k6 Cloud to Mode Integration
AI-first ETL from k6 Cloud into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads k6 Cloud into Mode
Datrise syncs k6 Cloud's test runs, thresholds, checks, metrics, virtual users, and response times into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
k6 Cloud: Load testing runs and performance thresholds.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How k6 Cloud entities map to Mode
| k6 Cloud entity | Mode object | Notes |
|---|---|---|
| test runs | k6_cloud_test_runs | id PK · custom fields → flattened columns for SQL and notebooks |
| 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 Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the k6 Cloud to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for k6 Cloud
Early access
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