The rapidly growing size of data and complexity of analytics present new challenges for large-scale data processing systems. Modern systems keep data partitions in memory for pipelined operators, and persist data across stages with wide dependencies on disks for fault tolerance. While processing can often scale well by splitting jobs into smaller tasks for better parallelism, all-to-all data transfer---called shuffle operations---become the scaling bottleneck when running many small tasks in multi-stage data analytics jobs. Our key observation is that this bottleneck is due to the superlinear increase in disk I/O operations as data volume increases.