Fully featured, commercially supported machine learning suites that can build Decision Trees in Hadoop are few and far between. Addressing this gap, Revolution Analytics recently enhanced its entire scalable analytics suite to run in Hadoop. In this talk, I will explain how our Decision Tree implementation exploits recent research reducing the computational complexity of decision tree estimation, allowing linear scalability with data size and number of nodes. This streaming algorithm processes data in chunks, allowing scaling unconstrained by aggregate cluster memory. The implementation supports both classification and regression and is fully integrated with the R statistical language and the rest of our advanced analytics and machine learning algorithms, as well as our interactive Decision Tree visualizer.