"Databricks and Snowflake are at it again, and the battleground is now SQL-based document parsing. In an intensifying race to dominate enterprise AI workloads with agent-driven automation, Databricks has added SQL-based AI parsing capabilities to its Agent Bricks framework, just days after Snowflake introduced a similar ability inside its Intelligence platform. The new abilities from Snowflake and Databricks are designed to help enterprises analyze unstructured data, preferably using agent-automated SQL, backed by their individual existing technologies, such as Cortex AISQL and Databricks' AI Functions."
"The ability to query unstructured data using relatively simpler yet automated methods compared to building and running costly ETL pipelines is a critical cog in the common goal that cloud data warehouses like Snowflake and Databricks share: help enterprises reduce cost and complexity by enabling unified queries across structured and unstructured data - a capability traditional warehouses lack as they are designed for analyzing structured data."
The new capability supports tables, figures, and diagrams with spatial metadata, making documents searchable and actionable in AI workflows. Databricks and Snowflake each introduced SQL-based AI parsing to enable agent-driven automation for enterprise AI workloads. Databricks exposed ai_parse_document in public preview as part of Agent Bricks' AI Functions to help build autonomous agents for use cases that handle documents. Both vendors aim to let enterprises query unstructured and structured data together using agent-automated SQL, lowering ETL costs and complexity while producing more accurate insights and faster decision-making. Existing technologies like Cortex AISQL and Databricks' AI Functions back these abilities.
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