Snowflake Money Pit

Snowflake isn’t your architecture—it’s just a tool. And treating it as anything more can lead to costly mistakes. In my last post, I explored the shift from traditional medallion architecture to more event-driven, contract-first designs. But there’s a deeper point worth emphasizing: understanding Snowflake’s role as a powerful compute engine, not a catch-all solution, is crucial for modern data engineering.

Too many teams lift-and-shift their on-prem or RDS workflows into Snowflake and expect magic. What they get instead is compute sprawl, unstable pipelines, and spiraling costs. This isn’t modern architecture; it’s legacy logic duct-taped to cloud-native billing.

Common Pitfalls

  • Using Views as a Core Layer: Views can be useful for lightweight logic or prototyping, but relying on them for production-grade layers—especially in high-traffic zones like Silver—pushes compute costs downstream, complicates observability, and limits caching and materialization strategies.
  • Defaulting to Auto-Refresh for Dynamic Tables: Dynamic tables are powerful, but using the AUTO refresh mode in production without strict orchestration or change detection often results in unnecessary compute triggers. Without clear boundaries and intent, what starts as a convenience can become a silent cost driver.

The Solution: Architect With Intention

Snowflake is powerful—but only if you treat it like the compute engine it is, not a catch-all data platform. Here’s how we’re rebuilding with clarity and control:

1. Immutable, Idempotent, Incremental Pipelines

  • Materialized Tables: All data layers—Bronze, Silver, and Gold—are materialized into physical tables, not views. This ensures consistency and avoids the pitfalls of re-computing logic.
  • Idempotent and Safe: Pipelines are fully idempotent and safe to rerun, minimizing compute and ensuring reliability.
  • Incremental Refresh: Data is incrementally refreshed to minimize compute resources and improve efficiency.

2. YAML-Driven Pipelines via Airflow + Astronomer DAG Factory

  • Declarative YAML: Define transformations declaratively with YAML to enforce lineage, schedule, and ownership in one place.
  • Dynamic DAGs: Dynamically generate DAGs for repeatability and scaling, promoting consistency and auditability.

3. Integrated Data Quality + Testing

  • Automated Validation: Plug into tools like Great Expectations or Soda to automate validation checks as part of the DAG, preventing bad data from cascading downstream.
  • Data Reliability: Enforce data quality checks to ensure clean handoffs from ingestion to analytics with trust.

4. Externalized Storage with Iceberg (Future State)

  • System of Record: Treat S3 as the system of record with Iceberg, using Snowflake purely as a compute and governance layer.
  • Full Control: Retain full control over partitioning, schema evolution, and versioning, ensuring portability and flexibility.

Why This Matters

Snowflake, like Presto, excels at distributed query execution. But it’s not magic—and it’s not free. Engineering leadership means asking hard questions:

  • Purpose: Whether the feature solves a real architectural need.
  • Cost: What it costs in credits and long-term team complexity.
  • Complexity: How cleanly it integrates into our ecosystem.
  • Failure Modes: What breaks, and how quickly we know it.

The problem isn’t Snowflake itself—it’s how teams approach it. If you rely on its features without understanding their trade-offs, you’re building on borrowed time and compounding costs. Snowflake isn’t a platform to blindly build on—it’s a tool, meant to be wielded with precision. Engineers need to own the architecture, not let the platform define it.

By treating Snowflake as a tool, you can drive down spend, increase data reliability, and enable real scale. This mindset fosters a culture of ownership and critical thinking, essential for modern data engineering.

Have thoughts or a different approach to using Snowflake intentionally?
I’d love to hear how other teams are evolving their architecture.