This is an inactive course webpage.
Lectures and Labs
- Lecture: THU 4-5:20pm in Wilson 214
- Lab – Section 1: TUE 1-2:20pm in Eads 016
- Lab – Section 2: TUE 4-5:20pm in January 110
Instructor: Marion Neumann
Office: Jolley Hall Room 222
Contact: Please use Piazza!
Office Hours: TUE 3-4pm or individual appointment (request via email – allow for 1-2 days to reply and schedule)
Try to avoid drop ins w/o appointment outside my office hours.
Head TA: Jonathan C (takes care of all grading issues – contact via Piazza or in his office hours)
TAs: Alexis, Arushee, Jordie, Kevin, Lorenzo, Patrick, Steven, Wentao, Zhibo
TA Office Hours:
Monday Sever 300 @ 2:30-4pm Alexis, Jonathan Wednesday Lopata 201 @ 2:30-4:30pm Lorenzo, Patrick Friday Lopata 201 @ 10am-2pm Wentao, Jordie, Steven Sunday Rudolph 201 @ 9-11am Kevin, Zhibo
This course provides a comprehensive introduction to applied parallel computing using the MapReduce programming model facilitating large scale data management and processing. There will be an emphasis on hands-on experience working with the Hadoop architecture, an open-source software framework written in Java for distributed storage and processing of very large data sets on computer clusters. Further, we will derive and discuss various algorithms to tackle big data applications and make use of related big data analysis tools from the Hadoop ecosystem, such as Pig, Hive, Impala, and Apache Spark to solve problems faced by enterprises today. Check the Roadmap for more detailed information.
Prerequisites: CSE 131 (solid background in programming with Java), CSE 247, and CSE 330 (basic knowledge in relational databases (RDMS), SQL, and AWS). Use this prerequisite check list if you are not sure.
This class counts towards the Certificate in Data Mining and Machine Learning as applications course.
This class uses materials from the Cloudera Developer Training for MapReduce, the Cloudera Data Analyst Training: Using Pig, Hive, and Impala with Hadoop, and the Cloudera Developer Training for Apache Spark, which are made available to Washington University through the Cloudera Academic Parntership program. Further contents are based on the “Mining of Massive Data Sets” book and class taught at Stanford by Jure Leskovec.
Please ask any questions related to the course materials and homework problems on Piazza. Other students might have the same questions 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.