 Live from Midtown Manhattan, it's theCUBE, covering Big Data, New York City, 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. Okay, welcome back everyone to our live special CUBE coverage in New York City in Manhattan. We're here in Hell's Kitchen for theCUBE's exclusive coverage of our Big Data NYC event and Strata data, which used to be called Strata Hadoop. It used to be Hadoop World, but our event, Big Data NYC, is our fifth year where we gather every year to see what's going on in Big Data, Big Data World and also produce all of our great research. I'm John Furrier, the co-host of theCUBE with Peter Burris, head of research. Our next guest is Itamar Anacorian, who's the chief marketing officer at Atunity. Welcome back to theCUBE, good to see you. Thank you very much. Good to see you back. So we've been covering Atunity for many, many years. We've had many conversations. You guys have had great success in Big Data. So congratulations on that. Thank you. And we're seeing data integration. We've been calling this for multiple years. That's not going away. People need to integrate more. But with cloud, there's been a real focus on accelerating the scale component with an emphasis on ease of use, data sovereignty, data as governance. So all these things are coming together. So the cloud has amplified what's going on in the Big Data world. And it's like, listen, get moving or you're out of business. It's pretty much been the mandate we've been seeing. So a lot of people have been reacting. What's your response at Atunity these days because you have successful piece parts with your product offering? What's the big update for you guys with respect to this big growth area? Thank you. First of all, the cloud and data lakes have been a major force, changing the data landscape and data management landscape for enterprises. And over the past few years, we've been working closely with some of the world's leading organizations across different industries as they deployed the first and then second and third iteration of the data lake and Big Data architectures. And one of the things of course we're all seeing is the move to the cloud whether we're seeing enterprises move completely to the cloud, kind of move their data lakes. That's where they build them or actually have a hybrid environment where part of the data lake and data works analytics environment is on-prem and part of it is in the cloud. The other thing we're seeing is that enterprises are starting to mix more of the traditional data lake, the cloud as the platform and streaming technologies is a way to enable all the modern data analytics that they need. And that's what we have been focusing on, on enabling them to use data across all these different technologies where and when they need it. So the sum of the parts is worth more if it's integrated together, it seems to be the positioning, which is great, it's what customers want, make it easier. What is the hard news that you guys have because you have some big news. Let's get to the news real quick. Thank you very much. Today we've announced, we're very excited about it. We've announced a new big release of our data integration platform. Our modern platform now brings together Attunity Replicate, Attunity Compose for Hive and Attunity Enterprise Manager or AEM. So these are products that we've evolved significantly invested a lot over the last few years to enable organizations to use data, make data available and available in real time across all these different platforms and then turn this data to be ready for analytics, especially in Hive and Hadoop environments on-prem and now also in the cloud. So today we've announced a major release with a lot of enhancements across the entire product line. So people might know you guys for the replicate piece. I know that this announcement was 6.0, but as you guys have the other piece part to this, really it's about modernization of kind of old school techniques. That's really been the driver of your success. What specifically in this announcement makes it, you know, really work well for people who are moving real time. They want to have good data access. What's the big aha for this, for the customers out there with attunity on this announcement? That's a great question. Thank you. First of all, is that we're bringing it all together. So as you mentioned over the past few years, attunity replicate has emerged as the choice of many Fortune 100 and other companies who are building modern architectures and moving data across different platforms to the cloud, to the lakes and they're doing it in a very efficient way. One of the things we've seen is that they needed the flexibility to adapt as they go through their journey to adapt different platforms and what we gave them with replicate was the flexibility to do so. So we gave them the flexibility, we gave them the performance to get the data and the efficiency to move only the changes of the data as they happen and to do that in a real time fashion. Now that's all great, but once the data gets to the data lake, how do you then turn it into valuable information? That's when we introduced Compose for Hive, which we talked about in our last session a few months ago, which basically takes the next stage in the pipeline, picking up incremental continuous data that is fed into the data lake and turning those into operational data store, historical data stores, data stores that's basically ready for analytics. What we've done with this release that we're really excited about is putting all of this together in a more integrated fashion, putting Attunity Enterprise Manager on top of it to manage larger scale environments so customers can move faster in deploying these solutions. As you think about the role that Attunity is going to play over time though, it's going to end up being part of a broader solution for how you handle your data. Imagine for a second, the patterns that your customers are deploying. What is Attunity typically being deployed with? That's a great question. First of all, we're definitely part of a large ecosystem for building the new data architecture, new data management with data integration being more than ever a key part of that bigger ecosystem because what we actually have today is more islands. We have more places where the data needs to go and to your point, more patterns in which the data moves. One of those patterns that we've seen significantly increase in demand and deployment is streaming. So where data used to be batch, right, now we're all talking about streaming. Kafka has emerged as a very common platform but not only Kafka. If you're on Amazon web services, you're using Kinesis. If you're in Azure, you're using Azure Event Hubs. So you have different streaming technologies. So that's spot of how they says evolve. How is that challenged? Because you just bring up a good point. I mean, with the big trend that customers want is they want either the same code bases in on-prem and if they have the hybrid, which means the gateway, if you will, to the public cloud, kind of, they want to have the same code base or move workloads between different clouds, multi-cloud. It seems to be the holy grail. We've identified it. We're taking the position that we think multi-cloud will be the preferred architecture going forward. I mean, not necessarily this year, but it's going to get there. But as a customer, I don't want to rebuild the employees and get skill development and retraining on Amazon, Azure, Google. I mean, each one has its own different path. You mentioned it. How do you talk to customers about that? Because they might be like, whoa, I want it, but how do I work in that environment? You guys have a solution for that? We do. And in fact, one of the things we've seen, to your point, we've seen the adoption of multiple clouds. And even if that adoption is staged, what we're seeing is more and more customers that are actually referring to the term locking in respect to the cloud. So do we put all the eggs in one cloud? Or do we allow ourselves the flexibility to move around and use different clouds and also mitigate our risk in that respect? So what we've done from that perspective is, first of all, when you use the Attunity Platform, we take away all the development complexity. So in the Attunity Platform, it is very easy to set up your data flows, your data pipelines. And it's all common and consistent. So whether you're working on prem, whether you're working on Amazon Web Services, on Azure, or on Google, or other platforms, it all looks and feels the same. So first of all, you solve the issue of the diversity, but also the complexity. Because what we've done is, this is one of the big things that Attunity has focused on was reducing the complexity, allowing to configure these data pipelines without development efforts and resources. One of the challenges, or one of the things you typically do to take complexity out is you do a better job of design up front. And I know that Attunity's got a tool set that starts to address some of these things. Take us a little bit through how your customers are starting to think in terms of designing flows, as opposed to just cobbling together things in a spoke way. How is that starting to change as customers gain experience with large data sets, the ability to, the need to aggregate them, the ability to present them to developers in different ways? So that's a great point. And again, one of the things we focused on is to make the process of developing or configuring these different data flows easy and modular. So first of all, in Attunity, you can set up different flows in different patterns, and you can then make them available to others for consumption. So some create the data ingestion, some create the data ingestion, and then create the transformation with Compose for Hive. And with Attunity Enterprise Manager, we've now also introduced APIs that allow you to create your own microservices, consuming and using the services enabled by the platform. So we provide more flexibility to put all these different solutions together. What's the biggest thing that you see from a customer standpoint from a problem that you solve? If you had to kind of lay it out here in the classic, hey, what problem do you solve? Because there are many, right? So take us through the key problem, and then if there's any secondary issues that you guys can address customers, that seems to be where the conversation starts. What are the key problems that you solve? So I think one of the major problems we solve is scale. Our customers that are deploying data lakes are trying to deploy and use data that is coming not from five or 10 or even 50 data sources. We are working at hundreds, going on thousands of data sources now. So that itself represents a major challenge to our customers, and we're addressing it by dramatically simplifying and making the process of setting those up very repeatable, very easy, and then providing the management facility. Because when you have hundreds or thousands, management becomes a bigger issue to operationalize it. So we invested a lot in the management facility. For those, from a monitoring, control, security, how do you secure it? The data lake is used by many different groups. So how do we allow each group to see and work only on what belongs to that group? So that's part of it. So again, the scale is the major thing there. The other one is real timeliness. So we talked about the move to streaming, and a lot of it is in order to enable streaming analytics, real-time analytics. All that's only as good as your data, so you need to capture data in real-time. And that's, of course, has been our claim to fame for a long time, being the leading independent provider of CDC. Changed it to capture technology. What we've done now, and also expanded significantly with the new release, version six, is creating universal database streaming. So we take databases, we take databases, all the enterprise databases, and we turn them into live streams. So when you think, by the way, about the most common way that people have used, customers have used to bring data into the lake from a database, it was Scoop. And Scoop is a great, easy software to use from an open source perspective, but it's scripting and batch. So you're building your new modern architecture with the tool that effectively is scripting and batch. What we do with CDC is we enable to take a database, and instead of the database being something you come to periodically to read it, we actually turn it into a live feed. So as the data changes in the database, we stream it, we make it available across all these different platforms. It changes the definition of what live streaming is. We're live streaming theCUBE, we're data streaming, and you get great data. So here's the question for you. This is a good topic, I love this topic. Peter and I talk about this all the time. And it's been addressed in the big data world, but it's kind of, you can see the pattern going, mainstream in society, globally, geopolitically, and also in society. Batch processing and data in motion are real time. When you're streaming, brings up this use case to the end customer, which is, this is where they've done it before, certainly store things in data lakes that's not going to go away. You're going to store stuff, but the real game is in motion. So how do you describe that to a customer when you go out and say, hey, you've been living in a batch world, but wake up to the real world called real time. How do you get them to align with, some people get it right away, I see that. Some people don't. How do you talk about that? Because that seems to be a real cultural thing going on right now or operational readiness from the customer standpoint. Can you just talk through your feeling on that? Yeah. So first of all, some of it gets lost in translation. And we see quite a few companies and even IT departments that when you talk, when they refer to real time or their business tells them we need real time, what they understand from it is, when you ask for the data, the response will be immediate. So you get real time access to the data, but the data is from last week. So you get real time access, but to last week's data. And that's what we try to do is to basically say, wait a second, when you mean real time, what does real time mean? And we start to understand what is the meaning of using last week's data versus or yesterday's data, whether the real time data, and that makes a big difference. We actually see that today the access, the availability, the ability to act on the real time data, that's the frontier of competitive differentiation. That's what makes the customer experience better. That's what makes the business more operationally efficient than the competition. So it's the data, not so much the process of what they used to do. Their version of real time is I respond it to you pretty quickly. Exactly, the other thing that's interesting is because we see it would again change the capture becoming a critical component of the modern data architecture. Traditionally we used to talk about different type of tools and technology. Now CDC itself is becoming a critical part of it. And the reason is that it serves and it answers a lot of fundamental needs that are now becoming critical. One is the need for real time data. The other one is efficiency. If you're moving to the cloud, and we talked about this earlier, if your data lake is going to be in the cloud, there's no way you're going to reload all your data because the bandwidth is going to get in the way. So you have to move only the delta. So you need the ability to capture and move only the delta. So CDC becomes fundamental both in enabling the real time as well as the efficient, the low impact data integration. You guys have a lot of partners, technology partners, global SIs, resellers, a bunch of different partnership levels. So the question I have for you to get your reaction and share your insight into is, okay, as the relationship to the customer who has the problem, what's in it for me? I want to move my business forward. I want to get into digital business. I need to get at my real time data as it's happening. Whether it's near real time or real time, that's evolution. But ultimately they have to move their developers down a certain path. They'll usually hire a partner. So the relationship between partners and you, the supplier to the customer has changed recently. How is that evolving? So first of all, it's evolving in several ways. We have invested on our part to make sure that we're building Trinity as a leading vendor in the ecosystem of the system integration consulting companies. We work with pretty much all the major global system integrators, as well as regional ones, boutique ones that focus on the emerging technologies, as well as get the modern analytic type platforms. We work a lot with plenty of them on major corporate data center level migrations to the cloud. So again, the motivations are different, but we invest a lot more. Are we more specialized in seeing more specialty? So we've been a technology and partner of choice to both Amazon and Microsoft for enabling, facilitating the data migration to the cloud. And they have, of course, their select or preferred or group of partners they work with. So we all come together to create these solutions. And what's the goals for Trinity as we wrap up here? I'll give you the last word. As you guys have this big announcement, you're bringing it all together. Integrating is key. It's always been your ethos in the company. Where is this next level? What's the next milestone for you guys? What do you guys see going forward? So first of all, we're going to continue to modernize. So we're really excited about the new announcement we did today. Replicate 6, AEM 6, compose, the new version of Compose for Hive that now also supports more data lakes, Autumn Wars, Cloudera, EMR. And the key point for us was expanding AEM to also enable analytics on the data we generate as data flows through our Trinity. So the old point is modernizing data integration, providing more intelligence in the process, reducing the complexity, and facilitating the automation end-to-end. So we're going to continue to, so in terms of automation is a big thing for us. Again, the point is you need to scale. You know, to scale, we want to generate things for you so you don't need to develop for every piece. So we automate the automation. Okay, the whole point is to deliver the solution faster. And the way we're going to do it is to continue to enhance each one of the products in its own space. If it's replication across systems, Compose for Hive transformations and pipeline automation and AEM for management, but also to create the integration between them. So again, for us is to create a platform that for our customers, they get more than the sum of the parts, they get the unique capabilities that we bring together in this platform. Itamar, thanks for coming on theCUBE. Appreciate you congratulating us on this opportunity. And you guys get bringing it all together, congratulations. Thank you very much for that to be here. This is theCUBE, live coverage, breaking it down here in New York City, in Manhattan. I'm John Furrier, Peter Burris. Be right back with more after this short break.