Rockfish Data announced an integration with Snowflake, the AI Data Cloud company, designed to help telecom operators and network technology providers accelerate the development and validation of Autonomous network operations. The solution combines Snowflake's AI Data Cloud with Rockfish's synthetic data generation platform to produce realistic, privacy-safe network telemetry and observability data directly within Snowflake. Organizations can test analytics systems, automation workflows, and AI models including AI Agents against rare and high-impact network conditions before deploying them into live environments.
Telecom networks are among the most complex and high-stakes systems in operation today. While operators collect vast amounts of telemetry and operational data, the scenarios that matter most are--network outages, congestion corridors, signaling storms, and edge-case subscriber behavior--which are often rare, incomplete, or too sensitive to share across teams and vendors. In addition to realizing highly performant and trustworthy autonomous networks, telcos need AI agents and digital twins that validate the impact of their actions before implementing changes into the network.
As a result, AI and automation systems are frequently validated only after encountering failures in production. Built for Operators and Network Vendors: Rockfish's integration with Snowflake is designed to serve both sides of the telecom ecosystem. For Telecom Operators: Validate against rare conditions - Test AI/ML models on outage scenarios and edge cases before customers are impacted; Test automation safely - Evaluate closed-loop automation without touching live networks; Enable controlled collaboration - Share privacy-safe, realistic datasets across internal teams and third parties; Accelerate AI deployment - Reduce friction when onboarding and validating network applications and closed loop AI-driven automations; Build realistic Digital Twins - Create and test Digital Twins with realistic data that emulates the operator specific implementations for training and modeling Autonomous Agents and cross domain closed loop automation.
For Network Equipment Providers and Software Vendors; Prove robustness at scale - Stress test and validate network applications, optimization solutions, and analytics tools; Reduce time to recreate failure cases for root cause analysis - Eliminate reliance on inconsistent or delayed customer-provided datasets; Shorten proof-of-concept cycles - Demonstrate system performance faster and accelerate adoption; Build and test AI Agents at scale - Accelerate AI Agent development lifecycles with robust network datasets for testing and training. Technical Capabilities: This solution enables organizations to: Preserve complex temporal and causal relationships in network data; Generate rare failures and stress scenarios on demand; Simulate realistic carrier-scale telemetry across observability, RAN, Transport and Network Core including the OSS and BSS operational domains; Produce privacy-safe datasets suitable for internal and cross-organizational collaboration. All synthetic data is generated and managed within Snowflake's AI Data Cloud, allowing seamless integration with existing analytics workflows, ML pipelines, and operational systems. This solution will be available soon on the Snowflake Marketplace.
Telecom operators and network technology providers can learn more at
