Systems | Development | Analytics | API | Testing

Containers have emerged as the modern approach to package code in any language to ensure portability and consistency across environments, especially for sophisticated AI/ML models and full-stack data-intensive apps. These types of modern data products frequently deal with massive amounts of proprietary data.

Today’s governmental and educational organizations can’t fully use the wealth of data they possess to improve citizen and student outcomes. Government agencies often deal with disparate and siloed data that can impact real-time decision-making. Securely exchanging information and collaborating on data remains an essential task in almost every agency strategy.

When it comes to data, state of the art is an ever-moving target. There’s also a lot of hype around “cutting edge” that isn’t always grounded in reality. At Snowflake, we’re able to see how cutting-edge companies are actually working with data on our platform.

Sports and gaming companies are forging ahead with the use of data science as a competitive differentiator. According to an industry report, the global AI in media and entertainment market size was valued at $10.87 billion in 2021 and is estimated to grow 26.9% annually until 2030.

In my last article, I outlined how we in Snowflake Support use contextual data about where our customers get stuck to improve the overall product experience. Now I’ll take you through how your organization can also implement these important feedback loops from support to product enhancements, to your company’s—and your user’s—benefit. Customers don’t wake up in the morning and decide they’d love to spend time with a Support team.

After 30 years of working in tech across Asia, I’ve seen a lot of ups and downs in these markets. Not long ago, I wrote a blog about what US and European software companies can do to ensure their success when opening for business in the region.