The data world has long been divided, with data engineers and data scientists working in silos. This fragmentation creates a long, difficult journey from raw data to machine learning models. We've unified these worlds through the Google Cloud and dbt partnership. In this session, we'll show you an end-to-end workflow that simplifies data to AI journey. The availability of dbt Cloud on Google Cloud Marketplace streamlines getting started, and its integration with BigQuery's new Apache Iceberg tables creates an open foundation. We'll also highlight how BigQuery DataFrames' integration with dbt Python models lets you perform complex data science at scale, all within a single, streamlined process. Join us to learn how to build a unified data and AI platform with dbt on Google Cloud.

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.

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.

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

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.