DatriseAI-first ETL

Mssql SQL Server Amazon Redshift

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

How Datrise loads Mssql SQL Server into Amazon Redshift

Datrise syncs Mssql SQL Server's records, events, and configuration objects into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

Mssql SQL Server: SaaS or API data source for analytics and warehouse sync.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Mssql SQL Server entities map to Amazon Redshift

Mssql SQL Server entityAmazon Redshift objectNotes
recordsmssql_sql_server_recordsid PK · custom fields → SUPER columns
eventsmssql_sql_server_eventsTIMESTAMPTZ events
configuration objectsmssql_sql_server_configuration_objectsid PK · linked to mssql_sql_server_records

FAQ

How does Datrise handle Mssql SQL Server's custom fields in Amazon Redshift?

Flexible values are stored as SUPER columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Redshift types.

How does the Mssql SQL Server to Amazon Redshift sync stay up to date?

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

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