DatriseAI-first ETL

Google Cloud Storage to Domo Integration

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

How Datrise loads Google Cloud Storage into Domo

Datrise syncs Google Cloud Storage's buckets, objects, prefixes, object metadata, generations, and storage classes into Domo as datasets in Domo's cloud store via connector. Flexible or custom fields land in flattened columns for Magic ETL, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses partitioned dataset updates rather than full replaces, so re-runs update only what changed. Domo dataset partitions keyed on load date. Domo stores its own copy of data, so Datrise sends incremental partitions to avoid re-uploading whole datasets.

Ideal for all-in-one cloud BI with built-in ETL.

Endpoints

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

Domo: Cloud BI platform combining data integration and executive dashboards.

How Google Cloud Storage entities map to Domo

Google Cloud Storage entityDomo objectNotes
bucketsgoogle_cloud_storage_f_bucketsid PK · custom fields → flattened columns for Magic ETL
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 Domo?

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

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

It runs incrementally — Datrise uses partitioned dataset updates rather than full replaces.

Related pipelines

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