Open data architecture powers DoorDash’s real-time logistics and agentic AI ambitions
As enterprises race to support machine learning, agentic workflows and analytics on the same infrastructure, open data architecture has emerged as the dividing line between platforms that scale and those that stall.
At petabyte scale, logistics platforms can’t afford a monolithic data stack. DoorDash Inc. — one of the world’s largest real-time logistics companies — has spent 10 years building a foundation on open architecture principles: open storage, open compute and compute-agnostic design. The result is an infrastructure capable of serving consumers, merchants and delivery workers simultaneously, while increasingly powering machine learning features and agentic AI workflows, according to Vaibhav “VJ” Jajoo (pictured, right), head of data engineering, data platform and business intelligence at DoorDash.
“What we have learned over time is that the machine user is outpacing the human user in consumption of analytics data,” Jajoo said. “The ML features, the feedback loops to production services or the AI agent workflows are outpacing the analytics user. When you do that, you cannot adopt a monolithic environment, which is holding you back and not letting you enable new use cases on top of it.”
