 Ready to start? All right. Good. Thank you. Well, first of all, thank you very much. I appreciate everyone. Come on in. We're waiting for you I appreciate you taking the time and you're very kind to come and join us for our for our first session this morning My name is Don Zevis. I'm one part of the the sales team for for Cintel Technologies And we've got a lot of different things to talk about today There's a by the way that we're at the five minutes yesterday My five minute came in like two minutes and thirty seconds So it was kind of like bang bang bang bang bang, but today we're certainly going to go into a lot more detail I'll give you a lot more than meat about Cintel and why it may make sense to look at us for some of your big data needs So we're putting one thing I should say before we get started We were putting the session together that we realized there was kind of one or two ways that we could go with this we can either make it very very technical and You know lots of formulas and squiggly lines and all that kind of stuff and that's probably has a market You know or we can make it we want to make it very interactive But I really wanted to kind of reach out to the people because the vast majority of people that I've talked to At this particular conference had problems. They were coming if they were end users They had an issue and I can't it doesn't do this or I can't get my my database to do this So we were developing the process we were developing the session for today I wanted to kind of speak to those people So the idea is when you leave today the question I would like to answer is it would it make sense to call Cintel about Something whatever that something is so something you've heard today seen today a slide something that I said Peaked your interest so I would encourage you to call us That doesn't you know, there's no obligation. There's nothing like that that share with us Whatever your issue is because we built this session to try to answer those questions. Is that okay? Everybody on board with that? This actually to 2013 no SQL data. We were actually here in the beginning We were actually here at the I believe the first one that they had and it's interesting that this industry and just Literally a few short years has grown to where it was now because only being named in 2010 You'd think you know, how do you get so many people on board all at once today's session? I want to specifically talk about how to process structured and unstructured big data with Jen Sonics No SQL database and our all data management system. That's that's a mouthful I got through it didn't make one mistake on it What I would also encourage you to do anybody's taken notes is I would encourage you to just write down our website address and Sometime today tomorrow when you find yourself with a free moment when it's fresh in your mind the things that you got from today's session Go and invest a few moments in our website. Take a look around get a sense of what we do Also, I would encourage you to go one step further and jot down our email address Now that comes to us that are here right now So if I say something or you hear something or you go to our booth and you get something You have a question if you send an email to that ultimately it will get to us here And we can connect with whoever has the question sometime over the next day or two and really attempt to answer it So again, we want to make this very very interactive for the people who are in the room that they can get the questions that they have Answered so let's get started There we go Let me start with a brief history because obviously Sintel's been around for some time. We actually were established in 1977 and if anyone was around in 1977 the IT industry was in its infancy the way we programmed You know we were kind of vacillating but not to not to date myself But I remember being in college and actually getting him in the punch cards I mean, I know anybody here is less than four. They're like, oh my god. You guys are dinosaurs They're punch cards You put information in the little holes that were in the cards and you slipped it in your computer And that's how you gave it the instructions to do whatever it's going to do the interesting thing about being established in 1977 for us is it gives us a rich here. It gives us very rich heritage a rich history Adaptability over the years because for the people that are in the no-sql industry right now in the last three years There's been many iterations of the next biggest thing since 1977 You look at all the firsts and where technology has gone and every one of those technology items has been followed by a first some Breakthrough that's in the industry Something that happened that changed the world and we've gone through many many many of those iterations to get here So it made us very adaptable and it gives us that history and a rich tradition of being capable to do these things We're an international IT technology company So if you're dealing in scenarios where you're dealing certainly out of the United States We have the capacity and the capability to assist you with that We also dealt with and do continue to deal with hardware and software and you're gonna see that's much more impressive We get to the pioneer line right there, but our specialty from our inception in 1977 was in the database management primarily in business and most specifically in the database management systems and its applications So this is one of my favorite lines I'm gonna point this one a pioneer in the development of the no-sql database. So let me share with you what that means Even though we've been around since 1977 we created our first no-sql processor in installation in 1985 Now at that time it certainly was nothing like it was today the industry didn't exist in 1985 We didn't even have a name for it because the no-sql name in any version of that wasn't even there We actually called it nsql. You know why the founder of our company's named Norman Really that was it week nsql meant norm sql. There wasn't a name We didn't know what it was all we knew was it was something different We had come across something that didn't exist before there was a process something that happened The platform that we operate on right now is actually introduced and launched in 2003 So in 2003 the platform now since that time it's gone through many changes and updates and changes In fact 90% of what you're going to look at right now on our product has happened only in the last three years Even though it's a product that was ultimately introduced in the 2000 why because it took that long for the industry to catch up All during that time when we were developing that no-sql product back in 2003 We just had problems. We had an issue a client would come to us and say hey, can you do this? And we did it or they'd say you know it'd be great if your thing did this well now it does Or if we could only get it to have work in this way and we created that it made that happen So we've created many many many versions which brings us to the amazing product that we have today Since its modest inception in 2003 because at that time it solved a lot of the data intensive issues and problems Using that no-sql database Our main products are broken into three areas We have our gen sonics light, which is approximately one terabyte. It's for a small business marketplace smaller medium-sized business We have our mediums a large marketplace in our gen sonics medium and ultimately we have our flagship Which is our enterprise item approximately 10 terabytes to multi petabytes, and that's for a large system installation But as we were developing this and again one of our claims to fame is we have the capacity to do structured and unstructured data On the same product in the gen sonics product Well at that time there wasn't even the hardware to support that our late large data warehouse appliance Was created because there was nothing that existed at the time It would allow us to do we do and now our three main products is a dbis intranet, which is our supply chain management That's how we cut our teeth. That's how we got into the world our dbis e-commerce systems and our dbis bi-analyticals So when we were creating this go back maybe a couple years in the past and a lot of this might be So some of the people have been very obvious, but let me tell you years ago before this whole industry took place This is a big deal because nobody knew exactly what it was in order to solve something you got to figure out What it is you've got to put some kind of parameter on it. You got to know where it begins You got to know where it ends We also realized very early on that the SQL databases weren't handling the big data at that time You had you know very easy structured data and the products that were out there would do a pretty good job of it Right you got rows and you got columns everything's kind of where it should be So a lot of the big players that were out there in the household names that are that are here in this wonderful Facility we're doing a pretty good job But when we realized relatively early on as we saw a trend starting to happen It was a grumbling and it was like you know what at some point in time We're got all these new things that are that are coming up That's cut that was the inception of the no SQL database concept and ultimately we had to start figuring out the differences Where the differences lie what what part falls into this category what part falls into that category and now how are we going to handle it? If we're going to come up with a solution a better real legitimate solution We're going to start plug some holes We're going to have to figure out why and our no SQL architecture was the mechanism by which we created those solutions So we started looking at defining big data. We said structured data 20 terabytes and above That's pretty straightforward. There's nothing. They're shaking there right on structured data at two terabytes in block Okay, that's nothing. They're shaking there Well, I'm a sales guy and this is the one that appeals to me the large data acquired from outside big data supply because that's Non-rescripted it could be little it could be big it all depending on the query that you put in If you could find out exactly say for example You owned a company and you wanted to know how many widgets that you sold on the day before Christmas On the East Coast when there was four inches of snow because for whatever reason you as a CEO wanted to know that Well, do you think that's an incredible amount of information that would be mined from the field that you have out there? Of course So that large data acquired from outside big data supplies for sales stats research general information And that started to drive what we started to create because now you drew a little picture for ourselves And we realized that the standard data Structured still less than 20 terabytes We saw that our big data also in this case Structured that 20 terabytes because of its size, but this was the explosion This was it this was what was going to change the face of the world because at that time a lot of the household things That we did a lot of the ways that we didn't really work You know if you I always like the idea that if 10 years ago if you use the women Twitter You think you're talking about a bird Because that at the time that language wasn't there there wasn't even the technology in place to do it There wasn't a big issue and now we're dealing with music and videos and movies and charts and graphs and anything that you can imagine And it's coming at us at an ever-increasing rate In an ever-increasing volume and an ever-increasing speed and now we're dealing with multi petabytes and everything That's going to be on the other side of that so as we're structuring this relationship as we're developing this product as we're going through We're recognizing that obviously these are areas we're gonna have to deal with these are going to be here But that's going to be the future So we started picking the failure we started looking where the weaknesses were again if you're gonna identify you're gonna solve something You best find out where the problems are it came out almost immediately the basic method methodology for the data connection was to join I Would take table a I would take table B and I would connect them together And that would ultimately give me the information that I'm looking for so I've made that made good sense Well, but when the tables got very very very large We couldn't do that anymore the technology wasn't in place They would allow that to happen very easily and now we've got duplications of data, which we're now making it even worse Now this seems very simplistic by today's standards But when this was really starting to take place the undercurrent the grumblings were huge So let's look at the customer table. I got a handy-dandy laser here somewhere there it is, right? So if you look at the customer table, all right, you've got ID name and city You've got an ID number assigned whatever they are you've got the name of the client You've got the city that they are so you've got four times three components So that means you've got three columns. You've got four rows very simple, right? You've got 12 You've got an order table here, which consists of the customer ID The order number that's been assigned to it and the amount of the sale I'm a sales guy, so it's about the money on that so I want to make sure now I know that I have 11 rows and I have three columns. That's 33 So the logic would be what if I was going to join those two tables together How many should I ultimately come out with well 12? Plus 33 should be 45 right make sense, right? It wasn't This is one example where the sum of the parts is actually more than what you originally started with Because now my number of components although I have five columns and I have 11 rows Now I'm at 55 and on top of that I still have to keep that information right there I still have to keep my customer table intact and I still have to keep my order table intact and Now I'm creating something on top of that which is greater than the sum of the parts that it starts with So does that look like I mean did I look like a process for destruction? Because what's going to happen ultimately is this is going to get larger that's going to get larger And that's going to get a management because the physical sizes of what we were dealing with were absolutely enormous So we figured that if we're going to make anything in the world, we'd better figure out a way to not have that And the not have that ultimately is the solution in a no SQL So the no SQL database works because it handles the unstructured data We talked about that the little bell that showed all the videos and the music and stuff All right the gensonic products handles the structured data as well The reason for that is we were designing it we were creating it when somebody came up and had a problem whether it was structured or unstructured We just solved the problem So we've got our no SQL database We've got our program and we're working on it and listen to this nice later over here comes to us and where's your first name is? Nelly so Nelly comes up to us and she says I got a problem with my unstructured data It's okay fine We went in there and did our thing and we fixed it and then he came and Ray has a problem with the structured data And we go in there We do our little thing right and we fix it and we're building it and building and building with so now We're building solutions for the unstructured and we're building structured a solution for the structured and we're doing it at the exact same time This wasn't a scenario We built the product that fit a hole that somebody else had it wasn't like and I don't know if they've been in from Oracle But because they got a great product of course But it was like we're gonna build something for all the stuff that Oracle can't do We're building something that doesn't exist and we were just basically taking the problems that you had and fixing them And then it went in and ultimately it manifests itself. Yes The recall structure instruction, you know what I don't know how they handle the questions and answers But I do have a slide that will answer that so when I get to them and have you sit right here And I'm gonna do it to you. Okay. I'm just funny with you, right? All right, but it had a we figured out a way so we figured I know we can handle that It's actually a cool slide. You're gonna dig it. I'll show you all right So it handles the structure, but we didn't have a joint table We didn't have a situation where the two parts coming together We're larger than the sum of the parts that would started with we also realize it could be scalable Because if I didn't have to create something to control that outer control expansion when you have the joint tables together We could easily increase it or decrease it by whatever he needed So if he needed it big because we're just pulling it out as we need it. It's big It doesn't have to force something together and make itself larger The other item is is that we know the four V's of big data. All right volume Bigger every single day. Here's a great thing for those that are taking notes right this down You probably heard I would come to see this guy. I'm not tech guy And I found out and I was told I believe this to be true by the way that yesterday the 20th of August regenerated more data More data the 20th of August then the entire history of mankind up until 1999 yesterday Let that resonate for a second what that means so that means between the history of dawn of man When we started putting stuff on cave walls because that's data up until 1999 the sum total of all that Was equal to yesterday So that means today Now it's the sum total of yesterday and the history of all mankind from 1999 until the dawn of man And that's today Let's tomorrow gonna hold what's it gonna look like in five years ten years volume velocity Historically the bigger it gets the slower it gets is a dog faster than the elephant Well, I'm not my little dog, but most dogs are All right, why historically the bigger it gets The slower it gets now on top of that. We've got to deal with variety remember I'm a little barrel here Because there might be a piece of information that comes now It's a photo and the next one that comes in is a is a document the next one comes in as a movie The next one comes in is Katy Perry's new album the next one that comes in is is is my Wall Street Journal And we don't have any choice. It just it comes in. That's the way it comes So now we're dealing with variety now on top of that. We're dealing with variability Some are big some are small some are fast some are slow and we're gonna accept it exactly the same way We're gonna gather it all down Identical so as a gensonic product was created. We're creating it around a marketplace. It's exploding in front of us We're just kind of accepting it as it comes and we're putting the items in place to take care of those situations So our gensonic data structure certainly is supported in the SQL network, but we have the opportunities and the ability to select How we're going to build what we build for you. We're gonna pick the one that we think is appropriate So if you come to us, and we believe that a network application is the best that's we're going to use why because we can Relationals the best we're gonna use it if it's vertical or graph whatever it is We have the capacity within our gensonic product to take your information and put it in the best possible Scenario the best possible framework that we can come up with now. What does that mean for you? Well, that's simple to see the application. Why reinvent the wheel if you got something to work stay with it Advantage of speed and simplicity and the results are ultimate savings. So if you're looking at a scenario where These the no less cruel databases are not the same. Why well because there are some people who's claimed the famous documents That's it. They do documents. They want to believe they do it better than everyone And I'm sure they've got some great pop graphs Somebody graphs they're claim the famous graph. They've built their product around historically a hole that someone else had That they were able to fill up and they do a good job at it. All right, the key values are structured The new SQLs that come into your database are going to be structured as we were developing our gen sonics product However, we recognize we didn't have that luxury We were getting things in different places in different times again. We weren't fooling the whole there is left open by someone else We were attempting to create something that didn't exist. So what we found is in the unstructured data management Most people are using an SQL database for the structure and that makes sense They're using the no SQL database of the unstructured that makes sense to right When they got their large structured data management that we're using their SQL database for their standard structure You know SQL database for their large structure And I'm a surface that makes sense. They're there somewhere embedded in those lines as a solution The drawback what we found the problem we were facing However is that requires two different types of databases. All right, two different types of systems two different types of expertise Two different types of hardware two different types of contractors two different types of providers. All right, and historically whenever you get multiple people It's that Murphy's law whatever can go wrong will and what I found is anytime I have more than one person involved If something goes wrong, they turn into an octopus It's never me in our scenario. It's always us We're the only ones we don't bring in two types of databases. We don't have two different types of systems We don't bring in additional levels of expertise because that's all provided in our large data warehouse product So our twenty two hundred twenty two notes 176 CPU cores one twenty two sixty four GB memory 150 terabytes that's a pretty impressive thing by the way I mean I when I to this day when I think of those numbers, it's impressive It uses the highly procedural no one SQL language So that makes it easy and it has all of the built-in storage management that you're going to be required now the interesting thing You can't really it doesn't do justice from that picture But it's about the size of what you were put into a big closet That's an amazing thing. So we start to talk about the capacity what this amazing device can do Yeah. Oh, no Absolutely yet, that's a good point that the particular one was photographed There's one of the options that we provide now certainly again being scared that you can make it as large or as small as necessary And what we found we were doing Trajections this particular projection was a trillion record SQL because we wanted to be able to monetize What we do when someone comes to us and says how much is it what you do we say this in the first It's why why do I need it? Why does it make any sense? I got whatever I have imperfect though it may be and I'm just going to keep using it So we came up with this they actually came up with this for me because I'm not smart about this kind of stuff So I said make it as simple as possible. Here's simple as possible Here's a trillion record transaction one a trillion record transaction to couldn't be simpler, right? You have an index with 51 terabytes of information transaction to as an index at 51 terabytes of information again couldn't be simpler Your data is broken down at 36 terabytes here 16 terabytes here 14 terabytes here 16 terabytes there Everybody makes sense to everybody nothing nothing. I'm shaking right here, right? When you put them together now because of the redundancy factor of the SQL database about how we talked about all the little charts and stuff Now I put the indexes together and now I have a hundred and two terabytes When I put the data together now I have 66 and ultimately what that requires me to do on top of this So all this I still gotta keep I now need to have a hundred and sixty eight terabytes of additional storage All right, so I said now I'm a sales guy. I'm not real smart and I said Oh, it's a big deal if it's a dollar a terabyte for storage. Who cares so hundred sixty eight dollars big deals No, so it makes no So I started doing some research and I got stuff from all over where I got some places as high as four thousand dollars All terabyte for storage. Now. Yeah, that includes everything. I'm sure there's stuff That's tacked on it and stuff people doing stuff people don't do and now all that's fine But even if you think four thousand dollars a terabyte of storage and you're going to add into that a hundred and sixty eight terabytes It doesn't take long before you realize there's a significant dollar value attached to this And if we were going to ever make any kind of enterprise any kind of going business concern out of what we're doing We better figure out a way where we do it Our trillion record dead gen sonics actual same thing same trillion for transaction one same training for a transaction two We're going to add them up and what do we have? Well, we don't need the redundancy of the tables We don't need to recreate the second one the second time. So we only have to have 51 terabytes We go to the next part. We don't need the redundancy of the 16. So we just need the 16 That's going to give us our 67 total, which is our gen sonic size of 117 terabytes total Nobody get that it's it's simple. All right, but we sat down that we would compare it to well The alternative would be in our SQL application now. We have an index of a hundred and two terabytes sixty six for the day We have a hundred and sixty eight whole their SQL size is three hundred and fifty two terabytes Necessary for that particular transaction Well, I'm gonna go back to the thing that I just said previously if I have to put that down to dollars and cents What am I ultimately going to get well? I'm going to get ultimately an increase of 200 percent between what we can do in terms of the space and what the joint does In terms of the space That's our large data warehouse appliance 2,200 now. Obviously as as Gord mentioned it can be large The only reason by the way for this I particularly like I'm gonna make the venture to guess that the vast majority of people this room or in some kind of office building probably All right, whatever they do is in an office building and I mentioned previously that Fits into a good-sized closet You don't have to have a floor. You don't have to have any kind of sophisticated Equipment you don't need chairs. You don't need a whole lot of other stuff That's there why because it doesn't require an enormous size you can make it larger if you choose But the actual product that is necessary to do the types of things we're talking about don't require an enormous amount of size This actually gives you some sizing and the people at a tech we keep this in because the techie people look at it and go So techie people go ahead go all right This this to me is the amazing part about it I would talk about velocity and speeds Volume the bigger you get historically the slower you get We actually figured we should probably have a slide that puts the money where the mouth is so that's a trillion transaction table run This is a program for an average table So we basically look for a single customer ID and we did a search within a trillion transactions to find it We came up with 1296 All right To get that 1296 took one point two five six seconds to search within one trillion Transactions it took me longer to say it Then it did for this particular product to do it So now we say well, let's push our luck. What if we did more, you know, because obviously that's even though trillion is a lot At some point time, it's gonna be bigger What would bigger look like and they said okay, we know what we're gonna do something different We're actually gonna have a transaction a sub transaction We're gonna enter a start date for a client and we're gonna have to put together an end date An end for a client would identify both of those things. We're gonna run the exact same thing We came out with 2106 total records of data, but here's the interesting thing Even though this one was a search through a trillion obviously because we're doing a beginning and an end We're gonna be searching through more From zero minutes one point two five six seconds to zero minutes One point two six five seconds if you mind that's what changed five to a six Even though the actual processes was significantly larger So when you look at speed related to volume, here's an application where we're doing more significantly more But we didn't have an abject increase in the speed associated with doing it one of the particular reasons We found is as we were looking at the development of the gen sonics product We had to figure out where again where the problems were where was the fly in the ointment? Let's just figure out what the problems are and fix that and the rest of the stuff will line up itself So we kind of mapped out an SQL process by steps. We took the gen sonics by steps And we realized right here in the beginning For whatever reason either it's another data store could come from a gen sonics But that SQL process couldn't take this crushing amount of information out at once The best that it could do is take some smaller chunk of it and it would take that smaller chunk And it would do something and it would take the findings and put it there Then it would go back to the data store and go back to us wherever it is And it would do that again it would take on another smaller chunk And it would do something to it and then ultimately it would take it and it would put it someplace else And it would do that and do that and do that over and over and over and over Depending on the size of the data store depending on the amount of the SQL device could actually come up with But it doesn't take long to look at and go that's a laborious process not only do you got five steps But you've got this constantly repeating step that it did is phased in based on the sizes of what you have side based on The size of the data store based on the size of the chunk you can take out we realize very early on in the gen sonics product We got something we don't need to do that. We can basically create our data store for lack of a better word And we can pull out just what we need. We don't have to go through a long laborous process We don't have to jiggle things around we don't have to do any special calisthenics We can just pull it right off the bat God, I love this picture What you're looking at right now is a 75 level deep explosion of Our gen sonics DBIS Now again, I'm not a tech guy and they're explaining this to me and they're saying everybody's in an hour and they're like, oh my god This is great. This is great. I'm like, well, why is this great? I know I'm not sure why it's great It looks cool, right? I mean you got two screens. You got a lot of stuff on there I was told that the in order to give example and this is and again This is something the floors and non-tech guys sales guy to build a 747 the Manufacturing documents for a 740. This is an airplane case that four five six hundred people is less than 75 levels deep To build an airplane That means if you took all the schematics and all the documents and all the parts and pieces you need to Manufactured that thing. It's less than 75 levels deep So when you look at capacity, it's enormous Not only again, we'll talk about scalability not necessarily for something. We're just going to use today Of course today is going to be important. Obviously we want to make sure yes. Oh, yeah, I know I love that I love that I like to believe by the way they name the first thing enterprise after Star Trek 2 because I'm a Star Trek geek So now what you have is any scenario that you have right now regardless of how complex it comes to us We have the capacity already to be able to digest and we have the capacity already to deal with it So now and the young lady who's standing right there if she was here, I'd say this is your slide This is the one that really kind of brings it all together because the explosion we believe is happening in the unstructured data Yes, we're getting more structured data that that stands to reason a lot of financial things and things that lend itself to rows and columns But right now the unstructured as I mentioned earlier if you use the word treat You know tweet ten years ago. You're talking about a bird There's somebody in their basement right now creating something that's going to change the world in the next three years We don't even know what exists right now We don't know the next thing. It's coming around the corner. So we're looking at a scalable product We're trying to factor in regardless of what happens. So our content manager, which is part of the offering Manages all that Manages all the files your emails your PDFs excels videos everything that you have Microsoft access database No matter what it is even with extended search times and for those that want to see this our demo in the booth will actually show you this Actually functioning I would encourage anyone who wants to see what that looks like to just invest a few moments in our booth And see it actually running because it is amazing. This really is the wave of the future We're not going to have the opportunity to be inherently selective of the what we do how we do with things of that nature We're going to get it kind of as it comes So what we also find is we're looking at Jen Sonics and we kind of put a little north there But our Jen Sonics came out of the idea of fast generation Sonic execution We knew that the speed was going to be the critical aspect of all the things associated with this Because ultimately that's that's that's where the time is time is money whether it's in computing time Calculation whatever that's ultimately where people are going to base their decision So our scientific intelligence the way we created our process Dictated that we look for something where science and intelligence be and we look at the pioneers that are involved in our industry Of which certainly which we are one of them that gives us the opportunity to have a great placement in the future of this particular industry Scientell where science and intelligence be I Thank you So before we get yes, sir Oh, absolutely. Yeah, I'm sorry. I was trying to get trying to get your question So I guess I just so I can make sure I understand what you're saying. Can you pull out both pieces of information equally the same is that Absolutely. Yeah inherently when we were accepting the data when it comes into us We have the ability to handle both of them not only how it comes in which is I believe which part of your answer But certainly how we extract it that answer your question Okay, what do you want to do I take a stab at it because I may be I'm not getting it, right? Oh Absolutely. Yes. Yeah, they would Right. Yes. Yeah, you want to see the original data that generated this statistic? It does. Yes. Yeah, absolutely That absolutely Yeah, and interestingly enough I mean again, I'll go back to what I said previously because I'm a sales guy I'm not a tech guy for me the value of all this and anybody in the room That's dealing with this as a problem is deciding the problem is how do I get the information that I want? Regardless of what that information is now in your case. It's you know, somebody said something negative in a tweet You want to go to the actual tweet that generated that that's the proof in the pudding That's the real value of being able to do this So regardless as long as whatever the source that comes in is what you can ultimately pull to whether it stales Statistics whether it's something related to what you're talking about. So but that's good any other questions It looks like we have a choir group today, but I appreciate that. Thank you very much for your time One thing that I would want to mention again If you have any questions, you're welcome to get to as my hope that you leave today's session and the number one thing You said is should we contact science tell do we have a product or a service was there something you sir? Heard or seen today that might solve a problem that you have more happy to do that In fact, we'll be having cigars and stuff up on the porch around seven o'clock tonight So I mean as a cigar fan is more than welcome to join me and we'll discuss no SQL technology to the nth degree