DatriseAI-first ETL

Contentful to Sisense Integration

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

How Datrise loads Contentful into Sisense

Datrise syncs Contentful's entries, content types, assets, spaces, locales, and publish events 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

Contentful: Headless CMS for structured content delivery.

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

How Contentful entities map to Sisense

Contentful entitySisense objectNotes
entriescontentful_entriesid PK · custom fields → flattened columns for the cube
content typescontentful_content_typesid PK · linked to contentful_entries
assetscontentful_assetsid PK · linked to contentful_entries
spacescontentful_spacesid PK · linked to contentful_entries

FAQ

How does Datrise handle Contentful'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 Contentful to Sisense sync stay up to date?

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

Related pipelines

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