I'm gonna fair everything fair anything Got it.   It's next to you. I got it. I got it. I got it. Don't worry.  It's half. It's half you might need to click it. I will hook up.  Yeah, I can move up to him. I can fish down here.  I'm running up to power running around Hello jump, I'm gonna get the sec getting it Learning power kick on me. Yeah. Yeah, I'm gonna shrink if need I think I'm okay I'll do I'm gonna drink Shaman running to me. Yeah, I'll fish him fish him Shaman drink it. Shaman drink it I got fiend on lock pumping If shaman is far, I fear lock okay riff and you sheep shaman Ready Going in I'm spreading Sham trinket no you got a spot I fear everything fear everything to spoil you now to spoil you now I'm running out. Of course. I can't stay bro. I can't stay I'm gonna get fit Nice I got fiend fiend again Shit Scared Yes me yes me What are everyone I'm next I'm next I'm fed. I'm dead. I'm dead. I'm dead. It's on you. It's on you. Maybe block. We ready Not yet. Not yet. Not yet. DR hold for me It's piercing I pierce everything I can fish him can fish him Fearing fearing fearing I think we go look right Yeah, sure, but be careful my cats are trolling me. I'm gonna fish him if I can get there Yep getting the second side Stunt stunned I can't fear I can't fear I'm going away Yeah, I see I'm in fear I'm gonna shield you you should be okay Half fear coming now Lock I see us me I'm trying to line the pet.  Careful You didn't click you didn't click you can still press Fearing and then just Okay, but Got it running running running I'm gonna fear everything fear anything Got it. Got it. Got it. Kill trauma.  It's next to you. I got it. I got it. I got it. Don't worry.  I've got fear releasing I have to bless him and bless him and bless him and bless him.   Wait, let me do some shots. Hold on Going under I can gush down I have to tremor Yeah, yeah Fear Got you going I kick shaman kick shaman. Yeah, I will fear me again. I can't empty can't empty Watch out riv I got I got feared down. I got feared down.  What is this? We got shaman trinket. That's it. We go lock here.  I'm going for me. I'm going lock is clean lock is clean Yeah Fear me fear me.  You're okay. I'm blinding I'm blinding Moving up I gotta read I gotta read I can fish down I'm going to some day. I'm trying to get there. I'm lining. I'm lining again. Yeah, sure. Sure. Sure. Burning power Hold on me. I'm I'm fine. I'm sitting I know we got nice. Nice. Nice. Chill. Chill. Chill. I'm sitting this okay. I have yes I just bow I shielded you I can I can face sham when you're ready.  I got stunned. That's okay. I'm modeling to sham Okay, okay. I fear I fear everything's fit Yes, yes, he's over the ship. Oh We can I can maybe read and go pala. No, I'm rooted. Yeah, sure. I'll get you shipping shipping lock ups. Okay Kick me. Kick me.  What's right? I spread out spread out for the uh, the sun I get we're here for Just chill chill chill chill back to it Jump look look at him.  I'm drinking sham is hot. I'm in a heart attack there. I can fear him Yeah I'm jumping again jumping again fair everything. Yes.  I super well. I get over here. I can stop the damage. I can stop the damage. I'm doing it. Yeah I'm talking I'm talking Watch out. You can block. Just do it. Do it. Do it. I have no manner.  What's right? Still running still running. Yeah Trying to drink now. I should get it. It's not everything. I'm thinking can drink really nice. No drinking pala's coming Yeah, just don't take damage now river. Okay. Yeah, I'm trying to drink Nice. Nice. Go pala go pal. Burning it. Um I should get both buns Look at me bro. Look at me No, no, I don't but I'm lying I'm here. It was position go. I am Looking looking I can watch I can watch nice Sending me Kill is us Go go go I Watch over if you got shield, but I don't know how strong it is not very 3k drinking Fearing Yeah, that's that was unreal You can still die watching


So, my name is Captain Pfeiffer and I work as a special analyst for ILRAE. As a special analyst, you do a lot of maps. For example, livestock distribution maps or poor livestock keeper maps. And then one day these people come into my office and say, let's integrate gender. And I look at them and I think, really? I was quite upset that they never really understood that what's the gender component of a livestock distribution. And I felt really misunderstood. But then I thought, let's take up this challenge. Let's challenge these people and show them, guys. I thought about it, maybe we can do some gender maps. So I went back to my office, put some thoughts in and decided, let's just do it. So, I arrived there and thought, how are we going to go about this? Crazy gender maps. And I started looking at many data, we had many frameworks. And I started to focus really on the concept of value chains. I used an economic model, which then I expanded with some gender concepts. And when this was nicely set, from there I defined which variable we're going to use and how we're going to go about that. So the data we ended up using was an OECD data, which looked at which policies are in place in every country and whether customary law was more important than a national policy. So, just if a woman might by law get access to them but men were never allowed in a country, that would be accounted for. Then we also used the so-called DHS data, that's the demographic health surveys from USAID. This is a women interview, that's a lot of women have been interviewed and there we have information on whether she owns land or doesn't. If her husband is actually allowed to beat her up. So there's quite a lot of information in this DHS that is individual data. We combined all the data and put it in a factor analysis. Factor analysis is just a method that reduces this big amount of data in something that you can handle. And we ended up for Africa, for example, with what we call six factors which you can also see as six different gender contexts. We could map those then and have an African map. It's an African map with gaps because we don't have data for all the countries to show these different contexts. So for example, we looked at labour in the household. A woman has not so much time to be involved in the value chains if she has a lot of children on a big family to take care of. So we saw that for example in Senegal, this is very, very important whereas this is much less important in East Africa. So it means if you come with a technology that's very time intensive it might not have so much success in Senegal whereas it might have more success in East Africa. Now we have these maps and we want to use them and how we use them is what we refer to as targeting. That means we try to understand the context where something has worked and if something has worked somewhere it might not work in the other place just because the context is different. Until today we looked at contexts mainly as biophysical like rainfall, soil quality. These maps now we have actually a new world opening to us because we can also look at what is the context, the gender context where a woman is moving in and therefore take this into account when we define what works where.


If there's a single transit innovation that has changed the world the most in the last century, it's probably the S-Bond and the city center tunnels that so often make such systems possible. And when talking about S-Bonds, no city comes to mind for me like Munich, which has 8 different S-Bond lines on top of a comprehensive U-Bond network. Together, these rail lines move nearly 2 million people per day pre-COVID over more than 200 stations and 500 kilometers of track. So let's dive in and see how this network achieves these very impressive numbers. Munich is a city region of over 3 million people and the largest city of the southern state of Bavaria in Germany. Like most major cities, it's got many key nodes, both from a transportation demand standpoint as well as a general city important standpoint. But none is quite as important as Munich Central Station or Munich-Helpenhof, which serves as a major intercity and regional transport hub as well as a local urban transport station. Munich East Station serves a similar, albeit significantly less important, role. Another major intercity transport hub lies to the northeast of the city center with Munich Airport. Like with many German cities and fairly unique among cities around the world, Munich has a massive messa, which is basically like an unbelievably big convention center where huge events and shows take place, stressing the rail system with thousands of visitors all at once. Now, speaking of large events in Germany, the very famous Bayern Munich has its famous Allianz Arena north of the city center, which also played host to a number of games in the 2006 World Cup and is one of my personal favorite arenas. And we can't forget the Olympic Park that was built for the 1972 Summer Olympics. Sitting across from that park is the BMW Museum, and the company actually has its headquarters and origins in the region. Of course, despite that, Munich still has an excellent public transport system, something which you can't necessarily say for North American motor towns. Managing to move people all over the city requires major connecting hubs, and none is quite as important as sending your tour, a major hub on the U-Bahn, as well as Odeon Splats, both of which are major urban rail hubs. Now that you've got the lay of the land, let's look at the specifics of the S-Bahn network, which is electrified at 15 kVac, as with the national network in Germany, and which operates over standard gauge tracks. The system has two different types of trains, the Class 420, first introduced in 1967, and the 423, first introduced in the late 90s, and now making up the majority of the trains used on the system. The Class 423s feature a modern, metro-esque design, lots of doors, as well as Jacob's bogies, which are shared between the carriages of the four-car units, which are often combined into longer 8 and 12 car trains. Interestingly, the Class 423s are also used in other German S-Bahn systems, such as in Frankfurt. If you were curious, the interior of the trains primarily consists of a lot of transverse seating, and on renovated units there are nice modern digital wayfinding screens. All trains have quite a bit of standing room for peak loads. What makes an S-Bahn and S-Bahn is usually the city center corridor, which in the case of Munich includes a roughly four-kilometer tunnel opened in 1972, connecting Munich Central and Munich East Stations, with Munich Central featuring dedicated underground S-Bahn platforms as well as four other underground stations. Interestingly, for maximum capacity on the two tracks in the tunnel, Central Station and Karlsplatz both implement the Spanish solution with platforms on both sides of the trains and an island in the middle, while Marionplatz implements the Spanish solution with tracks on two levels. This tunnel and central corridor, which is actually not even entirely underground, extends two stations to the west of the Hauptbahnhof. These additional stations are located where major roadways cross the giant rail corridor into Munich Central, almost all of the land in which is dedicated to intercity and regional trains as opposed to S-Bahn. The central corridor itself is used by all main S-Bahn services, with virtually all trains stopping at the eight central stations. During peak periods, there are 30 trains per hour operating through the core. That's about a train every two minutes. It should also be noted that all of the infrastructure on the S-Bahn is designed for larger, longer, and faster trains than are typically seen in urban metros, with speeds of up to 140 kilometers per hour in some sections. Outside of the center of the city, the vast majority of the trackage used by the S-Bahn system is on the surface, and lines extend far from the center to outlying villages and rural areas. These outer parts of the network frequently have single track sections and level crossings, but this is fine as the basic service level on single routes is typically around 3 trains per hour, or a train every 20 minutes, with some service levels on the furthest extents of the network reduced to 2 trains per hour. These single track sections are often next to double-tracked railways for longer distance regional, intercity, and even cargo trains, and sometimes the S-Bahn shares its tracks with other traffic in outlying areas. And of course, S-Bahn services are scheduled to optimize for transfers. Talking about specific routes, there are currently 8 services operated. S-1 branches in the north, with one branch going to the airport, and travels down to the core from the west, ending just beyond east station. Trains arriving at the branch point are often coupled together into a single set, and uncoupled headed in the reverse direction. S-2 is a little to the west of S-1, and also branches in the north, albeit with longer branches, before passing through the core and heading to the northeast. S-3 starts in the west, and travels east and southeast into and through the core to the east station, where trains actually reverse, like the Toei Urban Park Line in Tokyo, or some Zurich S-Bahn services, which do have a lot in common with the Munich S-Bahn, I mean these cities aren't that far apart. From east station, trains then exit westwards before turning to head south. S-4 is a mostly typical cross-city east-west line, however the service turns north of the eastern end to serve some towns. S-6 starts in the southwest along a major lake, before crossing the city and following along with the S-4 to its eastern terminus. As it turns out, the eastern part of S-4 is only operated during peak periods, while it operates as part of S-6 at all times. S-4 trains otherwise turn back early. S-7 essentially acts as an arc opening down, running from the southwest of the core, through the center, and then to the southeast, east of S-3's southern lake. S-8 starts in the southwest before passing through the core and heading north of the city, with a few tunneled sections that take it under villages before it merges with S-1 to serve the airport with a frequent service. The last route, S-20, does not operate all the time, and instead operates an infrequent service during weekdays from the south entirely on S-7 Trackage, and then to the west rather than through the core bypassing a number of stations. While the frequencies across the system may not seem super high, a large part of the network has higher frequencies where services overlap and combine to provide more, so services better than it might initially seem. Extra services are also filled in when capacity allows. On top of the impressive S-Bahn system, it's important to not forget that Munich also has an excellent U-Bahn network. Unlike the S-Bahn, which runs 24 hours on weekends, and the S-8, which runs 24 hours a day, 7 days a week, the U-Bahn closes late at night for work like most metros, though for less time than most, closing for just about 2 hours a night on weekends. The U-Bahn, like the S-Bahn, uses a standard train specification along all lines, with trains powered by a 750V DC bottom contact third rail operating on standard gauge tracks, and featuring a lot of transverse seating for a metro. Trains are fairly wide at 2.9 meters, and fairly long at around 120 meters with up to 6 cars, which means that the cars on the U-Bahn are actually slightly longer than the carriages used on the S-Bahn. There are 3 types of trains operating on this system, the A, B, and C, with the A trains being built from the 60s to the 80s, the B trains from the 80s to the 90s, and the C trains in the early 2000s up until present. There has been a newer revision in recent years, and I must say I am quite a fan of the look of the C type trains, they're just really modern and attractive. Now I should warn you that the Munich U-Bahn is weird, and quite unlike most other metro systems. To start, it effectively has three city-centered tunnels, each with two tracks which cross to form a triangle between where the lines intersect, rather than intersecting at a single station. However, all lines on the U-Bahn operate multiple services. One tunnel carries the U1 and U2 that branch out on either side onto their own alignments, another carries the U4 and U5 that split in the east but run together in the west with U4 turning back early, and a third tunnel carrying the U3 and U6 that again split on both the north and south of the city. The lines come together at the center with the central station connecting U1, U2, U4, and U5, Odian's Plats connecting U4, U5, U3, and U6, and Sendling Erzor connecting U1, U2, U3, and U6. U1 and U2 also intersect at different points with U3 outside of the core, and the same is true for U2 and U5. These connections are useful for moving the intercompatible trains as well as work equipment around the network for service, and such connections between lines is fairly common in many metro systems, but the connections here are actually also used in service for the limited U7 and U8 lines, which only operate during rush hour and Saturdays respectively. This allows the U7 to start on U1, going through the center, and then connecting onto U5 for a few stops. The U8 operates similarly but starts alongside U3 tracks before connecting onto U2 and joining the U7 at central station. Much like on the S-Bahn, many of the metro services do not operate all that frequently for a metro, but this as usual means that their interline sections do receive higher frequency, and this also enables the U7 and U8 services to slot in. The network also just isn't infrastructure heavy on the track side. Many stations have additional platforms such as wherever lines split apart and where tracks come together from one line to another, enabling lots of cross-platform transfers, some of which are actually timed in the schedule to have trains stopping at the same moment to enable connections. Interestingly, most of the network is below ground, but the U5 and U6 both are above ground adjacent to their depots, and the U6's above ground section continues north to its terminus, including through some essentially rural landscapes. I also have to mention that the U6 and U3 serve Allianz Arena and the Olympic Park respectively, which both have large stations with two island platforms and four tracks for extra capacity. Interestingly, in both cases there's a nearly one kilometer walk to the stations, which helps to spread people out so that trains don't become overwhelmed by a crushing wave of spectators. Now, sometimes North America gets the reputation of having a monopoly on crazy land use, but one very interesting example from Munich is the U2 line, which terminates with two stations at the Messe on its eastern end, which is pretty reasonable. But the station preceding these stations is a fully underground subway station right along a street consisting entirely of large single family homes with street parking, which is quite interesting. Now, Munich does have great transit today, and unlike some other cities in Europe, it's also actively working on a ton of major new projects. On the U-Bahn, U5 is being extended to a major S-Bahn and mainline rail station, and likely beyond, and the U6 is being extended to the south. There's also a plan for a new U9 line, which would reorganize the network, giving U3 and U6 dedicated tunnels through the center and making direct trips between central station and Allianz Arena and beyond possible, as the U9 tunnel will redirect either end of U6 through the Helpbahnhof. On the existing network, plans for platform screen doors are advancing, and this will help improve reliability on the network, which can be often compromised by its significant interlining. A new second S-Bahn tunnel is also under construction. It will be deeper than the existing one, and will bypass many central stations, from end to end, stopping at Central, Mariannplatz, and Ausbahnhof. A number of new S-Bahn services will be added using the new tunnel, and the S1 and S6 services will also be moved to it. There have also been discussions of a Ringbahn akin to in Berlin. It's still in its early days, but Munich already has the rail right of ways and connections to existing S-Bahn lines that could be the basis of such a line, so it could be built moderately easily, probably starting in the north where more of the infrastructure exists. Suffice to say, a lot is happening in Munich, and honestly, it's close to the top of my list for cities I need to visit next year, so I think we'll be talking more about the city soon. Thanks for watching, and I'll see you in the next one. A special thanks to Jacob, Julian, and Thomas for their on-the-ground footage from Munich for this video.


Live from New York City, it's The Cube at Big Data NYC 2014. Brought to you by headline sponsor, WAN Disco, with support from EMC, MarkLogic, and TerraData. Now, here is your host, Dave Vellante. Welcome back to Big Data NYC. This is The Cube. I'm Dave Vellante and I'm pleased to have Jim Campigli here. He's the Chief Product Officer and one of the co-founders of WAN Disco. Long time Cube alum. Jim, welcome back to The Cube. It's good to see you again. Thanks, Dave. Appreciate it. Yes, so we were just talking offline about the show down at Jab. It's a much bigger venue this year. You said about 6,000 people? About 6,000 people. I think they had, when they had it over here at the Hilton in Midtown, it was about 3,300, something like that. So it's a much bigger show, a lot more going on. So a lot of growth. I understand the average age is trending toward my age. Well, I mean, you know, you can imagine as Hadoop becomes more mainstream and enterprises adopt it and they expect it to have the same levels of reliability that they're used to in their data centers with the traditional relational databases like Oracle and DB2 and so forth. The people that are implementing those databases and maintaining those databases are probably about our age. You know, independent of WAN Disco and your active, active mojo, financial services seems to really be adopting Hadoop generally. And I want to come back and talk about WAN Disco specifically. But I mean, financial services has always led a lot of tech innovations. But why Hadoop? Why the action? Well, I think if you look at financial services, you look at any of the big banks, they really kind of fit the profile of the 3Vs, volume, variety and velocity. I mean, they've had it in spades for decades and they've had to come up with different approaches to handling that and addressing that. Some of the big banks that we're working with, for example, are trying to figure out, you know, whether or not they're making money in China. And they started discovering that, well, you know, the only way we can really drill down on some of these things is to look at a lot of the contracts we have and they discover the contracts are all in PDFs. Well, you can't really effectively store PDFs and look at that data in a traditional database. You've got to do something like that in Hadoop. A lot of image processing goes on in financial services. A lot of different types of data get handled in huge, massive volumes. So take that use case if we can. Can we just sort of stay on that a minute? So you've got data locked inside of, really valuable data locked inside of a PDF. Right. How do I go from that to a business outcome? Going from that to a business outcome basically means I'm able to go through those documents, understand what they mean and get a profile of whether or not they're working for the institution. And so I can... In aggregate. Yeah, and so... Maybe you'll change your legal policies, maybe you'll change the way you write contracts, the kind of business you do in different parts of the world, that kind of thing. And so talk about how Hadoop enables that versus a traditional technology. Is traditional technology too expensive? It doesn't have the capabilities? It doesn't have the... It doesn't handle the different types of data. You're not talking about fixed rows of data in rows and columns, that kind of thing. It's just not text and numbers. You're talking about a wide variety of data that gets processed. And if you just have something that you access as an attachment through a relational database, obviously there's no intelligence necessarily happening against that PDF document, for example. Now let's talk about the WAN disco piece in either financial services, Right. And then generally you're talking about... You guys focus on active-active 24 by 7 resiliency. Talk about that piece of it. And I'm wondering, are there other use cases that you're seeing for your technology? Yeah, so I mean obviously the thing that we've talked about most in relation to our active-active replication technology, the patented replication technology that we have, is the ability to maintain 24 by 7 availability globally. Effectively what we do is we enable the name node to scale. You can have multiple active name nodes in a cluster, and you can expand those name nodes across multiple data centers. So what you're effectively doing is you're creating a single namespace and then affect a single cluster over a wide area network. And the other thing that that does for you is it gives you the ability to have every application, regardless of where it's running, subject to what selective replication policies you've implemented, have land speed, local area network speed access, and read-write access to the same data. So what does that mean? Active-active replication by default means you're getting continuous hot backup of the data. The other thing it means is because it's full read-write, it means you don't have a read-only backup cluster. So that means you're getting full use of that hardware. Moving beyond that, what you can also do with it is multi-data center ingest, which is otherwise impossible. If you look at a global organization where data is being generated from hundreds of different sources all over the world, what happens today is you have to ingest at one location and very often move it to another for analysis. And what we're able to enable because we've got full read-write access across an entire implementation when you use us, regardless of what data center you're running at, we enable you to ingest and analyze anywhere. So anytime you've got data that's time critical, where accuracy is important, we would be the solution, especially in that kind of distributed scenario. Because typically what happens today is you load it at one location and then you use DCP or some other utility to copy it over to a central data center for analysis. And that means administrator overhead to monitor that data movement. And it also means that if any data is lost in transit, not only have you lost time engaging in that data movement, but you've also run the risk of having inaccurate results. And if you're doing something like gathering sensor data from industrial machinery, locomotives, oil and gas pipelines, monitoring whether or not pressure is building up too high in the jet engines, those kinds of things. Timeliness and accuracy are critical. You don't have time to wait several hours to move the data, make sure it all moved over there, and then hope it all moved over there to do an accurate analysis. We make all those problems go away because you can ingest and analyze anywhere. And people have this misconception, oh, I can run my analytical jobs against my backup cluster because it's not writing any data. Well, that's very often false. Most analytical applications will write data to the Hadoop cluster as part of their processing. It's very common to have a series of MapReduce jobs that write output that are to be picked up by the next job in the process. This kind of thing means that we're the only solution when you have that scenario. So the backup data is out of date in that scenario. Right. And you've also, if you try to do that, you've thrown your backup cluster out of sync with your primary cluster. And what that means to a bank, for example, is the regulators will shut you down until you get it sorted out. So is the high-speed data mover an oxymoron? Is the data mover dead? I think people are still going to use it, but I think anytime you have an application where you're gathering a lot of data from multiple sources and it's time critical and accuracy is important, it's probably not going to do what you need it to do. Yeah, and as volumes of data grow, I mean, the last thing I want to do is be moving data around. And with us, because of the active-active replication, you don't have to worry about it. We automatically do that. The data converges everywhere. And that means that you're not having to worry about clusters getting out of sync because we enforce that as well. But we also enforce selective replication. So if you only, for example, can have data reside in a certain country, if you're, I'll go back to the banks, if you're an international bank operating branches in Argentina, and Argentina says that data can't leave the country, then, of course, you know, you can't move the data out of Argentina, but what you can do with our software is you can access that data to do global roll-up analysis, for example. So we enable those kinds of use cases as well. Well, and that's sort of very much in parallel with the concept of Hadoop. Ship the five megabytes of code to petabyte of data. Don't try to move the data. Now, the value that you bring beyond that concept is 24 by 7, active-active. So I wonder if you can talk about, in a little bit more detail, sort of the customers that you're working with, you don't have to name names, but the types of, you know, maturity model, if you will, where do they start and where do they go and where do you want to take them? Where do they want to take you? In 2013 and 2014 is a lot of companies trying Hadoop out, getting a pilot project going, seeing some real value in it, but then, you know, wanting to deploy it in their data center, and then, of course, they've got internal audit requirements and regulatory requirements around availability, security, data access, all those things impact whether or not you can deploy Hadoop in a production environment and use it for anything that's business critical. So what's happening now is companies are looking for things that address reliability and security with Hadoop. The other thing that they're looking for, and that's financial services, and it's not just financial services, also industrial equipment manufacturers, the example I gave about all the different industrial sensors, the whole Internet of Things concept, they're really looking at this as well, and they're realizing that, you know, data is being generated all over the place. We really can't afford to run the risk of administrator error, time delays, using the approaches we're currently using, so we come in and address those problems. Do you have any competition? There are other solutions, and I'm not going to name names, that present themselves as being active-active, but when you drill down on it, they're really multi-master and eventual consistency solutions. So what I'm basically saying is if that application initiates a transaction, makes a change, and it's connected to one server, then that server in effect becomes the master at least for the life of that transaction, so it's responsible for replicating it to all the other servers in the implementation. If that server where that right transaction originated goes down during that process, what that means is that that transaction didn't get consistently replicated across the cluster, for example. Then you end up in scenarios where you've got to do a lot of consistency checking, the administrator has to get involved in re-syncing the implementation. You'll hear those solutions sometimes described as eventual consistency as well, not guaranteed consistency, but there are other solutions out there that claim to be active-active, but they're really multi-master. In our case, we're true peer-to-peer. In our situation, if you're connected to a particular server at any location, for example, you're updating a file, you're changing the metadata on the name node, we take care of replicating that consistently across the entire deployment across all your data centers, as well as the underlying data blocks. We also address that replication as well, but the point is that in that scenario, you don't run into the problems where you don't have consistent data access that's handled by us automatically, and if one of the nodes fails, for example, the node in particular, where the client initiated that transaction, let's say the name node that the client was connected to goes down before it's replicated to all the other nodes, the remaining nodes in the implementation take care of propagating it to maintain consistency, and then when any of the name nodes come back online, they re-sync automatically. The administrator doesn't have to do anything, and people use that not only to recover from outages, but if they want to take the servers offline for maintenance, and continue providing service to their users, they can do it with our software. They take the server that they're performing the maintenance on offline. The remaining servers continue supporting client requests as soon as it's restored, it automatically re-syncs with the rest of the cluster and you keep moving. So you can have 24x7 worldwide with our solution, which isn't the case with other products. So planned maintenance doesn't cause planned outages? Outages, yeah, no planned downtime. Do you remember the concept of, it's unplanned downtime and we have planned downtime, planned downtime is not okay in this day and age, but it's really all about recovery and continuous availability. And so what you're saying is you're eliminating a lot of the force aspects of recovery of alternative solutions. And you're guaranteeing data quality, data consistency. Interesting. Okay, let's see, what else? You mentioned business use cases, and it's true enough there's a lot of them evolving out there. I'm sure some of your other guests have been talking about them. What's happening in a lot of organizations right now is at the C level, the IT infrastructure folks are saying, we've got to get Hadoop in here. We know we're going to have requirements for it. And then they're saying the line of business people will come to you. So the IT infrastructure people are looking for something that's cost effective, number one. And number two, they know that they're going to get measured on quality of service. So how reliable is the implementation? How well does it perform? How well does it scale? All those kinds of things are going to be areas that they're going to have to deliver on. So other ways that we address that, and this makes this kind of a horizontal solution in that respect, all industries are going to have obviously the same set of requirements as they really deploy this for mission critical applications. Things that we do around this that are kind of byproducts of the active-active replication. Let's talk about keeping costs down. You don't have an investment in idle backup standby hardware waiting for a disaster to occur when you have to bring it into action. It's full read-write everywhere. It's continuously synchronized, so you have that going for you. The other thing that we're seeing a lot of companies use our software for is something called cluster zoning. So if you think about what we actually did, and I'm grossly oversimplifying here, when we applied our active-active replication to enable you to deploy a single Hadoop cluster over a wide area network, what we did is we added a new attribute on top of the rack ID. So if you look at Hadoop, every node in a Hadoop cluster has a rack ID associated with it. The name node knows the rack ID, and so the idea is for added resilience, you replicate in threes, and those three nodes that you're replicating to should preferably be on separate racks. Of course, that's within a single data center. So we took that notion up a step, and we said, well, we're going to add the attribute of a data center ID, and you can selectively replicate data across data centers, and you can also, rather than have that data center, if you will, live on the other side of the world, you can redefine that data center to be a zone within one location, and within that zone, you can selectively replicate the data, for example, that in-memory analytics applications that require the highest spec servers to be routed to. So what that means is if I've got intensive data ingest jobs, if I've got in-memory applications that are cranking through huge transaction volumes, whenever an application needs that data, it will be routed to those high-performance servers, and the rest of the cluster can be commodity boxes, if you will, that handle the run-of-the-mill, map-reduce, batch application kinds of things where they're not so time-critical. You know, if the job goes down, somebody restarts it, we get our results a few hours later, it's okay. And then you can, with that approach, you can maintain quality of service for all your users without being in a position that you would otherwise without this cluster-zoning capability of having to have all high-end servers because you don't know where that in-memory analytics application is going to come in at, and you end up spending a lot more money that way. With us, cluster-zoning enables customers to really reduce their hardware costs and the implementation. Interesting. And that cluster-zoning, Jim, happens all within a synchronous... Yeah, so you can think of it as it's almost like we've created a virtual cluster within a cluster. And, you know, you can do cluster-zoning, you can have separate data centers dedicated to different applications, or you can create this virtual cluster within a cluster within a single data center. And, you know, I've got high-end in-memory applications running at the same time as batch map-reduce jobs. The data that those high-end applications need is going to live on the high-spec servers. But I just need as many servers as I need to support those applications. I don't turn the whole cluster into a very expensive proposition with servers that are specced to support the most demanding applications. I can get away from that. So you're taking this, you know, active-active concept and now you and your customers are starting to find new ways to use it. Absolutely. What else are they asking you for that, you know, you're not delivering today that we should be thinking about in the future? Some of the other things that we're looking at, and we did a press release today, is integration of different Hadoop distributions. So we're working on products that are going to be coming out. We are actually demoing alpha versions of them this week at Strata over at the Javits Convention Center. One of the big ones is a unification solution that basically allows you to do what we do for, you know, a single distribution cluster over a wide-area network for a mix of different distributions where you could integrate Hortonworks and CloudEra, for example, or you could integrate even CloudEra and MapR. As long as that Hadoop compatible platform has an HDFS API, we would be able to plug into it and do the same thing we can do for the standard Apache compliant distros today. Interesting.  I'm sure you're happy you're not in that business anymore, but it's interesting to watch what's happening there, all the money that's being raised and the sort of, you know, the race to knock each other's heads together. You guys have picked a niche that is really specialized, not a ton of direct competition, but maybe other ways of doing it. How do you see that piece growing over time? I mean, today it's obviously a small percentage of the overall requirements, but as people bring on, you know, Hadoop into production systems, is it increasing as a percentage of that marketplace or is it just riding the tide of Hadoop growth? What do you see there? I think what's happening is both. It's riding the tide of Hadoop growth, but it's also increasing as a percentage. Companies discover that there's other value than just active-active replication over when and, you know, thinking of us as a failover and disaster recovery solution, a high-end one, albeit that nobody else can touch with the technology we have. They're starting to see value in things like cluster zoning, multi-data center ingest. If I'm a big industrial equipment manufacturer and I'm capturing all this data from sensors I've got attached to jet engines, appliances, locomotives, oil and gas pipelines, whatever it may be, I realize that I'm, you know, I'm collecting massive amounts of data and I want to be able to analyze it quickly because if there's any downtime, number one, I may miss critical information. If the data takes too long to move to the point of analysis, it loses its value. So they're starting to see that it's about more than just being up all the time. It's about being able to do these things in the center ingest and analyze anywhere. Keep your hardware cost down and maintain the highest quality of service for all your users' applications by doing cluster zoning. Just have the number of high-end servers you need to support the most demanding applications. And then, you know, we talked to a lot of companies where with what they're doing today, whenever they have to support, for example, massive amounts of data to load into the cluster, everything stops for half an hour. They just can do anything else. Zoning is something that could address that problem for them. But you guys have a huge play in Internet of Things, especially for mission-critical infrastructure. Now you're into issues of national security and obviously there's other sort of analytic applications, but is there real demand for that today or is it just sort of an issue? No, I think it's one of those things that you don't know what you're missing until you're aware of it. As people start to understand, I have this kind of problem and I'm just doing this. You know, I'm loading it here. I pay administrators to move it over here, monitor it, make sure it all gets there, deal with any data that's missing, make sure everything's consistent. It's taking a long time. Sometimes I have successes, sometimes I don't. With it, I miss critical information. They're just sort of living with it. But when you make them aware of what they can actually do, it just opens up a whole number of possibilities. We're working with different health agencies that have noticed things, different government health agencies that have noticed things like, when the power goes out in a certain region, all of a sudden food-borne illnesses start to rise. You can see why that would happen. It's one of those things you don't think about, but it's kind of intuitive why that would happen. Obviously, restaurants and grocery stores are still handing out that spoiled food, no matter what, trying to sell it. If government agencies can get in there and realize, we've got to stay on top of this, we've got to be prepared for people coming in the emergency room or whatever. All those kinds of use cases. Prepare for it, quantify it, maybe eliminate it. That kind of thing. Obviously, Internet of Things, industrial sensors, that kind of thing. Obviously, if you know right away that the pressure is building up in a gas pipeline, that a jet engine is indicating some other problem, you see it happening, you can do something about it right at the time. Jim, always fascinating conversations with the folks from WANDISCO. Really appreciate your time coming on theCUBE. We've got to go. We're going to set up for our Capital Markets event, which starts at four o'clock. Right here at the Hilton Times Square. Four to six is the Capital Markets event, and then we go into five-year celebration of theCUBE at Hadoop World. Jim, thanks so much for all your support and coming on theCUBE. I appreciate your time and thanks for interviewing me today. All right, you're welcome. Keep it right there, everybody. We will be back. We're going to take a break, and then we're going to go live at 4 p.m. East Coast time with the Capital Markets event. Jeff Kelly will be presenting new data, and then we've got a great panel, so thanks for watching, everybody. We'll be back right after this word.


This video is on using the map viewer on the Utah Geological Survey website to download and view geologic maps of your field area. First thing you need to do is go to geology.utah.gov and near the bottom of the page they have a button here that says Geologic Map Portal. Click on that and it opens the portal up. Then from that you can zoom into your field area, your area of interest, and click on the area that you'll be working in.  In the unit descriptions mode what will happen is eventually the unit description will pop up. So the unit I clicked on in this case was quartz mods mode, which indicates the little cottonwood stock in this area. However another nice feature about this website is it has a maps download tab up here. You can click on that and click on the same point. Instead of the unit description you get all of the maps that are under the point that you clicked. And here is the draper quad. This little arrow shows the next map. You have the Salt Lake City quad, which is a larger map. It says 100k here, which is larger than the 24k. That's a smaller area, more detail. Then you go to the larger map, which is the state of Utah. Less detail, much larger area. Not as good of a reference to site. And finally another skate version of the geologic map. This is a pretty good map to use, the 100k version. So I'm going to pick this one and just to see what my options are, I'm going to click on the PDF file. And from the PDF file I can get the reference information that I need. So I'm going to click open PDF version and should bring up what I need to see. Here we go. Here is the geologic map of the Salt Lake City 3x60. It not only includes little cottonwood canyons, but it also includes big cottonwood and all the other major canyons along the valley. This yellow area here is the valley. So what you're looking for when you want to reference something like this is the author and the version and stuff information. And what you'll have to do is zoom in and find the author information. It's usually at the bottom in the center. So it looks like there's the scale. There should be something near the top and sometimes it's even on the second page. Basically what you have to do is search around until you find the information on the author. Oh here we are. Geologic map of the Salt Lake City 3x60 Quadrant. So this part here is your title. There's your author Bruce Bryant and there's the date that it was published. And then for more information you have this miscellaneous investigation series and even a map number. All this stuff can be put into a citation machine and you can cite maps. I think the citation machine even has an option for for maps if you search for it. So then you can search for let's see if this map pops up. Geologic map of the Salt Lake City. Let me just type that in and see what we get. The whole range of things in the citation machine. So you find the appropriate one. This looks like a good one and it will produce a reasonable citation of that. Let's see here. And here's different information you can fill in. You can add author and stuff like that and create your citation and you get something like this. So obviously it helps to add detail to make these citations better. But you already have an APA citation just from viewing the interactive map. I don't recommend citing the map itself. It's not a good source. It's the individual maps that make this up that are good sources to cite. So make sure you cite those. Okay thank you.