k6 Cloud to Looker Integration
AI-first ETL from k6 Cloud into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads k6 Cloud into Looker
Datrise syncs k6 Cloud's test runs, thresholds, checks, metrics, virtual users, and response times into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.
Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.
Ideal for governed, version-controlled BI on a warehouse.
Endpoints
k6 Cloud: Load testing runs and performance thresholds.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How k6 Cloud entities map to Looker
| k6 Cloud entity | Looker object | Notes |
|---|---|---|
| test runs | k6_cloud_test_runs | id PK · custom fields → flattened columns (nested fields expanded for modeling) |
| thresholds | k6_cloud_thresholds | id PK · linked to k6_cloud_test_runs |
| checks | k6_cloud_checks | id PK · linked to k6_cloud_test_runs |
| metrics | k6_cloud_metrics | id PK · linked to k6_cloud_test_runs |
FAQ
How does Datrise handle k6 Cloud's custom fields in Looker?
Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.
How does the k6 Cloud to Looker sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.
Related pipelines
More destinations for k6 Cloud
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