In the modern era of AI-based innovations and cloud transitions, businesses face the challenge of managing complex processes across multiple systems — and the rate of change is only accelerating. At the same time, businesses need to ensure that the introduction of new innovations driven by accelerated business transformation doesn’t break existing processes and systems.

Generative AI has received the lion’s share of the press. With good reason—it’s revolutionizing the way we do work and do business. But it’s not the only game in town. Predictive AI also places a role across enterprise use cases like demand forecasting, maintenance, and customer experience. This blog will discuss these two types of AI: generative AI and predictive AI.

Are you ready to step into the digital world where the possibilities are boundless and the pace is exhilarating? The world is indeed embracing the digital revolution with open arms, and the gateway to this extraordinary universe lies in the palms of our hands. Yes, you guessed it right! Smartphones and mobile devices have become an integral part of our lives. As a result, this has led to the significant popularity of app development among businesses.

The way that companies govern data has evolved over the years. Previously, data governance processes focused on rigid procedures and strict controls over data assets. But now, with the data-driven culture, modern enterprises are adopting an agile approach toward data governance that primarily centers around data accessibility and empowering business users to take responsibility for governing and managing data.

When we initially started building our test recorder, we needed a way to validate the id attributes being used on the page. We would sometimes capture an id attribute in a recording, only to find that it failed when we used it in a test, because it didn’t meet specification. For instance, sometimes websites would use an id with a number in front, like this: That is technically invalid, at least in the HTML4 specification.

Discover the new Artifact Details page and Private Install page from Bitrise, offering detailed artifact information that's easy to find, and secure to share.

Using BigQuery DataFrames with CARTO visualization tools can help Python users access cloud-native analytics and generative AI services.

Welcome to the ultimate resource for mastering load testing! Whether you’re a non-technical business owner, a software engineer, a student, a product owner, a member of a web agency, or a DevOps professional, this article will show you how to approach load testing challenges head on. We’ll progress from the fundamentals to more in-depth strategies, all in a relaxed and easy-to-understand manner. Are you ready to ensure that your website runs smoothly even during peak traffic periods?

At a time when artificial intelligence (AI) and tools like generative AI (GenAI) and large language models (LLMs) have exploded in popularity, getting the most out of organizational data is critical to driving business value and carving out a competitive market advantage. To reach that goal, more businesses are turning toward hybrid cloud infrastructure – with data on-premises, in the cloud, or both – as a means to tap into valuable data.