Big Data

Big data is about processing, analysing and managing huge amounts of data to extract business value in the most effective manner.
Enterprise content management plays a critical role with big data and has responded to the need for content to be widely and quickly accessible as more users seek to extract value from their data. Often, these users are geographically dispersed and include suppliers and partners. As a result, distributing data geographically so that it is closer to users has become more important.
In the past, almost all the data an organisation dealt with were records in their databases. Each unit of data was small and the trick was sifting through huge collections of structured records, usually stored in SQL databases, to find the ones you wanted. But in the modern world, the analysis model has become dramatically extended. Some of the most valuable analysis being done these days is the massive parallel analysis of large unstructured files, whether huge web logs, financial data, or sensor information.
In some cases, the same data is being shared by human users and computational analytical environments. But the data performance requirements of these two audiences are diametrically opposed. Human analysis requires an extreme service level for delivering a single file or set of files to a single user so that even the most granular, high performance dataset can be delivered with integrity. By comparison, best performance for computational analytical environments relies on the simultaneous movement of many streams of data with the highest possible overall parallel throughput.
Video surveillance is a classic example of this challenge—human users need to see the entire picture captured by the camera while computational analysis slices each frame of this same video into tiles that are all fed into the analysis engine in parallel for later integration. The ideal big data storage architecture should be able to manage both these use cases in a single shared architecture.

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