DatriseAI-first ETL

Contentful to Databricks SQL Warehouse Integration

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

How Datrise loads Contentful into Databricks SQL Warehouse

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

Contentful: Headless CMS for structured content delivery.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Contentful entities map to Databricks SQL Warehouse

Contentful entityDatabricks SQL Warehouse objectNotes
entriescontentful_entriesid PK · custom fields → VARIANT or STRUCT columns
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 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 Contentful 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

Early access

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