The explosive rise of generative AI has prompted incredible excitement about its transformative potential, much like the advent of the Internet. But if like me, you’re old enough to remember what that looked like circa 1995, there was a lot we did not know at the time, creating uncertainty in both worlds of work and education on how to best leverage it, and whether providing unlimited access to employees or students was a good idea.
With the recent announcement of ThoughtSpot Sage, we launched a number of enhancements to our search capabilities including AI-generated answers, AI-powered search suggestions, and AI-assisted data modeling. In this article we will walk you through the steps we take to secure your data during the LLM interaction.
Keep track of new releases for our data connectors and data models for dbt with this regularly updated list.
Last week’s announcement of a data fabric approach from Microsoft was interesting to us on a number of levels, most notably for what it confirmed: Data Fabrics are now mainstream. If you’ve not heard why Data Fabrics are the next big thing, then here is a bit of history. Data Fabric is a concept that started to take hold over a decade ago with published research from Forrester.
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.
How to chart a roadmap to the pinnacle of data science.
