DatriseAI-first ETL

Confluence to Amazon Athena Integration

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

How Datrise loads Confluence into Amazon Athena

Datrise syncs Confluence's spaces, pages, blog posts, comments, attachments, and page versions into Amazon Athena as partitioned Parquet in S3 exposed as an Athena table. Flexible or custom fields land in struct/map columns in Parquet, and timestamps such as created, updated, and status changes are typed as timestamp.

Sync is incremental: Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog, so re-runs update only what changed. Hive-style partitioning by load date so Athena scans only new data. Athena bills per byte scanned and small files hurt, so Datrise compacts to right-sized Parquet rather than many tiny objects.

Ideal for serverless SQL over an S3 lake without a running warehouse.

Endpoints

Confluence: Team wiki and documentation workspace.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Confluence entities map to Amazon Athena

Confluence entityAmazon Athena objectNotes
spacesconfluence_spacesid PK · custom fields → struct/map columns in Parquet
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 Athena?

Flexible values are stored as struct/map columns in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Athena types.

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

It runs incrementally — Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog.

Related pipelines

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