DatriseAI-first ETL

Zoom Amazon Redshift

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

How Datrise loads Zoom into Amazon Redshift

Datrise syncs Zoom's meetings, participants, webinars, recordings, and usage reports into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

Zoom: Video meetings, webinars, and workplace collaboration.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Zoom entities map to Amazon Redshift

Zoom entityAmazon Redshift objectNotes
meetingszoom_meetingsid PK · custom fields → SUPER 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 Amazon Redshift?

Flexible values are stored as SUPER columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Redshift types.

How does the Zoom to Amazon Redshift sync stay up to date?

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

Early access

Connect Zoom to Amazon Redshift the easy way

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