 Okay, we're back. This is Dave Vellante wikibond.org. We're live from Stratoconf in Silicon Valley the heart of Silicon Valley Santa Clara the Santa Clara Convention Center. This is day two for us. The big keynote morning was today. We had some deep dive sessions yesterday at Stratoconf. Big meet-up last night a lot of energy here a Lot of sponsors a lot of practitioners Good show We were here last year as many of you know and we're really seeing the evolution of big data Both both Hadoop and outside of Hadoop and and I'm here with my co-host Jeff Kelly From wikibond.org our lead big data analyst and we've got Sandy Steyer Who is the founder founder co-founder co-founder of 1010 data executive vice president at the company? Sandy and I and Jeff first met last fall at strata a very interesting story Imagine if you had a trillion row Spreadsheet that's it's going to start this discussion. Is that really what you guys can deliver? Yeah, it's it's a spread it's very much like a spreadsheet in the sense that it is a visual tool You can see all your data in rows and columns the way you do in a spreadsheet And you can manipulate it the way you do in a spreadsheet so that you can add columns track columns do various kinds of Time series analysis and other kinds of things interactively the way you interact with a spreadsheet. Yeah, and this is sits on my laptop, right? Well the the browser in which it runs it's on your laptop. Yeah, but it's called cloud-based. This is the point right so So how big is the typical? Spreadsheet, I mean how much data are we talking? Oh, it varies from something that's really small to something that is you know the largest We have in a single table in a single spreadsheet. It's about a half a trillion rows. Yeah, okay So half a trillion rows is the tip of the bell curve So I Jeff has called you the oldest big data startup. You've been around for a while. Talk about that a little bit Well, you know we my co-founder and I were on Wall Street, and we decided in 2019-99 actually that we had had enough of that and so we started a company and we Decided to use some of the technology and techniques that we had always used on Wall Street And basically bring it to the world as a new product and the product really was what we've described It was an ability to interact with a tremendous amount of data in a very natural transparent open way As distinct from the way most databases work which are kind of like black boxes there They're data warehouses in the sense of a data of a warehouse where things are stored in there, but to use them You have to take them out In our case you go into the place and you can play with the stuff in the warehouse in a very natural way Okay, so you're bringing that give everybody talks about it Oh the big barrier one of the big barriers to big big data adoption is that you know a lot of people don't know how to use Hadoop or Program in each base and that environment, so you're say basically bringing tools to the masses That's right. It's well the masses the masses and in defining terms of the kinds of people who can use a Spreadsheet not not everybody in the world can use a spreadsheet and not everybody in the world should be using a spreadsheet You know there are people who run the meat department in a supermarket who shouldn't necessarily be using spreadsheets That's not their job, right, and that's not their interest The person who runs a company the CEO the CFO shouldn't necessarily be doing spreadsheets themselves But but there are a core group of people, you know the analysts both from the business side the application developers on the technology side That do and can use spreadsheets strategic plan is financial planners operational people I mean there's a lot of people who use spreadsheets to live and die by and those are the people who develop the applications that the rest of the World uses now used you came out of the financial services business. I presume that that's was that your initial customer base Our initial customers were in fact in financial services There's nothing about the product, which is particular to financial services, but because we knew people there sold to your friends it helped Friends greatens his enemies whoever you know it didn't really matter But we knew them so that was a that that was helpful and but since then we've expanded into other areas as well We have quite a number of retail customers We have business now in telecom and pharmaceuticals insurance other areas All right, I want to talk about let's talk about now. So talk about how some of the people or your customers are using the product I mean Yeah, you got retail customers doing point of sale data. They talk about some of those so there's a wide range of use Actually like a spreadsheet. Well, yes, although it's in arguably even wider because there was a limit to what you can do with a spreadsheet and You know It's not too much of a straight stretch to say there's no limit to what you can do with tent and data That's a little bit over the top, but there are companies that are using us for their enterprise data warehouse Really? So Everything they do is through tent and day all their data gets put into data all their business all their Operational reporting and and all their analysis is done through tent and data That is the biggest use and we're talking about very large companies. We're talking about companies that have you know that are 20 30 billion dollar companies I think the other the other end it's a good to departmental solution So if there's some department that needs to do some analysis that works for that, too so Can you share some names with us some of your customers? I'm sure you know the New York soccer change is a good customer They were really our first customer Most of the big banks Bank of America JP Morgan Chase credit Suisse Deutsche Bank In retail dollar general ride aid Auto zone GameStop It's a fairly extensive list. So I remember reading somewhere I was a case study in your website or something about dollar general using it for point of sale data and giving access to their customers Or their supply chain, right? So that's that's another interesting thing In addition to being used as the internal data warehouse at dollar general and by the way the same thing is true at ride aid We also are a great way for companies to share their data with other companies That is very atypical of a data warehouse. Normally a data warehouse is a closed thing that is meant to be used by The company itself and there's no way for an outsider to get access to it and because we are in the cloud and because we have this Spreadsheet interface it means that it's easy to use and so other people can use the data too with the proper permissions, of course Yeah, so I love what I hear so far. It's almost sounds too good to be true How do I get data into the the mega scale cloud spreadsheet? There are two ways most companies send us their data and if it's a lot of data that said that they send it on some sort of hard You know hard drive But then you know on an ongoing basis and in most cases the FTP serves very well And so some companies send us the data via FTP. We load it into our Database into our system, whatever you want to call it and then they get onto a web browser or through various other Connection points use 10-10 data as a database or as a spreadsheet depending on their their preference Okay, so So that's that gets it in there and then then you provide services. Is that a service for pay service? It's a service. It's a service. It's a managed database service. So we basically load the data set up the thing You know support the customers and well They really need to do is aside from getting us the data from wherever it's coming from Is use the product do the analysis build reports and do the stuff which presumably customer Companies are most interested in doing. Yeah, okay So it's a one-time charge or it's a sort of big one-time charge with a smaller one ongoing. How does that work? It's it's an ongoing charge. It's a service charge And so the you know, I guess in traditional software sales the way we'll typically work is there's a large upfront expense And there's a smaller maintenance fee right in our case the upfront expense is a little bit bigger than the ongoing expense But not much, you know There's a little bit of an extra expense to set things up But basically you pay the same amount every every month or every year depending on how you want to do it for as long as You use the system Yeah, I know Jeff you've got some questions So Jeff Kelly just did a study for those of you haven't seen it on big data You go to Wikibon look at big data market size of 1010 data actually you know came up, you know very well I know you guys don't report your revenues, but but Jeff maybe talk a little bit about Some of the questions that you have Well, I wonder if we could dig into the technology a little bit around the scalability both from you know how you enable a Spreadsheet to scale to that level and also how do you in terms of the user interface? How do you it seems to me they couldn't get a little unwieldy with that much data? I mean, how do you how do you simplify the user experience so that it doesn't feel like you're working with that much data? Well the answering the second question first we simplify it by making it look like a spreadsheet, which is a very visual comfortable Familiar paradigm to people so they see the dead you could scroll through it you have a half a trillion rows You can just scroll through it and look at every hit row if you want if you have the time You have to be immortal to do that But you know if that's what you want to do with your with your immortality then we're happy to fill the So you it's very familiar and comfortable It's interactive you do something you get a result immediately You don't have to wonder about how that we where their result came from you could see the effects immediately How we do that from a technology perspective? Obviously I can only talk about on certain a certain level otherwise I'd have to kill you and that's probably not a good thing to do on TV So But suffice it to say that we again we really use a combination of very old technology that's sort of been forgotten by the rest of the world and some newer things that we make use of new hardware obviously in interesting ways and We've just it's just we've been doing it for a long time, and we were our own users When we were on Wall Street, so you know we did our own technology Development for our own use I was really on the business side my co-founder Joe Kaplan was on the business side And so we kind of learned what it what it took to do things in a Wall Street and you know pretty demanding type of environment and We just replicated that and so it's a combination of techniques and You know some of them are are interesting some of them are probably proprietary to what we do But most of them are just it's a it's so you know it's it's the example I'll give is it's a it's a we're like a chef that puts together ingredients that you know pretty much is available on the supermarket shelves It's how you put them together that matters So I wonder if we could kind of draw on your experience you've been in this business for a long time So you've been in this well well before we were calling it big data. So my question would be You know how has the how have the requirements changed how have the use cases changed of analyzing large volumes of data over the last 12-13 years you've been around As you as you enter a kind of new markets beyond financial services To kind of take us through that evolution what well, I think that that a couple of things are changing first of all that There was always big data, but the data is getting bigger So that makes a difference and it's bigger enough that it you know The traditional technologies are beginning to fail, which is the whole reason why the strata confidence exists on some level The in terms of the way it's you is I think Competition is just the fact that people are beginning to look at things in a more sophisticated way means that kind of everybody has to You know if you're let's to use a retailer as an example You mentioned retail if a read if all your competitors are doing Let's say basket-level analysis that is to say they're looking at every single item that the store cell that the chain sells And they know that if somebody buys one product what other products do they buy at the same time? You know, that's a very detailed kind of analysis if if your cut if your competitors are doing that you better do it too and So there is I think there's just a it's a little bit of a feedback loop that the more people realize that You know, there is data to be analyzed and the sort of the early adopters begin to do that The later adopters have to follow suit and then the early adopters have to find something new to be early about and They have to you know to maintain their competitive edge And so it is it's again It's sort of a feedback loop that ultimately means that everybody is going to be doing this sort of thing Sandy, what do you make of this conference and these startups exploding you've been at it for ten years now and Becoming at it with a non-traditional approach, which I love Love, you know, different different ways of attacking problems But what do you make of this whole ecosystem and the development of and the hype around big data? Well, I love the hype obviously it does nothing but help us I think that in terms of people's and also it you know per the previous question It actually heightens people's Appreciation like they better be analyzing big data because it's what everybody's talking about So it drives customers to us and to other and our competitors as well, but it helps everyone I think that I think that you know a lot of the solutions that are being offered are very technical in nature I think they serve a very important purpose because the traditional relational database Has some limitations and these these alternatives I could do for things like that do You know do offer a solution to that problem I think we outwit for it and and yet still another solution, which is not quite as new on some level But it is new to a lot. You know to the young people at this conference I think what we're doing is seems pretty new because they weren't around 30 years when You know, there are many people who actually remember Some of the techniques that we're using IBM was using similar techniques 35 years ago 40 years ago That's pretty mainstream But even IBM stopped using those techniques and I think to their debt to well to the markets that are detriment Yeah, we were just talking to Billy Bosworth, you know, we've been around a long time We've seen it all before there's very little we we haven't seen it's just the way it's applied That's different and the way the market's absorbing it right is this nothing you want to the Sun on some level and so You know, I think but but but I think there is a you know in general technology nowadays has so much more attention Yeah, and so that's and that and that's you know, it's it's basically spilling over into the business world So that's a good thing when you have some piece of technology to offer and you have all this data that you need to do something with sandy Steyer 1010 data. Thanks for coming on the cube mega Spreadsheets in the cloud for people like us who actually want to use big data Thanks again. It was great perspectives and and good luck with everything Jeff Thank you, and we will be right back after this short break. Thank you Stratocomps