Sympower accelerates Europe's energy transition with forecasting on Databricks

Overview

Energy flexibility company Sympower built electricity demand forecasting models on a Databricks medallion architecture with Delta tables, Lakeflow Jobs and Unity Catalog to manage about 2 gigawatts of flexible capacity for European grid balancing, enabling rapid collaborative prototyping of new forecasting models.

The challenge

Sympower needed precise, scalable forecasting to balance the electricity grid amid rising volatility from renewable energy and growing demand from EVs and heat pumps, but lacked automated data lineage documentation and struggled to scale forecasting models and make data insights accessible across the company.

The solution

Sympower adopted the Databricks Data + AI Platform to scale its forecasting models, using Delta tables for data storage, Databricks Lakeflow Jobs to compose data pipeline stages, CI/CD pipelines synced to Databricks Repos, and migrated its Hive metastore and externally managed tables to Databricks Unity Catalog for governance and lineage.

Reported business value

Enabled Sympower's data team to rapidly and collaboratively prototype new forecasting models, improved data governance, lineage and real-time decision-making, and made data insights and self-service analytics accessible to more of the company via dashboards, notebook prototypes and SQL workspaces.