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.
When Jackie Edwards wrote “Keep On Running” back in 1965, he certainly wasn't thinking about the future of computing. But, it's the Spencer Davis Group grooves that is the soundtrack playing in my head when I think about Kubernetes and the business value it brings. Enabling your environment to “Keep On Running” is just one of many of Kubernetes’ value adds.
Customer experience is a key factor in competitive differentiation in the digital economy. The online business model has changed, and today, the focus has shifted from brands to customers. It would not be an exaggeration to say that customer experience plays a central role in any business model.
Regression testing is the process of verifying that if a change is made in one component of the product, the other components of the product continue to behave like it was before. It is a means to ensure that change in one functionality of the product doesn’t result in unintentional changes in other functionalities of the product.
When requirements change for your product, there arises a need to change not only the codebase but also the existing data that already lives in production. If you’re performing the changes locally, the whole process seems fairly simple. You test your new feature against a sparkling clean database, the test suite is green, and the feature looks great. Then you deploy, and everything goes to hell because you forgot that production was in a slightly different state.
Have you ever been neck-deep building a new feature? You're working at capacity. You need to test something out so you paste an API key into your source file with every intention of removing it later. But you forget. You push to GitHub. It's an easy mistake, and potentially a very expensive one. In this article, Julien Cretel explores the nuances of this kind of data leak, offers suggestions for recovery when leaks happen and gives us options for preventing them in the first place.
