Module Code: H7DA
Long Title Data Architecture
Title Data Architecture
Module Level: LEVEL 6
EQF Level: 5
EHEA Level: Short Cycle
Credits: 5
Module Coordinator: Paul Stynes
Module Author: Paul Stynes
Departments: School of Computing
Specifications of the qualifications and experience required of staff


Master’s degree in a computing 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 Compare different data stores, data models, query languages, data encoding techniques and knowledge discovery techniques
LO2 Summarise the constraints and trade-offs of a distributed shared-nothing architecture that is involved in the storage and retrieval of data
LO3 Analyse and design a data application architecture that integrates multiple disparate data systems that are optimised for different access patterns
LO4 Collaboratively implement an application data architecture
Dependencies
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

Learners should have attained the knowledge, skills and competence gained from stage 1 of the BSc (Hons) in Data Science
 

Module Content & Assessment

Indicative Content
Module Introduction Reliable, Scalable and Maintainable Applications
Fundamentals of data systems. Reliability–H/D, S/W and Human errors.. Scalability-Load, Performance.. Maintainability- Operability, Simplicity and Evolvability.
Data Models and Query Languages
Relational Vs Document models.. Query Languages for Data.. Graph-Like Data Models.
Storage and Retrieval
Data Structures.. Transaction Processing or Analytics.Column-Oriented Storage.
Encoding and Evolution
Formats for Encoding Data.. Modes of Data Flow..
Replication
Leaders and Followers.Problems with Replication Lag.. Multi-Leader Replication. . Leaderless Replication.
Partitioning and Transactions
Partitioning and Replication.. Partitioning of Key-Value Data.. Partitioning and Secondary Indexes.. Rebalancing Partitions.. Request Routing.. Transaction.. Weak Isolation Levels.. Serializability.
The trouble with Distributed Systems
Faults and Partial Failures. Unreliable Networks. Unreliable Clocks.
Consistency and Consensus
Consistent Guarantees.Linearizability.Ordering Guarantees. Distributed Transactions and Consensus.
Batch Processing
Batch Processing with Unix Tools.MapReduce and Distributed Filesystems.
Stream Processing
Transmitting Event Streams.Database and Streams.Processing Systems.
The future of Data Systems
Data Integration. Unbundling Databases. Predictive Analytics
Knowledge discovery
KDD. CRISP-DM
Assessment Breakdown%
Coursework60.00%
End of Module Assessment40.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: Yes
Assessment Description:
Ongoing independent and group class activities and feedback.
Assessment Type: Project % of total: 60
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
Students will work collaboratively to implement an application data architecture that integrates a variety of data systems with different data models and different query languages to access the data. Learners will integrate the data and apply data encoding techniques and knowledge discovery techniques to gain insight from the data.
Assessment Type: Easter Examination % of total: 40
Assessment Date: n/a Outcome addressed: 1,2,3
Non-Marked: No
Assessment Description:
The examination may include a mixture of theoretical, applied and interpretation questions.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
Reassessment Description
The repeat strategy for this module is a terminal assessment. Students will be afforded an opportunity to repeat the assessment at specified times throughout the year and all learning outcomes will be assessed in the repeat assessment.

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 Per Semester 2.00
Tutorial Other hours (Practical/Tutorial) 12 Per Semester 1.00
Independent Learning Independent learning (hours) 89 Per Semester 7.42
Total Weekly Contact Hours 3.00
 

Module Resources

Recommended Book Resources
  • Kleppmann, M.. (2017), Designing Data-Intensive Applications – The big ideas behind reliable, scalable and maintainable systems, California, O’Reilly Media Inc.
Supplementary Book Resources
  • Adkins, S., Belamaric, J., Giersch, V., Makogon, D. & Robinson, J.. (2015), OpenStack Cloud Application Development, Wrox.
This module does not have any article/paper resources
Other Resources
Discussion Note: