University of Technology Sydney

42893 Data Engineering Foundations

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2022 is available in the Archives.

UTS: Information Technology: Electrical and Data Engineering
Credit points: 2 cp
Result type: Grade and marks


This microcredential provides a comprehensive overview of the foundations of data engineering – a set of infrastructure platforms and capabilities that allows organisations to acquire, transform, store and curate data with the ultimate goal of realising value from data. Taking a broad perspective, the microcredential helps learners from diverse backgrounds to be better informed when working with data engineering teams or planning for data engineering as part of their team’s projects or operations. Beginning with an overview of a typical data value chain, the microcredential then introduces data infrastructure and data pipelines, alongside examples of implementation technologies. A range of issues around data quality, security, monitoring and governance are explored, with the ultimate goal of demonstrating how data engineering helps organisations extract and realise value from their data assets.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Apply basic data engineering principles to plan infrastructure and strategies that allow organisations to derive value from their data

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):

  • Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)

Teaching and learning strategies

This microcredential includes theoretical and practical components. Theoretical content will be presented as online learning modules for self-study in the LMS, supported by weekly, synchronous, one-hour online workshops facilitated by UTS academics. Regular formative quizzes embedded throughout the microcredential allow learners to gauge their progress and receive feedback on each module.

Small hands-on activities and case studies relating to the weekly topics will be provided to allow students to gain a deeper understanding of the theoretical concepts by engaging with data sets, case study examples, and working with some of the tools used in data engineering.

Content (topics)

  • Overview of the data value chain, stakeholders and data as a business asset.
  • Introduction to data pipelines and integrated data infrastructure engineering platforms.
  • Data curation, including large-scale data storage and management, and technologies for data security, monitoring and governance.
  • Realising value from data: an introduction to data optimisation, feature engineering, and packaging and delivery of data for different business users.


Assessment task 1: Data Engineering Case Study Presentation


Demonstrate basic data engineering principles as applied to a case study scenario.


This assessment task addresses the following subject learning objectives (SLOs):


This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):


Type: Presentation
Groupwork: Group, individually assessed
Weight: 100%

5-minute video, followed by Q&A

Minimum requirements

In order to pass the microcredential, a learner must achieve an overall mark of 50% or more.