>>Welcome back to the cubes. Continuing coverage of day, one of the snowflake summit 22 live from seizures forum in Las Vegas. I'm Lisa Martin. My co-host for the week is Dave ante, Dave and I are pleased to welcome Naga Raj Sastry to the program, the vice president of data and analytics at HCL technologies. Welcome. Great to have you. >>Same here. Thank you for inviting me here. >>Isn't it great to be back in person? >>Oh, love it. >>This the keynote this morning. I don't know if you had a chance to see it standing room only there was overflow rooms. People are ready for this, and it was a jam packed morning of announcements. >>Absolutely. >>Talk to us a little bit about the HCL snowflake partnership, but anybody in the audience who may not be familiar with HCL, give us a little bit of a background, vision, mission differentiation, and then that snowflake duo. >>Sure, sure. So let me first start off with, um, uh, talking about H at seal, we are 11.5 billion organization. Uh, we have three modes of working mode. One is everything to do with our infrastructure business and application services and maintenance mode. Two is anything that we do in the cutting edge, uh, ecosystem, whether it is cloud, whether it is application modernization, ERPs, uh, SA all of those put together is more to data. Analytics is part of our more to culture. Um, the whole ecosystem is called digital services business and, uh, within digital, uh, services, the one of the arms is data and analytics. We are about a billion dollars in terms of revenues from a data and analytics perspective, uh, of the 11 billion that I was talking to you about. And mode three is everything to do with our software services. So we have got our own software products, and that's a third of our business. So that's about HCL. So at C and, uh, snowflake relationship, we are a elite partner with snowflake. We are one of the fastest growing partners. We achieved the elite level within 18 months of us signing up as a snowflake partner. We're close to about 50 plus implementations worldwide, and, uh, about 800 people who are snowflake professionals within, within that CLE ecosystem, large customers that we serve. >>And how long have you been partners? >>Uh, about 18 to 20 months now. >>Okay. So, so the, during the last couple of tumultuous years, why snowflake, what was it about their vision, their strategy, their leadership that really to spoke to HCL as this is a partner for us? >>So, so one of the, uh, biggest things that we realized, uh, probably about four years ago was in terms of, you know, you had all the application databases or RDBMSs PPS, the huddle P ecosystems, which are getting expense systems, which were getting expensive, not in terms of the cost, but in terms of the pro processing times, the way the queries were getting created. And we knew that there was, there is something that is going to come and the people and the people. Yeah. >>And, uh, and we knew that, you know, there will be a hyperscaler that will come. And, uh, of course there was Azure was already there. AWS was there, Google was just picking it up. And at that point in time, we realized that, you know, there will be a cloud data warehouse because we had started reading about snowflake at that point in time. So fast forward a couple of years after that, and we realized that if we are to be in this business, you know, the, the right way of doing it is by getting partnering a partnering with the right tooling company. And snowflake brings that to table. We all know that now. And, uh, with, with what, what the keynote speakers were also saying, right, from 150 member team about five years ago in, uh, conference to about 12,000 people now. So you know that this is the right thing to do, and this is the right place to be at. So we, we devised a methodology in terms of saying that let's get into the partnership, let's get our resources trained and certified on the snowflake ecosystem. And let's take a point of view to our customers in terms of how data migrations and transformations have to be done in the snowflake arena. When >>You, when you think about your modes, you talked about modes one, two, and three. If I feel like snowflake touches on each of those, maybe not so much of the infrastructure and the apps, but although maybe going forward, it does increasingly. So, yeah, that's my question is where do you see snowflake vectoring into your modes? >>So it doesn in both in the first two modes, uh, and mode three also, uh, because, and I'll give you the reasons why mode one is predominantly because you can do application development on cloud yep. On the data cloud now, um, which basically means that I can have a qu application run on snowflake. Eventually that's the goal. Second is, uh, in, in more two, because it is a cloud data warehouse, it fits in exactly because the application data is in snowflake. I've got my, uh, regular data sets within snowflake. Both are talking to each other. There is zero, um, lapse time from a user perspective, >>It's a direct >>Tip. And then more three, the reason why I said more three was because software as a service or software services and products is because I can power by snowflake. I can implement that. So that's why it cuts across our entire ecosystem. >>The, the dig, the whole thing is called your dig business, correct? Yes. Is that right? So that's, this is the, the next wave of digital business that we're seeing here, cuz it's digital is data <laugh> right. That's really what it's about. It's about putting that data to work. >>So the president of our digital business, a BJA who was, who had done the, who had done a session in the, in the afternoon today, he says the D in the digital is data. >>There is right. >>And, uh, that's what we are seeing with our customers, large implementations that we do in this ecosystem. There is one other thing that we are focusing, uh, very heavily on is industrial solutions or industry led solutions. Like whether it is for healthcare, whether it's for retail or financial services, name, a vertical. And we have got our own capabilities around industrialized solutions that's fit that fit certain use cases. >>So in thinking about the D in, in digital is really data. If you think about the operating model for data, it's obviously evolved, you mentioned, had do, went to the cloud and all the data went to the cloud, but today it's, you've got an application development model, you got database, which is sort of hardened. And then you've got your data pipeline and your, your data stack and, and that's kind of the operating model. There's sort of siloed to a great degree. Mm-hmm <affirmative> how is that operating model changing as a result of, of data? So >>I answered it in two parts. Part is if you, if you realize over the years, what used to happen is you had a CIO in an organization or C more CIO, but, and then you had enterprise architecture teams, application development teams, support teams, and so on and so forth in the last 36 months. If you see there is an emergence of a new role, which is called the da chief data and analytics officer. So the data and analytics officer is a role that has been created. And the purpose of creating that role is to ensure that organizations will pull out our call out resources within the CIO organizations who are enterprise architects, who are data architects, who are application architects or security architects, and bring them under into the ecosystem of the data office from an operating model perspective. So that innovations can be driven. >>Data driven enterprises could be created and innovations can come through there. The other part of that is the use cases get prioritized when you start innovating. And then it is a factory model in terms of how those use cases get built, which is, which is, which is a no brainer in my mind, at least. But that is how the operating model is coming up from a people perspective, from a technology perspective. Also there is an operating model that is emerging. If you see all the hyperscalers that are there today, snowflake with its LA most latest and greatest announcements. If you see the way the industry is going, is everything will be housed into one ecosystem and the beauty of this entire thing. And if you, you are to, you'll be able to fathom it effectively, right? Because if you are, if I'm, multi-cloud kind of an environment and if I'm on snowflake, I don't care why, because I'm snowflake, which is, which can work around across the multi clouds. So my data is in one place >>Effectively. Yeah. It's interesting what you were saying about the chief data officer, the chief data officer, that role emerged out of the, the ashes, like a Phoenix of, of, you know, compliance data quality and, and healthcare and financial services and government, the highly regulated industries. And then it took a while, but it, it increasingly became, wow, this is a really front front of the board level role, if you will, you know, data, and now you're seeing it. It's it's, it is integrated with digital. >>Absolutely. And there is one other point, if you think about it, the emergence of the chief data officer came in because there were issues associated to data quality. Yeah. There were issues associated to data cataloging as to how data is cataloged. And there were issues in terms of trustability of the data. Now, the trustability of the data can be in two places. One is a data quality, Hey, bad data, garbage and garbage out. But then the other aspect of the trustability is in terms of, can I do the seven CS of data quality and say that, okay, I can hallmark this data platinum or gold or silver or bronze or UN hallmark data. And with snowflake, the advantage is if I, if you have a hallmark data set, that is a, say a platinum or a gold, and thanks to the virtual warehouse, the same data set gets penetrated across the enterprise. That's the beauty with which it comes. And then of course the metadata aspect of it, bringing in the technical metadata and the business metadata together for the purpose of creating the data catalogs is another key cool thing and enabled again by snowflake. >>What are some of it when you're in customer conversations, some of the myths or misconceptions that customers historically have typically been making when it comes to creating a data strategy, some of the misconceptions, and then what is your recommendation for those folks since every company, these days to be competitive has to be a data company. >>Yeah. So around data structures, the, the whole thought process has to be, uh, either do in the past, we used to go with, from source applications, we would gather requirements. Then we would figure out what sources are there, do a profiling of the data and then say, okay, the target data, data model should be this >>Too slow, >>Too slow right now, fast forward to the digital transformation. There is producers of data, which is basically that applications that are being modernized today are producers of data. They're actually telling you that I'm producing this kind of data. This is the kind of events that I'm producing. And this is my structure. Now the whole deal is I don't need to figure out what the requirements are. I know what the use case the application is going to be helping me with. So therefore the entire data model is supported. So, but at the same point in time, the newer generation applications that are getting created are not only created getting created in terms of the customer experience. Of course, that is very critical, but they're also taking into account aspects around metadata, the technical metadata associated within an application, the data quality rules or business rules that are implemented within an application, all of that is getting documented as a result, the whole timeline from source to profile to model, which used to be X number of days in the past is X minus, at least 20% now or 30% actually. So that is how the structures, uh, the data structures are coming into a play future futuristic thought process would be, there will be producers of data and there'll be consumers of data. Where is ETL then or ELT. Then there is not going to be any ETL or ELT because a producer is going to say that I'm producing the data for this. A consumer says that, okay, I wanna consume the data for this purpose. There, they meet through an API layer. So where is ETL eventually going to go away? >>Well, and those consumers of, if you think about the, the way it works today, the, the data operating model, if you will, the transaction systems and other systems draw off a bunch of exhaust, they gets thrown over the fence to the analytics system. They're not operation the data, the data pipeline, the data systems are not operationalized in a way that they need to be. And obviously Snowflake's trying to change that. >>So data >>That's a big change, please. >>Yeah. Sorry. Didn't mean to cut you off. My >>Apologies. No, no. I'm >>So data operations is a very, very critical aspect. And if you think about it holistically, we used to have ETL pipelines T pipelines. And then we used to have queries being written on top of metadata or PPS and HaLoop and all of that and reporting tools that would have number of reports that were created and certain self-service BI reports into the ecosystem. Now, when you think in terms of a cloud data warehouse, what is happening? Is this the way you are architecting your solution today in terms of data pipelines, those data pipelines are self manageable or self-healing do not need the number of people where there was no documentation in terms of what ETL pipelines were written in the past on certain ETL tools or why something is failing. Nobody knew why something was failing because these are age old code, but take it forward today. >>What happens is our organizations are migrating from on-prem to cloud and to the cloud data warehouse. And the overall cost of ownership is decreasing. The reason is the way we are implementing the data pipelines, the way the data operations are being done in terms of, you know, even before a pipeline is kicked, uh, or kicked in, then, you know, there is a check process to say whether the source application is ready or not ready. So such things, small, small things, which are part and parcel of the entire data operations lifecycle are taking the center stage as a result, self fueling mechanisms are coming in. And because of those self fueling mechanisms, metrics are being captured as a result, you know exactly where to focus on and where not to focus on as, as a result, the number of resources needed to support gets reduced. Cost of one service >>Is low, much higher trust self-service infrastructure, uh, data context in the hands of, of business users. Data is now more discoverable it's governed. So you can now create data products more quickly. So speed and scale become extremely important. >>Absolutely. And in fact, one of the things that, that, uh, that is changing is the way search is getting implemented here to in the past, you created an index and then, you know, the data is searchable, but now it is contextual search. Can I contextualize the entire search? Can I create a machine learning algorithm that will actually say that, okay, Nara as a persona was looking for this kind of data and then Nara as a person, or comes back again and looks for some different kind of data. Can the machine learning algorithm go and figure out, okay, what is, what is going on in a garage's mind? What is he trying to look at? And then, you know, improve the, the whole learnability of the, of the entire algorithm. That's how search is going to also take, get into a change kind of a scenario. >>Excellent NAAU garage. Thank you so much for joining us, talking about data modernization at speed, end scale HCL, what you're doing, what you're doing with snowflake, and the sounds like incredible power that you're enabling. And we're only just scratching the surface. I have a feeling there's a lot more under there that you guys are gonna uncover. >>Sure. So we have, we have a tool or an accelerator. We call it an accelerator in the HCL parlance, but just actually a tool. So when you think about data modernization onto snowflake, it is predominantly migrating the data set from your existing ecosystem onto snowflake. That is one aspect of it. The second aspect of it is the modernization of the ETL or E LT pipelines. The third aspect associated to the data that is there within this, these ecosystems is the reconciliation older application, uh, sorry, older legacy, uh, platform snowflake legacy platform gives me result. X does snowflake give me result X that kind of a reconciliation has to be done. Data reconciliation and testing. And then the third fourth layer associated is the reporting and visualization. So these four layers are part and parcel of something that we call as advantage. Migrate advantage migrate will convert your ter data, data, uh, model into a snowflake understandable data model automatically whether it's ter data, whether it is Oracle, extra data, green plum, <inaudible> you name a ecosystem. >>We have the mechanism to convert a data model from whatever it is into snowflake readable, understandable data model. The second aspect is the et L E L T pipeline. Whether you want to go from Informatica to DBT or Informatica to something else or data stage to something else doesn't matter. There is a, there is an algorithm, or there is a tool which is called the ETL pipeline. We call it gateway suit, gateway suit actually converts the code. It reads the code that is there on the left hand side, which is the legacy code, understands the logic, it reverse engineers and understands the logic. And then what it does is we use that understanding or that logic that has been called out into spark code or DBT or any other tool of your choice from a customer standpoint. That's the second layer. Third layer I talked about, which is basically data testing, automated data testing and data reconciliation and the last, but not the least is the reporting because older ways of reporting and visualization with, with current day reporting and visualization, which is more persona based, the art of visualization is something difficult or different in this, in this aspect, come over to our booth at 2 1, 1 4, and you'll see, uh, advantage migrate in the works >>Advantage. Migrate. There you go. Nero, thank you so much for joining us on the program and unpacking HCL, giving us really that technical dissection of what you guys are doing and together with snowflake. We appreciate your time. >>Thank you. My pleasure. Thank you >>For our guest and Dave ante. This is Lisa Martin live from the show floor of snowflake summit 22, Dave and I will be right back with our final guest of day one in just a minute.