IFrames were used, and are still used, in many embed frameworks. After much experimentation, including many unsuccessful platforms, iframes have proven themselves to be a simple and powerful way to let partners customize your experience. However, their limitations are also known: Consequently, platform teams have tested and continue to test alternatives. Certain platforms now exclude iframes entirely, while others combine iframes with non-iframe alternatives.

Within the domain of software quality assurance, functionality and user experience hold a paramount position, and visual testing becomes an indispensable protector of the digital environment. The complexity of modern apps is always growing, thus it's critical to guarantee pixel-perfect precision and smooth visual consistency across all platforms and devices.

Pharmaceutical industry leaders are adopting new artificial intelligence (AI) technologies and increasing process efficiency. The Infosys report on AI adoption shows that pharmaceuticals are among the most mature industries in Al adoption. In the same report, 40 percent of the respondents claimed their organizations had deployed Al and that it was working as expected. AI-powered features help them manage massive volumes of pharmaceutical data with great accuracy and speed.

Alright, I’m back. Time for part 2. In the first part, I covered how we handle bad data in batch processing. In particular, cutting out the bad data, replacing it, and running it again. But this strategy doesn’t work for immutable event streams as they are, well, immutable. You can’t cut out and replace bad data like you would in batch processed data sets.

Over one billion people worldwide live with some form of disability. Accessibility testing ensures that digital platforms are usable for everyone, including people with disabilities.

Many organizations struggle with fragmented testing approaches covering only individual components or features, leading to gaps in the system’s reliability and user experience.

Let’s say you’re leading a company that receives thousands of documents daily. These documents come in various formats like Excel, PDFs, CSVs, and more. And they differ in terms of layout. Before you can analyze the data, your team spends hours sorting, cleaning, and preparing these documents. Most of their time is spent preparing the documents for integration into business systems. Then, a colleague shares how intelligent document processing helped him save time and boost productivity.

Retrieval Augmented Generation (RAG) is an intelligent assistant that helps you find exactly what you’re looking for in a pile of medical records. Like an X-ray shows you hidden details inside the body, RAG helps you quickly extract precise information from complex data. RAG provides instant, accurate answers—often visualized in charts or summaries that require analysts to produce manually. RAG combines two AI capabilities—retrieval systems and generative models.

Sam Altman, the key visionary behind the popular adaption of Gen AI and essentially the father of ChatGPT, deemed it “unthinkable” to have products and services without AI integration in the future. I’m sure that among other beliefs that inspired him to make such claims, the need for intelligence beyond efficiency in the modern digital ecosystem was a key one. It only makes sense to rethink IT management in this context and replace our traditional methods with the benefits of AIOps.

BigQuery external datasets for Spanner let you browse, explore and query Spanner tables as if they were native BigQuery tables.