Enterprise big data-as-a-service

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 lifecycle of data analytics from movement to warehousing, however, is provided by the platform. The platform provides a way for the enterprise to translate its workloads into clusters of compute resources that meet its service level requirements.

Description

BACKGROUNDTechnical Field[0001]This disclosure relates generally to data analytics and, in particular, to a services platform upon which enterprises may execute their data analytic workloads in a secure and cost-efficient manner.Background of the Related Art[0002]“Big Data” refers to collections of data sets that are too large and complex for processing (e.g., analysis and visualization) using conventional database management tools or traditional data processing applications. On-premises “big data” analytic environments typically are predicated on enterprises “developing” or purchasing complete analytic infrastructure, whether via software, hardware (storage, compute, networking), or appliances. Subsequently, this infrastructure has to be deployed, maintained, and extended, requiring further significant investments and time delay. This dependence “slows” down the agility of the enterprises. Simple new projects for exploring analytics of newly procured data can take months if not lon...

AI Technical Summary

Benefits of technology

[0005]To address these problems, a services platform acts as an intermediary between an existing enterprise analytic environment, and one or more underlying cloud service providers. The platform provides an enterprise “big data-as-a-service” (“EBDaaS”), 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 (life-cycle) enterprise class manageability of the enterprise data assets, from data collection, aggregation, movement, staging and processing, all while provide service levels, security, access and governance.

[0007]In this manner, the platform integrates directly but seamlessly into the enterprise analytic stack, and existing analytics applications work as normal. The lifecycle of data analytics from movement to warehousing, however, is provided by the platform. The platform provides a way for the enterprise to translate its workloads into clusters of compute resources that meet its service level requirements at reasonable cost.

Problems solved by technology

Subsequently, this infrastructure has to be deployed, maintained, and extended, requiring further significant investments and time delay.

This dependence “slows” down the agility of the enterprises.

These cloud environments, however, typically are too complex for larger enterprises to understand, interact, provision, and manage In particular, such environments are designed for “self-serve” IT in smaller companies, or companies that have already built their businesses in the cloud.

To use these solutions, an end user typically has to manually pick piece parts of type of nodes, number of nodes, the type of database distribution, and the like, possibly without a good understanding of how such selections impact performance Also, it is difficult for an end user to estimate the overall cost of the cloud infrastructure, because most of the costing is based on per hour type-pricing.

The type of expertise that is required to manage the complexity (lifecycle of data, analytic workload translation, service level agreements, and the like) required for analytic type processing may not be available to larger enterprises, especially those that are new to the cloud.

Classic cloud environments, in contrast, are much more complex to scale up to large enterprise needs.

A further complication to transitioning on-premises analytic processing to the cloud is the difficulty in moving enterprise data to the cloud, and doing so in a secure and cost-effective manner.

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