. In case of well-known workload, where you will be running same queries regularly like daily reports having same set of queries processing similar size of data or daily ETL jobs et cetera. In this case, queries are fixed. Depending on the complexity of the queries, you can further divide it into low, medium, high resource required pools. Then try setting the budget to 1 GB, 4 GB, 8 GB on these pools by allocating the memory and setting the plan concurrency as per your requirement. Then run the query and measure the execution time. Try couple UP iterations by increasing and then decreasing the budget to find the best settings for your resource pools. For category of ad-hoc workload where there is no control over the number of users going to run the queries concurrently, or complexity of queries user going to submit. For this category, we cannot estimate, in advance, the optimum query budget. So for this category of workload, we have to use cascading resource pool settings where query starts on the pool based on the runtime they have set, then query resources moves to a secondary pool. This helps in preventing smaller queries waiting for resources, longer time when a big query consuming all resources and rendering for a longer time. Some important resource pool monitoring tables, analyze system, you can query resource cues table to find any transaction waiting for resources. You will also find on which resource pool transaction is waiting, how long it is waiting, how many queries are waiting on the pool. Resource pool status gives info on how many queries are in execution on each resource pool, how much memory in use and additional info. For resource consumption of a transaction which was already completed, you can play DC resource acquisitions to find how much memory a given transaction used per node. DC resource pool move table shows info on what our transactions moved from primary to secondary pool in case of cascading resource pools. DC resource rejections gives info on which node, which resource a given transaction failed or rejected. Query consumptions table gives info on how much CPU disk network resources a given transaction utilized. Till now, we discussed query plans and how to allocate resources for better query performance. It is possible for queries to perform slower when there is any resource contention. This contention can be within database or from system side. Here are some important system tables and queries which helps in finding resource contention. Table DC query execution gives the information on transaction level, how much time it took for each execution step. Like how much time it took for planning, resource allocation, actual execution etc. If the time taken is more in planning, which is mostly due to catalog contentions, you can play DC lock releases table as shown here to see how long transactions are waiting to acquire global catalog lock, how long transaction holding GCL x. Normally, GCL x acquire and release should be done within a couple of milliseconds. If the transactions are waiting for a few seconds to acquire GCL x or holding GCL x longer indicates some catalog contention, which may be due to too many concurrent queries or due to long running queries, or system services holding catalog mutexes and causing other transactions to queue up. A query is given here, particularly the system tables will help you further narrow down the contention. You can vary sessions table to find any long-running user queries. You can query system services table to find any service like analyze row counts, move out, merge operation and running for a long time. DC all evens table gives info on what are slower events happening. You can also query system resource usage table to find any particular system resource like CPU memory, disk IO or network throughput, saturating on any node. It is possible once slow node in the cluster could impact overall performance of queries negatively. To identify any slow node in the cluster, we use queries. Select one, and (mumbles) Clearly key one query just executes on initiative node. On a good node, kV one query returns within 50 milliseconds. As shown here, you can use a script to run this, select kV one query on all nodes in the cluster. You can repeat this test multiple times, say five to 10 times then reveal the time taken by this query on all nodes in all tech (mumbles) . If there is any one node taking more than a few seconds compared to other notes taking just milliseconds, then something is wrong with that node. To find what is going on with the node, which took more time for kV one query, run perf top. Perf top gives info on stopped only lister functions in which system spending most of the time. These functions can be counter functions or Vertica functions, as shown here. Based on their systemic spending most of the time we'll get some clue on what is going on with that code. Abhi will continue with the remaining part of the session. Over to you Abhi. >> Bir: Hey, thanks, Rakesh. My name is Abhimanu Thakur and today I will cover some performance cases which we had addressed recently in our customer clusters which we will be applying the best practices just showed by Rakesh. Now, to find where the performance problem is, it is always easy if we know where the problem is. And to understand that, like Rakesh just explained, the life of a query has different phases. The phases are pre execution, which is the planning, execution and post execution which is releasing all the required resources. This is something very similar to a plane taking a flight path where it prepares itself, gets onto the runway, takes off and lands back onto the runway. So, let's prepare our flight to take off. So, this is a use case which is from a dashboard application where the dashboard fails to refresh once in a while, and there is a batch of queries which are sent by the dashboard to the Vertica database. And let's see how we can be able to see where the failure is or where the slowness is. To reveal the dashboard application, these are very shortly queries, we need to see what were the historical executions and from the historical executions, we basically try to find where is the exact amount of time spent, whether it is in the planning phase, execution phase or in the post execution and if they are pretty consistent all the time, which means the plan has not changed in the execution which will also help us determine what is the memory used and if the memory budget is ideal. As just showed by Rakesh, the budget plays a very important role. So DC query executions, one-stop place to go and find your timings, whether it is a timing extra or is it execute plan or is it an abandoned plan. So, looking at the queries which we received and the times from the scrutinize, we find most of the time average execution, the execution is pretty consistent and there is some time, extra time spent in the planning phase which users of (mumbles) resource contention. This is a very simple matrix which you can follow to find if you have issues. So the system resource convention catalog contention and resource contention, all of these contribute mostly because of the concurrency. And let's see if we can drill down further to find the issue in these dashboard application queries. So, to get the concurrency, we pull out the number of queries issued, what is the max concurrency achieved, what are the number of threads, what is the overall percentage of query duration and all this data is available in the V advisor report. So, as soon as you provide scrutinize, we generate the V advisor report which helps us get complete insight of this data. So, based on this we definitely see there is very high concurrency and most of the queries finish in less than a second which is good. There are queries which go beyond 10 seconds and over a minute, but so definitely, the cluster had concurrency. What is more interesting is to find from this graph is... I'm sorry if this is not very readable, but the topmost line what you see is the Select and the bottom two or three lines are the create, drop and alters. So definitely this cluster is having a lot of DDL and DMLs being issued and what do they contribute is if there is a large DDL and DMLs, they cause catalog contention. So, we need to make sure that the batch, what we're sending is not causing too many catalog contention into the cluster which delays the complete plan face as the system resources are busy. And the same time, what we also analyze is the analyze tactics running every hour which is very aggressive, I would say. It should be scheduled to be need only so if a table has not changed drastically that's not scheduled analyzed tactics for the table. A couple more settings has shared by Rakesh is, it definitely plays a important role in the modeled and mode operations. So now, let's look at the budget of the query. The budget of the resource pool is currently at about two GB and it is the 75 percentile memory. Queries are definitely executing at that same budget, which is good and bad because these are dashboard queries, they don't need such a large amount of memory. The max memory as shown here from the capture data is about 20 GB which is pretty high. So what we did is, we found that there are some queries run by different user who are running in the same dashboard pool which should not be happening as dashboard pool is something like a premium pool or kind of a private run way to run your own private jet. And why I made that statement is as you see, resource pools are lik runways. You have different resource pools, different runways to cater different types of plane, different types of flights which... So, as you can manage your resource pools differently, your flights can take off and land easily. So, from this we did remind that the budget is something which could be well done. Now let's look... As we saw in the previous numbers that there were some resource weights and like I said, because resource pools are like your runways. So if you have everything ready, your plane is waiting just to get onto the runway to take off, you would definitely not want to be in that situation. So in this case, what we found is the coolest... There're quite a bit number of queries which have been waited in the pool and they waited almost a second and which can be avoided by modifying the the amount of resources allocated to the resource pool. So in this case, we increase the resource pool to provide more memory which is 80 GB and reduce the budget from two GB to one GB. Also making sure that the plan concurrency is increased to match the memory budget and also we moved the user who was running into the dashboard query pool. So, this is something which we have gone, which we found also in the resource pool is the execution parallelism and how this affects and what what number changes. So, execution parallelism is something which allocates the plan, allocates the number of threads, network buffers and all the data around it before even the query executes. And in this case, this pool had auto, which defaults to the core count. And so, dashboard queries not being too high on resources, they need to just get what they want. So we reduced the execution parallelism to eight and this drastically brought down the amount of threads which were needed without changing the time of execution. So, this is all what we saw how we could tune before the query takes off. Now, let's see what path we followed. This is the exact path what we followed. Hope of this diagram helps and these are the things which we took care of. So, tune your resource pool, adjust your execution parallelism based on the type of the queries the resource pool is catering to and match your memory sizes and don't be too aggressive on your resource budget. And see if you could replace your staging tables with temporary tables as they help a lot in reducing the DDLs and DMLs, reducing the catalog contention and the places where you cannot replace them with the truncate tables, reduce your analyzed statics duration and if possible, follow the best practices for a couple more operations. So moving on, let's let our query take a flight and see what best practices can be applied here. So this is another, I would say, very classic example of query where the query has been running and suddenly stops to fail. And if there is... I think most of the other seniors in a Join did not fit in memory. What does this mean? It basically means the inner table is trying to build a large Hash table, and it needs a lot of memory to fit. There are only two reasons why it could fail. One, your statics are outdated and your resource pool is not letting you grab all the memory needed. So in this particular case, the resource pool is not allowing all the memory it needs. As you see, the query acquire 180 GB of memory, and it failed. When looking at the... In most cases, you should be able to figure out the issue looking at the explained plan of the query as shared by Rakesh earlier. But in this case if you see, the explained plan looks awesome. There's no other operator like in a broadcast or outer V segment or something like that, it's just Join hash. So looking further we find into the projection. So inner is on segmented projection, the outer is segmented. Excellent. This is what is needed. So in this case, what we would recommend is go find further what is the cost. The cost to scan this row seems to be pretty high. There's the table DC query execution in their profiles in Vertica, which helps you drill down to every smallest amount of time, memory and what were the number of rows used by individual operators per pack. So, while looking into the execution engine profile details for this query, we found the amount of time spent is on the Join operator and it's the Join inner Hash table build time, which has taking huge amount of time. It's just waiting basically for the lower operators can and storage union to pass the data. So, how can we avoid this? Clearly, we can avoid it by creating a segmented projection instead of unsegmented projection on such a large table with one billion rows. Following the practice to create the projection... So this is a projection which was created and it was segmented on the column which is part of the select clause over here. Now, that plan looks nice and clean still, and the execution of this query now executes in 22 minutes 15 seconds and the most important you see is the memory. It executes in just 15 GB of memory. So, basically to what was done is the unsegmented projection which acquires a lot of memory per node is now not taking that much of memory and executing faster as it has been divided by the number of nodes per node to execute only a small share of data. But, the customer was still not happy as 22 minutes is still high. And let's see if we can tune it further to make the cost go down and execution time go down. So, looking at the explained plan again, like I said, most of the time, you could see the plan and say, "What's going on?" In this case, there is an inner re segment. So, how could we avoid the inner re segments? We can avoid the inner re segment... Most of the times, all the re segments just by creating the projection which are identically segmented which means your inner and outer both have the same amount, same segmentation clause. The same was done over here, as you see, there's now segment on sales ID and also ordered by sales ID which helps us execute the query drop from 22 minutes to eight minutes, and now the memory acquired is just equals to the pool budget which is 8 GB. And if you see, the most What is needed is the hash Join is converted into a merge Join being the ordered by the segmented clause and also the Join clause. So, what this gives us is, it has the new global data distribution and by changing the production design, we have improved the query performance. But there are times when you could not have changed the production design and there's nothing much which can be done. In all those cases, as even in the first case of Vertica after fail of the inner Join, the second Vertica replan (mumbles) spill to this operator. You could let the system degrade by acquiring 180 GB for whatever duration of minutes the query had. You could simply use this hand to replace and run the query in the very first go. Let the system have all the resources it needs. So, use hints wherever possible and filter disk is definitely your option where there're no other options for you to change your projection design. Now, there are times when you find that you have gone through your query plan, you have gone through every other thing and there's not much you see anywhere, but you definitely look at the query and you feel that, "Now, I think I can rewrite this query." And how what makes you decide that is you look at the query and you see that the same table has been accessed several times in my query plan, how can I rewrite this query to access my table just once? And in this particular use case, a very simple use case where a table is scanned three times for several different filters and then a union in Vertica union is kind of costly operator I would say, because union does not know what's the amount of data which should be coming from the underlying query. So we allocate a lot of resources to keep the union running. Now, we could simply replace all these unions by simple "Or" clause. So, simple "Or" clause changes the complete plan of the query and the cost drops down drastically. And now the optimizer almost know the exact amount of rows it has to process. So change, look at your query plans and see if you could make the execution in the profile or the optimizer do better job just by doing some small rewrites. Like if there are some tables frequently accessed you could even use a "With" clause which will do an early materialization and make use the better performance or for the union which I just shared and replace your left Joins with right Joins, use your (mumbles) like shade earlier for you changing your hash table types. This is the exact part what we have followed in this presentation. Hope this presentation was helpful in addressing, at least finding some performance issues in your queries or in your class test. So, thank you for listening to our presentation. Now we are ready for Q&A.

>> Narrator: Live from Orlando Florida, it's theCUBE! Covering ServiceNow, Knowledge17. Brought to you by ServiceNow. >> Hi everybody, we're back. This is theCUBE and we're live from Knowledge17, I'm Dave Vellante with Jeff Frick. Michael Dippolito, did I say that right? >> D'Ippolito, close enough. >> D'Ippolito, sorry about that. A fellow Italian, I should get that right. D'Ippolito is assistant Vice President of Run Services Delivery, infrastructure and operations for Nationwide Insurance. Nationwide is on your side. >> You got it. It's in our heads right? >> I remember that. >> What a great marketing campaign. Michael, great to see you, thanks for coming on theCUBE. >> Thank you, thanks for having me. >> So how's Knowledge going for ya? >> Very good, very good. I'm really excited about some of the new things coming out with the newest release that was just announced this morning. And as a matter of fact I'm ready to go back and say let's jump to that version right? Because it sounds really exciting. >> So where are you right now? Which version are you on? Are you on the Helsinki? >> We are on the Helsinki release now. We usually like to jump a couple and stay as current as we can, usually you know one release behind maybe but if we find there's good functionality in jumping one we'll do it. >> I want to come back and talk about that, because we like to pick your brains about what's the best practice there, but before we do maybe set up your role at Nationwide. >> Yeah, RunService is a pretty large organization for Nationwide, through acquisitions and through our legacy environments, we have lots of application systems, you know, keeping all those running is a monumental task. So, our group is kind of sitting mainly in the middle of the applications, the infrastructure, the process, and trying to help everything stay running smoothly. >> Okay and you started with IT service management change management, like most customers, is that right? And then, you've been evolving that. Can you talk about that a little bit? >> Yeah we just implemented, about a year ago actually, we installed a year ago. >> Okay. >> We went with the Fuji release that we implemented then we've already jumped to Helsinki, and we pretty much went all processes all at once and kind of a big bang. We actually did ask that management at first does a little bit of a pilot, but then we actually went through all the other ITSM functionality, big bang after that. >> Jeff: So you're all in. >> Michael: Yeah. >> So what was life like, you know, give us a before and after, and maybe take us through the business case and how that all came about. >> I'll give you a perfect example, I just kind of did an after action review for our senior management, on our previous platform, which was an on prem heavily customized platform, to take a release would require a year and a half with a lot of planning and about a million dollars. >> Jeff: To do an upgrade. >> To do an upgrade. (Jeff Laughing) This last release to Helsinki went about six weeks, and about $100,000. So, that's a huge business case right there. Being able to be in the cloud, not having to worry about the infrastructure ourselves, and really we drove a model of zero customization so we wanted to stay out of box as much as possible, just for that reason so we could take releases fast and stay current. >> Wow, I'm sure that benefits. >> In the, you know, was situtation, the cost was predominantly people cost, programming cost, license cost, maintenance, consultants? >> It was mostly hours of effort. >> Yeah. >> The amount of customization we had and then to retrofit and test all those changes back into the release from the vendor was a monumental task and we never want to get into that situation again. >> And so with the ServiceNow upgrade, it's not out of pocket cost as much, you're quantifying time, is that correct? >> Correct. >> Yeah okay. >> It's mostly our internal cost. >> You said the time it took was a year and a half and then, like a typical upgrade in ServiceNow is, >> Michael: Less than two months. >> Okay. >> For us to bring it in test it, exercise it, making sure all our customizations, or configurations actually I should say, are working well. And a lot of it is more just the change management around it, you know, putting out the word, the communications, doing a little bit of training, or whatever it takes to get ready for a smooth launch. >> And some of the upfront planning of that as well. Now, when we talk to customers, there seems to be, we heard today that 90% of customers are adopting service catalog, CMDB, I don't know. It's mixed, right? We hear some yes, some no. Maybe tell us your experiences. >> We have a huge focus on CMDB right now. We think that CMDB is basically the foundation to all your other processes to run more smoothly right? So good trustworthy data enables faster incident resolution, better problem solving, more rigorous change management so you asses your risk of change better. So really when we sold our CMDB project, we didn't sell it based on the CMDB, we sold it based on all those other things, >> All the benefits. >> That get a ramp off of it. You know, from doing that effort. So, we're putting a lot of effort on CMDB maturity. >> So you were talking before about some of the things you saw today in Jakarta that were of interest before we go there, you had mentioned you started with Fuji, and now you're on Helsinki. What was the, you didn't double leapfrog did you? Or did you? What's your upgrade strategy? You said you might be an N minus one, but you like to stay pretty current. What's your strategy in regards to upgrades? >> Right now, we're looking at trying to be N minus one >> Uh huh. >> and taking two per year. So looking at two releases a year. We're trying to plan our schedules around maybe spring and fall. So we organize our work and our patterns around that. But something like that. We haven't really solidified that yet. A lot of it depends on what we see coming up, and what we can take advantage of. Like for example, we're getting ready to implement Work Day. And we want to make sure we have great integration between Work Day and ServiceNow. Some of the things that Jakarta is going to offer us is going to integrate nicely into Work Day. So, we may jump to that version because of that. >> So we heard this morning that the big things, well CJ set up the big things in Jakarta were going to be performance, obviously everybody better performance, maybe some UX stuff in there too, vendor risk management, and then the software asset management, which got the big cheers and the whoohoo! >> Yeah. (Jeff chuckling) >> Yeah, so, what in Jakarta is appealing to you? >> This software as a management I'd say, is very interesting because we're looking at that very closely right now in terms of our strategy around that. The other one I really like is the performance analytics and the predictive analytics that are coming out. I'd really love to be able to benchmark ourselves against other companies in terms of how we're doing. I feel we beat ourselves up a lot internally around things like availability or performance. But then, when I look and talk to others, we're not so bad. (Jeff chuckling) We're actually doing pretty good. So it'd be nice to get that benchmarking. >> Right, right. >> And some of that trend analysis that's offered. And then, finally, how do we get into a more predictive analytics mode where we can prevent incidents from happening before they do? So that's key. >> It was interesting, listening to Farrell Hough this morning talk about sort of the evolution of automation. How do you look at automation? Some shops are afraid of automation, but it seems like the ServiceNow customers we talk to really can't go fast enough. What is your thought, and how are you evolving automation? >> Well, one of our key drivers right now is how do we increase the speed of delivery to the marketplace? But, we also have to stay safe and reliable, right? And the key to speed is through automation. You can't really get that speed if you're not highly automated. And, to be highly automated, you need really high trustworthy data. So that enables fast decision making, and accuracy. >> Jeff: And that ties back to your CMDB commitment. >> Exactly, so, that all entailed enables speed, which we really want because in today's world speed is everything in terms of how you're constantly adapting your systems of engagement out there with your customers. Constantly learning from their patterns and adjusting on the fly. And that requires new mindsets. >> So you start with IT service management, you've got HR as well, is that right? >> We don't have the HR model. Right now we're only IT service management. >> Okay, straight IT services. >> We're looking at other modules, as we speak. >> Okay, so you want to make sure you get the value out of the initial ITSM, and then, how do you see that, you know, evolving? What is the conversation like internally? Do the business lines say, wow, all of a sudden we're getting improved service, and how are you doing that? Or is it more of a push where you go out to the business and say hey, here are some ideas. How does that all work? >> I'll tell you what we're really starting to see is a really change in what's driving innovation. And it's more coming from IT versus, the former models where IT was kind of like the order taker, and the business came up with everything they needed. Now, with the pace of change with technology, new business models are coming from IT to the business. And we're actually almost seeing ourselves more of an IT company than we are an insurance company. And, you starting to see those patterns especially with things like, now we're talking about metered insurance for auto, right? So basically, pay by the mile insurance, versus paying the same rate for six months. With the data we're getting out of vehicles today we can adjust your rates on the fly as you drive. Why should you pay the same rate if your car sits in the garage all weekend, versus you take it out and drive it 200 miles, right? So with the kind of data, big data and analytics that are coming from the vehicles we can do that now. >> So how is that conversation taking place? Is it being initiated by somebody in the IT staff that says hey, did you know that we have this data and we can do this? Let's take it to the business unit. Or does the business unit saying, I just saw Flo, the competitor, sticking the little thing in the dashboard? (Michael chuckling) Can we do that too? You know, there's a lot of talk about IT taking a seat at the business table >> Right. >> But how have you seen it actually been executed inside of Nationwide? >> Actually what we're seeing is, the lines are very blurry now between IT and the business. Almost to where, we're just a team working together versus the silos you used to have, and throwing the ideas over the fence. So we actually have a team that their goal is strategy and innovation. They report up through our CIO, and then business line teams have similar organizations, and they all work in a matrix fashion together. So anybody can bring any type of idea to the table, regardless of who you report up through. And we take those into consideration and we look for partners, we've got partners coming to us all the time that want to join us in innovation. And so it doesn't have to be our own solution. It could just be us on the back end of somebody else's front end, right? So, there's a lot of interesting ideas coming at us. >> What's happening in the business Mike? I mean you've got, obviously you're supporting the big systems or claims, you've got your agents systems, but mobile has exploded onto the scene. >> Yes. >> How has that affected you? What are some of the drivers in the insurance business these days? >> Well, definitely we're in this digital world now so, mobile first is critical. Everything has to be mobile enabled. We have to think of our strategy in a digital way constantly so we have a whole digital strategy that we work on. The traditional models of agency sold insurance won't ever really go away, per se, but they are shrinking. You see the demands and needs of the millennials coming up, very differently and changing. You have to compete on price to get in the door. That's important, so again we're trying to find all those interaction or intercept points with our customers as they need us. People don't really like to think of insurance, it's not on top of mind in their day to day life. But, when certain events happen like oh, I'm going to get married, or I'm going to take a trip, or you know, those kinds of things. >> Jeff: Right, kid turns sixteen. >> Yeah, we have different ways to interact with our customers, and offer some solutions that meet their need at the time. >> Well it seems like you're right, to be competitive, you've got to have the right price for those that say okay, I've got to get insurance, I need to start somewhere, great, but are you able to, as an industry, sell value? I mean, increasingly you're seeing some companies I would say Nationwide is one, where you're selling value. >> Yeah. >> Is that a trend in the business? >> Absolutely, I'll give you an example. One of the things that, normally the insurance model used to be I buy insurance and I'm protected when something bad happens. then when something bad happens, you compensate me. You pay my claim. But what about, if we can help you prevent the bad thing from even happening? So with products like our Smart Home package that you can buy now with internet of things, we can put sensors on those hot water tanks or on those pipes, or connected to your alarm system so that maybe we could alert you when we see your pipe is about to break. >> Right so, we cover, as you know our audience, we cover big data a lot. And the data business, and the insurance business have come mashing together, right? You had mentioned before, Mike, in many regards you're becoming an IT company and digitization is all about data. And the data allows you guys to build new products, to offer new services, to be more competitive and at the end of the day it's all about speed. >> Correct, speed and then that helps drive that value equation, right? So it's not so much being the lowest price, although you have to have a good price to be in the game, but then after that how can you provide that value? >> I'm curious Mike, from an insurance point of view, where before the business was based on, you know you didn't have so much data, right? So you had some big swaths, Age, sex, smoker, not smoker, but now as you're able to get data to the individual level, how that changes the way you look at it? Because it's very different than just kind of aggregating to the bulk, and then the poor unfortunate soul who has a car wreck, you pay the claim. But now, like you said, you know if I'm driving on the weekends, or if I'm parking my car. How is that really shaping the way that you guys look at the marketplace and the opportunities? >> Well you know, in the old days, you used to be able to take basically a subset of data from the past, and make your decisions based on that. >> A subset of data from the past, I love that. >> Now we're taking all the data in real time. >> In real time. >> So that puts more demands on the need for the technologies to provide that. It's critical, like especially if we're going to change your rates daily on how we insure your car, we have to have all the data, all the time. >> I remember Abhi Mehta, one of our early big data CUBE interviews, he made the statement in 2010 he said, "Sampling is dead." And, now, some people will debate that but the point he was making is just the same one you just made Michael is that you've got that data coming in, streaming it in real time. Some consumers, you know, have an issue with sticking that little meter in their car, but ultimately, that's the trend. It's going to happen. >> And you know we're seeing, and you're probably seeing it in other businesses as well, if you can provide that value, customers will give you the access and the data, because they see a value in return. So, it's that value equation. If it's good enough, they'll give you the value, and they'll give you the data. >> Dave: Yeah, you see it every day in mobile apps, right? >> Correct. >> You know, you're in New York City trying to get somewhere and it's like, turn on location services and I can help you. >> When you download any app, there's a big screen that comes up and you say I accept at the bottom, and then it has access to your pictures, access to your location and you're free to hit that accept because you see the value in that application. >> It's a quid pro quo, you know it's interesting we had the author on yesterday, Pink, Daniel Pink? >> Jeff: Pink, Mr. Pink, yes. >> And he was pointing out, he said look there used to be that the brand used to have all the information, and now there's parody in information, but in many regards, this whole digitization is an attempt by the brand to provide, to use more data and to give the consumers more value, and to create differentiation in the marketplace, and that's kind of what you're describing in your business. Last question, what's on ServiceNow's to-do list? What do you want to see a year, year and a half in? >> Well, after we implemented, we partnered with ServiceNow in a project they call Inspire, and basically it's to, what are we going to do next? You know, that very question, how do we leverage now what we've implemented, and take advantage of what the platform has to offer? We see lots of opportunities, as a matter of fact our list is so long we just don't have the bandwidth to do it all (Jeff chuckling) and we have to prioritize, but we see a lot of integration points, we see a lot of APIs coming in, we are in a kind of a really big phase in automation right now, we're trying to automate as much as possible, so for our on prem technology, we really want to go into automated provisioning of our assets, which means being able to connect those into the CMDB as they're provisioned, all automatically, and we want to really shorten those cycle times for when we have to provision infrastructure and support our applications. So ServiceNow is setting us up to do just that. >> Inspire is a great program, it's one of the best freebies in the business, and it leads, it's a win win. The customer gets the best experts, they come in and obviously, the hope is they're going to buy more stuff from ServiceNow, and if the value's there you will. Why not? It's going to drive to the bottom line. >> Using cloud to provision on prem resources, I like that. (all laughing) >> Mike thanks very much for coming to theCUBE, it was really a pleasure having you. >> Thank you, thanks for having me. >> Jeff: Thanks for sharing the insight. >> Alright keep it right there buddy we'll be back with our next guest right after this short break, there's a CUBEr live from Knowledge, be right back. (techno music)

>> Voiceover: From San Jose, California, it's The Cube, covering big data Silicon Valley 2017. >> Welcome back, everyone. Live in Silicon Valley for BigData SV, BigData Silicon Valley. This is Silicon Angles, The Cube's event in Silicon Valley, with our companion event, BigData NYC, in conjunction with O'Reilly, Strata, Hadoop, Hadoop World, our eighth year. I'm John Furrier, my co-host Jeff Frick, breaking down all the action, and our superguest, Abhi Mehta, the CEO of Tresata. He's been on every year since 2010, and the CEO of very successful Tresata, building out the vertical approach in financial net health. Welcome back, good to see you. Thank you, John, always good to see you. >> The annual pilgrimage to have you on The Cube. >> Abhi: This is literally a pilgrimage. I was exchanging messages with your co-host here, and he was pinging me, saying, "You got to come here, you got to get to this thing." I made it. The pilgrimage is successful. >> Yeah, a lot's happened, right? Data's the new oil. We've heard it over again. You had the seminal first interview in 2010, calling the oil refineries the data refineries. Turns out that was true. We always love to talk about that prediction every time you're on, but it's so much going on now. You can't believe the shift. Certainly, Hadoop has got a nice little niche position as Batch, but real time processing, you've seen the convergence of Batch, and streaming, and all that good stuff in real time, with the advances of clouds, certainly, more compute, Intel processors are getting more powerful, 5G over the top, you have connective cars, smart cities, on and on, IoT, Internet of things, all powering this new deep learning and AI trend. Man, it is game changes. I see this as a step-up function. What's your thoughts? This is going to create more data, more action. >> I agree with you. I always remind myself, John, especially when I talk to you guys, and we were chatting about this right before we went on air, which is, as smart as we as humans are, trends repeat themself. I'll be talking about AI. We all went to school, and did things in AI, you know? The whole neural networks thing has not been new. It's almost like fashion. Bell bottoms come in fashion every 20 years. I will never be seen in them again. Hopefully, neither will you. AI seems to be like that. I think the thing that hasn't changed, and yes, absolutely agree with you, that as escrows shift, as you've said, almost at this point a decade ago, there's a fundamentally new technology escrow shift under way, and escrow shifts take time. We will look back at this 10 years saying it was literally the first, second inning of this new escrow shift. I think we are entering the second innings where the conversation around Batch, real time storage, databases, the stacks, is becoming less important, and AI and deep learnings are examples of it, conversations on, how can you leverage cheaper, better, faster technology to solve and answer unanswered problems is becoming interesting. I think the basics haven't changed though. What we have spoken with you for almost eight years remain the same. The three basics around every technology trend remain the same. I think you guys will agree with me. Let me just play it by you and you can either contest it or agree with me. Data is the new competitive effort. It is unequivocally clear that the new asset, the most valuable enterprise asset has become data, and we've seen it in data companies, Facebook, Google, Uber, Airbnb, they're all fundamentally data companies. Data is the new competitive effort. The more you have of it, the better off you are. I always love people who say, "Big Data, this is a bad term." It isn't, because big data, fundamentally, in those two words, defines the very pieces of what we built Tresata on, which is, the more data you have, and if you can process and extract intelligence from it, borrowing your term, extract signal from the noise, you can make a lot of money on it. I think that fundamental basic hasn't changed. >> Big Data, to me, was always about big storage kind of a view. We coined the term Fast Data on The Cube, so that now speaks to the real time. It's interesting. I just see that the four main new areas that are being talked about outside of the Big Data world are autonomous vehicles, smart cities, smart home, and media and entertainment, and each one of those, I would say that the data is the new weaponization. There's an article that was great this month called "Weaponizing AI," and it had to do with Breitbart, and the election, and that's media and entertainment. You've got Netflix, all these new companies. Data is content, content is data. It's a digital asset. This AI component fits into autonomous vehicles, it fits into media and entertainment, fits into smart cities, and smart home. >> You also raise a very interesting point. I think that we can take comfort in the fact that we have seen this happen. This is not an idea anymore, or it's not just a wild idea anymore, which is, we have seen massive disruption happen in consumer industries. Google has created a brand new industry in how to market stuff, could be any stuff. Facebook created a brand new way of not just being in touch with your friends globally, 'cause people have thousands of friends, not true, but also, how do you monetize deep preferences, right? A twist on deep learning, but deep, deep preferences. If I know what Jeff likes, I can market to him better. I think we're about to see, the industries you just mention, is, where will success come from in enterprise software? I always ask myself that question when I come to any of these conferences, Strata, others, there's now an AI conference. What will the disruption that we have seen happen in consumer industries, we'll just mention automobiles, media entertainment, et cetera, what is going to happen to enterprise software? I think the time is ripe in the next five years to see the emergence of massive scale creation. I actually don't think it'll get disrupted. I think we will see, just like with Facebook, Google, Uber, the creation of brand new industries in enterprise software. I think that's going to be interesting. >> Mark Cuban said at South by Southwest this week, where The Cube was with the AI lounge with Intel, he was on stage saying, "The first tech trillionaire "will come out of deep learning," and deep learning is kind of the underpins for AI, if you look at all the geek stuff. To your point that a new shift of opportunity, whether it comes in from the enterprise side, or consumer, or algorithmic side, is that there's never been a trillionaire. >> Abhi: No, there hasn't. >> I want to push back a little bit, because I don't think it always was that way with data. We used to have sampling. It was all about sophistication on sampling, and data was expensive to store, expensive to collect, and expensive to manage. I think that's where the significant change is. The economics of collecting, and storing, and analyzing are such that sampling is no longer the preferred method. To your point, it's the bigness. >> Absolutely, you know exactly where I stand on that. >> Jeff: Now it's an asset. >> You know exactly where I stand on that. I said on The Cube, at this point, almost a decade ago, sampling is dead, and it's for that particular reason. I think the reality is that it has become a very tricky area to be in. Buzzwords aside, whether it's deep learning, AI, streaming, Batch, doesn't matter, Flash, all buzzwords aside, the very interesting thing is, are we seeing, as a community, the emergence of new enterprise software business models? I think ours is an example. We are now six years old. We announced Tresata on The Cube. We have celebrated our significant milestones on The Cube. We'll announce today that we are now a valuable member of society in terms of you pay tax as a company, another big milestone for a company. We have never raised venture money. We had a broad view when we started that every single thing we have learned as a industry enterprise software, the stack, databases, storage, BI, algorithms are free. Dave was talking about this earlier this week. Algorithms, analytical tools, will all become free. What is this new class of enterprise software that creates value that can then be sold as value? Buyers, corporations are becoming smart to realize and say, "Maybe I can't hire people "as smart as some of the web industries "on this side of the coast, "but I can still hire good talent, the tool set is free. "Should I build versus buy?" It fundamentally changes the conversation. Databases is a $2 trillion industry. Where does that value shift to if databases are free? I think that's what is going to be interesting to see, is, what model creates the new enterprise software industry? What is that going to be? I do agree with Mark Cuban's statement, that the answer is going to lie in, if the building blocks are free and commoditized, you guys know exactly where I stand on that one, if the building blocks are commoditized, how do you add value in the building block? It comes from the point you made, industry knowledge, data, owning data and domain knowledge. If you can combine deep domain expertise to be an advanced application that solve business problems, people don't want to know if the data is stored in a free HDFS system, or in some other system, or quantum computing, people don't care. >> I got to get your take on the data layer because this is where it's come. We had a lot of guests on saying, with the cloud, you can rent things, algorithms are free, so essentially, commoditization has happened, which is a good thing, more compute, everything else is all great, all the goodness around that. You still own your data. The data layer seems to be the LAN grab, metadata. How do you cross-connect the data layer to be consistent fabric? >> Here's how we think of it, and this is something we haven't shared publicly yet, but I believe you see us talk a lot more about this. We believe there are three new layers in the technology fabric. There is what we call the hardware operating system. The battle has been won by a company that we all like a lot, Red Hat, I think mostly won. Then there is what we call the data operating system, what you call the data layer. I think there's a new layer emerging where people like us sit. We call it the analytics operating system. The data layer will commoditize as much as the hardware operating system, what I call the layer, commoditized. The data operating system fight is moot. Metadata should not be charged for. Massive data management, draining the swamp, whatever you want to call it, every single thing in the data operating system is a commodity where you need volumes, you all are businessmen, you need volumes, in the P times V game, you need volumes to sustain a profit business model. The interesting action, in my opinion, is going to come in the analytics operating system. You are now automating hardcore, what I call, finding intelligence questions, whether it's using deep learning, AI, or whatever other buzzword the industry dreams up in the next five years, whatever the buzzwords may be, immaterial, the layer that automates the extraction of intelligence from massive amounts of data sitting in the data layer, no matter who owns it, our opinion is, Tresata, as an enterprise software player, is not interested to be a data owner. That game, I can't play anymore, right? You guys are a content company, though. You guys are data owners, and you have incredible value in the data you're building. For us, it is, I want to be the tool builder for this next gold rush. If you need the tools to extract intelligence from your data, who's going to give you those tools? I think all that value sits in what we call the analytics operating system. The world hasn't seen enough players in it yet. >> This is an interesting mind bender, if you think about it. When you said, "analytics operating system," that rings a few bells and gets the hair standing on the back of my head up because we're in a systems world now. We kind of talk about this in The Cube where operating systems concepts are very much in play. If you look at this ecosystem and who's winning, who's losing, who's struggling, who's falling away, is, the winners are nailing the integration game, and they're nailing the functional game, I think, a core functional component of an operating environment, AKA, the cloud, AKA data. >> Agreed. >> Having those functional systems, as an operating system game. What is your view of what an analytics operating system? What are some of those components? I mean, most operating systems have a linker, loader, filer, all these things going on. What's your thoughts on this analytical operating system? What is it made of? >> It's made of three core components that we have now invested six years in. The first one is exactly what you said. We don't use the word integration. We now call it the same word, we have been saying it for six years, we call it the factory, but it's very similar, which is, the ability to go to a company or enterprises with unique data assets, and enrich, I will borrow your term, integrate, enrich. We call it the data factory, the automation of 90% of the workload to make data sitting in a swamp usable data, part one. We call that creation of a data asset, a nice twist or separation from the word data warehousing we all grew up on. That's number one, the ability to make raw data usable. It's actually quite hard. If you haven't built a company squarely on data, you have to be able to buy it because building is very hard, number one. Number two is what I call the infusion of domain-centric knowledge. Can industries and industry players take expert systems and convert them into machine systems? The moment we convert expert systems into machine systems, we can do automation at very large scale. As you can imagine, the ability to add value is exponentially higher for each of those tiers, from data asset to now infusion of domain knowledge, to take an expert into a machine system, but the value trade is incredibly large as well. If you actually have the system built out, you can afford to sell it for all the value. That's number two, the ability to take expert system, go to machine systems. Number three is the most interesting, and we are very early in it. I use the term on The Cube, I'm going to be more forward-thinking over here, which is automation. Today, the best we can do with leveraging incredibly smart machines, algorithms, at scale on massive amounts of data is augmenting humans. I do fundamentally believe, just like self-driving cars, that the era where software will automate a tremendous amount of business processes in all industries is upon us. How long it takes, I think we will see it in our lifetimes too. When you and I have both a little bit more gray hair, we're saying, "Remember, we said about that? "I think automation's going to come." I do believe automation will happen. Currently, it's all about augmentation, but I do believe that business-- >> John: Cubebots are coming. We're going to have some Cubebots. >> We will have Cubebots. >> John: Automated Cube broadcasting. >> John, we'll give them your magnificent hair, and they know they'll do it. I do believe automation of complex human processes, the era of enlightenment, is upon us, where we will be able to take incredibly manual activities, like hailing a car today, to complex activities, looking at transaction information, trading information, in split second time, even quicker than real time, and making the right trading decision to make sure that Jeff's kids go to college in a robo-advisor-like mode. It's all early, but the augmentation will transform to automation, and that will take some time to do them at three tiers in the AOS. >> Then, if we are successful at converting the expert to machine system, will the value of that expert system quickly be driven to zero due to the same factors that automation has added to many other things that have been sucked in? >> You guys always blow my mind. You always push my thinking when I come here. >> I just love the concept, but then, will the same economics that have driven asumtotically approaching zero costs, then now go to these expert systems? >> You know the answer. The answer is absolutely, yes. The question then becomes, how long of an era is it? What we have learned in technology is escrow shifts take time. This era of enlightenment, what I'm calling the era of enlightenment, that enterprise software is about to enable, and leaving aside all other buzzwords, whether it's deep learning, AI, machines, chatbot, doesn't matter, the era of enlightenment is absolute. I think there'll be two things. First of all, it'll take time to mature. Yes, whether it's 50 years, 40 years, or 30 years, does it, at some point, become it's own commodity? Absolutely. The marginal value we can deliver with a machine, at some point, does go to zero, because it commoditizes it, at scale, it commoditizes it, absolutely, but does that mean the next 30 years will not be a renaissance in enterprise software? Absolutely not. I think we will see ... Let's take the enterprise IT market, what, two to three trillion dollars a year? All of it is up for grabs, and we will see in the next 20, 30, 40, 50 years that, as it is up for grabs, tremendous amount of value will be re-traded and recreated in completely new industry models. I think that's the exciting part. I won't live for 50 years, so it's okay. >> I know we got a minute or so left. I want to get your thoughts on something that we're seeing here, The Cube this year pointed out. We've kind of teased around it, but again, Batch and real time process streaming, all that's coming together. The center of that's IoT data and AI, is causing product gaps. There are some gaps that are developing, either a pure play Batch player, or your real time, some people have been one or the other, some are integrating in. When you try to blend it together, there's product gaps, organizational gaps, and then process gaps. Can you talk about how companies are solving that? Because one supplier might have a great Batch solution, data lake, some might have streaming and whatnot. Now there seems to be more of an integrated approach, bringing those worlds together, but it's causing some gaps. How do companies figure that out? >> I believe there's only one way, in the near term, and then potentially even moreso in the long term, to bridge that divide that you talk about. There absolutely is a divide. It's been very interesting for us especially. I'll use our example to answer your question. We have a very advanced health analytics application to go after diabetes. The challenge is, in order to run it, not only do you need lots and lots of data, IoT, streamed, real time from sensors you wear on your body, you need that. Not only do you need the ability and processing power to crunch all that data, not only do you need the specific algorithms to find insights that were not findable before, the unanswered questions, but the last point, you need to be able to then deliver it across all channels so you can monetize it. That is a end-to-end, what I call, business process around data monetization. Our customers don't care about it. They come to Tresata and they say, "I love your predictive diabetes outcomes application. "I have rented the system from the cloud," Amazon, Azure, I think at this point, only two players. We don't see Google much in it. I'm sure they're doing something in it. We have rented you the wheels, and the steering, and the body, so if you want to put it together to run your car on the track, you could. Everything else is containerized by us. I call them advanced analytics applications. They're fully managed. They run on any environment that is given to them because they are resource ready, whatever environment they play in, and they are completely backwards and forwards integrated. I think you will see the emergence of a class of enterprise software, what we call advanced analytics applications, that actually take away the pain from enterprises to worry about those gaps, 'cause in our case, in that example I just gave you, yes, there are gaps, but we have done it enough off a automation cycle on the business process itself, that we can title with the gaps. >> Abhi, we got to go. Glad we could squeeze you in. >> Abhi: Thank you. >> Quick 30 seconds, the show this year, what are you seeing? What's the buzz coming out of? What's the meat, what's the buzz from the show here? What's the story? >> I continue to believe that we are in an era that will redefine what we have seen humans do. The people at the show continue to surprise me because the questions they've been asking over the last eight years have slightly changed. I'm done with buzzwords. I don't pay attention to buzzwords anymore. I see a maturation. I think I said it to you before. I see more bald heads and big pates. When I see that in shows like these, it gives me hope that, when people who grew up in a different escrow have borrowed a new escrow, the pace would strengthen. As always, phenomenal show, great community. The community's changing and looking different in a good way. >> We feel your pain in the buzzword. As we proceed down this epic digital transformation, over the top, 5G, autonomous vehicles, Big Data analytics, moving the needle, all this headroom, future proofing, AI, machine learning, thanks for sharing. >> Abhi: Thank you so much, as always. >> More buzzwords, more signal from the noise here on The Cube. I'm John Furrier, Jeff Frick, and George Gilbert will be back right after this short break. (electronic music)