DatriseAI-first ETL

Confluence to ClickHouse Integration

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

How Datrise loads Confluence into ClickHouse

Datrise syncs Confluence's spaces, pages, blog posts, comments, attachments, and page versions into ClickHouse as a MergeTree table per source entity. Flexible or custom fields land in JSON or Map columns, and timestamps such as created, updated, and status changes are typed as DateTime64.

Sync is incremental: Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge, so re-runs update only what changed. Partition by month and order by (entity id, updated-at) for fast range scans. ClickHouse deduplicates asynchronously on merge, so Datrise uses ReplacingMergeTree and FINAL-safe queries rather than assuming immediate upserts.

Ideal for high-volume event analytics that need sub-second aggregation.

Endpoints

Confluence: Team wiki and documentation workspace.

ClickHouse: Columnar OLAP engine for fast aggregations.

How Confluence entities map to ClickHouse

Confluence entityClickHouse objectNotes
spacesconfluence_spacesid PK · custom fields → JSON or Map 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 ClickHouse?

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

How does the Confluence to ClickHouse sync stay up to date?

It runs incrementally — Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge.

Related pipelines

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