Big Data Processing with Hadoop, Spark, or Both?

Hadoop and Spark are the frameworks used for data processing. Both Hadoop and Spark are maintained by Apache Software Foundation.

Both of these frameworks work on different principles. Hadoop is a common or popular name in the world of Big Data while Spark is still building a name for itself in a ”style”.


Hadoop is an Open Source software framework for storing data and running applications on a cluster. It is very useful in Big Data Analytics with great Volume, Velocity, Variety, and Value known as the 4V’s of Big Data. Data in various forms such as structured, semi-structured and unstructured data.

Hadoop has mainly four core components that work on different essential tasks for Big Data analysis.

  1. Distributed File System: Hadoop has its own distributed file system(HDFS) for storing the data.
  2. Map Reduce: Data store in the HDFS is processed or investigated using map-reduce.
  3. YARN: combines central resource manager that manages the node manager agents that monitors the processing of individual clustering nodes.
  4. Hadoop Common: It helps the user read and analyse the collected data in various systems

Hadoop is a great tool when it comes to big data analysis for applicability, flexibility, availability.

Spark :

Spark is a framework that provides a number of interconnected platforms systems and standards for big data analysis.

The important difference to be noted between Hadoop and Spark is that spark processes data through logical memory and RAM. While Hadoop works with disks. Spark is pretty much like Hadoop as a layer that can load data into memory with parallel analysis.

The major cor components of Spark :

  1. Spark Streaming: The real-time data streaming where data is in bulk. It helps you analyze the ocean of data.
  2. Spark Core: Distributes tasks and works with scheduling.
  3. Spark Machine Learning Library (MLlib): An extensive library of an analytic algorithm for spark clusters, adaptable to all other clusters Spark can work with.

Conclusion on Hadoop, Spark or Both?

When Hadoop is compared with spark, the spark is much speedier than Hadoop. Hadoop focuses on transferring data through hard disks, spark runs its operations through memory. Working through logical RAM increases the speed. Hence, the spark can handle data analysis quite faster than Hadoop. But, spark lacks a file system and it hence needs Hadoop. To get spark work without Hadoop one would need to go with the third party file organization system. However, this is complicating and since both Hadoop and spark are maintained by Apache Software Foundation it is implied that using spark on top of Hadoop is the best long time solution.

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