Amazon S3 to Tableau Integration
AI-first ETL from Amazon S3 into Tableau. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amazon S3 into Tableau
Datrise syncs Amazon S3's buckets, objects, prefixes, object metadata, versions, and storage classes into Tableau as warehouse tables or a refreshed .hyper extract. Flexible or custom fields land in flattened columns for Tableau fields, and timestamps such as created, updated, and status changes are typed as date/datetime fields.
Sync is incremental: Datrise uses incremental refresh of the tables behind a live connection or extract, so re-runs update only what changed. Date-partitioned facts to keep extract refresh quick. Tableau .hyper extracts snapshot data, so Datrise keeps the source tables incremental and lets you choose live vs extract.
Ideal for visual analytics and dashboards in Tableau.
Endpoints
Amazon S3: Object storage for files, exports, and data lakes.
Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.
How Amazon S3 entities map to Tableau
| Amazon S3 entity | Tableau object | Notes |
|---|---|---|
| buckets | s3_buckets | id PK · custom fields → flattened columns for Tableau fields |
| objects | s3_objects | id PK · linked to s3_buckets |
| prefixes | s3_prefixes | id PK · linked to s3_buckets |
| object metadata | s3_object_metadata | id PK · linked to s3_buckets |
FAQ
How does Datrise handle Amazon S3's custom fields in Tableau?
Flexible values are stored as flattened columns for Tableau fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Tableau types.
How does the Amazon S3 to Tableau sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the tables behind a live connection or extract.
Related pipelines
More destinations for Amazon S3
Early access
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