Microsoft Azure to Google BigQuery Integration
AI-first ETL from Microsoft Azure into Google BigQuery. Governed entities, incremental sync, typed landing tables.
How Datrise loads Microsoft Azure into Google BigQuery
Datrise syncs Microsoft Azure's subscriptions, resource groups, resources, usage records, costs, and activity logs 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 Azure: Azure subscription resources, usage, and billing.
Google BigQuery: Serverless analytics warehouse on GCP.
How Microsoft Azure entities map to Google BigQuery
| Microsoft Azure entity | Google BigQuery object | Notes |
|---|---|---|
| subscriptions | microsoft_azure_subscriptions | id PK · custom fields → JSON or nested/repeated (STRUCT) columns |
| resource groups | microsoft_azure_resource_groups | id PK · linked to microsoft_azure_subscriptions |
| resources | microsoft_azure_resources | id PK · linked to microsoft_azure_subscriptions |
| usage records | microsoft_azure_usage_records | id PK · linked to microsoft_azure_subscriptions |
FAQ
How does Datrise handle Microsoft Azure'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 Azure 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 Azure
- Microsoft Azure → Amazon Redshift
- Microsoft Azure → Databricks SQL Warehouse
- Microsoft Azure → ClickHouse
- Microsoft Azure → DuckDB
- Microsoft Azure → Amazon Athena
- Microsoft Azure → Amazon S3 Data Lake
- Microsoft Azure → Azure Data Lake Storage
- Microsoft Azure → Azure Synapse
- Microsoft Azure → Spreadsheets
- Microsoft Azure → Airtable
- Microsoft Azure → CSV Files
- Microsoft Azure → MongoDB
More sources for Google BigQuery
- Microsoft Dataverse → Google BigQuery
- Microsoft SharePoint → Google BigQuery
- Microsoft Teams → Google BigQuery
- Microsoft SQL Server → Google BigQuery
- My Hours → Google BigQuery
- n8n → Google BigQuery
- NetSuite SuiteAnalytics → Google BigQuery
- New York Times → Google BigQuery
- News API → Google BigQuery
- Newsdata → Google BigQuery
- Notion → Google BigQuery
- Okta → Google BigQuery
Early access
Connect Microsoft Azure to Google BigQuery the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.