One of the really interesting trends that I’m seeing in the marketplace relates to consultancies. Historically, the primary business model for consultancies was to sell their expertise and services. Every customer would get a unique service offering that was defined specifically for them.
As an all-encompassing marketplace, RapidAPI has become a popular option for developers and vendors alike. So what does it have to offer and how can you harness it effectively to get the most out of its features and benefits? On the surface, the concept of RapidAPI is simple; it aims to act as your one-stop-shop for a wide variety of RESTful APIs which can be deployed to tackle a plethora of potential projects.
We’ve set up several initiatives aimed at testing and improving the performance of GitLab, which is why the Quality team built a new tool to test GitLab's performance. Performance testing is an involved process and distinct from other testing disciplines. The strategies and tooling in this space are specialized and require dedicated resources to achieve results.
Ever feel like the world is moving by so rapidly, that it feels like something got left behind in the rush? That’s how we feel about being able to debug your applications easily. Technology has advanced rapidly, but the ease of debugging has not. With the introduction of serverless computing, the way enterprises are designed and how they build their production applications was changed.
Having spoken with many companies, I’ve learned that while they all monitor their application performance, infrastructure, product usage, conversion rates and a variety of other user experience parameters, very few monitor the actual transactions from their payment provider.
For many years, API Management (APIM) — and the adoption of API gateways — was the primary technology used to implement modern API use cases both inside and outside the data center.
This article is a continuation of Part I (A comprehensive guide to migrating from Python 2(Legacy Python) to Python 3), which details the changes, and improvements in Python 3, and why they are essential. The rest of the article describes automated tools, strategies, and the role of testing in the migration from Python 2 to 3.
My computer programming teacher had always told me that 10% of our time is spent developing 90% of our application, and the other 90% of our time finishing the last 10% of our project. Even with a good project plan and a concept that makes logical sense, most of our time will be consumed with fixing errors. Moreover, with JavaScript, our application can run without obvious errors preventing it from being run, so we have to employ several techniques to make sure everything is running smoothly.
This article discusses four bottlenecks in BigData applications and introduces a number of tools, some of which are new, for identifying and removing them. These bottlenecks could occur in any framework but a particular emphasis will be given to Apache Spark and PySpark.
