Systems | Development | Analytics | API | Testing

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.

Discover how to build a powerful data agent using ADK, BigQuery and CloudSQL. This video guides you through transforming unstructured data into structured knowledge, enabling intelligent applications. Watch along and learn how to create RAG pipelines, leverage Gemini for vector embeddings, and automate processes with Dataflow to achieve nuanced, context aware insights. Chapters: Speaker: Debi Cabrera Products Mentioned: BigQuery, CloudSQL, Agentverse, Gemini, Agent Development Kit.

You’ve moved your data to Google Cloud. Now it is time to make sure it’s accurate, secure, and cost effective. This video concludes our migration series by focusing on the critical steps following data transfer from Databricks, Teradata, Snowflake, Cloudera and many other platforms. You’ve moved your data to Google Cloud. Now it is time to make sure it’s accurate, secure, and cost effective. This video concludes our migration series by focusing on the critical steps following data transfer from Databricks, Teradata, Snowflake, Cloudera and many other platforms.

Following your migration assessment, it is time to execute the transfer of your data and SQL queries into Google Cloud. This video dives into the specific tools and services that simplify migrating workloads from Snowflake, Teradata, Cloudera, and Databricks into BigQuery, Dataproc, and Google Cloud Storage.

Embarking on a data lake or data warehouse migration to BigQuery can seem daunting, but a thorough assessment helps clarify the path forward. This video introduces Google Cloud's services and expert guidance for evaluating the cost and complexity of migrating your existing systems, providing a clear plan for your migration journey. Discover how initial assessments help estimate time, costs, and identify the best approach for a successful migration from Snowflake, Teradata, Cloudera, Databricks and more.

Transform messy JSON into a powerful AI recommendation engine, all within BigQuery. This demo showcases a low code workflow, using Data Prep's visual tools to clean raw data, BQML to generate Gemini powered vector embeddings, and a simple SQL query to find similar items. It's a look at how Google Cloud lets data analysts build end-to-end AI pipelines without complex ETL scripts. The entire project is open-source. Build it yourself!