In the dbt Labs on dbt series, you get a behind-the-scenes look at how dbt Labs uses data. Learn how dbt Labs thinks about the role of data, how data developers collaborate with business leaders, and the technical decisions we’ve made in our own dbt project.

In this session, Mark Matteucci, Enterprise Systems Architect at dbt Labs, and Mary Wleklinski, Senior Director of Revenue Marketing at dbt Labs share the rising importance of data in a challenging economic environment. You'll learn about a variety of dbt use cases across finance, G&A, and marketing and how we use data to solve tough business challenges. Learn about dbt Labs' journey and leave with ideas that you can implement in your dbt project today.

Register for Coalesce at https://coalesce.getdbt.com

In the dbt Labs on dbt series, get a behind-the-scenes look at how dbt Labs uses data. You’ll learn how dbt Labs thinks about the role of data, how data developers collaborate with business leaders, and the technical decisions we’ve made in our own dbt project.

In this session, Danny Lambert, Director of Marketing Operations at dbt Labs, shares how he partnered with marketing leaders and the data team to deliver clean, simple reporting that help everyone from campaign managers to the executive team make better decisions about marketing spend and optimization. Jakki Jakaj, Associate Manager of Integrated Marketing at dbt Labs, shares how her team uses this data every day to optimize marketing campaigns

Register for Coalesce at https://coalesce.getdbt.com/

The Lakehouse architecture has emerged as the ideal data architecture for the AI age. It unites the structured world of analytics with the rapidly evolving world of AI.But what really makes something a “Lakehouse” and why should you care? In this session, Databricks discusses the key components of a lakehouse, what you should look for when adapting this paradigm and how Databricks and dbt are together enabling analytics engineers, analysts and data scientists to collaborate together on a single unified platform.

Speaker: Thor List, Senior Field Engineering Manager, Databricks

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Seven years ago, in the early days of dbt, dbt Labs was building capabilities that made data developers more productive. dbt gained traction. It took off. Today, dbt is the standard for data transformation. It has never been easier to build and ship data products. This has created a new challenge – complexity. Join Heidi Brandenburg, Vice President of Engineering at dbt Labs, and Sai Maddali, Staff Product Manager at dbt Labs, in London to hear all about the latest releases in dbt and how they are helping organizations navigate complexity.

Speakers: Heidi Brandenburg, Vice President of Engineering, dbt Labs; Sai Maddali, Staff Product Manager, dbt Labs

Register for Coalesce at https://coalesce.getdbt.com

How a team of eight Analytics Engineers rolled out dbt across Virgin Media O2 to become a data driven company.

Speakers: Oliver Burt, Lead Analytics Engineer, Virgin Media O2; Jason Jones, Analytics Engineering Manager, Virgin Media O2

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Behind any good DataOps within a Modern Data Stack (MDS) architecture is a solid DevOps design! This is particularly pressing when building an MDS solution at scale, as reliability, quality and availability of data requires a very high degree of process automation while remaining fast, agile and resilient to change when addressing business needs.

While DevOps in Data Engineering is nothing new - for a broad-spectrum solution that includes data warehouse, BI, etc seemed either a bit out of reach due to overall complexity and cost - or simply overlooked due to perceived issues around scaling often attributed to the challenges of automation in CI/CD processes. However, this has been fast changing with tools such as dbt having super cool features which allow a very high degree of autonomy in the CI/CD processes with relative ease, with flexible and cutting edge features around pre-commits, Slim CI, etc.

In this session, Datatonic covers the challenges around building and deploying enterprise-grade MDS solutions for analytics at scale and how they have used dbt to address those - especially around near-complete autonomy to the CI/CD processes!

Speaker: Ash Sultan, Lead Data Architect, Datatonic

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Jai discusses how Snowflake enables distributed domains and data products with dbt and showcases a customer success story with Snowflake and dbt Cloud.

Speaker: Jai Parmar, AI & ML Senior Solutions Architect, Snowflake

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In this session, Alex Higgs unveils the potential of Data Vault 2.0, an often overlooked but powerful data warehousing method. Discover how it offers scalability, agility, and flexibility to your data solutions.

Key Highlights: - Explore the origins and essence of Data Vault 2.0 - Learn how Data Vault 2.0 streamlines big data solutions for scalability. - See how it integrates with dbt via AutomateDV for faster time to value. - Understand how AutomateDV simplifies Data Vault 2.0 data warehouses, freeing data teams from intricate SQL.

Speaker: Alex Higgs, Senior Consultant Data Engineer, Datavault

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Hear Benoit Perigaud explain how automation in dbt boosts productivity for large-scale operations. You'll learn how automation is significant throughout the dbt project life cycle, from helping you create projects, to executing rules and building observability on top of all transformations. This session is especially beneficial for data practitioners who manage many dbt projects (into the hundreds!), but it's also great information for anyone who may do so in the future.

Speaker: Benoit Perigaud, Staff Analytics Engineer, dbt Labs

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dbt packages are libraries for dbt. Packages can produce information about best practice for your dbt project (ex: dbt project evaluator) and cloud warehouse cost overviews. Unfortunately, all theses KPIs are stored in your data warehouse and it can be painful and expensive to create data visualization dashboards. This application build automatically dashboards from dbt packages that you are using. You just need to parameter your dbt Cloud API key - that's it! In this session, you'll learn how.

Speaker: Adrien Boutreau, Head of Analytics Engineers , Infinite Lambda

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Over the past four years, the data team at EQT has leveraged dbt and Snowflake to create a myriad of data products across the company. With a rapidly growing organization and increased demands for timely and accurate data, their immense monolithic dbt repository has become challenging to maintain. Learn about the best practices they are adopting to keep the platform in shape and scale with the business.

Speaker: Erik Lehto, Senior analytics engineer, EQT

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In analytics, generating a number is the easy part. Proving you got it right is much harder. In this talk, Christine Dixon walks through why you should strive to make your data pipelines reproducible and transparent, to ensure that you can always defend your data. She considers the ways in which dbt makes it easy, but also digs into some difficult technical scenarios and compromises you might need to make.

Speaker: Christine Dixon, Lead Analyst, Mantel Group

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The team at nib Health has internal projects that contain standardized packages for running a dbt project, such as pipeline management, data testing, and data modeling macros. In this talk, they share how they utilized the yaml documentation files in dbt to create standardized tagging for both data security (PII), project tags, and product domain tags that get pushed into Snowflake, Immuta, and Select Star.

Speaker: Pip Sidaway, Data Product Manager, nib

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Learn about Watercare's journey in implementing a modern data stack with a focus on self serving analytics in the water industry. The session covers the reasons behind Watercare's decision to implement a modern data stack, the problem of data conformity, and the tools they used to accelerate their data modeling process. Diego also discusses the benefits of using dbt, Snowflake, and Azure DevOps in data modeling. There is also a parallel drawn between analytics and Diego’s connection with jazz music.

Speaker: Diego Morales, Civil Industrial Engineer, Watercare

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Learn best practices for improving your data workflows at scale. In this session, the dbt Labs team shares tactical ideas for setting up CI for the first time and shipping with confidence, as well as tips to take your implementation to the next level.

Speaker: Joel Labes, Senior Developer Experience Advocate, dbt Labs

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In this presentation, Sarah and Darren discuss RMIT University's journey to implementing the modern data stack with dbt. They bring tales of their musical successes and misadventures, lessons learned with both music and data engineering, and how these seemingly disparate worlds overlap.

Speakers: Darren Ware, Senior Data Engineer, RMIT University; Sarah Taylor, Lead Data Engineer, RMIT University

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You've been there. Your pet data project somehow became super popular and now it's used internationally by people you never met, who are building data products themselves on what you build. Suddenly, you decide to change something in yours. Minutes later, you turned off Slack. Let's rewind for a second here. It could've been avoided. If only you had the change management in place. And the right automation. And made templates for others to be reused. We've been there. And we learned to start and improve all of these key factors that made dbt a success in deployments across regions, teams, and companies. In this session, Joris Van den Borre discusses a few learnings that got us here.

Speaker: Joris Van den Borre, Founder and CEO, Tropos.io

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Siemens has been revamping how it approaches data, looking to democratize data access to unlock faster innovation. It recently rolled out Siemens Data Cloud — a data mesh with Snowflake and dbt Cloud at its heart. The goal: ensure the people closest to the business problems were empowered to self-serve responsibly, without compromising on governance or creating silos.

This is the story of how Siemens has achieved success with dbt Cloud and a data mesh — and what the future holds in store.

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Showcasing how analytics engineers can add value to a business by simplifying complex asks into an easy to analyze format. This particular example looks at how we can achieve great insight into our product's performance from a standardized table structure and some clever macros.

Speaker: George Apps, Analytics Engineering, Travelperk

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TOCA Football, the largest operator of indoor soccer centers in North America, leverages accurate data to power analytics for over 30 training centers, providing everything from operational insights for executives to ball-by-ball analysis.

In 2020, the team adopted a cloud-native data stack with dbt to scale analytics enablement for the go-to-market org, including the company’s finance, strategy, operations, and marketing teams. By 2022, their lean team of four was struggling to gain visibility into the health and performance of their dbt models. So, what was the TOCA team to do? Two words: data observability.

In this talk, Sam Cvetkovski, Director, Data & Analytics discusses how TOCA built their larger data observability strategy to reduce model bloat, increase data accuracy, and boost stakeholder satisfaction with their team’s data products. She shares her biggest “aha!” moments, key challenges, and best practices for teams getting started on their dbt reliability journeys.

Speakers: Sam Cvetkovski, Director, Data & Analytics, TOCA Football; Barr Moses, Co-Founder & CEO, Monte Carlo

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Hear from Red Ventures about how through the creation of standard implementation playbooks, their data team is able to quickly roll out modern tools like dbt and Hightouch across businesses to drive growth.

Speakers: Tejas Manohar, cofounder/co-CEO, Hightouch; Brandon Beidel, Director of Data Engineering, Red Ventures

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Join the team from Moody's Analytics as they take you on a personal journey of optimizing their data pipelines for data quality and governance. Like many data practitioners, Ryan and Ravi understand the frustration and anxiety that comes with accidentally introducing bad code into production pipelines—they've spent countless hours putting out fires caused from these unexpected changes.

In this session, Ryan and Ravi recount their experiences with a previous data stack that lacked standardized testing methods and visibility into the impact of code changes on production data. They also share how their new data stack is safeguarded by Datafold's data diffing and continuous integration (CI) capabilities, which enables their team to work with greater confidence, peace of mind, and speed.

Speakers: Gleb Mezhanskiy, CEO, Datafold; Ravi Ramadoss, Director of Data Engineering, Moody's Analytics CRE; Ryan Kelly, Data Engineer, Moody's Analytics CRE

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In this session, the team at Rocket Money explains how they built a Quote-to-Cash (Q2C) system in dbt Cloud that the accounting team uses for end-of-month close. This was also used to pass Rocket Money's first SOX audit with zero deficiencies. The presentation will review how the team gathered requirements (for different products, payment providers, etc.) and built and tested all of their models.

Speaker: Amber Oar, Staff Analytics Engineer, Rocket Money

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Traditional data warehouses excel at churning through terabytes of data for historical analysis. But for real-time, business-critical use cases, traditional data warehouses can’t produce results fast enough—and they still rack up a huge bill in the process.

So when Ramp’s data engineering team needed to serve complex analytics queries on the critical path of their production application, they knew they needed a new tool for the job. Enter Materialize, the first operational data warehouse. Like a traditional data warehouse, Materialize centralizes the data from all of a business’s production systems, from application databases to SaaS tools. But unlike a traditional data warehouse, Materialize enables taking immediate and automatic action when that data changes. Queries that once took hours or minutes to run are up-to-date in Materialize within seconds.

This talk presents how Ramp is unlocking new real-time use cases using Materialize as their operational data warehouse. The best part? The team still uses dbt for data modeling and deployment management, just like they are able to with their traditional batch workloads.

Speakers: Nikhil Benesch, CTO, Materialize; Ryan Delgado, Staff Software Engineer, Data Platform, Ramp

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In this session, Vida Health’s senior director of data, mobile, and web engineering shares a story that can help other data and business leaders capitalize on the opportunities being created by current technology innovations, market realities, and real-world problems. This includes a playbook on how Vida Health uses modern data technologies like dbt Cloud, Fivetran, Looker, BigQuery, BigQueryML/dbtML, Vertex AI, LLMs, and more to put data in the driver’s seat to solve meaningful problems in complex industries like healthcare.

Speaker: Trenton Huey, Senior Director, Data and Frontend Engineering, Vida Health

Register for Coalesce at https://coalesce.getdbt.com