IAB206 Modern Data Management
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | IAB206 |
|---|---|
| Prerequisite(s): | (IFB104 or ITD104 or EGB103) and (IFB105 or IFB130 or ITD105) |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,118 |
| Domestic tuition unit fee | $4,104 |
| International unit fee | $4,788 |
Unit Outline: Semester 2 2024, Gardens Point, Internal
| Unit code: | IAB206 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (IFB104 or ITD104 or EGB103) and (IFB105 or IFB130 or ITD105) |
| Coordinator: | Sareh Sadeghianasl | s.sadeghianasl@qut.edu.au |
Overview
Introduce you to the technologies that can be used to address challenges in managing fast incoming, voluminous, and varied data that is increasingly being relied on to make decisions in today's business environment. You will develop practical skills in using modern data management technologies that will prepare you to be a data analyst, business analyst, solution architect, as well as enterprise architect.
Learning Outcomes
On successful completion of this unit you will be able to:
- Describe key issues for modern data management and identify corresponding technologies to address the issues, independent of any specific platform or framework.
- Evaluate various capabilities of modern data management technologies.
- Design and develop solutions to manage data using modern data management technologies.
- Work collaboratively with others to efficiently manage and deliver on projects related to the development of modern data management solutions for a client.
Content
This unit will introduce concepts and techniques needed to manage today's data at various stages of its use, including those technologies that can be used to (1) store and retrieve data at rest which may come in various formats using modern database technologies (such as NoSQL databases); (2) apply the necessary treatment of data while it is in-transit, such as streaming data processing and distributed ledger; and, (3) process a large volume of data using a parallel and distributed programming style (e.g. map-reduce algorithm and in-memory database). Furthermore, this unit also introduces fundamentals of modern data management concepts, such as transactional properties of a database and the structuredness (or lack thereof) of today's data.
Learning Approaches
This unit is available to be studied in either on-campus or online mode. The content of this unit will be delivered through a combination of pre-recorded lectures and computer tutorials. In addition to the QUT Canvas site, online messaging platform, such as Slack, will be used. Pre-recorded lectures will cover the theoretical aspects of the unit, while the tutorials will provide the opportunity for you to reinforce the knowledge you have learned from lectures by putting it into practice. A hands-on approach to learning is used whereby concepts learned are illustrated through worked examples and demonstrations. You are encouraged to work in groups to foster your ability to perform as part of a development team. You are encouraged to discuss the difficulties that you face in solving assessment tasks with your group partner(s).
Feedback on Learning and Assessment
Feedback for the assignments will be provided to support further learning.Students will receive formal feedback on assessment tasks from their tutor prior to the submission of the next assessment task. Teaching staff are available during the tutorials and consultation hours to clarify or elaborate on the assignment contents and to provide constructive feedback.
Assessment
Overview
The assessments in this unit measure your ability to apply knowledge related to modern data management technologies to address real-world scenarios informed by contemporary industry-based problems. The assessment of this unit consists of two Projects and an Examination (invigilated), all of which are to be completed during the teaching period. The assignments are to be completed in a group with some individual components. Assignments are to be submitted electronically through Canvas. For the assignments, marking guidelines will be made available to you via unit's Canvas site.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Project (applied)
NoSQL Database Design and Analysis
This group assignment will require you to create and retrieve data from a NoSQL database using queries. You will be asked to model and implement a NoSQL database based on a realistic industry-informed scenario using an open-source NoSQL platform. You will be asked to formulate queries to retrieve frequently-accessed information (in the context of the scenario you are given) from your database. You are expected to provide a comprehensive report detailing the outcomes of this project. An individual interview about your project will take place after its submission.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
This assessment evaluates your understanding of key concepts, techniques, challenges and trade-offs related to modern data management explored in lectures and tutorials. This assessment will be conducted during the teaching period.
Assessment: Project (applied)
Big Data Processing
This group assignment requires you to develop a prototype application that will retrieve data from a NoSQL database and process it using a big data processing model. The data processing application that you develop will be based on a realistic industry-informed scenario. You will be asked to undertake a number of tasks to demonstrate your understanding of basic concepts of big data processing. You will also be using the database to provide analytics solution. You are to provide a report detailing your findings.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
Readings (subject to change):
- Joe Celko. Joe Celko's Complete Guide to NoSQL: What Every SQL Professional Needs to Know about Non-Relational Databases. Morgan Kaufmann, Boston, 2014.
- Roger Wattenhofer. Distributed Ledger Technology: The Science of the Blockchain. CreateSpace Independent Publishing Platform. 2017.
- Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman. Mining of Massive Datasets. Stanford University. 2014.
Risk Assessment Statement
All commencing SEF students are required to complete the Mandatory Safety Induction
There are no extraordinary risks associated with the classroom/lecture activities in this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IN01 Bachelor of Information Technology
Unit Outline: Semester 2 2024, Online
| Unit code: | IAB206 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (IFB104 or ITD104 or EGB103) and (IFB105 or IFB130 or ITD105) |
Overview
Introduce you to the technologies that can be used to address challenges in managing fast incoming, voluminous, and varied data that is increasingly being relied on to make decisions in today's business environment. You will develop practical skills in using modern data management technologies that will prepare you to be a data analyst, business analyst, solution architect, as well as enterprise architect.
Learning Outcomes
On successful completion of this unit you will be able to:
- Describe key issues for modern data management and identify corresponding technologies to address the issues, independent of any specific platform or framework.
- Evaluate various capabilities of modern data management technologies.
- Design and develop solutions to manage data using modern data management technologies.
- Work collaboratively with others to efficiently manage and deliver on projects related to the development of modern data management solutions for a client.
Content
This unit will introduce concepts and techniques needed to manage today's data at various stages of its use, including those technologies that can be used to (1) store and retrieve data at rest which may come in various formats using modern database technologies (such as NoSQL databases); (2) apply the necessary treatment of data while it is in-transit, such as streaming data processing and distributed ledger; and, (3) process a large volume of data using a parallel and distributed programming style (e.g. map-reduce algorithm and in-memory database). Furthermore, this unit also introduces fundamentals of modern data management concepts, such as transactional properties of a database and the structuredness (or lack thereof) of today's data.
Learning Approaches
This unit is available to be studied in either on-campus or online mode. The content of this unit will be delivered through a combination of pre-recorded lectures and computer tutorials. In addition to the QUT Canvas site, online messaging platform, such as Slack, will be used. Pre-recorded lectures will cover the theoretical aspects of the unit, while the tutorials will provide the opportunity for you to reinforce the knowledge you have learned from lectures by putting it into practice. A hands-on approach to learning is used whereby concepts learned are illustrated through worked examples and demonstrations. You are encouraged to work in groups to foster your ability to perform as part of a development team. You are encouraged to discuss the difficulties that you face in solving assessment tasks with your group partner(s).
Feedback on Learning and Assessment
Feedback for the assignments will be provided to support further learning.Students will receive formal feedback on assessment tasks from their tutor prior to the submission of the next assessment task. Teaching staff are available during the tutorials and consultation hours to clarify or elaborate on the assignment contents and to provide constructive feedback.
Assessment
Overview
The assessments in this unit measure your ability to apply knowledge related to modern data management technologies to address real-world scenarios informed by contemporary industry-based problems. The assessment of this unit consists of two Projects and an Examination (invigilated), all of which are to be completed during the teaching period. The assignments are to be completed in a group with some individual components. Assignments are to be submitted electronically through Canvas. For the assignments, marking guidelines will be made available to you via unit's Canvas site.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Project (applied)
NoSQL Database Design and Analysis
This group assignment will require you to create and retrieve data from a NoSQL database using queries. You will be asked to model and implement a NoSQL database based on a realistic industry-informed scenario using an open-source NoSQL platform. You will be asked to formulate queries to retrieve frequently-accessed information (in the context of the scenario you are given) from your database. You are expected to provide a comprehensive report detailing the outcomes of this project. An individual interview about your project will take place after its submission.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
This assessment evaluates your understanding of key concepts, techniques, challenges and trade-offs related to modern data management explored in lectures and tutorials. This assessment will be conducted during the teaching period.
Assessment: Project (applied)
Big Data Processing
This group assignment requires you to develop a prototype application that will retrieve data from a NoSQL database and process it using a big data processing model. The data processing application that you develop will be based on a realistic industry-informed scenario. You will be asked to undertake a number of tasks to demonstrate your understanding of basic concepts of big data processing. You will also be using the database to provide analytics solution. You are to provide a report detailing your findings.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
Readings (subject to change):
- Joe Celko. Joe Celko's Complete Guide to NoSQL: What Every SQL Professional Needs to Know about Non-Relational Databases. Morgan Kaufmann, Boston, 2014.
- Roger Wattenhofer. Distributed Ledger Technology: The Science of the Blockchain. CreateSpace Independent Publishing Platform. 2017.
- Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman. Mining of Massive Datasets. Stanford University. 2014.
Risk Assessment Statement
All commencing SEF students are required to complete the Mandatory Safety Induction
There are no extraordinary risks associated with the classroom/lecture activities in this unit.