Bridging Operational Technology (OT) and Enterprise Analytics: A Framework for Integrating AVEVA PI with Cloud-Scale ELT Pipelines

Abstract

Industrial organizations struggle to bridge the gap between high frequency operational telemetry and cloud-scale enterprise analytics without sacrificing semantic integrity and incurring prohibitive computational costs. While the AVEVA PI System remains a mission-critical staple for contextualizing time-series data, traditional integration methods often trigger significant semantic loss during the transition to the cloud. This research introduces a reference framework utilizing Informatica IDMC and a layered Extract, Load, Transform (ELT) architecture to synchronize the PI System with Snowflake. Unlike standard migrations that flatten data into disconnected tags, this approach embeds operational semantics directly through the preservation of asset models and event context. By utilizing warehouse native Change Data Capture (CDC), specifically Snowflake Streams and Tasks, the framework replaces inefficient full refresh cycles with high performance incremental processing. To address the rigorous requirements of critical infrastructure, the architecture aligns with NIST SP 800-82 Revision 3 security standards and introduces a quantitative observability model based on Service Level Objectives (SLOs) for data freshness and reconciliation. I validated the framework through a utility. y scale implementation, which yielded a 95th percentile latency of 7.4 minutes and approximately 38% reduction in compute consumption compared to legacy methods. Ultimately, the aim of this research is to provide a practical, reproducible blueprint for organizations seeking to modernize industrial analytics while maintaining operational trust, performance, and continuity.