Over the course of my career in testing and quality, I have seen many different quality strategies. All of them have been narrowly focused on testing, with perhaps a sprinkle of DevOps tossed in. I started to wonder why this is, and when I dove a bit deeper I realized that all of them were written by testers. It’s a truism in the industry that you can’t test quality into a product. Why is it then that all of our quality strategies revolve around testing or development methodologies?
As an API provider, once you’ve decided to bring in revenue from your APIs, the next step is to figure out how you will price the usage. As with any business decision, there are plenty of ways to go about pricing an API. There are many short-term strategies to establish initial pricing and then iterate to find which pricing model and price points work best for your customers.
Connected-Stories NEXT is an end-to-end creative management platform built on Google Cloud to develop, serve, and optimize interactive video and display ads that scale across any channel.
Completely unrelated to the unfolding events at a prominent social media company, we thought it is a good idea to highlight some anti-patterns to measuring productivity, and a few things that should be common sense to do instead.
Load testing is one of the most common ways to test the resiliency of your applications. In this blog we show how recording production data with Speedscale and exporting to a K6 load tests gives you the best of both worlds. Whether or not it's important for your organization, there are clear benefits to be had from implementing these types of tests. By doing so, you can: When it comes to load testing, two of the most modern tools are Speedscale and K6. While there are many reasons for choosing one over the other, there are also benefits to using them together. If you want to know what the main differences are, check out the in-depth comparison.
Stream processing is about creating business value by applying logic to your data while it is in motion. Many times that involves combining data sources to enrich a data stream. Flink SQL does this and directs the results of whatever functions you apply to the data into a sink.
