Google Cloud Storage to Yellowfin Integration

AI-first ETL from Google Cloud Storage into Yellowfin. Governed entities, incremental sync, typed landing tables.

How Datrise loads Google Cloud Storage into Yellowfin

Datrise syncs Google Cloud Storage's buckets, objects, prefixes, object metadata, generations, and storage classes into Yellowfin as warehouse tables Yellowfin builds views on. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Yellowfin views reference columns by name, so Datrise lands stable, well-typed columns to keep reports valid.

Ideal for dashboards with automated data storytelling.

Endpoints

Google Cloud Storage: Object storage for files and data lake exports on GCP.

Yellowfin: BI suite with dashboards, automated insights, and data storytelling.

How Google Cloud Storage entities map to Yellowfin

Google Cloud Storage entity	Yellowfin object	Notes
buckets	`google_cloud_storage_f_buckets`	id PK · custom fields → flattened columns
objects	`google_cloud_storage_f_objects`	id PK · linked to google_cloud_storage_f_buckets
prefixes	`google_cloud_storage_f_prefixes`	id PK · linked to google_cloud_storage_f_buckets
object metadata	`google_cloud_storage_f_object_metadata`	id PK · linked to google_cloud_storage_f_buckets

FAQ

How does Datrise handle Google Cloud Storage's custom fields in Yellowfin?

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

How does the Google Cloud Storage to Yellowfin sync stay up to date?

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

Related pipelines

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