BigQuery Revolution: How Google’s Agentic Data Cloud Makes Data Think #BigQuery #AgenticAI #DataCloud #GoogleCloud #AIAgents
We break down Google's vision for AI-powered data infrastructure:
• Human Scale → Agent Scale analysis
• Why ETL pipelines are failing AI agents
• 3 Pillars: Universal Context, Agent-First Tools, Cross-Cloud Lakehouses
• Continuous Queries that never stop working
• Cross-cloud analysis (AWS, Azure, Iceberg)
• Built-in AI: Graphs, PDFs, hybrid search
• Remote Functions: Run AI agents on data without moving bytes
Data is no longer just "sitting there"—it's starting to reason, interpret, and drive action. Is your organization ready?
Chapters
00:00 - Introduction: The Leap to Agentic Data Cloud 00:52 - The Old Data Warehouse Problems 01:33 - The Messy Reality of ETL (Data Surgery) 02:12 - Why AI Agents Need a New Kind of Data Stack 03:11 - What is an Agentic Data Cloud? (The 3 Pillars) 04:08 - Powering Action with Continuous Queries 04:35 - Breaking Down Cloud Silos (AWS, Azure, and Iceberg) 05:04 - Built-in AI: Graphs, PDFs, and Hybrid Search 05:35 - Bringing AI to Data with Remote Functions 06:42 - 30x Growth: The Transformation is Happening Now 07:07 - Conclusion: When Data Starts Thinking