DatriseAI-first ETL

Snowflake to Microsoft SQL Server Integration

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

How Datrise loads Snowflake into Microsoft SQL Server

Datrise syncs Snowflake's databases, schemas, tables, views, query history, and warehouses 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

Snowflake: Cloud data platform with separated storage and compute.

Microsoft SQL Server: Microsoft relational DB with enterprise features.

How Snowflake entities map to Microsoft SQL Server

Snowflake entityMicrosoft SQL Server objectNotes
databasessnowflake_databasesid PK · custom fields → NVARCHAR(MAX) JSON columns
schemassnowflake_schemasid PK · linked to snowflake_databases
tablessnowflake_tablesid PK · linked to snowflake_databases
viewssnowflake_viewsid PK · linked to snowflake_databases

FAQ

How does Datrise handle Snowflake'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 Snowflake to Microsoft SQL Server sync stay up to date?

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

Related pipelines

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