Looker Usage Data Q&A

Looker Usage in Euno

In addition to ingesting metadata from your Looker instance, Euno also tracks usage activity across dashboards, looks, explores, views, tiles, and even individual fields. This rich usage history enables teams to make informed decisions about what is actively used, what may be deprecated, and how resources are accessed across the organization.

Utilize Looker Usage Data To:

• Identify underused resources to clean up dashboards, looks, or models that are no longer actively referenced.

• Highlight heavily used content to prioritize optimization, refactoring, or access reviews.

• Understand adoption and ownership by analyzing who queries what, from where, and how frequently.

Understanding the Types of Usage Data Captured in Euno

Euno collects and organizes Looker usage data into a few distinct signals, drawing from Looker’s System Activity API and enhancing it for deeper insights:

1. Query History

For all Looker resources—including views, explores, looks, tiles, dashboards, and fields—Euno tracks:

• Total Queries: The number of queries executed over 14-, 30-, and 60-day windows.

• Distinct Users: The number of unique users issuing those queries.

• Source Attribution: The context in which the query originated (e.g., from a dashboard tile or an explore).

• User List: A list of all users who have queried the resource.

Note: Euno includes both cached and non-cached queries. Cached queries are not filtered out, providing a more complete picture of usage—but potentially overestimating warehouse activity when cache hits are high.

Important: Euno not only collects total queries for dashboard tiles and other visualizations, but also propagates those queries back upstream to the associated explores, views, and fields. This means field-level and explore-level resources inherit accurate query activity metrics, even when they are not queried directly but are involved in dashboard or look execution. This propagation ensures a comprehensive view of actual field and model usage, helping teams make better-informed decisions during refactors or cleanups.

2. View Activity

For visual content such as dashboards and looks, Euno captures UI-level interactions:

• Views: The number of times a dashboard or look has been opened or accessed.

• Timeframes: View counts are tracked over 7- and 30-day windows.

These events represent frontend engagement with visual assets—even when no new queries are issued due to caching.

3. PDT Build Activity

For Persistent Derived Tables (PDTs) defined in looker_view files and marked as persistent:

• Total PDT Builds: The number of materialization events in the past 30 days.

• Total Build Time: The cumulative time spent building PDTs—useful for identifying slow or resource-intensive transformations.

Choosing the Right Signal for Your Use Case

• Use Query Activity when evaluating:

  • Warehouse cost

  • Query frequency

  • Backend performance

  • Data model usage across explores and fields (thanks to query propagation)

• Use View Activity when assessing:

  • Dashboard and look engagement

  • User adoption and content popularity

  • Organizational awareness and usage trends

• Use PDT Build Activity when evaluating:

  • Model performance and efficiency

  • Resource load from persistent transformations

  • Maintenance and freshness of derived data

Why Do Query and View Data Sometimes Differ?

Differences between query and view counts are normal and often reflect Looker's caching and backend mechanics:

• When a dashboard loads from cache, Euno records a view event, but no new query is sent to the database.

• Conversely, when a PDT refreshes or a model is recompiled, queries may be triggered without user interaction, resulting in query counts without corresponding views.

Understanding this difference helps you interpret usage signals more accurately, especially when balancing adoption metrics against infrastructure and cost considerations.