Hear how EF (Education First) modernized its data stack from a legacy SQL Server-based environment to a modern Snowflake and dbt-based data stack. Find out what challenges and key decisions were handled during the process, and learn how to incorporate some of their key takeaways into your work.

Fifth Third Bank transformed its MLOps using a feature store built with dbt Cloud. The result? Improved model governance, reduced risk and faster product innovation. In this session, learn how they defined features, automated pipelines and delivered real-time and historical feature views.

Platform engineers from global pharmaceutical company invites you to explore our journey in creating a Cloud native, Federated Data Platform using dbt Cloud, Snowflake, and Data Mesh. Discover how we established foundational tools, standards, and developed automation and self-service capabilities.

CHG Healthcare migrated 2000+ legacy MySQL jobs to dbt Cloud and Snowflake in record time. We'll share how Datafold used their AI-powered Migration Agent to migrate and refactor convoluted legacy code into dbt Cloud and Snowflake with full automatic validation, dramatically accelerating our modernization.

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.

Siemens Data Cloud runs over 1500 dbt Platform projects across teams and domains. But more projects can mean more silos and less visibility. Because dbt is designed to be project-scoped, getting a birds-eye view isn’t easy. That’s where the dbt Platform Admin API comes in. We’ll show how we used it to extract metadata and build a unified monitoring dashboard. You’ll learn how to track deployments, spot anomalies, and compare project health across your dbt landscape.

In the last few months, I’ve been working hard to onboard new data and analytics engineers. Along the way, I’ve learned which parts of dbt and dbt Cloud are hard to teach, what confuses people, and where tools or training fall short. I’d like to swap ideas and hear how others are handling this.

In this course, learn how to manage and monitor data platform costs using dbt's built-in tools. We’ll cover how to surface warehouse usage data, set up basic monitoring, and apply rule-based recommendations to optimize performance. You’ll also explore how cost insights fit naturally into the developer workflow—equipping you to make smarter decisions without leaving dbt. This course is for analytics engineers, data analysts, and data platform owners who have a foundational understanding of dbt and want to build more cost-effective data pipelines. Using these cost management and orchestration strategies, the internal dbt Labs Analytics team achieved significant savings: Our cloud compute bill was reduced by 9% by simply implementing dbt Fusion and state-aware orchestration. By understanding the impact of models on platform costs, the team reduced the number of models built in scheduled jobs by 35% and shaved 20% off of job execution times. After this course, you will be able to: Articulate how dbt development patterns impact data platform costs. Configure dbt Cloud to monitor warehouse compute spend. Use the dbt Cost Management dashboard to identify high-cost models and jobs. Apply specific optimization techniques, from materializations to advanced data modeling patterns, to reduce warehouse costs. Implement proactive strategies like dbt Fusion and state-aware orchestration to prevent future cost overruns. Prerequisites for this course include: dbt fundamentals What to bring: You will need to bring your own laptop to complete the hands-on exercises. We will provide all the other sandbox environments for dbt and data platform. Duration: 2 hours Fee: $200 Trainings and certifications are not offered separately and must be purchased with a Coalesce pass Trainings and certifications are not available for Coalesce Online passes