UC Irvine

Big Data is turning to be a key basis for the competition and growth among various businesses. The emerging need to store and process huge volumes of data has resulted in the appearance of different Big Data serving systems with fundamental differences. Big Data benchmarking is a means to assist users to pick the correct system to fulfill their applications' needs. It can also help the developers of these systems to make the correct decisions in building and extending them. While there have been several major efforts in benchmarking Big Data systems, there are still a number of challenges and unmet needs in this area. This dissertation is aimed at contributing to the performance evaluation of Big Data systems from two major aspects. First, it uses both new and existing benchmarks to do deep performance analysis of two major classes of modern Big Data systems, namely NoSQL request-serving systems and Big Data analytics platforms. As its second contribution, this dissertation looks at Big Data benchmarking from a new angle by comparing Big Data systems with respect to their features and functionality. This effort is specifically important in the context of increasing interest in using a unified system which is rich in functionality to serve different types of workloads, especially as the Big Data applications evolve and become more complex.