DatriseAI-first ETL

Microsoft Teams Google BigQuery

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

How Datrise loads Microsoft Teams into Google BigQuery

Datrise syncs Microsoft Teams's records, events, and configuration objects 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

Microsoft Teams: SaaS or API data source for analytics and warehouse sync.

Google BigQuery: Serverless analytics warehouse on GCP.

How Microsoft Teams entities map to Google BigQuery

Microsoft Teams entityGoogle BigQuery objectNotes
recordsmicrosoft_teams_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventsmicrosoft_teams_eventsTIMESTAMP events
configuration objectsmicrosoft_teams_configuration_objectsid PK · linked to microsoft_teams_records

FAQ

How does Datrise handle Microsoft Teams'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 Microsoft Teams 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.

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Early access

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