>> from the Silicon Angle Media Office in Boston, Massachusetts. It's the queue. Now here's your host. Still minimum. >> I'm stupid, Aman and this is a cube conversation from our Boston area studio. We spent a lot of time talking about digital transformation. Of course, At the center of that digital transformations data this segment We're going to be talking about the data integration platform. Joining me for that segment is Itamar on Cory on Who's the senior vice president of enterprise data Integration with Click. Thanks so much for joining me. >> Thanks to left me here. >> All right, so a zay just said, you know the customers, you know, digital information when you talked to any user, you know, there there's some that might say, Oh, there's a little bit of hyper I don't understand it, but really leveraging that data, you know, there are very few places that that is not core toe what they need to do, and if they're not doing it, they're competition will do it. So can you bring us inside a little bit? That customers you're talking to that, that you know where that fits into their business needs and you know how the data integration platform, you know, helps them solve that issue. >> Absolutely so, As you mentioned, the diesel transformation is driving a lot ofthe innovation, a lot off efforts by corporations and virtually any organization that we're talking. Toa seize data is a core component off, enabling the little transformation. The data creates new analytics, and there was toe power, the digital transformation, whether it's in making better decisions, whether it's embedding the analytics and the intelligence into business processes and custom applications to ever to reach the experience and make it better. So data becomes key, and the more data you can make available through the process, the faster you can make a development in the process. The faster you can adapt your process to accommodate the changes, the better it will be. So we're saying organization, virtually all of them looking to modernize their day, the strategy and the day, the platforms in order to accommodate these needs. >> Yeah, it's such a complex issue. We've we've been at, you know, chief data officer events way, talk about data initiatives. You know, we worry a little bit that the sea seats sometimes here it's like up. They heard data is the new oil and they came and they said, You know, according to the magazine I read, you need we need to have a date, a strategy, and give me the value of data. But, you know, where is the rubber hitting the road? You know what? What are some of those steps that they're taking? You know, how do I help, you know, get my arms around the data and that help make sure it can move along that spectrum from kind of the raw or two, you know, real value. >> I think you made a great point. Talking about the or to value our as we refer to it is a road to ready. And part of the whole innovation that we're seeing is the modernization of the platform where organizations are looking to tap into the tremendous amount of data that is available today. So a couple of things have happened first in the last decade. First of all, we have significantly more data. It is available and and then ever before, because of digitization, off data and new sources become available. But beyond that, we have the technology is the platforms that can both store in process large amounts of data. So we have foundations. But in the end, to make it happen, we need to get all the data to where we want to analyze it and find a way to put it together and turning from more row material into ready, material ready products that can be consumed. And that's really where the challenges and we're seeing. A lot of organizations, especially the CEO Seo the animals, architecture and First data architecture, teams on a journey to understand how to put together these kind of architectures and data systems. And that's where without data integration platform, we focused on accommodating the new challenges they have encountered in trying to make that happen. >> Yeah, help us unpack a little bit, You know, a here today. You know, it's the economy. Everything should work together when I rolled out. You know, in our company, you know, the industries leading serum, it's like, Oh, I've got hundreds of data sources and hundreds of tools I could put together, and it should be really easy for me to just, you know, allow my data to flow and get to the right place. But I always always find a lot a lot of times that that easy. But I've been having a hard time finding that so so >> that that's a good point. And if you cannot takes the bag, understand water, this side of the court challenges or the new needs that we're seeing because we talk about the transformation and more than analytics field by data being part of it. More analytics created a new type of challenges that didn't exist before and therefore kind of traditional data integration tools didn't do the job they didn't meet. Those model needs me very touched on a few of those. So, first of all, and people, when customers are implementing more than analytics many times where they refer to escape well they're trying to do is to do a I machine learning. We'LL use those terms and we talk about him but machine learning and I get smarter, the more data you give them. So it's all about the scale of data, and what we're seeing with customers is where if in the past data warehouse system, but if typically had five ten twenty, they the source is going into it. When I was saying one hundred X uh, times that number of sources. So we have customers that worked with five hundred six hundred, some over two thousand source of data feeding the data analytics system. So scale becomes a critical need and we talk about scale. You need the ability to bring data from hundreds or thousands of sources so systems efficiently with very low impact and ideally, do it also with less resources. Because again, you need to scale the second second chair and you ran in tow s to do with the fact that more than analytics for many organizations means real Time analytics or streaming analytics. So they wantto be ableto process data in real time. In response for that, to do that, you need away toe move data, capture it in real time and be able to make it available and do that in a very economic fashion. And then the third one is in order to deal with the scare in order to deal with the agility that the customers want. The question is, well, are they doing the analytics? And many of them are adopting the cloud, and we've been seeing multicoloured adoption. So in order to get data to the cloud. Now you're dealing with the challenge of efficiency. I have limited network band with. I have a lot of data that I need to move around. How can I move all of that and do that more efficiently? And, uh, the only thing that would add to that is that beyond that, the mechanics of how you move the data with scale, with efficiency even in real time there's also how you approach the process where the whole solution is to beware. What a join those the operations you can implement and accommodate any type of architecture. I need to have a platform that you may choose and we sink us was changed those overtime. So I need a breather to be agile and flexible. >> Yeah, well, ah, Lotto unpack there because, you know, I just made the comment. You know, if you talk about us humans, the more data we give them doesn't mean I'm actually going to get better. It's I need to We need to be able to have those tool ings in there to be able to have that data and help give me the insights, which then I could do on otherwise, you know, we understand most people. It's like if I have to make decisions or choices and I get more thrown at me, there's less and less likelihood that I can do on that on boy the Data Lakes. Yeah, I I remember the first time I heard Data Lakes. It was, you know, we talked about what infrastructure rebuilding, and now the last couple of years, the cloud public cloud tends to be a big piece of it. Even though we know data is goingto live everywhere, you know everything, not just public private ground. But EJ gets into a piece of it so that you know that the data integration platform, you know how easy it for customers get started on that We'LL talk about that diversity of everything else, you know, Where do they start? Give me a little bit of kind of customer journey, if you would. And maybe even if you have a customer example that that would be a great way to go illustrated. >> Absolutely so First of all, it's a journey, and I think that journey started quite a few years ago. I mean, do it is now over ten years old, and they were actually seeing a big change in shifting the market from what was initially the Duke ecosystem into a much brother sort of technology's, especially with the cloud in order to store and process large scales of data. So the journey customs we're going through with a few years, which were very experimental customers were trying trying it on for size. They were trying to understand how Toby the process around it, the solutions of them ivory batch oriented with may produce back in the early days off. But when you look at it today, it's a very it's already evolved significantly, and you're saying this big data systems needing to support different and diverse type off workloads. Some of them are michelle machine learning and sign. Some of them are streaming in the Olympics. Some of them are serving data for micro services toe parad, Egil applications. So there's a lot of need for the data in the journey, and what we're seeing is that customers as they move through this journey, they sometimes need to people and they need if they find you technology that come out and they had the ability to be able to accommodate, to adapt and adopt new technologies as they go through. It s so that's kind of the journey we have worked with our customers through. And as they evolved, once they figured it out, this scale came along. So it's very common to see a customer start with a smaller project and then scale it up. So for many of the cost me worked with, that's how it worked out. And you ask for an example. So one of her customers this month, the world's largest automotive companies, and they decided to have a strategy to turn what they believe is a huge asset they have, which is data. But the data is in a lot of silos across manufacturing facility supply facilities and others inventory and bring it all together into one place. Combined data with data to bring from the car itself and by having all the data in one place, be able to derive new insights into information that they they can use as well as potentially sale or monetizing other other ways. So as they got started, they initially start by running it out to set a number off their data data centers and their source of information manufacturing facilities. So they started small. But then very quickly, once they figured out they can do it fast and figure out the process to scale it. Today, there are over five hundred systems they have. Martha is over two hundred billion changes in data being fed daily. Okay, enter their Data lake. So it's a very, very large scale system. I feel we can talk about what it takes to put together something so big. >> Yeah. Don't pleaded. Please take the next step. That would that would be perfect. >> Okay, so I think whether the key things customers have to understand, uh, you were saying that the enterprise architecture teams is that when you need to scale, you need to change the way you think about things. And in the end of the day, there are two fundamental differences in the approach and the other light technology that enabled that. So we talked earlier about the little things help for the mind to understand. Now I'm going to focus on and hide it. Only two that should be easy to take away. First is that they're the move from bench to real time or from batch tow. The Delta to the changes. Traditionally, data integration was done in the best process. You reload the data today if you want to scale. If you want to work in a real time, you need to work based on the Delta on the change, the fundamental technology behind it. It's called change data capture, and it's like technology and approach. It allows you to find and identify only the changes on the enterprise data systems and imagine all the innovation you can get by capturing, imposing or the change is. First of all, you have a significant impact on the systems. Okay, so we can scale because you were moving less data. It's very efficient as you move the data around because it's only a fraction off the data, and it could be real time because again, you capturing the data as it changes. So they move from bitch to real time or to streaming data based on changes. The capture is fundamental, fundamental in creating a more than their integration environment. >> I'm assuming there's an initial load that has to go in something like that, >> correct. But he did that once and then for the rest of the time you're really moving onto the deltas. The second difference, toe one was get moving from batch toe streaming based on change. The capture and the second eyes how you approach building it, which is moving from a development. Let platform to automation. So through automation, you could take workloads that have traditionally being in the realm ofthe the developer and allow people with out development skills to be able to implement such solutions very quickly. So again, the move from developer toe toe configuration based automation based products or what we've done opportunity is First, we have been one of the pioneers in the innovators in change that I capture technology. So the platform that now it's part of the clique that integration plan from brings with it okay over fifteen years off innovation and optimization change their capture with the broader set of data sources that our support there, with lots of optimization ranging from data sources like sickle server and Oracle, the mainstream toe mainframes and to escape system. And then one of the key focus with the head is how do we take complex processes and ultimatum. So from a user perspective, you can click a few buttons, then few knobs, and you have the optimize solution available for making data moving data across that they're very sets off systems. So through moving on to the Delta and the automation, you allow this cape. >> So a lot of the systems I'm familiar with it's the metadata you know, comes in the system. I don't have to as an admin or somebody's setting that up. I don't have to do all of this or even if you think about you know, the way I think of photos these days. It used to be. I took photos and trying to sort them was, you know, ridiculous. Now, my, you know, my apple or Google, you know, normally facial recognition, but timestamp location, all those things I can sort it and find it. You know, it's built into the system >> absolutely in the metadata is critical to us to the whole process. First of all, because when you bring data from one system to another system, somebody's to understand their data. And the process of getting data into a lake and into a data warehouse is becoming a multi step day the pipeline, and in order to trust the data and understanding that you need to understand all the steps that they went through. And we also see different teams taking part in this process. So for it seemed to be able to pick up the data and work on it, it needs to understand its meta data. By the way, this is also where the click their integration platform bring together the unity software. Together with Click the catalyst, we'LL provide unique value proposition for you that because you have the ability to capture changed data as it changes, deliver that data virtually anywhere. Any data lake, any cloud platform, any analytic platform. And then we find the data to generate analytic ready data sets and together with the click data Catalyst, create derivative data sets and publish all of their for a catalogue that makes it really easy to understand which data exists and how to use it. So we have an end to end solution for streaming data pipelines that generate analytic data that data sets for the end of the day, wrote to ready an accelerated fashion. >> So, Itamar, your customers of the world that out, How did they measures Casesa? Their critical KP eyes is there You know some, you know, journey, you know, math that they help go along. You know what? What? What are some commonalities? >> So it's a great question. And naturally, for many organizations, it's about an arrow. I It's about total cost of ownership. It seeing result, as I mentioned earlier, agility and the timeto value is really changing. Customers are looking to get results within a matter of, if very few month and even sometimes weeks versus what it used to be, which is many months and sometimes even years. So again, the whole point is to do with much, much faster. So from a metric for success, what we're seeing his customers that buy our solution toe enable again large scale strategic initiatives where they have dozens to hundreds of data sources. One of the key metrics is how many data sources heavy onboard that heavy, made available. How many in the end of the data sets that already analytic ready have we published or made available Torrey Tor users and I'LL give you an example. Another example from one of for customers, very large corporation in the United States in the opportunity of after trying to move to the cloud and build a cloud Data Lake and analytic platform. In the two years they're able to move to two three data sets to the cloud after they try, they knew they'd integration platform okay, there. But they moved thirty day The sits within three months, so completely different result. And the other thing that they pointed out and actually talk about their solution is that unlike traditional data integration software, and they took an example of one of those traditional PTL platforms and they pointed out it takes seven months to get a new person skilled on that platform. Okay, with our data integration platform, they could do that in a matter of hours to a few days. So again, the ability to get results much faster is completely different. When you have that kind of software that goes back to a dimension about automation versus development based mouth now, >> it really seems like the industry's going through another step function, just as we saw from traditional data warehouses. Tto win. Who? Duke rolled out that just the order of magnitude, how long it took and the business value return Seems like we're we're going through yet another step function there. So final thing. Yeah, You know what? Some of the first things that people usually get started with any final takeaways you want to share? >> Sure. First, for what people are starting to work with. Is there usually selecting a platform of choice where they're gonna get started in respect of whether Iran analytics and the one take a way I'LL give customers is don't assume that the platform you chose is we're going to end up because new technologies come to market, a new options come. Customers are having mergers, acquisitions, so things change all the time. And as you plan, make sure you have the right infrastructure toe allow you two kind of people support and make changes as you move through the throw. These are innovation. So they may be key key takeaway. And the other one is make sure that you're feeling the right infrastructure that can accommodate speed in terms of real time accomodate scale. Okay, in terms of both enabling data legs, letting cloud data stores having the right efficiency to scale, and then anything agility in respect to being able to deploy solution much, much faster. Yeah, >> well, tomorrow I think that. That's some real important things to say. Well, we know that the only constant Internet industry is change on DH. Therefore, we need to have solutions that can help keep up with that on and be able to manage those environments. And, you know, the the role of is to be able to respond to those needs of the business fast. Because if I don't choose the right thing, the business will go elsewhere. Tara trying to fuck with Angelo. Thank you so much for sharing all the latest on the immigration data platforms. Thank you. Alright, Uh, always lots more on the cube dot Net comes to minimum is always thanks for watching.