Module Code: H8DIA
Long Title Data Intensive Architectures
Title Data Intensive Architectures
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 5
Module Coordinator: Horacio Gonzalez-Velez
Module Author: Margarete Silva
Departments: School of Computing
Specifications of the qualifications and experience required of staff

PhD in a computer science or cognate discipline. May have industry experience also. 

Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Critically compare and contrast multiple distributed system models and their associated enabling technologies.
LO2 Demonstrate in-depth knowledge of different types of processing on different data-intensive computational resources.
LO3 Identify and categorise platforms and software environments for cloud and cognitive computing.
LO4 Critically analyse the features of high performance computing platforms and how they enable parallel and distributed programming paradigms.
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).

No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

A level 8 degree or its equivalent in any discipline


Module Content & Assessment

Indicative Content
Principles of Cloud Computing Systems
Distributed systems, service models, ecosystems.
Non-functional characteristics of cloud systems
SLAs/QoS, Availability, Mobility, and Optimisation for Cloud
Data Analytics and Cognitive Computing
The Big Data Industry. Data Collection, Mining and Analytics on Clouds. Neuromorphic hardware and Cognitive Computing.
Computing Architectures
Multi/Many core, Clusters, Grids, and Clouds; NIST Model: Elastic provisioning, resource metering, pools, etc.
Cloud Infrastructures and Services
Computation, storage and general resource deployment; Public cloud services (e.g. AWS and GAE service offerings); Machine Learning support;
Clouds for Mobile and IOT services
Mobile devices and edge computing; Mobile clouds and colocation; Mobile networks; IoT interaction frameworks.
Clouds for Social Media and Mashup Services
Social media industrial applications; Social media networks and APIs; Graph analysis; Mashup architectures; Dynamic composition of services.
Structured parallel programming
Algorithmic skeletons and; Structured parallelism; scalable models; fine-grained vs. coarse-grained parallelisation
Parallel patterns for data-intensive computations
Data-enabled patterns and skeletons: map, reduce, broadcast, scan, gather scatter. MapReduce compute engine. MapReduce computations.
Data-intensive storage management
CAP Theorem; distributed file organisations: HDFS and Resilient distributed data sets;
Streams and Graphs
Structured sources; data streams; stream programming, libraries and applications; graphs; centrality and degrees; graph programming, libraries, and applications
Non Von Neumann architectures for machine learning
GPGPU; Neuromorphic hardware; TensorFlow; Cognitive services
Assessment Breakdown%


Full Time

Assessment Type: 430 % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning.
Assessment Type: Project % of total: 100
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
Produce a portfolio of studies that critically compare the data and computing architectures, programming models, and storage concepts.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
Reassessment Description
Reassessment of this module will be via a project.

NCIRL reserves the right to alter the nature and timings of assessment


Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom & Demonstrations (hours) 24 Every Week 24.00
Tutorial Other hours (Practical/Tutorial) 24 Every Week 24.00
Independent Learning Independent learning (hours) 77 Every Week 77.00
Total Weekly Contact Hours 48.00

Module Resources

Recommended Book Resources
  • Kai Hwang. (2017), Cloud Computing and Cognitive Computing: A Machine Learning Approach, MIT Press, p.624, [ISBN: 026203641X].
  • Jan Kunigk,Ian Buss,Lars George,Paul Wilkinson. (2019), Architecting Modern Data Platforms, O'Reilly Media, p.636, [ISBN: 149196927X].
Supplementary Book Resources
  • Bill Chambers,Matei Zaharia. Spark, [ISBN: 1491912219].
  • Ian Foster,Dennis B. Gannon. (2017), Cloud Computing for Science and Engineering, MIT Press, p.392, [ISBN: 9780262037242].
  • Martin Kleppmann. (2017), Designing Data-intensive Applications, Oreilly & Associates Incorporated, p.590, [ISBN: 1449373321].
  • Michael D. McCool,Arch D. Robison,James Reinders. (2012), Structured Parallel Programming, Elsevier, p.406, [ISBN: 0124159931].
  • K.C. Wang. (2018), Systems Programming in Unix/Linux, Springer, p.344, [ISBN: 3319924281].
  • Tom White. Hadoop: the Definitive Guide ; Storage and Analysis at Internet Scale, [ISBN: 1491901632].
Recommended Article/Paper Resources
  • J. Eckroth. (2018), A course on big data analytics, Journal of Parallel and Distributed Computi, 118, p.166.
  • J. Kolodziej, H. González-Vélez, H.D. Karatza. (2017), High-performance modelling and simulation for big data applications, Simulation Modelling Practice and Theory, 76, p.1-2.
  • R. Buyya, et al. (2017), A Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade, Working Draft: Distributed, Parallel, and Cluster Computing (cs.DC), arXiv:1711.09123 [cs.DC],, 43,
  • H. González-Vélez. (2010), A survey of algorithmic skeleton frameworks: high-level structured parallel programming enablers., Practice and Experience, 40(12), p.1135.
  • J. Dean, S. Ghemawat. (2010), MapReduce: a flexible data processing tool., Communications of the ACM, 53(1), p.72.
This module does not have any other resources
Discussion Note: