Dremio to Sisense Integration
AI-first ETL from Dremio into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads Dremio into Sisense
Datrise syncs Dremio's datasets, tables, reflections, query jobs, sources, and table schemas into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.
Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.
Ideal for embedded analytics on an in-memory engine.
Endpoints
Dremio: Lakehouse query engine over open table formats.
Sisense: Analytics platform with elastic data models and embedded analytics.
How Dremio entities map to Sisense
| Dremio entity | Sisense object | Notes |
|---|---|---|
| datasets | dremio_datasets | id PK · custom fields → flattened columns for the cube |
| 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 Sisense?
Flexible values are stored as flattened columns for the cube, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Sisense types.
How does the Dremio to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
Related pipelines
More destinations for Dremio
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