Snowplow to Tableau Integration

How Datrise loads Snowplow into Tableau

Datrise syncs Snowplow's events, users, sessions, contexts, page views, and event schemas into Tableau as warehouse tables or a refreshed .hyper extract. Flexible or custom fields land in flattened columns for Tableau fields, and timestamps such as created, updated, and status changes are typed as date/datetime fields.

Sync is incremental: Datrise uses incremental refresh of the tables behind a live connection or extract, so re-runs update only what changed. Date-partitioned facts to keep extract refresh quick. Tableau .hyper extracts snapshot data, so Datrise keeps the source tables incremental and lets you choose live vs extract.

Ideal for visual analytics and dashboards in Tableau.

Endpoints

Snowplow: Behavioral event pipeline with a strict schema.

Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.

How Snowplow entities map to Tableau

Snowplow entity	Tableau object	Notes
events	`snowplow_events`	date/datetime 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 Tableau?

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

How does the Snowplow to Tableau sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the tables behind a live connection or extract.

Related pipelines

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