 Live from New York, it's theCUBE. Covering theCUBE, New York City, 2018. Brought to you by SiliconANGLE Media and it's ecosystem partners. Welcome back to theCUBE here live in New York City for CUBE NYC. This is our feature presentation and program for two days live coverage of CUBE NYC and also Stratoconference going on right around the corner. I'm John Furrier, Dave Vellante. Our next guest is back with Amar Anacorian who's the atunity CMO. Good to see you again. Thanks for coming back in. Thank you very much. It's good to be back guys. It's been a year since we chatted with a lot's changed obviously in the market. We've talked about the shifts that's going on. You're seeing a revolution of the application developer market where new modern application programming techniques are being used. Infrastructure as code, DevOps. But the big story obviously is the impact of machine learning, which is now being bundled with AI, the future of AI. We had discussion last night, an event here. It's important, that's a huge trend. That's the catalyst. Yeah, we're seeing that as well. It's to your point as a catalyst. We're seeing an acceleration with the demand from enterprise customers. We're looking at ML and machine learning and AI as really was driving the next phase in the modern data analytic initiatives. And that's gonna start, starts with spin the wheel. So the modern analytics are spinning the need for modern platforms, data platforms, spinning the wheel for modern data integration, which is kind of where we come in and enabling the data for all of those initiatives. So we've absolutely seen a lot of more demand. I think it's kind of a change in the phase in the market from building lakes and populating them to actually using the data. When you start using the data, you also want more real-time data. You want the data to conform to the application you're using, like machine learning and AI. And we've seen really cool innovative applications too by customers from predictive maintenance, in large automotives to fraud-related activities in financial services to improving production lines in manufacturing. So a lot of applications where data gets collected, brought by the way, either to an on-premise or a cloud, now, data lake, where the machine learning and AI runs. So this is really your point. The machine learning and the appetite and the demand for the conversation hype is really an indicator that the setup phase is over and we're moving to get value out of the data. People are using it more so these data lakes and environments are being set up, hence the driving machine learning. They get that right? Is that... Yes, I think it goes back into that. It goes into the next phase of maturity, in a sense. So I think we're much like moving through a phase of learning how to build lakes. Now we're going through the phase of understanding how to build and better processes, methods of doing machine learning all the way from how we acquire the data to how we actually model and use it. So it kind of impacts this. That's actually the question. So if people scratch their head going, okay, my data lake didn't work out. So people, it's kind of as a test. It's like, if you're not set up operational right now, you're kind of behind. Are you seeing customers who have kind of botched the data lake or didn't get it right? And what do they do? Because they want to, again, get to this use case that you're referring to. You're seeing some folks who have botched the setup and are trying to figure out, still have not gotten there yet. Do you see that any market activity there? We've seen customers shift and pivots on a journey. So I think that as customers, we've seen customers getting on this journey from other analytics, they got started. They knew where they were starting. They had an idea of where they want to get to. How they get there is something that many of them are figuring out on the way. So we've actually been playing with this term of architecture in motion because we're seeing customers shift and change platforms and architectures on this path. So to your point, it's not necessarily about botching as much as experimenting, adapting and adopting. And Cloud's helped, too. Cloud is making a huge difference. Well, you kind of went to the early phases of so-called big data. You said, okay, throw it into a data lake, no scheme on right, a lot of unstructured data. And then it became, okay, how do we get value out of this? And then people realized, well, to get value out of it, I got to integrate it with other data sources. I got data sitting on mainframe platforms. I got data and all the other databases. So it took a while for people just to, they said, oh, hey, it's not as expensive to just put it into a data lake. So let's keep the data instead of throwing it away, who's actually throwing it away is kind of expensive, figuring out what to throw away and what not to throw away. And then the sort of trend of, all right, how do we get more value out of it? That's where you guys come in. You've seen the results. I mean, your numbers are pretty good. You guys have upgraded your sales force. You go to market pretty substantially. 47% revenue growth, 86% license growth. So that seems to be working. It's a combination, is my right, the combination of the tailwinds of the market trends, upgrading your, you go to market, your thoughts. Yeah, so that's a good point. It's a combination of a few things. I think the market demand, okay, is driving most of that growth. They move to the cloud and this less level phase of maturity in the data lakes requiring a real-time data, requiring much higher scales. That's what we're seeing with this kind of next phase. Those things are aligned very well with the core for what our product and technology does, which in the gestivate is about moving the delta. So our technology, right, identifies only the changes in a lot of different systems. So it allows you to get only the changes, the delta. That has become much more significant because if you're moving, if your data lake is in the cloud and you're bringing data, sourcing data, acquiring data from a lot of different locations, you're not gonna move all the data over the wire. You just wanna move the delta. And about scale, it's actually a interesting story. One of our customers, very large automotive company, and they were sourcing data from today, some of it run 500 and some data sources. And they got to get to month where they're moving 100 and even over 200 billion changes every month. And initially we're excited about moving the amount of changes. But then what dawned the bus is how much would they have moved if they didn't move only the changes? So the ability to move the change actually enabled that kind of data lake. There's no way they would have been able to bring all this data economically if they brought all the data, all the time into the lake. So if you want to do the machine learning, in their case predictive maintenance, and you need to source data from a lot of places, you gotta move the delta. So first of all, the reason for the growth is that the need, because of the changes in maturity and the move to the cloud, the need for the core way of moving only the delta became significant. To that, we've increased a lot in our investments in our sales force, pre-sale, post-sale. So that's a winning solution for you guys. It is, it is. And now by the way, it's the next products aligned with it, both in our alignment with multi-cloud environments, as well as with streaming the pipeline. So from generating a stream, so we focus on enhancing the product, from generating a stream out of databases, to delivering the stream to a much broader, by the way, set of environments, to refining the stream, which goes back into how do we now construct a dataset that can be more easily used for analytics. So we're expanding the product line also there. Yeah, we talked about last year, the pipeline, I think. So talk about the impact to your business on the partnership side. You guys have news, hard news today. Yes, very excited. With Google Cloud, talk about the news. What's the big news? Yeah, we're very excited about it. Again, Google has been a partner for us for a long time. And this morning, we've announced that we've expanded their offering for the Google Cloud platform for GCP. And it's really focused around enabling to deliver data to a wider set of data services on the Google Cloud platform. And of course, including the data lake. So we are seeing the data lake be wheeling around the cloud storage. So in the case of Google GCS, Google Cloud Storage. And now we've enhanced our support to deliver the data. For example, to GCS. Through GCS, you can get to BigQuery. You can get to the, we work with Dataproc. So again, there's a way to get a data and facilitate the building for Datalake on the Google Cloud platform. So we're very excited about that. And it kind of rounds up also our multi-cloud offering. So as I mentioned earlier about companies on a journey and adapting and adopting different platforms. Part of it is hybrid environment and also became a multi-cloud environment. So we have a very round up offering on Amazon, web services. Again, we've done a lot of work over the past year and more on Azure and now also Google. So whichever cloud you want to build your lake on, whatever data services you want to use around it, we at Trinity now can help you get a data there. So you guys are all in a multi-cloud for you guys. All in. You knocked down the big three in terms of just integration. Yeah, exactly. Exactly. So again, very excited about it. And we're seeing, and by the way, this is a response not just to round up the offering because it looks nice, it's because of customer demand. It's what we're doing with a Google as a partner and what we're seeing customers want to do. So we talked earlier about sort of people shoving stuff into the Datalake and then the wave of okay, now we got to get insights out of there. Part of that has been bringing together a multiple data sources, the enterprise data warehouses. There's always been a critical part of people's big data strategies. So where do you see it going now? This is sort of, we talked last night, we had an event here talking about AI and the future of AI and we sort of debated the same wine, new bottle or substantive changes. And clearly the consensus was this is something new. What do you see in terms of that AI layer coming in and what can we expect for the next phase of this sort of data trend? Yeah, so that's a great question. I think there's a couple of things we're seeing. First of all, there's more and more tooling and I think we're gonna start to see more applications come out, much more agility into that process. Again, we are focused on our side on the agility by allowing non-developers to make data available because without that the ability to scale and have data for AI is just not there. So the whole point of AI in ML is that you want more data. The more data you have, the smarter your model can become. So that means you need to scale the amount of systems and sources you fit in and again, what we've done is part of the innovation beyond moving the Delta was creating automation software that does not require developer skillset to set it up. So it allows you to scale much, much faster. Our customers are in hundreds and some of them are in thousands of data feeds today because of it. So that's part of it. The other one is real time. So starting to use AI with a more real time stream kind of way. For example, some of them can look at image processing as part of quality control on the production line. I have something takes a picture and I can correlate that picture is everything okay on the production line because I have reference data for the parts that went into that specific line which supplies it came from and I can do optimizations by using machine learning either for that plant or even more globally in my supply chain. So I think the combination of scale real time is part of what we'll see. So in that example you're doing a real time comparison with the steady state and with the actual state and understanding the Delta and then what taking action against that? So what we're doing is feeding the data that goes into that kind of model and engine. And what we're seeing is customers need more data to feed on that engine. You're seeing a plethora of addition more and more libraries and from image to others to do the machine learning to run the algorithm is they need the data from all the systems to correlate the model too. So that's what we're coming in. More data, better outcomes. So where are you guys going to be? Take us through your event calendar. Where can people find you out in the wild and events coming up? You got what's on the horizon for you guys? So there's a lot of stuff coming up. We're very excited about the roadmap, what we're doing in the product, how we're going to do it with customers. Again, we're significant investing in our extending our sales force and our product offering, giving the need we're seeing in the marketplace. So we're focused on this kind of idea of streaming pipeline. We're expanding, if you think about generating streams, delivering streams and refining streams. So we're focused on continuing to expand each and every one of those. So more sources, you're going to see from maternity, growth of more sources that we can bring. More targets or places where you deliver it from data lakes to cloud platforms. We're already in many of them, but continue to expand that. There's always new technologies coming out down to refinement, which is where we use engines like Hive Engine to Spark Engine and others to transform the data that we've landed and turn it into analytic data sets in different environments. Again, because we have a more diverse data lake environment. So we're going to continue to expand that. We're going to have a big focus on again, the global 2000 customer base. We have a very unique offering for them. And we're going to continue to meet you guys and be at big industry events from the big data events to cloud events and a lot of our partners because we're very aligned with them. Well, congratulations. You guys certainly have been leveling up and being ahead of the industry and the success shows it. Congratulations. Thanks for coming on. Thank you very much. Live here in New York City, I'm John Furrier, Dave Vellante, breaking down all the action happening in the big data machine learning, AI, now cloud world all coming together, coming together here in New York. We'll be right back with more after this short break.