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Editor’s Note: This is a transcript of a live Webcast. You can view the webcast in full here.
Eric Kavanagh: Ladies and gentleman, it’s time to get wise! It’s time for TechWise, a brand new show! My name is Eric Kavanagh. I’m going to be your moderator for our inaugural episode of TechWise. That’s exactly right. This is a partnership of Techopedia and the Bloor Group, of course, of Inside Analysis fame.
My name is Eric Kavanagh. I will be moderating this really interesting and involved event, folks. We’re going to be digging deep into the weave to understand what is going on with this big thing called Hadoop. What is the elephant in the room? It’s called Hadoop. We’re going to try to figure what it means and what’s going on with it.
First of all, big thank you to our sponsors, GridGain, Actian, Zettaset and DataTorrent. We’ll get a brief few words from each of them near the end of this event. We’ll also have a Q&A, so don’t be shy – send your questions in at any time.
We’ll dig into the details and throw the hard questions at our experts. And speaking of the experts, hey, there they are. So, we’re going to be hearing from our very own Dr. Robin Bloor, and folks, I’m very excited to have the legendary Ray Wang, principal analyst and founder of Constellation Research. He’s online today to give us his thoughts and he’s like Robin that he is incredibly diverse and really focuses on a lot of different areas and has the ability to synthesize them and to really understand what is going on out there in this whole field of information technology and data management.
So, there’s that little cute elephant. He’s at the beginning of the road, as you can see. It’s just beginning now, it’s just kind of starting, this whole Hadoop thing. Of course, back in 2006 or 2007, I suppose, is when it was released to the open-source community, but there have been a lot of things going on, folks. There have been huge developments. In fact, I want to bring up the story, so I’m going to do a quick desktop share, at least I think I am. Let’s do a quick desktop share.
I’m showing you this just crazy, crazy story folks. So Intel invested $740 million to buy 18 percent of Cloudera. I thought and I’m like, "Holy Christmas!" I started doing the math and it’s like, "It’s a valuation of $4.1 billion." Let’s think about this for a second. I mean, if WhatsApp is worth $2 billion, I suppose Cloudera might as well be worth $4.1 billion, right? I mean, why not? Some of these numbers are just out the window these days, folks. I mean, typically in terms of investment, you have EBITDA and all these other various mechanisms, multiples of revenue and so forth. Well, it will be one heck of a multiple of revenue to get to $4.1 billion for Cloudera, which is an awesome company. Don’t get me wrong – there are some very, very smart people over there including the guy who started the whole Hadoop craze, Doug Cutting, he is over there – a lot of very intelligent people that are doing a lot of really, really cool things, but the bottom line is that the $4.1 billion, that’s a lot of money.
So here is kind of a captive obvious moment of going through my head right now which is a chip, Intel. Their chip designers are bringing to see some Hadoop-optimized chip – I have to think so, folks. That’s just my guess. That’s just a rumor, coming from me, if you will, but it kind of makes sense. And what does this all mean?
So here is my theory. What is happening? A lot of this stuff is not new. Massive parallel processing is not terribly new. Parallel processing sure is not new. I’ve been in the world of supercomputing for a while. A lot of these things that are happening are not new, but there is the sort of general awareness that there is a new way to attack some of these problems. What I see happening, if you look at some of the big vendors of Cloudera or Hortonworks and some of these other guys, what they’re doing really if you boil it down to the most granular distilled level is application development. That’s what they’re doing.
They’re designing new applications – some of them involve business analytics; some of them just involve supercharging systems. One of our vendors who have talked about that, they do that kind of stuff all day, on the show today. But if it’s terribly new, again the answer is "not really," but there is big stuff happening, and personally, I think what’s going on with Intel making this huge investment is a market-making move. They look at the world today and see that it’s kind of a monopoly world today. There’s Facebook and they have beaten just the snot out of poor MySpace. LinkedIn has beaten the snot out of poor Who’s Who. So you look around and it’s one service that is dominating all these different spaces in our world today, and I think the idea is Intel is going to throw all their chips on Cloudera and try to elevate it to the top of the stack – that’s just my theory.
So folks, like I said, we are going to have a long Q&A session, so don’t be shy. Send your questions in at any time. You can do so using that Q&A component of your webcast console. And with that, I want to get to our content because we’ve got a lot of stuff to get through.
So, Robin Bloor, let me hand the keys over to you and the floor is yours.
Robin Bloor: OK, Eric, thanks for that. Let’s bring on the dancing elephants. It’s a curious thing, actually, that elephants are the only land mammals that can’t actually jump. All of these elephants in this particular graphic have got at least one foot on the ground, so I suppose it is feasible, but to a certain extent, these are obviously Hadoop elephants, so very, very capable.
The question, really, that I think has to be discussed and has to be discussed in all honesty. It has to be discussed before you go anywhere else, which is to really start talking about what Hadoop actually is.
One of the things that it absolutely is from the man-play basis is key-value store. We used to have key-value stores. We used to have them on IBM mainframe. We had them on the minicomputers; DEC VAX had IMS files. There were ISAM capabilities that were on pretty much every minicomputer you can get your hands on. But sometime around the late ’80s, Unix came in and Unix didn’t actually have any key-value store on it. When Unix developed it, they developed very swiftly. What happened really was that the database vendors, particularly Oracle, went steaming in there and they sold your databases to look after any data that you care to manage on Unix. Windows and Linux turned out to be the same. So, the industry went for best part of 20 years without a general-purpose key-value store. Well, it’s back now. Not only is it back, it’s scalable.
Now, I think really it’s the foundation of what Hadoop really is and to a certain degree, it determines where it’s going to go. What do we like about key-value stores? Those of you who are as old as I am and actually remember working with key-value stores realize that you could pretty much use them to informally set up a database, but only informally. You know the metadata quickly value stores in the program code, but you could actually make that an external file, and you could if you wanted to start treating a key-value store a little like a database. But of course it didn’t have all that recovery capability that a database has and it didn’t have an awful lot of things the databases have now got, but it was a really useful feature for developers and that’s one of the reasons I think that Hadoop has proved so popular – simply because it has been coders, programmers, developers who are quick to [adopt it]. They realized that not only is a key-value of the store but it’s a scale-out key-value store. It scales out pretty much indefinitely. I sent these scales out into thousands of servers, so that’s the really big thing about Hadoop, is that’s what it is.
It also has on top of it MapReduce, which is a parallelization algorithm, but actually that’s, in my opinion, not important. So, you know, Hadoop’s a chameleon. It’s not just a file system. I’ve seen various kinds of claims made for Hadoop: it’s a secret database; it’s no secret database; it’s a common store; it’s an analytical toolbox; it’s an ELT environment; it’s data cleansing tool; it’s a streaming platforms data warehouse; it’s an archive store; it’s a cure for cancer, and so on. Most of these things are really not true for vanilla Hadoop. Hadoop is probably a prototyping – it’s certainly a prototyping environment for a SQL database, but it doesn’t really have, if you put age space with age catalog over Hadoop, you’ve got something that looks like a database, but it’s not really what anyone would call a database in terms of capability. A lot of these capabilities, you can certainly get them on Hadoop. There are certainly a lot of them. In actual fact, you can get some source of Hadoop, but Hadoop itself is not what I would call operationally hardened, and therefore the deal about Hadoop, really I wouldn’t be on anything else, is that you kind of need to have third-party products to enhance it.
So, talking about you can only throw in a few lines as I’m talking Hadoop overreach. First of all, real-time query capability, well you know real-time is kind of business time, really, almost always performance critical otherwise. I mean, why would you engineer for real time? Hadoop doesn’t really do this. It does something that’s near real-time but it doesn’t really do real-time stuff. It does streaming, but it doesn’t do streaming in a way about I would call really mission-critical type application-streaming platforms can do. There’s a difference between a database and a clearable store. Synchronize it to over Hadoop gives you a clearable data store. That’s kind of like a database but it’s not the same as a database. Hadoop in its native form, in my opinion, doesn’t really qualify as a database at all because it’s short of quite a few things a database should have. Hadoop does a lot, but it doesn’t do it particularly well. Again, the capability’s there but we’re a ways away from actually having a fast capability in all of these areas.
The other thing to understand about Hadoop is, it’s kind of come a long way since it was developed. It was developed in the early days; it was developed when we had servers that actually only had one processor per server. We never had multi-core processors and it was built to run over grids, launch grids and severs. One of the design goals of Hadoop was to never lose the work. And that was really about disk failure, because if you have got hundreds of servers, then the likelihood is, if you’ve got disks on the servers, the likelihood is that you will get an uptime availability of something like 99.8. That means that you’ll get on average a failure of one of those servers once every 300 or 350 days, one day in a year. So if you had hundreds of these, the likelihood would be on any day of the year that you’d get a server failure.
Hadoop was built specifically to address that problem – so that, in the event that anything failed, it’s taking snapshots of everything that goes on, on every particular server and it can recover the batch job that’s running. And that was all that actually ever ran on Hadoop was batch jobs and that’s a really useful capability, it has to be said. Some of the batch jobs that were being run – particularly at Yahoo, where I think Hadoop was kind of born – would be running for two or three days, and if it failed after a day, you really didn’t want to lose the work that had been done. So that was the design point behind the availability on Hadoop. You wouldn’t call that high availability, but you could call it high availability for serial batch jobs. That’s probably the way to look at it. High availability is always configured according to work line characteristics. At the moment, Hadoop can only be configured for really serial batch jobs as regard to that kind of recovery. Enterprise high availability is probably best thought in terms of transactional LLP. I believe that if you’re not looking at it as kind of a real-time thing, Hadoop doesn’t do that yet. It’s probably a long ways away from doing that.
But here’s the beautiful thing about Hadoop. That graphic on the right-hand side that has got a list of vendors around the edge and all of the lines on it indicate connections between those vendors and other products in the Hadoop ecosystem. If you look at that, that is an incredibly impressive ecosystem. It’s quite remarkable. We obviously, we talk to a lot of vendors in terms of their capabilities. Amongst the vendors I’ve talked to, there are some really extraordinary capabilities of using Hadoop and in-memory, way of using Hadoop as a compressed archive, of using Hadoop as an ETL environment, and so on and so forth. But really, if you add the product to Hadoop itself, it works extremely well in a particular space. So while I’m being critical of native Hadoop, I’m not critical of Hadoop when you actually add some power to it. In my opinion, Hadoop’s popularity kind of guarantees its future. By that I mean, even if every line of code that’s written so far on Hadoop disappears, I don’t believe that the HDFS API will disappear. In other words, I think the file system, API, is here to stay, and possibly YARN, the scheduler that looks over it.
When you actually look at that, that’s a very important capability and I’ll kind of wax on about that in a minute, but the other thing that is, let’s say, exciting people about Hadoop is the whole open-source picture. So it’s worth going through what the open-source picture is in terms of what I regard as real capability. While Hadoop and all its components can certainly do what we call data lengths – or as I prefer to call it, a data reservoir – it’s certainly a very good staging area to drop data into the organization or to collect data in the organization – extremely good for sandboxes and for angling data. It’s very good as a prototyping development platform which you might implement at the end of the day, but you know as a development environment pretty much everything you want is there. As an archive store, it’s pretty much got everything you need, and of course it’s not expensive. I don’t think we should divorce either of these two things from Hadoop even though they’re not formally, if you like, components of Hadoop. The online wedge[?] has brought a vast amount of analytics into the open-source world and a lot of that analytics is now being run on Hadoop because that gives you a convenient environment in which you can actually take a lot of external data and just start playing at an analytical sandbox.
And then you’ve got the [?] open-source capabilities, both of which are machine learning. Both of those are extremely powerful in the sense that they implement powerful analytic algorithms. If you put these things together, you’ve got the kernels of some very, very important capability, which is in one way or another very likely to – whether it develops on its own or whether vendors come in to fill in the missing pieces – it’s very likely to continue for a long time and certainly I think the machine learning is already having a very big impact on the world.
The evolution of Hadoop, YARN changed everything. What had happened was MapReduce was pretty much welded to the early file system HDFS. When YARN was introduced, it created a scheduling capability in its first release. You don’t expect the extremely sophisticated scheduling from first release, but it did mean that it was now no longer necessarily a patch environment. It was an environment in which multiple jobs could be scheduled. As soon as that happened, there was a whole series of vendors who had kept away from Hadoop – they just came in and connected to it because then they could just look at it as the scheduling environment over a file system and they could address stuff to it. There are even database vendors that have implemented their databases on HDFS, because they just take the engine and just put it over at HDFS. With cascading and with YARN, it becomes a very interesting environment because you can create complex workflows over HDFS and this really means that you can start thinking of it as really a platform that can be running multiple jobs concurrently and is pushing itself towards the point of doing mission-critical stuff. If you’re going to do that, you’re going to probably need to buy some third-party components like security and so on and so forth, which Hadoop doesn’t actually have an audit account to fill in the gaps, but you get into the point where even with native open source you can do some interesting things.
In terms of where I think Hadoop is actually going to go, I personally believe that HDFS is going to become a default scale-out file system and therefore is going to become the OS, the operating system, for the grid for data flow. I think it has got a huge future in that and I don’t think it will be stopping there. And I think in actual fact the ecosystem just helps because pretty much everybody, all the vendors in the space, are actually integrating Hadoop in one way or another and they’re just enabling it. In terms of another point worth making, in terms of Hadoop overage, is it is not a very good platform plus the parallelization. If you actually look at what it’s doing, what it’s actually doing is it’s taking a snapshot regularly on every server as it’s executing its MapReduce jobs. If you were going to design for really fast parallelization, you wouldn’t be doing anything like that. In actual fact, you probably wouldn’t be using MapReduce on its own. MapReduce is only what I would say half capable of parallelism.
There are two approaches to parallelism: one is by pipelining processes and the other is by dividing data MapReduce and it does the division of data so there are a lot of jobs where MapReduce would not actually be the fastest way to do it, but it will give you parallelism and there’s no taking away from that. When you’ve got a lot of data, that kind of power isn’t usually as useful. YARN, as I’ve already said, is a very young scheduling capability.
Hadoop is, kind of drawing the line in the sand here, Hadoop is not a data warehouse. It’s so far from being a data warehouse that it’s almost an absurd suggestion to say that it is. In this diagram, what I’m showing along the top is a kind of data flow, going from a Hadoop data reservoir into a gargantuan scale-out database which is what we’ll actually do, an enterprise data warehouse. I’m showing legacy databases, feeding data into the data warehouse and offload activity creating offload databases from the data warehouse, but that is actually a picture that I’m starting to see emerge, and I would say this is like the first generation of what happens to the data warehouse with Hadoop. But if you look at the data warehouse yourself, you realize that underneath the data warehouse, you’ve got an optimizer. You’ve got distributed query workers over very many processes sitting over perhaps very many large number of disks. That’s what happens in a data warehouse. That’s actually kind of architecture that’s built for a data warehouse and it takes a good long time to build something like that, and Hadoop doesn’t have any of that at all. So Hadoop is not a data warehouse and it isn’t going to become one, in my opinion, anytime soon.
It does have this relative data reservoir, and it kind of looks interesting if you just look at the world as a series of events flowing into the organization. That’s what I’m showing on the left-hand side of this diagram. Having it go through a filtering and routing capability and the stuff that needs to go for streaming gets siphoned off of the streaming apps and everything else goes straight into the data reservoir where it’s prepared and cleansed, and then passed by ETL to either a single data warehouse or a logical data warehouse consisting of multiple engines. This is, in my opinion, a natural development line for Hadoop.
In terms of the ETW, one of the things that is worth kind of pointing out is that the data warehouse itself was actually moved – it’s not what it was. Certainly, nowadays, you expect there to be a hierarchical capability per hierarchical data of what people, or some people, call the documents in the data warehouse. That’s JSON. Possibly, network queries that’s graph databases, possibly analytics. So, what we’re moving towards is an ETW that has actually got a more complex workload than the ones that we’re used to. So that’s kind of interesting because in a way it means that the data warehouse is getting even more sophisticated, and because of that, it’s going to be even a longer time before Hadoop gets anywhere close to it. The meaning of data warehouse is extending, but it still includes optimization. You have to have an optimization capability, not just over queries now but over all of these activities.
That’s it, really. That’s all I wanted to say about Hadoop. I think I can hand on to Ray, who hasn’t got any slides, but he’s always good at talking.
Eric Kavanagh: I’ll take the slides. There’s our friend, Ray Wang. So, Ray, what are your thoughts on all this?
Ray Wang: Now, I think that was probably one of the most succinct and great histories of key-value stores and where Hadoop has gone in relationship to enterprise that are out, so I always learn a lot when listening to Robin.
Actually, I do have one slide. I can pop up one slide here.
Eric Kavanagh: Just go ahead and click on the, click start and go to share your desktop.
Ray Wang: Got it, there you go. I’ll actually share. You can see the app itself. Let’s see how it goes.
All this talk about Hadoop and then we go deep into the conversation about the technologies that are there and where Hadoop is heading, and a lot of times I just like to take it back up to really have the business discussion. A lot of the stuff that’s happening on the technology side is really this piece where we’ve been talking about data warehouses, information management, data quality, mastering that data, and so we tend to see this. So if you look at this graph here on the very bottom, that’s very interesting that the types of individuals we bump into that talk about Hadoop. We have the technologists and the data scientists that are geeking out, having lots of excitement, and it’s typically about data sources, right? How do we master the data sources? How do we get this into the right levels of quality? What do we do about the governance? What can we do to match different types of sources? How do we keep lineage? And all that kind of discussion. And how do we get more SQL out of our Hadoop? So that part is happening at this level.
Then at the information and orchestration side, this is where it gets interesting. We’re starting to tie the outputs of this insight that we’re getting or are we pulling it from back to business processes? How do we tie it back to any kind of metadata models? Are we connecting the dots between objects? And so the new verbs and discussions about how we use that data, moving from what we traditionally are in a world of CRUD: create, read, update, delete, to a world that is discussing about how do we engage or share or collaborate or like or pull something.
That’s where we’re starting to see a lot of the excitement and innovation, especially about how to pull this information and bring it to value. That is the technology-driven discussion below the red line. Above that red line, we’re getting the very questions that we always wanted to ask and one of them that we always bring up is like, for example, maybe the question in retail for you is like, "Why are red sweaters selling better in Alabama than blue sweaters in Michigan?" You could think about it and say, "That’s kind of interesting." You see that pattern. We ask that question, and we wonder, "Hey, what are we doing?" Maybe it’s about state schools – Michigan versus Alabama. OK, I get this, I see where we’re going. And so we’re starting to get the business side of the house, people in finance, people who have got traditional BI capabilities, people in marketing, and people in HR saying, "Where are my patterns?" How do we get to those patterns? And so we see another way of innovation on the Hadoop side. It’s really about how do we surface update insights faster. How do we make these kinds of connections? It goes all the way to the folks who are doing like, ad:tech that basically trying to connect ads and relevant content from anything from real-time bidding networks to contextual ads and ads placement and doing that on the fly.
So it’s interesting to watch this. You see the progression of Hadoop from, "Hey, here is the technology solution. Here is what we need to do to get this information out to people." Then as it crosses over the line of business portion, this is where it gets interesting. It’s the insight. Where is the performance? Where is the deduction? How are we predicting things? How do we take influence? And then bring that to that last level where we actually see another set of Hadoop innovations that are happening around decision systems and actions. What’s the next best action? So you know blue sweaters are selling better in Michigan. You’re sitting on a ton of blue sweaters in Alabama. The obvious thing is, "Yeah, well let’s get this shipped out there." How do we do it? What’s the next step? How do we tie that back in? Maybe the next best action, maybe it’s a suggestion, maybe it’s something that helps you prevent an issue, maybe it’s no action either, which is an action in itself. So we start seeing this kind of patterns emerge. And the beauty of this back to what you’re saying about key-value stores, Robin, is that it’s happening so fast. It’s happening in the way that we haven’t been thinking about it this way.
Probably I’d say in the last five years we picked up. We started thinking in terms of how we can leverage key-value stores again, but it’s just in the last five years, people are looking at this very differently and it’s like technology cycles are repeating itself in 40-year patterns, so this is kind of a funny thing where we’re looking at cloud and I’m just like mainframe time sharing. We’re looking at Hadoop and like key-value store – maybe it’s a data mart, less than a data warehouse – and so we start seeing these patterns again. What I’m trying to do right now is think about what were people doing 40 years ago? What approaches and techniques and methodologies were being applied that were limited by the technologies people had? That’s kind of driving this thought process. So as we go through the larger picture of Hadoop as a tool, when we go back and think about the business implications, this is kind of the path that we usually take people through so you can see what pieces, what parts are in the data decisions pathway. It’s just something that I wanted to share. It’s kind of a thinking that we’ve been using internally and hopefully adds to the discussion. So I’ll turn it over back to you, Eric.
Eric Kavanagh: That’s fantastic. If you can stick around for some Q&A. But I liked that you took it back to the business level because at the end of the day, it’s all about the business. It’s all about getting things done and making sure that you’re spending money wisely and that is one of the questions I saw already, so speakers may want to think about what is the TCL of going the Hadoop route. There is some sweet spot in between, for example, using office shelf tools to do things in some traditional way and using the new sets of tools, because again, think about it, a lot of this stuff is not new, it’s just sort of coalescing in a new way is, I guess, the best way to put it.
So let’s go ahead and introduce our friend, Nikita Ivanov. He is the founder and CEO of GridGain. Nikita, I’m going to go ahead and hand the keys to you, and I believe you’re out there. Can you hear me Nikita?
Nikita Ivanov: Yes, I’m here.
Eric Kavanagh: Excellent. So the floor is yours. Click on that slide. Use the down arrow, and take it away. Five minutes.
Nikita Ivanov: Which slide do I click?
Eric Kavanagh: Just click anywhere on that slide and then you use the down arrow on the keyboard to move. Just click on the slide itself and use the down arrow.
Nikita Ivanov: Alright so just a few quick slides about GridGain. What do we do in the context of this conversation? GridGain basically produce an in-memory computing software and part of the platform that we developed is in-memory Hadoop accelerator. In terms of Hadoop, we tend to think about ourselves as the Hadoop performance specialists. What we do, essentially, on top of our core in-memory computing platform that consists of technologies like data grid, memory streaming and computation grids would be able to plug-and-play Hadoop accelerator. That is very simple. It would be nice if we can develop some kind of plug-and-play solution that can be installed right in the Hadoop installation. If you, the developer of MapReduce, does need a boost without any need to write any new software or change of code or change, or basically have an all minimal configuration change in Hadoop cluster. That’s what we developed.
Fundamentally, the in-memory Hadoop accelerator is based on optimizing two components in the Hadoop ecosystem. If you think about Hadoop, it’s predominantly based on HDFS, which is the file system. The MapReduce, which is the framework to run the competitions in parallel on top of the file system. In order to optimize the Hadoop, we optimize both of these systems. We developed in-memory file system that is completely compatible, 100% compatible plug-and-play, with HDFS. You can run instead of HDFS, you can run on top of HDFS. And we also developed in-memory MapReduce that is plug-and-play compatible with Hadoop MapReduce, but there are a lot of optimizations on how the work flow of MapReduce and how the schedule on the MapReduce works.
If you look, for example on this slide, where we show the kind of stack of duplication. On the left side, you have your typical operating system with GDM and on top of this diagram you have the application center. In the middle you have the Hadoop. And Hadoop is again based on HDFS and the MapReduce. So this does represent on this diagram, that what’s what we’re kind of embedding into the Hadoop stack. Again, it’s plug-and-play; you don’t have to change any code. It just works the same way. On the next slide, we showed essentially how we optimized the MapReduce workflow. That’s probably the most interesting part because it gives you the most advantage when you run the MapReduce jobs.
The typical MapReduce, when you’re submitting the job, and on the left side there’s diagram, there’s usual application. So typically you are submitting the job and the job goes to a job tracker. It interacts with the Hadoop name node and the name node is actually the piece of software that manages the interaction with the digital files, and kind of keeps the directory of files and then the job tracker interacts with the task tracker on each individual node and the task tracker interacts with a Hadoop data node to get data from. So that’s basically a very kind of high-level overview of how your MapReduce job gets in the computers. As you can see what we do with our in-memory, Hadoop MapReduce will already completely bypass all this complex scheduling that takes a lot of time off your execution and go directly from client to GridGain data node and GridGain data node keeps all that e-memory for a blatantly fast, fast execution.
So all in all basically, we allow it to get anywhere from 5x up all the way to 100x performance increase on certain types of loads, especially for short leaf payloads where you literally measure every second. We can give you a dramatic boost in performance with literally no core change.
Alright, that’s all for me.
Eric Kavanagh: Yes, stick around for the Q&A. No doubt about it.
Let me hand it off to John Santaferraro. John, just click on that slide. Use the down arrow to move on.
John Santaferraro: Alright. Thanks a lot, Eric.
My perspective and Actian’s perspective really is that Hadoop is really about creating value and so this is an example from digital media. A lot of the data that is pumping into Hadoop right now has to do with digital media, digital marketing, and customer, so there is great opportunity – 226 billion dollars of retail purchases will be made online next year. Big data and Hadoop is about capturing new data to give you insight to get your share of that. How do you drive 14% higher marketing return and profits based on figuring out the right medium X and the right channels and the right digital marketing plan? How do you improve overall return on marketing investment? By the way, in 2017, what we ought to be thinking about when we look at Hadoop is the fact that CMO, chief marketing officer, spending in 2017 will outpace that of IT spending, and so it really is about driving value. Our view is that there are all kinds of noise being made on the left-hand side of this diagram, the data pouring into Hadoop.
Ultimately, our customers are wanting to create customer delight, competitive advantage, world-class risk management, disruptive new business models, and to do all of that to deliver transformational value. They are looking to capture all of this data in Hadoop and be able to do best-in-class kinds of things like discovery on that data without any limitations, no latency at any scale of the data that lives in there – moving from reactive to predictive kinds of analytics and doing everything dynamically instead of looking at data just as static. What pours into Hadoop? How do you analyze it when it arrives? Where do you put it to get the high-performance analytics? And ultimately moving everything down to a segment of one.
So what we’ve done at Actian in the Actian Analytics Platform, we have built an exoskeleton around Hadoop to give it all of these capabilities that you need so you are able to connect to any data source bringing it into Hadoop, delivering it as a data service wherever you need it. We have libraries of analytics and data blending and data enrichment kinds of operators that you literally drag and drop them so that you can build out these data and analytic workflows, and without ever doing any programming, we will push that workload via YARN right down to the Hadoop nodes so you can do high-performance data science natively on Hadoop. So all of your data prep, all of your data science happening on Hadoop highly parallelized, highly optimized, highly performance and then when you need to, you move it to the right via a high-speed connection over to our high-performance analytic engine, where you can do super-low latency kinds of analytics, and all of that delivering out these real-time kinds of analytics to users, machine-to-machine kinds of communication, and betting those on analytics and business processes, feeding big data apps or applications.
This is an example of telco churn, where at the top of this chart if you’re just building telco churn for example, where you have captured one kind of data and poured that into Hadoop, I’d be able to identify about 5% of your potential churn audience. As you move down this chart and add additional kinds of data sources, you do more complex kinds of analytics in the center column there. It allows you to act against that churn in a way that allows you to identify. You move from 5% identification up to 70% identification. So for telecommunications companies, for retail organizations, for any of the fast providers, anybody that has a customer base where there is a fear and a damage that is caused by churn.
This kind of analytics running on top of that exoskeleton-enabled version of Hadoop is what drives real value. What you can see here is that kind of value. This is an example taken from off of the annual report of a telecommunications company that shows their actual total subscribers, 32 million. Their existing churn rate which every telco reports 1.14, 4.3 million subscribers lost every year, costing them 1.14 billion dollars as well as 2.1 billion in revenue. This is a very modest example of how you generate value out of your data that lives in Hadoop, where you can see the potential cost of reacquisition where the potential here is to use Hadoop with the exoskeleton running analytics to basically help this telecommunications company save 160 million dollars as well as avoid 294 million in loss. That’s the kind of example that we think is driving Hadoop forward.
Eric Kavangh: Alright, fantastic. And Jim, let me go ahead and give the keys to you. So, Jim Vogt. If you would click on that slide and use the down arrow in your keyboard.
Jim Vogt: I got it. Great picture. OK, thank you very much. I’ll tell a little bit about Zettaset. We’ve been talking about Hadoop all afternoon here. What’s interesting about our company is that we basically spend our careers hardening new technology for the enterprise – being able to plug the gaps, if you will, in our new technology to allow it to be widely deployed within our enterprise operational environment. There are a couple of things happening in the market right now. It’s kind of like a big open pool party, right? But now the parents have come home. And basically we’re trying to bring this thing back to some sense of reality in terms of how you build a real infrastructure piece here that can be scalable, repeatable, non-resource intensive, and secure, most importantly secure. In the marketplace today, most people are still checking the tires on Hadoop. The main reason is, there is a couple of things. One is that within the open source itself, although it does some very useful things in terms of being able to blend data sources, being able to find structure data and very useful data sources, it really lacks for a lot of the hardening and enterprise features around security, higher availability and repeatability that people need to deploy not just a 10- or 20-node cluster, but a 2,000- and 20,000-node cluster – there are multiple clusters. What has been monetized in the last two years has been mainly pro-services around setting up these eval clusters. So there is a not a repeatable software process to actually actively deploy this into the marketplace.
So what we built in our software is a couple of things. We’re actually transparent into the distributions. At the end of the day, we don’t care if it’s CVH or HDP, it’s all open source. If you look at the raw Apache components that built those distributions, there is really no reason why you have to lock yourself into any one distribution. And so, we work across distributions.
The other thing is that we fill in the gaps transparently in terms of some of the things that are missing within the code itself, the open source. So we talked about HA. HA is great in terms of making no failover, but what happens if any of the active processes that you’re putting on these clusters fail? That could take it down or create a security hole, if you will. When we built software components into our solution, they all fall under an HA umbrella where we’re actively monitoring all the processes running on the cluster. If code roles goes down, you take the cluster down, so basically, meaning no failover is great, unless you’re actively monitoring all the processes running on the cluster, you don’t have true HA. And so that’s essential of what we developed here at Zettaset. And in a way that we’ve actually got a patent that has been issued on this and granted last November around this HA approach which is just quite novel and different from the open-source version and is much more hardened for the enterprise.
The second piece is being able to do real RBAC. People are talking about RBAC. They talk about other open-source projects. Why should you have to recreate all those entries and all those users and roles when they already exist in LDAP or in active directory? So we link those transparently and we fold all our processes not only under this RBAC umbrella, but also under the HA umbrella. They start to layer into this infrastructure encryption, encryption at data rest, state of motion, all the hardened security pieces that you really need to secure the information.
What is really driving this is our industries, which I have on the next slide, which profit finance and healthcare and have our compliances. You have to be able to protect this sets of data and you have to be able to do it on a very dynamic fashion because this data can be sitting anywhere across these parallel nodes and clusters and it can be duplicated and so forth, so essentially that’s the big umbrella that we built. The last piece that people need is they need to be able to put the pieces together. So having the analytics that John talked to and being able to get value out of data and do that through an open interface tapped into this infrastructure, that’s what we built in our software.
So the three cases that I had in here, and you guys are popping me along here were really around finance, healthcare and also cloud, where you’re having to deal with multi-tenant environments and essentially have to separate people’s sensitive data, so security and performance are key to this type of application whether its cloud or in a sensitive data environment.
The last slide here really talks to this infrastructure that we put together as a company is not just specific to Hadoop. It’s something that we can equally apply to other NoSQL technologies and that’s where we’re taking our company forward. And then we’re also going to pull in other open-source components, HBase and so forth, and secure those within that infrastructure in a way that you’re not tied to any one distribution. It’s like you truly have an open, secure and robust infrastructure for the enterprise. So that’s what we’re about and that’s what we’re doing to basically accelerate adoption of Hadoop so people get away from sending twenty-node clusters and actually have the confidence to employ a much larger environment that is more eyes on Hadoop and speeds the market along. Thank you.
Eric Kavanagh: That’s fantastic, great. Stick around for the Q&A. Finally, last but not the least, we’ve got Phu Hoang, CEO of DataTorrent. Let me go ahead and hand the keys to you. The keys are now yours. Click anywhere on that slide, use the down arrow on your keyboard to move them along.
Phu Hoang: Thank you so much.
So yes, I’m here to talk about DataTorrent and I actually think the story of DataTorrent is a great example of what Robin and Ray have been talking about through this session where they say that Hadoop is a great body of work, a great foundation. But it has a lot of goals. But the future is bright because the Hadoop ecosystem where more players are coming in are able to build and add value on top of that foundation to really bring it from storage to insights to action, and really that’s the story of DataTorrent.
What I’m going to talk about today is really about real-time big data screening processing. What you see, as I’m interacting with customers, I’ve never met a single customer that says to me, "Hey, my goal is to take action hours or days after my business events arrive." In fact, they all say they want to take action immediately after the events occur. The problem with the delay is that, that is what Hadoop is today with its MapReduce paradigm. To understand why, it’s worth revisiting the history of Hadoop.
I was leading much of Yahoo engineering when we hired Doug Cutting, the creator of Hadoop, and assigned over a hundred engineers to build out Hadoop to power our web search, advertising and data science processing. But Hadoop was built really as a back system to read and write and process these very large files. So while it’s great disruptive technology because of its massive scalability and high ability at no cost, it has a hole in that there is a lot of latency to process these large files. Now, it is fair to say that Hadoop is now becoming the plateau operating system that is truly computing and is gaining wide adoption across many enterprises. They are still using that same process of collecting events into large files, running these batch Hadoop jobs to get there inside the next day. What enterprise customers now want is that they want those exact same insights but they want to build to get these insights much earlier, and this will enable them to really act on these events as the event happens, not after maybe hours later after it has been back processed.
Eric Kavanagh: Do you want to be moving your slides forward, just out of curiosity?
Phu Hoang: Yeah it’s coming now. Let me illustrate that one example. In this example, using Hadoop in back-slope where you’re constantly engaging with files, first an organization might accumulate all the events for the full day, 24 hours’ worth of data. And then they batch process it, which may take another eight hours using MapReduce, and so now there is 32 hours of elapsed time before they get any insight. But with real-time stream processing, the events are coming in and are getting processed immediately, there is no accumulation time. Because we do all this processing, all in memory, the in-memory processing is also sub-second. All the time, you are reducing the elapsed time on 30 hours plus to something that is very small. If you’re reducing 30 hours to 10 hours, that’s valuable but if we can reduce it to a second, something profound happens. You can now act on your event while the event is still happening, and this gives enterprises the ability to understand what their products are doing, what their business is doing, what their users are doing in real time and react to it.
Let’s take a look at how this happens. Really, a combination of market forces and technology has enabled a solution like DataTorrent to come together, so from a market perspective, Hadoop is really becoming the de facto big data architecture as we said, right? In an IDC study in 2013, they say that by the end of this year, two-thirds of enterprises would have deployed Hadoop and for DataTorrent, whether that’s Apache Hadoop or any of our certified partners like Cloudera or Hortonworks, Hadoop is really clearly the choice for enterprise. From a technology perspective, and I think Robin and Ray alluded to this, Hadoop 2.0 was created to really enable Hadoop to extend to much more general cases than the batch MapReduce paradigm, and my co-founder, Amal, who was at Yahoo leading the development of Hadoop 2.0 really allows this layer of OS to have many more computation paradigms on top of it and real-time streaming is what we chose. By putting this layer of real-time streaming on top of YARN, you can really think of DataTorrent as the real-time equivalent of MapReduce. Whatever you can do in batch with MapReduce, you can now do in streaming with DataTorrent and we can process massive amount of data. We can slice and dice data in multiple dimensions. We have distributed computing and use YARN to give us resources. We have the full ecosystem of the open source Hadoop to enable fast application development.
Let me talk a little bit about the active capabilities of DataTorrent. In five minutes, it is hard for me to kind of give to you much in detail, but let me just discuss and re-differentiate it. First of all, sub-second scalable ingestions, right? This refers to DataTorrent’s platform to be able to take that in real-time from hundreds of data sources and begin to process them immediately. This is in direct contact to the back processing of MapReduce that is in Hadoop 1.0 and events can vary in size. They may be as simple as a line in the log file or they may be much more complex like CDR, call data record in the telcom industry. DataTorrent is able to scale the ingestion dynamically up or down depending on the incoming load, and we can deal with tens of millions of incoming events per second. The other major thing here, of course, is the processing itself which is in real-time ETL logic. So once the data is in motion, it is going to go into the ETL logic where you are doing a stack transform and load, and so on. And the logic is really executed by combining a series of what we call operators connected together in a data flow grab. We have open source of over 400 operators today to allow you to build applications very quickly. And they cover everything from input connectors to all kinds of message process to database drivers and connectors where you are to load to all kinds of information to unstream.
The combination of doing all these in memory and building the scale across hundreds of nodes really drive the superior performance. DataTorrent is able to process billions of events per second with sub-second latency.
The last piece that I’d like to highlight is the high-availability architecture. DataTorrent’s platform is fully post knowledge; that means that the platform automatically buffers the event and regularly checkpoints the state of the operators on the disk to ensure that there is possibly no problem. The applications can tell you in seconds with no data log and no human intervention. Simply put, data form processes billions of events and allots in data in seconds, it runs 24/7 and it never, ever goes down. The capabilities really set DataTorrent apart from the market and really make it the leading mission-critical, real-time analytics platform for enterprise. With that, we invite you to come visit our website and check us out.
Thanks.
Eric Kavanagh: Yeah, thank you so much. I’ll throw a question over to you, really a comment, and let you kind of expound upon it. I really think you’re on the ball here with this concept of turning over these operators and letting people use these operators almost like Legos to build big data applications. Can you kind of talk about what goes into the process of taking these operators and stitching them together, how do you actually do that?
Phu Hoang: That’s a great question. So first of all, these operators are in your standard application Java Logic. We supply 400 of them. They do all kinds of processing and so to build your application, you really are just connecting operators together into a data flow graph. In our customers, we find that they use a number of operators that we have in our library as well as they take their own job of custom logic and make it an operator so that they can substantiate that into a graph.
Eric Kavanagh: OK, good. I think it’s a good segue to bring in John Santaferraro from Actian because you guys have a slightly similar approach, it seems to me, in opening up a sort of management layer to be able to play around with different operators. Can you talk about what you do with respect to what tools we’re just talking about, John?
John Santaferraro: Yeah, exactly. We have a library of analytics operators as well as transformational operators, operators for blending and enriching data and it is very similar. You use a drag-and-drop interface to be able to stitch together these data flows or work flows, and even analytic workflows. So it’s everything from being able to connect to data, to be able to blend and enrich data, to be able to run data science or machine learning algorithms and then even being able to push that into a high-performance low-latency analytic engine. What we find is that it’s all built on the open-source nine project. So we capture a lot of the operators that they are developing and then we take all of that, and via YARN, very similar to what Phu described at DataTorrent, we push that down so that it is parallelized against all of the nodes in a Hadoop cluster. A lot of it is about making the data in Hadoop much more accessible to business users and less-skilled workers, somebody besides a data scientist.
Eric Kavanagh: OK, let me go bring in Nikita once again. I’m going to throw your five up as well. Can you kind of talk about how you approach this solution vis-à-vis what these two gentlemen just talked about? How does someone actually put this stuff together and make use from GridGain?
Nikita Ivanov: Well, I think the biggest difference between us and from practically the rest of them is we don’t require you to do any recording – you don’t have to do anything, it’s a plug-and-play. If you have an application today, it’s going to work faster. You don’t have to change code; you don’t have to do anything; you just have to install GridGain along the side of Hadoop cluster and that’s it. So that’s the biggest difference and we talked to our customers. There are different myriad of solutions today that ask you to change something: programming, doing your API, using your interfaces and whatnot. Ours is very simple. You don’t need to invest a lot of time into the Hadoop ecosystem, and whatever you used to do, the MapReduce or any of the tools continue to use. With GridGain, you don’t have to change any single line of code, it’s just going to work faster. That’s the biggest difference and that’s the biggest message for us.
Eric Kavanagh: Let’s get Jim back in here too. Jim, your quote is killing me. I had to write it down in between that. I’ll put it into some kind of deck, but the Hadoop ecosystem right now is like a pool party and the parents just came home. That is funny stuff man; that is brilliant. Can you kind of talk about how you guys come onto the scene? How do you actually implement this? How long does that take? How does all that work?
Jim Kaskade: Yes. So there are a couple of varieties depending on the target customer, but typically these days, you see evaluations where security is factored in, in some of these hardening requirements that I talked about. What has happened in some other cases, and especially last year where people had big plans to deploy, is that there was kind of a science project, if you will, or somebody was playing with the technology and had a cluster up and working and was working with it but then the security guy shows up, and if it is going to go on a live data center, it has to basically comply with the same requirements that we have for other equipment running in the data center, if it is going to be an infrastructure that we build out. Last year, we had even some banks that told us they were going to deploy 400 to 1,000 nodes last year and they’re still sitting on a 20-node cluster mainly because now a security person has been plugged in. They’ve got to be worried about financial compliance, about sets of information that is sitting on a cluster, and so forth. It varies by customer, but typically this is kind of what elongates the cycles and this is typical of a new technology where if you really want to deploy this in production environment, it really has to have some of these other pieces including the very valuable open-source pieces, right?
Eric Kavanagh: OK, good. Let’s see. I’m going to bring Phu back into the equation here. We’ve got a good question for you. One of the attendees is asking how is DataTorrent different from Storm or Kafka or the Redis infrastructure. Phu, are you out there? Hey, Phu, can you hear me? Maybe I’m mute.
Let’s bring Ray Wang back into this. Ray, you’ve seen a lot of these technologies and looked at how they worked. I really love this concept of turning over control or giving control to end users of the operators. I like to think of them as like really powerful Legos that they can use to kind of build some of these applications. Can you comment on that? What do you think about all that?
Ray Wang: Coming from my technical background, I’d say I’m scared – I was scared shitless! But honestly, I think it’s important, I mean, in order to get scale. There’s no way you can only put so many requests. Think about the old way we did data warehousing. In the business I had to file the request for a report so that they could match all the schemes. I mean, it’s ridiculous. So we do have to get to a way for the business side of the house and definitely become data jocks. We actually think that in this world, we’re going to see more digital artists and people that have the right skills, but also understand how to take that data and translate that into business value. And so these digital artisans, data artisans depending on how you look at this, are going to need both really by first having the curiosity and the right set of questions, but also the knowledge to know when the data set stinks. If I’m getting a false positive or a false negative, why is that happening?
I think a basic level of stats, a basic level of analytics, understanding that there’s going to be some training required. But I don’t think it’s going to be too hard. I think if you get the right folks that should be able to happen. You can’t democratize the whole decision-making process. I see that happening. We see that in a lot of companies. Some are financial services clients are doing that. Some of our retail folks are doing that, especially in the razor-thin margins that you are seeing in retail. I was definitely seeing that in high tech just around here in the valley. That’s just kind of how people are. It’s emerging that way but it’s going to take some time because these basic data skills are still lacking. And I think we need to combine that with some of the stuff that some of these guys are doing here on this webinar.
Eric Kavanagh: Well, you bring up a really good point. Like how many controls you want to give to the average end user. You don’t want to give an airplane cockpit to someone who’s driving a car for the first time. You want to be able to closely control what they have control over. I guess my excitement kind of stems around being able to do things yourself, but the key is you got to put the right person in that cockpit. You got to have someone who really knows what they’re doing. No matter what you hear from the vendor community folks, when somebody’s more powerful tools are extremely complex, I mean if you are talking about putting together a string of 13, 14, 15 operators to do a particular type of transformation on your data, there are not many people who could do that well. I think we’re going to have many, many more people who do that well because the tools are out there now and you can play with the stuff, and there is going to be a drive to be able to perfect that process or at least get good at it.
We did actually lose Phu, but he’s back on the line now. So, Phu, the question for you is how is DataTorrent different from, like, Storm or Kafka or Redis or some of these others?
Phu Hoang: I think that’s a great question. So, Redis of course is really an in-memory data store and we connect to Redis. We see ourselves as really a processing engine of data, of streaming data. Kafka again is a great bus messaging bus we use. It’s actually one of our favorite messaging bus, but someone has to do the big data processing across hundreds of nodes that is fault tolerant, that is scalable, and I repeat that as the job that we play. So, yes, we are similar to Storm, but I think that Storm is really developed a long time ago even before Hadoop, and it doesn’t have the enterprise-level thinking about scalability to the hundreds and millions, now even billions of events, nor does it really have the HA capability that I think enterprise requires.
Eric Kavanagh: Great. And you know, speaking of HA, I’ll use that as an excuse to bring Robin Bloor back into the conversation. We just talked about this yesterday. What do you mean by high availability? What do you mean by fault tolerance? What do you mean by real time, for example? These are terms that can be bent. We see this all time in the world of enterprise technology. It’s a good term that other people kind of glom onto and use and co-opt and move around and then suddenly things don’t mean quite what they used to. You know, Robin, one of my pet peeves is this whole universe of VOIP. It’s like "Why would we go down in quality? Isn’t it important to understand what people say to you and why that matters?" But I’ll just ask you to kind of comment on what you think. I’m still laughing about Ray’s comment that he’s scared shitless about giving these people. What do you think about that?
Ray Wang: Oh, I think it’s a Spider-man problem, isn’t it? With great power comes great responsibility. You really, in terms of the capabilities out there, I mean it changed me actually a long time ago. You know, I would give my ITs some of the capabilities that they have gotten now. We used to do it extraordinary amounts of what I would say was grunt work that the machines do right now and do it in parallel. They do things that we could never have imagined. I mean we would have understood mathematically, but we could never imagine doing. But there is some people understand data and Ray is completely right about this. The reason to be scared is that people will actually start getting wrong conclusions, that they will wrangle with the data and they will apply something extremely powerful and it will appear to suggest something and they will believe it without actually even being able to do anything as simple as have somebody doing audit on whether their result is actually a valid result. We used to do this all the time in the insurance company I used to work for. If anybody did any work, somebody always checks. Everything was checked by at least one person against the person who did it. These environments, the software is extremely strong but you got to have the discipline around it to use it properly. Otherwise, there’ll be tears before bedtime, won’t there?
Eric Kavanagh: I love that quote, that’s awesome. Let me see. I’m going to go ahead and throw just for this slide up here from GridGain, can you talk about, Nikita, when you come in to play, how do you actually get these application super charged? I mean, I understand what you are doing, but what does the process look like to actually get you embedded, to get you woven in and to get all that stuff running?
Nikita Ivanov: Well, the process is relatively simple. You essentially just need to install GridGain and make a small configuration change, just to let Hadoop know that there is now the HDFS if you want to use HDFS and you have to set up which way you want to use it. You can get it from BigTop, by the way. It’s probably the easiest way to install it if you’re using the Hadoop. That’s about it. With the new versions coming up, a little in about few weeks from now, by the end of May, we’re going to have even more simplified process for this. So the whole point of the in-memory Hadoop accelerator is to, do not code. Do not make any changes to your code. The only that you need to do is install it and have enough RAM in the cluster and off you go, so the process is very simple.
Eric Kavanagh: Let me bring John Santaferraro back in. We’ll take a couple more questions here. You know, John, you guys, we’ve been watching you from various perspectives of course. You were over at PEAR Excel; that got folded into Actian. Of course, Actian used to be called Ingres and you guys made a couple of other acquisitions. How are you stitching all of that stuff together? I realize you might not want to get too technical with this, but you guys have a lot of stuff now. You’ve got Data Rush. I’m not sure if it’s still the same name, but you got a whole bunch of different products that have been kind of woven together to create this platform. Talk about what’s going on there and how that’s coming along.
John Santaferraro: The good news is, Eric, that separately in the companies that we’re acquired Pervasive, PEAR Excel and even when Actian had developed, everybody developed their product with very similar architectures. Number one, they were open with regards to data and interacting with other platforms. Number two, everything was parallelized to run in a distributed environment. Number three, everything was highly optimized. What that allowed us to do is to very quickly make integration points, so that you can be creating these data flows already today. We have established the integration, so you create the data flows. You do your data blending and enriching right on Hadoop, everything parallelized, everything optimized. When you want, you move that over into our high-performance engines. Then, there’s already a high-performance connection between Hadoop and our massively parallel analytic engine that does these super-low-latency things like helping a bank recalculate and recast their entire risk portfolio every two minutes and feeding that into our real-time trading system or feeding it into some kind of a desktop for the wealth manager so they can respond to the most valuable customers for the bank.
We have already put those pieces together. There’s additional integration to be done. But today, we have the Actian Analytics Platform as our offering because a lot of that integration was ready to go. It has already been accomplished, so we’re stitching those pieces together to drive this entire analytic value chain from connecting the data, all of the processing that you do of it, any kind of analytics you want to run, and then using it to feed into these automated business processes so that you’re actually improving that activity over time. It’s all about this end-to-end platform that already exists today.
Eric Kavanagh: That’s pretty good stuff. And I guess, Jim, I’ll bring you back in for another couple of comments, and Robin, I want to bring you in for just one big question, I suppose. Folks, we will keep all these questions – we do pass them on to the people who participated in the event today. If you ever feel a question you asked was not answered, feel free to email yours truly. You should have some information on me and how to get ahold from me. Also, just now I put a link to the full deck with slides from non-sponsoring vendors. So we put the word out to all the vendors out there in the whole Hadoop space. We said, "Tell us what your story is; tell us what’s going on." It’s a huge file. It’s about 40-plus megabytes.
But Jim, let me bring you back in and just kind of talk about – again, I love this concept – where you’re talking about the pool party that comes to an end. Could you talk about how it is that you manage to stay on top on what’s happening in the open-source community? Because it’s a very fast-moving environment. But I think you guys have a pretty clever strategy of serving this sort of enterprise-hardening vendor that sits on top or kind of around that. Can you talk about your development cycles and how you stay on top of what’s happening?
Jim Vogt: Sure. It is pretty fast moving in terms of if you look at just a snapshot updates, but what we’re shipping in functionality today is about a year to a year and a half ahead of what we can get on security capabilities out to the community today. It’s not that they’re not going to get there; it just takes time. It’s a different process, it has contributors and so forth, and it just takes time. When we go to a customer, we need to be very well versed in the open source and very well versed in mainly the security things that we’re bringing. The reason that we’re actually issuing patents and submitting patents is that there is some real value in IP, intellectual property, around hardening these open-source components. When we support a customer, we have to support all the varying open-source components and all the varying distributions as we do, and we also need to have the expertise around the specific features that we’re adding to that open source to create the solution that we create. As a company, although we don’t want the customer to be a Hadoop expert, we don’t think you need to be a mechanic to drive the car. We need to be a mechanic that understands the car and how it works and understand what’s happening between our code and the open source code.
Eric Kavanagh: That’s great. Phu, I’ll give you one last question. Then Robin, I have one question for you and then we’ll wrap up, folks. We will archive this webcast. As I suggested, we’ll be up on insideanalysis.com. We’ll also go ahead and have some stuff up on Techopedia. A big thank you to those folks for partnering with us to create this cool new series.
But Phu … I remember watching the demo of the stuff and I was just frankly stunned at what you guys have done. Can you explain how it is that you can achieve that level of no failover?
Phu Hoang: Sure, I think it’s a great question. Really, the problem for us had three components. Number one is, you can’t lose the events that are moving from operator to operator in the Hadoop cluster. So we have to have event buffering. But even more importantly, inside your operators, you may have states that you’re calculating. Let’s say you’re actually counting money. There’s a subtotal in there, so if that node goes down and it’s in memory, that number is gone, and you can’t start from some point. Where would you start from?
So today, you have to actually do a regular checkpoint of your operator state down to this. You put that interval so it does not become a big overhead, but when a node goes down, it can come back up and be able to go back to exactly the right state where you last checkpointed and be able to bring in the events starting from that state. That allows you to therefore continue as if the event actually has never happened. Of course, the last one is to make sure that your application manager is also fault tolerant so that doesn’t go down. So all three factors need to be in place for you to say that you’re fully fault tolerant.
Eric Kavanagh: Yeah, that’s great. Let me go ahead and throw one last question over to Robin Bloor. So one of the attendees is asking, does anyone think that Hortonworks or another will get soaked up/invested in by a major player like Intel? I don’t think there’s any doubt about that. I’m not surprised, but I’m fascinated, I guess, that Intel jumped in before like an IBM or an Oracle, but I guess maybe the guys at IBM and Oracle think they’ve already got it covered by just co-opting what comes out of the open-source movement. What do you think about that?
Robin Bloor: It’s a very curious move. We should see in light of the fact that Intel already had its own Hadoop distribution and what it has effectively done is just passed that over to Cloudera. There aren’t many powers in the industry as large as Intel and it is difficult to know what your business model actually is if you have a Hadoop distribution, because it is difficult to know exactly what it is going to be used for in the future. In other words, we don’t know where the revenue streams are necessarily coming from.
With somebody like Intel, they just want a lot of processes to be solved. It is going to support their main business plan the more that Hadoop is used. It’s kind of easy to have a simplistic explanation of what Intel are up to. It’s not so easy to guess what they might choose to do in terms of putting code on chips. I’m not 100% certain whether they’re going to do that. I mean, it’s a very difficult thing to call that. Their next move at the hardware level, I think, is the system on a chip. When we go to the system on a chip, you may actually want to put some basic software on the chip, so to speak. So putting HDFS on there; that might make some sense. But I don’t think that that was what that money investment was about. I think all that money investment was about was just making sure that Intel had a hand in the game and is actually going forward.
In terms of who else is going to buy, that is also difficult to say. I mean, certainly the SAPs and Oracles of this world have got enough money to buy into this or IBM has got enough money to buy into it. But, you know, this is all open source. IBM never bought a Linux distribution, even though they plowed a lot of money into Linux. It didn’t break their hearts that they didn’t actually have a Linux distribution. They’re very happy to cooperate with Red Hat. I would say maybe Red Hat will buy one of these distributions, because they know how to make that business model work, but it’s difficult to say.
Eric Kavanagh: Yeah, great point. So folks, I’m going to go ahead and just share my desktop one last time here and just show you a couple of things. So after the event, check out Techopedia – you can see that on the left-hand side. Here’s a story that yours truly wrote, I guess a couple of months ago or a month and a half ago, I suppose. It really kind of spun out of a lot of the experience that we had talking with various vendors and trying to dig in to understanding what exactly is going on with the space because sometimes it can be kind of difficult to navigate the buzz words and the hype and the terminology and so forth.
Also a very big thank you to all of those who have been Tweeting. We had one heck of a Tweet stream here going today. So, thank you, all of you. You see that it just goes on and on and on. A lot of great Tweets on TechWise today.
This is the first of our new series, folks. Thank you so much for tuning in. We will let you know what’s going on for the next series sometime soon. I think we’re going to focus on analytics probably in June sometime. And folks, with that, I think we’re going to go ahead and close up our event. We will email you tomorrow with a link to the slides from today and we’re also going to email you the link to that full deck, which is a huge deck. We’ve got about twenty different vendors with their Hadoop story. We’re really trying to give you a sort of compendium of content around a particular topic. So for bedtime reading or whenever you’re interested, you can kind of dive in and try to get that strategic view of what’s going on here in the industry.
With that, we’re going to bid you farewell, folks. Thank you again so much. Go to insideanalysis.com and Techopedia to find more information about all this in the future and we’ll catch up to you next time. Bye, bye.
