For data science, data engineering, and machine learning teams, unlocking the value of unstructured data, such as images, videos, audio, and documents, has historically meant managing siloed data pipelines for structured and unstructured data, and for analytics and AI/ML use cases. Even when files remain in Cloud Storage, running inference requires teams to orchestrate external machine learning workloads, only to face the complex, brittle challenge of stitching those structured inference outputs back into the data warehouse for analysis. This post-hoc data-stitching introduces significant operational overhead and database misalignment.
ObjectRef and multimodal data analytics in BigQuery are now in General Availability (GA), establishing BigQuery as a comprehensive, multimodal agentic data platform.
With ObjectRef, BigQuery natively bridges the gap between Cloud Storage and your analytical engine. You can represent unstructured data as native ObjectRef columns directly alongside your traditional relational columns. This unlocks a massive advantage over standalone model APIs by enabling you to pass multimodal, multi-column inputs directly to Gemini models in a single SQL query or natural language query using conversational analytics. ObjectRef integrations with BigQuery AI, conversational analytics in BigQuery and Gemini Enterprise enable you to easily combine Cloud Storage files with rich, structured database context, metadata, timestamps and custom agent instructions.
You can now leverage BigQuery’s core strengths (fully serverless compute, low total cost of ownership, and built-in at-scale inference) to build, run, and govern powerful unstructured-to-structured, unstructured-to-unstructured and fully multimodal-to-multimodal pipelines without separate compute clusters. Crucially, the extracted features and annotations can be combined directly with the unstructured object itself by storing the ObjectRef in the same row. Using this combined data directly in conversational analytics and AI agents avoids the loss of nuanced data and context that typically occurs when customers transform unstructured files into purely structured formats. Customers are already using this approach to generate embeddings for multimodal vector search, perform at-scale classification using GenAI, and execute precise data and feature extraction from vast archives of documents and media, all via standard SQL.
