Amazon S3 → Azure Synapse
AI-first ETL from Amazon S3 into Azure Synapse. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amazon S3 into Azure Synapse
Datrise syncs Amazon S3's records, events, and configuration objects into Azure Synapse as a typed table per source entity. Flexible or custom fields land in NVARCHAR(MAX) JSON columns, and timestamps such as created, updated, and status changes are typed as datetime2.
Sync is incremental: Datrise uses COPY into staging, then a MERGE on stable id, so re-runs update only what changed. Hash distribution on the join id with date partitioning on facts. Synapse dedicated pools reward good hash-distribution choices, so Datrise distributes on entity ids to avoid data-movement-heavy joins.
Ideal for Azure analytics estates feeding Power BI.
Endpoints
Amazon S3: SaaS or API data source for analytics and warehouse sync.
Azure Synapse: Microsoft analytics workspace with SQL pools.
How Amazon S3 entities map to Azure Synapse
| Amazon S3 entity | Azure Synapse object | Notes |
|---|---|---|
| records | s3_records | id PK · custom fields → NVARCHAR(MAX) JSON columns |
| events | s3_events | datetime2 events |
| configuration objects | s3_configuration_objects | id PK · linked to s3_records |
FAQ
How does Datrise handle Amazon S3's custom fields in Azure Synapse?
Flexible values are stored as NVARCHAR(MAX) JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Azure Synapse types.
How does the Amazon S3 to Azure Synapse sync stay up to date?
It runs incrementally — Datrise uses COPY into staging, then a MERGE on stable id.
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Early access
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