I’m still buzzing from sharing the power of Euno at dbt Labs’ Coalesce a month ago. It was so motivating to see how Euno’s vision aligns with that of dbt Labs—from Tristan Handy’s introduction of the Analytics Development Lifecycle (ADLC) to the announcement of the “One dbt” ethos that supports the ADLC.
What I liked about One dbt is the focus on creating a common framework that:
- allows more people to safely contribute to the analytics workflow,
- lets them work in the tools that make the most sense for them,
- supports the acceleration of their work with AI.
You see, practicing analytics well takes more than just tools and tech—it demands an analytical workflow that unifies and empowers all teams within analytics.
This becomes even more apparent with AI becoming a part of analytics. The space for innovation lies in developing data modeling practices that offer a more comprehensive approach to semantic alignment and capturing business logic effectively across different layers.
In my view, Euno is here to manifest this vision. Our goal is to empower data teams not only to adopt workflows that boost their governance score and enable AI adoption, but also to anchor their data governance (specifically) in dbt™. This is the workflow we help our customers use:
#1 Utilization & Lineage Observability
Analytics engineers use Euno to automatically map all business logic crafted by analysts in one view, from dbt all the way to Looker and Tableau. For each table, metrics, field, and dashboard they can see actual usage and ask detailed questions about their entire data model:
- How often a model is used?
- Who uses it most?
- How many queries are connected to dbt upstream?
- What portion of models is documented?
- And so much more.
In other words, data teams can finally see how much of their most-used data models are truly governed. This step complements One dbt’s push for cross-platform flexibility so that data teams can “reuse data assets not only from different projects but also across different data platforms, breaking down silos and fostering collaboration across the organization’s entire data estate.”
#2 Let Analysts be Analysts
Analysts can continue to craft business logic as they go within the BI tools they love, where they bring valuable business context that other data teams might lack. Rather than diverting them from their core expertise, this approach encourages better collaboration between data and business teams.
#3 Shift Left to dbt
With the power to visualize the entire data stack, analytics engineers can now leverage Euno’s usage and governance insights to prioritize dbt modeling activities (semantics and transformations).
Then, they use our shift-left automation to promote only the most important definitions to the central data model in dbt, and sync changes across all BI tools for consistent reuse.
This step also aligns with dbt Labs’ efforts to centralize metadata across the ADLC. But more importantly, it cuts the grunt work for data teams and frees them up to focus on more strategic work.
Going forward
One dbt offers a long-awaited approach for data modeling. Whereas previous focus was on tools to visualize, build, and manage data models within dbt, now data teams are encouraged to extend their work across the BI ecosystem, enabling them to deliver trusted products as AI reshapes the industry.
Euno complements this vision, supporting a workflow where teams can see which models and metrics are in use across platforms, shifting only what matters to dbt. Data teams can use Euno to build a metrics layer in dbt and manage business logic as data scales—all by establishing a solid foundation with dbt.
Can’t wait for what’s to come.
