Building a Scalable ETL Pipeline with Apache Spark, Airflow, and Snowflake

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.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-21
DOI
https://doi.org/10.5281/zenodo.15125061
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Building a Scalable ETL Pipeline with Apache Spark, Airflow, and Snowflake

Zenodo (CERN European Organization for Nuclear Research)
Cloud Computing and Resource Management
article

Building a Scalable ETL Pipeline with Apache Spark, Airflow, and Snowflake

article en

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.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 38%
Cloud Computing and Resource Management
DOIOpen Access PDFOpenAlex
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Building a Scalable ETL Pipeline with Apache Spark, Airflow, and Snowflake · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS