Snowplow → Azure Synapse
AI-first ETL from Snowplow into Azure Synapse. Governed entities, incremental sync, typed landing tables.
How Datrise loads Snowplow into Azure Synapse
Datrise syncs Snowplow's records, events, and configuration objects into Azure Synapse 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 COPY into staging, then a MERGE on stable id, so re-runs update only what changed. Hash distribution on the join id with date partitioning on facts. Synapse dedicated pools reward good hash-distribution choices, so Datrise distributes on entity ids to avoid data-movement-heavy joins.
Ideal for Azure analytics estates feeding Power BI.
Endpoints
Snowplow: SaaS or API data source for analytics and warehouse sync.
Azure Synapse: Microsoft analytics workspace with SQL pools.
How Snowplow entities map to Azure Synapse
| Snowplow entity | Azure Synapse 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 Azure Synapse?
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 Azure Synapse types.
How does the Snowplow to Azure Synapse sync stay up to date?
It runs incrementally — Datrise uses COPY into staging, then a MERGE on stable id.
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Early access
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