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Spark vs. Hadoop

A Comparison between Apache Spark vs. Apache Hadoop

Spark 5 MIN READ July 13, 2021
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Frequently Asked Questions

Why Apache Spark is Faster than Apache Hadoop?

Apache Spark is faster than Apache Hadoop because it performs most computations in memory, uses a DAG execution engine for optimization, allows for data caching and reuse, offers built-in libraries for efficient processing, and provides a more streamlined API for faster development.

How Apache Spark is better than Apache Hadoop?

Apache Spark is better than Apache Hadoop because it offers faster processing with in-memory computations, optimized execution with a DAG engine, support for iterative and interactive processing through data caching, a rich set of built-in libraries, and a more user-friendly API for easier development.

How do Spark and Hadoop handle data processing differently?

Spark performs in-memory processing, which means it keeps data in memory as much as possible, resulting in faster computations. It utilizes a directed acyclic graph (DAG) execution engine and supports iterative and interactive processing through data caching.

On the other side, Hadoop, specifically its MapReduce framework, writes intermediate results to disk, resulting in slower processing. It processes data in a batch-oriented manner, dividing tasks into maps and reducing phases. Hadoop is well-suited for processing large-scale data sets efficiently.