Honing business data with AI real-time analytics – SiliconANGLE

A ground-up overhaul is currently underway in the realm of data architectures, and generative artificial is at its heart. Traditional databases no longer suffice; instead, modern applications demand enhanced data structures capable of accommodating AI integration seamlessly.

With data volumes rising exponentially, is real-time analytics the key to honing that binary gold to unlock new value points for businesses?

“It’s not just that there is a new category of applications, like chatbots and other things, that are being created, but every application now is getting enhanced with AI,” said Venkat Venkataramani (pictured, right), co-founder and chief executive officer of Rockset Inc. “The demands on the data architecture that allow people to build these AI applications quickly and efficiently at scale is the most important need of the hour.”

Venkataramani and Kyle Weller (left), head of product at Onehouse, spoke with theCUBE Research analysts Dave Vellante and John Furrier at the “Supercloud 6: AI Innovators” event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed empowering modern applications with real-time analytics, leveraging a search analytics database tailored for AI augmentation, and the imperative for agile and scalable data architecture.

Navigating uncharted territories: The rise of universal data lakehouses

Universal data lakehouses serve to unify data from diverse sources, catalyzing AI innovation as a result. The challenge, however, lies in bridging the gap between transactional databases, event streams and vast data lakes, necessitating a comprehensive solution.

Onehouse’s universal data lakehouse aims to streamline this process, enabling organizations to harness the full potential of their data without succumbing to the complexities of disparate systems, according to Weller.

“Everyone is trying to build better data structures and data structures and systems that can be ready for building AI applications,” he said. “You need to bring in change data capture or even just pull out any of the data that’s inside of your swamp of a data lake and add structure to this data governance, to this data performance optimization so it’s analytics-ready and ready train your ML models, build and create the vector embeddings, and store these in any downstream system that you need.”

As the industry transitions toward intelligent applications and real-time analytics, the traditional paradigm of data platforms is evolving toward decoupling data from storage, enabling unparalleled agility and scalability.

The “sixth data platform,” characterized by its ability to power intelligent apps and unify disparate data sources, represents a paradigm shift in cloud computing. By embracing AI-driven insights and contextual behavioral analysis, organizations can unlock new dimensions of efficiency and personalization in their operations, according to Venkataramani.

“If you look at these modern AI applications, the data architecture largely has two very important sides to it,” he said. “There’s this one side where you’re training, you’re either building new models or you are fine-tuning existing models with your datasets. There’s this whole infrastructure around how to aggregate all this data and use it to build efficient models.”

For businesses embarking on the AI journey, Rockset and Onehouse offer a compelling vision of streamlined data architecture and enhanced AI integration. Rockset’s unified search and analytics database promises accelerated time-to-market and scalability, while Onehouse’s universal data lakehouse simplifies the complexities of data management and governance, according to Weller and Venkataramani.

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of the “Supercloud 6: AI Innovators” event: 

Photo: SiliconANGLE