Building a Scalable ETL Pipeline with Apache Spark, Airflow, and Snowflake
Author(s): Ujjawal Nayak
Publication #: 2504004
Date of Publication: 21.03.2025
Country: USA
Pages: 1-3
Published In: Volume 11 Issue 2 March-2025
DOI: https://doi.org/10.5281/zenodo.15125062
Abstract
Extract, Transform, and Load (ETL) pipelines are critical in modern data engineering, enabling efficient data integration and analytics. This paper presents a scalable ETL pipeline leveraging Apache Spark for distributed data processing, Apache Airflow for workflow orchestration, and Snowflake as a cloud-based data warehouse. The proposed architecture ensures fault tolerance, cost efficiency, and high scalability, making it suitable for handling large-scale enterprise data workloads.
Keywords: ETL, Apache Spark, Airflow, Snowflake, Data Engineering, Scalable Architecture
Download/View Count: 123
Share this Article