• Proof of concept validates cloud-agnostic approach for network operators to scale AI-driven operations, deploy real-time analytics without rewriting code.

E Nokia and Databricks  announced the successful completion of a joint proof of concept demonstrating a unified, substrate-agnostic data platform designed to support AI-driven autonomous networks. The collaboration shows how telecommunication providers can simplify fragmented data environments and deploy real-time analytics at scale, enabling faster decision-making, improved network performance, and more efficient operations.

The PoC addresses a long-standing industry challenge: Telecom networks typically rely on hundreds of siloed operational and business support systems, each with its own data architecture, making it difficult to apply AI consistently across domains. To truly harness AI and multi-agent systems, operators need a common data platform that can run seamlessly across different cloud environments or on-premise infrastructure, without the need to rewrite code.

The POC confirmed Databricks and Nokia’s ability to develop a joint architecture that efficiently handles the massive scale and real-time ingestion speeds required to feed network data to AI agents for automated, cross-domain decision-making.

“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,” said Oguz Sunay, CTO AI and Autonomous Networks, Nokia.

“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,” said Nevash Pillay, Global Head of Telecommunications Industry, Databricks.

About the POC

Engineering teams from Nokia and Databricks focused on a real-time performance management use case, simulating analytics ingestion with an intent to scale quickly to match tier-1 operator scale in the cloud. Their work delivered several key technical breakthroughs designed to simplify how telecom operators build and run data-driven services across different environments:

• Cross-platform data pipelines, without coding complexity: Data pipelines were created once and deployed across different platforms without modification. In trials, the same data workflows ran seamlessly on both Databricks and an open-source stack based on Apache Flink, Kafka, and Iceberg, supporting real-time streaming, batch processing, and query-time data products.
• Vendor-neutral data logic design: To avoid lock-in to any single platform, Nokia engineers developed transformation logic using an abstract, platform-independent expression in Python. By separating the core logic from platform-specific connectors, the same data workflows could be reused across multiple environments.
• Automated deployment across environments: The teams validated a custom compiler that automatically adapted workflows at deployment. Based on the target environment, it translated the abstract logic into native formats — such as Delta Live Tables for Databricks or Flink SQL for open-source systems — and added the platform-specific connectors, eliminating manual rework and accelerating time to deployment.
• AI-powered creation of new data products: The project also showcased how AI can streamline operations. Using simple natural language prompts, an intelligent data fabric agent can generate new data products, request human validation, and deploy the pipeline automatically, resulting in faster innovation with less manual effort. In the agentic world, the same mechanism can be leveraged by other agents to create dynamic data products on demand by communicating (agent to agent) with the data fabric agent.
• Data fabric built for the agentic world:
  • Query-time data products computing derived metrics, applying filters, aggregating, enriching, or joining data on read instead of duplicating it.
  • Zero-copy sharing, making cross-domain data consumption lightweight and real-time.
  • A mechanism to selectively feed upper temporal layers in the cloud, where agents run retrospective tasks like root-cause analysis on past events.

Moving ahead

Nokia and Databricks plan to continue their collaboration around enhancing autonomous network capabilities, helping operators transition to a future where AI applications increasingly access, correlate, and act on large-scale network data in real time.