Outline

Overview

This unit of study covers the data engineering issues of building robust and scalable data processing pipelines. While data engineers may not be directly performing data analysis, they must have the technical knowledge and skillset to provide data analysts with appropriate data analytics architectures and to provide them with reliable and well-formed data that is ready to be analysed. Topics covered range from data ingestion from various sources including databases, text files and web services, to data cleaning and data transformation approaches, and the system architectures that allow the pipeline to run efficiently and automatically. Special consideration is given to building scalable data analysis solutions using a blend of Big Data processing techniques including data stream processing and distributed data processing platforms such as Apache Spark.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	COMP5310
Corequisites ?	None
Prohibitions ?	OCMP5339
Assumed knowledge ?	Proficiency in programming, especially Python, and in database querying with SQL; basic Unix scripting
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Lijun Chang, lijun.chang@sydney.edu.au
Lecturer(s)	Lijun Chang, lijun.chang@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final Examination Final exam; in-person written exam;	50%	Formal exam period	2 hours
Outcomes assessed:
Tutorial quiz	Tutorial quiz-1 Answer multiple-choice questions during tutorial	5%	Week 07	N/A
Outcomes assessed:
Assignment	Group Project Stage 1 Practical data integration and data analysis assignment.	10%	Week 08 Due date: 17 Apr 2025 at 23:59	N/A
Outcomes assessed:
Assignment	Group Project Stage 2 Practical data integration and data analysis assignment.	30%	Week 12 Due date: 22 May 2025 at 23:59	N/A
Outcomes assessed:
Tutorial quiz	Tutorial quiz-2 Answer multiple-choice questions during tutorial	5%	Week 12	N/A
Outcomes assessed:
= hurdle task ? = group assignment ? = AI allowed ?

Assessment summary

Tutorial quiz: There are two tutorial quizes conducted during tutorial time. These are short quizzes to test the material covered in the previous weeks.
Group project: The group project will examine your understanding of the concepts presented in the unit. The assignment task will measure your knowledge of data ingestion and processing pipeline, your skill in applying and evaluating these methods using Python, and your ability to communicate the results and conclusions professionally. This assignment will help you develop valuable communication and collaboration skills and allow you to contextualise your data engineering skills on real applied problems.
Final exams: The exam questions will test your understanding of the essential characteristics and theoretical properties of the methods covered. They will also assess your ability to make informed comparisons of data engineering methods, correctly interpret and analyse data engineering results, and formulate substantive conclusions based on these results.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).
As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85-100
Distinction	75-84
Credit	65-74
Pass	50-64
Fail	0-49	When you don’t meet the learning outcomes of the unit to a satisfactory standard. It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the final examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

For more information see sydney.edu.au/students/guide-to-grades .

For more information see guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

No extension for tutorial quiz

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to Data Engineering and Data Pipelines	Lecture (2 hr)
Week 02	Flat files vs Databases	Lecture and tutorial (3 hr)
Week 03	Web scraping	Lecture and tutorial (3 hr)
Week 04	Web API. NoSQL Databases	Lecture and tutorial (3 hr)
Week 05	Spatial data engineering	Lecture and tutorial (3 hr)
Week 06	Temporal data engineering	Lecture and tutorial (3 hr)
Week 07	Stream data processing	Lecture and tutorial (3 hr)
Week 09	Unstructured data: image and text	Lecture and tutorial (3 hr)
Week 10	Scalable Data Engineering - Part 1	Lecture and tutorial (3 hr)
Week 11	Scalable Data Engineering - Part 2	Lecture and tutorial (3 hr)
Week 12	Data Privacy and Security: Ensuring Data Protection and Compliance	Lecture and tutorial (3 hr)
Week 13	Review	Lecture (2 hr)

Attendance and class requirements

Students are expected to follow the weekly lectures either in-class or using the lecture recordings, and to attend the weekly tutorials.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

N/A

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Use appropriate Python libraries to automate data engineering activities on diverse kinds of data
LO2. Use Unix command line to manage and automate data engineering activities
LO3. Ingest, combine and summarise data from a variety of data models
LO4. Demonstrate experience with handling datasets of diverse kinds of data, including relational, semi-structured, time series, geo-location, image, text
LO5. Understand the main challenges in data engineering: data volume, variety, velocity, veracity, robustness, security
LO6. Demonstrate awareness of ethical and privacy issues when working with data
LO7. Evaluate approaches that store and process data, for correctness, efficiency, and ease-of-use.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This offering is the same as that of Semester 1, 2024.

Additional information

All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.

There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.

Computer programming assignments may also be checked by specialist code similarity detection software. These programs work in a similar way to TII in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

COMP5339: Data Engineering

Semester 1, 2025 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links