BigQuery - August 29th, 2023 [Feature]
## Feature [Data clean rooms](https://cloud.google.com/bigquery/docs/data-clean-rooms) is now in[preview](https://cloud.google.com/products/#product-launch-stages). Data clean rooms provide a secure environment in which multiple parties can share, join, and analyze their data assets without moving or revealing the underlying data. To learn more, see the following topics: * [Use data clean rooms](https://cloud.google.com/bigquery/docs/data-clean-rooms) * [Aggregation threshold for queries and views](https://cloud.google.com/bigquery/docs/privacy-policies) * [Aggregation threshold clause](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#agg%5Fthreshold%5Fclause) ## Feature [Duet AI in BigQuery](https://cloud.google.com/bigquery/docs/write-sql-duet-ai), an AI-powered collaborator in Google Cloud, can help you complete, generate, and explain SQL queries. This feature is in [preview](https://cloud.google.com/products/#product-launch-stages). ## Feature [BigQuery Studio](https://cloud.google.com/bigquery/docs/query-overview#bigquery-studio) is now in[preview](https://cloud.google.com/products/#product-launch-stages). BigQuery Studio offers features to make it easier for you to discover, explore, analyze, and run inference on data in BigQuery, including: * Python notebooks, powered by[Colab Enterprise](https://cloud.google.com/colab/docs/introduction). Notebooks provide one-click Python development runtimes, and built-in support for[BigQuery DataFrames](https://cloud.google.com/python/docs/reference/bigframes/latest). * Asset management and version history for notebooks and saved queries, powered by[Dataform](https://cloud.google.com/dataform). ## Feature [BigQuery DataFrames](https://cloud.google.com/python/docs/reference/bigframes/latest) is now in [preview](https://cloud.google.com/products/#product-launch-stages). BigQuery DataFrames is a Python API that you can use to analyze data and perform machine learning tasks in BigQuery. BigQuery DataFrames consists of the following parts: * `bigframes.pandas` implements a DataFrame API (with partial Pandas compatibility) on top of BigQuery. * `bigframes.ml` implements a Python API for BigQuery ML (with partial scikit-learn compatibility). Get started with BigQuery DataFrames by using the [BigQuery DataFrames quickstart](https://cloud.google.com/bigquery/docs/dataframes-quickstart). ## Feature The following Generative AI features are now [generally available](https://cloud.google.com/products/#product-launch-stages) (GA) in BigQuery ML: * Creating a [remote model](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-remote-model#remote%5Fservice%5Ftype) based on the [Vertex AI large language model (LLM) text-bison](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/models#foundation%5Fmodels). * Using the [ML.GENERATE\_TEXT function](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-text) with an LLM-based remote model to perform generative natural language tasks on text stored in BigQuery tables. Try these features with the [Generate text by using a remote model and the ML.GENERATE\_TEXT function](https://cloud.google.com/bigquery/docs/generate-text-tutorial) tutorial.