As learned in the first and second installments of this blog series, it can be quite useful to use Esri's Tapestry Segmentation to target specific populations that your hospital, system, or physicians serve. Tapestry Segmentation was designed specifically to understand your customer’s lifestyle choices – what they buy and how they spend their free time. This information gives Stratasan, and our clients, insights that help identify facility’s patient types, optimal sites for hospitals, physician offices, FSERS, and urgent care locations. We use the Tapestry Segmentation dataset to help our clients get higher response rates, focus on the most profitable growth opportunities, and invest their resources in the best ways possible.
We want to finish this series with a few niceties that we've built for ourselves to improve our process of delivering valuable software to our clients.
Here, we're going to cover how to integrate our Webpack builds into Django so that our site serves links to the newest version of our front-end assets.
Contrary to popular belief, software developers don’t hide behind their computers all day long and create ruthless memes. While I never subscribed to this line of thinking, to begin with, I got official confirmation of its inaccuracy this summer while interning at Stratasan.
Project Layout and Webpack Configuration
In the previous post, we introduced concepts around deploying front-end assets. If you haven't read it and are not knowledgeable in this area, we suggest you read it now.
In this post, we'll describe the basic file structure of our project and dive into our Webpack configuration.
A few weeks ago I had the pleasure to present to DjangoCon US 2016 about how the technology team at Stratasan delivers front-end assets. We've honed this process over the course of a year and are happy with the flexibility and simplicity it allows us. We believe providing concrete examples taken from our codebase will benefit the community.
You can find my slides here.
This is the first in a series of engineering posts where we take a deep look at the technical underpinnings of Stratasan's analytics platform. We hope you enjoy and perhaps learn something! This does assume a technical background—consider yourself warned :)
We've built Stratasan's analytics platform atop of Amazon Web Services and are big fans of the offerings AWS provides. Our application and worker servers are EC2 instances, application data is stored in an RDS Postgres database, Blackbird results and Canvas PDFs are stored in S3 and our considerable collection of healthcare data is stored and queried from a Redshift cluster.