Google Cloud SQL → Microsoft Power BI
AI-first ETL from Google Cloud SQL into Microsoft Power BI. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google Cloud SQL into Microsoft Power BI
Datrise syncs Google Cloud SQL's records, events, and configuration objects into Microsoft Power BI as star-schema-friendly tables for an Import or DirectQuery dataset. Flexible or custom fields land in flattened columns (nested fields expanded), and timestamps such as created, updated, and status changes are typed as date/time columns with a date table.
Sync is incremental: Datrise uses incremental-refresh windows aligned to Power BI's RangeStart/RangeEnd, so re-runs update only what changed. Date-partitioned fact tables matching incremental-refresh policies. Power BI measures and relationships live in the .pbix model, so Datrise keeps the underlying tables stable and star-schema-shaped.
Ideal for Microsoft-stack self-serve reporting.
Endpoints
Google Cloud SQL: SaaS or API data source for analytics and warehouse sync.
Microsoft Power BI: Microsoft business intelligence with datasets, reports, and semantic models.
How Google Cloud SQL entities map to Microsoft Power BI
| Google Cloud SQL entity | Microsoft Power BI object | Notes |
|---|---|---|
| records | google_cloud_sql_records | id PK · custom fields → flattened columns (nested fields expanded) |
| events | google_cloud_sql_events | date/time columns with a date table events |
| configuration objects | google_cloud_sql_configuration_objects | id PK · linked to google_cloud_sql_records |
FAQ
How does Datrise handle Google Cloud SQL's custom fields in Microsoft Power BI?
Flexible values are stored as flattened columns (nested fields expanded), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Microsoft Power BI types.
How does the Google Cloud SQL to Microsoft Power BI sync stay up to date?
It runs incrementally — Datrise uses incremental-refresh windows aligned to Power BI's RangeStart/RangeEnd.
Related pipelines
More destinations for Google Cloud SQL
- Google Cloud SQL → Tableau
- Google Cloud SQL → Apache Superset
- Google Cloud SQL → Metabase
- Google Cloud SQL → Amazon QuickSight
- Google Cloud SQL → Domo
- Google Cloud SQL → Sisense
- Google Cloud SQL → ThoughtSpot
- Google Cloud SQL → Qlik
- Google Cloud SQL → Mode
- Google Cloud SQL → Redash
- Google Cloud SQL → Chartio
- Google Cloud SQL → Holistics
More sources for Microsoft Power BI
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- Harvest Forecast → Microsoft Power BI
- Heap → Microsoft Power BI
Early access
Connect Google Cloud SQL to Microsoft Power BI 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.