Python-written data analytics applications can be modeled as and compiled into a directed acyclic graph (DAG) based workflow, where the nodes are fine-grained tasks and the edges are task dependencies. Such analytics workflow jobs are increasingly characterized by short, fine-grained tasks with large fan-outs. These characteristics make them well-suited for a new cloud computing model called serverless computing or Function-as-a-Service (FaaS), which has become prevalent in recent years. The auto-scaling property of serverless computing platforms accommodates short tasks and bursty workloads, while the pay-per-use billing model of serverless computing providers keeps the cost of short tasks low. In this paper, we thoroughly investigate the problem space of DAG scheduling in serverless computing. We identify and evaluate a set of techniques to make DAG schedulers serverless-aware. These techniques have been implemented in Wukong, a serverless, DAG scheduler attuned to AWS Lambda. Wukong provides decentralized scheduling through a combination of static and dynamic scheduling. We present the results of an empirical study in which Wukong is applied to a range of microbenchmark and real-world DAG applications. Results demonstrate the efficacy of Wukong in minimizing the performance overhead introduced by AWS Lambda --- Wukong achieves competitive performance compared to a serverful DAG scheduler, while improving the performance of real-world DAG jobs by as much as 3.1X at larger scale.