Module Details

Module Code: SOFT8026
Title: Data Driven Microservices
Long Title: Data Driven Microservices
NFQ Level: Advanced
Valid From: Semester 1 - 2017/18 ( September 2017 )
Duration: 1 Semester
Credits: 5
Field of Study: 4814 - Computer Software
Module Delivered in: 1 programme(s)
Module Description: Using a framework technology, such as Spring, this module will focus on developing skills that will simplify development of data centric applications. As part of this, the student will learn how to create an application composed of microservices and construct and orchestrate a data pipeline to perform tasks such as data ingest and real time data analytics.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Create and unit test a data streaming application created as a microservice and packaged in a container.
LO2 Deploy a data streaming application to a runtime environment.
LO3 Build a data pipeline for a particular use case such as real time data analytics.
LO4 Automate the deployment and scaling of data centric applications and pipelines.
LO5 Remotely manage microservice applications at runtime by performing health checks and gathering metrics.
Dependencies
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).
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
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.
SOFT8020 - App. Development Frameworks or another module that has introduced the student to the basics of Spring.
 
Indicative Content
Microservice architecture
History of micro-services - Service Oriented Architectures (SOA), Web Services, REST, Microservices. Monolithic service architecture – package and deployment methods i.e. ear and war; Enterprise Service Bus (ESB) – routing, choreography, transformation etc. Micro-service architecture – package, deployment and management. Micro-service characteristics.
Developing Microservices
Spring Cloud– features, configuration. Building micro-services using RESTful API and Web Services. Configuring microservices – YAML. Service Registration and discovery process: engines – Eureka, Consul, Zookeeper and others. Load balancing across services and Inter-process communication in the Cloud – Ribbon. Circuit breakers and fault tolerance in micro services – Netflix Hystrix and Zuul. Unit testing and troubleshooting applications.
Lightweight containerisation
Lightweight containerisation – compare to traditional server virtualisation, approaches, benefits and implementations i.e. Docker. Running micro-services in lightweight containers. Automating deployment, scaling and management of containerised applications – Kubernetes, Docker Swarm, Nomad, Kontena etc. Client side load balancing.
Create and deploy Streaming Applications
Spring Cloud stream. Streaming applications – imperative versus functional programming models. Streams – topologies, concurrency, partitioning and message delivery guarantees. Messaging middleware – publish/subscribe. Data Flow Server to create, deploy, destroy steams, tasks i.e. data streaming applications
Data Pipeline creation, orchestration and scaling
Big Data applications. Real time data analytics – using a framework technology. Data pipeline– create, tap, orchestrate and scale. Jobs – workflow. Application case study for twitter-analytics – consume data from a Twitter stream and compute analytics over data in transit. Predictive Model Mark-up Language (PMML).
Runtime operation
Fault tolerance – health probes. Resource management – memory, disk and CPU management. Runtime scaling of applications. Application versioning
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 50
Timing Week 8 Learning Outcomes 1,2
Assessment Description
An example project could be to use a framework technology to create, deploy and unit test a data streaming application in its runtime environment.
Assessment Type Project % of Total Mark 50
Timing Sem End Learning Outcomes 3,4,5
Assessment Description
As part of this project the student may be given a high level specification that will require them to set up a data pipeline. For example they may be create and orchestrate a data pipeline from Twitter and compute analytics over data in transit. In addition, they will have to demonstrate how to scale and manage the runtime operation of the application.
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.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lecture underpinning learning outcomes. Every Week 2.00 2
Lab Contact Lab supporting content delivered. Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Independent learning & study. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lecture underpinning learning outcomes. Every Week 2.00 2
Lab Contact Lab supporting content delivered. Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Independent learning & study. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • F. Gutierrez. (2017), Spring Cloud Data Flow, 1st. Apress, [ISBN: 9781484212394].
  • J. Carnell. (2017), Spring Microservices in Action, MEAP, [ISBN: 9781617293986].
Supplementary Book Resources
  • S. Newman. (2016), Building Microservices, O'Reilly Media, [ISBN: 9781491950357].
  • I. Nadareishvilli, R. Mitra, M. McLarty, M. Amundsen. (2016), Microservice Architecture : Aligning Principles, Practices, and Culture, O'Reilly Media, [ISBN: 9781491956250].
  • D. Vohra. (2016), Kubernetes Microservices with Docker, aPress, [ISBN: 9781484219065].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_KSDEV_8 Bachelor of Science (Honours) in Software Development 8 Mandatory