BigQuery has introduced AI.AGG(), a new function allowing natural-language instructions in SQL to summarize or synthesize millions of unstructured data rows. This function is invaluable for analyzing data where individual row analysis is insufficient, such as system logs or product reviews. AI.AGG() automates the process of extracting key insights from large volumes of text and even multimodal data. For instance, it can identify top feature requests from negative reviews or common user errors from system logs. The function efficiently handles large datasets by batching and aggregating data, simplifying context window management for users. It also supports both text and image inputs, enabling analysis of visual data like product photos. To ensure comprehensive analysis, it's best practice to pre-filter data and reduce input tokens before using AI.AGG(). Users can specify desired AI models for query execution, controlling the AI endpoint used. NULL values in input rows are automatically skipped, requiring careful handling of structured data to avoid silent data loss. AI.AGG() aims to provide partial results even with errors, and users can review job statistics for processing failures. This powerful function is currently in preview and available for all BigQuery users to explore.