| Module Title: | Data Intensive Applications |
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| Language of Instruction: | English |
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| Teaching & Learning Strategies: |
The course is taught by means of lectures and supervised practicals. The practical work consists of lab assignments and tutorials focusing on scalable datastores and data processing systems. The laboratory exercise topics (data models, data processing, analysis, etc) are designed to explore and analyse features of a variety of data intensive systems. |
| Module Aim: |
To develop the student’s knowledge of the design, operation and management of cloud and on-premises data storage and processing systems. |
| Learning Outcomes |
| On successful completion of this module the learner should be able to: |
| LO1 |
Organize and analyze data to discover patterns and tends. |
| LO2 |
Deploy and scale modern on-premises and cloud data stores and warehouses. |
| LO3 |
Evaluate and rationalize modern polyglot data architectures |
| Pre-requisite learning |
Module Recommendations
This is prior learning (or a practical skill) that is recommended before enrolment in this module.
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| 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
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| No Co-requisite modules listed |
Requirements
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 |
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1. Data Management in the Cloud
DaaS, DBaaS, Cloud-based DBMS Services, Security,
AWS, EMC, Azure
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2. Data Warehousing
OLAP, dimensions, measures, roll-up/drill-down,
dimension & fact tables, star schema,
data warehouse, data mart, materialized view
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3. Data Analytics
market basket analysis, classification, association
rules, clustering, decision trees, regression, neural
nets, genetic algorithms, big data, total data
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4. Scalable Datastores
Unstructured data, polyglot persistence, NoSQL, graph, document-oriented, columnar, key-value, NewSQL, Hadoop, Spark
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| Assessment Breakdown | % |
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| Continuous Assessment | 40.00% |
| Practical | 60.00% |
| Continuous Assessment |
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| Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
| Other |
Class test or written assignment (e.g. problem sheets, literature surveys, etc) |
1,2 |
15.00 |
Week 6 |
| Other |
Class test or written assignment (e.g. problem sheets, literature surveys, etc) |
2,3 |
25.00 |
Week 12 |
| Practical |
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| Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
| Practical/Skills Evaluation |
Laboratory assignments to be completed on weeks 3, 6 and 10. |
1,2,3 |
60.00 |
Week 10 |
| 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 |
| Lecture |
12 Weeks per Stage |
2.00 |
| Laboratory |
12 Weeks per Stage |
2.00 |
| Independent Learning Time |
15 Weeks per Stage |
5.13 |
| Total Hours |
125.00 |
Module Delivered In
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