The adoption of Agile methodologies in data warehousing has been slow, with the data community lagging behind software development. Key principles such as delivering value and frequent collaboration can be adapted to data science, but challenges remain, especially in applying software practices like test-driven development to ETL processes. The exploration of various models in data science further highlights the need for a tailored approach to Agile in this domain.
