Building a data platform processing 1B+ events daily

The UK electricity market processes an unimaginable number of events: we turn on the lights, dry our hair, or work on our laptop while charging our phone—these are all separate events which need to be handled by the electricity industry. Our client requested one thing: build a platform which can handle it all. Not a small request!

The problem

The UK electricity market operates on half-hourly settlement periods, requiring precise calculation and reconciliation of energy consumption across multiple market participants.

This creates significant technical challenges:

• Massive data volumes

  , exceeding 

  1 billion events daily

• Strict accuracy requirements

   for financial settlement

• Real-time processing needs

   across the entire market

• Complex coordination between multiple systems and stakeholders

The existing landscape required a platform that could handle scale, speed, and reliability simultaneously.

The solution

We designed and built a cloud-native, microservices-based platform on Microsoft Azure to process and aggregate energy market data at national scale.

The architecture included:

• Azure Databricks

   → high-throughput data processing

• Azure Data Factory

   → orchestration of data pipelines

• Azure Event Hubs & Service Bus

   → real-time data ingestion and messaging

• Azure Kubernetes Service (AKS)

   → scalable microservices infrastructure

• Cosmos DB & Azure SQL

   → storage and transactional processing

How does it work?

The platform enables:

1. Continuous ingestion of high-volume energy data
2. Real-time aggregation and transformation
3. Settlement-period calculations across the market
4. Specialised services for:Load shapingVolume allocationMarket-wide data aggregation

As the platform evolved, machine learning capabilities were introduced to support:

• Predictive analytics
• Advanced forecasting
• Automated model pipelines

The impact

The platform delivers:

• Scalability:

   Handles 

  1B+ events daily

   with room for growth

• High reliability:

   Designed for continuous, uninterrupted operation

• Accuracy:

   Supports critical financial settlement processes

• Future readiness:

   Enables advanced analytics and ML use cases

Key takeaways

At this scale, the challenge isn’t just processing data, it’s designing systems that remain reliable under constant load, flexible and maintainable over time. Cloud-native architecture makes this possible, but only when designed correctly from the start.