Systems | Development | Analytics | API | Testing

Teams often find themselves managing a big number of test cases, many of which are nearly identical except for variations in input data. This redundancy leads to a higher risk of missing critical test coverage. Test parameterization solves this challenge by allowing testers to re-run the same test logic with multiple data iterations - teams define parameters within a single test script and execute it dynamically via a dataset with different values.

Despite fears of job loss, QA professionals are leaning into AI faster than ever, according to Katalon’s newly released 2025 State of Software Quality Report. The report reveals that testers using AI tools are twice as likely to fear being replaced by them, a paradox that underscores the profession’s evolving relationship with automation.

Millions of users rushed to ChatGPT overnight, all craving Studio Ghibli-style art. What started as a fun trend quickly went viral, pushing OpenAI’s servers to their limits. The "Ghibli Trend" wasn't just another online craze — it became a live performance and load-testing scenario for OpenAI. Social media users began sharing Ghibli-inspired AI images, creating a massive buzz.

Pharmaceutical labeling is an ideal use case for AI because it’s a complex process that requires high levels of accuracy. Inaccurate labeling can result in: With recent breakthroughs in AI technology, pharmaceutical companies have rushed to explore its potential. But many have not seen the impact they expected. The problem isn’t the AI. It’s how pharma companies are using AI.

Adopting AI? Risky. Not adopting AI? Riskier… It’s an interesting conundrum illustrating a broader truth: playing it safe is sometimes risky. To succeed, companies need to take smart risks—and those who do often achieve the greatest rewards. But a risk that is smart for one organization may not be as smart for another, so what risk is right for you?

BigQuery data preparation is generally available and integrates with BigQuery pipelines and BigQuery repositories and Git.

We’re excited to share that self-serviceable continuous deployment (CD) pipelines are now generally available in Choreo. This feature builds on Choreo’s multi-environment, multi-cluster capabilities and gives platform teams even greater control over deployment workflows.

Despite the best efforts of many ML teams, most models still never make it to production due to disparate tooling, which often leads to fragmented data and ML pipelines and complex infrastructure management. Snowflake has continuously focused on making it easier and faster for customers to bring advanced models into production.