>> Narrator: Live from San Jose it's theCUBE. Presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media and its ecosystem partners. (upbeat music) >> Welcome back to San Jose everybody, this is theCUBE, the leader in live tech coverage and you're watching BigData SV, my name is Dave Vellante. In the early days of Hadoop everything was batch oriented. About four or five years ago the market really started to focus on real time and streaming analytics to try to really help companies affect outcomes while things were still in motion. Steve Wilks is here, he's the co-founder and CTO of a company called Stream, a firm that's been in this business for around six years. Steve welcome to theCUBE, good to see you. Thanks for coming on. >> Thanks Dave it's a pleasure to be here. >> So tell us more about that, you started about six years ago, a little bit before the market really started talking about real time and streaming. So what led you to that conclusion that you should co-found Steam way ahead of its time? >> It's partly our heritage. So the four of us that founded Stream, we were executives at GoldenGate Software. In fact our CEO Ali Kutay was the CEO of GoldenGate Software. So when we were acquired by Oracle in 2009, after having to work for Oracle for a couple years, we were trying to work out what to do next. And GoldenGate was replication software right? So it's moving data from one place to another. But customers would ask us in customer advisory boards, that data seems valuable, it's moving. Can you look at it while it's moving and analyze it while it's moving, get value out of that moving data? And so that was kind of set in our heads. And then we were thinking about what to do next, that was kind of the genesis of the idea. So the concept around Stream when we first started the company was we can't just give people streaming data, we need to give them the ability to process that data, analyze it, visualize it, play with it and really truly understand the data. As well as being able to collect it and move it somewhere else. And so the goal from day one was always to build a full end-to-end platform that did everything customers needed to do for streaming integration analytics out of the box. And that's what we've done after six years. >> I got to ask a really basic question, so you're talking about your experience at GoldenGate moving data from point a to point b and somebody said well why don't we put that to work. But is there change data or was it static data? Why couldn't I just analyze it in place? >> GoldenGate works on change data. >> Okay so that's why, there was changes going through. Why wait until it hits its target, let's do some work in real time and learn from that, get greater productivity. And now you guys have taken that to a new level. That new level being what? Modern tools, modern technologies? >> A platform built from the ground up to be inherently distributed, scalable, reliable with exactly one's processing guarantees. And to be a complete end-to-end platform. There's a recognition that the first part of being able to do streaming data integration or analytics is that you need to be able to collect the data right? And while change data captured from databases is the way to get data out of databases in a streaming fashion, you also have to deal with files and devices and message queues and anywhere else the data can reside. So you need a large number of different data collectors that all turn the enterprise data sources into streaming data. And similarly if you want to store data somewhere you need a large collection of target adapters that deliver to things. Not just on premise but also in the cloud. So things like Amazon S3 or the cloud databases like Redshift and Google BigQuery. So the idea was really that we wanted to give customers everything they need and that everything they need isn't trivial. It's not just, well we take Apache Kafka and then we stuff things into it and then we take things out. Pretty often, for example, you need to be able to enrich data and that means you need to be able to join streaming data with additional context information, reference data. And that reference data may come form a database or from files or somewhere else. So you can't call out to the database and maintain the speeds of streaming data. We have customers that are doing hundreds of thousands of events per second. So you can't call out to a database for every event and ask for records to enrich it with. And you can't even do that with an external cache because it's just not fast enough. So we built in an in-memory data grid as part of our platform. So you can join streaming data with the context information in real time without slowing anything down. So when you're thinking about doing streaming integration, it's more than just moving data around. It's ability to process it and get it in the right form, to be able to analyze it, to be able to do things like complex event processing on that data. And also to be able to visualize it and play with it is an essential part of the whole platform. >> So I wanted to ask you about end-to-end. I've seen a lot of products from larger, maybe legacy companies that will say it's end-to-end but what it really is, is a cobbled together pieces that they bought in and then, this is our end-to-end platform, but it's not unified. Or I've seen others "Well we've got an end-to-end platform" oh really, can I see the visualization? "Well we don't have visualization "we use this third party for visualization". So convince me that you're end-to-end. >> So our platform when you start with it you go into a UI, you can start building data flows. Those data flows start from connectors, we have all the connectors that you need to get your enterprise data. We have wizards to help you build those. And so now you have a data stream. Now you want to start processing that, we have SQL-based processing so you can do everything from filtering, transformation, aggregation, enrichment of data. If you want to load reference data into memory you use a cache component to drag that in, configure that. You now have data in-memory you can join with your streams. If you want to now take the results of all that processing and write it somewhere, use one of our target connectors, drag that in so you've got a data flow that's getting bigger and bigger, doing more and more processing. So now you're writing some of that data out to Kafka, oh I'm going to also add in another target adaptor write some of it into Azure Blob Storage and some of it's going to Amazon Redshift. So now you have a much bigger data flow. But now you say okay well I also want to do some analytics on that. So you take the data stream, you build another data flow that is doing some aggregation of a Windows, maybe some complex event processing, and then you use that dashboard builder to build a dashboard to visualize all of that. And that's all in one product. So it literally is everything you need to get value immediately. And you're right, the big vendors they have multiple different products and they're very happy to sell you consulting to put them all together. Even if you're trying to build this from open source and you know, organizations try and do that, you need five or six major pieces of open source, a lot of support in libraries, and a huge team of developers to just build a platform that you can start to build applications on. And most organizations aren't software platform companies, they're finance companies, oil and gas companies, healthcare companies. And they really want to focus on solving business problems and not on reinventing the wheel by building a software platform. So we can just go in there and say look; value immediately. And that really, really helps. >> So what are some of your favorite use cases, examples, maybe customer examples that you can share with me? >> So one of the great examples, one of my customers they have a lot of data in our HP non-stop system. And they needed to be able to get visibility into that immediately. And this was like order processing, supply chain, ERP data. And it would've taken a very large amount of time to do analytics directly on the HP nonstop. And finding resources to do that is hard as well. So they needed to get the data out and they need to get it into the appropriate place. And they recognize that use the right technology to ask the right question. So they wanted some of it in Hadoop so they could do some machine learning on that. They wanted some of it to go into Kafka so they could get real time analytics. And they wanted some of it to go into HBase so they could query it immediately and use that for reference purposes. So they utilized us to do change data capture against the HP nonstop, deliver that datastream out immediately into Kafka and also push some of it into HEFS and some of it into HBase. So they immediately got value out of that, because then they could also build some real-time analytics on it. It would sent out alerts if things were taking too long in their order processing system. And allowed them to get visibility directly into their process that they couldn't get before with much fewer resources and more modern technologies than they could have used before. So that's one example. >> Can I ask you a question about that? So you talked about Kafka, HBase, you talk about a lot of different open source projects. You've integrated those or you've got entries and exits into those? >> So we ship with Kafka as part of our product. It's an optional messaging bus. So, our platform has two different ways of moving data around. We have a high-speed, in-memory only message bus and that works almost network speed and it's great for a lot of different use cases. And that is what backs our data streams. So when you build a data flow, you have streams in between each step, that is backed by an in-memory bus. Pretty often though, in use cases, you need to be able to potentially rewind data for recovery purposes or have different applications running at different speeds and that's where a persistent message bus like Kafka comes in but you don't want to use a persistent message bus for everything because it's doing IO and it's slowing things down. So you typically use that at the beginning, at the sources, especially things like IOT where you can't rewind into them. Things like databases and files, you can rewind into them and replay and recover but IOT sources, you can't do that. So you would push that into a Kafka backed stream and then subsequent processing is in-memory. So we have that as part of our product. We also have Elastic as part of our product for results storage. You can switch to other results storage but that's our default. And we have a few other key components that are part of our product but then on the periphery, we have adapters integrate with a lot of the other things that you mentioned. So we have adapters to read and write HDFS, Hive, HBase, Across, Cloudera, Autumn Works, even MapR. So we have the MapR versions of the file system and MapR streams and MapR DB and then there's lots of other more proprietary connectors like CVC from Oracle, and SQL server, and MySQL and MariaDB. And then database connectors for delivery to virtually any JDBC compliant database. >> I took you down a tangent before you had a chance. You were going to give us another example. We're pretty much out of time but if you can briefly share either that or the last word, I'll give it to you. >> I think the last word would be that that is one example. We have lots and lots of other types of use cases that we do including things like: migrating data from on-premise to the cloud, being able to distribute log data, and being able to analyze that log data being able to do in-memory analytics and get real-time insights immediately and send alerts. It's a very comprehensive platform but each one of those use cases are very easy to develop on their own and you can do them very quickly. And of course as the use case expands within a customer, they build more and more and so they end up using the same platform for lots of different use cases within the same account. >> And how large is the company? How many people? >> We are around 70 people right now. >> 70 People and you're looking for funding? What rounds are you in? Where are you at with funding and revenue and all that stuff? >> Well I'd have to defer to my CEO for those questions. >> All right, so you've been around for what, six years you said? >> Yeah, we have a number of rounds of funding. We had initial seed funding then we had the investment by Summit Partners that carried us through for a while. Then subsequent investment from Intel Capital, Dell EMC, Atlantic Bridge. And that's where we are right now. >> Good, excellent. Steve, thanks so much for coming on theCUBE, really appreciate your time. >> Great, it's awesome. Thank you Dave. >> Great to meet you. All right, keep it right there everybody, we'll be back with our next guest. This is theCUBE. We're live from BigData SV in San Jose. We'll be right back. (techno music)