Systems | Development | Analytics | API | Testing

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.

For years, the traditional approach to automating tests relied heavily on scripted automation frameworks like Selenium, JUnit, and TestNG. While these tools are powerful and adaptable, they require a significant level of technical expertise. Testers need to write, debug, and maintain complex scripts, often leading to bottlenecks in the development process. Relying only on coded solutions limits test automation to a small portion of the team that has deep coding skills.

While many API tools are available on the market—such as enterprise service buses (ESBs) like Apigee and MuleSoft, or low-code solutions like Hasura and CData—few offer the level of flexibility that DreamFactory does. A recent project underscored just how dynamic this lightweight, enterprise-ready API generation tool can be. In this article, we'll dive into this unique project and explore how DreamFactory proved to be much more than just an API generator.