The world is undergoing a remarkable transformation fueled by data. Organizations have accumulated silos across their data infrastructure to support various workloads, languages, tools, and formats because of technology limitations. These silos can have major consequences in the form of greater operational burden, security vulnerabilities, increased total cost of ownership, incomplete insights, and reduced agility.

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.

At Snowflake Summit 2022, we introduced a new way of building apps with the Snowflake Native App Framework. Today, we are excited to bring the power of the Snowflake Native App Framework to developers around the world with the public preview in AWS. Developers can now start building and testing Snowflake Native Apps in their accounts in AWS. Distribution and monetization capabilities will be available in public preview on AWS later this year.

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.

Generative AI and large language models (LLMs) are revolutionizing many aspects of both developer and non-coder productivity with automation of repetitive tasks and fast generation of insights from large amounts of data. Snowflake users are already taking advantage of LLMs to build really cool apps with integrations to web-hosted LLM APIs using external functions, and using Streamlit as an interactive front end for LLM-powered apps such as AI plagiarism detection, AI assistant, and MathGPT.

Welcome to the third blog post in our series highlighting Snowflake’s data ingestion capabilities, covering the latest on Snowpipe Streaming (currently in public preview) and how streaming ingestion can accelerate data engineering on Snowflake.