As organizations increasingly adopt modern data stacks, the combination of dbt and AWS Analytics services emerged as a powerful pairing for analytics engineering at scale. This session will explore proven strategies and hard-learned lessons for optimizing this technology stack to use dbt-athena, dbt-redshift, and dbt-glue to deliver reliable, performant data transformations. We will also cover case studies, best practices, and modern lakehouse scenarios with Apache Iceberg and Amazon S3 Tables.

Iceberg is an open storage format for large analytical datasets that is now interoperable with most modern data platforms. But the setup is complicated, and caveats abound. Jeremy Cohen will tour the archipelago of Iceberg integrations — across data warehouses, catalogs, and dbt — and demonstrate the promise of cross platform dbt Mesh to provide flexibility and collaboration for data teams. The more the merrier.

Come see how dbt empowers teams to harness Apache Iceberg and Snowflake Open Catalog for reliable, scalable, and agile analytics pipelines

dbt Mesh allowed for monolithic dbt projects to be broken down into more consumable and governed smaller projects. Now, learn how cross-platform mesh will allow you to take this one step further with development across data platforms using Iceberg tables. After this course you will be able to: Identify ideal use cases dbt Mesh Configure cross-project references between data platforms Navigate dbt Catalog Prerequisites for this course include: dbt Fundamentals, specifically data models and building model dependencies dbt Model governance Various data platforms 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.

Learn how the Wikimedia Foundation implemented an on-prem, open source data lake to fund Wikipedia and the future of open knowledge. We'll discuss data architecture including challenges integrating open source tools, learnings from our implementation, how we achieved a 10x decrease in query run times, and more.

The dbt language is growing to support new workflows across both dbt Core and the dbt Fusion engine. In this session, we’ll walk through the latest updates to dbt—from sample mode to iceberg catalogs to UDFs—showing how they work across different engines. You’ll also learn how to track the roadmap, contribute to development, and stay connected to the future of dbt.

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.

What’s the big deal about Apache Iceberg anyway? "Might Iceberg solve problems for my team?" "I’m using Iceberg already, but I find it lacking in key areas!" If you have any of the above thoughts, this peer exchange is for you! Last year’s peer exchange on Apache Iceberg was standing room only given all the hype surrounding the open table format. However, when participants were asked asked when they might start testing Iceberg capabilities, most said: “wait at least a few months for the dust to settle”. So now we’re a year later, the dust has settled, adoption of Iceberg by analytics engineers continue to grow. But, there’s still some open questions and product integrations to be built. Join your peers in socially constructing knowledge that’ll inform you for the year to come and beyond!

dbt Mesh allowed for monolithic dbt projects to be broken down into more consumable and governed smaller projects. Now, learn how cross-platform mesh will allow you to take this one step further with development across data platforms using Iceberg tables. After this course you will be able to: Identify ideal use cases dbt Mesh Configure cross-project references between data platforms Navigate dbt Catalog Prerequisites for this course include: dbt Fundamentals, specifically data models and building model dependencies dbt Model governance Various data platforms 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