 At Big Data SV 2014 is brought to you by headline sponsors WAN Disco. We make Hadoop invincible and Actian accelerating Big Data 2.0. Hey welcome back everyone we're here live at the Hilton in Santa Clara in Silicon Valley for Big Data Silicon Valley or Big Data SV go to the hashtag Big Data SV I'm John Furrier the founder of Silicon Angle this is theCUBE our flagship program we go out to the events to extract the signal from the noise I'm joining my co-host today Jeff Frick with theCUBE and our next guest is Jim Hare product marketing at Actian analytics Actian is the emerging company that has a lot of a lot of a lot of secret jewels that will put together for Big Data Jim welcome to theCUBE. Thank you very much. We've had you guys on a bunch of times here in Silicon Valley big news with the company share with the folks first a little bit about the Actian history and how you fit into that how the the analytics piece of par Excel fits into it as part of an acquisition quick give a quick overview of what happened sure well Actian got its roots really in the operational database set at the house was a private equity firm that went off and really saw value in untapped unvalued resources and assets and has built the company on that foundation so most recently they acquired companies like pervasive and par Excel and where I fit into the equation is I was brought in to actually look at these assets and figure out how they could be combined in interesting ways to really unlock that value and the most recent announcement is around this Actian analytics platform where what we've done is looked at all these capabilities and combined them to really unlock that value and today if you look at Actian it's one company one voice one message one platform we're here in Silicon Valley absolutely a lot of innovation and you're seeing a lot of your moves being made in the big data space obviously we've been covering it you know really at the beginning and the Hadoop movement really kind of put a face to unstructured data taking data parking it away stirring it away and commodity hardware and exploding that out and analytics and insight etc etc and you start to see the maturation of that space and so big data just doesn't equal Hadoop anymore we had folks on from data stacks talking about Cassandra we've had folks that's saying hey we have sequel server which is you know sequel database of the database world meet meeting software meeting platforms is a really kind of hard thing to navigate so big data just doesn't mean Hadoop so I want you to explain to the folks out there from your perspective the role of Hadoop the role of other platforms and where it needs to go is it being unified is it going to continue to be dynamic what does that mean big data doesn't equal Hadoop what does that mean well if you look at it you know a lot of people were already dealing with lots of data with traditional sources you know they had data warehouses and databases and with the flurry of the volume of new data coming from new sources even sensor data with the Internet of Things there was no easy way to manage not only those new data types but also those different that volume of data so hence people were looking for new technologies to handle that kind of different types of data and Hadoop was explored and evaluated as an interesting way of not only handling that data but also becoming a low-cost storage mechanism really a data lake a landing zone for all this data and so I think people began to associate big data with simply Hadoop the reality is something far from it Hadoop is great for handling things such as when you need to again store that data you need to do things like cleanse it do some data science and maybe some data discovery to really find new relationships of that data but people now what they want to do is take some of these data and combine it with their on-premise enterprise data so how do you do that so if you look at it really comes down to acknowledgement that you have a collection of different platforms one of which is Hadoop for handling certain types of data again sort of being that low-cost data storage area you have your data warehouse so big data still involves that data warehouse and really sit in the middle is the need for an analytics platform to address another aspect of big data which is really providing the ability to handle more sophisticated analytics and do it at a low latency and also do it in much more price performance cost-effective way so with the acting analytics platform what we've created is the ability to interoperate and cooperate and complement and connect to these other types of platforms be it Hadoop be your data warehouse or even streaming data for that matter so it's really for people to completely solve the big data problem they really need to consider an ecosystem of all three types of platforms so John I wonder if you could share with us some customer examples or some of the things you're seeing out in the field that really take what you just said and put it into practice absolutely its value yeah in fact one of the customers that was speaking here at Strata this week was Evernote Evernote is one of the leaders in providing online capabilities for people to store notes videos you know just thinking about what I'm gonna get there you go so behind the scenes they actually use Hadoop to actually store a lot of the information both in terms of the assets that you're storing away as well as information about you as a customer and they're using the acting analytics platform to take a look at your usage patterns as well as you know the types of things you store to determine if perhaps you're ready to actually move from their free version to a paid subscription so that's how they're actually using our platform today so they really saw the value in having the complement of Hadoop and an analytics platform together one of the things that Strata conferences and we've been covering for many years is the notion of insights and insights is kind of like the holy grails the bumper sticker for what people want and you know IT has insights on the IT side and on the business side you're seeing a demand for business changing top-line revenue you know this is kind of the banter that's being kicked around so you know insights is not that easy so you know you know when you have you know a clean sheet of paper you're a consumer company maybe you can build a system from scratch but for most folks who want insights out of their business it's challenging could you just talk a little bit from your perspective how our customers really getting insights from the data lake the data landfill active data passive data all as neat data sources out there talk about the challenges and the opportunities around one laying the foundation for systems to do that and then how do you get insights good great question in fact if you look at our platform with our diagram you know I think people hear the word big data they naturally gravitate towards oh where are my data sources what's all out there what can I do with that when the reality is they need to be looking at the right-hand side which we call really the value you know what is it you could be doing with all this data in fact I challenge you know when I have the opportunity to speak with different clients I sort of challenge especially the business folks to say if you had unlimited access to all the data that's out there unlimited analytics what would you do differently and the challenge has been up to now is they felt so constrained and beaten down that they haven't been able to think outside the box now as a result of having faster analytics more information of their fingertips it's creating a revolution in terms of how people are looking at analytics and data to transform their businesses you know what we're seeing is several different industries I mentioned you know for instance Evernote there are other companies in the digital media and advertising space that are combining our location based information along with information about you know where what type of places not only you visit but also what kinds of places you shop and they're starting to marry up all this different kinds of data to really understand it and really create a personalized targeted advertising campaign directed at the individual versus sort of a broad segment of the population as an example so that's one change we're also seeing things in the financial services area where clearly you know fraud detection is one area that people are really trying to understand how you can detect and even prevent you know people from actually penetrating both in terms of you know be a credit card transaction fraud or simply employees actually taking assets inadvertently out of the company as well as in things such as risk management we have several large banks that are using our platform and prior to using Actian it was some cases taking a day for them to actually run the analytics to determine what the risk exposure was and two things occur one is that they may be making decisions that actually put them a situation where they're actually taken on more risk but more important is that they're not actually adapting to the market conditions as well so having lower latency analytics is what is really helping organizations be able to think differently about how they actually run their companies in fact if you think about it if it takes hours or even days to run some of these queries you know what was happening in the past the analysts would click simply click it off the job come back after lunch and figure out if it's still running and they're not able to think in terms of those interactive you know asking different questions of the data that's all changing it's interesting because we always talk about people processing tech and it's a combination of the three is how this thing continues to evolve and would you just outline basically now the text getting a little bit ahead of the of the people and how they'll have to change the way that they think about things because they've got so much capability to do things that before were not even possible absolutely one of things I would say is that I think in the area of big data the next element that hasn't been really tapped into and discussed is whole psychology of big data how is it transforming how the analyst you know use the tools and use analytics to do things differently in the organization yeah I think that will be the next way and the pressure to get it right the first time is significantly changed too right it's almost like an agile decision process making as opposed to trying to map out the exact structure of the question because it's going to take so long to get the answer now as you just said it's more of an iterative process where you can start explore get feedback rapidly explore a different direction deeper go a different way so it is a very different way to attack the problem than the kind of monolithic thing that used to mirror kind of software development absolutely and the other part of it is you know the market conditions are changing faster as well that's true so how do organizations you know react and change and you really use analytics to outperform and it differentiates them is it from their competitors so I want to ask you about back to the analytics piece talk about unifying earlier the demand when people look at the data they have a couple things in mind I want to get your perspective on seamless workflow and you seeing things like in memory kind of accelerate that within you know in memory kind of capabilities sure seamless workflow and then unified management around the analytics whether it's predictive prescription or whatever the analytics advanced analytics might be whether it's ingestion and to targeted analytics so so comment on the seamless workflow integration and then how do people manage it got you a good question what we've created as part of our platform is the ability for people to first of all start with connecting to those data sources and then from there really look at taking that and bringing it either into something like Hadoop but also the analytics platform or even their data warehouse where I'm going with this is the idea of actually creating workloads that are based on data flows where you're actually sort of first of all laying out what is that flow of information how will that actually flow through the various systems and then being able to sort of essentially embed that in the analytics process itself so up to now a lot of times what we have are these silos of information and people have to learn different tools different ways of actually moving the data analyzing it moving it on to the next step just imagine having the ability that you can actually drag and drop sort of define I want to take this data combine it with this other data transform it here's the kind of analytics I want to be able to apply to it and then even automate the actions based on that information this is what we call the data flow and this is one of the things that we offer as part of the acting and analytics platform it's part of our pervasive acquisition and we're going to see more of people wanting to actually have these workloads and work streams predefined so they can do faster analytical iterations be able to have one person define it and have those shared as assets across the organization and then that way they also don't have to get into becoming programmers like learning map reduce because that's one of the biggest challenges that we're seeing there's very few people that really understand to really even how to program and use it effectively so we're trying to abstract that up to your point John which is really to make it you know simple and easy for sort of the common users to be able to use these systems versus having to have people that are skilled experts one of the things we're hearing here at the Stratoconference and Big Data SV is the sequel on Hadoop or sequel because it sequels a pre-existing market obviously last some legacy in there obviously that's a transformation but it's also kind of a compatibility got to be compatible with that sequel how do you guys look at that trend does it affect you guys do you guys play into it you extend that what's your take on the current buzz around sequel on fill in the blank gotcha good if you look at it you know one of the things against for tying in with your previous point is the fact that people want to use the existing skills and knowledge that they already have what do people mostly know for a lot of cases to how to access data sequel so hence the reason that people are looking for capabilities how they can use sequel to tap into things like Hadoop what we've created is part of our platform I'll call it more of a and I guess Ecoskeleton system around Hadoop and what we mean by that is almost like the Iron Man where you know you have Robert Downey Jr. out there you know with the superhuman powers as a result of having the special armor what we've done with our platform is embellished and embraced Hadoop one of the challenges is that again people have to know map reduce and really understand the bowels of Hadoop to get the value out of it what we provide is the ability for people to analyze quickly the information on do figure out what the data is a value and then move it into a analytics platform for lower latency analytics so we do the data directly on Hadoop we simplify how people can bring that into an analytical database for faster processing and then the third part is being able to on demand reach out to Hadoop reach out to the data warehouse to get the freshest data so having a platform that actually embraces and plugs directly in Hadoop is where customers are really wanting because sitting on top of that are your traditional BI visualization tools that speak SQL and they want to be able to simply use SQL reach down into a platform access whatever data be it Hadoop be it in the data warehouse what are some of the things that people might not want to know that might want to know that don't know about Acti and what would you share with them so you guys have a pretty robust platform you guys put together some good corporate development kind of with this with the equity company underneath it a lot of cool platform features with some applications that with analytics for instance what should people know about acting that they might not be aware of that you'd like to share with them sure I think one of the things when people are looking for the fastest lowest latency platform for doing analytics they come to acting case in point is Amazon's redshift runs our platform we're the engine underneath that micro strategies cloud runs on the acting analytics platform so not only do we have a lot of enterprise and even medium-sized businesses run our platform to address interesting problems but when large organizations that are looking for a fast robust platform that's when they look at acting so I think that's one of the things I think to bring in case in point the other thing is that you know when you look at making a decision for an analytics platform up to now everybody's sort of have been I guess looking at things like hardware appliances you know that was sort of the rage for the longest time the problem is that hard work gets antiquated over time what we provide is a software platform that gives you the choice of running on whatever commodity hardware you want or running in the cloud so you have a lot of cases customers want to have that hybrid environment where they have some data and some analytics in the cloud some on some on premise we provide that kind of flexibility the other part of that is the way we've optimized our platform we take advantage of the new chip innovations so as new hardware rolls out faster chips compression all these things built into the chips we take full advantage of that so that's one of the major benefits of having our platform Jim I want to ask you obviously in the product side you have to look at the marketplace and understand what's going on in the market the business then look at the technology and kind of the value proposition kind of tie that together between engineering and the roadmap etc so I got to ask you obviously we talked about you know in memory we see cloud as an enabler from an infrastructure standpoint in terms of time to value you're seeing customers look at the cloud and the DevOps movement has really shown that application developers can do stuff pretty quickly absolutely having a platform like what you guys have in others is really a way to tap into that and scale it so I want to ask you what your take is when you talk to customers around their investment they have to build up they have to do more with less and now actually make real investments not just downsize or consolidate but now they have to kind of do more will bring your own device to where all kinds of new things are being invested so the question is about developers what is the enterprise look at from a developer standpoint in the old days remember the mainframe days yet in-house developers that kind of went away over the past couple years now there's a resurgence of okay I got to do this who's going to build it so people are hiring what is that developer what is the enterprise developer look like that that can take advantage of the things that you're building is it data science guys is it the you know classic front-end developers I mean enterprises are looking to figure out that developer equation it might not be that Microsoft developer it might not be that old school developer it might be a DevOps guy might be a JavaScript or Rails or Python or what's the profile of the kind of developer well I think the person would be looking at the analytics platform as an example is someone primarily is going to be more coming from an architectural background where they're looking to figure out our existing architecture perhaps is fine for today but we need to be looking for the future and what they're looking at in some cases is actually taking some of these capabilities and running in a cloud-based environment where they don't have to stand up their own hardware their own infrastructure they can actually build a prototype of this kind of environment in the cloud and then once they figure it out you know what that really should look like they'll actually bring that on premise so having a cloud as an example is a great way for organizations to really do proof of concepts try out concepts before they actually implement it and make the significant investment of the organization the other kind of a role we see is you know you mentioned you know it's sort of the developer the developer comes into play when you know it comes time to really say okay this is the technology now we want to start really building the applications and in some cases obviously it's going to be maybe the dupe in the case of an analytics platform it's you know commonly people that just sort of first of all know have dba skills but what we're seeing now is this really this evolution into this data scientist who's really being the owner of the analytics platform and really this sort of the overseer of all that data and really tying in the value of that data to the business use case it was interesting though I just met with one of our customers gywire this week and he was saying one of the biggest challenges he's facing is a lot of data scientists are coming to the market right now don't know sequel they know all these new technologies right they know are these predictive analytics right thing guys are but it almost reminds me of back to the future I mean people are forgetting the roots in terms of how you access all this other information that's already on premise and a value is through sequel so he's having the hardest time finding those kinds of skills it's a clash of the two worlds coming together you see the sequel guys trying to move into this new world and they're fluent but they also it's just a little bit different it's not that it's a square peg kind of in a round hole they got to figure it out so you're seeing kind of a blending a hybrid developing so you know it's interesting I always like to look at that because we're hearing from everyone we talk to all the big vendors the legacy guys to the new schools like and you know I need developers in the enterprise but it's not your classic old developer that you know that was weaned on you know maybe I have Java but there's other things going on like it's just it's just a major challenge right now and it reminds me of Jeff of the mainframe days back on the days where I run ahead in-house developers you know from whether it's from the cobalt days all the way down to essentially programming in-house apps but now you have a completely different app environment you could literally have thousands of apps in an enterprise so we find that kind of challenging we're trying to get some signal there so we appreciate that anything to add on the standards versus open source debate we had yesterday you know the open source is huge but you have to have some sort of proprietary advantage to build your business have some competitive advantage what's your take on that we always like to say open source is like free like a puppy right puppy's free but a lot more responsibility that comes along with that right in terms of get the Cooper scooper yes and all the training that goes along with it as well so some organizations are going to be happy to bring in open source and and deal with it at that level the reality is I think people are starting to at least acknowledge that yep having open source somewhere in the technology stack is great but you need all the enterprise readiness the enterprise hardening the things that really allow you to bring in the organization and really use it for production use cases I mean if you want to use open source a sort of again the pilot I'll call it the data science experiment you know the lab great but when you really want to try to bring it in you really need to make sure it's ready to be used in the enterprise because if you're betting your business on it it's got to work well it's awesome to have you here I wanted to get the last question for you before we break the next guest is summarize the show this moment in time where we big data SV Silicon Valley you know ton of innovation a lot new startups coming in we saw the show a lot of names we've never heard of some maybe around some might consolidate within the ecosystem and some might break out and be winners on their own valuations are high and some of the private companies and also you see the big guys looking at M&A market so a lot of growth opportunities clearly there's a lot of meat on the bone here so what's your take for the folks out there share with them what is the moment how would you summarize the big story here big data SV and strata conference in Silicon Valley what's the moment about if you look at the past couple years this show strata really appealed I'll call it more to the geek audience I mean it was people here learning about the new technologies this is the first time at a strata event that I've really seen an affliction point where it's really focused now not entirely but we're starting to see that on the business outcomes the business value where people are now wanting to really understand not only can we what you do with the data but what made it transformational you're also seeing for instance some of the sessions here talking about the the ethics and privacy issues as well so I really see this is sort of an affliction point where big data is really maturing we're really moving into what can we do with big data not focused on technology but much more on outcomes Jim thanks for coming in on the cube we appreciate acting in great platform you guys are coming out of the woodwork real smoking hot platform great buzz here at the show congratulations this is the cube big data SV where all the actions happening in Silicon Valley live at the Hilton right across the street from the strata conference we'll be right back with our next guest this is the cube I'm John with Jeff Frick here in live at the Hilton