Overview
Eneco needed a scalable way to process and analyse large volumes of energy data in near real time. We delivered a cloud-native platform that supports forecasting, improves reliability and enables better decision-making in energy trading.
The Challenge
Eneco operates in an energy market that is becoming increasingly complex.
As the share of renewable energy sources such as wind and solar grows, predictability decreases. Production depends on external factors like weather, making supply more volatile and harder to forecast.
This creates a direct business risk. Without accurate forecasting and timely insights, fluctuations in supply and demand can lead to significant financial losses.
At the same time, Eneco needed to improve its understanding of market trends and optimise trading strategies. This required access to reliable, near real-time data from multiple sources.
Several challenges shaped the need for a new approach:
High data volume and velocity
Data is generated continuously from production facilities, sensors and market sources. The platform needed to handle large volumes of data while processing it in near real time.
Scalability and reliability requirements
The system had to scale as data volumes grow, while maintaining high availability. Downtime or delays would directly impact trading decisions.
Integration of multiple data sources
Data needed to be collected, processed and combined from different systems in a consistent and reliable way.
Support for advanced analytics
The platform needed to support forecasting models and analytics, enabling Eneco to make informed decisions based on data.
The challenge was to design a platform that could handle complexity, scale with demand and provide timely, reliable insights to support critical business operations.
The Solution
We designed and implemented a cloud-native data platform tailored to Eneco’s energy trading needs.
The focus was on scalability, reliability and real-time data processing – ensuring the platform could support both current operations and future growth.
Phase 1: Building a cloud-native data foundation
The platform was built using modern cloud-native architecture, enabling flexibility and scalability from the outset.
Designed to handle growing data volumes, the platform can evolve without requiring major structural changes, providing a future-ready foundation for data-driven operations.
Phase 2: Enabling real-time data processing
Data pipelines were implemented to process inputs from sensors and market sources in near real time.
This ensures that insights are always up to date, allowing trading teams to respond quickly to changing conditions and make informed decisions based on current data.
Phase 3: Integrating analytics and decision support
Machine learning models were embedded into the platform to enable both real-time and time-based forecasting.
Dashboards and reporting tools provided clear, accessible insights, supporting both operational decisions and longer-term strategy.
This transformed raw data into actionable intelligence across the organisation.
Phase 4: Ensuring performance, resilience and continuous operation
The platform was designed for high performance and reliability, processing around 15GB of data daily and running more than 750 processes within a Kubernetes cluster.
Fault-tolerant architecture and scalable infrastructure ensure high availability, while monitoring and alerting systems detect issues and maintain data quality.
With continuous monitoring and 24/7 support, the platform operates as a stable, mission-critical system.
Transformation timeline
Phase 1
Building a cloud-native data foundation
Phase 2
Enabling real-time data processing
Phase 3
Integrating analytics and decision support
Phase 4
Ensuring performance, resilience and continuous operation
The Results
The platform has become a critical part of the client’s energy trading operations.
Improved reliability and platform performance
A modern, cloud-native architecture increased the reliability and maintainability of the data platform. This ensured stable operations in a high-demand trading environment.
Reduced costs through scalable infrastructure
Cloud-based scalability reduced infrastructure and operational costs by optimising resource usage and removing the need for rigid capacity planning.
More efficient data access and distribution
The introduction of “data as a service” improved how data is shared and accessed across the organisation. This increased efficiency and enabled teams to work with consistent, trusted data.
Stronger forecasting and strategic decision-making
Near real-time processing and integrated machine learning models improved forecasting accuracy and responsiveness to market changes. This enabled better-informed trading decisions, reduced risk and created a scalable foundation for future innovation.