Real-time Data Pipeline Optimization for Renewable Energy Forecasting

As a rapidly growing company in the renewable energy industry, we are looking to optimize our data engineering infrastructure to support real-time analytics in energy production forecasting. Our existing data pipeline needs enhancement to handle the increasing volume and velocity of data generated from our solar and wind farms. We aim to build a robust, real-time data pipeline that utilizes event streaming technologies, such as Apache Kafka, for high-throughput data ingestion and Spark for scalable data processing. Additionally, integrating Airflow for workflow management and dbt for transformation will ensure efficient data operations and observability. By leveraging Snowflake or BigQuery for scalable data warehousing, we aim to improve the accuracy and timeliness of our energy forecasts, crucial for efficient grid integration and operational planning. This project is vital for staying ahead in a competitive renewable energy market and ensuring compliance with evolving energy regulations.