- Master’s Project in collaboration with Dr. Polo Chau and a team of graduate students. - Designed a cluster of over 30 machines to reliably detect anomalies in power sensor data at scale. - Built a data ingestion pipeline using a Java multithreaded reverse proxy to write data to HBase. - Created a Spark Streaming analysis pipeline to perform anomaly detection at scale. - Results: Ingestion speeds of 400k data points per second on a 30 node cluster with linear scalability. - Published in International Parallel & Distributed Processing Symposium Workshop.