Applications of Software Architecture for Big Data (Coursera)

Applications of Software Architecture for Big Data can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics.

Course 3 of 3 in the Software Architecture for Big Data Specialization.

What You Will Learn

- Practice software engineering fundamentals; test first development, refactoring, continuous integration, and continuous delivery.

- Architect and create a big data or distributed system using rest collaboration, event collaboration, and batch processing.

- Create a performant, scalable distributed system that handles big data.

Syllabus

WEEK 1

Project Overview

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.

WEEK 2

MVP & Development Environment

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.

WEEK 3

Affixing Features

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.

WEEK 4

Scaling your MVP & Wrapping Up

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.