DatriseAI-first ETL

Harness to Amazon QuickSight Integration

AI-first ETL from Harness into Amazon QuickSight. Governed entities, incremental sync, typed landing tables.

How Datrise loads Harness into Amazon QuickSight

Datrise syncs Harness's pipelines, deployments, services, environments, execution logs, and approvals into Amazon QuickSight as warehouse tables or a SPICE-loaded dataset. Flexible or custom fields land in flattened columns for analyses, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental refresh of the tables behind SPICE or direct query, so re-runs update only what changed. Date-partitioned facts to bound SPICE refresh. QuickSight SPICE is an in-memory copy, so Datrise keeps the backing tables incremental so refreshes stay cheap.

Ideal for AWS-native dashboards with pay-per-session pricing.

Endpoints

Harness: Continuous delivery pipelines and deployment verification.

Amazon QuickSight: AWS serverless BI with SPICE and embedded analytics.

How Harness entities map to Amazon QuickSight

Harness entityAmazon QuickSight objectNotes
pipelinesharness_pipelinesid PK · custom fields → flattened columns for analyses
deploymentsharness_deploymentsid PK · linked to harness_pipelines
servicesharness_servicesid PK · linked to harness_pipelines
environmentsharness_environmentsid PK · linked to harness_pipelines

FAQ

How does Datrise handle Harness's custom fields in Amazon QuickSight?

Flexible values are stored as flattened columns for analyses, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon QuickSight types.

How does the Harness to Amazon QuickSight sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the tables behind SPICE or direct query.

Related pipelines

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