MapReduce has emerged as a promising architecture for large scale data analytics on commodity clusters. The rapid adoption of Hive, a SQL-like data processing language on Hadoop (an open source implementation of MapReduce), shows the increasing importance of processing structured data on MapReduce platforms. MapReduce offers several attractive properties such as the use of low-cost hardware, fault-tolerance, scalability, and elasticity. However, these advantages have required a substantial performance sacrifice. In this paper we introduce Clydesdale, a novel system for structured data processing on Hadoop - a popular implementation of MapReduce. We show that Clydesdale provides more than an order of magnitude in performance improvements compared to existing approaches without requiring any changes to the underlying platform. Clydesdale is aimed at workloads where the data fits a star schema. It draws on column oriented storage, tailored join-plans, and multi-core execution strategies and carefully fits them into the constraints of a typical MapReduce platform. Using the star schema benchmark, we show that Clydesdale is on average 38x faster than Hive. This demonstrates that MapReduce in general, and Hadoop in particular, is a far more compelling platform for structured data processing than previous results suggest. © 2012 ACM.