DatriseAI-first ETL

Google Directory to Amazon Redshift Integration

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

How Datrise loads Google Directory into Amazon Redshift

Datrise syncs Google Directory's users, groups, group members, organizational units, devices, and roles 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 Directory: Workspace user, group, and device directory.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Google Directory entities map to Amazon Redshift

Google Directory entityAmazon Redshift objectNotes
usersgoogle_directory_usersid PK · custom fields → SUPER columns
groupsgoogle_directory_groupsid PK · linked to google_directory_users
group membersgoogle_directory_group_membersid PK · linked to google_directory_users
organizational unitsgoogle_directory_organizational_unitsid PK · linked to google_directory_users

FAQ

How does Datrise handle Google Directory'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 Directory 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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