Real-Time Health Data Mesh Implementation for Enhanced Patient Insights

The ever-increasing volume of health data generated from various sources poses a significant challenge in ensuring timely and actionable insights for patient care. To address this, we propose the implementation of a real-time data mesh architecture. This project will utilize Apache Kafka for event streaming, Spark for large-scale data processing, and Snowflake for cloud-based data warehousing. With dbt and Airflow, we aim to streamline data transformations and orchestrations, ensuring data quality and observability are maintained. The solution will be deployed on platforms like BigQuery and Databricks to leverage their robust analytics capabilities. The primary goal is to enable healthcare providers to access and analyze critical patient data in real time, improving diagnostic accuracy and treatment efficiency. By adopting a data mesh approach, the project will empower data teams to manage and analyze data as a product, focusing on data ownership, domain-oriented architecture, and self-service data infrastructure. This initiative is expected to significantly enhance patient care delivery and operational efficiency, positioning our company as a leader in health data management.