 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 New York City, everybody. Hashtag, cube NYC, our special presentation. Used to be really focused on big data, that big data sort of evolving into artificial intelligence and automation and just new layers of innovation in the industry. Obviously, cloud has been a big topic of discussion this week. theCUBE is the leader in live tech coverage. My name is Dave Vellante and I'm here with my co-host, Peter Burris, along, LeMental is here, he's the senior manager of Solutions Marketing for Digital Business Automation at BMC alone. Thanks for coming on theCUBE, welcome. Thank you, it's good to be here. So digital business automation, digital means data. Peter and I talk about that all the time. If it's data these days, it's probably big data and these days, cloud. Absolutely, yes. Why cloud? Well, it's not a secret that cloud is becoming the platform of choice in many of the big data projects. When we talk about big data, cloud has a lot to offer. Big data means big amount of data, naturally movement of the data, massive need for computing and storage and this is exactly what cloud is providing. The other aspect is the flexibility of the cloud, being able to scale up and down as necessary, which fits, of course, the nature of big data projects. Also when we look at what the cloud vendors are doing these days, they're providing lots of new technologies, lots of new functionalities, specifically around big data, not only and this is very attractive for customers that are implementing big data and want to take advantage of all those new services that the cloud providers are including these days. The cloud's way of doing things isn't always the enterprise's way of doing things and it seems like the cloud way has the momentum but what are you seeing there in terms of alignment between the cloud model and the traditional enterprise model and how are those coming together? Well, there are many types of organizations today and what we see that many organizations are adopting cloud but it's not like they start a project today, move it completely to the cloud and forget completely from whatever they are running on premises. The reality is obviously more complex. In most of the organizations, we run into what is called a multi-cloud environment which is a combination of running some of the stuff on premises, some of the stuff in a private or a public cloud. Usually it's a combination of all of those. So one of the things that I'd like to introduce here is the idea, certainly the cloud's going to be very important and we think that increasingly the world's going to think not in terms of moving all their data to a cloud location but moving the cloud services to wherever the data resides. We believe pretty strongly and I want to test this with you that the big data community needs to start thinking more about an analytics capability, a strategic business capability centered on analytics that includes certainly big data but also includes increasingly BI, data warehousing and even reporting. If it's a capability, it means you have some predictability some certainty in how it's going to work otherwise it's just ad hoc. Talk a little bit about the idea of some of the foundational technologies to ensure that you get the control that you want over some of these jobs, over some of these data movements so that this array of analytic capabilities or technologies can become a strategic capability within the business. Yes, this is a very good point. Let's start by saying that big data is complex. Customers that are implementing big data are struggling because of all the different moving pieces in those projects. Injusting data from many sources, both on premises and the cloud, storing the data, processing it and finally of course the most important part of making it available for analytics and actually providing the value to the business. So in this reality of multiple steps, multiple sources, huge number of technologies that are being used, it creates a kind of a jungle, a jungle of a combination of infrastructure, data and applications. And in this process, the complexity is magnified when we take into consideration cloud, which again is providing lots of new capabilities but at the same time introducing new technologies in a pace, in a very high pace, probably faster than ever before, which makes it difficult for customers to actually assure that they can manage successfully their big data projects, run it successfully in production or show that SLAs are met, auditing governance, all of those management capabilities are important and this is where we believe that we can help. So if I had a paraphrase Einstein, let's see if I get it right, move as much data as you have to but no more, something along those lines, right? People don't want to necessarily move data, right? It's expensive and time consuming. So enter control M. What are your thoughts on data movement and that comment and where does control M fit? Well, what we've been doing with control M is for many years now, we automate and orchestrate processes, workflows running at the enterprise and we are platform agnostic. We don't care where those workflows are going to run. It can be on premises, it can be in one specific cloud vendor, it can be a combination of many of those and it's true also about big data projects and it's true also about big data technologies. So what we have been doing is really end-to-end automation of a workflow. Regardless of where it's running, which applications are being used, where you mentioned the ERP and data warehouse, file transfers always a part of one business process that from the customer perspective, they need to consider it and they need to ensure that these all well connected. And in a way that looks like a job. And I think this is an important point. At the end of the day, a capability in a digital sense or in a technology sense starts to look something like a job and now we're getting to the roots of where control M started. Exactly. So if we think about historically, writing jobs, certainly in the mainframe required, JCL and other types of things, scripting and some of the more modern platforms or some of the newer platforms or recent platforms, but how does this process of creating jobs and DevOps and data warehouse, or I'm sorry, big data come together, you know, these analytics capabilities. Yes, so what we have been doing in the last few years, obviously expanding the control M orchestration capabilities over big data technologies. So we have deep integration with Hadoop and Spark, obviously we have tens of customers if not more running it today in production. Also what we have introduced a couple of years ago and it's becoming more and more popular, it's what we call jobs as code. And jobs as code is really about allowing- Jobs as code. Jobs as code. Got it. Excuse my accent. No. So the idea behind jobs as code is really allowing our customers, developers within our customers to consume control M from within the code as a part of the CICD process. So when developing your application, they can easily call control M functions. We expose control M to developers so they can easily consume control M when they build new applications, call the control M functions to test the job, order it, do some other actions as necessary from within the code. And of course, developers like it. It assures that applications are being developed much faster, which is a must these days and definitely very true when it comes to big data. Okay, so the business impact is speed. That means time to value. Correct. Happy developers. Policy, improved policies. You know, a real true DevOps environment. Correct, and at the same time, everything is well audited and governed and that's our strength. So let's see, what's new here? You guys are across the street. What's that like? What are you showing? What's the conversation like with customers? Well yeah, I mean what we are showing is obviously the control M end to end capabilities to manage a workflow across multiple environments. We just had a session earlier today showing an IOT example running on the cloud. Control M on the edge. Well, this is who we are right now. And as well as of course showing advanced scheduling capabilities and orchestration capabilities of big data workflows. Great, so what should we expect? Going forward next six, nine months, I know you can't divulge your detailed roadmap, but what are the kinds of things that observers should be evaluating you on and maybe customers are pushing you on? Well, from our perspective, it's clear that we will continue to invest in three major areas. First one, big data. More capabilities and more integration around big data technologies. The second area is cloud and we are working very closely with many cloud vendors these days in order to support from within the product more and more cloud services. And the further area is DevOps and the whole concept of jobs is called. So if I can, we talk a lot about workloads and where workloads are going to run. But what you're trying to say is at the same time, imagine a workload with the appropriate set of controls so that actually becomes a job. So we want to be able to run big data jobs in the cloud, on premise, but have them look to the business like a real job with control, with certainty, with predictability, with all the resource consumptions associated with it. Exactly, with the SLA management and for constant capabilities to predict changes in the environment with archiving capabilities so you can keep track of what happened and when. Auditing obviously is very important. So it brings consistency, homogeneity, as you say, less prone for errors and less diversity in terms of the skill sets that I need to run each. Are we there yet where we can have that real same-same environment? Or are we close or are we still? I believe we are there and we have customers today. We have all kinds of customers, obviously, but we have customers that are running their all operation in the cloud, including the controlling for big data capabilities. One example I can share is Malwarebytes. Malwarebytes, they're in the business of detecting and remediating malware from millions of customers and endpoints around the world. In this process, they collect data. If I remember correctly, over 20 million records of data per day, analyze it, and the concept is obviously to fix and remediate problems as soon as possible. They are running all their business in the cloud. Actually, I found it quite interesting when we look at absorbing new technologies. When they listened to one of their presentations, Malwarebytes, and they described their big data journey, they said that very early in this journey, they took a decision that there will be two pillars as a foundation for the big data projects. One pillar is cloud, so running 100% in the cloud. The other pillar is the workload automation piece, what we call today digital business automation, and usage of ControlM to automate everything they have in their environment. The way they describe it is that ControlM is like a spine, that it doesn't matter which technology you put on it, ControlM will be orchestrating it end-to-end. And to them, cloud means public cloud. Is that right? Correct, correct. And if I remember correctly, they are now using a few multiple vendors. Oh, okay, so it's public cloud and it's multi-cloud, but not on-prem. Because why, they don't have data there? In this case, this specific customer from day one, as far as I know, started their big data projects. Well, it's mainly provided as a service, so it's national department of public cloud. But that's not the norm, unless they're a SaaS provider. Oh, right, I mean, that's kind of what they are. But if you think about it, one of the things that's going to be interesting is that, and you mentioned this, the idea of skills, that there are some disciplines that we associate with running good, discipline, high-quality IT. And there's a cloud operating model, which many people kind of say you can cut those corners. Now, CICD is really important, but bring together this notion of the merging of a discipline IT approach that still is associated with speed and flexibility and leads to things like job and control and whatnot. That as we bring these environments or these worlds together. Correct. And this is exactly the direction that we are taking. I mean, when you look at what ControlM is providing to our customers, is bringing to the table all those strong operational capabilities, which are a must. And it doesn't matter if it's a traditional data center or a completely innovative run, running completely in the cloud, running big data, do processes in the cloud. At the end of the day, you need all those operational capabilities in order to assure that it's running successfully. Well, Lon, thanks so much for coming by theCUBE. Give us the update on BMC, ControlM. We really appreciate your time. Thank you very much. You're welcome. All right, keep it right there, everybody. We'll be back from CUBE NYC. Dave Vellante from Peter Burris, right back.