The course is intended for individuals who want to build a production-quality software system that leverages big data.
You will apply the basics of software engineering and architecture to create a production-ready distributed system that handles big data. You will build data intensive, distributed system, composed of loosely coupled, highly cohesive applications.
This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:
MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder
MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
Overview
Syllabus
- Project Overview
- Welcome to Applications of Software Architecture for Big Data. This is a project-based course where you will apply the knowledge and skills gained from the entire Software Architecture for Big Data specialization. In this first week, you will learn about the expectations for the project as well as how to establish the features you need to get started with your own.
- MVP & Development Environment
- This week you will learn about the concept of a Minimum Viable Product (MVP) and 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.
- Affixing Features
- This week builds upon your previously created MVP. Now, you will learn how to create a database, populate the database as well as analyze the data in the database. The week ends with further elaboration on testing.
- Scaling your MVP & Wrapping Up
- This week you will add more features to your project, including collaborative messaging. You'll end this week by building a simple health check for production monitoring and discuss acceptance testing.
Taught by
Tyson Gern and Mike Barinek
