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50.043 Database Systems Course Handout

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Course Description

Database systems manage data which is at the heart of modern computing applications including artificial intelligence systems. This course covers (1) the fundamentals of relational databases, including ER diagram, relational model, SQL and relation normalization (2) DBMS internal techniques, e.g., storage and index, query optimization, transactions and (3) database applications, such as big data systems(Hadoop and Spark), NoSQL databases, or vector databases (TBD). Part 1 allows you to design/use databases. Part 2 enables you to be a data engineer and design/use databases efficiently. Part 3 gives you core ideas of other database systems.

The homeworks and exams will help you to understand the concepts, while the course project will allow students learn important problems that data management systems are designed to solve and experience the internal design and implementation of relational databases. Students will also have the chance to use big data systems on Amazon cloud (Amazon Web Service).

Learning Resources

The main resources are lecture slides, tutorial practices, and online documentations. There are no official textbooks. But the following are useful for reference and deeper understanding of some topics.

  1. Abraham Siberschatz, Henry Korth, S Sudarshan. Database System Concepts. 6th edition. (DSC)
  2. Raghu Ramakrishnan, Johannes Gehrke. Database management systems. 3rd edition (DBM)
  3. Hector Garcia-Molina, Jeffrey D. Ullman, Jennifer Widom. Database systems, the complete book. 2nd edition. (DS)

Instructors

  • Kenny Lu (kenny_lu@sutd.edu.sg) (Please send email to arrange)

  • Yanxia Qin (yanxia_qin@sutd.edu.sg) Office Hour: Monday 2:30-4:00 pm (please send email to arrange)

TAs

  • Matthaeus Choo (matthaeus_choo@mymail.sutd.edu.sg)

  • Afifah Mesha Putri (afifahmeshaputri_afifah@mymail.sutd.edu.sg)

Communication

If you have course/assignment/project related questions, please post it on the Discussion channel in eDimension.

Grading

Your final grade is computed as follows:

  1. Homework: 10% There will be 2 homework assignments, 5 points each.

  2. Project: 40% Group project, 3 students per group. Unless notifying the instructors otherwise, all group members have the same grade for the project.

  3. Mid-term quiz: 5% Held during class before Week 6. Format is similar to final exam. Only first four weeks' topics will be covered.

  4. Final exam: 40% Written exam on Week 14 covering all topics.

  5. Class participation: 3% If attendance rate >= 90%, get full participation marks. 90% < Attendance >= 70%, get 2 marks. 70% < Attend >= 50% to get 1 mark. Otherwise 0. Ask/answer 2 questions on eDimension, spot 1 mistake in slides, can make up for one missed class.

  6. Course Survey: 2% Can obtain survey marks only when attandance rate >= 70%.

Things you need to prepare

  • If you are using Windows 10 or Windows 11, please install ubuntu subsystems
  • If you are using Linux, it should be perfect.
  • If you are using Mac, please install homebrew.
  • Make sure Java >8 is installed and ant is installed.
  • Ubuntu: sudo apt install ant ant-contrib
  • Mac: brew install ant ant-contrib
  • When you have the AWS educate invitaiton email (before week 2). Please work on the AWS academy setup.

Project

Please refer to the project page.

Submission Policy and Plagiarism

  1. You will do the assignment/project on your own (own teams) and will not copy paste solutions from someone else.
  2. You will not post any solutions related to this course to a private/public repository that is accessible by the public/others.
  3. Students are allowed to have a private repository for their assignment which no one can access.
  4. For projects, students can only invite their partners as collaborators to a private repository.
  5. Failing to follow the Code of Honour will result in failing the course and/or being submitted to the University Disciplinary Committee. The consequences apply to both the person who shares their work and the person who copies the work.
  6. You must explicitly disclose how AI tools are used, and be responsible for your submissions.

Schedule (18 May 2026 - 24 August 2026)

Week (MM/DD) Lecture Cohort Reference Remarks
1 (5/18) Intro, ER Model ER Model DBM: Chapter 1-2,
DSC: Chapter 7
2 (5/25) Relational Model, Relational Algebra Relational Model, Relational Algebra DBM: Chapter 3-4,
DSC: Chapter 2 & 6		 Wed. cohort1 will be re-arranged due to PH.
3 (6/1) SQL SQL DBM: Chapter 5,
DSC: Chapter 2-4		 Mon. lec will be canceled due to PH. Project Team Submission (6/5 23:59)
4 (6/8) Functional Dependency, Normal Forms Functional Dependency, Normal Forms DBM: Chapter 19,
DSC: Chapter 8
5 (6/15) Storage, Index Strorage, Index DBM: Chapter 8-10,
DSC: Chapter 10-11		 Assignment 1 Submission (6/19 23:59)
6 (6/22) Query Operations Query Operations DBM: Chapter 12-14, DSC: Chapter 12 Project Lab 1 Submission ( 6/26 23:59)
7 (6/29) Recess Week
8 (7/6) Query Optimization Query Optimization DBM: Chapter 15 ,
DSC: Chapter 13		 Project Lab 2 Submission ( 7/10 23:59)
9 (7/13) Transaction Recovery and Concurrency Transactions DBM: Chapter 16-18,
DSC: Chapter 14-16
10 (7/20) HDFS, MapReduce HDFS, MapReduce Project Lab 3 Submission (7/24 23:59).
11 (7/27) Spark Spark
12 (8/3) Guest Lecture, NoSQL DBs TBD Assignment 2 Submission (8/7 23:59)
13 (8/10) Revision week - Project Lab 4 Submission (8/12 23:59)
14 (8/17) Exam week

Make Up and Alternative Assessment

Make ups for Final exam will be administered when there is an official Leave of Absence from OSA. There will be only one make up. There will be no make-up if students miss the make up test.