>>from around the globe. It's the queue with digital coverage of women transforming technology brought to you by VM Ware. >>Hi, this is Lisa Martin covering fifth Annual Women Transforming Technology WT two from my home in San Jose, California Because this is the first year than WT two has gone digital. Very excited to welcome next one of the speakers from the executive track. We have Charmaine Macquarie, president of Macquarie Group, but also offer C Suite Advisor. You know, Speaker Charmaine. Nice to start with you. >>An absolute pleasure. Thank you for having me. >>So you have an incredible background. You have been for two decades working with leaders and I read 27 industries, five continents and from some pretty big, well known brands Coca Cola, Johnson and Johnson, my particular favorite Starbucks. Tell me a little bit about your background in your career and how you came to be working with potential leaders >>early on in my career, I was working in politics, actually helping politicians understand their constituency and then how to communicate effectively with them and then went on into marketing. And really, what I say is that what I do is a conglomeration of all of my life experience working with leaders either in politics, in marketing and sales on a variety of industries, including gas and oil, and coming together and helping them understand how to communicate their message effectively. How have executive presence and ensuring that they're seeing, heard and remembered >>what? One of the things. One of the things that talking about being remembered especially now during a crisis that nobody has ever experienced before, when there are so much, so much concern and so much uncertainty. Um, I e. Read that you said effective communication is more than just words and phrases, especially in today's climate. What is effective communication? >>Effective communication is making sure that people hear your value, your value proposition, and that is really essential today. One of the things you want to do is that you want to elevate your visibility and when elevate the value that you bring to your organization. There are a number of competing priorities, and what you want organization to understand is what is it that you see that others don't see, and that is a part of your value proposition. How are you going to help the organization innovate through this time, and wanting to do that is really speaking about what is the value. What is it that it's gonna make the difference for the organization today with this crises and that will also take it further into the future. >>Tell me a little bit about this session that you did at women transforming technology the other day. 35 minutes. Interactive session. Since everything for this year's event was digital, I love the name of your session. Speak up, Stand out. We heard talking a little bit about when you first learned maybe last month, that this event was going digital. Did you change anything? Were there certain elements of your expertise and your recommendations that are now more even more important? Respect to visibility and value? >>Yes, So what? I changed it. What I changed and Waas. I really wanted to make it as a conversational as possible, because in this isolation it's easy to not feel seen or heard, and I want people to be able to elevate again their visibility and their ability to add value. So a couple of things that people can do is they can actually rewrite their narrative if they need to meaning if you believe that if you do not define yourself, others well, and their definition will inevitably be inadequate. So if you know that you are seen as a very quiet person and a person that is in the background and you want to have greater visibility, this is a great opportunity for you to rewrite that narrative and make yourself more visible. Meaning, I think, the expertise that you have again the insight that you have, making sure that you bring that to the table. You can do it in a number of formats. You could do it not only on a zoom call with your colleagues, but you can. Also, your email is heightened if you're using language and the language of leadership language that really hurts. People's here, and that creates a visual. So now you want to do to really make sure that using language that is very vivid and allows a person to touch, taste and feel what it is that you're saying, so that's one of the things that you can do. The other is say, Is that what I want to make sure that my clients are not well kept secrets. I want to make sure that in this time of isolation that they're finding opportunities to reach out. So most everyone is at home sheltering in place so people have more time on their hands in terms of reading your emails. When researchers found that there is a 26% increase and say your newsletters being read your emails being written, so now is the time that you could actually heighten that kind of communication. >>That's fascinating. Look that you said about making what you're communicating in an email. Maybe it's even texture over something like slack, vivid. Say, somebody has a great idea, I think. All right, so terms have changed. My job function is difference, or it's challenging to complete certain Give me some words that you think. So now you're saying people are actually focusing more on reading what you're saying, What are some vivid words that I could use if I had an interesting finance project or a marketing project that I wanted to raise the visibility of and gets them to really feel what I'm looking at? >>So when you speak about up in a finance project, one of the things you want to do is think about what is a story that could articulate those numbers that can tell the story with those numbers. So if you were saying, um, let's just make it as simple as possible. Two plus two equals four. Well, what you want to think about is what is it that is going to be different when you finished this project, or what is it that's gonna be? It's gonna shift in the marketplace. And so you want to create that visual? What does the future look like? And using examples of things that are very basic to our life today, as opposed to using really complicated language. Now is the time to have your language simple, having very clear and having very vivid. So you >>run it, Go ahead. Sorry. >>No, please go. Right. Yet >>I'm glad that you brought up simplicity because so often I think people think maybe I'm managing a project or I'm creating a methodology, and I think, really, it's just it's the simple. But we often second guess ourselves because I think I included in this. A lot of folks think it can't be that simple. It's got to be more complex I need to show, you know, like an episode of I'm picturing an Apple sort of the Big Bang theory, and Sheldon's talking about strength there. You need to make it complex to show your value. And but sometimes it's the simplest methodology. The simplest way of communicating that is the most effective. Do you find out that sometimes spokes, regardless of their level of executive nous, are challenged to really step back and look at the simple way to communicate with the simple answer? >>Absolutely. And simplicity is best, whether it's during this time period or even beyond this pandemic, but particularly now. So I don't know if anybody's ever seen the show. The marvelous is, um, I think it's amazing. Yeah, single and one of the things that she asked her husband, She goes, Well, honey, what do you do? And so I think, in the first episode, and he says, You know, I signed papers, I do this, I do that and he says, I really don't know what the hell I do. And I remember an incident with one of my clients, and I asked her, What does she do? She gave me her job title and I said, Okay, how many people work in your company? And she said, 49,000 people work here. I said, How many people do you think have the same title issue? If she goes well, you know, I'm sure at least a couple 1000. I said yes. So what distinguishes you? And so she wanted to talk about the title, which is like talking about acronyms at a company. And I said So, Really, What do you do? What we realized is that what she does is that she was responsible the fastest growing market segment in her company that articulates your value proposition that made a very visit vivid and very brought it to life. So people are able to understand when someone asked me, What do I do? I don't say that I'm an executive coach because you may have read an article last week that says all executive coach us up, that defines May. I wanted to find myself. My value is, I hope smart people get promoted when they get promoted, they communicate the big picture. So I help smart people get promote and communicate the big picture. I provide executive coaching senior level executives. I articulated my value. You know who I work with their smart people, that they're not smart. They're not working with me when they work with me and get promoted. Why? Because it communicate the big picture. Really? Simple one sent it. So what is the value? That is what really heightens your visibility and heightens your and levels. Level up your ability to be seen and heard in organizations. >>And, you know, I was looking at your website. You've been 98% success rate of folks that have worked with you that have been promoted within the following 18 months. What are some of the both hard and soft skills that you're looking for? So when you work, when you select clients to work with that, that demonstrate they are ready to be in the six weeks >>Well, there's a couple of things. One is that person has to be open and willing and not being volunteered by the organization, meaning saying you need to do you have to do this. If it is mandatory that someone work with an executive coach, that's not a winning proposition. The winning propositions That person is open and open to change and ready to make change. As I say to my clients, if you want everything to remain the same, I am not the coach for you because you're going to see change and you're going to see significant change. So that's one the other is preparing your organization for the kind of change is going to take place so that your organization begins with C and hear what you're doing different. So, for example, I would say to a client, if you're prepared to really step up and make the commitment to making the shift, you want to let people know what kind of shift that you're taking you're making so that they can begin to look for people like to look for success. They like to be able to reward you when you're successful, but you've got to let them know that you're there >>for that shift. >>So that's one of the things that's really important is that people be open to it and they'd be ready to take their spotlight. If you want to do it and remain behind the curtain, that's wonderful. This is not the work for you. >>It requires a little bit of vulnerability that, or maybe a lot of vulnerability to be able to do that, not easy, unless you're bringing a brown fan like I am talk to me about, especially in this time with covered 19 The uncertainty in every aspect of our lives. Every single aspect is it's dense and it's an emotional challenge. So do you find that it's harder for some folks, whether they're men or women, to do what your title says? You know? Speak up, let them know I'm coming. I'm on my way. How are you advising folks from a psychological perspective, to be able to do this? >>Well, I think there's a couple of things. One is that with the three questions I ask every client and those three questions are one. How do you see yourself? How do other people see you? And the third is, How do you want to be seen? So when you're able to answer, become introspective and answer those questions from the heart from your heart, then you can get really clear about what you want the world to know about you and how you want to show up. And it does require vulnerability. It requires you to look inward first for you to make that decision on how you want the world to see you. And then once you're able to make that, get that clarity and so it's process make getting that clarity. Then you can speak about that to the world. My thing is, is again. If you don't define yourself, others will, and their definition is inadequate. So when you define yourself, you know who you are and what you stand for. You can then shout that at the top of your lungs. But you don't really have to, because your actions will speak very clearly about what it is and who you say you are and how you want the world to see you. And you're always asking, am I can grow it? >>I love that about defining yourself so that others don't do it incorrectly. Talk to me about how somebody can develop their own communication style. How what are some of the steps that they need to recognize that, for example, if you see someone, anything there too bold or there to brush, or maybe dial it back a bit, especially because messages are getting read more now, which that process internally that I would need to take to develop and effective communication style. What is it >>that you need to do to to develop that effective communication style one? As I said, being able to define what that looks like for you and what that is may not be appropriate for every organization and every corporate culture. So you need to find immediate. Make sure either evaluate whether not you're in the right corporate culture so that you can be successful and or find a new one so that you could be successful once you have that, really, um, helping the people in your organization to make it easy for them to come to you. So by extending by extending yourself first, that is one of the things that I would say it would be really important in terms of stepping up during this time frame is saying, I feel really this is really let's say, someone has been felt really shaken by this really shaken by this. But I am determined so leverage this as an opportunity to really show up as my best self and show my greatest humanity. And I think that when we let people know what did it, where we're going and where we're headed, This far more easy for people to support you and provide you with the venues in which to exhibit who you are. This is a great time for you to volunteer A so much as possible to have that visibility. Because I think one of the questions you asked me earlier is how do you get hadn't become comfortable with this? You get comfortable with it by practising, Lady Gaga says. We're born that way, but we are. The only way that it happens with people that are really successful is because they practice >>something that is so interesting. Is during this time in particular, is getting is accountability, right? It's so easy right now more than ever to lose accountability. And I like that. You said that That's what I'm hearing when you say, you know, let people know that direction that you're going in. I think for the person you set that okay, I publicly said this, I need to be held that I need to hold myself accountable so that I deliver. I think there's a lot of power in that >>there is, and when you step up and articulate to the world. Well, you're about what it is that you're going to deliver your level of excellence. You hold yourself accountable because the person who is most important for you to be accountable to is yourself. Others come second, actually, sort of like being on the airplane in the mask. You've got to do it for you first. Because if you let yourself down, that's the that is the most horrific. And so stepping up to that is so much. There's so much power. And I believe that people provide you with a lot of grace when you do that and people know they can count on you. >>And that's so important knowing demonstrating your dependability in any situation. Sherman, I wish we had more time. It's been such a pleasure talking to you. Thank you for sharing your insight. I'm gonna be visible show value and the vetted and communication and accountable. Thank you so much for joining me. >>Have a wonderful day. You >>as well. And for Charmaine McCleery. I'm Lisa Martin. You're watching the Cube's coverage of the digital version of women transforming technology 2020 for now. >>Yeah, >>Yeah, yeah, yeah, yeah, yeah

>> Live from San Jose, in the heart of Silicon Valley, it's theCube. Covering Datawork Summit 2018. Brought to you by Hortonworks. >> Welcome back to theCube's live coverage of Dataworks here in San Jose, California. I'm your host, Rebecca Knight along with my cohost James Kobielus. We have with us Vimal Endiran. He is the Global Business Data Group Ecosystem Lead, at Accenture. He's coming to us straight from the Motor City. So, welcome Vimal. >> Thank you, thank you Rebecca. Thank you Jim. Looking forward to talk to you for the next ten minutes. >> So, before the cameras were rolling we were talking about how data veracity and how managers can actually know that the data that they're getting, that they're seeing, is trustworthy. What's your take on that right now? >> So, in the today's age the data is coming at you in a velocity that you never thought about, right. So today, the organizations are gathering data probably in the magnitude of petabytes. This is a new normal. We used to talk about gigs and now it's in petabytes. And the data coming in the form of images, video files, from the edge, you know edge devices, sensors, social media and everything. So, the amount of data, this is becoming the fuel for the new economy, right. So that companies, who can find a way to take advantage and figure out a way to use this data going to have a competitive advantage over their competitors. So, for that purpose, even though it's coming at that volume and velocity doesn't mean it's useful. So the thing is if they can find a way to make the data can be trustworthy, by the organization, and at the same time it's governed and secured. That's what's going to happen. It used to be it's called data quality, we call it when the structure it's okay, everything is maintained in SAP or some system. It's good it's coming to you. But now, you need to take advantage of the tools like machine learning, artificial intelligence, combining these algorithms and tool sets and abilities of people's mind, putting that in there and making it somewhat... Things can happen to itself at the same time it's trustworthy, we have offerings around that Accenture is developing place... It differs from industry to industry. Given the fact if the data coming in is something it's only worth for 15 seconds. After that it has no use other than understanding how to prevent something, from a sense of data. So, we have our offerings putting into place to make the data in a trustworthy, governed, secured, for an organization to use it and help the organization to get there. That's what we are doing. >> The standard user of your tools is it a data steward in the traditional sense or is it a data scientist or data engineer who's trying to, for example, compile a body of training data for use in building and training machine learning models? Do you see those kinds of customers for your data veracity offerings, that customer segment growing? >> Yes. We see both sides pretty much all walk of customers in our life. So, you hit the nail on the head, yes. We do see that type of aspects and also becoming, the data scientists you're also getting another set of people, the citizen data scientist. The people--- >> What is that? That's a controversial term. I've used that term on a number of occasions and a lot of my colleagues and peers in terms of other analysts bat me down and say, "No, that demeans the profession of data science by calling it..." But you tell me what how Accenture's defining that. >> The thing is, it's not demeaning. The fact is to become a citizen data scientist you need the help of data scientists. Basically, every time you need to build a model. And then you feed some data to learn. And then have an outcome to put that out. So you have a data scientist creating algorithms. What a citizen data scientist means, say if I'm not a data scientist, I should be able to take advantage of a model built for my business scenario, feed something data in, whatever I need to feed in, get an output and that program, that tool's going to tell me, go do this or don't do this, kind of things. So I become a data scientist by using a predefined model that's developed by an expert. Minds of many experts together. But rather than me going and hiring hundred experts, I go and buy a model and able to have one person maintain or tweak this model continuously. So, how can I enable that large volume of people by using more models. That's what-- >> If a predictive analytics tool that you would license from whatever vendor. If that includes prebuilt machine learning models for a particular tasks in it does that... Do you as a user of that tool, do you become automatically a citizen data scientist or do you need to do some actual active work with that model or data to live up to the notion of being a citizen data scientist? >> It's a good question. In my mind, I don't want to do it, my job is something else. To make something for the company. So, my job is not creating a model and doing that. My job is, I know my sets of data, I want to feed it in. I want to get the outcome that I can go and say increase my profit, increase my sales. That's what I want to do. So I may become a citizen data scientist without me knowing. I won't even be told that I'm using a model. I will take this set of data, feed it in here, it's going to tell you something. So, our data veracity point of view, we have these models built into some of platforms. That can be a tool from foreign works, taking advantage of the data storage tool or any other... In our own algorithms put in that helps you to create and maintain the data veracity to a scale of, if you say one to five, one is being low, five is being bad, to maintain at the five level. So that's the objective of that. >> So you're democratizing the tools of data science for the rest of us to solve real business problems. >> Right. >> So the data veracity aside, you're saying the user of these tools is doing something to manage, to correct or enhance or augment the data that's used to feed into these prebuilt models to achieve these outcomes? >> Yes. The augmented data, the feed data and the training data it comes out with an outcome to say, go do something. It tells you to perform something or do not perform. It's still an action. Comes out with an action to achieve a target. That's what it's going to be. >> You mention Hortonworks and since we are here at Dataworks and the Hortonworks show, tell us a little bit about your relationship with that company. >> Definitely. So Hortonworks is one of our premiere strategic partners. We've been the number one implementers, the partners for last two years in a row, implementing their technology across many of our clients. From partnership point of view, we have jointly developed offerings. What Accenture is best at, we're very good at industry knowledge. So with our industry knowledge and with their technology together what we're doing is we're creating some offerings that you can take to market. For example, we used to have data warehouses like using Teradata and older technology data warehouses. They're still good but at the same time, people also want to take the structured, unstructured data, images files and able to incorporate into the existing data warehouses. And how I can get the value out of the whole thing together. That's where Hortonworks' type of tools comes to play. So we have developed offerings called Modern Data Warehouse, taking advantage of your legacy systems you have plus this new data coming together and immediately you can create an analytics case, used case to do something. So, we have prebuilt programs and different scripts that take in different types of data. Moving into a data lake, Hortonworks data lake and then use your existing legacy data and all those together help you to create analytics use cases. So we have that called data modernization offering, we have one of that. Then we have-- >> So that's a prebuilt model for a specific vertical industry requirements or a specific business function, predictive analytics, anomaly detection and natural language processing, am I understanding correctly? >> Yes. We have industry based solutions as well but also to begin with, the data supply chain itself. To bring the data into the lake to use it. That's one of the offerings we play-- >> ...Pipeline and prepackaged models and rules and so forth. >> Right, prepackaged data ingestion, transformation, that prepackaged to take advantage with the new data sets along with your legacy data. That's one offering called data modernization offering. That to cloud. So, we can take to cloud. Hortonworks in a cloud it can be a joure, WS, HP, any cloud plus moving data. So that's one type of offering. Today actually we announced another offering jointly with Hortonworks, Atlas and Grainger Tool to help GDPR compliance. >> Will you explain what that tool does specifically to help customers with GDPR points. Does it work out of the box with Hortonworks data stewards studio? >> Well, to me I can get your answers from my colleagues who are much more technical on that but the fact is I can tell you functionally what the tool does is. >> Okay, please. >> So you, today the GDPR is basically, there's account regulations about you need to know about your personal data and you have your own destiny about your personal data. You can call the company and say, "Forget about me." If you are an EU resident. Or say, "Modify my data." They have to do it within certain time frame. If not they get fined. The fine can be up to 4% of the company's... So it's going to be a very large fine. >> Total revenue, yeah. >> So what we do is, basically take this tool. Put it in, working with Hortonworks this Atlas and Granger tool, we can go in and scan your data leak and we can scan at the metadata level and come into showcase. Then you know where is your personal data information about a consumer lies and now I know everything. Because what used to be in a legacy situation, the data originated someplace, somebody takes it and puts a system then somebody else downloads to an X file, somebody will put in an access data base and this kind of things. So now your data's pulling it across, you don't know where that lies. In this case, in the lake we can scan it, put this information, the meta data and the lineage information. Now, you immediately know where the data lies when somebody calls. Rebecca calls and says, "No longer use my information." I exactly know it's stored in this place in this table, in this column, let me go and take it out from here so that Rebecca doesn't exist anymore. Or whoever doesn't exist anymore. So that's the idea behind it. Also, we can catalog the entire data lake and we know not just personal information, other information, everything about other dimensions as well. And we can use it for our business advantage. So that's what we announced today. >> We're almost out of time but I want to finally ask you about talent because this is a pressing issue in Silicon Valley and beyond in really the tech industry, finding the right people, putting them in the right jobs and then keeping them happy there. So recruiting, retaining, what's Accenture's approach? >> This area, talent is the hardest one. >> Yes! >> Thanks to Hortonworks and Hortonworks point of view >> Send them to Detroit where the housing is far less expensive. >> Not a bad idea. >> Exactly! But the fact is-- >> We're both for Detroiters. >> What we did was, Hortonworks, Accenture has access to Hortonworks University, all their educational aspects. So we decided we're going to take that advantage and we going to enhance our talent by bringing the people from our... Retraining the people, taking the people to the new. People who know the legacy data aspects. So take them to see how we take the new world. So then we have a plan to use Hortonworks together the University, the materials and the people help, together we going to train about 500 people in different geos, 500 per piece and also our the development centers in India, Philippines, these places, so we have a larger plan to retrain the legacy into new. So, let's go and get people from out of the college and stuff, start building them from there, from an analyst to a consultant to a technical level and so that's the best way we are doing and actually the group I work with. Our group technology officer Sanjiv Vohra, he's basically in charge of training about 90,000 people on different technologies in and around that space. So the magnet is high but that's our approach to go and try and people and take it to that. >> Are you training them to be well rounded professionals in all things data or are you training them for specific specialties? >> Very, very good question. We do have this call master data architect program, so basically in the different levels after these trainings people go through specially you have to do so many projects, come back have an interview with a panel of people and you get certified, within the company, at certain level. At the master architect level you go and help a customer transform their data transformation, architecture vision where do you want to go to, that level. So we have the program with a university and that's the way we've taken it step by step to people to that level. >> Great. Vimal, thank you so much for coming on theCube. >> Thank you. >> It was really fun talking to you. >> Thank you so much, thank you for having me. Thank you. >> I'm Rebecca Knight for James Kobielus we will have more, well we actually will not be having any more coming up from Dataworks. This has been the Dataworks show. Thank you for tuning in. >> Signing off for now. >> And we'll see you next time.

(upbeat electronic music) >> Live from San Jose, in the heart of Silicon Valley, it's theCUBE. Covering Dataworks Summit 2018, brought to you by Hortonworks. (electronic whooshing) >> Welcome back to theCUBE's live coverage of Dataworks, here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We have two guests in this segment, we have Steve Wooledge, he is the VP of Product Marketing at Arcadia Data, and Satya Ramachandran, who is the VP of Engineering at Neustar. Thanks so much for coming on theCUBE. >> Our pleasure and thank you. >> So let's start out by setting the scene for our viewers. Tell us a little bit about what Arcadia Data does. >> Arcadia Data is focused on getting business value from these modern scale-out architectures, like Hadoop, and the Cloud. We started in 2012 to solve the problem of how do we get value into the hands of the business analysts that understand a little bit more about the business, in addition to empowering the data scientists to deploy their models and value to a much broader audience. So I think that's been, in some ways, the last mile of value that people need to get out of Hadoop and data lakes, is to get it into the hands of the business. So that's what we're focused on. >> And start seeing the value, as you said. >> Yeah, seeing is believing, a picture is a thousand words, all those good things. And what's really emerging, I think, is companies are realizing that traditional BI technology won't solve the scale and user concurrency issues, because architecturally, big data's different, right? We're on the scale-out, MPP architectures now, like Hadoop, the data complexity and variety has changed, but the BI tools are still the same, and you pull the data out of the system to put it into some little micro cube to do some analysis. Companies want to go after all the data, and view the analysis across a much broader set, and that's really what we enable. >> I want to hear about the relationship between your two companies, but Satya, tell us a little about Neustar, what you do. >> Neustar is an information services company, we are built around identity. We are the premiere identity provider, the most authoritative identity provider for the US. And we built a whole bunch of services around that identity platform. I am part of the marketing solutions group, and I head the analytics engineering for marketing solutions. The product that I work on helps marketers do their annual planning, as well as their campaign or tactical planning, so that they can fine tune their campaigns on an ongoing basis. >> So how do you use Arcadia Data's primary product? >> So we are a predictive analytics platform, the reporting solution, we use Arcadia for the reporting part of it. So we have multi terabytes of advertising data in our values, and so we use Arcadia to provide fast taxes to our customers, and also very granular and explorative analysis of this data. High (mumbles) and explorative analysis of this data. >> So you say you help your customers with their marketing campaigns, so are you doing predictive analytics? And are you during churn analysis and so forth? And how does Arcadia fit into all of that? >> So we get data and then they build an activation model, which tells how the marketing spent corresponds to the revenue. We not only do historical analysis, we also do predictive, in the sense that the marketers frequently done what-if analysis, saying that, what if I moved my budget from page search to TV? And how does it affect the revenue? So all of this modeling is built by Neustar, the modeling platform is built by the Neustar, but the last mile of taking these reports and providing this explorative analysis of the results, that is provided by the reporting solution, which is Arcadia. >> Well, I mean, the thing about data analytics, is that it really is going to revolutionize marketing. That famous marketing adage of, I know my advertising works, I just don't know which half, and now we're really going to be able to figure out which half. Can you talk a little bit about return on investment and what your clients see? >> Sure, we've got some major Fortune 500 companies that have said publicly that they've realized over a billion dollars of incremental value. And that could be across both marketing analytics, and how we better treat our messaging, our brand, to reach our intended audience. There's things like supply chain and being able to more realtime analyze what-if analysis for different routes, it's things like cyber security and stopping fraud and waste and things like that at a much grander scale than what was really possible in the past. >> So we're here at Dataworks and it's the Hortonworks show. Give us a sense of the degree of your engagement or partnership with Hortonworks and participation in their partner ecosystem. >> Yeah, absolutely. Hortonworks is one of our key partners, and what we did that's different architecturally, is we built our BI server directly into the data platforms. So what I mean by that is, we take the concept of a BI server, we install it and run it on the data nodes of Hortonworks Data Platform. We inherit the security directly out of systems like Apache Ranger, so that all that administration and scale is done at Hadoop economics, if you will, and it leverages the things that are already in place. So that has huge advantages both in terms of scale, but also simplicity, and then you get the performance, the concurrency that companies need to deploy out to like, 5,000 users directly on that Hadoop cluster. So, Hortonworks is a fantastic partner for us and a large number of our customers run on Hortonworks, as well as other platforms, such as Amazon Web Services, where Satya's got his system deployed. >> At the show they announced Hortonworks Data Platform 3.0. There's containerization there, there's updates to Hive to enable it to be more of a realtime analytics, and also a data warehousing engine. In Arcadia Data, do you follow their product enhancements, in terms of your own product roadmap with any specific, fixed cycle? Are you going to be leveraging the new features in HDP 3.0 going forward to add value to your customers' ability to do interactive analysis of this data in close to realtime? >> Sure, yeah, no, because we're a native-- >> 'Cause marketing campaigns are often in realtime increasingly, especially when you're using, you know, you got a completely digital business. >> Yeah, absolutely. So we benefit from the innovations happening within the Hortonworks Data Platform. So, because we're a native BI tool that runs directly within that system, you know, with changes in Hive, or different things within HDFS, in terms of performance or compression and things like that, our customers generally benefit from that directly, so yeah. >> Satya, going forward, what are some of the problems that you want to solve for your clients? What is their biggest pain points and where do you see Neustar? >> So, data is the new island, right? So, marketers, also for them now, data is the biggest, is what they're going after. They want faster analysis, they want to be able to get to insights as fast as they can, and they want to obviously get, work on as large amount of data as possible. The variety of sources is becoming higher and higher and higher, in terms of marketing. There used to be a few channels in '70s and '80s, and '90s kind of increased, now you have like, hundreds of channels, if not thousands of channels. And they want visibility across all of that. It's the ability to work across this variety of data, increasing volume at a very high speed. Those are high level challenges that we have at Neustar. >> Great. >> So the difference, marketing attribution analysis you say is one of the core applications of your solution portfolio. How is that more challenging now than it had been in the past? We have far more marketing channels, digital and so forth, then how does the state-of-the-art of marketing attribution analysis, how is it changing to address this multiplicity of channels and media for advertising and for influencing the customer on social media and so forth? And then, you know, can you give us a sense for then, what are the necessary analytical tools needed for that? We often hear about a social graph analysis or semantic analysis, or for behavioral analytics and so forth, all of this makes it very challenging. How can you determine exactly what influences a customer now in this day and age, where, you think, you know, Twitter is an influencer over the conversation. How can you nail that down to specific, you know, KPIs or specific things to track? >> So I think, from our, like you pointed out, the variety is increasing, right? And I think the marketers now have a lot more options than what they have, and that that's a blessing, and it's also a curse. Because then I don't know where I'm going to move my marketing spending to. So, attribution right now, is still sitting at the headquarters, it's kind of sitting at a very high level and it is answering questions. Like we said, with the Fortune 100 companies, it's still answering questions to the CMOs, right? Where attribution will take us, next step is to then lower down, where it's able to answer the regional headquarters on what needs to happen, and more importantly, on every store, I'm able to then answer and tailor my attribution model to a particular store. Let's take Ford for an example, right? Now, instead of the CMO suite, but, if I'm able to go to every dealer, and I'm able to personal my attribution to that particular dealer, then it becomes a lot more useful. The challenge there is it all needs to be connected. Whatever model we are working for the dealer, needs to be connected up to the headquarters. >> Yes, and that personalization, it very much leverages the kind of things that Steve was talking about at Arcadia. Being able to analyze all the data to find those micro, micro, micro segments that can be influenced to varying degrees, so yeah. I like where you're going with this, 'cause it very much relates to the power of distributing federated big data fabrics like Hortonworks' offers. >> And so it's streaming analytics is coming to forward, and it's been talked about for the past longest period of time, but we have real use cases for streaming analytics right now. Similarly, the large volumes of the data volumes is, indeed, becoming a lot more. So both of them are doing a lot more right now. >> Yes. >> Great. >> Well, Satya and Steve, thank you so much for coming on theCUBE, this was really, really fun talking to you. >> Excellent. >> Thanks, it was great to meet you. Thanks for having us. >> I love marketing talk. >> (laughs) It's fun. I'm Rebecca Knight, for James Kobielus, stay tuned to theCUBE, we will have more coming up from our live coverage of Dataworks, just after this. (upbeat electronic music)

>> Live from San Jose, in the heart of Silicon Valley, it's theCUBE. Covering DataWorks Summit 2018. Brought to you by Hortonworks. >> Welcome back everyone, you are watching day two of theCUBE's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight. I'm coming at you with my cohost Jame Kobielus. We're joined by Partha Seetala, he is the Chief Technology Officer at Robin Systems, thanks so much for coming on theCUBE. >> Pleasure to be here. >> You're a first timer, so we promise we don't bite. >> Actually I'm not, I was on theCUBE- >> Oh! >> At DockerCon in 2016. >> Oh well excellent, okay, so now you're a veteran, right. >> Yes, ma'am. >> So Robin Systems, as before the cameras were rolling, we were talking about it, it's about four years old, based here in San Jose, venture backed company. Tell us a little bit more about the company and what you do. >> Absolutely. First of all, thanks for hosting me here. Like you said, Robin is a Silicon Valley based company. Our focus is in allowing applications, such as big data, databases, no sequel and AI ML, to run within the Kubernetes platform. What we have built is a product that converges storage, complex storage, networking, application workflow management, along with Kubernetes to create a one click experience where users can get managed services kind of feel when they're deploying these applications. They can also do one click life cycle management on these apps. Our thesis has initially been to, instead of looking at this problem from an infrastructure up into application, to actually look at it from the applications down and then say, "Let the applications drive the underlying infrastructure to meet the user's requirements." >> Is that your differentiating factor, would you say? >> Yeah, I think it is because most of the folks out there today are looking at is as if it's a competent based play, it's like they want to bring storage to Kubernetes or networking to Kubernetes but the challenges are not really around storage and networking. If you talk to the operations folk they say that, "You know what? Those are underlying problems but my challenge is more along the lines of, okay, my CIO says the initiative is to make my applications mobile. They want go across to different Clouds. That's my challenge." The line of business user says, "I want to get a managed source experience." Yes, storage is the thing that you want to manage underneath, but I want to go and click and create my, let's say, an Oracle database or distributions log. >> In terms of the developer experience here, from the application down, give us a sense for how Robin Systems tooling your product enables that degree of specification of the application logic that will then get containerized within? >> Absolutely, like I said, we want applications to drive the infrastructure. What it means is that we, Robin is a software platform. We later ourselves on top of the machines that we sit on whether it is bare metal machines on premises, our VMs, or even an Azure, Google Cloud as well as AWs. Then we make the underlying compute, storage, network resources almost invisible. We treat it as a pool of resources. Now once you have this pool of resources, they can be attached to the applications that are being deployed as can inside containers. I mean, it's a software place, install on machines. Once it's installed, the experience now moves away from infrastructure into applications. You log in, you can see a portal, you have a lot of applications in that portal. We ship support for about 25 applications of some such. >> So these are templates? >> Yes. >> That the developer can then customize to their specific requirements? Or no? >> Absolutely, we ship reference templates for pretty much a wide variety of the most popular big data, no sequel, database, AI ML applications today. But again, as I said, it's a reference implementation. Typically customers take the reference recommendation and they enhance it or they use that to onboard their custom apps, for example, or the apps that we don't ship out of the box. So it's a very open, extensible platform but the goal being that whatever the application might be, in fact we keep saying that, if it runs somewhere else, it's runs on Robin, right? So the idea here is that you can bring anything, and we just, the flip of switch, you can make it a one click deploy, one click manage, one click mobile across Clouds. >> You keep mentioning this one click and this idea of it being so easy, so convenient, so seamless, is that what you say is the biggest concern of your customers? Is this ease and speed? Or what are some other things that are on their minds that you want to deliver? >> Right, so one click of course is a user experience part but what is the real challenge? The real challenges, there are a wide variety of tools being used by enterprises today. Even the data analytic pipeline, there's a lot across the data store, processor pipeline. Users don't want to deal with setting it up and keeping it up and running. They don't want that, they want to get the job done, right? Now when you only get the job done, you really want to hide the underlying details of those platforms and the best way to convey that, the best way to give that experience is to make it a single click experience from the UI. So I keep calling it all one click because that is the experience that you get to hide the underlying complexity for these apps. >> Does your environment actually compile executable code based on that one click experience? Or where does the compilation and containerization actually happen in your distributed architecture? >> Alright, so, I think the simplest- >> You're a prem based offering, right? You're not in the Cloud yourself? >> No, we are. We work on all the three big public clouds. >> Oh, okay. >> Whether it is Azure, AWS or Google. >> So your entire application is containerized itself for deployment into these Clouds? >> Yes, it is. >> Okay. >> So the idea here is let's simplify it significantly, right? You have Kubernetes today, it can run anywhere, on premises, in the public Cloud and so on. Kubernetes is a great platform for orchestrating containers but it is largely inaccessible to a certain class of data centric applications. >> Yeah. >> We make that possible. But our take is, just onboarding those applications on Kubernetes does not solve your CXO or you line of business user's problems. You ought to make the management, from an application point of view, not from a container management point of view, from an application point of view, a lot easier and that is where we kind of create this experience that I'm talking about, one click experience. >> Give us a sense for how, we're here at DataWorks and it's the Hortonworks show. Discuss with us your partnership with Hortonworks and you know, we've heard the announcement of HDP 3.0 and containerization support, just give us a rough sense for how you align or partner with Hortonworks in this area. >> Absolutely. It's kind of interesting because Hortonworks is a data management platform, if you think about it from that point of view and when we engaged with them first- So some of our customers have been using the product, Hortonworks, on top of Robin, so orchestrating Hortonworks, making it a lot easier to use. >> Right. >> One of the requirements was, "Are you certified with Hortonworks?" And the challenge that Hortonworks also had is they had never certified a container based deployment of Hortonworks before. They actually were very skeptical, you know, "You guys are saying all these things. Can you actually containerize and run Hortonworks?" So we worked with Hortonworks and we are, I mean if you go to the Hortonworks website, you'll see that we are the first in the entire industry who have been certified as a container based play that can actually deploy and manage Hortonworks. They have certified us by running a wide variety of tests, which they call the Q80 Test Suite, and when we got certified the only other players in the market that got that stamp of approval was Microsoft in Azure and EMC with Isilon. >> So you're in good company? >> I think we are in great company. >> You're certified to work with HTP 3.0 or the prior version or both? >> When we got certified we were still in the 2.X version of Hortonworks, HTP 3.0 is a more relatively newer version. But our plan is that we want to continue working with Hortonworks to get certified as they release the program and also help them because HTP 3.0 also has some container based orchestration and deployment so you want to help them provide the underlying infrastructure so that it becomes easier for beyond to spin up more containers. >> The higher level security and governance and all these things you're describing, they have to be over the Kubernetes layer. Hortonworks supports it in their data plane services portfolio. Does Robin Systems solutions portfolio tap in to any of that, or do you provide your own layer of sort of security and metadata management so forth? >> Yeah, so we don't want- >> In context of what you offer? >> Right, so we don't want to take away the security model that the application itself provides because might have step it up so that they are doing governance, it's not just logging in and auto control and things like this. Some governance is built into. We don't want to change that. We want to keep the same experience and the same workflow hat customers have so we just integrate with whatever security that the application has. We, of course, provide security in terms of isolating these different apps that are running on the Robin platform where the security or the access into the application itself is left to the apps themselves. When I say apps, I'm talking about Hortonworks. >> Yeah, sure. >> Or any other databases. >> Moving forward, as you think about ways you're going to augment and enhance and alter the Robin platform, what are some of the biggest trends that are driving your decision making around that in the sense of, as we know that companies are living with this deluge of data, how are you helping them manage it better? >> Sure. I think there are a few trends that we are closely watching. One is around Cloud mobility. CIOs want their applications along with their data to be available where their end users are. It's almost like follow the sun model, where you might have generated the data in one Cloud and at a different time, different time zone, you'll basically want to keep the app as well as data, moving. So we are following that very closely. How we can enable the mobility of data and apps a lot easier in that world. The other one is around the general AI ML workflow. One of the challenges there, of course, you have great apps like TensorFlow or Theano or Caffe, these are very good AI ML toolkits but one of the challenges that people face, is they are buying this very expensive, let's say NVIDIA DGX Box, this box costs about $150,000 each, how do you keep these boxes busy so that you're getting a good return on investment? It will require you to better manage the resources offered with these boxes. We are also monitoring that space and we're seeing that how can we take the Robin platform and how do you enable the better utilization of GPUs or the sharing of GPUs for running your AI ML kind of workload. >> Great. >> Those are, I think, two key trends that we are closely watching. >> We'll be discussing those at the next DataWorks Summit, I'm sure, at some other time in the future. >> Absolutely. >> Thank you so much for coming on theCUBE, Partha. >> Thank you. >> Thank you, my pleasure. Thanks. >> I'm Rebecca Knight for James Kobielus, We will have more from DataWorks coming up in just a little bit. (techno beat music)

>> Live from San Jose in the heart of Silicon Valley, it's theCUBE covering DataWorks Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks Summit here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We're joined by Rob Bearden. He is the CEO of Hortonworks. So thanks so much for coming on theCUBE again, Rob. >> Thank you for having us. >> So you just got off of the keynote on the main stage. The big theme is really about modern data architecture. So we're going to have this modern data architecture. What is it all about? How do you think about it? What's your approach? And how do you walk customers through this process? >> Well, there's a lot of moving parts in enabling a modern data architecture. One of the first steps is what we're trying to do is unlock the siloed transactional applications, and to get that data into a central architecture so you can get real time insights around the inclusive dataset. But what we're really trying to accomplish then within that modern data architecture is to bring all types of data whether it be real time streaming data, whether it be sensor data, IoT data, whether it be data that's coming from a connected core across the network, and to be able to bring all that data together in real time, and give the enterprise the ability to be able to take best in class action so that you get a very prescriptive outcome of what you want. So if we bring that data under management from point of origination and out on the edge, and then have the platforms that move that through its entire lifecycle, and that's our HDF platform, it gives the customer the ability to, after they capture it at the edge, move it, and then have the ability to process it as an event happens, a condition changes, various conditions come together, have the ability to process and take the exact action that you want to see performed against that, and then bring it to rest, and that's where our HDP platform comes into play where then all that data can be aggregated so you can have a holistic insight, and have real time interactions on that data. But then it then becomes about deploying those datasets and workloads on the tier that's most economically and architecturally pragmatic. So if that's on-prem, we make sure that we are architected for that on-prem deployment or private cloud or even across multiple public clouds simultaneously, and give the enterprise the ability to support each of those native environments. And so we think hybrid cloud architecture is really where the vast majority of our customers today and in the future, are going to want to be able to run and deploy their applications and workloads. And that's where our DataPlane Service Offering gives them the ability to have that hybrid architecture and the architectural latitude to move workloads and datasets across each tier transparently to what storage file format that they did or where that application is, and we provide all the tooling to match the complexity from doing that, and then we ensured that it has one common security framework, one common governance through its entire lifecycle, and one management platform to handle that entire lifecycle data. And that's the modern data architecture is to be able to bring all data under management, all types of data under management, and manage that in real time through its lifecycle til it comes at rest and deploy that across whatever architecture tier is most appropriate financially and from a performance on-cloud or prem. >> Rob, this morning at the keynote here in day one at DataWorks San Jose, you presented this whole architecture that you described in the context of what you call hybrid clouds to enable connected communities and with HDP, Hortonworks Data Platform 3.0 is one of the prime announcements, you brought containerization into the story. Could you connect those dots, containerization, connected communities, and HDP 3.0? >> Well, HDP 3.0 is really the foundation for enabling that hybrid architecture natively, and what's it done is it separated the storage from the compute, and so now we have the ability to deploy those workloads via a container strategy across whichever tier makes the most sense, and to move those application and datasets around, and to be able to leverage each tier in the deployment architectures that are most pragmatic. And then what that lets us do then is be able to bring all of the different data types, whether it be customer data, supply chain data, product data. So imagine as an industrial piece of equipment is, an airplane is flying from Atlanta, Georgia to London, and you want to be able to make sure you really understand how well is that each component performing, so that that plane is going to need service when it gets there, it doesn't miss the turnaround and leave 300 passengers stranded or delayed, right? Now with our Connected platform, we have the ability to take every piece of data from every component that's generated and see that in real time, and let the airlines make that real time. >> Delineate essentially. >> And ensure that we know every person that touched it and looked at that data through its entire lifecycle from the ground crew to the pilots to the operations team to the service. Folks on the ground to the reservation agents, and we can prove that if somehow that data has been breached, that we know exactly at what point it was breached and who did or didn't get to see it, and can prevent that because of the security models that we put in place. >> And that relates to compliance and mandates such as the Global Data Protection Regulation GDPR in the EU. At DataWorks Berlin a few months ago, you laid out, Hortonworks laid out, announced a new product called the Data Steward Studio to enable GDPR compliance. Can you give our listeners now who may not have been following the Berlin event a bit of an update on Data Steward Studio, how it relates to the whole data lineage, or set of requirements that you're describing, and then going forward what does Hortonworks's roadmap for supporting the full governance lifecycle for the Connected community, from data lineage through like model governance and so forth. Can you just connect a few dots that will be helpful? >> Absolutely. What's important certainly, driven by GDPR, is the requirement to be able to prove that you understand who's touched that data and who has not had access to it, and that you ensure that you're in compliance with the GDPR regulations which are significant, but essentially what they say is you have to protect the personal data and attributes of that data of the individual. And so what's very important is that you've got to be able to have the systems that not just secure the data, but understand who has the accessibility at any point in time that you've ever maintained that individual's data. And so it's not just about when you've had a transaction with that individual, but it's the rest of the history that you've kept or the multiple datasets that you may try to correlate to try to expand relationship with that customer, and you need to make sure that you can ensure not only that you've secured their data, but then you're protecting and governing who has access to it and when. And as importantly that you can prove in the event of a breach that you had control of that, and who did or did not access it, because if you can't prove any breach, that it was secure, and that no one breached it, who has or access to this not supposed to, you can be opened up for hundreds of thousands of dollars or even multiple millions of dollars of fines just because you can't prove that it was not accessed, and that's what the variety of our platforms, you mentioned Data Studio, is part of. DataPlane is one of the capabilities that gives us the ability. The core engine that does that is Atlas, and that's the open source governance platform that we developed through the community that really drives all the capabilities for governance that moves through each of our products, HDP, HDF, then of course, and DataPlane and Data Studio takes advantage of that and how it moves and replicates data and manages that process for us. >> One of the things that we were talking about before the cameras were rolling was this idea of data driven business models, how they are disrupting current contenders, new rivals coming on the scene all the time. Can you talk a little bit about what you're seeing and what are some of the most exciting and maybe also some of the most threatening things that you're seeing? >> Sure, in the traditional legacy enterprise, it's very procedural driven. You think about classic Encore ERP. It's worked very hard to have a very rigid, very structural procedural order to cash cycle that has not a great deal of flexibility. And it takes through a design process, it builds product, that then you sell product to a customer, and then you service that customer, and then you learn from that transaction different ways to automate or improve efficiencies in their supply chain. But it's very procedural, very linear. And in the new world of connected data models, you want to bring transparency and real time understanding and connectivity between the enterprise, the customer, the product, and the supply chain, and that you can take real time best in practice action. So for example you understand how well your product is performing. Is your customer using it correctly? Are they frustrated with that? Are they using it in the patterns and the frequency that they should be if they are going to expand their use and buy more, and if they're not, how do we engage in that cycle? How do we understand if they're going through a re-review and another buying of something similar that may not be with you for a different reason. And when we have real time visibility to our customer's interaction, understand our product's performance through its entire lifecycle, then we can bring real time efficiency with linking those together with our supply chain into the various relationships we have with our customers. To do that, it requires the modern data architecture, bringing data under management from the point it originates, whether it's from the product or the customer interacting with the company, or the customer interacting potentially with our ecosystem partners, mutual partners, and then letting the best in practice supply chain techniques, make sure that we're bringing the highest level of service and support to that entire lifecycle. And when we bring data under management, manage it through its lifecycle and have the historical view at rest, and leverage that across every tier, that's when we get these high velocity, deep transparency, and connectivity between each of the constituents in the value chain, and that's what our platforms give them the ability to do. >> Not only your platform, you guys have been in business now for I think seven years or so, and you shifted from being in the minds of many and including your own strategy from being the premier data at rest company in terms of the a Hadoop platform to being one of the premier data in motion companies. Is that really where you're going? To be more of a completely streaming focus, solution provider in a multi-cloud environment? And I hear a lot of Kafka in your story now that it's like, oh yeah, that's right, Hortonworks is big on Kafka. Can you give us just a quick sense of how you're making that shift towards low latency real time streaming, big data, or small data for that matter, with embedded analytics and machine learning? >> So, we have evolved from certainly being the leader in global data platforms with all the work that we do collaboratively, and in through the community, to make Hadoop an enterprise viable data platform that has the ability to run mission critical workloads and apps at scale, ensuring that it has all the enterprise facilities from security and governance and management. But you're right, we have expanded our footprint aggressively. And we saw the opportunity to actually create more value for our customers by giving them the ability to not wait til they bring data under management to gain an insight, because in that case, they're happened to be reactive post event post transaction. We want to give them the ability to shift their business model to being interactive, pre-event, pre-conditioned. The way to do that we learned was to be able to bring the data under management from the point of origination, and that's what we used MiNiFi and NiFi for, and then HDF, to move it through its lifecycle, and your point, we have the intellect, we have the insight, and then we have the ability then to process the best in class outcome based on what we know the variables are we're trying to solve for as that's happening. >> And there's the word, the phrase asset which of course is a transactional data paradigm plan, I hear that all over your story now in streaming. So, what you're saying is it's a completely enterprise-grade streaming environment from n to n for the new era of edge computing. Would that be a fair way of-- >> It's very much so. And our model and strategy has always been bring the other best in class engines for what they do well for their particular dataset. A couple of examples of that, one, you brought up Kafka, another is Spark. And they do what they do really well. But what we do is make sure that they fit inside an overall data architecture that then embodies their access to a much broader central dataset that goes from point of origination to point of rest on a whole central architecture, and then benefit from our security, governance, and operations model, being able to manage those engines. So what we're trying to do is eliminate the silos for our customers, and having siloed datasets that just do particular functions. We give them the ability to have an enterprise modern data architecture, we manage the things that bring that forward for the enterprise to have the modern data driven business models by bringing the governance, the security, the operations management, ensure that those workflows go from beginning to end seamlessly. >> Do you, go ahead. >> So I was just going to ask about the customer concerns. So here you are, you've now given them this ability to make these real time changes, what's sort of next? What's on their mind now and what do you see as the future of what you want to deliver next? >> First and foremost we got to make sure we get this right, and we really bring this modern data architecture forward, and make sure that we truly have the governance correct, the security models correct. One pane of glass to manage this. And really enable that hybrid data architecture, and let them leverage the cloud tier where it's architecturally and financially pragmatic to do it, and give them the ability to leg into a cloud architecture without risk of either being locked in or misunderstanding where the lines of demarcation of workloads or datasets are, and not getting the economies or efficiencies they should. And we solved that with DataPlane. So we're working very hard with the community, with our ecosystem and strategic partners to make sure that we're enabling the ability to bring each type of data from any source and deploy it across any tier with a common security, governance, and management framework. So then what's next is now that we have this high velocity of data through its entire lifecycle on one common set of platforms, then we can start enabling the modern applications to function. And we can go look back into some of the legacy technologies that are very procedural based and are dependent on a transaction or an event happening before they can run their logic to get an outcome because that grinds the customer in post world activity. We want to make sure that we're bringing that kind of, for example, supply chain functionality, to the modern data architecture, so that we can put real time inventory allocation based on the patterns that our customers go in either how they're using the product, or frustrations they've had, or success they've had. And we know through artificial intelligence and machine learning that there's a high probability not only they will buy or use or expand their consumption of whatever that they have of our product or service, but it will probably to these other things as well if we do those things. >> Predict the logic as opposed to procedural, yes, AI. >> And very much so. And so it'll be bringing those what's next will be the modern applications on top of this that become very predictive and enabler versus very procedural post to that post transaction. We're little ways downstream. That's looking out. >> That's next year's conference. >> That's probably next year's conference. >> Well, Rob, thank you so much for coming on theCUBE, it's always a pleasure to have you. >> Thank you both for having us, and thank you for being here, and enjoy the summit. >> We're excited. >> Thank you. >> We'll do. >> I'm Rebecca Knight for Jim Kobielus. We will have more from DataWorks Summit just after this. (upbeat music)

>> Live from San Jose, in the heart of Silicon Valley, it's theCUBE, overing DataWorks Summit 2018. Brought to you by Hortonworks. >> Welcome back everyone to day two of theCUBE's live coverage of DataWorks, here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host James Kobielus. We have two guests on this panel today, we have Tim Vincent, he is the VP of Cognitive Systems Software at IBM, and Steve Roberts, who is the Offering Manager for Big Data on IBM Power Systems. Thanks so much for coming on theCUBE. >> Oh thank you very much. >> Thanks for having us. >> So we're now in this new era, this Cognitive Systems era. Can you set the scene for our viewers, and tell our viewers a little bit about what you do and why it's so important >> Okay, I'll give a bit of a background first, because James knows me from my previous role as, and you know I spent a lot of time in the data and analytics space. I was the CTO for Bob running the analytics group up 'til about a year and a half ago, and we spent a lot of time looking at what we needed to do from a data perspective and AI's perspective. And Bob, when he moved over to the Cognitive Systems, Bob Picciano who's my current boss, Bob asked me to move over and really start helping build, help to build out more of a software, and more of an AI focus, and a workload focus on how we thinking of the Power brand. So we spent a lot of time on that. So when you talk about cognitive systems or AI, what we're really trying to do is think about how you actually couple a combination of software, so co-optimize software space and the hardware space specific of what's needed for AI systems. Because the act of processing, the data processing, the algorithmic processing for AI is very, very different then what you would have for traditional data workload. So we're spending a lot of time thinking about how you actually co-optimize those systems so you can actually build a system that's really optimized for the demands of AI. >> And is this driven by customers, is this driven by just a trend that IBM is seeing? I mean how are you, >> It's a combination of both. >> So a lot of this is, you know, there's a lot of thought put into this before I joined the team. So there was a lot of good thinking from the Power brand, but it was really foresight on things like Moore's Law coming to an end of it's lifecycle right, and the ramifications to that. And at the same time as you start getting into things like narrow NATS and the floating point operations that you need to drive a narrow NAT, it was clear that we were hitting the boundaries. And then there's new technologies such as what Nvidia produces with with their GPUs, that are clearly advantageous. So there's a lot of trends that were comin' together the technical team saw, and at the same time we were seeing customers struggling with specific things. You know how to actually build a model if the training time is going to be weeks, and months, or let alone hours. And one of the scenarios I like to think about, I was probably showing my age a bit, but went to a school called University of Waterloo, and when I went to school, and in my early years, they had a batch based system for compilation and a systems run. You sit in the lab at night and you submit a compile job and the compile job will say, okay it's going to take three hours to compile the application, and you think of the productivity hit that has to you. And now you start thinking about, okay you've got this new skill in data scientists, which is really, really hard to find, they're very, very valuable. And you're giving them systems that take hours and weeks to do what the need to do. And you know, so they're trying to drive these models and get a high degree of accuracy in their predictions, and they just can't do it. So there's foresight on the technology side and there's clear demand on the customer side as well. >> Before the cameras were rolling you were talking about how the term data scientists and app developers is used interchangeably, and that's just wrong. >> And actually let's hear, 'cause I'd be in this whole position that I agree with it. I think it's the right framework. Data science is a team sport but application development has an even larger team sport in which data scientists, data engineers play a role. So, yeah we want to hear your ideas on the broader application development ecosystem, and where data scientists, and data engineers, and sort, fall into that broader spectrum. And then how IBM is supporting that entire new paradigm of application development, with your solution portfolio including, you know Power, AI on Power? >> So I think you used the word collaboration and team sport, and data science is a collaborative team sport. But you're 100% correct, there's also a, and I think it's missing to a great degree today, and it's probably limiting the actual value AI in the industry, and that's had to be data scientists and the application developers interact with each other. Because if you think about it, one of the models I like to think about is a consumer-producer model. Who consumes things and who produces things? And basically the data scientists are producing a specific thing, which is you know simply an AI model, >> Machine models, deep-learning models. >> Machine learning and deep learning, and the application developers are consuming those things and then producing something else, which is the application logic which is driving your business processes, and this view. So they got to work together. But there's a lot of confusion about who does what. You know you see people who talk with data scientists, build application logic, and you know the number of people who are data scientists can do that is, you know it exists, but it's not where the value, the value they bring to the equation. And the application developers developing AI models, you know they exist, but it's not the most prevalent form fact. >> But you know it's kind of unbalanced Tim, in the industry discussion of these role definitions. Quite often the traditional, you know definition, our sculpting of data scientist is that they know statistical modeling, plus data management, plus coding right? But you never hear the opposite, that coders somehow need to understand how to build statistical models and so forth. Do you think that the coders of the future will at least on some level need to be conversant with the practices of building,and tuning, or training the machine learning models or no? >> I think it's absolutely happen. And I will actually take it a step further, because again the data scientist skill is hard for a lot of people to find. >> Yeah. >> And as such is a very valuable skill. And what we're seeing, and we are actually one of the offerings that we're pulling out is something called PowerAI Vision, and it takes it up another level above the application developer, which is how do you actually really unlock the capabilities of AI to the business persona, the subject matter expert. So in the case of vision, how do you actually allow somebody to build a model without really knowing what a deep learning algorithm is, what kind of narrow NATS you use, how to do data preparation. So we build a tool set which is, you know effectively a SME tool set, which allows you to automatically label, it actually allows you to tag and label images, and then as you're tagging and labeling images it learns from that and actually it helps automate the labeling of the image. >> Is this distinct from data science experience on the one hand, which is geared towards the data scientists and I think Watson Analytics among your tools, is geared towards the SME, this a third tool, or an overlap. >> Yeah this is a third tool, which is really again one of the co-optimized capabilities that I talked about, is it's a tool that we built out that really is leveraging the combination of what we do in Power, the interconnect which we have with the GPU's, which is the NVLink interconnect, which gives us basically a 10X improvement in bandwidth between the CPU and GPU. That allows you to actually train your models much more quickly, so we're seeing about a 4X improvement over competitive technologies that are also using GPU's. And if we're looking at machine learning algorithms, we've recently come out with some technology we call Snap ML, which allows you to push machine learning, >> Snap ML, >> Yeah, it allows you to push machine learning algorithms down into the GPU's, and this is, we're seeing about a 40 to 50X improvement over traditional processing. So it's coupling all these capabilities, but really allowing a business persona to something specific, which is allow them to build out AI models to do recognition on either images or videos. >> Is there a pre-existing library of models in the solution that they can tap into? >> Basically it allows, it has a, >> Are they pre-trained? >> No they're not pre-trained models that's one of the differences in it. It actually has a set of models that allow, it picks for you, and actually so, >> Oh yes, okay. >> So this is why it helps the business persona because it's helping them with labeling the data. It's also helping select the best model. It's doing things under the covers to optimize things like hyper-parameter tuning, but you know the end-user doesn't have to know about all these things right? So you're tryin' to lift, and it comes back to your point on application developers, it allows you to lift the barrier for people to do these tasks. >> Even for professional data scientists, there may be a vast library of models that they don't necessarily know what is the best fit for the particular task. Ideally you should have, the infrastructure should recommend and choose, under various circumstances, the models, and the algorithms, the libraries, whatever for you for to the task, great. >> One extra feature of PowerAI Enterprises is that it does include a way to do a quick visual inspection of a models accuracy with a small data sample before you invest in scaling over a cluster or large data set. So you can get a visual indicator as to the, whether the models moving towards accuracy or you need to go and test an alternate model. >> So it's like a dashboard, of like Gini coefficients and all that stuff, okay. >> Exactly it gives you a snapshot view. And the other thing I was going to mention, you guys talked about application development, data scientists and of course a big message here at the conference is, you know data science meets big data and the work that Hortonworks is doing involving the notion of container support in YARN, GPU awareness in YARN, bringing data science experience, which you can include the PowerAI capability that Tim was talking about, as a workload tightly coupled with Hadoop. And this is where our Power servers are really built, not for just a monolithic building block that always has the same ratio of compute and storage, but fit for purpose servers that can address either GPU optimized workloads, providing the bandwidth enhancements that Tim talked about with the GPU, but also day-to-day servers, that can now support two terrabytes of memory, double the overall memory bandwidth on the box, 44 cores that can support up to 176 threads for parallelization of Spark workloads, Sequel workloads, distributed data science workloads. So it's really about choosing the combination of servers that can really mix this evolving workload need, 'cause a dupe isn't now just map produced, it's a multitude of workloads that you need to be able to mix and match, and bring various capabilities to the table for a compute, and that's where Power8, now Power9 has really been built for this kind of combination workloads where you can add acceleration where it makes sense, add big data, smaller core, smaller memory, where it makes sense, pick and choose. >> So Steve at this show, at DataWorks 2018 here in San Jose, the prime announcement, partnership announced between IBM and Hortonworks was IHAH, which I believe is IBM Host Analytics on Hortonworks. What I want to know is that solution that runs inside, I mean it runs on top of HDP 3.0 and so forth, is there any tie-in from an offering management standpoint between that and PowerAI so you can build models in the PowerAI environment, and then deploy them out to, in conjunction with the IHAH, is there, going forward, I mean just wanted to get a sense of whether those kinds of integrations. >> Well the same data science capability, data science experience, whether you choose to run it in the public cloud, or run it in private cloud monitor on prem, it's the same data science package. You know PowerAI has a set of optimized deep-learning libraries that can provide advantage on power, apply when you choose to run those deployments on our Power system alright, so we can provide additional value in terms of these optimized libraries, this memory bandwidth improvements. So really it depends upon the customer requirements and whether a Power foundation would make sense in some of those deployment models. I mean for us here with Power9 we've recently announced a whole series of Linux Power9 servers. That's our latest family, including as I mentioned, storage dense servers. The one we're showcasing on the floor here today, along with GPU rich servers. We're releasing fresh reference architecture. It's really to support combinations of clustered models that can as I mentioned, fit for purpose for the workload, to bring data science and big data together in the right combination. And working towards cloud models as well that can support mixing Power in ICP with big data solutions as well. >> And before we wrap, we just wanted to wrap. I think in the reference architecture you describe, I'm excited about the fact that you've commercialized distributed deep-learning for the growing number of instances where you're going to build containerized AI and distributing pieces of it across in this multi-cloud, you need the underlying middleware fabric to allow all those pieces to play together into some larger applications. So I've been following DDL because you've, research lab has been posting information about that, you know for quite a while. So I'm excited that you guys have finally commercialized it. I think there's a really good job of commercializing what comes out of the lab, like with Watson. >> Great well a good note to end on. Thanks so much for joining us. >> Oh thank you. Thank you for the, >> Thank you. >> We will have more from theCUBE's live coverage of DataWorks coming up just after this. (bright electronic music)

>> Live, from San Jose, in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2018. Brought to you by Hortonworks. >> Welcome back everyone to theCUBE's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight, along with my cohost James Kobielus. We are joined by Mike McNamara, he is the Senior Product and Solutions Marketing at NetApp. Thanks so much for coming on theCUBE. >> Thanks for having me. >> You're a first timer, >> Yes, >> So this is very exciting! >> Happy to be here. >> Welcome. >> Thanks. >> So, before the cameras were rolling, we were talking about how NetApp has been in this space for a while, but is really just starting to be recognized as a player. So, talk a little bit about your company's evolution. >> Sure. So, in the whole analytic space, is something NetApp was in a long time ago, and then sort got out of it, and then over the last several years, we've gotten back in, and we recognize it's a huge opportunity for data storage, data management, if you look at IDC Data, massive, massive market, but, the opportunity for us, is like you know what, they're mainly using a direct attached storage model where compute and storage is tied together. And now, with data just exploding, and growing like crazy, it's always been growing, but now it seems like it's just growing like crazy now, that, and customers wanting to have data on-prem, but also being able to move it off to the cloud, we're like, hey this is a great opportunity for us to come in with a solution that's, external storage solution that can come in and show them the benefits of have a more reliable, have an opportunity to move their data off to the cloud, we've got great solutions with that, so it's gone well, but it's been a little bit different, like at this show, a lot of the people, the data scientists, data engineers, some who know us, some still don't like, so, NetApp, what do you guys do, and so it's a little bit of an education, 'cause it's not a traditional buyer, if you will, we look at them as influencers, but it's only one influence than we traditionally have sold to say Vice President of Infrastructure, as an example, or maybe a Director of Storage Admin, but most of those folks are not here, so we're, this is just kind of a new market for us that we're making inroads. >> How do data scientists, or do they influence the purchase of storage solutions, or data management solutions? >> Sure, so they want to have access to the data, they want to be able analyze it quickly and effectively, they want to make sure it's always available, you know, at their fingertips so to speak. We can help them by giving them very fast, very reliable solutions, and specially with our software, they want to do for example, do some virtual clone of that data, and just do some testing on that without impacting their production data, we can do that in a snap, so we can make their lives a lot easier, so we can show them how, hey, mister data scientist, we can make your life a little easier-- >> Or miss data scientist. >> Or miss, we were talking about that, >> There are a lot of women in this field. >> Yeah, yeah. >> More than we realize, and they're great. >> So we can help you do your job better, and then, that, him or her can then influence who's making the purchase decisions. >> Yeah, training sets, test sets, validation sets of data for the machine learning and analytics development pipeline, yes, you need a solid storage infrastructure to do it right. >> Absolutely. >> So, when you're getting inside the head of your potential buyer here, the VP of Infrastructure, or data admin, what is it that you're hearing from those people most, what are their concerns, what keeps them up at night, and where do you come in? >> Yeah, so one of the concerns is, often times, you're, hey, how do I, do you have a cloud storage, connected to the cloud, you know, I'm doing things on-prem now, but is there a path, so that's a big one. And we, NetApp, pride ourselves on being the most cloud-connected, all flash storage in the industry. So, that's a big focus, big push for us. If you saw our marketing, it shows data authority for the hybrid cloud, so we really honestly do, whether it's with Google, or Azure, or AWS, we know our software runs in those environments, it also runs on-premises, but because it's the same on-tap software, we can move data between those environments. So, we get a real good storage, so we can you know, boom, check the box, we got you covered if you want to utilize the cloud, and I think the next piece of that is just from a protecting, protecting the data, you know, again I said data is just growing so much, I want to make sure it's always available, and we can back it up and all that, and that's been a core, core strength, versus like a lot of these traditional solutions they've been using, these direct attached models, they just don't have anywhere near the enterprise-grade data protection that NetApp has always prided itself on, over many decades now. And so, we can help them do that, and quite honestly, a lot of people think, well you know, you guys are external storage, how do you compare versus direct attached storage from our total cost, that's another one. I can tell you definitively, and we've got data to back it up from a total cost of ownership point of view, because of the fact that, of the advantages we bring from, up-time, and you know from RAID, but you know, in a Hadoop environment, often times there's three copies of data. With our solution, a good piece of software, there's only one copy of your data, so have three versus one is a big saving, but even what we do with the data, compressing it, and compacting it, a lot of benefits. So, we do have honest to goodness, outwards to 50% better total cost of ownership, versus a DAS model. >> Do you use machine learning within your portfolio? I'm hearing of more stories, >> Great question, yeah. >> Incorporating machine learning to automate or facilitate more of the functions in the data protection or data management life-cycle. >> Yeah, that's a great question, and we do use, so we've got a piece of software which we call Active IQ, it was referred to as Ace Update, you may have, it may ring a bell, but to answer your question, so we've got thousands of thousands of NetApp systems out there, and those customers that allow us, we have, think of it as kind of a call home feature, where we're getting data back from all our installed customers, and then we will go and do predictive analytics, and do some machine learning on that data, so then we can go back to those customers and say, hey you know what, you've got this volume that's unprotected, you should protect this, or we can show them, if you were to move that data off into our cloud environment, here's maybe performance you would see, so we do do a lot of that predictive-- >> Predictive performance assessment, it sounds like there's anomaly detection in there as well. >> Anomaly as well, letting them know, hey, you know, it's time for this drive, it may fail on you, let's ship you out a new drive now before it happens, so yeah, a lot of, from an analytics, predictive analysis going on. And you know, it's a huge benefit to our customers. Huge benefit. >> I know you're also doing a push toward artificial intelligence, so I'd like to hear more about that, and then also, if there's any best practices that have emerged. >> Sure, sure, so yes. That is another big area, so it's kind of a logical progression from where we were, if you will, in the analytics space, data lakes, but now moving into artificial intelligence, which has always been around, but it's really taking more of a more prominent role, I mean just a quick fun fact, I read that, you know that at the royal wedding that recently happened, did you know that Amazon used artificial intelligence to help us, the TV viewer, identify who the guests were. >> Ooh. >> So, you know it's like, it's everywhere, right? And so for us, we see that trend, a ton of data that needs to be managed, and so we kind of look at it from the edge to the core, to the cloud, those three, not pillars, but directional ways, taking data from IOT centers at the edge, bring it into the core, doing training, and then if the customer so chooses, out to the cloud. So, yeah it is a big push for us now, and we're going a lot with Nvidia, is a key partner with us. >> Really? This is a bit futuristic, but I can see a role going forward for AI to look into large data volumes, like video objects, to find things like faces, and poses and gestures and so forth, and see, to use that intelligence to be able to reduce the data sets down to where it's reduced, to de-duplicate, so that you can use less storage and then you can re-construct the original video objects or whatever going forward, I mean as a potential use of AI within the storage efficiency. >> Yep, yeah you're right, and that again, like in the analytic space, how we roll our in-line efficiency capabilities and data protection, is you know, very important, and then being able to move the data off into the cloud, if the customer so chooses, or just wants to use the cloud. So yeah, some of the same benefits from cloud connectivity, performance and efficiency that analytics apply certainly to AI. You know, another fun fact too about AI, which might help us, you and I living in the Boston area, is that I've read IBM has a patent out to use AI in traffic signaling, so in conjunction with cameras, to get AI, so hopefully that, you know, that works well it could alleviate-- >> Lead them out of the Tip O'Neill tunnel easy. (laughing) >> You got it maybe worse in D.C. (laughing) >> I'd like to hear though, if you have any best practices that with this moving into AI, how are you experimenting with it, and how are you finding it used most efficiently and effectively. >> Yeah, so I think one way we are eating our own dog food, so to speak, in that we're using it internally, we're using it on our customers' data, as I was explaining to help look at trends, and do analysis. So that's one, and then it's other things, just you know, partnering with companies like Nvidia as well and coming out with a joint solution, so we're doing work with them on different solution areas. >> Great, great. Well, Mike thanks so much for coming on theCUBE, >> Thanks for having me! >> It was fun having you. >> You survived! >> Yes! (laughs) >> We'll look forward to many more CUBE conversations. >> Great to hear from NetApp, you're very much in the game. >> Indeed, indeed. >> Alright, thank you very much. >> I'm Rebecca Knight for James Kobielus, we will have more from theCUBE's coverage of DataWorks coming up in just a little bit. (electronic music)

>> Live from San Jose, in the heart of Silicone Valley, it's theCUBE. Covering Datawork Summit 2018. Brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of Dataworks Summit here in San Jose, California. I'm your host, Rebecca Knight, along with my cohost James Kobielus. We're joined by Scott Gnau, he is the chief technology officer at Hortonworks. Welcome back to theCUBE, Scott. >> Great to be here. >> It's always fun to have you on the show. So, you have really spent your entire career in the data industry. I want to start off at 10,000 feet, and just have you talk about where we are now, in terms of customer attitudes, in terms of the industry, in terms of where customers feel, how they're dealing with their data and how they're thinking about their approach in their business strategy. >> Well I have to say, 30 plus years ago starting in the data field, it wasn't as exciting as it is today. Of course, I always found it very exciting. >> Exciting means nerve-wracking. Keep going. >> Or nerve-wracking. But you know, we've been predicting it. I remember even you know, 10, 15 years ago before big data was a thing, it's like oh all this data's going to come, and it's going to be you know 10x what it is. And we were wrong. It was like 5000x, you know what it is. And I think the really exciting part is that data really used to be relegated frankly, to big companies as a derivative work of ERP systems, and so on and so forth. And while that's very interesting, and certainly enabled a whole level of productivity for industry, when you compare that to all of the data flying around everywhere today, whether it be Twitter feeds and even doing live polls, like we did in the opening session today. Data is just being created everywhere. And the same thing applies to that data that applied to the ERP data of old. And that is being able to harness, manage and understand that data is a new business creating opportunity. And you know, we were with some analysts the other day, and I think one of the more quoted things that came out of that when I was speaking with them, was really, like railroads and shipping in the 1800s and oil in the 1900s, data really is the wealth creator of this century. And so that creates a very nerve-wracking environment. It also creates an environment, a very agile and very important technological breakthroughs that enable those things to be turned into wealth. >> So thinking about that, in terms of where we are at this point in time and on the main stage this morning someone had likened it to the interstate highway system, that really revolutionized transportation, but also commerce. >> I love that actually. I may steal it in some of my future presentations. >> That's good but we'll know where you pilfered it. >> Well perhaps if data is oil the edge, in containerized applications and piping data, you know, microbursts of data across the internet of things, is sort of like the new fracking. You know, you're being able to extract more of this precious resource from the territory. >> Hopefully not quite as damaging to the environment. >> Maybe not. I'm sorry for environmentalist if I just offended you, I apologize. >> But I think you know, all of those analogies are very true, and I particularly like the interstate one this morning. Because when I think about what we've done in our core http platform, and I know Arun was here talking about all the great advances that we built into this, the kind of the core hadoop platform. Very traditional. Store data, analyze data but also bring in new kinds of algorithms, rapid innovation and so on. That's really great but that's kind of half of the story. In a device connected world, in a consumer centric world, capturing data at the edge, moving and processing data at the edge is the new normal, right? And so just like the interstate highway system actually created new ways of commerce because we could move people and things more efficiently, moving data and processing data more efficiently is kind of the second part of the opportunity that we have in this new deluge of data. And that's really where we've been with our Hortonworks data flow. And really saying that the complete package of managing data from origination at the edge all the way through analytic to decision that's triggered back at the edge is like the holy grail, right? And building a technology for that footprint, is why I'm certainly excited today. It's not the caffeine, it's just the opportunity of making all of that work. >> You know, one of the, I think the key announcement for me at this show, that you guys made on HDP 3.0 was containerization of more of the capabilities of your distributed environment so that these capabilities, in terms of processing. First of all, capturing and analyzing an moving that data, can be pushed closer to the end points. Can you speak a bit Scott, about this new capability or this containerization support? Within HDP 3.0 but really in your broader portfolio and where you're going with that in terms of addressing edge applications perhaps, autonomous vehicles or you know, whatever you might put into a new smart phone or whatever you put at the edge. Describe the potential containerizations to sort of break this ecosystem wide open. >> Yeah, I think there are a couple of aspects to containerization and by the way, we're like so excited about kind of the cloud first, containerized HDP 3.0 that we launched here today. There's a lot of great tech that our customers have been clamoring for that they can take advantage of. And it's really just the beginning, which again is part of the excitement of being in the technology space and certainly being part of Hortonworks. So containerization affords a couple of things. Certainly, agility. Agility in deploying applications. So, you know for 30 years we've built these enterprise software stacks that were very integrated, hugely complicated systems that could bring together multiple different applications, different workloads and manage all that in a multi-tendency kind of environment. And that was because we had to do that, right? Servers were getting bigger, they were more powerful but not particularly well distributed. Obviously in a containerized world, you now turn that whole paradigm on its head and you say, you know what? I'm just going to collect these three microservices that I need to do this job. I can isolate them. I can have them run in a server-less technology. I can actually allocate in the cloud servers to go run, and when they're done they go away. And I don't pay for them anymore. So thinking about kind of that from a software development deployment implementation perspective, there huge implications but the real value for customers is agility, right? I don't have to wait until next year to upgrade my enterprise software stack to take advantage of this new algorithm. I can simply isolate it inside of a container, have it run, and have it go away. And get the answer, right? And so when I think about, and a number of our keynotes this morning were talking about just kind of the exponential rate of change, this is really the net new norm. Because the only way we can do things faster, is in fact to be able to provide this. >> And it's not just microservices. Also orchestrating them through Kubernetes, and so forth, so they can be. >> Sure. That's the how versus yeah. >> Quickly deployed as an ensemble and then quickly de-provisioned when you don't need them anymore. >> Yeah so then there's obviously the cost aspect, right? >> Yeah. >> So if you're going to run a whole bunch of stuff or even if you have something as mundane as a really big merge join inside of hive. Let me spin up a thousand extra containers to go do that big thing, and then have them go away when it's done. >> And oh, by the way, you'll be deployed on. >> And only pay for it while I'm using it. >> And then you can possibly distribute those containers across different public clouds depending on what's most cost effective at any point in time Azure or AWS or whatever it might be. >> And I tease with Arun, you know the only thing that we haven't solved is for the speed of light, but we're working on it. >> In talking about how this warp speed change, being the new norm, can you talk about some of the most exciting use cases you've seen in terms of the customers and clients that are using Hortonworks in the coolest ways. >> Well I mean obviously autonomous vehicles is one that we all captured all of our imagination. 'Cause we understand how that works. But it's a perfect use case for this kind of technology. But the technology also applies in fraud detection and prevention. It applies in healthcare management, in proactive personalized medicine delivery, and in generating better outcomes for treatment. So, you know, all across. >> It will bind us in every aspect of our lives including the consumer realm increasingly, yeah. >> Yeah, all across the board. And you know one of the things that really changed, right, is well a couple things. A lot of bandwidth so you can start to connect these things. The devices themselves are particularly smart, so you don't any longer have to transfer all the data to a mainframe and then wait three weeks, sorry, wait three weeks for your answer and then come back. You can have analytic models running on and edge device. And think about, you know, that is really real time. And that actually kind of solves for the speed of light. 'Cause you're not waiting for those things to go back and forth. So there are a lot of new opportunities and those architectures really depend on some of the core tenets of ultimately containerization stateless application deployment and delivery. And they also depend on the ability to create feedback loops to do point-to-point and peer kinds of communication between devices. This is a whole new world of how data get moved and how the decisions around date movement get made. And certainly that's what we're excited about, building with the core components. The other implication of all of this, and we've know each other for a long time. Data has gravity. Data movements expensive. It takes time, frankly, you have to pay for the bandwidth and all that kind of stuff. So being able to play the data where it lies becomes a lot more interesting from an application portability perspective and with all of these new sensors, devices and applications out there, a lot more data is living its entire lifecycle in the cloud. And so being able to create that connective tissue. >> Or as being as terralexical on the edge. >> And even on the edge. >> In with machine learn, let me just say, butt in a second. One of the areas that we're focusing on increasingly in Wikibot in terms of our focus on machine learning at the edge, is more and more machine learning frameworks are coming into the browser world. Javascript for the most like tenser flow JS, you know more of this inferencing and training is going to happen inside your browser. That blows a lot of people's minds. It may not be heavy hitting machine learning, but it'll be good enough for a lot of things that people do in their normal life. Where you don't want to round trip back to the cloud. It's all happening right there, in you know, Chrome or whatever you happen to be using. >> Yeah and so the point being now, you know when I think about the early days, talking about scalability, I remember ship being my first one terabyte database. And then the first 10 terabyte database. Yeah, it doesn't sound very exciting. When I think about scalability of the future, it's really going to, scalability is not going to be defined as petabytes or exabytes under management. It's really going to be defined as petabytes or exabytes affected across a grid of storage and processing devices. And that's a whole new technology paradigm, and really that's kind of the driving force behind what we've been building and what we've been talking about at this conference. >> Excellent. >> So when you're talking about these things. I mean how much, are the companies themselves prepared, and do they have the right kind of talent to use the kinds of insights that you're able to extract? And then act on them in the real time. 'Cause you're talking about how this is saving a lot of the waiting around time. So is this really changing the way business gets done, and do companies have the talent to execute? >> Sure. I mean it's changing the way business gets done. We showed a quote on stage this morning from the CEO of Marriott, right? So, I think there a couple of pieces. One is business are increasingly data driven and business strategy is increasingly the data strategy. And so it starts from the top, kind of setting that strategy and understanding the value of that asset and how that needs to be leveraged to drive new business. So that's kind of one piece. And you know, obviously there are more and more folks kind of coming to the realization that that is important. The other thing that's been helpful is, you know, as with any new technology there's always kind of the startup shortage of resource and people start to spool up and learn. You know the really good news, and for the past 10 years I've been working with a number of different university groups. Parents are actually going to universities and demanding that the curriculum include data, and processing and big data and all of these technologies. Because they know that their children educated in that kind of a world, number one, they're going to have a fun job to go to everyday. 'Cause it's going to be something different everyday. But number two they're going to be employed for life. (laughing) >> Yeah. >> They will be solvent. >> Frankly the demand has actually created a catch up in supply that we're seeing. And of course, you know, as tools start to get more mature and more integrated, they also become a little bit easier to use. You know, less, there's a little bit easier deployment and so on. So a combination of, I'm seeing a really good supply, there really, obviously we invest in education through the community. And then frankly, the education system itself, and folks saying this is really the hot job of the next century. You know, I can be the new oil barren. Or I can be the new railroad captain. It's actually creating more supply which is also very helpful. >> Data's the heart of what I call the new stem cell. It's science, technology, engineering, mathematics that you want to implant in the brains of the young as soon as possible. I hear ya. >> Yeah, absolutely. >> Well Scott thanks so much for coming on. But I want to first also, we can't let you go without the fashion statement. You arrived on set wearing it. >> The elephants. >> I mean it was quite a look. >> Well I did it because then you couldn't see I was sweating on my brow. >> Oh please, no, no, no. >> 'Cause I was worried about this tough interview. >> You know one of the things I love about your logo, and I'll just you know, sounds like I'm fawning. The elephant is a very intelligent animal. >> It is indeed. >> My wife's from Indonesia. I remember going back one time they had Asian elephants at a one of these safari parks. And watching it perform, and then my son was very little then. The elephant is a very sensitive, intelligent animal. You don't realize 'till you're up close. They pick up all manner of social cues. I think it's an awesome symbol for a company that's all about data driven intelligence. >> The elephant never forgets. >> Yeah. >> That's what we know. >> That's right we never forget. >> Him forget 'cause he's got a brain. Or she, I'm sorry. He or she has a brain. >> And it's data driven. >> Yeah. >> Thanks very much. >> Great. Well thanks for coming on theCUBE. I'm Rebecca Knight for James Kobielus. We will have more coming up from Dataworks just after this. (upbeat music)

>> Live from San Jose, in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2018. Brought to you by HortonWorks. >> We are wrapping up Day One of coverage of Dataworks here in San Jose, California on theCUBE. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We have two guests for this last segment of the day. We have Sudhir Hasbe, who is the director of product management at Google and Ram Venkatesh, who is VP of Engineering at Hortonworks. Ram, Sudhir, thanks so much for coming on the show. >> Thank you very much. >> Thank you. >> So, I want to start out by asking you about a joint announcement that was made earlier this morning about using some Hortonworks technology deployed onto Google Cloud. Tell our viewers more. >> Sure, so basically what we announced was support for the Hortonworks DataPlatform and Hortonworks DataFlow, HDP and HDF, running on top of the Google Cloud Platform. So this includes deep integration with Google's cloud storage connector layer as well as it's a certified distribution of HDP to run on the Google Cloud Platform. >> I think the key thing is a lot of our customers have been telling us they like the familiar environment of Hortonworks distribution that they've been using on-premises and as they look at moving to cloud, like in GCP, Google Cloud, they want the similar, familiar environment. So, they want the choice to deploy on-premises or Google Cloud, but they want the familiarity of what they've already been using with Hortonworks products. So this announcement actually helps customers pick and choose like whether they want to run Hortonworks distribution on-premises, they want to do it in cloud, or they wat to build this hybrid solution where the data can reside on-premises, can move to cloud and build these common, hybrid architecture. So, that's what this does. >> So, HDP customers can store data in the Google Cloud. They can execute ephemeral workloads, analytic workloads, machine learning in the Google Cloud. And there's some tie-in between Hortonworks's real-time or low latency or streaming capabilities from HDF in the Google Cloud. So, could you describe, at a full sort of detail level, the degrees of technical integration between your two offerings here. >> You want to take that? >> Sure, I'll handle that. So, essentially, deep in the heart of HDP, there's the HDFS layer that includes Hadoop compatible file system which is a plug-able file system layer. So, what Google has done is they have provided an implementation of this API for the Google Cloud Storage Connector. So this is the GCS Connector. We've taken the connector and we've actually continued to refine it to work with our workloads and now Hortonworks has actually bundling, packaging, and making this connector be available as part of HDP. >> So bilateral data movement between them? Bilateral workload movement? >> No, think of this as being very efficient when our workloads are running on top of GCP. When they need to get at data, they can get at data that is in the Google Cloud Storage buckets in a very, very efficient manner. So, since we have fairly deep expertise on workloads like Apache Hive and Apache Spark, we've actually done work in these workloads to make sure that they can run efficiently, not just on HDFS, but also in the cloud storage connector. This is a critical part of making sure that the architecture is actually optimized for the cloud. So, at our skill and our customers are moving their workloads from on-premise to the cloud, it's not just functional parity, but they also need sort of the operational and the cost efficiency that they're looking for as they move to the cloud. So, to do that, we need to enable these fundamental disaggregated storage pattern. See, on-prem, the big win with Hadoop was we could bring the processing to where the data was. In the cloud, we need to make sure that we work well when storage and compute are disaggregated and they're scaled elastically, independent of each other. So this is a fairly fundamental architectural change. We want to make sure that we enable this in a first-class manner. >> I think that's a key point, right. I think what cloud allows you to do is scale the storage and compute independently. And so, with storing data in Google Cloud Storage, you can like scale that horizontally and then just leverage that as your storage layer. And the compute can independently scale by itself. And what this is allowing customers of HDP and HDF is store the data on GCP, on the cloud storage, and then just use the scale, the compute side of it with HDP and HDF. >> So, if you'll indulge me to a name, another Hortonworks partner for just a hypothetical. Let's say one of your customers is using IBM Data Science Experience to do TensorFlow modeling and training, can they then inside of HDP on GCP, can they use the compute infrastructure inside of GCP to do the actual modeling which is more compute intensive and then the separate decoupled storage infrastructure to do the training which is more storage intensive? Is that a capability that would available to your customers? With this integration with Google? >> Yeah, so where we are going with this is we are saying, IBM DSX and other solutions that are built on top of HDP, they can transparently take advantage of the fact that they have HDP compute infrastructure to run against. So, you can run your machine learning training jobs, you can run your scoring jobs and you can have the same unmodified DSX experience whether you're running against an on-premise HDP environment or an in-cloud HDP environment. Further, that's sort of the benefit for partners and partner solutions. From a customer standpoint, the big value prop here is that customers, they're used to securing and governing their data on-prem in their particular way with HDP, with Apache Ranger, Atlas, and so forth. So, when they move to the cloud, we want this experience to be seamless from a management standpoint. So, from a data management standpoint, we want all of their learning from a security and governance perspective to apply when they are running in Google Cloud as well. So, we've had this capability on Azure and on AWS, so with this partnership, we are announcing the same type of deep integration with GCP as well. >> So Hortonworks is that one pane of glass across all your product partners for all manner of jobs. Go ahead, Rebecca. >> Well, I just wanted to ask about, we've talked about the reason, the impetus for this. With the customer, it's more familiar for customers, it offers the seamless experience, But, can you delve a little bit into the business problems that you're solving for customers here? >> A lot of times, our customers are at various points on their cloud journey, that for some of them, it's very simple, they're like there's a broom coming by and the datacenter is going away in 12 months and I need to be in the cloud. So, this is where there is a wholesale movement of infrastructure from on-premise to the cloud. Others are exploring individual business use cases. So, for example, one of our large customers, a travel partner, so they are exploring their new pricing model and they want to roll out this pricing model in the cloud. They have on-premise infrastructure, they know they have that for a while. They are spinning up new use cases in the cloud typically for reasons of agility. So, if you, typically many of our customers, they operate large, multi-tenant clusters on-prem. That's nice for, so a very scalable compute for running large jobs. But, if you want to run, for example, a new version of Spark, you have to upgrade the entire cluster before you can do that. Whereas in this sort of model, what they can say is, they can bring up a new workload and just have the specific versions and dependency that it needs, independent of all of their other infrastructure. So this gives them agility where they can move as fast as... >> Through the containerization of the Spark jobs or whatever. >> Correct, and so containerization as well as even spinning up an entire new environment. Because, in the cloud, given that you have access to elastic compute resources, they can come and go. So, your workloads are much more independent of the underlying cluster than they are on-premise. And this is where sort of the core business benefits around agility, speed of deployment, things like that come into play. >> And also, if you look at the total cost of ownership, really take an example where customers are collecting all this information through the month. And, at month end, you want to do closing of books. And so that's a great example where you want ephemeral workloads. So this is like do it once in a month, finish the books and close the books. That's a great scenario for cloud where you don't have to on-premises create an infrastructure, keep it ready. So that's one example where now, in the new partnership, you can collect all the data through the on-premises if you want throughout the month. But, move that and leverage cloud to go ahead and scale and do this workload and finish the books and all. That's one, the second example I can give is, a lot of customers collecting, like they run their e-commerce platforms and all on-premises, let's say they're running it. They can still connect all these events through HDP that may be running on-premises with Kafka and then, what you can do is, in-cloud, in GCP, you can deploy HDP, HDF, and you can use the HDF from there for real-time stream processing. So, collect all these clickstream events, use them, make decisions like, hey, which products are selling better?, should we go ahead and give?, how many people are looking at that product?, or how many people have bought it?. That kind of aggregation and real-time at scale, now you can do in-cloud and build these hybrid architectures that are there. And enable scenarios where in past, to do that kind of stuff, you would have to procure hardware, deploy hardware, all of that. Which all goes away. In-cloud, you can do that much more flexibly and just use whatever capacity you have. >> Well, you know, ephemeral workloads are at the heart of what many enterprise data scientists do. Real-world experiments, ad-hoc experiments, with certain datasets. You build a TensorFlow model or maybe a model in Caffe or whatever and you deploy it out to a cluster and so the life of a data scientist is often nothing but a stream of new tasks that are all ephemeral in their own right but are part of an ongoing experimentation program that's, you know, they're building and testing assets that may be or may not be deployed in the production applications. That's you know, so I can see a clear need for that, well, that capability of this announcement in lots of working data science shops in the business world. >> Absolutely. >> And I think coming down to, if you really look at the partnership, right. There are two or three key areas where it's going to have a huge advantage for our customers. One is analytics at-scale at a lower cost, like total cost of ownership, reducing that, running at-scale analytics. That's one of the big things. Again, as I said, the hybrid scenarios. Most customers, enterprise customers have huge deployments of infrastructure on-premises and that's not going to go away. Over a period of time, leveraging cloud is a priority for a lot of customers but they will be in these hybrid scenarios. And what this partnership allows them to do is have these scenarios that can span across cloud and on-premises infrastructure that they are building and get business value out of all of these. And then, finally, we at Google believe that the world will be more and more real-time over a period of time. Like, we already are seeing a lot of these real-time scenarios with IoT events coming in and people making real-time decisions. And this is only going to grow. And this partnership also provides the whole streaming analytics capabilities in-cloud at-scale for customers to build these hybrid plus also real-time streaming scenarios with this package. >> Well it's clear from Google what the Hortonworks partnership gives you in this competitive space, in the multi-cloud space. It gives you that ability to support hybrid cloud scenarios. You're one of the premier public cloud providers and we all know about. And clearly now that you got, you've had the Hortonworks partnership, you have that ability to support those kinds of highly hybridized deployments for your customers, many of whom I'm sure have those requirements. >> That's perfect, exactly right. >> Well a great note to end on. Thank you so much for coming on theCUBE. Sudhir, Ram, that you so much. >> Thank you, thanks a lot. >> Thank you. >> I'm Rebecca Knight for James Kobielus, we will have more tomorrow from DataWorks. We will see you tomorrow. This is theCUBE signing off. >> From sunny San Jose. >> That's right.

>> Live from San Jose, in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We're joined by Stephanie McReynolds. She is the Vice President of Marketing at Alation. Thanks so much for, for returning to theCUBE, Stephanie. >> Thank you for having me again. >> So, before the cameras were rolling, we were talking about Kevin Slavin's talk on the main stage this morning, and talking about, well really, a background to sort of this concern about AI and automation coming to take people's jobs, but really, his overarching point was that we really, we shouldn't, we shouldn't let the algorithms take over, and that humans actually are an integral piece of this loop. So, riff on that a little bit. >> Yeah, what I found fascinating about what he presented were actual examples where having a human in the loop of AI decision-making had a more positive impact than just letting the algorithms decide for you, and turning it into kind of a black, a black box. And the issue is not so much that, you know, there's very few cases where the algorithms make the wrong decision. What happens the majority of the time is that the algorithms actually can't be understood by human. So if you have to roll back >> They're opaque, yeah. >> in your decision-making, or uncover it, >> I mean, who can crack what a convolutional neural network does, at a layer by layer, nobody can. >> Right, right. And so, his point was, if we want to avoid not just poor outcomes, but also make sure that the robots don't take over the world, right, which is where every like, media person goes first, right? (Rebecca and James laugh) That you really need a human in the loop of this process. And a really interesting example he gave was what happened with the 2015 storm, and he talked about 16 different algorithms that do weather predictions, and only one algorithm predicted, mis-predicted that there would be a huge weather storm on the east coast. So if there had been a human in the loop, we wouldn't have, you know, caused all this crisis, right? The human could've >> And this is the storm >> Easily seen. >> That shut down the subway system, >> That's right. That's right. >> And really canceled New York City for a few days there, yeah. >> That's right. So I find this pretty meaningful, because Alation is in the data cataloging space, and we have a lot of opportunity to take technical metadata and automate the collection of technical and business metadata and do all this stuff behind the scenes. >> And you make the discovery of it, and the analysis of it. >> We do the discovery of this, and leading to actual recommendations to users of data, that you could turn into automated analyses or automated recommendations. >> Algorithmic, algorithmically augmented human judgment is what it's all about, the way I see it. What do you think? >> Yeah, but I think there's a deeper insight that he was sharing, is it's not just human judgment that is required, but for humans to actually be in the loop of the analysis as it moves from stage to stage, that we can try to influence or at least understand what's happening with that algorithm. And I think that's a really interesting point. You know, there's a number of data cataloging vendors, you know, some analysts will say there's anywhere from 10 to 30 different vendors in the data cataloging space, and as vendors, we kind of have this debate. Some vendors have more advanced AI and machine learning capabilities, and other vendors haven't automated at all. And I think that the answer, if you really want humans to adopt analytics, and to be comfortable with the decision-making of those algorithms, you need to have a human in the loop, in the middle of that process, of not only making the decision, but actually managing the data that flows through these systems. >> Well, algorithmic transparency and accountability is an increasing requirement. It's a requirement for GDPR compliance, for example. >> That's right. >> That I don't see yet with Wiki, but we don't see a lot of solution providers offering solutions to enable more of an automated roll-up of a narrative of an algorithmic decision path. But that clearly is a capability as it comes along, and it will. That will absolutely depend on a big data catalog managing the data, the metadata, but also helping to manage the tracking of what models were used to drive what decision, >> That's right. >> And what scenario. So that, that plays into what Alation >> So we talk, >> And others in your space do. >> We call that data catalog, almost as if the data's the only thing that we're tracking, but in addition to that, that metadata or the data itself, you also need to track the business semantics, how the business is using or applying that data and that algorithmic logic, so that might be logic that's just being used to transform that data, or it might be logic to actually make and automate decision, like what they're talking about GDPR. >> It's a data artifact catalog. These are all artifacts that, they are derived in many ways, or supplement and complement the data. >> That's right. >> They're all, it's all the logic, like you said. >> And what we talk about is, how do you create transparency into all those artifacts, right? So, a catalog starts with this inventory that creates a foundation for transparency, but if you don't make those artifacts accessible to a business person, who might not understand what is metadata, what is a transformation script. If you can't make that, those artifacts accessible to a, what I consider a real, or normal human being, right, (James laughs) I love to geek out, but, (all laugh) at some point, not everyone is going to understand. >> She's the normal human being in this team. >> I'm normal. I'm normal. >> I'm the abnormal human being among the questioners here. >> So, yeah, most people in the business are just getting our arms around how do we trust the output of analytics, how do we understand enough statistics and know what to apply to solve a business problem or not, and then we give them this like, hairball of technical artifacts and say, oh, go at it. You know, here's your transparency. >> Well, I want to ask about that, that human that we're talking about, that needs to be in the loop at every stage. What, that, surely, we can make the data more accessible, and, but it also requires a specialized skill set, and I want to ask you about the talent, because I noticed on your LinkedIn, you said, hey, we're hiring, so let me know. >> That's right, we're always hiring. We're a startup, growing well. >> So I want to know from you, I mean, are you having difficulty with filling roles? I mean, what is at the pipeline here? Are people getting the skills that they need? >> Yeah, I mean, there's a wide, what I think is a misnomer is there's actually a wide variety of skills, and I think we're adding new positions to this pool of skills. So I think what we're starting to see is an expectation that true business people, if you are in a finance organization, or you're in a marketing organization, or you're in a sales organization, you're going to see a higher level of data literacy be expected of that, that business person, and that's, that doesn't mean that they have to go take a Python course and learn how to be a data scientist. It means that they have to understand statistics enough to realize what the output of an algorithm is, and how they should be able to apply that. So, we have some great customers, who have formally kicked off internal training programs that are data literacy programs. Munich Re Insurance is a good example. They spoke with James a couple of months ago in Berlin. >> Yeah, this conference in Berlin, yeah. >> That's right, that's right, and their chief data officer has kicked off a formal data literacy training program for their employees, so that they can get business people comfortable enough and trusting the data, and-- >> It's a business culture transformation initiative that's very impressive. >> Yeah. >> How serious they are, and how comprehensive they are. >> But I think we're going to see that become much more common. Pfizer has taken, who's another customer of ours, has taken on a similar initiative, and how do they make all of their employees be able to have access to data, but then also know when to apply it to particular decision-making use cases. And so, we're seeing this need for business people to get a little bit of training, and then for new roles, like information stewards, or data stewards, to come online, folks who can curate the data and the data assets, and help be kind of translators in the organization. >> Stephanie, will there be a need for a algorithm curator, or a model curator, to, you know, like a model whisperer, to explain how these AI, convolutional, recurrent, >> Yeah. >> Whatever, all these neural, how, what they actually do, you know. Would there be a need for that going forward? Another as a normal human being, who can somehow be bilingual in neural net and in standard language? >> I think, I think so. I mean, I think we've put this pressure on data scientists to be that person. >> Oh my gosh, they're so busy doing their job. How can we expect them to explain, and I mean, >> Right. >> And to spend 100% of their time explaining it to the rest of us? >> And this is the challenge with some of the regulations like GDPR. We aren't set up yet, as organizations, to accommodate this complexity of understanding, and I think that this part of the market is going to move very quickly, so as vendors, one of the things that we can do is continue to help by building out applications that make it easy for information stewardship. How do you lower the barrier for these specialist roles and make it easy for them to do their job by using AI and machine learning, where appropriate, to help scale the manual work, but keeping a human in the loop to certify that data asset, or to add additional explanation and then taking their work and using AI, machine learning, and automation to propagate that work out throughout the organization, so that everyone then has access to those explanations. So you're no longer requiring the data scientists to hold like, I know other organizations that hold office hours, and the data scientist like sits at a desk, like you did in college, and people can come in and ask them questions about neural nets. That's just not going to scale at today's pace of business. >> Right, right. >> You know, the term that I used just now, the algorithm or model whisperer, you know, the recommend-er function that is built into your environment, in similar data catalog, is a key piece of infrastructure to rank the relevance rank, you know, the outputs of the catalog or responses to queries that human beings might make. You know, the recommendation ranking is critically important to help human beings assess the, you know, what's going on in the system, and give them some advice about how to, what avenues to explore, I think, so. >> Yeah, yeah. And that's part of our definition of data catalog. It's not just this inventory of technical metadata. >> That would be boring, and dry, and useless. >> But that's where, >> For most human beings. >> That's where a lot of vendor solutions start, right? >> Yeah. >> And that's an important foundation. >> Yeah, for people who don't live 100% of their work day inside the big data catalog. I hear what you're saying, you know. >> Yeah, so people who want a data catalog, how you make that relevant to the business is you connect those technical assets, that technical metadata with how is the business actually using this in practice, and how can we have proactive recommendation or the recommendation engines, and certifications, and this information steward then communicating through this platform to others in the organization about how do you interpret this data and how do you use it to actually make business decisions. And I think that's how we're going to close the gap between technology adoption and actual data-driven decision-making, which we're not quite seeing yet. We're only seeing about 30, when they survey, only about 36% of companies are actually confident they're making data-driven decisions, even though there have been, you know, millions, if not billions of dollars that have gone into the data analytics market and investments, and it's because as a manager, I don't quite have the data literacy yet, and I don't quite have the transparency across the rest of the organization to close that trust gap on analytics. >> Here's my feeling, in terms of cultural transformations across businesses in general. I think the legal staff of every company is going to need to get real savvy on using those kinds of tools, like your catalog, with recommendation engines, to support e-discovery, or discovery of the algorithmic decision paths that were taken by their company's products, 'cause they're going to be called by judges and juries, under a subpoena and so forth, and so on, to explain all this, and they're human beings who've got law degrees, but who don't know data, and they need the data environment to help them frame up a case for what we did, and you know, so, we being the company that's involved. >> Yeah, and our politicians. I mean, anyone who's read Cathy's book, Weapons of Math Destruction, there are some great use cases of where, >> Math, M-A-T-H, yeah. >> Yes, M-A-T-H. But there are some great examples of where algorithms can go wrong, and many of our politicians and our representatives in government aren't quite ready to have that conversation. I think anyone who watched the Zuckerberg hearings you know, in congress saw the gap of knowledge that exists between >> Oh my gosh. >> The legal community, and you know, and the tech community today. So there's a lot of work to be done to get ready for this new future. >> But just getting back to the cultural transformation needed to be, to make data-driven decisions, one of the things you were talking about is getting the managers to trust the data, and we're hearing about what are the best practices to have that happen in the sense, of starting small, be willing to experiment, get out of the lab, try to get to insight right away. What are, what would your best advice be, to gain trust in the data? >> Yeah, I think the biggest gap is this issue of transparency. How do you make sure that everyone understands each step of the process and has access to be able to dig into that. If you have a foundation of transparency, it's a lot easier to trust, rather than, you know, right now, we have kind of like the high priesthood of analytics going on, right? (Rebecca laughs) And some believers will believe, but a lot of folks won't, and, you know, the origin story of Alation is really about taking these concepts of the scientific revolution and scientific process and how can we support, for data analysis, those same steps of scientific evaluation of a finding. That means that you need to publish your data set, you need to allow others to rework that data, and come up with their own findings, and you have to be open and foster conversations around data in your organization. One other customer of ours, Meijer, who's a grocery store in the mid-west, and if you're west coast or east coast-based, you might not have heard of them-- >> Oh, Meijers, thrifty acres. I'm from Michigan, and I know them, yeah. >> Gigantic. >> Yeah, there you go. Gigantic grocery chain in the mid-west, and, Joe Oppenheimer there actually introduced a program that he calls the social contract for analytics, and before anyone gets their license to use Tableau, or MicroStrategy, or SaaS, or any of the tools internally, he asks those individuals to sign a social contract, which basically says that I'll make my work transparent, I will document what I'm doing so that it's shareable, I'll use certain standards on how I format the data, so that if I come up with a, with a really insightful finding, it can be easily put into production throughout the rest of the organization. So this is a really simple example. His inspiration for that social contract was his high school freshman. He was entering high school and had to sign a social contract, that he wouldn't make fun of the teachers, or the students, you know, >> I love it. >> Very simple basics. >> Yeah, right, right, right. >> I wouldn't make fun of the teacher. >> We all need social contract. >> Oh my gosh, you have to make fun of the teacher. >> I think it was a little more formal than that, in the language, but that was the concept. >> That's violating your civil rights as a student. I'm sorry. (Stephanie laughs) >> Stephanie, always so much fun to have you here. Thank you so much for coming on. >> Thank you. It's a pleasure to be here. >> I'm Rebecca Knight, for James Kobielus. We'll have more of theCUBE's live coverage of DataWorks just after this.

>> Live from San Jose, in the heart of Silicon Valley, it's theCube. Covering DataWorks Summit 2018. Brought to you by Hortonworks. >> Welcome back to day two of theCube's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight along with my co-host James Kobielus. James, it's great to be here with you in the hosting seat again. >> Day two, yes. >> Exactly. So here we are, this conference, 2,100 attendees from 32 countries, 23 industries. It's a relatively big show. They do three of them during the year. One of the things that I really-- >> It's a well-established show too. I think this is like the 11th year since Yahoo started up the first Hadoop summit in 2008. >> Right, right. >> So it's an established event, yeah go. >> Exactly, exactly. But I really want to talk about Hortonworks the company. This is something that you had brought up in an analyst report before the show started and that was talking about Hortonworks' cash flow positivity for the first time. >> Which is good. >> Which is good, which is a positive sign and yet what are the prospects for this company's financial health? We're still not seeing really clear signs of robust financial growth. >> I think the signs are good for the simple reason they're making significant investments now to prepare for the future that's almost inevitable. And the future that's almost inevitable, and when I say the future, the 2020s, the decade that's coming. Most of their customers will shift more of their workloads, maybe not entirely yet, to public cloud environments for everything they're doing, AI, machine learning, deep learning. And clearly the beneficiaries of that trend will be the public cloud providers, all of whom are Hortonworks' partners and established partners, AWS, Microsoft with Azure, Google with, you know, Google Cloud Platform, IBM with IBM Cloud. Hortonworks, and this is... You know, their partnerships with these cloud providers go back several years so it's not a new initiative for them. They've seen the writing on the wall practically from the start of Hortonworks' founding in 2011 and they now need to go deeper towards making their solution portfolio capable of being deployable on-prem, in cloud, public clouds, and in various and sundry funky combinations called hybrid multi-clouds. Okay, so, they've been making those investments in those partnerships and in public cloud enabling the Hortonworks Data Platform. Here at this show, DataWorks 2018 here in San Jose, they've released the latest major version, HDP 3.0 of their core platform with a lot of significant enhancements related to things that their customers are increasingly doing-- >> Well I want to ask you about those enhancements. >> But also they have partnership announcements, the deep ones of integration and, you know, lift and shift of the Hortonworks portfolio of HDP with Hortonworks DataFlow and DataPlane Services, so that those solutions can operate transparently on those public cloud environments as the customers, as and when the customers choose to shift their workloads. 'Cause Hortonworks really... You know, like Scott Gnau yesterday, I mean just laid it on the line, they know that the more of the public cloud workloads will predominate now in this space. They're just making these speculative investments that they absolutely have to now to prepare the way. So I think this cost that they're incurring now to prepare their entire portfolio for that inevitable future is the right thing to do and that's probably why they still have not attained massive rock and rollin' positive cash flow yet but I think that they're preparing the way for them to do so in the coming decade. >> So their financial future is looking brighter and they're doing the right things. >> Yeah, yes. >> So now let's talk tech. And this is really where you want to be, Jim, I know you. >> Oh I get sleep now and I don't think about tech constantly. >> So as you've said, they're really doing a lot of emphasis now on their public cloud partnerships. >> Yes. >> But they've also launched several new products and upgrades to existing products, what are you seeing that excites you and that you think really will be potential game changers? >> You know, this is geeky but this is important 'cause it's at the very heart of Hortonworks Data Platform 3.0, containerization of more... When you're a data scientist, and you're building a machine learning model using data that's maintained, and is persisted, and processed within Hortonworks Data Platform or any other big data platform, you want the ability increasingly for developing machine learning, deep learning, AI in general, to take that application you might build while you're using TensorFlow models, that you build on HDP, they will containerize it in Docker and, you know, orchestrate it all through Kubernetes and all that wonderful stuff, and deploy it out, those AI, out to increasingly edge computing, mobile computing, embedded computing environments where, you know, the real venture capital mania's happening, things like autonomous vehicles, and you know, drones, and you name it. So the fact is that Hortonworks has made that in many ways the premier new feature of HDP 3.0 announced here this week at the show. That very much harmonizes with what their partners, where their partners are going with containerization of AI. IBM, one of their premier partners, very recently, like last month, I think it was, announced the latest version of IBM, what do they call it, IBM Cloud Private, which has embedded as a core feature containerization within that environment which is a prem-based environment of AI and so forth. The fact that Hortonworks continues to maintain close alignment with the capabilities that its public cloud partners are building to their respective portfolios is important. But also Hortonworks with its, they call it, you know, a single pane of glass, the DataPlane Services for metadata and monitoring and governance and compliance across this sprawling hybrid multi-cloud, these scenarios. The fact that they're continuing to make, in fact, really focusing on deep investments in that portfolio, so that when an IBM introduces or, AWS, whoever, introduces some new feature in their respective platforms, Hortonworks has the ability to, as it were, abstract above and beyond all of that so that the customer, the developer, and the data administrator, all they need to do, if they're a Hortonworks customer, is stay within the DataPlane Services and environment to be able to deploy with harmonized metadata and harmonized policies, and harmonized schemas and so forth and so on, and query optimization across these sprawling environments. So Hortonworks, I think, knows where their bread is buttered and it needs to stay on the DPS, DataPlane Services, side which is why a couple months ago in Berlin, Hortonworks made a, I think, the most significant announcement of the year for them and really for the industry, was that they announced the Data Steward Studio in Berlin. Tech really clearly was who addressed the GDPR mandate that was coming up but really did a stewardship as an end-to-end workflow for lots of, you know, core enterprise applications, absolutely essential. Data Steward Studio is a DataPlane Service that can operate across multi-cloud environments. Hortonworks is going to keep on, you know... They didn't have a DPS, DataPlane Services, announcements here in San Jose this week but you can best believe that next year at this time at this show, and in the interim they'll probably have a number of significant announcements to deepen that portfolio. Once again it's to grease the wheels towards a more purely public cloud future in which there will be Hortonworks DNA inside most of their customers' environments going forward. >> I want to ask you about themes of this year's conference. The thing is is that you were in Berlin at the last big Hortonworks DataWorks Summit. >> (speaks in foreign language) >> And really GDPR dominated the conversations because the new rules and regulations hadn't yet taken effect and companies were sort of bracing for what life was going to be like under GDPR. Now the rules are here, they're here to stay, and companies are really grappling with it, trying to understand the changes and how they can exist in this new regime. What would you say are the biggest themes... We're still talking about GDPR, of course, but what would you say are the bigger themes that are this week's conference? Is it scalability, is it... I mean, what would you say we're going, what do you think has dominated the conversations here? >> Well scalability is not the big theme this week though there are significant scalability announcements this week in the context of HDP 3.0, the ability to persist in a scale-out fashion across multi-cloud, billions of files. Storage efficiency is an important piece of the overall announcement with support for erasure coding, blah blah blah. That's not, you know, that's... Already, Hortonworks, like all of their cloud providers and other big data providers, provide very scalable environments for storage, workload management. That was not the hugest, buzzy theme in terms of the announcements this week. The buzz of course was HDP 3.0. Containerization, that's important, but you know, we just came out of the day two keynote. AI is not a huge focus yet for a lot of the Hortonworks customers who are here, the developers. They're, you know, most of their customers are not yet that far along in their deep learning journeys and whatever but they're definitely going there. There's plenty of really cool keynote discussions including the guy with the autonomous vehicles or whatever that, the thing we just came out of. That was not the predominant theme this week here in terms of the HDP 3.0. I think what it comes down to is that with HDP 3.0... Hive, though you tend to take it for granted, it's been in Hadoop from the very start, practically, Hive is now a full enterprise database and that's the core, one of the cores, of HDP 3.0. Hive itself, Hive 3.0 now is its version, is ACID compliant and that may be totally geeky to the most of the world but that enables it to support transactional applications. So more big data in every environment is supporting more traditional enterprise application, transactional applications that require like two-phase commit and all that goodness. The fact is, you know, Hortonworks have, from what I can see, is the first of the big data vendors to incorporate those enhancements to Hive 3.0 because they're so completely tuned in to the Hive environment in terms of a committer. I think in many ways that is the predominant theme in terms of the new stuff that will actually resonate with the developers, their customers here at the show. And with the, you know, enterprises in general, they can put more of their traditional enterprise application workloads on big data environments and specifically, Hortonworks hopes, its HDP 3.0. >> Well I'm excited to learn more here at the on theCube with you today. We've got a lot of great interviews lined up and a lot of interesting content. We got a great crew too so this is a fun show to do. >> Sure is. >> We will have more from day two of the.

>> From San Jose, in the heart of Silicon Valley, it's theCube. Covering DataWorks Summit 2018. Brought to you by Hortonworks. (upbeat music) >> Welcome back to theCUBE's live coverage of Data Works here in sunny San Jose, California. I'm your host Rebecca Knight along with my co-host James Kobielus. We're joined by Pandit Prasad. He is the analytics, projects, strategy, and management at IBM Analytics. Thanks so much for coming on the show. >> Thanks Rebecca, glad to be here. >> So, why don't you just start out by telling our viewers a little bit about what you do in terms of in relationship with the Horton Works relationship and the other parts of your job. >> Sure, as you said I am in Offering Management, which is also known as Product Management for IBM, manage the big data portfolio from an IBM perspective. I was also working with Hortonworks on developing this relationship, nurturing that relationship, so it's been a year since the Northsys partnership. We announced this partnership exactly last year at the same conference. And now it's been a year, so this year has been a journey and aligning the two portfolios together. Right, so Hortonworks had HDP HDF. IBM also had similar products, so we have for example, Big Sequel, Hortonworks has Hive, so how Hive and Big Sequel align together. IBM has a Data Science Experience, where does that come into the picture on top of HDP, so it means before this partnership if you look into the market, it has been you sell Hadoop, you sell a sequel engine, you sell Data Science. So what this year has given us is more of a solution sell. Now with this partnership we go to the customers and say here is NTN experience for you. You start with Hadoop, you put more analytics on top of it, you then bring Big Sequel for complex queries and federation visualization stories and then finally you put Data Science on top of it, so it gives you a complete NTN solution, the NTN experience for getting the value out of the data. >> Now IBM a few years back released a Watson data platform for team data science with DSX, data science experience, as one of the tools for data scientists. Is Watson data platform still the core, I call it dev ops for data science and maybe that's the wrong term, that IBM provides to market or is there sort of a broader dev ops frame work within which IBM goes to market these tools? >> Sure, Watson data platform one year ago was more of a cloud platform and it had many components of it and now we are getting a lot of components on to the (mumbles) and data science experience is one part of it, so data science experience... >> So Watson analytics as well for subject matter experts and so forth. >> Yes. And again Watson has a whole suit of side business based offerings, data science experience is more of a a particular aspect of the focus, specifically on the data science and that's been now available on PRAM and now we are building this arm from stack, so we have HDP, HDF, Big Sequel, Data Science Experience and we are working towards adding more and more to that portfolio. >> Well you have a broader reference architecture and a stack of solutions AI and power and so for more of the deep learning development. In your relationship with Hortonworks, are they reselling more of those tools into their customer base to supplement, extend what they already resell DSX or is that outside of the scope of the relationship? >> No it is all part of the relationship, these three have been the core of what we announced last year and then there are other solutions. We have the whole governance solution right, so again it goes back to the partnership HDP brings with it Atlas. IBM has a whole suite of governance portfolio including the governance catalog. How do you expand the story from being a Hadoop-centric story to an enterprise data-like story, and then now we are taking that to the cloud that's what Truata is all about. Rob Thomas came out with a blog yesterday morning talking about Truata. If you look at it is nothing but a governed data-link hosted offering, if you want to simplify it. That's one way to look at it caters to the GDPR requirements as well. >> For GDPR for the IBM Hortonworks partnership is the lead solution for GDPR compliance, is it Hortonworks Data Steward Studio or is it any number of solutions that IBM already has for data governance and curation, or is it a combination of all of that in terms of what you, as partners, propose to customers for soup to nuts GDPR compliance? Give me a sense for... >> It is a combination of all of those so it has a HDP, its has HDF, it has Big Sequel, it has Data Science Experience, it had IBM governance catalog, it has IBM data quality and it has a bunch of security products, like Gaurdium and it has some new IBM proprietary components that are very specific towards data (cough drowns out speaker) and how do you deal with the personal data and sensitive personal data as classified by GDPR. I'm supposed to query some high level information but I'm not allowed to query deep into the personal information so how do you blog those queries, how do you understand those, these are not necessarily part of Data Steward Studio. These are some of the proprietary components that are thrown into the mix by IBM. >> One of the requirements that is not often talked about under GDPR, Ricky of Formworks got in to it a little bit in his presentation, was the notion that the requirement that if you are using an UE citizen's PII to drive algorithmic outcomes, that they have the right to full transparency. It's the algorithmic decision paths that were taken. I remember IBM had a tool under the Watson brand that wraps up a narrative of that sort. Is that something that IBM still, it was called Watson Curator a few years back, is that a solution that IBM still offers, because I'm getting a sense right now that Hortonworks has a specific solution, not to say that they may not be working on it, that addresses that side of GDPR, do you know what I'm referring to there? >> I'm not aware of something from the Hortonworks side beyond the Data Steward Studio, which offers basically identification of what some of the... >> Data lineage as opposed to model lineage. It's a subtle distinction. >> It can identify some of the personal information and maybe provide a way to tag it and hence, mask it, but the Truata offering is the one that is bringing some new research assets, after GDPR guidelines became clear and then they got into they are full of how do we cater to those requirements. These are relatively new proprietary components, they are not even being productized, that's why I am calling them proprietary components that are going in to this hosting service. >> IBM's got a big portfolio so I'll understand if you guys are still working out what position. Rebecca go ahead. >> I just wanted to ask you about this new era of GDPR. The last Hortonworks conference was sort of before it came into effect and now we're in this new era. How would you say companies are reacting? Are they in the right space for it, in the sense of they're really still understand the ripple effects and how it's all going to play out? How would you describe your interactions with companies in terms of how they're dealing with these new requirements? >> They are still trying to understand the requirements and interpret the requirements coming to terms with what that really means. For example I met with a customer and they are a multi-national company. They have data centers across different geos and they asked me, I have somebody from Asia trying to query the data so that the query should go to Europe, but the query processing should not happen in Asia, the query processing all should happen in Europe, and only the output of the query should be sent back to Asia. You won't be able to think in these terms before the GDPR guidance era. >> Right, exceedingly complicated. >> Decoupling storage from processing enables those kinds of fairly complex scenarios for compliance purposes. >> It's not just about the access to data, now you are getting into where the processing happens were the results are getting displayed, so we are getting... >> Severe penalties for not doing that so your customers need to keep up. There was announcement at this show at Dataworks 2018 of an IBM Hortonwokrs solution. IBM post-analytics with with Hortonworks. I wonder if you could speak a little bit about that, Pandit, in terms of what's provided, it's a subscription service? If you could tell us what subset of IBM's analytics portfolio is hosted for Hortonwork's customers? >> Sure, was you said, it is a a hosted offering. Initially we are starting of as base offering with three products, it will have HDP, Big Sequel, IBM DB2 Big Sequel and DSX, Data Science Experience. Those are the three solutions, again as I said, it is hosted on IBM Cloud, so customers have a choice of different configurations they can choose, whether it be VMs or bare metal. I should say this is probably the only offering, as of today, that offers bare metal configuration in the cloud. >> It's geared to data scientist developers and machine-learning models will build the models and train them in IBM Cloud, but in a hosted HDP in IBM Cloud. Is that correct? >> Yeah, I would rephrase that a little bit. There are several different offerings on the cloud today and we can think about them as you said for ad-hoc or ephemeral workloads, also geared towards low cost. You think about this offering as taking your on PRAM data center experience directly onto the cloud. It is geared towards very high performance. The hardware and the software they are all configured, optimized for providing high performance, not necessarily for ad-hoc workloads, or ephemeral workloads, they are capable of handling massive workloads, on sitcky workloads, not meant for I turned this massive performance computing power for a couple of hours and then switched them off, but rather, I'm going to run these massive workloads as if it is located in my data center, that's number one. It comes with the complete set of HDP. If you think about it there are currently in the cloud you have Hive and Hbase, the sequel engines and the stories separate, security is optional, governance is optional. This comes with the whole enchilada. It has security and governance all baked in. It provides the option to use Big Sequel, because once you get on Hadoop, the next experience is I want to run complex workloads. I want to run federated queries across Hadoop as well as other data storage. How do I handle those, and then it comes with Data Science Experience also configured for best performance and integrated together. As a part of this partnership, I mentioned earlier, that we have progress towards providing this story of an NTN solution. The next steps of that are, yeah I can say that it's an NTN solution but are the product's look and feel as if they are one solution. That's what we are getting into and I have featured some of those integrations. For example Big Sequel, IBM product, we have been working on baking it very closely with HDP. It can be deployed through Morey, it is integrated with Atlas and Granger for security. We are improving the integrations with Atlas for governance. >> Say you're building a Spark machine learning model inside a DSX on HDP within IH (mumbles) IBM hosting with Hortonworks on HDP 3.0, can you then containerize that machine learning Sparks and then deploy into an edge scenario? >> Sure, first was Big Sequel, the next one was DSX. DSX is integrated with HDP as well. We can run DSX workloads on HDP before, but what we have done now is, if you want to run the DSX workloads, I want to run a Python workload, I need to have Python libraries on all the nodes that I want to deploy. Suppose you are running a big cluster, 500 cluster. I need to have Python libraries on all 500 nodes and I need to maintain the versioning of it. If I upgrade the versions then I need to go and upgrade and make sure all of them are perfectly aligned. >> In this first version will you be able build a Spark model and a Tesorflow model and containerize them and deploy them. >> Yes. >> Across a multi-cloud and orchestrate them with Kubernetes to do all that meshing, is that a capability now or planned for the future within this portfolio? >> Yeah, we have that capability demonstrated in the pedestal today, so that is a new one integration. We can run virtual, we call it virtual Python environment. DSX can containerize it and run data that's foreclosed in the HDP cluster. Now we are making use of both the data in the cluster, as well as the infrastructure of the cluster itself for running the workloads. >> In terms of the layers stacked, is also incorporating the IBM distributed deep-learning technology that you've recently announced? Which I think is highly differentiated, because deep learning is increasingly become a set of capabilities that are across a distributed mesh playing together as is they're one unified application. Is that a capability now in this solution, or will it be in the near future? DPL distributed deep learning? >> No, we have not yet. >> I know that's on the AI power platform currently, gotcha. >> It's what we'll be talking about at next year's conference. >> That's definitely on the roadmap. We are starting with the base configuration of bare metals and VM configuration, next one is, depending on how the customers react to it, definitely we're thinking about bare metal with GPUs optimized for Tensorflow workloads. >> Exciting, we'll be tuned in the coming months and years I'm sure you guys will have that. >> Pandit, thank you so much for coming on theCUBE. We appreciate it. I'm Rebecca Knight for James Kobielus. We will have, more from theCUBE's live coverage of Dataworks, just after this.

>> Live from San Jose in the heart of Silicon Valley, it's theCUBE, covering Data Works Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of Dataworks here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We're joined by Cindy Maike. She is the VP Industry Solutions and GM Insurance and Healthcare at Hortonworks. Thanks so much for coming on theCUBE, Cindy. >> Thank you, thank you, look forward to it. >> So, before the cameras were rolling we were talking about the business case for data, for data analytics. Walk our viewers through how you, how you think about the business case and your approach to sort of selling it. >> So, when you think about data and analytics, I mean, as industries we've been very good sometimes at doing kind of like the operational reporting. To me that's looking in the rearview mirror, something's already happened, but when you think about data and analytics, especially big data it's about what questions haven't I been able to answer. And, a lot of companies when they embark on it they're like, let's do it for technology's sake, but from a business perspective when we, as our industry GMs we are out there working with our customers it's like, what questions can't you answer today and how can I look at existing data on new data sources to actually help me answer questions. I mean, we were talking a little bit about the usage of sensors and so forth around telematics and the insurance industry, connected homes, connective lives, connected cars, those are some types of concepts. In other industries we're looking at industrial internet of things, so how do I actually make the operations more efficient? How do I actually deploy time series analysis to actually help us become more profitable? And, that's really where companies are about. You know, I think in our keynote this morning we were talking about new communities and it's what does that mean? How do we actually leverage data to either monetize new data sources or make us more profitable? >> You're a former insurance CFO, so let's delve into that use case a little bit and talk about the questions that I haven't asked yet. What are some of those and how are companies putting this thing to work? >> Yeah so, the insurance industry you know, it's kind of frustrating sometimes where as an insurance company you sit there and you always monitor what your combined ratio is, especially if you're a property casualty company and you go, yeah, but that tells me information like once a month, you know, but I was actually with a chief marketing officer recently and she's like, she came from the retail industry and she goes, I need to understand what's going on in my business on any given day. And so, how can we leverage better real time information to say, what customers are we interacting with? You know, what customers should we not be interacting with? And then you know, the last thing insurance companies want to do is go out and say, we want you as a customer and then you decline their business because they're not risk worthy. So, that's where we're seeing the insurance industry and I'll focus a lot on insurance here, but it's how do we leverage data to change that customer engagement process, look at connected ecosystems and it's a good time to be well fundamentally in the insurance industry, we're seeing a lot of use cases, but also in the retail industry, new data opportunities that are out there. We talked a little bit before the interview started on shrinkage and you know, the retail industry's especially in the food, any type of consumer type packages, we're starting to see the usage of sensors to actually help companies move fresh food around to reduce their shrinkage. You know, we've got. >> Sorry, just define shrinkage, 'cause I'm not even sure I understand, it's not that your gapple is getting smaller. It refers to perishable goods, you explain it. >> Right, so you're actually looking at, how do we make sure that my produce or items that are perishable, you know, I want to minimize the amount of inventory write offs that I have to do, so that would be the shrinkage and this one major retail chain is, they have a lot of consumer goods that they're actually saying, you know what, their shrinkage was pretty high, so they're now using sensors to help them monitor should we, do we need to move certain types of produce? Do we need to look at food before it expires you know, to make sure that we're not doing an inventory write off. >> You say sensors and it's kind of, are you referring to cameras taking photos of the produce or are you referring to other types of chemical analysis or whatever it might be, I don't know. >> Yeah, so it's actually a little bit of both. It's how do I actually you know, looking at certain types of products, so we all know when you walk into a grocery store or some type of department store, there's cameras all over the place, so it's not just looking at security, but it's also looking at you know, are those goods moving? And so, you can't move people around a store, but I can actually use the visualization and now with deep machine learning you can actually look at that and say, you know what, those bananas are getting a little ripe. We need to like move those or we need to help turn the inventory. And then, there's also things with bar coding you know, when you think of things that are on the shelves. So, how do I look at those bar codes because in the past you would've taken somebody down the isle. They would've like checked that, but no, now we're actually looking up the bar codes and say, do we need to move this? Do we need to put these things on sale? >> At this conference we're hearing just so much excitement and talk about data as the new oil and it is an incredible strategic asset, but you were also saying that it could become a liability. Talk about the point at which it becomes a liability. >> It becomes a liability when one, we don't know what to do with it, or we make decisions off of data data, so you think about you know, I'll give you an example, in the healthcare industry. You know, medical procedures have changed so immensely. The advancement in technology, precision medicine, but if we're making healthcare decisions on medical procedures from 10 years ago, so you really need to say how do I leverage you know, newer data stats, so over time if you make your algorithms based on data that's 10, 20 years old, it's good in certain things, but you know, you can make some bad business decisions if the data is not recent. So, that's when I talk about the liability aspect. >> Okay, okay, and then, thinking about how you talk with, collaborate with customers, what is your approach in the sense of how you help them think through their concerns, their anxieties? >> So, a lot of times it's really kind of understanding what's their business strategy. What are their financial, what are their operational goals? And you say, what can we look at from a data perspective, both data that we have today or data that we can acquire from new data sources to help them actually achieve their business goals and you know, specifically in the insurance industry we focus on top line growth with growing your premium or decreasing your combined ratio. So, what are the types of data sources and the analytical use cases that we can actually you know, use? See the exact same thing in manufacturing, so. >> And, have customer attitudes evolved over time since you've been in the industry? How would you describe their mindsets right now? >> I think we still have some industries that we struggle with, but it's actually you know, I mentioned healthcare, the way we're seeing data being used in the healthcare industry, I mean, it's about precision medicine. You look at gnomics research. It says that if people like 58 percent of the world's population would actually do a gnomics test if they could actually use that information. So, it's interesting to see. >> So, the struggle is with people's concern about privacy encroachment, is that the primary struggle? >> There's a little bit of that and companies are saying, you know, I want to make sure that it's not being used against me, but there was actually a recent article in Best Review, which is an insurance trade magazine, that says, you know, if I have, actually have a gnomic test can the insurance industry use that against me? So, I mean, there's still a little bit of concern. >> Which is a legitimate concern. >> It is, it is, absolutely and then also you know, we see globally with just you know, the General Data Protection act, the GDPR, you know, how are companies using my information and data? So you know, consumers have to be comfortable with the type of data, but outside of the consumer side there's so much data in the industry and you made the comment about you know, data's the new oil. I have a thing, against, with that is, but we don't use oil straight in a car, we don't use crude putting in a car, so once we do something with it which is the analytical side, then that's where we get the business end side. So, data for data's sake is just data. It's the business end sites is what's really important. >> Looking ahead at Hortonworks five, 10 years from now I mean, how much, how much will your business account for the total business of Hortonworks do you think, in the sense of as you've said, this is healthcare and insurance represents such huge potential possibilities and opportunities for the company? Where do you see the trajectory? >> The trajectory I believe is really in those analytical apps, so we were working with a lot of partners that are like you know, how do I accelerate those business value because like I said, it's like we're not just into data management, we're in the data age and what does that mean? It's like turning those things into business value and I've got to be able to I think from an industry perspective, you know be working with the right partners and then also customers because they lack some of the skillsets. So, who can actually accelerate the time to value of using data for profitability? >> Is your primary focus area at helping regulated industries with their data analytics challenges and using IOT or does it also cover unregulated? >> Unregulated as well. >> Are the analytics requirements different between regulated and unregulated in terms of the underlying capabilities they require in terms of predictive modeling, of governance and so forth and how does Hortonworks differentiate their response to those needs? >> Yeah, so it varies a little bit based upon their regulations. I mean, even if you look at life sciences, life sciences is very, very regulated on how long do I have to keep the data? How can I actually use the data? So, if you look at those industries that maybe aren't regulated as much, so we'll get away from financial services, highly regulated across all different areas, but I'll also look at say business insurance, not as much regulated as like you and I as consumers, because insurance companies can use any type of data to actually do the pricing and doing the underwriting and the actual claims. So, still regulated based upon the solvency, but not regulated on how we use it to evaluate risk. Manufacturing, definitely some regulation there from a work safety perspective, but you can use the data to optimize your yields you know, however you see fit. So, we see a mixture of everything, but I think from a Hortonworks perspective it's being able to share data across multiple industries 'cause we talk about connected ecosystems and connected ecosystems are really going to change business of the future. >> So, how so? I mean, especially in bringing it back to this conference, to Data Works, and the main stage this morning we heard so much about these connected communities and really it's all about the ecosystem, what do you see as the biggest change going forward? >> So, you look at, and I'll give you the context of the insurance industry. You look at companies like Arity, which is a division of All State, what they're doing actually working with the car manufacturers, so at some point in time you know, the automotive industry, General Motors tried this 20 years ago, they didn't quite get it with On Star and GMAC Insurance. Now, you actually have the opportunity with you know, maybe on the front man for the insurance industry. So, I can now start to collect the data from the vehicle. I'm using that for driving of the vehicle, but I can also use it to help a driver make safer driving. >> And upsize their experience of actually driving, making it more pleasant as well as safer. There's many layers of what can be done now with the same data. Some of those uses impinge or relate to regulated concern or mandatory concerns, then some are purely for competitive differentiation of the whole issue of experience. >> Right, and you think about certain aspects that the insurance industry just has you know, a negative connotation and we have an image challenge on what data can and cannot be used, so, but a lot of people opt in to an automotive manufacturer and share that type of data, so moving forward who's to say with the connected ecosystem I still have the insurance company in the background doing all the underwriting, but my distribution channel is now the car dealer. >> I love it, great. That's a great note to end on. Thanks so much for coming on theCUBE. Thank you Cindy. I'm Rebecca Knight for James Kobielus. We will have more from theCUBE's live coverage of Data Works in just a little bit. (upbeat music)

>> Live from San Jose in the heart of Silicon Valley, it's the Cube. Covering Dataworks Summit 2018 brought to you by Hortonworks. (upbeat music) >> Welcome back to The Cube's live coverage of Dataworks here in San Jose, California. I'm your host Rebecca Knight along with my co-host James Kobielus. We're joined by Peter Smails. He is the vice president of marketing at Imanis Data. Thanks so much for coming on The Cube. >> Thanks for having me, glad to be here. >> So you've been in the data storage solution industry for a long time, but you're new to Imanis, what made you jump? What was it about Imanis? >> Yep, so very easy to answer that. It's a hot market. So essentially what Imanis all about is we're an enterprise data management company. So the reason I jumped here is because if I put it in market context, if I take a small step back, I put it in market context, here's what happening. You've got your traditional application world, right? On prem typically already a mas based applications, that's the old world. New world is everybody's moving to the microservices based applications for IOT, for customer 360, for customer analysis, whatever you want. They're building these new modern applications. They're building those applications not in traditional RDMS, they're building them on microservices based architectures built on top of FEDOOP, or built on sequel databases. Those applications, as they go mainstream, and they go into production environments, they require data management. They require backup. They require backup and recovery. They require disaster recovery. They require archiving, etc. They require the whole plethora of data management capabilities. Nobody's touching that market. It's a blue ocean. So, that's why I'm here. >> Imanis as you were saying is one of the greatest little company no one's ever heard of. You've been around five years. (laughter) >> No, the company is not new. So, the thing that's exciting as a marketeer, what's exciting is that we're not sort of out there just pitching our wears untested technology. We have blue chip, we're getting into customers that people would die to get into. Big, blue chip companies because we're addressing a problem that's materialist. They roll out these new applications, they've got to have data management solutions for them. The company's been around five years. And I've only been on about a month, but what that's resulted is that over the last five years what they've had the opportunity, it's an enterprise product. And you don't build an enterprise product overnight. So they've had the last five years to really gestate the platform, gestate the technology, prove it in real world scenarios. And now, the opportunity for us as as a company is we're doubling down from a marketing standpoint. We're doubling down from the sales infrastructure standpoint. So the timing's right to essentially put this thing on the map, make sure everybody does know exactly what we do. Because we're solving a real world problem. >> Your backup and restore but much more. When you lay out the broad set of enterprise data and management capabilities, the mana state currently supports in your product portfolio on where you're going, on how you're going in terms of evolving in what you offer. >> Yeah, that's great. I love that question. So, think of us as the platform itself is this highly scalable distributed architecture. Okay, so we scale on multiple, and I'll come directly to your question. We scale on a number of different ways. One is we're infinitely scalable just in terms of computational power. So we're built for big data by definition. Number two is we're very, we scale very well from a storage efficiency standpoint. So we can store very large volumes of storage, which is a requirement. We also scale very much for the use case standpoint. So we support use cases throughout the life cycle. The one that gets all sort of the attention is obviously backup recovery. Because you have to protect your data. But if I look at it from a life cycle standpoint, our number use case is Test Def. So a lot of these organizations building these new apps now they want to spin up subsets of their data, cause they're supporting things like CICD. Okay, so they want to be able to do rapid testing and such. >> Develop Dev Opps and stuff like that. >> Yeah, Dev Opps and so worth. So, they need Test Def. So we help them automate the process and orchestrate the process of Test Def. Supporting things like sampling. I may have a one petabyte dataset, I'm not going to do Test Def against that. I want to do 10 percent of that and spin that up, and I want to do some masking of personal, PII data. So we can do masking and sampling against that Sport Test Def. We do backup and recovery. We do disaster recovery. So some customers, particularly in the big data space, they may for now say, well, I have replica so for some of this data it's not permanent data, it's transient data, but I do care about DR. So, DR is a key use case. We also do archiving. So if you just think of data through the life cycle, we support all of those. The piece in terms of where we're going is that what's truly unique, in addition to everything I just mentioned, is that we're the only data management platform that's machine learning based. Okay, so machine learning gets a lot of attention, and all that type of stuff, but we're actually delivering machine learning and abled capabilities today, so. >> And we discussed this before this interview. There's a bit of an anomaly detection. How exactly are you using machine learning? What value does it provide to a enterprise data administrator? They have ML inside your tool. >> Inside our platform, Great question. Very specifically, the product we're delivering today essentially there's a capability in the product called threat sets. Okay, so the number one use cases I mentioned is backup and recovery. So within backup and recovery, threat sense, what it will do with no user intervention whatsoever, what it will do is it will analyze your backups, as they go forward. And what it will do is it will learn what a normal pattern looks like across like 50 different metrics. The details of which I couldn't give you right now. But essentially, a whole bunch of different metrics that we look at to establish this is what a normal baseline looks like for you or for you, kind of thing. Great, that's number one. Number two is then we look and constantly analyze is anything occurring that is knocking things outside of that? Creating an anomaly, does something fall outside of that, and when it does, we're notifying the administrators. You might want to look at this, something could've happened. So the value very specifically is around ransomware typically one of the ways you're going to detect ransomware is you will see an anomaly in your backup set, because your data set will change materially. So we will be able to tell you, >> Cause somebody's holding for ransom is what you're saying. >> Correct, so something's going to happen in your data pattern. >> You've lost data that should be there, or whatever it might be. >> Correct, it could be that you lost data. Your change rate went way up, or something. >> Yeah, gotcha. >> There's any number of things that could trigger it. And then we let the administrator know, it happened here. So today we don't then turn around and just automatically solve that. But your point about where we're going. We've already broken the ice on delivering machine learning and abled data management. >> That might indicate you want to check point your backups to like a few days before this was detected. So the least you have, you know what data is most likely missing, so yeah, I understand. >> Bingo, that's exactly right now where we're going with that. As you could imagine, having a machine learning power data management platform at our core, how many different ways we can go with that. When do I backup? What data do I backup? How do I create the optimal RTO and IRPO? From a storage management standpoint, when do I put what data wear? There's all kinds of the whole library science of data management. The future of data management is machine learning based. There's too much data. There's too much complexity for humans to just be able to, you need to bring machine learning into the equation to help you harness the power of your data. We've broken the ice, we've got a long way to go. But we've got the platform to start with. And we've already introduced the first use case around this. And you can imagine all the places we can take this going forward. >> Very exciting. >> So you were the company that's using machine learning right now. What in your opinion will separate the winners from the losers? >> In terms of vendors, or in terms of the customers? >> Well, in terms of both. >> Yeah, let me answer that two ways. So, let me answer it sort of the inward/outward versus how we are unique. We are very unique, and since we're infinitely scalable, We are a single pane of glass for all of your distributed systems. We are very unique in terms of our multi-staged data reduction. And we're the only vendor that's doing, from a technology differentiation standpoint, we're the only vendor that's doing machine learning based stuff. >> Multi-stage data reduction, I want to break that down. What does that actually mean in practice? >> Sure, so we get the question frequently. Is that compression or duplication or is there something else in there? >> There's a couple different things actually. So why does that matter? So a lot of customers will ask a question, well by definition, no sequel or redo based environments, it's all based on replica, so how to back things up. First of all, replication isn't backup. So that's lesson number one. Point in time backup is very different than replication. Replication replicates bad data just as quickly as it replicates good. When you back up these very large data sets, you have to be incredibly efficient in how you do that. What we do with multi-stage data reduction is one, we will do de duplication, we'll do variable length, de duplication, we will do compression, we will do erasure coding, but the other thing that we'll also do in there, is what we call a global de plication pool. So when we're de duping your data, we're actually de duping it against a very large data set. So there's value in, this is where size matters. So the larger the data set, your data's all secured. But the larger the size of the data that I'm actually storing, the higher percentage I could get of de duplication. Because I've got a higher pool to reduce against. So the net result is we're incredibly efficient when you're talking about petabyte scale data management. We're incredibly efficient to the tune of 10 X easily 10 X over traditional de duplication, and multi X over technologies that are more current, if you will. So back to your question about, we are confident that we have a very strong head start. Our opportunity now is we got to drive why we're here. Cause we got to drive awareness. We got to make sure everybody knows who we are and how we're unique and how we're different. And you guys are great. Love being on The Cube. From a customer standpoint, the customers are going to win, and this is sort of a cliche, but it's true, the customer's the best harness of their data. They're the ones that are going to win. They're going to be more competitive, they're going to be able to find ways to be differentiated. And the only way they're going to do that is they're make the appropriate investments in their data infrastructure, in their data lakes, in their data management tool, so that they can harness all that data. >> Where do you see the future of your Hortonworks partnership going? So Hortonworks is, so we support a broad ecosystem. So, Hortonworks is just as important as any of our other data source partners. So, we are where we see that enfolding, is they're going to, we play an important part in, we feel our value, let me put it that way. We feel our value in helping Hortonworks, is as more and more organizations go mainstream with these applications. These are not corner cases anymore. This is not sort of in the lab. This is like the real deal. This is mainstream enterprises running business critical applications. The value we bring is you're not going to rely on those platforms without an enterprise data management solution that delivers what we deliver. So our value there is we can go to market, too. There's all kinds of ways we can go to market together. But net and that our value there is that we provide a very important enterprise data management capability that's important for customers that are deploying in these business critical environments. >> Great. >> Very good, as more of the data gets persisted out at the edge devices and the Internet of things, and so forth, what are the challenges in terms of protecting that data, backup and restore, de duplication, and so forth, and to what extent is your company's Imanis data maybe addressing those kinds of more distributed data management requirements going forward? Do you see that on the rise? Are you hearing that from customers? They want to do more of that? More of an edge cloud environment? Or is that way too far in the future? >> I don't think it's way too far in the future, but I do think there's an inside out. So my position on that is that it's not that there isn't edge work going on. What I would contend is that the big problem right now from an enterprise mainstreaming standpoint, is more getting the house is order, just your core house in order, from you move from sort of a traditional four wall data center to a hybrid cloud environment. Maybe not quite as edge. Combination of how do I leverage on prem and the cloud, so to speak. And how do I get the core data lake and the case of Hortonworks, how do I get that big data lake sorted out? You're touching on, I think, a longer discussion, which is where is the analysis going on? Where is the data going to persist? You know, where do you do some of that computational work? So you get all this information out at the edge. Does all that information end up going into the data lake? So, do you move the storage to where the lake is? Do you start pushing some of the lake functionale out to the edge where you have to then start doing some of the, so it's a much more complicated discussion. >> I know we had this discussion over lunch. This may be outside your wheelhouse, but let me just ask it anyway. We've seen more at Wikibon, I cover AI and distributed training and distributed inference and things so the edges are capturing the data and for more and more, there's a trend to where they're performing local training of their models, their embedded models, from the data they capture. But quite often, edge devices don't have a ton of storage and they're not going to retain that long. But some of that data will need to be archived. Will need to be persisted in a way and managed as a core resource, so we see that kind of requirement maybe not now, but in a few years time distributed training in persistence of that data, protection of that data, becoming a mainstream enterprise requirement. Where AI and machine learning, the whole pipeline is a concern. That's like I said, that's probably outside you guys wheelhouse. That's probably outside the realm for your customers But that kind of thing is coming out, as the likes of Hortonworks and IBM and everybody else, is starting to look at it and implement it, containerization of analytics and data management out to all these micro devices. >> Yes, and I think you're right there. And to your point about the, we're kind of going where the data is, if you will in volumes, kind of thing. And it's going that direction. And frankly, where we see that happening is, that's where the cloud plays a big role as well, because there's edge, but how do you get to the edge? You can get to the edge through the cloud. So, again, we run on AWS. We run on GCP, we run on Asher. So, to be clear, in terms of the data we can rotect, we got a broad portfolio, broad ecosystem of adute based big data, data sources that we support as well as no sequel. If they're running on AWS or GCP or Asher, we support ADLS, we support Asher's data lake stuff, HD Inside, we support a whole bunch of different things both from a cloud standpoint as on prem. Which is where we're seeing some of that edge work happening. >> Great, well Peter thank you so much for coming on The Cube. It's always a pleasure to have you on. >> Yes, thanks for having me and I look forward to being back sometime soon. >> We'll have you. >> Thank you both. >> When the time is right. >> Indeed, we will have more from The Cube's live coverage of Dataworks just after this. (upbeat music)

>> Live from San Jose in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks here in sunny San Jose, California. I'm your host Rebecca Knight along with my co-host James Kobielus. We're joined by Dan Potter. He is the VP Product Management at Attunity and also Ali Bajwah, who is the principal partner solutions engineer at Hortonworks. Thanks so much for coming on theCUBE. >> Pleasure to be here. >> It's good to be here. >> So I want to start with you, Dan, and have you tell our viewers a little bit about the company based in Boston, Massachusetts, what Attunity does. >> Attunity, we're a data integration vendor. We are best known as a provider of real-time data movement from transactional systems into data lakes, into clouds, into streaming architectures, so it's a modern approach to data integration. So as these core transactional systems are being updated, we're able to take those changes and move those changes where they're needed when they're needed for analytics for new operational applications, for a variety of different tasks. >> Change data capture. >> Change data capture is the heart of our-- >> They are well known in this business. They have changed data capture. Go ahead. >> We are. >> So tell us about the announcement today that Attunity has made at the Hortonworks-- >> Yeah, thank you, it's a great announcement because it showcases the collaboration between Attunity and Hortonworks and it's all about taking the metadata that we capture in that integration process. So we're a piece of a data lake architecture. As we are capturing changes from those source systems, we are also capturing the metadata, so we understand the source systems, we understand how the data gets modified along the way. We use that metadata internally and now we're built extensions to share that metadata into Atlas and to be able to extend that out through Atlas to higher data governance initiatives, so Data Steward Studio, into the DataPlane Services, so it's really important to be able to take the metadata that we have and to add to it the metadata that's from the other sources of information. >> Sure, for more of the transactional semantics of what Hortonworks has been describing they've baked in to HDP in your overall portfolios. Is that true? I mean, that supports those kind of requirements. >> With HTP, what we're seeing is you know the EDW optimization play has become more and more important for a lot of customers as they try to optimize the data that their EDWs are working on, so it really gels well with what we've done here with Attunity and then on the Atlas side with the integration on the governance side with GDPR and other sort of regulations coming into the play now, you know, those sort of things are becoming more and more important, you know, specifically around the governance initiative. We actually have a talk just on Thursday morning where we're actually showcasing the integration as well. >> So can you talk a little bit more about that for those who aren't going to be there for Thursday. GDPR was really a big theme at the DataWorks Berlin event and now we're in this new era and it's not talked about too, too much, I mean we-- >> And global business who have businesses at EU, but also all over the world, are trying to be systematic and are consistent about how they manage PII everywhere. So GDPR are those in EU regulation, really in many ways it's having ripple effects across the world in terms of practices. >> Absolutely and at the heart of understanding how you protect yourself and comply, I need to understand my data, and that's where metadata comes in. So having a holistic understanding of all of the data that resides in your data lake or in your cloud, metadata becomes a key part of that. And also in terms of enforcing that, if I understand my customer data, where the customer data comes from, the lineage from that, then I'm able to apply the protections of the masking on top of that data. So it's really, the GDPR effect has had, you know, it's created a broad-scale need for organizations to really get a handle on metadata so the timing of our announcement just works real well. >> And one nice thing about this integration is that you know it's not just about being able to capture the data in Atlas, but now with the integration of Atlas and Ranger, you can do enforcement of policies based on classifications as well, so if you can tag data as PCI, PII, personal data, that can get enforced through Ranger to say, hey, only certain admins can access certain types of data and now all that becomes possible once we've taken the initial steps of the Atlas integration. >> So with this collaboration, and it's really deepening an existing relationship, so how do you go to market? How do you collaborate with each other and then also service clients? >> You want to? >> Yeah, so from an engineering perspective, we've got deep roots in terms of being a first-class provider into the Hortonworks platform, both HDP and HDF. Last year about this time, we announced our support for acid merge capabilities, so the leading-edge work that Hortonworks has done in bringing acid compliance capabilities into Hive, was a really important one, so our change to data capture capabilities are able to feed directly into that and be able to support those extensions. >> Yeah, we have a lot of you know really key customers together with Attunity and you know maybe a a result of that they are actually our ISV of the Year as well, which they probably showcase on their booth there. >> We're very proud of that. Yeah, no, it's a nice honor for us to get that distinction from Hortonworks and it's also a proof point to the collaboration that we have commercially. You know our sales reps work hand in hand. When we go into a large organization, we both sell to very large organizations. These are big transformative initiatives for these organizations and they're looking for solutions not technologies, so the fact that we can come in, we can show the proof points from other customers that are successfully using our joint solution, that's really, it's critical. >> And I think it helps that they're integrating with some of our key technologies because, you know, that's where our sales force and our customers really see, you know, that as well as that's where we're putting in the investment and that's where these guys are also investing, so it really, you know, helps the story together. So with Hive, we're doing a lot of investment of making it closer and closer to a sort of real-time database, where you can combine historical insights as well as your, you know, real-time insights. with the new acid merge capabilities where you can do the inserts, updates and deletes, and so that's exactly what Attunity's integrating with with Atlas. We're doing a lot of investments there and that's exactly what these guys are integrating with. So I think our customers and prospects really see that and that's where all the wins are coming from. >> Yeah, and I think together there were two main barriers that we saw in terms of customers getting the most out of their data lake investment. One of them was, as I'm moving data into my data lake, I need to be able to put some structure around this, I need to be able to handle continuously updating data from multiple sources and that's what we introduce with Attunity composed for Hive, building out the structure in an automated fashion so I've got analytics-ready data and using the acid merge capabilities just made those updates much easier. The second piece was metadata. Business users need to have confidence that the data that they're using. Where did this come from? How is it modified? And overcoming both of those is really helping organizations make the most of those investments. >> How would you describe customer attitudes right now in terms of their approach to data because I mean, as we've talked about, data is the new oil, so there's a real excitement and there's a buzz around it and yet there's also so many high-profile cases of breeches and security concerns, so what would you say, is it that customers, are they more excited or are they more trepidatious? How would you describe the CIL mindset right now? >> So I think security and governance has become top of minds right, so more and more the serveways that we've taken with our customers, right, you know, more and more customers are more concerned about security, they're more concerned about governance. The joke is that we talk to some of our customers and they keep talking to us about Atlas, which is sort of one of the newer offerings on governance that we have, but then we ask, "Hey, what about Ranger for enforcement?" And they're like, "Oh, yeah, that's a standard now." So we have Ranger, now it's a question of you know how do we get our you know hooks into the Atlas and all that kind of stuff, so yeah, definitely, as you mentioned, because of GDPR, because of all these kind of issues that have happened, it's definitely become top of minds. >> And I would say the other side of that is there's real excitement as well about the possibilities. Now bringing together all of this data, AI, machine learning, real-time analytics and real-time visualization. There's analytic capabilities now that organizations have never had, so there's great excitement, but there's also trepidation. You know, how do we solve for both of those? And together, we're doing just that. >> But as you mentioned, if you look at Europe, some of the European companies that are more hit by GDPR, they're actually excited that now they can, you know, really get to understand their data more and do better things with it as a result of you know the GDPR initiative. >> Absolutely. >> Are you using machine learning inside of Attunity in a Hortonworks context to find patterns in that data in real time? >> So we enable data scientists to build those models. So we're not only bringing the data together but again, part of the announcement last year is the way we structure that data in Hive, we provide a complete historic data store so every single transaction that has happened and we send those transactions as they happen, it's at a big append, so if you're a data scientist, I want to understand the complete history of the transactions of a customer to be able to build those models, so building those out in Hive and making those analytics ready in Hive, that's what we do, so we're a key enabler to machine learning. >> Making analytics ready rather than do the analytics in the spring, yeah. >> Absolutely. >> Yeah, the other side to that is that because they're integrated with Atlas, you know, now we have a new capability called DataPlane and Data Steward Studio so the idea there is around multi-everything, so more and more customers have multiple clusters whether it's on-prem, in the cloud, so now more and more customers are looking at how do I get a single glass pane of view across all my data whether it's on-prem, in the cloud, whether it's IOT, whether it's data at rest, right, so that's where DataPlane comes in and with the Data Steward Studio, which is our second offering on top of DataPlane, they can kind of get that view across all their clusters, so as soon as you know the data lands from Attunity into Atlas, you can get a view into that across as a part of Data Steward Studio, and one of the nice things we do in Data Steward Studio is that we also have machine learning models to do some profiling, to figure out that hey, this looks like a credit card, so maybe I should suggest this as a tag of sensitive data and now the end user, the end administration has the option of you know saying that okay, yeah, this is a credit card, I'll accept that tag, or they can reject that and pick one of their own. >> Will any of this going forward of the Attunity CDC change in the capture capability be containerized for deployment to the edges in HDP 3.0? I mean, 'cause it seems, I mean for internetive things, edge analytics and so forth, change data capture, is it absolutely necessary to make the entire, some call it the fog computing, cloud or whatever, to make it a completely transactional environment for all applications from micro endpoint to micro endpoint? Are there any plans to do that going forward? >> Yeah, so I think what HDP 3.0 as you mentioned right, one of the key factors that was coming into play was around time to value, so with containerization now being able to bring third-party apps on top of Yarn through Docker, I think that's definitely an avenue that we're looking at. >> Yes, we're excited about that with 3.0 as well, so that's definitely in the cards for us. >> Great, well, Ali and Dan, thank you so much for coming on theCUBE. It's fun to have you here. >> Nice to be here, thank you guys. >> Great to have you. >> Thank you, it was a pleasure. >> I'm Rebecca Knight, for James Kobielus, we will have more from DataWorks in San Jose just after this. (techno music)

>> Live from San Jose in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We have with us Eric Herzog. He is the Chief Marketing Officer and VP of Global Channels at the IBM Storage Division. Thanks so much for coming on theCUBE once again, Eric. >> Well, thank you. We always love to be on theCUBE and talk to all of theCUBE analysts about various topics, data, storage, multi-cloud, all the works. >> And before the cameras were rolling, we were talking about how you might be the biggest CUBE alum in the sense of you've been on theCUBE more times than anyone else. >> I know I'm in the top five, but I may be number one, I have to check with Dave Vellante and crew and see. >> Exactly and often wearing a Hawaiian shirt. >> Yes. >> Yes, I was on theCUBE last week from CISCO Live. I was not wearing a Hawaiian shirt. And Stu and John gave me a hard time about why was not I wearing a Hawaiian shirt? So I make sure I showed up to the DataWorks show- >> Stu, Dave, get a load. >> You're in California with a tan, so it fits, it's good. >> So we were talking a little bit before the cameras were rolling and you were saying one of the points that is sort of central to your professional life is it's not just about the storage, it's about the data. So riff on that a little bit. >> Sure, so at IBM we believe everything is data driven and in fact we would argue that data is more valuable than oil or diamonds or plutonium or platinum or silver to anything else. It is the most viable asset, whether you be a global Fortune 500, whether you be a midsize company or whether you be Herzogs Bar and Grill. So data is what you use with your suppliers, with your customers, with your partners. Literally everything around your company is really built around the data so most effectively managing it and make sure, A, it's always performant because when it's not performant they go away. As you probably know, Google did a survey that one, two, after one, two they go off your website, they click somewhere else so has to be performant. Obviously in today's 365, 7 by 24 company it needs to always be resilient and reliable and it always needs to be available, otherwise if the storage goes down, guess what? Your AI doesn't work, your Cloud doesn't work, whatever workload, if you're more traditional, your Oracle, Sequel, you know SAP, none of those workloads work if you don't have a solid storage foundation underneath your data driven enterprise. >> So with that ethos in mind, talk about the products that you are launching, that you newly launched and also your product roadmap going forward. >> Sure, so for us everything really is that storage is this critical foundation for the data driven, multi Cloud enterprise. And as I've said before on theCube, all of our storage software's now Cloud-ified so if you need to automatically tier out to IBM Cloud or Amazon or Azure, we automatically will move the data placement around from one premise out to a Cloud and for certain customers who may be multi Cloud, in this case using multiple private Cloud providers, which happens due to either legal reasons or procurement reasons or geographic reasons for the larger enterprises, we can handle that as well. That's part of it, second thing is we just announced earlier today an artificial intelligence, an AI reference architecture, that incorporates a full stack from the very bottom, both servers and storage, all the way up through the top layer, then the applications on top, so we just launched that today. >> AI for storage management or AI for run a range of applications? >> Regular AI, artificial intelligence from an application perspective. So we announced that reference architecture today. Basically think of the reference architecture as your recipe, your blueprint, of how to put it all together. Some of the components are from IBM, such as Spectrum Scale and Spectrum Computing from my division, our servers from our Cloud division. Some are opensource, Tensor, Caffe, things like that. Basic gives you what the stack needs to be, and what you need to do in various AI workloads, applications and use cases. >> I believe you have distributed deep learning as an IBM capability, that's part of that stack, is that correct? >> That is part of the stack, it's like in the middle of the stack. >> Is it, correct me if I'm wrong, that's containerization of AI functionality? >> Right. >> For distributed deployment? >> Right. >> In an orchestrated Kubernetes fabric, is that correct? >> Yeah, so when you look at it from an IBM perspective, while we clearly support the virtualized world, the VM wares, the hyper V's, the KVMs and the OVMs, and we will continue to do that, we're also heavily invested in the container environment. For example, one of our other divisions, the IBM Cloud Private division, has announced a solution that's all about private Clouds, you can either get it hosted at IBM or literally buy our stack- >> Rob Thomas in fact demoed it this morning, here. >> Right, exactly. And you could create- >> At DataWorks. >> Private Cloud initiative, and there are companies that, whether it be for security purposes or whether it be for legal reasons or other reasons, don't want to use public Cloud providers, be it IBM, Amazon, Azure, Google or any of the big public Cloud providers, they want a private Cloud and IBM either A, will host it or B, with IBM Cloud Private. All of that infrastructure is built around a containerized environment. We support the older world, the virtualized world, and the newer world, the container world. In fact, our storage, allows you to have persistent storage in a container's environment, Dockers and Kubernetes, and that works on all of our block storage and that's a freebie, by the way, we don't charge for that. >> You've worked in the data storage industry for a long time, can you talk a little bit about how the marketing message has changed and evolved since you first began in this industry and in terms of what customers want to hear and what assuages their fears? >> Sure, so nobody cares about speeds and feeds, okay? Except me, because I've been doing storage for 32 years. >> And him, he might care. (laughs) >> But when you look at it, the decision makers today, the CIOs, in 32 years, including seven start ups, IBM and EMC, I've never, ever, ever, met a CIO who used to be a storage guy, ever. So, they don't care. They know that they need storage and the other infrastructure, including servers and networking, but think about it, when the app is slow, who do they blame? Usually they blame the storage guy first, secondarily they blame the server guy, thirdly they blame the networking guy. They never look to see that their code stack is improperly done. Really what you have to do is talk applications, workloads and use cases which is what the AI reference architecture does. What my team does in non AI workloads, it's all about, again, data driven, multi Cloud infrastructure. They want to know how you're going to make a new workload fast AI. How you're going to make their Cloud resilient whether it's private or hybrid. In fact, IBM storage sells a ton of technology to large public Cloud providers that do not have the initials IBM. We sell gobs of storage to other public Cloud providers, both big, medium and small. It's really all about the applications, workloads and use cases, and that's what gets people excited. You basically need a position, just like I talked about with the AI foundations, storage is the critical foundation. We happen to be, knocking on wood, let's hope there's no earthquake, since I've lived here my whole life, and I've been in earthquakes, I was in the '89 quake. Literally fell down a bunch of stairs in the '89 quake. If there's an earthquake as great as IBM storage is, or any other storage or servers, it's crushed. Boom, you're done! Okay, well you need to make sure that your infrastructure, really your data, is covered by the right infrastructure and that it's always resilient, it's always performing and is always available. And that's what IBM drives is about, that's the message, not about how many gigabytes per second in bandwidth or what's the- Not that we can't spew that stuff when we talk to the right person but in general people don't care about it. What they want to know is, "Oh that SAP workload took 30 hours and now it takes 30 minutes?" We have public references that will say that. "Oh, you mean I can use eight to ten times less storage for the same money?" Yes, and we have public references that will say that. So that's what it's really about, so storage is really more from really a speeds and feeds Nuremberger sort of thing, and now all the Nurembergers are doing AI and Caffe and TensorFlow and all of that, they're all hackers, right? It used to be storage guys who used to do that and to a lesser extent server guys and definitely networking guys. That's all shifted to the software side so you got to talk the languages. What can we do with Hortonworks? By the way we were named in Q1 of 2018 as the Hortonworks infrastructure partner of the year. We work with Hortonworks all time, at all levels, whether it be with our channel partners, whether it be with our direct end users, however the customer wants to consume, we work with Hortonworks very closely and other providers as well in that big data analytics and the AI infrastructure world, that's what we do. >> So the containerizations side of the IBM AI stack, then the containerization capabilities in Hortonworks Data Platform 3.0, can you give us a sense for how you plan to, or do you plan at IBM, to work with Hortonworks to bring these capabilities, your reference architecture, into more, or bring their environment for that matter, into more of an alignment with what you're offering? >> So we haven't an exact decision of how we're going to do it, but we interface with Hortonworks on a continual basis. >> Yeah. >> We're working to figure out what's the right solution, whether that be an integrated solution of some type, whether that be something that we do through an adjunct to our reference architecture or some reference architecture that they have but we always make sure, again, we are their partner of the year for infrastructure named in Q1, and that's because we work very tightly with Hortonworks and make sure that what we do ties out with them, hits the right applications, workloads and use cases, the big data world, the analytic world and the AI world so that we're tied off, you know, together to make sure that we deliver the right solutions to the end user because that's what matters most is what gets the end users fired up, not what gets Hortonworks or IBM fired up, it's what gets the end users fired up. >> When you're trying to get into the head space of the CIO, and get your message out there, I mean what is it, what would you say is it that keeps them up at night? What are their biggest pain points and then how do you come in and solve them? >> I'd say the number one pain point for most CIOs is application delivery, okay? Whether that be to the line of business, put it this way, let's take an old workload, okay? Let's take that SAP example, that CIO was under pressure because they were trying, in this case it was a giant retailer who was shipping stuff every night, all over the world. Well guess what? The green undershirts in the wrong size, went to Paducah, Kentucky and then one of the other stores, in Singapore, which needed those green shirts, they ended up with shoes and the reason is, they couldn't run that SAP workload in a couple hours. Now they run it in 30 minutes. It used to take 30 hours. So since they're shipping every night, you're basically missing a cycle, essentially and you're not delivering the right thing from a retail infrastructure perspective to each of their nodes, if you will, to their retail locations. So they care about what do they need to do to deliver to the business the right applications, workloads and use cases on the right timeframe and they can't go down, people get fired for that at the CIO level, right? If something goes down, the CIO is gone and obviously for certain companies that are more in the modern mode, okay? People who are delivering stuff and their primary transactional vehicle is the internet, not retail, not through partners, not through people like IBM, but their primary transactional vehicle is a website, if that website is not resilient, performant and always reliable, then guess what? They are shut down and they're not selling anything to anybody, which is to true if you're Nordstroms, right? Someone can always go into the store and buy something, right, and figure it out? Almost all old retailers have not only a connection to core but they literally have a server and storage in every retail location so if the core goes down, guess what, they can transact. In the era of the internet, you don't do that anymore. Right? If you're shipping only on the internet, you're shipping on the internet so whether it be a new workload, okay? An old workload if you're doing the whole IOT thing. For example, I know a company that I was working with, it's a giant, private mining company. They have those giant, like three story dump trucks you see on the Discovery Channel. Those things cost them a hundred million dollars, so they have five thousand sensors on every dump truck. It's a fricking dump truck but guess what, they got five thousand sensors on there so they can monitor and make sure they take proactive action because if that goes down, whether these be diamond mines or these be Uranium mines or whatever it is, it costs them hundreds of millions of dollars to have a thing go down. That's, if you will, trying to take it out of the traditional, high tech area, which we all talk about, whether it be Apple or Google, or IBM, okay great, now let's put it to some other workload. In this case, this is the use of IOT, in a big data analytics environment with AI based infrastructure, to manage dump trucks. >> I think you're talking about what's called, "digital twins" in a networked environment for materials management, supply chain management and so forth. Are those requirements growing in terms of industrial IOT requirements of that sort and how does that effect the amount of data that needs to be stored, the sophistication of the AI and the stream competing that needs to be provisioned? Can you talk to that? >> The amount of data is growing exponentially. It's growing at yottabytes and zettabytes a year now, not at just exabytes anymore. In fact, everybody on their iPhone or their laptop, I've got a 10GB phone, okay? My laptop, which happens to be a Power Book, is two terabytes of flash, on a laptop. So just imagine how much data's being generated if you're doing in a giant factory, whether you be in the warehouse space, whether you be in healthcare, whether you be in government, whether you be in the financial sector and now all those additional regulations, such as GDPR in Europe and other regulations across the world about what you have to do with your healthcare data, what you have to do with your finance data, the amount of data being stored. And then on top of it, quite honestly, from an AI big data analytics perspective, the more data you have, the more valuable it is, the more you can mine it or the more oil, it's as if the world was just oil, forget the pollution side, let's assume oil didn't cause pollution. Okay, great, then guess what? You would be using oil everywhere and you wouldn't be using solar, you'd be using oil and by the way you need more and more and more, and how much oil you have and how you control that would be the power. That right now is the power of data and if anything it's getting more and more and more. So again, you always have to be able to be resilient with that data, you always have to interact with things, like we do with Hortonworks or other application workloads. Our AI reference architecture is another perfect example of the things you need to do to provide, you know, at the base infrastructure, the right foundation. If you have the wrong foundation to a building, it falls over. Whether it be your house, a hotel, this convention center, if it had the wrong foundation, it falls over. >> Actually to follow the oil analogy just a little bit further, the more of this data you have, the more PII there is and it usually, and the more the workloads need to scale up, especially for things like data masking. >> Right. >> When you have compliance requirements like GDPR, so you want to process the data but you need to mask it first, therefore you need clusters that conceivably are optimized for high volume, highly scalable masking in real time, to drive the downstream app, to feed the downstream applications and to feed the data scientist, you know, data lakes, whatever, and so forth and so on? >> That's why you need things like Incredible Compute which IBM offers with the Power Platform. And why you need storage that, again, can scale up. >> Yeah. >> Can get as big as you need it to be, for example in our reference architecture, we use both what we call Spectrum Scale, which is a big data analytics workload performance engine, it has multiple threaded, multi tasking. In fact one of the largest banks in the world, if you happen to bank with them, your credit card fraud is being done on our stuff, okay? But at the same time we have what's called IBM Cloud Object Storage which is an object store, you want to take every one of those searches for fraud and when they find out that no one stole my MasterCard or the Visa, you still want to put it in there because then you mine it later and see patterns of how people are trying to steal stuff because it's all being done digitally anyway. You want to be able to do that. So you A, want to handle it very quickly and resiliently but then you want to be able to mine it later, as you said, mining the data. >> Or do high value anomaly detection in the moment to be able to tag the more anomalous data that you can then sift through later or maybe in the moment for realtime litigation. >> Well that's highly compute intensive, it's AI intensive and it's highly storage intensive on a performance side and then what happens is you store it all for, lets say, further analysis so you can tell people, "When you get your Am Ex card, do this and they won't steal it." Well the only way to do that, is you use AI on this ocean of data, where you're analyzing all this fraud that has happened, to look at patterns and then you tell me, as a consumer, what to do. Whether it be in the financial business, in this case the credit card business, healthcare, government, manufacturing. One of our resellers actually developed an AI based tool that can scan boxes and cans for faults on an assembly line and actually have sold it to a beer company and to a soda company that instead of people looking at the cans, like you see on the Food Channel, to pull it off, guess what? It's all automatically done. There's no people pulling the can off, "Oh, that can is damaged" and they're looking at it and by the way, sometimes they slip through. Now, using cameras and this AI based infrastructure from IBM, with our storage underneath the hood, they're able to do this. >> Great. Well Eric thank you so much for coming on theCUBE. It's always been a lot of fun talking to you. >> Great, well thank you very much. We love being on theCUBE and appreciate it and hope everyone enjoys the DataWorks conference. >> We will have more from DataWorks just after this. (techno beat music)