Domain-specific languages (DSLs) are programming languages tailored for specific tasks, offering a more focused approach to solving particular challenges. They facilitate better configuration, integration, and management of data workflows by providing a human-readable format that can be understood by non-engineers, such as business analysts or project managers. DSLs enable faster iteration and deployment, speeding up the process of testing and rolling out new features or making incremental adjustments in the data pipelines. Additionally, they support proper DevOps practices, including automation, continuous integration, and efficient deployment practices. DSLs are often used to standardize data pipelines and simplify data orchestration processes, allowing data engineers to focus on high-level design while leveraging abstraction provided by DSLs. YAML is a popular tool of choice for DSLs due to its human-readable nature, and custom DSLs can be developed to cater to specific organizational needs. Implementing modern data engineering practices with DSLs requires careful consideration of flexibility, technological stack, technical expertise, and data strategy and governance policies.