IEMS5730 Big Data Systems and Information Processing / Spring 2026

Announcements

Final Exam for ALL MSc students taking IEMS5730: May 4, Monday from 5:30pm to 8:30pm at SHB801.
A Make-up Lecture for IERG4330 and IEMS5730 will be held on April 20 (Monday) evening, 7:00pm to 10:00pm at SHB801.
Released: [Assignment #4 - GraphFrames, HBase]. Due: Mon, April 27, 23:59PM.
Released: [Assignment #3 - SparkSQL, Kafka, and Streaming]. Due: Tue, April 7, 23:59PM.
The details of final project has been released, please refer to Project and the Course Content in the Blackboard.
Released: [Assignment #2 - Pig, Hive and SparkRDD]. Due: Sun, March 1, 23:59PM.
Released: [Assignment #1 - Community Detection]. Due: Tue, February 10, 23:59PM.
The due date for HW0 is strict for all. Late-add student will NOT be granted extra time extension for submission. For HW submission of late-add students, please email your submission directly to the TAs via email.
Released: [Assignment #0 - Hadoop Cluster Setup]. Due: Sat, January 17, 23:59PM.
For IEMS students who have already taken ENGG5108, you only need to attend the alternative lectures from Week 1 to Week 4. Then the lectures will change back to the original schedule from Week 5.

Please pick the time slot(s) you CANNOT show up via: Google Form before 17:00, Jan 6, 2025.
Website account: bigdata, password: spring2026bigdata

Course Description

This course aims to provide students an understanding in the operating principles and hands-on experience with mainstream Big Data Computing systems. Open-source platforms for Big Data processing and analytics would be discussed. Topics to be covered include:

Programming models and design patterns for mainstream Big Data computational frameworks ;
System Architecture and Resource Management for Data-center-scale Computing ;
System Architecture and Programming Interface of Distributed Big Data stores ;
High-level Big Data Query languages and their processing systems ;
Operational and Programming tools for different stages of the Big Data processing pipeline including data collection/ ingestion, serialization and migration, workflow coordination.

Prerequisite: This course contains substantial hands-on components which require solid background in programming and hands-on operating systems experience. If you have never used a command-line interface to install/configure/manage an operating system, e.g. a linux-based one, you will need to pick-up the skills yourself and IT CAN BE VERY TIME-CONSUMING for you to complete the homeworks. (Students without the aforementioned required background may take several 10’s of hours to finish EACH homework assignment).

Please check Blackboard for important announcements, assignment submissions, grades, etc.

Course Assessment

The grade is based on the following components (tentative):

Homework & Programming Assignments (5 sets): 60%
Project with Presentation: 10%
Final Exam: 30%

Student/Faculty Expectations on Teaching and Learning

http://mobitec.ie.cuhk.edu.hk/StaffStudentExpectations.pdf

Academic Honesty

You are expected to do your own work and acknowledge the use of anyone else’s words or ideas. You MUST put down in your submitted work the names of people with whom you have had discussions.

Refer to http://www.cuhk.edu.hk/policy/academichonesty for details

When scholastic dishonesty is suspected, the matter will be turned over to the University authority for action.

You MUST include the following signed statement in all of your submitted homework, project assignments and examinations. Submission without a signed statement will not be graded.

I declare that the assignment here submitted is original except for source material explicitly acknowledged, and that the same or related material has not been previously submitted for another course. I also acknowledge that I am aware of University policy and regulations on honesty in academic work, and of the disciplinary guidelines and procedures applicable to breaches of such policy and regulations, as contained in the website http://www.cuhk.edu.hk/policy/academichonesty/.

Academic Honesty Slides from Associate Dean of Faculty of Engineering

Large Language Models (LLMs) Policy

You are NOT allowed to use any LLMs (e.g., ChatGPT, Claude etc.) in this course, unless it is explicitly approved by the instructor in advance. Anyone who uses LLMs for completing the homework will be treated as cheating.