BigQuery helps you build an autonomous data and AI platform from your organization. In this session, you’ll learn how BigQuery agentic intelligence is automating critical data workflows, including data preparation, analysis, predictions, model tuning, security, and governance. We’ll explore the BigQuery AI capabilities that all data practitioners can use to address data challenges in the AI era.

Learn how generative AI can enhance data workflows and analysis. This session demonstrates how to access and use Gen AI models in BigQuery, including new features like open source model support and structured output from Gemini. Accelerate your data analysis with the latest AI innovations available directly in BigQuery.

Discover the transformative power of Gemini in BigQuery, which is revolutionizing data analytics with AI-driven innovations. This session showcases capabilities designed to enhance workflows – streamlining data preparation and migrations, enabling advanced code generation, facilitating conversational data exploration, and optimizing workloads intelligently. Learn how these advancements simplify complex tasks, elevate productivity, and empower teams to unlock the full potential of their data.

BigQuery is unifying data management, analytics, governance, and AI. Join this session to learn about the latest innovations in BigQuery to help you get actionable insights from your multimodal data and accelerate AI innovation with a secure data foundation and new-gen AI-powered experiences. Hear how Mattel utilized BigQuery to create a no-code, shareable template for data processing, analytics, and AI modeling, leveraging their existing data and streamlining the entire workflow from ETL to AI implementation within a single platform.

This session showcases an end-to-end generative AI application on Google Cloud. We’ll demonstrate how to use Gemini 2.0 Flash to analyze user-uploaded images, extract features, and generate descriptions stored in AlloyDB. Then we’ll show you how to fine-tune Gemini 2.0 Flash with BigQuery and generate outfit recommendations with AlloyDB low-latency querying. Finally, we’ll use the output from Gemini 2.0 Flash and Imagen 3 to create visuals of the outfits and deploy the entire solution on Cloud Run.

Join us as we reveal how the AURA AI Suite—AURA-SECURE, AURA-FLEX, and AURA-OMNI—is transforming enterprise AI. Experience bespoke conversational solutions for both structured and unstructured data. Discover how private GPT models secure sensitive information and dynamic insights boost productivity, decision-making, and customer engagement. Plus, see how Google Cloud services like Vertex AI, BigQuery, and Kubernetes ensure seamless integration, accelerated performance, and robust security.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

Unlock the full potential of your data with Google's autonomous data and AI platform. This session explores how we're bringing the power of AI directly to your data, integrating multimodal data handling, an innovative AI Query Engine, and Gemini agents to enable seamless data integration, automated workflows, complex reasoning, and real-time insights. Join us to explore the latest advancements in BigQuery and Looker and build a data and AI strategy that drives your business forward.

In this hands-on lab, you'll explore data with BigQuery's intuitive table explorer and data insight features, enabling you to gain valuable insights without writing SQL queries from scratch. Learn how to generate key insights from order item data, query location tables, and interact with your data seamlessly. By the end, you’ll be equipped to navigate complex datasets and uncover actionable insights quickly and efficiently.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

Google's Data Cloud is a unified platform for the entire data lifecycle, from streaming with Managed Kafka, to ML feature creation in BigQuery, to global deployment via Bigtable. In this talk, we’ll give you a behind the scenes look at how Spotify's recommendation engine team uses Google's Data Cloud for their feature pipelines. Plus, we will demonstrate BigQuery AI Query Engine and how it streamlines feature development and testing. Finally, we'll explore new Bigtable capabilities that simplify application deployment and monitoring.

Unlock the potential of AI with high-performance, scalable lakehouses using BigQuery and Apache Iceberg. This session details how BigQuery leverages Google's infrastructure to supercharge Iceberg, delivering peak performance and resilience. Discover BigQuery's unified read/write path for rapid queries, superior storage management beyond simple compaction, and robust, high-throughput streaming pipelines. Learn how Spotify utilizes BigQuery's lakehouse architecture for a unified data source, driving analytics and AI innovation.

Redpanda, a leading Kafka API-compatible streaming platform, now supports storing topics in Apache Iceberg, seamlessly fusing low-latency streaming with data lakehouses using BigQuery and BigLake in GCP. Iceberg Topics eliminate complex & inefficient ETL between streams and tables, making real-time data instantly accessible for analysis in BigQuery This push-button integration eliminates the need for costly connectors or custom pipelines, enabling both simple and sophisticated SQL queries across streams and other datasets. By combining Redpanda and Iceberg, GCP customers gain a secure, scalable, and cost-effective solution that transforms their agility while reducing infrastructure and human capital costs.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

Managing BigQuery costs can be challenging, especially when juggling the different pricing models. In this session, we’ll dive into the obstacles companies face when using BigQuery at scale and how to overcome them. Highlighting real-world use cases, I’ll share how organizations have successfully optimized their BigQuery expenses by addressing inefficiencies in reservations. Attendees will learn practical strategies to reduce costs by up to 35% and discover how automatically adjusting the max slot setting can be a game-changer.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

Discover how semiautonomous agents reduce manual effort and accelerate business outcomes with AI-powered experiences for data discovery, data preparation, analysis, and insight generation.

Explore how the open source Data Migration Tool can help with end-to-end automation for Teradata to BigQuery migration, including schema migration, data migration, and schema & data validation.

Build fully integrated streaming pipelines on Google Cloud and learn how to leverage AlloyDB, Datastream, BigQuery, Looker, and Vertex AI for real-time data analysis.

Routine tasks such as data wrangling and pipeline maintenance often inhibit data teams from doing higher-value analysis and insights-led decision-making. This session showcases how intelligent data agents in BigQuery can help automate complex data engineering tasks. You’ll learn how to use natural language prompts to streamline data engineering tasks from ingestion and transformation, such as data cleaning, formatting, and loading results into BigQuery tables that accelerate the time to build and validate data pipelines.

Routine tasks such as data wrangling and pipeline maintenance often inhibit data teams from doing higher-value analysis and insights-led decision-making. This session showcases how intelligent data agents in BigQuery can help automate complex data engineering tasks. You’ll learn how to use natural language prompts to streamline data engineering tasks from ingestion and transformation, such as data cleaning, formatting, and loading results into BigQuery tables that accelerate the time to build and validate data pipelines.

Streamline data analytics workflows with AI assistance. Enhance data team productivity and reduce costs.

Learn to leverage the newest graph query capabilities to find hidden patterns, and build a Customer 360 graph for more personalized product recommendations.

Build an open, secure, and integrated AI data platform. Manage the end-to-end data life cycle with built-in governance.