| Title: | NoSQL Data Architectures |
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| Long Title: | NoSQL Data Architectures |
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| Field of Study: |
Computer Science
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| Valid From: |
Semester 1 - 2017/18 ( September 2017 ) |
| Module Coordinator: |
Sean McSweeney |
| Module Author: |
Ignacio Castineiras |
| Module Description: |
A NoSQL database provides a mechanism to store and retrieve data not modeled using tabular relations traditionally found in relational databases.
In this module, the learner will survey the main data models used in NoSQL databases, with a special emphasis on models that support distributed data architectures.
In addition, the learner will be equipped with the skills to store and query information in a NoSQL database. |
| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| LO1 |
Discuss how NoSQL data models and approaches can be applied to address challenges with Big Data. |
| LO2 |
Compare and contrast the ACID vs. BASE approaches (used respectively by relational and NoSQL data models) to store and retrieve data. |
| LO3 |
Survey the main key/value-based NoSQL families, identifying the uses cases most appealing to each of them. |
| LO4 |
Apply replication and sharding with a document-oriented NoSQL database to scale-out in a cluster. |
| LO5 |
Connect and synchronise a graph-based and a document-oriented NoSQL databases for a polyglot persistance-based solution. |
| Pre-requisite learning |
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list. |
| 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. You may not enrol on this module if you have not acquired the learning specified in this section.
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| No requirements listed |
Module Content & Assessment
| Indicative Content |
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Introduction: Big Data.
New data storage and data processing context.
Data Classification: 3 Vs.
Scalability problem: Difficulties to capture, store, search, analyse and visualise the data.
Emerging big data paradigm: New infrastructure (storage+compute cloud), data models and data manipulation techniques.
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Traditional Relational Model.
Relational Databases Management Systems (RDBMS): De facto standard.
Relational model: Transactional (ACID) properties, normalisation, expressive SQL language.
RDBMS limitations for big data: Impedance mismatch, rigid schema, inability to deal with distributed huge data volumes.
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NoSQL Alternative.
Paradigm arising to tackle problems RDBMS is not good at: Schema-less, high level data representation, scale-out distributed-based infrastructure.
Wide range of data models: Pure key/value, colummn-based, document oriented and graph-based. Polyglot persistance.
Lost of transactional properties: BASE approach.
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Key/value DBs.
Pure key/value pairs data model: Simple and neat interface. Survey of advantages and disadvantages with respect to RDBMS. Use cases.
Column-based DBs: More structured value pairs. Value gathering via supercolumns. Use cases.
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Document-oriented DBs.
Most popular and flexible data model. JSON documents. Use cases.
Consistency: Replica set.
Scaling out: Sharding. Clusters, configuration nodes, shards, chunk of data, shard key range, balancing backgroud operators.
Complex querying: Aggregation framework, aggregation commands command pipelines.
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Graph-based DBs.
Property graph data model: Nodes, relationships and properties.
Use cases.
Cypher query language.
Connection and synchronisation with other data models.
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| Assessment Breakdown | % |
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| Course Work | 100.00% |
| Course Work |
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| Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
| Project |
An example project would be to store and query some information using a relational and two key/value-based NoSQL databases. As part of the assessment the student may be expected to determine which key/value family is more appealing based on the information being queried. |
1,3 |
35.0 |
Week 7 |
| Project |
Given a distributed cluster to allocate a large dataset, store and query some information using a document-oriented and a graph-based NoSQL databases. Connect both databases for a polyglot persistance-based solution. Apply replication and sharding to demonstrate the scale-out approach followed. Assess the BASE transaction properties by disabling communication from/to some of the nodes. |
2,4,5 |
35.0 |
Week 10 |
| Short Answer Questions |
This assessment will examine the theoretical content delivered in class. |
1,2,3 |
30.0 |
Week 13 |
| No End of Module Formal Examination |
| Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
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The institute reserves the right to alter the nature and timings of assessment
Module Workload
| Workload: Full Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Lecture delivering theory underpinning learning outcomes. |
2.0 |
Every Week |
2.00 |
| Lab |
Practical computer-based lab supporting learning outcomes. |
2.0 |
Every Week |
2.00 |
| Independent Learning |
Independent Study. |
3.0 |
Every Week |
3.00 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
| Workload: Part Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Lecture delivering theory underpinning learning outcomes. |
2.0 |
Every Week |
2.00 |
| Lab |
Practical computer-based lab supporting learning outcomes. |
2.0 |
Every Week |
2.00 |
| Independent Learning |
Independent Study. |
3.0 |
Every Week |
3.00 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
Module Resources
| Recommended Book Resources |
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- Pramod J. Sadalage and Martin Fowler 2013, NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, Addison-Wesley [ISBN: 9780321826626]
- John Sharp et. al. 2013, Data Access for Highly-Scalable Solutions: Using SQL, NoSQL, and Polyglot Persistence, Microsoft patterns & practices [ISBN: 9781621140306]
| | Supplementary Book Resources |
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- Kristina Chodorow 2013, MongoDB: The Definitive Guide, 2nd Ed., O'Reilly Media [ISBN: 9781449344689]
- Kyle Banker 2012, MongoDB in Action, Manning Publication [ISBN: 9781935182870]
| | This module does not have any article/paper resources |
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| Other Resources |
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- Website: MongoDB documentation
- Website: Neo4j documentation
- Website: Cassandra documentation
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Module Delivered in
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