He currently holds 9 of 9 AWS certifications, is a Cloudbees-certified Jenkins Engineer, holds a CompTIA Security+ ce, and holds Google Cloud Professional Data Engineer and Google Associate Cloud Engineer certifications.
When he’s not geeking out at work, you’ll find him hanging out with his wife and two awesome boys, strumming a guitar, buried in a book, trying to learn a new language (as in, human language), building something in his shop, or tinkering with one from a collection of way too many microcontrollers.
BA Economics, 2005
University of Virginia, Charlottesville
BS Electrical Engineering, 2011
Virgina Commonwealth University (not completed)
This one’s short, sweet, and to the point. Here are my GCP Professional Data Engineer exam notes.
I’ve had the good fortune of traveling quite a bit recently. I’m no connoisseur, but I do like a good meal. As I come across a great restaurant, I’ll make a note of it here. Austin, TX Moonshine Grill: I had the Green Chile Macaroni and a friend had the Chicken & Waffles (both of us greatly enjoyed our meals). Atlanta, GA DBA Barbeque: I had the Archie Bunker (it was great).
My goal for Q1 2019 is to add the Certified Kubernetes Administrator certification to my resume. In regards to writing about previous certification experiences, I have either: written a general outline of the experience much later not written about it at all, or just posted my study notes After more or less every certification, people have asked me what I did to prepare for the certification. Generally, the answer has been some combination of “practical experience and watching the A Cloud Guru course.
I was able to squeeze in the beta AWS ML exam the week before Christmas. Given that it was several weeks ago, some of the other resources on Medium may be more informative, but I’ll throw my two cents out here for anyone who may be interested. Generally speaking, know about different types of machine learning models (particularly those supported by SageMaker) and in what sorts of situations they’re applicable. These include:
I have spent the last six months working on the migration of multiple (mostly) SQL-based data sources to a multitude of different AWS-based targets, ranging from conventional SQL backends to user stores like Cognito. Many (if not all) of these efforts involved joining and coalescing data from multiple sources (per single data set) to migrate to a single backend system. For the majority of the work, I used Jupyter notebooks – relying heavily on Pandas and Numpy – for source data analysis, transformation, and load into target systems (as well as the use of database-specific client connection libraries, including MySQL, MS SQL, and Redshift/Postgres).