Unifying storage for your data analytics workloads doesn‘t have to be hard. See how Google Cloud Storage brings your data closer to compute and meets your applications where they are, all while achieving exabyte scale, strong consistency, and lower costs. You'll get new product announcements and see enterprise customers present real-world solutions using Cloud Storage with BigQuery, Hadoop, Spark, Kafka, and more.

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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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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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In this game you will learn to build a BI dashboard with Looker Studio as the front end, powered by BigQuery on the back end, learn to use BigQuery to find data, build a time series model to forecast demand of multiple products using BigQuery ML, and create a basic report in Google Data Studio.

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Explore how Geotab harnesses BigQuery to fuel a robust data-driven culture. With more than 80% of our teams and over 1,000 data pipelines depending on BigQuery, we efficiently process petabytes of data every day. This session will unveil essential strategies for boosting BigQuery's efficiency and cost-effectiveness vital for handling large-scale data operations. Participants will gain valuable insights into refining BigQuery operations to meet extensive data management demands.

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Explore how BigQuery and BigLake power innovative data analytics solutions and walk through implementing data products powered by AI/ML for real-world applications. Learn from Google Cloud about the latest advancements in both technologies, with an emphasis on:

• BigLake's integration with Apache Iceberg for efficient AI/ML • How BigQuery serves as a foundational element in successful data mesh architectures

You will also hear from two leading customers, Trendyol and Snap, on their real-world journeys, demonstrating the transformative impact of these technologies on their analytics and AI initiatives at scale.

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Google Cloud is building the next generation of Observability solutions using Gemini and BigQuery. In this session, we’ll show you how we can remove fragmentation for your logs, metrics, traces, events, billing data sources using Google BigQuery on Google Cloud Operations Suite to perform Observability analytics. Targeted audience includes Developers, DevOps Engineers, SRE, and Cloud Architects.

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Join this session to learn how Gemini in BigQuery can help you accelerate time to insights by enhancing productivity, and optimizing the cost and performance of data workloads.

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The data-to-insights journey is often slow and inefficient due to the friction between specialized tools and analytics skills. Join us to learn about the new natural language-driven and collaborative user experience of BigQuery. Explore how you can use natural language to navigate various stages of the analytics lifecycle including data discovery, pipeline building, processing, query composition, visualization, and more.

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Spend less time prepping data and more time gaining insights with Gemini in BigQuery. In this session, you'll discover how to visually transform your data with AI for streamlined analysis. Witness a live demo of BigQuery data preparation. Seattle Children's will demonstrate the transformative effect of AI on data engineer productivity and accelerating development. Plus, get a sneak peek into the exciting roadmap of features including expanded connectivity, continuous integration and delivery workflows, and robust data quality.

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Adequately testing systems that use Google Cloud services can be a serious challenge. In this session we’ll show you how to shift testing to an API-first approach using Testcontainers. This approach helps us improve the feedback cycle and reliability for both our inner-dev loop and our competitive intelligence cycle. We’ll go through an end-to-end example that uses BigQuery and PubSub, Cloud Build, and Cloud Run. Examples will use Kotlin but it could be accomplished with other languages including Rust, Go, JavaScript, Python, Java, and more.

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In this session, we'll "break the ice" on the debate between Google BigQuery vs Snowflake for cloud data warehousing. From performance to scalability, we'll explore the key considerations to help you make the right choice for your data needs. Whether you're a Google Cloud customer or partner, this session will arm you with actionable insights to navigate the cloud data landscape confidently. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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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 mini course you will learn how to optimize performance and cost in BigQuery. You will learn how to optimize your queries and lower cost by optimizing your storage using partitioning and clustering. You will explore these techniques in a hands-on lab environment by exploring data inspired by a real customer use case.

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Customers increasingly want to run multiple analytics and artificial intelligence (AI) use cases on a single copy of their data spanning lakes and warehouses. However, the fragmented nature of today’s analytics and AI systems poses limits with distributed data and metadata across different engines. Google Cloud now offers a unified storage and metadata experience. Learn how these new capabilities in BigQuery can help unify the data and data sharing across engines and how HCA Healthcare is using them within its data ecosystem.

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Join us for this session where we will explore key MongoDB Atlas features including Data Federation, Atlas Triggers, and GraphQL API. We will also dive deep into MongoDB Atlas and Google Cloud integrations, such as Vertex AI and BigQuery, and how you can leverage these tools to build and scale your apps. Leave with actionable insights on how you can build with MongoDB Atlas on Google Cloud and explore what our Startup Program can do for you.

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Businesses need to predict what customers want and create personalized experiences to gain a competitive advantage and drive revenue. They need to deliver customized, tailored interactions that increase customer acquisition, improve loyalty and increase satisfaction. Join Fullstory’s Head of Data Products to learn how Data + Engineering teams can supercharge tools like DialogFlow and BigQuery with unprecedented behavioral data to accurately forecast and create experiences that outpace the competition and keep customers coming back for more. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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Python's dominance in data science streamlines workflows, but large-scale data processing challenges persist. Discover how BigQuery DataFrames, a Pandas and scikit-learn-like abstraction over the BigQuery engine, revolutionizes this process.

Join this session to learn about BigQuery DataFrames and witness how you can: - Effortlessly transform terabytes of data - Build efficient ML applications on massive datasets by leveraging large language models - Use your familiar Python environment

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In our session, learn how MSCI uses machine learning with Vertex AI, BigQuery and Cloud Run to enrich its datasets to help our clients gain insight into around 1 million asset locations to help manage climate-related risks. We’ll demonstrate how MSCI Geospatial can achieve an increase of 100 times in data processing efficiency, utilizing the scalability and cost-effectiveness of Google Cloud solutions.

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Attention developers! Are you struggling with the complexities of integrating Al/ML into your apps? Join this practical session where we'll explore how MongoDB Atlas and Google Cloud's offerings like Vertex Al, Gemini, Codey, BigQuery, and Dataflow, provide a comprehensive toolkit for developers. In completing this session, you'll have the tools and confidence to embark on your own Al/ML journey! By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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