Google Directory to Amazon S3 Data Lake Integration
AI-first ETL from Google Directory into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google Directory into Amazon S3 Data Lake
Datrise syncs Google Directory's users, groups, group members, organizational units, devices, and roles into Amazon S3 Data Lake as columnar Parquet objects partitioned per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.
Sync is incremental: Datrise uses writes new date partitions and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.
Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.
Endpoints
Google Directory: Workspace user, group, and device directory.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Google Directory entities map to Amazon S3 Data Lake
| Google Directory entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| users | google_directory_users | id PK · custom fields → nested struct/map fields in Parquet |
| groups | google_directory_groups | id PK · linked to google_directory_users |
| group members | google_directory_group_members | id PK · linked to google_directory_users |
| organizational units | google_directory_organizational_units | id PK · linked to google_directory_users |
FAQ
How does Datrise handle Google Directory's custom fields in Amazon S3 Data Lake?
Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon S3 Data Lake types.
How does the Google Directory to Amazon S3 Data Lake sync stay up to date?
It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.
Related pipelines
More destinations for Google Directory
- Google Directory → Azure Data Lake Storage
- Google Directory → Azure Synapse
- Google Directory → Spreadsheets
- Google Directory → Airtable
- Google Directory → CSV Files
- Google Directory → MongoDB
- Google Directory → Supabase
- Google Directory → Neon
- Google Directory → PlanetScale
- Google Directory → Amazon DynamoDB
- Google Directory → Looker
- Google Directory → Looker Studio
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- Impact → Amazon S3 Data Lake
Early access
Connect Google Directory to Amazon S3 Data Lake the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.