Data and Information Management, Big Data, Data Science

A Wider View

Hadoop's Next-Generation YARN As the undisputed pioneer of big data, Google established most of the key technologies underlying Hadoop and many of the NoSQL databases. The Google File System (GFS) allowed clusters of commodity servers to present their internal disk storage as a unified file system and inspired the Hadoop Distributed File System (HDFS). Google's column-oriented key value store BigTable influenced many NoSQL systems such as Apache HBase, Cassandra and HyperTable. And, of course, the Google Map-Reduce algorithm became the foundation computing model for Hadoop and was widely implemented in other NoSQL systems such as MongoDB.
5MB: MultiValue - December 19, 2012 Issue

Uniting Operations Research with Time-Based DB Performance Analysis Most Oracle performance analysis is now time-based. But it is "total time"-focused: Time to process a SQL statement, a batch process, or the CPU consumed plus Oracle wait time that occurred over an interval of time. This is a fantastic way to approach optimization because it is easy to monitor improvement and it is closer to what a user is experiencing. And, with just a couple twists, we can unite Operations Research (OR) queuing theory with the Oracle time-based approach, opening up an entirely new arena for performance analysis.
DBTA E-Edition - December 2011 Issue

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