Real-Time Data Pipeline for Renewable Energy Efficiency Optimization

In the rapidly evolving renewable energy sector, our company faces challenges in efficiently collecting and analyzing data from diverse sources such as solar panels, wind turbines, and energy storage systems. We aim to build a robust, real-time data pipeline that can ingest, process, and visualize data streams to optimize energy production and distribution. The project involves setting up an end-to-end data architecture using Apache Kafka for event streaming, Spark for data processing, and Snowflake or BigQuery for data warehousing. The system will leverage Airflow for orchestrating workflow processes and dbt for data transformation. By implementing this solution, we aim to improve energy yield forecasts, reduce operational costs, and enhance our capacity to integrate with distributed energy resources. The project is expected to be completed within 12-16 weeks, with a budget of $25,000 - $75,000.