DatriseAI-first ETL

Confluence to Amazon Redshift Integration

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

How Datrise loads Confluence into Amazon Redshift

Datrise syncs Confluence's spaces, pages, blog posts, comments, attachments, and page versions into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

Confluence: Team wiki and documentation workspace.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Confluence entities map to Amazon Redshift

Confluence entityAmazon Redshift objectNotes
spacesconfluence_spacesid PK · custom fields → SUPER 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 Amazon Redshift?

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

How does the Confluence to Amazon Redshift sync stay up to date?

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

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