I co-authored this one on the Google Cloud blog with Michael Stern, announcing the preview of Anthropic’s Claude models running against BigQuery data through BigQuery ML.
Read it here: BigQuery and Anthropic’s Claude: A powerful combination for data-driven insights (Google Cloud blog, September 2024)
What it does
The short version: an analyst who knows SQL can now run generation, summarization, translation, and classification directly against warehouse data — no data movement, no separate ML pipeline, no specialist ML background required.
That last part is the bit I care about most. A large amount of genuinely useful language-model work is blocked not by model capability but by plumbing: getting the data out, through an API, and back somewhere useful. Removing the plumbing changes who is able to do the work.
Three ways in
- BQML with SQL — call the model as a function inside a query
- Python in BigQuery Studio — for notebook-driven workflows
- BigQuery remote functions — for the cases needing custom logic in between
The examples
The article works through two: summarizing operational logs and returning recommended fixes, and translating museum art descriptions. They’re deliberately mundane — that’s the point. These are the kinds of tasks that pile up in a warehouse and never justify a dedicated ML project, which is exactly why making them a SQL function matters.
Full walkthrough on the Google Cloud blog.
Annie Xu 
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