Snowflake
Cloud-native data warehouse with separation of storage and compute.
Best T-Factor
Technology
T6
Weakest T-Factor
Traceability
T4
Architectural Position
Storage and query layer.
Objective Description
Snowflake is a cloud-based analytical data warehouse that separates storage from compute, enabling independent scaling. It supports structured and semi-structured data (JSON, Parquet, Avro) and provides multi-cluster shared data architecture. It operates on major cloud providers and exposes SQL as the primary interface.
Architectural Position
Storage and query layer. Typically positioned after ingestion pipelines (Fivetran, Airbyte, Kafka) and before BI tools (Tableau, Looker). Serves as the central analytical store in a modern data stack.
Use Case Fit
When to Use
- Centralized analytical workloads requiring concurrent access by many users
- Organizations needing to query semi-structured data alongside relational data
- Teams requiring data sharing across organizational boundaries without data movement
- Workloads with variable compute demand benefiting from auto-scaling
When NOT to Use
- Operational transactional workloads requiring row-level updates at high frequency
- Real-time streaming ingestion — Snowflake is not a streaming platform
- Organizations with strict data residency requirements incompatible with cloud deployment
- Small-scale workloads where cost-per-query economics are unfavorable
Anti-Patterns
Common misuse scenarios and overengineering risks.
Using Snowflake as a source-of-truth operational database — it is an analytical store
Storing raw, unmodeled data without a transformation layer, creating a data swamp
Ignoring clustering keys and result caching, leading to unnecessary compute costs
Treating Time Travel as a backup strategy rather than a short-term recovery mechanism