ThoughtSpot is taking Snowpark use cases to the next level with generative AI, connecting the dots between ML-powered insights and business action. If you’re new to Snowpark, this is Snowflake’s set of libraries and runtimes that securely deploy and process non-SQL code including Python, Java, and Scala.
Combining the power of Snowflake Snowpark and ThoughtSpot, developers and data professionals can create models, uncover insights, and build data apps using their preferred programming language. And you can do all of this without ever leaving the Snowflake environment, improving efficiency and reducing maintenance.
ThoughtSpot makes these predictions, models, and data points accessible and intuitive to every user—from business users to data scientists—through intuitive natural language search and generative AI-Powered Analytics. Here’s how it works.
ThoughtSpot for Snowpark enables users to:
Bring AI and ML use cases into production faster: Developers and data leaders can now put the power of AI and ML into the hands of business users and frontline decision-makers, empowering them to make better decisions and discover ROI from AI investments.
Build interactive, AI-powered data apps: Product leaders can use ThoughtSpot Everywhere and Snowpark to drive richer, search-driven embedded analytics experiences for all users.
Launch efficient data-to-decision pipelines: Snowpark makes it easy to build data pipelines that use familiar constructs and third-party data libraries. Meanwhile, ThoughtSpot provides a new, consumer-grade experience for business users to engage with those pipelines at scale. This collaboration allows users across the full spectrum of technical acumen to easily and efficiently go from data to decisions.
Four ways to use ThoughtSpot + Snowpark
We recently launched four new use cases that bridge the combined power of ThoughtSpot and Snowpark. In this video series, ThoughtSpot’s in-house Snowflake Data SuperHero and Senior Analytics Evangelist, Sonny Rivera, shows us the art of the possible with Snowpark and ThoughtSpot. Take a look:
Sentiment analysis
Apply Amazon Beauty product review data to perform sentiment analysis, process data with Snowpark Python, and visualize results via ThoughtSpot.
Time series forecasting
Extract powerful insights from data to guide key business decisions and dramatically improve sales forecasting.
Predictive churn analysis
Use Snowflake, Snowpark Python, and machine learning in ThoughtSpot to uncover insights that guide strategic decisions.
Loyalty classification and RFM analysis
Understand customer relationships using Snowflake Snowpark Python, visualized and analyzed using ThoughtSpot.
With live, AI-Powered Analytics from ThoughtSpot, text data in Snowflake, and the ability to execute custom analytics code with Snowflake Snowpark, any use case is just a live query away.
Start using AI-Powered Analytics for your Snowflake Data Cloud—try it for yourself.
PS: Find more ways to simplify your BI pipeline with Snowflake dynamic tables + ThoughtSpot.
