DatriseAI-first ETL

Zoom Databricks SQL Warehouse

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

How Datrise loads Zoom into Databricks SQL Warehouse

Datrise syncs Zoom's meetings, participants, webinars, recordings, and usage reports 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

Zoom: Video meetings, webinars, and workplace collaboration.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Zoom entities map to Databricks SQL Warehouse

Zoom entityDatabricks SQL Warehouse objectNotes
meetingszoom_meetingsid PK · custom fields → VARIANT or STRUCT columns
participantszoom_participantsid PK · linked to zoom_meetings
webinarszoom_webinarsid PK · linked to zoom_meetings
recordingszoom_recordingsid PK · linked to zoom_meetings

FAQ

How does Datrise handle Zoom'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 Zoom 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

Early access

Connect Zoom to Databricks SQL Warehouse the easy way

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