Systems | Development | Analytics | API | Testing

Modern enterprises generate data across hundreds of applications, databases, and cloud services. Moving this data efficiently between systems while maintaining quality and consistency requires robust Extract, Transform, Load (ETL) processes that form the backbone of every successful data strategy.

I recently tried using AI to create a digital twin of myself. I uploaded a photo, expecting a futuristic, slightly improved version of me… and what did I get in return? A picture of Kim Jong Un. Clearly, AI has a sense of humor—or a very different definition of “twin.” Forget Arnold Schwarzenegger and Danny DeVito.

Digital payments have moved from convenience to critical infrastructure on a global scale. Quality Assurance is the invisible backbone that protects customer trust, regulatory compliance and a bank’s reputation in this high-stakes environment.

APIs (Application Programming Interfaces) power nearly every modern digital experience, from mobile apps and online payments to AI-driven services and real-time data processing. As software systems increasingly rely on microservices and distributed architectures, the number of API interactions continues to grow, making reliability and performance more critical than ever.

Today, we are unveiling a new ThoughtSpot. Not because we needed a new logo. Because our brand needed to catch up to the company we already are. If you know ThoughtSpot, you know us as more than dashboards or static analytics. You know us as the place where every question has an actionable answer. We are an AI company. We lead in Agentic Analytics. And it is time for our brand to make that unmistakably clear. And yes, I am excited. We are all excited.

Start a conversation on your laptop, finish it on your phone. The context just follows you. That’s what cross-device AI sync delivers. No reloading history, no reintroducing yourself, just one continuous thread across every screen. It builds trust, reduces friction, and makes the assistant feel like a single, persistent presence. This post unpacks why users expect it, what makes it technically tricky, and what your system needs to get it right.

GPU underutilization costs enterprises millions annually, with expensive accelerators frequently running single workloads at a fraction of their capacity. According to ClearML’s 2025-2026 State of AI Infrastructure at Scale report, almost half (49.2%) of IT leaders at F1000 companies identified maximizing GPU efficiency across existing hardware, including shared compute and fractional GPUs, as their top priority for expanding AI infrastructure over the next 12-18 months.

What’s remarkable is how long Confluent competed at the highest level. Creating a category and type of application is hard; transitioning to cloud and surviving against hyper scalers is even harder. That alone is a huge achievement. Some see this as a pressured exit. But another way to look at it is as a strategic purchase by IBM to strengthen its position in enterprise data movement and integration.

iOS code signing is a critical step in app development, ensuring apps are secure and trusted by Apple and users. However, it often leads to frustrating errors that can disrupt development and delay app launches. Here’s what you need to know.

When Oracle updates hit, many IT teams brace for impact. Backlogs swell, manual checks slow releases, and a patch that should take hours can stretch into days. For enterprise teams running Oracle at scale, outdated testing tools can be inefficient, costly, and difficult to manage. At Oracle AI World, two global organizations shared the stories of how they moved past those bottlenecks.