Systems | Development | Analytics | API | Testing

APIs are vital in today’s digital transformation, but their seamless delivery is equally crucial. However, many organizations still fragment their API development and UI testing processes, leading to delayed issues detection, longer release times, and even missed bugs. This approach is unsustainable in a fast-paced environment where time-to-market and user experience are key. A unified strategy that integrates API management with UI testing is essential.

The recently published Independent Investigation of the National Health Service (NHS) in England offers valuable insights into the current challenges faced by one of the world’s largest healthcare systems. Among the many findings, the report highlights significant issues with patient access, quality of care and system performance.

For the last 10+ years, Docker has been one of the leading technology tools for helping developers build, run, update, and manage containers. It is one of the most widely used containerization tools available, able to work with Linux, Microsoft Windows, Apple MacOS, and all of the leading cloud service providers. With over 20 million monthly users, you’ve likely used Docker before, whether for your own projects or as part of a larger team.

Ukraine is a leading choice for IT outsourcing, thanks to its skilled talent and strong cultural alignment with Western clients. The country offers a deep pool of tech experts, a solid STEM (Science, Technology, Engineering, and Mathematics) foundation, and a thriving tech industry, making it a valuable option for businesses. When you work with Ukrainian firms, you get reliable software, smooth collaboration, and a shared drive for innovation.

The journey from a great idea for a Generative AI use case to deploying it in a production environment often resembles navigating a maze. Every turn presents new challenges—whether it’s technical hurdles, security concerns, or shifting priorities—that can stall progress or even force you to start over.

Computational chemistry relies on large volumes of complex data in order to provide insights into new applications, whether it’s for electric vehicles or new battery development. With the emergence of generative AI (GenAI), the rapid, scalable processing of this data has become possible and critical to investigate previously unexplored areas in catalysis and materials science.

How are AI governance and data governance related? Better still, what’s more important for an organization to focus on, AI-powered data governance or AI data governance? These are important questions, but before we answer these, let’s understand how AI and data governance are related to each other.

While data lakes make it easy to store and analyze a wide variety of data types, they can become data swamps without the proper documentation and governance. Until you solve the biggest data lake challenges — tackling exponential big data growth, costs, and management complexity — efficient and reliable data analytics will remain out of reach.

As part of gaining deeper insights into the rapidly evolving landscape of microservices testing and QA, I had the privilege of attending the Starwest 2024 conference, where one of the highlights was the insightful session led by Tariq King, CEO and Head of Test IO. His session on Full-Stack Testing for Microservices Architectures highlighted the growing leverage of microservices in modern applications and their seamless alignment with Agile and DevOps practices.

Prepare for software releases ahead of Black Friday, Cyber Monday, and beyond.