Jun 23, 2025

SHARE Powering AI at scale requires an infrastructure that can rapidly unlock the value of data. This is leading to a world where data lakes and graph databases are becoming valuable tools for driving analytic workloads. Philip Rathle at Neo4j, Tanya Bragin at ClickHouse and Nikita Shamgunov at Neon talk with theCUBE about analytic workloads. As this infrastructure matures, connectivity will be key, according to Nikita Shamgunov (pictured, right), chief executive officer at Neon Inc. “My observation is that there are three separate worlds,” he said. “One is the lakes for data and analytics. The other one is online transaction processing, and we tend to think about OLTP as databases powering financial transactions like those big systems, and then the world of applications. All of them are connecting and they’re connecting to drive businesses and enable AI. Every application still needs a database.” Shamgunov spoke with theCUBE’s John Furrier and Dave Vellante at theCUBE + NYSE Wired: Robotics & AI Infrastructure Leaders 2025 event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. He was joined by Tanya Bragin (center), vice president of product at ClickHouse Inc., and Philip Rathle (left), chief technology officer at Neo4j Inc., and they discussed the latest trends in application development and data lake architecture. Unification layer for analytic workloads The cost to build applications has fallen significantly, attributable to services offered by platforms such as Replit , Lovable and Bolt , according to Shamgunov. These are enabling vibe coding as a natural language interface with AI for application development, setting the stage for a unification layer that can drive velocity. “Now we have high velocity application development that is nothing that we’ve seen before,” Shamgunov said. “As those applications are getting more sophisticated, now we need that connectivity with the applications and application platform. You need an OLTP database to power the application, and then you need to have all the telemetry, observability and fundamental data to complete that cycle. I think we see an acceleration in the unification of all those three.” What role will the database market play in providing that unification layer? Rathle believes that a graph database management system can be a major factor in providing the connective tissue necessary for application deployment, as seen in a use case involving the Swedish fintech Klarna Group PLC . “Every AI application needs not only LLMs, not only vectors, but access to the real-time web and an enterprise knowledge graph that’s built with the database that’s designed from the ground up effectively for graphs,” Rathle said. “The biggest illustration I have for this is Klarna, who are known for those of us in enterprise AI circles. This application that they built homegrown provides intelligence for their employees, allowed them to unplug 1,200 SaaS apps from Salesforce to Workday by repatriating data from all the different silos into a connective tissue.” Industry practitioners are also seeing unification within data lake architecture. As data-driven analytical applications become more common, the open-source database management system ClickHouse is seeing combined support for observability and business intelligence, according to Bragin. “What’s interesting to me is that what we’re also seeing in an evolution of a single unified architecture for a data lake, not only for data warehousing and traditional BI, but also for observability,” Bragin noted. “Traditionally these stacks have been very separate … because historically it’s been two very different vertically integrated commercial tools, say Splunk for logging on the observability side and Oracle on the BI side. Now if you look at players in this space, both open source and commercial, everything is on top of object store, everything is evolving to open data lakes absolutely as a unifying layer.” Photo: SiliconANGLE