DatriseAI-first ETL

Mssql SQL Server Snowflake

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

How Datrise loads Mssql SQL Server into Snowflake

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

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

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

Snowflake: Cloud data warehouse with separated compute and storage.

How Mssql SQL Server entities map to Snowflake

Mssql SQL Server entitySnowflake objectNotes
recordsmssql_sql_server_recordsid PK · custom fields → VARIANT columns
eventsmssql_sql_server_eventsTIMESTAMP_TZ 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 Snowflake?

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

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

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

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