>> Live from Las Vegas, it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back, everyone to theCUBE's live coverage of Informatica World 2019 I'm your host, Rebecca Knight, along with my cohost, John Furrier. We are joined by Sudhir Hasbe. He is the director of product management at Google Cloud. Thank you so much for coming on theCUBE. >> Thank you for inviting me. (laughing) >> So, this morning we saw Thomas Kurian up on the main stage to announce the expanded partnership. Big story in Wall Street Journal. Google Cloud and Informatica Team Up to Tame Data. Tell us more about this partnership. >> So if you take a look at the whole journey of data within organizations, lot of data is still siloed in different systems within different environments. Could be a hybrid on-prem. It could be multi-cloud and all. And customers need this whole end-to-end experience where you can go ahead and take that data, move it to Cloud, do data cleansing on it, do data preparation. You want to be able to go ahead and govern the data, know what data you have, like a catalog. Informatica provides all of those capabilities. And if you look at Google Cloud, we have some highly differentiated services like Google BigQuery, which customers love across the globe, to go ahead and use for analytics. We can do large scale analytics. We have customers from few terabytes to 100-plus petabytes, and storing that amount of data in BigQuery, analyzing, getting value out of it. And from there, all the A.I. capabilities that we have built on top of it. This whole journey of taking data from wherever it is, moving it, cleansing it, and then actually getting value out of it with Big Query, as with our A.I. capabilities. That whole end-to-end experience is what customers need. And with this partnership, I think we are bringing all the key components our customers need together for a perfect fit. >> Sadhir, first of all, great to see you. Since Google Next, we just had a great event by the way this year, congratulations. >> Thanks. >> A lot of great momentum in the enterprise. Explain for a minute. What is the relationship, what is the partnership? Just take a quick minute to describe what it is with Informatica that you're doing. >> Yeah, that's great. I think if you take a look at it, you can bring two key areas together in this partnership. There's data management. How do you get data into Cloud, how do you govern it, manage it, understand it. And then there is analyze the data and AI. So the main thing that we're bring together is these two capabilities. What do I mean by that? The two key components that will be available for our customers is the Intelligent Cloud services from Informatica, which will be available on GCP, will run on GCP. This will basically make sure that the whole end-to-end capability for that platform, like data pipelines and data cleansing and preparation, everything is now available natively on GCP. That's one thing. What that will also do is, Informatica team has actually optimized the execution as part of this migration. What that means is, now you'll be able to use products like Data Cloud, Dataproc. You'll be able to use some of the AI capabilities in BigQuery to actually go do the data cleansing and preparation and process-- >> So when you say "execute", you mean "running." >> Yeah, just running software. >> Not executing, go to market, but executing software. >> Executing software. If you have a data pipeline, you can literally layer this Dataproc underneath to go ahead and run some of the key processes. >> And so the value to the customer is seamless-- >> Seamless integration. >> Okay, so as you guys get more enterprise savvy, and it's clear you guys are doing good work, and obviously Thomas has got the chops there. We've covered that on theCUBE many times. As you go forward, this Cloud formula seems to be taking shape. Amazon, Azure, Google, coming in, providing onboarding to Cloud and vice-versa, so those relationships. The customers are scratching their heads, going, "Okay, where do I fit in that?" So, when you talk to customers, how do you explain that? Because, unlike the old days in computer science and the computer industry, there was known practices. You built a data center, you provisioned some servers, you did some things. It was the general-purpose formula. But every company is different. Their journey's different. Their software legacy make-up's different. Could be born in the cloud with on-prem compliance needs. So, how do customers figure this out? What's the playbook? >> I think the big thing is this: There's a trend in the industry, across the board, to go ahead and be more data-driven, build a culture that is data-driven culture. And as customers are looking at it, what they are seeing is, "Hey, traditionally I was doing a lot of stuff. "Managing infrastructure. Let me go build a data center. "Let me buy machines." That is not adding that much value. It is because. "I need to go do that." That's why they did that. But the real value is, if I can get the data, I can go analyze it, I can get better decisions from it. If I can use machine learning to differentiate my services, that's where the value is. So, most customers are looking at it and saying, "Hey, I know what I need to do in the industry now, "is basically go ahead and focus more on insights "and less on infrastructure." But as doing this, the most important thing is, data is still, as you mentioned, siloed. It's different applications, different data centers, still sitting in different places. So, I think what is happening with what we announced today is making it easy to get that data into Google Cloud and then leveraging that to go ahead and get insights. That's where the focus is for us. And as you get more of these capabilities in the cloud as native services, from Infomatica and Google, customers can now focus more on how to derive value from the data. Putting the data into Cloud, cleansing it, and data preparation, and all of that, that becomes easier. >> Okay, so that brings the solution question to the table. With the solutions that you see with Infomatica, because again, they have a broad space, a horizontal, on-prem and cloud, and they have a huge customer base with enterprise, 25 years, and big data is their thing. What us case is their low-hanging fruit right now? Where are people putting their toe in the water? Where are they jumping full in? Where do you see that spectrum of solutions? >> Great question. There are two or three key scenarios that I see across the board with talking to a lot of customers. Even today, I spoke to a lot of customers at this show. And the first main thing I hear is this whole thing, modedernization of the data warehousing and analytics infrastructure. Lot of data is still siloed and stuck into these different data systems that are there within organizations. And, if you want to go ahead and leverage that data to build on top of the data, democratize it with everybody within the organization, or to leverage AI and machine learning on top of it, you need to unwind what you've done and just take that data and put into Cloud and all. I think modernization of data warehouses and analytics infrastructure is one key play across the IT systems and IT operations. >> Before you go on to the next one, I just want to drill down on that. Because one of the things we're hearing, obviously here and all of the places, is that if you constrain the data, machine learning and AI application ultimately fails. >> Yes. >> So, legacy silos. You mentioned that. But also regulatory things. I got to have privacy now, forget my customer, GDPR first-year anniversary, new regulatory things around, all kinds of data, nevermind outside the United States. But the cloud is appealing, of just throwing it in there as one thing. It's an agility lag issue. Because lagging is not good for AI. You want real-time data. You need to have it fast. How does a customer do that? Is it best to store it in the cloud first, on-premise, with mechanisms? What's your take on this? >> I think it's different in different scenarios. I talk a lot of customers on this. Not all data is restricted from going anywhere. I think there are some data sets you want to have good governance in place. For example, if you have PII data, if you have important customer information, you want to make sure that you take the right steps to govern it. You want to anonymize it. You want to make sure that the right amount of data, per the policies within the organization, only gets into the right systems. And I think this is where, also, the partnership is helpful, because with Infomatica, the tooling that they're provided, or as you mentioned over 25 years, allows customers to understand what these data sets are, what value they're providing. And so, you can do anonymization of data before it lands into Cloud and all of that. So I think one thing is the tooling around that, which is critical. And the second thing is, if you can identify data sets that are real-time, and they don't have business-critical or PII-critical data, that you're fine as a business process to be there, then you can derive a lot of data in real time from all the data sets. >> Tell me about Google's big capabilities, because you guys have a lot of internal power platform features. BigQuery is one of them. Is BigQuery the secret weapon? Is that the big power source for managing the data? >> I would just say: Our customers love BigQuery, primarily because of the capability it provides. There are different capabilities. Let me just list a few. One is: We can do analytics at scale. So as organizations grow, even if data sets are small within organization, what I have seen is, over a period of time, when you derive a lot of value from data, you will start collecting more data within organization. And so, you have to think about scale, whether you are starting with one terabyte or one petabyte or 100 petabytes, it doesn't matter. Analyzing data at scale is what we're really good at, at different types of scale. Second is: democratizing data. We have done a good job of making data available through different tooling, existing tooling that customers have invested in and our tooling, to make it available to everybody. AirAsia is a good example. They have been able to go ahead and give right insights to everybody within the organization, which has helped them go save 5 to 10% in their operational costs. So that's one great example of democratizing access to insights. The third big thing is machine learning and AI. We all know there are just lack of resources to do, at once, analytics with AI and machine learning in the industry. So our goal has been democratize it. Make it easy within an organization. So investments that we have done with BigQuery ML, where you can do machine learning with just simple SQL statements or AutoML tables, which basically allows you to just, within the UI, map and say, "That's table in BigQuery, here's a column that I want to predict, and just automatically figure out what model you want to create, and then we can use neural networks to go do that. I think that kind of investments is what customers love about it from the platform side. >> What about the partnership from a particular functional part of the company, marketing? There's the old adage: 50% of my marketing budget is wasted. I just don't know which one. This one could really change that. >> Exactly right. >> So talk a little bit about the impact of it on marketing. >> I think the main thing is, if you think about the biggest challenge that CMOs have within organizations is how do you better marketing analytics and optimize the spend? So, one of the thing that we're doing with the partnership is not just breaking the silos, getting the data in BigQuery, all of that side and data governance. But another thing is with master data management capability that Infomatica brings to table. Now you can have all of your data in BigQuery. You leverage the Customer 360 that MDM provides and now CMOs can actually say, "Hey, I have a complete view of my customer. "I can do better segmentation. I can do better targeting. "I can give them better service." So that is actually going to derive lot of value with our customers. >> I want to just touch on that once, see if I can get this right. What you just said, I think might be the question I was just about to ask, which is: What is unique about Google's analytical portfolio with Infomatica specifically? Because there's other cloud deals they have. They have Azure and AWS. What's unique about you guys and Infomatica? Was it that piece? >> Yeah, I think there are a few things. One is the whole end-to-end experience of basically getting the data, breaking the silos, doing data governance, this tight integration between our product portfolio, where now you can get a great experience within the native GCP environment. That's one. And then on the other side, Cloud for Marketing is a big, big initiative for us. We work with hundreds of thousand of customers across the globe on their marketing spend and optimizing their marketing. And this is one of the areas where we can work together to go ahead and help those CMOs to get more value from their marketing investments. >> One of the conversations we're having here on theCUBE, and really that we're having in the technology industry, is about the skills gap. I want to hear what you're doing at Google to tackle this problem. >> I think one of the big things that we're doing is just trying to-- I have this team internally. In planning, I use "radical simplicity." And radical simplicity is: How do we take things that we are doing today and make it extremely simple for the next generation of innovation that we're doing? All the investments and BigQuery ML, you SQL for mostly everything. One of the other things that we announced at Next was SQL for data flow, SQL pipelines. What that means is, instead of writing Beam or Java code to build data flow pipelines, now you can write SQL commands to go ahead and create a whole pipeline. Similarly, machine learning with SQL. This whole aspect of simplifying capabilities so that you can use SQL and then AutoML, that's one part of it. And the second, of course, we are working with different partners to go ahead and have a lot of training that is available online, where customers don't have to go take classes, like traditional classes, but just go online. All the assets are available, examples are available. One of the big things in BigQuery we have is we have 70-plus public data sets, where you can go, with BigQuery sandbox, without credit card, you can start using it. You can start trying it out. You can use 70-plus data sets that already available and start learning the product. So I think that should help drive more-- >> Google's a real cultural tech company, so you guys obviously based that from Stanford. Very academic field, so you do hire a lot of smart people. But there's a lot of people graduating middle school, high school, college. Berkeley just graduated their first, inaugural class in data science and analytics. What's the skills, specifically, that young kids or people who are either retraining should either reboot, hone, or dial up? Is there any things that you see from people that are successful inside Google? I mean, sometimes you don't have to have that traditional math background or computer science, although math does help; it's key. But what is your observation? What's your personal view on this? >> I think the biggest thing I've noticed is the passion for data. I fundamentally believe that, in the next three to five years, most organizations will be driven with data and insights. Machine learning and AI is going to become more and more important. So this understanding and having the passion for understanding data, answering questions based on data is the first thing that you need to have. And then you can learn the technologies and everything else. They will become simpler and easier to use. But the key thing is this passion for data and having this data-driven decision-making is the biggest thing, so my recommendation to everybody who is going to college today and learning is: Go learn more about how to make better decisions with data. Learn more about tooling around data. Focus on data, and then-- >> It's like an athlete. If you're not at the gym shooting hoops, if you don't love it, if you're not living it, you're probably not going to be any-- (laughing) It's kind of like that. >> Sudhir, thank you so much for coming on theCUBE. It's a pleasure talking to you. >> Thank you. Thanks a lot for having me. >> I'm Rebecca Knight for John Furrier. You are watching theCUBE. (techno music)

>> From our studios in the heart of Silicon Valley, Palo Alto, California. This is a CUBE Conversation. >> Hi everyone, welcome to this CUBE Conversation here in Palo Alto, I'm John Furrier. Host of theCUBE here in theCUBE studios. I'm joined with Ronen Schwartz. Senior Vice President and General Manager of Data Integration and Cloud Integration at Informatica, CUBE alumni, been on multiple times, here to do a preview round. Informatica World coming up as well as just catch up. Ronen, great to see you. >> Really happy to see you, you guys have a beautiful place here in Palo Alto. >> I know you live right around the corner so I'm expecting to see you come on multiple times and come in and share your commentary, but I want to get your thoughts, it's been a couple of months since we last chatted, interesting turn of events. If you go back just, you know, September of last year, and then you had Amazon Reinvent. They announced Outpost, multi-cloud starts hitting the scene, first it was hybrid. First it was all public cloud. But now the realization from customers is that this is now a fully blown up cloud world. It's cloud operations, it's just public cloud for unlimited cloud natives activity, on premise for existing workloads, and a complete re-architecture of the enterprise. >> Yes, and I think from Reinvent to Google Next just a week before, I agree with you. It's a world of hybrid and a world of multi-cloud. I think a lot of exciting announcements and a lot of changes, I think from my perspective what I see is that the Informatica customers are truly adopting cloud and hybrid and as data is growing, as data is changing the cloud is the place that they actually address this opportunity in the best way. >> So I know we've talked in the past. Your title is Data Integration, Cloud Integration. Obviously integration is the key point. You're starting to see APIs going to a whole other level, with Google they had acquired Apogee, which is an API marketplace, but with microservices and service meshes and Kubernetes momentum you're starting to see the advent of more programmability. This is a big trend, how is that impacting your world? Because at the end of the day you need the data. >> Yes, it actually means that you can do more things with the data in an easier way and also it means that you can actually share it with more users within the enterprise. I think that especially the whole ability to use containers, and Kubernetes is a great example of how you can do it, it's actually giving you unparalleled scale, as well as simplicity from the obstruction perspective. And it allows more and more developers to build more value from the data that they have. So data is actually in the core. Data is the foundation, and really a lot of this new technology allows you to build up from the data more valuable capabilities. I'm really happy that you're mentioning Apogee because one of the things that Google and Informatica notice together is the need for API to actually leverage data in a better way, and we strike a very strategic partnership that has gone into the market in the last few months allowing every user of Informatica Ipaas to basically publish APIs in a native experience from the Informatica Ipass directly to Apogee and vice versa, everything that you build in Informatica Cloud is basically automatically an API inside Apogee, so users get more value from data faster. >> So can you give an example, 'cause I think this is one of the things we saw at Google as a tell sign or the canary in the cole mine whatever trend parameter is that end to end CICD pipe lining, seamless execution in any environment seems to be the trend. What you're kind of getting at is this kind of cross integration, can you give an example of that Informatica Cloud to Apogee example of benefit to the customer or use case and why that's important. >> Yes, definitely, so if I'm a retailer or a manufacturer, I'm actually looking into automate processes. There is nothing better than deleting the Ipaas from Informatica to actually automate process anything from order to cash or inventory validation or even next best recommendation coming from some AI in the backend. Once you have created this process exposing this process as an API is actually allowing multiple other services. Multiple other capabilities to very easily leverage that, right, so this is basically what we're doing, so what an individual in the retailer is doing is they're actually defining this process of order to cash, and then they're publishing it as an API in one click, at that stage anybody anywhere can very very easily consume that API and basically use this process again and again. >> And that means what? Faster execution of application development? >> It means faster execution of application development. It also means consistency and basically scale so now you don't need to redevelop that. It's available as an API, you can reuse it again and again, so you do it in a consistent way, when you need to update you need to change, you need to modernize this process you modernize it once and use it again and again. >> Sorry to drill down on kind of the unique use case here, but this points to the integration challenges out there and the opportunities. Mentioned Google Next, Google Cloud. You've got a relationship with Amazon. This is part of your strategy for ecosystem. This is critical, integration is becoming Amit Walia was saying that you can compose. Have that foundation for the data and you compose your applications, but if you got to have a lot of composition, you need to have integration points, that's going to be either APIs or some sort of glue layer. This is huge, this is like the entire thesis of cloud architecture. >> Right, and the reality that our customers are facing is basically irrelative from multi-cloud, they will use a best of breed cloud for CRM, a best of breed cloud for ERP as well as a best of breed cloud for their data warehouse, their databases as well as their analytics, AI, et cetera. In that world, the only thing that is kind of common across this cloud is the data. And if you're actually able to allow the data to reside in the best place but you keep the metadata managed centrally by software like the one at Informatica is giving you are getting the best of breed of all of these offerings without actually paying a fine for that. >> So you guys are in a lot of magic quadrants out there in terms of categories of leadership and focus on data from day one. As you talk about your ecosystem, can you explain what that means because you're also an ecosystem partner of cloud players but you also have your own ecosystem. Talk about the ecosystem, how is it laid out? What's the update, what are some of the momentum points, can you share just an overview of how that's all happening? >> Yes, definitely, so when we're looking into our partnership with Microsoft Azure, with AWS, with JCP, we're not talking about just Informatica supporting the technologies that they build, we're talking about Informatica supporting the technologies that they're building as well as their ecosystem of partners. We're talking about an end-to-end solution that supports the entire ecosystem. What that actually translates to is Informatica building services that are giving best of breed experience for users within this cloud environment and really giving you the full power of data management integration, data quality. Master data management, data security. Data catalog across all of this cloud. In a way you're right, we can look at it in the same way as like we have an ecosystem and in that ecosystem we're seeing a lot of strategic partners that are very very large, definitely all of these cloud scales are key partners for us and for our customers, but we're also seeing a huge amount of smaller, innovative vendors that are joining this ecosystem, and Informatica World in May 20th is a great place to come and actually see these vendors. We're actually showing for the first time our AI and cloud ecosystem in one place and these vendors are coming and they're showing how are they leveraging Informatica technology to basically bring new value in AI, in machine learning, in analytics to their customers. If you ask me, like, what is Informatica doing to help them, we're basically making the data available in the best way for their offering, and that kind of allowed them to focus on their innovation rather than how do they work in the different places. >> Rowen, you got ahead of me on the Informatica World question, but you just brought it out, you're doing an innovation. Let's talk about Informatica World. Because again, this data, there's a lot of sessions, so you do the normal thing. We've covered multiple years there. Integration's the key point, what are, why should someone come to Informatica World if they're a customer or a prospect? Now, you mentioned the AI zone. What's the core theme that you're going to be seeing there from your group and from the company? >> Informatica World this year is an amazing place for people to come and see the latest that happens within the cloud and hybrid journey, a great place to actually see next generation analytics and all the innovation there, it is a great place to see customer 360 and master data management and how can that change your organization as well as an amazing place to see data security and data privacy and a lot of other innovations around data. But I would actually say that it's great to see everything that Informatica can share with you. It is a better place to see what our customers and our partners are sharing. And especially from a partnership perspective Informatica World 2019, you're actually going to see leaders from Google, you're going to see leaders from Microsoft, you're going to see leaders from AWS, the people that are leading the best data warehouses in the world the best analytics in the world as well as innovators like DataRobot and Databricks that are changing the world and are actually advancing technology very very fast. >> And the AI zone, there's a cloud and AI zone. I've seen them, I know it's here from the prep. What does that mean, what's someone, AI's going to be hot, I think that's a big theme. Getting clarity around, as Amit kind of shared with us on a previous interview. AI's hot because automation kind of left the blocking and tackling. But the value of creation is going to come from using the data, where's the, and it's not integrated, you can't get the data in. If it's not integrated, you can't leverage machine learning, so having access to data makes machine learning get great. The machine learning gets great, AI is great. So tell us what's going on with it. Give a little sneak preview. >> It's actually amazing what we can do leveraging the iron machine learning today, right? I wake up in the morning and I say Alexa, good morning, and I actually get back what's the weather and what's happening. I'm getting into my car, Google is telling me how fast will I get to the office or the first meeting. I left to come here and I knew exactly what's the best route to take. A lot of that is actually leveraging AI and machine learning, I think it's not a secret that the better your data is the better the machine can learn from the data. And if your data is not good, then learning can actually be really really bad. You know, sometimes I can use, like with my kids. If their learning books are bad, there's no way that they can actually get to the right answer. The same as data, data is so critical. What we're seeing is basically data engineers, data operation becoming a super strategic function to make AI and machine learning even possible. Your ability to collect enough data to make sure that the data is ready and clean for AI and machine learning is critical. And then once the AI and machine learning eventually contributed the automation, the decision making, the recommendation, you have to put it back in to the data pipes so that you are actually able to leverage them to do the right thing. >> You know, you, I think you nailed this one. We've talked about this before but I think more important than ever, data cleansing or data cleaning was always an afterthought in the old data warehouse world where well, we're not getting the answers we wanted so you kind of have to fail to figure out that the data sucks so you had to get the data to be better, now it's much more acute in the sense that people realize that you need quality data so there's now new capabilities to make sure there's a process for doing that on the front end, not on the back end. Talk about that dynamic, because this is something that is critical in the architecture, and how you think about data pipe-lining, data management, the things that you guys do, this is an important trend. Take a minute to explain that. >> Yes, I totally agree with you and I think that the rise of the importance of data quality, and it actually is coming also as part of the pattern of data governance and we want to make sure that the processes exist to make sure that the data that we make available for our AI research, for analytics, for our executives and data workers that this data is really the right data is critical. To actually support that, what we are seeing is people defining data governance process. What are the steps that the data needs to go before it is actually available for the next step? And what is nice today is that this is not people that the data needs to go through. These are processes, automation, that can actually drive data quality, it goes from things that are very very basic. Let's remove duplicate data, but also into the fact that you actually identify anomalies in the data and you ask the right questions so that that data doesn't go in. >> Is this the kind of topics that people will hear at Informatica World? >> Definitely, they will hear about how they can actually help the organization get the data right so that machine learning automation, and hyper growth is actually possible. >> You're excited about this market, aren't you? >> Super excited, I mean I think each and every one of us, we're going to see a lot of innovation coming out and I consider myself lucky that data is actually in the center of all of this innovation and that we're actually able to help the customers and our partners be successful with that. >> Yeah, you and I were talking before you came on camera, I wish I was 23 again right now, this is a great time to be in tech, everything's coming together. You got unlimited compute, machine learning's rocking and rolling, everyone's all kinds of diverse areas to play on, it's kind of intoxicating to be in this environment, isn't it? >> I totally agree, and I will add one additional thing to the reasons, agility. Like the fact that it all is available at your fingertip, and you can actually achieve so much with very little patience is really really amazing. >> This compose ability really as the new developer modernization renaissance. It's happening. >> Yes, yes, and as we usually say it all starts from the data. >> Okay, Ronen Schwartz, we're talking Informatica World but getting an update on what's going on because data integration, cloud integration, this is the number one activity people are spending their time on. You get it right, there's huge benefits. Ronen, thanks for coming in and sharing your insights, appreciate it. >> Hey, my pleasure. >> Okay, this is theCUBE, here for CUBE Conversation here in Palo Alto, California at theCUBE headquarters, I'm John Furrier Thanks for watching. (jazz music)