If you work in Quality Assurance testing, you know that automated browser testing saves every tester from hours upon hours of manual test work. But exactly what is automation testing, and how does it fit into a typical QA workflow? We’ve got the full explanation for you in this straightforward guide on the basics of automation testing, how it can transform your QA process, and how to get started with test automation tools like Ghost Inspector.

Financial planning and analysis (FP&A) is recognized as an essential tool for driving business success. At a time when agility has taken center stage, though, FP&A is giving way to the new discipline of extended planning and analysis (xP&A). Why? Because in an increasingly digital landscape where agility is paramount, actionable real-time feedback is essential.

Data is increasingly becoming critical for the public sector — from guiding decisions in higher education to enhancing citizen services and streamlining government operations. Government agencies are overwhelmed with data, whether it be structured, like incident logs, or unstructured, like satellite images. Harnessing the vast amount of data can become a burden for any organization, yet the insights have the potential to significantly improve quality of life and strengthen national security.

Artificial Intelligence (AI) is transforming industries, enhancing decision-making processes, and creating new opportunities across various domains. However, selecting the right type of AI for your specific needs can be challenging. Broadly, AI can be categorized into predictive (sometimes known as discriminative or traditional) AI and generative AI, each serving different purposes.

Did you know? In the past decade, mobile app usage has evolved and grown tremendously all around the world. Not only that, according to a recent study, the SaaS industry has progressed from $31.5 billion to an estimated $171.9 billion in 2022 as per the report published by Exploding Topics. That equates to around 5x growth in only seven years. That’s incredible, isn’t it?

Kubernetes has transformed how developers build, test, and deploy applications, providing a consistent platform for managing containerized workloads. Setting up efficient Kubernetes clusters is crucial for streamlining the development process, reducing time-to-market, and ensuring that applications run reliably from development to production.

Imagine you’re running a complex, distributed system—everything seems smooth until, without warning, a sudden failure brings the entire operation to a halt. What caused it? How can you prevent it from happening again? This is where chaos testing comes in. Chaos testing, or chaos engineering, is a proactive approach to discovering system weaknesses before they turn into catastrophic failures.

According to recent survey data from Cloudera, 88% of companies are already utilizing AI for the tasks of enhancing efficiency in IT processes, improving customer support with chatbots, and leveraging analytics for better decision-making. More and more enterprises are leveraging pre-trained models for various applications, from natural language processing to computer vision. For that reason, Cloudera is evaluating a new line of business: Cloudera Integrated Data and AI Exchange (InDaiX).

Historically, finance teams largely operated in the background, focusing on record-keeping, compliance, and ensuring that financial operations ran smoothly. Their primary responsibilities revolved around managing costs, balancing the books, and maintaining operational efficiency. In recent years, that has changed.

The debate between static and dynamically typed languages has long been a subject of contention among developers. Each approach offers its own set of advantages and disadvantages, significantly influencing the software development process. Dynamically typed languages like Ruby provide flexibility by allowing variables to be declared without corresponding types. This approach fosters rapid development and promotes an agile process.