My team was maintaining multiple Hadoop clusters on a high UCS hardware configuration powered by MapR. We were also maintaining a big cluster in a production environment and other clusters for development, QA, disaster recovery and POC. All clusters were configured with high availability. Multiple internal teams used to run their application jobs on our cluster. My team was responsible for managing and maintaining these clusters. We evaluated and implemented new big data and related tools introduced by Mapr. The goal was to make sure application customers using our cluster stay happy. Day to day jobs on our cluster include traditional Java MapReduce, Streaming, Pig, Hive, Mapr-Tables and other in-memory application jobs like spark for analytics on our company internal data. Nearly a total of 50 different use cases using Hadoop were implemented in our various clusters. At times we required getting support from Mapr on complex issues which could not be resolved by my team.