Big Data

Hadoop or Spark – Which one is Opt for my Business

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Hadoop contributes to being the open-source framework which helps in the storage and big data solutions or processing across the computers clusters in the distributed environment.

The technology is beneficial in scaling up from a single server to a plethora of machines in which each machine provides local storage and computation.

On the other hand, the spark is recognized to be the open-source cluster-computing which is useful for quick computation. It offers an interface for the programming of the whole clusters with the fault tolerance and parallelism of implicit data. The in-memory cluster computing is considered to be the primary feature of this technology which plays a vital role in enhancing the speed of the application.

Fitsly, have you ever thought about the future of Data Analytics like how it create impact with Business and all.


Hadoop is recognized to be the registered trademark of the Apache software foundation which makes use of the simple programming model for the performance of the prerequisite operation among the clusters.

All the modules, present in Hadoop are found to be designed with the aid of fundamental assumption which states that failures in hardware are found to be a common occurrence and should be dealt with the specific framework.

It is known to run the specific application with the aid of the MapReduce algorithm in which the processing of data is accomplished in various CPU nodes in parallel.

The Hadoop framework can develop a bunch of applications which also can run on specific computer clusters. In addition to this, it is capable of performing an accomplished statistical analysis for a wide array of data.

The basic part of Hadoop comprises of the storage part, which is referred to as Hadoop Distributed File system. The processing part, on the other hand, is known to be the MapReduce programming model.

Hadoop splits the files into large blocks after which it is distributed among the clusters. It is also useful in the transfer of package code into different nodes for the processing of data in parallel.

It is a prerequisite to process the approach dataset more efficiently and quickly.

The other modules of Hadoop are known to be Hadoop common which is essentially a bunch of the Java libraries as well as utilities, which are returned by the Hadoop modules. Such libraries offer an operating system level abstraction and file system. which comprise of the prerequisite scripts and java files for the starting of Hadoop.

Furthermore, the Hadoop Yarn is known to be a module which is beneficial for cluster resource management and job scheduling.


Spark happens to be one of the well-renowned technologies which are developed on the Hadoop MapReduce Module. It plays a vital role in the extension of the model for using different kinds of computations which are inclusive of Stream processing and interactive queries processing.

Apache software foundation introduced the same for boosting the speed of the process of Hadoop computational computing software.

It is known to have the cluster management of its own which makes use of Hadoop in two different ways, processing, and storage. As the cluster management is derived from the Spark, it makes use of Hadoop to facilitate storage only.

Spark or Hadoop: Which is useful for the business

Though Spark and Hadoop have earned a high reputation in the market, here are few major differences between both the technologies

Hadoop is considered to be an open-source framework in which a MapReduce algorithm is used. On the other hand, Spark is recognized to be the fastest cluster computing technology which brings an extension in the MapReduce model for using it effectively with different kinds of computations.

The MapReduce model of Hadoop is capable of writing and reading from the disk. Hence, it reduces the processing speed.

On the other hand, Spark is useful in bringing a reduction in the total count of read/write cycles to the disk as well as the storage of intermediate data-in-memory. Thus, it processes the speed at a faster rate.

Hadoop needs a bunch of experts for handling the codes at every operation. On the other hand, it is possible to program Spark easily with the aid of Resilient Distributed Dataset.

Hadoop stands out of the ordinary in handling the processing of batch effectively. Spark, on the other hand, is capable of handling real-time data effectively.

Hadoop happens to be the high latency computing framework that does not possess interactive mode. On the other hand, Spark is known to be low latency computing framework which is capable of processing the data interactively.

With the aid of Hadoop MapReduce, the developers can conduct the processing of data only in batch mode. On the other hand, Spark is capable of processing real-time data via Spark Streaming.

Hadoop is capable of handling different failures and faults. It is known to have a natural resilient towards different faults. Thus it is recognized to be a fault-tolerant system. On the other hand, RDD allows the spark to recover different partitions on the failed nodes.

 For Hadoop, an external job scheduler is essential for the scheduling of complex flows. On the other hand, spark possesses in-memory computation. Hence, it has flow scheduler of its own.

Hadoop involves a reduced cut off from the pocket. On the other hand, Spark needs a wide assortment of RAM for running the in-memory. Hence, it enhances the cluster and the prices at the same time.

Hadoop MapReduce enables the parallel processing for the huge array of data. It is known to break the larger chunk of data into smaller ones for processing it on various data nodes separately and collecting the results across different nodes automatically for returning the singular result. If the resultant dataset is larger in comparison to the available RAM, the Hadoop MapReduce helps in outperforming the Spark.

On the other hand, it is easier to user spark, as compared to Spark, as it is equipped with the user-friendly APIs for python, java, scala, Spark SQL, and python. Spark offers an option for performing batch processing, streaming, and machine learning in a similar cluster.