Euno

Expose dbt metrics to Looker users through Euno's automated sync, which creates measures within LookML views, enabling Looker users to dynamically query dbt metrics. Sync dbt metrics with Looker: https://lnkd.in/dzvT9N95

Govern business logic smarter, not harder. I had the opportunity to speak with dozens of data leaders at Big Data LDN and dbt Labs’ Coalesce. They all expressed a common challenge: How can business leaders trust data products when numbers often mismatch? Analytics Engineers also shared their frustration about "working blindly" with limited visibility into the clutter of dashboards and metrics and difficulty determining which ones matter. That’s why we built Euno: To provide end-to-end utilization and lineage visibility—from any warehouse, through dbt™, and all the way to the tables, fields, and dashboards in Tableau and Looker. With Euno data teams are able to gain insight into the **governance level** of their data products and data models. In other words, which data products rely on governed models and which are just exploratory work? → Three practices our customers add into their analytics workflow: ✩ Find out how much of your automatic refresh jobs are never used: Euno shows you detailed metrics on high- and low-usage data sources. Use it to identify unused tables that are constantly refreshed, and save your team money and direct resources where they’re needed most. ✩ Spot highly-used, ungoverned dashboards: Locate high-use dashboards not built on governed data models and prioritize the steps to consolidate logic into dbt. ✩ Use Euno’s 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.  The question no longer is whether you need to start governing your business logic. Sooner or later (especially if you want to ensure AI enablement) you won’t have a choice. It’s about giving your data team the power to see how your data model is used and how well it’s governed as you scale. *** How does your team identify which data products are governed and BI-certified?

Euno use case 4 out of 4 → Boost performance through rapid materialization: Fully automate the process to generate pre-aggregate models based on dbt Labs Metrics, optimizing slow query times & reducing compute costs. → Create summarized data tables → Select the exact columns to group by → Choose the appropriate join paths → Add filters for more refined data aggregation ✩ https://euno.ai/use-cases

ANALYTICS ENGINEERS' FAVORITE → Find highly-used, ungoverned dashboards: Use Euno to identify high-use dashboards not built on governed data models and prioritize the steps to consolidate dependencies and logic into dbt™.

Hallo Datenteams aus Berlin! I can't wait to speak at your dbt Labs meetup next week and answer what I’m sure is often one of your biggest dilemmas: Should we build this in Looker or dbt™? We’re all after that sweet spot where every tool that connects to our warehouse has access to a consistent data model. But achieving this without compromising our business analysts’ inventive spirit and swift delivery of data products? Easier said than done. The recipe seems straightforward: Looker becomes the go-to for visualization and data exploration, leaving the construction of data models, including transformations and even metrics, to dbt. But an important challenge remains: Business logic doesn't bloom behind the scenes by data engineers; it's developed by business analysts on the front lines, as part of their everyday tasks within their native BI environments like Looker. Despite dbt's game-changing impact on data modeling, issues like misused documentation, duplicate metrics, and siloed logic continue to hinder a unified source of truth. This talk proposes a 'reverse flow'—automating the promotion of business logic crafted by analysts within Looker to dbt—based on usage prioritization as the key business value indicator. Focused on empowering analytics engineers and analysts, this strategy fosters a much more streamlined collaboration across the data model evolution. I'll cover three things: → What’s the current status? When do data teams use Looker and when do they use dbt? → What are 3 overlooked challenges of scaling dbt when most analysts use Looker? → What's the best way to govern business logic with Looker and dbt? We have a full crowd, but check the first comment to see if you can grab a seat. Thanks to Eva Schreyer for the invite, and to the fellow organizers and speakers Lucas Silbernagel, Victoria Perez Mola, and Tim Hiebenthal. See you there ♡

DID YOU KNOW → Most automatic refresh jobs are never used. With detailed utilization metrics, Euno lets you gain insights into high-and low-usage data sources. By easily identifying unused tables that are constantly refreshed, you’ll save your team money and direct resources where they’re needed most.

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