Euno

EUNO TIP → Use our query language for governance insights: Define governance properties (e.g., connected to dbt?? depends on custom SQL?) to track the state of your data models and data products. Easily distinguish your certified data products from experimental, non-certified work.

If you want to be AI-ready, sooner or later, you’ll need to centralize your metrics. Here's why: → You need to build that layer that provides business context for your data. This is how AI tools can interpret the data, understand it, and give you trustworthy answers. → You need it to differentiate between the set of duplicates that are just experiments no one’s using and the actual governed metrics that reflect the definitions the business wants you to use, while also providing context. AI has definitely brought semantic layers back into the spotlight. Even though it’s been around for several years, the interest has significantly increased. The technology exists—dbt? is one example, but there are other standards. Some are open, some are not. The integrations exist, too. But I think the missing piece is the workflow: How do you make it work? How do you decide which metrics should go there? Who decides what belongs, and based on what? How do you use them? How do you even find them? Dozens of new metrics are created every day across multiple business domains. Does everything go into the semantic layer, or only ‘what matters’? *** AI adoption in analytics takes more than just tools and tech—it requires a collaborative workflow between data and business teams. One that supports the ongoing evolution of a governed semantic layer and the associated certified data products. This layer is key for reliable AI integration in your BI ecosystem, as it enables consistent data interpretation and allows LLM-based tools to accurately map business intent to data. Where do you stand? ***? Just a snippet from my panel at dbt Labs’ Coalesce a month ago. I had such a blast sharing my passion alongside my fantastic co-panelists. Tune in to the full panel recording in the first comment ↓

Shift left from Looker to dbt?: Leverage Euno to centralize your metrics. Automatically generate open-source dbt-semantic (MetricFlow) code from selected Looker/LookML measures, centralizing all your metrics in dbt's metrics mart. See Euno in action: https://lnkd.in/dzvT9N95

Euno use case 3 out of 4 → Eliminate analytics engineering bottlenecks: Free up analytics engineers from the grunt work backlog. With over 50% of their time spent keeping dbt? and the BI layer in sync, Euno automates repetitive and redundant data modeling tasks, so your team can focus on more strategic work ? ? https://euno.ai/use-cases

We asked Madison Schott of Learn Analytics Engineering: Hand to ?, what are the top 5 challenges for analytics engineers? (Besides explaining their job at family dinner.) Turns out, they’re not all technical. What are they? Euno what to do: → https://lnkd.in/dH4ngD2r

Euno use case 2 out of 4 → Build a source of truth for metrics in dbt?: Build a semantic layer in dbt Labs with minimal lift. Euno automatically codifies selected logic from measures created in your BI tools into dbt/MetricFlow and keeps this central metrics layer in sync across all your analytics tools from Looker to Tableau. ? https://euno.ai/use-cases

"OMG who knew Customer Engagements was this popular!" Track top queries and users of Tableau data sources (and their specific fields) as Euno propagates utilization scores from dashboards and charts upstream using column-level lineage. → More on Euno's usage scoring: https://lnkd.in/dtEjXKXM

A month ago at dbt Labs’ Coalesce, I had the chance to share how Euno is reshaping the way data teams work. I’ve been really excited to see how our vision aligns so well with dbt’s—and how it helps teams prepare for AI-driven analytics. In this quick article, I share how it all connects and why I believe this is just the beginning. Feel free to comment and share with your team ?

What if we told you... Euno visualizes your entire data model journey in one view—from dbt Labs all the way to Looker ?? From dbt sources → through dbt models and metrics → LookML Views and Explores → to Looker Looks, tiles, and dashboards. Explore both upstream and downstream dependencies for a comprehensive understanding. Make dbt work with Looker: https://lnkd.in/dzvT9N95

Euno use case 1 out of 4 → Centrally govern your entire data model: See what your analysts are up to with all your BI logic mapped out. Understand relationships, dependencies, and utilization patterns to easily determine which most used models should be promoted to dbt? and which least used should be cleaned. ? https://euno.ai/use-cases