Organizations leveraging cloud data warehouses like Snowflake require the ability to efficiently manage and optimize their data connections. Without this, data teams will face challenges with various use cases, such as workload distribution and environment testing. Recognizing the need for greater flexibility and control over data connections, ThoughtSpot developed a powerful new feature: Multiple Configurations per Connection.

Every company around the globe is trying to get in on the GenAI wave to simplify user experiences with natural language. And this is especially true in the realm of data and analytics. Imagine if you could enable all of your marketers to evaluate the performance of their campaigns with a simple question? Or, if you could provide all of your insurance risk managers with the ability to analyze the risk profile of their claims with the power of search and automated insights?

Over the past couple of years, ThoughtSpot and Google have collaborated on a series of seamless user experiences—enabling deployments on Google Cloud Platform, creating the ability to live query entire Google BigQuery analytics catalogs, and integrating key Looker Modeling functionality just to name a few. This type of co-innovation helps mutual customers get the most value out of their data.

Over the course of my career, I’ve had the opportunity to found and lead multiple technology companies with great teams. These companies have redefined their industries and empowered customers to work in different, better ways. At the core of these companies, however, has been a constant mission. A purpose. A north star that guides us over multiple decades.

Managing complex data pipelines is a major challenge for data-driven organizations looking to accelerate analytics initiatives. While AI-powered, self-service BI platforms like ThoughtSpot can fully operationalize insights at scale by delivering visual data exploration and discovery, it still requires robust underlying data management. Now, that’s changing. Snowflake's new dynamic tables feature redefines how BI and analytics teams approach data transformation pipelines.