Real-Time Data Infrastructure for Maritime Fleet Optimization

As a scale-up company in the Maritime & Marine sector, we face challenges in optimizing fleet operations due to the increasing complexity and volume of data generated by our vessels. We require a robust real-time data infrastructure that can handle large-scale data ingestion, processing, and analytics. The project involves setting up a data pipeline using Apache Kafka for event streaming, Spark for real-time analytics, and Snowflake for scalable data storage. Airflow will be utilized for workflow automation, while dbt (data build tool) will manage data transformations and modeling. Our goal is to provide a comprehensive dashboard and alert system that delivers insights on fuel consumption patterns, optimal routing based on weather and sea conditions, and predictive maintenance alerts to prevent costly downtimes. By doing so, we aim to reduce operational costs, improve fleet efficiency, and contribute to sustainability goals. The project requires close collaboration with our data science team to integrate machine learning models into the pipeline, facilitating continuous learning and improvement. If successful, this infrastructure will set the foundation for future expansion into more advanced analytics and predictive capabilities.