From Enterprise Data to AI-Driven Action
Move from data to insight, reasoning, and action with BigQuery — Google Cloud’s autonomous data-to-AI platform for analytics, AI, and agentic workloads.
PB+
Scale
AI-NATIVE
Analytics & Reasoning
99.99
Availability
{OVERVIEW}
Built for Data. Ready for AI Agents.
BigQuery brings enterprise analytics, AI, and intelligent data experiences into a unified platform. Its serverless architecture separates storage and compute for independent scalability, while built-in AI capabilities allow organizations to analyze structured and unstructured data, work with natural language, and build intelligent applications directly where their data lives. With Conversational Analytics, integrated AI functions, Data Agents, and business-aware context, BigQuery is evolving from a traditional analytics platform into a foundation for AI-driven and agentic decision-making. Google now describes BigQuery as an autonomous data-to-AI platform designed to move organizations from data to AI to action faster.
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Serverless, Fully Managed Architecture
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Conversational Analytics with Gemini
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Built-In AI Functions & BigQuery ML
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Data Engineering & Data Science Agents
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Structured & Unstructured Data Analysis
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Cross-Cloud Data & Lakehouse Capabilities
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Real-Time Streaming & Analytics
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Enterprise Governance & Business Context
{APPROACH}
Why BigQuery Changes the Data Game
- 01Ask Questions in Natural Language
- 02Run AI Directly Where Your Data Lives
- 03Bring Structured and Unstructured Data Together
- 04Build Data Agents Grounded in Business Context
- 05Scale Analytics Without Managing Infrastructure
- 06Move from Historical Insights to Intelligent Action
{OUR METHODOLOGY}
Core BigQuery Capabilities
Bring data ingestion, processing, analytics, machine learning, and AI into a unified environment. BigQuery automates much of the underlying infrastructure and data lifecycle, allowing teams to focus on extracting value from data rather than operating complex analytical systems. Google’s current BigQuery positioning explicitly expands the platform beyond the traditional enterprise data warehouse toward an autonomous data-to-AI platform.
Interact with enterprise data using natural language directly within BigQuery. Conversational Analytics uses Gemini-powered reasoning to understand questions, analyze data, perform multi-step analysis, and generate visual reports while grounding responses in enterprise data and business context. The capability reached general availability in BigQuery in 2026.
Bring AI and machine learning directly to enterprise data without creating unnecessary data movement. BigQuery supports built-in AI functions alongside BigQuery ML, allowing teams to use SQL to apply generative AI, classification, scoring, extraction, prediction, and other intelligent capabilities directly within analytical workflows.
Use specialized AI Agents to support data-intensive workflows. BigQuery’s expanding agentic capabilities include Conversational Analytics as well as Data Engineering and Data Science Agents designed to assist teams with analytics, data engineering, and data science tasks.
Analyze traditional tables together with unstructured information such as documents, images, audio, and other enterprise content. BigQuery’s expanding multimodal capabilities allow organizations to bring more business information into AI-powered analytical workflows instead of keeping structured and unstructured data in separate environments.
Give AI systems more than access to raw data. By combining metadata, business definitions, governance, and semantic context, organizations can ground analytical agents in information that reflects how the business actually operates. Google’s broader Agentic Data Cloud strategy increasingly emphasizes trusted enterprise context as a foundation for AI Agents.
Ingest and analyze streaming data alongside historical information to support live dashboards, operational analytics, anomaly detection, and AI-driven decisions based on current business signals.
Work across distributed data environments without forcing every dataset into a single storage model. BigQuery’s lakehouse capabilities extend analytics and AI across cloud and open data environments while providing a common foundation for intelligent applications.
From Data Analysis to Agentic Intelligence
BigQuery is no longer only about asking what happened. With natural-language analytics, business-aware context, integrated AI, and Data Agents, organizations can use enterprise data to understand why something happened, what is likely to happen next, and what action should follow. Google’s 2026 BigQuery direction increasingly connects Conversational Analytics with agentic workflows such as deeper research, root-cause analysis, and scheduled actions.
Ask
Use natural language to explore complex enterprise datasets without relying solely on manual SQL.
Understand
Ground analysis in metadata, trusted business definitions, and enterprise context.
Analyze
Combine structured, unstructured, historical, and real-time information to identify patterns and root causes.
Predict
Use machine learning and AI capabilities directly within BigQuery to support forecasting, scoring, and predictive use cases.
Act
Connect insights with AI Agents and business workflows to move from passive reporting toward proactive decision support.
BigQuery for the Agentic Data Cloud
Create a Trusted Data Foundation for AI Agents
Within Google Cloud’s Agentic Data Cloud, BigQuery provides the analytical foundation that helps AI applications and agents work with large-scale enterprise data. By connecting analytics, business context, real-time information, and AI capabilities, organizations can build agents that reason over governed enterprise information instead of relying on isolated datasets or generic model knowledge. Google’s Agentic Data Cloud is designed to bring models, analytics, and operational databases together as an AI-native system of action.
Enterprise Data at Scale
Analyze large and complex datasets within a serverless analytical environment.
Trusted Business Context
Ground AI-driven analysis in metadata, governance, and enterprise definitions.
AI Where the Data Lives
Apply Gemini-powered capabilities and machine learning without unnecessary data movement.
Agent-Ready Analytics
Use enterprise data as the analytical foundation for conversational experiences, Data Agents, and agentic workflows.
BIGQUERY WITH OREDATA
Modernize Your Data. Prepare for the Agentic Era.
Oredata helps organizations move beyond traditional data warehouse modernization and build BigQuery environments designed for analytics, AI, and emerging agentic workloads. Our approach combines data architecture, migration, performance optimization, governance, real-time analytics, AI enablement, and business context to ensure BigQuery becomes more than a data warehouse — it becomes a scalable foundation for enterprise intelligence.
FAQ
BigQuery is Google Cloud’s autonomous data-to-AI platform designed for large-scale analytics, machine learning, and AI workloads. It combines serverless data processing with built-in AI capabilities, allowing organizations to move from data ingestion and analysis toward AI-driven insights and intelligent actions within a governed environment.
BigQuery has expanded beyond traditional data warehousing with Conversational Analytics, Data Agents, built-in AI functions, multimodal data capabilities, and deeper integration with enterprise business context. These capabilities allow organizations to create AI experiences that can reason over trusted enterprise data rather than simply generate queries or reports.
Conversational Analytics allows users to ask questions about BigQuery data using natural language. Gemini-powered reasoning can interpret the question, perform multi-step analysis, query relevant data, and generate results and visualizations while working within BigQuery’s governed environment.
Data Agents are AI-powered capabilities designed to assist with data-related workflows. Google’s current BigQuery ecosystem includes conversational analytics and specialized agents supporting areas such as data engineering and data science, helping teams work with enterprise data more efficiently.
Yes. BigQuery supports capabilities for working with unstructured information alongside structured enterprise data. Google continues to expand AI functions and object-based data capabilities so organizations can analyze documents, images, audio, logs, and other content within AI-powered workflows.
BigQuery provides AI functions that allow teams to apply generative AI and Gemini-powered capabilities directly within data workflows. This enables organizations to perform tasks such as extraction, classification, summarization, semantic analysis, and other AI-assisted operations without unnecessarily moving enterprise data into separate systems.
BigQuery provides a core analytical foundation within Google Cloud’s Agentic Data Cloud. It enables agents and AI applications to analyze large-scale enterprise information while connecting analytical intelligence with governed business context and other parts of the enterprise data environment.
Yes. BigQuery supports streaming and near-real-time analytical workloads, allowing organizations to combine current business signals with historical data for dashboards, monitoring, operational intelligence, and AI-driven use cases.
Oredata supports organizations across BigQuery migration, data warehouse modernization, architecture design, query and cost optimization, real-time analytics, governance, machine learning, and AI enablement. As BigQuery evolves toward an autonomous data-to-AI platform, Oredata can also help organizations prepare their data and governance foundations for Conversational Analytics, Data Agents, and agentic AI use cases.
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Proven Transformations
