Google has made ObjectRef and multimodal data analytics in BigQuery generally available, extending BigQuery's handling of unstructured data stored in Cloud Storage.
The update lets users represent files such as images, video, audio and documents as native ObjectRef columns within BigQuery tables alongside structured data. Teams can therefore query structured and unstructured data together, rather than moving outputs between separate systems for analysis.
ObjectRef is a built-in BigQuery STRUCT data type that points to objects outside BigQuery. It allows organisations to keep large files in Cloud Storage while using BigQuery to analyse them alongside metadata, timestamps and other relational fields.
The move addresses a long-running challenge for data science, data engineering and machine learning teams. Many have relied on separate pipelines for structured and unstructured data, then had to feed inference outputs back into data warehouses for reporting and further analysis.
With the general release, users can pass multimodal inputs directly to Gemini models in a single SQL query or through conversational analytics. Extracted features and annotations can also be stored in the same row as the ObjectRef, keeping the original file linked to the structured output.
This is designed to reduce the loss of context that can occur when audio, images or documents are converted into purely structured formats. Customers are already using the system for multimodal vector search, large-scale classification and feature extraction from document and media archives through SQL.
The release also includes changes aimed at widening the range of use cases, including support for End User Credentials for ad hoc analysis, broader support for Cloud Storage metadata sources, and native array support for ObjectRefs so multiple files can be analysed in a single call.
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