solution: Real-Time Operational Data Lake for Energy Assets

One real-time foundation for every sensor, system, and site.

Energy companies generate massive volumes of operational data from SCADA, historians, IoT sensors, and field applications, but these datasets are often isolated across incompatible systems. Without a real-time, unified data foundation, organizations struggle to support analytics, automation, and decision-making at scale.

  • Operational data silos across SCADA, historians, PLCs, and field mobility tools.
  • No standardized data model for integrating time-series, event, and asset data.
  • Latency and batch processing delays limit real-time visibility for operations teams.
  • Difficulty supporting AI, optimization, and predictive analytics due to inconsistent data quality.
  • High cost and complexity associated with legacy on-prem systems and manual ETL processes.

Frame delivers a modern operational data lakehouse built on Databricks that unifies time-series, event, and asset data into a high-performance, scalable environment. This architecture enables real-time streaming pipelines, standardized asset models, and AI-ready data structures to support digital operations across the enterprise.

  • Unified ingestion of SCADA, historian, IoT, and operational data streams into a Databricks lakehouse.
  • Real-time data pipelines using Delta Live Tables and streaming architectures.
  • Standardized asset and event models enabling cross-site analytics and enterprise reporting.
  • Automated data quality, validation, and enrichment workflows to improve reliability of operational data.
  • Foundational platform enabling advanced analytics such as predictive maintenance, OEE, emissions tracking, and optimization models.

By modernizing their operational data infrastructure, organizations gain real-time situational awareness, better decision-making capabilities, and a scalable foundation for AI-driven transformation. This improves operational efficiency and reduces both cost and risk.

  • Faster, more accurate decision-making enabled by real-time operational visibility.
  • Reduced system complexity and cost through consolidation into a unified lakehouse.
  • Improved data quality supporting analytics, automation, and AI initiatives.
  • • Stronger operational resilience through standardized asset and event models.
  • A scalable data platform that supports future digital and AI use cases across the enterprise.
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