ABSTRACT
Chapter 7 dives into the creation of data pipelines – the end-to-end workflows that transform raw inputs into analysis-ready data. It frames pipelines through the classic ETL process. In the extract phase, researchers gather data from various sources. During transformation, they perform intensive data cleaning and integration – handling missing values, merging datasets, reshaping tables, and removing errors or duplicates. Finally, the clean data are loaded into a storage or analysis environment for further study. The chapter emphasizes automation and reproducibility: instead of ad hoc manual cleaning, pipelines should be implemented as scripts or workflows that can be rerun consistently. This ensures that if new data arrive or if someone else needs to replicate the process, the same steps yield the same results. It also advocates embedding quality checks into pipelines and using version control or testing to catch errors. By designing pipelines with rigor and transparency, researchers guard against the “garbage in, garbage out” problem: well-constructed data workflows lead to reliable analyses. In sum, the abstract portrays data pipelines as essential infrastructure for modern research, turning messy real-world data into trustworthy datasets through systematic, repeatable procedures that uphold data integrity.
