Driving Energy Transformation with Real-Time Data

Traditional wind energy systems fall short when batch-driven architectures slow the flow of insights. Turbines create data around the clock, yet legacy pipelines can drag, processing that data hours or even days later. By the time it’s ready for decisions, the opportunity to optimize outputs has passed, and any lingering performance issues have already negatively impacted the energy yield.

Join Ververica and Hivemind Technologies to discover how a streaming data lakehouse architecture transforms wind energy operations. Learn how Apache Flink® enables continuous sensor data ingestion from wind farms, powering real-time monitoring, automated optimization, and predictive decision-making capabilities.

What You’ll Discover:

• How stream processing enables instant response to changing environmental conditions and output curtailment
• Real-world implementation of Apache Flink® for continuous sensor data processing and complex event detection
• How to build automated prediction and control systems that optimize energy yield dynamically based on live turbine telemetry and weather data
• The positive business impact of moving from batch processing to real-time decision-making in renewable energy operations
• How to transform wind energy infrastructure from static forecasts to self-adjusting, intelligent systems with predictive maintenance and anomaly detection

Agenda

• Welcome & Introduction
• The challenges of batch-driven architectures in energy operations
• Real-world use case: Wind turbine optimization with Apache Flink®
• Building streaming data lakehouse architectures for scalability and intelligence
• Q&A with Ververica and Hivemind experts