The Six Five “On The Road” at Cloudera Evolve NYC. Hosts Daniel Newman and Patrick Moorhead are joined by Abhas Ricky, Chief Strategy Officer & Luke Roquet, Sr. VP, Product Marketing, Cloudera. In this conversation, they explore the key trends in modern data architecture and the benefits Cloudera’s modern data architecture is providing customers.
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Transcript:
Daniel Newman: Hey everyone. Welcome back to another Six Five “On the Road” here at Evolve NYC 2022, brought to you by Coquet, IBM, and Intel. I’m Daniel Newman, Principal Analyst, founding partner of Futurum Research, joined by my colleague Patrick Moorhead, Moor Insights & Strategy. Patrick, what a day.
Patrick Moorhead: No, I know. It’s been great. It’s so good to get back on the road again, and go to an event where we can be super active. I met customers. I met a lot of executives from Cloudera, and I actually gave part of the keynote today, which was fun. But, I know analysts won’t admit this, but I learn something every time I go somewhere. I might not give attribution to that person, but, you know… That’s supposed to be a joke, so…
Daniel Newman: Oh no, no, I got it.
Patrick Moorhead: Okay.
Daniel Newman: I was just waiting to see if I got some. No, I’m just kidding. But no, it was great to see you actually up on stage. We’ve been close for eight years. We started out our relationship at 2014 Dell Technologies World, hosting it together. That was the first time I’ve ever seen you give a solo presentation on stage.
Patrick Moorhead: How about that?
Daniel Newman: It’s not because you don’t do them. It’s just because I refused to watch. But I was here, I had to do it. No, in all seriousness.
Patrick Moorhead: Front row, too.
Daniel Newman: Good job. You kicked off for some really great thinkers for CEO of Cloudera, Rob Bearden, for our friend Rob Thomas from IBM, who also did a great job. We had Wordle there. I did Wordle, we had Wordle. But we have a great conversation. Actually this is the last one. So, if you’re chronologizing this thing, this is the last. I think we had seven today.
Patrick Moorhead: Yeah.
Daniel Newman: Great conversations. Data is the theme. Evolve the conference. All about evolving our data strategies and structures. We’ve got two great thinkers here. So, with that, I’m going to introduce Abhas. I’m going to introduce Luke, welcome to the show. Love for each of you just to give a quick hello, and tell everyone what you do for Cloudera.
Abhas Ricky: Yeah, Abhas. I’m responsible for corporate strategy. I’m the Chief Strategy Officer at Cloudera, and I’m based out of Seattle.
Daniel Newman: Excellent.
Luke Roquet: I’m Luke Roquet. I run product marketing, but I’ve been with Cloudera/Hortonworks for about eight years, primarily in a sales function. So, I moved into product marketing specifically to address our messaging and go to market strategy.
Patrick Moorhead: This is great. I mean I’m so old. I’ve had both your jobs. This is awesome.
Daniel Newman: Sales guy running product too.
Patrick Moorhead: No.
Daniel Newman: I love that. No, I mean I’m a sales guy, so…
Abhas Ricky: Well, I was in the field as well, and I was at Hortonworks as well.
Patrick Moorhead: I started with carrying a bag. So, maybe there’s something about selling it, moving to other things that… No, seriously, it’s great to have you here. I think that what we’d like to talk about here are data architecture trends. Hopefully, it’s obvious by now that in the infrastructure is hybrid. I’ve been on a holy tour talking about this. I never run into a customer who says that’s not the case. Maybe a new startup or something like that. But for the companies that you serve, the larger folks with a ton of data, they certainly have always been believers, but the tech really wasn’t there. Fundamentally, if you buy into hybrid architecture, you have to have a hybrid data architecture as well. So, maybe I’m leading the witness here, but Abhas, maybe we’ll start with you on how is this changing? What are the trends out there that are influencing it?
Abhas Ricky: Yeah, sure. So, it’s funny, I just came off a panel talking about this as well. So, there are three primary trends that we see. Number one, there’s a recent report that came out from McKinsey would says in the next 18 to 24 months, up to 90, in that case, 91% of the organization surveying, they have a plan to implement modern data architectures. The definition of modern data architectures vary. But the two common things that have come out is one is a data fabric. They need that because they want to have a unified layer for security governance [inaudible 00:03:58] a governance.
But then the second one is data mesh, because they want to have the ability for their data practitioners to treat data like a product. For simplistic terms, what we’re talking about is having Lego blocks that you can plug and play, and accelerate use cases, and go from delivering hundreds of use cases back in the day to a thousands of use cases. You do that at a price point of view choosing. But summarizing all of that is also the lake house trend that we’ve all seen coming through, which in simple terms, is multi-function analytics or life cycle analytics at scale.Tthat’s something that we as Cloudera or Hortonworks have been doing that for the better part of the decade. But it’s just become mainstream, and there have been more feature function capabilities that have been added to that.
Daniel Newman: So, in terms of as you’re sort of seeing all this proliferate, we’re seeing architectures change. That was a big theme of the day. I keep referring back to Pat telling us all our cloud sucks. So, here we are, we’re at this event. You’re thinking about this, you said messaging is sort of your focus.
Luke Roquet: Yeah.
Daniel Newman: When we talked about this as a sales guy, how are you of messaging the story of the build-out here, and then taking it from here to implementation for the customers?
Luke Roquet: Yeah, it’s a good question. So, I think part of the challenge for Cloudera is we’ve always offered industry-leading tech specifically for neighboring the hybrid world. But the way we talked about it didn’t resonate with our customers. That’s because we were trying to create our own terminology. The reality is the industry has convalesced around data fabric, data lakehouse, data mesh. For whatever reason, we had shied away from those terms. I think part of it is because the industry itself gets confused when it talks about those terms. But what we’ve decided to do is embrace what the industry’s talking about.
Clearly define what it is, and how our customers can implement it, but then also to help our customers in that journey, because there’s so much confusion around it. Specifically when you get to data mesh. Data mesh is a topic that means a lot of things to a lot of people. There are clear guiding principles, but in terms of how it’s implemented, and how customers are successful with it’s definitely a combination of people, process, and technology and probably most so, people and process. So, we’re working with our customers to help them enable that future reality.
Patrick Moorhead: So we use modern data architecture, throw it around, especially pundits like myself. But you’re on top of it upfront with customers, and actually doing this. Can you talk about some use cases, or some implementations on a so-called modern data architecture? How does that look?
Abhas Ricky: Yeah, sure. So, I’ll give it a try first. So, if we look at the people, process, technology, the framework that we have to understand is people includes everyone who was a data practitioner to a data builder, to a data owner. Processes that are all things about security, governance, documentation, all of those pieces. Then you have the technology there of. So, for majority of the customers data mesh is that technology, and the ability to take decisions in a proficient fashion using that technology as a framework is data fabric. So, majority of the organizations will start with a data fabric first, and they will start attacking majority of their fundamental use cases. For a retailer that can be Customer 360 because recommendation engines are super important for them for coupon management and/or pricing abilities for a manufacturer, preventative maintenance on the shop floor is even more critical than some of the other use cases.
They want to have the ability to get a better understanding of lineage, governance, and the associated lineage, which comes with the third party integration across multiple teams. But then when you come to data mesh, I think it’s still early in terms of implementation. There’s definitely a little bit of mesh washing going around. Every vendor out there is having a flavor of data mesh, they have a definition for that. But as I said, use cases that get accelerated because of data mesh can range all the way from a pharmaceutical, or a health provider having the ability to innovate faster, and also have pricing mechanisms real time. To also get into financial services companies who want to build a business within a business because that’s what disoriented domain architectures allow you to do.
The goal is every LOB IT analyst can then understand the terminologies and the requirements for their [inaudible 00:08:36] and build applications thereof fast enough through a simple platform data services, and data services API construct, rather than having to build it all over again and rewrite the code and refactoring. But the net out of that is the two value adds that these capabilities or architectures have. Data mesh definitely is time to market, and it significantly improves your service. Data fabric is definitely in risk reduction play, and definitely improves on cost reduction, their of as an outcome as well. Do you want to add something?
Luke Roquet: Yeah, it’s just important to note because there’s confusion about it. Customers wonder, should I have a data fabric, a data lakehouse, or a data mesh?
Daniel Newman: Right.
Luke Roquet: It’s not an “or” question.
Daniel Newman: Yeah.
Luke Roquet: It’s an “and” question, right? So, all three of them work interconnectively, but they’re focused on different personas in an organization. So, the data fabric conversation is really targeted towards the CTO, chief security officer, who are really focused on governance, secure security controls, audit lineage. They want to make sure that fabric’s in place to manage the ecosystem. The lakehouse is geared towards the domain practitioner, who doesn’t really care about everything else in the ecosystem except their data domain, and serving up business applications, data applications on their business data. Then the third is the data mesh, which is really more of a CDO conversation. It’s about how do we take enable data to transform our business? How do we monetize the data we have? How do we let different lines of business run autonomously? So, these are not mutually exclusive terms. They’re terms that our customers can, and do run together.
Abhas Ricky: The best way to think of that is you have the platform, then you have the data fabric with observability lineage replication capabilities, then you have the lakehouse as Luke mentioned, which has all the data services and the engines for a life cycle analytics. Then you have data mesh on top of that. That’s a visual representation to think of when you were to consider the total stack for any use case there of.
Daniel Newman: It’s the abstraction of the IAS pass SaaS services. This is your data version abstraction of the different data layers. You should draw that and use it for product.
Abhas Ricky: We have it, actually. It’s like that.
Daniel Newman: We missed that one. I didn’t see that. But let me ask a strategy question, since you have the strategy, you’ve got mesh, you’ve got lakehouse, you’ve got fabric, which by the way, I sometimes even feel like people use these somewhat interchangeably. So, you’ve given us how you explain them. How do you get this to land? How do you get customers to say, “A, we get it and B, Cloudera is the company we want to get it from?”
Luke Roquet: Yeah. I think because we adhere to what the industry analysts talk about in the definitions, we don’t try to go in charter. Here’s Cloudera’s definition of a fabric lakehouse mesh. When we talk about fabric, Forester has a very well defined pizza box of, What is the data fabric? So, we just layer on what we do on top of that defined data fabric. Same with we use Gartner’s definition of lakehouse. They have a very defined what is a lakehouse, what are the components, what makes a lakehouse? We layer onto that, and data mesh, again, there’s the core four principles of a data mesh that are widely accepted in the industry. But then how that looks is still, as I mentioned earlier, it’s still morphing, right?
So, we talked to customers about data meshes. It means a lot of things. We spend a lot of time here the last two days talking to customers about, “When you say data mesh, what does it mean to you?” How we enable a mesh for a bank which wants everything to be centralized, and for a manufacturer which wants every LLB to have its own data platform that works distinctively. So, data mesh is still one that, again, it’s not Cloudera forcing a definition on the market. It’s understanding the key principles that are established by the market, and then understand how our customers want to implement that so we can build the technology to enable it.
Abhas Ricky: I’ll add to that, which is, there was another part of your question, which is, “How do you land the message, and what’s the differentiating factor for Cloudera?” So, for lakehouse, for example, first of all, we have integration with the streaming site, and the CML as well. It’s purely open source because it’s supported by Iceberg. But even in the data mesh space, a lot of people will say, “Oh, it’s all about a mesh catalog.” Some people will say, “It’s much more than that because I want to use this as part of the data fabric as a technology layer.” So, one of the things that we’re saying is that; A) we’re agnostic to the Cloudera provider for your choice. B) were agnostic of the platform, and or workload deployment form factor of each choice because you have the ability to deploy wherever you want, at the price point that you want.
Then lastly, if you combine all of those pieces, we want to make sure that you have the ability to not only accelerate your use cases through the deployment form factor, but also you can integrate it with your existing systems. It’s not a rinse and replace mechanism, it’s just an ability to accelerate use cases there of.
Patrick Moorhead: Yeah, so, look, there’s a lot of moving pieces going on here, because not only are you transitioning from on-prem to CDP Cloud, you also have CDP One.
Luke Roquet: Yeah.
Patrick Moorhead: That also hits a broader activity. At the same time you’re educating people on difference between fabric and lakehouse, and every variation in between. You don’t necessarily have the industry agreed. Now, we have our own definitions.
Daniel Newman: Yes.
Patrick Moorhead: But we’ll take that Forester and Gardner have good definitions, too. But no really, hey, I really appreciate the time that you’ve spent with us and if nothing else, I’m hoping, and I know this will give your customers, your partners, a better idea of how you make the decisions that you make, and a peek into the future of where we’re looking when it comes to modern data architecture. We’d love to have you guys on the show again, to give us an update maybe six months, maybe a year.
Tell us how it’s going. We had a great conversation with Rob Beardon, and it’s amazing what the company has accomplished in the last two years. He shared some financials live here, which I was really happy that he did that.
Daniel Newman: He doesn’t have to.
Patrick Moorhead: No, he doesn’t have to. But it’s good to see that even in a time where you’re coming in to make those investments, to go cloud, and to go SaaS, you’re still a very profitable company. So, that looks great for the future, and looks like huge opportunities for the company. So, thank you for coming on the show.
Luke Roquet: Yeah, thank you for having us.
Abhas Ricky: Thank you for having us, and thank you for hosting.
Patrick Moorhead: Thank you very much. Yeah. So, this is Pat Moorehead with the Six Five signing off here with Luke, and Abhas, and my smiley and beautiful co-host Daniel Newman, signing off here from Evolve New York City Live. Take care.
Author Information
Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.
From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.
A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.
An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.
