Architecting High-Performance ETL Pipelines for Big Data Analytics in the Cloud

Authors: Santosh Vinnakota

DOI: https://doi.org/10.5281/zenodo.15054574

Short DOI: https://doi.org/g8837c

Country: USA

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Abstract: In the era of big data, organizations are increasingly leveraging cloud platforms to manage, process, and analyze vast amounts of data. Extract, Transform, Load (ETL) pipelines are critical components of data workflows, enabling the ingestion, transformation, and loading of data into analytics platforms. This paper presents a comprehensive approach to architecting high-performance ETL pipelines for big data analytics in the cloud, emphasizing scalability, efficiency, and cost-effectiveness. Key considerations such as data source integration, parallel processing, data transformation techniques, and optimization strategies are discussed. Real-world use cases and best practices are also highlighted to provide actionable insights.

Keywords: ETL, Big Data, Cloud Analytics, Data Processing, Data Engineering, Apache Spark, Azure Data Factory, AWS Glue, Data Lakes, Data Warehouses

Paper Id: 232253

Published On: 2022-04-06

Published In: Volume 10, Issue 2, March-April 2022

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