Snowplow to Amazon QuickSight Integration
AI-first ETL from Snowplow into Amazon QuickSight. Governed entities, incremental sync, typed landing tables.
How Datrise loads Snowplow into Amazon QuickSight
Datrise syncs Snowplow's events, users, sessions, contexts, page views, and event schemas 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
Snowplow: Behavioral event pipeline with a strict schema.
Amazon QuickSight: AWS serverless BI with SPICE and embedded analytics.
How Snowplow entities map to Amazon QuickSight
| Snowplow entity | Amazon QuickSight object | Notes |
|---|---|---|
| events | snowplow_events | date/time fields events |
| users | snowplow_users | id PK · linked to snowplow_events |
| sessions | snowplow_sessions | id PK · linked to snowplow_events |
| contexts | snowplow_contexts | id PK · linked to snowplow_events |
FAQ
How does Datrise handle Snowplow'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 Snowplow to Amazon QuickSight sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the tables behind SPICE or direct query.
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Early access
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