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BigQuery - August 29th, 2023 [Feature]



## Feature [Data clean rooms]( is now in[preview]( 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]( * [Aggregation threshold for queries and views]( * [Aggregation threshold clause]( ## Feature [Duet AI in BigQuery](, an AI-powered collaborator in Google Cloud, can help you complete, generate, and explain SQL queries. This feature is in [preview]( ## Feature [BigQuery Studio]( is now in[preview]( 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]( Notebooks provide one-click Python development runtimes, and built-in support for[BigQuery DataFrames]( * Asset management and version history for notebooks and saved queries, powered by[Dataform]( ## Feature [BigQuery DataFrames]( is now in [preview]( 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. * `` implements a Python API for BigQuery ML (with partial scikit-learn compatibility). Get started with BigQuery DataFrames by using the [BigQuery DataFrames quickstart]( ## Feature The following Generative AI features are now [generally available]( (GA) in BigQuery ML: * Creating a [remote model]( based on the [Vertex AI large language model (LLM) text-bison]( * Using the [ML.GENERATE\_TEXT function]( 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]( tutorial.