Real-Time Data Pipeline Optimization for Renewable Energy Forecasting

As a leader in the Renewable Energy sector, we are committed to harnessing data-driven insights to forecast energy production accurately. Our current challenge lies in the inefficient processing of large data volumes from various energy sources, leading to delays and inaccuracies in forecasts. This project aims to develop a robust, real-time data pipeline leveraging Apache Kafka for event streaming and Databricks for real-time analytics. By implementing data mesh principles and ensuring data observability with Airflow and dbt, we will streamline our data workflow, enhance collaboration across departments, and improve forecasting models with machine learning operations (MLOps). This initiative is crucial for optimizing energy resource allocation and responding swiftly to grid demands, thereby increasing operational efficiency and sustainability.