DatriseAI-first ETL

Google Drive Databricks SQL Warehouse

AI-first ETL from Google Drive into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Google Drive into Databricks SQL Warehouse

Datrise syncs Google Drive's files, folders, permissions, and revision metadata into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Google Drive: Cloud file storage often used for operational exports.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Google Drive entities map to Databricks SQL Warehouse

Google Drive entityDatabricks SQL Warehouse objectNotes
filesgoogle_drive_filesid PK · custom fields → VARIANT or STRUCT columns
foldersgoogle_drive_foldersid PK · linked to google_drive_files
permissionsgoogle_drive_permissionsid PK · linked to google_drive_files
revision metadatagoogle_drive_revision_metadataid PK · linked to google_drive_files

FAQ

How does Datrise handle Google Drive's custom fields in Databricks SQL Warehouse?

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

How does the Google Drive to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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