The Apache Hadoop® framework provides parallel processing and distributed data storage capabilities that data analytics applications can utilize to process massive sets of raw data. These Big Data applications typically run as a set of MapReduce jobs to take advantage of Hadoop's ease of service deployment and large-scale parallelism. Yet, Hadoop has not been adapted for multilevel secure (MLS) environments where data of different security classifications co-exist. To solve this problem, we have used the Security Enhanced Linux (SELinux) Linux kernel extension in a prototype cross-domain Hadoop on which multiple instances of Hadoop applications run at different sensitivity levels. Their accesses to Hadoop resources are constrained by the underlying MLS policy enforcement mechanism. A benefit of our prototype is its extension of the Hadoop Distributed File System to provide a cross-domain read-down capability for Hadoop applications without requiring complex Hadoop server components to be trustworthy.