Chatting with your Data: Conversational Analytics in BigQuery

2026-06-22 · Source: Data Engineering on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

BigQuery Conversational Analytics, powered by Gemini, transforms traditional data warehouses by enabling business users to query petabyte-scale datasets using natural language. This Google Cloud offering generates complex SQL, returns data, and provides geographic visualizations instantly, addressing bottlenecks in the traditional BI workflow. Unlike basic "Text-to-SQL" AI wrappers, it functions as a governed enterprise agent, integrating features like Dataplex Glossaries for business terminology, Parameterized Verified Queries for pre-approved SQL, and Financial Controls such as Maximum Bytes Billed to prevent costly table scans. The system requires explicit teaching of database schema and system instructions to control SQL generation, ensuring data accuracy and preventing dangerous outputs from misinterpreting business logic. A four-part video playlist demonstrates building and governing AI data agents from scratch, covering the AI reasoning pipeline, custom agent building, enterprise data governance, and automating multi-table relational joins.

Key takeaway

For Data Engineers, Cloud Architects, or BI Analysts building in Google Cloud, BigQuery Conversational Analytics shifts your focus from ad-hoc SQL to governing autonomous data pipelines. You should prioritize implementing robust governance features like Dataplex Glossaries and financial controls to prevent inaccurate data outputs or unexpected petabyte-scale billing. Get hands-on with building custom data agents, explicitly teaching them your database schema and business logic to ensure reliable, cost-effective natural language querying.

Key insights

Enterprise-grade conversational analytics requires robust governance to ensure accuracy and cost control over large datasets.

Principles

• Raw LLMs fail without business logic context.
• Strict governance is crucial for enterprise AI.
• Explicitly teach AI database schema.

Method

Build AI data agents by connecting tables, writing System Instructions, and implementing governance via Column Metadata, Dataplex Glossaries, and financial controls.

In practice

• Use Dataplex Glossaries to define business terms.
• Implement Parameterized Verified Queries for sensitive reports.
• Set Maximum Bytes Billed to control query costs.

Topics

• BigQuery Conversational Analytics
• Enterprise AI
• Data Governance
• Text-to-SQL
• Dataplex Glossaries
• Gemini

Best for: Data Engineer, AI Architect, Data Analyst

Related on AIssential

• How conversational analytics removes the BI bottleneck — Actionable insights, not just trivia, drive business value from conversational analytics.
• The Data Agent Stack - Part 1: What Is a Data Agent? — A data agent is a governed analysis loop that proves answers, not just generates SQL, by integrating context and validation.
• Turing Award Winner: Postgres, Disagreeing with Google, Future Problems | Mike Stonebraker — Specialized database architectures and strong data integrity are crucial for performance and correctness, especially as applications become complex.
• AI Agents and the Fight for Customer Data — Centralized, open data infrastructure is crucial for AI agents, despite vendor data access restrictions.
• Unlocking semantics for AI: How Mercedes-Benz Korea built trusted “Talk to Data” at scale — Unified, governed semantic layers are crucial for reliable, scalable enterprise "Talk to Data" AI, bridging BI and AI definitions.
• The modern data stack was built for humans asking questions. Google just rebuilt its for agents taking action. — Google's Agentic Data Cloud shifts data architecture from human-scale reporting to autonomous AI agent action.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.