Mixapart: decoupled analytics for shared storage systems

Data analytics and enterprise applications have very dif-ferent storage functionality requirements. For this rea-son, 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 si-los. We introduce MixApart, a scalable data processing framework for shared enterprise storage systems. With MixApart, a single consolidated storage back-end man-ages enterprise data and services all types of workloads, thereby lowering hardware costs and simplifying data management. In addition, MixApart enables the local storage performance required by analytics through an in-tegrated data caching and scheduling solution. Our pre-liminary evaluation shows that MixApart can be 45% faster than the traditional ingest-then-compute workflow used in enterprise IT analytics, while requiring one third of storage capacity when compared to HDFS.