A Systematic Review of Common Beginner Programming Mistakes in Data Engineering
Max Neuwinger, Dirk Riehle
TL;DR
Data engineering relies on correct and usable tooling, yet novice programmers frequently introduce errors that degrade data quality and analytics outcomes. The authors perform a systematic literature review of 21 publications from 2003 to 2024 and apply inductive thematic analysis to develop a taxonomy of common beginner mistakes that spans general programming and data-specific challenges. The study provides a transparent methodology, a quality-assured classification, and practical implications for tool design and educational strategies to reduce novice errors in data manipulation, algorithm selection, and data handling. Overall, the findings offer actionable guidance for educators, tool developers, and industry onboarding to improve early-career data engineering proficiency.
Abstract
The design of effective programming languages, libraries, frameworks, tools, and platforms for data engineering strongly depends on their ease and correctness of use. Anyone who ignores that it is humans who use these tools risks building tools that are useless, or worse, harmful. To ensure our data engineering tools are based on solid foundations, we performed a systematic review of common programming mistakes in data engineering. We focus on programming beginners (students) by analyzing both the limited literature specific to data engineering mistakes and general programming mistakes in languages commonly used in data engineering (Python, SQL, Java). Through analysis of 21 publications spanning from 2003 to 2024, we synthesized these complementary sources into a comprehensive classification that captures both general programming challenges and domain-specific data engineering mistakes. This classification provides an empirical foundation for future tool development and educational strategies. We believe our systematic categorization will help researchers, practitioners, and educators better understand and address the challenges faced by novice data engineers.