SPONSORED POST: As organizations race to harness the potential of AI, many are discovering that their existing data architectures are struggling to keep up. Traditional warehouses and lakes often lack the flexibility and speed required to support AI-driven analytics. The challenge lies in unifying diverse data sources, ensuring accessibility, and enabling advanced capabilities without adding complexity or creating bottlenecks.
In a new Q&A from The Register, host Tim Phillips talks to Geeta Banda of Google about how it is reimagining BigQuery as a unified data and AI platform. The conversation explores what it takes to design a data architecture ready for the AI era – one that can integrate structured and unstructured data, connect seamlessly with AI agents, and deliver insights faster to more people across the business.
Geeta explains BigQuery’s heritage and evolution, from its origins as a data warehouse to a platform capable of ingesting, transforming, and analyzing data in innovative ways. She outlines how BigQuery’s agentic AI approach goes beyond embedding machine learning models, enabling automated, context-aware insight generation that can boost speed, quality, and accessibility of analytics.
The session also tackles practical concerns: whether these capabilities are accessible to analysts and business users, how to avoid vendor lock-in, and what safeguards help control costs in environments with ad hoc querying. Real-world examples show how organizations are already using BigQuery to drive results, and the discussion closes with a look at the roadmap for further enhancements.
If you want to understand how to modernize your data architecture for AI, integrate with existing tools, and empower teams to generate insights without unnecessary friction, this video offers valuable guidance.
Watch the full Q&A now to learn how BigQuery can help you build a data architecture fit for the AI age. Watch here.
