Real-Time Data Pipeline Architecture for Predictive Maintenance in Medical Devices

In the rapidly evolving Medical Devices industry, ensuring the reliability and efficiency of equipment is crucial. Our company intends to build a sophisticated real-time data pipeline infrastructure to streamline predictive maintenance processes. The goal is to collect, process, and analyze data from various devices in real-time, enabling proactive maintenance and reducing the risk of unexpected failures. The project involves utilizing Apache Kafka for event streaming to ingest and process high-velocity data from device sensors. We will employ Apache Spark for data transformation and real-time analytics, Airflow for orchestrating complex data workflows, and dbt for data modeling. Snowflake and BigQuery will serve as our core data lakes, providing a scalable and flexible data storage solution. Databricks will be integrated to support MLOps and data observability features, ensuring a seamless transition from model development to deployment. This project not only aims to enhance the reliability of our medical devices but also to create a data mesh architecture that facilitates decentralized data ownership, promoting faster insights and increased agility across the organization.