A bad beginning... A testing bedtime story that even Crackanory would reject for transmission on the basis it was too crackpot and twisted to be credible. Gather closer my precious readers, sitting comfortably? Good, then I can begin. Abide me carefully because I am going to tell you the secrets of how to have a magic unicorn, hen’s teeth, Harry Potter, automate-the-lot, works every time, end-to-end automated checking solution..... Alas, no chickens, I am not, because there is no such thing. The End. Sleep well all.
It seems that everyone is trying to build a microservices based system these days. Some of those attempts succeed when others fail miserably. In this article, I will look at one thing that often connects the winners- the use of DevOps practices and culture.
It negatively affects all of us mentally and physiologically, yet we seem unable to stop. But are we really doomed to increasingly crumble under the weight of our workload, or can we change our ways to fit the agile world?
Distributed log technologies have matured in the last few years. In this article, I review the attributes of distributed log technologies, compare four of the most popular and suggest how to choose the one that's right for you.
Thinking of applying to work with us as a tester? This is a post to help you work out if we are a good fit for you. We'll do this by telling you how we work out if you are a good fit for us. Hopefully this will be of interest to anyone trying to evaluate if the benefits we offer are in line with the environment they want.
"Lets hand it over to QA”. This phrase is seen and heard a lot, more so when chucking stuff over the development waterfall edge but even now, this is an oft used term as a synonym for testing. Terminology, naming, what we call something or someone can have a powerful impact on how something is viewed. Even worse when terminology becomes interchangeable, even though what each represents is actually something very different.
Apache Spark is the major talking point in Big Data pipelines, boasting performance 10-100x faster than comparable tools. But how achievable are these speeds and what can you do to avoid memory errors? In this blog I will use a real example to introduce two mechanisms of data movement within Spark and demonstrate how they form the cornerstone of performance.
Spark is well known in Big Data for its incredible performance and expressive API. However, it just takes one small misstep to transform a massively parallel powerhouse into a pathetically poor performer. This post presents an example and the steps that can be taken to indentify the problem.