Real-Time Emergency Incident Data Pipeline Implementation

We are on a mission to enhance public safety by providing emergency services with a robust, real-time data infrastructure. Currently, emergency responders operate with fragmented data systems, leading to delays and inefficiencies in critical situations. Our project aims to build a fully-integrated, real-time data pipeline utilizing Apache Kafka for event streaming and Spark for data processing. The pipeline will ingest, process, and visualize incident data from various sources such as 911 calls, GPS data, and weather feeds. Using MLOps, we aim to incorporate predictive analytics that help anticipate resource needs and optimize dispatch strategies. We also plan to implement data observability practices to ensure data quality and reliability across the system. The solution will be deployed on Snowflake, leveraging its scalability and performance for managing large volumes of data. We expect the entire system to be orchestrated using Airflow to ensure seamless operations. Our goal is to provide emergency services with a unified platform that enhances both the speed and accuracy of their responses, ultimately saving lives.