Luminal is a framework designed to accelerate and simplify the development of systems-level data applications by using a typed, functional, and streaming-first approach. Instead of treating data processing as a series of ad-hoc scripts, Luminal models transformations as strongly typed building blocks that can be composed into reliable, scalable pipelines. The project emphasizes correctness and performance by requiring explicit types for the data flowing through transformations, reducing runtime surprises and allowing for highly optimized execution. It is particularly well-suited for data engineering workflows where large datasets must be processed incrementally, efficiently, and deterministically. The framework also includes a runtime capable of executing pipelines across multiple backends, making it flexible in cloud and local environments.
Features
- Strongly typed transformation model to ensure correctness at every stage
- Streaming-first processing engine optimized for large datasets
- Composable building blocks that form reliable and reusable pipelines
- Backend-flexible runtime capable of running locally or in cloud environments
- Functional programming style that simplifies reasoning and reduces side effects
- Modular project structure enabling scalable, maintainable data engineering workflows