Systems | Development | Analytics | API | Testing

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.

Learn about an architecture built on BigQuery Omni that reduces the overall cost of log analytics by 10x as compared to a traditional approach.

It’s no secret that more and more organizations are turning to solutions that can provide benefits of real time data to become more personalized and customer-centric, as well as make better business decisions.

Every QA tester knows: time is money. When something breaks down on your website or web application, it can cause major issues for users within minutes. Because you’re constantly working to prevent fires from spreading (or happening at all), it’s important to have numerous strategies at the ready to help you manage your time wisely. One of these QA testing timesavers is called smoke testing. But exactly what is smoke testing?