AWS and Beyond

We began this course with the goal of describing the range of data analytics tools available through AWS. We structured it based on an AWS recommendation for architecting systems that can derive insights from data.

Perspectives on AWS

While AWS has historically been a market leader in the cloud-computing space, many people find AWS to be complex (and much more challenging to use than buying products from Amazon.com). With this course, our goal was to describe AWS tools in practical and helpful ways. Developing this course required us to invest considerable time and effort.

Many ways to do the same things

One complexity we ran into was that the same use cases can be done with many AWS tools. For example, migrating data from one location to another can be done with code using AWS-provided libraries, AWS DataSync, AWS Glue, and more.

In the consumer world, this is similar to how Amazon has created and launched many versions and varieties of their Alexa virtual assistant technology. (And, of course, there are similar technologies from other companies also!)

When some companies provide a range of tools, it can feel easier to distinguish between the various options (for example, Apple and its smartphones). Within AWS, various services aren’t as well-integrated, differentiated, or documented as we might expect.

Marketing claims that might not be true

As experienced practitioners classically trained in computer science and engineering, we tend to want to look for the most effective and efficient approach to solving a problem. We saw the AWS marketing tagline claiming its suite of analytics tools is the “fastest way to get answers from all ...