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Fall 2017 Semester
Sep 26, 2017
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Information Select the desired Level or Schedule Type to find available classes for the course.

CS 4240 - Large-Scale Parallel Data Processing
Covers techniques for managing and analyzing very large data sets, with an emphasis on approaches that scale out effectively as more compute nodes are added. Introduces principles of distributed data management and strategies for problem-driven data partitioning through a selection of design patterns from various application domains, including graph analysis, databases, text processing, and data mining. Offers students an opportunity to obtain hands-on programming experience with modern big-data processing technology such as MapReduce, Spark, HBase, and cloud computing (this selection is subject to change as technology evolves).
4.000 Credit hours
4.000 Lecture hours

Levels: Undergraduate
Schedule Types: Lecture

Computer Science Department

Course Attributes:
Computer&Info Sci

Restrictions:
Must be enrolled in one of the following Levels:     
      Undergraduate

Prerequisites:
(Undergraduate level CS 3600 Minimum Grade of D- or Undergraduate level CS 3650 Minimum Grade of D- or Undergraduate level CS 5600 Minimum Grade of D- or Graduate level CS 5600 Minimum Grade of C-) and (Undergraduate level CS 4800 Minimum Grade of D- or Undergraduate level CS 5800 Minimum Grade of D- or Graduate level CS 5800 Minimum Grade of C-)

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