Amazon S3 → Spotfire
AI-first ETL from Amazon S3 into Spotfire. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amazon S3 into Spotfire
Datrise syncs Amazon S3's records, events, and configuration objects into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.
Ideal for interactive analytical visualization and data science.
Endpoints
Amazon S3: SaaS or API data source for analytics and warehouse sync.
Spotfire: Visual analytics platform for interactive dashboards and data science workflows.
How Amazon S3 entities map to Spotfire
| Amazon S3 entity | Spotfire object | Notes |
|---|---|---|
| records | s3_records | id PK · custom fields → flattened columns for visualizations |
| events | s3_events | date/time columns events |
| configuration objects | s3_configuration_objects | id PK · linked to s3_records |
FAQ
How does Datrise handle Amazon S3's custom fields in Spotfire?
Flexible values are stored as flattened columns for visualizations, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Spotfire types.
How does the Amazon S3 to Spotfire sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.
Related pipelines
More destinations for Amazon S3
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