CS 838: Big Data Systems

CS 838: Big Data Systems
Fall '16: Course Home Page

[ Home | Reading List | Schedule | Assignments | Piazza ]

Overview, Syllabus, Structure

This class will introduce you to key concepts and state-of-the-art in big data systems. Since cloud computing is a key enabler of these systems, we will spend the first 3-4 lectures on an overview of cloud architecture. We will then jump into big data systems, and explore them from the bottom up.

Specifically, we will cover these topics:

Basics of cloud architecture
Systems such as YARN and Spark, and execution frameworks such as Map-Reduce and Tez
High level frameworks such as Hive, Impala, Presto, and Spark SQL
Scheduling and resource management in cluster computing
Geo-distributed analytics
Systems for streaming and approximate queries
Systems for large scale distributed graph processing, machine learning, and deep learning.

Thus, this course will complement the earlier offering on cloud computing (Fall 2012), which focused on the storage, virtualization, distributed systems and networking issues.

Readings: The course will be paper reading-based. See the reading list here.

Assignments and project: While readings will cover the "theory" behind big data systems, a set of 3 hands-on assignments, coupled with a significant course project, will help students explore the "practical" side. See a tentative outline of planned assignments here.

Admin Details

Course prerequisites: The prerequisites for this course are (CS 564 or CS 764) and (CS 537 or CS 736), or equivalent courses. Both grads and undergrads are welcome to take this class. Feel free to talk to me first if you feel you may not be able to "handle" it.

Text: There is no required text for this course. The lectures will be based on discussing research papers. The entire paper reading list is available here.

Piazza: We will be using Piazza for outside-class Q&A and to discuss papers. The system is highly catered to getting you help fast and efficiently from classmates, and myself. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. If you have any problems or feedback for the developers, email team@piazza.com. Find our class page at: https://piazza.com/class#fall2016/cs838bigdata.

Grade distribution:

Participation in class and on Piazza - 5%
Mid-term exam - 20%
Final exam - 20%
Assignments/Projects - 55%

Class Time: MWF 1:00 PM - 2:15 PM

Location: Psychology 107

Instructor: Aditya Akella
Email: akella@cs.wisc.edu
Office: CS 7379
Office Hours: 2:30pm-3:30pm, Monday and Friday. Also by appointment.

Teaching assistants:

Arjun Singhvi
Office: CS 7372
Office Hours: Tu,F (4:00 - 5:00 PM)
Raajay Viswanathan
Office: CS 7376
Office Hours: Tu,Th (3:30 - 4:00 PM)
e-mail: cs838fall2016@gmail.com