DatriseAI-first ETL

Google Cloud Storage to GoodData Integration

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

How Datrise loads Google Cloud Storage into GoodData

Datrise syncs Google Cloud Storage's buckets, objects, prefixes, object metadata, generations, and storage classes into GoodData as warehouse tables GoodData maps into its logical data model. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date dimensions.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. GoodData's LDM maps datasets by keys, so Datrise lands stable primary and foreign id columns to keep the model valid.

Ideal for embedded, multi-tenant analytics.

Endpoints

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

GoodData: Composable analytics platform with headless BI and embedded dashboards.

How Google Cloud Storage entities map to GoodData

Google Cloud Storage entityGoodData objectNotes
bucketsgoogle_cloud_storage_f_bucketsid PK · custom fields → flattened columns
objectsgoogle_cloud_storage_f_objectsid PK · linked to google_cloud_storage_f_buckets
prefixesgoogle_cloud_storage_f_prefixesid PK · linked to google_cloud_storage_f_buckets
object metadatagoogle_cloud_storage_f_object_metadataid PK · linked to google_cloud_storage_f_buckets

FAQ

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

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 GoodData types.

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

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

Related pipelines

Early access

Connect Google Cloud Storage to GoodData the easy way

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