Snowplow → Microsoft SQL Server
AI-first ETL from Snowplow into Microsoft SQL Server. Governed entities, incremental sync, typed landing tables.
How Datrise loads Snowplow into Microsoft SQL Server
Datrise syncs Snowplow'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
Snowplow: SaaS or API data source for analytics and warehouse sync.
Microsoft SQL Server: Microsoft relational DB with enterprise features.
How Snowplow entities map to Microsoft SQL Server
| Snowplow entity | Microsoft SQL Server object | Notes |
|---|---|---|
| records | snowplow_records | id PK · custom fields → NVARCHAR(MAX) JSON columns |
| events | snowplow_events | datetime2 events |
| configuration objects | snowplow_configuration_objects | id PK · linked to snowplow_records |
FAQ
How does Datrise handle Snowplow'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 Snowplow 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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Early access
Connect Snowplow to Microsoft SQL Server the easy way
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