Optimizing Data Pipelines in Cloud-based Big Data Ecosystems: A Comparative Study of Modern ETL Tools

Abstract

The proliferation of big data and the widespread adoption of cloud computing have significantly transformed how organizations handle data ingestion, transformation, and analysis. In this evolving digital landscape, the optimization of data pipelines has become a cornerstone of operational efficiency and strategic decision-making. At the heart of this process lies the Extract, Transform, Load (ETL) mechanism, which plays a critical role in ensuring that data is processed and made analytics-ready in a timely, scalable, and cost-effective manner.

This paper conducts an in-depth comparative study of five modern ETL tools—Apache NiFi, Talend Data Integration, AWS Glue, Google Cloud Dataflow, and Azure Data Factory—with a focus on their performance within cloud-based big data ecosystems. The study evaluates each tool using six core metrics: latency, scalability, integration capabilities, streaming support, ease of use, and cost efficiency. By leveraging a combination of academic literature review, technical documentation, and industry benchmarks, the paper synthesizes both theoretical insights and practical findings.

The analysis is supported by detailed tables and visual graphs that compare latency performance and cost per data volume, offering a transparent and data-driven perspective on the suitability of each tool. The results highlight that while tools like AWS Glue and Google Cloud Dataflow outperform others in latency and scalability, open-source alternatives such as Apache NiFi provide unmatched flexibility and cost benefits for organizations seeking vendor-neutral solutions.

This study aims to guide data architects, engineers, and decision-makers in selecting the most appropriate ETL solution based on their cloud environment, data workload characteristics, and business priorities. The conclusions drawn underscore the importance of aligning ETL tool selection with the strategic goals of digital transformation, operational efficiency, and long-term scalability. Furthermore, the paper recommends future exploration into AI-enhanced ETL pipelines, containerized orchestration, and real-time observability as emerging frontiers in data engineering.