DatriseAI-first ETL

Amazon Amazon S3 Microsoft SQL Server

AI-first ETL from Amazon Amazon S3 into Microsoft SQL Server. Governed entities, incremental sync, typed landing tables.

How Datrise loads Amazon Amazon S3 into Microsoft SQL Server

Datrise syncs Amazon Amazon S3's records, events, and configuration objects into Microsoft SQL Server 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 a watermark on updated-at, applied with a MERGE statement, so re-runs update only what changed. Optional partitioned tables on a date partition function. SQL Server defaults to a case-insensitive collation, so Datrise preserves original casing in a metadata column to avoid silent key collisions.

Ideal for Microsoft-stack analytics and Power BI Import models.

Endpoints

Amazon Amazon S3: SaaS or API data source for analytics and warehouse sync.

Microsoft SQL Server: Microsoft relational DB with enterprise features.

How Amazon Amazon S3 entities map to Microsoft SQL Server

Amazon Amazon S3 entityMicrosoft SQL Server objectNotes
recordsamazon_s3_recordsid PK · custom fields → NVARCHAR(MAX) JSON columns
eventsamazon_s3_eventsdatetime2 events
configuration objectsamazon_s3_configuration_objectsid PK · linked to amazon_s3_records

FAQ

How does Datrise handle Amazon Amazon S3's custom fields in Microsoft SQL Server?

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 Microsoft SQL Server types.

How does the Amazon Amazon S3 to Microsoft SQL Server sync stay up to date?

It runs incrementally — Datrise uses a watermark on updated-at, applied with a MERGE statement.

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