Optimizing Data Processing Workflows Using Apache Spark on Cloud Platforms
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Optimizing Data Processing Workflows Using Apache Spark on Cloud Platforms
Authors:
Santosh Vinnakota
Software Engineer Advisor
Tennessee, USA
Abstract—Apache Spark has emerged as a dominant framework for big data processing, offering scalability, fault tolerance, and ease of use. Cloud platforms provide on-demand scalability and flexibility for Spark workloads. This paper explores techniques for optimizing data processing workflows using Apache Spark on cloud platforms such as AWS, Azure, and Google Cloud. We discuss resource allocation, data partitioning, caching strategies, cost optimization, and performance tuning to achieve efficient data processing. Through real-world use cases and benchmarks, we highlight best practices for enhancing Spark performance on cloud environments.
Keywords—Apache Spark, Cloud Computing, Data Processing, Optimization, Distributed Computing.