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Apache Spark or Hadoop: Which is the big data winner?

Apache Spark |  Hadoop  | Big Data

with the evolution of technology, data is present everywhere. Thanks to the internet, which has enabled inter-connectivity of millions of devices across the globe. There has been an unprecedented growth of data usage in the recent years which is likely to expand exponentially even further. Big Data is one such term which has taken the world by storm. Its growth has been incredible. This has aroused curiosity in the minds of many.

Big Data has superimposed traditional data processing applications with newer and refined data sets. Two of Big Data’s most trending technologies which are creating a furore among end users in the analytics world — Apache Spark and Hadoop. These two crucial frameworks which form a significant part of the Big Data family. Some people view these two technologies as major competitors in the Big Data space. Although it ain’t that easy to compare both since they are similar to each other in many aspects. Yet there are some areas in which Hadoop and Apache Spark don’t overlap

Apache Spark

Apache Spark is a Big Data framework which operates on distributed data collections. It furnishes in-memory computations for improved and quicker data processing over Map Reduce. It is a cluster-computing framework which is designed for faster data computations. It includes a huge variety of workloads which may be used for iterative, interactive and batch data computing. Apache Spark uses a hybrid processing framework by combining the various workloads for data processing and interactive queries together.


Hadoop is an open-source Big Data framework with a distributed data infrastructure. The distributed data is stored across multiple nodes within a cluster of commodity servers. It is an inexpensive software which is a fundamental need to most Big Data projects as it allows to store vast data sets across various cluster platforms. Initially, it was used for searching web pages and data collection purposes, but gradually it got recognized as a means to store distributed data sets across multiple servers. Over time, Hadoop has become a de factor model in the Big Data space.

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