DatriseAI-first ETL

Amazon Rds Spotfire

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

How Datrise loads Amazon Rds into Spotfire

Datrise syncs Amazon Rds'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 Rds: SaaS or API data source for analytics and warehouse sync.

Spotfire: Visual analytics platform for interactive dashboards and data science workflows.

How Amazon Rds entities map to Spotfire

Amazon Rds entitySpotfire objectNotes
recordsamazon_rds_recordsid PK · custom fields → flattened columns for visualizations
eventsamazon_rds_eventsdate/time columns events
configuration objectsamazon_rds_configuration_objectsid PK · linked to amazon_rds_records

FAQ

How does Datrise handle Amazon Rds'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 Rds to Spotfire sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.

Related pipelines

Early access

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