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• AUTHOR: codevision AI engine
• DZone IoT

This article explores the differences between Amazon EMRFS and HDFS, two storage options for big data processing on Amazon EMR. It explains how each system works, their strengths, and when to use them. The article is written by Satrajit Basu, an experienced data engineering expert. It is worth reading because it provides a clear comparison of two critical technologies for big data storage. Readers will learn how to choose between EMRFS and HDFS based on their specific needs and use cases.

Key facts

• Amazon EMRFS is an Amazon-specific file system that integrates with S3 for storage, while HDFS is a traditional distributed file system used in Hadoop environments.
• EMRFS allows computing and storage to scale independently, whereas HDFS stores data directly on the cluster’s machines, providing low-latency access.
• HDFS is ideal for applications requiring fast, iterative data reads, while EMRFS is better suited for large-scale data processing with durability and scalability.
• EMRFS offers cost efficiency because it doesn’t require provisioning core nodes, unlike HDFS, which has replication costs.
• The choice between EMRFS and HDFS depends on factors like data access patterns, latency requirements, and the need for durability.