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Overview

The Usage tab on an asset page gives you visibility into how a data asset is being queried — including query volume, performance, cost, and who is accessing it. This helps you understand adoption, spot expensive or slow queries, and identify the users and warehouses driving activity. You can access the Usage tab from any asset’s detail page, alongside Overview, Attribute, Metric, Lineage, Audit, Documentation, Conversation, and Custom.

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Summary metrics

At the top of the Usage tab, six summary cards give you an at-a-glance view of the asset’s usage over the selected period: Each card includes an expandable ... control for additional detail or drill-down.

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Queries tab

The Queries tab lists every query executed against the asset, under the Data Usage table.
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Filters

Use the filter buttons above the table to narrow down results:
  • All — every query recorded for this asset
  • Expensive — queries with the highest credit consumption
  • Slower — queries with the longest execution times
  • Popular — the most frequently run queries

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Table columns

You can search, sort, and filter the table using the icons in the top-right corner of the Data Usage panel (filter, sort, search, and overflow menu).
Use the Expensive or Slower filters to quickly identify queries that may be good candidates for optimization.

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Users tab

Switch to the Users tab to see a breakdown of asset usage by user rather than by individual query.
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Table columns

As with the Queries tab, you can filter (All), search, sort, and access additional options via the icons in the top-right of the panel.

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How usage data is collected

Usage data is extracted per data source on a scheduled basis and aggregated into the metrics shown on this tab. The underlying source queries differ by connector:
Usage is derived by joining SNOWFLAKE.ACCOUNT_USAGE.ACCESS_HISTORY (to identify which tables were touched) with SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY (for execution time, status, warehouse, and credits). This produces one summary row per table with total queries, unique users, unique warehouses, average/slowest execution time, success/failure counts, credits used, and a breakdown by query type (SELECT, INSERT, UPDATE, DELETE, MERGE).
Usage is derived from system.query.history, extracting the target table from the query text and aggregating the same set of signals as Snowflake (query count, unique users/warehouses, execution time, success/failure, query type breakdown).
Databricks does not expose table size or row count from Unity Catalog without running per-table commands (ANALYZE TABLE ... COMPUTE STATISTICS and DESCRIBE DETAIL). Volume-related metrics for Databricks assets may be limited as a result.
Usage and Performance data is not currently available for SQL Server sources.
Unlike Snowflake and Databricks, SQL Server does not retain a persistent, queryable history of past executions:
  • Query Store (sys.query_store_* views) is often disabled or not actively capturing recent workloads, so any data returned can be significantly out of date.
  • Dynamic Management Views (sys.dm_exec_query_stats, etc.) only reflect what’s currently cached in memory and reset on server restart.
  • Active session views (sys.dm_exec_requests, sys.dm_exec_sessions) only show queries running right now — there’s no way to look back in time.
As a result, the Usage tab for MSSQL assets won’t show populated metrics until a persistent tracking mechanism is available on the source side.

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Usage and asset criticality

Usage metrics also feed into an asset’s overall Criticality score, alongside freshness, dependency, volume, and metadata completeness. Usage makes up 30% of the criticality calculation, weighted as:
  • Unique users — 60%
  • Read query count — 40%
If an asset’s usage numbers look unexpectedly low, check whether it has few unique users or read queries relative to similar assets — this directly impacts its criticality ranking.

Lineage

See upstream and downstream dependencies for this asset

Audit

Review the change history for this asset