AI is becoming the rising tide that lifts a lot of boats, and the data storage industry is being carried along with the surge. Storage is moving from its past role as a background service, to the forefront of an enterprise data-platform strategy, and this is shaping the market response from major storage providers such as Dell Technologies Inc. as organizations increasingly turn to AI for driving business value.

AI demands advanced, high-performance data platforms solutions to ingest, store, manage and query data. This has led Dell to design offerings for this new reality. While storage can be overlooked in conversations about AI, enterprises are realizing that storage platforms with flexible data management and scalable performance can make a difference in the deployment of AI initiatives.

The recent “Making AI Real With Data,” “Future of Data Platforms” and “Oceans of Data” broadcasts on SiliconANGLE Media’s theCUBE underscored how storage technologies underlying data platforms are changing to meet customer needs for AI deployment. It is a transformation that is reshaping the role of storage architecture in IT today, as organizations leverage data for key AI initiatives.

“Our research indicates that every layer of the technology stack — from compute to storage to networking to the software layers — will be re-architected for AI-driven workloads and extreme parallelism,” wrote SiliconANGLE’s Dave Vellante and David Floyer in an analysis of a changing technology world. “We believe AI demands advanced, high-performance storage solutions.”

This feature is part of SiliconANGLE Media’s exploration of Dell’s efforts in enterprise AI and how its infrastructure solutions are evolving to meet rising demands. (* Disclosure below.)

Powering scientific discovery with AI and unstructured data

One of the most compelling examples of Dell’s unstructured data solutions in action can be found at Oregon State University’s College of Earth, Ocean and Atmospheric Sciences (CEOAS). Researchers there are harnessing AI to accelerate scientific discovery in oceanic and earth sciences, transforming how they process vast amounts of unstructured data. Dell’s advanced infrastructure underpins this work, enabling real-time analysis of critical environmental changes. 

“We really do focus on how the planet is being changed by the things that we do and the way we interact with it,” Christopher Sullivan, director of research and academic computing at CEOAS, told theCUBE. “We use a lot of data, obviously, to help us do that.”

The challenges for CEOAS mirror those faced by enterprises: massive volumes of unstructured data, diverse computational needs and the demand for real-time insights. By working with Dell, the university has built out flexible platform technologies tailored to specific scientific questions. Different data types require different infrastructure configurations, and Dell’s ability to support this variety allows the research teams to advance their mission.

As Sullivan noted, before AI, researchers were “data-rich and information-poor.” Today, metadata tagging, annotation and governance tools embedded in Dell’s infrastructure help turn unstructured content — from ocean sounds to plankton imagery — into actionable intelligence. Real-time processing at sea, powered by Dell PowerScale and PowerEdge systems with Nvidia GPUs, now allows scientists to analyze billions of data points on the spot rather than waiting until they return to shore.

These breakthroughs are not just about marine science. They demonstrate how Dell’s storage and compute solutions enable organizations to manage petabyte-scale workloads, apply AI workflows efficiently, and maintain data governance — the same imperatives enterprises face in industries from finance to healthcare.

Advanced platforms for enterprise storage

Whether in ocean science or enterprise AI, the challenge is the same: Too much data, too little time. Dell’s answer has been to engineer storage and compute platforms — most notably its Dell Data Lakehouse — that can unify silos and accelerate performance at scale.

In March of last year, Dell announced general availability of Dell Data Lakehouse, a hardware and advanced software platform that combines distributed query processing, powered by Starburst Data Inc., with scalable S3-compatible object storage. Designed to eliminate data silos and enable secure, federated querying, the solution accelerates time to insights by up to 90% and helps organizations centralize data more intelligently, according to Dell. The goal was to provide customers with ease of use, scalability and an ability to modernize, according to Vrashank Jain, product manager at Dell, who spoke with theCUBE.

“It gives us two really cool benefits,” he said. “One is it’s able to federate your query, which means you can go and access and process data regardless of location, no matter where it’s stored. All of your data silos now are certainly connected. And the second thing it does really well is it’s able to query data in a really high-performing manner on top of your data lake directly.”

Connecting silos and providing a measure of interoperability are key parts of Dell’s storage strategy. Dell has married the capabilities of Data Lakehouse with the solutions provided by PowerScale, the company’s scale-out, high-performance storage offering. This allows companies to ingest significant amounts of data, manage it and execute queries against the metadata that surrounds it for feeding real insights into AI applications. To accomplish this, Dell has made Apache Spark, a unified engine for large-scale data analytics, interoperable with Lakehouse.

“Our ethos with Lakehouse is we want to partner, we want to work with whatever the customers have and never have lock-in anywhere,” Jain said. “You can use your Spark engine to both ingest into our Lakehouse, as well as read data that’s in the Lakehouse with your engine.”

Open solutions for data analysis

Dell has extended its interoperability further through a partnership with Starburst. Dell selected Starburst as the federated query engine for the Data Lakehouse, enabling users to harness AI-driven insights without needing to move data. This is an attractive option in a world where data resides in hybrid or on-premises environments. Enterprises are looking for open, composable solutions to maintain control over their data platforms, and the Dell/Starburst collaboration provides another example of how open data lakehouse architectures are becoming the foundation for AI and analytics workloads.

“Starburst’s collaboration with Dell Technologies significantly validates its enterprise appeal,” said theCUBE Research’s Rob Strechay, in a recent analysis. “Starburst’s federated query engine allows enterprises to analyze data in place, reducing latency and cost, according to their reports, unlike traditional data warehouses that require ingestion, replication and transformation. This approach is particularly valuable in sectors with stringent regulatory and compliance requirements, such as financial services, healthcare and government.”

Starburst’s data lake analytics platform lets users connect to any data source, and Apache Iceberg’s open table format allows connectivity from a data lakehouse to any query engine. This helps transform cloud object stores into agile, AI-ready data layers. Dell has capitalized on this approach by leveraging Apache Iceberg as part of its joint work with Starburst. Originally developed at Netflix Inc., Iceberg provides the open foundation for Dell’s “Icehouse” architecture — a data lakehouse implementation that can be deployed across on-premises, hybrid and multicloud environments to meet diverse data management and analytics needs.

“An Icehouse is really just a data lakehouse where Iceberg is a first-class citizen,” according to Jain. “That means being able to continuously ingest data into Iceberg to finally help people modernize to the latest format.”

Solutions for structured and unstructured data

Modernizing to the latest format involves being able to leverage structured and unstructured data. Dell’s enhancements to its storage portfolio have included new solutions for both. The company has worked with its partners, notably Nvidia Corp., to integrate and manage data formats. Nvidia’s NeMo Retriever microservices sit on top of Dell’s PowerScale and Project Lightning to accelerate structured and unstructured data ingestion and Retrieval Augmented Generation, or RAG, workflows for multi-modal AI operations. RAG has become particularly important in the use of unstructured data, according to Jain. 

“We know that RAG is becoming pretty much the dominant use case in the enterprise,” he said. “It’s about discovering the unstructured data that you have across your landscape and being able to parse some of those complex data types, being able to find where that is with a ton of metadata around it. That’s becoming the secret sauce.”

Dell’s collaboration with Nvidia has also positioned storage as a central piece in driving performance for next-generation AI workloads. Last year, Dell PowerScale became the world’s first Ethernet-based storage to be certified for Nvidia DGX SuperPOD.

“You have to be able to scale easily, and you have to think differently from what was being done in the past,” said Premal Savla, senior director of product management, deep learning systems at Nvidia, during an interview with theCUBE. “What Dell brought to the table was a lot of capabilities around what it’s already been doing with the enterprise, and that allowed us to partner together to create a solution with Ethernet-enabled storage. The way to think about it is Nvidia DGX SuperPOD is a Ferrari, and to keep that Ferrari running on the racetrack, you need that data and that power, and that’s what PowerScale brings for us.”

Education customers for a shifting AI landscape

As Dell pursues its AI enterprise strategy, it is finding that customer education is a necessary and important part of the process. AI’s value proposition, its underlying science, and interoperability with other enterprise platforms are often key questions in the minds of many clients.

“We have to continue to talk to customers about the different aspects of the technology,” Savla said. “What is AI? How does storage play with AI as we move forward? As every new technology has come into market, there’s a place for education that comes in the front-end and that’s a big part of getting that technology adopted and making it easier to consume.”

To facilitate this process, Dell has focused on building a consulting services portfolio that keeps pace with the rapidly changing AI landscape. As part of the company’s announcements surrounding Dell Data Lakehouse last November, the company unveiled new services capabilities. These included optimization services for data cataloging and an ability to implement and orchestrate production-ready data pipelines, along with advisory consultations from Dell experts. 

“Dell has the vertical experts that you need to talk your data, to talk your language and walk through what that strategy should look like for your business,” said Elizabeth Carbone, senior marketing manager of unstructured data storage at Dell, in an interview with theCUBE. “I think that’s a real differentiator for us.”

Dell’s announcements around its storage portfolio underscore a central strategic objective for the company. It is managing to ride a massive wave of AI while continuing to serve its extensive customer base. That kind of balancing act would be a tall order for any company. With approximately 120,000 employees, 200,000-plus partners and nearly $100 billion in total revenue for fiscal 2025, Dell has emerged as a leading combatant in the battleground for enterprise AI. Its message is simple: Modernize now while adding what you need for AI.

“AI makes you think differently,” Savla explained. “The amount of processing power that it requires, the amount of data that is consumes is significant, so [customers] have to think through how this is going to be used as they move forward with whatever initiatives that they are taking with AI.”

To learn more about how Dell and Nvidia are teaming up to enable AI-ready platforms, unstructured storage and digital twins to accelerate AI initiatives, check out this exclusive eBook.

(* Disclosure: TheCUBE is a paid media partner for the “Making AI Real With Data,” Future of Data Platforms,” “Oceans of Data” events. Sponsors of theCUBE’s event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)

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