DatriseAI-first ETL

Confluence to Databricks SQL Warehouse Integration

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

How Datrise loads Confluence into Databricks SQL Warehouse

Datrise syncs Confluence's spaces, pages, blog posts, comments, attachments, and page versions 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

Confluence: Team wiki and documentation workspace.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Confluence entities map to Databricks SQL Warehouse

Confluence entityDatabricks SQL Warehouse objectNotes
spacesconfluence_spacesid PK · custom fields → VARIANT or STRUCT columns
pagesconfluence_pagesid PK · linked to confluence_spaces
blog postsconfluence_blog_postsid PK · linked to confluence_spaces
commentsconfluence_commentsid PK · linked to confluence_spaces

FAQ

How does Datrise handle Confluence'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 Confluence 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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