Cortex AISQL (public preview) transforms Snowflake SQL into an AI query language so users can build AI pipelines using familiar commands across multimodal data.

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Snowflake's Cortex AISQL uses AI to simplify unstructured data analysis, boosting insights & speed. No more waiting for data engineers!

Hex integrates with Snowflake Semantic Views, so you can model data in Snowflake to explore inside of Hex. Tap into Cortex and AISQL to speed up analytical workflows.

San Francisco, CA – The data cloud landscape is buzzing as Snowflake, a heavyweight in data warehousing and analytics, today pulled the wraps off two potentially transformative AI solutions: Cortex AISQL and Snowflake Intelligence. Revealed at the prestigious Snowflake Summit, these innovations are engineered to fundamentally alter how organizations interact with and derive intelligence from […] The post Snowflake Charts New AI Territory: Cortex AISQL & Snowflake Intelligence Poised to Reshape Data Analytics appeared first on MarkTechPost.

Snowflake Summit 2025 unveils central AI and analytics innovations, including Snowflake Intelligence, Cortex AISQL, and Adaptive Compute for enterprises.Snowflake has unveiled an extensive portfolio o...

Snowflake orchestrates its AI strategy through Cortex, unstructured data solutions and agents - SiliconANGLE

本記事は、Snowflake Advent Calendar 2025 の 1 日目です。2025年10月1日に開催した第5回Snowflake中部ユーザー会で発表した内容に加筆再構成したものになります。ですので、基本的に2025年9月末時点の情報に基づきます（一部最新情報反映あり）。AISQLは日々進化してくはずなので、最新情報は公式ドキュメントでご参照ください。!第5回Snowflake中部ユ

Funciones AI de Cortex, permisos por rol y las 4 trampas que te rompen el presupuesto cuando la tabla tiene millones de filas.

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With the advent of Large Language Models (LLMs), many database systems introduced semantic operators that enabled analytical queries over unstructured data (e.g. text, images, videos). Semantic operators typically incur high inference costs and latencies making semantic (AI) SQL queries challenging to apply on large scale datasets. At the same time, their semantic nature leads database engines to treat them as black boxes, making AISQL queries difficult to optimize. In this paper, we introduce Larch, a framework for optimizing the execution of semantic filters in AI SQL queries. Larch was inspired by two key observations: i) the high latency of semantic operators leaves significant room for computationally-heavy runtime optimization techniques, ii) unstructured data are typically accompanied by semantic information in the form of embeddings allowing for efficient semantic comparisons between AI_FILTER prompts and data values. Based on these two key observations, we present two Larch variants: Larch-A2C and Larch-Sel. Larch-A2C encodes arbitrary semantic filters expression tree using an embedding-augmented Gated Graph Neural Network and formulates the filter evaluation order as a Markov decision process. In contrast, Larch-Sel leverages a supervised learning model to predict filter selectivities, subsequently applying dynamic programming to find a near-optimal evaluation order for each input row. Evaluated across diverse real-world datasets and comprehensive synthetic workloads, both Larch variants always outperform existing semantic filter optimization techniques in terms of token usage. Our results demonstrate that Larch is robust across diverse workloads, reducing total token cost overhead by 3x-19x compared to Palimpzest and Quest.

Snowflake が生成 AI を活用し、SQL で非構造化データ分析を簡単に！データ分析の未来を解説。

Introduction Snowflake has released the new AI_SENTIMENT function as part of Cortex AISQL,...