Speedscale creates load tests from recorded traffic so generating load is pretty core to what we do. As a brief overview, we record traffic from your service in one environment and replay it in another, optionally increasing load several fold. During a replay the Speedscale load generator makes requests against the system under test (SUT), with the responses from external dependencies like APIs or a payment processor optionally mocked out for consistency. Your service is the SUT here. Currently the load generator runs as a single process, usually inside a pod in Kubernetes. So how fast is this thing, and how did we get to where we are today?

Digital transformation initiatives have been a top priority for several years in most companies. However, while such programmes are considered a necessity for the long-term survival of most businesses, they haven't always been prioritised or given the resources they deserve. That all changed with the COVID-19 pandemic. Today, according to analyst Gartner, customers expect companies to accelerate digital initiatives due to COVID-19 and all industries are experiencing widespread digitisation.

In this Postman load testing tutorial, you'll learn how to run a large scale load test in Kubernetes using your existing Postman collections. Because HTTP services don't have a graphical user interface, it's common to build collections of requests using Postman during the development process. These collections are useful for running quick functionality tests as you develop each endpoint. However, as the service grows you eventually need to test it in a more realistic way with larger volume. This is called a load or stress test. Speedscale is a Production Data Simulation Platform that includes this stress/load testing capability out of the box.

Tons of apps are out there, but most of them fail. Successful apps leverage data and provide users insights into it. In-app data exploration becomes the new standard in user experience (UX).

Without effective management and reuse, APIs will not deliver their digital transformation potential. There is an urgent need to make application programming interfaces (APIs) efficient and utilised more effectively. This need is being driven by the rapidly increased rate of digitisation that customers and business lines are demanding from CIOs and CTOs. APIs are a powerful tool that can be employed to deliver competitive advantage and market differentiation, the two biggest demands being placed on the technology team.

By combining traffic replay capabilities from Speedscale with observability from Datadog, SRE Teams can deploy with confidence. It makes sense to centralize your monitoring data into as few silos as possible. With this integration, Speedscale will push the results of various traffic replay conditions into Datadog so it can be combined with the other observability data. Being able to preview application performance by simulating production conditions allows better release decisions. Moreover, a baseline to compare production metrics can provide even earlier signals on degradation and scale problems. Speedscale joined the Datadog Marketplace so customers can shift-left the discovery of performance issues.

At its Worldwide Developer Conference in late May, Apple shared a sneak preview of what to expect in the autumn, including news around iOS 16. This gives app developers and testers a bit of time to get their heads around the implications, including new test requirements. Here are some of the highlights from the conference.

API Observability isn't exactly new, however it's popularity has seen rapid growth in the past few years in terms of popularity. API Observability using open source is different from regular API monitoring, as it allows you to get deeper and extract more valuable insights. Although it takes a bit more effort to set up, once you've got an observability infrastructure running it can be immensely helpful not only in catching errors and making debugging easier, but also in finding areas that can be optimized.