Implementing CI/CD in Data Engineering: Streamlining Data Pipelines for Reliable and Scalable Solutions

Authors: Tarun Parmar

DOI: https://doi.org/10.5281/zenodo.14762684

Short DOI: https://doi.org/g83jc6

Country: USA

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Abstract: Continuous Integration and Continuous Delivery (CI/CD) have become crucial practices in modern data engineering, streamlining the development and deployment of data pipelines. This study explores the implementation of CI/CD principles in data engineering, highlighting its benefits, methodologies, best practices, challenges, and future directions. By automating the building, testing, and deployment processes, the CI/CD ensures reliability, consistency, and efficiency in data pipeline development. The key steps in implementing CI/CD for data pipelines include version control, modular pipeline design, automated testing, continuous integration, artifact management, infrastructure such as code, and continuous delivery and deployment. Successful implementation requires careful planning, robust version-control systems, comprehensive automated testing, infrastructure-as-code practices, and effective monitoring and logging strategies. Challenges such as ensuring data quality, managing dependencies, and maintaining security and compliance were addressed. The paper also discusses emerging trends, including the adoption of serverless architectures, containerization, and integration of DataOps practices. The potential impact of AI and machine learning on CI/CD practices in data engineering was explored, highlighting areas for future research and development. This paper concludes by emphasizing the importance of CI/CD in building reliable, scalable, and efficient data pipelines, driving innovation, and productivity in modern data engineering practices.

Keywords: Continuous Integration (CI), Continuous Delivery (CD), Data Pipelines, Data Engineering, Automated Testing, Infrastructure as Code (IaC), DevOps

Paper Id: 232073

Published On: 2025-01-29

Published In: Volume 13, Issue 1, January-February 2025

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