The Need to Consider Hardware Selection when Designing Big Data Applications Supported by Metadata

Authors: 
Nathan Regola, David Cieslak, Nitesh V. Chawla
Citation: 
Big Data Management, Technologies, and Applications
Publication Date: 
October, 2013

The selection of hardware to support big data systems is complex. Even defining the term “big data” is difficult. “Big data” can mean a large volume of data in a database, a MapReduce cluster that processes data, analytics and reporting applications that must access large datasets to operate, algorithms that can effectively operate on large datasets, or even basic scripts that produce a needed resulted by leveraging data. Big data systems can be composed of many component systems. For these reasons, it appears difficult to create a universal, representative benchmark that approximates a “big data” workload. Along with the trend to utilize large datasets and sophisticated tools to analyze data, the trend of cloud computing
has emerged as an effective method of leasing compute time. This chapter explores some of the issues at the intersection of virtualized computing (since cloud computing often uses virtual machines), metadata stores, and big data. Metadata is important because it enables many applications and users to access datasets and effectively use them without relying on extensive knowledge from humans about the data.