Abstract: A services platform acts as an intermediary between an existing enterprise analytic environment, and one or more underlying cloud service providers. The platform provides enterprise “big data-as-a-service,” by which an enterprise can seamlessly and easily provision new capacity for processing its analytic workload, and migrate data sources (e.g., data warehouse marts, enterprise data warehouses, analytic sandboxes, and the like) to the cloud for processing. The platform provides end-to-end enterprise class manageability of enterprise data assets, from data collection, aggregation, movement, staging and processing, all while providing service levels, security, access and governance. The platform integrates directly but seamlessly into the enterprise analytic stack, and existing analytics applications work as normal. The platform provides a way for the enterprise to translate its workloads into clusters of compute resources that meet its service level requirements.

Abstract: A services platform acts as an intermediary between an existing enterprise analytic environment, and one or more underlying cloud service providers. The platform provides enterprise “big data-as-a-service,” by which an enterprise can seamlessly and easily provision new capacity for processing its analytic workload, and migrate data sources (e.g., data warehouse marts, enterprise data warehouses, analytic sandboxes, and the like) to the cloud for processing. The platform provides end-to-end enterprise class manageability of enterprise data assets, from data collection, aggregation, movement, staging and processing, all while providing service levels, security, access and governance. The platform integrates directly but seamlessly into the enterprise analytic stack, and existing analytics applications work as normal. The platform provides a way for the enterprise to translate its workloads into clusters of compute resources that meet its service level requirements.

Abstract: A services platform acts as an intermediary between an existing enterprise analytic environment, and one or more underlying cloud service providers. The platform provides enterprise “big data-as-a-service,” by which an enterprise can seamlessly and easily provision new capacity for processing its analytic workload, and migrate data sources (e.g., data warehouse marts, enterprise data warehouses, analytic sandboxes, and the like) to the cloud for processing. The platform provides end-to-end enterprise class manageability of enterprise data assets, from data collection, aggregation, movement, staging and processing, all while providing service levels, security, access and governance. The platform integrates directly but seamlessly into the enterprise analytic stack, and existing analytics applications work as normal. The platform provides a way for the enterprise to translate its workloads into clusters of compute resources that meet its service level requirements.

Abstract: A services platform acts as an intermediary between an existing enterprise analytic environment, and one or more underlying cloud service providers. The platform provides enterprise “big data-as-a-service,” by which an enterprise can seamlessly and easily provision new capacity for processing its analytic workload, and migrate data sources (e.g., data warehouse marts, enterprise data warehouses, analytic sandboxes, and the like) to the cloud for processing. The platform provides end-to-end enterprise class manageability of enterprise data assets, from data collection, aggregation, movement, staging and processing, all while providing service levels, security, access and governance. The platform integrates directly but seamlessly into the enterprise analytic stack, and existing analytics applications work as normal. The platform provides a way for the enterprise to translate its workloads into clusters of compute resources that meet its service level requirements.

Abstract: A services platform acts as an intermediary between an existing enterprise analytic environment, and one or more underlying cloud service providers. The platform provides enterprise “big data-as-a-service,” by which an enterprise can seamlessly and easily provision new capacity for processing its analytic workload, and migrate data sources (e.g., data warehouse marts, enterprise data warehouses, analytic sandboxes, and the like) to the cloud for processing. The platform provides end-to-end enterprise class manageability of enterprise data assets, from data collection, aggregation, movement, staging and processing, all while providing service levels, security, access and governance. The platform integrates directly but seamlessly into the enterprise analytic stack, and existing analytics applications work as normal. The platform provides a way for the enterprise to translate its workloads into clusters of compute resources that meet its service level requirements.

Abstract: A multi-user file storage service and system enable each user of a pre-subscribed user group to operate an arbitrary client node at an arbitrary geographic location, to communicate with a remote file server node via a wide area network and to access the files of the file group via the respective client node in communication with the remote file server node via the wide area network. More than one user of the pre-subscribed user group is permitted to access the file group at the remote file server node simultaneously. Illustratively, the integrity of the files at the remote file server node are maintained by controlling each access to each file at the remote file server node so that each access to files at the remote file server is performed, if at all, on a respective portion of each file as most recently updated at the remote file server node.