Vattenfall Hydro Germany operates more than 2.5 GW of pump storage plants in an energy market that now moves at second-level speed. With Databricks on Azure, the team built HySBAP, the Hydro Strategic Business Analytics Platform, to unify real-time SCADA streams, market signals, forecasts and operational data. The result is a governed intelligence platform that helps teams answer strategic questions in days instead of years.
The end of "good enough" predictions
Fifteen years ago, energy products were sold weeks or months in advance. Today, the energy system has changed drastically. "We moved from weekly products to daily, to 15-minute products, and now down to second-based settlements," explains Philipp Cüppers, Head of Data Innovation at Vattenfall Hydro Germany.
While Vattenfall's hydro assets are fully dispatchable, they operate in a market defined by uncertainty. The team needed to understand how hundreds of real-time sensor streams, weather patterns across Europe, and power prices from dozens of bidding regions all interacted. "Dashboards normally answer yesterday's questions, because it takes time to build them," says Cüppers. "We needed to provide answers for tomorrow. Asking questions in natural language is changing how data can be used for decision-making."
A Lakehouse for the energy transition
The platform ingests data from real-time Kafka streams, delivering SCADA machine data and trading signals, REST APIs with renewable forecasts and price curves, operational databases with metering, historian systems for hydrology data, and pan-European transparency platforms.
All data flows through a medallion architecture — raw, qualified, and curated layers — governed by Unity Catalog and stored in Delta Lake, giving teams a trusted foundation for dashboards, natural language exploration, and custom applications. The qualified layer applies business logic, deduplication, and interpolation at sub-second granularity; the curated layer produces actionable insights for strategic decision-making. Data Quality is monitored through automated checks based on DQX, which check for freshness, domain-specific outliers, and gaps in the data. On any given day, the platform writes close to 3 billion rows as data cascades through all layers.
The platform runs on a mature DTAP infrastructure with multiple VNET-integrated Databricks workspaces on Azure, automated CI/CD, and a custom Core Library with over 40 modules encoding domain-specific logic — from Protobuf deserialization to reservoir scenario modeling.
From complexity to accessibility
The team keeps engineering complexity under the hood while making insights accessible to everyone. Databricks Lakehouse and AI/BI Dashboards expose insights through production dashboards. Over a hundred automated Lakeflow Jobs orchestrate streaming and batch jobs with intelligent retry logic. For non-technical colleagues, Genie lets users query data in natural language.
Strategic capabilities: Answering tomorrow's questions
Digital Twin & Asset Simulation: A full asset simulator of the pump storage plants maps the physical reality of every turbine - ramp behavior, market signal response, and operational characteristics. This enables trading and optimization teams to test new algorithms against a virtual portfolio before risking real assets.
Carbon Impact Analysis: The team quantifies the operations' carbon footprint by combining European generation-mix data with plant-level metering at second-level granularity, answering whether their flexibility services actually help decarbonize the energy system.
Reservoir Scenario Modeling: Simulations project water-level trajectories under different dispatch strategies, balancing revenue optimization with operational and environmental constraints.
Grid Frequency Research: Machine learning approaches investigate how predictable grid frequency events correlate with market transitions, thereby understanding the real-world operational impact on the power grid.