DatriseAI-first ETL

Google Directory to Snowflake Integration

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

How Datrise loads Google Directory into Snowflake

Datrise syncs Google Directory's users, groups, group members, organizational units, devices, and roles into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

Google Directory: Workspace user, group, and device directory.

Snowflake: Cloud data warehouse with separated compute and storage.

How Google Directory entities map to Snowflake

Google Directory entitySnowflake objectNotes
usersgoogle_directory_usersid PK · custom fields → VARIANT 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 Snowflake?

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

How does the Google Directory to Snowflake sync stay up to date?

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

Early access

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