 From San Jose, California, it's theCUBE, covering Big Data Silicon Valley 2017. Welcome back, everyone. We are at Big Data Silicon Valley, running in conjunction with Strata and Hadoop World in San Jose. I'm George Gilbert and I'm joined by Raimi Stady. And Raimi was most recently CEO and founder of Altiscale. Hadoop is a service vendor. One of the few out there, not part of one of the public clouds and in keeping with all the great work they've done, they got snapped up by SAP. So Raimi, since we haven't seen you, I think on theCUBE, since that's happened, why don't you catch us up with all the good work that's gone on between you and SAP since then? Sure, so the acquisition closed back in September. So it's been about six months. And it's been a very busy six months. There's just a lot of blocking and tackling that needs to happen. So getting people on board, getting new laptops, a lot of good stuff, but certainly a huge effort for us was to open up a data center in Europe. We've long had demand to have that European presence, both because I think there's a lot of interest in Hadoop over in Europe itself, but also large multinational companies based in the US. It's important for them to have that European presence as well. So it was a natural thing to do as part of SAP. So kind of first order of business was to expand over into Europe. So that was a big exercise. We've actually had some good traction on the sales side. So we're getting new customers, larger customers, more demanding customers, which has been a good challenge to the team. So let's pause for a minute and sort of unpack for folks what Altiscale offered, the core services that were here in the US and now you've extended to Europe. Right, so our core platform is kind of Hadoop, Hive and Spark as a service in the cloud. And so we would offer HDFS and Yarn from Hadoop, Spark and Hive kind of well integrated. And we would offer that as a cloud service. So you would just get an account log in, store stuff in HDFS, run your Spark programs. And the way we encourage people to think about it is very often I think vendors have trained folks in the big data space to think about nodes. How many nodes am I gonna get? What kind of nodes am I gonna get? And the way we really force people to think twice about Hadoop and what Hadoop as a service means is to say, why are you asking that? You don't need to know about nodes. Just store stuff, run your jobs. We worry about nodes. And that, you know, once people kind of understood, you know, just how much complexity that takes out of their lives and how that just enables them to truly focus on using these technologies to get business value rather than operating them. You know, there's that aha moment in the sales cycle where people say, yeah, that's what I want. I want Hadoop as a service. So that's been our value proposition from the beginning. And it's remained, you know, quite constant and even coming into SAP, that's not changing, you know, one bit. So just to be clear then, it's like a lot of the operational responsibilities sort of you took control over so that when you say like, don't worry about nodes, it's customer pours X amount of data into storage, which in your case would be HDFS. And then compute is independent of that. They need, you spin up however many or however much capacity they need with Spark, for instance, to process it or Hive. Right. Okay, so it sounds like- And all on demand? Yeah, so it sounds like it's, how close to like the BigQuery or Athena services, Athena on AWS or BigQuery on Google where you're not aware of any servers either for storage or for compute. How- Yeah, I mean, I think that's a very good comparable. It's very much like Athena in BigQuery where you just store stuff in tables and you issue queries and you don't worry about how much compute, you know, and managing it. I think by throwing Spark in the equation and yarn more generally, right, we can handle a broader range of use cases. So, for example, you don't have to store file and data tables, you can store them into HDFS files which is good for processing log data, for example. And with Spark, for example, you have access to a lot of machine learning algorithms that are a little bit harder to run in the context of, say, Athena. So I think it's the same model in terms of it's, you know, fully operated for you but a broader platform in terms of its capabilities. Okay, so now let's talk about what SAP brought to the table and how that changed the use cases that were appropriate for Alta Scale. You know, starting at the data layer. Yeah, so I mean, I think the, certainly from the business perspective, SAP brings a large, very engaged customer base that, you know, is eager to embrace kind of a data-driven mindset and culture and is looking for a partner to help them do that, right? And so that's been great to be in that environment. SAP has a number of additional technologies that we've been integrating into the Alta Scale offering. So one of them is Vora, which is kind of an interactive SQL engine. It also has a time series capabilities and graph capabilities and search capabilities. So it has a lot of additive capabilities, if you will, to what we have at Alta Scale. And it also integrates very deeply into HANA itself. And so we now have that Vora technology available as a service at Alta Scale. Let me make sure so that everyone understands, and so I understand too, is that so that you can issue queries from HANA and they can, you know, beyond just simple SQL queries, they can handle the time series and predictive analytics and access data sort of seamlessly that's in Hadoop. Or can it go the other way as well? It's both ways. So you can, you know, from HANA, you can essentially federate out into Vora and through that access data that's in a Hadoop cluster. But it's also the other way around. A lot of times there's an analyst who really lives in the big data world, right? They're in the Hadoop world, but they want to join in data that's sitting in a HANA database, you know, might be dimensions in a warehouse or customer details even in a transactional system. And so, you know, that Hadoop-based analyst, you know, now has access to data that's out in those HANA databases. Do you have some lighthouse accounts that are working with this already? Yes, we do. Yes, we do. Okay, I guess that was the diplomatic way of saying yes, but no comment. All right, so tell us more about SAP's big data stack today and how that might evolve. Yeah, I mean, of course now, especially in the cloud, we've got, you know, the Spark Hadoop, you know, Hive offering that that we have and then Vora sitting on top of that. There's an offering called predictive analytics which is Spark-based predictive analytics. Is that something that came from you or is that? That's an SAP thing, right? Okay. So, and this is what's been great about the acquisition is that, you know, SAP does have a lot of technologies that we can now integrate. And it brings new capabilities to our customer base. So, you know, those three are kind of pretty key. And then there's something called data services as well which allows us to move data easily in and out of, you know, HANA and other data stores. Is this the ability to federate queries between, you know, Hadoop and HANA and then migration of the data between the stores? Does that change the economics of how much data people, SAP customers, maintain and sort of what type of apps they can build on it now that they might, you know, it's economically sort of feasible to store a lot more data? Well, yes and no. I mean, I think the context of Altascale, both before and after the acquisition is very often there's what you might call a big data source, right? It could be your web logs. It could be some, you know, IoT generated log data. It could be social media streams. You know, this is data that's, you know, doesn't have a lot of structure coming in. It's fairly voluminous. It doesn't very naturally go into a SQL database, you know, and that's kind of the sweet spot for the big data technologies like Kadeep and Spark. So those data, you know, come into your big data environment, you can transform it, you can do some data quality on it and then you can eventually stage it out into something like a HANA data mart where, you know, to make it available for reporting. But obviously there's stuff that you can do on the larger data set in Hadeep as well. And so in a way, yes, you can now team, if you will, those huge data sources that, you know, weren't practical to put into, you know, into a HANA database, say. If you were to prioritize in the context of sort of the application SAP, applications SAP focused on, would you be sort of, would the highest priority use case be IoT related stuff where, you know, it was just prohibitive to put it in HANA since it's mostly in memory. But, you know, SAP is exposed to tons of that type of data which would seem to most naturally have affinity to alter scale. Yeah, so, I mean, IoT is a big initiative and is a great use case, you know, for big data. But, you know, the financial services industry as another example, you know, is fairly down the path, you know, using Hadeep technologies for many different use cases. And so that's also an opportunity for us. So let me pop back up, you know, before we have to wrap. With AltaScale as part of the SAP portfolio, have the two companies sort of gone to customers with more transformational options that, you know, you'll sell together? Yeah, we have, in fact, AltaScale actually is no longer called AltaScale, right? We're part of a portfolio of products, you know, known as the SAP Cloud Platform. So, you know, under the Cloud Platform were the big data services. The SAP Cloud Platform, you know, is all about business transformation and business innovation. And so we bring to that portfolio the ability to now bring the types of data sources that I just discussed, you know, to bear on these transformative efforts. And so, you know, we fit into some momentum SAP already has, right, to help companies drive change. Okay. So, and along those lines, which might be, I mean, we know the financial services has been, has done a lot of work with, and I guess telcos as well. What are some of the other verticals that look like they're primed to fall, you know, with this type of transformational artwork? Yeah, so you mentioned one, which I kind of call manufacturing, right? And there tends to be two kind of different use cases there. One of them I call kind of the shop floor thing where you're collecting a lot of sensor data, you know, out of a manufacturing facility with the goal of increasing yield, typically. So you've got the shop floor and then you've got, I think, more commonly discussed, you know, measuring stuff out in the field. You've got a product, you know, out in the field, bringing the telemetry back, doing things like predictive maintenance. So I think manufacturing, you know, is a big sector ready to go for big data. And healthcare is another one. People pulling together electronic medical records, you know, trying to combine that with clinical outcomes. And I think the big focus there is to drive towards kind of outcome-based models, even on the payment side. And big data is really valuable to try to assess, you know, kind of outcomes in an aggregate way. Okay, we're going to have to leave it on that note, but we will tune back in at, I guess, Sapphire or TechEd, whichever of the SAP shows is coming up next to get an update. Sapphire's next? Then TechEd. Okay. With that, this is George Gilbert and Raimi Stata. We will be back in a few moments with another segment. We're here at Big Data Silicon Valley, running in conjunction with Strata and Hadoop World. Stay tuned, we'll be right back.