Data is the driving force of the world’s modern economies, but data teams are struggling to meet demand to support generative AI (GenAI), including rapid data volume growth and the increasing complexity of data pipelines. More than 88% of software engineers, data scientists, and SQL analysts surveyed say they are turning to AI for more effective bug-fixing and troubleshooting. And 84% of engineers who use AI said it frees up their time to focus on high-value activities.

Briefly explained: Chrome is built upon Chromium, an open-source project maintain by Google. Chromium is the base for Chrome and also other browsers like Microsoft Edge and Opera. Let's explore the differences between Chrome vs Chromium in depth!

Data practitioners are consistently asked to deliver more with less, and although most executives recognize the value of innovating with data, the reality is that most data teams spend the majority of their time responding to support tickets for data access, performance and troubleshooting, and other mundane activities. At the heart of this backlog of requests is this: data is hard to work with, and it’s made even harder when users need to work to get or find what they need.

As compute gets increasingly powerful, the fact of the matter is: most AI workloads do not require the entire capacity of a single GPU. Computing power required across the model development lifecycle looks like a normal bell curve – with some compute required for data processing and ingestion, maximum firepower for model training and fine-tuning, and stepped-down requirements for ongoing inference.

Artificial Intelligence (AI) is here, and it has the potential to revolutionize industries, enhance customer experiences, and drive business efficiencies. But with great power comes great responsibility — ensuring that AI use is ethical is paramount to building and maintaining customer trust. At Tricentis, we’re committed to responsible AI practices. At the core of this commitment are data privacy, continuous improvement, and accessible design.

Broadly defined, the supply chain management process (SCM) refers to the coordination of all activities amongst participants in the supply chain, such as sourcing and procurement of raw materials, manufacturing, distribution center coordination, and sales.

In today’s fast-paced business environment, having control over your data can be the difference between success and stagnation. Leaning on Master Data Management (MDM), the creation of a single, reliable source of master data, ensures the uniformity, accuracy, stewardship, and accountability of shared data assets.

The modern data-driven approach comes with a host of benefits. A few major ones include better insights, more informed decision-making, and less reliance on guesswork. However, some undesirable scenarios can occur in the process of generating, accumulating, and analyzing data. One such scenario involves organizational data scattered across multiple storage locations. In such instances, each department’s data often ends up siloed and largely unusable by other teams.

With organizations prioritizing data-driven decision-making, the amount of collected and stored data is reaching historic highs. Meanwhile, organizations are democratizing access across all functions to convert this data into actionable insights. Since more users will work with sensitive data, ensuring secure access is more important than ever. Organizations must regulate and maintain the relationship between their data assets and users. Why?

Performance testing is a form of software testing that focuses on how a system running the system performs under a particular load. This type of test is not about finding software bugs or defects. Different performance testing types measures according to benchmarks and standards. Performance testing gives developers the diagnostic information they need to eliminate bottlenecks. In this article you will learn about.