DatriseAI-first ETL

Public API to Amazon QuickSight Integration

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

How Datrise loads Public API into Amazon QuickSight

Datrise syncs Public API's records, response payloads, pagination cursors, request parameters, schemas, and fetch timestamps 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

Public API: Generic REST endpoint pulled on a schedule.

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

How Public API entities map to Amazon QuickSight

Public API entityAmazon QuickSight objectNotes
recordspublic_api_recordsid PK · custom fields → flattened columns for analyses
response payloadspublic_api_response_payloadsid PK · linked to public_api_records
pagination cursorspublic_api_pagination_cursorsid PK · linked to public_api_records
request parameterspublic_api_request_parametersid PK · linked to public_api_records

FAQ

How does Datrise handle Public API'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 Public API 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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