Dremio to Looker Studio Integration

How Datrise loads Dremio into Looker Studio

Datrise syncs Dremio's datasets, tables, reflections, query jobs, sources, and table schemas into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

Dremio: Lakehouse query engine over open table formats.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How Dremio entities map to Looker Studio

Dremio entity	Looker Studio object	Notes
datasets	`dremio_datasets`	id PK · custom fields → flattened columns for chart 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 Looker Studio?

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

How does the Dremio to Looker Studio sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

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