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Unit of study_

DATA3404: Scalable Data Management

Semester 1, 2021 [Normal day] - Remote

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

Unit code DATA3404
Academic unit Computer Science
Credit points 6
Prohibitions
? 
INFO3504 OR INFO3404
Prerequisites
? 
DATA2001 OR DATA2901 OR ISYS2120 OR INFO2120 OR INFO2820
Corequisites
? 
None
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 Uwe Roehm, uwe.roehm@sydney.edu.au
Lecturer(s) Alan Fekete, alan.fekete@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final Examination
Final examination; online; short-release and timed
55% Formal exam period 2 hours
Outcomes assessed: LO8 LO3 LO4 LO6 LO2 LO5
Small test hurdle task Oral Exam (Barrier)
Oral 'in-vivo' test after the exam about the exam content; hurdle task
5% Formal exam period 10 min
Outcomes assessed: LO2 LO3 LO4 LO5 LO6 LO8
Presentation group assignment Presentation of DB Concepts
video presentation of some data processing concept
5% Multiple weeks 5 min
Outcomes assessed: LO2 LO3 LO4 LO5 LO6
Assignment group assignment Assignment
Practical programming/tuning assignment.
15% Week 12 ca 4 Weeks
Outcomes assessed: LO1 LO8 LO7
Small test Weekly Homework Quizzes
Weekly quiz on DB-engine concepts taught in this unit.
20% Weekly ca 20 min per week
Outcomes assessed: LO1 LO4 LO3 LO2
hurdle task = hurdle task ?
group assignment = group assignment ?
Type D final exam = Type D 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 (data storage and indexing, query processing and optimization, data distribution and replication).
  • Presentation of DB Concepts: A short video presentation on a selected content topic, to be prepared by students in pairs in a selected week, and shared online with the other students of the same tutorial class.
  • Assignment: Practical programming/tuning assignment.
  • 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.

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

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:

Late submission penalty for the practical assignment: -20% of the awarded marks per day late; minimum 0% after 5 days. Homework quizzes can be done anytime during the week they are released, up-to their respective deadline.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
Multiple weeks Project Work (practical assignment) during last third of semester - own time Independent study (40 hr) LO1 LO5 LO7 LO8
revision of weekly material and working on weekly tutorial exercises (homework) Independent study (40 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
study of a selected topic of the week for the presentation task Independent study (4 hr) LO2 LO3 LO4 LO5 LO6
Week 01 1. Organisation and Administrativa; 2. Architecture of Database Systems Lecture (4 hr) LO1
Week 02 Storage Engines: physical data organisation Lecture and tutorial (4 hr) LO2
Week 03 Disk-based Index Structures: Tree indexes Lecture and tutorial (4 hr) LO3
Week 04 Disk-based Index Structures: Extensible Hashing & Bitmap Index Lecture and tutorial (4 hr) LO3
Week 05 Introduction to Query Processing and External Sorting Lecture and tutorial (4 hr) LO4
Week 06 Query Execution and Join Algorithms Lecture and tutorial (4 hr) LO4
Week 07 Query Optimization Lecture and tutorial (4 hr) LO4
Week 08 Distributed Data Management Lecture and tutorial (4 hr) LO2 LO5
Week 09 Distributed Computation and Data Processing Lecture and tutorial (4 hr) LO5 LO6
Week 10 Dataflow Platforms Lecture and tutorial (4 hr) LO5 LO6
Week 11 Data Stream Processing Lecture and tutorial (4 hr) LO5
Week 12 NoSQL Lecture and tutorial (4 hr) LO1 LO2
Week 13 UoS review Lecture and tutorial (4 hr) LO2 LO3 LO4 LO5 LO6 LO7

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 assignment is 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 the tutorials of Week 12, and based on this demo, the group’s assignment mark 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 handout.

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 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.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Based on student feedback from last year, tutorials have been extended to two hours this semester. Assessment tasks have been re-weighted to put more emphasize on progressive content revision.

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.