Dagster's introduction of the `dg` tool and Components marks a significant advancement in data tooling, allowing for the creation of well-organized projects and the development of domain-specific languages (DSLs) through YAML-based configurations. DSLs, like SQL, offer a focused, high-level abstraction tailored to specific domains, which makes them powerful despite their limited scope. Dagster Components enable users to abstract complex processes, such as data extraction and transformation, into reusable, declarative building blocks, simplifying workflow implementation without delving into low-level technical details. This approach contrasts with traditional imperative data pipeline tools by modeling systems as collections of assets rather than sequences of steps. Components allow for a customizable user interface that balances abstraction and flexibility, empowering data teams to work more efficiently while maintaining clarity and consistency. Although DSLs cannot encapsulate all complexities of data systems, Dagster's design allows for the integration of DSLs with imperative Python code, providing a scalable platform that supports both abstraction and detailed implementation.