The exponential growth in the pursuit of knowledge gleaned from data relationships that are expressed naturally as large and complex graphs is fueling new parallel machine learning algorithms. The nature of these computations is iterative and data-dependent. Recently, frameworks have emerged to perform these computations in a distributed manner at commercial scale. But feeding data to these frameworks is a huge challenge in itself. Since graph construction is a data-parallel problem, Hadoop is well-suited for this task but lacks some elements that would make things easier for data scientists that do not have domain expertise in distributed systems engineering. We developed GraphBuilder, a scalable graph construction software library for Apache Hadoop, to address this gap. GraphBuilder offloads many of the complexities of graph construction, including graph formation, tabulation, compression, transformation, partitioning, output formatting, and serialization. It is written in Java for ease of programming and scales using the MapReduce parallel programming model. We describe the motivation for GraphBuilder, its architecture, and present two case studies that provide a preliminary evaluation.