DatriseAI-first ETL

Google Cloud Storage F Mode

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

How Datrise loads Google Cloud Storage F into Mode

Datrise syncs Google Cloud Storage F's records, events, and configuration objects 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 F: SaaS or API data source for analytics and warehouse sync.

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

How Google Cloud Storage F entities map to Mode

Google Cloud Storage F entityMode objectNotes
recordsgoogle_cloud_storage_f_recordsid PK · custom fields → flattened columns for SQL and notebooks
eventsgoogle_cloud_storage_f_eventstemporal columns events
configuration objectsgoogle_cloud_storage_f_configuration_objectsid PK · linked to google_cloud_storage_f_records

FAQ

How does Datrise handle Google Cloud Storage F'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 F 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 F to Mode the easy way

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