DatriseAI-first ETL

Google Cloud Storage F Amazon Redshift

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

How Datrise loads Google Cloud Storage F into Amazon Redshift

Datrise syncs Google Cloud Storage F's records, events, and configuration objects into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

Google Cloud Storage F: SaaS or API data source for analytics and warehouse sync.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Google Cloud Storage F entities map to Amazon Redshift

Google Cloud Storage F entityAmazon Redshift objectNotes
recordsgoogle_cloud_storage_f_recordsid PK · custom fields → SUPER columns
eventsgoogle_cloud_storage_f_eventsTIMESTAMPTZ 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 Amazon Redshift?

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

How does the Google Cloud Storage F to Amazon Redshift sync stay up to date?

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

Early access

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