DatriseAI-first ETL

k6 Cloud to Databricks SQL Warehouse Integration

AI-first ETL from k6 Cloud into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads k6 Cloud into Databricks SQL Warehouse

Datrise syncs k6 Cloud's test runs, thresholds, checks, metrics, virtual users, and response times into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

k6 Cloud: Load testing runs and performance thresholds.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How k6 Cloud entities map to Databricks SQL Warehouse

k6 Cloud entityDatabricks SQL Warehouse objectNotes
test runsk6_cloud_test_runsid PK · custom fields → VARIANT or STRUCT columns
thresholdsk6_cloud_thresholdsid PK · linked to k6_cloud_test_runs
checksk6_cloud_checksid PK · linked to k6_cloud_test_runs
metricsk6_cloud_metricsid PK · linked to k6_cloud_test_runs

FAQ

How does Datrise handle k6 Cloud's custom fields in Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the k6 Cloud to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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