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.

Spoiler alert: you can’t! But new approaches to teaming, tracking, and tech are helping data teams shift from cost centers to strategic powerhouses. We’ll share how Hex customers are leading this change, how AI is evolving the space, and actionable takeaways for all data roles.

Continue the conversation from Building High-Quality AI Workflows with the dbt MCP Server in this interactive roundtable. We’ll explore how dbt’s MCP Server connects governed data to AI systems, dive deeper into the practical use cases, and talk through how organizations can adopt AI safely and effectively. This is your chance to: Ask detailed technical questions and get answers from product experts. Share your team’s challenges and learn from peers who are experimenting with MCP. Explore what’s possible today and influence where we go next. Attendance at the breakout session is encouraged but not required. Come ready to join the discussion and leave with new ideas!

Join us for a roundtable discussion where we’ll go deeper into the ideas shared in the dbt development in the age of AI: Improving the developer experience in the dbt platform. Bring your questions, share your take, and connect with peers and presenters. Note attendance in the breakout session is not required.

AI is transforming data teams - maybe you're anxious, excited, or kind of bummed. I'll share how I went from AI skeptic to enthusiast, how analytics engineering is evolving, and how data teams can finally deliver on promises we've been making for years.

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!

In this session we will cover the strategic and intentional tools that Workday took a bet on to create a true AI focused roadmap for data and analytics. You’ll learn about the strategy, alignment, tooling, and architecture put in place, covering both the journey & the technology.

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.

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.

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.

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.

AI isn’t just a lab experiment at J.Crew. It’s already driving results. This session unpacks how a few early wins with AI are being scaled across the company. You'll learn how to spot issues early on and change approaches when necessary to ensure AI is useful across your organization, and what it takes to turn pilot projects into repeatable systems, including the training, tools, and metrics that matter. If you’re trying to apply AI beyond your data team to your business users, this could serve as your playbook.

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.

Discover how dbt Catalog brings structure and clarity to your data ecosystem by turning metadata into a powerful asset. In this session, we’ll dive into how the dbt Catalog helps data teams and their stakeholders explore, trust, and act on data faster—by connecting lineage, documentation, and usage in one place. Learn what’s available today, what’s coming next, and how dbt Catalog fits into the broader vision for metadata in the AI era.

Discover how to turn your customer data into actionable insights using Generative AI. In this session, you'll see how Fivetran and dbt make it easy to prepare data from platforms like HubSpot and Zendesk, and how to use that data to power a Gen AI app that answers your business questions in real time.

Step into a dynamic, interactive session where you'll experience the data transformation journey from multiple angles: Data Engineer, Manager, Analytics VP, and Chief Data Officer. This immersive tabletop exercise isn’t your typical panel or demo—it’s a high-empathy, scenario-driven experience designed to build cross-role understanding and alignment across the modern data stack. Each scene drops you into a real-world challenge—whether it's data trust issues, managing cost pressures, or preparing for an AI initiative—and forces a go/no-go decision with your peers. You’ll explore how your choices impact others across the org, from the technical trenches to the boardroom. Whether you're a practitioner, leader, or executive, this session will help you see data not just as pipelines and dashboards, but as a 360-degree opportunity to drive business change. Walk away with a clear picture of the capabilities your team needs (without naming products) and a roadmap for building champions across your org.

In this session, you’ll learn how dbt’s Semantic Layer and MCP Server enable safe, AI-powered access to data through natural language interfaces. As business users start exploring data conversationally, the analyst role is evolving—from writing queries to curating trusted logic, guiding usage, and enabling scalable self-service. We’ll break down what this shift looks like and how dbt helps analysts stay at the center of decision-making.

Setting up a dbt project the right way ensures maintainability, scalability, and efficiency. Learn how Bumble uses dbt to reduce tech debt and lay the foundation not only for analytics, but also for machine learning and data products.

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.

• Mat-adata Yoga is currently full * If you are still interested in this class, complete THIS FORM to be added to the waitlist. The Coalesce team will be in touch via email if a spot opens up. Start your Coalesce day by untangling more than just your joins. In this structured yoga flow, we’ll stretch out the tension that’s been building from waiting on AI analytics to actually work. Just like data teams use Euno to unlock the power of the metadata they already have for governance and AI, this session will surface the strength you didn’t realize you’ve been storing. No yoga experience (or data catalog) required. Just bring yourself and a willingness to commit a few commits to your own wellbeing. As our guru/CEO likes to say: clear mind, clear context. Hosted by Euno 💜

The future of AI is here. Join AI and data industry thought leader Ashley Kramer from OpenAI as she shares how AI-powered development and intelligent systems act as force multipliers for organizations—and how to confidently embrace these accelerants at scale. In the second half of the keynote, she'll be joined by a panel of product leaders from across the data stack for a discussion on the future of analytics in an AI-driven world and how dbt and ecosystem partners are innovating to rewrite what’s possible: turning yesterday's science fiction into today's reality. For our Coalesce Online attendees, join us on Slack in #coalesce-2025 to stay connected during keynote!

Learn why dbt is the best place for analysts to build reliable data products quickly, combining structured workflows with context-aware AI. We will explore how dbt Canvas, dbt Studio, and more give analysts the visibility, control, and flexibility they need to move from exploration to production without relying on engineers. You will also see how AI agents in dbt accelerate development by recommending logic, surfacing relevant models, and helping troubleshoot issues—making self-service both faster and more trusted.

Aura Minerals cut pipeline migration time by 87%, transforming a 45-hour manual process into a 6-hour lift with relatively little manual oversight. In this session, learn how the team used AI tools to accelerate their shift from a complex PySpark environment to a streamlined, modern dbt workflow. You’ll walk away with a clear view of the strategy, the tools they used, and what it looks like to modernize your data stack with AI as a force multiplier.

Get hands-on with dbt Copilot, the AI assistant built into dbt that helps you move faster and write better code. In this workshop, you’ll learn how to use copilot to generate sql, create data tests and documentation, and build semantic models and metrics —all within your existing workflow. What to bring: You must bring your own laptop to complete the hands-on exercises. We will provide all the other sandbox environments for dbt and data platform.