Attention: This article was published over 2 years ago, and the information provided may be aged or outdated. While some topics are evergreen, technology moves fast, so please keep that in mind as you read the post.Has your Python application been running a bit slow lately? We’ve all been there! Luckily, continuous profiling can help you catch those sneaky performance …
In every software application I’ve ever worked on, no matter the industry or maturity of the team or number of weeks in a sprint, there have been three questions that always come up: What is the best way to center a
? Should we use tabs or spaces? How should we implement complex workflows?
It was with that third question in mind that I stumbled upon a link in HackerNews a few weeks back on Temporal.io announcing that its .NET SDK is now in alpha.
If you aren’t in the know, Temporal is a library that lets you describe workflows as code. It’s available in multiple languages, but the Python flavor is the most popular.
So, I took a leap of faith, tried out the Temporal .NET SDK, and decided to recap my thoughts for you all as a blog. I’ll walk through, at a high level, what the Temporal approach is, the implications of workflows at the different zones of enterprise architecture, and where I see Temporal being useful in a large organization’s software strategy.
This year, the client I work with has started exploring the offerings in Google Cloud Platform (GCP) after investing years into the Microsoft Azure cloud. The opportunity has allowed me to explore a few new technologies that this post will cover and that you will hopefully find interesting.
In short, I was tasked with transforming Pub/Sub to Firestore Database in GCP. The scenario explored in this post takes an FHIR healthcare data feed, extracts telephone and email information, and then stores that in a Firestore database.
In this video, Alex presents an introduction to data science “big ideas” relevant to the model, an explanation of its data modeling process, and a demonstration of the real-life machine learning solution implemented with Python, Postgres SQL, and H2O (an open-source machine learning algorithm). Multiple data sources and technologies were combined to create an accurate model that allows farmers and recreationists to make actionable insights about the future. A few questions and answers round out the discussion.
In this post, l explain how we used Visual Studio Code’s Development Container feature as a stepping stone in our long-term effort to achieve Collaborative Infrastructure as Code. This one step in the process gave a versioned, repeatable working environment and allowed us time to determine the next steps in the effort to achieve IaC.