University of Toronto
Data analytics and enterprise applications have very different storage functionality requirements. For this reason, enterprise deployments of data analytics are on a separate storage silo. This may generate additional costs and inefficiencies in data management, e.g., whenever data needs to be archived, copied, or migrated across silos. The authors introduce MixApart, a scalable data processing framework for shared enterprise storage systems. With MixApart, a single consolidated storage back-end manages enterprise data and services all types of workloads, thereby lowering hardware costs and simplifying data management.