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Subject: Data Analytics
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Keywords: Big Data, Big Data Analytics, Cloudera, Hadoop, MapReduce, Shared storage
Document Price: Free
Document Date: 2011-10
Document Number:
Author(s): John Webster
# of Pages: 17
Short Description:
We’re seeing dramatic growth in the use of Big Data database architectures (Hadoop MapReduce for example). While these are best known in the context of web-based applications and development activities, they are no longer confined to the web. Cloudera, EMC Greenplum, IBM Netezza, NoSQL, and ParAccel are all examples of database architectures that are being
Full Summary:
We’re seeing dramatic growth in the use of Big Data database architectures (Hadoop MapReduce for example). While these are best known in the context of web-based applications and development activities, they are no longer confined to the web. Cloudera, EMC Greenplum, IBM Netezza, NoSQL, and ParAccel are all examples of database architectures that are being used increasingly in “Big Data” business analytics applications within corporate data centers.
However, the SAN and NAS systems typically found in corporate data centers are typically shunned by Big Data analytics practitioners. Yet there is a case to be made for shared storage here, certainly as a secondary storage facility that offers data protection and data persistence services, and possibly more. This presentation by John Webster makes the case for using shared storage in support of Big Data analytics.
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SNW Fall 2011 Presentation: Shared Storage for Shared Nothing
We’re seeing dramatic growth in the use of Big Data database architectures (Hadoop MapReduce for example). While these are best known in the context of web-based applications and development activities, they are no longer confined to the web. Cloudera, EMC Greenplum, IBM Netezza, NoSQL, and ParAccel are all examples of database architectures that are being used increasingly in “Big Data” business analytics applications within corporate data centers.
However, the SAN and NAS systems typically found in corporate data centers are typically shunned by Big Data analytics practitioners. Yet there is a case to be made for shared storage here, certainly as a secondary storage facility that offers data protection and data persistence services, and possibly more. This presentation by John Webster makes the case for using shared storage in support of Big Data analytics.