BIG DATA: 4) The Big Data Toolbox: C. Auxiliary Tools ============================ 1. Cloud Services. Computational services: • Amazon Elastic Compute Cloud (Amazon EC2) • Google Prediction API & BigQuery (as both were initially offered as part of the discontinued Google Labs program, Google may choose to commercialize them as domainspecific rather than generic data-crunching services, as with the Earth Builder geo-spatial applications) Data collections: • Factual (diverse) • InfoChimps (diverse) • Windows Azure Marketplace DataMarket (diverse) • Hoovers (business) • Urban Mapping (geographic) • Xignite (finance) There are also a number of companies that offer Cloud-based database systems (mainly relational) that are more likely to be used for social or mobile enterprise applications than Big Data storage, processing or analytics. These include: • Database • Amazon Relational Database Service (RDS) • Microsoft SQL Azure • Xeround Caveats ===== In addition to addressing concerns common to the Cloud model in general (like privacy, efficiency, vendor lock-in, interactivity, etc.), one needs in particular to carefully weigh the unique challenges of working remotely with very large data sets. Such sets are expensive and slow to move around, and can tax even the best network capabilities. For example, with a T1 (1.544Mbps) connection, it would take a minimum of 82 days to upload one terabyte of data, and a minimum of two weeks with a 10Mbps connection, which is why Amazon AWS proposes shipping portable storage devices instead, with Amazon then using its high-speed internal network (bypassing the Internet) to get the data to its final Amazon destination. to be contd . . .
Posted on: Tue, 21 Jan 2014 05:15:22 +0000
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