APIs have never had more connections and requests for data. With variable data types, changing programming languages, and a demand for high performance, even with an increased focus on authentication mechanisms and data security, it’s never been more critical to figure out the efficiency of your systems. Enter GraphQL and gRPC.

APIs might not always be in the spotlight, but they’re the unsung heroes of just about every modern software project. APIs make it possible for different apps, services, and platforms to talk to each other seamlessly. When an API falters, so does the entire system. That’s where REST API testing comes in. It’s not just a box to check off—it’s the key to making sure your services are dependable, secure, and prepared to handle the unexpected.

In almost all industries, a standard set of KPIs helps to guide teams on whether they are doing the right things in the right ways, with the right outcomes. In software development, this is something that can sometimes go untouched, allowing ourselves and fellow developers to continually work on our code and platforms without making sure we are paying attention to what matters. Some development frameworks, such as Agile, have some KPIs baked directly into them.

Observability is a critical element of modern software development, unlocking awareness across complex and distributed systems with ease. This has allowed developers to monitor, understand, and debug their applications effectively, leveraging existing resources for more efficient lifecycle management and iteration. In the context of Python, observability is an engine for boosting and maintaining the performance, reliability, and stability of the implementation.

Performance tests, end-to-end tests, integration tests. There are many different types of tests you can run on your infrastructure. One of the most overlooked kinds is load testing. Failure to include load tests in your supply chain can be detrimental, as you will be missing out on a number of benefits. Some of the big advantages of load testing Kubernetes are.

With the promise of auto-provisioning and self-healing, Kubernetes environments can be an attractive option for hosting your application platform. However, with increasing budget restrictions, the competitive cloud providers and offerings, and the need to do more with less, engineers are looking to get a handle on their resource utilization.

Large Language Models, or LLMs, have become a near-ubiquitous technology in recent years. Promising the ability to generate human-like content with simple and direct prompts, LLMs have been integrated across a diverse array of systems, purposes, and functions, including content generation, image identification and curation, and even heuristics-based performance testing for APIs and other software components.

Microservices are now the architecture of choice for many developers when crafting cloud-native applications. A microservices application is a collection of loosely coupled services that communicate with each other, enhancing collaboration, maintainability, scalability, and deployment. There are several options for enabling this communication between microservices. When it comes to Python, gRPC and REST are two extremely popular directions to go.

Memory leaks happen when a program fails to release memory it no longer needs, and can be a big issue for developers and system administrators alike, as the gradual depletion of available memory often makes for complex troubleshooting and debugging. Given how the consequences of a memory leak can range from decreased system performance to outright crashes, it’s crucial to isolate the root cause of the leak quickly and efficiently.