Brad is a self-taught technologist, consulting as a principal-level architect with a focus in app modernization and security informed by years of experience in application development. Those years include exclusive focus on individual contributor (IC) contributions, as well as responsibilities leading and shepherding teams and various types of projects and initiatives, both formally and informally.
Brad is the author of The Definitive Guide to AWS Infrastructure Automation in addition to magazine articles in Linux Pro Magazine and ADMIN Magazine. He enjoys speaking and presenting publicly on technology-related topics when an opportunity presents itself.
In addition to the work-related aspects of his career, he plans to author another AWS-focused book and produce his first online course focusing on cloud platform automation in the coming months.
In the previous post of this series, we tackled how to land inbound emails routed to an entire domain using SES, a Lambda function, and an S3 bucket. As the whole point of these posts is parsing image-based documents of invoices using AWS Textract, you’re probably wondering how we get from files in S3 to magic, OCR-extracted text. This post gets us most of the way there, addressing some points of improvement on our original solution. In addition, we add the function to our serverless application in this post that actually gets our extracted text back from Textract. In a future post, we’ll stand up a DynamoDB table for storing our outputs and then look at ways to interact with the data we’ve stored there.
I recently set up AWS SSO on an engagement. In this instance, I was setting it up using AWS Managed Active Directory as the identity provider. My first impression was how incredibly easy it was to set up the integration of SSO with the Managed AD. The SSO console basically prompts you for your Managed AD instance from a dropdown list. You connect it, and that’s it. AWS Managed AD comes with an OU already set up for you that SSO federates with.
I recently had the good fortune to take on a really fun project at work. First off, the client was incredibly easy to work with, which makes any project (even something I might consider tedious and boring, like migration work) a win in my book. In any case, this wasn’t a boring project – the client asked us to roll out Cloud Custodian across their entire AWS footprint – which at this point consists of an AWS Organization with a decent number of accounts (and more to follow).
In the first post of this series, we looked at a solution to allow us to define a serverless, email-based workflow to extract relevant information from auto maintenance invoices. Even in this age of accelerated digital transformation, there are still many scenarios in business and life where we receive data that is not in a machine-friendly format; we are building this solution to address these kinds of situations. We use the Serverless Framework to build the core of this solution. We create a few resources and configuration items in this post manually, but you are certainly free to manage these elements with something like CloudFormation or Terraform if desired. This post focuses on the resources highlighted in the figure below, where we design a solution to land incoming emails with S3 and SES, and sort them with a Lambda function:
Earlier this year, I tried to consolidate all of my automotive maintenance histories into a database-backed system that was the lowest-friction means possible for me to keep up with my records. At the time, I settled on building out a solution using Airtable. I was able to set up a solution very quickly. I am honestly quite happy with the outcome, except that I am still manually keying in records to either the Airtable app or on their site based on the paper records that my auto shop gives me on every visit. Ideally, I would like a solution that handles the data extraction from the paper records I get from my shop and stores it in a structured format that I can easily consume.