Type-Safe Rust Data Pipelines

What problem does it solve?

It prevents runtime data pipeline failures and silent bugs by making data correctness enforceable by Rust’s type system rather than relying on conventions or late validation.

Core Features & Use Cases

• Newtype for domain correctness: Define distinct ID and meaning-carrying types so you cannot accidentally mix values like CustomerId and OrderId.
• Phantom types for stage-checked workflows: Model data lifecycle stages (e.g., Raw → Validated → Ready) so only the right operations are available at the right time.
• Trait bounds for safe operations: Restrict transformations and computations (e.g., numeric ops only on numeric column types) to avoid invalid calls.
• Typestate for ordered builders: Enforce correct construction steps in query builders or multi-stage setup so terminal operations only exist when prerequisites are met.
• Use cases: Building typed analytics pipelines, validating/cleaning data before querying, and implementing safe, composable transformations for production-grade Rust data engineering.

Quick Start

Use the skill to implement your first end-to-end typed pipeline in Rust by converting raw inputs into stage-checked representations, applying type-restricted transforms, and generating only valid queries.