DatriseAI-first ETL

Retently Amazon QuickSight

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

How Datrise loads Retently into Amazon QuickSight

Datrise syncs Retently's records, events, and configuration objects into Amazon QuickSight as warehouse tables or a SPICE-loaded dataset. Flexible or custom fields land in flattened columns for analyses, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental refresh of the tables behind SPICE or direct query, so re-runs update only what changed. Date-partitioned facts to bound SPICE refresh. QuickSight SPICE is an in-memory copy, so Datrise keeps the backing tables incremental so refreshes stay cheap.

Ideal for AWS-native dashboards with pay-per-session pricing.

Endpoints

Retently: SaaS or API data source for analytics and warehouse sync.

Amazon QuickSight: AWS serverless BI with SPICE and embedded analytics.

How Retently entities map to Amazon QuickSight

Retently entityAmazon QuickSight objectNotes
recordsretently_recordsid PK · custom fields → flattened columns for analyses
eventsretently_eventsdate/time fields events
configuration objectsretently_configuration_objectsid PK · linked to retently_records

FAQ

How does Datrise handle Retently's custom fields in Amazon QuickSight?

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

How does the Retently to Amazon QuickSight sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the tables behind SPICE or direct query.

Related pipelines

Early access

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