>> Narrator: Covering DataWorks Summit, Europe 2017, brought to you by Hortonworks. >> Welcome back to Munich everybody. This is The Cube. We're here live at DataWorks Summit, and we are the live leader in tech coverage. Steve Roberts is here as the offering manager for big data on power systems for IBM. Steve, good to see you again. >> Yeah, good to see you Dave. >> So we're here in Munich, a lot of action, good European flavor. It's my second European, formerly Hadoop Summit, now DataWorks. What's your take on the show? >> I like it. I like the size of the venue. It's the ability to interact and talk to a lot of the different sponsors and clients and partners, so the ability to network with a lot of people from a lot of different parts of the world in a short period of time, so it's been great so far and I'm looking forward to building upon this and towards the next DataWorks Summit in San Jose. >> Terri Virnig VP in your organization was up this morning, had a keynote presentation, so IBM got a lot of love in front of a fairly decent sized audience, talking a lot about the sort of ecosystem and that's evolving, the openness. Talk a little bit about open generally at IBM, but specifically what it means to your organization in the context of big data. >> Well, I am from the power systems team. So we have an initiative that we have launched a couple years ago called Open Power. And Open Power is a foundation of participants innovating from the power processor through all aspects, through accelerators, IO, GPUs, advanced analytics packages, system integration, but all to the point of being able to drive open power capability into the market and have power servers delivered not just through IBM, but through a whole ecosystem of partners. This compliments quite well with the Apache, Hadoop, and Spark philosophy of openness as it relates to software stack. So our story's really about being able to marry the benefits of open ecosystem for open power as it relates to the system infrastructure technology, which drives the same time to innovation, community value, and choice for customers as it relates to a multi-vendor ecosystem and coupled with the same premise as it relates to Hadoop and Spark. And of course, IBM is making significant contributions to Spark as part of the Apache Spark community and we're a key active member, as is Hortonworks with the ODPi organization forwarding the standards around Hadoop. So this is a one, two combo of open Hadoop, open Spark, either from Hortonworks or from IBM sitting on the open power platform built for big data. No other story really exists like that in the market today, open on open. >> So Terri mentioned cognitive systems. Bob Picciano has recently taken over and obviously has some cognitive chops, and some systems chops. Is this a rebranding of power? Is it sort of a layer on top? How should we interpret this? >> No, think of it more as a layer on top. So power will now be one of the assets, one of the sort of member family of the cognitive systems portion on IBM. System z can also be used as another great engine for cognitive in certain clients, certain use cases where they want to run cognitive close to the data and they have a lot of data sitting on System z. So power systems as a server really built for big data and machine learning, in particular our S822LC for high performance computing. This is a server which is landing very well in the deep learning, machine learning space. It offers the Tesla P100 GPU and with the NVIDIA NVLink technology can offer up to 2.8x bandwidth benefits CPU to GPU over what would be available through a PCIe Intel combination today. So this drives immediate value when you need to ensure that not just you're exploiting GPUs, but you of course need to move your data quickly from the processor to the GPU. >> So I was going to ask you actually, sort of what make power so well suited for big data and cognitive applications, particularly relative to Intel alternatives. You touched on that. IBM talks a lot about Moore's Law starting to hit its peak, that innovation is going to come from other places. I love that narrative 'cause it's really combinatorial innovation that's going to lead us in the next 50 years, but can we stay on that thread for a bit? What makes power so substantially unique, uniquely suited and qualified to run cognitive systems and big data? >> Yeah, it actually starts with even more of the fundamentals of the power processors. The power processor has eight threads per core in contrast to Intel's two threads per core. So this just means for being able to parallelize your workloads and workloads that come up in the cognitive space, whether you're running complex queries and need to drive SQL over a lot of parallel pipes or you're writing iterative computation, the same data set as when you're doing model training, these can all benefit from highly parallelized workloads, which can benefit from this 4x thread advantage. But of course to do this, you also need large, fast memory, and we have six times more cache per core versus Broadwell, so this just means you have a lot of memory close to the processor, driving that throughput that you require. And then on top of that, now we get to the ability to add accelerators, and unique accelerators such as I mentioned the NVIDIA in the links scenario for GPU or using the open CAPI as an approach to attach FPGA or Flash to get access speeds, processor memory access speeds, but with an attached acceleration device. And so this is economies of scale in terms of being able to offload specialized compute processing to the right accelerator at the right time, so you can drive way more throughput. The upper bounds are driving workload through individual nodes and being able to balance your IO and compute on an individual node is far superior with the power system server. >> Okay, so multi-threaded, giant memories, and this open CAPI gives you primitive level access I guess to a memory extension, instead of having to-- >> Yeah, pluggable accelerators through this high speed memory extension. >> Instead of going through, what I often call the horrible storage stack, aka SCSI, And so that's cool, some good technology discussion there. What's the business impact of all that? What are you seeing with clients? >> Well, the business impact is not everyone is going to start with supped up accelerated workloads, but they're going to get there. So part of the vision that clients need to understand is to begin to get more insights from their data is, it's hard to predict where your workloads are going to go. So you want to start with a server that provides you some of that upper room for growth. You don't want to keep scaling out horizontally by requiring to add nodes every time you need to add storage or add more compute capacity. So firstly, it's the flexibility, being able to bring versatile workloads onto a node or a small number of nodes and be able to exploit some of these memory advantages, acceleration advantages without necessarily having to build large scale out clusters. Ultimately, it's about improving time to insights. So with accelerators and with large memory, running workloads on a similar configured clusters, you're simply going to get your results faster. For example, recent benchmark we did with a representative set of TPC-DS queries on Hortonworks running on Linux and power servers, we're able to drive 70% more queries per hour over a comparable Intel configuration. So this is just getting more work done on what is now similarly priced infrastructure. 'Cause power family is a broad family that now includes 1U, 2U, scale out servers, along with our 192 core horsepowers for enterprise grade. So we can directly price compete on a scale out box, but we offer a lot more flexible choice as clients want to move up in the workload stack or to bring accelerators to the table as they start to experiment with machine learning. >> So if I understand that right, I can turn two knobs. I can do the same amount of work for less money, TCO play. Or, for the same amount of money, I can do more work. >> Absolutely >> Is that fair? >> Absolutely, now in some cases, especially in the Hadoop space, the size of your cluster is somewhat gated by how much storage you require. And if you're using the classic scale up storage model, you're going to have so many nodes no matter what 'cause you can only put so much storage on the node. So in that case, >> You're scaling storage. >> Your clusters can look the same, but you can put a lot more workload on that cluster or you can bring in IBM, a solution like IBM Spectrum Scale our elastic storage server, which allows you to essentially pull that storage off the nodes, put it in a storage appliance, and at that point, you now have high speed access to storage 'cause of course the network bandwidth has increased to the point that the performance benefit of local storage is no longer really a driving factor to a classic Hadoop deployment. You can get that high speed access in a storage appliance mode with the resiliency at far less cost 'cause you don't need 3x replication, you just have about a 30% overhead for the software erasure coding. And now with your compete nodes, you can really choose and scale those nodes just for your workload purposes. So you're not bound by the number of nodes equal total storage required by storage per node, which is a classic, how big is my cluster calculation. That just doesn't work if you get over 10 nodes, 'cause now you're just starting to get to the point where you're wasting something right? You're either wasting storage capacity or typically you're wasting compute capacity 'cause you're over provisioned on one side or the other. >> So you're able to scale compute and storage independent and tune that for the workload and grow that resource efficiently, more efficiently? >> You can right size the compute and storage for your cluster, but also importantly is you gain the flexibility with that storage tier, that data plan can be used for other non-HDFS workloads. You can still have classic POSIX applications or you may have new object based applications and you can with a single copy of the data, one virtual file system, which could also be geographically distributed, serving both Hadoop and non-Hadoop workloads, so you're saving then additional replicas of the data from being required by being able to onboard that onto a common data layer. >> So that's a return on asset play. You got an asset that's more fungible across the application portfolio. You can get more value out of it. You don't have to dedicate it to this one workload and then over provision for another one when you got extra capacity sitting here. >> It's a TCO play, but it's also a time saver. It's going to get you time to insight faster 'cause you don't have to keep moving that data around. The time you spend copying data is time you should be spending getting insights from the data, so having a common data layer removes that delay. >> Okay, 'cause it's HDFS ready I don't have to essentially move data from my existing systems into this new stovepipe. >> Yeah, we just present it through the HDFS API as it lands in the file system from the original application. >> So now, all this talk about rings of flexibility, agility, etc, what about cloud? How does cloud fit into this strategy? What do are you guys doing with your colleagues and cohorts at Bluemix, aka SoftLayer. You don't use that term anymore, but we do. When we get our bill it says SoftLayer still, but any rate, you know what I'm talking about. The cloud with IBM, how does it relate to what you guys are doing in power systems? >> Well the cloud is still, really the born on the cloud philosophy of IBM software analytics team is still very much the motto. So as you see in the data science experience, which was launched last year, born in the cloud, all our analytics packages whether it be our BigInsights software or our business intelligence software like Cognos, our future generations are landing first in the cloud. And of course we have our whole arsenal of Watson based analytics and APIs available through the cloud. So what we're now seeing as well as we're taking those born in the cloud, but now also offering a lot of those in an on-premise model. So they can also participate in the hybrid model, so data science experience now coming on premise, we're showing it at the booth here today. Bluemix has a on premise version as well, and the same software library, BigInsights, Cognos, SPSS are all available for on prem deployment. So power is still ideal place for hosting your on prem data and to run your analytics close to the data, and now we can federate that through hybrid access to these elements running in the cloud. So the focus is really being able to, the cloud applications being able to leverage the power and System z's based data through high speed connectors and being able to build hybrid configurations where you're running your analytics where they most make sense based upon your performance requirements, data security and compliance requirements. And a lot of companies, of course, are still not comfortable putting all their jewels in the cloud, so typically there's going to be a mix and match. We are expanding the footprint for cloud based offerings both in terms of power servers offered through SoftLayer, but also through other cloud providers, Nimbix is a partner we're working with right now who actually is offering our Power AI package. Power AI is a package of open source, deep learning frameworks, packaged by IBM, optimized for Power in an easily deployed package with IBM support available. And that's, could be deployed on premise in a power server, but also available on a pay per drink purpose through the Nimbix cloud. >> All right, we covered a lot of ground here. We talked strategy, we talked strategic fit, which I guess is sort of a adjunct to strategy, we talked a little bit about the competition and where you differentiate, some of the deployment models, like cloud, other bits and pieces of your portfolio. Can we talk specifically about the announcements that you have here at this event, just maybe summarize for use? >> Yeah, no absolutely. As it relates to IBM, and Hadoop, and Spark, we really have the full stack support, the rich analytics capabilities that I was mentioning, deep insight, prescriptive insights, streaming analytics with IBM Streams, Cognos Business Intelligence, so this set of technologies is available for both IBMs, Hadoop stack, and Hortonworks Hadoop stack today. Our BigInsights and IOP offering, is now out for tech preview, their next release their 4.3 release, is available for technical preview will be available for both Linux on Intel, Linux on power towards the end of this month, so that's kind of one piece of new Hadoop news at the analytics layer. As it relates to power systems, as Hortonworks announced this morning, HDP 2.6 is now available for Linux on power, so we've been partnering closely with Hortonworks to ensure that we have an optimized story for HDP running on power system servers as the data point I shared earlier with the 70% improved queries per hour. At the storage layer, we have a work in progress to certify Hortonworks, to certify Spectrum Scale file system, which really now unlocks abilities to offer this converged storage alternative to the classic Hadoop model. Spectrum Scale actually supports and provides advantages in both a classic Hadoop model with local storage or it can provide the flexibility of offering the same sort of multi-application support, but in a scale out model for storage that it also has the ability to form a part of a storage appliance that we call Elastic Storage Server, which is a combination of power servers and high density storage enclosures, SSD or spinning disk, depending upon the, or flash, depending on the configuration, and that certification will now have that as an available storage appliance, which could underpin either IBM Open Platform or HDP as a Hadoop data leg. But as I mentioned, not just for Hadoop, really for building a common data plane behind mixed analytics workloads that reduces your TCO through converged storage footprint, but more importantly, provides you that flexibility of not having to create data copies to support multiple applications. >> Excellent, IBM opening up its portfolio to the open source ecosystem. You guys have always had, well not always, but in the last 20 years, major, major investments in open source. They continue on, we're seeing it here. Steve, people are filing in. The evening festivities are about to begin. >> Steve: Yeah, yeah, the party will begin shortly. >> Really appreciate you coming on The Cube, thanks very much. >> Thanks a lot Dave. >> You're welcome. >> Great to talk to you. >> All right, keep it right there everybody. John and I will be back with a wrap up right after this short break, right back.