Systems | Development | Analytics | API | Testing

Your reports are only as good as your data. When your team spends hours building dashboards and answering leadership questions, mismatched or messy data can quickly erode trust. That’s why data preparation-cleaning, organizing, and aligning your data through structured preparation processes is the first (and most critical) step before you can analyze trends, track goals, or forecast results.

After years of feedback from customers and quiet development, we officially launched a powerful new set of advanced analytics features during our May 7th launch event. This release marks a major milestone in our mission to make Business Intelligence (BI) accessible to everyone. We call it DIY BI (Do-It-Yourself Business Intelligence): the idea that anyone, regardless of technical skill, can explore data, find answers, and improve performance without relying on complex tools or a busy data analyst.

By 2025, many QA teams have doubled down on automation. They’re running more tests than ever, collecting more results, and generating more data. And yet… they’re still facing the same challenges. Bugs are still found late in the cycle, release decisions are still delayed, which leads to production costs inflating. Even with all the dashboards and reports in place, there’s often still one big question left hanging: Where to focus on next?

Have you ever wondered how apps like Google Maps predict traffic, or how Netflix knows exactly what you want to watch next? Or better yet, how can chatbots (like ChatGPT!) carry on conversations almost like humans? The magic behind it all? AI models. But what exactly is an AI model? Is it some complex algorithm sitting in a dark server room somewhere? Or is it the new digital brain behind today’s smartest tools? In simple terms, AI models are like trained minds.

Software testing methodologies are structured approaches that determine how testing activities are planned, executed, and managed throughout the software development lifecycle. With increasing complexity in modern applications, implementing the right testing methodology is crucial for delivering high-quality software products on time and within budget. This guide explores various software testing methodologies and their practical applications in today’s development environments.

At the heart of any enterprise data management strategy is the driving principle of infrastructure as code (IaC), the practice of defining, provisioning and managing infrastructure through code, and a foundational approach for enabling continuous delivery (CD).

Git and Perforce P4 are two powerful options for source code management, but choosing between them, or using both, depends on your specific use case and priorities. When evaluting these solutions, there's a lot to consider. So, we've broken down things to cover everything you need to know about Git vs. Perforce P4 in the table of contents below. Follow along or jump to the section that interests you the most.

As a long-established ERP, SAP has earned its trust by helping users streamline and improve financial operations. But the global economy is in a state of rapid flux, making financial navigation challenging. Further, the SAP ecosystem’s technical intricacies add another layer of complexity for those without specialized expertise in managing financial decisions.

AI.GENERATE_TABLE() in BigQuery lets you convert unstructured data into a structured table based on the provided prompt and table schema.