Gemini in BigQuery overview

This document describes how Gemini in BigQuery, which is partof theGemini for Google Cloud product suite,provides AI-powered assistance to help you work with your data.

AI assistance with Gemini in BigQuery

Gemini in BigQuery provides AI assistance to helpyou do the following:

• Explore and understand your data with data insights. Data insights offers an automated, intuitive way to uncover patterns and perform statistical analysis by using insightful queries that are generated from the metadata of your tables. This feature is especially helpful in addressing the cold-start challenges of early data exploration. For more information, seeGenerate data insights in BigQuery.
• Discover, transform, query, and visualize data with BigQuery data canvas. You can use natural language with Gemini in BigQuery, to find, join, and query table assets, visualize results, and seamlessly collaborate with others throughout the entire process. For more information, seeAnalyze with data canvas.
• Get assisted SQL and Python data analysis. You can use Gemini in BigQuery to generate or suggest code in either SQL or Python, and to explain an existing SQL query. You can also use natural language queries to begin data analysis. To learn how to generate, complete, and summarize code, see the following documentation:
  
  • SQL code assist
    • Use the SQL generation tool
    • Prompt to generate SQL queries
    • Generate SQL queries with Gemini Cloud Assist (Preview)
    • Convert comments to SQL (Preview)
    • Complete a SQL query (Preview)
    • Explain a SQL query
  • Python code assist
    • Generate Python code with the code generation tool
    • Generate Python code with Gemini Cloud Assist (Preview)
    • Python code completion
    • Generate BigQuery DataFrames Python code (Preview)
• Prepare data for analysis. Data preparation in BigQuery gives you context aware, AI-generated transformation recommendations to cleanse data for analysis. For more information, seePrepare data with Gemini.
• Customize your SQL translations with translation rules. (Preview) Create Gemini-enhanced translation rules to customize your SQL translations when using theinteractive SQL translator. You can describe changes to the SQL translation output using natural language prompts or specify SQL patterns to find and replace. For more information, seeCreate a translation rule.

Gemini for Google Cloud doesn't use your prompts or itsresponses as data to train its models without your express permission. For moreinformation about how Google uses your data, seeHow Gemini for Google Cloud uses your data.

As an early-stage technology, Gemini for Google Cloud products can generate output that seems plausible but is factually incorrect. We recommend that you validate all output from Gemini for Google Cloud products before you use it. For more information, seeGemini for Google Cloud and responsible AI.

For information about security, privacy and compliance, seeSecurity, privacy, and compliance for Gemini in BigQuery.

Note: Gemini in BigQuery is part of Gemini for Google Cloud and doesn't support the same compliance and security offerings as BigQuery. You should only set up Gemini in BigQuery for BigQuery projects that don't requirecompliance offerings that aren't supported by Gemini for Google Cloud. For information about how to turn off or prevent access to Gemini in BigQuery, seeTurn off Gemini for Google Cloud products.

Pricing

SeeGemini for Google Cloud pricing.

Quotas and limits

For quotas and limits that apply to Gemini in BigQuery,seeGemini for Google Cloud quotas and limits.

Where to interact with Gemini in BigQuery

After youset up Gemini in BigQuery,you can use Gemini in BigQuery to do the followingin BigQuery Studio:

• Togenerate data insights,go to theInsights tab for a table entry,where you can identify patterns, assess quality, and run statisticalanalysis across your BigQuery data.
• To use data canvas,create a data canvas or use data canvasfrom a table or query to explore data assets with natural language andshare your canvases.
• To use natural language to generateSQLqueries orPythoncode, usecommentsin code or theSQL generationtool. You canalso receivesuggestions with autocomplete whiletyping.
• To prepare data for analysis, in theCreate new list, selectData preparation. For more information, seeOpen the data preparation editor in BigQuery.

Set up Gemini in BigQuery

For detailed setup steps, seeSet up Gemini in BigQuery.

How Gemini in BigQuery uses your data

In order to provide accurate results, Gemini inBigQuery requires access to both yourCustomer Data and metadatain BigQuery for enhanced features. Enabling Geminiin BigQuery grants Gemini permission to accessthis data, which includes your tables and query history. Geminiin BigQuery doesn't use your data to train or fine-tune itsmodels. For more information on how Gemini uses your data, seeSecurity, privacy, and compliance for Geminiin BigQuery.

Enhanced features in Gemini in BigQuery are the following:

• SQL generation tool
• Prompt to generate SQL queries
• Convert comments to SQL
• Complete a SQL query
• Explain a SQL query
• Generate python code
• Python code completion
• Data canvas
• Data preparation
• Data insights

Locations

For information about where Gemini processes your data, seeGemini serving locations.

What's next

• See the latest enhancements and fixes inrelease notes.
• Learn how toset up Gemini in BigQuery.
• Learn how towrite queries with Gemini assistance.
• Learn more aboutGoogle Cloud compliance.
• Learn aboutsecurity, privacy, and compliance for Gemini in BigQuery.
• Learn more abouthow Gemini for Google Cloud uses your data