A course on big data analytics

被引：11
|
作者：
Eckroth, Joshua [1 ]
机构：
[1] Stetson Univ, DeLand, FL 32723 USA
来源：
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING | 2018年 / 118卷
关键词：
Curriculum; Undergraduate education; Big data; Cloud computing; PARALLEL;
D O I：
10.1016/j.jpdc.2018.02.019
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This report details a course on big data analytics designed for undergraduate junior and senior computer science students. The course is heavily focused on projects and writing code for big data processing. It is designed to help students learn parallel and distributed computing frameworks and techniques commonly used in industry. The curriculum includes a progression of projects requiring increasingly sophisticated big data processing ranging from data preprocessing with Linux tools, distributed processing with Hadoop MapReduce and Spark, and database queries with Hive and Google's BigQuery. We discuss hardware infrastructure and experimentally evaluate the cost/benefit of an on-premise server versus Amazon's Elastic MapReduce. Finally, we showcase outcomes of our course in terms of student engagement and anonymous student feedback. (C) 2018 Elsevier Inc. All rights reserved.
引用
收藏
页码：166 / 176
页数：11
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