Amazon S3 to Microsoft Power BI Integration
AI-first ETL from Amazon S3 into Microsoft Power BI. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amazon S3 into Microsoft Power BI
Datrise syncs Amazon S3's buckets, objects, prefixes, object metadata, versions, and storage classes 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
Amazon S3: Object storage for files, exports, and data lakes.
Microsoft Power BI: Microsoft business intelligence with datasets, reports, and semantic models.
How Amazon S3 entities map to Microsoft Power BI
| Amazon S3 entity | Microsoft Power BI object | Notes |
|---|---|---|
| buckets | s3_buckets | id PK · custom fields → flattened columns (nested fields expanded) |
| objects | s3_objects | id PK · linked to s3_buckets |
| prefixes | s3_prefixes | id PK · linked to s3_buckets |
| object metadata | s3_object_metadata | id PK · linked to s3_buckets |
FAQ
How does Datrise handle Amazon S3'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 Amazon S3 to Microsoft Power BI sync stay up to date?
It runs incrementally — Datrise uses incremental-refresh windows aligned to Power BI's RangeStart/RangeEnd.
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Early access
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