maheshsv / big-data-mapreduce-course

Big Data, MapReduce, Spark, PySpark, Java @ Santa Clara University, Fall 2016

Santa Clara University

Big-Data-MapReduce-Course

Course Information

• Fall 2016: Graduate Business, Leavey School of Business
• Course MSIS 2627: Big Data Modeling & Analytics
• Big-Data/MapReduce Course @ Santa Clara University
• Class duration: September 19 - December 8, 2016
• Class hours:
  • Monday 5:45pm - 7:00pm PST
  • Wednesday 5:45pm - 7:00pm PST
• Class room: Lucas Hall 208
• Office: 321 T, Lucas Hall
• Required books and papers (all resources are online):
  • 1. A
 Very
Brief 
Introduction
 to 
MapReduce by Diana 
MacLean
  • 2. Introduction to MapReduce by Mahmoud Parsian
  • 3. Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer
  • 4. Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman

Syllabus

Midterm Exam: TBDL, 2016 from 5:45pm to 7:00pm PST

Final Exam: Monday, December 5, 2016 from 5:45pm-7:45pm PST

Description

The main focus of this class is to cover the following concepts:

• Concepts of Big Data
• Distributed File Systems
• Distributed Computing
• Distributed and Parallel Algorithms
• MapReduce Paradigm
• Scale-out Architectures (using Hadoop, Spark, PySpark)
• Apache Spark: http://spark.apache.org/
• Use Spark, Py-Spark, Hadoop, and Java to teach MapReduce and distributed computing

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My latest book:

Data Algorithms Book

Data Algorithms: Recipes for Scaling up with Hadoop and Spark