CDij

CDij is a domain‑specific programming language designed for concise representation of computational workflows. It was introduced by the Cognitive Data Integration Group at the University of Eindhoven in 2015 as a way to streamline the translation of declarative data‑processing logic into executable code. CDij’s syntax is intentionally minimalist, borrowing elements from both JSON and YAML to favor readability while enabling machine‑readable configuration. The language centers on a set of core primitives—data sources, transformations, aggregations, and sinks—each expressed as a simple key/value mapping. Users declare the data flow in a top‑level “pipeline” block, and CDij automatically infers dependencies, resolves execution order, and guarantees data integrity.

The language is compiled to an intermediate representation that can be targeted by several execution engines, including the open‑source CDij Runtime and the commercial CDij Cloud Service. Built‑in operators cover common tasks such as filtering, mapping, pivoting, and statistical summarization, while the extension system allows developers to register custom functions in languages such as Python, Java and Rust. CDij’s design emphasizes reproducibility; its pipeline definitions are fully versioned and can be stored in git repositories. The language is distributed under the Apache 2.0 license, encouraging both academic and industrial usage.

Key applications of CDij include scientific data processing, ETL for business intelligence, and automated machine‑learning pipelines. Its growing ecosystem features libraries for bioinformatics, finance, and real‑time analytics, and there are community‑contributed connectors for a wide range of databases and cloud storage services. Documentation and tutorials are hosted on the project’s GitHub repository, and an active discussion forum supports troubleshooting and feature requests. Overall, CDij offers a lightweight, expressive alternative to traditional workflow description languages, particularly suited for users who value compactness, clarity, and cross‑platform portability.