Learn about real-time AI-powered insights with BigQuery continuous queries, and how this new feature is poised to revolutionize data engineering by empowering event-driven and AI-driven data pipelines with Vertex AI, Pub/Sub, and Bigtable – all through the familiar language of Cloud SQL. Learn about how UPS was able to use big data on millions of shipped packages to reduce package theft, their work on more efficient claims processing, and why they are looking to BigQuery to accelerate time to insights and smarter business outcomes.

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Join us to learn how to activate the full potential of your data with AI in BigQuery. Take an in-depth look at how BigQuery's core integration with generative AI models like Gemini, coupled with its petabyte-scale analytics capabilities, enables new possibilities for gaining insights from your data. Learn how to derive insights from your untapped and unstructured data such as images, documents, and audio files, and explore BigQuery vector search and multi-modal embeddings, all powered by Google's industry-leading AI capabilities in BigQuery using simple Cloud SQL queries. You will also learn how Unilever is creating a data strategy that allows data teams to scale efficiently and rapidly experiment with AI models and gen AI use cases.

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More customers are moving their critical workloads to Cloud SQL for SQL Server to take advantage of built-in high availability, data protection, security, and integration with other Google Cloud services. In this session, we’ll explore new capabilities that make it easier than ever to manage and scale your SQL Server databases with Cloud SQL, and we’ll dig into how to minimize downtime during migration using the Database Migration Service.

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Get a behind-the-scenes look at Walmart's data and AI platform. We'll dissect their use of BigQuery, Spark, and large language models to run complex multi-modal data pipelines. We will deep dive into the choices with various engines (SQL, pySPARK) and technologies along with the corresponding tradeoffs. Gain exclusive insights to implement into your own projects.

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Learn to connect large language models (LLMs) to real-world data and external knowledge sources with LangChain's open-source capabilities integrated with Vertex AI. Explore use cases such as natural language queries for interacting with Cloud SQL databases, complex workflow automation using Gemini function calling, interactive data-mapping and analysis, and enhanced chatbots using real-time data and tool integrations. Harness LangChain's flexibility with Gemini in Vertex AI's reasoning engine to unlock the true potential of generative AI and push boundaries.

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BigQuery allows you to generate multimodal embeddings and perform vector searches directly on your data without complex preprocessing steps. Simplify the process of finding relevant data, identifying patterns and trends, and clustering similar objects together.

Learn how to generate embeddings using familiar BigQuery SQL syntax with multimodal inputs (text, images, audio). We’ll then review how to use BigQuery’s vector search capabilities to explore data in new and innovative ways, leading to faster decision-making and improved insights.

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There are many cloud-based migration services for enterprise applications, but not all get you priority boarding. Join this session to: - Know how the largest enterprises optimally run Microsoft and Linux workloads on Google Cloud - Uncover how the world's largest enterprises effectively run on Google Cloud - Learn how to use custom machine types and sole-tenant nodes to optimize the performance and costs for your environment - Get the latest product releases, including Workload Manager for SQL Server and managed OpenShift, and more

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In this session we’ll create an Apigee proxy that will talk to our containerized workload on Google Cloud Run and Google Cloud SQL, where our business logic will be stored. We’ll explore Apigee capabilities like security and monetization, and build developer resources. We will also cover the concept of “treating your APIs as products.”

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Learn how to manage complex, large-scale Oracle or SQL database migrations and embrace a smooth, efficient journey with our database migration framework. Built on best practices and powered by cutting-edge automation tools, our team of experts takes the stress out of migrating your critical data. It all starts with our free Database Migration Assessment (DMA) , our brand-new first-party tool. Our framework empowers you with a repeatable process for future migrations, giving you the confidence to tackle any database move with ease.

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In this game you will create and manage permissions for Google Cloud resources, run structured queries on BigQuery and Cloud SQL, create several VPC networks and VM instances and test connectivity across networks, and monitor a Google Compute Engine VM instance with Cloud Monitoring.

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In this session, you’ll learn how to deploy a fully-functional Retrieval-Augmented Generation (RAG) application to Google Cloud using open-source tools and models from Ray, HuggingFace, and LangChain. You’ll learn how to augment it with your own data using Ray on Google Kubernetes Engine (GKE) and Cloud SQL’s pgvector extension, deploy any model from HuggingFace to GKE, and rapidly develop your LangChain application on Cloud Run. After the session, you’ll be able to deploy your own RAG application and customize it to your needs.

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A hands-on coding session and deep dive for connecting Gemini to real-world systems, data, and APIs with Function Calling and Reasoning Engine. Function calling helps developers build online generative AI applications that have access to the latest data and information. We'll dive into practical use cases like using natural language to interact with SQL databases, automating complex workflows, and enhancing your chatbots with real-time data. You'll be equipped to connect LLMs to any API or system and extend the capabilities of what LLMs can do.

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Join us for an insightful session that explores the exciting future of Google Cloud‘s managed databases, including Cloud SQL, AlloyDB, and Spanner. Vector search capabilities deeply integrated into operational databases enable powerful enterprise generative AI apps. Additionally, learn how AI has the potential to revolutionize the way applications interact with databases. We will delve into exciting frontiers: Natural language processing in databases, and app migration with large language model-powered code migration.

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As a small or midsize business, adopting generative AI may come with challenges around security, cost, and support resources. Google Cloud databases such as AlloyDB and Cloud SQL can help bridge the gap between gen AI models and the data sitting in your databases. We’ll review Google Cloud database options and learn about their reliability, scalability, and openness benefits. Then we'll dive into some gen AI application examples to see how they were built and how they use data from your database to provide accurate responses.

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Ready to take your MySQL applications to the next level? Join us as we explore how leading enterprises across diverse industries are leveraging Cloud SQL for MySQL to handle their most demanding workloads and deep-dive some exciting innovations with vector search in MySQL. In this session, we'll dive deep into advanced features of Cloud SQL Enterprise Plus, including improved performance and availability, plus learn practical tips and tricks to tune your MySQL database for maximum performance.

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LangChain is the most popular open-source framework for building LLM-based apps. Google Cloud is the easiest place to deploy LangChain apps to production. In this session technical practitioners will learn how to combine LangChain on Cloud Run with Cloud SQL's pgvector for vector storage and Vertex Endpoints to create generative AI applications.

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Join this session to learn the latest innovations for BigQuery to support all data, be it structured or unstructured, across multiple and open data formats, and cross-clouds; all workloads, be they Cloud SQL, Spark, or Python; and built-in AI, to supercharge the work of data teams and unlock generative AI across new use cases. Learn how you can take advantage of BigQuery, a single, unified data platform that combines capabilities including data processing, streaming, and governance.

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Developers choose PostgreSQL for its power, ecosystem, and enterprise-grade features. In this session, unlock best practices for building apps of all kinds with PostgreSQL. We'll cover Google Kubernetes Engine deployments, pgvector for generative AI development, performance optimization with caching, essential observability strategies, and more.

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Customers use Cloud SQL to run their business-critical applications. In this session, we will give you a comprehensive understanding of the various capabilities of Cloud SQL and best practices to maximize business continuity for the applications. The session will deep dive into Enterprise Plus edition features, how Cloud SQL achieves near-zero downtime maintenance, behaviors that affect availability and mitigations, all of which will prepare you to be an expert in configuring and monitoring Cloud SQL for maximum availability.

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In this game you will create and manage permissions for Google Cloud resources, run structured queries on BigQuery and Cloud SQL, create several VPC networks and VM instances and test connectivity across networks, and monitor a Google Compute Engine VM instance with Cloud Monitoring.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.