Nokia and Databricks completed a proof of concept for a unified, substrate-agnostic data platform aimed at supporting AI-driven autonomous networks. The test was meant to show how operators can simplify their data environments for real-time analytics at scale without rewriting code.
The setup targets a familiar telco problem: fragmented OSS/BSS stacks, multiple vendors, and mismatched data architectures. Nokia and Databricks are pitching a shared layer that can run across cloud environments or on-premises infrastructure while avoiding code rewrites.
Real-time performance management was the test case
During the proof of concept, engineering teams from both companies focused on real-time performance management. The work was intended to scale quickly to match tier-1 operator scale in the cloud.
Nokia and Databricks frame the AI argument
Oguz Sunay, CTO of AI and Autonomous Networks at Nokia, said teaming up with Databricks represents a big step toward building the data foundations required for next-generation autonomous networks. He said a common, flexible data platform across cloud environments can help operators accelerate AI adoption and build more efficient, resilient, and sustainable networks.
Nevash Pillay, Global Head of Telecommunications Industry at Databricks, said telecom operators need a more consistent way to use their data and that the collaboration shows how a unified data platform can simplify operations and unlock AI value across network domains.
Read for network automation teams
For operators building autonomous-network roadmaps, the useful part here is not the branding but the architecture: Nokia and Databricks are testing a data layer that is meant to span cloud and on-premises environments without rewriting code. That is the kind of implementation detail procurement and platform teams will benchmark against existing OSS/BSS and analytics estates.