Spark vs. Hadoop MapReduce: Which Big Data Framework Is Better?

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Are you looking for an extensive data framework to help you manage data and expand your business? Then this might be the single most important article you read today. This article will explore the two most popular processing frameworks in the market right now and help you decide which one is the right fit for your business.

Apache Hadoop

Hadoop is a robust platform that uses the MapReduce framework to break data down into blocks and then assigns them across a cluster to process them across computers and combine the results.

Hadoop is known for its high fault tolerance because it replicates data across clusters then pulls data from healthy sets to rebuild data lost or damaged because of faulty hardware.

Apache Spark

Spark is a versatile framework that processes enormous amounts of data by splitting up workloads across different nodes but does it much faster than Hadoop simply because it uses RAM to do its work.

The Spark engine is known as the Swiss army knife of frameworks, which is the single biggest reason. Spark software development is gaining traction, and MapReduce for batch processing and real-time stream processing.

Which Framework Is Better?

Both frameworks are frequently used in tandem and work fantastic when used to complement one another. However, there are clear parameters where one is better than the other.

Spark Is The Winner In Performance

Spark runs 100 times faster in memory and ten times faster on disk because it’s not concerned with input-output concerns every time it executes part of a MapReduce task. Hadoop lacks the cyclical connection between MapReduce steps, while Spark’s DAGs have better optimization between degrees.

Spark Is More Cost-Effective

Both frameworks are open-source and free to use. However, Spark requires large RAM to function, while Hadoop requires more memory on disk to work.

This makes Hadoop seem cheaper in the short run. However, optimized for compute time, Spark ends up performing the same tasks much faster than Hadoop. This is critical as you pay for computers peruse on the cloud.

Spark Is Easier To Maintain And Use

Hadoop MapReduce is known to be more challenging to program and doesn’t have an interactive mode. Spark has an interactive manner and comes with much simpler building blocks and easier to write user-defined functions with its pre-built APIs for Java, Scala, and Python.

Spark Can Handle Real-Time Data Processing

Hadoop MapReduce is fantastic at batch processing, but you’ll find it lacking when it comes to real-time processing. 

Spark implementation ensures a one-size-fits-all platform rather than splitting your tasks across different platforms like in Hadoop.

Still Haven’t Made Up Your Mind?

Well, it’s certainly not easy to find the right solution to your processing needs.

At Knoldus, we have 10+ years of experience helping Fortune 500 companies find the perfect data solutions for their unique needs. Reach us at and follow our movements at @Knolspeak for the latest in cutting-edge digital engineering.

Also published on Medium.

Written by 

Diksha Yadav, Marketing executive having 1-year experience. She likes to explore new places and like to do photography.