If you’re struggling with sluggish Python applications, you’re not alone—slow response times can frustrate users and hinder overall performance. At Keyhole Software, we excel at transforming underperforming Python applications into high-speed, efficient systems. Leveraging advanced performance testing and optimization techniques, we ensure your applications not only meet but exceed user expectations. Here’s how Keyhole’s expert team can elevate your Python …
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 issues before they slow everything down. It’s like having a constant health check for your code. In this blog, we’ll explain how continuous profiling works—and why it’s a game-changer for keeping your Python apps fast and …
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.
