DTSA 5714 Applications of Software Architecture for Big Data
- Specialization: Software Architecture for Big Data
- Instructor: Tyson Gern and Mike Barinek
- Prior knowledge needed: Software engineering or big data experience
Learning Outcomes
- Build and maintain a distributed system, practicing software engineering fundamentals; test first development, refactoring, continuous integration, and continuous delivery.
- Write custom performance tests to measure the characteristics of a distributed system.
- Integrate the appropriate data store(s) to meet performance requirements.
- Scale datastores to meet the performance needs of distributed systems.
Course Content
In this module, we will introduce a project where you can apply some of the concepts from Fundamentals of Software Architecture for Big Data. You will learn about the expectations for the project as well as how to establish features for the project.
In this module you will learn about the concept of a Minimum Viable Product (MVP), how to incrementally add features to the MVP. Additionally, we will show you how to get going with a development environment and set up appropriate tests.
This module builds upon an MVP created in the previous module. Here we show you how to create a database, populate the database as well as analyze the data in the database. The module ends by elaborating on testing.
Here we add more features to the project inclusive of collaborative messaging. We end things off by building a simple health check for production monitoring and discussing acceptance testing.
You will complete a peer reviewed final project worth 30% of your grade. You must attempt the final in order to earn a grade in the course. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.
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