In the recent years the field of sublinear algorithms has led to new theoretical developments in algorithms for massive data processing. The goal of this workshop is to discuss recent work and new challenges in sublinear algorithms and how they relate to Big Data and massively parallel computing. Among multiple research areas represented at this workshop primary focus will be given to algorithms for MapReduce, streaming, distributed machine learning, property testing and communication complexity. An in-depth coverage of these topics will be given through 5 invited keynotes and tutorials accompanied by 15-20 talks by the leading experts in the area with broad representation from both academia and industry. We highly encourage attendance by graduate students who will have an opportunity to showcase their work during a poster session.