Conference proceeding
Raspberry HadooPI: A Low-Cost, Hands-On Laboratory in Big Data and Analytics (Abstract Only)
Proceedings of the 46th ACM Technical Symposium on Computer Science Education, pp 687-687
24 Feb 2015
Abstract
Educating STEM students in the techniques of massively parallel computing anticipates a growing current and future need for scientists, engineers, and analysts who are facile with Big Data. Using very low cost hardware (Raspberry Pi) and free software (Hadoop) we are exposing students to distributed computing while limiting expense. We anticipate that micro-cluster labs and projects will give students hands on experience necessary so they can be prepared to use these methods in real world applications. A series of lessons and projects were developed to teach Hadoop and MapReduce, and were extended into STAR (Students Tackling Advanced Research) summer competitive research projects.
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Details
- Title
- Raspberry HadooPI
- Creators
- Kenneth Fox - Drexel University, Philadelphia, PA, USAWilliam Mongan - Drexel UniversityJeffrey Popyack - Drexel University
- Publication Details
- Proceedings of the 46th ACM Technical Symposium on Computer Science Education, pp 687-687
- Conference
- SIGCSE '15: The 46th ACM Technical Symposium on Computer Science Education
- Series
- ACM Conferences
- Publisher
- ACM
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- College of Computing and Informatics
- Other Identifier
- 991021870302404721