Nokia and Databricks Forge the Data Backbone for Self-Driving Networks

ESPOO, Finland – June 24, 2026 – In a world increasingly reliant on seamless connectivity, the telecommunications industry has long chased the vision of a “self-driving” network—one that can predict faults, optimize performance, and heal itself with minimal human touch. The challenge has never been a lack of data, but rather an inability to make sense of it. Today, Nokia and Databricks announced a collaboration that strikes at the heart of this problem, promising to build the unified data foundation required to finally make truly autonomous networks a reality.

The companies revealed the successful completion of a proof of concept (PoC) demonstrating a single, cloud-agnostic data platform designed for the immense scale of modern telecom operations. While corporate partnerships are common, this one is different. It’s not just about integrating two products; it’s about architecting a solution to a decades-old problem that has held back network evolution: data fragmentation. For years, I’ve analyzed company reports showing operators wrestling with hundreds of siloed systems, each speaking its own language. This announcement suggests a practical way to translate that chaos into a common tongue that AI can understand and act upon.

From Data Swamps to a Unified Lakehouse

The concept of an Autonomous Network (AN) is often categorized by the TM Forum on a scale from Level 0 (fully manual) to Level 5 (fully autonomous). While many operators have reached Level 3, achieving conditional automation in specific domains, the leap to Level 4—a highly autonomous, AI-led system—has been stymied. The primary barrier is the “data swamp,” where valuable information from operational and business support systems lies isolated and inaccessible for holistic analysis.

This is where the collaboration’s architecture becomes critical. Databricks brings its “Lakehouse” platform to the table, a hybrid model that combines the massive storage capacity and flexibility of a data lake with the performance and governance of a traditional data warehouse. For a telco, this means being able to pour torrents of real-time network performance data, customer usage patterns, and equipment logs into a single, governed environment. It addresses the core challenge of harnessing diverse data for complex AI workloads without having to stitch together dozens of disparate tools.

“Telecom operators are managing increasingly complex networks and need a more consistent way to harness their data,” said Nevash Pillay, Global Head of Telecommunications Industry at Databricks. “Our collaboration with Nokia demonstrates how a unified data platform can help simplify operations and unlock the value of AI across network domains.”

The PoC validated that this unified approach can be deployed across any cloud provider or even on-premise infrastructure without rewriting the underlying code. This flexibility is crucial for global operators who operate in different regulatory environments and want to avoid being locked into a single cloud vendor’s ecosystem.

AI Building AI: The Engine of Automation

Beyond just unifying data, the PoC showcased several technical innovations that read less like an engineering report and more like a blueprint for the future of network operations. One of the most significant breakthroughs is a “custom compiler” that automates deployment. In essence, it acts as a universal translator, taking high-level, platform-independent logic written in Python and automatically converting it into the native format for the target environment, whether it's Databricks’ own Delta Live Tables or an open-source stack. This eliminates immense manual effort and accelerates the time it takes to roll out new analytics and AI-driven services.

Perhaps even more transformative is the demonstration of an “intelligent data fabric agent.” This AI-powered agent can create and deploy new “data products”—curated, reusable datasets for specific tasks—using simple natural language prompts. A network engineer could theoretically ask the system to “create a real-time view of 5G uplink performance in the downtown core” and the agent would generate the necessary data pipeline automatically. This not only democratizes data access but also lays the groundwork for a future where autonomous AI agents can create their own data streams on the fly to solve problems.

“Teaming up with Databricks represents a big step as we work toward building the types of data foundations required for next-generation autonomous networks,” said Oguz Sunay, CTO AI and Autonomous Networks at Nokia. “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.”

A New Playing Field in the Race for Network Intelligence

This partnership doesn’t exist in a vacuum. The race to deliver network intelligence is heating up. Nokia’s competitors, including Ericsson and Huawei, are aggressively pushing their own automation platforms. Ericsson has expanded its Intelligent Automation Platform to unify RAN and core network automation, while Huawei is pursuing an “AgenticRAN” architecture to embed AI directly into network functions. At the same time, cloud giants like Google Cloud, with whom Nokia also has a partnership, are vying for a larger share of the telecom IT budget with their powerful AI and data analytics toolkits.

The Nokia-Databricks approach carves out a distinct position by focusing squarely on the universal data layer. Instead of a vertically integrated solution, it offers a horizontal platform that promises interoperability and freedom from vendor lock-in—a compelling proposition for operators managing multi-vendor networks.

The financial stakes are enormous. Industry analysis suggests that a successful transition to autonomous networks could unlock an estimated $800 million in value per operator through cost savings and new revenue. With telcos planning to invest an average of $87 million each over the next five years in this area, the return on investment is projected to be between 1.7x and 3.4x. This collaboration is a direct play for that market, offering a tangible solution to the data problem that has long been the biggest drag on realizing those returns.

By tackling the foundational issue of data fragmentation, Nokia and Databricks are not just selling a new technology; they are providing an enabling platform that could finally help the industry cross the chasm from conditional automation to true network autonomy. This move paves the way for the hyper-complex, ultra-reliable networks that future technologies like 6G and the wider AI-driven economy will demand, ensuring the digital world’s backbone is not only strong, but intelligent.