DatriseAI-first ETL

Zoom Google BigQuery

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

How Datrise loads Zoom into Google BigQuery

Datrise syncs Zoom's meetings, participants, webinars, recordings, and usage reports into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Zoom: Video meetings, webinars, and workplace collaboration.

Google BigQuery: Serverless analytics warehouse on GCP.

How Zoom entities map to Google BigQuery

Zoom entityGoogle BigQuery objectNotes
meetingszoom_meetingsid PK · custom fields → JSON or nested/repeated (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 Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Zoom to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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