Heterogeneity is a growing concern for scheduling on the cloud. Hardware is increasingly heterogeneous (e.g., GPUs, FPGAs, diverse I/O devices), emphasizing the need to build schedulers that identify the internal structure of applications and utilize available hardware resources to their full potential. In this paper we present our initial efforts to build a scheduler that tackles heterogeneity (in hardware and in software) as a primary concern. Our scheduler, HCl (Heterogeneous Cluster), models applications as annotated directed acyclic graphs (DAGs), where each node represents a task. It maps tasks onto hardware nodes, also organized in DAGs. Initial results using application models based on TPC-DS queries running on Apache Spark show that HCl can improve significantly upon approaches that do not consider heterogeneity and generate schedules that approach the critical path in length.