A 55-minute dive into the “Serverless” framework, an open-source tool that aims to simplify building, packaging, and deploying serverless applications across multiple cloud providers and platforms like AWS, GCP, Azure, and Kubernetes.
Using Groovy 4: GINQ for the Win
In my last blog post Back in the Groovy 4, I briefly mentioned Groovy-Integrated Query (GINQ). I’ve been wanting to write about how I would use this new feature, and I decided to take this opportunity to do so.
In this post, I will be describing two examples in which I used GINQ. The first requirement I faced on a recent project of mine and demonstrating how I used GINQ to fulfill it. A quick disclaimer: this is not a tutorial on GINQ. This blog is merely a discussion of how I’ve used GINQ and how I plan on making it part of my toolkit.
Angular, Blazor, React: A Quick Look
In the world of technology and development, there are many different types of frontend frameworks that are used. In this post, we’ll discuss two of the biggest ones, Angular and React, and one of the newer ones, Blazor.
Getting Started With Stripe Elements Integration
Recently, I was involved with a project that required the client to collect payments from their customers. This brought up many questions on the best approach to process orders to accomplish this.
We wondered, is a Stripe Elements integration the answer, or can we avoid fees and process payments ourselves? For the vast majority of companies, ourselves included, yes, Stripe is the answer. The complexity of Payment Card Industry (PCI) Compliance alone makes this a no-brainer. In addition, the development of payment infrastructure is very time-consuming and costly.
In this blog, I will discuss Stripe Elements and its integration and the best way to implement them with React. I will cover basic Stripe information, how to set up a Stripe account, and a brief code overview to help you get on your way to incorporating Stripe…
[Video] Data Mining/Science: Supervised Time-Series Model
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.