Fall 2020 - CMPT 756 G100

Systems For Big Data (3)

Class Number: 6673

Delivery Method: In Person


  • Course Times + Location:

    Tu 9:30 AM – 12:20 PM

  • Prerequisites:

    Operating Systems (CMPT 300) and Data Base Systems (CMPT 354), or equivalents.



From health care to social media the world generates a tremendous amount of data every day, often too much to be processed on a single computer or even some-times a single data centre. In this graduate seminar we will learn about technologies and systems behind Big Data. In particular, we will discuss what challenges exist in processing and storing massive amounts of data. We will explore how these challenges are being solved in real-world systems as well as the limitations inherent in these designs. The evolution of these technologies will be explored by reading both current and historically significant research papers. Students with credit for CMPT 886 when offered as a Special Topics course in Big Data may not take this course for further credit.


This course will survey a variety of tools essential for developing data analytics that run at scale in modern data centres. Approximation techniques are often required to handle high volumes and velocities of data. Agile development methods allow teams to adjust their products to changes in data variety and customer needs. Latency and throughput can be improved by tailoring implementations to GPUs and wide-scale distribution. You navigate these complex choices guided by the high-level overview of the design space of distributed applications, producing scalable, fault-tolerant, efficient analytic tools.


  • Agile software development
  • Instrumenting and monitoring performance
  • Cloud computing
  • Acceleration using GPU and cloud resources
  • Designing approximation algorithms
  • Reliability and consistency in distributed systems
  • The design space of distributed applications


  • Tentative grading breakdown: • Assignments (30%) • Term Project (50%) • Quizzes (20%) The Term Project, as well as some assignments, will be a group project. This breakdown is tentative. The grading and assignment policy will be finalized in the first week of class. Students must attain an overall passing grade on the Term Project in order to obtain a clear pass (C- or better). 100%



More Effective Agile: A Roadmap for Software Leaders, Steve McConnell, Construx Press, Aug. 24 2019, , Up-to-date volume on practicing agile.
ISBN: 978-1733518215

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:


SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html


Teaching at SFU in fall 2020 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112).