Geographical redundancy is a key pillar of a resilient data architecture. With BigQuery cross-region dataset replication and managed disaster recovery, you can ensure your mission-critical apps remain available even in the unlikely event of a region-level infrastructure outage. Learn how this built-in capability protects your data and workloads against regional outages and ensures uninterrupted data access for your organization.

Join Deutsche Bank and Deloitte as we show you how our GenAI-powered chatbot is enhancing customer engagement, driving efficiency, and changing the face of banking. In this fascinating session, we'll demonstrate how the chatbot, integrated into the Deutsche Bank customer-facing banking app, provides accurate product and service information, guides customers to the right transactions, and seamlessly connects to live agents as needed. We'll also review insights and best practices for implementing your own chatbot solutions on Google Cloud.

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.

See how a small team can leverage Google Cloud to serve millions. As the former lead of Flutter and mobile-specialist, I started with little knowledge of Cloud. Using Google's Dart and Google Cloud, we've built a successful service across Cloud Run, Compute Engine, Big Query, Storage, CDN and more and here to share our learnings.

Are you ready to get hands-on with Google Cloud’s AI tools? In this 2 hour gHack, you will work in teams of 4. Together you will build a Formula E Race Analysis System from scratch using a variety of our AI and Data tools. Teams will work together to build the solution by searching, learning and collaborating together to find the answers needed. 3-2-1 lights out and away we go!

Discover how to break free from the cycle of endless AI Proofs of Concept (POC) and unlock scalable, enterprise-wide impact. In this session, we’ll explore proven strategies for operationalizing AI, including leveraging cloud-native solutions like Vertex AI, building robust MLOps pipelines, and defining measurable ROI tied to business goals. Through real-world examples and actionable insights, learn how to overcome common scaling challenges, drive cultural adoption, and future-proof your AI strategy for sustained innovation and success.

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.

Building product recommendation system/chat bot using LLMs is simple... on paper. In reality, simple RAG covers only the simplest scenarios. To cover more complicated one, you may want to learn about such things as a conversation graph, logical and semantic routing, hybrid search etc. In this talk I share lessons and tricks we have learn during building product recommendation system using Gemini.

Small teams often struggle to unlock the full potential of their data due to limited resources and a lack of specialized expertise. In this session, we’ll show you how BigQuery and Looker make advanced analytics accessible and easy for teams of any size. Learn how to seamlessly integrate, analyze, and visualize your data to drive data-driven decisions and achieve your business goals.

This session provides a comprehensive guide to building a secure and unified AI lakehouse on BigQuery with the power of open source software (OSS). We’ll explore essential components, including data ingestion, storage, and management; AI and machine learning workflows; pipeline orchestration; data governance; and operational efficiency. Learn about the newest features that support both Apache Spark and Apache Iceberg.

This session explores the business drivers for ESG data in the current business climate and delves into the technical architecture of a robust ESG data management solution. Discover how to integrate AI and data agents with core enterprise systems, like SAP and Cortex, and incorporate crucial third-party ESG data from sources like ESG Book to enhance business decision-making. We will also showcase real-world examples of how to integrate Google Cloud with platforms like Watershed to build a complete ESG data ecosystem. 

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.

Are you a data scientist or developer using Python to build AI models and generative AI applications? Learn how BigQuery can supercharge Python data science workflows with capabilities that give you the productivity of Python and allow BigQuery to handle core processing. Offloading Python processing enables large-scale data analysis and seamless production deployments along the data-to-AI journey. Find out how Deutsche Telekom modernized their machine learning platform with a radically simplified infrastructure and increased developer productivity.

Modern analytics and AI workloads demand a unified storage layer for structured and unstructured data. Learn how Cloud Storage simplifies building data lakes based on Apache Iceberg. We’ll discuss storage best practices and new capabilities that enable high performance and cost efficiency. We’ll also guide you through real-world examples, including Iceberg data lakes with BigQuery or third-party solutions, data preparation for AI pipelines with Dataproc and Apache Spark, and how customers have built unified analytics and AI solutions on Cloud Storage.

A clean energy platform that connects residents, businesses, and other consumers with clean energy facilities that lower their electric costs, centralized data from CRM and billing systems in BigQuery for churn analysis. Historically, account managers manually reviewed cancellation cases, parsing emails and call transcripts to categorize reasons using a complex 65-category system, later condensed into 16 actionable insights. By leveraging Google Gemini, we automated this process, training the LLM to analyze customer interactions and assign accurate categories, streamlining operations and enhancing retention strategies.

Unlock the power of AI-assisted coding in BigQuery with this hands-on lab. Learn how to generate SQL queries using natural language prompts, utilize BigQuery's code explanation and transformation features, and collaborate with Gemini to review, debug, and optimize your SQL code. Whether you're looking to streamline query development or troubleshoot issues, this session will enhance your ability to write and refine code efficiently using Gemini's intelligent capabilities in BigQuery.

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!

Are you ready to get hands-on with Google Cloud’s AI tools? In this 2 hour gHack, you will work in teams of 4. Together you will build a Formula E Race Analysis System from scratch using a variety of our AI and Data tools. Teams will work together to build the solution by searching, learning and collaborating together to find the answers needed. 3-2-1 lights out and away we go!

Learn how the legendary retail brand accelerated AI adoption by building a GCP Data Platform and conducting enterprise-wide data transformation program. We’ll demonstrate how Big Query and other GCP services liberated the data from legacy environments and became the foundation for AI Factory initiative. We will highlight the challenges and solutions for data quality control, enterprise-wide stakeholder alignment, and business users engagement on the road to data value realization.

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.

Explore how infrastructure, data analytics, cybersecurity, and intelligence at the edge converge to support mission-critical operations on the frontlines. Hear from public sector agencies about how they’re applying technology to their mission to protect and defend our nation.

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.

Learn from industry leaders how they migrated their platforms to BigQuery for business transformation. They’ll share how they simplified the migration process, unified their data for better insights from analytics and AI, and accelerated AI adoption with BigQuery and Vertex AI. Discover how they achieved real-world results, such as driving business growth, reducing costs, and improving customer experiences.

NVIDIA GPUs accelerate batch ETL workloads at significant cost savings and performance. In this session, we will delve into optimizing Apache Spark on GCP Dataproc using the G2 accelerator-optimized series with L4 GPUs via RAPIDS Accelerator For Apache Spark, showcasing up to 14x speedups and 80% cost reductions for Spark applications. We will demonstrate this acceleration through a reference AI architecture on financial transaction fraud detection, and go through performance measurements.

Unstructured data makes up the majority of all new data; a trend that's been growing exponentially since 2018. At these volumes, vector embeddings require indexes to be trained so that nearest neighbors can be efficiently approximated, avoiding the need for exhaustive lookups. However, training these indexes puts intense demand on vector databases to maintain a high ingest throughput. In this session, we will explain how the NVIDIA cuVS library is turbo charging vector database ingest with GPUs, providing speedups from 5-20x and improving data readiness.

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 Google’s interconnected ecosystem of Google Cloud platform and specialty solutions can address the needs and challenges of resource-constrained IT teams. We’ll delve into practical use cases and demonstrate how Google Cloud’s specialized business intelligence platform (Looker) and security solutions (Google Security Operations, Mandiant) can help your business improve efficiency and reduce costs while improving your security posture.

Modernize business processes and ignite innovation. Google Cloud infrastructure uniquely offers unmatched performance, scalability, and reliability for your mission-critical SAP workloads, especially for RISE with SAP. Learn how enterprises are future-proofing their SAP landscapes with Google Cloud, unlocking data and accelerating business outcomes. Discover best practices to transform your SAP deployments into a foundation for innovation. Don’t miss these real-world customer success stories.

Learn how to leverage Gemini's long context for creating and interpreting data analytics queries using Agentic Retrieval Augmented Generation with NL2SQL techniques and reasoning.

This talk will demonstrate how the SAP user community can use Looker/Explore Assistant Chatbot to explore data insights into SAP ERP data stored on Google Cloud's BigQuery using natural language prompts. We will discuss the challenge of accessing and analyzing SAP data - ETL, Complex Data Model, introduction to Generative AI and Large Language Models (LLMs), and Looker Explore Assistant and Chatbot This presentation will illustrate how SAP users can leverage Looker and Explore Assistant Chatbot to gain insights into their SAP ERP data residing on Google Cloud's BigQuery, using natural language prompts. We will address common challenges in accessing and analyzing SAP data, such as ETL processes and complex data models. Additionally, we will provide an introduction to Generative AI and Large Language Models (LLMs), as well as an overview of Looker Explore Assistant and Chatbot's capabilities.

Are you ready to get hands-on with Google Cloud’s AI tools? In this 2 hour gHack, you will work in teams of 4. Together you will build a Formula E Race Analysis System from scratch using a variety of our AI and Data tools. Teams will work together to build the solution by searching, learning and collaborating together to find the answers needed. 3-2-1 lights out and away we go!

In this hands-on lab, you'll learn how to build a powerful business intelligence (BI) dashboard using Looker Studio and BigQuery. Discover how to upload and query data, create reports datasets, and run scheduled queries to uncover valuable insights from large service usage logs. With your dashboard, you'll gain the ability to identify trends, optimize operations, and make data-driven decisions to improve efficiency and service quality.

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!