Rachel is a member of the Analytics and Monitoring (AM) Team and is responsible for building and managing monitoring and streaming analytics solutions for HPC operational data within NCCS in order to help decision makers formulate and implement informed decisions. She helps manage Kafka and Elasticsearch services for HPC metrics and logs data, stood up an Elasticsearch cluster for service monitoring, and will be contributing to future analytics and monitoring efforts for new HPC compute resources at ORNL.
She obtained her B.S in Biology in 2014 from Furman University where she focused on botany, ecological systems, data collection, and analysis techniques. After graduating, she was a biological technician with the National Park Service in the Southwest before returning East to pursue technical skills in Geographic Information Systems. In 2017, she began a Post Bachelor’s Research Associate position in the Geographic Information Sciences Group (Now NSET) at ORNL and began learning about big data in geographic research. Her work on the PlanetSense team utilized python scripting to extract, clean, and update large amounts of Points of Interest (POI) data in an ElasticSearch cluster. She was in charge of creating and managing a graph database for POI category information using Neo4j which is now used for POI data enrichment. This sparked her interest in further pursuing data science, analytics, and data engineering at the big data scale, leading her to HPC.