Introductory guide to automated data engineering practices. It discusses best practice as well considerations.

Reproducible Analytical Pipelines (RAPs) represent an approach in the statistical and analytical process, combining software engineering practices to build data sets that automatically refresh. The main benefit being that they enhance reproducibility, auditability, efficiency, and quality. RAPs aim to automate analytical processes, minimizing manual interventions and utilizing open-source software, preferably R or Python. These pipelines are designed to improve analysis quality, increase trust among users, streamline processes, and enhance business continuity. This guide offers best practice on how to implement RAP as well as what pitfalls to avoid. A good introduction read when first learning about automated data engineering.