Module Title:Data Engineering
Language of Instruction:English
Credits: 5
NFQ Level:8
Module Delivered In 3 programme(s)
Teaching & Learning Strategies: This module is 100% delivered interactively within a laboratory setting (on online, as needed).
Module Aim: To provide an overview of modern data engineering practices, tools, and methods.
Learning Outcomes
On successful completion of this module the learner should be able to:
LO1 Clean and wrangle data from multiple sources into a usable state.
LO2 Organize the collection, processing, and storage of data from different data sources.
LO3 Design and build ETL and ELT processes and pipelines.
Pre-requisite learning
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.

No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed.
No requirements listed

Module Content & Assessment

Indicative Content
Data Formats
Understanding internet data-types: MIME, quoted-printable, Base64 (and others). Data Sources: TXT, CSV, JSON, Web Data, APIs, ERP, CRM, Databases. Structured data, Semi-structured data, and unstructured data.
Data Storage
SQL Databases, Document Databases, Graph Databases, Data Warehouses, Data Lakes, Dataframes.
Extract, Transform, and Load and Extract, Load, and Transform: data cleaning, munging, parsing, converting, mining, and saving.
Data Platforms
Big Data, Map Reduce, Cloud-scale data, distributed data processing, Data pipelines, Parallel Computation Platforms, Scaling Issues/Concerns.
Assessment Breakdown%
No Continuous Assessment
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project TBD 1 35.00 n/a
Project TBD 2 20.00 n/a
Project TBD 3 45.00 n/a
No Practical
No End of Module Formal Examination

SETU Carlow Campus reserves the right to alter the nature and timings of assessment


Module Workload

Workload: Full Time
Workload Type Frequency Average Weekly Learner Workload
Laboratory 12 Weeks per Stage 2.00
Estimated Learner Hours 15 Weeks per Stage 6.73
Total Hours 125.00

Module Delivered In

Programme Code Programme Semester Delivery
CW_KCCGD_B Bachelor of Science (Honours) in Computer Games Development 8 Group Elective 1
CW_KCCYB_B Bachelor of Science (Honours) in Cyber Crime and IT Security 8 Elective
CW_KCSOF_B Bachelor of Science (Honours) in Software Development 8 Group Elective 1
Discussion Note: First draft of one of the elective modules for final year undergrad offerings.