DatriseAI-first ETL

Google Cloud Storage to Mode Integration

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

How Datrise loads Google Cloud Storage into Mode

Datrise syncs Google Cloud Storage's buckets, objects, prefixes, object metadata, generations, and storage classes into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.

Ideal for SQL-first analysis with Python and R notebooks.

Endpoints

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

Mode: Collaborative analytics workspace for SQL, Python, and shared reports.

How Google Cloud Storage entities map to Mode

Google Cloud Storage entityMode objectNotes
bucketsgoogle_cloud_storage_f_bucketsid PK · custom fields → flattened columns for SQL and notebooks
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 Mode?

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

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

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

Related pipelines

Early access

Connect Google Cloud Storage to Mode the easy way

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