Nokia and Databricks have successfully completed a joint proof of concept (PoC) demonstrating a unified, substrate-agnostic data platform aimed at supporting AI-driven autonomous networks.
The collaboration highlights how telecom operators can simplify fragmented data environments and deploy real-time analytics at scale, enabling faster decision-making, improved network performance and greater operational efficiency.
Addressing data fragmentation challenges
Telecom networks typically depend on hundreds of operational and business support systems, each with distinct data architectures. This fragmentation often makes it difficult to deploy artificial intelligence consistently across network domains.
The joint PoC was designed to address this challenge by creating a common data platform capable of operating seamlessly across cloud environments and on-premise infrastructure without requiring code modifications.
The companies said the architecture demonstrated the ability to efficiently handle large-scale data volumes and real-time ingestion speeds needed to support AI agents and automated cross-domain decision-making.
Enabling next-generation autonomous networks
Nokia, CTO AI and Autonomous Networks, Oguz Sunay, said, “Teaming up with Databricks represents a big step as we work toward building the types of data foundations required for next-generation autonomous networks. By enabling a common, flexible data platform across cloud environments, we can help operators accelerate the adoption of AI and create more efficient, resilient and sustainable networks.”
Databricks, Global Head of Telecommunications Industry, Nevash Pillay, said, “Telecom operators are managing increasingly complex networks and need a more consistent way to harness their data. Our collaboration with Nokia demonstrates how a unified data platform can help simplify operations and unlock the value of AI across network domains.”
Key technical breakthroughs
Engineering teams from Nokia and Databricks focused on a real-time performance management use case, simulating analytics ingestion with the objective of scaling to meet tier-1 telecom operator requirements in cloud environments.
The PoC delivered several technological advancements, including the ability to create data pipelines once and deploy them across different platforms without modification. During trials, identical workflows operated seamlessly on both Databricks and an open-source technology stack based on Apache Flink, Kafka and Iceberg.
The project also introduced a vendor-neutral approach to data logic design. Nokia engineers developed transformation logic using an abstract, platform-independent Python-based framework, allowing workflows to be reused across multiple environments while reducing dependency on any single platform.
Automated deployment and AI-driven innovation
The companies also validated a custom compiler capable of automatically adapting workflows during deployment. Depending on the target environment, the compiler translated abstract logic into native formats such as Delta Live Tables for Databricks or Flink SQL for open-source platforms, eliminating manual rework and accelerating deployment timelines.
Another key feature demonstrated during the project was AI-powered creation of new data products. Using natural language prompts, an intelligent data fabric agent can generate data products, seek human validation and automatically deploy pipelines, reducing manual effort and accelerating innovation.
The system also supports agent-to-agent communication, enabling AI agents to dynamically create and consume data products as needed.
Building a data fabric for the AI era
The PoC showcased a data fabric architecture designed for autonomous and agentic AI environments. Key capabilities include query-time data products that compute derived metrics without duplicating data, zero-copy data sharing for real-time cross-domain access, and mechanisms to selectively feed cloud-based analytical layers used for tasks such as root-cause analysis.
Moving ahead
Nokia and Databricks said they plan to continue collaborating on technologies that enhance autonomous network capabilities.
The companies aim to help telecom operators transition toward AI-driven networks where applications can access, correlate and act on large-scale network data in real time, supporting more intelligent and automated operations.