Systems | Development | Analytics | API | Testing

Learn more about measures here. In this BigQuery tutorial, you’ll learn how to define graph measures with MEASURE, query them with AGG, and reuse calculations across different groupings. Follow a simple music dataset to fix double counting and explore the relationships behind the results. Chapters: Speaker: Annie Wang Products Mentioned: BigQuery, Gemini Live API, Agent Development Kit.

In this video, Mazlum Tosun walks Martin Omander through building and deploying an AI data agent. Watch along as the team takes a BigQuery database (the company's data goldmine) and open it for plain English questions, using Agent Development Kit (ADK) and Model Context Protocol (MCP). Resource links: Speakers: Martin Omander, Mazlum Tosun Products Mentioned: BigQuery, Agent Development Kit, Cloud Run.

Try it out here. The BigQuery Agent Analytics Block, now available in the Looker Marketplace, provides an analytics solution designed to give you immediate visibility into your AI agent's behavior and performance.

Join this session to learn about Agent Development Kit (ADK) and Model Context Protocol (MCP) integration methods that standardize how agents connect to your data while removing the need to build custom database connectors from scratch. Discover how to build agents with the ADK that accesses BigQuery for analysis, Google Maps for geospatial insights, and AlloyDB for transactions – all in a single workflow. Learn how to implement agent operations (AgentOps) for deep observability into both agent performance and cost with a single line of code.

Python UDFs now Generally Available (GA), you can run custom Python code natively, securely, and serverlessly inside your SQL statements. Write standard SQL, import libraries like BeautifulSoup, or run machine learning tokenizers with zero infrastructure management. Speaker: Products Mentioned: BigQuery, Python User Defined Function.

Check out the repo here. Manually vetting hundreds of project requests is a thing of the past. Imagine receiving every proposal with a built in risk score, resource check, and "Go/No-Go" recommendation—delivered in seconds. Join Kevin Blanco as he demonstrates how to build a powerful multi-agent system that seamlessly integrates Google Cloud and Asana. Watch along and see a real world example of automating an infrastructure request, returning instant historical pattern analysis and live workspace context without any manual steps.

Join Paul Ramsey, Product Manager at Google, for a demonstration of AlloyDB’s new Lakehouse Federation capability. Using a fictional financial services firm, Cymbal Investments, we show how analysts can research S&P 500 trends by combining real-time vector search with data in BigQuery and Google Cloud Lakehouse. In this video, you will see: Learn how AlloyDB enables cloud and AI transformation for your data platform.

For many, running generative AI over massive datasets has felt out of reach due to costs and slow processing times. Others settle for traditional ML techniques that require specialized skill sets and often deliver lower-quality results. With optimized mode for BigQuery AI functions, you can now get LLM-quality results at a fraction of the cost and at BigQuery speeds. In this video, we’ll show you how BigQuery uses model distillation and embeddings to process massive datasets, reducing query latency and token consumption.

Did you know that BigQuery can run GQL queries? BigQuery Graph easily uncovers connections in your datasets, alongside your relational SQL queries.

Did you know you can call a Gemini model directly from a SQL query in BigQuery? In this hands-on codelab, Ayo and Annie do exactly that, and use it to solve a real problem: converting messy, unstructured text into clean, structured data at scale. This is Episode 1 of our multi-part series where we build a fully functional, data-aware AI agent on Google Cloud. *What we cover:* Chapters: Speakers: Ayo Adedeji, Annie Wang Products Mentioned: Gemini, BigQuery.