MGT 560M Home (FL18)

This is an inactive course webpage.


Lectures and Labs

  • Lectures will be held on FRI (Oct 19, Oct 26, Nov 2) 8:30-11:30am in Emerson Auditorium in Knight Hall (Room 110).
  • Labs will be held in two sections:
    • Section 1 meets at
      • 1-4pm FRI on Oct 19, Oct 26, and Nov 2 in Eads 016
    • Section 2 meets at
      • 1-4pm on SAT Oct 20 in Bauer 330
      • 9am-12pm on Oct 27 in Bauer 330
      • 9am-12pm on Nov 3 in Bauer 210

Instructor: Marion Neumann
Office: Jolley Hall Room 222
Contact: Please use Piazza!
Office Hours: TUE 11:30am-12:30pm and 3-4pm

TAs: Jonathan (Head TA), Eric, Erik, Lan, Jonny, Steven, Yachen, Zac

WED 12-2pm (Erik, Jonathan) in Urbauer 215
THU 2-4pm (Lan) in Jolley 224
SUN 1-3pm (Eric, Jonny) in Jolley 408

This course introduces Hadoop, and related technologies supported by the Apache Foundation, as the current standard in facilitating storage of vast amounts of heterogeneous data across commodity servers. Students will learn about projects supported by the Apache Foundations, including Hadoop, YARN, MapReduce, Sqoop, Hive, Pig, and Spark. Each of these plays a unique role in the development of clusters of commodity servers, managing vast amounts of structured and unstructured data, parallel processing, organizing data for analysis, and developing queries for reports. Through hands-on examples using relevant data, students develop competencies in these technologies, realizing the challenges and opportunities of Big Data.

Prerequisites: MGT560G


Course Outline and Materials (Lectures, Labs, Assignments, and Reading)

Grades on Canvas

Resources and HowTos


Please ask any questions related to the course materials and homework problems on Piazza. I cannot promise to monitor Piazza 24/7, but other students might have the same questions and/or are able to provide a quick answer.
Any public postings of (partial or full) solutions to homework problems (written or in form of source or pseudo code) will result in a grade of zero for that particular problem for ALL students in the course.