Snowflake renews its analytics approach with Cortex AISQL and SnowConvert AI. Cortex AISQL brings generative AI to SQL queries while SnowConvert AI ensures rapid migrations to Snowflake.
Today, the Snowflake platform encompasses the entire data cycle, but the company’s roots lie in analytics. Snowflake hasn’t forgotten its origin: during its conference in San Francisco, it announces new tools that help organizations extract insights from data faster. Cortex AISQL brings generative AI directly into SQL queries, while SnowConvert AI automates the transition from legacy systems.
AI in SQL
With Cortex AISQL, currently in public preview, users can invoke AI functions within their familiar SQL syntax. This makes it possible to analyze unstructured sources such as text, images, and audio alongside structured data. The technology is integrated into Snowflake’s existing SQL engine and supports models from OpenAI, Anthropic, and Meta, among others. Cortex AISQL delivers up to 60 percent cost savings when filtering large datasets and accelerates query performance by 30 to 70 percent.
Analysts can now enrich customer tables with chats, link image material to sensor data, or combine sales figures with social media data. Snowflake states that this AI functionality can be deployed without additional tools or code and provides access to insights that would otherwise only be accessible through data scientists. For business users who don’t speak the language of data scientists, Snowflake Intelligence translates data into human language.
Rapid Migrations
Many companies are still stuck with legacy environments that hinder AI adoption. Snowflake welcomes these companies with open arms, and with SnowConvert AI. This solution automates the migration process from existing data environments. AI supports not only database migrations but also the migration of ETL tools and BI reports. Thanks to agentive automation, organizations shorten implementation time by a factor of two to three.
Snowflake emphasizes that this reduces risks, costs, and manual work in migrations. By accelerating conversion, validation, and testing, companies can transition to a modern data architecture faster without disrupting their workflows.
At the Core
The announcements fit into Snowflake’s broader picture of unifying data analysis, AI, and platform modernization in one environment, with which it aims to distinguish itself in the competitive analytics sector. For this, Snowflake goes back to the core of the platform.
The company now also makes it possible to process open data formats such as Apache Iceberg and renews its warehouse architecture. This brings improved performance, with Snowflake claiming doubled speed for analytical workloads.