DatriseAI-first ETL

Confluence to Snowflake Integration

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

How Datrise loads Confluence into Snowflake

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

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

Confluence: Team wiki and documentation workspace.

Snowflake: Cloud data warehouse with separated compute and storage.

How Confluence entities map to Snowflake

Confluence entitySnowflake objectNotes
spacesconfluence_spacesid PK · custom fields → VARIANT 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 Snowflake?

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

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

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

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