DatriseAI-first ETL

Harness to Sisense Integration

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

How Datrise loads Harness into Sisense

Datrise syncs Harness's pipelines, deployments, services, environments, execution logs, and approvals into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.

Ideal for embedded analytics on an in-memory engine.

Endpoints

Harness: Continuous delivery pipelines and deployment verification.

Sisense: Analytics platform with elastic data models and embedded analytics.

How Harness entities map to Sisense

Harness entitySisense objectNotes
pipelinesharness_pipelinesid PK · custom fields → flattened columns for the cube
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 Sisense?

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

How does the Harness to Sisense sync stay up to date?

It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.

Related pipelines

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