Check out our team’s takeaways from dbt Coalesce 2023 and see why dbt Labs continues to set the standard for community events.
Every year, analytics engineers and other data pros flock to dbt Coalesce to elevate their skills, connect with the dbt community, and discover the latest product updates. It’s one of the industry’s most unique and refreshing events. From puppy playpens, yacht parties, and virtual rock climbing to jam-packed educational sessions and major dbt Cloud innovations—dbt Coalesce 2023 was one for the books.
Data Clymer proudly sponsored this year’s event and sent a full crew of #Clymers to participate. We’ve asked our team to share their main takeaways and favorite moments. Whether you missed this year’s conference or want to relive the magic, enjoy this dbt Coalesce 2023 recap!
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Question 1
What were your biggest takeaways from dbt Coalesce 2023?
Data is poised for growth
The data and analytics industry is still extremely nascent. dbt is paving the way for analytics engineering best practices with the enterprise-grade, differentiating features they’re rolling out. There is so much room to discover and set new industry best practices. It’s an exciting time to be in this space.
—Mike Musi, Solutions Engineer
Innovation in dbt cloud
"dbt is making huge investments in dbt Cloud, solidifying its role as a key component of the modern data stack. New features such as dbt Mesh, dbt Explorer, and the dbt Semantic Layer are likely to drive more widespread cloud adoption. Yet even with these new cloud-specific feature announcements, dbt remains committed to supporting dbt Core under the Apache 2.0 license, which is really exciting for the open source community."
— Noah Snider, Senior Cloud Data Engineer
Skill sets are evolving
"As the focus on extracting value from data grows, the need for analytics engineers to closely collaborate with business stakeholders becomes even more pressing. We're also seeing increased emphasis on areas like prompt-engineering, data governance, semantic layers, and observability."
—Pat Ross, Solution Architect
A universal need for data
"The customer sessions all told a similar challenging story: their data was a mess, they didn’t trust it, and they needed help building new systems to centralize and report on their data. They decided to move to a modern data stack and dbt was a critical tool in helping them reach their goals."
—Brian Albers, Chief Operating Officer
Widespread use of dbt
"I gained an appreciation for dbt’s market presence and relative value in the data ecosystem. Data professionals from many different industries attended Coalesce, including small brands and household name brands. It was impressive to hear and see how these brands use and value dbt."
—Mike Runyon, VP of Sales
Community growth and engagement
"dbt has been a pioneer within the data and analytics engineering communities. Coalesce is a testament to their influence in this area. The dbt community actively engages and wants to learn and grow. The technology partners and consultants who attend Coalesce—both as sponsors and as attendees—are an indicator of the dbt ecosystem's growth and the importance of dbt in the modern data stack."
—Jesse McCabe, VP of Marketing
Question 2
What dbt product updates are you most excited about and why?
dbt Mesh and the dbt Semantic Layer
"dbt Mesh gives analytics engineers a clean, visual way to reference models built in different domains. This improves model readability, scalability, and security, helping organizations better manage their data modeling complexity as they scale. The dbt Semantic Layer promises to centralize metrics definitions within organizations, making it their single source of truth. This also reduces complexity as organizations continue adding analytics use cases on top of their metrics."
—Mike Musi, Solutions Engineer
New integrations
"The dbt Semantic Layer allows users to build metrics within the dbt interface, all while enabling software engineering best practices like version control and CI/CD. The Google Sheets integration is really exciting! Keep in mind that there are a limited number of direct integrations, so make sure your BI tool is currently supported if you are considering implementing the dbt semantic layer."
— Noah Snider, Senior Cloud Data Engineer
Support for streaming tables and materialized views
"This feature promises to be a cost-efficient solution for those needing near-real time data model refreshes to support reporting use cases. I'm also excited about the semantic layer. It will not only boost self-service analytics and reporting but also ensure consistency in terms, definitions, and calculations. Another upshot? The growing integration of AI tools with semantic layers is on the horizon. For AI to truly amplify its role in analytics, clear metrics and table definitions are vital."
—Pat Ross, Solution Architect
Enabling mature data strategies
"dbt has made significant updates to support data practitioners with mature data strategies. dbt Mesh provides the ability to better manage and support multiple dbt projects across data teams and improve data lineage capabilities. The dbt community is thrilled about the semantic layer GA release and how it will help organizations better organize their data and drive consistency across various tools."
—Jesse McCabe, VP of Marketing
Question 3
What major themes stood out for data teams?
Managing complexity
"dbt is placing a strong focus on how to better support more complex data initiatives and organizations with more mature data strategies. As part of this focus, dbt is making commercial investments to boost dbt Cloud's growth. These improved capabilities make dbt better able to support an analytics engineer’s overall data journey."
—Jesse McCabe, VP of Marketing
Working faster and more collaboratively
"Data mesh is all about enabling data teams to operate like software teams. Specialized teams can manage and deliver data products, while organizations can sidestep centralization bottlenecks. When teams treat data as a product, there's inherent accountability, leading to improvements in quality, reliability, and trustworthiness of data. Smaller, more domain-focused teams can work much faster and more creatively than large, centralized teams. Tools like dbt Explorer will play a pivotal role in this shift."
— Pat Ross, Solution Architect
Data teams as a revenue driver
"Many companies are being driven to reduce costs due to economic headwinds. Now more than ever, data leaders must align their teams with revenue goals and high-level business objectives. A key part of this is ensuring that data products don't just stay in the data warehouse and remain unused. A whole new suite of activation tools (Census, Hightouch, and Rudderstack, to name a few) are helping ensure that data teams’ outputs are easily integrated into the workflows of ops and revenue teams."
— Noah Snider, Senior Cloud Data Engineer
Accountability at scale
"dbt’s new features put a conceptual fence around data domains that clearly delineate where one team’s responsibility ends and another’s begins. Given the abundance of early-stage data catalog and observability vendors, data quality and proactive governance are more valuable than ever. It’s good to see the industry is investing in tools that empower data teams to manage trust and quality."
— Mike Musi, Solution Engineer
Collaboration is key
"True to its name, collaboration shone at Coalesce—for the Data Clymer team, as well as other vendors and attendees. We met with several companies building products off dbt or seeking to partner with the greater community. It’s great to see everyone working together, communicating, and sharing ideas."
— Brian Albers, Chief Operating Officer
Governing data
"How can data teams manage complexity and effectively govern their data? This is a priority for dbt and a space for lots of new emerging technologies. Vendors focused on data cataloging, data lineage, and data governance filled the activation hall. In addition, dbt is adding a suite of new tools focused on managing this complexity, including cross product refs and dbt explorer."
— Noah Snider, Senior Cloud Data Engineer
Question 4
What was your favorite session? What did you learn?
How Canadian Football League’s data team runs winning marketing plays
"CFL is a mature data organization that is using best-in-class tools (including RudderStack) to centralize data collection, modeling, and analysis on behalf of their nine teams. This is a highly efficient, cost-effective way to equip teams with advanced analytics for revenue optimization. It was great to compare CFL’s journey with the Big Ten and the numerous professional sports teams Data Clymer works with."
—Mike Musi, Solution Engineer
Fixing the data engineering lifecycle
"One thing that really stood out in this panel discussion was the increased importance for engineers to develop 'soft skills' such as communication, empathy, and stakeholder management. Due to the increased ubiquity of cloud computing and the advent of modern data warehousing, technical skills that used to be valuable (Apache Hadoop, for example) are becoming obsolete and commoditized. Although technical skills are still important, the key differentiator of a good engineer vs a great one usually boils down to the softer skills."
— Noah Snider, Senior Cloud Data Engineer
Not Just X’s and O’s: How Sports Teams are Adopting the Modern Data Stack
"Data Clymer led a well-attended panel discussion with the Indiana Pacers, the Seattle Seahawks, and the NBA. Data analytics in sports are a big area of focus. It was fascinating to learn how major league teams enhance their fan experiences and businesses with modern data technologies. I learned more about their respective migration challenges and how dbt mitigates risk and accelerates the process."
— Mike Runyon, VP of Sales
How FanDuel migrated a mature data organization
"FanDuel’s performance metrics from the 2022 to 2023 Super Bowl were remarkable! They halved runtime durations, achieved a perfect SLA record, and handled a 40% transaction volume increase. They attributed their success to transitioning from Redshift to a Databricks + dbt combo. This highlights the value of pivoting from an ETL to an ELT pipeline and underscores the efficiency of first depositing raw data in a lake or warehouse, then executing transformations—a strategy I wholeheartedly endorse."
— Pat Ross, Solution Architect
Multiple AI/LLM Sessions
"Several sessions highlighted the great opportunity for companies to go 'from feeling stuck to making bucks' with their data. I'm excited about helping teams modernize their stacks and prepare for AI and large language models (LLM). I heard people say LLM more in the week at Coalesce than reading articles over the past year!"
— Brian Albers, CFO/COO
We're Already Counting the Days till dbt Coalesce 2024...
Before we wrap up, we wanted to give a major shout-out to a few of our technology partners, customers, and attendees who made our time in San Diego extra special.
- dbt Labs for hosting a top-notch event from beginning to end, and Tristan Handy for giving us a reason to explain who Doogie Howser is to millennials
- Census for making the outdoors extra special with campfires, custom patches, and a fabulous travel giveaway
- Sigma for powering our #ClymOn leaderboard with its awesome Sigma visualization tool
- Christian Renfro and Jacob Emerson for kicking butt at The Climb and driving a heated VR competition!
- Jared Chavez, Paimon Jaberi, and Keelan Smithers for a great discussion on the modern data stack in sports (and especially Jared for teaching us what a ‘Pacer’ is)
- Kate Schiffelbein and Lindsay Murphy for teaming up with Pat Ross to discuss what AI tools mean for AE’s
- Trenton Huey for his ongoing support and an outstanding talk on Vida’s healthcare analytics journey
- Last but not least, Monte Carlo for the sweet sunglasses and Secoda for puppy playtime!
Until next year…#ClymOn!
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