Transforming the Data Landscape: Understanding the Intersection of AI and Data Platforms
Saket Saurabh, co-founder and Chief Executive Officer of NextLeft, joins host John Furrier at theCUBE NYC Wired edition in a compelling session on the evolving role of data platforms amidst the rising influence of generative AI. In this insightful conversation, they explore the burgeoning trends around company mergers and the implications for technology enterprises navigating the newly forming AI-driven landscape.
Saurabh brings expertise as a leader in the data integration space, analyzing the recent moves by major players such as Confluent, dbt Labs, and Fivetran, with key insights provided by theCUBE's research and hosting team. The discussion reveals how these transformations indicate a shift from traditional analytics models toward more integrated real-time data solutions to harness the power of AI applications.
Key takeaways include Saurabh's analysis of the challenges companies encounter in merging analytics with generative AI solutions and the strategic moves needed to remain competitive. According to Saurabh, a critical understanding of platform convergence is essential, highlighting the necessity for integration solutions that facilitate seamless data flow across multiple environments. This deep dive unravels complex market dynamics and provides clarity on the future direction of data technologies.
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Saket Saurabh, Nexla
In this episode of theCUBE's Mixture of Experts series, John Furrier interviews Ginniee Singh, an advisor to Spiked AI. Broadcasting from the New York Stock Exchange Studio, this discussion delves into the evolving role of AI in business, focusing on how Spiked AI aims to revolutionize sales processes and address cognitive workload challenges.
Singh draws from their background in high-tech and advisory roles to discuss their passion for mentoring Gen Z and the transformative potential of Spiked AI. With insights from theCUBE Research and video host John Furrier, the conversation highlights Singh's expertise in integrating advanced analytics into traditional sales models.
The discussion offers valuable insights into reducing the cognitive workload in sales and the importance of real-time Customer Relationship Management updates. According to Singh, embracing tools that merge technology and human interaction can significantly enhance business operations, especially for emerging ventures such as Spiked AI.
>> Hello, welcome back to theCUBE at our New York Stock Exchange CUBE Studio. Of course, we have our studio on the West Coast in Palo Alto, connecting Wall Street and Silicon Valley. I'm John Furrier, host of theCUBE. It's our Mixture of Expert series. We go out to our CUBE alumni and experts to bring them in to unpack some of the trends, the news. We've got a returning CUBE alumni, many-time CUBE alumni, Saket Saurabh, who's the co-founder and CEO of NextLeft. Saket, thank you for coming back and making the time on short notice to hit some of the news around the big data world, the data platforms, which we've talked about in the past. Thanks for coming on.>> Yeah, John. Thank you for having me. It's an exciting time here, so much happening.>> We just had a week at Dreamforce, which is Salesforce's event. And obviously, agentic is the hottest thing. They're now calling Agentforce. You're seeing companies like Confluent, rumor is they're up for sale, not making the streaming market. Obviously, streaming data is super important, but the role of databases, plural, in this new data layer is super important. And so, you start to see the lines forming of the pre-SaaS era databases and now the post-SaaS, now agentic and generative AI data platforms emerging. And one of the things that we are covering, and it's still ongoing, is the news of two companies merging. And you've got dbt Labs, companies both we've covered in depth as well. Really, the pioneers in pushing the envelope on streaming data, getting data ready for the data lakes. You got Confluent, as I mentioned earlier, but you're starting to see the consolidation. Those two firms is a significant milestone or indicator of where the market is. I want to get your thoughts on this. You wrote a very interesting LinkedIn post, which got our attention. They turned into competition. They're coopetition, frenemies, now enemies. The pieces are moving on the board. Can you explain from your view what's happening? Again, Confluent not growing as fast. There's rumors that they're going to try to sell and Fivetran and dbt merging. What does this mean? What does it pointing to?>> So, I think one of the things to remember is that overarching everything, every action is the AI revolution that we are going through. And every company is really thinking their future with AI being the dominant force. So, the applications are what we are all serving as technology companies and AI applications is the main driver. And to build an AI application, you are taking the model and passing information to that, that's the context. So, if you are not going to be the winner as a model company, which is very hard of course, then you have to be the one building the context, owning the context. And that is basically driving all the companies to say, "Well, how do I really build that context? I need to stitch data from many places." And that's really brought the heat or the focus on integration companies, that's why Informatica got acquired. And if you look specifically at FiveTran and dbt, FiveTran $6 billion last valuation, dbt $4.2 billion. So, these are giant companies by valuation, right? But dbt is solving the problem of creating and managing and running SQL. And that itself is useful, but it is not a $4 billion worth useful. And I think... Yeah, so->> So, I want to ask you, because one of the things you brought up in your post is they can't go alone. Fivetran needs what dbt had and dbt with FiveTran has. But the comment that jumped out on me was that the modern data stack is dying. And the point was everything was built for analytics, you mentioned that. So, I want to talk about that because analytics and gen AI are merging, so it's hard to be gen AI when you were optimized for analytics and it's hard to be analytics if you're optimized too much for gen AI. It's a Venn diagram, right? So, I want to ask you, with the platform wave, with integrations, what I'm seeing is, and I'd love to get your reaction on this, so I agree with you by the way on that, but I want to get your reaction. It seems like what Salesforce and what enterprises are trying to do is be more like a Palantir, to be more of a platform where they can get a lot of different data. I use Palantir as a random example, but they claim to have that kind of view. Is that accurate? Is that on point? Is it platforms have to have everything? And if so, what is the new modern stack? I mean, their modern legacy, a term we've been using on theCUBE lately, if it exists, modern legacy. Pre-gen AI, I guess that's modern legacy, but the new modern seems to be platforms. What's your reaction to that? Thoughts?>> Yeah, platform's certainly the new modern. I would differentiate slightly the fact that Salesforce, as a business, as a CRM and they are trying to figure out how they become the platform. Fivetran, Snowflake, Databricks, these our data platforms from the beginning. But the big change from the analytics world to the AI world is analytics was about dumping the data in one place and building your dashboards. AI is about stitching a lot of data in real time and feeding it to the AI. And that sort of changes slightly because now we are not just talking about data, which is in tables, but documents and unstructured data and all of that together. So, we have moved up from simply data, which is structured to context and context is a lot more beyond that. And context is everything that you understand that you can want the AI to know, so it can help you solve your problem.>> Yeah, context was a big theme in agents. So, how does this translate to agentic? Because context is something that we see as super valuable in the role of agents, plural. Your thoughts?>> Yeah, two things on the agentic side. So, now we are talking about more converged integration, not just about dumping data, which is one pattern. So, think about MuleSoft was one way of doing integration. Informatica, ETL was one way of doing it, Fivetran was one way of doing it. We're talking convergence is needed to really feed AI. When it comes to agentic, the other part that also becomes important is being able to take actions. It's not just about you dropping the data here, but based on this data, you want to take action, you want to trigger some workflow and all of that stuff. So, there's another part to agentic.
So, agentic has two parts in my mind that are really, really important. One is give the context to AI because AI can reason for you, it can plan for you, you can figure out the steps. Great. Then, it can execute those steps for you. And to execute the steps it needs another capability, and if you look at the Fivetran announcement, they also talked about a prior acquisition called Census, as the hinge that will help them maybe on that front.>> Yeah. So, on the mergers, less about innovations, more about survival. What do you think that the resulting entity will look like? Will it be the same together? Will there be carnage? Will there be any synergies? Where's the overlap on the product portfolio? What's your thoughts?>> Yeah, I think the slight precursor to the story is that both Snowflake and Databricks went ahead and announced integration solutions in the prior conferences this year, which means that they were getting into that Fivetran territory that we will do the pipelining. And traditionally, the data storage and the pipelining companies have been good friends, but not necessarily competed at that level. So, what Fivetran has done is like, "Okay, we will have the integration space and we'll own dbt," which is a very important part of transforming data. And then, they're coming out with their own data lake as well. So, I think what is happening from that perspective is that the Fivetran survival question was that, for me to be relevant, I need to have all of these pieces. I cannot be just the pipeline itself, but then I want to be the data store as well. I want to be the data lake. But then, that I think flips it back to the other companies to say, "Well, is Fivetran a full-on competitor to you?" Because now they are coming up and saying, "Store your data in me. I'm not just a transfer mechanism.">> So, obviously, in Silicon Valley, I talk to a lot of folks, and obviously, here in New York now on the financial side. Consolidation like this is often viewed as a positive. On one side of the coin, you can say, "Hey, you put them together and make it better," that's more of a private equity kind of vibe. But really, on the Silicon Valley side, you'd be like, "Okay, there's no buyers." So, my feeling is that the music's kind of stopped, the musical chairs is happening. Who would buy these companies? Databricks, Snowflake, Salesforce, Palantir? So, it feels like to me a little bit of the musical chairs, the music's kind of stopped. So, they kind look at each other and say, "Why don't we just combine both in the same portfolio of a16z?" So, it's like, okay, better together. That's a sign that there might not be a buying market for these data companies pre-gen AI and you're out competing. What's your thoughts on this?>> Yeah, I think when you're a $5 billion company, the set of companies that can buy you for a Fivetran or dbt is very, very small and it has really fit in. And if those companies are trying to... Or your primary partners are typically your acquirers. And if they're competing with you, then it's a hard one to expect. I do think that it's good in one way to consolidate, but let's put the customer first. The customer is, in many companies, in many enterprises is not just on Snowflake, they're also on Databricks. They use both the platforms in many cases. They're not just on AWS, they're also on GCP or Azure. So, the fact that each vendor has their own integration platform now, AWS and GCP. And now, Fivetran was one neutral is like, "Hey, no matter what you do, I can cover for you."
I think the customer might end up questioning like, okay, so now in future, fast forward, is Fivetran integration or pipelining going to be first class for their own data lake and then not as first class for others? Where will they innovate first? And maybe it's cheaper if you use Fivetran with their data lake and not if you use it with somebody else. I think there is going to be, for the customers, a cost question as to how this consolidation impacts them from a pricing perspective, how it also is an innovation question, how this consolidation maybe slows down the innovation because they need... And large enterprises will need more than one platform, that's definitely the case. So, I think that's where the real question for us would be. And my guess is customers may not necessarily be happy with->> Well, I really appreciate you coming in on this breaking analysis of these moves. They're reading the tea leaves, but I really appreciate your commentary. How's business going with you? NextLeft, you're in the space, you're in the integration, the new platforms are emerging, large-scale supercomputers are booming, NVIDIA, AMD. I mean the infrastructure is thundering away with power, like horsepower.>> Yeah, I mean, as I said, data has become super important. Connecting and orchestrating data to make context is super important. So, we are right in the center of it. We were one of the companies to do converge, so we support applications, documents. Now, we just announced support for video files to be processed in our system, all adding to the context. So, I think a lot of important problems to solve and enable, ultimately, the end user. We are the means to the end for them. And for us, certainly we watch the market very, very closely, but staying true to the customer. And I think giving a very viable alternative to customers and we feel pretty good about that.>> Saket, thank you so much for coming in remote. Appreciate it. Thank you. Good to see you. Hope everything stays well.>> .>> All right. Thanks for coming on. Okay. I'm John Furrier with theCUBE. We're breaking down the news as the data market continues, the mixture of experts are weighing in here on theCUBE in the NYC Wired community. As the world's changing, architectural shifts are causing customers to rethink and repackage their infrastructure, their data infrastructure, and how applications in the context is going to be a very, very big conversation around the context of data. Contextual data in the age of agentic will be a super important topic. We'll certainly revisit it many, many times in the next year, of course, and many more to come. I'm John Furrier, your host. Thanks for watching.
>> Hello, welcome back to theCUBE at our New York Stock Exchange CUBE Studio. Of course, we have our studio on the West Coast in Palo Alto, connecting Wall Street and Silicon Valley. I'm John Furrier, host of theCUBE. It's our Mixture of Expert series. We go out to our CUBE alumni and experts to bring them in to unpack some of the trends, the news. We've got a returning CUBE alumni, many-time CUBE alumni, Saket Saurabh, who's the co-founder and CEO of NextLeft. Saket, thank you for coming back and making the time on short notice to hit some of the news around the big data world, the data platforms, which we've talked about in the past. Thanks for coming on.>> Yeah, John. Thank you for having me. It's an exciting time here, so much happening.>> We just had a week at Dreamforce, which is Salesforce's event. And obviously, agentic is the hottest thing. They're now calling Agentforce. You're seeing companies like Confluent, rumor is they're up for sale, not making the streaming market. Obviously, streaming data is super important, but the role of databases, plural, in this new data layer is super important. And so, you start to see the lines forming of the pre-SaaS era databases and now the post-SaaS, now agentic and generative AI data platforms emerging. And one of the things that we are covering, and it's still ongoing, is the news of two companies merging. And you've got dbt Labs, companies both we've covered in depth as well. Really, the pioneers in pushing the envelope on streaming data, getting data ready for the data lakes. You got Confluent, as I mentioned earlier, but you're starting to see the consolidation. Those two firms is a significant milestone or indicator of where the market is. I want to get your thoughts on this. You wrote a very interesting LinkedIn post, which got our attention. They turned into competition. They're coopetition, frenemies, now enemies. The pieces are moving on the board. Can you explain from your view what's happening? Again, Confluent not growing as fast. There's rumors that they're going to try to sell and Fivetran and dbt merging. What does this mean? What does it pointing to?>> So, I think one of the things to remember is that overarching everything, every action is the AI revolution that we are going through. And every company is really thinking their future with AI being the dominant force. So, the applications are what we are all serving as technology companies and AI applications is the main driver. And to build an AI application, you are taking the model and passing information to that, that's the context. So, if you are not going to be the winner as a model company, which is very hard of course, then you have to be the one building the context, owning the context. And that is basically driving all the companies to say, "Well, how do I really build that context? I need to stitch data from many places." And that's really brought the heat or the focus on integration companies, that's why Informatica got acquired. And if you look specifically at FiveTran and dbt, FiveTran $6 billion last valuation, dbt $4.2 billion. So, these are giant companies by valuation, right? But dbt is solving the problem of creating and managing and running SQL. And that itself is useful, but it is not a $4 billion worth useful. And I think... Yeah, so->> So, I want to ask you, because one of the things you brought up in your post is they can't go alone. Fivetran needs what dbt had and dbt with FiveTran has. But the comment that jumped out on me was that the modern data stack is dying. And the point was everything was built for analytics, you mentioned that. So, I want to talk about that because analytics and gen AI are merging, so it's hard to be gen AI when you were optimized for analytics and it's hard to be analytics if you're optimized too much for gen AI. It's a Venn diagram, right? So, I want to ask you, with the platform wave, with integrations, what I'm seeing is, and I'd love to get your reaction on this, so I agree with you by the way on that, but I want to get your reaction. It seems like what Salesforce and what enterprises are trying to do is be more like a Palantir, to be more of a platform where they can get a lot of different data. I use Palantir as a random example, but they claim to have that kind of view. Is that accurate? Is that on point? Is it platforms have to have everything? And if so, what is the new modern stack? I mean, their modern legacy, a term we've been using on theCUBE lately, if it exists, modern legacy. Pre-gen AI, I guess that's modern legacy, but the new modern seems to be platforms. What's your reaction to that? Thoughts?>> Yeah, platform's certainly the new modern. I would differentiate slightly the fact that Salesforce, as a business, as a CRM and they are trying to figure out how they become the platform. Fivetran, Snowflake, Databricks, these our data platforms from the beginning. But the big change from the analytics world to the AI world is analytics was about dumping the data in one place and building your dashboards. AI is about stitching a lot of data in real time and feeding it to the AI. And that sort of changes slightly because now we are not just talking about data, which is in tables, but documents and unstructured data and all of that together. So, we have moved up from simply data, which is structured to context and context is a lot more beyond that. And context is everything that you understand that you can want the AI to know, so it can help you solve your problem.>> Yeah, context was a big theme in agents. So, how does this translate to agentic? Because context is something that we see as super valuable in the role of agents, plural. Your thoughts?>> Yeah, two things on the agentic side. So, now we are talking about more converged integration, not just about dumping data, which is one pattern. So, think about MuleSoft was one way of doing integration. Informatica, ETL was one way of doing it, Fivetran was one way of doing it. We're talking convergence is needed to really feed AI. When it comes to agentic, the other part that also becomes important is being able to take actions. It's not just about you dropping the data here, but based on this data, you want to take action, you want to trigger some workflow and all of that stuff. So, there's another part to agentic.
So, agentic has two parts in my mind that are really, really important. One is give the context to AI because AI can reason for you, it can plan for you, you can figure out the steps. Great. Then, it can execute those steps for you. And to execute the steps it needs another capability, and if you look at the Fivetran announcement, they also talked about a prior acquisition called Census, as the hinge that will help them maybe on that front.>> Yeah. So, on the mergers, less about innovations, more about survival. What do you think that the resulting entity will look like? Will it be the same together? Will there be carnage? Will there be any synergies? Where's the overlap on the product portfolio? What's your thoughts?>> Yeah, I think the slight precursor to the story is that both Snowflake and Databricks went ahead and announced integration solutions in the prior conferences this year, which means that they were getting into that Fivetran territory that we will do the pipelining. And traditionally, the data storage and the pipelining companies have been good friends, but not necessarily competed at that level. So, what Fivetran has done is like, "Okay, we will have the integration space and we'll own dbt," which is a very important part of transforming data. And then, they're coming out with their own data lake as well. So, I think what is happening from that perspective is that the Fivetran survival question was that, for me to be relevant, I need to have all of these pieces. I cannot be just the pipeline itself, but then I want to be the data store as well. I want to be the data lake. But then, that I think flips it back to the other companies to say, "Well, is Fivetran a full-on competitor to you?" Because now they are coming up and saying, "Store your data in me. I'm not just a transfer mechanism.">> So, obviously, in Silicon Valley, I talk to a lot of folks, and obviously, here in New York now on the financial side. Consolidation like this is often viewed as a positive. On one side of the coin, you can say, "Hey, you put them together and make it better," that's more of a private equity kind of vibe. But really, on the Silicon Valley side, you'd be like, "Okay, there's no buyers." So, my feeling is that the music's kind of stopped, the musical chairs is happening. Who would buy these companies? Databricks, Snowflake, Salesforce, Palantir? So, it feels like to me a little bit of the musical chairs, the music's kind of stopped. So, they kind look at each other and say, "Why don't we just combine both in the same portfolio of a16z?" So, it's like, okay, better together. That's a sign that there might not be a buying market for these data companies pre-gen AI and you're out competing. What's your thoughts on this?>> Yeah, I think when you're a $5 billion company, the set of companies that can buy you for a Fivetran or dbt is very, very small and it has really fit in. And if those companies are trying to... Or your primary partners are typically your acquirers. And if they're competing with you, then it's a hard one to expect. I do think that it's good in one way to consolidate, but let's put the customer first. The customer is, in many companies, in many enterprises is not just on Snowflake, they're also on Databricks. They use both the platforms in many cases. They're not just on AWS, they're also on GCP or Azure. So, the fact that each vendor has their own integration platform now, AWS and GCP. And now, Fivetran was one neutral is like, "Hey, no matter what you do, I can cover for you."
I think the customer might end up questioning like, okay, so now in future, fast forward, is Fivetran integration or pipelining going to be first class for their own data lake and then not as first class for others? Where will they innovate first? And maybe it's cheaper if you use Fivetran with their data lake and not if you use it with somebody else. I think there is going to be, for the customers, a cost question as to how this consolidation impacts them from a pricing perspective, how it also is an innovation question, how this consolidation maybe slows down the innovation because they need... And large enterprises will need more than one platform, that's definitely the case. So, I think that's where the real question for us would be. And my guess is customers may not necessarily be happy with->> Well, I really appreciate you coming in on this breaking analysis of these moves. They're reading the tea leaves, but I really appreciate your commentary. How's business going with you? NextLeft, you're in the space, you're in the integration, the new platforms are emerging, large-scale supercomputers are booming, NVIDIA, AMD. I mean the infrastructure is thundering away with power, like horsepower.>> Yeah, I mean, as I said, data has become super important. Connecting and orchestrating data to make context is super important. So, we are right in the center of it. We were one of the companies to do converge, so we support applications, documents. Now, we just announced support for video files to be processed in our system, all adding to the context. So, I think a lot of important problems to solve and enable, ultimately, the end user. We are the means to the end for them. And for us, certainly we watch the market very, very closely, but staying true to the customer. And I think giving a very viable alternative to customers and we feel pretty good about that.>> Saket, thank you so much for coming in remote. Appreciate it. Thank you. Good to see you. Hope everything stays well.>> .>> All right. Thanks for coming on. Okay. I'm John Furrier with theCUBE. We're breaking down the news as the data market continues, the mixture of experts are weighing in here on theCUBE in the NYC Wired community. As the world's changing, architectural shifts are causing customers to rethink and repackage their infrastructure, their data infrastructure, and how applications in the context is going to be a very, very big conversation around the context of data. Contextual data in the age of agentic will be a super important topic. We'll certainly revisit it many, many times in the next year, of course, and many more to come. I'm John Furrier, your host. Thanks for watching.
