Real-Time Data Pipeline Optimization for Energy Storage Systems

As a growing SME in the Energy Storage industry, we are facing challenges in managing the increasing influx of data generated by our storage systems. Our current batch processing methods are proving inadequate for timely insights and decision-making. The objective of this project is to design and implement a robust real-time data pipeline that can handle the scale and complexity of our operations. By integrating technologies such as Apache Kafka for event streaming and Apache Spark for real-time analytics, the solution will facilitate immediate data processing and provide actionable insights. We also plan to incorporate data observability tools to monitor data quality and performance, ensuring reliable outputs. This project will ultimately support our strategic goals of optimizing energy usage and reducing operational costs by enabling faster, data-driven decisions. The solution will be built on a scalable architecture using platforms like Databricks and Snowflake, ensuring future expandability as our needs evolve.