Peter has been researching and solving leading-edge distributed computational problems for nearly 20 years. This began with intelligent agent systems; he tracked high-performance computing and their developments in both Grid and Cloud. More recently Peter has been closely following and working with Big Data, MapReduce, NoSQL and realtime streaming analysis. Peter is a Data Scientist, Trainer and Researcher who enjoys problems of scale and complexity.
He combines the skill-sets of Data Engineer and Analyst and is as happy building fast real-time Kafka / Spark data pipelines as he is doing time series decomposition and building customer profiles. By fast, Peter has worked at 1 million events per second (80B events per day) with a total data warehouse size of 15PB. In his spare time Peter is a keen but talentless mountain biker, he tends to fall off a lot
Talks I've Given
Anomaly Detection: A breakdown of Twitter’s Seasonal Hybrid ESD
Featuring Peter Tillotson
In a world of deep learning statistical techniques are out of fashion but can still be very effective tools. Twitter’s open source anomaly detection project uses a statistical technique call Seasonal Hybrid ESD.data-learning seasonal-hybrid-esd twitter data-science-fest