Imagine being at the helm of a project, excited to launch an innovative software application, but then... the testing phase reveals critical flaws that could have been caught earlier. This experience is a stark reminder of how vital a cohesive test plan can be. Join me as we explore how a visual test plan builder can transform your approach to crafting robust test plans, ensuring quality at every step of the way!

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.

Organizations generate vast amounts of data daily during various business operations. For example, whenever a customer checks out out at a retail outlet, data such as the customer identifier, retail outlet identifier, time of check out, list of purchased items, and the total sales value can be captured in the Point of Sales (PoS) system. Similarly, field sales staff may record possible sales opportunities in spreadsheets.

The report highlights platforms like Fivetran that are crucial for modernizing marketing data infrastructure in today’s fast-paced, budget-conscious market.

Organizations are generating more data than ever before, but accessing it and gaining insights from it can be challenging. Fivetran and Snowflake simplify and speed up the process.

Over the last decade, many IT leaders have felt confident they were ready for the API evolution. Afterall, they invested a lot into it. Traditional API management platforms promised to deliver on transformational challenges, and offer cornerstone capabilities – API cataloguing, specification support, authentication, authorization permissions, security, policy management, and even developer portals.

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.

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.

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.