Systems | Development | Analytics | API | Testing

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.

Manufacturers today are implementing a range of new technologies to increase operational efficiency and create visibility and flexibility across value chains. These include robotics, automation, data analytics, IoT, and artificial intelligence (AI) and machine learning (ML), according to Deloitte. Company leaders hope these innovations will help them create more productive and resilient supply chains, improve production quality and efficiency, and mitigate risks.