A walkthrough of building a fully self-contained retail data pipeline using only Snowflake-native features: Snowpipe for continuous ingestion, Streams and Task Trees for CDC-driven processing, Dynamic Tables for auto-refreshing analytics, and Time Travel for data recovery. Key design decisions covered include using VARCHAR landing tables, APPEND_ONLY vs standard streams, TRY_ cast functions for validation, MERGE-based incremental aggregation, and three-warehouse RBAC separation. The result is a production-grade pipeline with no Airflow, no Lambda, and no external scheduler.
Table of contents
The Problem This Project SolvesArchitecture — Five Layers, One SystemSnowpipe — Ingestion Without a SchedulerStreams and Task Trees — CDC-Driven AutomationDynamic Tables — The Better Materialized ViewTime Travel — Recovery as a Native FeatureKey Technical LessonsWhy This Architecture MattersSort: