Outline

Overview

This unit of study provides a comprehensive overview of the internal mechanisms data science platforms and of the systems that manage large data collections. These skills are needed for successful performance tuning and to understand the scalability challenges faced by when processing Big Data. This unit builds upon the second' year DATA2001 - 'Data Science - Big Data and Data Diversity' and correspondingly assumes a sound understanding of SQL and data analysis tasks. The first part of this subject focuses on mechanisms for large-scale data management. It provides a deep understanding of the internal components of a data management platform. Topics include: physical data organization and disk-based index structures, query processing and optimisation, and database tuning. The second part focuses on the large-scale management of big data in a distributed architecture. Topics include: distributed and replicated databases, information retrieval, data stream processing, and web-scale data processing. The unit will be of interest to students seeking an introduction to data management tuning, disk-based data structures and algorithms, and information retrieval. It will be valuable to those pursuing such careers as Software Engineers, Data Engineers, Database Administrators, and Big Data Platform specialists.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	DATA2001 OR DATA2901 OR ISYS2120 OR INFO2120 OR INFO2820
Corequisites ?	None
Prohibitions ?	INFO3504 OR INFO3404
Assumed knowledge ?	This unit of study assumes that students have previous knowledge of database structures and of SQL. The prerequisite material is covered in DATA2001 or ISYS2120. Familiarity with a programming language (e.g. Java or C) is also expected
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Josh Burridge, joshua.burridge@sydney.edu.au
Lecturer(s)	Josh Burridge, joshua.burridge@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final Examination Final examination; online; short-release and timed	55%	Formal exam period	2 hours
Outcomes assessed:
Presentation	Presentation of DB Concepts video presentation of some data processing concept	5%	Multiple weeks	5 min
Outcomes assessed:
Assignment	Major Assignment: Task 1 Implementation and analysis of queries in SQL and Apache Spark.	10%	Week 05	Variable - Code + Report
Outcomes assessed:
Assignment	Major Assignment: Task 1 Resubmission Resubmission of Task 1 aspects graded 'Revision Required'	0%	Week 08	Variable - Code + Report
Outcomes assessed:
Assignment	Major Assignment: Task 2 More complex queries and performance tuning in SQL and Apache Spark.	20%	Week 10	Variable - Code + Report
Outcomes assessed:
Assignment	Major Assignment: Task 2 Resubmission Resubmission of Task 2 aspects graded 'Revision Required'	0%	Week 13	Variable - Code + Report
Outcomes assessed:
Small test	Weekly Homework Quizzes Weekly quiz on DB-engine concepts taught in this unit.	10%	Weekly	~20 min per week
Outcomes assessed:
= hurdle task ? = group assignment ? = Type B final exam ?

Assessment summary

Weekly Homework Quizzes: Short weekly online quizzes on the content-of-the-week to be answered by students progressively as homework in Canvas; includes electronic review questions on the concepts taught in this unit.
Presentation of DB Concepts: A short video presentation on a selected content topic, to be prepared by students in groups in a selected week, and shared online with the other students of the same tutorial class.
Major Assignment: Task 1: Practical programming/tuning assignment.
Major Assignment: Task 2: Practical programming/tuning assignment extending Task 1.
Final Examination: Understanding of all of this unit’s material is reviewed.

Detailed information for each assessment can be found on Canvas.

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	Awarded when you demonstrate the learning outcomes for this unit at an exceptional standard, representing complete or close to complete mastery of the material.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for this unit at an excellent standard, representing excellence, but substantial less than complete mastery.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for this unit at a good standard, representing creditable performance that goes beyond routine knowledge and understanding, but less than excellence.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for this unit at an acceptable standard, representing at least routine knowledge over a spectrum of topics and important ideas and concepts taught in this unit of study.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

Minimum Pass Requirement:

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 written 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 guide to grades.

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:

Major Assignment Tasks 1 and 2 offer the possibility of correction and resubmission. This resubmission is only available for original submissions made on time or at most 1 day after the due date. Late resubmission will incur normal late penalties from the resubmission due date combined with any original submission late penalty. Weekly Quizzes release sample solutions after the due date - no late submissions may be accepted for these assessments. Video Presentations are required for activities in your weekly tutorials - no late submissions may be accepted for these assessments.

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.

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

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

Weekly schedule

WK	Topic	Learning activity
Multiple weeks	Project Work (practical assignments) throughout semester - own time	Independent study (55 hr)
	revision of weekly material and working on weekly tutorial exercises (homework)	Independent study (30 hr)
	Study of a selected topic of the week for the presentation task	Independent study (4 hr)
	Reviewing topic videos prior to lecture. 1 hr/week for weeks 2-12.	Independent study (11 hr)
Week 01	1. Organisation and Administrivia; 2. Unit-in-brief	Lecture (2 hr)
Week 02	Storage Engines & Computing Architecture	Lecture (2 hr)
Week 02	Platform & Software Access: Postgres & Databricks. SQL revision.	Tutorial (2 hr)
Week 03	Distributed Data Science Platforms	Lecture (2 hr)
Week 03	Data processing in Apache Spark I	Tutorial (2 hr)
Week 04	Distributed Data Processing	Lecture (2 hr)
Week 04	Data processing in Apache Spark II	Tutorial (2 hr)
Week 05	Indexing: Trees, Hashes, and Bitmaps	Lecture (2 hr)
Week 05	Indexing	Tutorial (2 hr)
Week 06	Introduction to Query Processing, Relational Algebra, and External Sorting	Lecture (2 hr)
Week 06	Reading SQL Execution Plans, Relational Algebra, External Sorting	Tutorial (2 hr)
Week 07	Query Execution and Join Algorithms	Lecture (2 hr)
Week 07	Join algorithms	Tutorial (2 hr)
Week 08	Query Optimisation	Lecture (2 hr)
Week 08	Query optimisation, further relational algebra	Tutorial (2 hr)
Week 09	Distributed Data Platform Queries	Lecture (2 hr)
Week 09	Execution plans in Apache Spark: analysing Directed Acyclic Graphs, optimising Apache Spark queries	Tutorial (2 hr)
Week 10	Distributed Data Platform Optimisation	Lecture (2 hr)
Week 10	Nodes, partitioning, replication, and consistency in Hadoop/HDFS.	Tutorial (2 hr)
Week 11	Data Stream Management Systems	Lecture (2 hr)
Week 11	Apache Spark Streaming	Tutorial (2 hr)
Week 12	NoSQL	Lecture (2 hr)
Week 12	NoSQL: Running MongoDB	Tutorial (2 hr)
Week 13	Unit Review	Lecture (2 hr)
Week 13	NoSQL: MongoDB Query Optimisation	Tutorial (2 hr)

Attendance and class requirements

Students are expected to follow the weekly lectures either in-class or using the lecture recordings, and to work through the weekly tutorial material.

The practical assignments are group work where all team members are expected to actively participate and to divide the work fairly among the team members. The individual mark awarded for the group assignment is conditional on a team member being able to explain any part of the group submission to the tutor or the lecturer if asked. In particular, groups will have to demo their submissions in tutorials, and based on this demo, the group’s assignment marks will be scaled for each team member based on the individual level of contribution. Further details of this participation scaling will be defined on the assignment handouts.

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.

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. demonstrate experience with using/tuning data science platforms such as Apache Spark
LO2. understand different physical data organisations including data partitioning and data replication
LO3. understand disk-based indexing structures such as B-Trees, extensible hashing and bitmap indexes
LO4. understand the principles of query processing and query optimization
LO5. understand the principles of (distributed) data science platforms.
LO6. understand data sharding algorithms and data replication protocols
LO7. make effective physical data design decisions
LO8. identify a performance problem and be able to effectively tune the performance of a (distributed) data processing system

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.

Based on student feedback from last year, practical skills have been brought forward in the semester to allow better alignment between lectures and assessment work.

Additional information

Computer programming assignments may be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see http://theory.stanford.edu/~aiken/moss/) . These programs work in a similar way to TurnItIn 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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

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

Media

Student links

About us

Connect

Current students

DATA3404: Scalable Data Management

Semester 1, 2022 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

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

Media

Student links

About us

Connect