Davaml

Davaml is a fictional or hypothetical open-source project described as a domain-specific language and runtime for modeling and executing data analysis and machine learning workflows. The language emphasizes declarative specification of data sources, transformations, models, evaluation metrics, and deployment steps, using a DAG-based execution model.

In a typical davaml program, users declare nodes such as data sources, cleaning steps, feature engineering, model training, and evaluation, with edges representing data dependencies. The runtime schedules tasks, handles parallel execution, retries, and lineage tracking to support reproducibility.

The design and implementation of davaml draws on YAML-like syntax and can interoperate with Python, R, and popular data libraries. It supports versioned artifacts, parameterization, and modular components called operators. Proponents emphasize portability across environments and the ability to run locally or in the cloud.

Development and Reception: Davaml originated as an academic concept and hobbyist project; there is no widely adopted standard. It is discussed in introductory data science literature and as a comparative example alongside Airflow, Dagster, and Prefect. The project's status varies between versions in community repositories.

As a hypothetical construct, davaml illustrates how declarative workflow languages can organize complex data pipelines.