Real-time stream processing has long been one of the toughest challenges in modern data architecture—balancing performance, cost, and operational complexity. But as AI-powered, real-time applications emerge—from anomaly detection and personalization to agentic data systems—the need for simpler, scalable stream processing has never been greater.
In this session, the Google Cloud engineering team reveals how they’re redefining stream processing with BigQuery continuous queries — a serverless, SQL-native capability designed to power dynamic, event-driven architectures at Google scale. Learn how this approach makes it possible to build complex, AI-enhanced pipelines using just SQL, without the burden of managing Flink, Spark, or complex orchestration frameworks.
What you’ll learn:
- How BigQuery continuous queries bring real-time AI inference and stream processing together in one unified model.
- The architectural decisions and trade-offs behind building Google-scale, low-latency stream systems.
- How SQL can now orchestrate end-to-end streaming workflows, from ingestion to enrichment to activation.
- Real-world examples of AI-driven, real-time use cases built entirely on SQL.
- How Google Cloud’s serverless architecture minimizes cost and operational overhead.
If you’re an engineer, architect, or data practitioner exploring real-time AI and streaming pipelines, this talk offers a first look at how Google Cloud is making streaming as simple and powerful as SQL.
Nick Orlove is a BigQuery product manager, focused on making data and insights available to customers in real-time. He's been at Google for >8 years and in his off time focuses on his 1 year old daughter, running, wood working, and the great outdoors.
Andriy Vityuk is a Software Engineer at Google with a decade of experience in large-scale data systems. For the past four years, he has been a core member of the BigQuery team, currently contributing to the development of Continuous Query execution to enable real-time insights from streaming data. He previously worked on time series analysis in BigQuery. Andriy's technical interests revolve around designing and building robust real-time big data systems.
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