To enrich and model Snowplow event data using Azure Data Factory:

Data ingestion: Start by streaming your Snowplow events into Azure Data Lake Storage or Blob Storage as your foundation.

Pipeline creation: Create Data Factory pipelines to orchestrate comprehensive ETL processes that clean, validate, and enrich the raw Snowplow data with additional context such as customer demographics, product catalogs, or external data sources.

Transformation: Use Data Factory's mapping data flows to apply business rules, perform complex transformations, and create enriched datasets ready for analytics.

The enriched data can feed both your data warehouse for historical analysis and Snowplow Signals for real-time operational use cases, ensuring consistent data quality across your entire customer data infrastructure.