As organizations strive to identify and realise the value in Big Data, many now seek more agile and capable analytic systems. Some of the business drivers are to improve customer retention, increasing operational efficiencies, influence product development and quality, and to gain a competitive advantage. While many have piloted Hadoop as a data repository for simple workloads, Hadoop is not just a storage platform for big data, as it also serves as the computational platform for business analytics. Hadoop is an ecosystem of several services rather than a single product, and is designed for storing and processing Petabytes of data in a linear scale-out model. Each service in the Hadoop ecosystem may use different technologies to ingest, store, process and visualize data. As organizations look at accelerating their analytics workloads beyond traditional batch processing into real time event streaming and in-memory analytics, they realize that current architectures are not as flexible and do not perform well as a multi-tenant platform. This limitation leads to deploying separate clusters for each workload, and duplicating data across these clusters.
October 23, 2015
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