We describe how multi-national retailer ADEO successfully employed database virtualization to migrate all workloads of a complex Enterprise Data Warehouse (EDW) from a legacy Teradata system to Google BigQuery. We demonstrate the generality of the technology and the approach.

One can audit SQL applications by running SQL programs over sequences of persistent snapshots, but care is needed to avoid wasteful duplicate computation. This paper describes the design, implementation, and performance of RID, the first language-independent optimization framework that eliminates duplicate computations in SQL programs running over low-level snapshots by exploiting snapshot metadata efficiently.

A large class of modern distributed file systems treat metadata services as an independent system component, separately from data servers. The availability of the metadata service is key to the availability of the overall system. Given the high rates of failures observed in large-scale data centers, distributed file systems usually incorporate high-availability (HA) features. A typical approach in the development of distributed file systems is to design and develop metadata services from the ground up, at significant cost in terms of complexity and time, often leading to functional shortcomings. Our motivation in this paper was to improve on this state of things by defining a general-purpose architecture for HA metadata services (which we call RMS) that can be easily incorporated and reused in new or existing file systems, reducing development time. Taking two prominent distributed file systems as case studies, PVFS and HDFS, we developed RMS variants that improve on functional shortcomings of the original HA solutions, while being easy to build and test. Our extensive evaluation of the RMS variant of HDFS shows that it does not incur an overall performance or availability penalty compared to the original implementation.

Resource allocation policies in public Clouds are today largely agnostic to requirements that distributed applications have from their underlying infrastructure. As a result, assumptions about data-center topology that are built-into distributed data-intensive applications are often violated, impacting performance and availability goals. In this paper we describe a management system that discovers a limited amount of information about Cloud allocation decisions - in particular VMs of the same user that are collocated on a physical machine - so that data-intensive applications can adapt to those decisions and achieve their goals. Our distributed discovery process is based on either application-level techniques (measurements) or a novel lightweight and privacy-preserving Cloud management API proposed in this paper. Using the distributed Hadoop file system as a case study we show that VM collocation in a Cloud setup occurs in commercial platforms and that our methodologies can handle its impact in an effective, practical, and scalable manner.