Dremio to Chartio Integration
AI-first ETL from Dremio into Chartio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Dremio into Chartio
Datrise syncs Dremio's datasets, tables, reflections, query jobs, sources, and table schemas into Chartio as SQL tables a visual-SQL explorer connects to. Flexible or custom fields land in flattened columns for visual SQL, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Visual-SQL tools build joins from your schema, so Datrise lands clearly related tables with stable id columns.
Ideal for drag-and-drop charting over a database.
Endpoints
Dremio: Lakehouse query engine over open table formats.
Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.
How Dremio entities map to Chartio
| Dremio entity | Chartio object | Notes |
|---|---|---|
| datasets | dremio_datasets | id PK · custom fields → flattened columns for visual SQL |
| 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 Chartio?
Flexible values are stored as flattened columns for visual SQL, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Chartio types.
How does the Dremio to Chartio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for Dremio
Early access
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