As highlighted in the Quantum-Safeness of WSO2 Products blog, there is an urgent need for alternative asymmetric encryption methods. Post-quantum algorithms (Q) are gradually gaining prominence but require time to establish trust within the industry. Therefore, combining classical (C) and post-quantum (Q) Key Encapsulation Mechanisms (KEMs) ensures resilience against classical and quantum attacks. The Ballerina Swan Lake Update 9 release adds support for post-quantum secure end-to-end encryption.

In August, we wrote about how in a future where distributed data architectures are inevitable, unifying and managing operational and business metadata is critical to successfully maximizing the value of data, analytics, and AI. One of the most important innovations in data management is open table formats, specifically Apache Iceberg, which fundamentally transforms the way data teams manage operational metadata in the data lake.

Slowly but surely, HTTP/2 has become the preferred protocol for transferring data files between clients and servers. In contrast, HTTP/1, which once stood as the sole method for loading web applications, is now falling short, particularly in terms of user experience. Since 2015, HTTP/2 APIs have taken the lead with lightning-fast server responses.

Whether you agree with Elon's prediction or not, it's hard to ignore AI's far-ranging impact, especially on how we approach work. Over the last two years, we have seen AI progress rapidly, leaving many of us wondering, "Will AI replace my job?" It's a question that software engineers have also been grappling with. As ironic as it may seem, the people writing the code driving the technology revolution face the same uncertainty about whether AI might replace them in the future.

Many vendors have started calling their platforms “low-code” or “no-code.” Competitors often pit the terms against each other. But what do low-code and no-code really mean? And what's the difference between the two? Both platforms offer improvements over traditional high-code approaches. The biggest differences are the target user groups.

The problem with test data is that it can become stale very quickly. This is either through its use from testing or from the fact that it is naturally aging in the test environments. This is not just an issue for performance testing, although the volumes of data sometimes required for performance testing do make it harder. This also affects functional testing as well as batch testing and business acceptance testing amongst others.

In recent years, machine learning operations (MLOps) have become the standard practice for developing, deploying, and managing machine learning models. MLOps standardizes processes and workflows for faster, scalable, and risk-free model deployment, centralizing model management, automating CI/CD for deployment, providing continuous monitoring, and ensuring governance and release best practices.

Every software application undergoes rigorous functional and non-functional testing to meet business requirements. When we talk about delivering top-tier software, performance is key. How fast does it run? How stable is it under pressure? Can it scale with growing demand? Does it remain reliable when pushed to its limits? These questions are at the core of performance testing, and that’s where benchmark testing steps into the spotlight.

We’re excited to unveil a sneak peek of the upcoming NeoLoad 2024.3 release, which will be generally available this November. One of the most anticipated features in this release is remote terminal emulation (RTE) support — a game-changer for teams responsible for the performance of mainframe and other legacy systems.