Dremio to Tableau Integration
AI-first ETL from Dremio into Tableau. Governed entities, incremental sync, typed landing tables.
How Datrise loads Dremio into Tableau
Datrise syncs Dremio's datasets, tables, reflections, query jobs, sources, and table 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
Dremio: Lakehouse query engine over open table formats.
Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.
How Dremio entities map to Tableau
| Dremio entity | Tableau object | Notes |
|---|---|---|
| datasets | dremio_datasets | id PK · custom fields → flattened columns for Tableau fields |
| tables | dremio_tables | id PK · linked to dremio_datasets |
| reflections | dremio_reflections | id PK · linked to dremio_datasets |
| query jobs | dremio_query_jobs | id PK · linked to dremio_datasets |
FAQ
How does Datrise handle Dremio'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 Dremio 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
More destinations for Dremio
Early access
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