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.

Delivering trusted data at scale doesn’t have to mean ballooning costs or endless rework. In this session, we’ll explore how state-aware orchestration, powered by Fusion, drives leaner, smarter pipelines in the dbt platform. We’ll cover advanced configurations for even greater efficiency, practitioner tips that save resources, and testing patterns that cut redundancy. The result: faster runs, lower spend, and more time for impactful 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.

AI and dbt unlocks the potential for any data analyst to work like full-stack dbt developers. But without the right guardrails, that freedom can quickly turn into chaos and technical debt. At WHOOP, we embraced analyst autonomy, and scaled it responsibly. In this session, you’ll learn how we empowered analysts to build in dbt while protecting data quality, staying aligned with the broader team, and avoiding technical debt. If you’re looking to give analysts more ownership without giving up control, this session will show you how to get there.

Eliminate 80% of the manual effort spent writing dbt tests, chasing noisy alerts, and fixing data issues. In this session, you'll see how data teams are using an AI Data SRE that detects, triages, and resolves issues across the entire data stack. We’ll cover everything from AI architecture to optimised incident management–and even show an agent writing production-ready PRs!

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.

Manual modeling is out. Multi-agent systems are in. In this session, Dylan shows how his team at Mammoth Growth paired dbt with autonomous agents to handle complex modeling tasks. The result was days of work completed in minutes. You'll see how the system works, what it solved, and how it's reshaped their internal development workflows.

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.

With 700+ monthly BI users, Cribl scales self-service through governance, SDLC workflows, and smart AI practices. Join Priya Gupta and Chris Merrick to see how Git, Omni’s dbt integration, and a $20 auto-doc hack to enrich 100+ models helps deliver fast and trusted AI-powered insights.

Modern data teams are tasked with bringing AI into the analytics stack in a way that is trustworthy, scalable, and deeply integrated. In this session, we’ll show how the dbt MCP server connects LLMs and agents to structured and governed data to power use cases for development, discovery, and querying.

At Virgin Media O2, we believe that strong processes and culture matter more than any individual tool. In this talk, we’ll share how we’ve applied DevOps and software engineering principles to transform our data capabilities and enable true data modernization at scale. We’ll take you behind the scenes of how these practices shaped the design and delivery of our enterprise Data Mesh, with dbt at its core, empowering our teams to move faster, build trust in data, and fully embrace a modern, decentralized approach.

In this session we’ll show how we integrated CI/CD dbt jobs to validate data and run tests on every merge request. Attendees will walk away with a blueprint for implementing CI/CD for dbt, lessons learned from our journey and best practices to keep data quality high without slowing down development.

Self-serve analytics promises speed. But without clear guidance, it often leads to hidden obstacles like cluttered dashboards, runaway costs, and a loss of trust in the data. Add AI to the mix, and those faults become fractures. In this session, we'll unpack why self-serve efforts stall, with lessons from real-world teams at Shopify and Tableau. We’ll also explore how BetterHelp uses dbt alongside Euno’s lineage and usage insights to declutter, cut compute costs, and determine which data assets and reports teams (and AI agents) can trust.

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.

See how AI is shaping developer tools for the next generation. AWS Kiro provides a powerful general purpose IDE great for a wide variety of programming languages and tasks. Pairing Kiro with the dbt MCP Server makes this even more powerful by providing access to dbt specific functionality, context and agents. In addition to this, interact with an external agent deployed in Amazon Bedrock AgentCore.

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.

Learn how a small team at Dexcom used dbt to unify hundreds of global manufacturing data tables into 30 analytics-ready models—delivering sub-15-minute freshness and complex processing at scale. The result: faster, smarter manufacturing decisions that support the timely delivery of technology that has transformed how people manage diabetes and track their glucose.

Merging two large organizations with different tools, teams, and data practices is never simple. At Virgin Media O2, we used dbt to help bring consistency to that complexity, building a hybrid data mesh that supported self-serve analytics across domains. In this session, we’ll share how we gave teams clearer ownership, put governance in place using dbt, and set up secure data sharing through GCP’s Analytics Hub. If you’re working in a federated or fast-changing environment, this session offers practical lessons for making self-serve work at scale.

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.

Let's get meshy! Learn how to evolve your organization's dbt project(s) to more effectively manage distributed data at scale by adopting data mesh as a grounding philosophy. This is your roadmap of "socio" strategies & "technical" dbt features to implement following the four principles of data mesh.