A comparative analysis of Jenkins as a data pipeline tool in relation to dedicated data pipeline frameworks

This paper presents a comparative analysis of Jenkins as a data pipeline tool in comparison to other dedicated frameworks, including Apache Airflow and Prefect. Jenkins is an open-source tool that is employed for the implementation of continuous integration and delivery (CI/CD) pipelines. It is a highly popular tool that offers robust automation capabilities and a comprehensive range of plugins. Nevertheless, the software is deficient in certain features that would be more pertinent to data engineering, encompassing activities pertaining to data pipelines, monitoring, error handling, and scaling. Some of the tools, such as Apache Airflow and Prefect, are specifically designed for the implementation of data pipelines. The software supports the ingestion, transformation, and loading (ETL) of data natively, which is an appropriate naming convention for this feature. Such tools possess additional functionalities that are designed to address potential errors in order to maintain data integrity and productivity. This study compares and contrasts the functionalities, integration, performance, and community aspects of Jenkins and other specialized data pipeline tools. It aims to determine whether Jenkins can be effectively utilized to address data pipeline tasks or if there are specific end-to-end tools that may offer superior performance. The findings of this study will be invaluable for data engineers and decision-makers seeking to identify the optimal tool for their data pipeline-related tasks.