Real-Time Data Pipeline Optimization for Patient Monitoring Systems

As an enterprise in the Digital Health sector, we aim to revolutionize our patient monitoring systems by optimizing data processing and analytics. The current infrastructure lacks the scalability and real-time processing capabilities essential for modern healthcare services. This project will deploy a sophisticated data pipeline leveraging Apache Kafka for event streaming, Spark for processing, and Snowflake for scalable storage. We will integrate Airflow for orchestrating complex workflows and use dbt to manage data transformations efficiently. Additionally, we will incorporate data observability tools to ensure data accuracy and lineage tracking. The solution will adopt a data mesh architecture, facilitating decentralized data ownership and domain-oriented data management, which aligns with our strategic vision of delivering real-time insights to healthcare professionals. This project will enable us to provide timely, actionable insights leading to better patient outcomes, reduce system downtimes, and enhance operational efficiency.