Microsoft SharePoint to Google BigQuery Integration
AI-first ETL from Microsoft SharePoint into Google BigQuery. Governed entities, incremental sync, typed landing tables.
How Datrise loads Microsoft SharePoint into Google BigQuery
Datrise syncs Microsoft SharePoint's sites, lists, list items, documents, libraries, and permissions 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 SharePoint: Document libraries and team site content.
Google BigQuery: Serverless analytics warehouse on GCP.
How Microsoft SharePoint entities map to Google BigQuery
| Microsoft SharePoint entity | Google BigQuery object | Notes |
|---|---|---|
| sites | microsoft_sharepoint_sites | id PK · custom fields → JSON or nested/repeated (STRUCT) columns |
| lists | microsoft_sharepoint_lists | id PK · linked to microsoft_sharepoint_sites |
| list items | microsoft_sharepoint_list_items | id PK · linked to microsoft_sharepoint_sites |
| documents | microsoft_sharepoint_documents | id PK · linked to microsoft_sharepoint_sites |
FAQ
How does Datrise handle Microsoft SharePoint'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 SharePoint 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
More destinations for Microsoft SharePoint
- Microsoft SharePoint → Amazon Redshift
- Microsoft SharePoint → Databricks SQL Warehouse
- Microsoft SharePoint → ClickHouse
- Microsoft SharePoint → DuckDB
- Microsoft SharePoint → Amazon Athena
- Microsoft SharePoint → Amazon S3 Data Lake
- Microsoft SharePoint → Azure Data Lake Storage
- Microsoft SharePoint → Azure Synapse
- Microsoft SharePoint → Spreadsheets
- Microsoft SharePoint → Airtable
- Microsoft SharePoint → CSV Files
- Microsoft SharePoint → MongoDB
More sources for Google BigQuery
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- Microsoft SQL Server → Google BigQuery
- My Hours → Google BigQuery
- n8n → Google BigQuery
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- New York Times → Google BigQuery
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- Onesignal → Google BigQuery
Early access
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