dlt was built to address the limitations of Singer and Meltano by designing a more accessible and user-friendly abstraction for data teams, allowing them to focus on solving problems rather than wrestling with frameworks. Data teams are looking for pipeline tools that enable them to easily import, use, and get production-ready pipelines without requiring them to learn new paradigms or frameworks. dlt achieves this by providing Python libraries that can be used directly, making it easier for users to build pipelines without the need for extensive knowledge of framework patterns, project structures, and configuration management. In contrast, Singer was designed as a competitive move but lacked significant engineering investment, while Meltano built upon Stitch's foundation but retained some of its fundamental flaws. The comparison highlights that dlt is more suitable for data teams who value simplicity and ease of use, allowing them to build production-ready pipelines in a fraction of the time it takes with other tools.