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Using BigQuery object tables, we analyze unstructured Cloud Storage data with generative AI models and join the results with BigQuery structured data.

Managed disaster recovery in BigQuery offers automated failover of compute and storage to a different region and a new cross-regional SLA.

Watch how to use a multimodal LLM such as Gemini to analyze data in a BigQuery data lake and automatically generate schema layouts.

By combining speech-to-text with BigQuery's analytics capabilities, you can understand things like customer sentiment and recurring product issues.

New BigQuery functions monitor skew between training and serving data, and monitor drift in serving data over time, all using SQL.

Support for model fine-tuning in BigQuery takes input text (the prompt) and the expected output, and teaches the model to mimic the behavior.

You can now query Delta Lake tables stored in Cloud Storage or Amazon Web Services S3 directly from BigQuery, without exporting or copying the data.

BigQuery supports multimodal embedding generation via Vertex AI models, and for structured data with PCA, Autoencoder or Matrix Factorization models.

BigQuery data canvas improves data analytics with a natural language-driven experience for data discovery, preparation, querying, and visualization.

With Gemini 1.0 Pro Vision in BigQuery via Vertex AI, you can analyze images and videos by combining them with your own text prompts.