DatriseAI-first ETL

Okta to Databricks SQL Warehouse Integration

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

How Datrise loads Okta into Databricks SQL Warehouse

Datrise syncs Okta's users, groups, applications, system log events, factors, and group memberships 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

Okta: Identity provider for SSO and user lifecycle.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Okta entities map to Databricks SQL Warehouse

Okta entityDatabricks SQL Warehouse objectNotes
usersokta_usersid PK · custom fields → VARIANT or STRUCT columns
groupsokta_groupsid PK · linked to okta_users
applicationsokta_applicationsid PK · linked to okta_users
system log eventsokta_system_log_eventsTIMESTAMP events

FAQ

How does Datrise handle Okta'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 Okta 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.

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Early access

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